From b8ec3645390422675ca053ada38951ba2cb963d9 Mon Sep 17 00:00:00 2001 From: Walter Hugo Lopez Pinaya Date: Sun, 26 Feb 2023 15:32:16 +0000 Subject: [PATCH 1/4] [WIP] Fixed example Signed-off-by: Walter Hugo Lopez Pinaya --- .../generative/3d_ddpm/3d_ddpm_tutorial.ipynb | 617 ++++++++++-------- .../generative/3d_ddpm/3d_ddpm_tutorial.py | 35 +- 2 files changed, 381 insertions(+), 271 deletions(-) diff --git a/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.ipynb b/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.ipynb index 7ea3b769..7e65962c 100644 --- a/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.ipynb +++ b/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.ipynb @@ -23,7 +23,6 @@ "metadata": {}, "outputs": [], "source": [ - "!python -c \"import monai\" || pip install -q \"monai-weekly[gdown, nibabel, tqdm, ignite]\"\n", "!python -c \"import matplotlib\" || pip install -q matplotlib\n", "%matplotlib inline" ] @@ -38,37 +37,46 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 2, "id": "cdea37d5", "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": [ - "MONAI version: 1.1.dev2248\n", - "Numpy version: 1.23.3\n", - "Pytorch version: 1.8.0+cu111\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: 3400bd91422ccba9ccc3aa2ffe7fecd4eb5596bf\n", - "MONAI __file__: /media/walter/Storage/Projects/GenerativeModels/venv/lib/python3.8/site-packages/monai/__init__.py\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: NOT INSTALLED or UNKNOWN VERSION.\n", - "Pillow version: 9.2.0\n", + "scikit-image version: 0.19.3\n", + "Pillow version: 9.3.0\n", "Tensorboard version: 2.11.0\n", - "gdown version: NOT INSTALLED or UNKNOWN VERSION.\n", - "TorchVision version: 0.9.0+cu111\n", + "gdown version: 4.6.0\n", + "TorchVision version: 0.14.1+cu117\n", "tqdm version: 4.64.1\n", - "lmdb version: NOT INSTALLED or UNKNOWN VERSION.\n", - "psutil version: 5.9.3\n", - "pandas version: NOT INSTALLED or UNKNOWN VERSION.\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: NOT INSTALLED or UNKNOWN VERSION.\n", - "mlflow version: NOT INSTALLED or UNKNOWN VERSION.\n", - "pynrrd version: NOT INSTALLED or UNKNOWN VERSION.\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", @@ -129,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "c38b4c33", "metadata": {}, "outputs": [ @@ -137,7 +145,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "/tmp/tmplnn_oq6n\n" + "/tmp/tmpk32kv7za\n" ] } ], @@ -157,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "515d8583", "metadata": {}, "outputs": [], @@ -175,10 +183,18 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 5, "id": "f640d7ac", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + ": Class `AddChannel` has been deprecated since version 0.8. please use MetaTensor data type and monai.transforms.EnsureChannelFirst instead.\n" + ] + } + ], "source": [ "train_transform = Compose(\n", " [\n", @@ -205,42 +221,62 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 6, "id": "ddd61e60", "metadata": { "lines_to_next_cell": 2 }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Task01_BrainTumour.tar: 7.09GB [06:45, 18.8MB/s] " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-02-26 14:41:20,822 - INFO - Downloaded: /tmp/tmpk32kv7za/Task01_BrainTumour.tar\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "2022-11-28 14:36:36,241 - INFO - Verified 'Task01_BrainTumour.tar', md5: 240a19d752f0d9e9101544901065d872.\n", - "2022-11-28 14:36:36,242 - INFO - File exists: /tmp/tmplnn_oq6n/Task01_BrainTumour.tar, skipped downloading.\n", - "2022-11-28 14:36:36,242 - INFO - Non-empty folder exists in /tmp/tmplnn_oq6n/Task01_BrainTumour, skipped extracting.\n" + "2023-02-26 14:41:28,972 - INFO - Verified 'Task01_BrainTumour.tar', md5: 240a19d752f0d9e9101544901065d872.\n", + "2023-02-26 14:41:28,973 - INFO - Writing into directory: /tmp/tmpk32kv7za.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Loading dataset: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 388/388 [03:39<00:00, 1.77it/s]\n" + "Loading dataset: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 388/388 [03:32<00:00, 1.82it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2022-11-28 14:40:23,825 - INFO - Verified 'Task01_BrainTumour.tar', md5: 240a19d752f0d9e9101544901065d872.\n", - "2022-11-28 14:40:23,825 - INFO - File exists: /tmp/tmplnn_oq6n/Task01_BrainTumour.tar, skipped downloading.\n", - "2022-11-28 14:40:23,826 - INFO - Non-empty folder exists in /tmp/tmplnn_oq6n/Task01_BrainTumour, skipped extracting.\n" + "2023-02-26 14:45:14,070 - INFO - Verified 'Task01_BrainTumour.tar', md5: 240a19d752f0d9e9101544901065d872.\n", + "2023-02-26 14:45:14,071 - INFO - File exists: /tmp/tmpk32kv7za/Task01_BrainTumour.tar, skipped downloading.\n", + "2023-02-26 14:45:14,071 - INFO - Non-empty folder exists in /tmp/tmpk32kv7za/Task01_BrainTumour, skipped extracting.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Loading dataset: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 96/96 [00:53<00:00, 1.80it/s]\n" + "Loading dataset: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 96/96 [00:53<00:00, 1.79it/s]\n" ] } ], @@ -268,13 +304,13 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 7, "id": "bffb4abc", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -316,15 +352,14 @@ " spatial_dims=3,\n", " in_channels=1,\n", " out_channels=1,\n", - " model_channels=128,\n", - " attention_resolutions=[8],\n", + " num_channels=[128, 128, 256, 256],\n", + " attention_levels=[False, False, False, True],\n", + " num_head_channels=[128, 128, 256, 256],\n", " num_res_blocks=1,\n", - " channel_mult=[1, 1, 2, 2],\n", - " num_heads=1,\n", ")\n", "model.to(device)\n", "\n", - "scheduler = DDPMScheduler(num_train_timesteps=1000)\n", + "scheduler = DDPMScheduler(num_train_timesteps=1000, beta_schedule=\"scaled_linear\", beta_start=0.0015, beta_end=0.0195)\n", "\n", "inferer = DiffusionInferer(scheduler)\n", "\n", @@ -351,62 +386,37 @@ "name": "stderr", "output_type": "stream", "text": [ - "Epoch 0: 100%|████████████| 25/25 [00:57<00:00, 2.29s/it, loss=0.836]\n", - "Epoch 1: 100%|████████████| 25/25 [00:58<00:00, 2.34s/it, loss=0.505]\n", - "Epoch 2: 100%|████████████| 25/25 [00:59<00:00, 2.37s/it, loss=0.279]\n", - "Epoch 3: 100%|████████████| 25/25 [00:59<00:00, 2.39s/it, loss=0.145]\n", - "Epoch 4: 100%|███████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.0792]\n", - "Epoch 5: 100%|███████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.0471]\n", - "Epoch 6: 100%|███████████| 25/25 [01:00<00:00, 2.43s/it, loss=0.0272]\n", - "Epoch 7: 100%|███████████| 25/25 [01:01<00:00, 2.44s/it, loss=0.0175]\n", - "Epoch 8: 100%|███████████| 25/25 [01:01<00:00, 2.45s/it, loss=0.0166]\n", - "Epoch 9: 100%|███████████| 25/25 [01:00<00:00, 2.44s/it, loss=0.0107]\n", - "Epoch 10: 100%|██████████| 25/25 [01:00<00:00, 2.43s/it, loss=0.0129]\n", - "Epoch 11: 100%|██████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.0123]\n", - "Epoch 12: 100%|██████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.0108]\n", - "Epoch 13: 100%|█████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.00742]\n", - "Epoch 