From 6724012e79b7bbfbad40b6370a26b64a7cb66941 Mon Sep 17 00:00:00 2001 From: Wakame <85863413+wakameds@users.noreply.github.com> Date: Fri, 24 Sep 2021 14:54:57 +1000 Subject: [PATCH 01/66] test to use github --- Musk_RCNN.ipynb | 45 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 45 insertions(+) create mode 100644 Musk_RCNN.ipynb diff --git a/Musk_RCNN.ipynb b/Musk_RCNN.ipynb new file mode 100644 index 0000000000..2dd2431209 --- /dev/null +++ b/Musk_RCNN.ipynb @@ -0,0 +1,45 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Musk-RCNN.ipynb", + "provenance": [], + "collapsed_sections": [], + "mount_file_id": "1sn2UhFSOZZoyAj5L2RL8YS_7_cthikKn", + "authorship_tag": "ABX9TyMYHL6VaZp3qc8YabzK+PtV", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "PzblyaBIFZqB" + }, + "source": [ + "" + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file From 137b0db651bef59427c127c3bbb617b824b5274c Mon Sep 17 00:00:00 2001 From: Wakame <85863413+wakameds@users.noreply.github.com> Date: Fri, 24 Sep 2021 14:56:42 +1000 Subject: [PATCH 02/66] test --- Musk_RCNN.ipynb | 19 ++++++++++++++----- 1 file changed, 14 insertions(+), 5 deletions(-) diff --git a/Musk_RCNN.ipynb b/Musk_RCNN.ipynb index 2dd2431209..884dcdb0eb 100644 --- a/Musk_RCNN.ipynb +++ b/Musk_RCNN.ipynb @@ -7,8 +7,7 @@ "provenance": [], "collapsed_sections": [], "mount_file_id": "1sn2UhFSOZZoyAj5L2RL8YS_7_cthikKn", - "authorship_tag": "ABX9TyMYHL6VaZp3qc8YabzK+PtV", - "include_colab_link": true + "authorship_tag": "ABX9TyPugfeUj1s/MDTpavLUB9ID" }, "kernelspec": { "name": "python3", @@ -23,11 +22,10 @@ { "cell_type": "markdown", "metadata": { - "id": "view-in-github", - "colab_type": "text" + "id": "t84bc17tKTmU" }, "source": [ - "\"Open" + "test" ] }, { @@ -40,6 +38,17 @@ ], "execution_count": null, "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "AQNtK-9zKSTc" + }, + "source": [ + "" + ], + "execution_count": null, + "outputs": [] } ] } \ No newline at end of file From c24a76d61b56370487bdfa7e35cfc309b9ac0e8f Mon Sep 17 00:00:00 2001 From: Wakame <85863413+wakameds@users.noreply.github.com> Date: Fri, 24 Sep 2021 14:59:51 +1000 Subject: [PATCH 03/66] =?UTF-8?q?Colaboratory=20=E3=82=92=E4=BD=BF?= =?UTF-8?q?=E7=94=A8=E3=81=97=E3=81=A6=E4=BD=9C=E6=88=90=E3=81=97=E3=81=BE?= =?UTF-8?q?=E3=81=97=E3=81=9F?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- Musk_RCNN.ipynb | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/Musk_RCNN.ipynb b/Musk_RCNN.ipynb index 884dcdb0eb..363e4ef125 100644 --- a/Musk_RCNN.ipynb +++ b/Musk_RCNN.ipynb @@ -7,7 +7,8 @@ "provenance": [], "collapsed_sections": [], "mount_file_id": "1sn2UhFSOZZoyAj5L2RL8YS_7_cthikKn", - "authorship_tag": "ABX9TyPugfeUj1s/MDTpavLUB9ID" + "authorship_tag": "ABX9TyPugfeUj1s/MDTpavLUB9ID", + "include_colab_link": true }, "kernelspec": { "name": "python3", @@ -19,6 +20,16 @@ "accelerator": "GPU" }, "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, { "cell_type": "markdown", "metadata": { From cee0e3f512dfdabbe3055020b58e5e2dabdab895 Mon Sep 17 00:00:00 2001 From: Wakame <85863413+wakameds@users.noreply.github.com> Date: Fri, 24 Sep 2021 15:00:47 +1000 Subject: [PATCH 04/66] =?UTF-8?q?Colaboratory=20=E3=82=92=E4=BD=BF?= =?UTF-8?q?=E7=94=A8=E3=81=97=E3=81=A6=E4=BD=9C=E6=88=90=E3=81=97=E3=81=BE?= =?UTF-8?q?=E3=81=97=E3=81=9F?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- Musk_RCNN.ipynb | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/Musk_RCNN.ipynb b/Musk_RCNN.ipynb index 363e4ef125..691956baa5 100644 --- a/Musk_RCNN.ipynb +++ b/Musk_RCNN.ipynb @@ -7,7 +7,7 @@ "provenance": [], "collapsed_sections": [], "mount_file_id": "1sn2UhFSOZZoyAj5L2RL8YS_7_cthikKn", - "authorship_tag": "ABX9TyPugfeUj1s/MDTpavLUB9ID", + "authorship_tag": "ABX9TyNlCc5GW5jggKkr0jGOvNVB", "include_colab_link": true }, "kernelspec": { @@ -39,6 +39,15 @@ "test" ] }, + { + "cell_type": "markdown", + "metadata": { + "id": "iL1WKwmyLTz7" + }, + "source": [ + "test" + ] + }, { "cell_type": "code", "metadata": { From b4e4ee52e2be5f33b56c28a0927de4f660d80e4f Mon Sep 17 00:00:00 2001 From: Wakame <85863413+wakameds@users.noreply.github.com> Date: Fri, 24 Sep 2021 15:16:29 +1000 Subject: [PATCH 05/66] =?UTF-8?q?Colaboratory=20=E3=82=92=E4=BD=BF?= =?UTF-8?q?=E7=94=A8=E3=81=97=E3=81=A6=E4=BD=9C=E6=88=90=E3=81=97=E3=81=BE?= =?UTF-8?q?=E3=81=97=E3=81=9F?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit From 33c5b6fe8476dcf0288c5ac7ced9be2239d85e56 Mon Sep 17 00:00:00 2001 From: Wakame <85863413+wakameds@users.noreply.github.com> Date: Fri, 24 Sep 2021 15:40:05 +1000 Subject: [PATCH 06/66] test --- recognition/s4633139/Musk_RCNN.ipynb | 61 ++++++++++++++++++++++++++++ 1 file changed, 61 insertions(+) create mode 100644 recognition/s4633139/Musk_RCNN.ipynb diff --git a/recognition/s4633139/Musk_RCNN.ipynb b/recognition/s4633139/Musk_RCNN.ipynb new file mode 100644 index 0000000000..fc010b4420 --- /dev/null +++ b/recognition/s4633139/Musk_RCNN.ipynb @@ -0,0 +1,61 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Musk-RCNN.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "t84bc17tKTmU" + }, + "source": [ + "test" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iL1WKwmyLTz7" + }, + "source": [ + "test" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "PzblyaBIFZqB" + }, + "source": [ + "" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "AQNtK-9zKSTc" + }, + "source": [ + "" + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file From cf32feeaedd8fae7b86e22d3f8057ed5ea61dcb1 Mon Sep 17 00:00:00 2001 From: Wakame <85863413+wakameds@users.noreply.github.com> Date: Fri, 24 Sep 2021 16:05:07 +1000 Subject: [PATCH 07/66] ss --- recognition/s4633139/aa.txt | 1 + 1 file changed, 1 insertion(+) create mode 100644 recognition/s4633139/aa.txt diff --git a/recognition/s4633139/aa.txt b/recognition/s4633139/aa.txt new file mode 100644 index 0000000000..58fc08447a --- /dev/null +++ b/recognition/s4633139/aa.txt @@ -0,0 +1 @@ +sssss From 0e8fc4fccc9bd4edbacfe31f94dfca2cddddbe0c Mon Sep 17 00:00:00 2001 From: Wakame <85863413+wakameds@users.noreply.github.com> Date: Fri, 8 Oct 2021 15:43:29 +1000 Subject: [PATCH 08/66] Delete recognition/s4633139 directory --- recognition/s4633139/Musk_RCNN.ipynb | 61 ---------------------------- recognition/s4633139/aa.txt | 1 - 2 files changed, 62 deletions(-) delete mode 100644 recognition/s4633139/Musk_RCNN.ipynb delete mode 100644 recognition/s4633139/aa.txt diff --git a/recognition/s4633139/Musk_RCNN.ipynb b/recognition/s4633139/Musk_RCNN.ipynb deleted file mode 100644 index fc010b4420..0000000000 --- a/recognition/s4633139/Musk_RCNN.ipynb +++ /dev/null @@ -1,61 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "name": "Musk-RCNN.ipynb", - "provenance": [], - "collapsed_sections": [] - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - }, - "accelerator": "GPU" - }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "t84bc17tKTmU" - }, - "source": [ - "test" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "iL1WKwmyLTz7" - }, - "source": [ - "test" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "PzblyaBIFZqB" - }, - "source": [ - "" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "AQNtK-9zKSTc" - }, - "source": [ - "" - ], - "execution_count": null, - "outputs": [] - } - ] -} \ No newline at end of file diff --git a/recognition/s4633139/aa.txt b/recognition/s4633139/aa.txt deleted file mode 100644 index 58fc08447a..0000000000 --- a/recognition/s4633139/aa.txt +++ /dev/null @@ -1 +0,0 @@ -sssss From ff4c5b8dd92e013a3190e52f5a6529bdc3319e65 Mon Sep 17 00:00:00 2001 From: Wakame <85863413+wakameds@users.noreply.github.com> Date: Fri, 8 Oct 2021 16:07:59 +1000 Subject: [PATCH 09/66] Delete Musk_RCNN.ipynb --- Musk_RCNN.ipynb | 74 ------------------------------------------------- 1 file changed, 74 deletions(-) delete mode 100644 Musk_RCNN.ipynb diff --git a/Musk_RCNN.ipynb b/Musk_RCNN.ipynb deleted file mode 100644 index 691956baa5..0000000000 --- a/Musk_RCNN.ipynb +++ /dev/null @@ -1,74 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "name": "Musk-RCNN.ipynb", - "provenance": [], - "collapsed_sections": [], - "mount_file_id": "1sn2UhFSOZZoyAj5L2RL8YS_7_cthikKn", - "authorship_tag": "ABX9TyNlCc5GW5jggKkr0jGOvNVB", - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - }, - "accelerator": "GPU" - }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "t84bc17tKTmU" - }, - "source": [ - "test" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "iL1WKwmyLTz7" - }, - "source": [ - "test" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "PzblyaBIFZqB" - }, - "source": [ - "" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "AQNtK-9zKSTc" - }, - "source": [ - "" - ], - "execution_count": null, - "outputs": [] - } - ] -} \ No newline at end of file From d9ae2072e122720092934dc35d362a4d46c9f228 Mon Sep 17 00:00:00 2001 From: Wakame <85863413+wakameds@users.noreply.github.com> Date: Fri, 8 Oct 2021 16:11:33 +1000 Subject: [PATCH 10/66] upload dataloader --- recognition/s4633139/Dataloader.ipynb | 1 + 1 file changed, 1 insertion(+) create mode 100644 recognition/s4633139/Dataloader.ipynb diff --git a/recognition/s4633139/Dataloader.ipynb b/recognition/s4633139/Dataloader.ipynb new file mode 100644 index 0000000000..5eb3f16a23 --- /dev/null +++ b/recognition/s4633139/Dataloader.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Dataloader.ipynb","provenance":[],"collapsed_sections":[],"mount_file_id":"1sn2UhFSOZZoyAj5L2RL8YS_7_cthikKn","authorship_tag":"ABX9TyNkwzaLVu64OaORBKup58Jo"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","metadata":{"id":"vlhQO-g6Y2ah","executionInfo":{"status":"ok","timestamp":1633672076114,"user_tz":-600,"elapsed":10346,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["import os\n","from PIL import Image\n","import matplotlib.pyplot as plt\n","import glob\n","\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","\n","import torch.utils.data\n","from torchvision import datasets, transforms"],"execution_count":2,"outputs":[]},{"cell_type":"code","metadata":{"id":"WuSova9HHAzc","colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"status":"ok","timestamp":1633672105488,"user_tz":-600,"elapsed":26753,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"05fb16af-8927-4381-d504-febf63d348d5"},"source":["#define dataset for img and mask\n","file_dir = \"/content/drive/MyDrive/ColabGitHub/PatternFlow/recognition/s46331391_Unet/ISIC2018_Task1-2_Training_Data\"\n","os.chdir(file_dir)\n","\n","img_path = './ISIC2018_Task1-2_Training_Input_x2'\n","mask_path = './ISIC2018_Task1_Training_GroundTruth_x2'\n","\n","imgs = [file for file in sorted(os.listdir(img_path)) if file.endswith('.jpg')]\n","masks = [file for file in sorted(os.listdir(mask_path)) if file.endswith('.png')]\n","\n","imgs[0]"],"execution_count":3,"outputs":[{"output_type":"execute_result","data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'ISIC_0000000.jpg'"]},"metadata":{},"execution_count":3}]},{"cell_type":"code","metadata":{"id":"JhoipyIyXvBB","executionInfo":{"status":"ok","timestamp":1633672109919,"user_tz":-600,"elapsed":253,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["from torch.utils.data import Dataset\n","from PIL import Image\n","\n","class Unet_dataset(Dataset):\n"," def __init__(self,\n"," img_dir = './ISIC2018_Task1-2_Training_Input_x2',\n"," mask_dir = './ISIC2018_Task1_Training_GroundTruth_x2', \n"," img_transforms=None,\n"," mask_transforms= None):\n"," \n"," self.img_dir = img_dir\n"," self.mask_dir = mask_dir\n"," self.img_transforms = img_transforms\n"," self.mask_transforms = mask_transforms\n","\n"," self.imgs = [file for file in sorted(os.listdir(self.img_dir)) if file.endswith('.jpg')]\n"," self.masks = [file for file in sorted(os.listdir(self.mask_dir)) if file.endswith('.png')]\n","\n"," #meke dataloader\n"," def load_img(self, idx):\n"," img_path = os.path.join(self.img_dir, self.imgs[idx])\n"," img = Image.open(img_path).convert('RGB')\n"," return img\n","\n"," def load_mask(self, idx):\n"," mask_path = os.path.join(self.mask_dir, self.masks[idx])\n"," mask = Image.open(mask_path).convert('L')\n"," return mask\n","\n"," def __getitem__(self, idx):\n"," img = self.load_img(idx)\n"," mask = self.load_mask(idx)\n","\n"," if self.img_transforms is not None:\n"," img = self.img_transforms(img)\n","\n"," if self.mask_transforms is not None:\n"," mask = self.mask_transforms(mask)\n","\n"," return img, mask\n","\n"," def __len__(self):\n"," return len(self.imgs)"],"execution_count":4,"outputs":[]},{"cell_type":"code","metadata":{"id":"YFzDGbXcE_p8","executionInfo":{"status":"ok","timestamp":1633672112761,"user_tz":-600,"elapsed":276,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["#img_path = os.path.join(root, \"ISIC2018_Task1-2_Training_Input_x2\", imgs[idx])\n","import torchvision.transforms as transforms\n","img_tfs = transforms.Compose([\n"," transforms.Resize((64,64)),\n"," transforms.ToTensor(),\n"," transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n"," ]\n"," )\n","\n","mask_tfs = transforms.Compose([\n"," transforms.Resize((64,64)),\n"," transforms.ToTensor(),\n"," ]\n"," )"],"execution_count":5,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":302},"id":"zx1QLG_3l_nB","executionInfo":{"status":"ok","timestamp":1633672115791,"user_tz":-600,"elapsed":1189,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"1fd9dd2e-3a27-4ed5-e81b-bc34773b19c0"},"source":["dataset = Unet_dataset(img_transforms=img_tfs, mask_transforms=mask_tfs)\n","img, mask = dataset.__getitem__(20)\n","\n","import matplotlib.pyplot as plt\n","plt.imshow(img.permute(1,2,0))"],"execution_count":6,"outputs":[{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":6},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAPsAAAD7CAYAAACscuKmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO19bawtV3ne8947Y/bE7IPPIb63vsfYJsbCRaGYyCWgRBGBErlJFP4glA9VtELyj6YVUVMFaKUqqVqJ/MnHjyqSVdLwIw2QTxCNklAXVFWNHC4BEr4cf9QOvjb3ODnHORuYDTP3rP7Y+5x53mfOrLt9P/YxmfeRru7MXmvWWrNm1pn3Xe/7Pq+llBAIBP7+49RJDyAQCKwHsdgDgZEgFnsgMBLEYg8ERoJY7IHASBCLPRAYCa5qsZvZfWb2sJk9ambvuVaDCgQC1x52pXZ2MzsN4K8AvAXAUwA+BeAnUkpfvHbDCwQC1wrFVVz7OgCPppQeBwAz+yCAtwIYXOyTGzfTjTedAwDcUJaZpr3Acfq0HR0b6NhcNX+uZdTkqVPD9UB/+w4uDbfPx/r3kstOXaHsxG20rQyR+8vNwUF3ePq0r3eJytIBBgtPne5uIMm9pEvdQOyUTmQHnoNLrUwWTXLvw3O6ez2Nx38g9U7xO+HHwWe579qV+pbxdf6dSFKvOz+45B9o2zTUBjVyyT+YRGV22q+fb31rUffrf3cB82/sHfswrmaxbwP4Cp0/BeB7cxfceNM5/Mi//CAA4Laz25ma/kam00lXUnRlhfy9KPhupKyqjj/uzQA9h3pf2qc2S7qukcXIZa6v5wHua3fXl82b4+sBMgd1d7ix4evtU1lbY7Cw2uhuoJF7afe7gRTV8B9vnoPZbuPK2tmsa7+RgWydOTosafxFLRNedTddyITwmT4nN45muCwHHop/J+Q+qfN6d8eV7ex05xU10u7PXL2m7Caymp5xZX99YTF3/+P9bxsc63XfoDOz+83svJmd/+bX9653d4FAYABX82W/AOBldH7r8jeHlNIDAB4AgJe94t50z6tfDQC48xXDA6nlDzz/xZxOu+OJfGlYMyikjL8uBf2J0+9RQ5LTTL7sXJk+Jr2vaw7TFf+88keoVjGb62W+SPzFnm4M15uJ5FDQJ5znTbtqW5KyphgGP89dP1nzdos6lnHwmLn9VsWZ7rB60fAwmtw88hh1HJnny/PP7+2s1ou683rvdmmjO2/pY15fkAez1c1VseWLzixX3ic/OMEQrubL/ikAd5nZy83sBgA/DuCjV9FeIBC4jrjiL3tKqTWzfwXgjwGcBvDrKaUvXLORBQKBa4qrEeORUvpDAH94jcYSCASuI65qsT9/JBxqfkVPW+5QZnSmVfVj3WHm0ynpoXVO59VN34FxqN7sdu1FUeImc5PPerq2z3qp6qhuyFTW01dpQgoZSDWkf8t8uDnQMtrvqNjCMZN6/DylXzdXPF7pa8674BDwXNGz0I35lvd4rnBnnlFJBzMav77f5cD4i22vmPO+SClz9fTF5cGwBTTcZQOBsSAWeyAwEqxXjO+k+OcFFjPdgFXMJlNNI/2wgwyL+Or0UgyY+XrIOLaw6K6GEB7yXMqGfIJy5qRWxPOh6e2ZGIebdGXuOnW+ofmpRDyvyaVij6/LqDz6Mjqz36rvTTt8ykNUVYBNtfOc842WNZkybj+z0vg6Nh/nnlnPQehQ18u4AsaXPRAYCWKxBwIjQSz2QGAkWKvOfpAS6nqhqdbiTsj6seo3rEOVqjfi+DJtIxtkN4CeOYnbHNCzem3IOW8R9Nxg6U+vG77UY1Ncz4WV54DG1dN5aWBqamN9kK+rZE5nfHPqWsxjzLiictmWbHBM2F2WzXfSFd/nTF2tcTxadRGmuBJ1w86o4l53zjyXXEBUPTA/bcYdvLdyDzchLHT2QGD0iMUeCIwEaxXjU+pEY41sYxFRJR4nKWXMZixy9rzCquGyK0HJfcufzOG4I6B5rjtWNaEZsL2VGbNTJowcbeY+m4xXG3sY8hjVK9HNqYyDRVAO395QcyaNsSees2rA45DxTkgEV1XDRSrSz7XWozZ7qhebdKXMmd74BmQ+eL5zHqJORdN3mE96fA2L0lMZtpT4sgcCI0Es9kBgJFirGH/q1ClMllu/KoLXmUAHFwCwoidVbgd1VTE+57XFO9ETaW9CZXPti+5TWZiYk4HvWcVsRs/Lj9rgcWgwjVN5pH0WwXlneq7zwSKsFDm6JrrRZsc/+IrYyVStIcYq79moQU5EmVJ4tiaUA7vlufdPx6H0ZIxi4FhXlqqtboy8A0/15pnnntUVBxBf9kBgJIjFHgiMBLHYA4GRYM2mt0Qc2d52wDqUmnicXpTzImKdMqMjIdcGX9eLLOoOp8wrIDqdi5oSnXqSifJyPIo0H+o1yKfqMTbEkl3ofDDRb8/dqzucD4bA+T0HtQC2NCdFmXEfI/RMb6Sz5ui/+X1pn/ZlJenwTSZCbTCyEsA89y4NECarKTI7AzQW3q/alHeH90FUn2+W9sjUSwLQIb7sgcBIEIs9EBgJToCDbiGLaCAMS1U9D7oBcVFNaE40E/HKVV01S4uaqwayxaipkFFKGZtZ1JOqGfASU7MNi4hzYepvOHMKiYG9rC85E+aAKtPLkMMnSgLCxBPsISZtMGd9LliEvel6/BFs5htuwqtJwhzi3h3pIGd+5HeQTaTF85iroUGrt6FzKJTxz/cXpQeXJGcZIb7sgcBIEIs9EBgJYrEHAiPBmk1vB2iXymMpKUE5eitH0uiIFTK6snqROh2Yf9eccANRUgBQsOmG2ivFRZMH3PMw5f0HJScYyMDa0y95P0JdXanvmTN/ST12T9bowQGdXfcm2gE3T21zy2Vx9fX2dylj7NQPhMkr+DkVMqm5LRjOC+D4/OUl43dJx+j60px51A6TgOzLXgpnqy2ULUTfn6OK0jebY+WmH1sq/pYhjr/sl93Mft3Mdszs8/Tblpl93MweWf6/ebl2AoHAyWIVMf43ANwnv70HwIMppbsAPLg8DwQCL2BcVoxPKf1vM7tDfn4rgDcujz8A4JMA3n3Z3uzUkSylHl01iU6ajtZJPVRPxX0n5mjUEYtpbCKRNpzXmYrZfE5tqDrh+M7lPrlvTRHE8qjjsRC5iUXmiUiEc05txWYtJcrIcMu5vkgVmGTE+J6IPyAKT9XMdKYb8KqpvXq87izWqwmQ1a2cvM/mO22Dq6lexs+Xr8uQvreih7hUaMwbmIkC7Iv414+84mxK6Znl8VcBnL3CdgKBwJpw1bvxKaWEhbfMsTCz+83svJmd/8b+31xtd4FA4ApxpbvxF83slpTSM2Z2C4CdoYoppQcAPAAA29/1PalYdtnbiWaRRUUgJlMgEV93gF2bumM7IMJlHckyAShO9BVvKeeMJeKs43STMpcllsXzjPfbXCmRaX74nqeZHfdextsBnj8Vn5vMrjV7lrWzbsCtkOZNRGXzHdAxWzh03jKegqy+5UhAOCuqqkb8Puozc+8Il+2LqM6DlB19pohuMinHGubJ66mHi5fw0sG196D7KIB3LI/fAeAjV9hOIBBYE1Yxvf0WgD8F8Eoze8rM3gngfQDeYmaPAPgny/NAIPACxiq78T8xUPTmazyWQCBwHbFWDzqjDnu88XSsHlIF6UKs86ojUk22uB5vPJ1vskeXDjLDS+/2EjL1Juy1pc1nZpy9vVg/620slAPHMi7GruyquFRZosxOmcWSSUV6LoU0xF3fMRNWFGRTm9f+Znaf6NwDS7GXFlvHb7S0F31fbSZcrh3S+2t9yYbtfq4oY2bd3aW9CWHHmJ7tGlEi06F9o56Zb6BfP8ir8KALBAJ/PxCLPRAYCdZLXtFxV+Q7Ftna1WWzhZhtcnzwjrhgKOBE0JPshogn1ARIZUp24NQQcQHk4Iyag1/UO4096M4Ml+HJrvP6y0/6inRz9ZafuKm7t66NXiJYupmeukKRQmUxbBMtieCt3vMTWe3SGKmHauof/P6F7rpSXPkKZoBwqaZ8dFG7213XikthVR3v4QZ4L8tqqyuc7fuKzjNTg2k4RwCGwdqVOhFOtxYvwunMIogveyAwEsRiDwRGgljsgcBIsFad/eDgAPNDm5vYcdhFsRS9iF0ZHedFTkeX8yHDSs681iNpHFCotAk2r82UV5xcaxt1FR3gzq/VJZbNRlrGGw0NmbVkkLOmG4jmettlOx0NpNXNFM4pnEnZPCHGh7aRjQrS+9td335NPr5F1YX+1ZzcDeKeLO64xV43QSUl5VMu+4bsvZVSn5C7b89iR3o6E1toxKTLiyDNZ6bRQSM53Tgub3mLL3sgMBbEYg8ERoL1etAdAMVhGJJGgzE32+2+rGZSB8ckIG3QcaPmOxadhtpD33uPMSfp1olpIpYxPx0u+jKONlPxeYhMrVLZkcRz9QSryZRV7Hcd7Fz4sh/jk50oXMhEtvvdjTbMXrHt7XwVTUIrOk/TsGmrm+Ryuu3rcW4l9Xo8c4aKukmei3daQQ+x2BC7FovnGRK3YrPrS7U1d2vCB1iTvtJSm+rdyfemIj6fu9TlqhrRo+6L+4eFg9Hm8WUPBMaCWOyBwEiw5kCYAxRLIaltZeeVtrAL3amnYya50MGzZKa8asUQf1fG+63njDSwWy6OX54bT0R1tiyoJ5UTTimoohAyssK58nkxvqW0rntPPnp0vPOZz/jOniSxXggB61nnbTcnU8Dm/t2u3ozE4tmT4qFHE+uk7sLLt9Nym4q8eWLG/HTkLlltCSkf7cAXM69qtFU3jhm/VpXvq6D7LKWsrLo2G3loFb0kLiVYjscuk6U4Bw0e84XLST4IMT4QGD1isQcCI0Es9kBgJFirzn66OIXp1kJnm07Vk4oOhZyA3eaYULFVTztWp8T04dTS4SAmn+5ITUHU5l4mtZKLehMvOUdcIESVEyacJD20rb29hwkg6pnsfTSdy96s7vToQmyds7rT5/d2vb5dX/rb7pjH/jnfV3lTp8s+9twjrmzIgqnWxi38+dHxGXyHK5teuOPoeHLmThqgN9+xyasuZO/A5QInM9wZ0e2nHRt6edbvkdRbfK423W4s5Ub3zPS9Yo59zSXgUlRxyquMGVisj6iXCn06OBi8Jr7sgcBIEIs9EBgJ1ktecZA6+4G4GFWUFyiXMokF2qk6S/FxhpubRXoV1XPBBiw5Md+5Bu6wCWZfzSUXOs81NpMBQEM3xBlN23nGVlN7XaB+ohPP9x997Oj42Se96W3n2c/SmRf9WFD12Y3+ztUrn+vOVeLkNviZ5erN8A1XdvNzXzw63mbxdldMkfNuQlohr6iImGNCJPJF/bQfyHZnVpzJKKc1ifziAVjTe1yS7iiPxXlm6vvSZkgpXBt8jVQ8DDA7SGF6CwRGj1jsgcBIEIs9EBgJ1kxecQnzJUd5UUj0E+kgWxtCQMCEfFSk6jBZVtTy5iozTbqSPubYA9gFcrrizG2oFXGrUz5LVd4aNqk1w/VmRDyx69kx5o99/uh478v/9+j4yWcecvUeo+NcJuPcbWaCwVzyvwG6/d65aNHg4Mf5X3f6+xRfdPVYw96Q71ex25nsdh0LiB/JubtJyd54tR/jVvfCVHe6Iu9ae7Gr18jMNUyKoowptBRcugDlqOcb1ffqWpjezOxlZvYJM/uimX3BzN61/H3LzD5uZo8s/9+8XFuBQODksIoY3wL42ZTSqwC8HsBPm9mrALwHwIMppbsAPLg8DwQCL1CskuvtGQDPLI9nZvYlLASPtwJ447LaBwB8EsC7c20d4AD1YdSbyiF0OhPB8hyJOU4k1JTBTJ0mfOouYogj1sQWxNzcE+URG1Ahcp5Om15bQUummmbXKxvzx4hbjYgnChH76gvdjbKoDgD7jx4vun8WHizGq8rDTn8srvVIHehYPeP2BspygVsKfjlZ29Lp5nvZEjPiOfLsc0Fv0sZ8RlFvpX+xWhCJhorPzJtXdqpAuSmuk5zKKpNGi+d7JjfKgaK1pOyqlvbpU5euUcpmM7sDwGsBPATg7PIPAQB8FcDZgcsCgcALACsvdjN7MYDfBfAzKSXnMpBSShjgwzGz+83svJmd//rXlWo1EAisCystdjMrsVjov5lS+r3lzxfN7JZl+S3wG7BHSCk9kFK6N6V07403xh5eIHBSuKzObmYG4P0AvpRS+iUq+iiAdwB43/L/j1yurVMwVMsuNaVtblSsnbCprBK9nEkmRaXp88Mftq1bB70czh3UArZKX9rvjN1spZ2WCzkvmfCpt22nU+7ueDfYxx//ZHdMv6tZi/8yq7zF47otU4/P9S/9wBZJP0U2QR8Ra709UyqB3yQdx5BZsdfejM1mvpXpmc4IWMt7W5F/tWdHUlLM7rhU3viB96r3znJEZi0240Mf8zRselvFWvx9AP4ZgL80s8N9nn+HxSL/sJm9E8CTAN6+QluBQOCEsMpu/P/BcJ6JN1/b4QQCgeuF9RJOni5QbCyEs0LIK5igQcUaJnxw4pC0TxmCeiIQm95Y3FILSTsUroWO0w/wZASK1oeKSWF32BPftrvKE2LpaISgYrZPBBUXPFnD/sBxz9uQjnMvQTtwfLn2h5Q0CQbLZSsa9K7LeeHlvAFzpsJitzN7ntn0cnZddj1Mhcm02Do+b1ktkYoVhbZVOjn8TnDzSlrJZKhT8TJdNmqZXbjwjQ8ERoJY7IHASLBmMf40is1FIAhnvwQAzCgbZkb0ZRFZpaEz5K0m9OSYrrglzNlk1VWL+5uyC1FOVNesn04U82Wa5ekQ5VRd+bgrfxHvkOsOvBsHHevONCtY3MaqQTF6fqxNdgl2zlAR/+GBvrUv1sTEYdF5pHEbynL/9KWOOKPc8zPnpv+MytZ0TFliK0kvVe9THgC9UVoLHOjVe08zz/0of4AFeUUgMHrEYg8ERoJY7IHASLBmwsmDI5e1akPMG6ziiDLO6g+TRmjOLDavKbmE43Zngj9pgx2kKiGtZN3NEVMqNzy12Whq6ozjIHvoNdSB8tK7FL+ttw8ONa96bs6TjW/7+MTLfWh7rKdnggKz4GzXvEWyKikjAExxw9FxebqbuL1LnjzTeeE9+1Xfxm53N6WYQSuyo7FnnN5zu8d8/r6Ucxu29JLV8nJOpzkakMNxXaOot0Ag8O2LWOyBwEiwVjE+IR2ZDFQUm2YiHZiLi01XlV7DnnYZ8dyZv6QJJgxQEX/Cpj0mshBTyixn2iO71lw89JxwRx0UMsqWzqdnfCThzc92x+fo95z5S/nj2CzFUyzaCjKUaFcsug+BRXr1uuN4KJ36lt60OjOqocAdQOZfiOE49Xi914nglbjJtWR6q7bELEfRWO0u6X0aREV2ubJ6/jMcX/ZAYCSIxR4IjASx2AOBkWC9Ovsp4NDKoBoHWxXUXbYZUqhk9JzyuEcM0Rx/rJ6obP6aXfBlmnLtqJ4oinM611xyfD5RznoCZanGXNvgDQjRIfl2xHLo4Fx/pYxvh7vO52lbH9QhlMevnssNE1Bemg/WY5y77R+48+pct1sx2fAPbcJ7SBQt11b+DayYvXQumzz0gvOz1b2amglN9v1zL5c7KpZZ0vFlDwRGgljsgcBIsF4PukudB91cROSGZLFKbTzFwHFOFtMmWKpicUjk/Uz2J8dTXw+Y8oC8eD5kAuxdRzL4XIPeiDBhunW7K5viU0fHt5EIK3R9ztym98xi8SxTj7Waa21qez5gs6K+0P51ORisx1NcnpEXkPW32hsq5xWVkf5Wyktc73TGw/qCRNVt09PZ4NA2SR3Nqct7yQoOzyPqLRAYPWKxBwIjwVrF+NOnT2M6XcinGqjCm5eFuNcVw5KNB18n7bOHHlerM8E0Og7Mjq+n29lsTVDPwFwgjOPao98nqtdsdalEp9vf7YrO3fxod/Lsnx8d3iN9rZqSiUX3kxTVc+C50ullET+3a8+oL/jopWarO283ZSd92s3KJu24t8JR3pI7ZikedC1RhbPjXSPvztylBNNRLy9MIcYHAqNHLPZAYCSIxR4IjATrNb2dPgUszUYT5WvnKDLVt5lkMjPiIZ0X8F5t7MxUKe8l6+UZJZX4Bp7XJHKTpXrvsRXH1fMV56Rxlq0vq4ouNK+iRM3nJJUxa/oaEccaa4ZTxJUph+K1Bke66XxToF8vRRXXvZOO1brLGvbmM5684tyru+OZhEJuUtRbS7NQyD7L4V4V4M22AFCc7WayoF2SRsIpG+q7kBxSxf6yjYPh7/dlv+xmNjGzPzOzz5nZF8zsF5a/v9zMHjKzR83sQ2Z2w+XaCgQCJ4dVxPhvAnhTSuk1WGzq3mdmrwfwiwB+OaX0Ciz+oL7z+g0zEAhcLVbJ9ZYAfG15Wi7/JQBvAvCTy98/AODnAfxarq2D9hLqiwshsWKSd3giCs2KOiNphgNVCmFd4DbUO41NZSzuqyjNJo1eCin2fsMwWErbk3txHl1qPhlotN73omND54XYYCr2xnqmE1wrPOLqMeVFn6yhQ47GPGeyuxKclfMh/vpzGIaqGkP89TlxX0V87Ha9V7e/drDvGZnbNK0YyOuxUj2VRj3b2T32dwCYUtpivc9iqfhZJqHWqvnZTy8zuO4A+DiAxwA8l1I67PMp9Pn5A4HACwgrLfaU0qWU0j0AbgXwOgB3r9qBmd1vZufN7PzX9v/2CocZCASuFs/L9JZSeg7AJwC8AcBNZnYoj9wK4MLANQ+klO5NKd374o2XXtVgA4HAleOyOruZ3QygSSk9Z2YVgLdgsTn3CQBvA/BBAO8A8JHLtkUdqpsqm9dUlWVdeZ9NaBrKlcEQecWeKG+s6/c45Qd8RxtVXpl7PmO+U3KMTXazZQ55UdBasg9W2145bOtOm5reRDrec15n5yGr/loN1FOSCz5/FFeGXJQh7xHwXWZS6/XuZcg7Wftls9ymlLW7vOHjy6Zb3XyXZTfKVsj+OT13KQ+0mHXnLS8MXSQV7WKIzfiwhYy37Eom4lsAfMDMTmMhCXw4pfQxM/sigA+a2X8C8BkA71+hrUAgcEJYZTf+LwD0tiBTSo9job8HAoFvA6w3ZbMZykPbmchR80yaYzaPlSyOq4mOeezkzlyTJB3N1H2MOeVzMiaPT/tij7+cXUtkzGdpLC7VlERJVUSu0Oz4CK3J7eRBd6EzZs2e89szOSIHxhCRhZa9Wsq+PNCeiuCsiek4csmOGDyunCffZOBYx9GziJJ5c6KlDaVpvp2UDUntzHNVSyhkWbPJjiLnVIznHN9ity3qxctjNwy/tOEbHwiMBLHYA4GRYL1U0gcHaJaiSW+XlOSqnhcUSTPlTieQFyJnMyFGo9xyHLjCrL7qJZfZPXc0diuK+Cr1ubRUUsZBPjXloaqkswmL9bUnjG4b3qnvxL7Z53wgzF/TsWoyPKzcLjiP6mYp4x3tP6VjVQVy4jMLuy6ASOrxS6z+Y7w5zQqPtsHjPfMibyKebnduJeWGt35UHPBConVvYfFuvL7g/HIyB52+jLQDr+QV5ROL2TK7ikCYQCDw9wOx2AOBkSAWeyAwEqzX9HbKUKpNbIkcD3vjHJioonB4l/WU6kn7dOwIITU6iWw36rnmxpghjlwZOXsS6+nCsllSBFVbiL9XTcmNSRfMDVf11yEyxlyQnqZ9HtKPexFlBPVcy3HbM1i3V6fKizge+junqf5uma1yo+thcsb3wBzzbBlTnZpNsFrWJ49cQpIa8J5OqxbApUk7hc4eCARisQcCI8F6OehgR+JpT1Rnm0xGBq9IDahn3ntsdoEuVHI5ZrYgWVJFdeanm0gaVEeigWFwm8pzz5aaVt29yNxW0Ph7gTbM19d6Abohu05B+srmzWJOenY43JgfBc9wLitXDmyWy5nXNOss182Z3nJBPaviYTre/ubfubJz+13EkvIBNuo+uYTmB2CPvR6PYnF8PZ0sF4clbbTLdZEyn+/4sgcCI0Es9kBgJIjFHgiMBOt1l0Wne6lOU2TClVxuNjqrznktsqmZBMAryxUTwpOm2IoCyHsJpdiCOOKOdau5cgxwbjo1s2RTTrO5jQ7FxFjQ5JXyCFu6sCDf3GJTXG6JbF1fAr6d+cDvgB9+Jkt1Fqyzr9qGjnczU8YYMsPpdUrSUZCLbCMMpRU9bLf/k9kLqsQ+yNe5d0n3tTL200OSlFMZ8or4sgcCI0Es9kBgJFivB93pUyiXUT1qesvBVSVZpuqJsMxsITIPeZ01JP1PRaRqM15yzDnQkHyrt8JivFoRmXCjF7nEJrWLTG7v6813O4NYkcs5TXa/XqopHq+MkVvk8WvEGjJl9UCZSqKsJmj0XTtQT02AzCOvbQyNWbnn30DHd97ycl+4efvRYSkPzamfzEsok7qX4Vhkz7vcihyw8i1w+OKGGB8IBGKxBwIjwXo96C4dAIepi4SHKxeo0bIbGskytegCUw4cmIl4O6WdepaXM65wqglM2cWLjnsebsxjp3TUQ25h8GJ9sdUNrNUwE6I2rne84FqTiD/b78qaPV+Ph6Ei7ZDYrc+IUzKtmhpKX7gmUzZEoqEBM/wm6Rh5XJzZ5Luk3saLycNQUjzx+1KL2jQlvayoyZ5Q+hdrRim7GpHxOXsve1hmpXZ9d5bWJ0sHx9ReIL7sgcBIEIs9EBgJYrEHAiPBmqPe0ClVGX24l3ZpgGtdvd/m005nmpzxewJMLFlkXK6KjE6tfPaHUAIMl2pKxpjlq6CxbJFJcLeV/Q0mlZ/KDZDJru6R4ndgbzUlfOCrhvjfFTldmadbI9v4ztQkxXsJPMVT+UZtvqi7g7mEEt526Rtd+6dv6Nre/m7fGaUQr7bvdEVTipisa3+nrMM7YhYlQ2U+eN8zmt3umTW071SKKx8Hcu77gE/sPbmIzLv0zWHG1JW/7Mu0zZ8xs48tz19uZg+Z2aNm9iEzu+FybQQCgZPD8xHj3wXgS3T+iwB+OaX0CizCiN95LQcWCASuLVYS483sVgA/AuA/A/g3ZmYA3gTgJ5dVPgDg5wH8Wq6dhISm51O2AHsHaXB/vdtREkgYgjtryJw3EdF6c0BerFSuzPDGNwMOej0zSMacxyK/qiuOH58ldZGz53VXWO6JR5dLwdoJ0/N9b4TlRsUAABbgSURBVJLJedDxuXLLMXj46rj1HXTMjyJnXlN1gsscz9wtXgSfTo/nbgeAcxudEsFpnKqz264eNrs2qjvudkVF1SkURSE8/dRmy7zxyp3C77fI8U3dvd8lm6Qliqp2ATMix1fLwkwkzKpf9l8B8HPAUYKwlwJ4LqV02P1TALaPuzAQCLwwcNnFbmY/CmAnpfTpK+nAzO43s/Nmdv5r+8NUSIFA4PpiFTH++wD8mJn9MBabuBsAfhXATWZWLL/utwK4cNzFKaUHADwAALff+ZqMm34gELieWCU/+3sBvBcAzOyNAP5tSumnzOy3AbwNwAcBvAPARy7bG7FXVKooMkSncdFts+HYq5ZsV23tO2D1Z3Pj+N8Brzf3cpuVxx+rDsanOsFal8GmlWZAfweA6RaRKZzzmu7+xe7mtm7vdNtW7IZt9cjRcS0Wuuml7viV9PvDvlouwMqRTPIIlRiCy9Rt15nbbrm1O97SfGtM2C6EJtNOu9y6u9PFp3fc7uoVmzSS0rdRVp3eX+8LpeVAvgPdx3He2/v+HW6JybStugtdHjkADXUw0ei7wzLTbHcdrsap5t1YbNY9ioUO//6raCsQCFxnPC+nmpTSJwF8cnn8OIDXXfshBQKB64E1c9ClI4405Vxgc5tav6ptSrHTdsf1RZE/SUbOicuu7xt9WfGdNI6/kQuZlp6lqEyqn9598rgybBBODBTZd0riXC36UEUibrtFoqrYShy5RP2IK9t87vgx6XNhIVPNZiyS5yLseFia9nmKTnSvbn91d7x1u9Qkk5oQBxYkuhecqqnykzolkZnFdqBLrQT0I9bQDjDnqXmtYLut2mo5pRmZ1OTZbtAY9+UFXyUbWfjGBwIjQSz2QGAkWG8gjJ3qtpZFGuLUSsr9Vg04SLWFFx4dEYJmuaQ2G07jJPWydMYD3BjT3rY9HeZE9UxXLomrurHRhdWG73y60QnK7Rbt8qrkSDrEza33xqqf6/whOCSkF8BBx8OCtddC7pZ6my96SXfNa33p5lbXe7nRCfyTqegkJJLrGKfbZ7s2nBulF+NLehEaSR3G5BJTiU5pybLDqpHGJ3lOPv9S+HfV5fZy9Rpqpcdjd7gwTkUW10Bg9IjFHgiMBLHYA4GRYO3pn9rDLqXnaada9QZVDlgtVC9nVauno7Kuz9FDF4frqZdfw6ocm9ckAMml1oWAb24yXJRLCV2T+aeVKKySPOoKinqbaqrhs0SSIGVt/UdHx08/3pE/PA0P1l5vk7IhssibX3yXr3d3p+1Xd7zCl027soJ7E7aQ6h92ur6mw5pw3m0KrWw1YpJzIEvqsHqXNk10/+ROGpczuYqXHJne2h3vWe6eO0e9ieLPbba73qbbHnqWXrqEIcSXPRAYCWKxBwIjwXrTP+EUiqVNSQNQ2ASmPG0cqOEc15R4gq+Rcw5S4HQ7vUAVOtasmc4iwxwD2jn9IPThrr+ZBtCQ9Ngz5w2MqxVTUFF2+tAZNhXuetF39uVuhubbfrbOlPcdHd+583tHx5Ov+XHw9N9220tcWTHp1Ily3o1xunmHr0d8b9NNb8CriIivLKk3nZwJpfYS0r+CZrwg5ahStQYs4nsUryDTnnpEcsCS0zH9mzV7uhPdiwtexK+2+YUciLZCPktssxTrUxu88YHA6BGLPRAYCWKxBwIjwfp545fQaDBWcVTP7fkGLtFq0q+M/ykXse49yZFoiN2M9bMhDnnAm81698mF6kpLjc5I99RUvXzeIwEhF2Im8CjOnPX1aFyFkEEUxHB5z9u6SX7lXG5mQjqwED6wKaskf+fJhnd1raiv6qzEzpFpsiXSEo1Y4wdayGYQR1PWpFNP5WUpide90hA+JqXQZ0Zm19kOR6+JS+xFMptti8stR+Px77veplvSZtN8qubSpc6erp5wMhAIfJsjFnsgMBKsVYy/dHAJs8PwtpmIMrkQMPZMYiuFSJVsClLRdyjqrQe6LhcBVw145AFeJVEp25nNtGvmeZ9xGiA/V5yZutK+mRONriuEZGF+O3muiT40PdOVtXf/Y7pIUh/xg+mpPN0PBdlIq0rpK0gV2FKGOiKNqLq+WrF/1XuduFtqwgB6oCXrUGIaY06KSc+Fszvscb7zdaQKzMW8ho1u/qtNIc4g1asm77p6Vzzt6Jn1wjUPVZnrxEEXCAS+jRCLPRAYCdbrQWd25EGnYjaLgZlNX+/FlvFO011qx0BNwQw5qmf18qsGHJ0UbcbTyZ1OJOBi//gLG82HRSKc7vazZO08DIWQoahIVJddX5zpJstZLqSrzf1u/HN5GLu7na5UUllRKg008enJDjZHJWWmFNPJ8fWAfkAUDcqfZtIzuXN9FHTMsTSNqAn8wjQSTLNLW/zznc5dtNj18zGrKE3UVi5U6njElz0QGAlisQcCI0Es9kBgJFizzg6US+VISSVZZ+/p0WzKYk871VfZvCbedT1zSv+SfueawodUKN4TUPWsyVGEM5+gqKhVy7onRWGJt5Qj7ZA2XNQez5s6nXE9MVc1FKXG91JomB5vEIiiu0VEmD6VlXh+0XEpualnNEHNrNtXaHu2zm43YVL5h1GR19mMrptfkJwDZP4qtmW+ebw5VZn2SKpaJpyfu7z8vDc0IcIOPO1f8IrSSmPLh3xWy/k5VQx/v1fNz/4EFq/VJQBtSuleM9sC8CEAdwB4AsDbU0p7Q20EAoGTxfMR438wpXRPSune5fl7ADyYUroLwIPL80Ag8ALF1YjxbwXwxuXxB7DIAffu3AXpIKFZ2opm4sXmxOIMDzubmlSodJdl+Nqd+JwJaMkRweXEeJa6ewQbLoWUdEAyIgfoqLMUnIlHisg+xs2rqbAYOF50ePyNz3syLAXaSCPs2bd7sRPBC+2NRFr1jCvoJal7Nkau6ARtV9RsdNdNLpIXnpi1Sn5oWzLhw4537n2cs8ojprFyQz37aMTUSEPDmm+KuXSDX6YBF9FrEAiTAPyJmX3azO5f/nY2pfTM8virAM4ef2kgEHghYNUv+/enlC6Y2RkAHzezL3NhSimZ2bF/UpZ/HO4HgJteun1clUAgsAas9GVPKV1Y/r8D4PexSNV80cxuAYDl/zsD1z6QUro3pXTvjdOXXptRBwKB543LftnN7EYAp1JKs+XxDwH4jwA+CuAdAN63/P8jl2vrFAzVUgHqkS4wpIwJIlkXlBRl/jpR6Fm3cuqfsFCUrK9mk7Flyrharp6WsbmwHK7nIudy7XNknhS5ORB1mGnZuX0lW8yRbjbu2XQ3Uze+kZKU1Jno5Y3bXGEWT+mLbkCno+Bk0sw8UUlf6PYHml0xm5EZsefmzX1lnpk7lckq92nvg59ZLwow0/dRD8NRb6u8smcB/L4tQucKAP89pfRHZvYpAB82s3cCeBLA21ceWSAQWDsuu9hTSo8DeM0xv/8tgDdfj0EFAoFrj/V60J0CikMPJxXVfU5bB7b4lGxaEmsGi5nqucb8D6xCzPbV/EVjyols1MYZzVfMYraKvs70Nljk+1KzGZsitTI1UmdSUzuIWMlmUZ5jcXBzUrFaxpjngkkpet6R7JVXqz2WdYjuuJqISYpla3lorCaAUzCJedE55dUZ984NMakNeWb2mEnoUJ5F7dQtEunlJXbBd2q2PeSxy+gZ4RsfCIwEsdgDgZEgFnsgMBKslzfe0OnqmTxtGhHXkipH5Cg4Izo7q3i1ptbl9qneNOPO2ruO1CTeA9BgMN5/UAdTNpupzs7j2iOvBb0Vt/8gT5DNkZxiWvVENq/1ggcHvE97rC/DqrLP5+wal3POVSepjEsy05VTZrvxnRWZpAOu7lniZ1d2HlLtW9k7KGiuSnUlZvNjxpXbzc8Vumh7v10p2lgO5NTw9zu+7IHASBCLPRAYCdYqxh+khPnSRqNWlmInE9VEUUgkzXkSSYgElIlOyjhjed54Kaw4HRGLtxelL5K71RLCJBqzDLGF+z13L0qAMeDFNROmARY5e15nOfGcu6ZnqKYmVjWYYFHJRJtcNBs1OmFbrcxbmclv7SxqFOnW7irrYzdBky1vS+WoNMizrprj5fiiN3HkRZgZIz8MjYp0xCT67izbt8z3O77sgcBIEIs9EBgJ1rsbn5LfjiZwVkr1HCpY5GenKtlJz9B7ezGTm1f+cJfuVRoZELObjMdfbob1Ot7orZkIQZ38HC+cNMoWCnZOE7mvbXh3W9qn63hTPefJp3DBOvz81NJCorWKvhWfk0tes+kZ7AuauB7JhdvAJo56HTDVa0THLIkhpBXzx5yDdejB9DfceUvflzDBiXs1M2moCuFYbOtF5XS61/ER4sseCIwEsdgDgZEgFnsgMBKsVWdPBwdoDm0vms+NObdVqWHPJC6S5GMufbF6tfFJeexhr6Kak4asRMrD6DIs9zYPusMmo/M6fVgVQLLZzWuxqRFf+ZR03lIjufh06gfC3bHurTo665R6K2x+5BTTvR2bikkr5UZpz6SmBnsvLT2YssfA2R06U2ct9bY6JbgsvGtmcZbC/WQeZzvkiccvzDkfIlgN7MfoGDk6Ube3hnICLLo+JJzEIOLLHgiMBLHYA4GRYL3kFTCUyy57VgVOmaQXstiT4Znz7lJSNGTKEvPaJCPGO685am+iJrri2EMAnltcTStDD0O9pRhT9R4j09CcRN9mVyekq1dMJY0yqUc8RlVj3JzKuFywETuZ9cjwOvG5nvkO6h7zx7KNmfd+m5H3W7V9TirTmEhlaMRsW07YFOkLW045rZrGdseYzN6AmuaKRfee9ZlNpGTJ0/cjFz9zVBhifCAQiMUeCIwEsdgDgZFgvVFvBwl1j8xvBQyYoZQ3vnWukQKyu9SktCv5Q5Y7IJMCeaiRntqZIbR0ppX58b8vOu8OlWt9Sp6eDXXe1n6yWrZDnVGbWndzrHqqDsn3ptFsg7nqdD7IJlXVfu9gb5dzxNHzk3suSdef7ShLR3dYkF9qJebGlm60lYfLuvJEcrgVlPSMI/P0uVe8v5Eho2R9vtE9Et4zyhGIDiC+7IHASBCLPRAYCdZrejM7imxqxcziSBgGIuMAiF3IFzUsEqpFispmFGlVt+KGR+KdesZNWLobMgfCi+dzsZc40SynCuQIJHZdOJsrYg7ywpkRfb3JVifSZqRKRxDS4wZkAoxmuKxl6VzFeK7Xcy07/rKyx5lOprJSJpXTfhHbRinkgwXluu69fTyPwns4SO6hhCP7x1fTNljlqfX9YDVV36vl803pYLCflb7sZnaTmf2OmX3ZzL5kZm8wsy0z+7iZPbL8f/PyLQUCgZPCqmL8rwL4o5TS3VikgvoSgPcAeDCldBeAB5fngUDgBYpVsri+BMAPAPjnAJBS+haAb5nZWwG8cVntAwA+CeDd+dYSjoSkTIBIK7KeE0FnbrvZt0Fb69XZ4VzwBe226q66Ui4zeDeaxVuNReE2RUlwolgtxBPct8t81FNXaNdX8zrRnLSDboPi4SUiPnszsvee7g67jLrqDTjk6aiZdxkbEoBCugB7p/WeET2Mes9PajklgZNE90YefM0PY+Y7KLaPz7KqmFMTOlfNgPcl4INk+DGpttLQyzRXK9LyQrOr46B7OYBnAfw3M/uMmf3XZermsymlZ5Z1vopFttdAIPACxSqLvQDwPQB+LaX0WgBfh4jsKaWEAa9cM7vfzM6b2fmvf23vuCqBQGANWGWxPwXgqZTSQ8vz38Fi8V80s1sAYPn/znEXp5QeSCndm1K698YXxx5eIHBSWCU/+1fN7Ctm9sqU0sNY5GT/4vLfOwC8b/n/Ry7b1sHBEQd328uZxId+WC0pQJMN0lfFSOLURA0VY654TiWkKaTYdCXjd3wPpPLVT/t6u7td31PxuMpQnPvop0w11vsnYmpqC3K9Ix2y2hAdlb3T1I2QBtIy2aK6bZEC20sJzZ5rGW9A9kDrpZeqaN9ln2ZExltU1EHlZ67ht4L09B6ZI+np9b4q3DRG3beg5vdy20mZyD+3L8JmWxliMbwFQ4McNr2tamf/1wB+08xuAPA4gH+BhVTwYTN7J4AnAbx9xbYCgcAJYKXFnlL6LIB7jyl687UdTiAQuF5YrwddIi+sHo95d1yI+9ukJNmpYROMiPsk29QiMHLNmkjRlLudu1bvN9cem0vU7JThZhtqI4cy5+ImPbSeAL07lPloZyTGz4SDjs1+s2GzE6dd6gX1sKmJRV8hjaiGvBLltKTOK828S6pS1YpagwGoludMnf5meHbUXOoCmzLPk+ejpzXRWDheqdbcXoRiwxt163ZR9+BqPegCgcC3P2KxBwIjQSz2QGAkWKvOXn/rm/jCE48C6JMuMKpe1Fun1Dh3Qolcaim0SNXtEqx7kplF9LOtnkJIIDbKKZkAe2mHiaCjlXAnNhcW0ndVDIQ1qb8sK32yv1EwOQjp5fW+kFdceLI7FhfWCfOkF918zEXZnJJ7ayU86ezrWT/NHcg9k94/EbvknBTdhvYf6kfFvZeOdW9iKB9yK/Yvjrhr9P1zHfi+564qc9tnzJmVOFFTfzXz0GvKZnonmg3/nj59cZFL+htfH15X8WUPBEaCWOyBwEhgC7f2NXVm9iwWDjjfCeBv1tbx8XghjAGIcShiHB7Pdxy3p5RuPq5grYv9qFOz8yml45x0RjWGGEeMY53jCDE+EBgJYrEHAiPBSS32B06oX8YLYQxAjEMR4/C4ZuM4EZ09EAisHyHGBwIjwVoXu5ndZ2YPm9mjZrY2Nloz+3Uz2zGzz9Nva6fCNrOXmdknzOyLZvYFM3vXSYzFzCZm9mdm9rnlOH5h+fvLzeyh5fP50JK/4LrDzE4v+Q0/dlLjMLMnzOwvzeyzZnZ++dtJvCPXjbZ9bYvdzE4D+C8A/imAVwH4CTN71Zq6/w0A98lvJ0GF3QL42ZTSqwC8HsBPL+dg3WP5JoA3pZReA+AeAPeZ2esB/CKAX04pvQLAHoB3XudxHOJdWNCTH+KkxvGDKaV7yNR1Eu/I9aNtTymt5R+ANwD4Yzp/L4D3rrH/OwB8ns4fBnDL8vgWAA+vayw0ho8AeMtJjgXAdwD4cwDfi4XzRnHc87qO/d+6fIHfBOBjAOyExvEEgO+U39b6XAC8BMD/w3Iv7VqPY51i/DaAr9D5U8vfTgonSoVtZncAeC2Ah05iLEvR+bNYEIV+HMBjAJ5LKR1GZazr+fwKgJ9DR5720hMaRwLwJ2b2aTO7f/nbup/LdaVtjw065KmwrwfM7MUAfhfAz6SUXFjcusaSUrqUUroHiy/r6wDcfb37VJjZjwLYSSl9et19H4PvTyl9DxZq5k+b2Q9w4Zqey1XRtl8O61zsFwC8jM5vXf52UliJCvtaw8xKLBb6b6aUfu8kxwIAKaXnAHwCC3H5JjM7jM1cx/P5PgA/ZmZPAPggFqL8r57AOJBSurD8fwfA72PxB3Ddz+WqaNsvh3Uu9k8BuGu503oDgB8H8NE19q/4KBYU2MCKVNhXCzMzAO8H8KWU0i+d1FjM7GYzu2l5XGGxb/AlLBb929Y1jpTSe1NKt6aU7sDiffhfKaWfWvc4zOxGM5seHgP4IQCfx5qfS0rpqwC+YmavXP50SNt+bcZxvTc+ZKPhhwH8FRb64b9fY7+/BeAZLDgtnsJid/elWGwMPQLgfwLYWsM4vh8LEewvAHx2+e+H1z0WAP8IwGeW4/g8gP+w/P27APwZgEcB/DaAF63xGb0RwMdOYhzL/j63/PeFw3fzhN6RewCcXz6bPwCwea3GER50gcBIEBt0gcBIEIs9EBgJYrEHAiNBLPZAYCSIxR4IjASx2AOBkSAWeyAwEsRiDwRGgv8PZIlY8pxM7eUAAAAASUVORK5CYII=\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]}]} \ No newline at end of file From 68cdb62e83156b2aef1b7f8930f7e821be8a299a Mon Sep 17 00:00:00 2001 From: wakahide23 Date: Fri, 8 Oct 2021 07:00:13 +0000 Subject: [PATCH 11/66] test2 --- recognition/s4633139/UNet.ipynb | 1 + 1 file changed, 1 insertion(+) create mode 100644 recognition/s4633139/UNet.ipynb diff --git a/recognition/s4633139/UNet.ipynb b/recognition/s4633139/UNet.ipynb new file mode 100644 index 0000000000..21cf81919d --- /dev/null +++ b/recognition/s4633139/UNet.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"UNet.ipynb","provenance":[],"collapsed_sections":[],"mount_file_id":"1sn2UhFSOZZoyAj5L2RL8YS_7_cthikKn","authorship_tag":"ABX9TyNdbWoFJtjjLepg4aq3aQR1"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","metadata":{"id":"vXc844rDosCM"},"source":["# UNET"]},{"cell_type":"code","metadata":{"id":"vlhQO-g6Y2ah"},"source":["import os\n","from PIL import Image\n","import matplotlib.pyplot as plt\n","import glob\n","\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","\n","import torch.utils.data\n","from torchvision import datasets, transforms"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"WuSova9HHAzc","colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"status":"ok","timestamp":1633585012559,"user_tz":-600,"elapsed":280,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"376c0ef2-eca7-473b-fa7a-3f52df4964e7"},"source":["#define dataset for img and mask\n","file_dir = \"/content/drive/MyDrive/ColabGitHub/s4633131-ISICs-UNET/ISIC2018_Task1-2_Training_Data\"\n","os.chdir(file_dir)\n","\n","img_path = './ISIC2018_Task1-2_Training_Input_x2'\n","mask_path = './ISIC2018_Task1_Training_GroundTruth_x2'\n","\n","imgs = [file for file in sorted(os.listdir(img_path)) if file.endswith('.jpg')]\n","masks = [file for file in sorted(os.listdir(mask_path)) if file.endswith('.png')]\n","\n","imgs[0]"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'ISIC_0000000.jpg'"]},"metadata":{},"execution_count":24}]},{"cell_type":"code","metadata":{"id":"JhoipyIyXvBB"},"source":["from torch.utils.data import Dataset\n","from PIL import Image\n","\n","class Unet_dataset(Dataset):\n"," def __init__(self,\n"," img_dir = './ISIC2018_Task1-2_Training_Input_x2',\n"," mask_dir = './ISIC2018_Task1_Training_GroundTruth_x2', \n"," img_transforms=None,\n"," mask_transforms= None):\n"," \n"," self.img_dir = img_dir\n"," self.mask_dir = mask_dir\n"," self.img_transforms = img_transforms\n"," self.mask_transforms = mask_transforms\n","\n"," self.imgs = [file for file in sorted(os.listdir(self.img_dir)) if file.endswith('.jpg')]\n"," self.masks = [file for file in sorted(os.listdir(self.mask_dir)) if file.endswith('.png')]\n","\n"," #meke dataloader\n"," def load_img(self, idx):\n"," img_path = os.path.join(self.img_dir, self.imgs[idx])\n"," img = Image.open(img_path).convert('RGB')\n"," return img\n","\n"," def load_mask(self, idx):\n"," mask_path = os.path.join(self.mask_dir, self.masks[idx])\n"," mask = Image.open(mask_path).convert('L')\n"," return mask\n","\n"," def __getitem__(self, idx):\n"," img = self.load_img(idx)\n"," mask = self.load_mask(idx)\n","\n"," if self.img_transforms is not None:\n"," img = self.img_transforms(img)\n","\n"," if self.mask_transforms is not None:\n"," mask = self.mask_transforms(mask)\n","\n"," return img, mask\n","\n"," def __len__(self):\n"," return len(self.imgs)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"YFzDGbXcE_p8"},"source":["#img_path = os.path.join(root, \"ISIC2018_Task1-2_Training_Input_x2\", imgs[idx])\n","import torchvision.transforms as transforms\n","img_tfs = transforms.Compose([\n"," transforms.Resize((64,64)),\n"," transforms.ToTensor(),\n"," transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n"," ]\n"," )\n","\n","mask_tfs = transforms.Compose([\n"," transforms.Resize((64,64)),\n"," transforms.ToTensor(),\n"," ]\n"," )"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":302},"id":"zx1QLG_3l_nB","executionInfo":{"status":"ok","timestamp":1633587996487,"user_tz":-600,"elapsed":1209,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"3285e87c-32b1-4864-af1c-88564e2a97f6"},"source":["dataset = Unet_dataset(img_transforms=img_tfs, mask_transforms=mask_tfs)\n","img, mask = dataset.__getitem__(20)\n","\n","import matplotlib.pyplot as plt\n","plt.imshow(img.permute(1,2,0))"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":94},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YPWydbZkaCl5","executionInfo":{"status":"ok","timestamp":1633587620112,"user_tz":-600,"elapsed":282,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"70f9f721-128f-473a-e4b1-8213957000d3"},"source":[""],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(64, 64, 1)"]},"metadata":{},"execution_count":86}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6wyVgya8TX9x","executionInfo":{"status":"ok","timestamp":1633585081765,"user_tz":-600,"elapsed":380,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"719db019-13e3-406b-c030-9aecd3c347a4"},"source":["img_transforms(Image.open(os.path.join(file_dir, img_path, imgs[0])).convert('RGB')).shape"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["torch.Size([3, 64, 64])"]},"metadata":{},"execution_count":34}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0gENtcRuWWk5","executionInfo":{"status":"ok","timestamp":1633585053171,"user_tz":-600,"elapsed":1373,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"50fbee1c-9211-4b03-df2e-1252937da309"},"source":["img_transforms"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Compose(\n"," Resize(size=(64, 64), interpolation=bilinear, max_size=None, antialias=None)\n"," ToTensor()\n"," Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",")"]},"metadata":{},"execution_count":31}]},{"cell_type":"code","metadata":{"id":"xItKH6hLVkE9"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"VFiXmwWjSeXi"},"source":["import torch\n","import torch.nn as nn\n","import torchvision.transforms.functional as TF"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"nzGxgjkBeFXR"},"source":["class DConv(nn.Module):\n"," def __init__(self, in_channels, out_channels):\n"," super(DConv, self).__init__()\n"," self.conv = nn.Sequential(\n"," nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.ReLU(inplace=True),\n"," nn.Conv2d(out_channels, out_channels, kernel_size=3, stride = 1, padding=1, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.ReLU(inplace=True),\n"," )\n","\n"," def forward(self, x):\n"," return self.conv(x)\n","\n","\n","class Unet(nn.Module):\n"," def __init__(self, in_channels=3, out_channels=1, feature_size=[64, 128, 256, 512],):\n"," super(Unet, self).__init__()\n"," self.downsample = nn.ModuleList()\n"," self.upsample = nn.ModuleList()\n"," self.pool = nn.MaxPool2d(kernel_size=2, stride=2)\n","\n"," #Downscale part\n"," for feature in feature_size:\n"," self.downsample.append(DConv(in_channels, feature))\n"," in_channels = feature\n","\n"," #Upscale part\n"," for feature in reversed(feature_size):\n"," self.upsample.append(nn.ConvTranspose2d(feature*2, feature, kernel_size=2, stride =2))\n"," self.upsample.append(DConv(feature*2, feature))\n","\n"," self.bottleneck = DConv(feature_size[-1], feature_size[-1]*2)\n"," self.final_conv = nn.Conv2d(feature_size[0], out_channels, kernel_size=1)\n","\n","\n"," def forward(self, x):\n"," skip_connections = []\n","\n"," for down in self.downsample:\n"," x = down(x)\n"," skip_connections.append(x)\n"," x = self.pool(x)\n","\n"," x = self.bottleneck(x)\n"," skip_connections = skip_connections[: : -1]\n","\n"," for idx in range(0, len(self.upsample), 2):\n"," x = self.upsample[idx](x)\n"," skip_connection = skip_connections[idx//2]\n","\n"," if x.shape != skip_connection.shape:\n"," x = TF.resize(x, size=skip_connection.shape[2:])\n","\n"," concatnate_skip = torch.cat((skip_connection, x), dim=1)\n"," x = self.upsample[idx+1](concatnate_skip)\n"," \n"," return self.final_conv(x)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"dNMXUs-qViYi"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"EhJChHsfViUi"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"m7ZMGrmOViRp"},"source":[""],"execution_count":null,"outputs":[]}]} \ No newline at end of file From 1a3384b2c1a29a8847b827cd776df56be32ef1c3 Mon Sep 17 00:00:00 2001 From: wakahide23 Date: Fri, 15 Oct 2021 04:59:26 +0000 Subject: [PATCH 12/66] upload colab.file from Colab --- recognition/s4633139/UNet.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/recognition/s4633139/UNet.ipynb b/recognition/s4633139/UNet.ipynb index 21cf81919d..88301ea97b 100644 --- a/recognition/s4633139/UNet.ipynb +++ b/recognition/s4633139/UNet.ipynb @@ -1 +1 @@ -{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"UNet.ipynb","provenance":[],"collapsed_sections":[],"mount_file_id":"1sn2UhFSOZZoyAj5L2RL8YS_7_cthikKn","authorship_tag":"ABX9TyNdbWoFJtjjLepg4aq3aQR1"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","metadata":{"id":"vXc844rDosCM"},"source":["# UNET"]},{"cell_type":"code","metadata":{"id":"vlhQO-g6Y2ah"},"source":["import os\n","from PIL import Image\n","import matplotlib.pyplot as plt\n","import glob\n","\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","\n","import torch.utils.data\n","from torchvision import datasets, transforms"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"WuSova9HHAzc","colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"status":"ok","timestamp":1633585012559,"user_tz":-600,"elapsed":280,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"376c0ef2-eca7-473b-fa7a-3f52df4964e7"},"source":["#define dataset for img and mask\n","file_dir = \"/content/drive/MyDrive/ColabGitHub/s4633131-ISICs-UNET/ISIC2018_Task1-2_Training_Data\"\n","os.chdir(file_dir)\n","\n","img_path = './ISIC2018_Task1-2_Training_Input_x2'\n","mask_path = './ISIC2018_Task1_Training_GroundTruth_x2'\n","\n","imgs = [file for file in sorted(os.listdir(img_path)) if file.endswith('.jpg')]\n","masks = [file for file in sorted(os.listdir(mask_path)) if file.endswith('.png')]\n","\n","imgs[0]"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'ISIC_0000000.jpg'"]},"metadata":{},"execution_count":24}]},{"cell_type":"code","metadata":{"id":"JhoipyIyXvBB"},"source":["from torch.utils.data import Dataset\n","from PIL import Image\n","\n","class Unet_dataset(Dataset):\n"," def __init__(self,\n"," img_dir = './ISIC2018_Task1-2_Training_Input_x2',\n"," mask_dir = './ISIC2018_Task1_Training_GroundTruth_x2', \n"," img_transforms=None,\n"," mask_transforms= None):\n"," \n"," self.img_dir = img_dir\n"," self.mask_dir = mask_dir\n"," self.img_transforms = img_transforms\n"," self.mask_transforms = mask_transforms\n","\n"," self.imgs = [file for file in sorted(os.listdir(self.img_dir)) if file.endswith('.jpg')]\n"," self.masks = [file for file in sorted(os.listdir(self.mask_dir)) if file.endswith('.png')]\n","\n"," #meke dataloader\n"," def load_img(self, idx):\n"," img_path = os.path.join(self.img_dir, self.imgs[idx])\n"," img = Image.open(img_path).convert('RGB')\n"," return img\n","\n"," def load_mask(self, idx):\n"," mask_path = os.path.join(self.mask_dir, self.masks[idx])\n"," mask = Image.open(mask_path).convert('L')\n"," return mask\n","\n"," def __getitem__(self, idx):\n"," img = self.load_img(idx)\n"," mask = self.load_mask(idx)\n","\n"," if self.img_transforms is not None:\n"," img = self.img_transforms(img)\n","\n"," if self.mask_transforms is not None:\n"," mask = self.mask_transforms(mask)\n","\n"," return img, mask\n","\n"," def __len__(self):\n"," return len(self.imgs)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"YFzDGbXcE_p8"},"source":["#img_path = os.path.join(root, \"ISIC2018_Task1-2_Training_Input_x2\", imgs[idx])\n","import torchvision.transforms as transforms\n","img_tfs = transforms.Compose([\n"," transforms.Resize((64,64)),\n"," transforms.ToTensor(),\n"," transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n"," ]\n"," )\n","\n","mask_tfs = transforms.Compose([\n"," transforms.Resize((64,64)),\n"," transforms.ToTensor(),\n"," ]\n"," )"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":302},"id":"zx1QLG_3l_nB","executionInfo":{"status":"ok","timestamp":1633587996487,"user_tz":-600,"elapsed":1209,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"3285e87c-32b1-4864-af1c-88564e2a97f6"},"source":["dataset = Unet_dataset(img_transforms=img_tfs, mask_transforms=mask_tfs)\n","img, mask = dataset.__getitem__(20)\n","\n","import matplotlib.pyplot as plt\n","plt.imshow(img.permute(1,2,0))"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":94},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YPWydbZkaCl5","executionInfo":{"status":"ok","timestamp":1633587620112,"user_tz":-600,"elapsed":282,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"70f9f721-128f-473a-e4b1-8213957000d3"},"source":[""],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(64, 64, 1)"]},"metadata":{},"execution_count":86}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6wyVgya8TX9x","executionInfo":{"status":"ok","timestamp":1633585081765,"user_tz":-600,"elapsed":380,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"719db019-13e3-406b-c030-9aecd3c347a4"},"source":["img_transforms(Image.open(os.path.join(file_dir, img_path, imgs[0])).convert('RGB')).shape"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["torch.Size([3, 64, 64])"]},"metadata":{},"execution_count":34}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0gENtcRuWWk5","executionInfo":{"status":"ok","timestamp":1633585053171,"user_tz":-600,"elapsed":1373,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"50fbee1c-9211-4b03-df2e-1252937da309"},"source":["img_transforms"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Compose(\n"," Resize(size=(64, 64), interpolation=bilinear, max_size=None, antialias=None)\n"," ToTensor()\n"," Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",")"]},"metadata":{},"execution_count":31}]},{"cell_type":"code","metadata":{"id":"xItKH6hLVkE9"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"VFiXmwWjSeXi"},"source":["import torch\n","import torch.nn as nn\n","import torchvision.transforms.functional as TF"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"nzGxgjkBeFXR"},"source":["class DConv(nn.Module):\n"," def __init__(self, in_channels, out_channels):\n"," super(DConv, self).__init__()\n"," self.conv = nn.Sequential(\n"," nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.ReLU(inplace=True),\n"," nn.Conv2d(out_channels, out_channels, kernel_size=3, stride = 1, padding=1, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.ReLU(inplace=True),\n"," )\n","\n"," def forward(self, x):\n"," return self.conv(x)\n","\n","\n","class Unet(nn.Module):\n"," def __init__(self, in_channels=3, out_channels=1, feature_size=[64, 128, 256, 512],):\n"," super(Unet, self).__init__()\n"," self.downsample = nn.ModuleList()\n"," self.upsample = nn.ModuleList()\n"," self.pool = nn.MaxPool2d(kernel_size=2, stride=2)\n","\n"," #Downscale part\n"," for feature in feature_size:\n"," self.downsample.append(DConv(in_channels, feature))\n"," in_channels = feature\n","\n"," #Upscale part\n"," for feature in reversed(feature_size):\n"," self.upsample.append(nn.ConvTranspose2d(feature*2, feature, kernel_size=2, stride =2))\n"," self.upsample.append(DConv(feature*2, feature))\n","\n"," self.bottleneck = DConv(feature_size[-1], feature_size[-1]*2)\n"," self.final_conv = nn.Conv2d(feature_size[0], out_channels, kernel_size=1)\n","\n","\n"," def forward(self, x):\n"," skip_connections = []\n","\n"," for down in self.downsample:\n"," x = down(x)\n"," skip_connections.append(x)\n"," x = self.pool(x)\n","\n"," x = self.bottleneck(x)\n"," skip_connections = skip_connections[: : -1]\n","\n"," for idx in range(0, len(self.upsample), 2):\n"," x = self.upsample[idx](x)\n"," skip_connection = skip_connections[idx//2]\n","\n"," if x.shape != skip_connection.shape:\n"," x = TF.resize(x, size=skip_connection.shape[2:])\n","\n"," concatnate_skip = torch.cat((skip_connection, x), dim=1)\n"," x = self.upsample[idx+1](concatnate_skip)\n"," \n"," return self.final_conv(x)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"dNMXUs-qViYi"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"EhJChHsfViUi"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"m7ZMGrmOViRp"},"source":[""],"execution_count":null,"outputs":[]}]} \ No newline at end of file +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"UNet.ipynb","provenance":[],"collapsed_sections":[],"mount_file_id":"1UdH3PIDr4uawfSNGUl8ntSrq2Lbo_uQQ","authorship_tag":"ABX9TyNiaLFhg+HbcKfrWJAzQit8"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"vlhQO-g6Y2ah","executionInfo":{"status":"ok","timestamp":1634273373551,"user_tz":-600,"elapsed":306,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["import os\n","from PIL import Image\n","import matplotlib.pyplot as plt\n","import glob\n","\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","\n","import torch.utils.data\n","from torchvision import datasets, transforms"],"execution_count":60,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"7vcUuTeHs59d"},"source":["# Data preparation\n","\n","\n","1. get path\n","2. dataloader for img and mask\n","1. split into test and validation\n","\n","\n","\n"]},{"cell_type":"code","metadata":{"id":"WuSova9HHAzc","executionInfo":{"status":"ok","timestamp":1634270416375,"user_tz":-600,"elapsed":13732,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["#define dataset for img and mask\n","file_dir = \"/content/drive/MyDrive/ColabGitHub/ISIC2018_Task1-2_Training_Data\"\n","os.chdir(file_dir)\n","\n","img_path = './ISIC2018_Task1-2_Training_Input_x2'\n","mask_path = './ISIC2018_Task1_Training_GroundTruth_x2'\n","\n","imgs = [file for file in sorted(os.listdir(img_path)) if file.endswith('.jpg')]\n","masks = [file for file in sorted(os.listdir(mask_path)) if file.endswith('.png')]"],"execution_count":2,"outputs":[]},{"cell_type":"code","metadata":{"id":"StdLOrNGt3Id","executionInfo":{"status":"ok","timestamp":1634270422062,"user_tz":-600,"elapsed":341,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["from torchvision import datasets\n","from torchvision.datasets import ImageFolder\n","from torch.utils.data import dataloader, random_split, Subset\n","from torchvision.transforms import transforms\n"],"execution_count":3,"outputs":[]},{"cell_type":"code","metadata":{"id":"JhoipyIyXvBB","executionInfo":{"status":"ok","timestamp":1634270424835,"user_tz":-600,"elapsed":1106,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["from torch.utils.data import Dataset\n","from PIL import Image\n","\n","class Unet_dataset(Dataset):\n"," def __init__(self,\n"," img_dir = './ISIC2018_Task1-2_Training_Input_x2',\n"," mask_dir = './ISIC2018_Task1_Training_GroundTruth_x2', \n"," img_transforms=None,\n"," mask_transforms= None,\n"," train_ratio = 0.5):\n"," \n"," self.img_dir = img_dir\n"," self.mask_dir = mask_dir\n"," self.img_transforms = img_transforms\n"," self.mask_transforms = mask_transforms\n","\n"," self.imgs = [file for file in sorted(os.listdir(self.img_dir)) if file.endswith('.jpg')]\n"," self.masks = [file for file in sorted(os.listdir(self.mask_dir)) if file.endswith('.png')]\n","\n"," self.train_ratio = train_ratio\n","\n"," #meke dataloader\n"," def load_data(self, idx):\n"," img_path = os.path.join(self.img_dir, self.imgs[idx])\n"," mask_path = os.path.join(self.mask_dir, self.masks[idx])\n"," img = Image.open(img_path).convert('RGB')\n"," mask = Image.open(mask_path).convert('L')\n"," return img, mask\n","\n"," def __getitem__(self, idx):\n"," img, mask = self.load_data(idx)\n"," #transformation\n"," if self.img_transforms is not None:\n"," img = self.img_transforms(img)\n"," if self.mask_transforms is not None:\n"," mask = self.mask_transforms(mask)\n"," return img, mask\n","\n"," def __len__(self):\n"," return len(self.imgs)"],"execution_count":4,"outputs":[]},{"cell_type":"code","metadata":{"id":"vpwsi1o-xnYw","executionInfo":{"status":"ok","timestamp":1634270429179,"user_tz":-600,"elapsed":316,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["from torch.utils.data import DataLoader\n","import torchvision.transforms as transforms\n","\n","img_tfs = transforms.Compose([\n"," transforms.Resize((64,64)),\n"," transforms.ToTensor(),\n"," transforms.Normalize(mean=[0, 0, 0], std=[1, 1, 1]),\n"," ]\n"," )\n","\n","mask_tfs = transforms.Compose([\n"," transforms.Resize((64,64)),\n"," transforms.ToTensor(),\n"," ]\n"," )\n","\n","dataset = Unet_dataset(img_transforms=img_tfs, mask_transforms=mask_tfs)\n","\n","#shuffle index\n","sample_size = len(imgs)\n","train_size = int(sample_size*0.8)\n","test_size = sample_size - train_size\n","\n","#train and test set\n","train_set, test_set = torch.utils.data.random_split(dataset, [train_size, test_size], generator=torch.Generator().manual_seed(123))\n","\n","#dataloader\n","train_loader= DataLoader(train_set, batch_size=64, shuffle = True)\n","test_loader= DataLoader(test_set, batch_size=64, shuffle=False)"],"execution_count":5,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"cN3J5wTpx_HX"},"source":["# Unet"]},{"cell_type":"code","metadata":{"id":"VFiXmwWjSeXi","executionInfo":{"status":"ok","timestamp":1634270481930,"user_tz":-600,"elapsed":20,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["import torch\n","import torch.nn as nn"],"execution_count":8,"outputs":[]},{"cell_type":"code","metadata":{"id":"nzGxgjkBeFXR","executionInfo":{"status":"ok","timestamp":1634270481931,"user_tz":-600,"elapsed":19,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["class twotimes_conv(nn.Module):\n"," def __init__(self, in_channels, out_channels):\n"," super(twotimes_conv, self).__init__()\n"," self.conv = nn.Sequential(\n"," nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.ReLU(inplace=True),\n"," nn.Conv2d(out_channels, out_channels, kernel_size=3, stride = 1, padding=1, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.ReLU(inplace=True),\n"," )\n","\n"," def forward(self, x):\n"," return self.conv(x)\n","\n","\n","class Unet(nn.Module):\n"," def __init__(self, in_channels=3, out_channels=1, feature_size=None):\n"," super(Unet, self).__init__()\n"," self.downsample = nn.ModuleList()\n"," self.upsample = nn.ModuleList()\n"," self.pool = nn.MaxPool2d(kernel_size=2, stride=2)\n"," self.feature_size = None\n","\n"," #Downsample frame\n"," for feature in feature_size:\n"," self.downsample.append(twotimes_conv(in_channels, feature))\n"," in_channels = feature\n","\n"," #Upsample frame\n"," for feature in reversed(feature_size):\n"," #Deconvolution\n"," self.upsample.append(nn.ConvTranspose2d(feature*2, feature, kernel_size=2, stride =2))\n"," #Convolution\n"," self.upsample.append(twotimes_conv(feature*2, feature))\n","\n"," #Bottleneck frame\n"," self.bottleneck = twotimes_conv(feature_size[-1], feature_size[-1]*2)\n"," self.final_conv = nn.Conv2d(feature_size[0], out_channels, kernel_size=1)\n","\n","\n"," def forward(self, x):\n"," skip_connections = []\n","\n"," #Downsampling steps\n"," for down_i in self.downsample:\n"," x = down_i(x)\n"," #preserve location\n"," skip_connections.append(x)\n"," x = self.pool(x)\n","\n"," #Bottle neck part\n"," x = self.bottleneck(x)\n"," skip_connections = skip_connections[: : -1]\n","\n"," #Upsampling steps\n"," for idx in range(0, len(self.upsample), 2):\n"," x = self.upsample[idx](x)\n"," skip_connection = skip_connections[idx//2]\n","\n"," if x.shape != skip_connection.shape:\n"," x = torchvision.transforms.resize(x, size=skip_connection.shape[2:])\n"," \n"," #where + what\n"," concatnate_skip = torch.cat((skip_connection, x), dim=1)\n"," x = self.upsample[idx+1](concatnate_skip)\n"," \n"," x = self.final_conv(x)\n"," output = F.sigmoid(x)\n"," \n"," return output"],"execution_count":9,"outputs":[]},{"cell_type":"code","metadata":{"id":"oDhAF_WYyeJm","executionInfo":{"status":"ok","timestamp":1634271792485,"user_tz":-600,"elapsed":490,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["#dice coef\n","def dice_coef(pred, target):\n"," batch_size = len(pred)\n"," somooth = 1.\n","\n"," pred_flat = pred.view(batch_size, -1)\n"," target_flat = target.view(batch_size, -1)\n","\n"," intersection = (pred_flat*target_flat).sum()\n"," dice_coef = (2.*intersection+somooth)/(pred_flat.sum()+target_flat.sum()+somooth)\n"," return dice_coef\n","\n","\n","#loss\n","def dice_loss(pred, target):\n"," dice_loss = 1 - dice_coef(pred, target)\n"," return dice_loss"],"execution_count":52,"outputs":[]},{"cell_type":"code","metadata":{"id":"dNMXUs-qViYi","executionInfo":{"status":"ok","timestamp":1634270491165,"user_tz":-600,"elapsed":457,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["import torch.optim as optim\n","\n","#set parameters\n","feature_size=[64, 128, 256, 512]\n","optimizer = optim.Adam(model.parameters(), lr=0.001)\n","EPOCHS = 30\n","\n","model = Unet(feature_size=feature_size)"],"execution_count":12,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JrGpP3KMPzpe","executionInfo":{"status":"ok","timestamp":1634205931646,"user_tz":-600,"elapsed":24149591,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"70b6a5b8-887c-4489-cc8a-f6718ec3dd50"},"source":["from tqdm import tqdm\n","\n","TRAIN_LOSS = []\n","TRAIN_DICE = []\n","TEST_LOSS =[]\n","TEST_DICE = []\n","\n","for epoch in range(1, EPOCHS+1):\n"," print('EPOCH {}/{}'.format(epoch, EPOCHS))\n"," running_loss = 0\n"," running_dicecoef = 0\n"," running_loss_test = 0\n"," running_dicecoef_test = 0\n"," BATCH_NUM = len(train_loader)\n"," BATCH_NUM_TEST = len(test_loader)\n","\n"," #training\n"," with tqdm(train_loader, unit='batch') as tbatch:\n"," for batch_idx, (x, y) in enumerate(tbatch):\n"," tbatch.set_description(f'Batch: {batch_idx}')\n","\n"," optimizer.zero_grad()\n"," output = model(x)\n"," loss = dice_loss(output, y)\n"," dicecoef = dice_coef(output, y)\n"," loss.backward()\n"," optimizer.step()\n","\n"," running_loss += loss.item()\n"," running_dicecoef += dicecoef.item()\n","\n"," tbatch.set_postfix(loss=loss.item(), dice_coef=dicecoef.item())\n","\n"," epoch_loss = running_loss/BATCH_NUM\n"," epoch_dicecoef = running_dicecoef/BATCH_NUM\n"," TRAIN_LOSS.append(epoch_loss)\n"," TRAIN_DICE.append(epoch_dicecoef)\n","\n"," #test\n"," with tqdm(test_loader, unit='batch') as tsbatch:\n"," for batch_idx, (x, y) in enumerate(tsbatch):\n"," tsbatch.set_description(f'Batch: {batch_idx}')\n"," output_test = model(x)\n"," loss_test = dice_loss(output_test, y)\n"," dicecoef_test = dice_coef(output_test, y)\n"," tsbatch.set_postfix(loss=loss_test.item(), dice_coef=dicecoef_test.item())\n","\n"," running_loss_test += loss_test.item()\n"," running_dicecoef_test += dicecoef_test.item()\n","\n"," TEST_LOSS.append(running_loss_test/BATCH_NUM_TEST)\n"," TEST_DICE.append(running_dicecoef_test/BATCH_NUM_TEST)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["EPOCH 1/30\n"]},{"output_type":"stream","name":"stderr","text":["Batch: 0: 0%| | 0/33 [00:04"]},"metadata":{},"execution_count":40},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"4Rh-uq05TG-A"},"source":["# Model Evaluation"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":279},"id":"7ZBk4w3xIA8L","executionInfo":{"status":"ok","timestamp":1634206091827,"user_tz":-600,"elapsed":419,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"a2ff558d-5b76-4ebd-8b09-2514182ad30b"},"source":["import matplotlib.pyplot as plt\n","import numpy as np\n","\n","X = np.arange(1,EPOCHS+1)\n","\n","plt.plot(X, TRAIN_LOSS, marker='.', markersize=10, label='train')\n","plt.plot(X, TEST_LOSS, marker='.', markersize=10, label='test')\n","plt.xlabel('Epochs')\n","plt.ylabel('Dice Loss')\n","plt.xticks(X)\n","plt.legend()\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":279},"id":"Y80RZXzKRPpc","executionInfo":{"status":"ok","timestamp":1634206126932,"user_tz":-600,"elapsed":1066,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"8e714aaa-5868-4270-f7ca-54a5b827fb4d"},"source":["plt.plot(X, TRAIN_DICE, marker='.', markersize=10, label='train')\n","plt.plot(X, TEST_DICE, marker='.', markersize=10, label='test')\n","plt.xlabel('Epochs')\n","plt.ylabel('Dice Coeffeciency')\n","plt.xticks(X)\n","plt.legend()\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":373},"id":"V3Ahm7ecTST4","executionInfo":{"status":"ok","timestamp":1634270514838,"user_tz":-600,"elapsed":8784,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"44a27d39-7951-47f3-8484-132efc3bba3a"},"source":["#load model\n","new_model = Unet(feature_size=feature_size)\n","\n","filename = \"Unet_ISIC1.pth\"\n","checkpoint = torch.load(filename)\n","\n","new_model.load_state_dict(checkpoint)\n","\n","p = new_model(x)[0]\n","plt.imshow(p.detach().squeeze(), cmap='gray')"],"execution_count":13,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /pytorch/c10/core/TensorImpl.h:1156.)\n"," return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n","/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:1805: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n"," warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n"]},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":13},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"BZYl7y1XIJFr"},"source":[""],"execution_count":null,"outputs":[]}]} \ No newline at end of file From a6b3311a8eaad5f6be1b762f7e0b9eefcaccd340 Mon Sep 17 00:00:00 2001 From: wakahide23 Date: Sun, 17 Oct 2021 06:46:49 +0000 Subject: [PATCH 13/66] Improved Unet file from Colab --- recognition/s4633139/IUNet.ipynb | 1 + 1 file changed, 1 insertion(+) create mode 100644 recognition/s4633139/IUNet.ipynb diff --git a/recognition/s4633139/IUNet.ipynb b/recognition/s4633139/IUNet.ipynb new file mode 100644 index 0000000000..26f8f85ff1 --- /dev/null +++ b/recognition/s4633139/IUNet.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"IUNet.ipynb","provenance":[],"collapsed_sections":[],"mount_file_id":"1UdH3PIDr4uawfSNGUl8ntSrq2Lbo_uQQ","authorship_tag":"ABX9TyPXanSKnpIqYcGKvPCydodW"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"vlhQO-g6Y2ah","executionInfo":{"status":"ok","timestamp":1634452298249,"user_tz":-600,"elapsed":13266,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["import os\n","from PIL import Image\n","import glob\n","import matplotlib.pyplot as plt\n","\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","\n","import torch.utils.data\n","from torchvision import datasets, transforms"],"execution_count":3,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"7vcUuTeHs59d"},"source":["# Data preparation\n","\n","\n","1. get path\n","2. dataloader for img and mask\n","1. split into test and validation\n","\n","\n","\n"]},{"cell_type":"code","metadata":{"id":"gPYa8ZVvXk2d","executionInfo":{"status":"ok","timestamp":1634452300911,"user_tz":-600,"elapsed":4,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["import os\n","from PIL import Image\n","import glob\n","import matplotlib.pyplot as plt\n","\n","#define dataset for img and mask\n","file_dir = \"./ISIC2018_Task1-2_Training_Data\"\n","os.chdir(file_dir)"],"execution_count":4,"outputs":[]},{"cell_type":"code","metadata":{"id":"mhX77-qIYdGT","executionInfo":{"status":"ok","timestamp":1634452302191,"user_tz":-600,"elapsed":1283,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["#path\n","img_path = './ISIC2018_Task1-2_Training_Input_x2'\n","mask_path = './ISIC2018_Task1_Training_GroundTruth_x2'\n","imgs = [file for file in sorted(os.listdir(img_path)) if file.endswith('.jpg')]\n","masks = [file for file in sorted(os.listdir(mask_path)) if file.endswith('.png')]"],"execution_count":5,"outputs":[]},{"cell_type":"code","metadata":{"id":"StdLOrNGt3Id","executionInfo":{"status":"ok","timestamp":1634452302191,"user_tz":-600,"elapsed":4,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["from torchvision import datasets\n","from torchvision.datasets import ImageFolder\n","from torch.utils.data import dataloader, random_split, Subset\n","from torchvision.transforms import transforms"],"execution_count":6,"outputs":[]},{"cell_type":"code","metadata":{"id":"JhoipyIyXvBB","executionInfo":{"status":"ok","timestamp":1634452306163,"user_tz":-600,"elapsed":17,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["from torch.utils.data import Dataset\n","from PIL import Image\n","\n","class Unet_dataset(Dataset):\n"," def __init__(self,\n"," img_dir = './ISIC2018_Task1-2_Training_Input_x2',\n"," mask_dir = './ISIC2018_Task1_Training_GroundTruth_x2', \n"," img_transforms=None,\n"," mask_transforms= None):\n"," \n"," self.img_dir = img_dir\n"," self.mask_dir = mask_dir\n"," self.img_transforms = img_transforms\n"," self.mask_transforms = mask_transforms\n","\n"," self.imgs = [file for file in sorted(os.listdir(self.img_dir)) if file.endswith('.jpg')]\n"," self.masks = [file for file in sorted(os.listdir(self.mask_dir)) if file.endswith('.png')]\n","\n"," #meke dataloader\n"," def load_data(self, idx):\n"," img_path = os.path.join(self.img_dir, self.imgs[idx])\n"," mask_path = os.path.join(self.mask_dir, self.masks[idx])\n"," img = Image.open(img_path).convert('RGB')\n"," mask = Image.open(mask_path).convert('L')\n"," return img, mask\n","\n"," def __getitem__(self, idx):\n"," img, mask = self.load_data(idx)\n"," #transformation\n"," if self.img_transforms is not None:\n"," img = self.img_transforms(img)\n"," if self.mask_transforms is not None:\n"," mask = self.mask_transforms(mask)\n"," return img, mask\n","\n"," def __len__(self):\n"," return len(self.imgs)"],"execution_count":7,"outputs":[]},{"cell_type":"code","metadata":{"id":"vpwsi1o-xnYw","executionInfo":{"status":"ok","timestamp":1634452307860,"user_tz":-600,"elapsed":5,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["from torch.utils.data import DataLoader\n","import torchvision.transforms as transforms\n","\n","\n","#transformation\n","img_tfs = transforms.Compose([\n"," transforms.Resize((128,128)),\n"," transforms.ToTensor(),\n"," transforms.Normalize(mean=[0, 0, 0], std=[1, 1, 1]),\n"," ]\n"," )\n","\n","mask_tfs = transforms.Compose([\n"," transforms.Resize((128,128)),\n"," transforms.ToTensor(),\n"," ]\n"," )\n","\n","dataset = Unet_dataset(img_transforms=img_tfs, mask_transforms=mask_tfs)\n","\n","\n","#shuffle index\n","sample_size = len(dataset.imgs)\n","train_size = int(sample_size*0.8)\n","test_size = sample_size - train_size\n","\n","#train and test set\n","train_set, test_set = torch.utils.data.random_split(dataset, [train_size, test_size], generator=torch.Generator().manual_seed(123))\n","\n","#dataloader\n","train_loader= DataLoader(train_set, batch_size=64, shuffle = True)\n","test_loader= DataLoader(test_set, batch_size=64, shuffle=False)"],"execution_count":8,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"cN3J5wTpx_HX"},"source":["# Unet"]},{"cell_type":"code","metadata":{"id":"VFiXmwWjSeXi","executionInfo":{"status":"ok","timestamp":1634452310344,"user_tz":-600,"elapsed":391,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["import torch\n","import torch.nn as nn\n","import torch.nn.functional as F"],"execution_count":9,"outputs":[]},{"cell_type":"code","metadata":{"id":"nzGxgjkBeFXR","executionInfo":{"status":"ok","timestamp":1634452312281,"user_tz":-600,"elapsed":454,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["class Context(nn.Module):\n"," def __init__(self, in_channels, out_channels):\n"," super(Context, self).