diff --git a/tutorials/generative/2d_vqvae_transformer/2d_vqvae_transformer_tutorial.ipynb b/tutorials/generative/2d_vqvae_transformer/2d_vqvae_transformer_tutorial.ipynb new file mode 100644 index 00000000..584e5894 --- /dev/null +++ b/tutorials/generative/2d_vqvae_transformer/2d_vqvae_transformer_tutorial.ipynb @@ -0,0 +1,1862 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "dc826984", + "metadata": {}, + "source": [ + "# Vector Quantized Variational Autoencoders and Transformers with MedNIST Dataset\n", + "\n", + "This tutorial illustrates how to use MONAI for training a Vector Quantized Variational Autoencoder (VQVAE)[1] and a transformer model on 2D images.\n", + "\n", + "This is a two step process:\n", + "- We will train our VQVAE model to be able to reconstruct the input images.\n", + "- This will be followed by using the trained VQVAE model to encode images to feed into the transformer network to train.\n", + "\n", + "We will work with the MedNIST dataset available on MONAI\n", + "(https://docs.monai.io/en/stable/apps.html#monai.apps.MedNISTDataset). In order to train faster, we will select just one of the available classes (\"HeadCT\"), resulting in a training set with 7999 2D images.\n", + "\n", + "[1] - [Oord et al. \"Neural Discrete Representation Learning\"](https://arxiv.org/abs/1711.00937)\n", + "\n", + "\n", + "### Setup imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "5e2e2865", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/media/walter/Storage/Projects/GenerativeModels/venv/lib/python3.10/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-02-17 14:01:31,235 - A matching Triton is not available, some optimizations will not be enabled.\n", + "Error caught was: No module named 'triton'\n", + "MONAI version: 1.2.dev2304\n", + "Numpy version: 1.23.5\n", + "Pytorch version: 1.13.1+cu117\n", + "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n", + "MONAI rev id: 9a57be5aab9f2c2a134768c0c146399150e247a0\n", + "MONAI __file__: /media/walter/Storage/Projects/GenerativeModels/venv/lib/python3.10/site-packages/monai/__init__.py\n", + "\n", + "Optional dependencies:\n", + "Pytorch Ignite version: 0.4.10\n", + "ITK version: 5.3.0\n", + "Nibabel version: 4.0.2\n", + "scikit-image version: 0.19.3\n", + "Pillow version: 9.3.0\n", + "Tensorboard version: 2.11.0\n", + "gdown version: 4.6.0\n", + "TorchVision version: 0.14.1+cu117\n", + "tqdm version: 4.64.1\n", + "lmdb version: 1.4.0\n", + "psutil version: 5.9.4\n", + "pandas version: 1.5.3\n", + "einops version: 0.6.0\n", + "transformers version: 4.21.3\n", + "mlflow version: 2.1.1\n", + "pynrrd version: 1.0.0\n", + "\n", + "For details about installing the optional dependencies, please visit:\n", + " https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n", + "\n" + ] + } + ], + "source": [ + "# Copyright 2020 MONAI Consortium\n", + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "# http://www.apache.org/licenses/LICENSE-2.0\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License.\n", + "import os\n", + "import tempfile\n", + "import shutil\n", + "import time\n", + "\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import torch\n", + "from torch.nn import L1Loss, CrossEntropyLoss\n", + "import torch.nn.functional as F\n", + "from monai import transforms\n", + "from monai.apps import MedNISTDataset\n", + "from monai.config import print_config\n", + "from monai.data import DataLoader, Dataset\n", + "from monai.utils import first, set_determinism\n", + "from tqdm import tqdm\n", + "from ignite.utils import convert_tensor\n", + "\n", + "from generative.networks.nets import VQVAE, DecoderOnlyTransformer\n", + "from generative.utils.ordering import Ordering\n", + "from generative.utils.enums import OrderingType\n", + "\n", + "print_config()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "5eecf5fa", + "metadata": {}, + "outputs": [], + "source": [ + "# for reproducibility purposes set a seed\n", + "set_determinism(42)" + ] + }, + { + "cell_type": "markdown", + "id": "eeeb2157", + "metadata": {}, + "source": [ + "### Setup a data directory and download dataset\n", + "\n", + "Specify a `MONAI_DATA_DIRECTORY` variable, where the data will be downloaded. If not\n", + "specified a temporary directory will be used." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "44d781fc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/tmp/tmpe6_z3kbl\n" + ] + } + ], + "source": [ + "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n", + "root_dir = tempfile.mkdtemp() if directory is None else directory\n", + "print(root_dir)" + ] + }, + { + "cell_type": "markdown", + "id": "f7b331a2", + "metadata": {}, + "source": [ + "### Download training data" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d89063f8", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "MedNIST.tar.gz: 59.0MB [00:08, 7.24MB/s] " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-02-17 14:01:39,832 - INFO - Downloaded: /tmp/tmpe6_z3kbl/MedNIST.tar.gz\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-02-17 14:01:39,905 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n", + "2023-02-17 14:01:39,905 - INFO - Writing into directory: /tmp/tmpe6_z3kbl.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading dataset: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 47164/47164 [00:14<00:00, 3309.92it/s]\n" + ] + } + ], + "source": [ + "train_data = MedNISTDataset(root_dir=root_dir, section=\"training\", download=True, seed=0)\n", + "train_datalist = [{\"image\": item[\"image\"]} for item in train_data.data if item[\"class_name\"] == \"HeadCT\"]\n", + "image_size = 64\n", + "train_transforms = transforms.Compose(\n", + " [\n", + " transforms.LoadImaged(keys=[\"image\"]),\n", + " transforms.EnsureChannelFirstd(keys=[\"image\"]),\n", + " transforms.ScaleIntensityRanged(keys=[\"image\"], a_min=0.0, a_max=255.0, b_min=0.0, b_max=1.0, clip=True),\n", + " transforms.RandAffined(\n", + " keys=[\"image\"],\n", + " rotate_range=[(-np.pi / 36, np.pi / 36), (-np.pi / 36, np.pi / 36)],\n", + " translate_range=[(-1, 1), (-1, 1)],\n", + " scale_range=[(-0.05, 0.05), (-0.05, 0.05)],\n", + " spatial_size=[image_size, image_size],\n", + " padding_mode=\"zeros\",\n", + " prob=0.5,\n", + " ),\n", + " ]\n", + ")\n", + "train_ds = Dataset(data=train_datalist, transform=train_transforms)\n", + "train_loader = DataLoader(train_ds, batch_size=64, shuffle=True, num_workers=4)" + ] + }, + { + "cell_type": "markdown", + "id": "19ce954e", + "metadata": {}, + "source": [ + "### Visualse some examples from the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "510f986a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot 3 examples from the training set\n", + "check_data = first(train_loader)\n", + "fig, ax = plt.subplots(nrows=1, ncols=3)\n", + "for image_n in range(3):\n", + " ax[image_n].imshow(check_data[\"image\"][image_n, 0, :, :], cmap=\"gray\")\n", + " ax[image_n].axis(\"off\")" + ] + }, + { + "cell_type": "markdown", + "id": "acda5546", + "metadata": {}, + "source": [ + "### Download Validation Data" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "cde9bca8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-02-17 14:01:58,883 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n", + "2023-02-17 14:01:58,883 - INFO - File exists: /tmp/tmpe6_z3kbl/MedNIST.tar.gz, skipped downloading.\n", + "2023-02-17 14:01:58,884 - INFO - Non-empty folder exists in /tmp/tmpe6_z3kbl/MedNIST, skipped extracting.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading dataset: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5895/5895 [00:01<00:00, 3366.35it/s]\n" + ] + } + ], + "source": [ + "val_data = MedNISTDataset(root_dir=root_dir, section=\"validation\", download=True, seed=0)\n", + "val_datalist = [{\"image\": item[\"image\"]} for item in train_data.data if item[\"class_name\"] == \"HeadCT\"]\n", + "val_transforms = transforms.Compose(\n", + " [\n", + " transforms.LoadImaged(keys=[\"image\"]),\n", + " transforms.EnsureChannelFirstd(keys=[\"image\"]),\n", + " transforms.ScaleIntensityRanged(keys=[\"image\"], a_min=0.0, a_max=255.0, b_min=0.0, b_max=1.0, clip=True),\n", + " ]\n", + ")\n", + "val_ds = Dataset(data=val_datalist, transform=val_transforms)\n", + "val_loader = DataLoader(val_ds, batch_size=64, shuffle=True, num_workers=4)" + ] + }, + { + "cell_type": "markdown", + "id": "ba5fac10", + "metadata": {}, + "source": [ + "## VQVAE Training\n", + "The first step is to train a VQVAE network - once this is done we can use the trained vqvae model to encode the 2d images to generate the inputs required for the transformer" + ] + }, + { + "cell_type": "markdown", + "id": "cec13fba", + "metadata": {}, + "source": [ + "### Define network, optimizer and losses" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "8bc82d96", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using cuda\n" + ] + }, + { + "data": { + "text/plain": [ + "VQVAE(\n", + " (encoder): Sequential(\n", + " (0): Convolution(\n", + " (conv): Conv2d(1, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n", + " (adn): ADN(\n", + " (A): ReLU()\n", + " )\n", + " )\n", + " (1): VQVAEResidualUnit(\n", + " (conv1): Convolution(\n", + " (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (adn): ADN(\n", + " (D): Dropout(p=0.1, inplace=False)\n", + " (A): ReLU()\n", + " )\n", + " )\n", + " (conv2): Convolution(\n", + " (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " )\n", + " )\n", + " (2): VQVAEResidualUnit(\n", + " (conv1): Convolution(\n", + " (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (adn): ADN(\n", + " (D): Dropout(p=0.1, inplace=False)\n", + " (A): ReLU()\n", + " )\n", + " )\n", + " (conv2): Convolution(\n", + " (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " )\n", + " )\n", + " (3): Convolution(\n", + " (conv): Conv2d(256, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n", + " (adn): ADN(\n", + " (D): Dropout(p=0.1, inplace=False)\n", + " (A): ReLU()\n", + " )\n", + " )\n", + " (4): VQVAEResidualUnit(\n", + " (conv1): Convolution(\n", + " (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (adn): ADN(\n", + " (D): Dropout(p=0.1, inplace=False)\n", + " (A): ReLU()\n", + " )\n", + " )\n", + " (conv2): Convolution(\n", + " (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " )\n", + " )\n", + " (5): VQVAEResidualUnit(\n", + " (conv1): Convolution(\n", + " (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (adn): ADN(\n", + " (D): Dropout(p=0.1, inplace=False)\n", + " (A): ReLU()\n", + " )\n", + " )\n", + " (conv2): Convolution(\n", + " (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " )\n", + " )\n", + " (6): Convolution(\n", + " (conv): Conv2d(256, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " )\n", + " )\n", + " (quantizer): VectorQuantizer(\n", + " (quantizer): EMAQuantizer(\n", + " (embedding): Embedding(256, 32)\n", + " )\n", + " )\n", + " (decoder): Sequential(\n", + " (0): Convolution(\n", + " (conv): Conv2d(32, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " )\n", + " (1): VQVAEResidualUnit(\n", + " (conv1): Convolution(\n", + " (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (adn): ADN(\n", + " (D): Dropout(p=0.1, inplace=False)\n", + " (A): ReLU()\n", + " )\n", + " )\n", + " (conv2): Convolution(\n", + " (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " )\n", + " )\n", + " (2): VQVAEResidualUnit(\n", + " (conv1): Convolution(\n", + " (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (adn): ADN(\n", + " (D): Dropout(p=0.1, inplace=False)\n", + " (A): ReLU()\n", + " )\n", + " )\n", + " (conv2): Convolution(\n", + " (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " )\n", + " )\n", + " (3): Convolution(\n", + " (conv): ConvTranspose2d(256, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n", + " (adn): ADN(\n", + " (D): Dropout(p=0.1, inplace=False)\n", + " (A): ReLU()\n", + " )\n", + " )\n", + " (4): VQVAEResidualUnit(\n", + " (conv1): Convolution(\n", + " (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (adn): ADN(\n", + " (D): Dropout(p=0.1, inplace=False)\n", + " (A): ReLU()\n", + " )\n", + " )\n", + " (conv2): Convolution(\n", + " (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " )\n", + " )\n", + " (5): VQVAEResidualUnit(\n", + " (conv1): Convolution(\n", + " (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (adn): ADN(\n", + " (D): Dropout(p=0.1, inplace=False)\n", + " (A): ReLU()\n", + " )\n", + " )\n", + " (conv2): Convolution(\n", + " (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " )\n", + " )\n", + " (6): Convolution(\n", + " (conv): ConvTranspose2d(256, 1, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n", + " )\n", + " )\n", + ")" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "print(f\"Using {device}\")\n", + "vqvae_model = VQVAE(\n", + " spatial_dims=2,\n", + " in_channels=1,\n", + " out_channels=1,\n", + " num_res_layers=2,\n", + " num_levels=2,\n", + " downsample_parameters=((2, 4, 1, 1), (2, 4, 1, 1)),\n", + " upsample_parameters=((2, 4, 1, 1, 0), (2, 4, 1, 1, 0)),\n", + " num_channels=(256,256),\n", + " num_res_channels=(256,256),\n", + " num_embeddings=256,\n", + " embedding_dim=32,\n", + ")\n", + "vqvae_model.to(device)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "675d2618", + "metadata": {}, + "outputs": [], + "source": [ + "optimizer = torch.optim.Adam(params=vqvae_model.parameters(), lr=1e-4)\n", + "l1_loss = L1Loss()" + ] + }, + { + "cell_type": "markdown", + "id": "19ad3fd0", + "metadata": {}, + "source": [ + "### VQVAE Model training\n", + "We will run our model for 100 epochs" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "42a56f13", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 0: 100%|█████████████████| 125/125 [00:30<00:00, 4.11it/s, recons_loss=0.088, quantization_loss=1.2e-5]\n", + "Epoch 1: 100%|███████████████| 125/125 [00:30<00:00, 4.16it/s, recons_loss=0.0402, quantization_loss=1.08e-5]\n", + "Epoch 2: 100%|███████████████| 125/125 [00:30<00:00, 4.10it/s, recons_loss=0.0333, quantization_loss=1.02e-5]\n", + "Epoch 3: 100%|███████████████| 125/125 [00:30<00:00, 4.04it/s, recons_loss=0.0292, quantization_loss=9.15e-6]\n", + "Epoch 4: 100%|███████████████| 125/125 [00:31<00:00, 3.94it/s, recons_loss=0.0274, quantization_loss=8.31e-6]\n", + "Epoch 5: 100%|███████████████| 125/125 [00:31<00:00, 4.00it/s, recons_loss=0.0264, quantization_loss=9.04e-6]\n", + "Epoch 6: 100%|█████████████████| 125/125 [00:31<00:00, 4.00it/s, recons_loss=0.025, quantization_loss=9.8e-6]\n", + "Epoch 7: 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recons_loss=0.0206, quantization_loss=1.57e-5]\n", + "Epoch 16: 100%|██████████████| 125/125 [00:32<00:00, 3.88it/s, recons_loss=0.0209, quantization_loss=1.85e-5]\n", + "Epoch 17: 100%|██████████████| 125/125 [00:32<00:00, 3.89it/s, recons_loss=0.0201, quantization_loss=1.83e-5]\n", + "Epoch 18: 100%|██████████████| 125/125 [00:32<00:00, 3.91it/s, recons_loss=0.0204, quantization_loss=1.83e-5]\n", + "Epoch 19: 100%|██████████████| 125/125 [00:31<00:00, 3.91it/s, recons_loss=0.0203, quantization_loss=1.89e-5]\n", + "Epoch 20: 100%|██████████████| 125/125 [00:32<00:00, 3.90it/s, recons_loss=0.0201, quantization_loss=1.87e-5]\n", + "Epoch 21: 100%|██████████████| 125/125 [00:31<00:00, 3.93it/s, recons_loss=0.0197, quantization_loss=1.85e-5]\n", + "Epoch 22: 100%|██████████████| 125/125 [00:31<00:00, 3.91it/s, recons_loss=0.0197, quantization_loss=1.76e-5]\n", + "Epoch 23: 100%|███████████████| 125/125 [00:31<00:00, 3.92it/s, recons_loss=0.0193, quantization_loss=1.9e-5]\n", + "Epoch 24: 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recons_loss=0.0182, quantization_loss=2.02e-5]\n", + "Epoch 33: 100%|██████████████| 125/125 [00:31<00:00, 3.93it/s, recons_loss=0.0186, quantization_loss=2.06e-5]\n", + "Epoch 34: 100%|██████████████| 125/125 [00:31<00:00, 3.91it/s, recons_loss=0.0187, quantization_loss=1.92e-5]\n", + "Epoch 35: 100%|██████████████| 125/125 [00:32<00:00, 3.90it/s, recons_loss=0.0182, quantization_loss=2.33e-5]\n", + "Epoch 36: 100%|██████████████| 125/125 [00:31<00:00, 3.92it/s, recons_loss=0.0177, quantization_loss=2.37e-5]\n", + "Epoch 37: 100%|██████████████| 125/125 [00:31<00:00, 3.93it/s, recons_loss=0.0178, quantization_loss=2.46e-5]\n", + "Epoch 38: 100%|██████████████| 125/125 [00:31<00:00, 3.92it/s, recons_loss=0.0177, quantization_loss=2.37e-5]\n", + "Epoch 39: 100%|██████████████| 125/125 [00:31<00:00, 3.93it/s, recons_loss=0.0181, quantization_loss=2.09e-5]\n", + "Epoch 40: 100%|██████████████| 125/125 [00:31<00:00, 3.92it/s, recons_loss=0.0187, quantization_loss=1.99e-5]\n", + "Epoch 41: 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recons_loss=0.017, quantization_loss=2.13e-5]\n", + "Epoch 50: 100%|███████████████| 125/125 [00:32<00:00, 3.90it/s, recons_loss=0.017, quantization_loss=1.89e-5]\n", + "Epoch 51: 100%|██████████████| 125/125 [00:31<00:00, 3.92it/s, recons_loss=0.0171, quantization_loss=2.11e-5]\n", + "Epoch 52: 100%|███████████████| 125/125 [00:31<00:00, 3.92it/s, recons_loss=0.018, quantization_loss=1.88e-5]\n", + "Epoch 53: 100%|██████████████| 125/125 [00:31<00:00, 3.91it/s, recons_loss=0.0174, quantization_loss=1.82e-5]\n", + "Epoch 54: 100%|███████████████| 125/125 [00:31<00:00, 3.91it/s, recons_loss=0.017, quantization_loss=2.51e-5]\n", + "Epoch 55: 100%|██████████████| 125/125 [00:31<00:00, 3.92it/s, recons_loss=0.0168, quantization_loss=1.95e-5]\n", + "Epoch 56: 100%|██████████████| 125/125 [00:31<00:00, 3.92it/s, recons_loss=0.0168, quantization_loss=1.96e-5]\n", + "Epoch 57: 100%|███████████████| 125/125 [00:31<00:00, 3.92it/s, recons_loss=0.0172, quantization_loss=1.6e-5]\n", + "Epoch 58: 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recons_loss=0.0168, quantization_loss=2.27e-5]\n", + "Epoch 67: 100%|██████████████| 125/125 [00:31<00:00, 3.92it/s, recons_loss=0.0164, quantization_loss=2.07e-5]\n", + "Epoch 68: 100%|██████████████| 125/125 [00:31<00:00, 3.91it/s, recons_loss=0.0162, quantization_loss=2.12e-5]\n", + "Epoch 69: 100%|██████████████| 125/125 [00:31<00:00, 3.92it/s, recons_loss=0.0162, quantization_loss=2.33e-5]\n", + "Epoch 70: 100%|███████████████| 125/125 [00:31<00:00, 3.91it/s, recons_loss=0.0162, quantization_loss=2.5e-5]\n", + "Epoch 71: 100%|██████████████| 125/125 [00:31<00:00, 3.91it/s, recons_loss=0.0168, quantization_loss=2.34e-5]\n", + "Epoch 72: 100%|██████████████| 125/125 [00:31<00:00, 3.92it/s, recons_loss=0.0171, quantization_loss=2.01e-5]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 73: 100%|██████████████| 125/125 [00:31<00:00, 3.92it/s, recons_loss=0.0166, quantization_loss=2.05e-5]\n", + "Epoch 74: 100%|██████████████| 125/125 [00:31<00:00, 3.92it/s, recons_loss=0.0165, quantization_loss=2.36e-5]\n", + "Epoch 75: 100%|██████████████| 125/125 [00:31<00:00, 3.91it/s, recons_loss=0.0161, quantization_loss=1.96e-5]\n", + "Epoch 76: 100%|██████████████| 125/125 [00:31<00:00, 3.92it/s, recons_loss=0.0161, quantization_loss=2.22e-5]\n", + "Epoch 77: 100%|██████████████| 125/125 [00:31<00:00, 3.92it/s, recons_loss=0.0166, quantization_loss=2.06e-5]\n", + "Epoch 78: 100%|██████████████| 125/125 [00:31<00:00, 3.92it/s, recons_loss=0.0161, quantization_loss=2.05e-5]\n", + "Epoch 79: 100%|██████████████| 125/125 [00:31<00:00, 3.92it/s, recons_loss=0.0161, quantization_loss=1.79e-5]\n", + "Epoch 80: 100%|██████████████| 125/125 [00:32<00:00, 3.90it/s, recons_loss=0.0162, quantization_loss=2.33e-5]\n", + "Epoch 81: 100%|██████████████| 125/125 [00:31<00:00, 3.92it/s, recons_loss=0.0163, quantization_loss=1.92e-5]\n", + "Epoch 82: 100%|██████████████| 125/125 [00:31<00:00, 3.92it/s, recons_loss=0.0162, quantization_loss=2.08e-5]\n", + "Epoch 83: 100%|██████████████| 125/125 [00:32<00:00, 3.90it/s, recons_loss=0.0162, quantization_loss=2.08e-5]\n", + "Epoch 84: 100%|███████████████| 125/125 [00:31<00:00, 3.92it/s, recons_loss=0.0162, quantization_loss=1.9e-5]\n", + "Epoch 85: 100%|██████████████| 125/125 [00:31<00:00, 3.92it/s, recons_loss=0.0158, quantization_loss=1.92e-5]\n", + "Epoch 86: 100%|██████████████| 125/125 [00:31<00:00, 3.92it/s, recons_loss=0.0157, quantization_loss=1.78e-5]\n", + "Epoch 87: 100%|███████████████| 125/125 [00:31<00:00, 3.91it/s, recons_loss=0.0157, quantization_loss=2.1e-5]\n", + "Epoch 88: 100%|██████████████| 125/125 [00:31<00:00, 3.91it/s, recons_loss=0.0157, quantization_loss=1.92e-5]\n", + "Epoch 89: 100%|██████████████| 125/125 [00:31<00:00, 3.92it/s, recons_loss=0.0158, quantization_loss=1.94e-5]\n", + "Epoch 90: 100%|██████████████| 125/125 [00:31<00:00, 3.92it/s, recons_loss=0.0164, quantization_loss=1.93e-5]\n", + "Epoch 91: 100%|██████████████| 125/125 [00:31<00:00, 3.92it/s, recons_loss=0.0164, quantization_loss=1.94e-5]\n", + "Epoch 92: 100%|██████████████| 125/125 [00:31<00:00, 3.91it/s, recons_loss=0.0162, quantization_loss=2.09e-5]\n", + "Epoch 93: 100%|██████████████| 125/125 [00:31<00:00, 3.92it/s, recons_loss=0.0156, quantization_loss=1.86e-5]\n", + "Epoch 94: 100%|██████████████| 125/125 [00:31<00:00, 3.92it/s, recons_loss=0.0155, quantization_loss=2.16e-5]\n", + "Epoch 95: 100%|██████████████| 125/125 [00:32<00:00, 3.89it/s, recons_loss=0.0158, quantization_loss=2.12e-5]\n", + "Epoch 96: 100%|███████████████| 125/125 [00:31<00:00, 3.92it/s, recons_loss=0.016, quantization_loss=1.94e-5]\n", + "Epoch 97: 100%|██████████████| 125/125 [00:31<00:00, 3.92it/s, recons_loss=0.0162, quantization_loss=2.08e-5]\n", + "Epoch 98: 100%|██████████████| 125/125 [00:31<00:00, 3.92it/s, recons_loss=0.0157, quantization_loss=2.28e-5]\n", + "Epoch 99: 100%|██████████████| 125/125 [00:31<00:00, 3.92it/s, recons_loss=0.0153, quantization_loss=2.18e-5]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train completed, total time: 3202.491312265396.\n" + ] + } + ], + "source": [ + "n_epochs = 100\n", + "val_interval = 10\n", + "epoch_recon_loss_list = []\n", + "epoch_quant_loss_list = []\n", + "val_recon_epoch_loss_list = []\n", + "intermediary_images = []\n", + "n_example_images = 4\n", + "\n", + "total_start = time.time()\n", + "for epoch in range(n_epochs):\n", + " vqvae_model.train()\n", + " epoch_loss = 0\n", + " progress_bar = tqdm(enumerate(train_loader), total=len(train_loader), ncols=110)\n", + " progress_bar.set_description(f\"Epoch {epoch}\")\n", + " for step, batch in progress_bar:\n", + " images = batch[\"image\"].to(device)\n", + " optimizer.zero_grad(set_to_none=True)\n", + "\n", + " # model outputs reconstruction and the quantization error\n", + " reconstruction, quantization_loss = vqvae_model(images=images)\n", + "\n", + " recons_loss = l1_loss(reconstruction.float(), images.float())\n", + "\n", + " loss = recons_loss + quantization_loss\n", + "\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " epoch_loss += recons_loss.item()\n", + "\n", + " progress_bar.set_postfix(\n", + " {\n", + " \"recons_loss\": epoch_loss / (step + 1),\n", + " \"quantization_loss\": quantization_loss.item() / (step + 1),\n", + " }\n", + " )\n", + " epoch_recon_loss_list.append(epoch_loss / (step + 1))\n", + " epoch_quant_loss_list.append(quantization_loss.item() / (step + 1))\n", + "\n", + " if (epoch + 1) % val_interval == 0:\n", + " vqvae_model.eval()\n", + " val_loss = 0\n", + " with torch.no_grad():\n", + " k = 0\n", + " for val_step, batch in enumerate(val_loader, start=1):\n", + " k += 1\n", + " if k == 3:\n", + " break\n", + " images = batch[\"image\"].to(device)\n", + "\n", + " reconstruction, quantization_loss = vqvae_model(images=images)\n", + "\n", + " # get the first sample from the first validation batch for\n", + " # visualizing how the training evolves\n", + " if val_step == 1:\n", + " intermediary_images.append(reconstruction[:n_example_images, 0])\n", + "\n", + " recons_loss = l1_loss(reconstruction.float(), images.float())\n", + "\n", + " val_loss += recons_loss.item()\n", + "\n", + " val_loss /= val_step\n", + " val_recon_epoch_loss_list.append(val_loss)\n", + "\n", + "total_time = time.time() - total_start\n", + "print(f\"train completed, total time: {total_time}.