14: 100%|█████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00805]\n", - "Epoch 15: 100%|█████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.00876]\n", - "Epoch 16: 100%|█████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.00957]\n", - "Epoch 17: 100%|█████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.00693]\n", - "Epoch 18: 100%|█████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.00775]\n", - "Epoch 19: 100%|█████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.00958]\n", - "Epoch 20: 100%|█████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.00941]\n", - "Epoch 21: 100%|█████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.00811]\n", - "Epoch 22: 100%|█████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.00768]\n", - "Epoch 23: 100%|█████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.00656]\n", - "Epoch 24: 100%|██████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.0071]\n", - "Epoch 25: 100%|█████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.00618]\n", - "Epoch 26: 100%|█████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.00562]\n", - "Epoch 27: 100%|█████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.00616]\n", - "Epoch 28: 100%|█████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.00688]\n", - "Epoch 29: 100%|█████████| 25/25 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[01:00<00:00, 2.42s/it, loss=0.00418]\n", - "Epoch 43: 100%|█████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.00366]\n", - "Epoch 44: 100%|█████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.00558]\n", - "Epoch 45: 100%|█████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.00472]\n", - "Epoch 46: 100%|███████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.004]\n", - "Epoch 47: 100%|█████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.00502]\n", - "Epoch 48: 100%|█████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.00601]\n", - "Epoch 49: 100%|█████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.00437]\n", - "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [11:40<00:00, 1.43it/s]\n" + "Epoch 0: 100%|████████████| 25/25 [00:15<00:00, 1.58it/s, loss=0.831]\n", + "Epoch 1: 100%|████████████| 25/25 [00:15<00:00, 1.63it/s, loss=0.493]\n", + "Epoch 2: 100%|████████████| 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\n", 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\n", 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"Epoch 35: 100%|█████████| 25/25 [00:16<00:00, 1.56it/s, loss=0.00243]\n", + "Epoch 36: 100%|█████████| 25/25 [00:16<00:00, 1.56it/s, loss=0.00341]\n", + "Epoch 37: 100%|█████████| 25/25 [00:16<00:00, 1.56it/s, loss=0.00235]\n", + "Epoch 38: 100%|█████████| 25/25 [00:16<00:00, 1.56it/s, loss=0.00242]\n", + "Epoch 39: 100%|█████████| 25/25 [00:16<00:00, 1.56it/s, loss=0.00258]\n", + "Epoch 40: 100%|██████████| 25/25 [00:16<00:00, 1.56it/s, loss=0.0023]\n", + "Epoch 41: 100%|█████████| 25/25 [00:15<00:00, 1.57it/s, loss=0.00243]\n", + "Epoch 42: 100%|█████████| 25/25 [00:16<00:00, 1.56it/s, loss=0.00233]\n", + "Epoch 43: 100%|█████████| 25/25 [00:15<00:00, 1.57it/s, loss=0.00254]\n", + "Epoch 44: 100%|█████████| 25/25 [00:15<00:00, 1.56it/s, loss=0.00266]\n", + "Epoch 45: 100%|█████████| 25/25 [00:15<00:00, 1.56it/s, loss=0.00234]\n", + "Epoch 46: 100%|█████████| 25/25 [00:15<00:00, 1.57it/s, loss=0.00193]\n", + "Epoch 47: 100%|█████████| 25/25 [00:15<00:00, 1.56it/s, loss=0.00262]\n", + "Epoch 48: 100%|█████████| 25/25 [00:15<00:00, 1.56it/s, loss=0.00291]\n", + "Epoch 49: 100%|█████████| 25/25 [00:15<00:00, 1.57it/s, loss=0.00224]\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:20<00:00, 49.17it/s]\n" ] }, { "data": { - "image/png": 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" ] @@ -485,62 +470,37 @@ "name": "stderr", "output_type": "stream", "text": [ - "Epoch 100: 100%|████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.00424]\n", - "Epoch 101: 100%|████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.00393]\n", - "Epoch 102: 100%|████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.00318]\n", - "Epoch 103: 100%|████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.00484]\n", - "Epoch 104: 100%|████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.00632]\n", - "Epoch 105: 100%|████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.00684]\n", - "Epoch 106: 100%|████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.00324]\n", - "Epoch 107: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00562]\n", - "Epoch 108: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00503]\n", - "Epoch 109: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00367]\n", - "Epoch 110: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00438]\n", - "Epoch 111: 100%|████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.00331]\n", - "Epoch 112: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00511]\n", - "Epoch 113: 100%|█████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.0031]\n", - "Epoch 114: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00622]\n", - "Epoch 115: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00327]\n", - "Epoch 116: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00508]\n", - "Epoch 117: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00662]\n", - "Epoch 118: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00366]\n", - "Epoch 119: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00303]\n", - "Epoch 120: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00387]\n", - "Epoch 121: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00322]\n", - "Epoch 122: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00455]\n", - "Epoch 123: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00363]\n", - "Epoch 124: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00339]\n", - "Epoch 125: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00274]\n", - "Epoch 126: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00358]\n", - "Epoch 127: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00445]\n", - "Epoch 128: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00413]\n", - "Epoch 129: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00432]\n", - "Epoch 130: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00384]\n", - "Epoch 131: 100%|█████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.0039]\n", - "Epoch 132: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00349]\n", - "Epoch 133: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00343]\n", - "Epoch 134: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00443]\n", - "Epoch 135: 100%|█████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.0028]\n", - "Epoch 136: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00284]\n", - "Epoch 137: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00312]\n", - "Epoch 138: 100%|████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.00356]\n", - "Epoch 139: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00404]\n", - "Epoch 