__init__()\n"," self.context = nn.Sequential(\n"," nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.Dropout2d(p=0.3),\n"," nn.LeakyReLU(negative_slope=0.02, inplace=True),\n"," nn.Conv2d(in_channels, out_channels, kernel_size=3, stride = 1, padding=1, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.LeakyReLU(negative_slope=0.02, inplace=True),\n"," )\n","\n"," def forward(self, x):\n"," x = self.context(x) + x\n"," return x\n","\n","\n","class Localization(nn.Module):\n"," def __init__(self, in_channels, out_channels):\n"," super(Localization, self).__init__()\n"," self.localization = nn.Sequential(\n"," nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.LeakyReLU(negative_slope=0.02, inplace=True),\n"," nn.Conv2d(out_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.LeakyReLU(negative_slope=0.02, inplace=True),\n"," )\n"," \n"," def forward(self, x):\n"," return self.localization(x)\n","\n","\n","class Upsampling(nn.Module):\n"," def __init__(self, in_channels, out_channels):\n"," super(Upsampling, self).__init__()\n"," self.upsampling = nn.Sequential(\n"," nn.Upsample(scale_factor=2, mode='nearest'),\n"," nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.LeakyReLU(negative_slope=0.02, inplace=True),\n"," )\n"," \n"," def forward(self, x):\n"," return self.upsampling(x)\n","\n","\n","class Segment(nn.Module):\n"," def __init__(self, in_channels, out_channels):\n"," super(Segment, self).__init__()\n"," self.segment = nn.Sequential(\n"," nn.Conv2d(in_channels, out_channels=1, kernel_size=1, stride=1, padding=0, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.LeakyReLU(negative_slope=0.02, inplace=True)\n"," )\n"," \n"," def forward(self, x):\n"," return self.segment(x)\n","\n","\n","class Conv2(nn.Module):\n"," def __init__(self, in_channels, out_channels):\n"," super(Conv2, self).__init__()\n"," self.conv2 = nn.Sequential(\n"," nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=2, padding=1, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.LeakyReLU(negative_slope=0.02, inplace=True),\n"," )\n","\n"," def forward(self, x):\n"," return self.conv2(x)\n","\n","\n","class ImprovedUnet(nn.Module):\n"," def __init__(self, in_channels=3, out_channels=1, feature_size=[16, 32, 64, 128]):\n"," super(ImprovedUnet, self).__init__()\n"," self.Conv1 = nn.Conv2d(in_channels=3, out_channels=feature_size[0], kernel_size=3, stride=1, padding=1, bias=False) \n"," self.Downs = nn.ModuleList()\n"," self.Convs = nn.ModuleList()\n"," self.Ups = nn.ModuleList()\n"," self.Segmentations = nn.ModuleList()\n","\n"," self.upscale = nn.Upsample(scale_factor=2, mode='nearest')\n"," self.bottleneck = Context(feature_size[-1]*2, feature_size[-1]*2)\n","\n","\n"," #Downsampling frame\n"," for feature in feature_size:\n"," self.Downs.append(Context(feature, feature))\n"," self.Convs.append(Conv2(feature, feature*2))\n","\n"," #Upsampleing frame\n"," for feature in reversed(feature_size):\n"," #Upsample\n"," self.Ups.append(Upsampling(feature*2, feature))\n","\n"," #Localization\n"," if feature != feature_size[0]:\n"," self.Ups.append(Localization(feature*2, feature))\n"," else:\n"," self.Ups.append(Localization(feature*2, feature*2))\n"," \n"," #Segmentation\n"," self.Segmentations.append(Segment(feature, 1))\n","\n"," self.final_conv = nn.Conv2d(feature_size[0]*2, out_channels, kernel_size=1, stride=1, bias=False)\n"," \n","\n"," def forward(self, x):\n"," skip_connections = []\n"," segmentation_layers = []\n","\n"," x = self.Conv1(x)\n","\n"," #Downsampling steps\n"," for i, (context_i, conv_i) in enumerate(zip(self.Downs, self.Convs)):\n"," x = context_i(x)\n"," #preserve location\n"," skip_connections.append(x)\n"," x = conv_i(x)\n","\n"," x = self.bottleneck(x) + x\n"," skip_connections = skip_connections[: : -1]\n","\n"," #Upsampling steps\n"," for idx in range(0, len(self.Ups), 2):\n"," #upsample\n"," x = self.Ups[idx](x)\n","\n"," #localization\n"," skip_connection = skip_connections[idx//2]\n"," concatnate_skip = torch.cat((skip_connection, x), dim=1)\n"," x = self.Ups[idx+1](concatnate_skip)\n","\n"," #segmentation\n"," if idx == 2 or idx == 4:\n"," x_segment = self.Segmentations[idx//2](x)\n"," segmentation_layers.append(x_segment)\n","\n"," seg_scale1 = self.upscale(segmentation_layers[0])\n"," seg_scale2 = self.upscale(segmentation_layers[1]+seg_scale1)\n","\n"," x = self.final_conv(x)\n"," x = x + seg_scale2\n","\n"," output = F.sigmoid(x)\n"," \n"," return output"],"execution_count":10,"outputs":[]},{"cell_type":"code","metadata":{"id":"otdcbBK7g4fW","executionInfo":{"status":"ok","timestamp":1634452313629,"user_tz":-600,"elapsed":2,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["feature_size=[16, 32, 64, 128]"],"execution_count":11,"outputs":[]},{"cell_type":"code","metadata":{"id":"oDhAF_WYyeJm","executionInfo":{"status":"ok","timestamp":1634452314967,"user_tz":-600,"elapsed":5,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["#dice coef\n","def dice_coef(pred, target):\n"," batch_size = len(pred)\n"," somooth = 1.\n","\n"," pred_flat = pred.view(batch_size, -1)\n"," target_flat = target.view(batch_size, -1)\n","\n"," intersection = (pred_flat*target_flat).sum()\n"," dice_coef = (2.*intersection+somooth)/(pred_flat.sum()+target_flat.sum()+somooth)\n"," return dice_coef\n","\n","#loss\n","def dice_loss(pred, target):\n"," dice_loss = 1 - dice_coef(pred, target)\n"," return dice_loss"],"execution_count":12,"outputs":[]},{"cell_type":"code","metadata":{"id":"dNMXUs-qViYi","executionInfo":{"status":"ok","timestamp":1634452317681,"user_tz":-600,"elapsed":377,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["import torch.optim as optim\n","\n","#set parameters\n","feature_size=[16, 32, 64, 128]\n","model = ImprovedUnet(feature_size=feature_size)\n","\n","optimizer = optim.Adam(model.parameters(), lr=0.001)\n","EPOCHS = 15"],"execution_count":13,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JrGpP3KMPzpe","executionInfo":{"status":"ok","timestamp":1634439533392,"user_tz":-600,"elapsed":9774961,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"917497df-4479-47d2-9ae4-ea56435fd75f"},"source":["from tqdm import tqdm\n","\n","TRAIN_LOSS = []\n","TRAIN_DICE = []\n","TEST_LOSS =[]\n","TEST_DICE = []\n","\n","for epoch in range(1, EPOCHS+1):\n"," print('EPOCH {}/{}'.format(epoch, EPOCHS))\n"," running_loss = 0\n"," running_dicecoef = 0\n"," running_loss_test = 0\n"," running_dicecoef_test = 0\n"," BATCH_NUM = len(train_loader)\n"," BATCH_NUM_TEST = len(test_loader)\n","\n"," #training\n"," with tqdm(train_loader, unit='batch') as tbatch:\n"," for batch_idx, (x, y) in enumerate(tbatch):\n"," tbatch.set_description(f'Batch: {batch_idx}')\n","\n"," optimizer.zero_grad()\n"," output = model(x)\n"," loss = dice_loss(output, y)\n"," dicecoef = dice_coef(output, y)\n"," loss.backward()\n"," optimizer.step()\n","\n"," running_loss += loss.item()\n"," running_dicecoef += dicecoef.item()\n","\n"," tbatch.set_postfix(loss=loss.item(), dice_coef=dicecoef.item())\n","\n"," epoch_loss = running_loss/BATCH_NUM\n"," epoch_dicecoef = running_dicecoef/BATCH_NUM\n"," TRAIN_LOSS.append(epoch_loss)\n"," TRAIN_DICE.append(epoch_dicecoef)\n","\n"," #test\n"," with tqdm(test_loader, unit='batch') as tsbatch:\n"," for batch_idx, (x, y) in enumerate(tsbatch):\n"," tsbatch.set_description(f'Batch: {batch_idx}')\n"," output_test = model(x)\n"," loss_test = dice_loss(output_test, y)\n"," dicecoef_test = dice_coef(output_test, y)\n"," tsbatch.set_postfix(loss=loss_test.item(), dice_coef=dicecoef_test.item())\n","\n"," running_loss_test += loss_test.item()\n"," running_dicecoef_test += dicecoef_test.item()\n","\n"," TEST_LOSS.append(running_loss_test/BATCH_NUM_TEST)\n"," TEST_DICE.append(running_dicecoef_test/BATCH_NUM_TEST)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["EPOCH 1/15\n"]},{"output_type":"stream","name":"stderr","text":["Batch: 0: 0%| | 0/33 [00:43torchviz) (3.7.4.3)\n","Building wheels for collected packages: torchviz\n"," Building wheel for torchviz (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for torchviz: filename=torchviz-0.0.2-py3-none-any.whl size=4151 sha256=80aa29d36737c5d4b81edcddc53bec07221514dc0c26ab4de9e6c23bf49714c5\n"," Stored in directory: /root/.cache/pip/wheels/04/38/f5/dc4f85c3909051823df49901e72015d2d750bd26b086480ec2\n","Successfully built torchviz\n","Installing collected packages: torchviz\n","Successfully installed torchviz-0.0.2\n"]}]},{"cell_type":"code","metadata":{"id":"oKvcU7lyeujb"},"source":["from torchviz import make_dot\n","make_dot(output, params=dict(model.named_parameters(), ))"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"zG5sYuWORuIO","executionInfo":{"status":"ok","timestamp":1634452208764,"user_tz":-600,"elapsed":490,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["#save model\n","filename = \"Unet_ISIC2.pth\"\n","torch.save(model.state_dict(), filename)"],"execution_count":169,"outputs":[]},{"cell_type":"code","metadata":{"id":"EhJChHsfViUi","colab":{"base_uri":"https://localhost:8080/","height":319},"executionInfo":{"status":"ok","timestamp":1634205941940,"user_tz":-600,"elapsed":1094,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"2a6204e7-1325-46f0-cdbf-fee476cdd087"},"source":["model.eval()\n","p = model(x)[0]\n","plt.imshow(p.detach().squeeze(), cmap='gray')"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:1805: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n"," warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n"]},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":40},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"4Rh-uq05TG-A"},"source":["# Model Evaluation"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":279},"id":"7ZBk4w3xIA8L","executionInfo":{"status":"ok","timestamp":1634439698477,"user_tz":-600,"elapsed":391,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"519836e3-612d-4bcf-ccdb-b92349a2d6cb"},"source":["import matplotlib.pyplot as plt\n","import numpy as np\n","\n","X = np.arange(1,EPOCHS+1)\n","\n","plt.plot(X, TRAIN_LOSS, marker='.', markersize=10, label='train')\n","plt.plot(X, TEST_LOSS, marker='.', markersize=10, label='test')\n","plt.xlabel('Epochs')\n","plt.ylabel('Dice Loss')\n","plt.xticks(X)\n","plt.legend()\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":279},"id":"Y80RZXzKRPpc","executionInfo":{"status":"ok","timestamp":1634439703085,"user_tz":-600,"elapsed":429,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"e5830c99-2e63-46c5-beb6-a95b3f9ce19b"},"source":["plt.plot(X, TRAIN_DICE, marker='.', markersize=10, label='train')\n","plt.plot(X, TEST_DICE, marker='.', markersize=10, label='test')\n","plt.xlabel('Epochs')\n","plt.ylabel('Dice Coeffeciency')\n","plt.xticks(X)\n","plt.legend()\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"BZYl7y1XIJFr","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1634452335129,"user_tz":-600,"elapsed":5640,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"615ef612-6f7c-486a-f79e-99c1290af99f"},"source":["for batch in test_loader:\n"," x, y = batch\n"," print(x.shape, y.shape)\n"," break"],"execution_count":14,"outputs":[{"output_type":"stream","name":"stdout","text":["torch.Size([64, 3, 128, 128]) torch.Size([64, 1, 128, 128])\n"]}]},{"cell_type":"code","metadata":{"id":"V3Ahm7ecTST4"},"source":["#load model\n","new_model = ImprovedUnet(feature_size=feature_size)\n","\n","filename = \"Unet_ISIC2.pth\"\n","checkpoint = torch.load(filename)\n","\n","new_model.load_state_dict(checkpoint)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":285},"id":"BIAOsDaLtliA","executionInfo":{"status":"ok","timestamp":1634452356740,"user_tz":-600,"elapsed":923,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"6e9d7e79-98ef-467d-dcdd-b75c9a67d2a7"},"source":["plt.imshow(x[0].permute(1,2,0))"],"execution_count":17,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":17},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":302},"id":"LXKpDfDRjlkR","executionInfo":{"status":"ok","timestamp":1634448826907,"user_tz":-600,"elapsed":933,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"035c4c92-79d4-46d2-a20b-d0be012ff0d1"},"source":["alpha = 5\n","seg_img = x[0].clone()\n","image_r = seg_img[0]\n","image_r = image_r*(1-alpha*pred_mask)+(pred_mask*pred_mask*alpha)\n","segment_image = image_r.detach().squeeze()\n","seg_img[0] = segment_image\n","plt.imshow(seg_img.permute(1,2,0))"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":157},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]}]} \ No newline at end of file From 6b55cbda43528eef1dc72a0686d23d35bcdb75a8 Mon Sep 17 00:00:00 2001 From: Wakame <85863413+wakameds@users.noreply.github.com> Date: Sun, 17 Oct 2021 20:56:21 +1000 Subject: [PATCH 14/66] upload improved Unet model file --- recognition/s4633139/ImprovedUNet.py | 169 ++++++++++++++++++ .../{UNet.ipynb => UNetjupyter.ipynb} | 0 2 files changed, 169 insertions(+) create mode 100644 recognition/s4633139/ImprovedUNet.py rename recognition/s4633139/{UNet.ipynb => UNetjupyter.ipynb} (100%) diff --git a/recognition/s4633139/ImprovedUNet.py b/recognition/s4633139/ImprovedUNet.py new file mode 100644 index 0000000000..01d44d0d39 --- /dev/null +++ b/recognition/s4633139/ImprovedUNet.py @@ -0,0 +1,169 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class Context(nn.Module): + """ + context module + """ + def __init__(self, in_channels, out_channels): + super(Context, self).__init__() + self.context = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False), + nn.BatchNorm2d(out_channels), + nn.Dropout2d(p=0.3), + nn.LeakyReLU(negative_slope=0.02, inplace=True), + nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False), + nn.BatchNorm2d(out_channels), + nn.LeakyReLU(negative_slope=0.02, inplace=True), + ) + + def forward(self, x): + x = self.context(x) + x + return x + + +class Localization(nn.Module): + """ + localization module + """ + def __init__(self, in_channels, out_channels): + super(Localization, self).__init__() + self.localization = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False), + nn.BatchNorm2d(out_channels), + nn.LeakyReLU(negative_slope=0.02, inplace=True), + nn.Conv2d(out_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=False), + nn.BatchNorm2d(out_channels), + nn.LeakyReLU(negative_slope=0.02, inplace=True), + ) + + def forward(self, x): + return self.localization(x) + + +class Upsampling(nn.Module): + """ + upsampling module + """ + def __init__(self, in_channels, out_channels): + super(Upsampling, self).__init__() + self.upsampling = nn.Sequential( + nn.Upsample(scale_factor=2, mode='nearest'), + nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False), + nn.BatchNorm2d(out_channels), + nn.LeakyReLU(negative_slope=0.02, inplace=True), + ) + + def forward(self, x): + return self.upsampling(x) + + +class Segment(nn.Module): + """ + segmentation layer + """ + def __init__(self, in_channels, out_channels): + super(Segment, self).__init__() + self.segment = nn.Sequential( + nn.Conv2d(in_channels, out_channels=1, kernel_size=1, stride=1, padding=0, bias=False), + nn.BatchNorm2d(out_channels), + nn.LeakyReLU(negative_slope=0.02, inplace=True) + ) + + def forward(self, x): + return self.segment(x) + + +class Conv2(nn.Module): + """ + convolution stride=2 + """ + def __init__(self, in_channels, out_channels): + super(Conv2, self).__init__() + self.conv2 = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=2, padding=1, bias=False), + nn.BatchNorm2d(out_channels), + nn.LeakyReLU(negative_slope=0.02, inplace=True), + ) + + def forward(self, x): + return self.conv2(x) + + +class IUNet(nn.Module): + """ + Improved Unet (International MICCAI Brainlesion Workshop(pp. 287-297). Springer, Cham.) + """ + def __init__(self, in_channels=3, out_channels=1, feature_size=[16, 32, 64, 128]): + super(IUNet, self).__init__() + self.Conv1 = nn.Conv2d(in_channels=3, out_channels=feature_size[0], kernel_size=3, stride=1, padding=1, bias=False) + self.Downs = nn.ModuleList() + self.Convs = nn.ModuleList() + self.Ups = nn.ModuleList() + self.Segmentations = nn.ModuleList() + + self.upscale = nn.Upsample(scale_factor=2, mode='nearest') + self.bottleneck = Context(feature_size[-1] * 2, feature_size[-1] * 2) + + #Downsampling frame + for feature in feature_size: + self.Downs.append(Context(feature, feature)) + self.Convs.append(Conv2(feature, feature * 2)) + + #Upsampleing frame + for feature in reversed(feature_size): + #Upsampling + self.Ups.append(Upsampling(feature * 2, feature)) + + #Localization + if feature != feature_size[0]: + self.Ups.append(Localization(feature * 2, feature)) + else: + self.Ups.append(Localization(feature * 2, feature * 2)) + + #Segmentation + self.Segmentations.append(Segment(feature, 1)) + + self.final_conv = nn.Conv2d(feature_size[0] * 2, out_channels, kernel_size=1, stride=1, bias=False) + + def forward(self, x): + skip_connections = [] + segmentation_layers = [] + idxs = [idx for idx in range(0, len(self.Ups),2)] + + x = self.Conv1(x) + + #Downsampling steps + for i, (context_i, conv_i) in enumerate(zip(self.Downs, self.Convs)): + x = context_i(x) + #preserve location + skip_connections.append(x) + x = conv_i(x) + + x = self.bottleneck(x) + x + skip_connections = skip_connections[:: -1] + + #Upsampling steps + for idx in range(0, len(self.Ups), 2): + #upsampling + x = self.Ups[idx](x) + + #localization + skip_connection = skip_connections[idx // 2] + concatnate_skip = torch.cat((skip_connection, x), dim=1) + x = self.Ups[idx + 1](concatnate_skip) + + #segmentation + if idx == 2 or idx == 4: + x_segment = self.Segmentations[idx // 2](x) + segmentation_layers.append(x_segment) + + seg_scale1 = self.upscale(segmentation_layers[0]) + seg_scale2 = self.upscale(segmentation_layers[1] + seg_scale1) + x = self.final_conv(x) + x = x + seg_scale2 + output = F.sigmoid(x) + + return output \ No newline at end of file diff --git a/recognition/s4633139/UNet.ipynb b/recognition/s4633139/UNetjupyter.ipynb similarity index 100% rename from recognition/s4633139/UNet.ipynb rename to recognition/s4633139/UNetjupyter.ipynb From 02e566223b6823bae9eb5c88e3a32a02bca0588f Mon Sep 17 00:00:00 2001 From: Wakame <85863413+wakameds@users.noreply.github.com> Date: Sun, 17 Oct 2021 21:03:23 +1000 Subject: [PATCH 15/66] upload the file for criterion for the improved UNet --- recognition/ISICs_Unet/README.md | 153 +++++++++++-------------------- 1 file changed, 52 insertions(+), 101 deletions(-) diff --git a/recognition/ISICs_Unet/README.md b/recognition/ISICs_Unet/README.md index 549f2535f2..f2c009212e 100644 --- a/recognition/ISICs_Unet/README.md +++ b/recognition/ISICs_Unet/README.md @@ -1,101 +1,52 @@ -# Segment the ISICs data set with the U-net - -## Project Overview -This project aim to solve the segmentation of skin lesian (ISIC2018 data set) using the U-net, with all labels having a minimum Dice similarity coefficient of 0.7 on the test set[Task 3]. - -## ISIC2018 -![ISIC example](imgs/example.jpg) - -Skin Lesion Analysis towards Melanoma Detection - -Task found in https://challenge2018.isic-archive.com/ - - -## U-net -![UNet](imgs/uent.png) - -U-net is one of the popular image segmentation architectures used mostly in biomedical purposes. The name UNet is because it’s architecture contains a compressive path and an expansive path which can be viewed as a U shape. This architecture is built in such a way that it could generate better results even for a less number of training data sets. - -## Data Set Structure - -data set folder need to be stored in same directory with structure same as below -```bash -ISIC2018 - |_ ISIC2018_Task1-2_Training_Input_x2 - |_ ISIC_0000000 - |_ ISIC_0000001 - |_ ... - |_ ISIC2018_Task1_Training_GroundTruth_x2 - |_ ISIC_0000000_segmentation - |_ ISIC_0000001_segmentation - |_ ... -``` - -## Dice Coefficient - -The Sørensen–Dice coefficient is a statistic used to gauge the similarity of two samples. - -Further information in https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient - -## Dependencies - -- python 3 -- tensorflow 2.1.0 -- pandas 1.1.4 -- numpy 1.19.2 -- matplotlib 3.3.2 -- scikit-learn 0.23.2 -- pillow 8.0.1 - - -## Usages - -- Run `train.py` for training the UNet on ISIC data. -- Run `evaluation.py` for evaluation and case present. - -## Advance - -- Modify `setting.py` for custom setting, such as different batch size. -- Modify `unet.py` for custom UNet, such as different kernel size. - -## Algorithm - -- data set: - - The data set we used is the training set of ISIC 2018 challenge data which has segmentation labels. - - Training: Validation: Test = 1660: 415: 519 = 0.64: 0.16 : 0.2 (Training: Test = 4: 1 and in Training, further split 4: 1 for Training: Validation) - - Training data augmentations: rescale, rotate, shift, zoom, grayscale -- model: - - Original UNet with padding which can keep the shape of input and output same. - - The first convolutional layers has 16 output channels. - - The activation function of all convolutional layers is ELU. - - Without batch normalization layers. - - The inputs is (384, 512, 1) - - The output is (384, 512, 1) after sigmoid activation. - - Optimizer: Adam, lr = 1e-4 - - Loss: dice coefficient loss - - Metrics: accuracy & dice coefficient - -## Results - -Evaluation dice coefficient is 0.805256724357605. - -plot of train/valid Dice coefficient: - -![img](imgs/train_and_valid_dice_coef.png) - -case present: - -![case](imgs/case%20present.png) - -## Reference -Manna, S. (2020). K-Fold Cross Validation for Deep Learning using Keras. [online] Medium. Available at: https://medium.com/the-owl/k-fold-cross-validation-in-keras-3ec4a3a00538 [Accessed 24 Nov. 2020]. - -zhixuhao (2020). zhixuhao/unet. [online] GitHub. Available at: https://github.com/zhixuhao/unet. - -GitHub. (n.d.). NifTK/NiftyNet. [online] Available at: https://github.com/NifTK/NiftyNet/blob/a383ba342e3e38a7ad7eed7538bfb34960f80c8d/niftynet/layer/loss_segmentation.py [Accessed 24 Nov. 2020]. - -Team, K. (n.d.). Keras documentation: Losses. [online] keras.io. Available at: https://keras.io/api/losses/#creating-custom-losses [Accessed 24 Nov. 2020]. - -262588213843476 (n.d.). unet.py. [online] Gist. Available at: https://gist.github.com/abhinavsagar/fe0c900133cafe93194c069fe655ef6e [Accessed 24 Nov. 2020]. - -Stack Overflow. (n.d.). python - Disable Tensorflow debugging information. [online] Available at: https://stackoverflow.com/questions/35911252/disable-tensorflow-debugging-information [Accessed 24 Nov. 2020]. +# Segmenting ISICs with U-Net + +COMP3710 Report recognition problem 3 (Segmenting ISICs data set with U-Net) solved in TensorFlow + +Created by Christopher Bailey (45576430) + +## The problem and algorithm +The problem solved by this program is binary segmentation of the ISICs skin lesion data set. Segmentation is a way to label pixels in an image according to some grouping, in this case lesion or non-lesion. This translates images of skin to masks representing areas of concern for skin lesions. + +U-Net is a form of autoencoder where the downsampling path is expected to learn the features of the image and the upsampling path learns how to recreate the masks. Long skip connections between downpooling and upsampling layers are utilised to overcome the bottleneck in traditional autoencoders allowing feature representations to be recreated. + +## How it works +A four layer padded U-Net is used, preserving skin features and mask resolution. The implementation utilises Adam as the optimizer and implements Dice distance as the loss function as this appeared to give quicker convergence than other methods (eg. binary cross-entropy). + +The utilised metric is a Dice coefficient implementation. My initial implementation appeared faulty and was replaced with a 3rd party implementation which appears correct. 3 epochs was observed to be generally sufficient to observe Dice coefficients of 0.8+ on test datasets but occasional non-convergence was observed and could be curbed by increasing the number of epochs. Visualisation of predictions is also implemented and shows reasonable correspondence. Orange bandaids represent an interesting challenge for the implementation as presented. + +### Training, validation and testing split +Training, validation and testing uses a respective 60:20:20 split, a commonly assumed starting point suggested by course staff. U-Net in particular was developed to work "with very few training images" (Ronneberger et al, 2015) The input data for this problem consists of 2594 images and masks. This split appears to provide satisfactory results. + +## Using the model +### Dependencies required +* Python3 (tested with 3.8) +* TensorFlow 2.x (tested with 2.3) +* glob (used to load filenames) +* matplotlib (used for visualisations, tested with 3.3) + +### Parameter tuning +The model was developed on a GTX 1660 TI (6GB VRAM) and certain values (notably batch size and image resolution) were set lower than might otherwise be ideal on more capable hardware. This is commented in the relevant code. + +### Running the model +The model is executed via the main.py script. + +### Example output +Given a batch size of 1 and 3 epochs the following output was observed on a single run: +Era | Loss | Dice coefficient +--- | ---- | ---------------- +Epoch 1 | 0.7433 | 0.2567 +Epoch 2 | 0.3197 | 0.6803 +Epoch 3 | 0.2657 | 0.7343 +Testing | 0.1820 | 0.8180 + + +### Figure 1 - example visualisation plot +Skin images in left column, true mask middle, predicted mask right column +![Visualisation of predictions](visual.png) + +## References +Segments of code in this assignment were used from or based on the following sources: +1. COMP3710-demo-code.ipynb from Guest Lecture +1. https://www.tensorflow.org/tutorials/load_data/images +1. https://www.tensorflow.org/guide/gpu +1. Karan Jakhar (2019) https://medium.com/@karan_jakhar/100-days-of-code-day-7-84e4918cb72c From dd7e85dddb3b57b7e6ab1a548db3686f5cb79c68 Mon Sep 17 00:00:00 2001 From: Wakame <85863413+wakameds@users.noreply.github.com> Date: Sun, 17 Oct 2021 21:05:29 +1000 Subject: [PATCH 16/66] upload the dataloader and criterion files for the improved UNet --- recognition/s4633139/IUNet_criterion.py | 17 +++++++++++ recognition/s4633139/IUNet_dataloader.py | 38 ++++++++++++++++++++++++ 2 files changed, 55 insertions(+) create mode 100644 recognition/s4633139/IUNet_criterion.py create mode 100644 recognition/s4633139/IUNet_dataloader.py diff --git a/recognition/s4633139/IUNet_criterion.py b/recognition/s4633139/IUNet_criterion.py new file mode 100644 index 0000000000..187e198143 --- /dev/null +++ b/recognition/s4633139/IUNet_criterion.py @@ -0,0 +1,17 @@ +#dice coefficient +def dice_coef(pred, target): + batch_size = len(pred) + somooth = 1. + + pred_flat = pred.view(batch_size, -1) + target_flat = target.view(batch_size, -1) + + intersection = (pred_flat*target_flat).sum() + dice_coef = (2.*intersection+somooth)/(pred_flat.sum()+target_flat.sum()+somooth) + return dice_coef + + +#loss +def dice_loss(pred, target): + dice_loss = 1 - dice_coef(pred, target) + return dice_loss \ No newline at end of file diff --git a/recognition/s4633139/IUNet_dataloader.py b/recognition/s4633139/IUNet_dataloader.py new file mode 100644 index 0000000000..789027a1d5 --- /dev/null +++ b/recognition/s4633139/IUNet_dataloader.py @@ -0,0 +1,38 @@ +import os +from torch.utils.data import Dataset +from PIL import Image + +os.chdir("./ISIC2018_Task1-2_Training_Data") + +class UNet_dataset(Dataset): + def __init__(self, + img_dir='./ISIC2018_Task1-2_Training_Input_x2', + mask_dir='./ISIC2018_Task1_Training_GroundTruth_x2', + img_transforms=None, + mask_transforms=None, + ): + + self.img_dir = img_dir + self.mask_dir = mask_dir + self.img_transforms = img_transforms + self.mask_transforms = mask_transforms + self.imgs = [file for file in sorted(os.listdir(self.img_dir)) if file.endswith('.jpg')] + self.masks = [file for file in sorted(os.listdir(self.mask_dir)) if file.endswith('.png')] + + def load_data(self, idx): + img_path = os.path.join(self.img_dir, self.imgs[idx]) + mask_path = os.path.join(self.mask_dir, self.masks[idx]) + img = Image.open(img_path).convert('RGB') + mask = Image.open(mask_path).convert('L') + return img, mask + + def __getitem__(self, idx): + img, mask = self.load_data(idx) + if self.img_transforms is not None: + img = self.img_transforms(img) + if self.mask_transforms is not None: + mask = self.mask_transforms(mask) + return img, mask + + def __len__(self): + return len(self.imgs) \ No newline at end of file From dcb8e90b6fcf6b45b70f35a4d41ff1355d25d087 Mon Sep 17 00:00:00 2001 From: Wakame <85863413+wakameds@users.noreply.github.com> Date: Sun, 17 Oct 2021 21:08:51 +1000 Subject: [PATCH 17/66] upload the files to train model and to evaluate the performance for the improved Unet --- recognition/s4633139/IUNet_train_test.py | 61 ++++++++++++++++++++++++ recognition/s4633139/visualse.py | 43 +++++++++++++++++ 2 files changed, 104 insertions(+) create mode 100644 recognition/s4633139/IUNet_train_test.py create mode 100644 recognition/s4633139/visualse.py diff --git a/recognition/s4633139/IUNet_train_test.py b/recognition/s4633139/IUNet_train_test.py new file mode 100644 index 0000000000..573d6f96fe --- /dev/null +++ b/recognition/s4633139/IUNet_train_test.py @@ -0,0 +1,61 @@ +from IUNet_criterion import dice_coef, dice_loss +from tqdm import tqdm + + +def model_train_test(model, optimizer, EPOCHS, train_loader, test_loader): + """ + function for model training and test + :return: list of train and test dice coefficients and dice losses by epochs + """ + TRAIN_LOSS = [] + TRAIN_DICE = [] + TEST_LOSS =[] + TEST_DICE = [] + + for epoch in range(1, EPOCHS+1): + print('EPOCH {}/{}'.format(epoch, EPOCHS)) + running_loss = 0 + running_dicecoef = 0 + running_loss_test = 0 + running_dicecoef_test = 0 + BATCH_NUM = len(train_loader) + BATCH_NUM_TEST = len(test_loader) + + #train + with tqdm(train_loader, unit='batch') as tbatch: + for batch_idx, (x, y) in enumerate(tbatch): + tbatch.set_description(f'Batch: {batch_idx}') + + optimizer.zero_grad() + output = model(x) + loss = dice_loss(output, y) + dicecoef = dice_coef(output, y) + loss.backward() + optimizer.step() + + running_loss += loss.item() + running_dicecoef += dicecoef.item() + + tbatch.set_postfix(loss=loss.item(), dice_coef=dicecoef.item()) + + epoch_loss = running_loss/BATCH_NUM + epoch_dicecoef = running_dicecoef/BATCH_NUM + TRAIN_LOSS.append(epoch_loss) + TRAIN_DICE.append(epoch_dicecoef) + + #test + with tqdm(test_loader, unit='batch') as tsbatch: + for batch_idx, (x, y) in enumerate(tsbatch): + tsbatch.set_description(f'Batch: {batch_idx}') + output_test = model(x) + loss_test = dice_loss(output_test, y) + dicecoef_test = dice_coef(output_test, y) + tsbatch.set_postfix(loss=loss_test.item(), dice_coef=dicecoef_test.item()) + + running_loss_test += loss_test.item() + running_dicecoef_test += dicecoef_test.item() + + TEST_LOSS.append(running_loss_test/BATCH_NUM_TEST) + TEST_DICE.append(running_dicecoef_test/BATCH_NUM_TEST) + + return TRAIN_DICE, TEST_DICE, TRAIN_LOSS, TEST_LOSS \ No newline at end of file diff --git a/recognition/s4633139/visualse.py b/recognition/s4633139/visualse.py new file mode 100644 index 0000000000..fe0038c605 --- /dev/null +++ b/recognition/s4633139/visualse.py @@ -0,0 +1,43 @@ +import matplotlib.pyplot as plt +import numpy as np + +def dice_coef_vis(EPOCHS, TRAIN_COEFS, TEST_COEFS): + X = np.arange(1, EPOCHS+1) + plt.plot(X, TRAIN_COEFS, marker='.', markersize=10, label='train') + plt.plot(X, TEST_COEFS, marker='.', markersize=10, label='test') + plt.xlabel('Epochs') + plt.ylabel('Dice coefficient') + plt.xticks(X) + plt.legend() + plt.show() + + +def dice_loss_vis(EPOCHS, TRAIN_LOSS, TEST_LOSS): + X = np.arange(1, EPOCHS+1) + plt.plot(X, TRAIN_LOSS, marker='.', markersize=10, label='train') + plt.plot(X, TEST_LOSS, marker='.', markersize=10, label='test') + plt.xlabel('Epochs') + plt.ylabel('Dice Loss') + plt.xticks(X) + plt.legend() + plt.show() + + +def pred_mask(img, pred_mask, alpha=5): + seg_img = img.clone() + image_r = seg_img[0] + image_r = image_r*(1-alpha*pred_mask)+(pred_mask*pred_mask*alpha) + segmentation = image_r.detach().squeeze() + seg_img[0] = segmentation + plt.imshow(seg_img.permute(1,2,0)) + plt.show() + + +def segment_pred_mask(img, pred_mask, alpha=0.5): + seg_img = img.clone() + image_r = seg_img[0] + image_r = image_r*(1-alpha*pred_mask)+(pred_mask*pred_mask*alpha) + segment_img_r = image_r.detach().squeeze() + seg_img[0] = segment_img_r + plt.imshow(seg_img.permute(1,2,0)) + plt.show() \ No newline at end of file From 30d2aaff9f98d64d6f6b4ad0b8080ca7d98196a9 Mon Sep 17 00:00:00 2001 From: Wakame <85863413+wakameds@users.noreply.github.com> Date: Sun, 17 Oct 2021 21:09:44 +1000 Subject: [PATCH 18/66] upload the main files for the improved Unet --- recognition/s4633139/main.py | 76 ++++++++++++++++++++++++++++++++++++ 1 file changed, 76 insertions(+) create mode 100644 recognition/s4633139/main.py diff --git a/recognition/s4633139/main.py b/recognition/s4633139/main.py new file mode 100644 index 0000000000..46cad93ada --- /dev/null +++ b/recognition/s4633139/main.py @@ -0,0 +1,76 @@ +from IUNet_dataloader import UNet_dataset +from ImprovedUNet import IUNet +from IUNet_train_test import model_train_test +from visualse import dice_coef_vis, segment_pred_mask + +import torch +from torch.utils.data import DataLoader, Dataset, random_split +import torchvision.transforms as transforms +import torch.optim as optim + + +def main(): + """ + execute model training and return dice coefficient plots + """ + + #PARAMETERS + FEATURE_SIZE=[16, 32, 64, 128] + IN_CHANEL=3 + OUT_CHANEL=1 + + IMG_TF = transforms.Compose([ + transforms.Resize((FEATURE_SIZE[-1], FEATURE_SIZE[-1])), + transforms.ToTensor(), + transforms.Normalize(mean=[0, 0, 0], std=[1, 1, 1]), + ]) + + MASK_TF = transforms.Compose([ + transforms.Resize((FEATURE_SIZE[-1],FEATURE_SIZE[-1])), + transforms.ToTensor(), + ]) + + BATCH_SIZE = 64 + EPOCHS = 15 + LR = 0.001 + + #DATA PREPARATION + dataset = UNet_dataset(img_transforms=IMG_TF, mask_transforms=MASK_TF) + + #shuffle index + sample_size = len(dataset.imgs) + train_size = int(sample_size * 0.8) + test_size = sample_size - train_size + + #train and test set + train_set, test_set = random_split(dataset, [train_size, test_size], generator=torch.Generator().manual_seed(123)) + + #data loader + train_loader = DataLoader(train_set, batch_size=BATCH_SIZE, shuffle=True) + test_loader = DataLoader(test_set, batch_size=BATCH_SIZE, shuffle=False) + + #MODEL + model = IUNet(in_channels=IN_CHANEL, out_channels=OUT_CHANEL, feature_size=FEATURE_SIZE) + optimizer = optim.Adam(model.parameters(), lr=LR) + + #train,test + TRAIN_DICE, TEST_DICE, TRAIN_LOSS, TEST_LOSS = model_train_test(model, optimizer, EPOCHS, train_loader, test_loader) + + #plot dice coefficient + dice_coef_vis(EPOCHS, TRAIN_DICE, TEST_DICE) + + #segmentation + for batch in train_loader: + x, y = batch + break + + img = x[0] + model.eval() + pred_mask = model(x)[0] + segment_pred_mask(img, pred_mask, alpha=0.5) + +if __name__ == main(): + main() + + + From 990299f2786f017493b4852fa2762624913cb271 Mon Sep 17 00:00:00 2001 From: Wakame <85863413+wakameds@users.noreply.github.com> Date: Mon, 18 Oct 2021 08:55:26 +1000 Subject: [PATCH 19/66] remove UNetjupyter.ipynb --- recognition/s4633139/UNetjupyter.ipynb | 1 - 1 file changed, 1 deletion(-) delete mode 100644 recognition/s4633139/UNetjupyter.ipynb diff --git a/recognition/s4633139/UNetjupyter.ipynb b/recognition/s4633139/UNetjupyter.ipynb deleted file mode 100644 index 88301ea97b..0000000000 --- a/recognition/s4633139/UNetjupyter.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"UNet.ipynb","provenance":[],"collapsed_sections":[],"mount_file_id":"1UdH3PIDr4uawfSNGUl8ntSrq2Lbo_uQQ","authorship_tag":"ABX9TyNiaLFhg+HbcKfrWJAzQit8"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"vlhQO-g6Y2ah","executionInfo":{"status":"ok","timestamp":1634273373551,"user_tz":-600,"elapsed":306,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["import os\n","from PIL import Image\n","import matplotlib.pyplot as plt\n","import glob\n","\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","\n","import torch.utils.data\n","from torchvision import datasets, transforms"],"execution_count":60,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"7vcUuTeHs59d"},"source":["# Data preparation\n","\n","\n","1. get path\n","2. dataloader for img and mask\n","1. split into test and validation\n","\n","\n","\n"]},{"cell_type":"code","metadata":{"id":"WuSova9HHAzc","executionInfo":{"status":"ok","timestamp":1634270416375,"user_tz":-600,"elapsed":13732,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["#define dataset for img and mask\n","file_dir = \"/content/drive/MyDrive/ColabGitHub/ISIC2018_Task1-2_Training_Data\"\n","os.chdir(file_dir)\n","\n","img_path = './ISIC2018_Task1-2_Training_Input_x2'\n","mask_path = './ISIC2018_Task1_Training_GroundTruth_x2'\n","\n","imgs = [file for file in sorted(os.listdir(img_path)) if file.endswith('.jpg')]\n","masks = [file for file in sorted(os.listdir(mask_path)) if file.endswith('.png')]"],"execution_count":2,"outputs":[]},{"cell_type":"code","metadata":{"id":"StdLOrNGt3Id","executionInfo":{"status":"ok","timestamp":1634270422062,"user_tz":-600,"elapsed":341,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["from torchvision import datasets\n","from torchvision.datasets import ImageFolder\n","from torch.utils.data import dataloader, random_split, Subset\n","from torchvision.transforms import transforms\n"],"execution_count":3,"outputs":[]},{"cell_type":"code","metadata":{"id":"JhoipyIyXvBB","executionInfo":{"status":"ok","timestamp":1634270424835,"user_tz":-600,"elapsed":1106,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["from torch.utils.data import Dataset\n","from PIL import Image\n","\n","class Unet_dataset(Dataset):\n"," def __init__(self,\n"," img_dir = './ISIC2018_Task1-2_Training_Input_x2',\n"," mask_dir = './ISIC2018_Task1_Training_GroundTruth_x2', \n"," img_transforms=None,\n"," mask_transforms= None,\n"," train_ratio = 0.5):\n"," \n"," self.img_dir = img_dir\n"," self.mask_dir = mask_dir\n"," self.img_transforms = img_transforms\n"," self.mask_transforms = mask_transforms\n","\n"," self.imgs = [file for file in sorted(os.listdir(self.img_dir)) if file.endswith('.jpg')]\n"," self.masks = [file for file in sorted(os.listdir(self.mask_dir)) if file.endswith('.png')]\n","\n"," self.train_ratio = train_ratio\n","\n"," #meke dataloader\n"," def load_data(self, idx):\n"," img_path = os.path.join(self.img_dir, self.imgs[idx])\n"," mask_path = os.path.join(self.mask_dir, self.masks[idx])\n"," img = Image.open(img_path).convert('RGB')\n"," mask = Image.open(mask_path).convert('L')\n"," return img, mask\n","\n"," def __getitem__(self, idx):\n"," img, mask = self.load_data(idx)\n"," #transformation\n"," if self.img_transforms is not None:\n"," img = self.img_transforms(img)\n"," if self.mask_transforms is not None:\n"," mask = self.mask_transforms(mask)\n"," return img, mask\n","\n"," def __len__(self):\n"," return len(self.imgs)"],"execution_count":4,"outputs":[]},{"cell_type":"code","metadata":{"id":"vpwsi1o-xnYw","executionInfo":{"status":"ok","timestamp":1634270429179,"user_tz":-600,"elapsed":316,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["from torch.utils.data import DataLoader\n","import torchvision.transforms as transforms\n","\n","img_tfs = transforms.Compose([\n"," transforms.Resize((64,64)),\n"," transforms.ToTensor(),\n"," transforms.Normalize(mean=[0, 0, 0], std=[1, 1, 1]),\n"," ]\n"," )\n","\n","mask_tfs = transforms.Compose([\n"," transforms.Resize((64,64)),\n"," transforms.ToTensor(),\n"," ]\n"," )\n","\n","dataset = Unet_dataset(img_transforms=img_tfs, mask_transforms=mask_tfs)\n","\n","#shuffle index\n","sample_size = len(imgs)\n","train_size = int(sample_size*0.8)\n","test_size = sample_size - train_size\n","\n","#train and test set\n","train_set, test_set = torch.utils.data.random_split(dataset, [train_size, test_size], generator=torch.Generator().manual_seed(123))\n","\n","#dataloader\n","train_loader= DataLoader(train_set, batch_size=64, shuffle = True)\n","test_loader= DataLoader(test_set, batch_size=64, shuffle=False)"],"execution_count":5,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"cN3J5wTpx_HX"},"source":["# Unet"]},{"cell_type":"code","metadata":{"id":"VFiXmwWjSeXi","executionInfo":{"status":"ok","timestamp":1634270481930,"user_tz":-600,"elapsed":20,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["import torch\n","import torch.nn as nn"],"execution_count":8,"outputs":[]},{"cell_type":"code","metadata":{"id":"nzGxgjkBeFXR","executionInfo":{"status":"ok","timestamp":1634270481931,"user_tz":-600,"elapsed":19,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["class twotimes_conv(nn.Module):\n"," def __init__(self, in_channels, out_channels):\n"," super(twotimes_conv, self).__init__()\n"," self.conv = nn.Sequential(\n"," nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.ReLU(inplace=True),\n"," nn.Conv2d(out_channels, out_channels, kernel_size=3, stride = 1, padding=1, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.ReLU(inplace=True),\n"," )\n","\n"," def forward(self, x):\n"," return self.conv(x)\n","\n","\n","class Unet(nn.Module):\n"," def __init__(self, in_channels=3, out_channels=1, feature_size=None):\n"," super(Unet, self).__init__()\n"," self.downsample = nn.ModuleList()\n"," self.upsample = nn.ModuleList()\n"," self.pool = nn.MaxPool2d(kernel_size=2, stride=2)\n"," self.feature_size = None\n","\n"," #Downsample frame\n"," for feature in feature_size:\n"," self.downsample.append(twotimes_conv(in_channels, feature))\n"," in_channels = feature\n","\n"," #Upsample frame\n"," for feature in reversed(feature_size):\n"," #Deconvolution\n"," self.upsample.append(nn.ConvTranspose2d(feature*2, feature, kernel_size=2, stride =2))\n"," #Convolution\n"," self.upsample.append(twotimes_conv(feature*2, feature))\n","\n"," #Bottleneck frame\n"," self.bottleneck = twotimes_conv(feature_size[-1], feature_size[-1]*2)\n"," self.final_conv = nn.Conv2d(feature_size[0], out_channels, kernel_size=1)\n","\n","\n"," def forward(self, x):\n"," skip_connections = []\n","\n"," #Downsampling steps\n"," for down_i in self.downsample:\n"," x = down_i(x)\n"," #preserve location\n"," skip_connections.append(x)\n"," x = self.pool(x)\n","\n"," #Bottle neck part\n"," x = self.bottleneck(x)\n"," skip_connections = skip_connections[: : -1]\n","\n"," #Upsampling steps\n"," for idx in range(0, len(self.upsample), 2):\n"," x = self.upsample[idx](x)\n"," skip_connection = skip_connections[idx//2]\n","\n"," if x.shape != skip_connection.shape:\n"," x = torchvision.transforms.resize(x, size=skip_connection.shape[2:])\n"," \n"," #where + what\n"," concatnate_skip = torch.cat((skip_connection, x), dim=1)\n"," x = self.upsample[idx+1](concatnate_skip)\n"," \n"," x = self.final_conv(x)\n"," output = F.sigmoid(x)\n"," \n"," return output"],"execution_count":9,"outputs":[]},{"cell_type":"code","metadata":{"id":"oDhAF_WYyeJm","executionInfo":{"status":"ok","timestamp":1634271792485,"user_tz":-600,"elapsed":490,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["#dice coef\n","def dice_coef(pred, target):\n"," batch_size = len(pred)\n"," somooth = 1.\n","\n"," pred_flat = pred.view(batch_size, -1)\n"," target_flat = target.view(batch_size, -1)\n","\n"," intersection = (pred_flat*target_flat).sum()\n"," dice_coef = (2.*intersection+somooth)/(pred_flat.sum()+target_flat.sum()+somooth)\n"," return dice_coef\n","\n","\n","#loss\n","def dice_loss(pred, target):\n"," dice_loss = 1 - dice_coef(pred, target)\n"," return dice_loss"],"execution_count":52,"outputs":[]},{"cell_type":"code","metadata":{"id":"dNMXUs-qViYi","executionInfo":{"status":"ok","timestamp":1634270491165,"user_tz":-600,"elapsed":457,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["import torch.optim as optim\n","\n","#set parameters\n","feature_size=[64, 128, 256, 512]\n","optimizer = optim.Adam(model.parameters(), lr=0.001)\n","EPOCHS = 30\n","\n","model = Unet(feature_size=feature_size)"],"execution_count":12,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JrGpP3KMPzpe","executionInfo":{"status":"ok","timestamp":1634205931646,"user_tz":-600,"elapsed":24149591,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"70b6a5b8-887c-4489-cc8a-f6718ec3dd50"},"source":["from tqdm import tqdm\n","\n","TRAIN_LOSS = []\n","TRAIN_DICE = []\n","TEST_LOSS =[]\n","TEST_DICE = []\n","\n","for epoch in range(1, EPOCHS+1):\n"," print('EPOCH {}/{}'.format(epoch, EPOCHS))\n"," running_loss = 0\n"," running_dicecoef = 0\n"," running_loss_test = 0\n"," running_dicecoef_test = 0\n"," BATCH_NUM = len(train_loader)\n"," BATCH_NUM_TEST = len(test_loader)\n","\n"," #training\n"," with tqdm(train_loader, unit='batch') as tbatch:\n"," for batch_idx, (x, y) in enumerate(tbatch):\n"," tbatch.set_description(f'Batch: {batch_idx}')\n","\n"," optimizer.zero_grad()\n"," output = model(x)\n"," loss = dice_loss(output, y)\n"," dicecoef = dice_coef(output, y)\n"," loss.backward()\n"," optimizer.step()\n","\n"," running_loss += loss.item()\n"," running_dicecoef += dicecoef.item()\n","\n"," tbatch.set_postfix(loss=loss.item(), dice_coef=dicecoef.item())\n","\n"," epoch_loss = running_loss/BATCH_NUM\n"," epoch_dicecoef = running_dicecoef/BATCH_NUM\n"," TRAIN_LOSS.append(epoch_loss)\n"," TRAIN_DICE.append(epoch_dicecoef)\n","\n"," #test\n"," with tqdm(test_loader, unit='batch') as tsbatch:\n"," for batch_idx, (x, y) in enumerate(tsbatch):\n"," tsbatch.set_description(f'Batch: {batch_idx}')\n"," output_test = model(x)\n"," loss_test = dice_loss(output_test, y)\n"," dicecoef_test = dice_coef(output_test, y)\n"," tsbatch.set_postfix(loss=loss_test.item(), dice_coef=dicecoef_test.item())\n","\n"," running_loss_test += loss_test.item()\n"," running_dicecoef_test += dicecoef_test.item()\n","\n"," TEST_LOSS.append(running_loss_test/BATCH_NUM_TEST)\n"," TEST_DICE.append(running_dicecoef_test/BATCH_NUM_TEST)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["EPOCH 1/30\n"]},{"output_type":"stream","name":"stderr","text":["Batch: 0: 0%| | 0/33 [00:04"]},"metadata":{},"execution_count":40},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"4Rh-uq05TG-A"},"source":["# Model Evaluation"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":279},"id":"7ZBk4w3xIA8L","executionInfo":{"status":"ok","timestamp":1634206091827,"user_tz":-600,"elapsed":419,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"a2ff558d-5b76-4ebd-8b09-2514182ad30b"},"source":["import matplotlib.pyplot as plt\n","import numpy as np\n","\n","X = np.arange(1,EPOCHS+1)\n","\n","plt.plot(X, TRAIN_LOSS, marker='.', markersize=10, label='train')\n","plt.plot(X, TEST_LOSS, marker='.', markersize=10, label='test')\n","plt.xlabel('Epochs')\n","plt.ylabel('Dice Loss')\n","plt.xticks(X)\n","plt.legend()\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":279},"id":"Y80RZXzKRPpc","executionInfo":{"status":"ok","timestamp":1634206126932,"user_tz":-600,"elapsed":1066,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"8e714aaa-5868-4270-f7ca-54a5b827fb4d"},"source":["plt.plot(X, TRAIN_DICE, marker='.', markersize=10, label='train')\n","plt.plot(X, TEST_DICE, marker='.', markersize=10, label='test')\n","plt.xlabel('Epochs')\n","plt.ylabel('Dice Coeffeciency')\n","plt.xticks(X)\n","plt.legend()\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYgAAAEGCAYAAAB/+QKOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOydeXxU1dn4v08me9gCJGxhJyAgCoorLrigYP25tr7aajdbWqvWamurb1trte1r+1bb175WX9dWW2utrVulCoqIRfZFVgNhkSRsgSRs2Wee3x/nhsxMZiYDZJiQPN/P537m3nPPPfe5c2bOc89znvMcUVUMwzAMI5yUZAtgGIZhtE9MQRiGYRgRMQVhGIZhRMQUhGEYhhERUxCGYRhGRFKTLUBb0bt3bx0yZEiyxTAMwziuWLp06W5VzYt0rsMoiCFDhrBkyZJki2EYhnFcISKfRjtnJibDMAwjIqYgDMMwjIiYgjAMwzAi0mHGICLR0NBAaWkptbW1yRYl4WRmZlJQUEBaWlqyRTEMo4PQoRVEaWkpXbt2ZciQIYhIssVJGKrKnj17KC0tZejQockWxzCMDkKHNjHV1tbSq1evDq0cAESEXr16dYqekmF0BvwB5b11O3n0vQ28t24n/kBygqp26B4E0OGVQxOd5TkN43jFH1DmFO1izbZ9jO3fjcmj8vGltPzf+gPKTc8sZEVJFTX1frLSfYwf2IMXbj4jYv5E0uEVhGEYRrKJp9Gvrm9kW1Utb63cxpItldT7A166nxUlVcwp2sVFo/scU7lNQSSYqqoqXnzxRb71rW8d1nWXXXYZL774Ij169EiQZIbR+Tict/h48sWbd/YnO1m+tYqaBj/gGv1Fmyv47OMf0RAIUFZZQ2V1Q1S5a+r9rNm2zxREMjmcH0W8VFVV8fvf/76FgmhsbCQ1NfrXP2PGjKO6r2EYocRrujkcE0943vTUFAbmZnHZSf3YsbeWsqoayiprKKmsaTGO0BhQtlZUc1JBd04u6EH/HlkM6JHF9r21PPrehkPKBECBlxeX0K97JleM709Gqi+h31UTpiA8EmX3u+eee9i4cSPjx48nLS2NzMxMcnNz+eSTT1i/fj1XXXUVJSUl1NbWcscddzB9+nSgOXTIgQMHmDZtGueccw4fffQRAwYM4PXXXycrK6utHt0wjmvifbF7bUUZSz+tpK6x2XSzeEsFP3x1FaP7dSMlRUgRWL9jfwsTz+LNFXz9+cV0y0xjf20j+2ob2FfTyK79tSFv/nWNAYrLD/Loe8Xkdc1gQI8sxg7ozqi+XXn/k13U+5uVRHa6j1999qQWvQJ/QPlwQ3lIW9S/eya+FOHuV1byy7eL+OJZg/nCGYPokZ3e5i+1wUhHWXJ04sSJGh6Lad26dYwePRqAn765hrXb9kW9vrK6nuJdBwhW8ikCI/K7kJudHvGaMf278ZP/NzamXFu2bOHyyy9n9erVzJkzh8985jOsXr36kDtqRUUFPXv2pKamhtNOO40PPviAXr16hSiIESNGsGTJEsaPH891113HFVdcwY033tjiXsHPaxjHkkSYbuK9b6QXu59fNY5PduxjzbZ9rNm2l9Xb9lG+v+5oHpHMtBTyu2bSNTOVbplpdMtKpbSyhjVh7YoA376okDunjGxVzmgvoE3f09pt+xjjfU8pAvOK9/D0vzcxp6icdJ+Qm53O3toG6hoCR/xSKyJLVXVipHPWg/CorvMT7kkWUJeem9129zn99NND5io8+uijvPrqqwCUlJSwYcMGevXqFXLN0KFDGT9+PACnnnoqW7ZsaTuBDOMoSYTppil/a8rkrVXbWba1ktqG5rf9jzbu4YKH5wDgSxEK87twbmFv0n0pvLa8jFqvBwGQle7jl9eM45zCPPwBJaDK3PXl3Pf6amoamvNlp/v43Q0TWrztv7duJ7f/ZTnV9f6QMk8q6B6Sz5civHDzGS0a/WgNuS9FuGh0nxb3O6ewN+cU9qZ4134eeHMdczeUHzqXiMHsTqMgWnvTj1TR2ek+fnrl2DYdGMrJyTm0P2fOHN59913mz59PdnY2kydPjjiXISMj49C+z+ejpqamzeQxjKNlTtGuFgOwS7ZU8uA/1zCmX3cQ91a9bntL083STyt5Yf4WLhrdhy4ZqeRkpJKemtJCmWSm+RjaO5trTilg8+6DbCw/QPGug+w+ELlXcPHoPtx+4QhG9e1KZpqz1/s9m3+4gvrMSf1DGuprTing1eVlLfJNHpXf4j6TR+UzfmCPuPJGa/SPhBH5XZk4JJcPN5QT/F5bU+9nbRsOZncaBdEah1PRh0PXrl3Zv39/xHN79+4lNzeX7OxsPvnkExYsWHBU9zKMeGgrM09tg5931+3kkZnrQwZUAer9Af7wUdQo0oeoawxw/5truf/NtYfS0lNTyPClcKCu8VDjV9PgZ+32/ax9ax3dMlMZkd+FC0blocCbH287NK4A7sXuhtMHcvLAUA/AeN/iD+dt/3B7Bm3J2P7dyEr3tei9jOnfrc3uYQrCI1EV3atXLyZNmsSJJ55IVlYWffo0a/apU6fyxBNPMHr0aEaNGsWZZ555tI9hGDE5XA+dcEUiwKItFby6rIwZq7azv66RHlmppKYIjUE22qw01/s+e3gvmoY55xXv5qdvrgkx3WSkpnDzpKEMzcvhQF0jB+sa2V/XyPyNe1hZujdEHgG+cf4wfjD1hEMTQ/0BZVtVTdwvdvG+xR/O235b9gwOh0S91AbTaQapOwOd7XmNZuLpFTT6A7y6vIwfv7Y61A6flsIvrz2Zy0/qR0pKc8MbauJJoWeXDDSgbNtbS066j6kn9uPqCQM4fWhPvvzcojYdg4hm8o00DhBpQPdYzzhOFm3x7LEGqU1BdCA62/Majki++P26Z3LhCfns3FfHtr017Nhby859tS0cMYJJEeielea89gS27D7YIv/Y/t34+rnDuGRsH7LTmw0Q8TZUh5OvvYSb6OgkzYtJRKYC/wP4gKdV9aGw84OBZ4E8oAK4UVVLvXNfAn7kZf2Zqv4xkbIaxtGSiImWrVHb4OfRdzewcHPFoYlYdY0Btuyp5vn5nzKwZzb9umdy9vDe9O+RSVV1PS8vKQ2x2af7Urh6Qn/yu2VSWV1PVXUDq0r3tlAOAkwd25erJgxoIUdbm26Sads3mkmYghARH/AYMAUoBRaLyBuqujYo26+B51X1jyJyIfBfwE0i0hP4CTARN4lwqXdtZaLkNYyjIVFvvJGUTmMgwAdF5fxz5XbeXbczxAzTRJMv/rcvKmxR3sbygy3k/MU1J4XIGc19sy0HQFsjWbZ9o5lE9iBOB4pVdROAiLwEXAkEK4gxwF3e/vvAa97+pcAsVa3wrp0FTAX+kkB5DeOI8AeUR2dvYOGmPTRNlK2u97Ps00pmf7KTKWP6HnG54aajblmp1NT5OVDvp0d2GleO70//7lk8/sHGFo352AiNebxv5sdiANRo/yRSQQwASoKOS4EzwvJ8DFyDM0NdDXQVkV5Rrm3RrxWR6cB0gEGDBrWZ4IYRD6WV1by8pJS/LSlh+96W81dqGwN8+y8rmHZiXy4cnc95I/PolpnWqikqEFC27a3h70tLWbylggZ/s+mofH89Zw/vxfTzhjFpRG/SfG7OwPxNe9rUk8dMPAYk3831e8D/isiXgblAGdCyvxwFVX0SeBLcIHUiBDQ6N+GN+dnDe/N+0S5eWlzCh94s1nML87hq/AD+OH9LyFt8emoKJw/szuyiXfxjeRmpKcLEwbns2OcGjGsbAmSkpTC4Zw7TxvVlU7mbALap/GCLeQVNCHDWsF4hjX+iGnMz8RiJVBBlwMCg4wIv7RCqug3Xg0BEugDXqmqViJQBk8OunZNAWRPGkYb7Bvjtb3/L9OnTyc5uw1gfHZhEx/lpKqsxoPTvnsm3LyzkcxMLKMjNxh9QPi6tijgGAbB8ayWzP9nFGyu2UVrVPBO+tiFA0c79FO3cz4AeWYzI78IZQ3sxIr8LVdX1/G52cYiyiDYOYI25kQgSqSAWA4UiMhSnGK4HPh+cQUR6AxWqGgDuxXk0AbwD/EJEcr3jS7zziSXghw2zYMdK6HsSFE6BlKMLqxst3Hc8/Pa3v+XGG280BREHbT1IrKr8acGWEBNPY0DxCXzn4kJuv7AwpNzW3uInDunJxCE9yUzz8ZtZ60PCIwhw+4WF3HXJSILxB5R/F++2cQAjaSRMQahqo4jchmvsfcCzqrpGRB4AlqjqG7hewn+JiOJMTLd611aIyIM4JQPwQNOAdcII+OGFq6FsCdRXQ3o2DJgIN716VEoiONz3lClTyM/P5+WXX6auro6rr76an/70pxw8eJDrrruO0tJS/H4/P/7xj9m5cyfbtm3jggsuoHfv3rz//vtt+LAdjzlFu1hRUnXIxNNa4LJIvQ1VZemnlcxcu5NZa3eytaK6xXUBBZ9I1LALrb3FRwuPcPLA7i3y2jiAkWwSOgahqjOAGWFp9wXtvwK8EuXaZ2nuURw9/7oHdqyKfr66AnZ/Aur5h9cfhC0fwuPnQHbPyNf0HQfTHop8zuOhhx5i9erVrFixgpkzZ/LKK6+waNEiVJUrrriCuXPnUl5eTv/+/XnrrbcAF6Ope/fuPPLII7z//vv07t37SJ64U7C3poE5Rbv4/ZyNLdw9q+v93PXyx0wcnMuIPl0ozO/KiPwuDO2dwy1/WhrmHZRGQ6OfqppG0n0pTBrRi8kj8/jb0tK4TDzxcrjeQR3OdJSAXnrS6YjP5JHsQer2Q/2BZuXQhAZcejQFcZjMnDmTmTNnMmHCBAAOHDjAhg0bOPfcc/nud7/LD37wAy6//HLOPffcNrlfRyH8bX9o7xzeLyrn3bU7WbylgsaA0jUjFV+KhKzaleYTRvbpQllVDR9u2H0oiig4s05TTucdVMeZw3ryxbOGcN7IPLpkpOIPKMXlB9rUxNOpewUJ6qUntYHuiM8UROdREK286VP0Nvz9q67n0ER6Dlz23zBqapuIoKrce++9fOMb32hxbtmyZcyYMYMf/ehHXHTRRdx3330RSuh8NI0tNMX8D27YR/bpwvTzhnHR6D6MG9A9ZjygRn+ArRXVFO86wB/nb2Fe8Z6Q+wgwaXhvLhvX71CaeQe1MWtfh63zwV/vjusPQuli1xAe6X8sUQ10vGyY5Z6hwTNH1h90shzPzxRE51EQrVE4xVVCeKUUTjmqYoPDfV966aX8+Mc/5gtf+AJdunShrKyMtLQ0Ghsb6dmzJzfeeCM9evTg6aefDrm2M5uYnvpwEws27TkU9kFxPYMHrzyR608PnfsSqzFP9aUwLK8Lw/K64EsRlm+timuW8HHTmLeTN84WqMKnH8HyP8Gqv0GgIfR8QzW89wCkZcGQcyEl5fDKXvpcS6WzdT58+DCc+hXokhd6TVt+Tw21sPCJZuXQRP1BeP/ncGAHDDwTeo90zxXPvf0NsOJFKFkIjbXN5R2t0jlCTEE0keJzGnrDLDdW0Xdcm/zJgsN9T5s2jc9//vOcddZZAHTp0oU//elPFBcXc/fdd5OSkkJaWhqPP/44ANOnT2fq1Kn079+/0w1Sryrdy8OziphTVN7iXKNfIy4fGW9j3uFmCbeHN87wxq/PWFj5V1jxZ6jYBOldYeh5Tlk0Bi14lZIKlZvh+SsgdyicchOM/wJ07Ru5QfXXw5Z/w/p3YMNMqIqw5oS/3jXQ7/8ccvKdLH3GQv4YWPIM7PrENepH+j2pwro3YOaP3f3FBxo0/iU+90xv3uGOM3tAwemwZwPs3w6NdZCaAd0HwgmfgX1lUFUCe0vc+XBTN7h63bHqmCsIi+bagegIz1u0Yz+/mbWet9fsoEd2GheP7sOMVdvjCvt8OHSoENFF/4JXvgINQQ1vWg589tlj06A0KajSJa7hlZTmBnPwOa7RH/3/IDUzsiK7/kX45C1Y9kf4dJ5rYAsvhb1boWKzK9OXBuld3Nu0vw7SsmHo+dB9gHvjDn6LT8uGc7/rPneugZ2rYdc6d104adnw2efi/562fwxv/yd8+m+ncKY8AB/9ruUz3fgPqNwCJQtg6wLY+B7s2xahwBToXgA9BjqF0WMg1O6FZc839yDAKdJrn4GxV8VbK3Fja1Ib7Y7wgechvXJ4dPYG3vh4GznpqXzn4kK+es5QctJT2V55gG6lcyj0b2KDbxj7CiYf9dv+cW86OlAOmz+Aje+7t9mGsGVoGw7Cv74P25bD4LOh4DTXeMUqM957H7pHrSt/2fPurb5JKagfUtLc+N3Er4SWGa2XfvJ/uG13sVMUS/8Adfuar/PXQ00ljJwKp3/NKZ60TCfj7g0tG+hz7gyV1d8I7/wnLHoSgmehNFTDO/c4c9AJl0NO78jP3vckmPMLZyrL7gmfeQRO+RL4UmH4hZGfqfcIt024ET74Fbz/i9B7IzD5BzD5npbfe3lR8zOl+CDQ6HosWbkw7PzIv5UEYArCaFPimc0caYZyY0DJSBW+cd5wvnHeMHJz0l3mgJ8/ZfwSf/piUhprCKRm4ctYiPAqbnpNgmlPHjKpGa4By+gBu1a7PJk9IH+0a6ib7PDgGmhJgbm/ciaLlDQYcAoMOhOKZ0PFRqdUoplZIpmt+p4EZ9wCpYugZBFsXxF6zxDZG+Hg7pbpKT73th7tjb33CLjkQfdm/8EvCW1QgQGnwoiLQ8uLxzTc1JCv+FOoI0pKGtRVO3PQP++CIee43s7qv7s6r/d6LwE/IHDWrXDe3ZDVI1SGWM8E7rtLzw5zgsmGfuMjf0fhz5SeA29+25niTv2K67lkJj6ybodXEKp6aHnCjkx7MBVGms08bkB3fnbView5WM/OfbWU769j0eaKkPULGgNKaorwi2tO4tpTCkIL3TALKV1CaqMzIaQ0Vh+7AbvDte23tTJZ/Y/QAdjGWthbCvnd4MIfw/ALmhuYaHLWH4CtC53p5tOPnDkk2MZdfxA2z4X/Ge/s/uk5bqvd6+4daGzOt3W+23wZ0H8CnHkLDDzDnfvnd1o2fn3HHfmz958QuUGNVGY8DTREd0S58R9Qvg7WvAZrX4MZ3wu9zl/vzF6f+XXLHlG8HK4TTKRn+uY814uZ/5j7nf2//3G/gQS+wHRoBZGZmcmePXvo1atXh1YSqsqePXvIzMxMqhxzinax9NPKQ4vRVNf7Wbi5gim/mRuSL3y+AnhrC1eGm0lqYfHTzlwSTH21+0MkWkFsmOX9ob371x903iWLn4GRl0JGV9eY+tJdo9sWA8UNNW5MYdUrsP5fEQYsBcZeA+eFNWLR3qIzu8PIS9wGMPtnMPfXhL6Zq/MiamqQD+yCfaXNyiGYU74Ml/3K9WaaCPid6aUtPQAT4VUYq7fRd5zbLvwRzLgbFj8Veq0GIveI2uLe8ZKeDZf8DMZcBa/fCn++Frr0gbr9sXuDR0GHVhAFBQWUlpZSXt7SE6ajkZmZSUFBQesZE8Tqsr088ObakJXKmph2Yl9uPHMw+V0zyO+WyeLNe/j2Syuiu5k21jm79oePwP5t3qBncLkKH78EPYfBmCudCSARNJkYgmmshX/d7bYmUlLdW3VDNYca3vqDbnByybNw6pdDZQzvaQy7wA16rvobrPsn1O+HLn1h1DRnDgr2+jnat+gBEyO8mec4k0XwtdHmBY2aFqocmu7d1h6ACfIqbPV7EnEmrI9fbNseUTz3jpeCifCNufCPb8DaV5vTE+AO26EVRFpaGkOHDk22GB2aLbsP8vCs9bz58TZyMnxk+JRJupyxsoU1OoRFqafy2VMLmDSieS7HBSf0iexmOiLXDU7O/bVz+Rt4Jlz1e/j3b5rfJNOyoFt/52r495vdwN3pX3N22czubdfdVoV922lhA0/NhDO+Cb0L3R+ybn+zmaYs1IsOf50zV7x9r8ufPxryRrkJYxWbXA8pxQeImx+Q0Q3GXgnjPufmBEDkXsmxeDNvC5PI0ZKIMuMhQXOi2pTUDOgzxpnEgn+jbewO26HdXI3EsWt/Lb97r5i/LNpKmi+Fr507lK+ePYiyR6cytO4TMqmjlgw2Z5zA6O+/hy819F3E39jIqg9eoXbrcrIKTmJcz0ZSPnzY+ZUPmAgX/tC9WYsEvXEHvUkiUPwuLPg9bHrfvcFn94TaKtfwHk13u7HeeQAtfQ6yerpeQ2td+Ehv3KlZcNrNrvewax3sWgtVW1veT3ww6Q44/wfOMyeYSM9+tG/R8ZaZiHsfLxwPzx6tl3ft4bk3x3JzNQVhtEqwZ9LQ3jms276P5+ZtocEf4IbTB3H7RSPI75oJRW+jr3wVCRoz0NQM5Jy7YPAk9/admuFMMv+8A3as9swyXgCNfuOdDXjExU4xxMuudfDOD52veTBH8Gfh4G54+UvO5HPOnTD5P2Hj7Pga03jGIN570M3yDXd3vOCHcP7d4aUaRnTaaIKkzYMwjphgz6TgMYPLT+rH9y4ZxZDeOS6hsR6W/iFEOQBIYx3M+a9W7qJOcUy+58i68fmjnfvmxtmEdrcPujkCIy+NT+HsWA0v3QD7d8I1T8FJ17n0eMwc8drMm+YjtLV92+h8JGqcJghTEB2Mtl5VbU7RLpZ9Wklt0OBzZloKV08Y4JSDv8ENGM/9lTOfhA8op2bBBf8J/cc7JdJY6wZj174WeqPGetdAj5p2ZIJG8jNHXKiH3Rvg/O/H7pms+yf8Y7rzLf/qv5y//eESj838eLBvG8cPCR6nMQXRgWjrVdUO1DXyu9kbQpQDQF1DgHVllVxU/z7MecjFnek/Aab9GhY81rLxO+vW0LealFQontW2b9GRGt7+p8CYK2Deo/DnzzoT1nl3w6jLAHVvXts/hj0bYdVfnVL4jz9Dt36t3u6IOQZvfYbRVtgYRAfivXU7uf0vy9skbtHc9eXc+49VlFXVkJ6inIPzTFqrg8lNreOn3d8iZ/9m18Bd8EMXAiHagHJ445fwGPph926sd4HjPnzYKbO8MYC6Hk+TSSwnH769HDK6HPn9DeM4xMYgOglrtu2jJsKqags374lbQeytbuDBt9byytJShufl8PLXTyP7r59jaN06sqgDhBRRNHM0THvBxa8JDtEcT5f3WPu4p6a7gHEn3+BCKLx3f8vAaQ0HXTyhY+1SaRjtGFMQHYh6fyDcax+Ap+ZupmjHAa6bOJCLx+STkeoa4vDxiroGPz95cy0VB+u59YLh3H5hIZmbZqG6AZGmSJiK+jKQi+6DEy47cmGT4ePuS3UB4So3O9NYAv3HDaMjkFAFISJTgf/BRVV7WlUfCjs/CPgj0MPLc4+qzhCRIcA6oMjLukBVv5lIWds9rcT5eW7eZv53djFdM4RzAsspDLjIp7v7nMfpI/L4x7Iybn1xGbnZaVw1YQDXnlLAL2asOzRekZIC/gCM7tuV5758GicO6O4KLl3U0jPJX+/CKB+Ngkgm/cabJ5FhxEHCFISI+IDHgClAKbBYRN5Q1bVB2X4EvKyqj4vIGGAGMMQ7t1FVI4Q67ITEsNn7SeFnb63luXlbuGR0b34feBDKljZHPu2yEJnyKndNGcW/i3fz8pIS/rxgK8/N20KKcGilNn/ArdT2nSkjm5XDwd0uYFw4x3tjap5EhhEXiexBnA4Uq+omABF5CbgSCFYQCjTFrO0ORFpRw4i07u2WD/E/cR7r9mdy/oEGPp+fzYjaWmTHykNx+YMjn/pGTeX8kXmcPzKPyoP13PnyihartTX6lfU79nPp2L7ORv/8le6zz4nOLNNRGlPzJDKMuEikghgAlAQdlwJnhOW5H5gpIrcDOUBQoHeGishyYB/wI1X9MPwGIjIdmA4waNCg8NMdhw0zW6x7qxpgz+6d1Dd2Z2xuJnldal3sIA0dpI4U+TQ3J52bzhzMos0VkQPm7dkIL1wF1ZWuIR10ZsdrTJMV58cwjiMOY4XwhHAD8AdVLQAuA14QkRRgOzBIVScAdwEvikiL1TFU9UlVnaiqE/Py8sJPdwyK34UVf0YJncdQQyY/afgKlTfMIO+ueTB9josPn54TVoC6SWm7N4SkNq3LnJ3uQ3DusOMH9mBy7m54bhrUHYAvvwlDJjU3puff7T6Pd+VgGEZcJLIHUQYMDDou8NKCuRmYCqCq80UkE+itqruAOi99qYhsBEYCnWuiw7Ln4c3voHknUFQFA+uKyaSeWtL5WIfzza99k5MH92rOH25bT8t2kU8rS+Dxs11soXPugrRMfCnCCzefEbouc5cSfH+4zEVM/cq/IP+E5D27YRhJJ5EKYjFQKCJDcYrheuDzYXm2AhcBfxCR0UAmUC4ieUCFqvpFZBhQCGxKoKztC1W3sMuHv4bhF/HByf/Nba8UcXrjUsbIp6zVwSz0ncqj1WELukSzrR/cDTN/6JZwXPU3+MzDMPzC0HWZN8+FF25wS1p+8XXIHZKURzcMo/2QMAWhqo0ichvwDs6F9VlVXSMiDwBLVPUN4LvAUyJyJ27A+suqqiJyHvCAiDQAAeCbqlqRKFnbFY118PptsOplmHATXP4bVs7ZwoF6ZTanMJtTAJCAsnbbvpYT4CLZ1rv2gWufhvFfgLe+6zyiTvwsTHnQjU+sfgXWvAo9R8CXXndLTxqG0elJ6DwIVZ2Bc10NTrsvaH8tMCnCdX8H/p5I2dolNZXw15tgy4cu7PW53wMRctJbVlPICmzxMvwCuOUjtwDPhw87pSDilpaUFLeeQk4HHcsxDOOwsZnUySR48luXvm5B+crNcPWTbsYvbmGeJ+duJN2Xgi9FqG0IWoFtVP7h3zMtEy64161lO+O7TgZwEVh3fNymyxUahnF8YwoiWYRPfkPdymI3/t296QO1DX6+8cJS9tU28sotZ1G+v655QPkow3hTvduNdQRj4SYMwwjCFESy2DDLUw7BS1SmuzEIQFX54aurWb61ise/cAonFfQAOOyorFGJtH7C8T5D2jCMNiXZ8yA6LztWej2HIBpq3Rs88My/N/P3ZaV85+JCpo1LwPoETS6x6TmAuM/jfYa0YRhtivUgkkXfk9zCOYGG5jTvDX5O0S5+MWMd007sy7cvLEzM/S3chGEYrWAKIlnkj3FhMcTnBoi9GEcbe5zF7Y8vYFTfbjx83cmkHM04Q2tYuAnDMGJgCiJZzMN9pBQAACAASURBVH4QJNVNWjuwE/qOY++AyXz9iQWk+1J46ounkh3BvdUwDONYYS1QMihZ7CbCnftdOPWLADT6A9z+xyWUVFbz4tfPpCA3O8lCGobR2TEFcawJBODtH0CXvvgn3cmcdTtZs20fq8v2Mnd9OQ9dM47ThvRMtpSGYRitKwgReRgvTMYxkKfjs+plKFtK4Mrfc9MLa1hRUnUo5Ha/7pl8buLAVgowDMM4NsTj5roOeFJEForIN0Wke6KF6rDUHYBZP4H+p/B+xoUhygFgb00Dc4p2JVFAwzCMZlpVEKr6tKpOAr6IWw50pYi8KCIXJFq4Dse/fwMHdsDUh1iz/QA19aGL+9TU+1m7bV+ShDMMwwglroly3vrSJ3jbbuBj4C5vGVEjHio/dbGWxn0OBp3B2P7dSPOFfv1HFIDPMAwjQcQzBvEb4HJgNvALVV3knfqliBQlUrgOxaz7XMTUi+8H4MxhvQAlRVxIpKMKwGcYhpEA4vFiWolbE/pghHOnt7E8HZMt89yyn5Pvhe4FALyw4FPq/crdl44iENC2CcBnGIbRhsSjIKqC84lID2Cyqr6mqnsTJllHIeCHt++BbgVw9rcBqKqu5/fvF3PhCfncesGIJAtoGIYRmXjGIH4SrAhUtQr4SeJE6mCs+LMLzDflpy6cBvDY+8UcqGvkB1NtzWfDMNov8SiISHlsgl081O6D9x6AgWfAidcCUFpZzR8/+pRrTylgVN+uSRbQMAwjOvE09EtE5BHgMe/4VmBp4kTqADStFDfvN3CwHK5/0S3tCTwycz0icNclI5MspGEYRmzi6UHcDtQDf/W2OpySaBURmSoiRSJSLCL3RDg/SETeF5HlIrJSRC4LOnevd12RiFwa3+O0A5pWinvly7B1gQvpPftnEHBzHF5dUcZXJg2lX/esZEtqGIYRk1Z7EJ73UovGvTW8uROPAVOAUmCxiLyhqmuDsv0IeFlVHxeRMcAMYIi3fz0wFugPvCsiI1U1dGZZe6RppbiGGnccaHTHG2bx0LyedM9K45bJw5Mro2EYRhzEMw9iJPA93CzqQ/lV9cJWLj0dKFbVTV45LwFXAsEKQoGmmWHdgW3e/pXAS6paB2wWkWKvvPmtyZt0Iq0UV1/Np2sWMHf9afzoM6PpnpWWHNkMwzAOg3jGIP4GPAE8DRzOG/wAoCTouBQ4IyzP/cBMEbkdyAEuDrp2Qdi1A8JvICLTgekAgwYNOgzREkjfk9yEuKDOjqZl89ymrgzokcVNZw1OonCGYRjxE88YRKOqPq6qi1R1adPWRve/AfiDqhYAlwEviEjc62Sr6pOqOlFVJ+bl5bWRSEdJ3iinHFLSaFrreXf3cTy/eyTfu3QkGam2pKdhGMcH8fQg3hSRbwGv4gaoAVDVilauKwOCY1cXeGnB3AxM9cqbLyKZQO84r22fLHoKSIH/91vYt52G/LFc+3oaJ/TL5MqTW3SCDMMw2i3xKIgveZ93B6UpMKyV6xYDhSIyFNe4Xw98PizPVuAi4A8iMhrIBMqBN4AXPffa/kAhsIj2Tu1eWPY8nHgNTLgRgOf/vZmtVWt5/prxiV1f2jAMo42Jx4tp6JEUrKqNInIb8A7gw1t0SEQeAJao6hvAd4GnROROnNL5sqoqsEZEXsYNaDcCtx4XHkzLnof6/XD2bQDsq23gf2dv4JwRvTlvZDsxgRmGYcRJPF5M2cBdwCBVnS4ihcAoVf1na9eq6gyc62pw2n1B+2uBSVGu/Tnw89bu0W7wN8CCJ2DwOdB/AgBPzNlIZXUD90yzkBqGYRx/xDMg/BxuotzZ3nEZ8LOESXS8svZ12FcKZ9+GP6C8sqSUJ+du4oyhPRndz9Z4MAzj+CMeBTFcVX8FNACoajVgxvRgVN1iQL0K8Y+4hJueWcg9/1hJY0BZWVrFTc8sxB/QZEtpGIZxWMSjIOpFJAs3RoCIDCfIm8kAPp0H21fAWd9izvrdLN9aRaOnEGoaAqwoqbK1pg3DOO6IK9w38DYwUET+DLwHfD+hUh1vfPS/kN0LTr6BNdv2UdNga00bhnH8E48X0ywRWQaciTMt3aGquxMu2fHC7mJY/y84/weQlsXY/t3wpUiIScnWmjYM43gkag9CRE7wPk8BBgPbcbGSBnlpBsCCx8CXAad9HYDzR+bhE8EnTptm21rThmEcp8TqQdyFi3P0cIRzCrQWrK/jc3APrHgRTv4P6OLmOZRW1lDvD3DjGYPo0y3T1po2DOO4JaqCUNXp3ucFx06c44wlz0BjLZzZvDzGvI3O+vaVc4YyPK9LsiQzDMM4alodpBaRW0WkR9BxrhebqXPTUAuLnoQRUyC/eSLcR8V76Nstk2G9c5IonGEYxtETjxfT11W1qulAVSuBrydOpOOEVS+75US9sBoAgYAyf9Mezh7eCxEzKRmGcXwTj4LwSVBr560Ul544kY4DVGH+Y9BnHAw9/1DyJzv2U3GwnrNH9E6icIZhGG1DPNFc3wb+KiL/5x1/w0vrvBS/B+WfwNX/B0E9hY+88YdJI3olSzLDMIw2Ix4F8QOcUrjFO56FW12u8zL/d9C1H4y9JiR5XvFuhvXOoV/3rCQJZhiG0XbEM1EuICJ/AGaralHiRWrn7FgFm+bAxfdDarOlrcEfYNHmCq6aYIsCGYbRMYjHi+kKYAWeWUlExovIG4kWrN0R8EPR2/DaLW5i3ISbQk6vLK3iYL2fSTb+YBhGByHeWEynA1UAqroCOKJFhI5bAn544Wp45SuuB6F+eOWrLt1jXvEeROCsYTb+YBhGxyAeBdGgqnvD0jpX7OoNs6BsCTRUu+NAozveMOtQlnnFuxnTrxu5OZ3bwcswjI5DPApijYh8HufuWigivwM+SrBc7YsdK6G+OjStvtr1JnDRWpdvreLs4dZ7MAyj4xCPgrgdGItbA+JFYC/wnUQK1e7oexKkZYempWdD33EALPm0gnp/wOY/GIbRoYgVzfUFb/frqvpDVT3N236kqrXxFC4iU0WkSESKReSeCOd/IyIrvG29iFQFnfMHnUvuoHjhFMgf3XycngMDJrp03PhDaopw+pCeSRLQMAyj7Ynl5nqqiPQHvioizxO2zKiqVsQq2Jtx/RgwBSgFFovIG6q6NqiMO4Py3w5MCCqiRlXHx/0kiSTFB6d80Y07nHELDJvslEOKD3AT5CYM6kFORjzTSgzDMI4PYrVoT+BWjxsGLCVUQaiXHovTgWJV3QQgIi8BVwJro+S/Aecx1T7Zthwyu8Olv4CU5o7X3uoGVpft5bYLC5MonGEYRtsTawziTVUdDTyrqsNUdWjQ1ppyABgAlAQdl3ppLRCRwTjX2dlByZkiskREFojIVVGum+7lWVJeXh6HSEdB2VLof0qIcgBYsHkPAYVJNkBtGEYHI5aCeMX7HHkM5LgeeEVVgxdzHqyqE4HPA78VkeHhF6nqk6o6UVUn5uXlJU66hhrYuQYGnNri1EfFu8lK8zFhUG7i7m8YhpEEYpmYUkTkP4GRInJX+ElVfaSVssuAgUHHBV5aJK4Hbg1OUNUy73OTiMzBjU9sbOWeiWH7Sjc5LoKCmLdxD6cN7Ul6ajwOYYZhGMcPsVq16wE/Tol0jbC1xmKgUESGiki6V14LbyRv7etcYH5QWq6IZHj7vYFJRB+7SDxlS93ngNCluHftq6V41wGb/2AYRock1pKjRcAvRWSlqv7rcAtW1UYRuQ14B/DhxjLWiMgDwBJVbVIW1wMvqWrw7OzRwP+JSACnxB4K9n465pQthW4F0LVvSPJHG/cAMGm4zX8wDKPjEY9f5jIReQbor6rTRGQMcJaqPtPahao6A5gRlnZf2PH9Ea77CBgXh2zHhrKlLXoP4MJrdM9KY0z/bkkQyjAMI7HEYzj/A64X0N87Xk9nmkldXQGVm1uMP6gqH23cw1nDeuFLseVFDcPoeMSjIHqr6stAAJzpCDc20TkoW+Y+wxTE1opqyqpqONtWjzMMo4MSj4I4KCK98CK4isiZuHhMnYMyb45g/9BJ3fOK3fjD2Tb+YBhGByWeMYi7cN5Hw0VkHpAHfDahUrUnypZC3gmQEeq4NW/jbvp0y2B4Xk6SBDMMw0gs8Sw5ukxEzgdG4cJtFKlqQ8Ilaw+oOgUxcmpIciCgzN+4h8kj8xCx8QfDMDomrSoIEUkDbgHO85LmiMj/dQolUbUVqne38GAq2rmfioP1nGXzHwzD6MDEY2J6HEgDfu8d3+SlfS1RQrUbDk2QCx2gnle8G8DWnzYMo0MTj4I4TVVPDjqeLSIfJ0qgdkXZUvBlQJ+xIckfbdzD0N459O+RlSTBDMMwEk88Xkz+4EB5IjKMzuLmum059DsZfGmHkhr8ARZu2mPhNQzD6PDE04O4G3hfRDbhBqkHA19JqFTtAX+jUxCnfDEkeWXpXg7W+8291TCMDk88XkzviUghzosJnBdTXWLFagfsLoKG6hbjDx954w82QG0YRkcnqoIQkRsBUdUXPIWw0ku/SUT8qvrisRIyKUQboN64mzH9utEzJz0JQhmGYRw7Yo1B3A68GiH9H8B3EyNOO6JsqVtitKdbPM8fUGas2s7izZUU5GbhD2grBRiGYRzfxFIQaap6IDxRVQ/i3F47NmVLXe9BBH9AuemZhdz51xX4VflgfTk3PbPQlIRhGB2aWAoiS0RaxJEQka5Ax7av1FfDzrWHzEtzinaxoqSKusYAAHWNAVaUVDGnaFcypTQMw0gosRTEM8ArIjK4KUFEhgAveec6LjtClxhds20fNfWhnr019X7WbtuXDOkMwzCOCbFWlPu1iBwA5opIFy/5AG51t8ePiXTJommAur8LsTG2fzey0n1UBymJrHSfLRRkGEaHJuZEOVV9QlUHA0OAIao6uMMrB3AKovtA6NoHgMmj8hk/sMeh09npPsYP7MHkUfnJktAwDCPhxDOTGlXdr6r7Ey1MuyFsiVFfivA/108A4PyRefzuhgm8cPMZtpKcYRgdmrgUxJEiIlNFpEhEikXkngjnfyMiK7xtvYhUBZ37kohs8LYvJVLOEA7ugcotLeY/bKuqAeALZwziotF9TDkYhtHhiSfUxhEhIj7gMWAKUAosFpE3VHVtUx5VvTMo/+3ABG+/J/ATYCJuJbul3rWViZL3ENuiLzEKMKhXdsJFMAzDaA+02oMQkWwR+bGIPOUdF4rI5XGUfTpQrKqbVLUe5/10ZYz8NwB/8fYvBWapaoWnFGYBU6Ne2ZaULQVJgX6hS4w2KYiBuaYgDMPoHMRjYnoOqAPO8o7LgJ/Fcd0AoCTouNRLa4HnSjsUmH0414rIdBFZIiJLysvL4xApDg4tMdolJLm0sppeOenkZCSs02UYhtGuiEdBDFfVXwENAKpajYvq2pZcD7yiqocVRlxVn1TViao6MS8v7+ilaFpiNGwFOXA9iIKe1nswDKPzEI+CqBeRLNxYAN7aEPFEcy0DBgYdF3hpkbieZvPS4V7bdlR9CtV7Wow/AJRU1DDIFIRhGJ2IeBTET4C3gYEi8mfgPeD7cVy3GCgUkaEiko5TAm+EZxKRE4BcYH5Q8jvAJSKSKyK5wCVeWmKJEsG10R+grKqGgbm2gpxhGJ2HeNaDmCUiy4AzcaalO1R1dxzXNYrIbbiG3Qc8q6prROQBYImqNimL64GXVFWDrq0QkQdxSgbgAVWtOKwnOxLKlkFqJuSPCUnevrcWf0CtB2EYRqeiVQUhIlcDs1X1Le+4h4hcpaqvtXatqs4AZoSl3Rd2fH+Ua58Fnm3tHm1K2bIWS4wClDR5MJmCMAyjExGXiUlV9zYdqGoVzuzUsfA3wvYVh+IvBVNS6c2BMAVhGEYnIh4FESlPx/P1LP8k4hKj4DyYfClCv+6ZSRDMMAwjOcSjIJaIyCMiMtzbHgGWJlqwY86hAeoIPYiKGvr3yCTVl9DIJIZhGO2KeFq824F64K/eVgfcmkihkkLZUsjscWiJ0WC2VlSbeckwjE5HPF5MB4EWgfY6HGXLDi0xGk5pZTUXj+6TBKEMwzCSR1QFISK/VdXviMibeJPkglHVKxIq2bGk/iDsWgujprU4dbCukd0H6s2DyTCMTkesHsQL3uevj4UgSWV76BKjwTR5MJmCMAyjsxFrydGl3ucHIpLn7bdRRLx2RisD1GAuroZhdD5iDlKLyP0ishsoAtaLSLmI3BfrmuOOgB8++SdkdHfjEIHQeIGH1oEwBWEYRicjqoIQkbuAScBpqtpTVXOBM4BJInJntOuOKwJ+eOFq2LoA6vbC37/qjoOURElFNTnpPnKz02IUZBiG0fGI1YO4CbhBVTc3JajqJuBG4IuJFuyYsGEWlC7m0Bh8/UEoW+LSPUoqqhnYMxuJ4N1kGIbRkYmlINIiBeXzxiE6xuv0jpXQUBOaVl8NO1YdOiyprLYBasMwOiWxFET9EZ47fuh7EqSHNf7p2dB3HACqautAGIbRaYnl5nqyiOyLkC5AxwhKVDgFBkx0ZqX6aqccBkx06cDuA/XUNPhtHQjDMDolsdxcfcdSkKSQ4oObXnVjDjtWuZ5D4RSXTpAHUy/rQRiG0fnoeFFZD5cUH4ya6rYwSpsmyeWagjAMo/Nh4UljsHWPUxAFpiAMw+iEmIKIwdaKavK7ZpCV3vGtbYZhGOGYgoiBubgahtGZSaiCEJGpIlIkIsUiEjFkuIhcJyJrRWSNiLwYlO4XkRXe9kYi5YyGubgahtGZSdggtYj4gMeAKUApsFhE3lDVtUF5CoF7gUmqWiki+UFF1Kjq+ETJ1xr1jQG2760xF1fDMDotiexBnA4Uq+omVa0HXgKuDMvzdeAxVa0EUNVdCZTnsNhWVUNALcy3YRidl0QqiAFASdBxqZcWzEhgpIjME5EFIhLsa5opIku89Ksi3UBEpnt5lpSXt20kclsHwjCMzk6y50GkAoXAZKAAmCsi41S1ChisqmUiMgyYLSKrVHVj8MWq+iTwJMDEiRNbrHp3NFiYb8MwOjuJ7EGUAQODjgu8tGBKgTdUtcGLGrsepzBQ1TLvcxMwB5iQQFlbUFJRQ5pP6NOtY0QVMQzDOFwSqSAWA4UiMlRE0oHrgXBvpNdwvQdEpDfO5LRJRHJFJCMofRKwlmNISUU1BbnZ+FIszLdhGJ2ThJmYVLVRRG4D3gF8wLOqukZEHgCWqOob3rlLRGQt4AfuVtU9InI28H8iEsApsYeCvZ+OBTYHwjCMzk5CxyBUdQYwIyztvqB9Be7ytuA8HwHjEilba2ytqGbcgO7JFMEwDCOp2EzqCOyrbaCqusEGqA3D6NSYgohASYW5uBqGYZiCiECJubgahmGYgohESYVbp9rWgTAMozNjCiICWyuq6ZaZSvfstGSLYhiGkTRMQUSgpLLalhk1DKPTYwoiAlsrqs28ZBhGp8cURBiBgFJaaetAGIZhmIIIY9f+OuobAxSYgjAMo5NjCiKMpjDf1oMwDKOzYwoijK17vElytpKcYRidHFMQYZRUViMCA0xBGIbRyTEFEcbWimr6dsskI9WXbFEMwzCSiimIMEoqLMy3YRgGmIJoQUmFubgahmGAKYgQahv87NhXa5PkDMMwMAURQlmVC9I3qJcNUBuGYZiCCGJr0zoQ1oMwDMMwBRFMqa0DYRiGcYiEKggRmSoiRSJSLCL3RMlznYisFZE1IvJiUPqXRGSDt30pkXI2sbWimozUFPK6ZhyL2xmGYbRrUhNVsIj4gMeAKUApsFhE3lDVtUF5CoF7gUmqWiki+V56T+AnwERAgaXetZWJkhecB9PAntmISCJvYxiGcVyQyB7E6UCxqm5S1XrgJeDKsDxfBx5ravhVdZeXfikwS1UrvHOzgKkJlBVwPQgzLxmGYTgSqSAGACVBx6VeWjAjgZEiMk9EFojI1MO4FhGZLiJLRGRJeXn5UQmrqm6SnIXYMAzDAJI/SJ0KFAKTgRuAp0SkR7wXq+qTqjpRVSfm5eUdlSB7axrYX9dos6gNwzA8EqkgyoCBQccFXlowpcAbqtqgqpuB9TiFEc+1bcohF1dTEIZhGEBiFcRioFBEhopIOnA98EZYntdwvQdEpDfO5LQJeAe4RERyRSQXuMRLSxglFd4kOVMQhmEYQAK9mFS1UURuwzXsPuBZVV0jIg8AS1T1DZoVwVrAD9ytqnsARORBnJIBeEBVKxIlK1gPwjAMI5yEKQgAVZ0BzAhLuy9oX4G7vC382meBZxMpXzAlldX0zEmnS0ZCvxLDMIzjhmQPUrcbzIPJMAwjFFMQHrYOhGEYRiimIAB/QCmrsnUgDMMwgjEFAezYV0uDX60HYRiGEYQpCGDrHoviahiGEY4pCJwHE9g6EIZhGMGYgsANUPtShH49MpMtimEYRrvBFAROQfTrnkmaz74OwzCMJjp9i+gPKKvK9iLAe+t24g9oskUyDMNoF3RqBeEPKDc9s5CN5Qcpqazh9r8s56ZnFpqSMAzDoJMriDlFu1hRUnXouLrez4qSKuYU7YpxlWEYRuegUyuINdv2UVPvD0mrqfezdtu+JElkGIbRfujUCmJs/25kpftC0rLSfYzp3y1JEhmGYbQfOrWCmDwqn/EDe5Cd7kOA7HQf4wf2YPKo/GSLZhiGkXQ6dWxrX4rwws1nMKdoF2u37WNM/25MHpWPL0WSLZphGEbS6dQKApySuGh0Hy4a3SfZohiGYbQrOrWJyTAMw4iOKQjDMAwjIqYgDMMwjIiYgjAMwzAiYgrCMAzDiIiodoy4QyJSDnx6FEX0Bna3YT4r08q0Mq3M9lhmOINVNS/iGVW1zSnJJW2Zz8q0Mq1MK7M9lnk4m5mYDMMwjIiYgjAMwzAiYgqimSfbOJ+VaWVamVZmeywzbjrMILVhGIbRtlgPwjAMw4iIKQjDMAwjMolwjTqeNuBZYBewupV8A4H3gbXAGuCOKPkygUXAx16+n7ZSrg9YDvyzlXxbgFXAClpxaQN6AK8AnwDrgLMi5BnlldW07QO+E6PMO73nWQ38BciMku8OL8+a8PIifddAT2AWsMH7zI2S73NemQFgYitl/rf37CuBV73vI1K+B708K4CZQP/WfhPAdwHF+Z1HKvN+oCzoe70sVpnA7Z6sa4BfRSnzr0HlbQFWxHj28cCCpt8JcHqUfCcD873f1JtAt1i/8wj1dGKUfCH1FKO8SHUULW94PU2MlC9KHUUrM7yevhitzAh1FK3M8HpaEyVfpDqKVmaLeiJKGwMMBRYCxZ4s6UfdPrZVQ3u8bsB5wCm0riD6Aad4+12B9cCYCPkE6OLtp3kVdmaMcu8CXiQ+BdE7zmf6I/A1bz8d6NFKfh+wAzdhJtL5AcBmIMs7fhn4coR8J+KUQzYulPy7wIhY37X3h7vH278H+GWUfKNxSm0OoQoiUt5LgFRv/5cxyuwWtP9t4IlYvwnvT/wObkJm7yhl3g98L57fGXCB9x1leMf5rf0egYeB+2KUOROY5u1f5n1fkfItBs739r8KPBjrdx6hnv43Sr6QeopRXqQ6ipY3vJ6ej5QvSh1FKzOknmLki1RHrbYFXj39KkqZkeoo2v1b1BNR2hjc//J6L/0J4JZ42otYW6c3ManqXKAijnzbVXWZt78f92Y+IEI+VdUD3mGat0X0BBCRAuAzwNNHJn3EMrvjGoRnPHnqVbWqlcsuAjaqaqyZ6KlAloik4hTAtgh5RgMLVbVaVRuBD4Brmk5G+a6vxCk0vM+rIuVT1XWqWhR+wyh5Z3r3B/emVhAlX/Di4zl49RTjN/Eb4Ptx5GtBlLy3AA+pap2XZ1esMkVEgOtwPbhoZSruLROgO7AtSr6RwFxvfxZwrVdmtN95eD1NiZQvvJ6ilReljqLlDa+ngzH+i+F1FO//Nlq+SHUUs8ygenoqSr5IdRStzBb1FKONuRBnOWiqo6vCn/Nw6fQK4kgQkSHABJzmjnTeJyIrcN36WaoaMR/wW9yPORDHbRWYKSJLRWR6jHxDgXLgORFZLiJPi0hOK2Vfj9foRLyxahnwa2ArsB3Yq6ozI2RdDZwrIr1EJBv3djSwlXv3UdXt3v4OoK1Xbvoq8K9oJ0Xk5yJSAnwBuC9GviuBMlX9OI573iYiK0XkWRHJjZFvJO77WigiH4jIaa2Uey6wU1U3xMjzHeC/vWf6NXBvlHxrcI0+OLNQi3oK+51HrafW/g9x5GtRR+F5o9VTcL7W6ijC/SPWU1i+mHUU5Zla1FNYvph1FJY3Yj2FtzHARqAqSOmWEkERHi6mIA4TEekC/B1nX98XKY+q+lV1PFAAnC4iJ0Yo53Jgl6oujfPW56jqKcA04FYROS9KvlScOeFxVZ0AHMSZBKI9TzpwBfC3GHlycT/SoUB/IEdEbgzPp6rrcKaCmcDbOBurv/VHO3S9EqW3dSSIyA+BRuDPMe75Q1Ud6OW5LUo52cB/EkOBBPE4MBxnZ96OMzVEIxVn2z8TuBt42Xv7jMYNxFDkHrcAd3rPdCdeTzICXwW+JSJLcSaN+uCTsX7nwfUUz/8hVr5IdRQpb6R6Cs7nlRG1jiKUGbGeIuSLWkcxnj2kniLki1pHEfJGrKfwNgY4Idp3f1QcrY2qI2zAEFoZg9Bme987wF2HUfZ9RLZJ/xdOy2/BvZFVA3+Ks8z7I5XpnesLbAk6Phd4K0ZZVwIzW7nf54Bngo6/CPw+Djl/AXwr1ncNFAH9vP1+QFGsOiFsDCJaXuDLuMG97HjqGRgUJtehvMA43JvaFm9rxPWm+rZSZvizhh+/DVwQdLwRyIvyPKnATpwpJtY99tI8v0mAfXE8+0hgUazfeaR6ipQvUj1FyxeljmL+x5rqKTxfK3XUWplDIpXZSh1Fe6aQeopSZrQ6ak3OkHoKSr8Pp7x20zyucxbwTmv/0dY260HEiffW8AywTlUfiZEvT0R6ePtZwBScB0QIqnqvqhao6hCciWe2qrZ4K/fKyRGRrk37uAG+1ZHySnITjwAAA+ZJREFUquoOoERERnlJF+E8I6IRz1vpVuBMEcn2voeLcDbSSLLme5+DcOMPL7ZS9hvAl7z9LwGvt5K/VURkKs50d4WqVsfIVxh0eCUR6glAVVepar6qDvHqqxQ3oLgjQpn9gg6vJko9ebyGGwRFREbiHAqiReS8GPhEVUtjlAdubOh8b/9CnNdRC4LqKQX4EW5QM9bvPFI9xfN/iFhepDqKkTdSPYXki1ZHuMY6UpmR6inS80Sro2jPfqieYnyXLeooxrO3qKcobcw6nBfUZ71L2+S/dFTapSNsuMZxO9CA+1HdHCXfObhudZO73SEXxrB8J+HcVlfifnT3xSHDZGJ4MQHDcC5tTW5tP2ylvPE497mVuB94bpR8OcAeoHscMv4U98dcDbyA59URId+HOIX0MXBRa9810At4D9eQvYvrzkfKd7W3X4f7078To8xioCSonp6Iku/v3vOsxLkQDojnN4HnURalzBdwLokrcY1qvxhypgN/8mRYhmssIt4b+APwzTi+z3OApd73vxA4NUq+O3CeMuuBh2h+o434O49QT9Oi5Auvp4VR8kWqo2j3Dq+nqyLli1JH0coMr6cro+SLVEdR24Lgeopx70h1FC1vi3oiShuDaycWed/t34jyHz2czUJtGIZhGBExE5NhGIYREVMQhmEYRkRMQRiGYRgRMQVhGIZhRMQUhGEYhhERUxCG0Qoi4heRFUFb1JnpR1D2EBGJNVfCMJJGarIFMIzjgBp1YQ0Mo1NhPQjDOEJEZIuI/EpEVonIIhEZ4aUPEZHZXiC497xZ5YhIHxF5VUQ+9razvaJ8IvKUiKwRkZne7FhE5NsistYr56UkPabRiTEFYRitkxVmYvqPoHN7VXUcbn2E33ppvwP+qKon4YLLPeqlPwp8oKon48JArPHSC4HHVHUsUIUXehsXZHGCV843E/VwhhENm0ltGK0gIgdUtUuE9C3Ahaq6SUTSgB2q2ktEduNCbDR46dtVtbeIlOOCuNUFlTEEFxK+0Dv+AZCmqj8TkbeBA7hwKa9p8xoAhnFMsB6EYRwdGmX/cKgL2vfTPDb4GeAxXG9jsbjFmgzjmGEKwjCOjv8I+pzv7X+Ei9ALboGbD73993BrATQt+NI9WqFe9M6Bqvo+8APcymMtejGGkUjsjcQwWifLW72ribdVtcnVNVdEVuJ6ATd4abfjVvS7G7e631e89DuAJ0XkZlxP4RZclNVI+IA/eUpEgEf1/7d3xyYAgEAMAJdy/9ViYxkRxEa4WyLkU/z5dSw8ZYOAS2uDGEl2Pxzga05MAFQaBACVBgFAJSAAqAQEAJWAAKASEABUE8+MejK78F3VAAAAAElFTkSuQmCC\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":373},"id":"V3Ahm7ecTST4","executionInfo":{"status":"ok","timestamp":1634270514838,"user_tz":-600,"elapsed":8784,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"44a27d39-7951-47f3-8484-132efc3bba3a"},"source":["#load model\n","new_model = Unet(feature_size=feature_size)\n","\n","filename = \"Unet_ISIC1.pth\"\n","checkpoint = torch.load(filename)\n","\n","new_model.load_state_dict(checkpoint)\n","\n","p = new_model(x)[0]\n","plt.imshow(p.detach().squeeze(), cmap='gray')"],"execution_count":13,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /pytorch/c10/core/TensorImpl.h:1156.)