\")" + ] + }, + { + "cell_type": "markdown", + "id": "86d238c9", + "metadata": {}, + "source": [ + "### VQVE Loss Curve" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "96730fbb", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.style.use(\"ggplot\")\n", + "plt.title(\"Learning Curves\", fontsize=20)\n", + "plt.plot(np.linspace(1, n_epochs, n_epochs), epoch_recon_loss_list, color=\"C0\", linewidth=2.0, label=\"Train\")\n", + "plt.plot(\n", + " np.linspace(val_interval, n_epochs, int(n_epochs / val_interval)),\n", + " val_recon_epoch_loss_list,\n", + " color=\"C1\",\n", + " linewidth=2.0,\n", + " label=\"Validation\",\n", + ")\n", + "plt.yticks(fontsize=12)\n", + "plt.xticks(fontsize=12)\n", + "plt.xlabel(\"Epochs\", fontsize=16)\n", + "plt.ylabel(\"Loss\", fontsize=16)\n", + "plt.legend(prop={\"size\": 14})\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "e61de2f8", + "metadata": {}, + "source": [ + "### Plotting evolution of reconstruction performance" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "bccef846", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot every evaluation as a new line and example as columns\n", + "val_samples = np.linspace(val_interval, n_epochs, int(n_epochs / val_interval))\n", + "fig, ax = plt.subplots(nrows=len(val_samples), ncols=1, sharey=True)\n", + "fig.set_size_inches(18, 30)\n", + "for image_n in range(len(val_samples)):\n", + " reconstructions = torch.reshape(intermediary_images[image_n], (64 * n_example_images, 64)).T\n", + " ax[image_n].imshow(reconstructions.cpu(), cmap=\"gray\")\n", + " ax[image_n].set_xticks([])\n", + " ax[image_n].set_yticks([])\n", + " ax[image_n].set_ylabel(f\"Epoch {val_samples[image_n]:.0f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "ffa58261", + "metadata": {}, + "source": [ + "### Plot reconstructions of final trained vqvae model" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "d6efa4c9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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5OOaYY7Bx40YsWrRor9svyfHFrvHWrVuxePFiHHLIIUGMU1bwxwAxY8aMfHUI/vjHP2LVqlXo379/EC+Qp5Pdfffdwe+nT5+evlhK3bp18eGHHxaYX59nL+jFAb4M+qtSpQouv/xyrFixIp99165dwQTVq1cvtG7dGs899xwmTZpU4DmVNTg/mVm0aBGuv/76fL9v0qQJ+vXrh7feegt33XVXYHvmmWfyxQsAwPDhw9GiRQvccccdeOqppwo8jnnz5uHTTz8t2UmYCsPPfvYzVKtWDTfddFOqCRf3PezSpQt69eqFxYsX44Ybbsj3+02bNmHnzp0Avqxt0Lp1azz//PN4+OGHg989/PDDmDNnDo4++mj07t27xOf08ssvFyh35nnR8jwgwJfzEQC88847JdpXmzZtUKNGDUyaNAkffPBB+vfPPvusUDki7++XXHJJvuPcs2dP+n/uJT2+Cy+8EFWqVMHtt9+e/s9HHj//+c/xySef4MILL0xTMbOEZQJi6NChGDFiBEaMGJHm5D/99NOoU6cO7rzzzuC3Y8aMwY033ojrr78eS5YsQbt27bBixYq0TsEjjzySb/snnngiFixYgCFDhqBPnz6oVq0aOnTogKFDh6b2f/zjHxg6dCg6d+6MKlWqoE+fPujTpw/atGmDe++9F9/61rfQvn17DBkyBEcffTR2796Nd955B3PmzEH9+vWxbNkyAP8fADVo0CCcccYZQZ2BvIIbzzzzzL6/qMVg5MiRuPHGG/HDH/4QM2fORKtWrbBy5Uo88cQTOP300zFx4sR869xxxx044YQT8N3vfhdPPfVUWmfgkUcewfDhwzFp0qQgcKtKlSp49NFHMXjwYJxyyino1asXOnbsiJycHLz77rtYsGABVq9ejffffz+YGE32aNSoEcaOHYtbb70Vv//973H99dcX+z0Evoxg79evH66++mo88sgj6NevH5IkwcqVK/Hss89i2bJlaNasGSpVqoS//vWvGDRoEM455xwMHz4cbdq0wfLly/H444+jevXq+Nvf/rbXQMQYEyZMwF133YXevXujRYsWqF27NlatWoXJkyejWrVqQQ2Unj17IicnB7fccgs2bdqUxgZ8//vfL5IbvUqVKrjsssvw61//Gp06dcKIESPw+eefY+rUqWjYsGEaYMx8+9vfxpw5czBhwgS0atUKw4cPR/369bF+/XrMmDED3/rWt1IJsSTH16xZM9xyyy343ve+h86dO+Pss89G/fr1MXv2bMybNw9t2rQp8KMtExzg1MavBCJ1BkaNGlXgOgXVB+B81cmTJyc9evRIcnJykpo1ayann356snz58gK39frrrycnnXRScthhhyW5ublJ3759k1mzZhWa/7p9+/Zk7NixSaNGjZKDDjoo33Fu3LgxOe+885LDDz88qVy5coE5uq+++moyatSopEmTJknVqlWT2rVrJ+3bt08uvvjiZPr06fmOceHChcngwYOTww47LDnssMOSE088MZk7d26BObgx9lZnYM2aNYWuU9A+CrtGb7zxRjJ06NCkfv36SU5OTtK5c+fk7rvvjt7XpUuXJiNGjEhq1qyZ5OTkJD169EieeOKJ5MYbb0wAJI899li+dTZu3JhceeWVSfv27ZNDDz00yc3NTVq2bJmcccYZyYQJE5Ldu3cX6bqY8k1BcwizYcOGJCcnJ8nJyUk2bNiQ/r247+FHH32U/PSnP02OPvropFq1aknNmjWTDh06JFdffXWyY8eO4LfLli1LLrzwwqRBgwbJwQcfnDRo0CC54IILkmXLluXbbuwdK+idmT9/fjJ27NjkuOOOS2rXrp0ccsghSYsWLZLRo0cnr732Wr5tPP3000mPHj2S3Nzc9FrlvetFmUP27NmTXH/99Unz5s2TKlWqJI0bN06uuOKKZMeOHdG6Kw888EDSp0+fpEaNGkm1atWSZs2aJeeff37y8ssvl8rxTZkyJRk0aFBSq1atpGrVqkmLFi2SK664ItmyZUu+35akpkx5pFKSZLgt13+4//77MWbMGNx3331B9T9Tvrngggvw4IMPYtmyZSWuHmmMMVnAMQOmXLNnz54Co6+nT5+OiRMnol27dv4QMMaYveCYAVOu2bVrFxo3boz+/fujTZs2OPjgg/HGG29g6tSpqFq1Ku64444DfYjGGFPm8ceAKddUqVIFY8eOxYwZM/Diiy/i008/Rb169XDWWWfhqquu2meVzIwxpiLhmAFjjDEm4zhmwBhjjMk4/hgwxhhjMo4/BowxxpiMU+QAwoKqRRlTlqhSpUqwrO2PuTZ8rNzorl27gmVuha1VCbmLYiz8RqvG8XKsdazWs9+XTVz2FV+1B70x5QWdA/jdLk6LaH7vdb3YNrlPg84dXBK6IOwZMMYYYzKOPwaMMcaYjOM6A6bCoC66ww47LFhmt5n2WGcpoGrVqoGNpYBNmzYFtkMPPTQdH3xw+Drxsrrsdu/eXaiNj0Wlj4oAuzZjblWFf1scl6sxpYm+r7yscwDbtNVyTCrk9z62v+LMY3vDngFjjDEm4/hjwBhjjMk4lglMhWHbtm3BcvXq1YNldjOrey03NzcdH3nkkYGNXXibN28ObFu3bk3HO3fuLPR41A3I2QzqzmN3om6zIlBSd7+lAbO/iGUG6XN4yCGHpOOaNWsWut5nn31W6LJmMPH8pMfCEoLKEvxbliKLgj0DxhhjTMbxx4AxxhiTcfwxYIwxxmQcxwyYCgOn+RUEa3Sq39WoUSMd169fP7AdddRR6ZhTdwCgefPm6Vh1P05DXLNmTWBbsWJFOl63bl1g41TGrBGLJ9gXqYXlJV2xOCmY5ZX9fX9LK3aldu3a6bht27aBjeOPNJ6A9X6NYeLYpPXr1wc2rkK6cePGwMZxSpqSuDfsGTDGGGMyjj8GjDHGmIxTKYnlUBBuVGTKOuqm17QbdvHHqnrF0N+xS79u3bqBrWXLlum4ffv2ga1p06bpWJsmcbrili1bAtuNN95YpOMsS2ijooro4jYVi5gsozaeEzRNOFYtkFMSa9WqFdgaNWqUjvXf3gYNGqRjneM++eSTdLx9+/bA9uc//xkx7BkwxhhjMo4/BowxxpiM448BY4wxJuM4tdBUGLikMJC/HCdrfaq18W+1BDD/Vjsh8rJ2JVuyZEk6fumllwIbdyzTWIMmTZqkY9YHs0B5SfUzFZviPHv8LmtcAG+HfweEMU6aIsjpgzqP8XzEKdFAGGvQuHHjvR47Y8+AMcYYk3H8MWCMMcZkHKcWmgqDpv2p255TCzUFKNbti119mgbI+9T1eFndjuzqU/chH7eew4cffojyhucOU94oTmphzBajqFJEbJuxNEedDz/66KPofuwZMMYYYzKOPwaMMcaYjOOPAWOMMSbjOLXQVBg0XVC1+FhcAP9Wt8Pam6Ydsu6nGiDHF8S0PU1H4mOpUqUKyjtZ6LhnKhaxZ7SkHQ6Lo/3H5pXYsfDcUZz4BcCeAWOMMSbz+GPAGGOMyTiWCQ4gRe12FXMF7dixI7Cx+1ur5XFqHRB2tdJ91KlTJx1zZz5dT6v+cSpcTk5OYOOOWpr2wi71WEdBdf3z8t46D8bcbexSi7nXdP8xSms75R3LAqYisS9kr6LOTaW1v4LIzoxkjDHGmALxx4AxxhiTcfwxYIwxxmQcxwwcQFhvZx1el1Xrr169eoFjIF6O8rPPPguWOS5AU+22bduWjlWj4uNWG++DYwT0WDWeQPdf2Hqqn3388cfpWOMujDEl46vo1LFYmZLq3bFulvu702VJ0w5LemwljSco7v7sGTDGGGMyjj8GjDHGmIxjmeAA8umnn6ZjrXrXoEGDdKzufq6Cp2l/nLKmNq1mxy79Xbt2BbZYhz/ev7r3a9SoUeD2AeCQQw4p8Dj1t1qRj49bJRNLAybr7O9Ut321nZi7f1+43/e3vFBSSlMKiGHPgDHGGJNx/DFgjDHGZBx/DBhjjDEZxzEDB5BYGiBr8arnb926NR1zGV8gTMNTXZ5LBav90EMPDWys4WunPk4L1JgB3oduk2MY9Fg4FkCPm9McVT/jfZRl3c+YA0FpxRPodmLlu2OadlH3r7+LlRqPdQYsatfAssz+mtfsGTDGGGMyjj8GjDHGmIxjmeAAwilzmgbIy5oSyGmHnK4HhO53lRfq168fLL/77rvpWDscsvtdUwQ5JVLdd5xayL/T36pMwKmVKi+wTddjyUL3Z0wW2Bdpd0rMTa/VRPkd1TRhlhVj3Vh1f0VNpyuOrbzKBvsKewaMMcaYjOOPAWOMMSbj+GPAGGOMyTiOGTiAsC6mmj2jOjnHE3DXPkW19/Xr1wfLtWrVSsdNmjQJbB9++GE61vTFRo0apeP3338/sG3ZsiUdq15Yt27ddKwpidzhUPVCLjmscRC8Pz1OY8oaxUl9K2laXFHXi2n2up7GJjVv3jwdcxdTANi4cWM65jRo3Y7uY8eOHQWO9beaeszEYgZ0vdh2YjELZYnSLEVtz4AxxhiTcfwxYIwxxmQcywRlBHX3bN++PR2rTMDuf3XFs3vriCOOCGzqQjr11FPTsbr6Hn744XTcokWLwDZy5Mh0/MEHHwS2d955Jx0feeSRgY3dcm+++WZge/HFFwvdJssi2rWQKy6qhGDMvqKkHe9K67ex/fN8ofNKLH2PpTvtoqrdQXneUamS1+3atWtga9euXYHHCQCbNm0qdP88H7733nuBbfXq1emY5UYgnBN07uDlklZKPNC4a6ExxhhjSg1/DBhjjDEZxx8DxhhjTMaplBQxZ6Vhw4b7+lgyB5fPjaX5HH/88YHtpJNOSsesswHAlClT0vGGDRsCG6cEAsBRRx2Vjo899tjA1rZt23Q8ZMiQwMapQ9OnTw9sJ554YjrW8sex1L9169alY44fAML4hVmzZgU2TkGKpWdWFDQ9tDxQEeeOmGZf0jTA2D50m6zva4oclwfWlOE2bdqkY9XQWYvn1GIgf5ry4Ycfno65PDoAdOzYMR23atUqsPF5rF27NrBx3NIxxxwT2GrXrp2ONdaA45R0Ppo5c2Y6Xr58eWDjOAQt+V7U+6K/Lc1Uv6+KHoumgSv2DBhjjDEZxx8DxhhjTMYpszKBurA4vUxTYtjFoykpsW5b7F7bvHlzYOMKWwDw0UcfFXgsun+trFezZs1CbeyKP+GEEwIbX292yQHAaaedlo7Z1QWErvJXX301sGkKEG9XJYSYy51d+uq25uv29ttvF7pNrkYIhG7I2L5VJvjzn/+cjqdOnVroNoHw+mtKJrtW9ZXYuXNnOla3nz6n+xrLBGWDkkoBsfkoVvVOOwM2bdo0Hasrnl3sLAvoeirbcXdSlQl07uJKgjo/8bGqhMFSxLZt2wIby6a6Hl8brYYYmys5tXDu3LmBbcKECen45ZdfLnQ9PR5952NyTlFlgv0hL+xt7rBnwBhjjMk4/hgwxhhjMo4/BowxxpiMU2ZjBoqjzWqqSWHb0d+xLlSnTp3Apil7XI5Ttb3WrVunY02n4zSb008/PbCx9qV6DpfVVB2MUwJ1f6zDaYlh1eEee+yxdLx06dLAxrofnwMAdOrUKR2rtrh48eJ0rLo8a+96DTlOgM8PANq3b1/gcek+5syZE9iuvvrqYHnRokXpWGMkYh3TuLujPpd6jvsaxwyUDWKphfpsxzru8XPfsmXLwNajR4903KVLl8DG8TAaN8TaO8csAUCzZs3SscbtcAwBxw8A+bsPcjnxFStWBDaeuzQugc/xuOOOC2yc+rZs2bJCt6n/FnDMlqZSdujQIR3rO8/n+MQTTwS2Bx54IFjm+VH/yeTjiWn/B7oTomMGjDHGGBPFHwPGGGNMxvHHgDHGGJNxymwLY83lZ40sloOqsC6v+hVrTaqRab0C/q1uZ/DgwelYdT/WxVivA4Bp06alY81rZb1/yZIlgU1bEzOsQaomxVqi/lbjAv70pz+lY44tAIBf/epX6Vg1fL5usWvK8QMA8O6776bj119/PbBxieXu3bsHtgEDBqTj/v37B7ZHHnkkWL7nnnvS8X333RfYYvEjHBegcSf6LJqKQ0nbFOt8xM+Ivrsnn3xyOuYaALrMcUlAWOtDW/pyGXLVybkOiJ4TvwP6nOt8yLFKGm+0cuXKQrfD8UAa08S/1feK53+tecDxBFrKnOOEONYJCMu8n3322YFNf/uXv/wlHWt8AcdsaKvnotajKAsljj2TGWOMMRnHHwPGGGNMximzMoG64tlVom6iWJlZdlVrSgq7vtT1ol2s2BXUrVu3wNa4ceN0/OyzzwY2Tgts165dYONOgW+88UZgmz17djpmNxgAnHXWWelY5RS+bmpTF9agQYPSsaYnsauRu4IBYfqOpgHy9V+zZk1g45Q9Td1k2UDvIf9WJYQXXnghHV900UWFHicA/OIXv0jHRx99dGD7+c9/no41BedrX/taOtZ7YZmg7FPUDoNqK6k7Vtfjd7Jz586BjbvxqRzHaXEqE7A8p88rp/ppR0E+FnX987KWBNdzYrlD5xmWMHTuYLQ8OpeE5/LvQJgyrRIjy58qTbL0oeXRWUIYOHBgYNM5/ic/+Uk6Vqnnf//3f9Oxzmv67whTVAlhf6UgeiYzxhhjMo4/BowxxpiM448BY4wxJuOU2ZgB1WI5LkBt1atXT8ea1hNri8m6m8YhaHwB6zaqaXPq3+TJkwMbp8uoDsflNx988MHAxmV+x48fH9i4PK7C2r/uT6/Nxo0b0/HFF18c2J577rl0rPEbnK75ox/9KLD9z//8Tzr+4IMPCj1OTXNkbU1LLPP+Y+fA6ZAAcMoppwTLvXv3TscjRowIbHwvOI0ICHVPTYcyZZ+Y5lpSPTYWK6JzB88Bq1atCmzLly9Px2+99VZgY/1bn2XW27UcMM9P2u43dr7FacvMZcc1hZdLCWsreH5/NE6K2wirvq/xBQzHQsVKFWv6Nr/XTz31VGBT7Z9bzI8ZM6bQY7n33nuDZY6T0viBWEo849RCY4wxxuwX/DFgjDHGZJwyKxPE0gdj6Rrqvitq10K16f55O9OnTw9s8+fPT8fq/uYqg1ohj1Pvnn766cD2gx/8IB1r1zd2abNEAuRPF2L0nL797W+n4y1btgS2J598Mh1rSiTLIjfccENg4wpgKlNwlzTtpsauTb2GLOfoemzTFCd1X3LK07nnnhvYvv/976dj7cR49913p2N1NaqEYvKzP9ycJU0fLOo2Y/OKuntVRmTXNafsAmE3QJ3XuLKgViFl97++85xSq7IWp+hxVT/dpqKV/f785z+nYz0nrgrKYyA8X3XNr169Oh3ru8zXVFOkGT0HvvYqIfCyplYvXLgwWObrpmmI3/zmN9Mxp0cCwD//+c90rP/GlIZkU5rvlj0DxhhjTMbxx4AxxhiTcfwxYIwxxmScMhszoNoHa0GqfXMaiOpCrFur1sRaH6eAAGGJYSAsu6taOKfSaEoKp8hoSiCXvOzXr19gY12KtTTdDndFBMLrpKU5ueQuEHZFu/POOwMbl0lVHY67fWkJ1TZt2hS6HpcRVf2M741eXz5f1WdZV9V7rx0OX3311XSs5U7HjRuXjn/6058GNo7R4A6Kpmjsj3KqpbGPWLdBhTVs3TfHxgDhXKIaPs8PGrfD+3jzzTcDW9u2bdNxixYtAhvHGOlcyd1B9bg59kBT+TjtDwhL8mqqHXdA1X3MmjUrHWtsDuvfGs/A90ZjK/g+aQwPzwkaW6HzBaP/HnAapMaEDB8+PB2PHDkysHGc0pw5cwJbLCalNEoVFydVFLBnwBhjjMk8/hgwxhhjMk6ZlQk0hatGjRrpWN077EJq1KhRYOvVq1c61mpYjLqFzjzzzGCZ3f/qQmPXlzJjxox0/NprrwW2M844Ix1feOGFgS3mhuS0Fz0WduHHUo4A4IILLkjH6tpk96Juh8+XKwACQMuWLQs9thjs0tL1+FlQ9yEfp7pguaMhANSrVy8da0omp4ueeOKJge3KK69Mx0uXLg1ssa5s5kv2RWphcVygsf2xXKXPRLNmzdIxd67UZe2AWadOnWCZ09b0eeG5jCUvIJwDYi5tlTR5PtS0P5Yc165dG9g6depU4L4B4MgjjwyWTz311HSscy7DqYRAKIWobMrvr0o2fL9VYmTU3R6Tenib+jzpPjjVUSs+xuYOTt9muREAVq5cWeix8TNbXHd/QdsoCvYMGGOMMRnHHwPGGGNMxvHHgDHGGJNxihwzoGl5rIOppsHam6a3sRbEcQBAqNurNsw21YY5zeb8888PbJdeemk61lQW7o43derUwPbrX/86WOZuhHq+fB5cHhcIy95qXALrd1pml0t1cocwIOx01rp168D26KOPpmPV3bRTH2ubep/0+jMa+8BwbIWWFGUdLpYepLZYXACnR6rOqRowb1fLhr700kvpWMs/8/P1u9/9LrBxrIfGufD+YqVIVQ/m61YRyh0XpzxwUUsHF2c9fg/0PrDer3E7nJrKqXRAOB9q9z3tesnasJba5nlUj43TdDt37hzY+D3QeS3WCZE1e353gFD71xiJY489NljWGCOG74XGQbBurjED/NzrfBQrRx+7v/xOxkq1a2xBrOSxzjN8//Wadu3aNR1z2WIgnEv03wY+x5LGDBQXewaMMcaYjOOPAWOMMSbjFFkmUJczu5HVbcIunVhqB1ek0+3oeuzuGTx4cGDjrnJa9Y4rzbEsAIQpbFxFEMjfpet73/teOl6wYEFgW7VqVTpW9zNX9nvllVcCG7va1BW0fv36dDx79uzAxtdNz5clhS5dugQ2doUDYUqSut74eFQW4G6L6k7ke6/PBbvpVIZg167KC5988kmhNu4mpu7hWOqSuuVY3tBqa3ydvv71rwe2Pn36pONnn302sPEzq/IGu4v1uGPvTHkkllpYnLRDnYMKW09dxSzDqLudZcTevXsHNn5G9Tj5/s2cOTOwafopy0x6fvxOalVMrsTZvn37wMbvhKa68TLPTQrLEEAocer7qdeU5yB9Xnnu0m6sLBPo3MVzh1ZOjKVaM/pe83HqPeQ5T99PrYLKxxq7FirpcnoqzxVAeG2ee+65wMbP7P6o4AnYM2CMMcZkHn8MGGOMMRnHHwPGGGNMxilyzIBqtawvqRZT1NQo1ZS5U56mgXDHPe4gCMRLdXIJ2meeeSawcRc71d04RgAALrvssnSsXQQfeOCBdKzdxQYNGpSOtbsYrzdhwoTAxufE+iAQlhX+wx/+ENhYB9QyyVo2ldHrzTETo0aNCmyccqXrcQoWd0gDiq6Fq+6n589wzICmBOozy8+pPnt8bJoOxc+Gas5nnXVWOtbrzelRGpMRS7Nk9pdeuC8prRRBXtb4AX4OWacFwvRbLqMLAD169EjHqmHrM8JwBzruPAgAp512WrDMJWp17uBYJdWi+bnQ+Cp+XvUd5JRaLg8OhHMnl2oHgAYNGqRjTnkE8l8bRkss//Of/0zHc+fODWyHH354OtZ0an1fGT5ffS44NicWv6bPDK8Xi1EAwhiC2DOr8SI8H2uHWY4/mj9/fmCLzQ98HqU5P9gzYIwxxmQcfwwYY4wxGafIMoG6UWJVn2IuDrape6tjx47p+Ec/+lFgY3fakCFDCj1O7aLHbmvtJsapYNpRimUJIHQdX3HFFYGNU2k0PYi3q12q2rVrl45POeWUwMYpinrc7HZkaQUI0xD79esX2J544olCj1tTBMeNG1fgsQBhupK6IblLmab2xe49uwF1PXZ76vPE21FXqj6z7M7T7bCctW7dusDGqYaajtWzZ890zC5QIHSfxlzOsQqEWUblR15WyemYY45Jx+qm53umnUv52n/wwQeBjV36ixYtCmzs/tZKl9w5FAifQ+44CoTPmp4vpyLfdNNNgY0lR61qyBUJNWWOqwzqu8vvp3YO1evNx81VT4Gwi59eGz4ePV89VobfH/33ho9N3yWuDqvnwNuMvZ9AOLdodUK+v1oNkucOlbe7deuWjnXO4+3EZMQYxa1caM+AMcYYk3H8MWCMMcZkHH8MGGOMMRmnyDEDsU5ymrLB2kysrLB2+OOSm6zF6rLq8qx9X3PNNYGN9de1a9cGNk4Xue666wKbdp+64YYb0rGW2OSUHNXXOUVHSx5zug5reUCoP6uWyel0rDsBYdqhrqclNjkW4eabbw5sfI01zZI7oWkaF6dyahpVrDRorGsh64Aaa8DPnsaLaElV1vo0voDvm+p+nOo4cODAwMb3TTVBfi41XZLPQ9+RWNndLBHTeNXG+neHDh0C25FHHpmONZ2NO87dddddhdo0TZZjbDS+iMsPA8CSJUvSsaatcmqYxrhwSi2nMgJhzIJ2f2VNXTV01qa1tHZxyvM+//zz6ZjPDwjjBHQe5Xcylsqp80NM34+VOed3XucOvk46HxQnboevsd5Dniu1gySnenPJcyCcj0qrnPfesGfAGGOMyTj+GDDGGGMyTpFlAnXpsLtf3S/sulCXZ8zdw+ld3EVO98euaAD45S9/mY7VncZuI00J4cpkQ4cODWyaWnPllVcWup177703Hcc6WmmaDZ+TVkBUFzvD6XvqBmRXtbq6unbtGixzxTyVMC6//PJ0rGkvvA/t8MfuRXWbx2QCRq9hLI2Vq/xpl0Z9vvhZiKXKakU1Tt3S+8QygUo9sYqHLJHF3J5Zlgz03GPuUX4nteopL2sa3rRp09JxrDuoul/79++fjrkiZ0Hw+8MVD4HwfdF3iWUvnTv43VYJKlatjyvk6fzA0qTKMLoPrrqokgJXAlUJI1YtNiYv83q6DZ4TStoBVP8NK05lTLbpc8nufk0t54qEnAK/t23uK+wZMMYYYzKOPwaMMcaYjOOPAWOMMSbjFDlmIKbjajwBa1GqzbK2p1rphg0b0nHdunUDG6fZaOoZpx2yfq/Hpvvjkses3wP5U5AYLtULAN/+9rfTMZc4BsISwP/6178CG2uZer6Mprpxmk+rVq0CG6cTsh4KhOWegTAlSvf/2muvpWO996wDamwHpz1qeV5+ZlTLY11MtUxeT/Uzvm9amlmPjZ8b1R35WPW4OX6DO10CYaqhpl/xsek7wvvXDnF8fTXlKUvE0qT0OeA4Gk1Z5uXYfKS6Mb+fqulyyWONqdH7ybEqWiqZl5988snANnHixHSsqY38LMfiSvRd4vdO