140: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00249]\n", - "Epoch 141: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00366]\n", - "Epoch 142: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00258]\n", - "Epoch 143: 100%|█████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.0043]\n", - "Epoch 144: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00336]\n", - "Epoch 145: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00294]\n", - "Epoch 146: 100%|██████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.004]\n", - "Epoch 147: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00445]\n", - "Epoch 148: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00492]\n", - "Epoch 149: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00558]\n", - 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"Epoch 60: 100%|█████████| 25/25 [00:15<00:00, 1.57it/s, loss=0.00201]\n", + "Epoch 61: 100%|█████████| 25/25 [00:15<00:00, 1.57it/s, loss=0.00213]\n", + "Epoch 62: 100%|█████████| 25/25 [00:15<00:00, 1.56it/s, loss=0.00255]\n", + "Epoch 63: 100%|█████████| 25/25 [00:15<00:00, 1.56it/s, loss=0.00208]\n", + "Epoch 64: 100%|██████████| 25/25 [00:15<00:00, 1.57it/s, loss=0.0017]\n", + "Epoch 65: 100%|█████████| 25/25 [00:15<00:00, 1.57it/s, loss=0.00194]\n", + "Epoch 66: 100%|█████████| 25/25 [00:16<00:00, 1.56it/s, loss=0.00162]\n", + "Epoch 67: 100%|██████████| 25/25 [00:15<00:00, 1.57it/s, loss=0.0015]\n", + "Epoch 68: 100%|█████████| 25/25 [00:16<00:00, 1.56it/s, loss=0.00171]\n", + "Epoch 69: 100%|█████████| 25/25 [00:15<00:00, 1.56it/s, loss=0.00203]\n", + "Epoch 70: 100%|█████████| 25/25 [00:15<00:00, 1.57it/s, loss=0.00164]\n", + "Epoch 71: 100%|█████████| 25/25 [00:15<00:00, 1.56it/s, loss=0.00183]\n", + "Epoch 72: 100%|█████████| 25/25 [00:15<00:00, 1.57it/s, loss=0.00226]\n", + "Epoch 73: 100%|██████████| 25/25 [00:15<00:00, 1.56it/s, loss=0.0018]\n", + "Epoch 74: 100%|█████████| 25/25 [00:15<00:00, 1.56it/s, loss=0.00177]\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:20<00:00, 49.03it/s]\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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loss=0.00447]\n", - "Epoch 162: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00388]\n", - "Epoch 163: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00272]\n", - "Epoch 164: 100%|█████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.0067]\n", - "Epoch 165: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00339]\n", - "Epoch 166: 100%|████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.00398]\n", - "Epoch 167: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00268]\n", - "Epoch 168: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00433]\n", - "Epoch 169: 100%|█████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.0033]\n", - "Epoch 170: 100%|████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.00299]\n", - "Epoch 171: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00389]\n", - "Epoch 172: 100%|████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.00417]\n", - "Epoch 173: 100%|████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.00322]\n", - "Epoch 174: 100%|████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.00202]\n", - "Epoch 175: 100%|████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.00368]\n", - "Epoch 176: 100%|████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.00476]\n", - "Epoch 177: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00273]\n", - "Epoch 178: 100%|████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.00343]\n", - "Epoch 179: 100%|████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.00233]\n", - "Epoch 180: 100%|████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.00397]\n", - "Epoch 181: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00288]\n", - "Epoch 182: 100%|████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.00424]\n", - "Epoch 183: 100%|████████| 25/25 [01:00<00:00, 2.42s/it, loss=0.00272]\n", - "Epoch 184: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00545]\n", - "Epoch 185: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00417]\n", - "Epoch 186: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00437]\n", - "Epoch 187: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00328]\n", - "Epoch 188: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00438]\n", - "Epoch 189: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00451]\n", - "Epoch 190: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00464]\n", - "Epoch 191: 100%|██████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.003]\n", - "Epoch 192: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00295]\n", - "Epoch 193: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00268]\n", - "Epoch 194: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00368]\n", - "Epoch 195: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00321]\n", - "Epoch 196: 100%|█████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.0035]\n", - "Epoch 197: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00393]\n", - "Epoch 198: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00359]\n", - "Epoch 199: 100%|████████| 25/25 [01:00<00:00, 2.41s/it, loss=0.00286]\n", - 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"Epoch 85: 100%|█████████| 25/25 [00:15<00:00, 1.56it/s, loss=0.00213]\n", + "Epoch 86: 100%|█████████| 25/25 [00:15<00:00, 1.56it/s, loss=0.00242]\n", + "Epoch 87: 100%|█████████| 25/25 [00:15<00:00, 1.56it/s, loss=0.00208]\n", + "Epoch 88: 100%|█████████| 25/25 [00:15<00:00, 1.56it/s, loss=0.00228]\n", + "Epoch 89: 100%|█████████| 25/25 [00:16<00:00, 1.56it/s, loss=0.00189]\n", + "Epoch 90: 100%|█████████| 25/25 [00:15<00:00, 1.56it/s, loss=0.00175]\n", + "Epoch 91: 100%|█████████| 25/25 [00:15<00:00, 1.56it/s, loss=0.00224]\n", + "Epoch 92: 100%|█████████| 25/25 [00:16<00:00, 1.56it/s, loss=0.00193]\n", + "Epoch 93: 100%|██████████| 25/25 [00:15<00:00, 1.57it/s, loss=0.0018]\n", + "Epoch 94: 100%|█████████| 25/25 [00:16<00:00, 1.55it/s, loss=0.00167]\n", + "Epoch 95: 100%|█████████| 25/25 [00:15<00:00, 1.57it/s, loss=0.00145]\n", + "Epoch 96: 100%|█████████| 25/25 [00:15<00:00, 1.57it/s, loss=0.00163]\n", + "Epoch 97: 100%|█████████| 25/25 [00:15<00:00, 1.56it/s, loss=0.00166]\n", + "Epoch 98: 100%|█████████| 25/25 [00:16<00:00, 1.56it/s, loss=0.00141]\n", + "Epoch 99: 100%|█████████| 25/25 [00:15<00:00, 1.56it/s, loss=0.00139]\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:20<00:00, 49.35it/s]\n" ] }, { "data": { - "image/png": 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\n", 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" ] @@ -619,13 +638,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "train completed, total time: 14937.766214132309.\n" + "train completed, total time: 2558.3661634922028.