\n"," return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n","/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:1805: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n"," warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n"]},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":13},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"BZYl7y1XIJFr"},"source":[""],"execution_count":null,"outputs":[]}]} \ No newline at end of file From a489568618bf7a0837b940fc18d605e6a1cceb22 Mon Sep 17 00:00:00 2001 From: Wakame <85863413+wakameds@users.noreply.github.com> Date: Mon, 18 Oct 2021 09:00:27 +1000 Subject: [PATCH 20/66] remove dataloader ipynb --- recognition/s4633139/Dataloader.ipynb | 1 - 1 file changed, 1 deletion(-) delete mode 100644 recognition/s4633139/Dataloader.ipynb diff --git a/recognition/s4633139/Dataloader.ipynb b/recognition/s4633139/Dataloader.ipynb deleted file mode 100644 index 5eb3f16a23..0000000000 --- a/recognition/s4633139/Dataloader.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Dataloader.ipynb","provenance":[],"collapsed_sections":[],"mount_file_id":"1sn2UhFSOZZoyAj5L2RL8YS_7_cthikKn","authorship_tag":"ABX9TyNkwzaLVu64OaORBKup58Jo"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","metadata":{"id":"vlhQO-g6Y2ah","executionInfo":{"status":"ok","timestamp":1633672076114,"user_tz":-600,"elapsed":10346,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["import os\n","from PIL import Image\n","import matplotlib.pyplot as plt\n","import glob\n","\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","\n","import torch.utils.data\n","from torchvision import datasets, transforms"],"execution_count":2,"outputs":[]},{"cell_type":"code","metadata":{"id":"WuSova9HHAzc","colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"status":"ok","timestamp":1633672105488,"user_tz":-600,"elapsed":26753,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"05fb16af-8927-4381-d504-febf63d348d5"},"source":["#define dataset for img and mask\n","file_dir = \"/content/drive/MyDrive/ColabGitHub/PatternFlow/recognition/s46331391_Unet/ISIC2018_Task1-2_Training_Data\"\n","os.chdir(file_dir)\n","\n","img_path = './ISIC2018_Task1-2_Training_Input_x2'\n","mask_path = './ISIC2018_Task1_Training_GroundTruth_x2'\n","\n","imgs = [file for file in sorted(os.listdir(img_path)) if file.endswith('.jpg')]\n","masks = [file for file in sorted(os.listdir(mask_path)) if file.endswith('.png')]\n","\n","imgs[0]"],"execution_count":3,"outputs":[{"output_type":"execute_result","data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'ISIC_0000000.jpg'"]},"metadata":{},"execution_count":3}]},{"cell_type":"code","metadata":{"id":"JhoipyIyXvBB","executionInfo":{"status":"ok","timestamp":1633672109919,"user_tz":-600,"elapsed":253,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["from torch.utils.data import Dataset\n","from PIL import Image\n","\n","class Unet_dataset(Dataset):\n"," def __init__(self,\n"," img_dir = './ISIC2018_Task1-2_Training_Input_x2',\n"," mask_dir = './ISIC2018_Task1_Training_GroundTruth_x2', \n"," img_transforms=None,\n"," mask_transforms= None):\n"," \n"," self.img_dir = img_dir\n"," self.mask_dir = mask_dir\n"," self.img_transforms = img_transforms\n"," self.mask_transforms = mask_transforms\n","\n"," self.imgs = [file for file in sorted(os.listdir(self.img_dir)) if file.endswith('.jpg')]\n"," self.masks = [file for file in sorted(os.listdir(self.mask_dir)) if file.endswith('.png')]\n","\n"," #meke dataloader\n"," def load_img(self, idx):\n"," img_path = os.path.join(self.img_dir, self.imgs[idx])\n"," img = Image.open(img_path).convert('RGB')\n"," return img\n","\n"," def load_mask(self, idx):\n"," mask_path = os.path.join(self.mask_dir, self.masks[idx])\n"," mask = Image.open(mask_path).convert('L')\n"," return mask\n","\n"," def __getitem__(self, idx):\n"," img = self.load_img(idx)\n"," mask = self.load_mask(idx)\n","\n"," if self.img_transforms is not None:\n"," img = self.img_transforms(img)\n","\n"," if self.mask_transforms is not None:\n"," mask = self.mask_transforms(mask)\n","\n"," return img, mask\n","\n"," def __len__(self):\n"," return len(self.imgs)"],"execution_count":4,"outputs":[]},{"cell_type":"code","metadata":{"id":"YFzDGbXcE_p8","executionInfo":{"status":"ok","timestamp":1633672112761,"user_tz":-600,"elapsed":276,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["#img_path = os.path.join(root, \"ISIC2018_Task1-2_Training_Input_x2\", imgs[idx])\n","import torchvision.transforms as transforms\n","img_tfs = transforms.Compose([\n"," transforms.Resize((64,64)),\n"," transforms.ToTensor(),\n"," transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n"," ]\n"," )\n","\n","mask_tfs = transforms.Compose([\n"," transforms.Resize((64,64)),\n"," transforms.ToTensor(),\n"," ]\n"," )"],"execution_count":5,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":302},"id":"zx1QLG_3l_nB","executionInfo":{"status":"ok","timestamp":1633672115791,"user_tz":-600,"elapsed":1189,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"1fd9dd2e-3a27-4ed5-e81b-bc34773b19c0"},"source":["dataset = Unet_dataset(img_transforms=img_tfs, mask_transforms=mask_tfs)\n","img, mask = dataset.__getitem__(20)\n","\n","import matplotlib.pyplot as plt\n","plt.imshow(img.permute(1,2,0))"],"execution_count":6,"outputs":[{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":6},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]}]} \ No newline at end of file From 44edc737e3a996f918d4ac425ffd8bd9f79a720d Mon Sep 17 00:00:00 2001 From: Wakame <85863413+wakameds@users.noreply.github.com> Date: Mon, 18 Oct 2021 09:06:54 +1000 Subject: [PATCH 21/66] upload the results obtained from IUnet --- recognition/s4633139/dice_coefficient.png | Bin 0 -> 16615 bytes recognition/s4633139/dice_loss.png | Bin 0 -> 16024 bytes recognition/s4633139/image.png | Bin 0 -> 78043 bytes recognition/s4633139/seg.png | Bin 0 -> 76624 bytes 4 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 recognition/s4633139/dice_coefficient.png create mode 100644 recognition/s4633139/dice_loss.png create mode 100644 recognition/s4633139/image.png create mode 100644 recognition/s4633139/seg.png diff --git a/recognition/s4633139/dice_coefficient.png b/recognition/s4633139/dice_coefficient.png new file mode 100644 index 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z33%jIpK~0R13u{vH|$k#SRf+z0q+H#1MX7Q*X*bBz+H0~!XFX}xB6DHw*l8xy&m~o zaB>#71pI|9Oa5F$#>^QXs`@JM2f)j~Yrq=tb>@$c9BzbzqomzmB3L1BdK||=2plPh z$P(~(z;CMRdj6XRv3jU6R1x_Qa8Xsiewfd>1?UmX&p%bw_Y(GUwEQ!3!pAD1lhs*5 z#p*Hp+u$7;%mW9n2l6KReH;YGK?wYxdKJ7CAR>K&0C-AOzi5Xqh{$RC{WNg#kcaVO zz=uWT`@mllmIQwUoD>n?T~w}3k0DsZ+!eT@I6(%q^eWkiv%3a8y`8` z2nTbgT}k~uK4t>xO^@R^2!VeC?l6t%HFzdbn{D=K}l&~KE3h*7^pMX2&h==!+q66==iF^_GTVObc=l>}1HN(l@1FJ`Y z58(HJ{|251{)*uBo(n#JrwP7@DZx7W1gAR@mIw8g^;5LW<1(d-O`|tzuPyhe`07*qoM6N<$f_+S1UH||9 literal 0 HcmV?d00001 From 27f0ba3bab4304c32f584bab11b22d25844e33d9 Mon Sep 17 00:00:00 2001 From: wakahide23 Date: Mon, 18 Oct 2021 07:30:47 +0000 Subject: [PATCH 22/66] revised colab file with segmentation part --- recognition/s4633139/IUNet.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/recognition/s4633139/IUNet.ipynb b/recognition/s4633139/IUNet.ipynb index 26f8f85ff1..165f64bec1 100644 --- a/recognition/s4633139/IUNet.ipynb +++ b/recognition/s4633139/IUNet.ipynb @@ -1 +1 @@ -{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"IUNet.ipynb","provenance":[],"collapsed_sections":[],"mount_file_id":"1UdH3PIDr4uawfSNGUl8ntSrq2Lbo_uQQ","authorship_tag":"ABX9TyPXanSKnpIqYcGKvPCydodW"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"vlhQO-g6Y2ah","executionInfo":{"status":"ok","timestamp":1634452298249,"user_tz":-600,"elapsed":13266,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["import os\n","from PIL import Image\n","import glob\n","import matplotlib.pyplot as plt\n","\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","\n","import torch.utils.data\n","from torchvision import datasets, transforms"],"execution_count":3,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"7vcUuTeHs59d"},"source":["# Data preparation\n","\n","\n","1. get path\n","2. dataloader for img and mask\n","1. split into test and validation\n","\n","\n","\n"]},{"cell_type":"code","metadata":{"id":"gPYa8ZVvXk2d","executionInfo":{"status":"ok","timestamp":1634452300911,"user_tz":-600,"elapsed":4,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["import os\n","from PIL import Image\n","import glob\n","import matplotlib.pyplot as plt\n","\n","#define dataset for img and mask\n","file_dir = \"./ISIC2018_Task1-2_Training_Data\"\n","os.chdir(file_dir)"],"execution_count":4,"outputs":[]},{"cell_type":"code","metadata":{"id":"mhX77-qIYdGT","executionInfo":{"status":"ok","timestamp":1634452302191,"user_tz":-600,"elapsed":1283,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["#path\n","img_path = './ISIC2018_Task1-2_Training_Input_x2'\n","mask_path = './ISIC2018_Task1_Training_GroundTruth_x2'\n","imgs = [file for file in sorted(os.listdir(img_path)) if file.endswith('.jpg')]\n","masks = [file for file in sorted(os.listdir(mask_path)) if file.endswith('.png')]"],"execution_count":5,"outputs":[]},{"cell_type":"code","metadata":{"id":"StdLOrNGt3Id","executionInfo":{"status":"ok","timestamp":1634452302191,"user_tz":-600,"elapsed":4,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["from torchvision import datasets\n","from torchvision.datasets import ImageFolder\n","from torch.utils.data import dataloader, random_split, Subset\n","from torchvision.transforms import transforms"],"execution_count":6,"outputs":[]},{"cell_type":"code","metadata":{"id":"JhoipyIyXvBB","executionInfo":{"status":"ok","timestamp":1634452306163,"user_tz":-600,"elapsed":17,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["from torch.utils.data import Dataset\n","from PIL import Image\n","\n","class Unet_dataset(Dataset):\n"," def __init__(self,\n"," img_dir = './ISIC2018_Task1-2_Training_Input_x2',\n"," mask_dir = './ISIC2018_Task1_Training_GroundTruth_x2', \n"," img_transforms=None,\n"," mask_transforms= None):\n"," \n"," self.img_dir = img_dir\n"," self.mask_dir = mask_dir\n"," self.img_transforms = img_transforms\n"," self.mask_transforms = mask_transforms\n","\n"," self.imgs = [file for file in sorted(os.listdir(self.img_dir)) if file.endswith('.jpg')]\n"," self.masks = [file for file in sorted(os.listdir(self.mask_dir)) if file.endswith('.png')]\n","\n"," #meke dataloader\n"," def load_data(self, idx):\n"," img_path = os.path.join(self.img_dir, self.imgs[idx])\n"," mask_path = os.path.join(self.mask_dir, self.masks[idx])\n"," img = Image.open(img_path).convert('RGB')\n"," mask = Image.open(mask_path).convert('L')\n"," return img, mask\n","\n"," def __getitem__(self, idx):\n"," img, mask = self.load_data(idx)\n"," #transformation\n"," if self.img_transforms is not None:\n"," img = self.img_transforms(img)\n"," if self.mask_transforms is not None:\n"," mask = self.mask_transforms(mask)\n"," return img, mask\n","\n"," def __len__(self):\n"," return len(self.imgs)"],"execution_count":7,"outputs":[]},{"cell_type":"code","metadata":{"id":"vpwsi1o-xnYw","executionInfo":{"status":"ok","timestamp":1634452307860,"user_tz":-600,"elapsed":5,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["from torch.utils.data import DataLoader\n","import torchvision.transforms as transforms\n","\n","\n","#transformation\n","img_tfs = transforms.Compose([\n"," transforms.Resize((128,128)),\n"," transforms.ToTensor(),\n"," transforms.Normalize(mean=[0, 0, 0], std=[1, 1, 1]),\n"," ]\n"," )\n","\n","mask_tfs = transforms.Compose([\n"," transforms.Resize((128,128)),\n"," transforms.ToTensor(),\n"," ]\n"," )\n","\n","dataset = Unet_dataset(img_transforms=img_tfs, mask_transforms=mask_tfs)\n","\n","\n","#shuffle index\n","sample_size = len(dataset.imgs)\n","train_size = int(sample_size*0.8)\n","test_size = sample_size - train_size\n","\n","#train and test set\n","train_set, test_set = torch.utils.data.random_split(dataset, [train_size, test_size], generator=torch.Generator().manual_seed(123))\n","\n","#dataloader\n","train_loader= DataLoader(train_set, batch_size=64, shuffle = True)\n","test_loader= DataLoader(test_set, batch_size=64, shuffle=False)"],"execution_count":8,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"cN3J5wTpx_HX"},"source":["# Unet"]},{"cell_type":"code","metadata":{"id":"VFiXmwWjSeXi","executionInfo":{"status":"ok","timestamp":1634452310344,"user_tz":-600,"elapsed":391,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["import torch\n","import torch.nn as nn\n","import torch.nn.functional as F"],"execution_count":9,"outputs":[]},{"cell_type":"code","metadata":{"id":"nzGxgjkBeFXR","executionInfo":{"status":"ok","timestamp":1634452312281,"user_tz":-600,"elapsed":454,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["class Context(nn.Module):\n"," def __init__(self, in_channels, out_channels):\n"," super(Context, self).__init__()\n"," self.context = nn.Sequential(\n"," nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.Dropout2d(p=0.3),\n"," nn.LeakyReLU(negative_slope=0.02, inplace=True),\n"," nn.Conv2d(in_channels, out_channels, kernel_size=3, stride = 1, padding=1, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.LeakyReLU(negative_slope=0.02, inplace=True),\n"," )\n","\n"," def forward(self, x):\n"," x = self.context(x) + x\n"," return x\n","\n","\n","class Localization(nn.Module):\n"," def __init__(self, in_channels, out_channels):\n"," super(Localization, self).__init__()\n"," self.localization = nn.Sequential(\n"," nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.LeakyReLU(negative_slope=0.02, inplace=True),\n"," nn.Conv2d(out_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.LeakyReLU(negative_slope=0.02, inplace=True),\n"," )\n"," \n"," def forward(self, x):\n"," return self.localization(x)\n","\n","\n","class Upsampling(nn.Module):\n"," def __init__(self, in_channels, out_channels):\n"," super(Upsampling, self).__init__()\n"," self.upsampling = nn.Sequential(\n"," nn.Upsample(scale_factor=2, mode='nearest'),\n"," nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.LeakyReLU(negative_slope=0.02, inplace=True),\n"," )\n"," \n"," def forward(self, x):\n"," return self.upsampling(x)\n","\n","\n","class Segment(nn.Module):\n"," def __init__(self, in_channels, out_channels):\n"," super(Segment, self).__init__()\n"," self.segment = nn.Sequential(\n"," nn.Conv2d(in_channels, out_channels=1, kernel_size=1, stride=1, padding=0, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.LeakyReLU(negative_slope=0.02, inplace=True)\n"," )\n"," \n"," def forward(self, x):\n"," return self.segment(x)\n","\n","\n","class Conv2(nn.Module):\n"," def __init__(self, in_channels, out_channels):\n"," super(Conv2, self).__init__()\n"," self.conv2 = nn.Sequential(\n"," nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=2, padding=1, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.LeakyReLU(negative_slope=0.02, inplace=True),\n"," )\n","\n"," def forward(self, x):\n"," return self.conv2(x)\n","\n","\n","class ImprovedUnet(nn.Module):\n"," def __init__(self, in_channels=3, out_channels=1, feature_size=[16, 32, 64, 128]):\n"," super(ImprovedUnet, self).__init__()\n"," self.Conv1 = nn.Conv2d(in_channels=3, out_channels=feature_size[0], kernel_size=3, stride=1, padding=1, bias=False) \n"," self.Downs = nn.ModuleList()\n"," self.Convs = nn.ModuleList()\n"," self.Ups = nn.ModuleList()\n"," self.Segmentations = nn.ModuleList()\n","\n"," self.upscale = nn.Upsample(scale_factor=2, mode='nearest')\n"," self.bottleneck = Context(feature_size[-1]*2, feature_size[-1]*2)\n","\n","\n"," #Downsampling frame\n"," for feature in feature_size:\n"," self.Downs.append(Context(feature, feature))\n"," self.Convs.append(Conv2(feature, feature*2))\n","\n"," #Upsampleing frame\n"," for feature in reversed(feature_size):\n"," #Upsample\n"," self.Ups.append(Upsampling(feature*2, feature))\n","\n"," #Localization\n"," if feature != feature_size[0]:\n"," self.Ups.append(Localization(feature*2, feature))\n"," else:\n"," self.Ups.append(Localization(feature*2, feature*2))\n"," \n"," #Segmentation\n"," self.Segmentations.append(Segment(feature, 1))\n","\n"," self.final_conv = nn.Conv2d(feature_size[0]*2, out_channels, kernel_size=1, stride=1, bias=False)\n"," \n","\n"," def forward(self, x):\n"," skip_connections = []\n"," segmentation_layers = []\n","\n"," x = self.Conv1(x)\n","\n"," #Downsampling steps\n"," for i, (context_i, conv_i) in enumerate(zip(self.Downs, self.Convs)):\n"," x = context_i(x)\n"," #preserve location\n"," skip_connections.append(x)\n"," x = conv_i(x)\n","\n"," x = self.bottleneck(x) + x\n"," skip_connections = skip_connections[: : -1]\n","\n"," #Upsampling steps\n"," for idx in range(0, len(self.Ups), 2):\n"," #upsample\n"," x = self.Ups[idx](x)\n","\n"," #localization\n"," skip_connection = skip_connections[idx//2]\n"," concatnate_skip = torch.cat((skip_connection, x), dim=1)\n"," x = self.Ups[idx+1](concatnate_skip)\n","\n"," #segmentation\n"," if idx == 2 or idx == 4:\n"," x_segment = self.Segmentations[idx//2](x)\n"," segmentation_layers.append(x_segment)\n","\n"," seg_scale1 = self.upscale(segmentation_layers[0])\n"," seg_scale2 = self.upscale(segmentation_layers[1]+seg_scale1)\n","\n"," x = self.final_conv(x)\n"," x = x + seg_scale2\n","\n"," output = F.sigmoid(x)\n"," \n"," return output"],"execution_count":10,"outputs":[]},{"cell_type":"code","metadata":{"id":"otdcbBK7g4fW","executionInfo":{"status":"ok","timestamp":1634452313629,"user_tz":-600,"elapsed":2,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["feature_size=[16, 32, 64, 128]"],"execution_count":11,"outputs":[]},{"cell_type":"code","metadata":{"id":"oDhAF_WYyeJm","executionInfo":{"status":"ok","timestamp":1634452314967,"user_tz":-600,"elapsed":5,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["#dice coef\n","def dice_coef(pred, target):\n"," batch_size = len(pred)\n"," somooth = 1.\n","\n"," pred_flat = pred.view(batch_size, -1)\n"," target_flat = target.view(batch_size, -1)\n","\n"," intersection = (pred_flat*target_flat).sum()\n"," dice_coef = (2.*intersection+somooth)/(pred_flat.sum()+target_flat.sum()+somooth)\n"," return dice_coef\n","\n","#loss\n","def dice_loss(pred, target):\n"," dice_loss = 1 - dice_coef(pred, target)\n"," return dice_loss"],"execution_count":12,"outputs":[]},{"cell_type":"code","metadata":{"id":"dNMXUs-qViYi","executionInfo":{"status":"ok","timestamp":1634452317681,"user_tz":-600,"elapsed":377,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["import torch.optim as optim\n","\n","#set parameters\n","feature_size=[16, 32, 64, 128]\n","model = ImprovedUnet(feature_size=feature_size)\n","\n","optimizer = optim.Adam(model.parameters(), lr=0.001)\n","EPOCHS = 15"],"execution_count":13,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JrGpP3KMPzpe","executionInfo":{"status":"ok","timestamp":1634439533392,"user_tz":-600,"elapsed":9774961,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"917497df-4479-47d2-9ae4-ea56435fd75f"},"source":["from tqdm import tqdm\n","\n","TRAIN_LOSS = []\n","TRAIN_DICE = []\n","TEST_LOSS =[]\n","TEST_DICE = []\n","\n","for epoch in range(1, EPOCHS+1):\n"," print('EPOCH {}/{}'.format(epoch, EPOCHS))\n"," running_loss = 0\n"," running_dicecoef = 0\n"," running_loss_test = 0\n"," running_dicecoef_test = 0\n"," BATCH_NUM = len(train_loader)\n"," BATCH_NUM_TEST = len(test_loader)\n","\n"," #training\n"," with tqdm(train_loader, unit='batch') as tbatch:\n"," for batch_idx, (x, y) in enumerate(tbatch):\n"," tbatch.set_description(f'Batch: {batch_idx}')\n","\n"," optimizer.zero_grad()\n"," output = model(x)\n"," loss = dice_loss(output, y)\n"," dicecoef = dice_coef(output, y)\n"," loss.backward()\n"," optimizer.step()\n","\n"," running_loss += loss.item()\n"," running_dicecoef += dicecoef.item()\n","\n"," tbatch.set_postfix(loss=loss.item(), dice_coef=dicecoef.item())\n","\n"," epoch_loss = running_loss/BATCH_NUM\n"," epoch_dicecoef = running_dicecoef/BATCH_NUM\n"," TRAIN_LOSS.append(epoch_loss)\n"," TRAIN_DICE.append(epoch_dicecoef)\n","\n"," #test\n"," with tqdm(test_loader, unit='batch') as tsbatch:\n"," for batch_idx, (x, y) in enumerate(tsbatch):\n"," tsbatch.set_description(f'Batch: {batch_idx}')\n"," output_test = model(x)\n"," loss_test = dice_loss(output_test, y)\n"," dicecoef_test = dice_coef(output_test, y)\n"," tsbatch.set_postfix(loss=loss_test.item(), dice_coef=dicecoef_test.item())\n","\n"," running_loss_test += loss_test.item()\n"," running_dicecoef_test += dicecoef_test.item()\n","\n"," TEST_LOSS.append(running_loss_test/BATCH_NUM_TEST)\n"," TEST_DICE.append(running_dicecoef_test/BATCH_NUM_TEST)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["EPOCH 1/15\n"]},{"output_type":"stream","name":"stderr","text":["Batch: 0: 0%| | 0/33 [00:43torchviz) (3.7.4.3)\n","Building wheels for collected packages: torchviz\n"," Building wheel for torchviz (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for torchviz: filename=torchviz-0.0.2-py3-none-any.whl size=4151 sha256=80aa29d36737c5d4b81edcddc53bec07221514dc0c26ab4de9e6c23bf49714c5\n"," Stored in directory: /root/.cache/pip/wheels/04/38/f5/dc4f85c3909051823df49901e72015d2d750bd26b086480ec2\n","Successfully built torchviz\n","Installing collected packages: torchviz\n","Successfully installed torchviz-0.0.2\n"]}]},{"cell_type":"code","metadata":{"id":"oKvcU7lyeujb"},"source":["from torchviz import make_dot\n","make_dot(output, params=dict(model.named_parameters(), ))"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"zG5sYuWORuIO","executionInfo":{"status":"ok","timestamp":1634452208764,"user_tz":-600,"elapsed":490,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["#save model\n","filename = \"Unet_ISIC2.pth\"\n","torch.save(model.state_dict(), filename)"],"execution_count":169,"outputs":[]},{"cell_type":"code","metadata":{"id":"EhJChHsfViUi","colab":{"base_uri":"https://localhost:8080/","height":319},"executionInfo":{"status":"ok","timestamp":1634205941940,"user_tz":-600,"elapsed":1094,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"2a6204e7-1325-46f0-cdbf-fee476cdd087"},"source":["model.eval()\n","p = model(x)[0]\n","plt.imshow(p.detach().squeeze(), cmap='gray')"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:1805: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n"," warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n"]},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":40},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"4Rh-uq05TG-A"},"source":["# Model Evaluation"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":279},"id":"7ZBk4w3xIA8L","executionInfo":{"status":"ok","timestamp":1634439698477,"user_tz":-600,"elapsed":391,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"519836e3-612d-4bcf-ccdb-b92349a2d6cb"},"source":["import matplotlib.pyplot as plt\n","import numpy as np\n","\n","X = np.arange(1,EPOCHS+1)\n","\n","plt.plot(X, TRAIN_LOSS, marker='.', markersize=10, label='train')\n","plt.plot(X, TEST_LOSS, marker='.', markersize=10, label='test')\n","plt.xlabel('Epochs')\n","plt.ylabel('Dice Loss')\n","plt.xticks(X)\n","plt.legend()\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":279},"id":"Y80RZXzKRPpc","executionInfo":{"status":"ok","timestamp":1634439703085,"user_tz":-600,"elapsed":429,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"e5830c99-2e63-46c5-beb6-a95b3f9ce19b"},"source":["plt.plot(X, TRAIN_DICE, marker='.', markersize=10, label='train')\n","plt.plot(X, TEST_DICE, marker='.', markersize=10, label='test')\n","plt.xlabel('Epochs')\n","plt.ylabel('Dice Coeffeciency')\n","plt.xticks(X)\n","plt.legend()\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"BZYl7y1XIJFr","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1634452335129,"user_tz":-600,"elapsed":5640,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"615ef612-6f7c-486a-f79e-99c1290af99f"},"source":["for batch in test_loader:\n"," x, y = batch\n"," print(x.shape, y.shape)\n"," break"],"execution_count":14,"outputs":[{"output_type":"stream","name":"stdout","text":["torch.Size([64, 3, 128, 128]) torch.Size([64, 1, 128, 128])\n"]}]},{"cell_type":"code","metadata":{"id":"V3Ahm7ecTST4"},"source":["#load model\n","new_model = ImprovedUnet(feature_size=feature_size)\n","\n","filename = \"Unet_ISIC2.pth\"\n","checkpoint = torch.load(filename)\n","\n","new_model.load_state_dict(checkpoint)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":285},"id":"BIAOsDaLtliA","executionInfo":{"status":"ok","timestamp":1634452356740,"user_tz":-600,"elapsed":923,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"6e9d7e79-98ef-467d-dcdd-b75c9a67d2a7"},"source":["plt.imshow(x[0].permute(1,2,0))"],"execution_count":17,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":17},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":302},"id":"LXKpDfDRjlkR","executionInfo":{"status":"ok","timestamp":1634448826907,"user_tz":-600,"elapsed":933,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"035c4c92-79d4-46d2-a20b-d0be012ff0d1"},"source":["alpha = 5\n","seg_img = x[0].clone()\n","image_r = seg_img[0]\n","image_r = image_r*(1-alpha*pred_mask)+(pred_mask*pred_mask*alpha)\n","segment_image = image_r.detach().squeeze()\n","seg_img[0] = segment_image\n","plt.imshow(seg_img.permute(1,2,0))"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":157},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]}]} \ No newline at end of file +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"IUNet.ipynb","provenance":[],"collapsed_sections":[],"mount_file_id":"1UdH3PIDr4uawfSNGUl8ntSrq2Lbo_uQQ","authorship_tag":"ABX9TyMhn0gJSue2GPm8/W30Ob7Y"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"vlhQO-g6Y2ah","executionInfo":{"status":"ok","timestamp":1634536832538,"user_tz":-600,"elapsed":12962,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["import os\n","from PIL import Image\n","import glob\n","import matplotlib.pyplot as plt\n","\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","\n","import torch.utils.data\n","from torchvision import datasets, transforms"],"execution_count":1,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"7vcUuTeHs59d"},"source":["# Data preparation\n","\n"]},{"cell_type":"code","metadata":{"id":"gPYa8ZVvXk2d","executionInfo":{"status":"ok","timestamp":1634536832542,"user_tz":-600,"elapsed":9,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["import os\n","from PIL import Image\n","import glob\n","import matplotlib.pyplot as plt\n","\n","#define dataset for img and mask\n","file_dir = \"/content/drive/MyDrive/ColabGitHub/ISIC2018_Task1-2_Training_Data\"\n","os.chdir(file_dir)"],"execution_count":2,"outputs":[]},{"cell_type":"code","metadata":{"id":"StdLOrNGt3Id","executionInfo":{"status":"ok","timestamp":1634536854321,"user_tz":-600,"elapsed":9,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["from torchvision import datasets\n","from torchvision.datasets import ImageFolder\n","from torch.utils.data import dataloader, random_split, Subset\n","from torchvision.transforms import transforms"],"execution_count":4,"outputs":[]},{"cell_type":"code","metadata":{"id":"JhoipyIyXvBB","executionInfo":{"status":"ok","timestamp":1634536854321,"user_tz":-600,"elapsed":7,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["from torch.utils.data import Dataset\n","from PIL import Image\n","\n","class Unet_dataset(Dataset):\n"," def __init__(self,\n"," img_dir = './ISIC2018_Task1-2_Training_Input_x2',\n"," mask_dir = './ISIC2018_Task1_Training_GroundTruth_x2', \n"," img_transforms=None,\n"," mask_transforms= None):\n"," \n"," self.img_dir = img_dir\n"," self.mask_dir = mask_dir\n"," self.img_transforms = img_transforms\n"," self.mask_transforms = mask_transforms\n","\n"," self.imgs = [file for file in sorted(os.listdir(self.img_dir)) if file.endswith('.jpg')]\n"," self.masks = [file for file in sorted(os.listdir(self.mask_dir)) if file.endswith('.png')]\n","\n"," #meke dataloader\n"," def load_data(self, idx):\n"," img_path = os.path.join(self.img_dir, self.imgs[idx])\n"," mask_path = os.path.join(self.mask_dir, self.masks[idx])\n"," img = Image.open(img_path).convert('RGB')\n"," mask = Image.open(mask_path).convert('L')\n"," return img, mask\n","\n"," def __getitem__(self, idx):\n"," img, mask = self.load_data(idx)\n"," #transformation\n"," if self.img_transforms is not None:\n"," img = self.img_transforms(img)\n"," if self.mask_transforms is not None:\n"," mask = self.mask_transforms(mask)\n"," return img, mask\n","\n"," def __len__(self):\n"," return len(self.imgs)"],"execution_count":5,"outputs":[]},{"cell_type":"code","metadata":{"id":"vpwsi1o-xnYw","executionInfo":{"status":"ok","timestamp":1634536856022,"user_tz":-600,"elapsed":4,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["from torch.utils.data import DataLoader\n","import torchvision.transforms as transforms\n","\n","\n","#transformation\n","img_tfs = transforms.Compose([\n"," transforms.Resize((128,128)),\n"," transforms.ToTensor(),\n"," transforms.Normalize(mean=[0, 0, 0], std=[1, 1, 1]),\n"," ]\n"," )\n","\n","mask_tfs = transforms.Compose([\n"," transforms.Resize((128,128)),\n"," transforms.ToTensor(),\n"," ]\n"," )\n","\n","dataset = Unet_dataset(img_transforms=img_tfs, mask_transforms=mask_tfs)\n","\n","\n","#shuffle index\n","sample_size = len(dataset.imgs)\n","train_size = int(sample_size*0.8)\n","test_size = sample_size - train_size\n","\n","#train and test set\n","train_set, test_set = torch.utils.data.random_split(dataset, [train_size, test_size], generator=torch.Generator().manual_seed(123))\n","\n","#dataloader\n","train_loader= DataLoader(train_set, batch_size=64, shuffle = True)\n","test_loader= DataLoader(test_set, batch_size=64, shuffle=False)"],"execution_count":6,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"cN3J5wTpx_HX"},"source":["# Improved Unet"]},{"cell_type":"code","metadata":{"id":"VFiXmwWjSeXi","executionInfo":{"status":"ok","timestamp":1634536856023,"user_tz":-600,"elapsed":4,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["import torch\n","import torch.nn as nn\n","import torch.nn.functional as F"],"execution_count":7,"outputs":[]},{"cell_type":"code","metadata":{"id":"nzGxgjkBeFXR","executionInfo":{"status":"ok","timestamp":1634536862998,"user_tz":-600,"elapsed":410,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["class Context(nn.Module):\n"," def __init__(self, in_channels, out_channels):\n"," super(Context, self).__init__()\n"," self.context = nn.Sequential(\n"," nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.Dropout2d(p=0.3),\n"," nn.LeakyReLU(negative_slope=0.02, inplace=True),\n"," nn.Conv2d(in_channels, out_channels, kernel_size=3, stride = 1, padding=1, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.LeakyReLU(negative_slope=0.02, inplace=True),\n"," )\n","\n"," def forward(self, x):\n"," x = self.context(x) + x\n"," return x\n","\n","\n","class Localization(nn.Module):\n"," def __init__(self, in_channels, out_channels):\n"," super(Localization, self).__init__()\n"," self.localization = nn.Sequential(\n"," nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.LeakyReLU(negative_slope=0.02, inplace=True),\n"," nn.Conv2d(out_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.LeakyReLU(negative_slope=0.02, inplace=True),\n"," )\n"," \n"," def forward(self, x):\n"," return self.localization(x)\n","\n","\n","class Upsampling(nn.Module):\n"," def __init__(self, in_channels, out_channels):\n"," super(Upsampling, self).__init__()\n"," self.upsampling = nn.Sequential(\n"," nn.Upsample(scale_factor=2, mode='nearest'),\n"," nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.LeakyReLU(negative_slope=0.02, inplace=True),\n"," )\n"," \n"," def forward(self, x):\n"," return self.upsampling(x)\n","\n","\n","class Segment(nn.Module):\n"," def __init__(self, in_channels, out_channels):\n"," super(Segment, self).__init__()\n"," self.segment = nn.Sequential(\n"," nn.Conv2d(in_channels, out_channels=1, kernel_size=1, stride=1, padding=0, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.LeakyReLU(negative_slope=0.02, inplace=True)\n"," )\n"," \n"," def forward(self, x):\n"," return self.segment(x)\n","\n","\n","class Conv2(nn.Module):\n"," def __init__(self, in_channels, out_channels):\n"," super(Conv2, self).__init__()\n"," self.conv2 = nn.Sequential(\n"," nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=2, padding=1, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.LeakyReLU(negative_slope=0.02, inplace=True),\n"," )\n","\n"," def forward(self, x):\n"," return self.conv2(x)\n","\n","\n","class ImprovedUnet(nn.Module):\n"," def __init__(self, in_channels=3, out_channels=1, feature_size=[16, 32, 64, 128]):\n"," super(ImprovedUnet, self).__init__()\n"," self.Conv1 = nn.Conv2d(in_channels=3, out_channels=feature_size[0], kernel_size=3, stride=1, padding=1, bias=False) \n"," self.Downs = nn.ModuleList()\n"," self.Convs = nn.ModuleList()\n"," self.Ups = nn.ModuleList()\n"," self.Segmentations = nn.ModuleList()\n","\n"," self.upscale = nn.Upsample(scale_factor=2, mode='nearest')\n"," self.bottleneck = Context(feature_size[-1]*2, feature_size[-1]*2)\n","\n","\n"," #Downsampling frame\n"," for feature in feature_size:\n"," self.Downs.append(Context(feature, feature))\n"," self.Convs.append(Conv2(feature, feature*2))\n","\n"," #Upsampleing frame\n"," for feature in reversed(feature_size):\n"," #Upsample\n"," self.Ups.append(Upsampling(feature*2, feature))\n","\n"," #Localization\n"," if feature != feature_size[0]:\n"," self.Ups.append(Localization(feature*2, feature))\n"," else:\n"," self.Ups.append(Localization(feature*2, feature*2))\n"," \n"," #Segmentation\n"," self.Segmentations.append(Segment(feature, 1))\n","\n"," self.final_conv = nn.Conv2d(feature_size[0]*2, out_channels, kernel_size=1, stride=1, bias=False)\n"," \n","\n"," def forward(self, x):\n"," skip_connections = []\n"," segmentation_layers = []\n","\n"," x = self.Conv1(x)\n","\n"," #Downsampling steps\n"," for i, (context_i, conv_i) in enumerate(zip(self.Downs, self.Convs)):\n"," x = context_i(x)\n"," #preserve location\n"," skip_connections.append(x)\n"," x = conv_i(x)\n","\n"," x = self.bottleneck(x) + x\n"," skip_connections = skip_connections[: : -1]\n","\n"," #Upsampling steps\n"," for idx in range(0, len(self.Ups), 2):\n"," #upsample\n"," x = self.Ups[idx](x)\n","\n"," #localization\n"," skip_connection = skip_connections[idx//2]\n"," concatnate_skip = torch.cat((skip_connection, x), dim=1)\n"," x = self.Ups[idx+1](concatnate_skip)\n","\n"," #segmentation\n"," if idx == 2 or idx == 4:\n"," x_segment = self.Segmentations[idx//2](x)\n"," segmentation_layers.append(x_segment)\n","\n"," seg_scale1 = self.upscale(segmentation_layers[0])\n"," seg_scale2 = self.upscale(segmentation_layers[1]+seg_scale1)\n","\n"," x = self.final_conv(x)\n"," x = x + seg_scale2\n","\n"," output = F.sigmoid(x)\n"," \n"," return output"],"execution_count":8,"outputs":[]},{"cell_type":"code","metadata":{"id":"otdcbBK7g4fW","executionInfo":{"status":"ok","timestamp":1634536865757,"user_tz":-600,"elapsed":3,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["feature_size=[16, 32, 64, 128]\n","device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"],"execution_count":9,"outputs":[]},{"cell_type":"code","metadata":{"id":"oDhAF_WYyeJm","executionInfo":{"status":"ok","timestamp":1634536867513,"user_tz":-600,"elapsed":3,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["#dice coef\n","def dice_coef(pred, target):\n"," batch_size = len(pred)\n"," somooth = 1.\n","\n"," pred_flat = pred.view(batch_size, -1)\n"," target_flat = target.view(batch_size, -1)\n","\n"," intersection = (pred_flat*target_flat).sum()\n"," dice_coef = (2.*intersection+somooth)/(pred_flat.sum()+target_flat.sum()+somooth)\n"," return dice_coef\n","\n","#loss\n","def dice_loss(pred, target):\n"," dice_loss = 1 - dice_coef(pred, target)\n"," return dice_loss"],"execution_count":10,"outputs":[]},{"cell_type":"code","metadata":{"id":"dNMXUs-qViYi","executionInfo":{"status":"ok","timestamp":1634536882245,"user_tz":-600,"elapsed":9838,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["import torch.optim as optim\n","\n","#set parameters\n","feature_size=[16, 32, 64, 128]\n","model = ImprovedUnet(feature_size=feature_size)\n","model = model.to(device)\n","\n","optimizer = optim.Adam(model.parameters(), lr=0.001)\n","EPOCHS = 50"],"execution_count":11,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JrGpP3KMPzpe","executionInfo":{"status":"ok","timestamp":1634529930671,"user_tz":-600,"elapsed":1700198,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"d305c055-fbf6-4433-efae-2d8caf9980ea"},"source":["from tqdm import tqdm\n","\n","TRAIN_LOSS = []\n","TRAIN_DICE = []\n","TEST_LOSS =[]\n","TEST_DICE = []\n","\n","for epoch in range(1, EPOCHS+1):\n"," print('EPOCH {}/{}'.format(epoch, EPOCHS))\n"," running_loss = 0\n"," running_dicecoef = 0\n"," running_loss_test = 0\n"," running_dicecoef_test = 0\n"," BATCH_NUM = len(train_loader)\n"," BATCH_NUM_TEST = len(test_loader)\n","\n"," #training\n"," with tqdm(train_loader, unit='batch') as tbatch:\n"," for batch_idx, (x, y) in enumerate(tbatch):\n"," x, y = x.to(device), y.to(device)\n"," tbatch.set_description(f'Batch: {batch_idx}')\n","\n"," optimizer.zero_grad()\n"," output = model(x)\n"," loss = dice_loss(output, y)\n"," dicecoef = dice_coef(output, y)\n"," loss.backward()\n"," optimizer.step()\n","\n"," running_loss += loss.item()\n"," running_dicecoef += dicecoef.item()\n","\n"," tbatch.set_postfix(loss=loss.item(), dice_coef=dicecoef.item())\n","\n"," epoch_loss = running_loss/BATCH_NUM\n"," epoch_dicecoef = running_dicecoef/BATCH_NUM\n"," TRAIN_LOSS.append(epoch_loss)\n"," TRAIN_DICE.append(epoch_dicecoef)\n","\n"," #test\n"," with tqdm(test_loader, unit='batch') as tsbatch:\n"," for batch_idx, (x, y) in enumerate(tsbatch):\n"," x, y = x.to(device), y.to(device)\n"," tsbatch.set_description(f'Batch: {batch_idx}')\n"," output_test = model(x)\n"," loss_test = dice_loss(output_test, y)\n"," dicecoef_test = dice_coef(output_test, y)\n"," tsbatch.set_postfix(loss=loss_test.item(), dice_coef=dicecoef_test.item())\n","\n"," running_loss_test += loss_test.item()\n"," running_dicecoef_test += dicecoef_test.item()\n","\n"," TEST_LOSS.append(running_loss_test/BATCH_NUM_TEST)\n"," TEST_DICE.append(running_dicecoef_test/BATCH_NUM_TEST)"],"execution_count":null,"outputs":[{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["EPOCH 1/50\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["Batch: 0: 0%| | 0/33 [00:05torchviz) (3.7.4.3)\n"]}]},{"cell_type":"code","metadata":{"id":"oKvcU7lyeujb"},"source":["from torchviz import make_dot\n","make_dot(output, params=dict(model.named_parameters(), ))"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"zG5sYuWORuIO"},"source":["#save model\n","filename = \"Unet_ISIC2.pth\"\n","torch.save(model.state_dict(), filename)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"EhJChHsfViUi"},"source":["model.eval()\n","p = model(x)[0]\n","p = p.to('cpu')\n","plt.imshow(p.detach().squeeze(), cmap='gray')"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"4Rh-uq05TG-A"},"source":["#Evaluation"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":281},"id":"7ZBk4w3xIA8L","executionInfo":{"status":"ok","timestamp":1634530179125,"user_tz":-600,"elapsed":599,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"3d36229a-f06c-4097-81ef-ba14fcb527b8"},"source":["import matplotlib.pyplot as plt\n","import numpy as np\n","\n","X = np.arange(1,EPOCHS+1)\n","X_tick = np.arange(0,EPOCHS+1,5)\n","\n","plt.plot(X, TRAIN_LOSS, marker='.', markersize=10, label='train')\n","plt.plot(X, TEST_LOSS, marker='.', markersize=10, label='test')\n","plt.xlabel('Epochs')\n","plt.ylabel('Dice Loss')\n","plt.xticks(X_tick)\n","plt.legend()\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":279},"id":"Y80RZXzKRPpc","executionInfo":{"status":"ok","timestamp":1634530190434,"user_tz":-600,"elapsed":528,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"04523ef5-c82f-4b02-ec9a-79f64eb243a2"},"source":["plt.plot(X, TRAIN_DICE, marker='.', markersize=10, label='train')\n","plt.plot(X, TEST_DICE, marker='.', markersize=10, label='test')\n","plt.xlabel('Epochs')\n","plt.ylabel('Dice Coeffecient')\n","plt.xticks(X_tick)\n","plt.legend()\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"f0oyHRCkQ331"},"source":["# Segmentation"]},{"cell_type":"code","metadata":{"id":"V3Ahm7ecTST4","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1634541481872,"user_tz":-600,"elapsed":940,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"7607bfba-d530-42f2-dc0c-4f752e538500"},"source":["#load model\n","new_model = ImprovedUnet(feature_size=feature_size)\n","\n","filename = \"Unet_ISIC2.pth\"\n","checkpoint = torch.load(filename)\n","\n","new_model.load_state_dict(checkpoint)"],"execution_count":137,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":137}]},{"cell_type":"code","metadata":{"id":"BZYl7y1XIJFr","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1634541488207,"user_tz":-600,"elapsed":4036,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"a28afd14-4fc1-4013-f790-2a64af890567"},"source":["for batch in test_loader:\n"," x, y = batch\n"," print(x.shape, y.shape)\n"," break"],"execution_count":138,"outputs":[{"output_type":"stream","name":"stdout","text":["torch.Size([64, 3, 128, 128]) torch.Size([64, 1, 128, 128])\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8mo7-6wPsboG","executionInfo":{"status":"ok","timestamp":1634541495022,"user_tz":-600,"elapsed":4545,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"6839af64-3b89-4a4f-b8ba-6b7db113e9ae"},"source":["p = new_model(x)"],"execution_count":139,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:1805: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n"," warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n"]}]},{"cell_type":"code","metadata":{"id":"O-lkaeAvKayG","executionInfo":{"status":"ok","timestamp":1634541510585,"user_tz":-600,"elapsed":454,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["def segment_pred_mask(imgs=x, pred_masks=p, idx=0, alpha=10):\n"," seg_img = x[idx].clone()\n"," image_r = seg_img[0] #C: red\n"," image_r = image_r*(1-alpha*p[idx])+(p[idx]*p[idx]*alpha)\n"," segment_image = image_r.detach().squeeze()\n"," seg_img[0] = segment_image\n"," return seg_img"],"execution_count":140,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":666},"id":"KPJtsairAkc6","executionInfo":{"status":"ok","timestamp":1634541514338,"user_tz":-600,"elapsed":1288,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"660e1be6-dae2-478b-e0c0-633a0cadbc77"},"source":["#visualise\n","import matplotlib.pyplot as plt\n","\n","n_col = 4\n","n_row = 6\n","\n","def plot_gallery(images=x, mask=y, pred_mask = p, n_row=n_row, n_col=n_col):\n"," idxs = n_col*n_row\n"," plt.figure(figsize=(1.5*n_col, 1.5*n_row))\n"," plt.subplots_adjust(bottom=0, left=0.01, right=0.99, top=0.9, hspace=0.35) #adjust layout parameters\n"," plt.suptitle('Segmentation', fontsize=15)\n","\n"," for i in range(0, idxs, 4):\n"," #image\n"," plt.subplot(n_row, n_col, i+1)\n"," plt.imshow(x[i].permute(1,2,0))\n"," plt.title('image', fontsize = 10)\n"," plt.axis('off')\n","\n"," #target mask\n"," plt.subplot(n_row, n_col, i+2)\n"," plt.imshow(y[i].detach().squeeze(), cmap='gray')\n"," plt.title('target mask', fontsize = 10)\n"," plt.axis('off')\n"," \n"," #predicted mask\n"," plt.subplot(n_row, n_col, i+3)\n"," plt.imshow(p[i].detach().squeeze(), cmap='gray')\n"," plt.title('predicted mask', fontsize = 10)\n"," plt.axis('off')\n","\n"," #segmentation\n"," seg_img = segment_pred_mask(imgs=x, pred_masks=p, idx=i, alpha=0.5)\n"," plt.subplot(n_row, n_col, i+4)\n"," plt.imshow(seg_img.permute(1,2,0))\n"," plt.title('segmentation', fontsize = 10)\n"," plt.axis('off')\n","\n","plot_gallery()\n","plt.show()"],"execution_count":141,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"9GxVJPOpKK-G","executionInfo":{"status":"ok","timestamp":1634541523500,"user_tz":-600,"elapsed":786,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["seg_img= segment_pred_mask(imgs=x, pred_masks=p, idx=10, alpha=10)"],"execution_count":142,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":302},"id":"2qNx8jmVJxbF","executionInfo":{"status":"ok","timestamp":1634541525522,"user_tz":-600,"elapsed":13,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"510d6761-517c-4f90-d345-064a87ce6859"},"source":["plt.imshow(seg_img.permute(1,2,0))"],"execution_count":143,"outputs":[{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":143},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"d29ABXmtBDPc","executionInfo":{"status":"ok","timestamp":1634520579438,"user_tz":-600,"elapsed":470,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"51258823-64d3-499f-8cd2-26868e33c009"},"source":["print(torch.__version__)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["1.9.0+cu111\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XwYGPWVxBLjQ","executionInfo":{"status":"ok","timestamp":1634520681440,"user_tz":-600,"elapsed":470,"user":{"displayName":"Wakayama 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45e5f023fb416c954283d707ae17aa57e5b7c75f Mon Sep 17 00:00:00 2001 From: Wakame <85863413+wakameds@users.noreply.github.com> Date: Tue, 19 Oct 2021 18:24:00 +1000 Subject: [PATCH 27/66] Revised code in the files and add comments --- recognition/s4633139/IUNet_criterion.py | 9 +++ recognition/s4633139/IUNet_dataloader.py | 9 +++ recognition/s4633139/IUNet_train_test.py | 13 +++- recognition/s4633139/ImprovedUNet.py | 33 ++++---- recognition/s4633139/main.py | 33 ++++---- recognition/s4633139/visualse.py | 98 ++++++++++++++++++++---- 6 files changed, 146 insertions(+), 49 deletions(-) diff --git a/recognition/s4633139/IUNet_criterion.py b/recognition/s4633139/IUNet_criterion.py index 187e198143..653097b751 100644 --- a/recognition/s4633139/IUNet_criterion.py +++ b/recognition/s4633139/IUNet_criterion.py @@ -1,3 +1,12 @@ +# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ +# Copyright (c) 2021, H.WAKAYAMA, All rights reserved. +# File: IUNet_criterion.py +# Author: Hideki WAKAYAMA +# Contact: h.wakayama@uq.net.au +# Platform: macOS Big Sur Ver 11.2.1, Pycharm pro 2021.1 +# Time: 19/10/2021, 15:47 +# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ + #dice coefficient def dice_coef(pred, target): batch_size = len(pred) diff --git a/recognition/s4633139/IUNet_dataloader.py b/recognition/s4633139/IUNet_dataloader.py index 789027a1d5..7cb47b3e49 100644 --- a/recognition/s4633139/IUNet_dataloader.py +++ b/recognition/s4633139/IUNet_dataloader.py @@ -1,3 +1,12 @@ +# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ +# Copyright (c) 2021, H.WAKAYAMA, All rights reserved. +# File: IUNet_dataloader.py +# Author: Hideki WAKAYAMA +# Contact: h.wakayama@uq.net.au +# Platform: macOS Big Sur Ver 11.2.1, Pycharm pro 2021.1 +# Time: 19/10/2021, 15:47 +# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ + import os from torch.utils.data import Dataset from PIL import Image diff --git a/recognition/s4633139/IUNet_train_test.py b/recognition/s4633139/IUNet_train_test.py index 573d6f96fe..30276ab1e0 100644 --- a/recognition/s4633139/IUNet_train_test.py +++ b/recognition/s4633139/IUNet_train_test.py @@ -1,10 +1,17 @@ +# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ +# Copyright (c) 2021, H.WAKAYAMA, All rights reserved. +# File: IUNet_train_test.py +# Author: Hideki WAKAYAMA +# Contact: h.wakayama@uq.net.au +# Platform: macOS Big Sur Ver 11.2.1, Pycharm pro 2021.1 +# Time: 19/10/2021, 15:47 +# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ + from IUNet_criterion import dice_coef, dice_loss from tqdm import tqdm - def model_train_test(model, optimizer, EPOCHS, train_loader, test_loader): - """ - function for model training and test + """function for model training and test :return: list of train and test dice coefficients and dice losses by epochs """ TRAIN_LOSS = [] diff --git a/recognition/s4633139/ImprovedUNet.py b/recognition/s4633139/ImprovedUNet.py index 01d44d0d39..c519f5a493 100644 --- a/recognition/s4633139/ImprovedUNet.py +++ b/recognition/s4633139/ImprovedUNet.py @@ -1,12 +1,19 @@ +# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ +# Copyright (c) 2021, H.WAKAYAMA, All rights reserved. +# File: ImprovedUNet.py +# Author: Hideki WAKAYAMA +# Contact: h.wakayama@uq.net.au +# Platform: macOS Big Sur Ver 11.2.1, Pycharm pro 2021.1 +# Time: 19/10/2021, 15:47 +# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ + import torch import torch.nn as nn import torch.nn.functional as F class Context(nn.Module): - """ - context module - """ + """context""" def __init__(self, in_channels, out_channels): super(Context, self).__init__() self.context = nn.Sequential( @@ -25,9 +32,7 @@ def forward(self, x): class Localization(nn.Module): - """ - localization module - """ + """localization""" def __init__(self, in_channels, out_channels): super(Localization, self).__init__() self.localization = nn.Sequential( @@ -44,9 +49,7 @@ def forward(self, x): class Upsampling(nn.Module): - """ - upsampling module - """ + """upsampling""" def __init__(self, in_channels, out_channels): super(Upsampling, self).__init__() self.upsampling = nn.Sequential( @@ -61,9 +64,7 @@ def forward(self, x): class Segment(nn.Module): - """ - segmentation layer - """ + """segmentation layer""" def __init__(self, in_channels, out_channels): super(Segment, self).__init__() self.segment = nn.Sequential( @@ -77,9 +78,7 @@ def forward(self, x): class Conv2(nn.Module): - """ - convolution stride=2 - """ + """convolution stride=2""" def __init__(self, in_channels, out_channels): super(Conv2, self).__init__() self.conv2 = nn.Sequential( @@ -93,9 +92,7 @@ def forward(self, x): class IUNet(nn.Module): - """ - Improved Unet (International MICCAI Brainlesion Workshop(pp. 287-297). Springer, Cham.) - """ + """Improved Unet (International MICCAI Brainlesion Workshop(pp. 287-297). Springer, Cham.)""" def __init__(self, in_channels=3, out_channels=1, feature_size=[16, 32, 64, 128]): super(IUNet, self).__init__() self.Conv1 = nn.Conv2d(in_channels=3, out_channels=feature_size[0], kernel_size=3, stride=1, padding=1, bias=False) diff --git a/recognition/s4633139/main.py b/recognition/s4633139/main.py index 46cad93ada..7da4fbd383 100644 --- a/recognition/s4633139/main.py +++ b/recognition/s4633139/main.py @@ -1,20 +1,28 @@ +# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ +# Copyright (c) 2021, H.WAKAYAMA, All rights reserved. +# File: main.py +# Author: Hideki WAKAYAMA +# Contact: h.wakayama@uq.net.au +# Platform: macOS Big Sur Ver 11.2.1, Pycharm pro 2021.1 +# Time: 19/10/2021, 17:30 +# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ + from IUNet_dataloader import UNet_dataset from ImprovedUNet import IUNet from IUNet_train_test import model_train_test -from visualse import dice_coef_vis, segment_pred_mask +from visualse import dice_coef_vis, segment_pred_mask, plot_gallery import torch from torch.utils.data import DataLoader, Dataset, random_split import torchvision.transforms as transforms import torch.optim as optim +import matplotlib.pyplot as plt def main(): - """ - execute model training and return dice coefficient plots - """ + """execute model training and return dice coefficient plots""" - #PARAMETERS + #PARAMETERS# FEATURE_SIZE=[16, 32, 64, 128] IN_CHANEL=3 OUT_CHANEL=1 @@ -49,7 +57,7 @@ def main(): train_loader = DataLoader(train_set, batch_size=BATCH_SIZE, shuffle=True) test_loader = DataLoader(test_set, batch_size=BATCH_SIZE, shuffle=False) - #MODEL + #MODEL# model = IUNet(in_channels=IN_CHANEL, out_channels=OUT_CHANEL, feature_size=FEATURE_SIZE) optimizer = optim.Adam(model.parameters(), lr=LR) @@ -61,16 +69,13 @@ def main(): #segmentation for batch in train_loader: - x, y = batch + images, masks = batch break - img = x[0] model.eval() - pred_mask = model(x)[0] - segment_pred_mask(img, pred_mask, alpha=0.5) - -if __name__ == main(): - main() - + pred_masks = model(images) + plot_gallery(images, masks, pred_masks, n_row=6, n_col=4) +if __name__ == main(): + main() \ No newline at end of file diff --git a/recognition/s4633139/visualse.py b/recognition/s4633139/visualse.py index fe0038c605..ca62384be3 100644 --- a/recognition/s4633139/visualse.py +++ b/recognition/s4633139/visualse.py @@ -1,7 +1,26 @@ +# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ +# Copyright (c) 2021, H.WAKAYAMA, All rights reserved. +# File: visualse.py +# Author: Hideki WAKAYAMA +# Contact: h.wakayama@uq.net.au +# Platform: macOS Big Sur Ver 11.2.1, Pycharm pro 2021.1 +# Time: 19/10/2021, 17:30 +# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ + import matplotlib.pyplot as plt import numpy as np + def dice_coef_vis(EPOCHS, TRAIN_COEFS, TEST_COEFS): + """ + function for dice coefficient + :param + EPOCHS(array): epochs + TRAIN_COEFS(array): train dice coefficients + TEST_COEFS(array): test dice coefficients + :return + plot with dice coefficients by epochs + """ X = np.arange(1, EPOCHS+1) plt.plot(X, TRAIN_COEFS, marker='.', markersize=10, label='train') plt.plot(X, TEST_COEFS, marker='.', markersize=10, label='test') @@ -13,6 +32,15 @@ def dice_coef_vis(EPOCHS, TRAIN_COEFS, TEST_COEFS): def dice_loss_vis(EPOCHS, TRAIN_LOSS, TEST_LOSS): + """ + function for dice loss + :param + EPOCHS(array): epochs + TRAIN_LOSS(array): train dice losses + TEST_LOSS(array): test dice losses + :return + plot with dice loss by epochs + """ X = np.arange(1, EPOCHS+1) plt.plot(X, TRAIN_LOSS, marker='.', markersize=10, label='train') plt.plot(X, TEST_LOSS, marker='.', markersize=10, label='test') @@ -23,21 +51,63 @@ def dice_loss_vis(EPOCHS, TRAIN_LOSS, TEST_LOSS): plt.show() -def pred_mask(img, pred_mask, alpha=5): - seg_img = img.clone() +def segment_pred_mask(imgs, pred_masks, idx, alpha): + """ + function to make a covered image with the predicted mask + :param imgs(tensor[B,C,W,H]): 3 channels image + :param pred_masks(tensor[B,C,W,H]): predicted mask + :param idx(int): image index + :param alpha(float): ratio for segmentation + :return: segmentation image + """ + seg_img = imgs[idx].clone() image_r = seg_img[0] - image_r = image_r*(1-alpha*pred_mask)+(pred_mask*pred_mask*alpha) - segmentation = image_r.detach().squeeze() - seg_img[0] = segmentation - plt.imshow(seg_img.permute(1,2,0)) - plt.show() + image_r = image_r*(1-alpha*pred_masks[idx])+(pred_masks[idx]*pred_masks[idx]*alpha) + segment_image = image_r.detach().squeeze() + seg_img[0] = segment_image + return seg_img -def segment_pred_mask(img, pred_mask, alpha=0.5): - seg_img = img.clone() - image_r = seg_img[0] - image_r = image_r*(1-alpha*pred_mask)+(pred_mask*pred_mask*alpha) - segment_img_r = image_r.detach().squeeze() - seg_img[0] = segment_img_r - plt.imshow(seg_img.permute(1,2,0)) +def plot_gallery(images, masks, pred_masks, n_row=6, n_col=4): + """ + function to generate gallery + :parameters + images(tensor[B,C,W,H]): images + masks(tensor[B,C,W,H]): target masks + pred_masks(tensor[B,C,W,H]): predicted masks + n_row: number of the row for the gallery + n_col: number of the column for the gallery + :return + gallery images + """ + idxs = n_col * n_row + plt.figure(figsize=(1.5 * n_col, 1.5 * n_row)) + plt.subplots_adjust(bottom=0, left=0.01, right=0.99, top=0.9, hspace=0.35) # adjust layout parameters + plt.suptitle('Segmentation', fontsize=15) + + for i in range(0, idxs, 4): + # image + plt.subplot(n_row, n_col, i + 1) + plt.imshow(images[i].permute(1, 2, 0)) + plt.title('image', fontsize=10) + plt.axis('off') + + # target mask + plt.subplot(n_row, n_col, i + 2) + plt.imshow(masks[i].detach().squeeze(), cmap='gray') + plt.title('target mask', fontsize=10) + plt.axis('off') + + # predicted mask + plt.subplot(n_row, n_col, i + 3) + plt.imshow(pred_masks[i].detach().squeeze(), cmap='gray') + plt.title('predicted mask', fontsize=10) + plt.axis('off') + + # segmentation + seg_img = segment_pred_mask(imgs=images, pred_masks=pred_masks, idx=i, alpha=0.5) + plt.subplot(n_row, n_col, i + 4) + plt.imshow(seg_img.permute(1, 2, 0)) + plt.title('segmentation', fontsize=10) + plt.axis('off') plt.show() \ No newline at end of file From e6d3857768a57ce84285ad437c1dde590d776e49 Mon Sep 17 00:00:00 2001 From: wakame <85863413+wakameds@users.noreply.github.com> Date: Tue, 19 Oct 2021 19:37:11 +1000 Subject: [PATCH 28/66] Create README.md Create README.md --- recognition/s4633139/README.md | 87 ++++++++++++++++++++++++++++++++++ 1 file changed, 87 insertions(+) create mode 100644 recognition/s4633139/README.md diff --git a/recognition/s4633139/README.md b/recognition/s4633139/README.md new file mode 100644 index 0000000000..28b2f8be1a --- /dev/null +++ b/recognition/s4633139/README.md @@ -0,0 +1,87 @@ +# Improved UNet for ISIC2018 image segmentation + +## Objective +This project is a practical work for COMP3710 in 2021. The project objective is to implement the improved UNet for ISIC2018 image segmentation. + +## Model Architecture +UNet is the model for biomedical image segmentation. [1] Figure1 shows the improved UNet architecture. [2] The improved model newly added the context module, 3x3 convolution layer with stride = 2 instead of max pooling layer, localization module, and segmentation layer extracted from localization layer. + +In downsampling part, context module works as residual blocks and the output from the module is concatenate to the input for the localization modules in upsampling block. 3x3 stride 2 convolution works for downsample block. In upsampling, segmentation layer outputted from localization is added to the next segmentation layer. + +