9XzepqYla1ocxwxoDBXft1jnULXxdjQOIRZPUNi+dVnn/5guX5ySx3zddP+8nS1btgQ2vm96L2JxAnwspRlTZM+AMcYYk3H8MWCMMcZknCLLBJzCBYTukFiqh7pb2GWmaT7c4W/KlCmBTSsSFmYbNWpUYOOqYn379g1s2tWPufPOO4NlrhA1c+bMwMYuM3VhLViwIB1r90F2k6lbjmUKdTEzWvGQu6Kpa1HT4titra5Ovt96Tnys+lxw5UK9v7wddS2yu0slG3bfqguYt7lmzZrApm5Ivv7qLmZXn15vrm6n6Yv87KuLkPev15d/q1IAyxSa0pVl+JrpvMJpW1w5EAglMK1Qx+ldmrLMz5N2O+Tqccry5cuD5alTp6ZjfV/5fXnzzTcLtcU618UkN5U+Yi5tfgf1XXrppZeCZZZC1MXN+1TXPK/HsjAQnkf9+vUDG0s2sW6WSuzfqdjzpPNDrAoqL2taK5+/zgG8zdg9LM75fhXsGTDGGGMyjj8GjDHGmIzjjwFjjDEm4xQ5ZkC1EEa1YdXlGNZQVDfmTliqof/tb38r9Fi4c5524uKUxKuuuiqwxbp7cUogADz33HPpWPUk1oxU6+L0Nu50BoQ6kaalcTlkTXVjjVnT6ViXOuWUUwKbphpyiVNNO+RSqMccc0xgY01WU4BYu9Vj49+qTs73W1Oz+PqqtsfXTe+hdojj50Q10VgqKV8njftg9Lnne8Gpb0DY6VKfZ9YyVZ/MEjFtVK8L38+///3vgY3fyc6dOwc27vp5/vnnB7ZXXnklHQ8ePDiw8XO3aNGiwPaXv/wlWOYup/r88nnENG3V3ovanS6WLqnxKPyc69ys8Td8rKqFx+ISOOZH4394rlZ4/zF9X228f33P+Bw1RU/np1iJZ77GGtvB8Soad8JoLAnv46t0IiwO9gwYY4wxGccfA8YYY0zG8ceAMcYYk3HKrCA5d+7cYJk1bM0HZk3lvPPOC2wnnnhiOlbtnVm1alWwrLrf7bffno65DSgQlu7V1sdHHnlkOtZylFxmWNvfss6pefd9+vRJx6pRsSaqJURbtGgRLLPWp/ps06ZN07Fq+JxjvXbt2sDGcRGqkbEOp9eJ76GWfmV9UnU/1h1VA1Xdk3VXjRHg66jr8Xlo/ASjcS6Maresbeq159oNWWthXNQSxLGy1FzbAwhz5PX+cQyI1igZMWJEOtZ4EH6X169fH9iaNGkSLHN9E66lAsRrdvAzGSuXq3p+TG/md0nnFY7xad++fWDTuYRrImisA7+j+tzz86wxAzyv63Mfq0PC84rGVsRqm7AtVn5Y7bGSxzo/cWwQxywBYayDxhTFYoVisTRfpQaBPQPGGGNMxvHHgDHGGJNxyqxMoFLATTfdlI5vvPHGwMZuOXUvsTSg6SLsmuHURQBYtmxZsHz22Wen45NPPjmwcWqRlqvljncsdQChS2vdunWB7fXXXy9w+0B4vroeyxLKUUcdFSxziqDKFOyyVBcpu/jV1ccpV7FuluqG433EXKLqzlMXKdOwYcNgOSbLsAsxlg4Vc/dzWWogfBZ1Pb73KvXwOWU5tTBGzFWr7vb58+enY00R5HurbmROC1OpjKVBve+aUjto0KBC98FdPjUVVl3HDMsLmibL29T9tWnTJh1rGWG+FpoGfeyxxwbLPHdoSmCsdDK/2zpXx0r+xrav51jY/lTO4P3HUhKB8H3VuYuPR4+br6lKk/ycxmRTPZaill8urmRgz4AxxhiTcfwxYIwxxmQcfwwYY4wxGafMCpKqy3B5YC6bCQC//vWv07HqdYzqOVye91e/+lVg031wqqPq5JyW+N3vfjew3Xbbben4m9/8ZmDj9CRN0eM0lG984xuBjVs2axtQTldRPV01Ql5X99+6det0rOfLrZG5tTMQpv288847gY3TbrSsL+tuqt/xNvWcWHfTmBAtlczb0RQg3qdqkNxKVo+NdUiNCWHNTp9n1iBjbU9jemjWiKUd6vVlOGbgjjvuCGxcsrtHjx6BjTVe1Zu57Pbdd98d2PQZ4edAbZx6xqWRAaB79+7pWMuVc5yCptPx86qxV5xerM8dv1v63PFxAuE8o3MH3yd9z2NlffXaFLZNvdc8r8dScdUW09R1H7H3kH+r94Lnp1i8k8Zd8G9j8ROlWarYngFjjDEm4/hjwBhjjMk4ZVYm0PQRdpW88MILgY0rhXF1PgC4/PLL07GmywwfPjwdX3TRRYFt3rx5wTK7yjWVaODAgek4ltrHkgEALF68OB3/4he/CGycTqguOnZZdu3aNbBxiopWDdMKWMOGDUvHU6ZMCWyxzoiclhfrWKkuQnZ9cfoTEEomWmGM3bXq2uTj1NQdlQ041VFtvE91ycYqArKLMNbNMpYuqefE2/kqFcUqGkVNqVL4WnMHQSCcS84555zAxl0M1d3OLv1+/foFtiVLlgTLXHVQq2Ry6rG+5+eee2461nRJTidkyQII3c/6LPOzps9dTGrRuZPTdnX/sap/LL/GUuZilQT1HeTtxGSCffUu8T71uPle6PnyHBjrkri/sGfAGGOMyTj+GDDGGGMyjj8GjDHGmIxTZmMGNE2LU900lYXLdk6YMCGwrVixIh1z/AAQauqq+/Xq1StYZp2cu6ABYUng++67L7BxCdwBAwYEthkzZqRj1YwY1hWBUPfUbbJu3r9//8CmOtTYsWPTsXZtXLhwYTrWOAyO59CYAdb+NSWRtTWNCeHj1hQc3o5ep02bNqVjLh9b0G+5NLWmirG+rzqnxiIwnD6px83b1HiCmD7Ktli55SxT1O6Git4HTsPTTqVcclffAb7XHTt2DGz6TvL7++KLLwY2TtONPduaJsvPjD4jrEVr6vHmzZvTsb67sRLZmpY8ZMiQdKxllHmfug9+l/V94fPXexgrVVxSfT3WBTO2rPuLPYt83HpNuaS0xknx/opTjvirYM+AMcYYk3H8MWCMMcZknDIrE3BnPgBYvnx5Ola3LS+vX78+sHGKIG8DCOUGTcHRCoTs3lK3LrvJOnToENi4U6Cm8rBNU4445UndS+wO5+6GatNUJT3uzp07p+Pf/OY3ge2BBx4o8Dj1eNjNCoRuTz0n3r+69Dl9UV257JbTDoq1atVKx5quqNvh1EJ1rbLrTSWq9u3bF/g7AHjuuecK3T/LIupajMkE7FqMpUpljZJKA0wsNVTd9DNnzkzHXMVQ96f3T9PweG5hNz2QvxsiwzImdxsEwmdSt7l06dJ0rO59lsC0Ux4vq1Smy8cff3w61vfsySefTMfauZTRdEnev97PmIwX65LIxJ6fvVXyU1mxqPC8psfNnS9VJuDncn+lF9szYIwxxmQcfwwYY4wxGccfA8YYY0zGKbMxA6pFc3c8TiUEwhSc+vXrBzZObdFtHnHEEelYtTvVlFlHVu2fdUjtTMjocR933HHpWMudss6o8RNcOnn69OmBjTV7LVWsWibrZJpKyTqgpkuyXql6KV9TPV++F6z1A2HqlKY5cjlXjYPg8s96z1Qf5pQn1ff5WqiWyWVhVffjmBROqwRCvTSWDqWaK+9Dr6/ZO7FUrJgtlqKm8S+xWAONOeHU41hHQ50D+HnWdGqO49H1+P3U+CpON9Z5jMusa1qwxupwTJV2e4yVROfzj3URjKXiqobO11/fXdbi9d5zrIGen8Y68G/1uHn/+r5ySXb9N4bLVmvMAN8bxwwYY4wxZr/gjwFjjDEm45RZmUDdsezW1TQPXlY3EbuGNAWF3XBHH310YBs9enSw/Oqrr6bjBx98MLBx9z/tDMgu73vuuSewXXvttYWux5IGbx8Iz6NLly6B7cc//nE61kposfQZlVBYmmA5Awjd/epOYymC3ftA6E5Tlyz/ljuyFfTbwo5bXfGaEsnd1dTtyzLFSSedFNi4why7fAHg8ccfL3AbSqyimT7PlgYODLFueDp38P3TdOLmzZsHy+wq54qVQFgVk7uoAqGLWaUznruaNm0a2LijokqMLVu2TMfqtubqiCp1cMVBIJQRVFLg4/7kk08CG7u/dT2+3voOsGwQ6wyo8G9jVUf3JhPws6FSD//bpPP4sccem441tZ2rvOrcFWNvaZAlxZ4BY4wxJuP4Y8AYY4zJOP4YMMYYYzJOmY0Z2N9ozECLFi2CZdZ+OnXqFNg4hU/1ddYLuUwoEKal6Xpt27ZNx5oSyJqV2jhF8Le//W1gu/7661EYmmrH2rh2HtOSzwxr/6xPAqG+r/vjGBE+dyDU7zSWhI9NU6y0TCunIaruxmVbL7nkksDGaT+///3vYfYdsTTAGCUtNVvUtK1YFz2NFTnhhBOCZe54yKm/QDjvaCowd7XTWCjW+7WsML93mlrI2rvGQfBz/uyzzwY2TQXu2bNnOtaYHo4F0LmD30mOLQDCa6rb5GXV/vk81Mb3Rm2xeUWfGV7W2BKeV0477bTAxiniDz/8cGDja1PSzoT6O95OcWML7BkwxhhjMo4/BowxxpiMY5ngP0ybNi1Y1nSdM888Mx137949sLHra8WKFYGNU4euuOKKwPbQQw+lY01JYffehx9+GNjYLdi6devAxtv54Q9/GNjUnfmzn/0sHavL8Pnnn0/H6m5ftGhRodtktyen8gGhK06rn8W6mzHqPmT3pab8aMc2dtmpbdSoUYXuc+7cuel46tSpRTpOUzL2VdpUae+bU9a4khyQ/504+eST07FKCPzeaQVLTnfjDqNAmPqnbutY+h67yrXKHy9rKiN35wTC907T4ni+0PeaO7yqhMC/1WvIx633iecVnR/42IojPehcwnaVNwYNGpSOdT6eNGlSOp4xY0ah2yypTFCa74s9A8YYY0zG8ceAMcYYk3H8MWCMMcZkHMcM/AfVqP7whz8Ey1OmTEnHF1xwQWDjcqScDgSEnfJUs+rbt286Zj27oGVGO/cxHGugZXX5WIDwnHgMhCmRp556aqE27hgJAK+99lo61mvasGHDdKxpgKwXqn7HpYO1FGmsM6BqknzdTjnllMDGXdi0ZOzll1+ejjXFy5Rviqq5xlK41DZr1qxgmd87fZfatWuXjjVmgOcL1d5Zt9bnnju3amwO/1bL+PJ7ph1Hly1bFixznMLq1asDG6c761zFnRE1zZK1f507eF7TNEeOu9AUwVjMAKM2nYN4/7179w5snHY+e/bswHbrrbemYy2BXtRnrzipsV8lhsCeAWOMMSbj+GPAGGOMyTiWCf4Dp7wA+St3sWv8v/7rvwIbVw/UzmNsU9cTp++ojVMSY52/FHaZaXqkpstcfPHF6Vjd/WPGjEnHmp7E3QcbN24c2DjtUdP32MWurnh207ObEwhTrDQdilH3YY8ePYJlTs/S9CBOl/zWt74V2Ph8tRNj3bp1Cz0eU3EojqtW03S5Wx3PI0CYisZV/YCwCiq/H0A4X+h8wC587cYXq/DI0h3LF0Ao8QFhF9c33ngjsB1zzDHpWCu7NmrUKB1r5USWAFWOYze+zmM8V+u94N+qFMDnr/KCdnvke8MVBwHgiSeeSMcqL/M8F+uEGOuYWZpVBmPYM2CMMcZkHH8MGGOMMRnHHwPGGGNMxnHMwH9QXVx1P0b1He5G+OabbwY2Lg+smiBr2qpFd+zYMR2r9h7T/VivU+1bNbM1a9ak44ULFwY21tQ5fgEA+vXrl465exoALFiwoNBjY/1OYyRYL9T4hViKFduGDh0a2DRmglO8brvttsB2++23p2Pu3qbHo3qhUw33TmmmP5VF9nY+nN6mejen4nJsARA+a5y+BgBt2rRJx7G4HY034hgCTV8+/vjj07F2Q9X3nN8ljdXh2AO18TvJsThAPIWY33N9nvh8NV2S19O4C45f4PkWyN9Flq/jLbfcEtgef/zxdKwpmRyLoPf+QJQcjmHPgDHGGJNx/DFgjDHGZBzLBP9BXejqXmMXj6YdsrtNO/yx62/dunWB7aWXXkrH2gmRXWbt27cPbOxC0zQ8rvjFbjAAuPnmm4NlPo8+ffoENk7DO+KIIwIbp9288sorgY1dlpquw9dUZQJeVomGXaJctREA2rZtm471HnL6EwD87ne/S8d///vfAxufo0oRfI21qmFFc3nvC7J2jWKySOy5Vzcyy3jr168PbPze8TsAAN26dUvHLVu2DGws/6nbnG21a9cObNOnTw+WWdbU+YGre6pUx++ophby3BlLEdTrxPOhvrs8H6rrnyVVrYjKcicA3H333emY521Fq0GWNSkghj0DxhhjTMbxx4AxxhiTcfwxYIwxxmQcxwz8B9XyVE9jnUq7T7EWrqVBWYvSlETWBD/66KPAdu+996bjYcOGBTZOg9QUnLPOOisdDxkyJLCdcMIJwTLrgNoxjeMJtBQpx0jodWLNbuPGjYGNz1HTFTkuQG2sSer15fN/8MEHA9tvfvObYJk1Sj0n1SEZ7rSmGqw+CyY/FT21sDgUJ54gVoKXYwi0lDrHymiKIMe/aJpsp06d0rGWEdZ4nJiGzx0HNe2RNXXV9/mc9L3ifcRiHbiEMxCeo6ZS8vzE8y0APPTQQ8Eyx3vFyj/H5hGlrL0H9gwYY4wxGccfA8YYY0zGqZQUMfdB3aqmdNHuYuwy02vPVa60+97YsWPTsbrpWUIAQpflP/7xj8DG1cjOPvvswMYV+tRlxjZ1mbGLMFbhUeH0TO4QBoRSx+LFi4u8zfKKppiVB7I2dxRHFilq6plS1G3q7zjdV6sDsku9efPmgU0rIPJ2VWJkKa1Vq1aBjecrPTZO29VKgkysa6uut3r16nT8/PPPB7b58+cX+LuCtqMSTkmIPRcltRWHvc0d9gwYY4wxGccfA8YYY0zG8ceAMcYYk3EcM3AAYX1d0162bduWjrXkMJft1NRC7r61atWqwKYd9n70ox+lY03JYd2vbt26gY01Qi73CYRxAtqVjFOgtIzxiy++mI5XrlwZ2DhGQssB87LGXVREymPMgKa3lbWUqvJASeMQ9Hdsi+ngqstr51SeZ7QcMqcCa2wQb0djFhjtkvjOO++k4xUrVhRqW7t2bWDjjqNaYp7R1Em9bqWh4e+LuIDirOeYAWOMMcZE8ceAMcYYk3H8MWCMMcZkHMcMlBFUv+N4AtXd2Ka3j0uBfvjhh4FN22ty+VGNPeAaBbw/3Y6WFOWSv1pSlDV9PV/O69X98W+1LTJrfSXN2S5PlMeYAc8d/8/+1o1jNtXJixprAITvJM85QDg/6LzCpXu1PDsv7969O7Dx/KDHwss6x/E2Y3EBe7v2sbllX9QE2Bc4ZsAYY4wxUfwxYIwxxmQcdy08gLDLW91p7IZTdzun/aiLjrej3f8UTuFT9zu7u9Sdx+mDnLqj6+k2C9sGELrwVHqIdW9j96GuZ8oGZdl1ur+JnXtJr0tJtxlzfe9NcuM5IZayF+viF3PFx1L7VGKMrafSQGHr7Y2YFFDY78ob9gwYY4wxGccfA8YYY0zG8ceAMcYYk3EcM3AA0RQ6htPwVLPn9BnV5LhUsJbnVT0rVlaY9X7dB5c11hQgLp3MaYZA/vNgYumDfL66Dd6/xl2YskF51lHLA6XVGrek+4zd31jJY91/UdOES3pOxUkXjO2joj7P9gwYY4wxGccfA8YYY0zGsUxwAIm5zdmmrjZ228ckBF1PO5HFYAlDq3rxdjSt6KOPPkrHmvbI7rWYu1+3Gaswpsum7OHUwn3LvkhXVPZHdc+i7qO0zre8PIelVZlyb9gzYIwxxmQcfwwYY4wxGccfA8YYY0zGseB6AOFuhJqi9+mnn6Zj1f45hU5tdevWTceqGWmqX40aNdKxpjny/hVOAdKOilwSePv27YGNf6vrcRyC2mIlTfn8Y78zB47yos2WZcpSt8PyRFE7MZbl89sXZaoLwp4BY4wxJuP4Y8AYY4zJOJYJDiCxqlbs8s7JyQlsnJan7n2WGzZv3hzYYqmF27ZtC5bZ3V+zZs3obxmWMDS1kI91586dgY1TBGMVCLXKIG8zVu3MHDgqisv5QFKWuh0eCMrSOe6LVL+SShilmfJpz4AxxhiTcfwxYIwxxmQcfwwYY4wxGccxAwcQ1r9VF+L0un//+9+BjVP7WNsHQg39sMMOi+6fuw9qXAITixGIoal+rOnH9P3idB/k7Wg6pJ4/xx5oKifHYcTuhXaC5HRNXa+kWiIv748ysPuasqY/m69ORUjZO9Ds6ziQWLn7grBnwBhjjMk4/hgwxhhjMo5lAlNhiEkdQJjOqC40TrtUG3dR5K6MQLziI8skKpnE3P8sA1UEnFpY8ShPaYmlQWnIIjEZsTgu/VjX2qJKsQVRsWYdY4wxxhQbfwwYY4wxGccfA8YYY0zGccyAqTAUJ7VPbazTcwoiEGqCWiqZ0xljXRM1zfHII49Mx61btw5svPy1r32t0G0aY/YNsZge1elj2j9vpzg2RuecI444Ih03bdo0sLVq1Sod169fv9BtFoQ9A8YYY0zG8ceAMcYYk3EqJUUscdawYcN9fSzGfCW2b98eLGu3RXb/a1VHdvGrW65OnTrpmFMJAaBBgwbp+PDDDw9sLAXosfCxrlixIrC9+uqr6fidd94JbO+++y7KGyp1FLULW3FSuCp61bvS6pRXXq/Ngb6/sX8mOS1ZO8NyxVKtXsrzRa1atQIbzyvVq1cPbFyhVeeHpUuXpmOdK9atW1fg8edhz4AxxhiTcfwxYIwxxmQcfwwYY4wxGcephabCoNr0li1bgmUuJdyoUaPA1qtXr3Tcpk2bwMYapXZw3LRpUzpev359YFu4cGE6fv/99wMbdzvUlESObdD4hYpOScvcllctvKiURhe78sz+Po9YrIXG//Ts2TMdc9ofEO+qynPJ1q1bA9uCBQvSsc4rvJ52X+Xj5m6rRcGeAWOMMSbj+GPAGGOMyThOLTQVBq0cqHA6YaxroXYN/Oyzz9KxuuW4U6K+Srwce81i1RDV9t577xW6nbJK1lILYy7mfWEr6bHsi/V03bJ8D4t6LAUtMzF3PKcB6pxT1E6F2n0w1tU0Nuds2LAhuh97BowxxpiM448BY4wxJuP4Y8AYY4zJOE4tNBUGTfvLzc0NlrkcqKbzxTqPcSyClhvleALV8ng5pvsdaI27LFERUgv3xTns79TCr3I9y8s9LK1j4bgA1en5Pdf5geeV4sRoFDXWoLjX054BY4wxJuP4Y8AYY4zJOJYJTIWB0/yA/K55TgvUFEF22cVSd1ReYClCXX3szlPXHrsWY5Ql93dJKU5apTFlkZI+p7FnP+buj6UI8rHsLSWyONgzYIwxxmQcfwwYY4wxGccfA8YYY0zGKXI5YmOMMcZUTOwZMMYYYzKOPwaMMcaYjOOPAWOMMSbj+GPAGGOMyTj+GDDGGGMyjj8GjDHGmIzjjwFjjDEm4/hjwBhjjMk4/hgwxhhjMs7/ARSwI6/UoM4MAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(nrows=1, ncols=2)\n", + "ax[0].imshow(images[0, 0].detach().cpu(), vmin=0, vmax=1, cmap=\"gray\")\n", + "ax[0].axis(\"off\")\n", + "ax[0].title.set_text(\"Inputted Image\")\n", + "ax[1].imshow(reconstruction[0, 0].detach().cpu(), vmin=0, vmax=1, cmap=\"gray\")\n", + "ax[1].axis(\"off\")\n", + "ax[1].title.set_text(\"Reconstruction\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "b40490ea", + "metadata": {}, + "source": [ + "## Transformer Training\n", + "Now that a vqvae model has been trained, we can use this model to encode the data into its discrete latent representations. These inputs can then be flattened into a 1D sequence for the transformer to learn in an autoregressive manor.\n", + "\n", + "Training can be done in 2 ways:\n", + "- Loading in the original images and then encoding these images on the fly during training using the vqvae model, the advantage of this is we can augment training data during training that is then encoded, however this will slow down training and is more memory intensive.\n", + "- Before training the transformer we encode all the training data first and save the discrete encodings. These latent codes are then loaded and fed to the transformer for training.\n", + "\n", + "For this tutorial we will use the first appraoch and use the vqvae network to encode the data during the training cycle" + ] + }, + { + "cell_type": "markdown", + "id": "ca886d3e", + "metadata": {}, + "source": [ + "### Datasets\n", + "We can use the same dataloader with augmentations as used for training the VQVAE model. However given the memory intensive nature of Transformer models we will need to reduce the batch size" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "9c888aa5", + "metadata": {}, + "outputs": [], + "source": [ + "train_loader = DataLoader(train_ds, batch_size=8, shuffle=True, num_workers=4)\n", + "val_loader = DataLoader(val_ds, batch_size=8, shuffle=True, num_workers=4)" + ] + }, + { + "cell_type": "markdown", + "id": "c11cccc3", + "metadata": {}, + "source": [ + "### Latent sequence ordering\n", + "We need to define an ordering of which we convert our 2D latent space into a 1D sequence. For this we will use a simple raster scan." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "d95e1cc1", + "metadata": {}, + "outputs": [], + "source": [ + "spatial_shape = next(iter(train_loader))[\"image\"].shape[2:]" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "8afcb16e", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "# Get spatial dimensions of data\n", + "# We divide the spatial shape by 4 as the vqvae downsamples the image by a factor of 4 along each dimension\n", + "spatial_shape = next(iter(train_loader))[\"image\"].shape[2:]\n", + "spatial_shape = (int(spatial_shape[0]/4),int(spatial_shape[1]/4))\n", + "\n", + "ordering = Ordering(ordering_type=OrderingType.RASTER_SCAN.value,\n", + " spatial_dims=2,\n", + " dimensions=(1,) + spatial_shape)\n", + "\n", + "sequence_ordering = ordering.get_sequence_ordering()\n", + "revert_sequence_ordering = ordering.get_revert_sequence_ordering()" + ] + }, + { + "cell_type": "markdown", + "id": "295e1970", + "metadata": {}, + "source": [ + "## Define Network, optimizer and losses" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "acaa850a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DecoderOnlyTransformer(\n", + " (model): TransformerWrapper(\n", + " (token_emb): TokenEmbedding(\n", + " (emb): Embedding(256, 64)\n", + " )\n", + " (pos_emb): AbsolutePositionalEmbedding(\n", + " (emb): Embedding(256, 64)\n", + " )\n", + " (post_emb_norm): Identity()\n", + " (emb_dropout): Dropout(p=0.0, inplace=False)\n", + " (project_emb): Identity()\n", + " (attn_layers): Decoder(\n", + " (layers): ModuleList(\n", + " (0): ModuleList(\n", + " (0): ModuleList(\n", + " (0): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", + " (1): None\n", + " (2): None\n", + " )\n", + " (1): Attention(\n", + " (to_q): Linear(in_features=64, out_features=512, bias=False)\n", + " (to_k): Linear(in_features=64, out_features=512, bias=False)\n", + " (to_v): Linear(in_features=64, out_features=512, bias=False)\n", + " (dropout): Dropout(p=0.0, inplace=False)\n", + " (to_out): Linear(in_features=512, out_features=64, bias=False)\n", + " )\n", + " (2): Residual()\n", + " )\n", + " (1): ModuleList(\n", + " (0): ModuleList(\n", + " (0): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", + " (1): None\n", + " (2): None\n", + " )\n", + " (1): FeedForward(\n", + " (ff): Sequential(\n", + " (0): Sequential(\n", + " (0): Linear(in_features=64, out_features=256, bias=True)\n", + " (1): GELU(approximate='none')\n", + " )\n", + " (1): Identity()\n", + " (2): Dropout(p=0.0, inplace=False)\n", + " (3): Linear(in_features=256, out_features=64, bias=True)\n", + " )\n", + " )\n", + " (2): Residual()\n", + " )\n", + " (2): ModuleList(\n", + " (0): ModuleList(\n", + " (0): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", + " (1): None\n", + " (2): None\n", + " )\n", + " (1): Attention(\n", + " (to_q): Linear(in_features=64, out_features=512, bias=False)\n", + " (to_k): Linear(in_features=64, out_features=512, bias=False)\n", + " (to_v): Linear(in_features=64, out_features=512, bias=False)\n", + " (dropout): Dropout(p=0.0, inplace=False)\n", + " (to_out): Linear(in_features=512, out_features=64, bias=False)\n", + " )\n", + " (2): Residual()\n", + " )\n", + " (3): ModuleList(\n", + " (0): ModuleList(\n", + " (0): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", + " (1): None\n", + " (2): None\n", + " )\n", + " (1): FeedForward(\n", + " (ff): Sequential(\n", + " (0): Sequential(\n", + " (0): Linear(in_features=64, out_features=256, bias=True)\n", + " (1): GELU(approximate='none')\n", + " )\n", + " (1): Identity()\n", + " (2): Dropout(p=0.0, inplace=False)\n", + " (3): Linear(in_features=256, out_features=64, bias=True)\n", + " )\n", + " )\n", + " (2): Residual()\n", + " )\n", + " (4): ModuleList(\n", + " (0): ModuleList(\n", + " (0): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", + " (1): None\n", + " (2): None\n", + " )\n", + " (1): Attention(\n", + " (to_q): Linear(in_features=64, out_features=512, bias=False)\n", + " (to_k): Linear(in_features=64, out_features=512, bias=False)\n", + " (to_v): Linear(in_features=64, out_features=512, bias=False)\n", + " (dropout): Dropout(p=0.0, inplace=False)\n", + " (to_out): Linear(in_features=512, out_features=64, bias=False)\n", + " )\n", + " (2): Residual()\n", + " )\n", + " (5): ModuleList(\n", + " (0): ModuleList(\n", + " (0): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", + " (1): None\n", + " (2): None\n", + " )\n", + " (1): FeedForward(\n", + " (ff): Sequential(\n", + " (0): Sequential(\n", + " (0): Linear(in_features=64, out_features=256, bias=True)\n", + " (1): GELU(approximate='none')\n", + " )\n", + " (1): Identity()\n", + " (2): Dropout(p=0.0, inplace=False)\n", + " (3): Linear(in_features=256, out_features=64, bias=True)\n", + " )\n", + " )\n", + " (2): Residual()\n", + " )\n", + " (6): ModuleList(\n", + " (0): ModuleList(\n", + " (0): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", + " (1): None\n", + " (2): None\n", + " )\n", + " (1): Attention(\n", + " (to_q): Linear(in_features=64, out_features=512, bias=False)\n", + " (to_k): Linear(in_features=64, out_features=512, bias=False)\n", + " (to_v): Linear(in_features=64, out_features=512, bias=False)\n", + " (dropout): Dropout(p=0.0, inplace=False)\n", + " (to_out): Linear(in_features=512, out_features=64, bias=False)\n", + " )\n", + " (2): Residual()\n", + " )\n", + " (7): ModuleList(\n", + " (0): ModuleList(\n", + " (0): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", + " (1): None\n", + " (2): None\n", + " )\n", + " (1): FeedForward(\n", + " (ff): Sequential(\n", + " (0): Sequential(\n", + " (0): Linear(in_features=64, out_features=256, bias=True)\n", + " (1): GELU(approximate='none')\n", + " )\n", + " (1): Identity()\n", + " (2): Dropout(p=0.0, inplace=False)\n", + " (3): Linear(in_features=256, out_features=64, bias=True)\n", + " )\n", + " )\n", + " (2): Residual()\n", + " )\n", + " (8): ModuleList(\n", + " (0): ModuleList(\n", + " (0): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", + " (1): None\n", + " (2): None\n", + " )\n", + " (1): Attention(\n", + " (to_q): Linear(in_features=64, out_features=512, bias=False)\n", + " (to_k): Linear(in_features=64, out_features=512, bias=False)\n", + " (to_v): Linear(in_features=64, out_features=512, bias=False)\n", + " (dropout): Dropout(p=0.0, inplace=False)\n", + " (to_out): Linear(in_features=512, out_features=64, bias=False)\n", + " )\n", + " (2): Residual()\n", + " )\n", + " (9): ModuleList(\n", + " (0): ModuleList(\n", + " (0): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", + " (1): None\n", + " (2): None\n", + " )\n", + " (1): FeedForward(\n", + " (ff): Sequential(\n", + " (0): Sequential(\n", + " (0): Linear(in_features=64, out_features=256, bias=True)\n", + " (1): GELU(approximate='none')\n", + " )\n", + " (1): Identity()\n", + " (2): Dropout(p=0.0, inplace=False)\n", + " (3): Linear(in_features=256, out_features=64, bias=True)\n", + " )\n", + " )\n", + " (2): Residual()\n", + " )\n", + " (10): ModuleList(\n", + " (0): ModuleList(\n", + " (0): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", + " (1): None\n", + " (2): None\n", + " )\n", + " (1): Attention(\n", + " (to_q): Linear(in_features=64, out_features=512, bias=False)\n", + " (to_k): Linear(in_features=64, out_features=512, bias=False)\n", + " (to_v): Linear(in_features=64, out_features=512, bias=False)\n", + " (dropout): Dropout(p=0.0, inplace=False)\n", + " (to_out): Linear(in_features=512, out_features=64, bias=False)\n", + " )\n", + " (2): Residual()\n", + " )\n", + " (11): ModuleList(\n", + " (0): ModuleList(\n", + " (0): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", + " (1): None\n", + " (2): None\n", + " )\n", + " (1): FeedForward(\n", + " (ff): Sequential(\n", + " (0): Sequential(\n", + " (0): Linear(in_features=64, out_features=256, bias=True)\n", + " (1): GELU(approximate='none')\n", + " )\n", + " (1): Identity()\n", + " (2): Dropout(p=0.0, inplace=False)\n", + " (3): Linear(in_features=256, out_features=64, bias=True)\n", + " )\n", + " )\n", + " (2): Residual()\n", + " )\n", + " (12): ModuleList(\n", + " (0): ModuleList(\n", + " (0): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", + " (1): None\n", + " (2): None\n", + " )\n", + " (1): Attention(\n", + " (to_q): Linear(in_features=64, out_features=512, bias=False)\n", + " (to_k): Linear(in_features=64, out_features=512, bias=False)\n", + " (to_v): Linear(in_features=64, out_features=512, bias=False)\n", + " (dropout): Dropout(p=0.0, inplace=False)\n", + " (to_out): Linear(in_features=512, out_features=64, bias=False)\n", + " )\n", + " (2): Residual()\n", + " )\n", + " (13): ModuleList(\n", + " (0): ModuleList(\n", + " (0): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", + " (1): None\n", + " (2): None\n", + " )\n", + " (1): FeedForward(\n", + " (ff): Sequential(\n", + " (0): Sequential(\n", + " (0): Linear(in_features=64, out_features=256, bias=True)\n", + " (1): GELU(approximate='none')\n", + " )\n", + " (1): Identity()\n", + " (2): Dropout(p=0.0, inplace=False)\n", + " (3): Linear(in_features=256, out_features=64, bias=True)\n", + " )\n", + " )\n", + " (2): Residual()\n", + " )\n", + " (14): ModuleList(\n", + " (0): ModuleList(\n", + " (0): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", + " (1): None\n", + " (2): None\n", + " )\n", + " (1): Attention(\n", + " (to_q): Linear(in_features=64, out_features=512, bias=False)\n", + " (to_k): Linear(in_features=64, out_features=512, bias=False)\n", + " (to_v): Linear(in_features=64, out_features=512, bias=False)\n", + " (dropout): Dropout(p=0.0, inplace=False)\n", + " (to_out): Linear(in_features=512, out_features=64, bias=False)\n", + " )\n", + " (2): Residual()\n", + " )\n", + " (15): ModuleList(\n", + " (0): ModuleList(\n", + " (0): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", + " (1): None\n", + " (2): None\n", + " )\n", + " (1): FeedForward(\n", + " (ff): Sequential(\n", + " (0): Sequential(\n", + " (0): Linear(in_features=64, out_features=256, bias=True)\n", + " (1): GELU(approximate='none')\n", + " )\n", + " (1): Identity()\n", + " (2): Dropout(p=0.0, inplace=False)\n", + " (3): Linear(in_features=256, out_features=64, bias=True)\n", + " )\n", + " )\n", + " (2): Residual()\n", + " )\n", + " (16): ModuleList(\n", + " (0): ModuleList(\n", + " (0): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", + " (1): None\n", + " (2): None\n", + " )\n", + " (1): Attention(\n", + " (to_q): Linear(in_features=64, out_features=512, bias=False)\n", + " (to_k): Linear(in_features=64, out_features=512, bias=False)\n", + " (to_v): Linear(in_features=64, out_features=512, bias=False)\n", + " (dropout): Dropout(p=0.0, inplace=False)\n", + " (to_out): Linear(in_features=512, out_features=64, bias=False)\n", + " )\n", + " (2): Residual()\n", + " )\n", + " (17): ModuleList(\n", + " (0): ModuleList(\n", + " (0): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", + " (1): None\n", + " (2): None\n", + " )\n", + " (1): FeedForward(\n", + " (ff): Sequential(\n", + " (0): Sequential(\n", + " (0): Linear(in_features=64, out_features=256, bias=True)\n", + " (1): GELU(approximate='none')\n", + " )\n", + " (1): Identity()\n", + " (2): Dropout(p=0.0, inplace=False)\n", + " (3): Linear(in_features=256, out_features=64, bias=True)\n", + " )\n", + " )\n", + " (2): Residual()\n", + " )\n", + " (18): ModuleList(\n", + " (0): ModuleList(\n", + " (0): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", + " (1): None\n", + " (2): None\n", + " )\n", + " (1): Attention(\n", + " (to_q): Linear(in_features=64, out_features=512, bias=False)\n", + " (to_k): Linear(in_features=64, out_features=512, bias=False)\n", + " (to_v): Linear(in_features=64, out_features=512, bias=False)\n", + " (dropout): Dropout(p=0.0, inplace=False)\n", + " (to_out): Linear(in_features=512, out_features=64, bias=False)\n", + " )\n", + " (2): Residual()\n", + " )\n", + " (19): ModuleList(\n", + " (0): ModuleList(\n", + " (0): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", + " (1): None\n", + " (2): None\n", + " )\n", + " (1): FeedForward(\n", + " (ff): Sequential(\n", + " (0): Sequential(\n", + " (0): Linear(in_features=64, out_features=256, bias=True)\n", + " (1): GELU(approximate='none')\n", + " )\n", + " (1): Identity()\n", + " (2): Dropout(p=0.0, inplace=False)\n", + " (3): Linear(in_features=256, out_features=64, bias=True)\n", + " )\n", + " )\n", + " (2): Residual()\n", + " )\n", + " (20): ModuleList(\n", + " (0): ModuleList(\n", + " (0): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", + " (1): None\n", + " (2): None\n", + " )\n", + " (1): Attention(\n", + " (to_q): Linear(in_features=64, out_features=512, bias=False)\n", + " (to_k): Linear(in_features=64, out_features=512, bias=False)\n", + " (to_v): Linear(in_features=64, out_features=512, bias=False)\n", + " (dropout): Dropout(p=0.0, inplace=False)\n", + " (to_out): Linear(in_features=512, out_features=64, bias=False)\n", + " )\n", + " (2): Residual()\n", + " )\n", + " (21): ModuleList(\n", + " (0): ModuleList(\n", + " (0): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", + " (1): None\n", + " (2): None\n", + " )\n", + " (1): FeedForward(\n", + " (ff): Sequential(\n", + " (0): Sequential(\n", + " (0): Linear(in_features=64, out_features=256, bias=True)\n", + " (1): GELU(approximate='none')\n", + " )\n", + " (1): Identity()\n", + " (2): Dropout(p=0.0, inplace=False)\n", + " (3): Linear(in_features=256, out_features=64, bias=True)\n", + " )\n", + " )\n", + " (2): Residual()\n", + " )\n", + " (22): ModuleList(\n", + " (0): ModuleList(\n", + " (0): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", + " (1): None\n", + " (2): None\n", + " )\n", + " (1): Attention(\n", + " (to_q): Linear(in_features=64, out_features=512, bias=False)\n", + " (to_k): Linear(in_features=64, out_features=512, bias=False)\n", + " (to_v): Linear(in_features=64, out_features=512, bias=False)\n", + " (dropout): Dropout(p=0.0, inplace=False)\n", + " (to_out): Linear(in_features=512, out_features=64, bias=False)\n", + " )\n", + " (2): Residual()\n", + " )\n", + " (23): ModuleList(\n", + " (0): ModuleList(\n", + " (0): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", + " (1): None\n", + " (2): None\n", + " )\n", + " (1): FeedForward(\n", + " (ff): Sequential(\n", + " (0): Sequential(\n", + " (0): Linear(in_features=64, out_features=256, bias=True)\n", + " (1): GELU(approximate='none')\n", + " )\n", + " (1): Identity()\n", + " (2): Dropout(p=0.0, inplace=False)\n", + " (3): Linear(in_features=256, out_features=64, bias=True)\n", + " )\n", + " )\n", + " (2): Residual()\n", + " )\n", + " )\n", + " )\n", + " (norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", + " (to_logits): Linear(in_features=64, out_features=256, bias=True)\n", + " )\n", + ")" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "\n", + "transformer_model = DecoderOnlyTransformer(\n", + " num_tokens= 256, # must be equal to num_embeddings input of VQVAE\n", + " max_seq_len=spatial_shape[0]*spatial_shape[1],\n", + " attn_layers_dim=64,\n", + " attn_layers_depth=12,\n", + " attn_layers_heads=8,\n", + ")\n", + "transformer_model.to(device)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "c64b1237", + "metadata": {}, + "outputs": [], + "source": [ + "optimizer = torch.optim.Adam(params=transformer_model.parameters(), lr=1e-3)\n", + "ce_loss = CrossEntropyLoss()" + ] + }, + { + "cell_type": "markdown", + "id": "ad0849c3", + "metadata": {}, + "source": [ + "First we will define a function to allow us to generate random samples from the transformer. This will allow us to keep track of training progress as well to see how samples look during the training cycle" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "eedfc55e", + "metadata": {}, + "outputs": [], + "source": [ + "@torch.no_grad()\n", + "def generate(\n", + " net,\n", + " vqvae_model,\n", + " starting_tokens,\n", + " seq_len,\n", + " **kwargs\n", + "):\n", + " \n", + " progress_bar = iter(range(seq_len))\n", + "\n", + " latent_seq = starting_tokens.long()\n", + " for _ in progress_bar:\n", + " # if the sequence context is growing too long we must crop it at block_size\n", + " if latent_seq.size(1) <= net.max_seq_len:\n", + " idx_cond = latent_seq\n", + " else:\n", + " idx_cond = latent_seq[:, -net.max_seq_len :]\n", + "\n", + " # forward the model to get the logits for the index in the sequence\n", + " logits = net(x=idx_cond)\n", + " # pluck the logits at the final step and scale by desired temperature\n", + " logits = logits[:, -1, :]\n", + " # optionally crop the logits to only the top k options\n", + "\n", + " \n", + " # apply softmax to convert logits to (normalized) probabilities\n", + " probs = F.softmax(logits, dim=-1)\n", + " # remove the chance to be sampled the BOS token\n", + " probs[:, vqvae_model.num_embeddings-1] = 0\n", + "\n", + " # sample from the distribution\n", + " idx_next = torch.multinomial(probs, num_samples=1)\n", + " latent_seq = torch.cat((latent_seq, idx_next), dim=1)\n", + "\n", + " latent_seq = latent_seq[:, 1:]\n", + " \n", + " return latent_seq" + ] + }, + { + "cell_type": "markdown", + "id": "a54894d1", + "metadata": {}, + "source": [ + "### Transformer Model Training\n", + "We will train the model for 100 epochs" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "34364372", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 0: 100%|████████████████████████████████████████████████| 999/999 [00:55<00:00, 17.87it/s, ce_loss=3.58]\n", + "Epoch 1: 100%|████████████████████████████████████████████████| 999/999 [00:56<00:00, 17.72it/s, ce_loss=3.05]\n", + "Epoch 2: 100%|████████████████████████████████████████████████| 999/999 [00:55<00:00, 17.84it/s, ce_loss=2.86]\n", + "Epoch 3: 100%|████████████████████████████████████████████████| 999/999 [00:56<00:00, 17.81it/s, ce_loss=2.76]\n", + "Epoch 4: 100%|█████████████████████████████████████████████████| 999/999 [00:56<00:00, 17.79it/s, ce_loss=2.7]\n", + "Epoch 5: 100%|████████████████████████████████████████████████| 999/999 [00:57<00:00, 17.46it/s, ce_loss=2.65]\n", + "Epoch 6: 100%|████████████████████████████████████████████████| 999/999 [00:56<00:00, 17.69it/s, ce_loss=2.61]\n", + "Epoch 7: 100%|████████████████████████████████████████████████| 999/999 [00:56<00:00, 17.72it/s, ce_loss=2.57]\n", + "Epoch 8: 100%|████████████████████████████████████████████████| 999/999 [00:57<00:00, 17.47it/s, ce_loss=2.54]\n", + "Epoch 9: 100%|████████████████████████████████████████████████| 999/999 [00:56<00:00, 17.70it/s, ce_loss=2.52]\n", + "Epoch 10: 100%|████████████████████████████████████████████████| 999/999 [00:55<00:00, 17.90it/s, ce_loss=2.5]\n", + "Epoch 11: 100%|███████████████████████████████████████████████| 999/999 [00:56<00:00, 17.61it/s, ce_loss=2.48]\n", + "Epoch 12: 100%|███████████████████████████████████████████████| 999/999 [00:56<00:00, 17.61it/s, ce_loss=2.46]\n", + "Epoch 13: 100%|███████████████████████████████████████████████| 999/999 [00:57<00:00, 17.41it/s, ce_loss=2.45]\n", + "Epoch 14: 100%|███████████████████████████████████████████████| 999/999 [00:57<00:00, 17.48it/s, ce_loss=2.44]\n", + "Epoch 15: 100%|███████████████████████████████████████████████| 999/999 [00:56<00:00, 17.77it/s, ce_loss=2.42]\n", + "Epoch 16: 100%|███████████████████████████████████████████████| 999/999 [00:57<00:00, 17.44it/s, ce_loss=2.41]\n", + "Epoch 17: 100%|████████████████████████████████████████████████| 999/999 [00:56<00:00, 17.61it/s, ce_loss=2.4]\n", + "Epoch 18: 100%|███████████████████████████████████████████████| 999/999 [00:57<00:00, 17.47it/s, ce_loss=2.39]\n", + "Epoch 19: 100%|███████████████████████████████████████████████| 999/999 [00:57<00:00, 17.45it/s, ce_loss=2.38]\n", + "Epoch 20: 100%|███████████████████████████████████████████████| 999/999 [00:56<00:00, 17.62it/s, ce_loss=2.37]\n", + "Epoch 21: 100%|███████████████████████████████████████████████| 999/999 [00:56<00:00, 17.59it/s, ce_loss=2.37]\n", + "Epoch 22: 100%|███████████████████████████████████████████████| 999/999 [00:56<00:00, 17.65it/s, ce_loss=2.36]\n", + "Epoch 23: 100%|███████████████████████████████████████████████| 999/999 [00:56<00:00, 17.80it/s, ce_loss=2.35]\n", + "Epoch 24: 100%|███████████████████████████████████████████████| 999/999 [00:56<00:00, 17.65it/s, ce_loss=2.35]\n", + "Epoch 25: 100%|███████████████████████████████████████████████| 999/999 [00:56<00:00, 17.67it/s, ce_loss=2.34]\n", + "Epoch 26: 100%|███████████████████████████████████████████████| 999/999 [00:56<00:00, 17.75it/s, ce_loss=2.33]\n", + "Epoch 27: 100%|███████████████████████████████████████████████| 999/999 [00:55<00:00, 17.90it/s, ce_loss=2.33]\n", + "Epoch 28: 100%|███████████████████████████████████████████████| 999/999 [00:56<00:00, 17.70it/s, ce_loss=2.32]\n", + "Epoch 29: 100%|███████████████████████████████████████████████| 999/999 [00:55<00:00, 17.91it/s, ce_loss=2.32]\n", + "Epoch 30: 100%|███████████████████████████████████████████████| 999/999 [00:56<00:00, 17.69it/s, ce_loss=2.31]\n", + "Epoch 31: 100%|███████████████████████████████████████████████| 999/999 [00:56<00:00, 17.75it/s, ce_loss=2.31]\n", + "Epoch 32: 100%|████████████████████████████████████████████████| 999/999 [00:55<00:00, 17.87it/s, ce_loss=2.3]\n", + "Epoch 33: 100%|███████████████████████████████████████████████| 999/999 [00:55<00:00, 17.84it/s, ce_loss=2.29]\n", + "Epoch 34: 100%|███████████████████████████████████████████████| 999/999 [00:56<00:00, 17.56it/s, ce_loss=2.29]\n", + "Epoch 35: 100%|███████████████████████████████████████████████| 999/999 [00:56<00:00, 17.59it/s, ce_loss=2.29]\n", + "Epoch 36: 100%|███████████████████████████████████████████████| 999/999 [00:58<00:00, 17.18it/s, ce_loss=2.28]\n", + "Epoch 37: 100%|███████████████████████████████████████████████| 999/999 [00:58<00:00, 17.20it/s, ce_loss=2.28]\n", + "Epoch 38: 100%|███████████████████████████████████████████████| 999/999 [00:58<00:00, 17.02it/s, ce_loss=2.28]\n", + "Epoch 39: 100%|███████████████████████████████████████████████| 999/999 [00:58<00:00, 17.05it/s, ce_loss=2.27]\n", + "Epoch 40: 100%|███████████████████████████████████████████████| 999/999 [00:57<00:00, 17.37it/s, ce_loss=2.27]\n", + "Epoch 41: 100%|███████████████████████████████████████████████| 999/999 [00:58<00:00, 17.18it/s, ce_loss=2.26]\n", + "Epoch 42: 100%|███████████████████████████████████████████████| 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"Epoch 51: 100%|███████████████████████████████████████████████| 999/999 [00:58<00:00, 16.99it/s, ce_loss=2.23]\n", + "Epoch 52: 100%|███████████████████████████████████████████████| 999/999 [00:58<00:00, 17.07it/s, ce_loss=2.23]\n", + "Epoch 53: 100%|███████████████████████████████████████████████| 999/999 [00:57<00:00, 17.36it/s, ce_loss=2.23]\n", + "Epoch 54: 100%|███████████████████████████████████████████████| 999/999 [00:57<00:00, 17.33it/s, ce_loss=2.23]\n", + "Epoch 55: 100%|███████████████████████████████████████████████| 999/999 [00:58<00:00, 17.08it/s, ce_loss=2.22]\n", + "Epoch 56: 100%|███████████████████████████████████████████████| 999/999 [00:58<00:00, 17.09it/s, ce_loss=2.22]\n", + "Epoch 57: 100%|███████████████████████████████████████████████| 999/999 [00:58<00:00, 17.15it/s, ce_loss=2.22]\n", + "Epoch 58: 100%|███████████████████████████████████████████████| 999/999 [00:57<00:00, 17.22it/s, ce_loss=2.22]\n", + "Epoch 59: 100%|███████████████████████████████████████████████| 999/999 [00:58<00:00, 17.21it/s, ce_loss=2.21]\n", + "Epoch 60: 100%|███████████████████████████████████████████████| 999/999 [00:58<00:00, 17.05it/s, ce_loss=2.21]\n", + "Epoch 61: 100%|███████████████████████████████████████████████| 999/999 [00:58<00:00, 17.00it/s, ce_loss=2.21]\n", + "Epoch 62: 100%|███████████████████████████████████████████████| 999/999 [00:58<00:00, 17.03it/s, ce_loss=2.21]\n", + "Epoch 63: 100%|████████████████████████████████████████████████| 999/999 [00:58<00:00, 17.16it/s, ce_loss=2.2]\n", + "Epoch 64: 100%|████████████████████████████████████████████████| 999/999 [00:58<00:00, 17.10it/s, ce_loss=2.2]\n", + "Epoch 65: 100%|████████████████████████████████████████████████| 999/999 [00:58<00:00, 17.18it/s, ce_loss=2.2]\n", + "Epoch 66: 100%|████████████████████████████████████████████████| 999/999 [00:58<00:00, 17.09it/s, ce_loss=2.2]\n", + "Epoch 67: 100%|███████████████████████████████████████████████| 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"Epoch 92: 100%|███████████████████████████████████████████████| 999/999 [00:56<00:00, 17.65it/s, ce_loss=2.15]\n", + "Epoch 93: 100%|███████████████████████████████████████████████| 999/999 [00:57<00:00, 17.34it/s, ce_loss=2.15]\n", + "Epoch 94: 100%|███████████████████████████████████████████████| 999/999 [00:56<00:00, 17.73it/s, ce_loss=2.15]\n", + "Epoch 95: 100%|███████████████████████████████████████████████| 999/999 [00:57<00:00, 17.39it/s, ce_loss=2.15]\n", + "Epoch 96: 100%|███████████████████████████████████████████████| 999/999 [00:56<00:00, 17.57it/s, ce_loss=2.15]\n", + "Epoch 97: 100%|███████████████████████████████████████████████| 999/999 [00:58<00:00, 16.98it/s, ce_loss=2.14]\n", + "Epoch 98: 100%|███████████████████████████████████████████████| 999/999 [00:58<00:00, 17.15it/s, ce_loss=2.14]\n", + "Epoch 99: 100%|███████████████████████████████████████████████| 999/999 [00:57<00:00, 17.41it/s, ce_loss=2.14]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train completed, total time: 6049.573261499405.