\n" ] } ], "source": [ - "n_epochs = 200\n", - "val_interval = 50\n", + "n_epochs = 150\n", + "val_interval = 25\n", "epoch_loss_list = []\n", "val_epoch_loss_list = []\n", "\n", @@ -644,8 +663,13 @@ " # Generate random noise\n", " noise = torch.randn_like(images).to(device)\n", "\n", + " # Create timesteps\n", + " timesteps = torch.randint(\n", + " 0, inferer.scheduler.num_train_timesteps, (images.shape[0],), device=images.device\n", + " ).long()\n", + "\n", " # Get model prediction\n", - " noise_pred = inferer(inputs=images, diffusion_model=model, noise=noise)\n", + " noise_pred = inferer(inputs=images, diffusion_model=model, noise=noise, timesteps=timesteps)\n", "\n", " loss = F.mse_loss(noise_pred.float(), noise.float())\n", "\n", @@ -666,7 +690,12 @@ " noise = torch.randn_like(images).to(device)\n", " with torch.no_grad():\n", " with autocast(enabled=True):\n", - " noise_pred = inferer(inputs=images, diffusion_model=model, noise=noise)\n", + " timesteps = torch.randint(\n", + " 0, inferer.scheduler.num_train_timesteps, (images.shape[0],), device=images.device\n", + " ).long()\n", + "\n", + " # Get model prediction\n", + " noise_pred = inferer(inputs=images, diffusion_model=model, noise=noise, timesteps=timesteps)\n", " val_loss = F.mse_loss(noise_pred.float(), noise.float())\n", "\n", " val_epoch_loss += val_loss.item()\n", @@ -678,7 +707,7 @@ " image = image.to(device)\n", " scheduler.set_timesteps(num_inference_steps=1000)\n", " with autocast(enabled=True):\n", - " image = inferer.sample(input_noise=noise, diffusion_model=model, scheduler=scheduler)\n", + " image = inferer.sample(input_noise=image, diffusion_model=model, scheduler=scheduler)\n", "\n", " plt.figure(figsize=(2, 2))\n", " plt.imshow(image[0, 0, :, :, 15].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n", @@ -700,7 +729,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "id": "c7520419", "metadata": { "lines_to_next_cell": 2 @@ -708,7 +737,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -746,7 +775,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 11, "id": "092eb6a0", "metadata": { "lines_to_next_cell": 2 @@ -756,7 +785,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:51<00:00, 19.50it/s]\n" + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:51<00:00, 19.27it/s]\n" ] } ], @@ -770,13 +799,13 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 12, "id": "5dc3e69d", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -789,11 +818,81 @@ "plt.style.use(\"default\")\n", "plotting_image_0 = np.concatenate([image[0, 0, :, :, 15].cpu(), np.flipud(image[0, 0, :, 24, :].cpu().T)], axis=1)\n", "plotting_image_1 = np.concatenate([np.flipud(image[0, 0, 15, :, :].cpu().T), np.zeros((32, 32))], axis=1)\n", - "plt.imshow(np.concatenate([plotting_image_0, plotting_image_1], axis=0), vmin=0, vmax=1, cmap=\"gray\")\n", + "plt.imshow(np.concatenate([plotting_image_0, plotting_image_1], axis=0), cmap=\"gray\")\n", "plt.tight_layout()\n", "plt.axis(\"off\")\n", "plt.show()" ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "9329ec38", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[[[[-0.0118, -0.0161, -0.0204, ..., -0.0177, -0.0213, -0.0121],\n", + " [-0.0221, -0.0141, -0.0138, ..., -0.0157, -0.0150, -0.0172],\n", + " [-0.0207, -0.0173, -0.0138, ..., -0.0152, -0.0128, -0.0035],\n", + " ...,\n", + " [-0.0197, -0.0170, -0.0155, ..., -0.0220, -0.0165, -0.0158],\n", + " [-0.0122, -0.0134, -0.0122, ..., -0.0125, -0.0143, -0.0174],\n", + " [-0.0097, -0.0158, -0.0132, ..., -0.0090, -0.0218, -0.0054]],\n", + "\n", + " [[-0.0135, -0.0153, -0.0159, ..., -0.0154, -0.0136, -0.0122],\n", + " [-0.0202, -0.0044, -0.0154, ..., -0.0104, -0.0127, -0.0237],\n", + " [-0.0146, -0.0061, -0.0209, ..., -0.0099, -0.0035, -0.0175],\n", + " ...,\n", + " [-0.0141, -0.0155, -0.0132, ..., -0.0117, -0.0034, -0.0124],\n", + " [-0.0151, -0.0135, -0.0084, ..., -0.0099, -0.0103, -0.0147],\n", + " [-0.0154, -0.0138, -0.0188, ..., -0.0135, -0.0183, -0.0186]],\n", + "\n", + " [[-0.0140, -0.0153, -0.0128, ..., -0.0140, -0.0157, -0.0100],\n", + " [-0.0080, -0.0141, -0.0195, ..., -0.0190, -0.0130, -0.0143],\n", + " [-0.0109, -0.0158, -0.0169, ..., -0.0086, -0.0183, -0.0083],\n", + " ...,\n", + " [-0.0169, -0.0170, -0.0210, ..., -0.0166, -0.0115, -0.0055],\n", + " [-0.0139, -0.0102, -0.0239, ..., -0.0054, -0.0090, -0.0142],\n", + " [-0.0142, -0.0163, -0.0141, ..., -0.0292, -0.0181, -0.0136]],\n", + "\n", + " ...,\n", + "\n", + " [[-0.0163, -0.0123, -0.0178, ..., -0.0183, -0.0132, -0.0167],\n", + " [-0.0114, -0.0064, -0.0112, ..., -0.0125, -0.0078, -0.0170],\n", + " [-0.0226, -0.0162, -0.0194, ..., -0.0125, -0.0053, -0.0162],\n", + " ...,\n", + " [-0.0061, -0.0163, -0.0128, ..., -0.0163, -0.0257, -0.0110],\n", + " [-0.0109, -0.0073, -0.0129, ..., -0.0172, -0.0035, -0.0134],\n", + " [-0.0187, -0.0208, -0.0168, ..., -0.0154, -0.0192, -0.0203]],\n", + "\n", + " [[-0.0140, -0.0088, -0.0152, ..., -0.0193, -0.0113, -0.0151],\n", + " [-0.0126, -0.0144, -0.0108, ..., -0.0057, -0.0083, -0.0140],\n", + " [-0.0204, -0.0089, -0.0156, ..., -0.0183, -0.0147, -0.0155],\n", + " ...,\n", + " [-0.0176, -0.0158, -0.0191, ..., -0.0175, -0.0069, -0.0122],\n", + " [-0.0103, -0.0158, -0.0040, ..., -0.0104, -0.0180, -0.0171],\n", + " [-0.0099, -0.0032, -0.0117, ..., -0.0122, -0.0135, -0.0179]],\n", + "\n", + " [[ 0.0144, -0.0134, -0.0216, ..., -0.0157, -0.0157, -0.0201],\n", + " [-0.0065, -0.0194, -0.0155, ..., -0.0205, -0.0168, -0.0207],\n", + " [-0.0139, -0.0150, -0.0128, ..., -0.0134, -0.0197, -0.0205],\n", + " ...,\n", + " [-0.0083, -0.0041, -0.0108, ..., -0.0177, -0.0169, -0.0201],\n", + " [-0.0153, -0.0141, -0.0211, ..., -0.0135, -0.0171, -0.0232],\n", + " [-0.0652, -0.0062, -0.0201, ..., -0.0215, -0.0135, -0.0569]]]]],\n", + " device='cuda:0')" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "image" + ] } ], "metadata": { diff --git a/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.py b/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.py index 7c3d3ce3..06a19b0b 100644 --- a/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.py +++ b/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.py @@ -25,7 +25,6 @@ # ## Setup environment # %% -# !python -c "import monai" || pip install -q "monai-weekly[gdown, nibabel, tqdm, ignite]" # !python -c "import matplotlib" || pip install -q matplotlib # %matplotlib inline @@ -150,15 +149,14 @@ spatial_dims=3, in_channels=1, out_channels=1, - model_channels=128, - attention_resolutions=[8], + num_channels=[128, 128, 256, 256], + attention_levels=[False, False, False, True], + num_head_channels=[128, 128, 256, 256], num_res_blocks=1, - channel_mult=[1, 1, 2, 2], - num_heads=1, ) model.to(device) -scheduler = DDPMScheduler(num_train_timesteps=1000) +scheduler = DDPMScheduler(num_train_timesteps=1000, beta_schedule="scaled_linear", beta_start=0.0015, beta_end=0.0195) inferer = DiffusionInferer(scheduler) @@ -169,8 +167,8 @@ # ### Model training # %% -n_epochs = 200 -val_interval = 50 +n_epochs = 150 +val_interval = 25 epoch_loss_list = [] val_epoch_loss_list = [] @@ -189,8 +187,13 @@ # Generate random noise noise = torch.randn_like(images).to(device) + # Create timesteps + timesteps = torch.randint( + 0, inferer.scheduler.num_train_timesteps, (images.shape[0],), device=images.device + ).long() + # Get model prediction - noise_pred = inferer(inputs=images, diffusion_model=model, noise=noise) + noise_pred = inferer(inputs=images, diffusion_model=model, noise=noise, timesteps=timesteps) loss = F.mse_loss(noise_pred.float(), noise.float()) @@ -211,7 +214,12 @@ noise = torch.randn_like(images).to(device) with torch.no_grad(): with autocast(enabled=True): - noise_pred = inferer(inputs=images, diffusion_model=model, noise=noise) + timesteps = torch.randint( + 0, inferer.scheduler.num_train_timesteps, (images.shape[0],), device=images.device + ).long() + + # Get model prediction + noise_pred = inferer(inputs=images, diffusion_model=model, noise=noise, timesteps=timesteps) val_loss = F.mse_loss(noise_pred.float(), noise.float()) val_epoch_loss += val_loss.item() @@ -223,7 +231,7 @@ image = image.to(device) scheduler.set_timesteps(num_inference_steps=1000) with autocast(enabled=True): - image = inferer.sample(input_noise=noise, diffusion_model=model, scheduler=scheduler) + image = inferer.sample(input_noise=image, diffusion_model=model, scheduler=scheduler) plt.figure(figsize=(2, 2)) plt.imshow(image[0, 0, :, :, 15].cpu(), vmin=0, vmax=1, cmap="gray") @@ -270,7 +278,10 @@ plt.style.use("default") plotting_image_0 = np.concatenate([image[0, 0, :, :, 15].cpu(), np.flipud(image[0, 0, :, 24, :].cpu().T)], axis=1) plotting_image_1 = np.concatenate([np.flipud(image[0, 0, 15, :, :].cpu().T), np.zeros((32, 32))], axis=1) -plt.imshow(np.concatenate([plotting_image_0, plotting_image_1], axis=0), vmin=0, vmax=1, cmap="gray") +plt.imshow(np.concatenate([plotting_image_0, plotting_image_1], axis=0), cmap="gray") plt.tight_layout() plt.axis("off") plt.show() + +# %% +image From c975de410cb0371ea15364f080ffe3c1123144ac Mon Sep 17 00:00:00 2001 From: Walter Hugo Lopez Pinaya Date: Sun, 26 Feb 2023 19:01:19 +0000 Subject: [PATCH 2/4] Fixed tutorial Signed-off-by: Walter Hugo Lopez Pinaya --- .../generative/3d_ddpm/3d_ddpm_tutorial.ipynb | 457 +++++++----------- .../generative/3d_ddpm/3d_ddpm_tutorial.py | 19 +- 2 files changed, 192 insertions(+), 284 deletions(-) diff --git a/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.ipynb b/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.ipynb index 7e65962c..dd5a86f5 100644 --- a/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.ipynb +++ b/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.ipynb @@ -151,7 +151,8 @@ ], "source": [ "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n", - "root_dir = tempfile.mkdtemp() if directory is None else directory\n", + "root_dir = \"/tmp/tmpk32kv7za\"\n", + "# root_dir = tempfile.mkdtemp() if directory is None else directory\n", "print(root_dir)" ] }, @@ -227,56 +228,36 @@ "lines_to_next_cell": 2 }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Task01_BrainTumour.tar: 7.09GB [06:45, 18.8MB/s] " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-02-26 14:41:20,822 - INFO - Downloaded: /tmp/tmpk32kv7za/Task01_BrainTumour.tar\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-02-26 14:41:28,972 - INFO - Verified 'Task01_BrainTumour.tar', md5: 240a19d752f0d9e9101544901065d872.\n", - "2023-02-26 14:41:28,973 - INFO - Writing into directory: /tmp/tmpk32kv7za.\n" + "2023-02-26 15:37:24,411 - INFO - Verified 'Task01_BrainTumour.tar', md5: 240a19d752f0d9e9101544901065d872.\n", + "2023-02-26 15:37:24,412 - INFO - File exists: /tmp/tmpk32kv7za/Task01_BrainTumour.tar, skipped downloading.\n", + "2023-02-26 15:37:24,412 - INFO - Non-empty folder exists in /tmp/tmpk32kv7za/Task01_BrainTumour, skipped extracting.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Loading dataset: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 388/388 [03:32<00:00, 1.82it/s]\n" + "Loading dataset: 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"output_type": "stream", "text": [ - "Loading dataset: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 96/96 [00:53<00:00, 1.79it/s]\n" + "Loading dataset: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 96/96 [01:00<00:00, 1.58it/s]\n" ] } ], @@ -285,13 +266,13 @@ " root_dir=root_dir, task=\"Task01_BrainTumour\", transform=train_transform, section=\"training\", download=True\n", ")\n", "\n", - "train_loader = DataLoader(train_ds, batch_size=16, shuffle=True, num_workers=8)\n", + "train_loader = DataLoader(train_ds, batch_size=8, shuffle=True, num_workers=8)\n", "\n", "val_ds = DecathlonDataset(\n", " root_dir=root_dir, task=\"Task01_BrainTumour\", transform=val_transform, section=\"validation\", download=True\n", ")\n", "\n", - "val_loader = DataLoader(val_ds, batch_size=16, shuffle=False, num_workers=8)" + "val_loader = DataLoader(val_ds, batch_size=8, shuffle=False, num_workers=8)" ] }, { @@ -352,10 +333,10 @@ " spatial_dims=3,\n", " in_channels=1,\n", " out_channels=1,\n", - " num_channels=[128, 128, 256, 256],\n", - " attention_levels=[False, False, False, True],\n", - " num_head_channels=[128, 128, 256, 256],\n", - " num_res_blocks=1,\n", + " num_channels=[256, 256, 512],\n", + " attention_levels=[False, False, True],\n", + " num_head_channels=[256, 256, 512],\n", + " num_res_blocks=2,\n", ")\n", "model.to(device)\n", "\n", @@ -386,37 +367,37 @@ "name": "stderr", "output_type": "stream", "text": [ - "Epoch 0: 100%|████████████| 25/25 [00:15<00:00, 1.58it/s, loss=0.831]\n", - "Epoch 1: 100%|████████████| 25/25 [00:15<00:00, 1.63it/s, loss=0.493]\n", - "Epoch 2: 100%|████████████| 25/25 [00:15<00:00, 1.61it/s, loss=0.266]\n", - "Epoch 3: 100%|████████████| 25/25 [00:15<00:00, 1.62it/s, loss=0.134]\n", - 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"Epoch 15: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00237]\n", + "Epoch 16: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00388]\n", + "Epoch 17: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00504]\n", + "Epoch 18: 100%|█████████| 49/49 [01:07<00:00, 1.37s/it, loss=0.00314]\n", + "Epoch 19: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00338]\n", + "Epoch 20: 100%|██████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.0027]\n", + "Epoch 21: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00242]\n", + "Epoch 22: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00218]\n", + "Epoch 23: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00219]\n", + "Epoch 24: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00291]\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [01:12<00:00, 13.82it/s]\n" ] }, { "data": { - "image/png": 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\n", 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loss=0.00341]\n", - "Epoch 37: 100%|█████████| 25/25 [00:16<00:00, 1.56it/s, loss=0.00235]\n", - "Epoch 38: 100%|█████████| 25/25 [00:16<00:00, 1.56it/s, loss=0.00242]\n", - "Epoch 39: 100%|█████████| 25/25 [00:16<00:00, 1.56it/s, loss=0.00258]\n", - "Epoch 40: 100%|██████████| 25/25 [00:16<00:00, 1.56it/s, loss=0.0023]\n", - "Epoch 41: 100%|█████████| 25/25 [00:15<00:00, 1.57it/s, loss=0.00243]\n", - "Epoch 42: 100%|█████████| 25/25 [00:16<00:00, 1.56it/s, loss=0.00233]\n", - "Epoch 43: 100%|█████████| 25/25 [00:15<00:00, 1.57it/s, loss=0.00254]\n", - "Epoch 44: 100%|█████████| 25/25 [00:15<00:00, 1.56it/s, loss=0.00266]\n", - "Epoch 