+ +

+ +

+ Figure1: The improved UNet model architecture[2] +

+ + +This repository includes the below files for the improved UNet: +* **IUNet_criterion.py:** This file consists of the two criterion functions: dice coefficient and dice loss. The functions are utilised for training and evaluating the model through the forward and backpropagation steps. +* **IUNet_dataloader.py:** This file is concerning data preparation for UNet model, which works for data loading, data transformation, preparing data loader. +* **IUNet_train_test.py:** This file works to train the model and to assess the segmentation performance with dice coefficient and dice loss. The function in the file returns lists recorded the criteria values by epochs: TRAIN_DICE, TEST_DICE, TRAIN_LOSS, TEST_LOSS. +* **ImprovedUNet.py:** This file includes the classes to build the improved UNet model. The classes with Context, Localization, Up-sampling, Segmentation, and Convolution to down-sampling, and Improved Unet are provided. In this model, Sigmoid function for the binary classification between mask and non-mask areas is utilised instead of softmax function. +* **main.py:** The file performs all procedures for the project, data preparation, training model, and model evaluation. The file includes the parameters with the improved UNet: FEATURE_SIZE, IN_CHANEL, OUT_CHANEL, IMG_TF, MASK_TF, BATCH_SIZE, EPOCHS, and LR. The image size is resized into 128x128 as the initial parameter. Also, random_split() is set to split the dataset into train set and test set by 80:20. The file applies Adam as an optimizer. +* **visualise.py:** The file contains the four functions for plotting the test result and training dice coefficient and dice loss by epochs, and output the segmented images with the predicted mask. + +## Dataset +ISIC 2018 Task1 is a dataset with skin cancer images shared by the International Skin Imaging Collaboration (ISIC). [3] The dataset consists of 2594 images and 2594 mask images, respectively. The dataset is split into the train set and test set by train ratio = 0.8. + +## Usage model +“main.py” calls all files in the repository to train the model and evaluate the performance. ISIC dataset is needed to be set in the same directory, including main.py. + + +### Dependency +The model training and evaluation was executed under the environment. +* Pytorch 1.9.0+cu111 +* Python 3.7.12 +* Matplotlib 3.3.4 + + +## Results +### Dice coefficient and loss +The figure is about train and test dice coefficient and losses by 50 epochs. The test dice coefficient was approximately 0.85 and the test accuracy was stable after 15 epochs. + +