\n" + ] + } + ], + "source": [ + "n_epochs = 100\n", + "val_interval = 10\n", + "epoch_ce_loss_list = []\n", + "val_ce_epoch_loss_list = []\n", + "intermediary_images = []\n", + "vqvae_model.eval()\n", + "\n", + "total_start = time.time()\n", + "for epoch in range(n_epochs):\n", + " transformer_model.train()\n", + " epoch_loss = 0\n", + " progress_bar = tqdm(enumerate(train_loader), total=len(train_loader), ncols=110)\n", + " progress_bar.set_description(f\"Epoch {epoch}\")\n", + " for step, batch in progress_bar:\n", + "\n", + " images = batch[\"image\"].to(device)\n", + " # Encode images using vqvae and transformer to 1D sequence\n", + " quantizations = vqvae_model.index_quantize(images)\n", + " quantizations = quantizations.reshape(quantizations.shape[0], -1)\n", + " quantizations = quantizations[:, sequence_ordering]\n", + "\n", + " # Pad input to give start of sequence token\n", + " quantizations = F.pad(quantizations, (1, 0), \"constant\", 255) # pad with 0 i.e. vocab size of vqvae\n", + " quantizations = quantizations.long()\n", + "\n", + " quantizations_input = convert_tensor(quantizations[:, :-1], device, non_blocking=True)\n", + " quantizations_target = convert_tensor(quantizations[:, 1:], device, non_blocking=True)\n", + "\n", + " optimizer.zero_grad(set_to_none=True)\n", + "\n", + " # model outputs\n", + " logits = transformer_model(x=quantizations_input).transpose(1, 2)\n", + "\n", + " loss = ce_loss(logits, quantizations_target)\n", + "\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " epoch_loss += loss.item()\n", + "\n", + " progress_bar.set_postfix(\n", + " {\n", + " \"ce_loss\": epoch_loss / (step + 1),\n", + " }\n", + " )\n", + " epoch_ce_loss_list.append(epoch_loss / (step + 1))\n", + "\n", + "\n", + " if (epoch + 1) % val_interval == 0:\n", + " transformer_model.eval()\n", + " val_loss = 0\n", + " with torch.no_grad():\n", + " for val_step, batch in enumerate(val_loader, start=1):\n", + "\n", + " images = batch[\"image\"].to(device)\n", + " # Encode images using vqvae and transformer to 1D sequence\n", + " quantizations = vqvae_model.index_quantize(images)\n", + " quantizations = quantizations.reshape(quantizations.shape[0], -1)\n", + " quantizations = quantizations[:, sequence_ordering]\n", + "\n", + " # Pad input to give start of sequence token\n", + " quantizations = F.pad(quantizations, (1, 0), \"constant\", 255) # pad with 255 i.e. vocab size of vqvae\n", + " quantizations = quantizations.long()\n", + "\n", + " quantizations_input = convert_tensor(quantizations[:, :-1], device, non_blocking=True)\n", + " quantizations_target = convert_tensor(quantizations[:, 1:], device, non_blocking=True)\n", + "\n", + " # model outputs\n", + " logits = transformer_model(x=quantizations_input).transpose(1, 2)\n", + "\n", + " loss = ce_loss(logits, quantizations_target)\n", + "\n", + " # Generate a random sample to visualise progress\n", + " if val_step == 1:\n", + " starting_token = 255 * torch.ones((1, 1), device=device)\n", + " generated_latent = generate(transformer_model, vqvae_model, starting_token, spatial_shape[0]*spatial_shape[1])\n", + " generated_latent = generated_latent[0]\n", + " vqvae_latent = generated_latent[revert_sequence_ordering]\n", + " vqvae_latent = vqvae_latent.reshape((1,)+spatial_shape)\n", + " decoded = vqvae_model.decode_samples(vqvae_latent)\n", + " intermediary_images.append(decoded[:, 0])\n", + "\n", + " val_loss += loss.item()\n", + "\n", + " val_loss /= val_step\n", + " val_ce_epoch_loss_list.append(val_loss)\n", + "\n", + "total_time = time.time() - total_start\n", + "print(f\"train completed, total time: {total_time}.\")" + ] + }, + { + "cell_type": "markdown", + "id": "1100d2c4", + "metadata": {}, + "source": [ + "### Transformer Loss Curve" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "7fd86e1e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.style.use(\"ggplot\")\n", + "plt.title(\"Learning Curves\", fontsize=20)\n", + "plt.plot(np.linspace(1, n_epochs, n_epochs), epoch_ce_loss_list, color=\"C0\", linewidth=2.0, label=\"Train\")\n", + "plt.plot(\n", + " np.linspace(val_interval, n_epochs, int(n_epochs / val_interval)),\n", + " val_ce_epoch_loss_list,\n", + " color=\"C1\",\n", + " linewidth=2.0,\n", + " label=\"Validation\",\n", + ")\n", + "plt.yticks(fontsize=12)\n", + "plt.xticks(fontsize=12)\n", + "plt.xlabel(\"Epochs\", fontsize=16)\n", + "plt.ylabel(\"Loss\", fontsize=16)\n", + "plt.legend(prop={\"size\": 14})\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "67e4eebf", + "metadata": {}, + "source": [ + "### Plot evoluation of Generated Samples" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "391f5417", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot every evaluation as a new line and example as columns\n", + "val_samples = np.linspace(val_interval, n_epochs, int(n_epochs / val_interval))\n", + "print(len(val_samples))\n", + "fig, ax = plt.subplots(nrows=len(val_samples), ncols=1, sharey=True)\n", + "fig.set_size_inches(12, 30)\n", + "for image_n in range(len(val_samples)):\n", + " reconstructions = intermediary_images[image_n][0]\n", + " ax[image_n].imshow(reconstructions.cpu(), cmap=\"gray\")\n", + " ax[image_n].set_xticks([])\n", + " ax[image_n].set_yticks([])\n", + " ax[image_n].set_ylabel(f\"Epoch {val_samples[image_n]:.0f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "0fffe05f", + "metadata": {}, + "source": [ + "### Generating samples from the trained model" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "1fdbabce", + "metadata": {}, + "outputs": [], + "source": [ + "samples = []\n", + "for i in range(5):\n", + " starting_token = 255 * torch.ones((1, 1), device=device)\n", + " generated_latent = generate(transformer_model, vqvae_model, starting_token, spatial_shape[0]*spatial_shape[1])\n", + " generated_latent = generated_latent[0]\n", + " vqvae_latent = generated_latent[revert_sequence_ordering]\n", + " vqvae_latent = vqvae_latent.reshape((1,)+spatial_shape)\n", + " decoded = vqvae_model.decode_samples(vqvae_latent)\n", + " samples.append(decoded[:, 0])" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "63bf0adb", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(nrows=1, ncols=5)\n", + "for i in range(5):\n", + " ax[i].imshow(samples[i][0].detach().cpu(), vmin=0, vmax=1, cmap=\"gray\")\n", + " ax[i].axis(\"off\")\n", + " ax[i].title.set_text(\"Sample \" + str(i))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "82147d1f", + "metadata": {}, + "source": [ + "### Cleanup data directory\n", + "\n", + "Remove directory if a temporary was used." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "2bb33a5d", + "metadata": {}, + "outputs": [], + "source": [ + "if directory is None:\n", + " shutil.rmtree(root_dir)" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,py:percent" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials/generative/2d_vqvae_transformer/2d_vqvae_transformer_tutorial.py b/tutorials/generative/2d_vqvae_transformer/2d_vqvae_transformer_tutorial.py new file mode 100644 index 00000000..be387a2f --- /dev/null +++ b/tutorials/generative/2d_vqvae_transformer/2d_vqvae_transformer_tutorial.py @@ -0,0 +1,552 @@ +# --- +# jupyter: +# jupytext: +# formats: ipynb,py:percent +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.3' +# jupytext_version: 1.14.4 +# kernelspec: +# display_name: Python 3 (ipykernel) +# language: python +# name: python3 +# --- + +# %% [markdown] +# # Vector Quantized Variational Autoencoders and Transformers with MedNIST Dataset +# +# This tutorial illustrates how to use MONAI for training a Vector Quantized Variational Autoencoder (VQVAE)[1] and a transformer model on 2D images. +# +# This is a two step process: +# - We will train our VQVAE model to be able to reconstruct the input images. +# - This will be followed by using the trained VQVAE model to encode images to feed into the transformer network to train. +# +# We will work with the MedNIST dataset available on MONAI +# (https://docs.monai.io/en/stable/apps.html#monai.apps.MedNISTDataset). In order to train faster, we will select just one of the available classes ("HeadCT"), resulting in a training set with 7999 2D images. +# +# [1] - [Oord et al. "Neural Discrete Representation Learning"](https://arxiv.org/abs/1711.00937) +# +# +# ### Setup imports + +# %% +# Copyright 2020 MONAI Consortium +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import os +import tempfile +import shutil +import time + + +import matplotlib.pyplot as plt +import numpy as np +import torch +from torch.nn import L1Loss, CrossEntropyLoss +import torch.nn.functional as F +from monai import transforms +from monai.apps import MedNISTDataset +from monai.config import print_config +from monai.data import DataLoader, Dataset +from monai.utils import first, set_determinism +from tqdm import tqdm +from ignite.utils import convert_tensor + +from generative.networks.nets import VQVAE, DecoderOnlyTransformer +from generative.utils.ordering import Ordering +from generative.utils.enums import OrderingType + +print_config() + +# %% +# for reproducibility purposes set a seed +set_determinism(42) + +# %% [markdown] +# ### Setup a data directory and download dataset +# +# Specify a `MONAI_DATA_DIRECTORY` variable, where the data will be downloaded. If not +# specified a temporary directory will be used. + +# %% +directory = os.environ.get("MONAI_DATA_DIRECTORY") +root_dir = tempfile.mkdtemp() if directory is None else directory +print(root_dir) + +# %% [markdown] +# ### Download training data + +# %% +train_data = MedNISTDataset(root_dir=root_dir, section="training", download=True, seed=0) +train_datalist = [{"image": item["image"]} for item in train_data.data if item["class_name"] == "HeadCT"] +image_size = 64 +train_transforms = transforms.Compose( + [ + transforms.LoadImaged(keys=["image"]), + transforms.EnsureChannelFirstd(keys=["image"]), + transforms.ScaleIntensityRanged(keys=["image"], a_min=0.0, a_max=255.0, b_min=0.0, b_max=1.0, clip=True), + transforms.RandAffined( + keys=["image"], + rotate_range=[(-np.pi / 36, np.pi / 36), (-np.pi / 36, np.pi / 36)], + translate_range=[(-1, 1), (-1, 1)], + scale_range=[(-0.05, 0.05), (-0.05, 0.05)], + spatial_size=[image_size, image_size], + padding_mode="zeros", + prob=0.5, + ), + ] +) +train_ds = Dataset(data=train_datalist, transform=train_transforms) +train_loader = DataLoader(train_ds, batch_size=64, shuffle=True, num_workers=4) + +# %% [markdown] +# ### Visualse some examples from the dataset + +# %% +# Plot 3 examples from the training set +check_data = first(train_loader) +fig, ax = plt.subplots(nrows=1, ncols=3) +for image_n in range(3): + ax[image_n].imshow(check_data["image"][image_n, 0, :, :], cmap="gray") + ax[image_n].axis("off") + +# %% [markdown] +# ### Download Validation Data + +# %% +val_data = MedNISTDataset(root_dir=root_dir, section="validation", download=True, seed=0) +val_datalist = [{"image": item["image"]} for item in train_data.data if item["class_name"] == "HeadCT"] +val_transforms = transforms.Compose( + [ + transforms.LoadImaged(keys=["image"]), + transforms.EnsureChannelFirstd(keys=["image"]), + transforms.ScaleIntensityRanged(keys=["image"], a_min=0.0, a_max=255.0, b_min=0.0, b_max=1.0, clip=True), + ] +) +val_ds = Dataset(data=val_datalist, transform=val_transforms) +val_loader = DataLoader(val_ds, batch_size=64, shuffle=True, num_workers=4) + +# %% [markdown] +# ## VQVAE Training +# The first step is to train a VQVAE network - once this is done we can use the trained vqvae model to encode the 2d images to generate the inputs required for the transformer + +# %% [markdown] +# ### Define network, optimizer and losses + +# %% +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +print(f"Using {device}") +vqvae_model = VQVAE( + spatial_dims=2, + in_channels=1, + out_channels=1, + num_res_layers=2, + num_levels=2, + downsample_parameters=((2, 4, 1, 1), (2, 4, 1, 1)), + upsample_parameters=((2, 4, 1, 1, 0), (2, 4, 1, 1, 0)), + num_channels=(256,256), + num_res_channels=(256,256), + num_embeddings=256, + embedding_dim=32, +) +vqvae_model.to(device) + +# %% +optimizer = torch.optim.Adam(params=vqvae_model.parameters(), lr=1e-4) +l1_loss = L1Loss() + +# %% [markdown] +# ### VQVAE Model training +# We will run our model for 100 epochs + +# %% +n_epochs = 100 +val_interval = 10 +epoch_recon_loss_list = [] +epoch_quant_loss_list = [] +val_recon_epoch_loss_list = [] +intermediary_images = [] +n_example_images = 4 + +total_start = time.time() +for epoch in range(n_epochs): + vqvae_model.train() + epoch_loss = 0 + progress_bar = tqdm(enumerate(train_loader), total=len(train_loader), ncols=110) + progress_bar.set_description(f"Epoch {epoch}") + for step, batch in progress_bar: + images = batch["image"].to(device) + optimizer.zero_grad(set_to_none=True) + + # model outputs reconstruction and the quantization error + reconstruction, quantization_loss = vqvae_model(images=images) + + recons_loss = l1_loss(reconstruction.float(), images.float()) + + loss = recons_loss + quantization_loss + + loss.backward() + optimizer.step() + + epoch_loss += recons_loss.item() + + progress_bar.set_postfix( + { + "recons_loss": epoch_loss / (step + 1), + "quantization_loss": quantization_loss.item() / (step + 1), + } + ) + epoch_recon_loss_list.append(epoch_loss / (step + 1)) + epoch_quant_loss_list.append(quantization_loss.item() / (step + 1)) + + if (epoch + 1) % val_interval == 0: + vqvae_model.eval() + val_loss = 0 + with torch.no_grad(): + k = 0 + for val_step, batch in enumerate(val_loader, start=1): + k += 1 + if k == 3: + break + images = batch["image"].to(device) + + reconstruction, quantization_loss = vqvae_model(images=images) + + # get the first sample from the first validation batch for + # visualizing how the training evolves + if val_step == 1: + intermediary_images.append(reconstruction[:n_example_images, 0]) + + recons_loss = l1_loss(reconstruction.float(), images.float()) + + val_loss += recons_loss.item() + + val_loss /= val_step + val_recon_epoch_loss_list.append(val_loss) + +total_time = time.time() - total_start +print(f"train completed, total time: {total_time}.") + +# %% [markdown] +# ### VQVE Loss Curve + +# %% +plt.style.use("ggplot") +plt.title("Learning Curves", fontsize=20) +plt.plot(np.linspace(1, n_epochs, n_epochs), epoch_recon_loss_list, color="C0", linewidth=2.0, label="Train") +plt.plot( + np.linspace(val_interval, n_epochs, int(n_epochs / val_interval)), + val_recon_epoch_loss_list, + color="C1", + linewidth=2.0, + label="Validation", +) +plt.yticks(fontsize=12) +plt.xticks(fontsize=12) +plt.xlabel("Epochs", fontsize=16) +plt.ylabel("Loss", fontsize=16) +plt.legend(prop={"size": 14}) +plt.show() + +# %% [markdown] +# ### Plotting evolution of reconstruction performance + +# %% +# Plot every evaluation as a new line and example as columns +val_samples = np.linspace(val_interval, n_epochs, int(n_epochs / val_interval)) +fig, ax = plt.subplots(nrows=len(val_samples), ncols=1, sharey=True) +fig.set_size_inches(18, 30) +for image_n in range(len(val_samples)): + reconstructions = torch.reshape(intermediary_images[image_n], (64 * n_example_images, 64)).T + ax[image_n].imshow(reconstructions.cpu(), cmap="gray") + ax[image_n].set_xticks([]) + ax[image_n].set_yticks([]) + ax[image_n].set_ylabel(f"Epoch {val_samples[image_n]:.0f}") + + +# %% [markdown] +# ### Plot reconstructions of final trained vqvae model + +# %% +fig, ax = plt.subplots(nrows=1, ncols=2) +ax[0].imshow(images[0, 0].detach().cpu(), vmin=0, vmax=1, cmap="gray") +ax[0].axis("off") +ax[0].title.set_text("Inputted Image") +ax[1].imshow(reconstruction[0, 0].detach().cpu(), vmin=0, vmax=1, cmap="gray") +ax[1].axis("off") +ax[1].title.set_text("Reconstruction") +plt.show() + +# %% [markdown] +# ## Transformer Training +# Now that a vqvae model has been trained, we can use this model to encode the data into its discrete latent representations. These inputs can then be flattened into a 1D sequence for the transformer to learn in an autoregressive manor. +# +# Training can be done in 2 ways: +# - Loading in the original images and then encoding these images on the fly during training using the vqvae model, the advantage of this is we can augment training data during training that is then encoded, however this will slow down training and is more memory intensive. +# - Before training the transformer we encode all the training data first and save the discrete encodings. These latent codes are then loaded and fed to the transformer for training. +# +# For this tutorial we will use the first appraoch and use the vqvae network to encode the data during the training cycle + +# %% [markdown] +# ### Datasets +# We can use the same dataloader with augmentations as used for training the VQVAE model. However given the memory intensive nature of Transformer models we will need to reduce the batch size + +# %% +train_loader = DataLoader(train_ds, batch_size=8, shuffle=True, num_workers=4) +val_loader = DataLoader(val_ds, batch_size=8, shuffle=True, num_workers=4) + +# %% [markdown] +# ### Latent sequence ordering +# We need to define an ordering of which we convert our 2D latent space into a 1D sequence. For this we will use a simple raster scan. + +# %% +spatial_shape = next(iter(train_loader))["image"].shape[2:] + +# %% +# Get spatial dimensions of data +# We divide the spatial shape by 4 as the vqvae downsamples the image by a factor of 4 along each dimension +spatial_shape = next(iter(train_loader))["image"].shape[2:] +spatial_shape = (int(spatial_shape[0]/4),int(spatial_shape[1]/4)) + +ordering = Ordering(ordering_type=OrderingType.RASTER_SCAN.value, + spatial_dims=2, + dimensions=(1,) + spatial_shape) + +sequence_ordering = ordering.get_sequence_ordering() +revert_sequence_ordering = ordering.get_revert_sequence_ordering() + + +# %% [markdown] +# ## Define Network, optimizer and losses + +# %% +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + +transformer_model = DecoderOnlyTransformer( + num_tokens= 256, # must be equal to num_embeddings input of VQVAE + max_seq_len=spatial_shape[0]*spatial_shape[1], + attn_layers_dim=64, + attn_layers_depth=12, + attn_layers_heads=8, +) +transformer_model.to(device) + +# %% +optimizer = torch.optim.Adam(params=transformer_model.parameters(), lr=1e-3) +ce_loss = CrossEntropyLoss() + + +# %% [markdown] +# First we will define a function to allow us to generate random samples from the transformer. This will allow us to keep track of training progress as well to see how samples look during the training cycle + +# %% +@torch.no_grad() +def generate( + net, + vqvae_model, + starting_tokens, + seq_len, + **kwargs +): + + progress_bar = iter(range(seq_len)) + + latent_seq = starting_tokens.long() + for _ in progress_bar: + # if the sequence context is growing too long we must crop it at block_size + if latent_seq.size(1) <= net.max_seq_len: + idx_cond = latent_seq + else: + idx_cond = latent_seq[:, -net.max_seq_len :] + + # forward the model to get the logits for the index in the sequence + logits = net(x=idx_cond) + # pluck the logits at the final step and scale by desired temperature + logits = logits[:, -1, :] + # optionally crop the logits to only the top k options + + + # apply softmax to convert logits to (normalized) probabilities + probs = F.softmax(logits, dim=-1) + # remove the chance to be sampled the BOS token + probs[:, vqvae_model.num_embeddings-1] = 0 + + # sample from the distribution + idx_next = torch.multinomial(probs, num_samples=1) + latent_seq = torch.cat((latent_seq, idx_next), dim=1) + + latent_seq = latent_seq[:, 1:] + + return latent_seq + +# %% [markdown] +# ### Transformer Model Training +# We will train the model for 100 epochs + +# %% +n_epochs = 100 +val_interval = 10 +epoch_ce_loss_list = [] +val_ce_epoch_loss_list = [] +intermediary_images = [] +vqvae_model.eval() + +total_start = time.time() +for epoch in range(n_epochs): + transformer_model.train() + epoch_loss = 0 + progress_bar = tqdm(enumerate(train_loader), total=len(train_loader), ncols=110) + progress_bar.set_description(f"Epoch {epoch}") + for step, batch in progress_bar: + + images = batch["image"].to(device) + # Encode images using vqvae and transformer to 1D sequence + quantizations = vqvae_model.index_quantize(images) + quantizations = quantizations.reshape(quantizations.shape[0], -1) + quantizations = quantizations[:, sequence_ordering] + + # Pad input to give start of sequence token + quantizations = F.pad(quantizations, (1, 0), "constant", 255) # pad with 0 i.e. vocab size of vqvae + quantizations = quantizations.long() + + quantizations_input = convert_tensor(quantizations[:, :-1], device, non_blocking=True) + quantizations_target = convert_tensor(quantizations[:, 1:], device, non_blocking=True) + + optimizer.zero_grad(set_to_none=True) + + # model outputs + logits = transformer_model(x=quantizations_input).transpose(1, 2) + + loss = ce_loss(logits, quantizations_target) + + loss.backward() + optimizer.step() + + epoch_loss += loss.item() + + progress_bar.set_postfix( + { + "ce_loss": epoch_loss / (step + 1), + } + ) + epoch_ce_loss_list.append(epoch_loss / (step + 1)) + + + if (epoch + 1) % val_interval == 0: + transformer_model.eval() + val_loss = 0 + with torch.no_grad(): + for val_step, batch in enumerate(val_loader, start=1): + + images = batch["image"].to(device) + # Encode images using vqvae and transformer to 1D sequence + quantizations = vqvae_model.index_quantize(images) + quantizations = quantizations.reshape(quantizations.shape[0], -1) + quantizations = quantizations[:, sequence_ordering] + + # Pad input to give start of sequence token + quantizations = F.pad(quantizations, (1, 0), "constant", 255) # pad with 255 i.e. vocab size of vqvae + quantizations = quantizations.long() + + quantizations_input = convert_tensor(quantizations[:, :-1], device, non_blocking=True) + quantizations_target = convert_tensor(quantizations[:, 1:], device, non_blocking=True) + + # model outputs + logits = transformer_model(x=quantizations_input).transpose(1, 2) + + loss = ce_loss(logits, quantizations_target) + + # Generate a random sample to visualise progress + if val_step == 1: + starting_token = 255 * torch.ones((1, 1), device=device) + generated_latent = generate(transformer_model, vqvae_model, starting_token, spatial_shape[0]*spatial_shape[1]) + generated_latent = generated_latent[0] + vqvae_latent = generated_latent[revert_sequence_ordering] + vqvae_latent = vqvae_latent.reshape((1,)+spatial_shape) + decoded = vqvae_model.decode_samples(vqvae_latent) + intermediary_images.append(decoded[:, 0]) + + val_loss += loss.item() + + val_loss /= val_step + val_ce_epoch_loss_list.append(val_loss) + +total_time = time.time() - total_start +print(f"train completed, total time: {total_time}.") + +# %% [markdown] +# ### Transformer Loss Curve + +# %% +plt.style.use("ggplot") +plt.title("Learning Curves", fontsize=20) +plt.plot(np.linspace(1, n_epochs, n_epochs), epoch_ce_loss_list, color="C0", linewidth=2.0, label="Train") +plt.plot( + np.linspace(val_interval, n_epochs, int(n_epochs / val_interval)), + val_ce_epoch_loss_list, + color="C1", + linewidth=2.0, + label="Validation", +) +plt.yticks(fontsize=12) +plt.xticks(fontsize=12) +plt.xlabel("Epochs", fontsize=16) +plt.ylabel("Loss", fontsize=16) +plt.legend(prop={"size": 14}) +plt.show() + +# %% [markdown] +# ### Plot evoluation of Generated Samples + +# %% +# Plot every evaluation as a new line and example as columns +val_samples = np.linspace(val_interval, n_epochs, int(n_epochs / val_interval)) +print(len(val_samples)) +fig, ax = plt.subplots(nrows=len(val_samples), ncols=1, sharey=True) +fig.set_size_inches(12, 30) +for image_n in range(len(val_samples)): + reconstructions = intermediary_images[image_n][0] + ax[image_n].imshow(reconstructions.cpu(), cmap="gray") + ax[image_n].set_xticks([]) + ax[image_n].set_yticks([]) + ax[image_n].set_ylabel(f"Epoch {val_samples[image_n]:.0f}") + + +# %% [markdown] +# ### Generating samples from the trained model + +# %% +samples = [] +for i in range(5): + starting_token = 255 * torch.ones((1, 1), device=device) + generated_latent = generate(transformer_model, vqvae_model, starting_token, spatial_shape[0]*spatial_shape[1]) + generated_latent = generated_latent[0] + vqvae_latent = generated_latent[revert_sequence_ordering] + vqvae_latent = vqvae_latent.reshape((1,)+spatial_shape) + decoded = vqvae_model.decode_samples(vqvae_latent) + samples.append(decoded[:, 0]) + +# %% +fig, ax = plt.subplots(nrows=1, ncols=5) +for i in range(5): + ax[i].imshow(samples[i][0].detach().cpu(), vmin=0, vmax=1, cmap="gray") + ax[i].axis("off") + ax[i].title.set_text("Sample " + str(i)) +plt.show() + +# %% [markdown] +# ### Cleanup data directory +# +# Remove directory if a temporary was used. + +# %% +if directory is None: + shutil.rmtree(root_dir)