45: 100%|█████████| 25/25 [00:15<00:00, 1.56it/s, loss=0.00234]\n", - "Epoch 46: 100%|█████████| 25/25 [00:15<00:00, 1.57it/s, loss=0.00193]\n", - "Epoch 47: 100%|█████████| 25/25 [00:15<00:00, 1.56it/s, loss=0.00262]\n", - "Epoch 48: 100%|█████████| 25/25 [00:15<00:00, 1.56it/s, loss=0.00291]\n", - "Epoch 49: 100%|█████████| 25/25 [00:15<00:00, 1.57it/s, loss=0.00224]\n", - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:20<00:00, 49.17it/s]\n" + "Epoch 25: 100%|███████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.002]\n", + "Epoch 26: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00265]\n", + "Epoch 27: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00256]\n", + "Epoch 28: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00223]\n", + "Epoch 29: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00263]\n", + "Epoch 30: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00259]\n", + "Epoch 31: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00268]\n", + "Epoch 32: 100%|██████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.0024]\n", + "Epoch 33: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00203]\n", + "Epoch 34: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00245]\n", + "Epoch 35: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00163]\n", + "Epoch 36: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00214]\n", + "Epoch 37: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00204]\n", + "Epoch 38: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00298]\n", + "Epoch 39: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00194]\n", + "Epoch 40: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00208]\n", + "Epoch 41: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00216]\n", + "Epoch 42: 100%|██████████| 49/49 [01:06<00:00, 1.37s/it, loss=0.0022]\n", + "Epoch 43: 100%|██████████| 49/49 [01:06<00:00, 1.37s/it, loss=0.0017]\n", + "Epoch 44: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00186]\n", + "Epoch 45: 100%|██████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.0021]\n", + "Epoch 46: 100%|█████████| 49/49 [01:06<00:00, 1.37s/it, loss=0.00274]\n", + "Epoch 47: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00186]\n", + "Epoch 48: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00191]\n", + "Epoch 49: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00198]\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [01:12<00:00, 13.84it/s]\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -470,37 +451,37 @@ "name": "stderr", "output_type": "stream", "text": [ - "Epoch 50: 100%|█████████| 25/25 [00:15<00:00, 1.56it/s, loss=0.00205]\n", - "Epoch 51: 100%|██████████| 25/25 [00:15<00:00, 1.57it/s, loss=0.0023]\n", - "Epoch 52: 100%|█████████| 25/25 [00:15<00:00, 1.56it/s, loss=0.00365]\n", - "Epoch 53: 100%|█████████| 25/25 [00:15<00:00, 1.57it/s, loss=0.00247]\n", - "Epoch 54: 100%|█████████| 25/25 [00:15<00:00, 1.56it/s, loss=0.00286]\n", - "Epoch 55: 100%|█████████| 25/25 [00:16<00:00, 1.55it/s, loss=0.00221]\n", - "Epoch 56: 100%|█████████| 25/25 [00:15<00:00, 1.56it/s, loss=0.00232]\n", - "Epoch 57: 100%|█████████| 25/25 [00:15<00:00, 1.57it/s, loss=0.00312]\n", - "Epoch 58: 100%|█████████| 25/25 [00:15<00:00, 1.56it/s, loss=0.00266]\n", - "Epoch 59: 100%|█████████| 25/25 [00:15<00:00, 1.57it/s, loss=0.00224]\n", - "Epoch 60: 100%|█████████| 25/25 [00:15<00:00, 1.57it/s, loss=0.00201]\n", - "Epoch 61: 100%|█████████| 25/25 [00:15<00:00, 1.57it/s, loss=0.00213]\n", - "Epoch 62: 100%|█████████| 25/25 [00:15<00:00, 1.56it/s, loss=0.00255]\n", - "Epoch 63: 100%|█████████| 25/25 [00:15<00:00, 1.56it/s, loss=0.00208]\n", - "Epoch 64: 100%|██████████| 25/25 [00:15<00:00, 1.57it/s, loss=0.0017]\n", - "Epoch 65: 100%|█████████| 25/25 [00:15<00:00, 1.57it/s, loss=0.00194]\n", - "Epoch 66: 100%|█████████| 25/25 [00:16<00:00, 1.56it/s, loss=0.00162]\n", - "Epoch 67: 100%|██████████| 25/25 [00:15<00:00, 1.57it/s, loss=0.0015]\n", - "Epoch 68: 100%|█████████| 25/25 [00:16<00:00, 1.56it/s, loss=0.00171]\n", - "Epoch 69: 100%|█████████| 25/25 [00:15<00:00, 1.56it/s, loss=0.00203]\n", - "Epoch 70: 100%|█████████| 25/25 [00:15<00:00, 1.57it/s, loss=0.00164]\n", - "Epoch 71: 100%|█████████| 25/25 [00:15<00:00, 1.56it/s, loss=0.00183]\n", - "Epoch 72: 100%|█████████| 25/25 [00:15<00:00, 1.57it/s, loss=0.00226]\n", - "Epoch 73: 100%|██████████| 25/25 [00:15<00:00, 1.56it/s, loss=0.0018]\n", - "Epoch 74: 100%|█████████| 25/25 [00:15<00:00, 1.56it/s, loss=0.00177]\n", - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:20<00:00, 49.03it/s]\n" + "Epoch 50: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00254]\n", + "Epoch 51: 100%|█████████| 49/49 [01:07<00:00, 1.37s/it, loss=0.00186]\n", + "Epoch 52: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00189]\n", + "Epoch 53: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00236]\n", + "Epoch 54: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00156]\n", + "Epoch 55: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00257]\n", + "Epoch 56: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00204]\n", + "Epoch 57: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00165]\n", + "Epoch 58: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00235]\n", + "Epoch 59: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00196]\n", + "Epoch 60: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00201]\n", + "Epoch 61: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00214]\n", + "Epoch 62: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00208]\n", + "Epoch 63: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00145]\n", + "Epoch 64: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00141]\n", + "Epoch 65: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00188]\n", + "Epoch 66: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00154]\n", + "Epoch 67: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00163]\n", + "Epoch 68: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00176]\n", + "Epoch 69: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00232]\n", + "Epoch 70: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00178]\n", + "Epoch 71: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00187]\n", + "Epoch 72: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00134]\n", + "Epoch 73: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00161]\n", + "Epoch 74: 100%|██████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.0019]\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [01:12<00:00, 13.85it/s]\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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loss=0.00131]\n", + "Epoch 85: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00233]\n", + "Epoch 86: 100%|█████████| 49/49 [01:06<00:00, 1.37s/it, loss=0.00221]\n", + "Epoch 87: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00219]\n", + "Epoch 88: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00285]\n", + "Epoch 89: 100%|█████████| 49/49 [01:07<00:00, 1.37s/it, loss=0.00185]\n", + "Epoch 