+ +

+ +

+ Figure2. Dice coefficient +

+ + +The test dice loss was stable at roughly 0.15 while train loss declined after epoch 15. + +

+ +

+ +

+ Figure3. Dice loss +

+ + +### Segmentation +The trained UNet predict the mask from the image. The segmentations in the right-hand side column are the images covered with the predicted mask. + +

+ +

+ +

+ Figure4. Segmentation +

+ + +## References +[1] Ronneberger, O., Fischer, P., & Brox, T. (2015, October). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). Springer, Cham. https://arxiv.org/abs/1505.04597 + +[2] Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., & Maier-Hein, K. H. (2017, September). Brain tumor segmentation and radiomics survival prediction: Contribution to the brats 2017 challenge. In International MICCAI Brainlesion Workshop (pp. 287-297). Springer, Cham. https://arxiv.org/pdf/1802.10508v1.pdf + +[3] ISIC 2018 Task1 https://paperswithcode.com/dataset/isic-2018-task-1 + + + + From 89d50e3d2389bc620297cf0a56cb5b42a6620874 Mon Sep 17 00:00:00 2001 From: Wakame <85863413+wakameds@users.noreply.github.com> Date: Wed, 20 Oct 2021 13:05:18 +1000 Subject: [PATCH 29/66] Revised code in the files --- .../{IUNet_criterion.py => criterion.py} | 2 +- .../{IUNet_dataloader.py => dataloader.py} | 2 +- recognition/s4633139/{main.py => driver.py} | 38 ++++++++------ .../s4633139/{ImprovedUNet.py => model.py} | 4 +- ...IUNet_train_test.py => model_train_val.py} | 50 ++++++++++--------- recognition/s4633139/visualse.py | 28 ++++++++--- 6 files changed, 74 insertions(+), 50 deletions(-) rename recognition/s4633139/{IUNet_criterion.py => criterion.py} (96%) rename recognition/s4633139/{IUNet_dataloader.py => dataloader.py} (98%) rename recognition/s4633139/{main.py => driver.py} (65%) rename recognition/s4633139/{ImprovedUNet.py => model.py} (99%) rename recognition/s4633139/{IUNet_train_test.py => model_train_val.py} (50%) diff --git a/recognition/s4633139/IUNet_criterion.py b/recognition/s4633139/criterion.py similarity index 96% rename from recognition/s4633139/IUNet_criterion.py rename to recognition/s4633139/criterion.py index 653097b751..6b597eab54 100644 --- a/recognition/s4633139/IUNet_criterion.py +++ b/recognition/s4633139/criterion.py @@ -1,6 +1,6 @@ # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ # Copyright (c) 2021, H.WAKAYAMA, All rights reserved. -# File: IUNet_criterion.py +# File: criterion.py # Author: Hideki WAKAYAMA # Contact: h.wakayama@uq.net.au # Platform: macOS Big Sur Ver 11.2.1, Pycharm pro 2021.1 diff --git a/recognition/s4633139/IUNet_dataloader.py b/recognition/s4633139/dataloader.py similarity index 98% rename from recognition/s4633139/IUNet_dataloader.py rename to recognition/s4633139/dataloader.py index 7cb47b3e49..24d8fce742 100644 --- a/recognition/s4633139/IUNet_dataloader.py +++ b/recognition/s4633139/dataloader.py @@ -1,6 +1,6 @@ # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ # Copyright (c) 2021, H.WAKAYAMA, All rights reserved. -# File: IUNet_dataloader.py +# File: dataloader.py # Author: Hideki WAKAYAMA # Contact: h.wakayama@uq.net.au # Platform: macOS Big Sur Ver 11.2.1, Pycharm pro 2021.1 diff --git a/recognition/s4633139/main.py b/recognition/s4633139/driver.py similarity index 65% rename from recognition/s4633139/main.py rename to recognition/s4633139/driver.py index 7da4fbd383..8a4b13e066 100644 --- a/recognition/s4633139/main.py +++ b/recognition/s4633139/driver.py @@ -1,15 +1,15 @@ # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ # Copyright (c) 2021, H.WAKAYAMA, All rights reserved. -# File: main.py +# File: driver.py # Author: Hideki WAKAYAMA # Contact: h.wakayama@uq.net.au # Platform: macOS Big Sur Ver 11.2.1, Pycharm pro 2021.1 # Time: 19/10/2021, 17:30 # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ -from IUNet_dataloader import UNet_dataset -from ImprovedUNet import IUNet -from IUNet_train_test import model_train_test +from dataloader import UNet_dataset +from model import IUNet +from model_train_val import model_train_val from visualse import dice_coef_vis, segment_pred_mask, plot_gallery import torch @@ -22,7 +22,7 @@ def main(): """execute model training and return dice coefficient plots""" - #PARAMETERS# + #PARAMETERS FEATURE_SIZE=[16, 32, 64, 128] IN_CHANEL=3 OUT_CHANEL=1 @@ -39,43 +39,51 @@ def main(): ]) BATCH_SIZE = 64 - EPOCHS = 15 + EPOCHS = 1 LR = 0.001 + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + #DATA PREPARATION dataset = UNet_dataset(img_transforms=IMG_TF, mask_transforms=MASK_TF) #shuffle index sample_size = len(dataset.imgs) - train_size = int(sample_size * 0.8) - test_size = sample_size - train_size + train_size = int(sample_size * 0.5) + split_size = sample_size - train_size + + val_size = split_size//2 + test_size = split_size - val_size - #train and test set - train_set, test_set = random_split(dataset, [train_size, test_size], generator=torch.Generator().manual_seed(123)) + #train, validation, test + train_set, split_set = random_split(dataset, [train_size, split_size], generator=torch.Generator().manual_seed(123)) + val_set, test_set = random_split(split_set, [val_size, test_size], generator=torch.Generator().manual_seed(123)) #data loader train_loader = DataLoader(train_set, batch_size=BATCH_SIZE, shuffle=True) + val_loader = DataLoader(val_set, batch_size=BATCH_SIZE, shuffle=False) test_loader = DataLoader(test_set, batch_size=BATCH_SIZE, shuffle=False) - #MODEL# + #MODEL model = IUNet(in_channels=IN_CHANEL, out_channels=OUT_CHANEL, feature_size=FEATURE_SIZE) + model = model.to(device) optimizer = optim.Adam(model.parameters(), lr=LR) #train,test - TRAIN_DICE, TEST_DICE, TRAIN_LOSS, TEST_LOSS = model_train_test(model, optimizer, EPOCHS, train_loader, test_loader) + TRAIN_DICE, VAL_DICE, VAL_LOSS, VAL_LOSS = model_train_val(model, optimizer, EPOCHS, train_loader, val_loader) #plot dice coefficient - dice_coef_vis(EPOCHS, TRAIN_DICE, TEST_DICE) + dice_coef_vis(EPOCHS, TRAIN_DICE, VAL_DICE) #segmentation - for batch in train_loader: + for batch in test_loader: images, masks = batch break model.eval() pred_masks = model(images) - plot_gallery(images, masks, pred_masks, n_row=6, n_col=4) + plot_gallery(images, masks, pred_masks, n_row=5, n_col=4) if __name__ == main(): main() \ No newline at end of file diff --git a/recognition/s4633139/ImprovedUNet.py b/recognition/s4633139/model.py similarity index 99% rename from recognition/s4633139/ImprovedUNet.py rename to recognition/s4633139/model.py index c519f5a493..38210b1f6c 100644 --- a/recognition/s4633139/ImprovedUNet.py +++ b/recognition/s4633139/model.py @@ -1,6 +1,6 @@ # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ # Copyright (c) 2021, H.WAKAYAMA, All rights reserved. -# File: ImprovedUNet.py +# File: model.py # Author: Hideki WAKAYAMA # Contact: h.wakayama@uq.net.au # Platform: macOS Big Sur Ver 11.2.1, Pycharm pro 2021.1 @@ -161,6 +161,6 @@ def forward(self, x): seg_scale2 = self.upscale(segmentation_layers[1] + seg_scale1) x = self.final_conv(x) x = x + seg_scale2 - output = F.sigmoid(x) + output = torch.sigmoid(x) return output \ No newline at end of file diff --git a/recognition/s4633139/IUNet_train_test.py b/recognition/s4633139/model_train_val.py similarity index 50% rename from recognition/s4633139/IUNet_train_test.py rename to recognition/s4633139/model_train_val.py index 30276ab1e0..c34c365c4d 100644 --- a/recognition/s4633139/IUNet_train_test.py +++ b/recognition/s4633139/model_train_val.py @@ -1,36 +1,39 @@ # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ # Copyright (c) 2021, H.WAKAYAMA, All rights reserved. -# File: IUNet_train_test.py +# File: model_train_val.py # Author: Hideki WAKAYAMA # Contact: h.wakayama@uq.net.au # Platform: macOS Big Sur Ver 11.2.1, Pycharm pro 2021.1 # Time: 19/10/2021, 15:47 # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ -from IUNet_criterion import dice_coef, dice_loss +from criterion import dice_coef, dice_loss from tqdm import tqdm +import torch -def model_train_test(model, optimizer, EPOCHS, train_loader, test_loader): - """function for model training and test - :return: list of train and test dice coefficients and dice losses by epochs +def model_train_val(model, optimizer, EPOCHS, train_loader, val_loader): + """function for model training and validation + :return: list of train and validation dice coefficients and dice losses by epochs """ TRAIN_LOSS = [] TRAIN_DICE = [] - TEST_LOSS =[] - TEST_DICE = [] + VAL_LOSS =[] + VAL_DICE = [] + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") for epoch in range(1, EPOCHS+1): print('EPOCH {}/{}'.format(epoch, EPOCHS)) running_loss = 0 running_dicecoef = 0 - running_loss_test = 0 - running_dicecoef_test = 0 + running_loss_val = 0 + running_dicecoef_val = 0 BATCH_NUM = len(train_loader) - BATCH_NUM_TEST = len(test_loader) + BATCH_NUM_VAL = len(val_loader) #train with tqdm(train_loader, unit='batch') as tbatch: for batch_idx, (x, y) in enumerate(tbatch): + x, y = x.to(device), y.to(device) tbatch.set_description(f'Batch: {batch_idx}') optimizer.zero_grad() @@ -50,19 +53,20 @@ def model_train_test(model, optimizer, EPOCHS, train_loader, test_loader): TRAIN_LOSS.append(epoch_loss) TRAIN_DICE.append(epoch_dicecoef) - #test - with tqdm(test_loader, unit='batch') as tsbatch: - for batch_idx, (x, y) in enumerate(tsbatch): - tsbatch.set_description(f'Batch: {batch_idx}') - output_test = model(x) - loss_test = dice_loss(output_test, y) - dicecoef_test = dice_coef(output_test, y) - tsbatch.set_postfix(loss=loss_test.item(), dice_coef=dicecoef_test.item()) + #validation + with tqdm(val_loader, unit='batch') as valbatch: + for batch_idx, (x, y) in enumerate(valbatch): + x, y = x.to(device), y.to(device) + valbatch.set_description(f'Batch: {batch_idx}') + output_val = model(x) + loss_val = dice_loss(output_val, y) + dicecoef_val = dice_coef(output_val, y) + valbatch.set_postfix(loss=loss_val.item(), dice_coef=dicecoef_val.item()) - running_loss_test += loss_test.item() - running_dicecoef_test += dicecoef_test.item() + running_loss_val += loss_val.item() + running_dicecoef_val += dicecoef_val.item() - TEST_LOSS.append(running_loss_test/BATCH_NUM_TEST) - TEST_DICE.append(running_dicecoef_test/BATCH_NUM_TEST) + VAL_LOSS.append(running_loss_val/BATCH_NUM_VAL) + VAL_DICE.append(running_dicecoef_val/BATCH_NUM_VAL) - return TRAIN_DICE, TEST_DICE, TRAIN_LOSS, TEST_LOSS \ No newline at end of file + return TRAIN_DICE, VAL_DICE, TRAIN_LOSS, VAL_LOSS \ No newline at end of file diff --git a/recognition/s4633139/visualse.py b/recognition/s4633139/visualse.py index ca62384be3..b8892576bc 100644 --- a/recognition/s4633139/visualse.py +++ b/recognition/s4633139/visualse.py @@ -11,19 +11,19 @@ import numpy as np -def dice_coef_vis(EPOCHS, TRAIN_COEFS, TEST_COEFS): +def dice_coef_vis(EPOCHS, TRAIN_COEFS, VAL_COEFS): """ function for dice coefficient :param EPOCHS(array): epochs TRAIN_COEFS(array): train dice coefficients - TEST_COEFS(array): test dice coefficients + VAL_COEFS(array): validation dice coefficients :return plot with dice coefficients by epochs """ X = np.arange(1, EPOCHS+1) plt.plot(X, TRAIN_COEFS, marker='.', markersize=10, label='train') - plt.plot(X, TEST_COEFS, marker='.', markersize=10, label='test') + plt.plot(X, VAL_COEFS, marker='.', markersize=10, label='validation') plt.xlabel('Epochs') plt.ylabel('Dice coefficient') plt.xticks(X) @@ -31,19 +31,19 @@ def dice_coef_vis(EPOCHS, TRAIN_COEFS, TEST_COEFS): plt.show() -def dice_loss_vis(EPOCHS, TRAIN_LOSS, TEST_LOSS): +def dice_loss_vis(EPOCHS, TRAIN_LOSS, VAL_LOSS): """ function for dice loss :param EPOCHS(array): epochs TRAIN_LOSS(array): train dice losses - TEST_LOSS(array): test dice losses + VAL_LOSS(array): validation dice losses :return plot with dice loss by epochs """ X = np.arange(1, EPOCHS+1) plt.plot(X, TRAIN_LOSS, marker='.', markersize=10, label='train') - plt.plot(X, TEST_LOSS, marker='.', markersize=10, label='test') + plt.plot(X, VAL_LOSS, marker='.', markersize=10, label='validation') plt.xlabel('Epochs') plt.ylabel('Dice Loss') plt.xticks(X) @@ -51,6 +51,18 @@ def dice_loss_vis(EPOCHS, TRAIN_LOSS, TEST_LOSS): plt.show() +def eval_dice_coef(target, pred_masks, idx): + batch_size = len(pred_masks) + somooth = 1. + + pred_flat = pred_masks.view(batch_size, -1) + target_flat = target.view(batch_size, -1) + + intersection = (pred_flat*target_flat) + dice_coef = (2.*intersection.sum(dim=1)+somooth)/(pred_flat.sum(dim=1)+target_flat.sum(dim=1)+somooth) + return dice_coef[idx] + + def segment_pred_mask(imgs, pred_masks, idx, alpha): """ function to make a covered image with the predicted mask @@ -68,7 +80,7 @@ def segment_pred_mask(imgs, pred_masks, idx, alpha): return seg_img -def plot_gallery(images, masks, pred_masks, n_row=6, n_col=4): +def plot_gallery(images, masks, pred_masks, n_row=5, n_col=4): """ function to generate gallery :parameters @@ -108,6 +120,6 @@ def plot_gallery(images, masks, pred_masks, n_row=6, n_col=4): seg_img = segment_pred_mask(imgs=images, pred_masks=pred_masks, idx=i, alpha=0.5) plt.subplot(n_row, n_col, i + 4) plt.imshow(seg_img.permute(1, 2, 0)) - plt.title('segmentation', fontsize=10) + plt.title('dice_coef: {:.2f}'.format(eval_dice_coef(masks, pred_masks, i)), fontsize=10) plt.axis('off') plt.show() \ No newline at end of file From ca4b900aa666455b73a06a05e2375e2c8dc006c2 Mon Sep 17 00:00:00 2001 From: Wakame <85863413+wakameds@users.noreply.github.com> Date: Wed, 20 Oct 2021 13:22:18 +1000 Subject: [PATCH 30/66] Revised code --- recognition/s4633139/model.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/recognition/s4633139/model.py b/recognition/s4633139/model.py index 38210b1f6c..6eabad5253 100644 --- a/recognition/s4633139/model.py +++ b/recognition/s4633139/model.py @@ -4,7 +4,7 @@ # Author: Hideki WAKAYAMA # Contact: h.wakayama@uq.net.au # Platform: macOS Big Sur Ver 11.2.1, Pycharm pro 2021.1 -# Time: 19/10/2021, 15:47 +# Time: 20/10/2021, 13:09 # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ import torch @@ -153,7 +153,7 @@ def forward(self, x): x = self.Ups[idx + 1](concatnate_skip) #segmentation - if idx == 2 or idx == 4: + if idx != idxs[0] and idx != idxs[-1]: x_segment = self.Segmentations[idx // 2](x) segmentation_layers.append(x_segment) From 5fa2afd06bcccc9d48a4b3879e2bf96a74b3aacc Mon Sep 17 00:00:00 2001 From: wakame <85863413+wakameds@users.noreply.github.com> Date: Wed, 20 Oct 2021 14:14:23 +1000 Subject: [PATCH 31/66] Update README.md Revised README.md --- recognition/s4633139/README.md | 90 ++++++++++++++++++++++------------ 1 file changed, 60 insertions(+), 30 deletions(-) diff --git a/recognition/s4633139/README.md b/recognition/s4633139/README.md index 28b2f8be1a..ecc7aaf88d 100644 --- a/recognition/s4633139/README.md +++ b/recognition/s4633139/README.md @@ -1,12 +1,15 @@ # Improved UNet for ISIC2018 image segmentation +The project is the practical work for COMP3710 in 2021. This report will summarise the information with the improved UNet model in the repository. + ## Objective -This project is a practical work for COMP3710 in 2021. The project objective is to implement the improved UNet for ISIC2018 image segmentation. +The project objective is to implement the improved UNet for ISIC2018 image segmentation. UNet is a developed model for biomedical image segmentation, which automatically identifies the tumour area.[1] The automatic image segmentation without objective will support the medical and experimental works, while the higher accurate image segmentation performance is also required. This project aimed to implement the improved UNet model for Brain tumours into the ISIC2018 image dataset. + ## Model Architecture -UNet is the model for biomedical image segmentation. [1] Figure1 shows the improved UNet architecture. [2] The improved model newly added the context module, 3x3 convolution layer with stride = 2 instead of max pooling layer, localization module, and segmentation layer extracted from localization layer. +Figure 1 shows the improved UNet architecture for Brain tumours.[2] The improved model utilises the context module, 3x3 convolution layer with stride = 2 instead of max-pooling layer, localisation module, and segmentation layer extracted from localisation layer. In down-sampling part, context block works as residual blocks of ResNet. The context block consists of 3x3 convolution layer, batch normalisation layer, dropout layer, and activity function layer. LeakyReLu is applied as the activation function in the model. The output through the context block is concatenated to the input for the localisation modules in up-sampling block. After that, the output features are decreased with 3x3 convolution layer for the following context block. -In downsampling part, context module works as residual blocks and the output from the module is concatenate to the input for the localization modules in upsampling block. 3x3 stride 2 convolution works for downsample block. In upsampling, segmentation layer outputted from localization is added to the next segmentation layer. +In up-sampling, the concatenated input is fed into the localization block. Then, the output from the localization is fed into the convolutional layer to transform into a segmentation layer to add to the next segmentation layer and the up-sampling block, respectively.

@@ -16,72 +19,99 @@ In downsampling part, context module works as residual blocks and the output fro Figure1: The improved UNet model architecture[2]

- -This repository includes the below files for the improved UNet: -* **IUNet_criterion.py:** This file consists of the two criterion functions: dice coefficient and dice loss. The functions are utilised for training and evaluating the model through the forward and backpropagation steps. -* **IUNet_dataloader.py:** This file is concerning data preparation for UNet model, which works for data loading, data transformation, preparing data loader. -* **IUNet_train_test.py:** This file works to train the model and to assess the segmentation performance with dice coefficient and dice loss. The function in the file returns lists recorded the criteria values by epochs: TRAIN_DICE, TEST_DICE, TRAIN_LOSS, TEST_LOSS. -* **ImprovedUNet.py:** This file includes the classes to build the improved UNet model. The classes with Context, Localization, Up-sampling, Segmentation, and Convolution to down-sampling, and Improved Unet are provided. In this model, Sigmoid function for the binary classification between mask and non-mask areas is utilised instead of softmax function. -* **main.py:** The file performs all procedures for the project, data preparation, training model, and model evaluation. The file includes the parameters with the improved UNet: FEATURE_SIZE, IN_CHANEL, OUT_CHANEL, IMG_TF, MASK_TF, BATCH_SIZE, EPOCHS, and LR. The image size is resized into 128x128 as the initial parameter. Also, random_split() is set to split the dataset into train set and test set by 80:20. The file applies Adam as an optimizer. -* **visualise.py:** The file contains the four functions for plotting the test result and training dice coefficient and dice loss by epochs, and output the segmented images with the predicted mask. +In terms of the loss function, dice loss is utilised for UNet. The loss function is represented as + +

+ +

+ +Dice coefficient is represented as + +

+ +

+ +Dice coefficient measures the similarity between the target mask and the predicted mask from the model. + + +## Files +This repository includes the below files for the improved UNet. + +**criterion.py:** This file consists of the two criterion functions: dice coefficient and dice loss. The functions are utilised for training and evaluating the model through the forward and backpropagation steps. + +**dataloader.py:** This file is concerning data preparation for UNet model, which works for data loading, image augmentation, transformation into data loader. + +**model_train_val.py:** This file works to train the model and to assess the segmentation performance with dice coefficient and dice loss. The function in the file returns lists recorded the criteria values by epochs: TRAIN_DICE, VAL_DICE, TRAIN_LOSS, VAL_LOSS. + +**model.py:** This file includes the classes to build the improved UNet model. The classes with Context, Localization, Up-sampling, Segmentation, and Convolution to down-sampling, and Improved UNet are provided. In this model, Sigmoid function for the binary classification between mask and non-mask areas is utilised instead of softmax function. + +**driver.py:** The file performs all procedures for the project, data preparation, training model, and model evaluation. The file includes the parameters with the improved UNet: FEATURE_SIZE, IN_CHANEL, OUT_CHANEL, IMG_TF, MASK_TF, BATCH_SIZE, EPOCHS, and LR. The image size is resized into 128x128 as the initial parameter. Also, random_split() is set to split the dataset into train set, validation set, and test set by 50:25:25. Adam is applied as an optimizer to train the model. + +**visualise.py:** The file contains the five functions for plotting the test result and training dice coefficient and dice loss by epochs, and output the segmented images with the predicted mask. + ## Dataset -ISIC 2018 Task1 is a dataset with skin cancer images shared by the International Skin Imaging Collaboration (ISIC). [3] The dataset consists of 2594 images and 2594 mask images, respectively. The dataset is split into the train set and test set by train ratio = 0.8. +ISIC 2018 Task1 is a dataset with skin cancer images shared by the International Skin Imaging Collaboration (ISIC). [3] The dataset consists of 2594 images and mask images, respectively. The dataset is split into the train set, validation, and test set by ratio: 0.5: 0.25: 0.25. + -## Usage model -“main.py” calls all files in the repository to train the model and evaluate the performance. ISIC dataset is needed to be set in the same directory, including main.py. +## How to run +“driver.py” calls all files in the repository to train the model and to evaluate the performance. ISIC dataset is needed to be set in the same directory including the files. After that, put the command ‘python driver.py’ in the terminal and execute the command. -### Dependency +## Dependency The model training and evaluation was executed under the environment. * Pytorch 1.9.0+cu111 * Python 3.7.12 * Matplotlib 3.3.4 + ## Results -### Dice coefficient and loss -The figure is about train and test dice coefficient and losses by 50 epochs. The test dice coefficient was approximately 0.85 and the test accuracy was stable after 15 epochs. +#### Dice coefficient and loss +The figure is about train and validation dice coefficient and losses by 50 epochs. The validation dice coefficient was approximately 0.85 and it was stable after 15 epochs. +

- +

- Figure2. Dice coefficient +Figure2. Dice coefficient

-The test dice loss was stable at roughly 0.15 while train loss declined after epoch 15. +The validation dice loss was stable at roughly 0.15 while train loss declined after epoch 15. +

- +

+

- Figure3. Dice loss +Figure3. Dice loss

-### Segmentation -The trained UNet predict the mask from the image. The segmentations in the right-hand side column are the images covered with the predicted mask. + +#### Segmentation +The trained UNet predict the mask from the image in test set. The segmentations in the right-hand side column are the images covered with the predicted mask. The dice coefficient of the image is provided in the label. The dice coefficients in the figure recorded over 0.87. +