90: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00162]\n", + "Epoch 91: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00149]\n", + "Epoch 92: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00151]\n", + "Epoch 93: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00176]\n", + "Epoch 94: 100%|█████████| 49/49 [01:06<00:00, 1.37s/it, loss=0.00218]\n", + "Epoch 95: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00161]\n", + "Epoch 96: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00128]\n", + "Epoch 97: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00179]\n", + "Epoch 98: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00178]\n", + "Epoch 99: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00222]\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [01:12<00:00, 13.85it/s]\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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loss=0.00151]\n", - "Epoch 112: 100%|█████████| 25/25 [00:16<00:00, 1.56it/s, loss=0.0019]\n", - "Epoch 113: 100%|████████| 25/25 [00:15<00:00, 1.56it/s, loss=0.00235]\n", - "Epoch 114: 100%|████████| 25/25 [00:16<00:00, 1.56it/s, loss=0.00207]\n", - "Epoch 115: 100%|████████| 25/25 [00:16<00:00, 1.56it/s, loss=0.00266]\n", - "Epoch 116: 100%|████████| 25/25 [00:15<00:00, 1.56it/s, loss=0.00258]\n", - "Epoch 117: 100%|████████| 25/25 [00:16<00:00, 1.56it/s, loss=0.00305]\n", - "Epoch 118: 100%|████████| 25/25 [00:15<00:00, 1.57it/s, loss=0.00162]\n", - "Epoch 119: 100%|████████| 25/25 [00:15<00:00, 1.56it/s, loss=0.00176]\n", - "Epoch 120: 100%|█████████| 25/25 [00:16<00:00, 1.55it/s, loss=0.0019]\n", - "Epoch 121: 100%|████████| 25/25 [00:15<00:00, 1.57it/s, loss=0.00147]\n", - "Epoch 122: 100%|████████| 25/25 [00:16<00:00, 1.56it/s, loss=0.00175]\n", - "Epoch 123: 100%|████████| 25/25 [00:15<00:00, 1.57it/s, loss=0.00146]\n", - "Epoch 124: 100%|████████| 25/25 [00:15<00:00, 1.57it/s, loss=0.00189]\n", - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:20<00:00, 48.74it/s]\n" + "Epoch 100: 100%|████████| 49/49 [01:06<00:00, 1.37s/it, loss=0.00169]\n", + "Epoch 101: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00171]\n", + "Epoch 102: 100%|████████| 49/49 [01:06<00:00, 1.37s/it, loss=0.00143]\n", + "Epoch 103: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00149]\n", + "Epoch 104: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00151]\n", + "Epoch 105: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.0022]\n", + "Epoch 106: 100%|████████| 49/49 [01:06<00:00, 1.37s/it, loss=0.00145]\n", + "Epoch 107: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00187]\n", + "Epoch 108: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.0019]\n", + "Epoch 109: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00197]\n", + "Epoch 110: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00168]\n", + "Epoch 111: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00203]\n", + "Epoch 112: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00156]\n", + "Epoch 113: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00186]\n", + "Epoch 114: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00139]\n", + "Epoch 115: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00139]\n", + "Epoch 116: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00154]\n", + "Epoch 117: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00216]\n", + "Epoch 118: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00135]\n", + "Epoch 119: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00175]\n", + "Epoch 120: 100%|████████| 49/49 [01:06<00:00, 1.37s/it, loss=0.00199]\n", + "Epoch 121: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00192]\n", + "Epoch 122: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00159]\n", + "Epoch 123: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.0015]\n", + "Epoch 124: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00138]\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [01:12<00:00, 13.87it/s]\n" ] }, { "data": { - "image/png": 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\n", 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loss=0.00151]\n", - "Epoch 137: 100%|████████| 25/25 [00:15<00:00, 1.56it/s, loss=0.00196]\n", - "Epoch 138: 100%|█████████| 25/25 [00:15<00:00, 1.56it/s, loss=0.0018]\n", - "Epoch 139: 100%|████████| 25/25 [00:15<00:00, 1.56it/s, loss=0.00153]\n", - "Epoch 140: 100%|████████| 25/25 [00:15<00:00, 1.57it/s, loss=0.00209]\n", - "Epoch 141: 100%|████████| 25/25 [00:15<00:00, 1.57it/s, loss=0.00153]\n", - "Epoch 142: 100%|████████| 25/25 [00:15<00:00, 1.57it/s, loss=0.00129]\n", - "Epoch 143: 100%|████████| 25/25 [00:16<00:00, 1.56it/s, loss=0.00165]\n", - "Epoch 144: 100%|████████| 25/25 [00:16<00:00, 1.56it/s, loss=0.00173]\n", - "Epoch 145: 100%|████████| 25/25 [00:15<00:00, 1.57it/s, loss=0.00125]\n", - "Epoch 146: 100%|████████| 25/25 [00:15<00:00, 1.57it/s, loss=0.00216]\n", - "Epoch 147: 100%|█████████| 25/25 [00:15<00:00, 1.57it/s, loss=0.0011]\n", - "Epoch 148: 100%|████████| 25/25 [00:16<00:00, 1.55it/s, loss=0.00161]\n", - "Epoch 149: 100%|████████| 25/25 [00:15<00:00, 1.56it/s, loss=0.00154]\n", - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:20<00:00, 48.85it/s]\n" + "Epoch 125: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00136]\n", + "Epoch 126: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00136]\n", + "Epoch 127: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00139]\n", + "Epoch 128: 100%|████████| 49/49 [01:06<00:00, 1.37s/it, loss=0.00156]\n", + "Epoch 129: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00145]\n", + "Epoch 130: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.0019]\n", + "Epoch 131: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00232]\n", + "Epoch 132: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00161]\n", + "Epoch 133: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00184]\n", + "Epoch 134: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00142]\n", + "Epoch 135: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00175]\n", + "Epoch 136: 100%|████████| 49/49 [01:06<00:00, 1.37s/it, loss=0.00131]\n", + "Epoch 137: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00162]\n", + "Epoch 138: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00165]\n", + "Epoch 139: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00159]\n", + "Epoch 140: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00151]\n", + "Epoch 141: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00201]\n", + "Epoch 142: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00159]\n", + "Epoch 143: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00163]\n", + "Epoch 144: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00116]\n", + "Epoch 145: 100%|█████████| 49/49 [01:06<00:00, 1.37s/it, loss=0.0016]\n", + "Epoch 146: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00159]\n", + "Epoch 147: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00157]\n", + "Epoch 148: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00125]\n", + "Epoch 149: 100%|████████| 