- +

+

- Figure4. Segmentation +Figure4. Segmentation

+ ## References [1] Ronneberger, O., Fischer, P., & Brox, T. (2015, October). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). Springer, Cham. https://arxiv.org/abs/1505.04597 [2] Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., & Maier-Hein, K. H. (2017, September). Brain tumor segmentation and radiomics survival prediction: Contribution to the brats 2017 challenge. In International MICCAI Brainlesion Workshop (pp. 287-297). Springer, Cham. https://arxiv.org/pdf/1802.10508v1.pdf [3] ISIC 2018 Task1 https://paperswithcode.com/dataset/isic-2018-task-1 - - - - From e8ccecad5c13217c21e706b25e7bb6deebb79046 Mon Sep 17 00:00:00 2001 From: Wakame <85863413+wakameds@users.noreply.github.com> Date: Wed, 20 Oct 2021 14:47:38 +1000 Subject: [PATCH 32/66] add comments in the file --- recognition/s4633139/criterion.py | 18 +++++++- recognition/s4633139/model_train_val.py | 15 ++++-- recognition/s4633139/visualse.py | 61 +++++++++++++++---------- 3 files changed, 65 insertions(+), 29 deletions(-) diff --git a/recognition/s4633139/criterion.py b/recognition/s4633139/criterion.py index 6b597eab54..b0b3813b99 100644 --- a/recognition/s4633139/criterion.py +++ b/recognition/s4633139/criterion.py @@ -4,11 +4,19 @@ # Author: Hideki WAKAYAMA # Contact: h.wakayama@uq.net.au # Platform: macOS Big Sur Ver 11.2.1, Pycharm pro 2021.1 -# Time: 19/10/2021, 15:47 +# Time: 20/10/2021, 09:52 # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ #dice coefficient def dice_coef(pred, target): + """ + function to compute the dice coefficient + param---- + pred(tensor[B,C,W,H]): predicted mask images + target(tensor[B,C,W,H]: target mask images + return--- + dice coefficient + """ batch_size = len(pred) somooth = 1. @@ -22,5 +30,13 @@ def dice_coef(pred, target): #loss def dice_loss(pred, target): + """ + function to compute dice loss + param---- + pred(tensor[B,C,W,H]): predicted mask images + target(tensor[B,C,W,H]): target mask images + return---- + dice loss + """ dice_loss = 1 - dice_coef(pred, target) return dice_loss \ No newline at end of file diff --git a/recognition/s4633139/model_train_val.py b/recognition/s4633139/model_train_val.py index c34c365c4d..f553b0eef9 100644 --- a/recognition/s4633139/model_train_val.py +++ b/recognition/s4633139/model_train_val.py @@ -4,7 +4,7 @@ # Author: Hideki WAKAYAMA # Contact: h.wakayama@uq.net.au # Platform: macOS Big Sur Ver 11.2.1, Pycharm pro 2021.1 -# Time: 19/10/2021, 15:47 +# Time: 20/10/2021, 09:52 # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ from criterion import dice_coef, dice_loss @@ -12,9 +12,18 @@ import torch def model_train_val(model, optimizer, EPOCHS, train_loader, val_loader): - """function for model training and validation - :return: list of train and validation dice coefficients and dice losses by epochs """ + function for model training and validation + :param---- + model: model + optimizer: optimizer + EPOCHS(int):number of epochs + train_loader: train loader + val_loader: validation loader + :return---- + list of train and validation dice coefficients and dice losses by epochs + """ + TRAIN_LOSS = [] TRAIN_DICE = [] VAL_LOSS =[] diff --git a/recognition/s4633139/visualse.py b/recognition/s4633139/visualse.py index b8892576bc..50d7288abc 100644 --- a/recognition/s4633139/visualse.py +++ b/recognition/s4633139/visualse.py @@ -4,7 +4,7 @@ # Author: Hideki WAKAYAMA # Contact: h.wakayama@uq.net.au # Platform: macOS Big Sur Ver 11.2.1, Pycharm pro 2021.1 -# Time: 19/10/2021, 17:30 +# Time: 20/10/2021, 12:49 # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ import matplotlib.pyplot as plt @@ -14,11 +14,11 @@ def dice_coef_vis(EPOCHS, TRAIN_COEFS, VAL_COEFS): """ function for dice coefficient - :param + param---- EPOCHS(array): epochs TRAIN_COEFS(array): train dice coefficients VAL_COEFS(array): validation dice coefficients - :return + return---- plot with dice coefficients by epochs """ X = np.arange(1, EPOCHS+1) @@ -34,11 +34,11 @@ def dice_coef_vis(EPOCHS, TRAIN_COEFS, VAL_COEFS): def dice_loss_vis(EPOCHS, TRAIN_LOSS, VAL_LOSS): """ function for dice loss - :param + param---- EPOCHS(array): epochs TRAIN_LOSS(array): train dice losses VAL_LOSS(array): validation dice losses - :return + return---- plot with dice loss by epochs """ X = np.arange(1, EPOCHS+1) @@ -52,25 +52,36 @@ def dice_loss_vis(EPOCHS, TRAIN_LOSS, VAL_LOSS): def eval_dice_coef(target, pred_masks, idx): - batch_size = len(pred_masks) - somooth = 1. + """ + function to return dice coefficient of the image + param---- + target(tensor[B,C,W,H]):target mask images + pred_masks:(tensor[B,C,W,H]):predicted mask images + idx(int): index + return---- + dice coefficient + """ + batch_size = len(pred_masks) + somooth = 1. - pred_flat = pred_masks.view(batch_size, -1) - target_flat = target.view(batch_size, -1) + pred_flat = pred_masks.view(batch_size, -1) + target_flat = target.view(batch_size, -1) - intersection = (pred_flat*target_flat) - dice_coef = (2.*intersection.sum(dim=1)+somooth)/(pred_flat.sum(dim=1)+target_flat.sum(dim=1)+somooth) - return dice_coef[idx] + intersection = (pred_flat*target_flat) + dice_coef = (2.*intersection.sum(dim=1)+somooth)/(pred_flat.sum(dim=1)+target_flat.sum(dim=1)+somooth) + return dice_coef[idx] def segment_pred_mask(imgs, pred_masks, idx, alpha): """ function to make a covered image with the predicted mask - :param imgs(tensor[B,C,W,H]): 3 channels image - :param pred_masks(tensor[B,C,W,H]): predicted mask - :param idx(int): image index - :param alpha(float): ratio for segmentation - :return: segmentation image + param---- + imgs(tensor[B,C,W,H]): 3 channels image + pred_masks(tensor[B,C,W,H]): predicted mask + idx(int): image index + alpha(float): ratio for segmentation + return---- + segmentation image """ seg_img = imgs[idx].clone() image_r = seg_img[0] @@ -83,14 +94,14 @@ def segment_pred_mask(imgs, pred_masks, idx, alpha): def plot_gallery(images, masks, pred_masks, n_row=5, n_col=4): """ function to generate gallery - :parameters - images(tensor[B,C,W,H]): images - masks(tensor[B,C,W,H]): target masks - pred_masks(tensor[B,C,W,H]): predicted masks - n_row: number of the row for the gallery - n_col: number of the column for the gallery - :return - gallery images + parameters---- + images(tensor[B,C,W,H]): images + masks(tensor[B,C,W,H]): target masks + pred_masks(tensor[B,C,W,H]): predicted masks + n_row: number of the row for the gallery + n_col: number of the column for the gallery + return---- + gallery images """ idxs = n_col * n_row plt.figure(figsize=(1.5 * n_col, 1.5 * n_row)) From 3413697171ec96881d601ef0f10d9a2af01f3174 Mon Sep 17 00:00:00 2001 From: wakame <85863413+wakameds@users.noreply.github.com> Date: Fri, 22 Oct 2021 14:05:27 +1000 Subject: [PATCH 33/66] Update README.md revise README.md --- recognition/s4633139/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/recognition/s4633139/README.md b/recognition/s4633139/README.md index ecc7aaf88d..5786d3b024 100644 --- a/recognition/s4633139/README.md +++ b/recognition/s4633139/README.md @@ -7,7 +7,7 @@ The project objective is to implement the improved UNet for ISIC2018 image segme ## Model Architecture -Figure 1 shows the improved UNet architecture for Brain tumours.[2] The improved model utilises the context module, 3x3 convolution layer with stride = 2 instead of max-pooling layer, localisation module, and segmentation layer extracted from localisation layer. In down-sampling part, context block works as residual blocks of ResNet. The context block consists of 3x3 convolution layer, batch normalisation layer, dropout layer, and activity function layer. LeakyReLu is applied as the activation function in the model. The output through the context block is concatenated to the input for the localisation modules in up-sampling block. After that, the output features are decreased with 3x3 convolution layer for the following context block. +Figure 1 shows the improved UNet architecture for Brain tumours.[2] The improved model utilises the context module, 3x3 convolution layer with stride = 2 instead of max-pooling layer, localisation module, and segmentation layer extracted from localisation layer. In down-sampling part, context block works as residual blocks of ResNet. The context block consists of 3x3 convolution layer, batch normalisation layer, dropout layer, and activity function layer. LeakyReLu is applied as the activation function in the model. The output through the context block is concatenated to the input for the localisation modules in up-sampling block. After that, the output features are decreased with 3x3 convolution layer for the following context block. In up-sampling, the concatenated input is fed into the localization block. Then, the output from the localization is fed into the convolutional layer to transform into a segmentation layer to add to the next segmentation layer and the up-sampling block, respectively. From 1c3f4a8fb3c09c1c9c101dbcfa5bda7e03c3fe98 Mon Sep 17 00:00:00 2001 From: Wakame <85863413+wakameds@users.noreply.github.com> Date: Fri, 22 Oct 2021 14:14:08 +1000 Subject: [PATCH 34/66] change epoch num from 1 to 20 --- recognition/s4633139/driver.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/recognition/s4633139/driver.py b/recognition/s4633139/driver.py index 8a4b13e066..b31d798904 100644 --- a/recognition/s4633139/driver.py +++ b/recognition/s4633139/driver.py @@ -39,7 +39,7 @@ def main(): ]) BATCH_SIZE = 64 - EPOCHS = 1 + EPOCHS = 20 LR = 0.001 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") From b9e5ea66dd9cded0ea8545d8083b52a5b75f9974 Mon Sep 17 00:00:00 2001 From: Wakame <85863413+wakameds@users.noreply.github.com> Date: Fri, 22 Oct 2021 14:16:53 +1000 Subject: [PATCH 35/66] Revise README.md --- recognition/s4633139/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/recognition/s4633139/README.md b/recognition/s4633139/README.md index 5786d3b024..524e880dc5 100644 --- a/recognition/s4633139/README.md +++ b/recognition/s4633139/README.md @@ -58,7 +58,7 @@ ISIC 2018 Task1 is a dataset with skin cancer images shared by the International “driver.py” calls all files in the repository to train the model and to evaluate the performance. ISIC dataset is needed to be set in the same directory including the files. After that, put the command ‘python driver.py’ in the terminal and execute the command. -## Dependency +## Dependencies The model training and evaluation was executed under the environment. * Pytorch 1.9.0+cu111 * Python 3.7.12 From b38403e336bff44f5170ac2af836e26241cb82c1 Mon Sep 17 00:00:00 2001 From: Wakame <85863413+wakameds@users.noreply.github.com> Date: Fri, 24 Sep 2021 15:40:05 +1000 Subject: [PATCH 36/66] test --- recognition/s4633139/Musk_RCNN.ipynb | 61 ++++++++++++++++++++++++++++ 1 file changed, 61 insertions(+) create mode 100644 recognition/s4633139/Musk_RCNN.ipynb diff --git a/recognition/s4633139/Musk_RCNN.ipynb b/recognition/s4633139/Musk_RCNN.ipynb new file mode 100644 index 0000000000..fc010b4420 --- /dev/null +++ b/recognition/s4633139/Musk_RCNN.ipynb @@ -0,0 +1,61 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Musk-RCNN.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "t84bc17tKTmU" + }, + "source": [ + "test" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iL1WKwmyLTz7" + }, + "source": [ + "test" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "PzblyaBIFZqB" + }, + "source": [ + "" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "AQNtK-9zKSTc" + }, + "source": [ + "" + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file From 68f05cebd95c72f4173664bebba3e2a2baabd8a4 Mon Sep 17 00:00:00 2001 From: Wakame <85863413+wakameds@users.noreply.github.com> Date: Fri, 24 Sep 2021 14:59:51 +1000 Subject: [PATCH 37/66] test to upload with Colaboratory --- Musk_RCNN.ipynb | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/Musk_RCNN.ipynb b/Musk_RCNN.ipynb index 884dcdb0eb..363e4ef125 100644 --- a/Musk_RCNN.ipynb +++ b/Musk_RCNN.ipynb @@ -7,7 +7,8 @@ "provenance": [], "collapsed_sections": [], "mount_file_id": "1sn2UhFSOZZoyAj5L2RL8YS_7_cthikKn", - "authorship_tag": "ABX9TyPugfeUj1s/MDTpavLUB9ID" + "authorship_tag": "ABX9TyPugfeUj1s/MDTpavLUB9ID", + "include_colab_link": true }, "kernelspec": { "name": "python3", @@ -19,6 +20,16 @@ "accelerator": "GPU" }, "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, { "cell_type": "markdown", "metadata": { From 5e755b2e8fc270fd317de901647936fabced1b5b Mon Sep 17 00:00:00 2001 From: Wakame <85863413+wakameds@users.noreply.github.com> Date: Fri, 24 Sep 2021 15:00:47 +1000 Subject: [PATCH 38/66] =?UTF-8?q?Colaboratory=20=E3=82=92=E4=BD=BF?= =?UTF-8?q?=E7=94=A8=E3=81=97=E3=81=A6=E4=BD=9C=E6=88=90=E3=81=97=E3=81=BE?= =?UTF-8?q?=E3=81=97=E3=81=9F?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- Musk_RCNN.ipynb | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/Musk_RCNN.ipynb b/Musk_RCNN.ipynb index 363e4ef125..691956baa5 100644 --- a/Musk_RCNN.ipynb +++ b/Musk_RCNN.ipynb @@ -7,7 +7,7 @@ "provenance": [], "collapsed_sections": [], "mount_file_id": "1sn2UhFSOZZoyAj5L2RL8YS_7_cthikKn", - "authorship_tag": "ABX9TyPugfeUj1s/MDTpavLUB9ID", + "authorship_tag": "ABX9TyNlCc5GW5jggKkr0jGOvNVB", "include_colab_link": true }, "kernelspec": { @@ -39,6 +39,15 @@ "test" ] }, + { + "cell_type": "markdown", + "metadata": { + "id": "iL1WKwmyLTz7" + }, + "source": [ + "test" + ] + }, { "cell_type": "code", "metadata": { From a8d59eae89b953804e9c9d1fc98a715252c0454a Mon Sep 17 00:00:00 2001 From: Wakame <85863413+wakameds@users.noreply.github.com> Date: Fri, 8 Oct 2021 15:43:29 +1000 Subject: [PATCH 39/66] Delete recognition/s4633139 directory --- recognition/s4633139/Musk_RCNN.ipynb | 61 ---------------------------- 1 file changed, 61 deletions(-) delete mode 100644 recognition/s4633139/Musk_RCNN.ipynb diff --git a/recognition/s4633139/Musk_RCNN.ipynb b/recognition/s4633139/Musk_RCNN.ipynb deleted file mode 100644 index fc010b4420..0000000000 --- a/recognition/s4633139/Musk_RCNN.ipynb +++ /dev/null @@ -1,61 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "name": "Musk-RCNN.ipynb", - "provenance": [], - "collapsed_sections": [] - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - }, - "accelerator": "GPU" - }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "t84bc17tKTmU" - }, - "source": [ - "test" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "iL1WKwmyLTz7" - }, - "source": [ - "test" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "PzblyaBIFZqB" - }, - "source": [ - "" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "AQNtK-9zKSTc" - }, - "source": [ - "" - ], - "execution_count": null, - "outputs": [] - } - ] -} \ No newline at end of file From 9d6ce0e45bfa2c5e10fd9d5791e98ac218b71086 Mon Sep 17 00:00:00 2001 From: Wakame <85863413+wakameds@users.noreply.github.com> Date: Fri, 8 Oct 2021 16:11:33 +1000 Subject: [PATCH 40/66] upload dataloader --- recognition/s4633139/Dataloader.ipynb | 1 + 1 file changed, 1 insertion(+) create mode 100644 recognition/s4633139/Dataloader.ipynb diff --git a/recognition/s4633139/Dataloader.ipynb b/recognition/s4633139/Dataloader.ipynb new file mode 100644 index 0000000000..5eb3f16a23 --- /dev/null +++ b/recognition/s4633139/Dataloader.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Dataloader.ipynb","provenance":[],"collapsed_sections":[],"mount_file_id":"1sn2UhFSOZZoyAj5L2RL8YS_7_cthikKn","authorship_tag":"ABX9TyNkwzaLVu64OaORBKup58Jo"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","metadata":{"id":"vlhQO-g6Y2ah","executionInfo":{"status":"ok","timestamp":1633672076114,"user_tz":-600,"elapsed":10346,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["import os\n","from PIL import Image\n","import matplotlib.pyplot as plt\n","import glob\n","\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","\n","import torch.utils.data\n","from torchvision import datasets, transforms"],"execution_count":2,"outputs":[]},{"cell_type":"code","metadata":{"id":"WuSova9HHAzc","colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"status":"ok","timestamp":1633672105488,"user_tz":-600,"elapsed":26753,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"05fb16af-8927-4381-d504-febf63d348d5"},"source":["#define dataset for img and mask\n","file_dir = \"/content/drive/MyDrive/ColabGitHub/PatternFlow/recognition/s46331391_Unet/ISIC2018_Task1-2_Training_Data\"\n","os.chdir(file_dir)\n","\n","img_path = './ISIC2018_Task1-2_Training_Input_x2'\n","mask_path = './ISIC2018_Task1_Training_GroundTruth_x2'\n","\n","imgs = [file for file in sorted(os.listdir(img_path)) if file.endswith('.jpg')]\n","masks = [file for file in sorted(os.listdir(mask_path)) if file.endswith('.png')]\n","\n","imgs[0]"],"execution_count":3,"outputs":[{"output_type":"execute_result","data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'ISIC_0000000.jpg'"]},"metadata":{},"execution_count":3}]},{"cell_type":"code","metadata":{"id":"JhoipyIyXvBB","executionInfo":{"status":"ok","timestamp":1633672109919,"user_tz":-600,"elapsed":253,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["from torch.utils.data import Dataset\n","from PIL import Image\n","\n","class Unet_dataset(Dataset):\n"," def __init__(self,\n"," img_dir = './ISIC2018_Task1-2_Training_Input_x2',\n"," mask_dir = './ISIC2018_Task1_Training_GroundTruth_x2', \n"," img_transforms=None,\n"," mask_transforms= None):\n"," \n"," self.img_dir = img_dir\n"," self.mask_dir = mask_dir\n"," self.img_transforms = img_transforms\n"," self.mask_transforms = mask_transforms\n","\n"," self.imgs = [file for file in sorted(os.listdir(self.img_dir)) if file.endswith('.jpg')]\n"," self.masks = [file for file in sorted(os.listdir(self.mask_dir)) if file.endswith('.png')]\n","\n"," #meke dataloader\n"," def load_img(self, idx):\n"," img_path = os.path.join(self.img_dir, self.imgs[idx])\n"," img = Image.open(img_path).convert('RGB')\n"," return img\n","\n"," def load_mask(self, idx):\n"," mask_path = os.path.join(self.mask_dir, self.masks[idx])\n"," mask = Image.open(mask_path).convert('L')\n"," return mask\n","\n"," def __getitem__(self, idx):\n"," img = self.load_img(idx)\n"," mask = self.load_mask(idx)\n","\n"," if self.img_transforms is not None:\n"," img = self.img_transforms(img)\n","\n"," if self.mask_transforms is not None:\n"," mask = self.mask_transforms(mask)\n","\n"," return img, mask\n","\n"," def __len__(self):\n"," return len(self.imgs)"],"execution_count":4,"outputs":[]},{"cell_type":"code","metadata":{"id":"YFzDGbXcE_p8","executionInfo":{"status":"ok","timestamp":1633672112761,"user_tz":-600,"elapsed":276,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["#img_path = os.path.join(root, \"ISIC2018_Task1-2_Training_Input_x2\", imgs[idx])\n","import torchvision.transforms as transforms\n","img_tfs = transforms.Compose([\n"," transforms.Resize((64,64)),\n"," transforms.ToTensor(),\n"," transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n"," ]\n"," )\n","\n","mask_tfs = transforms.Compose([\n"," transforms.Resize((64,64)),\n"," transforms.ToTensor(),\n"," ]\n"," )"],"execution_count":5,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":302},"id":"zx1QLG_3l_nB","executionInfo":{"status":"ok","timestamp":1633672115791,"user_tz":-600,"elapsed":1189,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"1fd9dd2e-3a27-4ed5-e81b-bc34773b19c0"},"source":["dataset = Unet_dataset(img_transforms=img_tfs, mask_transforms=mask_tfs)\n","img, mask = dataset.__getitem__(20)\n","\n","import matplotlib.pyplot as plt\n","plt.imshow(img.permute(1,2,0))"],"execution_count":6,"outputs":[{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":6},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]}]} \ No newline at end of file From 00436fa7e831db772798db44b6f3f00bd598e599 Mon Sep 17 00:00:00 2001 From: Wakame <85863413+wakameds@users.noreply.github.com> Date: Fri, 8 Oct 2021 16:07:59 +1000 Subject: [PATCH 41/66] Delete Musk_RCNN.ipynb --- Musk_RCNN.ipynb | 74 ------------------------------------------------- 1 file changed, 74 deletions(-) delete mode 100644 Musk_RCNN.ipynb diff --git a/Musk_RCNN.ipynb b/Musk_RCNN.ipynb deleted file mode 100644 index 691956baa5..0000000000 --- a/Musk_RCNN.ipynb +++ /dev/null @@ -1,74 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "name": "Musk-RCNN.ipynb", - "provenance": [], - "collapsed_sections": [], - "mount_file_id": "1sn2UhFSOZZoyAj5L2RL8YS_7_cthikKn", - "authorship_tag": "ABX9TyNlCc5GW5jggKkr0jGOvNVB", - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - }, - "accelerator": "GPU" - }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "t84bc17tKTmU" - }, - "source": [ - "test" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "iL1WKwmyLTz7" - }, - "source": [ - "test" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "PzblyaBIFZqB" - }, - "source": [ - "" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "AQNtK-9zKSTc" - }, - "source": [ - "" - ], - "execution_count": null, - "outputs": [] - } - ] -} \ No newline at end of file From a4ee4bc8b9b76035a4ee6234384f428c6c3c9ced Mon Sep 17 00:00:00 2001 From: wakahide23 Date: Fri, 8 Oct 2021 07:00:13 +0000 Subject: [PATCH 42/66] test2 --- recognition/s4633139/UNet.ipynb | 1 + 1 file changed, 1 insertion(+) create mode 100644 recognition/s4633139/UNet.ipynb diff --git a/recognition/s4633139/UNet.ipynb b/recognition/s4633139/UNet.ipynb new file mode 100644 index 0000000000..21cf81919d --- /dev/null +++ b/recognition/s4633139/UNet.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"UNet.ipynb","provenance":[],"collapsed_sections":[],"mount_file_id":"1sn2UhFSOZZoyAj5L2RL8YS_7_cthikKn","authorship_tag":"ABX9TyNdbWoFJtjjLepg4aq3aQR1"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","metadata":{"id":"vXc844rDosCM"},"source":["# UNET"]},{"cell_type":"code","metadata":{"id":"vlhQO-g6Y2ah"},"source":["import os\n","from PIL import Image\n","import matplotlib.pyplot as plt\n","import glob\n","\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","\n","import torch.utils.data\n","from torchvision import datasets, transforms"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"WuSova9HHAzc","colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"status":"ok","timestamp":1633585012559,"user_tz":-600,"elapsed":280,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"376c0ef2-eca7-473b-fa7a-3f52df4964e7"},"source":["#define dataset for img and mask\n","file_dir = \"/content/drive/MyDrive/ColabGitHub/s4633131-ISICs-UNET/ISIC2018_Task1-2_Training_Data\"\n","os.chdir(file_dir)\n","\n","img_path = './ISIC2018_Task1-2_Training_Input_x2'\n","mask_path = './ISIC2018_Task1_Training_GroundTruth_x2'\n","\n","imgs = [file for file in sorted(os.listdir(img_path)) if file.endswith('.jpg')]\n","masks = [file for file in sorted(os.listdir(mask_path)) if file.endswith('.png')]\n","\n","imgs[0]"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'ISIC_0000000.jpg'"]},"metadata":{},"execution_count":24}]},{"cell_type":"code","metadata":{"id":"JhoipyIyXvBB"},"source":["from torch.utils.data import Dataset\n","from PIL import Image\n","\n","class Unet_dataset(Dataset):\n"," def __init__(self,\n"," img_dir = './ISIC2018_Task1-2_Training_Input_x2',\n"," mask_dir = './ISIC2018_Task1_Training_GroundTruth_x2', \n"," img_transforms=None,\n"," mask_transforms= None):\n"," \n"," self.img_dir = img_dir\n"," self.mask_dir = mask_dir\n"," self.img_transforms = img_transforms\n"," self.mask_transforms = mask_transforms\n","\n"," self.imgs = [file for file in sorted(os.listdir(self.img_dir)) if file.endswith('.jpg')]\n"," self.masks = [file for file in sorted(os.listdir(self.mask_dir)) if file.endswith('.png')]\n","\n"," #meke dataloader\n"," def load_img(self, idx):\n"," img_path = os.path.join(self.img_dir, self.imgs[idx])\n"," img = Image.open(img_path).convert('RGB')\n"," return img\n","\n"," def load_mask(self, idx):\n"," mask_path = os.path.join(self.mask_dir, self.masks[idx])\n"," mask = Image.open(mask_path).convert('L')\n"," return mask\n","\n"," def __getitem__(self, idx):\n"," img = self.load_img(idx)\n"," mask = self.load_mask(idx)\n","\n"," if self.img_transforms is not None:\n"," img = self.img_transforms(img)\n","\n"," if self.mask_transforms is not None:\n"," mask = self.mask_transforms(mask)\n","\n"," return img, mask\n","\n"," def __len__(self):\n"," return len(self.imgs)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"YFzDGbXcE_p8"},"source":["#img_path = os.path.join(root, \"ISIC2018_Task1-2_Training_Input_x2\", imgs[idx])\n","import torchvision.transforms as transforms\n","img_tfs = transforms.Compose([\n"," transforms.Resize((64,64)),\n"," transforms.ToTensor(),\n"," transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n"," ]\n"," )\n","\n","mask_tfs = transforms.Compose([\n"," transforms.Resize((64,64)),\n"," transforms.ToTensor(),\n"," ]\n"," )"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":302},"id":"zx1QLG_3l_nB","executionInfo":{"status":"ok","timestamp":1633587996487,"user_tz":-600,"elapsed":1209,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"3285e87c-32b1-4864-af1c-88564e2a97f6"},"source":["dataset = Unet_dataset(img_transforms=img_tfs, mask_transforms=mask_tfs)\n","img, mask = dataset.__getitem__(20)\n","\n","import matplotlib.pyplot as plt\n","plt.imshow(img.permute(1,2,0))"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":94},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YPWydbZkaCl5","executionInfo":{"status":"ok","timestamp":1633587620112,"user_tz":-600,"elapsed":282,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"70f9f721-128f-473a-e4b1-8213957000d3"},"source":[""],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(64, 64, 1)"]},"metadata":{},"execution_count":86}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6wyVgya8TX9x","executionInfo":{"status":"ok","timestamp":1633585081765,"user_tz":-600,"elapsed":380,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"719db019-13e3-406b-c030-9aecd3c347a4"},"source":["img_transforms(Image.open(os.path.join(file_dir, img_path, imgs[0])).convert('RGB')).shape"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["torch.Size([3, 64, 64])"]},"metadata":{},"execution_count":34}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0gENtcRuWWk5","executionInfo":{"status":"ok","timestamp":1633585053171,"user_tz":-600,"elapsed":1373,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"50fbee1c-9211-4b03-df2e-1252937da309"},"source":["img_transforms"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Compose(\n"," Resize(size=(64, 64), interpolation=bilinear, max_size=None, antialias=None)\n"," ToTensor()\n"," Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",")"]},"metadata":{},"execution_count":31}]},{"cell_type":"code","metadata":{"id":"xItKH6hLVkE9"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"VFiXmwWjSeXi"},"source":["import torch\n","import torch.nn as nn\n","import torchvision.transforms.functional as TF"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"nzGxgjkBeFXR"},"source":["class DConv(nn.Module):\n"," def __init__(self, in_channels, out_channels):\n"," super(DConv, self).__init__()\n"," self.conv = nn.Sequential(\n"," nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.ReLU(inplace=True),\n"," nn.Conv2d(out_channels, out_channels, kernel_size=3, stride = 1, padding=1, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.ReLU(inplace=True),\n"," )\n","\n"," def forward(self, x):\n"," return self.conv(x)\n","\n","\n","class Unet(nn.Module):\n"," def __init__(self, in_channels=3, out_channels=1, feature_size=[64, 128, 256, 512],):\n"," super(Unet, self).__init__()\n"," self.downsample = nn.ModuleList()\n"," self.upsample = nn.ModuleList()\n"," self.pool = nn.MaxPool2d(kernel_size=2, stride=2)\n","\n"," #Downscale part\n"," for feature in feature_size:\n"," self.downsample.append(DConv(in_channels, feature))\n"," in_channels = feature\n","\n"," #Upscale part\n"," for feature in reversed(feature_size):\n"," self.upsample.append(nn.ConvTranspose2d(feature*2, feature, kernel_size=2, stride =2))\n"," self.upsample.append(DConv(feature*2, feature))\n","\n"," self.bottleneck = DConv(feature_size[-1], feature_size[-1]*2)\n"," self.final_conv = nn.Conv2d(feature_size[0], out_channels, kernel_size=1)\n","\n","\n"," def forward(self, x):\n"," skip_connections = []\n","\n"," for down in self.downsample:\n"," x = down(x)\n"," skip_connections.append(x)\n"," x = self.pool(x)\n","\n"," x = self.bottleneck(x)\n"," skip_connections = skip_connections[: : -1]\n","\n"," for idx in range(0, len(self.upsample), 2):\n"," x = self.upsample[idx](x)\n"," skip_connection = skip_connections[idx//2]\n","\n"," if x.shape != skip_connection.shape:\n"," x = TF.resize(x, size=skip_connection.shape[2:])\n","\n"," concatnate_skip = torch.cat((skip_connection, x), dim=1)\n"," x = self.upsample[idx+1](concatnate_skip)\n"," \n"," return self.final_conv(x)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"dNMXUs-qViYi"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"EhJChHsfViUi"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"m7ZMGrmOViRp"},"source":[""],"execution_count":null,"outputs":[]}]} \ No newline at end of file From 9e81c7dde878acbefac989ef0acda86bb5a25149 Mon Sep 17 00:00:00 2001 From: wakahide23 Date: Fri, 15 Oct 2021 04:59:26 +0000 Subject: [PATCH 43/66] upload colab.file from Colab --- recognition/s4633139/UNet.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/recognition/s4633139/UNet.ipynb b/recognition/s4633139/UNet.ipynb index 21cf81919d..88301ea97b 100644 --- a/recognition/s4633139/UNet.ipynb +++ b/recognition/s4633139/UNet.ipynb @@ -1 +1 @@ -{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"UNet.ipynb","provenance":[],"collapsed_sections":[],"mount_file_id":"1sn2UhFSOZZoyAj5L2RL8YS_7_cthikKn","authorship_tag":"ABX9TyNdbWoFJtjjLepg4aq3aQR1"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","metadata":{"id":"vXc844rDosCM"},"source":["# UNET"]},{"cell_type":"code","metadata":{"id":"vlhQO-g6Y2ah"},"source":["import os\n","from PIL import Image\n","import matplotlib.pyplot as plt\n","import glob\n","\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","\n","import torch.utils.data\n","from torchvision import datasets, transforms"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"WuSova9HHAzc","colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"status":"ok","timestamp":1633585012559,"user_tz":-600,"elapsed":280,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"376c0ef2-eca7-473b-fa7a-3f52df4964e7"},"source":["#define dataset for img and mask\n","file_dir = \"/content/drive/MyDrive/ColabGitHub/s4633131-ISICs-UNET/ISIC2018_Task1-2_Training_Data\"\n","os.chdir(file_dir)\n","\n","img_path = './ISIC2018_Task1-2_Training_Input_x2'\n","mask_path = './ISIC2018_Task1_Training_GroundTruth_x2'\n","\n","imgs = [file for file in sorted(os.listdir(img_path)) if file.endswith('.jpg')]\n","masks = [file for file in sorted(os.listdir(mask_path)) if file.endswith('.png')]\n","\n","imgs[0]"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'ISIC_0000000.jpg'"]},"metadata":{},"execution_count":24}]},{"cell_type":"code","metadata":{"id":"JhoipyIyXvBB"},"source":["from torch.utils.data import Dataset\n","from PIL import Image\n","\n","class Unet_dataset(Dataset):\n"," def __init__(self,\n"," img_dir = './ISIC2018_Task1-2_Training_Input_x2',\n"," mask_dir = './ISIC2018_Task1_Training_GroundTruth_x2', \n"," img_transforms=None,\n"," mask_transforms= None):\n"," \n"," self.img_dir = img_dir\n"," self.mask_dir = mask_dir\n"," self.img_transforms = img_transforms\n"," self.mask_transforms = mask_transforms\n","\n"," self.imgs = [file for file in sorted(os.listdir(self.img_dir)) if file.endswith('.jpg')]\n"," self.masks = [file for file in sorted(os.listdir(self.mask_dir)) if file.endswith('.png')]\n","\n"," #meke dataloader\n"," def load_img(self, idx):\n"," img_path = os.path.join(self.img_dir, self.imgs[idx])\n"," img = Image.open(img_path).convert('RGB')\n"," return img\n","\n"," def load_mask(self, idx):\n"," mask_path = os.path.join(self.mask_dir, self.masks[idx])\n"," mask = Image.open(mask_path).convert('L')\n"," return mask\n","\n"," def __getitem__(self, idx):\n"," img = self.load_img(idx)\n"," mask = self.load_mask(idx)\n","\n"," if self.img_transforms is not None:\n"," img = self.img_transforms(img)\n","\n"," if self.mask_transforms is not None:\n"," mask = self.mask_transforms(mask)\n","\n"," return img, mask\n","\n"," def __len__(self):\n"," return len(self.imgs)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"YFzDGbXcE_p8"},"source":["#img_path = os.path.join(root, \"ISIC2018_Task1-2_Training_Input_x2\", imgs[idx])\n","import torchvision.transforms as transforms\n","img_tfs = transforms.Compose([\n"," transforms.Resize((64,64)),\n"," transforms.ToTensor(),\n"," transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n"," ]\n"," )\n","\n","mask_tfs = transforms.Compose([\n"," transforms.Resize((64,64)),\n"," transforms.ToTensor(),\n"," ]\n"," )"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":302},"id":"zx1QLG_3l_nB","executionInfo":{"status":"ok","timestamp":1633587996487,"user_tz":-600,"elapsed":1209,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"3285e87c-32b1-4864-af1c-88564e2a97f6"},"source":["dataset = Unet_dataset(img_transforms=img_tfs, mask_transforms=mask_tfs)\n","img, mask = dataset.__getitem__(20)\n","\n","import matplotlib.pyplot as plt\n","plt.imshow(img.permute(1,2,0))"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":94},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YPWydbZkaCl5","executionInfo":{"status":"ok","timestamp":1633587620112,"user_tz":-600,"elapsed":282,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"70f9f721-128f-473a-e4b1-8213957000d3"},"source":[""],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(64, 64, 1)"]},"metadata":{},"execution_count":86}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6wyVgya8TX9x","executionInfo":{"status":"ok","timestamp":1633585081765,"user_tz":-600,"elapsed":380,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"719db019-13e3-406b-c030-9aecd3c347a4"},"source":["img_transforms(Image.open(os.path.join(file_dir, img_path, imgs[0])).convert('RGB')).shape"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["torch.Size([3, 64, 64])"]},"metadata":{},"execution_count":34}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0gENtcRuWWk5","executionInfo":{"status":"ok","timestamp":1633585053171,"user_tz":-600,"elapsed":1373,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"50fbee1c-9211-4b03-df2e-1252937da309"},"source":["img_transforms"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Compose(\n"," Resize(size=(64, 64), interpolation=bilinear, max_size=None, antialias=None)\n"," ToTensor()\n"," Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",")"]},"metadata":{},"execution_count":31}]},{"cell_type":"code","metadata":{"id":"xItKH6hLVkE9"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"VFiXmwWjSeXi"},"source":["import torch\n","import torch.nn as nn\n","import torchvision.transforms.functional as TF"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"nzGxgjkBeFXR"},"source":["class DConv(nn.Module):\n"," def __init__(self, in_channels, out_channels):\n"," super(DConv, self).__init__()\n"," self.conv = nn.Sequential(\n"," nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.ReLU(inplace=True),\n"," nn.Conv2d(out_channels, out_channels, kernel_size=3, stride = 1, padding=1, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.ReLU(inplace=True),\n"," )\n","\n"," def forward(self, x):\n"," return self.conv(x)\n","\n","\n","class Unet(nn.Module):\n"," def __init__(self, in_channels=3, out_channels=1, feature_size=[64, 128, 256, 512],):\n"," super(Unet, self).__init__()\n"," self.downsample = nn.ModuleList()\n"," self.upsample = nn.ModuleList()\n"," self.pool = nn.MaxPool2d(kernel_size=2, stride=2)\n","\n"," #Downscale part\n"," for feature in feature_size:\n"," self.downsample.append(DConv(in_channels, feature))\n"," in_channels = feature\n","\n"," #Upscale part\n"," for feature in reversed(feature_size):\n"," self.upsample.append(nn.ConvTranspose2d(feature*2, feature, kernel_size=2, stride =2))\n"," self.upsample.append(DConv(feature*2, feature))\n","\n"," self.bottleneck = DConv(feature_size[-1], feature_size[-1]*2)\n"," self.final_conv = nn.Conv2d(feature_size[0], out_channels, kernel_size=1)\n","\n","\n"," def forward(self, x):\n"," skip_connections = []\n","\n"," for down in self.downsample:\n"," x = down(x)\n"," skip_connections.append(x)\n"," x = self.pool(x)\n","\n"," x = self.bottleneck(x)\n"," skip_connections = skip_connections[: : -1]\n","\n"," for idx in range(0, len(self.upsample), 2):\n"," x = self.upsample[idx](x)\n"," skip_connection = skip_connections[idx//2]\n","\n"," if x.shape != skip_connection.shape:\n"," x = TF.resize(x, size=skip_connection.shape[2:])\n","\n"," concatnate_skip = torch.cat((skip_connection, x), dim=1)\n"," x = self.upsample[idx+1](concatnate_skip)\n"," \n"," return self.final_conv(x)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"dNMXUs-qViYi"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"EhJChHsfViUi"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"m7ZMGrmOViRp"},"source":[""],"execution_count":null,"outputs":[]}]} \ No newline at end of file +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"UNet.ipynb","provenance":[],"collapsed_sections":[],"mount_file_id":"1UdH3PIDr4uawfSNGUl8ntSrq2Lbo_uQQ","authorship_tag":"ABX9TyNiaLFhg+HbcKfrWJAzQit8"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"vlhQO-g6Y2ah","executionInfo":{"status":"ok","timestamp":1634273373551,"user_tz":-600,"elapsed":306,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["import os\n","from PIL import Image\n","import matplotlib.pyplot as plt\n","import glob\n","\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","\n","import torch.utils.data\n","from torchvision import datasets, transforms"],"execution_count":60,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"7vcUuTeHs59d"},"source":["# Data preparation\n","\n","\n","1. get path\n","2. dataloader for img and mask\n","1. split into test and validation\n","\n","\n","\n"]},{"cell_type":"code","metadata":{"id":"WuSova9HHAzc","executionInfo":{"status":"ok","timestamp":1634270416375,"user_tz":-600,"elapsed":13732,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["#define dataset for img and mask\n","file_dir = \"/content/drive/MyDrive/ColabGitHub/ISIC2018_Task1-2_Training_Data\"\n","os.chdir(file_dir)\n","\n","img_path = './ISIC2018_Task1-2_Training_Input_x2'\n","mask_path = './ISIC2018_Task1_Training_GroundTruth_x2'\n","\n","imgs = [file for file in sorted(os.listdir(img_path)) if file.endswith('.jpg')]\n","masks = [file for file in sorted(os.listdir(mask_path)) if file.endswith('.png')]"],"execution_count":2,"outputs":[]},{"cell_type":"code","metadata":{"id":"StdLOrNGt3Id","executionInfo":{"status":"ok","timestamp":1634270422062,"user_tz":-600,"elapsed":341,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["from torchvision import datasets\n","from torchvision.datasets import ImageFolder\n","from torch.utils.data import dataloader, random_split, Subset\n","from torchvision.transforms import transforms\n"],"execution_count":3,"outputs":[]},{"cell_type":"code","metadata":{"id":"JhoipyIyXvBB","executionInfo":{"status":"ok","timestamp":1634270424835,"user_tz":-600,"elapsed":1106,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["from torch.utils.data import Dataset\n","from PIL import Image\n","\n","class Unet_dataset(Dataset):\n"," def __init__(self,\n"," img_dir = './ISIC2018_Task1-2_Training_Input_x2',\n"," mask_dir = './ISIC2018_Task1_Training_GroundTruth_x2', \n"," img_transforms=None,\n"," mask_transforms= None,\n"," train_ratio = 0.5):\n"," \n"," self.img_dir = img_dir\n"," self.mask_dir = mask_dir\n"," self.img_transforms = img_transforms\n"," self.mask_transforms = mask_transforms\n","\n"," self.imgs = [file for file in sorted(os.listdir(self.img_dir)) if file.endswith('.jpg')]\n"," self.masks = [file for file in sorted(os.listdir(self.mask_dir)) if file.endswith('.png')]\n","\n"," self.train_ratio = train_ratio\n","\n"," #meke dataloader\n"," def load_data(self, idx):\n"," img_path = os.path.join(self.img_dir, self.imgs[idx])\n"," mask_path = os.path.join(self.mask_dir, self.masks[idx])\n"," img = Image.open(img_path).convert('RGB')\n"," mask = Image.open(mask_path).convert('L')\n"," return img, mask\n","\n"," def __getitem__(self, idx):\n"," img, mask = self.load_data(idx)\n"," #transformation\n"," if self.img_transforms is not None:\n"," img = self.img_transforms(img)\n"," if self.mask_transforms is not None:\n"," mask = self.mask_transforms(mask)\n"," return img, mask\n","\n"," def __len__(self):\n"," return len(self.imgs)"],"execution_count":4,"outputs":[]},{"cell_type":"code","metadata":{"id":"vpwsi1o-xnYw","executionInfo":{"status":"ok","timestamp":1634270429179,"user_tz":-600,"elapsed":316,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["from torch.utils.data import DataLoader\n","import torchvision.transforms as transforms\n","\n","img_tfs = transforms.Compose([\n"," transforms.Resize((64,64)),\n"," transforms.ToTensor(),\n"," transforms.Normalize(mean=[0, 0, 0], std=[1, 1, 1]),\n"," ]\n"," )\n","\n","mask_tfs = transforms.Compose([\n"," transforms.Resize((64,64)),\n"," transforms.ToTensor(),\n"," ]\n"," )\n","\n","dataset = Unet_dataset(img_transforms=img_tfs, mask_transforms=mask_tfs)\n","\n","#shuffle index\n","sample_size = len(imgs)\n","train_size = int(sample_size*0.8)\n","test_size = sample_size - train_size\n","\n","#train and test set\n","train_set, test_set = torch.utils.data.random_split(dataset, [train_size, test_size], generator=torch.Generator().manual_seed(123))\n","\n","#dataloader\n","train_loader= DataLoader(train_set, batch_size=64, shuffle = True)\n","test_loader= DataLoader(test_set, batch_size=64, shuffle=False)"],"execution_count":5,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"cN3J5wTpx_HX"},"source":["# Unet"]},{"cell_type":"code","metadata":{"id":"VFiXmwWjSeXi","executionInfo":{"status":"ok","timestamp":1634270481930,"user_tz":-600,"elapsed":20,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["import torch\n","import torch.nn as nn"],"execution_count":8,"outputs":[]},{"cell_type":"code","metadata":{"id":"nzGxgjkBeFXR","executionInfo":{"status":"ok","timestamp":1634270481931,"user_tz":-600,"elapsed":19,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["class twotimes_conv(nn.Module):\n"," def __init__(self, in_channels, out_channels):\n"," super(twotimes_conv, self).__init__()\n"," self.conv = nn.Sequential(\n"," nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.ReLU(inplace=True),\n"," nn.Conv2d(out_channels, out_channels, kernel_size=3, stride = 1, padding=1, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.ReLU(inplace=True),\n"," )\n","\n"," def forward(self, x):\n"," return self.conv(x)\n","\n","\n","class Unet(nn.Module):\n"," def __init__(self, in_channels=3, out_channels=1, feature_size=None):\n"," super(Unet, self).__init__()\n"," self.downsample = nn.ModuleList()\n"," self.upsample = nn.ModuleList()\n"," self.pool = nn.MaxPool2d(kernel_size=2, stride=2)\n"," self.feature_size = None\n","\n"," #Downsample frame\n"," for feature in feature_size:\n"," self.downsample.append(twotimes_conv(in_channels, feature))\n"," in_channels = feature\n","\n"," #Upsample frame\n"," for feature in reversed(feature_size):\n"," #Deconvolution\n"," self.upsample.append(nn.ConvTranspose2d(feature*2, feature, kernel_size=2, stride =2))\n"," #Convolution\n"," self.upsample.append(twotimes_conv(feature*2, feature))\n","\n"," #Bottleneck frame\n"," self.bottleneck = twotimes_conv(feature_size[-1], feature_size[-1]*2)\n"," self.final_conv = nn.Conv2d(feature_size[0], out_channels, kernel_size=1)\n","\n","\n"," def forward(self, x):\n"," skip_connections = []\n","\n"," #Downsampling steps\n"," for down_i in self.downsample:\n"," x = down_i(x)\n"," #preserve location\n"," skip_connections.append(x)\n"," x = self.pool(x)\n","\n"," #Bottle neck part\n"," x = self.bottleneck(x)\n"," skip_connections = skip_connections[: : -1]\n","\n"," #Upsampling steps\n"," for idx in range(0, len(self.upsample), 2):\n"," x = self.upsample[idx](x)\n"," skip_connection = skip_connections[idx//2]\n","\n"," if x.shape != skip_connection.shape:\n"," x = torchvision.transforms.resize(x, size=skip_connection.shape[2:])\n"," \n"," #where + what\n"," concatnate_skip = torch.cat((skip_connection, x), dim=1)\n"," x = self.upsample[idx+1](concatnate_skip)\n"," \n"," x = self.final_conv(x)\n"," output = F.sigmoid(x)\n"," \n"," return output"],"execution_count":9,"outputs":[]},{"cell_type":"code","metadata":{"id":"oDhAF_WYyeJm","executionInfo":{"status":"ok","timestamp":1634271792485,"user_tz":-600,"elapsed":490,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["#dice coef\n","def dice_coef(pred, target):\n"," batch_size = len(pred)\n"," somooth = 1.\n","\n"," pred_flat = pred.view(batch_size, -1)\n"," target_flat = target.view(batch_size, -1)\n","\n"," intersection = (pred_flat*target_flat).sum()\n"," dice_coef = (2.*intersection+somooth)/(pred_flat.sum()+target_flat.sum()+somooth)\n"," return dice_coef\n","\n","\n","#loss\n","def dice_loss(pred, target):\n"," dice_loss = 1 - dice_coef(pred, target)\n"," return dice_loss"],"execution_count":52,"outputs":[]},{"cell_type":"code","metadata":{"id":"dNMXUs-qViYi","executionInfo":{"status":"ok","timestamp":1634270491165,"user_tz":-600,"elapsed":457,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["import torch.optim as optim\n","\n","#set parameters\n","feature_size=[64, 128, 256, 512]\n","optimizer = optim.Adam(model.parameters(), lr=0.001)\n","EPOCHS = 30\n","\n","model = Unet(feature_size=feature_size)"],"execution_count":12,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JrGpP3KMPzpe","executionInfo":{"status":"ok","timestamp":1634205931646,"user_tz":-600,"elapsed":24149591,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"70b6a5b8-887c-4489-cc8a-f6718ec3dd50"},"source":["from tqdm import tqdm\n","\n","TRAIN_LOSS = []\n","TRAIN_DICE = []\n","TEST_LOSS =[]\n","TEST_DICE = []\n","\n","for epoch in range(1, EPOCHS+1):\n"," print('EPOCH {}/{}'.format(epoch, EPOCHS))\n"," running_loss = 0\n"," running_dicecoef = 0\n"," running_loss_test = 0\n"," running_dicecoef_test = 0\n"," BATCH_NUM = len(train_loader)\n"," BATCH_NUM_TEST = len(test_loader)\n","\n"," #training\n"," with tqdm(train_loader, unit='batch') as tbatch:\n"," for batch_idx, (x, y) in enumerate(tbatch):\n"," tbatch.set_description(f'Batch: {batch_idx}')\n","\n"," optimizer.zero_grad()\n"," output = model(x)\n"," loss = dice_loss(output, y)\n"," dicecoef = dice_coef(output, y)\n"," loss.backward()\n"," optimizer.step()\n","\n"," running_loss += loss.item()\n"," running_dicecoef += dicecoef.item()\n","\n"," tbatch.set_postfix(loss=loss.item(), dice_coef=dicecoef.item())\n","\n"," epoch_loss = running_loss/BATCH_NUM\n"," epoch_dicecoef = running_dicecoef/BATCH_NUM\n"," TRAIN_LOSS.append(epoch_loss)\n"," TRAIN_DICE.append(epoch_dicecoef)\n","\n"," #test\n"," with tqdm(test_loader, unit='batch') as tsbatch:\n"," for batch_idx, (x, y) in enumerate(tsbatch):\n"," tsbatch.set_description(f'Batch: {batch_idx}')\n"," output_test = model(x)\n"," loss_test = dice_loss(output_test, y)\n"," dicecoef_test = dice_coef(output_test, y)\n"," tsbatch.set_postfix(loss=loss_test.item(), dice_coef=dicecoef_test.item())\n","\n"," running_loss_test += loss_test.item()\n"," running_dicecoef_test += dicecoef_test.item()\n","\n"," TEST_LOSS.append(running_loss_test/BATCH_NUM_TEST)\n"," TEST_DICE.append(running_dicecoef_test/BATCH_NUM_TEST)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["EPOCH 1/30\n"]},{"output_type":"stream","name":"stderr","text":["Batch: 0: 0%| | 0/33 [00:04"]},"metadata":{},"execution_count":40},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"4Rh-uq05TG-A"},"source":["# Model Evaluation"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":279},"id":"7ZBk4w3xIA8L","executionInfo":{"status":"ok","timestamp":1634206091827,"user_tz":-600,"elapsed":419,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"a2ff558d-5b76-4ebd-8b09-2514182ad30b"},"source":["import matplotlib.pyplot as plt\n","import numpy as np\n","\n","X = np.arange(1,EPOCHS+1)\n","\n","plt.plot(X, TRAIN_LOSS, marker='.', markersize=10, label='train')\n","plt.plot(X, TEST_LOSS, marker='.', markersize=10, label='test')\n","plt.xlabel('Epochs')\n","plt.ylabel('Dice Loss')\n","plt.xticks(X)\n","plt.legend()\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":279},"id":"Y80RZXzKRPpc","executionInfo":{"status":"ok","timestamp":1634206126932,"user_tz":-600,"elapsed":1066,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"8e714aaa-5868-4270-f7ca-54a5b827fb4d"},"source":["plt.plot(X, TRAIN_DICE, marker='.', markersize=10, label='train')\n","plt.plot(X, TEST_DICE, marker='.', markersize=10, label='test')\n","plt.xlabel('Epochs')\n","plt.ylabel('Dice Coeffeciency')\n","plt.xticks(X)\n","plt.legend()\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":373},"id":"V3Ahm7ecTST4","executionInfo":{"status":"ok","timestamp":1634270514838,"user_tz":-600,"elapsed":8784,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"44a27d39-7951-47f3-8484-132efc3bba3a"},"source":["#load model\n","new_model = Unet(feature_size=feature_size)\n","\n","filename = \"Unet_ISIC1.pth\"\n","checkpoint = torch.load(filename)\n","\n","new_model.load_state_dict(checkpoint)\n","\n","p = new_model(x)[0]\n","plt.imshow(p.detach().squeeze(), cmap='gray')"],"execution_count":13,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /pytorch/c10/core/TensorImpl.h:1156.)\n"," return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n","/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:1805: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n"," warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n"]},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":13},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"BZYl7y1XIJFr"},"source":[""],"execution_count":null,"outputs":[]}]} \ No newline at end of file From 3877806e3b827e6ab28dd1a842804a7f88c8273c Mon Sep 17 00:00:00 2001 From: wakahide23 Date: Sun, 17 Oct 2021 06:46:49 +0000 Subject: [PATCH 44/66] Improved Unet file from Colab --- recognition/s4633139/IUNet.ipynb | 1 + 1 file changed, 1 insertion(+) create mode 100644 recognition/s4633139/IUNet.ipynb diff --git a/recognition/s4633139/IUNet.ipynb b/recognition/s4633139/IUNet.ipynb new file mode 100644 index 0000000000..26f8f85ff1 --- /dev/null +++ b/recognition/s4633139/IUNet.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"IUNet.ipynb","provenance":[],"collapsed_sections":[],"mount_file_id":"1UdH3PIDr4uawfSNGUl8ntSrq2Lbo_uQQ","authorship_tag":"ABX9TyPXanSKnpIqYcGKvPCydodW"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"vlhQO-g6Y2ah","executionInfo":{"status":"ok","timestamp":1634452298249,"user_tz":-600,"elapsed":13266,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["import os\n","from PIL import Image\n","import glob\n","import matplotlib.pyplot as plt\n","\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","\n","import torch.utils.data\n","from torchvision import datasets, transforms"],"execution_count":3,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"7vcUuTeHs59d"},"source":["# Data preparation\n","\n","\n","1. get path\n","2. dataloader for img and mask\n","1. split into test and validation\n","\n","\n","\n"]},{"cell_type":"code","metadata":{"id":"gPYa8ZVvXk2d","executionInfo":{"status":"ok","timestamp":1634452300911,"user_tz":-600,"elapsed":4,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["import os\n","from PIL import Image\n","import glob\n","import matplotlib.pyplot as plt\n","\n","#define dataset for img and mask\n","file_dir = \"./ISIC2018_Task1-2_Training_Data\"\n","os.chdir(file_dir)"],"execution_count":4,"outputs":[]},{"cell_type":"code","metadata":{"id":"mhX77-qIYdGT","executionInfo":{"status":"ok","timestamp":1634452302191,"user_tz":-600,"elapsed":1283,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["#path\n","img_path = './ISIC2018_Task1-2_Training_Input_x2'\n","mask_path = './ISIC2018_Task1_Training_GroundTruth_x2'\n","imgs = [file for file in sorted(os.listdir(img_path)) if file.endswith('.jpg')]\n","masks = [file for file in sorted(os.listdir(mask_path)) if file.endswith('.png')]"],"execution_count":5,"outputs":[]},{"cell_type":"code","metadata":{"id":"StdLOrNGt3Id","executionInfo":{"status":"ok","timestamp":1634452302191,"user_tz":-600,"elapsed":4,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["from torchvision import datasets\n","from torchvision.datasets import ImageFolder\n","from torch.utils.data import dataloader, random_split, Subset\n","from torchvision.transforms import transforms"],"execution_count":6,"outputs":[]},{"cell_type":"code","metadata":{"id":"JhoipyIyXvBB","executionInfo":{"status":"ok","timestamp":1634452306163,"user_tz":-600,"elapsed":17,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["from torch.utils.data import Dataset\n","from PIL import Image\n","\n","class Unet_dataset(Dataset):\n"," def __init__(self,\n"," img_dir = './ISIC2018_Task1-2_Training_Input_x2',\n"," mask_dir = './ISIC2018_Task1_Training_GroundTruth_x2', \n"," img_transforms=None,\n"," mask_transforms= None):\n"," \n"," self.img_dir = img_dir\n"," self.mask_dir = mask_dir\n"," self.img_transforms = img_transforms\n"," self.mask_transforms = mask_transforms\n","\n"," self.imgs = [file for file in sorted(os.listdir(self.img_dir)) if file.endswith('.jpg')]\n"," self.masks = [file for file in sorted(os.listdir(self.mask_dir)) if file.endswith('.png')]\n","\n"," #meke dataloader\n"," def load_data(self, idx):\n"," img_path = os.path.join(self.img_dir, self.imgs[idx])\n"," mask_path = os.path.join(self.mask_dir, self.masks[idx])\n"," img = Image.open(img_path).convert('RGB')\n"," mask = Image.open(mask_path).convert('L')\n"," return img, mask\n","\n"," def __getitem__(self, idx):\n"," img, mask = self.load_data(idx)\n"," #transformation\n"," if self.img_transforms is not None:\n"," img = self.img_transforms(img)\n"," if self.mask_transforms is not None:\n"," mask = self.mask_transforms(mask)\n"," return img, mask\n","\n"," def __len__(self):\n"," return len(self.imgs)"],"execution_count":7,"outputs":[]},{"cell_type":"code","metadata":{"id":"vpwsi1o-xnYw","executionInfo":{"status":"ok","timestamp":1634452307860,"user_tz":-600,"elapsed":5,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["from torch.utils.data import DataLoader\n","import torchvision.transforms as transforms\n","\n","\n","#transformation\n","img_tfs = transforms.Compose([\n"," transforms.Resize((128,128)),\n"," transforms.ToTensor(),\n"," transforms.Normalize(mean=[0, 0, 0], std=[1, 1, 1]),\n"," ]\n"," )\n","\n","mask_tfs = transforms.Compose([\n"," transforms.Resize((128,128)),\n"," transforms.ToTensor(),\n"," ]\n"," )\n","\n","dataset = Unet_dataset(img_transforms=img_tfs, mask_transforms=mask_tfs)\n","\n","\n","#shuffle index\n","sample_size = len(dataset.imgs)\n","train_size = int(sample_size*0.8)\n","test_size = sample_size - train_size\n","\n","#train and test set\n","train_set, test_set = torch.utils.data.random_split(dataset, [train_size, test_size], generator=torch.Generator().manual_seed(123))\n","\n","#dataloader\n","train_loader= DataLoader(train_set, batch_size=64, shuffle = True)\n","test_loader= DataLoader(test_set, batch_size=64, shuffle=False)"],"execution_count":8,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"cN3J5wTpx_HX"},"source":["# Unet"]},{"cell_type":"code","metadata":{"id":"VFiXmwWjSeXi","executionInfo":{"status":"ok","timestamp":1634452310344,"user_tz":-600,"elapsed":391,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["import torch\n","import torch.nn as nn\n","import torch.nn.functional as F"],"execution_count":9,"outputs":[]},{"cell_type":"code","metadata":{"id":"nzGxgjkBeFXR","executionInfo":{"status":"ok","timestamp":1634452312281,"user_tz":-600,"elapsed":454,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["class Context(nn.Module):\n"," def __init__(self, in_channels, out_channels):\n"," super(Context, self).__init__()\n"," self.context = nn.Sequential(\n"," nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.Dropout2d(p=0.3),\n"," nn.LeakyReLU(negative_slope=0.02, inplace=True),\n"," nn.Conv2d(in_channels, out_channels, kernel_size=3, stride = 1, padding=1, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.LeakyReLU(negative_slope=0.02, inplace=True),\n"," )\n","\n"," def forward(self, x):\n"," x = self.context(x) + x\n"," return x\n","\n","\n","class Localization(nn.Module):\n"," def __init__(self, in_channels, out_channels):\n"," super(Localization, self).__init__()\n"," self.localization = nn.Sequential(\n"," nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.LeakyReLU(negative_slope=0.02, inplace=True),\n"," nn.Conv2d(out_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.LeakyReLU(negative_slope=0.02, inplace=True),\n"," )\n"," \n"," def forward(self, x):\n"," return self.localization(x)\n","\n","\n","class Upsampling(nn.Module):\n"," def __init__(self, in_channels, out_channels):\n"," super(Upsampling, self).__init__()\n"," self.upsampling = nn.Sequential(\n"," nn.Upsample(scale_factor=2, mode='nearest'),\n"," nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.LeakyReLU(negative_slope=0.02, inplace=True),\n"," )\n"," \n"," def forward(self, x):\n"," return self.upsampling(x)\n","\n","\n","class Segment(nn.Module):\n"," def __init__(self, in_channels, out_channels):\n"," super(Segment, self).__init__()\n"," self.segment = nn.Sequential(\n"," nn.Conv2d(in_channels, out_channels=1, kernel_size=1, stride=1, padding=0, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.LeakyReLU(negative_slope=0.02, inplace=True)\n"," )\n"," \n"," def forward(self, x):\n"," return self.segment(x)\n","\n","\n","class Conv2(nn.Module):\n"," def __init__(self, in_channels, out_channels):\n"," super(Conv2, self).__init__()\n"," self.conv2 = nn.Sequential(\n"," nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=2, padding=1, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.LeakyReLU(negative_slope=0.02, inplace=True),\n"," )\n","\n"," def forward(self, x):\n"," return self.conv2(x)\n","\n","\n","class ImprovedUnet(nn.Module):\n"," def __init__(self, in_channels=3, out_channels=1, feature_size=[16, 32, 64, 128]):\n"," super(ImprovedUnet, self).__init__()\n"," self.Conv1 = nn.Conv2d(in_channels=3, out_channels=feature_size[0], kernel_size=3, stride=1, padding=1, bias=False) \n"," self.Downs = nn.ModuleList()\n"," self.Convs = nn.ModuleList()\n"," self.Ups = nn.ModuleList()\n"," self.Segmentations = nn.ModuleList()\n","\n"," self.upscale = nn.Upsample(scale_factor=2, mode='nearest')\n"," self.bottleneck = Context(feature_size[-1]*2, feature_size[-1]*2)\n","\n","\n"," #Downsampling frame\n"," for feature in feature_size:\n"," self.Downs.append(Context(feature, feature))\n"," self.Convs.append(Conv2(feature, feature*2))\n","\n"," #Upsampleing frame\n"," for feature in reversed(feature_size):\n"," #Upsample\n"," self.Ups.append(Upsampling(feature*2, feature))\n","\n"," #Localization\n"," if feature != feature_size[0]:\n"," self.Ups.append(Localization(feature*2, feature))\n"," else:\n"," self.Ups.append(Localization(feature*2, feature*2))\n"," \n"," #Segmentation\n"," self.Segmentations.append(Segment(feature, 1))\n","\n"," self.final_conv = nn.Conv2d(feature_size[0]*2, out_channels, kernel_size=1, stride=1, bias=False)\n"," \n","\n"," def forward(self, x):\n"," skip_connections = []\n"," segmentation_layers = []\n","\n"," x = self.Conv1(x)\n","\n"," #Downsampling steps\n"," for i, (context_i, conv_i) in enumerate(zip(self.Downs, self.Convs)):\n"," x = context_i(x)\n"," #preserve location\n"," skip_connections.append(x)\n"," x = conv_i(x)\n","\n"," x = self.bottleneck(x) + x\n"," skip_connections = skip_connections[: : -1]\n","\n"," #Upsampling steps\n"," for idx in range(0, len(self.Ups), 2):\n"," #upsample\n"," x = self.Ups[idx](x)\n","\n"," #localization\n"," skip_connection = skip_connections[idx//2]\n"," concatnate_skip = torch.cat((skip_connection, x), dim=1)\n"," x = self.Ups[idx+1](concatnate_skip)\n","\n"," #segmentation\n"," if idx == 2 or idx == 4:\n"," x_segment = self.Segmentations[idx//2](x)\n"," segmentation_layers.append(x_segment)\n","\n"," seg_scale1 = self.upscale(segmentation_layers[0])\n"," seg_scale2 = self.upscale(segmentation_layers[1]+seg_scale1)\n","\n"," x = self.final_conv(x)\n"," x = x + seg_scale2\n","\n"," output = F.sigmoid(x)\n"," \n"," return output"],"execution_count":10,"outputs":[]},{"cell_type":"code","metadata":{"id":"otdcbBK7g4fW","executionInfo":{"status":"ok","timestamp":1634452313629,"user_tz":-600,"elapsed":2,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["feature_size=[16, 32, 64, 128]"],"execution_count":11,"outputs":[]},{"cell_type":"code","metadata":{"id":"oDhAF_WYyeJm","executionInfo":{"status":"ok","timestamp":1634452314967,"user_tz":-600,"elapsed":5,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["#dice coef\n","def dice_coef(pred, target):\n"," batch_size = len(pred)\n"," somooth = 1.\n","\n"," pred_flat = pred.view(batch_size, -1)\n"," target_flat = target.view(batch_size, -1)\n","\n"," intersection = (pred_flat*target_flat).sum()\n"," dice_coef = (2.*intersection+somooth)/(pred_flat.sum()+target_flat.sum()+somooth)\n"," return dice_coef\n","\n","#loss\n","def dice_loss(pred, target):\n"," dice_loss = 1 - dice_coef(pred, target)\n"," return dice_loss"],"execution_count":12,"outputs":[]},{"cell_type":"code","metadata":{"id":"dNMXUs-qViYi","executionInfo":{"status":"ok","timestamp":1634452317681,"user_tz":-600,"elapsed":377,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["import torch.optim as optim\n","\n","#set parameters\n","feature_size=[16, 32, 64, 128]\n","model = ImprovedUnet(feature_size=feature_size)\n","\n","optimizer = optim.Adam(model.parameters(), lr=0.001)\n","EPOCHS = 15"],"execution_count":13,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JrGpP3KMPzpe","executionInfo":{"status":"ok","timestamp":1634439533392,"user_tz":-600,"elapsed":9774961,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"917497df-4479-47d2-9ae4-ea56435fd75f"},"source":["from tqdm import tqdm\n","\n","TRAIN_LOSS = []\n","TRAIN_DICE = []\n","TEST_LOSS =[]\n","TEST_DICE = []\n","\n","for epoch in range(1, EPOCHS+1):\n"," print('EPOCH {}/{}'.format(epoch, EPOCHS))\n"," running_loss = 0\n"," running_dicecoef = 0\n"," running_loss_test = 0\n"," running_dicecoef_test = 0\n"," BATCH_NUM = len(train_loader)\n"," BATCH_NUM_TEST = len(test_loader)\n","\n"," #training\n"," with tqdm(train_loader, unit='batch') as tbatch:\n"," for batch_idx, (x, y) in enumerate(tbatch):\n"," tbatch.set_description(f'Batch: {batch_idx}')\n","\n"," optimizer.zero_grad()\n"," output = model(x)\n"," loss = dice_loss(output, y)\n"," dicecoef = dice_coef(output, y)\n"," loss.backward()\n"," optimizer.step()\n","\n"," running_loss += loss.item()\n"," running_dicecoef += dicecoef.item()\n","\n"," tbatch.set_postfix(loss=loss.item(), dice_coef=dicecoef.item())\n","\n"," epoch_loss = running_loss/BATCH_NUM\n"," epoch_dicecoef = running_dicecoef/BATCH_NUM\n"," TRAIN_LOSS.append(epoch_loss)\n"," TRAIN_DICE.append(epoch_dicecoef)\n","\n"," #test\n"," with tqdm(test_loader, unit='batch') as tsbatch:\n"," for batch_idx, (x, y) in enumerate(tsbatch):\n"," tsbatch.set_description(f'Batch: {batch_idx}')\n"," output_test = model(x)\n"," loss_test = dice_loss(output_test, y)\n"," dicecoef_test = dice_coef(output_test, y)\n"," tsbatch.set_postfix(loss=loss_test.item(), dice_coef=dicecoef_test.item())\n","\n"," running_loss_test += loss_test.item()\n"," running_dicecoef_test += dicecoef_test.item()\n","\n"," TEST_LOSS.append(running_loss_test/BATCH_NUM_TEST)\n"," TEST_DICE.append(running_dicecoef_test/BATCH_NUM_TEST)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["EPOCH 1/15\n"]},{"output_type":"stream","name":"stderr","text":["Batch: 0: 0%| | 0/33 [00:43torchviz) (3.7.4.3)\n","Building wheels for collected packages: torchviz\n"," Building wheel for torchviz (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for torchviz: filename=torchviz-0.0.2-py3-none-any.whl size=4151 sha256=80aa29d36737c5d4b81edcddc53bec07221514dc0c26ab4de9e6c23bf49714c5\n"," Stored in directory: /root/.cache/pip/wheels/04/38/f5/dc4f85c3909051823df49901e72015d2d750bd26b086480ec2\n","Successfully built torchviz\n","Installing collected packages: torchviz\n","Successfully installed torchviz-0.0.2\n"]}]},{"cell_type":"code","metadata":{"id":"oKvcU7lyeujb"},"source":["from torchviz import make_dot\n","make_dot(output, params=dict(model.named_parameters(), ))"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"zG5sYuWORuIO","executionInfo":{"status":"ok","timestamp":1634452208764,"user_tz":-600,"elapsed":490,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["#save model\n","filename = \"Unet_ISIC2.pth\"\n","torch.save(model.state_dict(), filename)"],"execution_count":169,"outputs":[]},{"cell_type":"code","metadata":{"id":"EhJChHsfViUi","colab":{"base_uri":"https://localhost:8080/","height":319},"executionInfo":{"status":"ok","timestamp":1634205941940,"user_tz":-600,"elapsed":1094,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"2a6204e7-1325-46f0-cdbf-fee476cdd087"},"source":["model.eval()\n","p = model(x)[0]\n","plt.imshow(p.detach().squeeze(), cmap='gray')"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:1805: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n"," warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n"]},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":40},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"4Rh-uq05TG-A"},"source":["# Model Evaluation"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":279},"id":"7ZBk4w3xIA8L","executionInfo":{"status":"ok","timestamp":1634439698477,"user_tz":-600,"elapsed":391,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"519836e3-612d-4bcf-ccdb-b92349a2d6cb"},"source":["import matplotlib.pyplot as plt\n","import numpy as np\n","\n","X = np.arange(1,EPOCHS+1)\n","\n","plt.plot(X, TRAIN_LOSS, marker='.', markersize=10, label='train')\n","plt.plot(X, TEST_LOSS, marker='.', markersize=10, label='test')\n","plt.xlabel('Epochs')\n","plt.ylabel('Dice Loss')\n","plt.xticks(X)\n","plt.legend()\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":279},"id":"Y80RZXzKRPpc","executionInfo":{"status":"ok","timestamp":1634439703085,"user_tz":-600,"elapsed":429,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"e5830c99-2e63-46c5-beb6-a95b3f9ce19b"},"source":["plt.plot(X, TRAIN_DICE, marker='.', markersize=10, label='train')\n","plt.plot(X, TEST_DICE, marker='.', markersize=10, label='test')\n","plt.xlabel('Epochs')\n","plt.ylabel('Dice Coeffeciency')\n","plt.xticks(X)\n","plt.legend()\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"BZYl7y1XIJFr","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1634452335129,"user_tz":-600,"elapsed":5640,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"615ef612-6f7c-486a-f79e-99c1290af99f"},"source":["for batch in test_loader:\n"," x, y = batch\n"," print(x.shape, y.shape)\n"," break"],"execution_count":14,"outputs":[{"output_type":"stream","name":"stdout","text":["torch.Size([64, 3, 128, 128]) torch.Size([64, 1, 128, 128])\n"]}]},{"cell_type":"code","metadata":{"id":"V3Ahm7ecTST4"},"source":["#load model\n","new_model = ImprovedUnet(feature_size=feature_size)\n","\n","filename = \"Unet_ISIC2.pth\"\n","checkpoint = torch.load(filename)\n","\n","new_model.load_state_dict(checkpoint)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":285},"id":"BIAOsDaLtliA","executionInfo":{"status":"ok","timestamp":1634452356740,"user_tz":-600,"elapsed":923,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"6e9d7e79-98ef-467d-dcdd-b75c9a67d2a7"},"source":["plt.imshow(x[0].permute(1,2,0))"],"execution_count":17,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":17},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":302},"id":"LXKpDfDRjlkR","executionInfo":{"status":"ok","timestamp":1634448826907,"user_tz":-600,"elapsed":933,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"035c4c92-79d4-46d2-a20b-d0be012ff0d1"},"source":["alpha = 5\n","seg_img = x[0].clone()\n","image_r = seg_img[0]\n","image_r = image_r*(1-alpha*pred_mask)+(pred_mask*pred_mask*alpha)\n","segment_image = image_r.detach().squeeze()\n","seg_img[0] = segment_image\n","plt.imshow(seg_img.permute(1,2,0))"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":157},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]}]} \ No newline at end of file From b6bcf3a31ac46b0d75de1d1edaa8ec2688d36b31 Mon Sep 17 00:00:00 2001 From: wakahide23 Date: Mon, 18 Oct 2021 07:30:47 +0000 Subject: [PATCH 45/66] revised colab file with segmentation part --- recognition/s4633139/IUNet.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/recognition/s4633139/IUNet.ipynb b/recognition/s4633139/IUNet.ipynb index 26f8f85ff1..165f64bec1 100644 --- a/recognition/s4633139/IUNet.ipynb +++ b/recognition/s4633139/IUNet.ipynb @@ -1 +1 @@ -{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"IUNet.ipynb","provenance":[],"collapsed_sections":[],"mount_file_id":"1UdH3PIDr4uawfSNGUl8ntSrq2Lbo_uQQ","authorship_tag":"ABX9TyPXanSKnpIqYcGKvPCydodW"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"vlhQO-g6Y2ah","executionInfo":{"status":"ok","timestamp":1634452298249,"user_tz":-600,"elapsed":13266,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["import os\n","from PIL import Image\n","import glob\n","import matplotlib.pyplot as plt\n","\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","\n","import torch.utils.data\n","from torchvision import datasets, transforms"],"execution_count":3,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"7vcUuTeHs59d"},"source":["# Data preparation\n","\n","\n","1. get path\n","2. dataloader for img and mask\n","1. split into test and validation\n","\n","\n","\n"]},{"cell_type":"code","metadata":{"id":"gPYa8ZVvXk2d","executionInfo":{"status":"ok","timestamp":1634452300911,"user_tz":-600,"elapsed":4,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["import os\n","from PIL import Image\n","import glob\n","import matplotlib.pyplot as plt\n","\n","#define dataset for img and mask\n","file_dir = \"./ISIC2018_Task1-2_Training_Data\"\n","os.chdir(file_dir)"],"execution_count":4,"outputs":[]},{"cell_type":"code","metadata":{"id":"mhX77-qIYdGT","executionInfo":{"status":"ok","timestamp":1634452302191,"user_tz":-600,"elapsed":1283,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["#path\n","img_path = './ISIC2018_Task1-2_Training_Input_x2'\n","mask_path = './ISIC2018_Task1_Training_GroundTruth_x2'\n","imgs = [file for file in sorted(os.listdir(img_path)) if file.endswith('.jpg')]\n","masks = [file for file in sorted(os.listdir(mask_path)) if file.endswith('.png')]"],"execution_count":5,"outputs":[]},{"cell_type":"code","metadata":{"id":"StdLOrNGt3Id","executionInfo":{"status":"ok","timestamp":1634452302191,"user_tz":-600,"elapsed":4,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["from torchvision import datasets\n","from torchvision.datasets import ImageFolder\n","from torch.utils.data import dataloader, random_split, Subset\n","from torchvision.transforms import transforms"],"execution_count":6,"outputs":[]},{"cell_type":"code","metadata":{"id":"JhoipyIyXvBB","executionInfo":{"status":"ok","timestamp":1634452306163,"user_tz":-600,"elapsed":17,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["from torch.utils.data import Dataset\n","from PIL import Image\n","\n","class Unet_dataset(Dataset):\n"," def __init__(self,\n"," img_dir = './ISIC2018_Task1-2_Training_Input_x2',\n"," mask_dir = './ISIC2018_Task1_Training_GroundTruth_x2', \n"," img_transforms=None,\n"," mask_transforms= None):\n"," \n"," self.img_dir = img_dir\n"," self.mask_dir = mask_dir\n"," self.img_transforms = img_transforms\n"," self.mask_transforms = mask_transforms\n","\n"," self.imgs = [file for file in sorted(os.listdir(self.img_dir)) if file.endswith('.jpg')]\n"," self.masks = [file for file in sorted(os.listdir(self.mask_dir)) if file.endswith('.png')]\n","\n"," #meke dataloader\n"," def load_data(self, idx):\n"," img_path = os.path.join(self.img_dir, self.imgs[idx])\n"," mask_path = os.path.join(self.mask_dir, self.masks[idx])\n"," img = Image.open(img_path).convert('RGB')\n"," mask = Image.open(mask_path).convert('L')\n"," return img, mask\n","\n"," def __getitem__(self, idx):\n"," img, mask = self.load_data(idx)\n"," #transformation\n"," if self.img_transforms is not None:\n"," img = self.img_transforms(img)\n"," if self.mask_transforms is not None:\n"," mask = self.mask_transforms(mask)\n"," return img, mask\n","\n"," def __len__(self):\n"," return len(self.imgs)"],"execution_count":7,"outputs":[]},{"cell_type":"code","metadata":{"id":"vpwsi1o-xnYw","executionInfo":{"status":"ok","timestamp":1634452307860,"user_tz":-600,"elapsed":5,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["from torch.utils.data import DataLoader\n","import torchvision.transforms as transforms\n","\n","\n","#transformation\n","img_tfs = transforms.Compose([\n"," transforms.Resize((128,128)),\n"," transforms.ToTensor(),\n"," transforms.Normalize(mean=[0, 0, 0], std=[1, 1, 1]),\n"," ]\n"," )\n","\n","mask_tfs = transforms.Compose([\n"," transforms.Resize((128,128)),\n"," transforms.ToTensor(),\n"," ]\n"," )\n","\n","dataset = Unet_dataset(img_transforms=img_tfs, mask_transforms=mask_tfs)\n","\n","\n","#shuffle index\n","sample_size = len(dataset.imgs)\n","train_size = int(sample_size*0.8)\n","test_size = sample_size - train_size\n","\n","#train and test set\n","train_set, test_set = torch.utils.data.random_split(dataset, [train_size, test_size], generator=torch.Generator().manual_seed(123))\n","\n","#dataloader\n","train_loader= DataLoader(train_set, batch_size=64, shuffle = True)\n","test_loader= DataLoader(test_set, batch_size=64, shuffle=False)"],"execution_count":8,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"cN3J5wTpx_HX"},"source":["# Unet"]},{"cell_type":"code","metadata":{"id":"VFiXmwWjSeXi","executionInfo":{"status":"ok","timestamp":1634452310344,"user_tz":-600,"elapsed":391,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["import torch\n","import torch.nn as nn\n","import torch.nn.functional as F"],"execution_count":9,"outputs":[]},{"cell_type":"code","metadata":{"id":"nzGxgjkBeFXR","executionInfo":{"status":"ok","timestamp":1634452312281,"user_tz":-600,"elapsed":454,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["class Context(nn.Module):\n"," def __init__(self, in_channels, out_channels):\n"," super(Context, self).__init__()\n"," self.context = nn.Sequential(\n"," nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.Dropout2d(p=0.3),\n"," nn.LeakyReLU(negative_slope=0.02, inplace=True),\n"," nn.Conv2d(in_channels, out_channels, kernel_size=3, stride = 1, padding=1, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.LeakyReLU(negative_slope=0.02, inplace=True),\n"," )\n","\n"," def forward(self, x):\n"," x = self.context(x) + x\n"," return x\n","\n","\n","class Localization(nn.Module):\n"," def __init__(self, in_channels, out_channels):\n"," super(Localization, self).__init__()\n"," self.localization = nn.Sequential(\n"," nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.LeakyReLU(negative_slope=0.02, inplace=True),\n"," nn.Conv2d(out_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.LeakyReLU(negative_slope=0.02, inplace=True),\n"," )\n"," \n"," def forward(self, x):\n"," return self.localization(x)\n","\n","\n","class Upsampling(nn.Module):\n"," def __init__(self, in_channels, out_channels):\n"," super(Upsampling, self).__init__()\n"," self.upsampling = nn.Sequential(\n"," nn.Upsample(scale_factor=2, mode='nearest'),\n"," nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.LeakyReLU(negative_slope=0.02, inplace=True),\n"," )\n"," \n"," def forward(self, x):\n"," return self.upsampling(x)\n","\n","\n","class Segment(nn.Module):\n"," def __init__(self, in_channels, out_channels):\n"," super(Segment, self).__init__()\n"," self.segment = nn.Sequential(\n"," nn.Conv2d(in_channels, out_channels=1, kernel_size=1, stride=1, padding=0, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.LeakyReLU(negative_slope=0.02, inplace=True)\n"," )\n"," \n"," def forward(self, x):\n"," return self.segment(x)\n","\n","\n","class Conv2(nn.Module):\n"," def __init__(self, in_channels, out_channels):\n"," super(Conv2, self).__init__()\n"," self.conv2 = nn.Sequential(\n"," nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=2, padding=1, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.LeakyReLU(negative_slope=0.02, inplace=True),\n"," )\n","\n"," def forward(self, x):\n"," return self.conv2(x)\n","\n","\n","class ImprovedUnet(nn.Module):\n"," def __init__(self, in_channels=3, out_channels=1, feature_size=[16, 32, 64, 128]):\n"," super(ImprovedUnet, self).__init__()\n"," self.Conv1 = nn.Conv2d(in_channels=3, out_channels=feature_size[0], kernel_size=3, stride=1, padding=1, bias=False) \n"," self.Downs = nn.ModuleList()\n"," self.Convs = nn.ModuleList()\n"," self.Ups = nn.ModuleList()\n"," self.Segmentations = nn.ModuleList()\n","\n"," self.upscale = nn.Upsample(scale_factor=2, mode='nearest')\n"," self.bottleneck = Context(feature_size[-1]*2, feature_size[-1]*2)\n","\n","\n"," #Downsampling frame\n"," for feature in feature_size:\n"," self.Downs.append(Context(feature, feature))\n"," self.Convs.append(Conv2(feature, feature*2))\n","\n"," #Upsampleing frame\n"," for feature in reversed(feature_size):\n"," #Upsample\n"," self.Ups.append(Upsampling(feature*2, feature))\n","\n"," #Localization\n"," if feature != feature_size[0]:\n"," self.Ups.append(Localization(feature*2, feature))\n"," else:\n"," self.Ups.append(Localization(feature*2, feature*2))\n"," \n"," #Segmentation\n"," self.Segmentations.append(Segment(feature, 1))\n","\n"," self.final_conv = nn.Conv2d(feature_size[0]*2, out_channels, kernel_size=1, stride=1, bias=False)\n"," \n","\n"," def forward(self, x):\n"," skip_connections = []\n"," segmentation_layers = []\n","\n"," x = self.Conv1(x)\n","\n"," #Downsampling steps\n"," for i, (context_i, conv_i) in enumerate(zip(self.Downs, self.Convs)):\n"," x = context_i(x)\n"," #preserve location\n"," skip_connections.append(x)\n"," x = conv_i(x)\n","\n"," x = self.bottleneck(x) + x\n"," skip_connections = skip_connections[: : -1]\n","\n"," #Upsampling steps\n"," for idx in range(0, len(self.Ups), 2):\n"," #upsample\n"," x = self.Ups[idx](x)\n","\n"," #localization\n"," skip_connection = skip_connections[idx//2]\n"," concatnate_skip = torch.cat((skip_connection, x), dim=1)\n"," x = self.Ups[idx+1](concatnate_skip)\n","\n"," #segmentation\n"," if idx == 2 or idx == 4:\n"," x_segment = self.Segmentations[idx//2](x)\n"," segmentation_layers.append(x_segment)\n","\n"," seg_scale1 = self.upscale(segmentation_layers[0])\n"," seg_scale2 = self.upscale(segmentation_layers[1]+seg_scale1)\n","\n"," x = self.final_conv(x)\n"," x = x + seg_scale2\n","\n"," output = F.sigmoid(x)\n"," \n"," return output"],"execution_count":10,"outputs":[]},{"cell_type":"code","metadata":{"id":"otdcbBK7g4fW","executionInfo":{"status":"ok","timestamp":1634452313629,"user_tz":-600,"elapsed":2,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["feature_size=[16, 32, 64, 128]"],"execution_count":11,"outputs":[]},{"cell_type":"code","metadata":{"id":"oDhAF_WYyeJm","executionInfo":{"status":"ok","timestamp":1634452314967,"user_tz":-600,"elapsed":5,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["#dice coef\n","def dice_coef(pred, target):\n"," batch_size = len(pred)\n"," somooth = 1.\n","\n"," pred_flat = pred.view(batch_size, -1)\n"," target_flat = target.view(batch_size, -1)\n","\n"," intersection = (pred_flat*target_flat).sum()\n"," dice_coef = (2.*intersection+somooth)/(pred_flat.sum()+target_flat.sum()+somooth)\n"," return dice_coef\n","\n","#loss\n","def dice_loss(pred, target):\n"," dice_loss = 1 - dice_coef(pred, target)\n"," return dice_loss"],"execution_count":12,"outputs":[]},{"cell_type":"code","metadata":{"id":"dNMXUs-qViYi","executionInfo":{"status":"ok","timestamp":1634452317681,"user_tz":-600,"elapsed":377,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["import torch.optim as optim\n","\n","#set parameters\n","feature_size=[16, 32, 64, 128]\n","model = ImprovedUnet(feature_size=feature_size)\n","\n","optimizer = optim.Adam(model.parameters(), lr=0.001)\n","EPOCHS = 15"],"execution_count":13,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JrGpP3KMPzpe","executionInfo":{"status":"ok","timestamp":1634439533392,"user_tz":-600,"elapsed":9774961,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"917497df-4479-47d2-9ae4-ea56435fd75f"},"source":["from tqdm import tqdm\n","\n","TRAIN_LOSS = []\n","TRAIN_DICE = []\n","TEST_LOSS =[]\n","TEST_DICE = []\n","\n","for epoch in range(1, EPOCHS+1):\n"," print('EPOCH {}/{}'.format(epoch, EPOCHS))\n"," running_loss = 0\n"," running_dicecoef = 0\n"," running_loss_test = 0\n"," running_dicecoef_test = 0\n"," BATCH_NUM = len(train_loader)\n"," BATCH_NUM_TEST = len(test_loader)\n","\n"," #training\n"," with tqdm(train_loader, unit='batch') as tbatch:\n"," for batch_idx, (x, y) in enumerate(tbatch):\n"," tbatch.set_description(f'Batch: {batch_idx}')\n","\n"," optimizer.zero_grad()\n"," output = model(x)\n"," loss = dice_loss(output, y)\n"," dicecoef = dice_coef(output, y)\n"," loss.backward()\n"," optimizer.step()\n","\n"," running_loss += loss.item()\n"," running_dicecoef += dicecoef.item()\n","\n"," tbatch.set_postfix(loss=loss.item(), dice_coef=dicecoef.item())\n","\n"," epoch_loss = running_loss/BATCH_NUM\n"," epoch_dicecoef = running_dicecoef/BATCH_NUM\n"," TRAIN_LOSS.append(epoch_loss)\n"," TRAIN_DICE.append(epoch_dicecoef)\n","\n"," #test\n"," with tqdm(test_loader, unit='batch') as tsbatch:\n"," for batch_idx, (x, y) in enumerate(tsbatch):\n"," tsbatch.set_description(f'Batch: {batch_idx}')\n"," output_test = model(x)\n"," loss_test = dice_loss(output_test, y)\n"," dicecoef_test = dice_coef(output_test, y)\n"," tsbatch.set_postfix(loss=loss_test.item(), dice_coef=dicecoef_test.item())\n","\n"," running_loss_test += loss_test.item()\n"," running_dicecoef_test += dicecoef_test.item()\n","\n"," TEST_LOSS.append(running_loss_test/BATCH_NUM_TEST)\n"," TEST_DICE.append(running_dicecoef_test/BATCH_NUM_TEST)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["EPOCH 1/15\n"]},{"output_type":"stream","name":"stderr","text":["Batch: 0: 0%| | 0/33 [00:43torchviz) (3.7.4.3)\n","Building wheels for collected packages: torchviz\n"," Building wheel for torchviz (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for torchviz: filename=torchviz-0.0.2-py3-none-any.whl size=4151 sha256=80aa29d36737c5d4b81edcddc53bec07221514dc0c26ab4de9e6c23bf49714c5\n"," Stored in directory: /root/.cache/pip/wheels/04/38/f5/dc4f85c3909051823df49901e72015d2d750bd26b086480ec2\n","Successfully built torchviz\n","Installing collected packages: torchviz\n","Successfully installed torchviz-0.0.2\n"]}]},{"cell_type":"code","metadata":{"id":"oKvcU7lyeujb"},"source":["from torchviz import make_dot\n","make_dot(output, params=dict(model.named_parameters(), ))"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"zG5sYuWORuIO","executionInfo":{"status":"ok","timestamp":1634452208764,"user_tz":-600,"elapsed":490,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["#save model\n","filename = \"Unet_ISIC2.pth\"\n","torch.save(model.state_dict(), filename)"],"execution_count":169,"outputs":[]},{"cell_type":"code","metadata":{"id":"EhJChHsfViUi","colab":{"base_uri":"https://localhost:8080/","height":319},"executionInfo":{"status":"ok","timestamp":1634205941940,"user_tz":-600,"elapsed":1094,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"2a6204e7-1325-46f0-cdbf-fee476cdd087"},"source":["model.eval()\n","p = model(x)[0]\n","plt.imshow(p.detach().squeeze(), cmap='gray')"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:1805: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n"," warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n"]},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":40},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAPsAAAD7CAYAAACscuKmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAARwklEQVR4nO3dfYxc1XnH8e/P7zYYGwfXdfC2NmCBwComWRkco4qXQg1KUyeKUCLUWJWj/SeVCKRKTStVTdVGJFJCErWq4oQ0/BECxAk1IglvLighqhybmheDY+wYI+zYXqAmpA1+f/rHXN/MjHa945n7srvn95FWc869MzuPdubZc+49556riMDMxr8JdQdgZtVwspslwslulggnu1kinOxmiXCymyWip2SXtFLSDkm7JK0tKigzK566HWeXNBF4BbgB2AtsBj4eES8XF56ZFWVSD69dBuyKiN0Aku4H/hwYNtkleQaPWckiQkNt76Ubfz7welN9b7bNzEahXlr2jkgaAAbKfh8zO71ekn0f0NdUX5BtaxER64B14G68WZ166cZvBhZLWiRpCvAx4OFiwjKzonXdskfEcUl/BTwGTAS+FREvFRaZmRWq66G3rt7M3Xiz0pVxNt7MxhAnu1kinOxmiSh9nN3GD6n1UNBLmo0tbtnNEuFkN0uEk90sET5mH4Pmzp2bl7/2ta+17Nu0adOQz2suA/z4xz/Oyz/84Q9b9k2ePDkvr1+/Pi8vWbKk5Xm33nprXv7JT37SUexWH7fsZolwspslwtNlx4Cbbrqppf6jH/2opkiGt2HDhpb6qlWraorEPF3WLHFOdrNEuBs/SvX39+flzZs31xhJd5oPPR577LG87Fl35XM33ixxTnazRDjZzRLhY/YanXfeeS31wcHBvNx+hdlY9uKLL+blq6++umXfO++8U3U4456P2c0S52Q3S4S78RX7xCc+kZfvvffeGiOpxze+8Y2W+sCA7x9SNHfjzRLnZDdLhJPdLBFevKIEEydOzMt33HFHy74vfvGLVYczqixfvrzuEJI1Yssu6VuSBiVta9o2R9ITknZmj+eWG6aZ9aqTbvy3gZVt29YCGyNiMbAxq5vZKNbR0JukhcAjEbEkq+8AromI/ZLmA09HxMUd/J4kht4uuuiivLxt27aWfVOnTq06nFFl9+7dLfULL7ywpkjGr6KH3uZFxP6sfACY1+XvMbOK9HyCLiLidC22pAHAMyfMatZtsh+UNL+pGz843BMjYh2wDtLpxl955ZV5OfVue7tFixa11Jsv+PHCFuXqthv/MLA6K68GNpzmuWY2CnQy9PZd4L+AiyXtlbQGuAu4QdJO4E+yupmNYiN24yPi48Psur7gWMysRL7qrQTNizW03zLJWt199915uX22oXXHV72ZJc7JbpYId+NLcPjw4bzsobfTO3HiRF6eNMnXZRXB3XizxDnZzRLhZDdLhA+SSjB58uS6Qxgzmhf6aF8r39Nni+WW3SwRTnazRLgbXwB3P4sxYUJr29M8LGe9c8tulggnu1ki3I0vQPvZ9/F0B9YquRtfLrfsZolwspslwslulggfsxdgypQpLXUPvXXn2muvbak//vjjNUUyPrllN0uEk90sEV68ogDt3fjf/va3ebn5Qg87vXfffbelPmPGjJoiGdu8eIVZ4pzsZolwspslwkNvBWif1unpst2ZPn16S933gStWJ7d/6pP0lKSXJb0k6bZs+xxJT0jamT2eW364ZtatTrrxx4HPRMSlwFXApyRdCqwFNkbEYmBjVjezUeqMh94kbQD+Jfu5pum2zU9HxMUjvHZc9sXa1zs/duxYTZGML0ePHs3LXn+/c4UMvUlaCFwBbALmRcT+bNcBYF4P8ZlZyTo+QSfpbOD7wKcj4p22kycxXKstaQAY6DVQM+tNRy27pMk0Ev07EfGDbPPBrPtO9jg41GsjYl1E9EdEfxEBm1l3RmzZ1WjC7wG2R8SXm3Y9DKwG7soeN5QS4Rgwe/bsukMYl5qnIa9d+7vzv3fddVcd4Yx5nXTjVwB/Abwo6bls29/SSPIHJa0BXgNuKSdEMyvCiMkeEc8Aw80Sub7YcMysLL7qrQArVqxoqT/zzDM1RZIGz1A8PV/1ZpY4J7tZInwhTJeau5IrV66sMZL0tC8I4vXlO+OW3SwRTnazRDjZzRLhY/YuNR+zL1++vMZI0rNnz56Wel9fXz2BjDFu2c0S4WQ3S4Rn0HWpefjnrbfeatk3a9asqsNJmmfUtfIMOrPEOdnNEuFkN0uEh966NG3atLw8c+bMGiMx64xbdrNEONnNEuFufJduvPHGvDxhgv9n2ujnb6lZIpzsZonwDLoubd++PS9fcsklNUZinkHXyjPozBLnZDdLhJPdLBEeeuvSOeecU3cIZmdkxJZd0jRJP5f0vKSXJH0u275I0iZJuyQ9IGnKSL/LzOrTSTf+CHBdRFwOLAVWSroK+AJwd0RcBBwC1pQXppn1asRkj4b/zaqTs58ArgPWZ9vvBVaVEuEodeTIkfzHqhURLT/WmU7vzz4xu4PrIPAE8Evg7Yg4nj1lL3B+OSGaWRE6SvaIOBERS4EFwDKg41kkkgYkbZG0pcsYzawAZzT0FhFvA08By4HZkk6dzV8A7BvmNesioj8i+nuK1Mx60snZ+LmSZmfl6cANwHYaSf/R7GmrgQ1lBTka+ZixPidPnmz5sc50Ms4+H7hX0kQa/xwejIhHJL0M3C/pn4CtwD0lxmlmPRox2SPiBeCKIbbvpnH8bmZjgGfQnYHmq6ua16CzanmxkO74r2aWCCe7WSLcjT8DzWfeDx8+XGMkaWtfrKL5VlwnTpyoOpwxwy27WSKc7GaJcLKbJcLH7F3avHlzXr7gggtqjMQ+/OEP5+X169ef5plpc8tulggnu1ki3I3vUvNwj9Xr85//fF52N354btnNEuFkN0uEk90sET5m79KSJUvqDsEyfX19dYcwJrhlN0uEk90sEe7Gd2nq1Kl1h2CZSZP8Ne6EW3azRDjZzRLh/k+Xdu7cmZcXLlzYsq99cQUr14EDB+oOYUxwy26WCCe7WSKc7GaJUJW3L5I0bu6VNHfu3Lz82muvteybPn161eEkbc6cOXn50KFDNUYyOkTEkCeNOm7Zs9s2b5X0SFZfJGmTpF2SHpA0pahgzax4Z9KNv43GDR1P+QJwd0RcBBwC1hQZmJkVq6NuvKQFwL3APwN3AH8GvAH8fkQcl7Qc+IeI+NMRfs+46cY3D681r0cH8P73v7/qcJLmoc5WvXbjvwJ8Fjh1f9z3AG9HxPGsvhc4v6cIzaxUndyf/YPAYEQ8280bSBqQtEXSlm5eb2bF6GQG3QrgQ5JuBqYB5wBfBWZLmpS17guAfUO9OCLWAetgfHXjzcaaTu7PfidwJ4Cka4C/johbJX0P+ChwP7Aa2FBinKNO87mOJ598smWfj9ltNOplUs3fAHdI2kXjGP6eYkIyszKc0YUwEfE08HRW3g0sKz4kMyuDZ9AV4AMf+EBL/Wc/+1lNkaSh/Ts7YYJnfTfreQadmY1tTnazRHjxigJs37595CdZYfbtG3KU10bglt0sEU52s0Q42c0S4aG3ArSvW3706NG87Cuyijd79uyW+q9//euaIhmdPPRmljgnu1ki3I0vQPsMrmPHjg27z3rnQ6PTczfeLHFOdrNEONnNEuHpsgVoP+9x8uTJvOxj9mL89Kc/rTuEMc/fRLNEONnNEuGhtxK88soreXnx4sU1RjK2NX83J0+enJdPnDhRRzhjhofezBLnZDdLhLvxJbjwwgvz8o4dO1r2TZw4sepwxozmUQyAs88+Oy+/++67VYczZrkbb5Y4J7tZIpzsZonwMXvJPvnJT7bUv/71r+dlz65rPRY/66yzWvZV+d0cT4Y7Zu9ouqykPcBvgBPA8YjolzQHeABYCOwBbomIQ0UEa2bFO5Om5dqIWBoR/Vl9LbAxIhYDG7O6mY1SHXXjs5a9PyLebNq2A7gmIvZLmg88HREXj/B7kuuXtS+08MILL+Tlyy677LTPTcFHPvKRvPzQQw/VGMn40evQWwCPS3pW0kC2bV5E7M/KB4B5PcZoZiXq9BLXqyNin6TfA56Q9IvmnRERw7Xa2T+HgaH2mVl1OmrZI2Jf9jgIPETjVs0Hs+472ePgMK9dFxH9Tcf6ZlaDEVt2SWcBEyLiN1n5RuAfgYeB1cBd2eOGMgMdq9rPiaxYsSIv79q1q2Xf3LlzK4lpNGlfA97K00k3fh7wUHbyaBJwX0Q8Kmkz8KCkNcBrwC3lhWlmvRox2SNiN3D5ENvfAq4vIygzK55n0NVo2rRpLfUDBw7k5VmzZlUdTiXar2zr6+vLy7/61a+qDmdc8lVvZolzspslwslulgivG1+jw4cPt9TnzfvdJMRXX301L8+fP7+ymMrQvEDkl770pZZ9Pk6vjlt2s0Q42c0S4aG3UWrGjBl5uX3Ryve+9715ebQugHH06NG8vGrVqrz86KOPtjzPC1QUz0NvZolzspslwt34MaC9qz516tS8/Oabb7bsa+7+V+ngwYMt9WXLluXl119/PS+7214+d+PNEudkN0uEk90sET5mH+MmTWqdBPnGG2/k5ZkzZ+blrVu3tjyveeHLm2++uWVf8z3Wms8XHDt2rOV5t99+e16+7777WvYdOXJkxNitHD5mN0uck90sEe7GjzPDrT3f7efc/Ps8bDY2uBtvljgnu1kinOxmifDiFeNM0cfVPk4fP9yymyXCyW6WCCe7WSI6SnZJsyWtl/QLSdslLZc0R9ITknZmj+eWHayZda/Tlv2rwKMRcQmNW0FtB9YCGyNiMbAxq5vZKDXiDDpJs4DngAui6cmSdgDXRMT+7JbNT0fExSP8Lp/aNStZLzPoFgFvAP8uaaukb2a3bp4XEfuz5xygcbdXMxulOkn2ScD7gH+LiCuA/6Oty561+EO22pIGJG2RtKXXYM2se50k+15gb0RsyurraST/waz7TvY4ONSLI2JdRPRHRH8RAZtZd0ZM9og4ALwu6dTx+PXAy8DDwOps22pgQykRmlkhOrrEVdJS4JvAFGA38Jc0/lE8CPwB8BpwS0T8zwi/xyfozEo23Ak6X89uNs74enazxDnZzRLhZDdLhJPdLBFOdrNEONnNEuFkN0tE1WvQvUljAs55WblOoyEGcBztHEerM43jD4fbUemkmvxNpS11z5UfDTE4DsdRZRzuxpslwsluloi6kn1dTe/bbDTEAI6jneNoVVgctRyzm1n13I03S0SlyS5ppaQdknZJqmw1WknfkjQoaVvTtsqXwpbUJ+kpSS9LeknSbXXEImmapJ9Lej6L43PZ9kWSNmWfzwOSppQZR1M8E7P1DR+pKw5JeyS9KOm5U0uo1fQdKW3Z9sqSXdJE4F+Bm4BLgY9LurSit/82sLJtWx1LYR8HPhMRlwJXAZ/K/gZVx3IEuC4iLgeWAislXQV8Abg7Ii4CDgFrSo7jlNtoLE9+Sl1xXBsRS5uGuur4jpS3bHtEVPIDLAcea6rfCdxZ4fsvBLY11XcA87PyfGBHVbE0xbABuKHOWIAZwH8DV9KYvDFpqM+rxPdfkH2BrwMeAVRTHHuA89q2Vfq5ALOAV8nOpRUdR5Xd+POB15vqe7Ntdal1KWxJC4ErgE11xJJ1nZ+jsVDoE8Avgbcj4nj2lKo+n68AnwVOZvX31BRHAI9LelbSQLat6s+l1GXbfYKO0y+FXQZJZwPfBz4dEe/UEUtEnIiIpTRa1mXAJWW/ZztJHwQGI+LZqt97CFdHxPtoHGZ+StIfN++s6HPpadn2kVSZ7PuAvqb6gmxbXTpaCrtokibTSPTvRMQP6owFICLeBp6i0V2eLenU9RJVfD4rgA9J2gPcT6Mr/9Ua4iAi9mWPg8BDNP4BVv259LRs+0iqTPbNwOLsTOsU4GM0lqOuS+VLYUsScA+wPSK+XFcskuZKmp2Vp9M4b7CdRtJ/tKo4IuLOiFgQEQtpfB/+MyJurToOSWdJmnmqDNwIbKPizyXKXra97BMfbScabgZeoXF8+HcVvu93gf3AMRr/PdfQODbcCOwEngTmVBDH1TS6YC/QuH/ec9nfpNJYgD8CtmZxbAP+Ptt+AfBzYBfwPWBqhZ/RNcAjdcSRvd/z2c9Lp76bNX1HlgJbss/mP4Bzi4rDM+jMEuETdGaJcLKbJcLJbpYIJ7tZIpzsZolwspslwslulggnu1ki/h9tyVjKU07/QwAAAABJRU5ErkJggg==\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"4Rh-uq05TG-A"},"source":["# Model Evaluation"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":279},"id":"7ZBk4w3xIA8L","executionInfo":{"status":"ok","timestamp":1634439698477,"user_tz":-600,"elapsed":391,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"519836e3-612d-4bcf-ccdb-b92349a2d6cb"},"source":["import matplotlib.pyplot as plt\n","import numpy as np\n","\n","X = np.arange(1,EPOCHS+1)\n","\n","plt.plot(X, TRAIN_LOSS, marker='.', markersize=10, label='train')\n","plt.plot(X, TEST_LOSS, marker='.', markersize=10, label='test')\n","plt.xlabel('Epochs')\n","plt.ylabel('Dice Loss')\n","plt.xticks(X)\n","plt.legend()\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":279},"id":"Y80RZXzKRPpc","executionInfo":{"status":"ok","timestamp":1634439703085,"user_tz":-600,"elapsed":429,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"e5830c99-2e63-46c5-beb6-a95b3f9ce19b"},"source":["plt.plot(X, TRAIN_DICE, marker='.', markersize=10, label='train')\n","plt.plot(X, TEST_DICE, marker='.', markersize=10, label='test')\n","plt.xlabel('Epochs')\n","plt.ylabel('Dice Coeffeciency')\n","plt.xticks(X)\n","plt.legend()\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"BZYl7y1XIJFr","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1634452335129,"user_tz":-600,"elapsed":5640,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"615ef612-6f7c-486a-f79e-99c1290af99f"},"source":["for batch in test_loader:\n"," x, y = batch\n"," print(x.shape, y.shape)\n"," break"],"execution_count":14,"outputs":[{"output_type":"stream","name":"stdout","text":["torch.Size([64, 3, 128, 128]) torch.Size([64, 1, 128, 128])\n"]}]},{"cell_type":"code","metadata":{"id":"V3Ahm7ecTST4"},"source":["#load model\n","new_model = ImprovedUnet(feature_size=feature_size)\n","\n","filename = \"Unet_ISIC2.pth\"\n","checkpoint = torch.load(filename)\n","\n","new_model.load_state_dict(checkpoint)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":285},"id":"BIAOsDaLtliA","executionInfo":{"status":"ok","timestamp":1634452356740,"user_tz":-600,"elapsed":923,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"6e9d7e79-98ef-467d-dcdd-b75c9a67d2a7"},"source":["plt.imshow(x[0].permute(1,2,0))"],"execution_count":17,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":17},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":302},"id":"LXKpDfDRjlkR","executionInfo":{"status":"ok","timestamp":1634448826907,"user_tz":-600,"elapsed":933,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"035c4c92-79d4-46d2-a20b-d0be012ff0d1"},"source":["alpha = 5\n","seg_img = x[0].clone()\n","image_r = seg_img[0]\n","image_r = image_r*(1-alpha*pred_mask)+(pred_mask*pred_mask*alpha)\n","segment_image = image_r.detach().squeeze()\n","seg_img[0] = segment_image\n","plt.imshow(seg_img.permute(1,2,0))"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":157},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]}]} \ No newline at end of file +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"IUNet.ipynb","provenance":[],"collapsed_sections":[],"mount_file_id":"1UdH3PIDr4uawfSNGUl8ntSrq2Lbo_uQQ","authorship_tag":"ABX9TyMhn0gJSue2GPm8/W30Ob7Y"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"vlhQO-g6Y2ah","executionInfo":{"status":"ok","timestamp":1634536832538,"user_tz":-600,"elapsed":12962,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["import os\n","from PIL import Image\n","import glob\n","import matplotlib.pyplot as plt\n","\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","\n","import torch.utils.data\n","from torchvision import datasets, transforms"],"execution_count":1,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"7vcUuTeHs59d"},"source":["# Data preparation\n","\n"]},{"cell_type":"code","metadata":{"id":"gPYa8ZVvXk2d","executionInfo":{"status":"ok","timestamp":1634536832542,"user_tz":-600,"elapsed":9,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["import os\n","from PIL import Image\n","import glob\n","import matplotlib.pyplot as plt\n","\n","#define dataset for img and mask\n","file_dir = \"/content/drive/MyDrive/ColabGitHub/ISIC2018_Task1-2_Training_Data\"\n","os.chdir(file_dir)"],"execution_count":2,"outputs":[]},{"cell_type":"code","metadata":{"id":"StdLOrNGt3Id","executionInfo":{"status":"ok","timestamp":1634536854321,"user_tz":-600,"elapsed":9,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["from torchvision import datasets\n","from torchvision.datasets import ImageFolder\n","from torch.utils.data import dataloader, random_split, Subset\n","from torchvision.transforms import transforms"],"execution_count":4,"outputs":[]},{"cell_type":"code","metadata":{"id":"JhoipyIyXvBB","executionInfo":{"status":"ok","timestamp":1634536854321,"user_tz":-600,"elapsed":7,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["from torch.utils.data import Dataset\n","from PIL import Image\n","\n","class Unet_dataset(Dataset):\n"," def __init__(self,\n"," img_dir = './ISIC2018_Task1-2_Training_Input_x2',\n"," mask_dir = './ISIC2018_Task1_Training_GroundTruth_x2', \n"," img_transforms=None,\n"," mask_transforms= None):\n"," \n"," self.img_dir = img_dir\n"," self.mask_dir = mask_dir\n"," self.img_transforms = img_transforms\n"," self.mask_transforms = mask_transforms\n","\n"," self.imgs = [file for file in sorted(os.listdir(self.img_dir)) if file.endswith('.jpg')]\n"," self.masks = [file for file in sorted(os.listdir(self.mask_dir)) if file.endswith('.png')]\n","\n"," #meke dataloader\n"," def load_data(self, idx):\n"," img_path = os.path.join(self.img_dir, self.imgs[idx])\n"," mask_path = os.path.join(self.mask_dir, self.masks[idx])\n"," img = Image.open(img_path).convert('RGB')\n"," mask = Image.open(mask_path).convert('L')\n"," return img, mask\n","\n"," def __getitem__(self, idx):\n"," img, mask = self.load_data(idx)\n"," #transformation\n"," if self.img_transforms is not None:\n"," img = self.img_transforms(img)\n"," if self.mask_transforms is not None:\n"," mask = self.mask_transforms(mask)\n"," return img, mask\n","\n"," def __len__(self):\n"," return len(self.imgs)"],"execution_count":5,"outputs":[]},{"cell_type":"code","metadata":{"id":"vpwsi1o-xnYw","executionInfo":{"status":"ok","timestamp":1634536856022,"user_tz":-600,"elapsed":4,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["from torch.utils.data import DataLoader\n","import torchvision.transforms as transforms\n","\n","\n","#transformation\n","img_tfs = transforms.Compose([\n"," transforms.Resize((128,128)),\n"," transforms.ToTensor(),\n"," transforms.Normalize(mean=[0, 0, 0], std=[1, 1, 1]),\n"," ]\n"," )\n","\n","mask_tfs = transforms.Compose([\n"," transforms.Resize((128,128)),\n"," transforms.ToTensor(),\n"," ]\n"," )\n","\n","dataset = Unet_dataset(img_transforms=img_tfs, mask_transforms=mask_tfs)\n","\n","\n","#shuffle index\n","sample_size = len(dataset.imgs)\n","train_size = int(sample_size*0.8)\n","test_size = sample_size - train_size\n","\n","#train and test set\n","train_set, test_set = torch.utils.data.random_split(dataset, [train_size, test_size], generator=torch.Generator().manual_seed(123))\n","\n","#dataloader\n","train_loader= DataLoader(train_set, batch_size=64, shuffle = True)\n","test_loader= DataLoader(test_set, batch_size=64, shuffle=False)"],"execution_count":6,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"cN3J5wTpx_HX"},"source":["# Improved Unet"]},{"cell_type":"code","metadata":{"id":"VFiXmwWjSeXi","executionInfo":{"status":"ok","timestamp":1634536856023,"user_tz":-600,"elapsed":4,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["import torch\n","import torch.nn as nn\n","import torch.nn.functional as F"],"execution_count":7,"outputs":[]},{"cell_type":"code","metadata":{"id":"nzGxgjkBeFXR","executionInfo":{"status":"ok","timestamp":1634536862998,"user_tz":-600,"elapsed":410,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["class Context(nn.Module):\n"," def __init__(self, in_channels, out_channels):\n"," super(Context, self).__init__()\n"," self.context = nn.Sequential(\n"," nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.Dropout2d(p=0.3),\n"," nn.LeakyReLU(negative_slope=0.02, inplace=True),\n"," nn.Conv2d(in_channels, out_channels, kernel_size=3, stride = 1, padding=1, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.LeakyReLU(negative_slope=0.02, inplace=True),\n"," )\n","\n"," def forward(self, x):\n"," x = self.context(x) + x\n"," return x\n","\n","\n","class Localization(nn.Module):\n"," def __init__(self, in_channels, out_channels):\n"," super(Localization, self).__init__()\n"," self.localization = nn.Sequential(\n"," nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.LeakyReLU(negative_slope=0.02, inplace=True),\n"," nn.Conv2d(out_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.LeakyReLU(negative_slope=0.02, inplace=True),\n"," )\n"," \n"," def forward(self, x):\n"," return self.localization(x)\n","\n","\n","class Upsampling(nn.Module):\n"," def __init__(self, in_channels, out_channels):\n"," super(Upsampling, self).__init__()\n"," self.upsampling = nn.Sequential(\n"," nn.Upsample(scale_factor=2, mode='nearest'),\n"," nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.LeakyReLU(negative_slope=0.02, inplace=True),\n"," )\n"," \n"," def forward(self, x):\n"," return self.upsampling(x)\n","\n","\n","class Segment(nn.Module):\n"," def __init__(self, in_channels, out_channels):\n"," super(Segment, self).__init__()\n"," self.segment = nn.Sequential(\n"," nn.Conv2d(in_channels, out_channels=1, kernel_size=1, stride=1, padding=0, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.LeakyReLU(negative_slope=0.02, inplace=True)\n"," )\n"," \n"," def forward(self, x):\n"," return self.segment(x)\n","\n","\n","class Conv2(nn.Module):\n"," def __init__(self, in_channels, out_channels):\n"," super(Conv2, self).__init__()\n"," self.conv2 = nn.Sequential(\n"," nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=2, padding=1, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.LeakyReLU(negative_slope=0.02, inplace=True),\n"," )\n","\n"," def forward(self, x):\n"," return self.conv2(x)\n","\n","\n","class ImprovedUnet(nn.Module):\n"," def __init__(self, in_channels=3, out_channels=1, feature_size=[16, 32, 64, 128]):\n"," super(ImprovedUnet, self).__init__()\n"," self.Conv1 = nn.Conv2d(in_channels=3, out_channels=feature_size[0], kernel_size=3, stride=1, padding=1, bias=False) \n"," self.Downs = nn.ModuleList()\n"," self.Convs = nn.ModuleList()\n"," self.Ups = nn.ModuleList()\n"," self.Segmentations = nn.ModuleList()\n","\n"," self.upscale = nn.Upsample(scale_factor=2, mode='nearest')\n"," self.bottleneck = Context(feature_size[-1]*2, feature_size[-1]*2)\n","\n","\n"," #Downsampling frame\n"," for feature in feature_size:\n"," self.Downs.append(Context(feature, feature))\n"," self.Convs.append(Conv2(feature, feature*2))\n","\n"," #Upsampleing frame\n"," for feature in reversed(feature_size):\n"," #Upsample\n"," self.Ups.append(Upsampling(feature*2, feature))\n","\n"," #Localization\n"," if feature != feature_size[0]:\n"," self.Ups.append(Localization(feature*2, feature))\n"," else:\n"," self.Ups.append(Localization(feature*2, feature*2))\n"," \n"," #Segmentation\n"," self.Segmentations.append(Segment(feature, 1))\n","\n"," self.final_conv = nn.Conv2d(feature_size[0]*2, out_channels, kernel_size=1, stride=1, bias=False)\n"," \n","\n"," def forward(self, x):\n"," skip_connections = []\n"," segmentation_layers = []\n","\n"," x = self.Conv1(x)\n","\n"," #Downsampling steps\n"," for i, (context_i, conv_i) in enumerate(zip(self.Downs, self.Convs)):\n"," x = context_i(x)\n"," #preserve location\n"," skip_connections.append(x)\n"," x = conv_i(x)\n","\n"," x = self.bottleneck(x) + x\n"," skip_connections = skip_connections[: : -1]\n","\n"," #Upsampling steps\n"," for idx in range(0, len(self.Ups), 2):\n"," #upsample\n"," x = self.Ups[idx](x)\n","\n"," #localization\n"," skip_connection = skip_connections[idx//2]\n"," concatnate_skip = torch.cat((skip_connection, x), dim=1)\n"," x = self.Ups[idx+1](concatnate_skip)\n","\n"," #segmentation\n"," if idx == 2 or idx == 4:\n"," x_segment = self.Segmentations[idx//2](x)\n"," segmentation_layers.append(x_segment)\n","\n"," seg_scale1 = self.upscale(segmentation_layers[0])\n"," seg_scale2 = self.upscale(segmentation_layers[1]+seg_scale1)\n","\n"," x = self.final_conv(x)\n"," x = x + seg_scale2\n","\n"," output = F.sigmoid(x)\n"," \n"," return output"],"execution_count":8,"outputs":[]},{"cell_type":"code","metadata":{"id":"otdcbBK7g4fW","executionInfo":{"status":"ok","timestamp":1634536865757,"user_tz":-600,"elapsed":3,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["feature_size=[16, 32, 64, 128]\n","device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"],"execution_count":9,"outputs":[]},{"cell_type":"code","metadata":{"id":"oDhAF_WYyeJm","executionInfo":{"status":"ok","timestamp":1634536867513,"user_tz":-600,"elapsed":3,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["#dice coef\n","def dice_coef(pred, target):\n"," batch_size = len(pred)\n"," somooth = 1.\n","\n"," pred_flat = pred.view(batch_size, -1)\n"," target_flat = target.view(batch_size, -1)\n","\n"," intersection = (pred_flat*target_flat).sum()\n"," dice_coef = (2.*intersection+somooth)/(pred_flat.sum()+target_flat.sum()+somooth)\n"," return dice_coef\n","\n","#loss\n","def dice_loss(pred, target):\n"," dice_loss = 1 - dice_coef(pred, target)\n"," return dice_loss"],"execution_count":10,"outputs":[]},{"cell_type":"code","metadata":{"id":"dNMXUs-qViYi","executionInfo":{"status":"ok","timestamp":1634536882245,"user_tz":-600,"elapsed":9838,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["import torch.optim as optim\n","\n","#set parameters\n","feature_size=[16, 32, 64, 128]\n","model = ImprovedUnet(feature_size=feature_size)\n","model = model.to(device)\n","\n","optimizer = optim.Adam(model.parameters(), lr=0.001)\n","EPOCHS = 50"],"execution_count":11,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JrGpP3KMPzpe","executionInfo":{"status":"ok","timestamp":1634529930671,"user_tz":-600,"elapsed":1700198,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"d305c055-fbf6-4433-efae-2d8caf9980ea"},"source":["from tqdm import tqdm\n","\n","TRAIN_LOSS = []\n","TRAIN_DICE = []\n","TEST_LOSS =[]\n","TEST_DICE = []\n","\n","for epoch in range(1, EPOCHS+1):\n"," print('EPOCH {}/{}'.format(epoch, EPOCHS))\n"," running_loss = 0\n"," running_dicecoef = 0\n"," running_loss_test = 0\n"," running_dicecoef_test = 0\n"," BATCH_NUM = len(train_loader)\n"," BATCH_NUM_TEST = len(test_loader)\n","\n"," #training\n"," with tqdm(train_loader, unit='batch') as tbatch:\n"," for batch_idx, (x, y) in enumerate(tbatch):\n"," x, y = x.to(device), y.to(device)\n"," tbatch.set_description(f'Batch: {batch_idx}')\n","\n"," optimizer.zero_grad()\n"," output = model(x)\n"," loss = dice_loss(output, y)\n"," dicecoef = dice_coef(output, y)\n"," loss.backward()\n"," optimizer.step()\n","\n"," running_loss += loss.item()\n"," running_dicecoef += dicecoef.item()\n","\n"," tbatch.set_postfix(loss=loss.item(), dice_coef=dicecoef.item())\n","\n"," epoch_loss = running_loss/BATCH_NUM\n"," epoch_dicecoef = running_dicecoef/BATCH_NUM\n"," TRAIN_LOSS.append(epoch_loss)\n"," TRAIN_DICE.append(epoch_dicecoef)\n","\n"," #test\n"," with tqdm(test_loader, unit='batch') as tsbatch:\n"," for batch_idx, (x, y) in enumerate(tsbatch):\n"," x, y = x.to(device), y.to(device)\n"," tsbatch.set_description(f'Batch: {batch_idx}')\n"," output_test = model(x)\n"," loss_test = dice_loss(output_test, y)\n"," dicecoef_test = dice_coef(output_test, y)\n"," tsbatch.set_postfix(loss=loss_test.item(), dice_coef=dicecoef_test.item())\n","\n"," running_loss_test += loss_test.item()\n"," running_dicecoef_test += dicecoef_test.item()\n","\n"," TEST_LOSS.append(running_loss_test/BATCH_NUM_TEST)\n"," TEST_DICE.append(running_dicecoef_test/BATCH_NUM_TEST)"],"execution_count":null,"outputs":[{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["EPOCH 1/50\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["Batch: 0: 0%| | 0/33 [00:05torchviz) (3.7.4.3)\n"]}]},{"cell_type":"code","metadata":{"id":"oKvcU7lyeujb"},"source":["from torchviz import make_dot\n","make_dot(output, params=dict(model.named_parameters(), ))"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"zG5sYuWORuIO"},"source":["#save model\n","filename = \"Unet_ISIC2.pth\"\n","torch.save(model.state_dict(), filename)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"EhJChHsfViUi"},"source":["model.eval()\n","p = model(x)[0]\n","p = p.to('cpu')\n","plt.imshow(p.detach().squeeze(), cmap='gray')"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"4Rh-uq05TG-A"},"source":["#Evaluation"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":281},"id":"7ZBk4w3xIA8L","executionInfo":{"status":"ok","timestamp":1634530179125,"user_tz":-600,"elapsed":599,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"3d36229a-f06c-4097-81ef-ba14fcb527b8"},"source":["import matplotlib.pyplot as plt\n","import numpy as np\n","\n","X = np.arange(1,EPOCHS+1)\n","X_tick = np.arange(0,EPOCHS+1,5)\n","\n","plt.plot(X, TRAIN_LOSS, marker='.', markersize=10, label='train')\n","plt.plot(X, TEST_LOSS, marker='.', markersize=10, label='test')\n","plt.xlabel('Epochs')\n","plt.ylabel('Dice Loss')\n","plt.xticks(X_tick)\n","plt.legend()\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":279},"id":"Y80RZXzKRPpc","executionInfo":{"status":"ok","timestamp":1634530190434,"user_tz":-600,"elapsed":528,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"04523ef5-c82f-4b02-ec9a-79f64eb243a2"},"source":["plt.plot(X, TRAIN_DICE, marker='.', markersize=10, label='train')\n","plt.plot(X, TEST_DICE, marker='.', markersize=10, label='test')\n","plt.xlabel('Epochs')\n","plt.ylabel('Dice Coeffecient')\n","plt.xticks(X_tick)\n","plt.legend()\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"f0oyHRCkQ331"},"source":["# Segmentation"]},{"cell_type":"code","metadata":{"id":"V3Ahm7ecTST4","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1634541481872,"user_tz":-600,"elapsed":940,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"7607bfba-d530-42f2-dc0c-4f752e538500"},"source":["#load model\n","new_model = ImprovedUnet(feature_size=feature_size)\n","\n","filename = \"Unet_ISIC2.pth\"\n","checkpoint = torch.load(filename)\n","\n","new_model.load_state_dict(checkpoint)"],"execution_count":137,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":137}]},{"cell_type":"code","metadata":{"id":"BZYl7y1XIJFr","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1634541488207,"user_tz":-600,"elapsed":4036,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"a28afd14-4fc1-4013-f790-2a64af890567"},"source":["for batch in test_loader:\n"," x, y = batch\n"," print(x.shape, y.shape)\n"," break"],"execution_count":138,"outputs":[{"output_type":"stream","name":"stdout","text":["torch.Size([64, 3, 128, 128]) torch.Size([64, 1, 128, 128])\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8mo7-6wPsboG","executionInfo":{"status":"ok","timestamp":1634541495022,"user_tz":-600,"elapsed":4545,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"6839af64-3b89-4a4f-b8ba-6b7db113e9ae"},"source":["p = new_model(x)"],"execution_count":139,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:1805: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n"," warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n"]}]},{"cell_type":"code","metadata":{"id":"O-lkaeAvKayG","executionInfo":{"status":"ok","timestamp":1634541510585,"user_tz":-600,"elapsed":454,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["def segment_pred_mask(imgs=x, pred_masks=p, idx=0, alpha=10):\n"," seg_img = x[idx].clone()\n"," image_r = seg_img[0] #C: red\n"," image_r = image_r*(1-alpha*p[idx])+(p[idx]*p[idx]*alpha)\n"," segment_image = image_r.detach().squeeze()\n"," seg_img[0] = segment_image\n"," return seg_img"],"execution_count":140,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":666},"id":"KPJtsairAkc6","executionInfo":{"status":"ok","timestamp":1634541514338,"user_tz":-600,"elapsed":1288,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"660e1be6-dae2-478b-e0c0-633a0cadbc77"},"source":["#visualise\n","import matplotlib.pyplot as plt\n","\n","n_col = 4\n","n_row = 6\n","\n","def plot_gallery(images=x, mask=y, pred_mask = p, n_row=n_row, n_col=n_col):\n"," idxs = n_col*n_row\n"," plt.figure(figsize=(1.5*n_col, 1.5*n_row))\n"," plt.subplots_adjust(bottom=0, left=0.01, right=0.99, top=0.9, hspace=0.35) #adjust layout parameters\n"," plt.suptitle('Segmentation', fontsize=15)\n","\n"," for i in range(0, idxs, 4):\n"," #image\n"," plt.subplot(n_row, n_col, i+1)\n"," plt.imshow(x[i].permute(1,2,0))\n"," plt.title('image', fontsize = 10)\n"," plt.axis('off')\n","\n"," #target mask\n"," plt.subplot(n_row, n_col, i+2)\n"," plt.imshow(y[i].detach().squeeze(), cmap='gray')\n"," plt.title('target mask', fontsize = 10)\n"," plt.axis('off')\n"," \n"," #predicted mask\n"," plt.subplot(n_row, n_col, i+3)\n"," plt.imshow(p[i].detach().squeeze(), cmap='gray')\n"," plt.title('predicted mask', fontsize = 10)\n"," plt.axis('off')\n","\n"," #segmentation\n"," seg_img = segment_pred_mask(imgs=x, pred_masks=p, idx=i, alpha=0.5)\n"," plt.subplot(n_row, n_col, i+4)\n"," plt.imshow(seg_img.permute(1,2,0))\n"," plt.title('segmentation', fontsize = 10)\n"," plt.axis('off')\n","\n","plot_gallery()\n","plt.show()"],"execution_count":141,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"9GxVJPOpKK-G","executionInfo":{"status":"ok","timestamp":1634541523500,"user_tz":-600,"elapsed":786,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["seg_img= segment_pred_mask(imgs=x, pred_masks=p, idx=10, alpha=10)"],"execution_count":142,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":302},"id":"2qNx8jmVJxbF","executionInfo":{"status":"ok","timestamp":1634541525522,"user_tz":-600,"elapsed":13,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"510d6761-517c-4f90-d345-064a87ce6859"},"source":["plt.imshow(seg_img.permute(1,2,0))"],"execution_count":143,"outputs":[{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":143},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"d29ABXmtBDPc","executionInfo":{"status":"ok","timestamp":1634520579438,"user_tz":-600,"elapsed":470,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"51258823-64d3-499f-8cd2-26868e33c009"},"source":["print(torch.__version__)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["1.9.0+cu111\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XwYGPWVxBLjQ","executionInfo":{"status":"ok","timestamp":1634520681440,"user_tz":-600,"elapsed":470,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"55b03581-ae27-4f77-94e5-9911ca207fd5"},"source":["!python --version"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Python 3.7.12\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1NiyOl3MBXb8","executionInfo":{"status":"ok","timestamp":1634520779592,"user_tz":-600,"elapsed":489,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"7b9abc74-9e0c-4df8-e10b-1385fc9dfd24"},"source":["!nvidia-smi -L"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["GPU 0: Tesla P100-PCIE-16GB (UUID: GPU-3a7e6110-e668-9d4e-bb61-02d20470e114)\n"]}]}]} \ No newline at end of file From ca87d30ac24fe5910b9d7b22a40a1f8d2f6d428a Mon Sep 17 00:00:00 2001 From: Wakame <85863413+wakameds@users.noreply.github.com> Date: Sun, 17 Oct 2021 20:56:21 +1000 Subject: [PATCH 46/66] upload improved Unet model file --- recognition/s4633139/ImprovedUNet.py | 169 ++++++++++++++++++ .../{UNet.ipynb => UNetjupyter.ipynb} | 0 2 files changed, 169 insertions(+) create mode 100644 recognition/s4633139/ImprovedUNet.py rename recognition/s4633139/{UNet.ipynb => UNetjupyter.ipynb} (100%) diff --git a/recognition/s4633139/ImprovedUNet.py b/recognition/s4633139/ImprovedUNet.py new file mode 100644 index 0000000000..01d44d0d39 --- /dev/null +++ b/recognition/s4633139/ImprovedUNet.py @@ -0,0 +1,169 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class Context(nn.Module): + """ + context module + """ + def __init__(self, in_channels, out_channels): + super(Context, self).__init__() + self.context = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False), + nn.BatchNorm2d(out_channels), + nn.Dropout2d(p=0.3), + nn.LeakyReLU(negative_slope=0.02, inplace=True), + nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False), + nn.BatchNorm2d(out_channels), + nn.LeakyReLU(negative_slope=0.02, inplace=True), + ) + + def forward(self, x): + x = self.context(x) + x + return x + + +class Localization(nn.Module): + """ + localization module + """ + def __init__(self, in_channels, out_channels): + super(Localization, self).__init__() + self.localization = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False), + nn.BatchNorm2d(out_channels), + nn.LeakyReLU(negative_slope=0.02, inplace=True), + nn.Conv2d(out_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=False), + nn.BatchNorm2d(out_channels), + nn.LeakyReLU(negative_slope=0.02, inplace=True), + ) + + def forward(self, x): + return self.localization(x) + + +class Upsampling(nn.Module): + """ + upsampling module + """ + def __init__(self, in_channels, out_channels): + super(Upsampling, self).__init__() + self.upsampling = nn.Sequential( + nn.Upsample(scale_factor=2, mode='nearest'), + nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False), + nn.BatchNorm2d(out_channels), + nn.LeakyReLU(negative_slope=0.02, inplace=True), + ) + + def forward(self, x): + return self.upsampling(x) + + +class Segment(nn.Module): + """ + segmentation layer + """ + def __init__(self, in_channels, out_channels): + super(Segment, self).__init__() + self.segment = nn.Sequential( + nn.Conv2d(in_channels, out_channels=1, kernel_size=1, stride=1, padding=0, bias=False), + nn.BatchNorm2d(out_channels), + nn.LeakyReLU(negative_slope=0.02, inplace=True) + ) + + def forward(self, x): + return self.segment(x) + + +class Conv2(nn.Module): + """ + convolution stride=2 + """ + def __init__(self, in_channels, out_channels): + super(Conv2, self).__init__() + self.conv2 = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=2, padding=1, bias=False), + nn.BatchNorm2d(out_channels), + nn.LeakyReLU(negative_slope=0.02, inplace=True), + ) + + def forward(self, x): + return self.conv2(x) + + +class IUNet(nn.Module): + """ + Improved Unet (International MICCAI Brainlesion Workshop(pp. 287-297). Springer, Cham.) + """ + def __init__(self, in_channels=3, out_channels=1, feature_size=[16, 32, 64, 128]): + super(IUNet, self).__init__() + self.Conv1 = nn.Conv2d(in_channels=3, out_channels=feature_size[0], kernel_size=3, stride=1, padding=1, bias=False) + self.Downs = nn.ModuleList() + self.Convs = nn.ModuleList() + self.Ups = nn.ModuleList() + self.Segmentations = nn.ModuleList() + + self.upscale = nn.Upsample(scale_factor=2, mode='nearest') + self.bottleneck = Context(feature_size[-1] * 2, feature_size[-1] * 2) + + #Downsampling frame + for feature in feature_size: + self.Downs.append(Context(feature, feature)) + self.Convs.append(Conv2(feature, feature * 2)) + + #Upsampleing frame + for feature in reversed(feature_size): + #Upsampling + self.Ups.append(Upsampling(feature * 2, feature)) + + #Localization + if feature != feature_size[0]: + self.Ups.append(Localization(feature * 2, feature)) + else: + self.Ups.append(Localization(feature * 2, feature * 2)) + + #Segmentation + self.Segmentations.append(Segment(feature, 1)) + + self.final_conv = nn.Conv2d(feature_size[0] * 2, out_channels, kernel_size=1, stride=1, bias=False) + + def forward(self, x): + skip_connections = [] + segmentation_layers = [] + idxs = [idx for idx in range(0, len(self.Ups),2)] + + x = self.Conv1(x) + + #Downsampling steps + for i, (context_i, conv_i) in enumerate(zip(self.Downs, self.Convs)): + x = context_i(x) + #preserve location + skip_connections.append(x) + x = conv_i(x) + + x = self.bottleneck(x) + x + skip_connections = skip_connections[:: -1] + + #Upsampling steps + for idx in range(0, len(self.Ups), 2): + #upsampling + x = self.Ups[idx](x) + + #localization + skip_connection = skip_connections[idx // 2] + concatnate_skip = torch.cat((skip_connection, x), dim=1) + x = self.Ups[idx + 1](concatnate_skip) + + #segmentation + if idx == 2 or idx == 4: + x_segment = self.Segmentations[idx // 2](x) + segmentation_layers.append(x_segment) + + seg_scale1 = self.upscale(segmentation_layers[0]) + seg_scale2 = self.upscale(segmentation_layers[1] + seg_scale1) + x = self.final_conv(x) + x = x + seg_scale2 + output = F.sigmoid(x) + + return output \ No newline at end of file diff --git a/recognition/s4633139/UNet.ipynb b/recognition/s4633139/UNetjupyter.ipynb similarity index 100% rename from recognition/s4633139/UNet.ipynb rename to recognition/s4633139/UNetjupyter.ipynb From 037dd581831c450fb001f903d361ac7dcbb3077a Mon Sep 17 00:00:00 2001 From: Wakame <85863413+wakameds@users.noreply.github.com> Date: Sun, 17 Oct 2021 21:05:29 +1000 Subject: [PATCH 47/66] upload the dataloader and criterion files for the improved UNet --- recognition/s4633139/IUNet_criterion.py | 17 +++++++++++ recognition/s4633139/IUNet_dataloader.py | 38 ++++++++++++++++++++++++ 2 files changed, 55 insertions(+) create mode 100644 recognition/s4633139/IUNet_criterion.py create mode 100644 recognition/s4633139/IUNet_dataloader.py diff --git a/recognition/s4633139/IUNet_criterion.py b/recognition/s4633139/IUNet_criterion.py new file mode 100644 index 0000000000..187e198143 --- /dev/null +++ b/recognition/s4633139/IUNet_criterion.py @@ -0,0 +1,17 @@ +#dice coefficient +def dice_coef(pred, target): + batch_size = len(pred) + somooth = 1. + + pred_flat = pred.view(batch_size, -1) + target_flat = target.view(batch_size, -1) + + intersection = (pred_flat*target_flat).sum() + dice_coef = (2.*intersection+somooth)/(pred_flat.sum()+target_flat.sum()+somooth) + return dice_coef + + +#loss +def dice_loss(pred, target): + dice_loss = 1 - dice_coef(pred, target) + return dice_loss \ No newline at end of file diff --git a/recognition/s4633139/IUNet_dataloader.py b/recognition/s4633139/IUNet_dataloader.py new file mode 100644 index 0000000000..789027a1d5 --- /dev/null +++ b/recognition/s4633139/IUNet_dataloader.py @@ -0,0 +1,38 @@ +import os +from torch.utils.data import Dataset +from PIL import Image + +os.chdir("./ISIC2018_Task1-2_Training_Data") + +class UNet_dataset(Dataset): + def __init__(self, + img_dir='./ISIC2018_Task1-2_Training_Input_x2', + mask_dir='./ISIC2018_Task1_Training_GroundTruth_x2', + img_transforms=None, + mask_transforms=None, + ): + + self.img_dir = img_dir + self.mask_dir = mask_dir + self.img_transforms = img_transforms + self.mask_transforms = mask_transforms + self.imgs = [file for file in sorted(os.listdir(self.img_dir)) if file.endswith('.jpg')] + self.masks = [file for file in sorted(os.listdir(self.mask_dir)) if file.endswith('.png')] + + def load_data(self, idx): + img_path = os.path.join(self.img_dir, self.imgs[idx]) + mask_path = os.path.join(self.mask_dir, self.masks[idx]) + img = Image.open(img_path).convert('RGB') + mask = Image.open(mask_path).convert('L') + return img, mask + + def __getitem__(self, idx): + img, mask = self.load_data(idx) + if self.img_transforms is not None: + img = self.img_transforms(img) + if self.mask_transforms is not None: + mask = self.mask_transforms(mask) + return img, mask + + def __len__(self): + return len(self.imgs) \ No newline at end of file From 825725ede684385430ca083374593e6fbf059212 Mon Sep 17 00:00:00 2001 From: Wakame <85863413+wakameds@users.noreply.github.com> Date: Sun, 17 Oct 2021 21:08:51 +1000 Subject: [PATCH 48/66] upload the files to train model and to evaluate the performance for the improved Unet --- recognition/s4633139/IUNet_train_test.py | 61 ++++++++++++++++++++++++ recognition/s4633139/visualse.py | 43 +++++++++++++++++ 2 files changed, 104 insertions(+) create mode 100644 recognition/s4633139/IUNet_train_test.py create mode 100644 recognition/s4633139/visualse.py diff --git a/recognition/s4633139/IUNet_train_test.py b/recognition/s4633139/IUNet_train_test.py new file mode 100644 index 0000000000..573d6f96fe --- /dev/null +++ b/recognition/s4633139/IUNet_train_test.py @@ -0,0 +1,61 @@ +from IUNet_criterion import dice_coef, dice_loss +from tqdm import tqdm + + +def model_train_test(model, optimizer, EPOCHS, train_loader, test_loader): + """ + function for model training and test + :return: list of train and test dice coefficients and dice losses by epochs + """ + TRAIN_LOSS = [] + TRAIN_DICE = [] + TEST_LOSS =[] + TEST_DICE = [] + + for epoch in range(1, EPOCHS+1): + print('EPOCH {}/{}'.format(epoch, EPOCHS)) + running_loss = 0 + running_dicecoef = 0 + running_loss_test = 0 + running_dicecoef_test = 0 + BATCH_NUM = len(train_loader) + BATCH_NUM_TEST = len(test_loader) + + #train + with tqdm(train_loader, unit='batch') as tbatch: + for batch_idx, (x, y) in enumerate(tbatch): + tbatch.set_description(f'Batch: {batch_idx}') + + optimizer.zero_grad() + output = model(x) + loss = dice_loss(output, y) + dicecoef = dice_coef(output, y) + loss.backward() + optimizer.step() + + running_loss += loss.item() + running_dicecoef += dicecoef.item() + + tbatch.set_postfix(loss=loss.item(), dice_coef=dicecoef.item()) + + epoch_loss = running_loss/BATCH_NUM + epoch_dicecoef = running_dicecoef/BATCH_NUM + TRAIN_LOSS.append(epoch_loss) + TRAIN_DICE.append(epoch_dicecoef) + + #test + with tqdm(test_loader, unit='batch') as tsbatch: + for batch_idx, (x, y) in enumerate(tsbatch): + tsbatch.set_description(f'Batch: {batch_idx}') + output_test = model(x) + loss_test = dice_loss(output_test, y) + dicecoef_test = dice_coef(output_test, y) + tsbatch.set_postfix(loss=loss_test.item(), dice_coef=dicecoef_test.item()) + + running_loss_test += loss_test.item() + running_dicecoef_test += dicecoef_test.item() + + TEST_LOSS.append(running_loss_test/BATCH_NUM_TEST) + TEST_DICE.append(running_dicecoef_test/BATCH_NUM_TEST) + + return TRAIN_DICE, TEST_DICE, TRAIN_LOSS, TEST_LOSS \ No newline at end of file diff --git a/recognition/s4633139/visualse.py b/recognition/s4633139/visualse.py new file mode 100644 index 0000000000..fe0038c605 --- /dev/null +++ b/recognition/s4633139/visualse.py @@ -0,0 +1,43 @@ +import matplotlib.pyplot as plt +import numpy as np + +def dice_coef_vis(EPOCHS, TRAIN_COEFS, TEST_COEFS): + X = np.arange(1, EPOCHS+1) + plt.plot(X, TRAIN_COEFS, marker='.', markersize=10, label='train') + plt.plot(X, TEST_COEFS, marker='.', markersize=10, label='test') + plt.xlabel('Epochs') + plt.ylabel('Dice coefficient') + plt.xticks(X) + plt.legend() + plt.show() + + +def dice_loss_vis(EPOCHS, TRAIN_LOSS, TEST_LOSS): + X = np.arange(1, EPOCHS+1) + plt.plot(X, TRAIN_LOSS, marker='.', markersize=10, label='train') + plt.plot(X, TEST_LOSS, marker='.', markersize=10, label='test') + plt.xlabel('Epochs') + plt.ylabel('Dice Loss') + plt.xticks(X) + plt.legend() + plt.show() + + +def pred_mask(img, pred_mask, alpha=5): + seg_img = img.clone() + image_r = seg_img[0] + image_r = image_r*(1-alpha*pred_mask)+(pred_mask*pred_mask*alpha) + segmentation = image_r.detach().squeeze() + seg_img[0] = segmentation + plt.imshow(seg_img.permute(1,2,0)) + plt.show() + + +def segment_pred_mask(img, pred_mask, alpha=0.5): + seg_img = img.clone() + image_r = seg_img[0] + image_r = image_r*(1-alpha*pred_mask)+(pred_mask*pred_mask*alpha) + segment_img_r = image_r.detach().squeeze() + seg_img[0] = segment_img_r + plt.imshow(seg_img.permute(1,2,0)) + plt.show() \ No newline at end of file From 77891e0f77678c412ecd1364c3407278cf709f94 Mon Sep 17 00:00:00 2001 From: Wakame <85863413+wakameds@users.noreply.github.com> Date: Sun, 17 Oct 2021 21:09:44 +1000 Subject: [PATCH 49/66] upload the main files for the improved Unet --- recognition/s4633139/main.py | 76 ++++++++++++++++++++++++++++++++++++ 1 file changed, 76 insertions(+) create mode 100644 recognition/s4633139/main.py diff --git a/recognition/s4633139/main.py b/recognition/s4633139/main.py new file mode 100644 index 0000000000..46cad93ada --- /dev/null +++ b/recognition/s4633139/main.py @@ -0,0 +1,76 @@ +from IUNet_dataloader import UNet_dataset +from ImprovedUNet import IUNet +from IUNet_train_test import model_train_test +from visualse import dice_coef_vis, segment_pred_mask + +import torch +from torch.utils.data import DataLoader, Dataset, random_split +import torchvision.transforms as transforms +import torch.optim as optim + + +def main(): + """ + execute model training and return dice coefficient plots + """ + + #PARAMETERS + FEATURE_SIZE=[16, 32, 64, 128] + IN_CHANEL=3 + OUT_CHANEL=1 + + IMG_TF = transforms.Compose([ + transforms.Resize((FEATURE_SIZE[-1], FEATURE_SIZE[-1])), + transforms.ToTensor(), + transforms.Normalize(mean=[0, 0, 0], std=[1, 1, 1]), + ]) + + MASK_TF = transforms.Compose([ + transforms.Resize((FEATURE_SIZE[-1],FEATURE_SIZE[-1])), + transforms.ToTensor(), + ]) + + BATCH_SIZE = 64 + EPOCHS = 15 + LR = 0.001 + + #DATA PREPARATION + dataset = UNet_dataset(img_transforms=IMG_TF, mask_transforms=MASK_TF) + + #shuffle index + sample_size = len(dataset.imgs) + train_size = int(sample_size * 0.8) + test_size = sample_size - train_size + + #train and test set + train_set, test_set = random_split(dataset, [train_size, test_size], generator=torch.Generator().manual_seed(123)) + + #data loader + train_loader = DataLoader(train_set, batch_size=BATCH_SIZE, shuffle=True) + test_loader = DataLoader(test_set, batch_size=BATCH_SIZE, shuffle=False) + + #MODEL + model = IUNet(in_channels=IN_CHANEL, out_channels=OUT_CHANEL, feature_size=FEATURE_SIZE) + optimizer = optim.Adam(model.parameters(), lr=LR) + + #train,test + TRAIN_DICE, TEST_DICE, TRAIN_LOSS, TEST_LOSS = model_train_test(model, optimizer, EPOCHS, train_loader, test_loader) + + #plot dice coefficient + dice_coef_vis(EPOCHS, TRAIN_DICE, TEST_DICE) + + #segmentation + for batch in train_loader: + x, y = batch + break + + img = x[0] + model.eval() + pred_mask = model(x)[0] + segment_pred_mask(img, pred_mask, alpha=0.5) + +if __name__ == main(): + main() + + + From 73dd28160ff4ccac10bcd3a4af3cc2cea0505ee9 Mon Sep 17 00:00:00 2001 From: Wakame <85863413+wakameds@users.noreply.github.com> Date: Mon, 18 Oct 2021 08:55:26 +1000 Subject: [PATCH 50/66] remove UNetjupyter.ipynb --- recognition/s4633139/UNetjupyter.ipynb | 1 - 1 file changed, 1 deletion(-) delete mode 100644 recognition/s4633139/UNetjupyter.ipynb diff --git a/recognition/s4633139/UNetjupyter.ipynb b/recognition/s4633139/UNetjupyter.ipynb deleted file mode 100644 index 88301ea97b..0000000000 --- a/recognition/s4633139/UNetjupyter.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"UNet.ipynb","provenance":[],"collapsed_sections":[],"mount_file_id":"1UdH3PIDr4uawfSNGUl8ntSrq2Lbo_uQQ","authorship_tag":"ABX9TyNiaLFhg+HbcKfrWJAzQit8"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"vlhQO-g6Y2ah","executionInfo":{"status":"ok","timestamp":1634273373551,"user_tz":-600,"elapsed":306,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["import os\n","from PIL import Image\n","import matplotlib.pyplot as plt\n","import glob\n","\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","\n","import torch.utils.data\n","from torchvision import datasets, transforms"],"execution_count":60,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"7vcUuTeHs59d"},"source":["# Data preparation\n","\n","\n","1. get path\n","2. dataloader for img and mask\n","1. split into test and validation\n","\n","\n","\n"]},{"cell_type":"code","metadata":{"id":"WuSova9HHAzc","executionInfo":{"status":"ok","timestamp":1634270416375,"user_tz":-600,"elapsed":13732,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["#define dataset for img and mask\n","file_dir = \"/content/drive/MyDrive/ColabGitHub/ISIC2018_Task1-2_Training_Data\"\n","os.chdir(file_dir)\n","\n","img_path = './ISIC2018_Task1-2_Training_Input_x2'\n","mask_path = './ISIC2018_Task1_Training_GroundTruth_x2'\n","\n","imgs = [file for file in sorted(os.listdir(img_path)) if file.endswith('.jpg')]\n","masks = [file for file in sorted(os.listdir(mask_path)) if file.endswith('.png')]"],"execution_count":2,"outputs":[]},{"cell_type":"code","metadata":{"id":"StdLOrNGt3Id","executionInfo":{"status":"ok","timestamp":1634270422062,"user_tz":-600,"elapsed":341,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["from torchvision import datasets\n","from torchvision.datasets import ImageFolder\n","from torch.utils.data import dataloader, random_split, Subset\n","from torchvision.transforms import transforms\n"],"execution_count":3,"outputs":[]},{"cell_type":"code","metadata":{"id":"JhoipyIyXvBB","executionInfo":{"status":"ok","timestamp":1634270424835,"user_tz":-600,"elapsed":1106,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["from torch.utils.data import Dataset\n","from PIL import Image\n","\n","class Unet_dataset(Dataset):\n"," def __init__(self,\n"," img_dir = './ISIC2018_Task1-2_Training_Input_x2',\n"," mask_dir = './ISIC2018_Task1_Training_GroundTruth_x2', \n"," img_transforms=None,\n"," mask_transforms= None,\n"," train_ratio = 0.5):\n"," \n"," self.img_dir = img_dir\n"," self.mask_dir = mask_dir\n"," self.img_transforms = img_transforms\n"," self.mask_transforms = mask_transforms\n","\n"," self.imgs = [file for file in sorted(os.listdir(self.img_dir)) if file.endswith('.jpg')]\n"," self.masks = [file for file in sorted(os.listdir(self.mask_dir)) if file.endswith('.png')]\n","\n"," self.train_ratio = train_ratio\n","\n"," #meke dataloader\n"," def load_data(self, idx):\n"," img_path = os.path.join(self.img_dir, self.imgs[idx])\n"," mask_path = os.path.join(self.mask_dir, self.masks[idx])\n"," img = Image.open(img_path).convert('RGB')\n"," mask = Image.open(mask_path).convert('L')\n"," return img, mask\n","\n"," def __getitem__(self, idx):\n"," img, mask = self.load_data(idx)\n"," #transformation\n"," if self.img_transforms is not None:\n"," img = self.img_transforms(img)\n"," if self.mask_transforms is not None:\n"," mask = self.mask_transforms(mask)\n"," return img, mask\n","\n"," def __len__(self):\n"," return len(self.imgs)"],"execution_count":4,"outputs":[]},{"cell_type":"code","metadata":{"id":"vpwsi1o-xnYw","executionInfo":{"status":"ok","timestamp":1634270429179,"user_tz":-600,"elapsed":316,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["from torch.utils.data import DataLoader\n","import torchvision.transforms as transforms\n","\n","img_tfs = transforms.Compose([\n"," transforms.Resize((64,64)),\n"," transforms.ToTensor(),\n"," transforms.Normalize(mean=[0, 0, 0], std=[1, 1, 1]),\n"," ]\n"," )\n","\n","mask_tfs = transforms.Compose([\n"," transforms.Resize((64,64)),\n"," transforms.ToTensor(),\n"," ]\n"," )\n","\n","dataset = Unet_dataset(img_transforms=img_tfs, mask_transforms=mask_tfs)\n","\n","#shuffle index\n","sample_size = len(imgs)\n","train_size = int(sample_size*0.8)\n","test_size = sample_size - train_size\n","\n","#train and test set\n","train_set, test_set = torch.utils.data.random_split(dataset, [train_size, test_size], generator=torch.Generator().manual_seed(123))\n","\n","#dataloader\n","train_loader= DataLoader(train_set, batch_size=64, shuffle = True)\n","test_loader= DataLoader(test_set, batch_size=64, shuffle=False)"],"execution_count":5,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"cN3J5wTpx_HX"},"source":["# Unet"]},{"cell_type":"code","metadata":{"id":"VFiXmwWjSeXi","executionInfo":{"status":"ok","timestamp":1634270481930,"user_tz":-600,"elapsed":20,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["import torch\n","import torch.nn as nn"],"execution_count":8,"outputs":[]},{"cell_type":"code","metadata":{"id":"nzGxgjkBeFXR","executionInfo":{"status":"ok","timestamp":1634270481931,"user_tz":-600,"elapsed":19,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["class twotimes_conv(nn.Module):\n"," def __init__(self, in_channels, out_channels):\n"," super(twotimes_conv, self).__init__()\n"," self.conv = nn.Sequential(\n"," nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.ReLU(inplace=True),\n"," nn.Conv2d(out_channels, out_channels, kernel_size=3, stride = 1, padding=1, bias=False),\n"," nn.BatchNorm2d(out_channels),\n"," nn.ReLU(inplace=True),\n"," )\n","\n"," def forward(self, x):\n"," return self.conv(x)\n","\n","\n","class Unet(nn.Module):\n"," def __init__(self, in_channels=3, out_channels=1, feature_size=None):\n"," super(Unet, self).__init__()\n"," self.downsample = nn.ModuleList()\n"," self.upsample = nn.ModuleList()\n"," self.pool = nn.MaxPool2d(kernel_size=2, stride=2)\n"," self.feature_size = None\n","\n"," #Downsample frame\n"," for feature in feature_size:\n"," self.downsample.append(twotimes_conv(in_channels, feature))\n"," in_channels = feature\n","\n"," #Upsample frame\n"," for feature in reversed(feature_size):\n"," #Deconvolution\n"," self.upsample.append(nn.ConvTranspose2d(feature*2, feature, kernel_size=2, stride =2))\n"," #Convolution\n"," self.upsample.append(twotimes_conv(feature*2, feature))\n","\n"," #Bottleneck frame\n"," self.bottleneck = twotimes_conv(feature_size[-1], feature_size[-1]*2)\n"," self.final_conv = nn.Conv2d(feature_size[0], out_channels, kernel_size=1)\n","\n","\n"," def forward(self, x):\n"," skip_connections = []\n","\n"," #Downsampling steps\n"," for down_i in self.downsample:\n"," x = down_i(x)\n"," #preserve location\n"," skip_connections.append(x)\n"," x = self.pool(x)\n","\n"," #Bottle neck part\n"," x = self.bottleneck(x)\n"," skip_connections = skip_connections[: : -1]\n","\n"," #Upsampling steps\n"," for idx in range(0, len(self.upsample), 2):\n"," x = self.upsample[idx](x)\n"," skip_connection = skip_connections[idx//2]\n","\n"," if x.shape != skip_connection.shape:\n"," x = torchvision.transforms.resize(x, size=skip_connection.shape[2:])\n"," \n"," #where + what\n"," concatnate_skip = torch.cat((skip_connection, x), dim=1)\n"," x = self.upsample[idx+1](concatnate_skip)\n"," \n"," x = self.final_conv(x)\n"," output = F.sigmoid(x)\n"," \n"," return output"],"execution_count":9,"outputs":[]},{"cell_type":"code","metadata":{"id":"oDhAF_WYyeJm","executionInfo":{"status":"ok","timestamp":1634271792485,"user_tz":-600,"elapsed":490,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["#dice coef\n","def dice_coef(pred, target):\n"," batch_size = len(pred)\n"," somooth = 1.\n","\n"," pred_flat = pred.view(batch_size, -1)\n"," target_flat = target.view(batch_size, -1)\n","\n"," intersection = (pred_flat*target_flat).sum()\n"," dice_coef = (2.*intersection+somooth)/(pred_flat.sum()+target_flat.sum()+somooth)\n"," return dice_coef\n","\n","\n","#loss\n","def dice_loss(pred, target):\n"," dice_loss = 1 - dice_coef(pred, target)\n"," return dice_loss"],"execution_count":52,"outputs":[]},{"cell_type":"code","metadata":{"id":"dNMXUs-qViYi","executionInfo":{"status":"ok","timestamp":1634270491165,"user_tz":-600,"elapsed":457,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["import torch.optim as optim\n","\n","#set parameters\n","feature_size=[64, 128, 256, 512]\n","optimizer = optim.Adam(model.parameters(), lr=0.001)\n","EPOCHS = 30\n","\n","model = Unet(feature_size=feature_size)"],"execution_count":12,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JrGpP3KMPzpe","executionInfo":{"status":"ok","timestamp":1634205931646,"user_tz":-600,"elapsed":24149591,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"70b6a5b8-887c-4489-cc8a-f6718ec3dd50"},"source":["from tqdm import tqdm\n","\n","TRAIN_LOSS = []\n","TRAIN_DICE = []\n","TEST_LOSS =[]\n","TEST_DICE = []\n","\n","for epoch in range(1, EPOCHS+1):\n"," print('EPOCH {}/{}'.format(epoch, EPOCHS))\n"," running_loss = 0\n"," running_dicecoef = 0\n"," running_loss_test = 0\n"," running_dicecoef_test = 0\n"," BATCH_NUM = len(train_loader)\n"," BATCH_NUM_TEST = len(test_loader)\n","\n"," #training\n"," with tqdm(train_loader, unit='batch') as tbatch:\n"," for batch_idx, (x, y) in enumerate(tbatch):\n"," tbatch.set_description(f'Batch: {batch_idx}')\n","\n"," optimizer.zero_grad()\n"," output = model(x)\n"," loss = dice_loss(output, y)\n"," dicecoef = dice_coef(output, y)\n"," loss.backward()\n"," optimizer.step()\n","\n"," running_loss += loss.item()\n"," running_dicecoef += dicecoef.item()\n","\n"," tbatch.set_postfix(loss=loss.item(), dice_coef=dicecoef.item())\n","\n"," epoch_loss = running_loss/BATCH_NUM\n"," epoch_dicecoef = running_dicecoef/BATCH_NUM\n"," TRAIN_LOSS.append(epoch_loss)\n"," TRAIN_DICE.append(epoch_dicecoef)\n","\n"," #test\n"," with tqdm(test_loader, unit='batch') as tsbatch:\n"," for batch_idx, (x, y) in enumerate(tsbatch):\n"," tsbatch.set_description(f'Batch: {batch_idx}')\n"," output_test = model(x)\n"," loss_test = dice_loss(output_test, y)\n"," dicecoef_test = dice_coef(output_test, y)\n"," tsbatch.set_postfix(loss=loss_test.item(), dice_coef=dicecoef_test.item())\n","\n"," running_loss_test += loss_test.item()\n"," running_dicecoef_test += dicecoef_test.item()\n","\n"," TEST_LOSS.append(running_loss_test/BATCH_NUM_TEST)\n"," TEST_DICE.append(running_dicecoef_test/BATCH_NUM_TEST)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["EPOCH 1/30\n"]},{"output_type":"stream","name":"stderr","text":["Batch: 0: 0%| | 0/33 [00:04"]},"metadata":{},"execution_count":40},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"4Rh-uq05TG-A"},"source":["# Model Evaluation"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":279},"id":"7ZBk4w3xIA8L","executionInfo":{"status":"ok","timestamp":1634206091827,"user_tz":-600,"elapsed":419,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"a2ff558d-5b76-4ebd-8b09-2514182ad30b"},"source":["import matplotlib.pyplot as plt\n","import numpy as np\n","\n","X = np.arange(1,EPOCHS+1)\n","\n","plt.plot(X, TRAIN_LOSS, marker='.', markersize=10, label='train')\n","plt.plot(X, TEST_LOSS, marker='.', markersize=10, label='test')\n","plt.xlabel('Epochs')\n","plt.ylabel('Dice Loss')\n","plt.xticks(X)\n","plt.legend()\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":279},"id":"Y80RZXzKRPpc","executionInfo":{"status":"ok","timestamp":1634206126932,"user_tz":-600,"elapsed":1066,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"8e714aaa-5868-4270-f7ca-54a5b827fb4d"},"source":["plt.plot(X, TRAIN_DICE, marker='.', markersize=10, label='train')\n","plt.plot(X, TEST_DICE, marker='.', markersize=10, label='test')\n","plt.xlabel('Epochs')\n","plt.ylabel('Dice Coeffeciency')\n","plt.xticks(X)\n","plt.legend()\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":373},"id":"V3Ahm7ecTST4","executionInfo":{"status":"ok","timestamp":1634270514838,"user_tz":-600,"elapsed":8784,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"44a27d39-7951-47f3-8484-132efc3bba3a"},"source":["#load model\n","new_model = Unet(feature_size=feature_size)\n","\n","filename = \"Unet_ISIC1.pth\"\n","checkpoint = torch.load(filename)\n","\n","new_model.load_state_dict(checkpoint)\n","\n","p = new_model(x)[0]\n","plt.imshow(p.detach().squeeze(), cmap='gray')"],"execution_count":13,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /pytorch/c10/core/TensorImpl.h:1156.)\n"," return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n","/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:1805: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n"," warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n"]},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":13},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"BZYl7y1XIJFr"},"source":[""],"execution_count":null,"outputs":[]}]} \ No newline at end of file From 6eda6197e393782af41133be3ac12727e04e83e7 Mon Sep 17 00:00:00 2001 From: Wakame <85863413+wakameds@users.noreply.github.com> Date: Mon, 18 Oct 2021 09:00:27 +1000 Subject: [PATCH 51/66] remove dataloader ipynb --- recognition/s4633139/Dataloader.ipynb | 1 - 1 file changed, 1 deletion(-) delete mode 100644 recognition/s4633139/Dataloader.ipynb diff --git a/recognition/s4633139/Dataloader.ipynb b/recognition/s4633139/Dataloader.ipynb deleted file mode 100644 index 5eb3f16a23..0000000000 --- a/recognition/s4633139/Dataloader.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Dataloader.ipynb","provenance":[],"collapsed_sections":[],"mount_file_id":"1sn2UhFSOZZoyAj5L2RL8YS_7_cthikKn","authorship_tag":"ABX9TyNkwzaLVu64OaORBKup58Jo"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","metadata":{"id":"vlhQO-g6Y2ah","executionInfo":{"status":"ok","timestamp":1633672076114,"user_tz":-600,"elapsed":10346,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["import os\n","from PIL import Image\n","import matplotlib.pyplot as plt\n","import glob\n","\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","\n","import torch.utils.data\n","from torchvision import datasets, transforms"],"execution_count":2,"outputs":[]},{"cell_type":"code","metadata":{"id":"WuSova9HHAzc","colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"status":"ok","timestamp":1633672105488,"user_tz":-600,"elapsed":26753,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"05fb16af-8927-4381-d504-febf63d348d5"},"source":["#define dataset for img and mask\n","file_dir = \"/content/drive/MyDrive/ColabGitHub/PatternFlow/recognition/s46331391_Unet/ISIC2018_Task1-2_Training_Data\"\n","os.chdir(file_dir)\n","\n","img_path = './ISIC2018_Task1-2_Training_Input_x2'\n","mask_path = './ISIC2018_Task1_Training_GroundTruth_x2'\n","\n","imgs = [file for file in sorted(os.listdir(img_path)) if file.endswith('.jpg')]\n","masks = [file for file in sorted(os.listdir(mask_path)) if file.endswith('.png')]\n","\n","imgs[0]"],"execution_count":3,"outputs":[{"output_type":"execute_result","data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'ISIC_0000000.jpg'"]},"metadata":{},"execution_count":3}]},{"cell_type":"code","metadata":{"id":"JhoipyIyXvBB","executionInfo":{"status":"ok","timestamp":1633672109919,"user_tz":-600,"elapsed":253,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["from torch.utils.data import Dataset\n","from PIL import Image\n","\n","class Unet_dataset(Dataset):\n"," def __init__(self,\n"," img_dir = './ISIC2018_Task1-2_Training_Input_x2',\n"," mask_dir = './ISIC2018_Task1_Training_GroundTruth_x2', \n"," img_transforms=None,\n"," mask_transforms= None):\n"," \n"," self.img_dir = img_dir\n"," self.mask_dir = mask_dir\n"," self.img_transforms = img_transforms\n"," self.mask_transforms = mask_transforms\n","\n"," self.imgs = [file for file in sorted(os.listdir(self.img_dir)) if file.endswith('.jpg')]\n"," self.masks = [file for file in sorted(os.listdir(self.mask_dir)) if file.endswith('.png')]\n","\n"," #meke dataloader\n"," def load_img(self, idx):\n"," img_path = os.path.join(self.img_dir, self.imgs[idx])\n"," img = Image.open(img_path).convert('RGB')\n"," return img\n","\n"," def load_mask(self, idx):\n"," mask_path = os.path.join(self.mask_dir, self.masks[idx])\n"," mask = Image.open(mask_path).convert('L')\n"," return mask\n","\n"," def __getitem__(self, idx):\n"," img = self.load_img(idx)\n"," mask = self.load_mask(idx)\n","\n"," if self.img_transforms is not None:\n"," img = self.img_transforms(img)\n","\n"," if self.mask_transforms is not None:\n"," mask = self.mask_transforms(mask)\n","\n"," return img, mask\n","\n"," def __len__(self):\n"," return len(self.imgs)"],"execution_count":4,"outputs":[]},{"cell_type":"code","metadata":{"id":"YFzDGbXcE_p8","executionInfo":{"status":"ok","timestamp":1633672112761,"user_tz":-600,"elapsed":276,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}}},"source":["#img_path = os.path.join(root, \"ISIC2018_Task1-2_Training_Input_x2\", imgs[idx])\n","import torchvision.transforms as transforms\n","img_tfs = transforms.Compose([\n"," transforms.Resize((64,64)),\n"," transforms.ToTensor(),\n"," transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n"," ]\n"," )\n","\n","mask_tfs = transforms.Compose([\n"," transforms.Resize((64,64)),\n"," transforms.ToTensor(),\n"," ]\n"," )"],"execution_count":5,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":302},"id":"zx1QLG_3l_nB","executionInfo":{"status":"ok","timestamp":1633672115791,"user_tz":-600,"elapsed":1189,"user":{"displayName":"Wakayama Hideki","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgX8nPmXxUwee3ETuvhR0wO18p4u7HNRL2KAZ97=s64","userId":"16567349205826503190"}},"outputId":"1fd9dd2e-3a27-4ed5-e81b-bc34773b19c0"},"source":["dataset = Unet_dataset(img_transforms=img_tfs, mask_transforms=mask_tfs)\n","img, mask = dataset.__getitem__(20)\n","\n","import matplotlib.pyplot as plt\n","plt.imshow(img.permute(1,2,0))"],"execution_count":6,"outputs":[{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":6},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}}]}]} \ No newline at end of file From bf3fda8acbf99f11f1e78c49d7f23883c4f58dd4 Mon Sep 17 00:00:00 2001 From: Wakame <85863413+wakameds@users.noreply.github.com> Date: Mon, 18 Oct 2021 09:06:54 +1000 Subject: [PATCH 52/66] upload the results obtained from IUnet --- recognition/s4633139/dice_coefficient.png | Bin 0 -> 16615 bytes recognition/s4633139/dice_loss.png | Bin 0 -> 16024 bytes recognition/s4633139/image.png | Bin 0 -> 78043 bytes recognition/s4633139/seg.png | Bin 0 -> 76624 bytes 4 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 recognition/s4633139/dice_coefficient.png create mode 100644 recognition/s4633139/dice_loss.png create mode 100644 recognition/s4633139/image.png create mode 100644 recognition/s4633139/seg.png diff --git a/recognition/s4633139/dice_coefficient.png b/recognition/s4633139/dice_coefficient.png new file mode 100644 index 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28c807ab619dd5dfd10386ae864fa19c47169186 Mon Sep 17 00:00:00 2001 From: Wakame <85863413+wakameds@users.noreply.github.com> Date: Tue, 19 Oct 2021 18:24:00 +1000 Subject: [PATCH 57/66] Revised code in the files and add comments --- recognition/s4633139/IUNet_criterion.py | 9 +++ recognition/s4633139/IUNet_dataloader.py | 9 +++ recognition/s4633139/IUNet_train_test.py | 13 +++- recognition/s4633139/ImprovedUNet.py | 33 ++++---- recognition/s4633139/main.py | 33 ++++---- recognition/s4633139/visualse.py | 98 ++++++++++++++++++++---- 6 files changed, 146 insertions(+), 49 deletions(-) diff --git a/recognition/s4633139/IUNet_criterion.py b/recognition/s4633139/IUNet_criterion.py index 187e198143..653097b751 100644 --- a/recognition/s4633139/IUNet_criterion.py +++ b/recognition/s4633139/IUNet_criterion.py @@ -1,3 +1,12 @@ +# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ +# Copyright (c) 2021, H.WAKAYAMA, All rights reserved. +# File: IUNet_criterion.py +# Author: Hideki WAKAYAMA +# Contact: h.wakayama@uq.net.au +# Platform: macOS Big Sur Ver 11.2.1, Pycharm pro 2021.1 +# Time: 19/10/2021, 15:47 +# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ + #dice coefficient def dice_coef(pred, target): batch_size = len(pred) diff --git a/recognition/s4633139/IUNet_dataloader.py b/recognition/s4633139/IUNet_dataloader.py index 789027a1d5..7cb47b3e49 100644 --- a/recognition/s4633139/IUNet_dataloader.py +++ b/recognition/s4633139/IUNet_dataloader.py @@ -1,3 +1,12 @@ +# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ +# Copyright (c) 2021, H.WAKAYAMA, All rights reserved. +# File: IUNet_dataloader.py +# Author: Hideki WAKAYAMA +# Contact: h.wakayama@uq.net.au +# Platform: macOS Big Sur Ver 11.2.1, Pycharm pro 2021.1 +# Time: 19/10/2021, 15:47 +# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ + import os from torch.utils.data import Dataset from PIL import Image diff --git a/recognition/s4633139/IUNet_train_test.py b/recognition/s4633139/IUNet_train_test.py index 573d6f96fe..30276ab1e0 100644 --- a/recognition/s4633139/IUNet_train_test.py +++ b/recognition/s4633139/IUNet_train_test.py @@ -1,10 +1,17 @@ +# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ +# Copyright (c) 2021, H.WAKAYAMA, All rights reserved. +# File: IUNet_train_test.py +# Author: Hideki WAKAYAMA +# Contact: h.wakayama@uq.net.au +# Platform: macOS Big Sur Ver 11.2.1, Pycharm pro 2021.1 +# Time: 19/10/2021, 15:47 +# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ + from IUNet_criterion import dice_coef, dice_loss from tqdm import tqdm - def model_train_test(model, optimizer, EPOCHS, train_loader, test_loader): - """ - function for model training and test + """function for model training and test :return: list of train and test dice coefficients and dice losses by epochs """ TRAIN_LOSS = [] diff --git a/recognition/s4633139/ImprovedUNet.py b/recognition/s4633139/ImprovedUNet.py index 01d44d0d39..c519f5a493 100644 --- a/recognition/s4633139/ImprovedUNet.py +++ b/recognition/s4633139/ImprovedUNet.py @@ -1,12 +1,19 @@ +# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ +# Copyright (c) 2021, H.WAKAYAMA, All rights reserved. +# File: ImprovedUNet.py +# Author: Hideki WAKAYAMA +# Contact: h.wakayama@uq.net.au +# Platform: macOS Big Sur Ver 11.2.1, Pycharm pro 2021.1 +# Time: 19/10/2021, 15:47 +# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ + import torch import torch.nn as nn import torch.nn.functional as F class Context(nn.Module): - """ - context module - """ + """context""" def __init__(self, in_channels, out_channels): super(Context, self).__init__() self.context = nn.Sequential( @@ -25,9 +32,7 @@ def forward(self, x): class Localization(nn.Module): - """ - localization module - """ + """localization""" def __init__(self, in_channels, out_channels): super(Localization, self).__init__() self.localization = nn.Sequential( @@ -44,9 +49,7 @@ def forward(self, x): class Upsampling(nn.Module): - """ - upsampling module - """ + """upsampling""" def __init__(self, in_channels, out_channels): super(Upsampling, self).__init__() self.upsampling = nn.Sequential( @@ -61,9 +64,7 @@ def forward(self, x): class Segment(nn.Module): - """ - segmentation layer - """ + """segmentation layer""" def __init__(self, in_channels, out_channels): super(Segment, self).__init__() self.segment = nn.Sequential( @@ -77,9 +78,7 @@ def forward(self, x): class Conv2(nn.Module): - """ - convolution stride=2 - """ + """convolution stride=2""" def __init__(self, in_channels, out_channels): super(Conv2, self).__init__() self.conv2 = nn.Sequential( @@ -93,9 +92,7 @@ def forward(self, x): class IUNet(nn.Module): - """ - Improved Unet (International MICCAI Brainlesion Workshop(pp. 287-297). Springer, Cham.) - """ + """Improved Unet (International MICCAI Brainlesion Workshop(pp. 287-297). Springer, Cham.)""" def __init__(self, in_channels=3, out_channels=1, feature_size=[16, 32, 64, 128]): super(IUNet, self).__init__() self.Conv1 = nn.Conv2d(in_channels=3, out_channels=feature_size[0], kernel_size=3, stride=1, padding=1, bias=False) diff --git a/recognition/s4633139/main.py b/recognition/s4633139/main.py index 46cad93ada..7da4fbd383 100644 --- a/recognition/s4633139/main.py +++ b/recognition/s4633139/main.py @@ -1,20 +1,28 @@ +# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ +# Copyright (c) 2021, H.WAKAYAMA, All rights reserved. +# File: main.py +# Author: Hideki WAKAYAMA +# Contact: h.wakayama@uq.net.au +# Platform: macOS Big Sur Ver 11.2.1, Pycharm pro 2021.1 +# Time: 19/10/2021, 17:30 +# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ + from IUNet_dataloader import UNet_dataset from ImprovedUNet import IUNet from IUNet_train_test import model_train_test -from visualse import dice_coef_vis, segment_pred_mask +from visualse import dice_coef_vis, segment_pred_mask, plot_gallery import torch from torch.utils.data import DataLoader, Dataset, random_split import torchvision.transforms as transforms import torch.optim as optim +import matplotlib.pyplot as plt def main(): - """ - execute model training and return dice coefficient plots - """ + """execute model training and return dice coefficient plots""" - #PARAMETERS + #PARAMETERS# FEATURE_SIZE=[16, 32, 64, 128] IN_CHANEL=3 OUT_CHANEL=1 @@ -49,7 +57,7 @@ def main(): train_loader = DataLoader(train_set, batch_size=BATCH_SIZE, shuffle=True) test_loader = DataLoader(test_set, batch_size=BATCH_SIZE, shuffle=False) - #MODEL + #MODEL# model = IUNet(in_channels=IN_CHANEL, out_channels=OUT_CHANEL, feature_size=FEATURE_SIZE) optimizer = optim.Adam(model.parameters(), lr=LR) @@ -61,16 +69,13 @@ def main(): #segmentation for batch in train_loader: - x, y = batch + images, masks = batch break - img = x[0] model.eval() - pred_mask = model(x)[0] - segment_pred_mask(img, pred_mask, alpha=0.5) - -if __name__ == main(): - main() - + pred_masks = model(images) + plot_gallery(images, masks, pred_masks, n_row=6, n_col=4) +if __name__ == main(): + main() \ No newline at end of file diff --git a/recognition/s4633139/visualse.py b/recognition/s4633139/visualse.py index fe0038c605..ca62384be3 100644 --- a/recognition/s4633139/visualse.py +++ b/recognition/s4633139/visualse.py @@ -1,7 +1,26 @@ +# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ +# Copyright (c) 2021, H.WAKAYAMA, All rights reserved. +# File: visualse.py +# Author: Hideki WAKAYAMA +# Contact: h.wakayama@uq.net.au +# Platform: macOS Big Sur Ver 11.2.1, Pycharm pro 2021.1 +# Time: 19/10/2021, 17:30 +# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ + import matplotlib.pyplot as plt import numpy as np + def dice_coef_vis(EPOCHS, TRAIN_COEFS, TEST_COEFS): + """ + function for dice coefficient + :param + EPOCHS(array): epochs + TRAIN_COEFS(array): train dice coefficients + TEST_COEFS(array): test dice coefficients + :return + plot with dice coefficients by epochs + """ X = np.arange(1, EPOCHS+1) plt.plot(X, TRAIN_COEFS, marker='.', markersize=10, label='train') plt.plot(X, TEST_COEFS, marker='.', markersize=10, label='test') @@ -13,6 +32,15 @@ def dice_coef_vis(EPOCHS, TRAIN_COEFS, TEST_COEFS): def dice_loss_vis(EPOCHS, TRAIN_LOSS, TEST_LOSS): + """ + function for dice loss + :param + EPOCHS(array): epochs + TRAIN_LOSS(array): train dice losses + TEST_LOSS(array): test dice losses + :return + plot with dice loss by epochs + """ X = np.arange(1, EPOCHS+1) plt.plot(X, TRAIN_LOSS, marker='.', markersize=10, label='train') plt.plot(X, TEST_LOSS, marker='.', markersize=10, label='test') @@ -23,21 +51,63 @@ def dice_loss_vis(EPOCHS, TRAIN_LOSS, TEST_LOSS): plt.show() -def pred_mask(img, pred_mask, alpha=5): - seg_img = img.clone() +def segment_pred_mask(imgs, pred_masks, idx, alpha): + """ + function to make a covered image with the predicted mask + :param imgs(tensor[B,C,W,H]): 3 channels image + :param pred_masks(tensor[B,C,W,H]): predicted mask + :param idx(int): image index + :param alpha(float): ratio for segmentation + :return: segmentation image + """ + seg_img = imgs[idx].clone() image_r = seg_img[0] - image_r = image_r*(1-alpha*pred_mask)+(pred_mask*pred_mask*alpha) - segmentation = image_r.detach().squeeze() - seg_img[0] = segmentation - plt.imshow(seg_img.permute(1,2,0)) - plt.show() + image_r = image_r*(1-alpha*pred_masks[idx])+(pred_masks[idx]*pred_masks[idx]*alpha) + segment_image = image_r.detach().squeeze() + seg_img[0] = segment_image + return seg_img -def segment_pred_mask(img, pred_mask, alpha=0.5): - seg_img = img.clone() - image_r = seg_img[0] - image_r = image_r*(1-alpha*pred_mask)+(pred_mask*pred_mask*alpha) - segment_img_r = image_r.detach().squeeze() - seg_img[0] = segment_img_r - plt.imshow(seg_img.permute(1,2,0)) +def plot_gallery(images, masks, pred_masks, n_row=6, n_col=4): + """ + function to generate gallery + :parameters + images(tensor[B,C,W,H]): images + masks(tensor[B,C,W,H]): target masks + pred_masks(tensor[B,C,W,H]): predicted masks + n_row: number of the row for the gallery + n_col: number of the column for the gallery + :return + gallery images + """ + idxs = n_col * n_row + plt.figure(figsize=(1.5 * n_col, 1.5 * n_row)) + plt.subplots_adjust(bottom=0, left=0.01, right=0.99, top=0.9, hspace=0.35) # adjust layout parameters + plt.suptitle('Segmentation', fontsize=15) + + for i in range(0, idxs, 4): + # image + plt.subplot(n_row, n_col, i + 1) + plt.imshow(images[i].permute(1, 2, 0)) + plt.title('image', fontsize=10) + plt.axis('off') + + # target mask + plt.subplot(n_row, n_col, i + 2) + plt.imshow(masks[i].detach().squeeze(), cmap='gray') + plt.title('target mask', fontsize=10) + plt.axis('off') + + # predicted mask + plt.subplot(n_row, n_col, i + 3) + plt.imshow(pred_masks[i].detach().squeeze(), cmap='gray') + plt.title('predicted mask', fontsize=10) + plt.axis('off') + + # segmentation + seg_img = segment_pred_mask(imgs=images, pred_masks=pred_masks, idx=i, alpha=0.5) + plt.subplot(n_row, n_col, i + 4) + plt.imshow(seg_img.permute(1, 2, 0)) + plt.title('segmentation', fontsize=10) + plt.axis('off') plt.show() \ No newline at end of file From 3f95c847922a1771cd25edd88264b26be306dc98 Mon Sep 17 00:00:00 2001 From: Wakame <85863413+wakameds@users.noreply.github.com> Date: Wed, 20 Oct 2021 13:05:18 +1000 Subject: [PATCH 58/66] Revised code in the files --- .../{IUNet_criterion.py => criterion.py} | 2 +- .../{IUNet_dataloader.py => dataloader.py} | 2 +- recognition/s4633139/{main.py => driver.py} | 38 ++++++++------ .../s4633139/{ImprovedUNet.py => model.py} | 4 +- ...IUNet_train_test.py => model_train_val.py} | 50 ++++++++++--------- recognition/s4633139/visualse.py | 28 ++++++++--- 6 files changed, 74 insertions(+), 50 deletions(-) rename recognition/s4633139/{IUNet_criterion.py => criterion.py} (96%) rename recognition/s4633139/{IUNet_dataloader.py => dataloader.py} (98%) rename recognition/s4633139/{main.py => driver.py} (65%) rename recognition/s4633139/{ImprovedUNet.py => model.py} (99%) rename recognition/s4633139/{IUNet_train_test.py => model_train_val.py} (50%) diff --git a/recognition/s4633139/IUNet_criterion.py b/recognition/s4633139/criterion.py similarity index 96% rename from recognition/s4633139/IUNet_criterion.py rename to recognition/s4633139/criterion.py index 653097b751..6b597eab54 100644 --- a/recognition/s4633139/IUNet_criterion.py +++ b/recognition/s4633139/criterion.py @@ -1,6 +1,6 @@ # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ # Copyright (c) 2021, H.WAKAYAMA, All rights reserved. -# File: IUNet_criterion.py +# File: criterion.py # Author: Hideki WAKAYAMA # Contact: h.wakayama@uq.net.au # Platform: macOS Big Sur Ver 11.2.1, Pycharm pro 2021.1 diff --git a/recognition/s4633139/IUNet_dataloader.py b/recognition/s4633139/dataloader.py similarity index 98% rename from recognition/s4633139/IUNet_dataloader.py rename to recognition/s4633139/dataloader.py index 7cb47b3e49..24d8fce742 100644 --- a/recognition/s4633139/IUNet_dataloader.py +++ b/recognition/s4633139/dataloader.py @@ -1,6 +1,6 @@ # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ # Copyright (c) 2021, H.WAKAYAMA, All rights reserved. -# File: IUNet_dataloader.py +# File: dataloader.py # Author: Hideki WAKAYAMA # Contact: h.wakayama@uq.net.au # Platform: macOS Big Sur Ver 11.2.1, Pycharm pro 2021.1 diff --git a/recognition/s4633139/main.py b/recognition/s4633139/driver.py similarity index 65% rename from recognition/s4633139/main.py rename to recognition/s4633139/driver.py index 7da4fbd383..8a4b13e066 100644 --- a/recognition/s4633139/main.py +++ b/recognition/s4633139/driver.py @@ -1,15 +1,15 @@ # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ # Copyright (c) 2021, H.WAKAYAMA, All rights reserved. -# File: main.py +# File: driver.py # Author: Hideki WAKAYAMA # Contact: h.wakayama@uq.net.au # Platform: macOS Big Sur Ver 11.2.1, Pycharm pro 2021.1 # Time: 19/10/2021, 17:30 # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ -from IUNet_dataloader import UNet_dataset -from ImprovedUNet import IUNet -from IUNet_train_test import model_train_test +from dataloader import UNet_dataset +from model import IUNet +from model_train_val import model_train_val from visualse import dice_coef_vis, segment_pred_mask, plot_gallery import torch @@ -22,7 +22,7 @@ def main(): """execute model training and return dice coefficient plots""" - #PARAMETERS# + #PARAMETERS FEATURE_SIZE=[16, 32, 64, 128] IN_CHANEL=3 OUT_CHANEL=1 @@ -39,43 +39,51 @@ def main(): ]) BATCH_SIZE = 64 - EPOCHS = 15 + EPOCHS = 1 LR = 0.001 + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + #DATA PREPARATION dataset = UNet_dataset(img_transforms=IMG_TF, mask_transforms=MASK_TF) #shuffle index sample_size = len(dataset.imgs) - train_size = int(sample_size * 0.8) - test_size = sample_size - train_size + train_size = int(sample_size * 0.5) + split_size = sample_size - train_size + + val_size = split_size//2 + test_size = split_size - val_size - #train and test set - train_set, test_set = random_split(dataset, [train_size, test_size], generator=torch.Generator().manual_seed(123)) + #train, validation, test + train_set, split_set = random_split(dataset, [train_size, split_size], generator=torch.Generator().manual_seed(123)) + val_set, test_set = random_split(split_set, [val_size, test_size], generator=torch.Generator().manual_seed(123)) #data loader train_loader = DataLoader(train_set, batch_size=BATCH_SIZE, shuffle=True) + val_loader = DataLoader(val_set, batch_size=BATCH_SIZE, shuffle=False) test_loader = DataLoader(test_set, batch_size=BATCH_SIZE, shuffle=False) - #MODEL# + #MODEL model = IUNet(in_channels=IN_CHANEL, out_channels=OUT_CHANEL, feature_size=FEATURE_SIZE) + model = model.to(device) optimizer = optim.Adam(model.parameters(), lr=LR) #train,test - TRAIN_DICE, TEST_DICE, TRAIN_LOSS, TEST_LOSS = model_train_test(model, optimizer, EPOCHS, train_loader, test_loader) + TRAIN_DICE, VAL_DICE, VAL_LOSS, VAL_LOSS = model_train_val(model, optimizer, EPOCHS, train_loader, val_loader) #plot dice coefficient - dice_coef_vis(EPOCHS, TRAIN_DICE, TEST_DICE) + dice_coef_vis(EPOCHS, TRAIN_DICE, VAL_DICE) #segmentation - for batch in train_loader: + for batch in test_loader: images, masks = batch break model.eval() pred_masks = model(images) - plot_gallery(images, masks, pred_masks, n_row=6, n_col=4) + plot_gallery(images, masks, pred_masks, n_row=5, n_col=4) if __name__ == main(): main() \ No newline at end of file diff --git a/recognition/s4633139/ImprovedUNet.py b/recognition/s4633139/model.py similarity index 99% rename from recognition/s4633139/ImprovedUNet.py rename to recognition/s4633139/model.py index c519f5a493..38210b1f6c 100644 --- a/recognition/s4633139/ImprovedUNet.py +++ b/recognition/s4633139/model.py @@ -1,6 +1,6 @@ # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ # Copyright (c) 2021, H.WAKAYAMA, All rights reserved. -# File: ImprovedUNet.py +# File: model.py # Author: Hideki WAKAYAMA # Contact: h.wakayama@uq.net.au # Platform: macOS Big Sur Ver 11.2.1, Pycharm pro 2021.1 @@ -161,6 +161,6 @@ def forward(self, x): seg_scale2 = self.upscale(segmentation_layers[1] + seg_scale1) x = self.final_conv(x) x = x + seg_scale2 - output = F.sigmoid(x) + output = torch.sigmoid(x) return output \ No newline at end of file diff --git a/recognition/s4633139/IUNet_train_test.py b/recognition/s4633139/model_train_val.py similarity index 50% rename from recognition/s4633139/IUNet_train_test.py rename to recognition/s4633139/model_train_val.py index 30276ab1e0..c34c365c4d 100644 --- a/recognition/s4633139/IUNet_train_test.py +++ b/recognition/s4633139/model_train_val.py @@ -1,36 +1,39 @@ # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ # Copyright (c) 2021, H.WAKAYAMA, All rights reserved. -# File: IUNet_train_test.py +# File: model_train_val.py # Author: Hideki WAKAYAMA # Contact: h.wakayama@uq.net.au # Platform: macOS Big Sur Ver 11.2.1, Pycharm pro 2021.1 # Time: 19/10/2021, 15:47 # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ -from IUNet_criterion import dice_coef, dice_loss +from criterion import dice_coef, dice_loss from tqdm import tqdm +import torch -def model_train_test(model, optimizer, EPOCHS, train_loader, test_loader): - """function for model training and test - :return: list of train and test dice coefficients and dice losses by epochs +def model_train_val(model, optimizer, EPOCHS, train_loader, val_loader): + """function for model training and validation + :return: list of train and validation dice coefficients and dice losses by epochs """ TRAIN_LOSS = [] TRAIN_DICE = [] - TEST_LOSS =[] - TEST_DICE = [] + VAL_LOSS =[] + VAL_DICE = [] + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") for epoch in range(1, EPOCHS+1): print('EPOCH {}/{}'.format(epoch, EPOCHS)) running_loss = 0 running_dicecoef = 0 - running_loss_test = 0 - running_dicecoef_test = 0 + running_loss_val = 0 + running_dicecoef_val = 0 BATCH_NUM = len(train_loader) - BATCH_NUM_TEST = len(test_loader) + BATCH_NUM_VAL = len(val_loader) #train with tqdm(train_loader, unit='batch') as tbatch: for batch_idx, (x, y) in enumerate(tbatch): + x, y = x.to(device), y.to(device) tbatch.set_description(f'Batch: {batch_idx}') optimizer.zero_grad() @@ -50,19 +53,20 @@ def model_train_test(model, optimizer, EPOCHS, train_loader, test_loader): TRAIN_LOSS.append(epoch_loss) TRAIN_DICE.append(epoch_dicecoef) - #test - with tqdm(test_loader, unit='batch') as tsbatch: - for batch_idx, (x, y) in enumerate(tsbatch): - tsbatch.set_description(f'Batch: {batch_idx}') - output_test = model(x) - loss_test = dice_loss(output_test, y) - dicecoef_test = dice_coef(output_test, y) - tsbatch.set_postfix(loss=loss_test.item(), dice_coef=dicecoef_test.item()) + #validation + with tqdm(val_loader, unit='batch') as valbatch: + for batch_idx, (x, y) in enumerate(valbatch): + x, y = x.to(device), y.to(device) + valbatch.set_description(f'Batch: {batch_idx}') + output_val = model(x) + loss_val = dice_loss(output_val, y) + dicecoef_val = dice_coef(output_val, y) + valbatch.set_postfix(loss=loss_val.item(), dice_coef=dicecoef_val.item()) - running_loss_test += loss_test.item() - running_dicecoef_test += dicecoef_test.item() + running_loss_val += loss_val.item() + running_dicecoef_val += dicecoef_val.item() - TEST_LOSS.append(running_loss_test/BATCH_NUM_TEST) - TEST_DICE.append(running_dicecoef_test/BATCH_NUM_TEST) + VAL_LOSS.append(running_loss_val/BATCH_NUM_VAL) + VAL_DICE.append(running_dicecoef_val/BATCH_NUM_VAL) - return TRAIN_DICE, TEST_DICE, TRAIN_LOSS, TEST_LOSS \ No newline at end of file + return TRAIN_DICE, VAL_DICE, TRAIN_LOSS, VAL_LOSS \ No newline at end of file diff --git a/recognition/s4633139/visualse.py b/recognition/s4633139/visualse.py index ca62384be3..b8892576bc 100644 --- a/recognition/s4633139/visualse.py +++ b/recognition/s4633139/visualse.py @@ -11,19 +11,19 @@ import numpy as np -def dice_coef_vis(EPOCHS, TRAIN_COEFS, TEST_COEFS): +def dice_coef_vis(EPOCHS, TRAIN_COEFS, VAL_COEFS): """ function for dice coefficient :param EPOCHS(array): epochs TRAIN_COEFS(array): train dice coefficients - TEST_COEFS(array): test dice coefficients + VAL_COEFS(array): validation dice coefficients :return plot with dice coefficients by epochs """ X = np.arange(1, EPOCHS+1) plt.plot(X, TRAIN_COEFS, marker='.', markersize=10, label='train') - plt.plot(X, TEST_COEFS, marker='.', markersize=10, label='test') + plt.plot(X, VAL_COEFS, marker='.', markersize=10, label='validation') plt.xlabel('Epochs') plt.ylabel('Dice coefficient') plt.xticks(X) @@ -31,19 +31,19 @@ def dice_coef_vis(EPOCHS, TRAIN_COEFS, TEST_COEFS): plt.show() -def dice_loss_vis(EPOCHS, TRAIN_LOSS, TEST_LOSS): +def dice_loss_vis(EPOCHS, TRAIN_LOSS, VAL_LOSS): """ function for dice loss :param EPOCHS(array): epochs TRAIN_LOSS(array): train dice losses - TEST_LOSS(array): test dice losses + VAL_LOSS(array): validation dice losses :return plot with dice loss by epochs """ X = np.arange(1, EPOCHS+1) plt.plot(X, TRAIN_LOSS, marker='.', markersize=10, label='train') - plt.plot(X, TEST_LOSS, marker='.', markersize=10, label='test') + plt.plot(X, VAL_LOSS, marker='.', markersize=10, label='validation') plt.xlabel('Epochs') plt.ylabel('Dice Loss') plt.xticks(X) @@ -51,6 +51,18 @@ def dice_loss_vis(EPOCHS, TRAIN_LOSS, TEST_LOSS): plt.show() +def eval_dice_coef(target, pred_masks, idx): + batch_size = len(pred_masks) + somooth = 1. + + pred_flat = pred_masks.view(batch_size, -1) + target_flat = target.view(batch_size, -1) + + intersection = (pred_flat*target_flat) + dice_coef = (2.*intersection.sum(dim=1)+somooth)/(pred_flat.sum(dim=1)+target_flat.sum(dim=1)+somooth) + return dice_coef[idx] + + def segment_pred_mask(imgs, pred_masks, idx, alpha): """ function to make a covered image with the predicted mask @@ -68,7 +80,7 @@ def segment_pred_mask(imgs, pred_masks, idx, alpha): return seg_img -def plot_gallery(images, masks, pred_masks, n_row=6, n_col=4): +def plot_gallery(images, masks, pred_masks, n_row=5, n_col=4): """ function to generate gallery :parameters @@ -108,6 +120,6 @@ def plot_gallery(images, masks, pred_masks, n_row=6, n_col=4): seg_img = segment_pred_mask(imgs=images, pred_masks=pred_masks, idx=i, alpha=0.5) plt.subplot(n_row, n_col, i + 4) plt.imshow(seg_img.permute(1, 2, 0)) - plt.title('segmentation', fontsize=10) + plt.title('dice_coef: {:.2f}'.format(eval_dice_coef(masks, pred_masks, i)), fontsize=10) plt.axis('off') plt.show() \ No newline at end of file From f1030710e9cb0f17bf6d8537cbe1b12f6729e860 Mon Sep 17 00:00:00 2001 From: wakame <85863413+wakameds@users.noreply.github.com> Date: Tue, 19 Oct 2021 19:37:11 +1000 Subject: [PATCH 59/66] Create README.md Create README.md --- recognition/s4633139/README.md | 87 ++++++++++++++++++++++++++++++++++ 1 file changed, 87 insertions(+) create mode 100644 recognition/s4633139/README.md diff --git a/recognition/s4633139/README.md b/recognition/s4633139/README.md new file mode 100644 index 0000000000..28b2f8be1a --- /dev/null +++ b/recognition/s4633139/README.md @@ -0,0 +1,87 @@ +# Improved UNet for ISIC2018 image segmentation + +## Objective +This project is a practical work for COMP3710 in 2021. The project objective is to implement the improved UNet for ISIC2018 image segmentation. + +## Model Architecture +UNet is the model for biomedical image segmentation. [1] Figure1 shows the improved UNet architecture. [2] The improved model newly added the context module, 3x3 convolution layer with stride = 2 instead of max pooling layer, localization module, and segmentation layer extracted from localization layer. + +In downsampling part, context module works as residual blocks and the output from the module is concatenate to the input for the localization modules in upsampling block. 3x3 stride 2 convolution works for downsample block. In upsampling, segmentation layer outputted from localization is added to the next segmentation layer. + +

+ +

+ +

+ Figure1: The improved UNet model architecture[2] +

+ + +This repository includes the below files for the improved UNet: +* **IUNet_criterion.py:** This file consists of the two criterion functions: dice coefficient and dice loss. The functions are utilised for training and evaluating the model through the forward and backpropagation steps. +* **IUNet_dataloader.py:** This file is concerning data preparation for UNet model, which works for data loading, data transformation, preparing data loader. +* **IUNet_train_test.py:** This file works to train the model and to assess the segmentation performance with dice coefficient and dice loss. The function in the file returns lists recorded the criteria values by epochs: TRAIN_DICE, TEST_DICE, TRAIN_LOSS, TEST_LOSS. +* **ImprovedUNet.py:** This file includes the classes to build the improved UNet model. The classes with Context, Localization, Up-sampling, Segmentation, and Convolution to down-sampling, and Improved Unet are provided. In this model, Sigmoid function for the binary classification between mask and non-mask areas is utilised instead of softmax function. +* **main.py:** The file performs all procedures for the project, data preparation, training model, and model evaluation. The file includes the parameters with the improved UNet: FEATURE_SIZE, IN_CHANEL, OUT_CHANEL, IMG_TF, MASK_TF, BATCH_SIZE, EPOCHS, and LR. The image size is resized into 128x128 as the initial parameter. Also, random_split() is set to split the dataset into train set and test set by 80:20. The file applies Adam as an optimizer. +* **visualise.py:** The file contains the four functions for plotting the test result and training dice coefficient and dice loss by epochs, and output the segmented images with the predicted mask. + +## Dataset +ISIC 2018 Task1 is a dataset with skin cancer images shared by the International Skin Imaging Collaboration (ISIC). [3] The dataset consists of 2594 images and 2594 mask images, respectively. The dataset is split into the train set and test set by train ratio = 0.8. + +## Usage model +“main.py” calls all files in the repository to train the model and evaluate the performance. ISIC dataset is needed to be set in the same directory, including main.py. + + +### Dependency +The model training and evaluation was executed under the environment. +* Pytorch 1.9.0+cu111 +* Python 3.7.12 +* Matplotlib 3.3.4 + + +## Results +### Dice coefficient and loss +The figure is about train and test dice coefficient and losses by 50 epochs. The test dice coefficient was approximately 0.85 and the test accuracy was stable after 15 epochs. + +

+ +

+ +

+ Figure2. Dice coefficient +

+ + +The test dice loss was stable at roughly 0.15 while train loss declined after epoch 15. + +

+ +

+ +

+ Figure3. Dice loss +

+ + +### Segmentation +The trained UNet predict the mask from the image. The segmentations in the right-hand side column are the images covered with the predicted mask. + +

+ +

+ +

+ Figure4. Segmentation +

+ + +## References +[1] Ronneberger, O., Fischer, P., & Brox, T. (2015, October). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). Springer, Cham. https://arxiv.org/abs/1505.04597 + +[2] Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., & Maier-Hein, K. H. (2017, September). Brain tumor segmentation and radiomics survival prediction: Contribution to the brats 2017 challenge. In International MICCAI Brainlesion Workshop (pp. 287-297). Springer, Cham. https://arxiv.org/pdf/1802.10508v1.pdf + +[3] ISIC 2018 Task1 https://paperswithcode.com/dataset/isic-2018-task-1 + + + + From 284223d023e611ddded475f083ddf3b9e09827c2 Mon Sep 17 00:00:00 2001 From: Wakame <85863413+wakameds@users.noreply.github.com> Date: Wed, 20 Oct 2021 13:22:18 +1000 Subject: [PATCH 60/66] Revised code --- recognition/s4633139/model.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/recognition/s4633139/model.py b/recognition/s4633139/model.py index 38210b1f6c..6eabad5253 100644 --- a/recognition/s4633139/model.py +++ b/recognition/s4633139/model.py @@ -4,7 +4,7 @@ # Author: Hideki WAKAYAMA # Contact: h.wakayama@uq.net.au # Platform: macOS Big Sur Ver 11.2.1, Pycharm pro 2021.1 -# Time: 19/10/2021, 15:47 +# Time: 20/10/2021, 13:09 # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ import torch @@ -153,7 +153,7 @@ def forward(self, x): x = self.Ups[idx + 1](concatnate_skip) #segmentation - if idx == 2 or idx == 4: + if idx != idxs[0] and idx != idxs[-1]: x_segment = self.Segmentations[idx // 2](x) segmentation_layers.append(x_segment) From bc18688f8c46ce859f036cc46bea3c110ed6443e Mon Sep 17 00:00:00 2001 From: Wakame <85863413+wakameds@users.noreply.github.com> Date: Wed, 20 Oct 2021 14:47:38 +1000 Subject: [PATCH 61/66] add comments in the file --- recognition/s4633139/criterion.py | 18 +++++++- recognition/s4633139/model_train_val.py | 15 ++++-- recognition/s4633139/visualse.py | 61 +++++++++++++++---------- 3 files changed, 65 insertions(+), 29 deletions(-) diff --git a/recognition/s4633139/criterion.py b/recognition/s4633139/criterion.py index 6b597eab54..b0b3813b99 100644 --- a/recognition/s4633139/criterion.py +++ b/recognition/s4633139/criterion.py @@ -4,11 +4,19 @@ # Author: Hideki WAKAYAMA # Contact: h.wakayama@uq.net.au # Platform: macOS Big Sur Ver 11.2.1, Pycharm pro 2021.1 -# Time: 19/10/2021, 15:47 +# Time: 20/10/2021, 09:52 # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ #dice coefficient def dice_coef(pred, target): + """ + function to compute the dice coefficient + param---- + pred(tensor[B,C,W,H]): predicted mask images + target(tensor[B,C,W,H]: target mask images + return--- + dice coefficient + """ batch_size = len(pred) somooth = 1. @@ -22,5 +30,13 @@ def dice_coef(pred, target): #loss def dice_loss(pred, target): + """ + function to compute dice loss + param---- + pred(tensor[B,C,W,H]): predicted mask images + target(tensor[B,C,W,H]): target mask images + return---- + dice loss + """ dice_loss = 1 - dice_coef(pred, target) return dice_loss \ No newline at end of file diff --git a/recognition/s4633139/model_train_val.py b/recognition/s4633139/model_train_val.py index c34c365c4d..f553b0eef9 100644 --- a/recognition/s4633139/model_train_val.py +++ b/recognition/s4633139/model_train_val.py @@ -4,7 +4,7 @@ # Author: Hideki WAKAYAMA # Contact: h.wakayama@uq.net.au # Platform: macOS Big Sur Ver 11.2.1, Pycharm pro 2021.1 -# Time: 19/10/2021, 15:47 +# Time: 20/10/2021, 09:52 # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ from criterion import dice_coef, dice_loss @@ -12,9 +12,18 @@ import torch def model_train_val(model, optimizer, EPOCHS, train_loader, val_loader): - """function for model training and validation - :return: list of train and validation dice coefficients and dice losses by epochs """ + function for model training and validation + :param---- + model: model + optimizer: optimizer + EPOCHS(int):number of epochs + train_loader: train loader + val_loader: validation loader + :return---- + list of train and validation dice coefficients and dice losses by epochs + """ + TRAIN_LOSS = [] TRAIN_DICE = [] VAL_LOSS =[] diff --git a/recognition/s4633139/visualse.py b/recognition/s4633139/visualse.py index b8892576bc..50d7288abc 100644 --- a/recognition/s4633139/visualse.py +++ b/recognition/s4633139/visualse.py @@ -4,7 +4,7 @@ # Author: Hideki WAKAYAMA # Contact: h.wakayama@uq.net.au # Platform: macOS Big Sur Ver 11.2.1, Pycharm pro 2021.1 -# Time: 19/10/2021, 17:30 +# Time: 20/10/2021, 12:49 # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ import matplotlib.pyplot as plt @@ -14,11 +14,11 @@ def dice_coef_vis(EPOCHS, TRAIN_COEFS, VAL_COEFS): """ function for dice coefficient - :param + param---- EPOCHS(array): epochs TRAIN_COEFS(array): train dice coefficients VAL_COEFS(array): validation dice coefficients - :return + return---- plot with dice coefficients by epochs """ X = np.arange(1, EPOCHS+1) @@ -34,11 +34,11 @@ def dice_coef_vis(EPOCHS, TRAIN_COEFS, VAL_COEFS): def dice_loss_vis(EPOCHS, TRAIN_LOSS, VAL_LOSS): """ function for dice loss - :param + param---- EPOCHS(array): epochs TRAIN_LOSS(array): train dice losses VAL_LOSS(array): validation dice losses - :return + return---- plot with dice loss by epochs """ X = np.arange(1, EPOCHS+1) @@ -52,25 +52,36 @@ def dice_loss_vis(EPOCHS, TRAIN_LOSS, VAL_LOSS): def eval_dice_coef(target, pred_masks, idx): - batch_size = len(pred_masks) - somooth = 1. + """ + function to return dice coefficient of the image + param---- + target(tensor[B,C,W,H]):target mask images + pred_masks:(tensor[B,C,W,H]):predicted mask images + idx(int): index + return---- + dice coefficient + """ + batch_size = len(pred_masks) + somooth = 1. - pred_flat = pred_masks.view(batch_size, -1) - target_flat = target.view(batch_size, -1) + pred_flat = pred_masks.view(batch_size, -1) + target_flat = target.view(batch_size, -1) - intersection = (pred_flat*target_flat) - dice_coef = (2.*intersection.sum(dim=1)+somooth)/(pred_flat.sum(dim=1)+target_flat.sum(dim=1)+somooth) - return dice_coef[idx] + intersection = (pred_flat*target_flat) + dice_coef = (2.*intersection.sum(dim=1)+somooth)/(pred_flat.sum(dim=1)+target_flat.sum(dim=1)+somooth) + return dice_coef[idx] def segment_pred_mask(imgs, pred_masks, idx, alpha): """ function to make a covered image with the predicted mask - :param imgs(tensor[B,C,W,H]): 3 channels image - :param pred_masks(tensor[B,C,W,H]): predicted mask - :param idx(int): image index - :param alpha(float): ratio for segmentation - :return: segmentation image + param---- + imgs(tensor[B,C,W,H]): 3 channels image + pred_masks(tensor[B,C,W,H]): predicted mask + idx(int): image index + alpha(float): ratio for segmentation + return---- + segmentation image """ seg_img = imgs[idx].clone() image_r = seg_img[0] @@ -83,14 +94,14 @@ def segment_pred_mask(imgs, pred_masks, idx, alpha): def plot_gallery(images, masks, pred_masks, n_row=5, n_col=4): """ function to generate gallery - :parameters - images(tensor[B,C,W,H]): images - masks(tensor[B,C,W,H]): target masks - pred_masks(tensor[B,C,W,H]): predicted masks - n_row: number of the row for the gallery - n_col: number of the column for the gallery - :return - gallery images + parameters---- + images(tensor[B,C,W,H]): images + masks(tensor[B,C,W,H]): target masks + pred_masks(tensor[B,C,W,H]): predicted masks + n_row: number of the row for the gallery + n_col: number of the column for the gallery + return---- + gallery images """ idxs = n_col * n_row plt.figure(figsize=(1.5 * n_col, 1.5 * n_row)) From 5c0b40d65a53d7eba5ee4469b62ba0678011c30d Mon Sep 17 00:00:00 2001 From: wakame <85863413+wakameds@users.noreply.github.com> Date: Wed, 20 Oct 2021 14:14:23 +1000 Subject: [PATCH 62/66] Update README.md Revised README.md --- recognition/s4633139/README.md | 90 ++++++++++++++++++++++------------ 1 file changed, 60 insertions(+), 30 deletions(-) diff --git a/recognition/s4633139/README.md b/recognition/s4633139/README.md index 28b2f8be1a..ecc7aaf88d 100644 --- a/recognition/s4633139/README.md +++ b/recognition/s4633139/README.md @@ -1,12 +1,15 @@ # Improved UNet for ISIC2018 image segmentation +The project is the practical work for COMP3710 in 2021. This report will summarise the information with the improved UNet model in the repository. + ## Objective -This project is a practical work for COMP3710 in 2021. The project objective is to implement the improved UNet for ISIC2018 image segmentation. +The project objective is to implement the improved UNet for ISIC2018 image segmentation. UNet is a developed model for biomedical image segmentation, which automatically identifies the tumour area.[1] The automatic image segmentation without objective will support the medical and experimental works, while the higher accurate image segmentation performance is also required. This project aimed to implement the improved UNet model for Brain tumours into the ISIC2018 image dataset. + ## Model Architecture -UNet is the model for biomedical image segmentation. [1] Figure1 shows the improved UNet architecture. [2] The improved model newly added the context module, 3x3 convolution layer with stride = 2 instead of max pooling layer, localization module, and segmentation layer extracted from localization layer. +Figure 1 shows the improved UNet architecture for Brain tumours.[2] The improved model utilises the context module, 3x3 convolution layer with stride = 2 instead of max-pooling layer, localisation module, and segmentation layer extracted from localisation layer. In down-sampling part, context block works as residual blocks of ResNet. The context block consists of 3x3 convolution layer, batch normalisation layer, dropout layer, and activity function layer. LeakyReLu is applied as the activation function in the model. The output through the context block is concatenated to the input for the localisation modules in up-sampling block. After that, the output features are decreased with 3x3 convolution layer for the following context block. -In downsampling part, context module works as residual blocks and the output from the module is concatenate to the input for the localization modules in upsampling block. 3x3 stride 2 convolution works for downsample block. In upsampling, segmentation layer outputted from localization is added to the next segmentation layer. +In up-sampling, the concatenated input is fed into the localization block. Then, the output from the localization is fed into the convolutional layer to transform into a segmentation layer to add to the next segmentation layer and the up-sampling block, respectively.

@@ -16,72 +19,99 @@ In downsampling part, context module works as residual blocks and the output fro Figure1: The improved UNet model architecture[2]

- -This repository includes the below files for the improved UNet: -* **IUNet_criterion.py:** This file consists of the two criterion functions: dice coefficient and dice loss. The functions are utilised for training and evaluating the model through the forward and backpropagation steps. -* **IUNet_dataloader.py:** This file is concerning data preparation for UNet model, which works for data loading, data transformation, preparing data loader. -* **IUNet_train_test.py:** This file works to train the model and to assess the segmentation performance with dice coefficient and dice loss. The function in the file returns lists recorded the criteria values by epochs: TRAIN_DICE, TEST_DICE, TRAIN_LOSS, TEST_LOSS. -* **ImprovedUNet.py:** This file includes the classes to build the improved UNet model. The classes with Context, Localization, Up-sampling, Segmentation, and Convolution to down-sampling, and Improved Unet are provided. In this model, Sigmoid function for the binary classification between mask and non-mask areas is utilised instead of softmax function. -* **main.py:** The file performs all procedures for the project, data preparation, training model, and model evaluation. The file includes the parameters with the improved UNet: FEATURE_SIZE, IN_CHANEL, OUT_CHANEL, IMG_TF, MASK_TF, BATCH_SIZE, EPOCHS, and LR. The image size is resized into 128x128 as the initial parameter. Also, random_split() is set to split the dataset into train set and test set by 80:20. The file applies Adam as an optimizer. -* **visualise.py:** The file contains the four functions for plotting the test result and training dice coefficient and dice loss by epochs, and output the segmented images with the predicted mask. +In terms of the loss function, dice loss is utilised for UNet. The loss function is represented as + +

+ +

+ +Dice coefficient is represented as + +

+ +

+ +Dice coefficient measures the similarity between the target mask and the predicted mask from the model. + + +## Files +This repository includes the below files for the improved UNet. + +**criterion.py:** This file consists of the two criterion functions: dice coefficient and dice loss. The functions are utilised for training and evaluating the model through the forward and backpropagation steps. + +**dataloader.py:** This file is concerning data preparation for UNet model, which works for data loading, image augmentation, transformation into data loader. + +**model_train_val.py:** This file works to train the model and to assess the segmentation performance with dice coefficient and dice loss. The function in the file returns lists recorded the criteria values by epochs: TRAIN_DICE, VAL_DICE, TRAIN_LOSS, VAL_LOSS. + +**model.py:** This file includes the classes to build the improved UNet model. The classes with Context, Localization, Up-sampling, Segmentation, and Convolution to down-sampling, and Improved UNet are provided. In this model, Sigmoid function for the binary classification between mask and non-mask areas is utilised instead of softmax function. + +**driver.py:** The file performs all procedures for the project, data preparation, training model, and model evaluation. The file includes the parameters with the improved UNet: FEATURE_SIZE, IN_CHANEL, OUT_CHANEL, IMG_TF, MASK_TF, BATCH_SIZE, EPOCHS, and LR. The image size is resized into 128x128 as the initial parameter. Also, random_split() is set to split the dataset into train set, validation set, and test set by 50:25:25. Adam is applied as an optimizer to train the model. + +**visualise.py:** The file contains the five functions for plotting the test result and training dice coefficient and dice loss by epochs, and output the segmented images with the predicted mask. + ## Dataset -ISIC 2018 Task1 is a dataset with skin cancer images shared by the International Skin Imaging Collaboration (ISIC). [3] The dataset consists of 2594 images and 2594 mask images, respectively. The dataset is split into the train set and test set by train ratio = 0.8. +ISIC 2018 Task1 is a dataset with skin cancer images shared by the International Skin Imaging Collaboration (ISIC). [3] The dataset consists of 2594 images and mask images, respectively. The dataset is split into the train set, validation, and test set by ratio: 0.5: 0.25: 0.25. + -## Usage model -“main.py” calls all files in the repository to train the model and evaluate the performance. ISIC dataset is needed to be set in the same directory, including main.py. +## How to run +“driver.py” calls all files in the repository to train the model and to evaluate the performance. ISIC dataset is needed to be set in the same directory including the files. After that, put the command ‘python driver.py’ in the terminal and execute the command. -### Dependency +## Dependency The model training and evaluation was executed under the environment. * Pytorch 1.9.0+cu111 * Python 3.7.12 * Matplotlib 3.3.4 + ## Results -### Dice coefficient and loss -The figure is about train and test dice coefficient and losses by 50 epochs. The test dice coefficient was approximately 0.85 and the test accuracy was stable after 15 epochs. +#### Dice coefficient and loss +The figure is about train and validation dice coefficient and losses by 50 epochs. The validation dice coefficient was approximately 0.85 and it was stable after 15 epochs. +

- +

- Figure2. Dice coefficient +Figure2. Dice coefficient

-The test dice loss was stable at roughly 0.15 while train loss declined after epoch 15. +The validation dice loss was stable at roughly 0.15 while train loss declined after epoch 15. +

- +

+

- Figure3. Dice loss +Figure3. Dice loss

-### Segmentation -The trained UNet predict the mask from the image. The segmentations in the right-hand side column are the images covered with the predicted mask. + +#### Segmentation +The trained UNet predict the mask from the image in test set. The segmentations in the right-hand side column are the images covered with the predicted mask. The dice coefficient of the image is provided in the label. The dice coefficients in the figure recorded over 0.87. +

- +

+

- Figure4. Segmentation +Figure4. Segmentation

+ ## References [1] Ronneberger, O., Fischer, P., & Brox, T. (2015, October). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). Springer, Cham. https://arxiv.org/abs/1505.04597 [2] Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., & Maier-Hein, K. H. (2017, September). Brain tumor segmentation and radiomics survival prediction: Contribution to the brats 2017 challenge. In International MICCAI Brainlesion Workshop (pp. 287-297). Springer, Cham. https://arxiv.org/pdf/1802.10508v1.pdf [3] ISIC 2018 Task1 https://paperswithcode.com/dataset/isic-2018-task-1 - - - - From ad96dd5306f1fd90d6479478afa53434842c1895 Mon Sep 17 00:00:00 2001 From: Wakame <85863413+wakameds@users.noreply.github.com> Date: Fri, 22 Oct 2021 14:14:08 +1000 Subject: [PATCH 63/66] change epoch num from 1 to 20 --- recognition/s4633139/driver.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/recognition/s4633139/driver.py b/recognition/s4633139/driver.py index 8a4b13e066..b31d798904 100644 --- a/recognition/s4633139/driver.py +++ b/recognition/s4633139/driver.py @@ -39,7 +39,7 @@ def main(): ]) BATCH_SIZE = 64 - EPOCHS = 1 + EPOCHS = 20 LR = 0.001 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") From ad202dcfa44a00a82c14a77012336eb2cdb7d5d8 Mon Sep 17 00:00:00 2001 From: wakame <85863413+wakameds@users.noreply.github.com> Date: Fri, 22 Oct 2021 14:05:27 +1000 Subject: [PATCH 64/66] Update README.md revise README.md --- recognition/s4633139/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/recognition/s4633139/README.md b/recognition/s4633139/README.md index ecc7aaf88d..5786d3b024 100644 --- a/recognition/s4633139/README.md +++ b/recognition/s4633139/README.md @@ -7,7 +7,7 @@ The project objective is to implement the improved UNet for ISIC2018 image segme ## Model Architecture -Figure 1 shows the improved UNet architecture for Brain tumours.[2] The improved model utilises the context module, 3x3 convolution layer with stride = 2 instead of max-pooling layer, localisation module, and segmentation layer extracted from localisation layer. In down-sampling part, context block works as residual blocks of ResNet. The context block consists of 3x3 convolution layer, batch normalisation layer, dropout layer, and activity function layer. LeakyReLu is applied as the activation function in the model. The output through the context block is concatenated to the input for the localisation modules in up-sampling block. After that, the output features are decreased with 3x3 convolution layer for the following context block. +Figure 1 shows the improved UNet architecture for Brain tumours.[2] The improved model utilises the context module, 3x3 convolution layer with stride = 2 instead of max-pooling layer, localisation module, and segmentation layer extracted from localisation layer. In down-sampling part, context block works as residual blocks of ResNet. The context block consists of 3x3 convolution layer, batch normalisation layer, dropout layer, and activity function layer. LeakyReLu is applied as the activation function in the model. The output through the context block is concatenated to the input for the localisation modules in up-sampling block. After that, the output features are decreased with 3x3 convolution layer for the following context block. In up-sampling, the concatenated input is fed into the localization block. Then, the output from the localization is fed into the convolutional layer to transform into a segmentation layer to add to the next segmentation layer and the up-sampling block, respectively. From ffcec4072785082b91a953f3bd2ebc6dc1e3f9ab Mon Sep 17 00:00:00 2001 From: Wakame <85863413+wakameds@users.noreply.github.com> Date: Fri, 22 Oct 2021 14:16:53 +1000 Subject: [PATCH 65/66] Revise README.md --- recognition/s4633139/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/recognition/s4633139/README.md b/recognition/s4633139/README.md index 5786d3b024..524e880dc5 100644 --- a/recognition/s4633139/README.md +++ b/recognition/s4633139/README.md @@ -58,7 +58,7 @@ ISIC 2018 Task1 is a dataset with skin cancer images shared by the International “driver.py” calls all files in the repository to train the model and to evaluate the performance. ISIC dataset is needed to be set in the same directory including the files. After that, put the command ‘python driver.py’ in the terminal and execute the command. -## Dependency +## Dependencies The model training and evaluation was executed under the environment. * Pytorch 1.9.0+cu111 * Python 3.7.12 From fcb61389f20bbacfc09edfa15daf0d6062174005 Mon Sep 17 00:00:00 2001 From: Wakame <85863413+wakameds@users.noreply.github.com> Date: Wed, 17 Nov 2021 20:36:01 +1000 Subject: [PATCH 66/66] Remove commit 02e5662 --- recognition/ISICs_Unet/README.md | 153 +++++++++++-------------------- 1 file changed, 52 insertions(+), 101 deletions(-) diff --git a/recognition/ISICs_Unet/README.md b/recognition/ISICs_Unet/README.md index 549f2535f2..f2c009212e 100644 --- a/recognition/ISICs_Unet/README.md +++ b/recognition/ISICs_Unet/README.md @@ -1,101 +1,52 @@ -# Segment the ISICs data set with the U-net - -## Project Overview -This project aim to solve the segmentation of skin lesian (ISIC2018 data set) using the U-net, with all labels having a minimum Dice similarity coefficient of 0.7 on the test set[Task 3]. - -## ISIC2018 -![ISIC example](imgs/example.jpg) - -Skin Lesion Analysis towards Melanoma Detection - -Task found in https://challenge2018.isic-archive.com/ - - -## U-net -![UNet](imgs/uent.png) - -U-net is one of the popular image segmentation architectures used mostly in biomedical purposes. The name UNet is because it’s architecture contains a compressive path and an expansive path which can be viewed as a U shape. This architecture is built in such a way that it could generate better results even for a less number of training data sets. - -## Data Set Structure - -data set folder need to be stored in same directory with structure same as below -```bash -ISIC2018 - |_ ISIC2018_Task1-2_Training_Input_x2 - |_ ISIC_0000000 - |_ ISIC_0000001 - |_ ... - |_ ISIC2018_Task1_Training_GroundTruth_x2 - |_ ISIC_0000000_segmentation - |_ ISIC_0000001_segmentation - |_ ... -``` - -## Dice Coefficient - -The Sørensen–Dice coefficient is a statistic used to gauge the similarity of two samples. - -Further information in https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient - -## Dependencies - -- python 3 -- tensorflow 2.1.0 -- pandas 1.1.4 -- numpy 1.19.2 -- matplotlib 3.3.2 -- scikit-learn 0.23.2 -- pillow 8.0.1 - - -## Usages - -- Run `train.py` for training the UNet on ISIC data. -- Run `evaluation.py` for evaluation and case present. - -## Advance - -- Modify `setting.py` for custom setting, such as different batch size. -- Modify `unet.py` for custom UNet, such as different kernel size. - -## Algorithm - -- data set: - - The data set we used is the training set of ISIC 2018 challenge data which has segmentation labels. - - Training: Validation: Test = 1660: 415: 519 = 0.64: 0.16 : 0.2 (Training: Test = 4: 1 and in Training, further split 4: 1 for Training: Validation) - - Training data augmentations: rescale, rotate, shift, zoom, grayscale -- model: - - Original UNet with padding which can keep the shape of input and output same. - - The first convolutional layers has 16 output channels. - - The activation function of all convolutional layers is ELU. - - Without batch normalization layers. - - The inputs is (384, 512, 1) - - The output is (384, 512, 1) after sigmoid activation. - - Optimizer: Adam, lr = 1e-4 - - Loss: dice coefficient loss - - Metrics: accuracy & dice coefficient - -## Results - -Evaluation dice coefficient is 0.805256724357605. - -plot of train/valid Dice coefficient: - -![img](imgs/train_and_valid_dice_coef.png) - -case present: - -![case](imgs/case%20present.png) - -## Reference -Manna, S. (2020). K-Fold Cross Validation for Deep Learning using Keras. [online] Medium. Available at: https://medium.com/the-owl/k-fold-cross-validation-in-keras-3ec4a3a00538 [Accessed 24 Nov. 2020]. - -zhixuhao (2020). zhixuhao/unet. [online] GitHub. Available at: https://github.com/zhixuhao/unet. - -GitHub. (n.d.). NifTK/NiftyNet. [online] Available at: https://github.com/NifTK/NiftyNet/blob/a383ba342e3e38a7ad7eed7538bfb34960f80c8d/niftynet/layer/loss_segmentation.py [Accessed 24 Nov. 2020]. - -Team, K. (n.d.). Keras documentation: Losses. [online] keras.io. Available at: https://keras.io/api/losses/#creating-custom-losses [Accessed 24 Nov. 2020]. - -262588213843476 (n.d.). unet.py. [online] Gist. Available at: https://gist.github.com/abhinavsagar/fe0c900133cafe93194c069fe655ef6e [Accessed 24 Nov. 2020]. - -Stack Overflow. (n.d.). python - Disable Tensorflow debugging information. [online] Available at: https://stackoverflow.com/questions/35911252/disable-tensorflow-debugging-information [Accessed 24 Nov. 2020]. +# Segmenting ISICs with U-Net + +COMP3710 Report recognition problem 3 (Segmenting ISICs data set with U-Net) solved in TensorFlow + +Created by Christopher Bailey (45576430) + +## The problem and algorithm +The problem solved by this program is binary segmentation of the ISICs skin lesion data set. Segmentation is a way to label pixels in an image according to some grouping, in this case lesion or non-lesion. This translates images of skin to masks representing areas of concern for skin lesions. + +U-Net is a form of autoencoder where the downsampling path is expected to learn the features of the image and the upsampling path learns how to recreate the masks. Long skip connections between downpooling and upsampling layers are utilised to overcome the bottleneck in traditional autoencoders allowing feature representations to be recreated. + +## How it works +A four layer padded U-Net is used, preserving skin features and mask resolution. The implementation utilises Adam as the optimizer and implements Dice distance as the loss function as this appeared to give quicker convergence than other methods (eg. binary cross-entropy). + +The utilised metric is a Dice coefficient implementation. My initial implementation appeared faulty and was replaced with a 3rd party implementation which appears correct. 3 epochs was observed to be generally sufficient to observe Dice coefficients of 0.8+ on test datasets but occasional non-convergence was observed and could be curbed by increasing the number of epochs. Visualisation of predictions is also implemented and shows reasonable correspondence. Orange bandaids represent an interesting challenge for the implementation as presented. + +### Training, validation and testing split +Training, validation and testing uses a respective 60:20:20 split, a commonly assumed starting point suggested by course staff. U-Net in particular was developed to work "with very few training images" (Ronneberger et al, 2015) The input data for this problem consists of 2594 images and masks. This split appears to provide satisfactory results. + +## Using the model +### Dependencies required +* Python3 (tested with 3.8) +* TensorFlow 2.x (tested with 2.3) +* glob (used to load filenames) +* matplotlib (used for visualisations, tested with 3.3) + +### Parameter tuning +The model was developed on a GTX 1660 TI (6GB VRAM) and certain values (notably batch size and image resolution) were set lower than might otherwise be ideal on more capable hardware. This is commented in the relevant code. + +### Running the model +The model is executed via the main.py script. + +### Example output +Given a batch size of 1 and 3 epochs the following output was observed on a single run: +Era | Loss | Dice coefficient +--- | ---- | ---------------- +Epoch 1 | 0.7433 | 0.2567 +Epoch 2 | 0.3197 | 0.6803 +Epoch 3 | 0.2657 | 0.7343 +Testing | 0.1820 | 0.8180 + + +### Figure 1 - example visualisation plot +Skin images in left column, true mask middle, predicted mask right column +![Visualisation of predictions](visual.png) + +## References +Segments of code in this assignment were used from or based on the following sources: +1. COMP3710-demo-code.ipynb from Guest Lecture +1. https://www.tensorflow.org/tutorials/load_data/images +1. https://www.tensorflow.org/guide/gpu +1. Karan Jakhar (2019) https://medium.com/@karan_jakhar/100-days-of-code-day-7-84e4918cb72c