49/49 [01:06<00:00, 1.37s/it, loss=0.00133]\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [01:12<00:00, 13.86it/s]\n" ] }, { "data": { - "image/png": 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rc9JzSMXbbltad0RCza+SC4Rhz1SdEi5/C7A0Zq/V7HuAHp599OgSRkn7VWqz01dLvlTIKIfVTmirraNeLB4iafZ4hxzKUpJ6KzIaoFTH0RbvRY2sutlUiPaIo0XE9moq8By1nU0DlMGLhPseqcIc+SJbFOPBr8rIETEX2qkh8moPYfIapits6UqrBbcDNyhxzAyLxXOczMqcjlponWhHtM3oScVSla12q0pE71zAs1DlrHjwjO+2baeJx8M5vZudSmyYuW2DFAb5IAWx+jGhKTtS2+nVAdQ4LR70yV3aFg1TeVLQi7d6Nj5rLj70UIK1SbrZrQJYYX3f28+fP63ve/vx48ekflQtcrgkKpMRTSyfJsHbmPR+Z+TlBy/hCbb4HsGrF6TntmoopmTiuO+8u5NCiVPCcU7kwThAUOAo2JKDFVrvHKxCxEglm9nDvbp930+2IqsaHUR8j+whSAyt1zsSBiLivSPcNvbqca1UAnp95e+q/lIhkOg729JRGSUTznaoB4yR96TcCFE4qRabv1WAD0Acj8dvj4RLGb05exT/Iw+dz9d53nXTPN7k79WZ2h3Bf1XFqf1gvaYx0xRy5PVCYN74qkdeY8uvHU1YfM9KrtG3281+/fo1xdNOp9P0yljsO5s9Pg5DSZPyAjX0wFKQfwPpHjrvvOs555joBOq+tqrjlI2ogeqSMFUEbnck/WCa6DUs2jk24tp4mkRsmsaGYZhuYjczO51O34K73qEDgFe8J8FYCnpeoCdBmKAlDlkkCTWdd90bGw54Rwu8lIQp7eLVzXWp2fJsPOWlkJ69eD6fHw6mlqimaNeFHR22P9lQ9yaWtx0hGVPtT9l/kQrPGf8lzkHOs9d2elLZU8f8nElv4T4TqzwNrMSwHcfR+r63f//917qum15zAUQnPzxpofWzRIOEYU82chRy0pAnDPBUMNeRUsUlk+z1l5/dE4WqonK821lhwgzDMP3xc7RfgVVO36RsRv6OO8batp0cF34lrhrNXFdKYnqhHy/wq1B7MIoL4n8k+bTNNSEQJbAeWKjxUnUceRGxNORdnLWIOLe9m4RvUulYWmFVmj0GXtlOXOJRR0Z7dBxK24nrkXqL8kd99z5HZdTuyyOv58lDunOcld8i8Eps+jB3Lw8GqO97+/z8tOv1aj9+/JhOCfPKVEkYhWAYrN7HcUwSWiUckwztYNspJTVykYQSienZsWzv8vcorzoeSkAIgM/Pz+nuyjVfgabtL8XT32DPe6n8pFeVGCBRiWSIzIQoLBIRxZN2LF3wfQ7mEFCJWCoVPRJ6Dx2ACmZpqP17FZ5ORLM/g4OB6LrOfv36ZX3fT4835iP6ajehjJztyGkjZ8ILwbBE8WwrRRRYVuRuIcj1IfWd+8FeMP9hZ0tP2iyVhnPCRhE2J2JEkuv1aj9//rS2ba3v++n51XiPMwea+c4yr0zvfhLA82J50lKhGEwqS25PgvCxMQ1Se21LEZJji7wn7yGShOyMgHS32+P7muEpczmvxCYntFNgYnAwm0n38fHxEFpRFaV2I0+Wd/KEgQlL7Yhw+1Q6RnYitzG1MBiRMwUtwf3M2bhqy6pTcr/f7evra3JOeF95DSyVik95g31qQtiLHsdxcmKGYZjeHqDvyjN7fPMAG/MlEkS/RwFgbaPnTHiho5RkjurzrnvpGSydmYg4dcSSL6WK15CIT1PNSyrKeZGILzZNM6mQrvv/JZI4Osa2o9pg3lEoJUM0sephah5VxZ6K5f+6N54KNkdee5QPaTynhIkIUwd2oXcEbk11nJvfErzEWVGM4zjtR2OQMbm8+8IS0VPNuueaMyc8aaTeewpMuJL96shh8rzmKK8uHDUhoI5BRI3VRnb2q/F0G9EDe8C82odhsN+/f9swDNNbNfkWVdiRfO8v21ZeW9nGi4gQ9VGlYcoh4b5pfXzdO1BrVqaOWcLxYQ9WzVtIwC3wEonokZonBk4Mq2w+mHA6naYjZMfj0S6Xy8MTrVKxtyjGxp8V/EYC7wmw3lE1L/Sjux24zo6QOkecXk0FlYSaxrNtdby3xNO3+LTyVCdzAWeeAA6d8EFafaMoH/nHJCk5PGk3Vw3Dy1VJqEipUVznviIdv2dFCZkimRKOP79KEm6+s5Ij2pyOz03LBIVDw+3Cy4UgQfX0NcqJTtN4HiWn0TajnNTJHXYq9JoSSyViJEG1zZrmb8Si+5prOj43jzfxsIMilY3TPZfL5ds9yymbzJOQniftfc55uXwCmu+YKyEf1+WpbC/N34bFz0d8ZqcjlQ2SwVPEzoS+KgLtjc7oKfm0bylyqjOiUCLqS9C9eqP6X2XzbYlFRHw2CXMnqCEp1aPlv5zkQjm5NCnJ5OVjyccSMXJOtG8p/O0kNDNrxsJebPm0g1J4zgfbgCwdeYtQr5cEvb16PftMyeMdS/McDi/vWgRMHUl7lhbjNkTRCMbLwjdr2pessj1ppiT0SBmVz2n0vmnPiYmOpJVIz0gVa3uW4tkmVQledgysFiVeeyTpxvHxAUk5InrlRM5EVEZ0/VVEeDcCAn+9atbrJXmXSJUc0UrV/Vp53xVvq5o9SZZTi7VISSptR029JfvJ3vcc0Via/00kfOrB2KUDtMbA5sp41uSlPPhcPgBmwd9EuAhr9GH1nZWatEs7Mie/kqikjam98VT6KG+ufTksEQq5vGtJ5Ln5qw2/pQ3NSZQtjylp3K60rtJ0KUdmq3HzogBzyqzNuxaqiZgz/qNYHfBKNcsDvwY5orKj33L559QFlDhvtWXPyVNL6NVeCvlslEzCGnG4KEC9tNxUPk89lpTvBfm9NKVkiea5pP+bn75JVVwrWbYy2tewpaLfUiSpJZJX9py8pWXOcbZSC6WkrlK8Pji4Y4fNCGjv2LEldom44y2wE3HHW2An4o63wE7EHW+BnYg73gI7EXe8BXYi7ngL7ETc8RbYibjjLfAfTNUg+A5FWscAAAAASUVORK5CYII=\n", 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" ] @@ -638,7 +619,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "train completed, total time: 2558.3661634922028.\n" + "train completed, total time: 10538.769879579544.\n" ] } ], @@ -737,7 +718,7 @@ "outputs": [ { "data": { - "image/png": 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rr74aNM1WuPr06aM777xTkvdmDddee62+/PLLoDpgwzC0fv16DR8+XC+88IIkbznJE088EXSsDh06+Ns2YcIE/wiv2+3W8uXLNWrUKP82BeWbc/e7777zl2Ccbi7i8847TzfffLMkad68eXrllVeCLv7bt2+f/vWvf2n16tXauXNn0NRnDRo00NGjR/Xxxx9rxowZucow/vjjD/3nP/+RJDVs2DDoZicAig4XqAEocomJiacNF4FuueUW/fOf/5QktWnTRpMnT9a///1vrVu3Tr169VLt2rXlcDi0f/9+/wjZ1VdfrVGjRgUdZ8yYMbrnnnuUmpqqG2+8UbVq1ZJhGNq/f79sNpsmTZqktm3b6pVXXpHH49E999yjWrVq6c033zTdTRPCMWbMGK1du1ZJSUmaNm2aZs6cqerVq2v//v1KT0/XmDFjlJ2dHfId3fIyduxYXXzxxXriiSe0c+dOjRkzRg6HQ9WqVZPD4dDff//t/4g/IiJCw4YN03333ZdrvuNevXrpoosu0q+//qpFixbpiy++UIUKFZSenq7MzEz17t1bV111ldasWSPJG4LDvcWxz5VXXqmYmBj9+eefkrwXofXo0eO02z/22GM6fPiwVqxYoZdeekmvv/66atWqpbS0NB06dEgej0cRERF65plngsLujTfeqB9//FGff/65Xn75Zb322muqWrWqypUrp+TkZP/0cFWqVNG0adPy9VgAhI+wC6DIZWVl6ffffz/rdqdOBda/f3+1adNGb775plavXq19+/bJ4/GoatWqatq0qfr3768rr7wy18wAXbt21dy5c/X6669r06ZNOnjwoKpWrap+/fpp8ODBuuiiiyR5bwf7xhtv6MiRI4qMjDzjlF0lQdWqVfXxxx9r5syZSkhI0P79+3X8+HG1bdtWQ4YMUadOnfTyyy9LUoFmU7jmmmt0+eWX67PPPtO3336rP/74wx8CK1SooIYNG6pz587q37//aef8dTgcevPNN/Xqq68qISFBe/fuVXZ2ti644AL1799ft956a9DNILKysoJKVMIRFRWlnj17+ksOrrrqKn/Jx+m2f/XVV/XVV1/p008/1aZNm7Rnzx7FxMSoUaNG6ty5s26++eZcd7izWq2aOnWqrr76an322WfasmWLDh8+rIMHDyomJkatWrVSly5ddNtttwVdUAigaFmMgs7tAgAoMZ599lnNnTtXTZo00WeffVbczQGAIkfNLgCUIoZhKDU19bTrfVOA1apV61w1CQCKFWEXAEqJJ554Qq1atdKAAQPynL923759/hrYwrr4CwDMjrALAKVEu3btlJGRoZ07d+rRRx8NmnotMTFR9957r5xOpypVqqT+/fsXY0sB4NyhZhcASpEJEybo3XffleS9CKxWrVpyuVzau3evJO/UbK+88kqBpiADgJKEsAsApcx3332n999/X3/++acOHDggm82mOnXqqFOnTho8eLD/hg4AUBYQdgEAAFBqUbMLAACAUoubSuTh0KHTT9tTGKxWiypXjtHRo2nyeBhYPxP6Kjz0V3jor9DRV+Ghv8JDf4WOvjqpatXyIW3HyG4xsFotslgsslrzfwejsoK+Cg/9FR76K3T0VXjor/DQX6Gjr8JH2AUAAECpRdgFAABAqUXYBQAAQKlF2AUAAECpRdgFAABAqUXYBQAAQKlF2AUAAECpRdgFAABAqUXYBQAAQKlF2AUAAECpRdgFAABAqUXYBQAAQKlF2AUAAECpRdgFAABAqUXYBQAAQKlF2AUAAECpZS/uBpR1G7cf1icr/1K3VrV0Zes6xd0cAACQh2eeeUpLlnwR0raPPfakevfuV+Bzdu7cVi1bttaMGa8X+FhlGWG3mC35cbeSDp3QgpV/EXYBADCpYcPu1o033hy07M47b1f9+g00fvzTQctr1qxVKOecNestlStXrlCOVZYRdouZy+2RJKVnuWQYhiwWSzG3CAAAnKpmzVp5htjIyCg1bdrsjPsahiG32y27PbzYdbbjIjTU7BYzu/VkuHV7jGJsCQAAKAyzZ/9XnTu31U8/rdfo0SPVvXsnLV++xL9+yZIvdM89w9SnT3d1795Jt956o15//VWlp6cFHadz57a67767/T/PnDlDHTq01o4dO/T666/pxhv76sorL9Mtt/TXe++9JcMgR+SFkd1iZrOdfL/hdhuy24qxMQAAoNC88cZruvjiSzRkyJ2qU6euJOnjjz/Q//3fVHXteoWGDbtbdrtdq1Z9p7femqPdu3dp0qQppz2ew+GQJE2cOFEVK1bWww8/LkmaN2+WXn31JVWoUFF9+lxb9A+shCHsFjN7QNh1uj2KFGkXAIDSoHz5ON1776igZcePH9Nll12uJ598RhEREZKk1q3batOmX7RyZYLS09PPWqcbFRWlJ5+cKJfLWwpZsWIl3XHHQK1Z8wNhNw+E3WJmtwWUMeTU7wIAUNKs+/2gFn73lzKz3fna32q1yFOE5XxRETb1v/x8tW1arcjOcaqOHTvlWnbnnffkuW29evW0deuvOnBgvxo0OP+Mx+3du3fQz7Vq1ZbkDdLIjbBbzALLGFxuam0AACXT0h93ad+R9OJuxhkt+XH3OQ27lStXybXs4MEDmjt3ltavX6vDhw/K6XQGrTeMsw98VasW/BhsNu+nwh4Pg2Z5IewWM0fAyK6LJykAoIS6pv15+tTkI7vXtK9XZMfPy6mzL2RmZuree+/SwYMHdOutt6tt20tVvnx5WSwWzZo1U6tWfRfScZm5KTyE3WLGyC4AoDRo27RavkdN7XarKlWKUXJymr8OtTRav36t9u37Wzfd9E8NH35v0LpTR3hReEw99dj8+fPVp08fNW/eXF26dNHUqVOVnZ192u2PHj2qp556SldeeaVatGihq666Sq+++uoZ9ylu9qDZGErvHzgAAGWdr8ygYsWKQct///03bdiwTpLkdudvZBynZ9qR3YULF2r8+PEaN26cevTooe3bt2vcuHE6fvy4Jk6cmGt7j8eje+65R8nJyZo0aZLq1q2rzZs36/HHH9fhw4f1xBNPFMOjOLvAeXadhF0AAEqtiy9urujoclqw4GPVrXueKleurM2bN2rZssUaMOAWffjhu1q6dLEcjgjVr9+guJtbaph2ZHfGjBnq06ePhgwZojp16qhbt24aNWqU5s+fr3379uXaPjExURs3btTIkSN12WWXqW7duurdu7euvfZaff7558XwCEJjP2WeXQAAUDpVrlxFzz47TVWrVtPkyU/p8cfHatu2PzR16su65Zbb1KhRY3322QJ98slHxd3UUsWUI7u7du3Snj179MADDwQt79atm5566imtWrVKAwYMyHNf3xWJPg6Hw9RXJ9oCL1BjZBcAgBLj++/X57n8jjuG6447hue5rk2bdpo16608182b994Zj3/HHcM1fPgIf32zT7ly5U7bFph0ZDcxMVGSVLdu3aDlNWrUkMPh8K8P1LBhQ1166aWaO3eu9uzZI0n69ddf9eWXX+of//hH0Tc6nxxcoAYAAFBkTDmym5KSIkmKjY0NWm6xWBQTE+Nff6rXXntN999/v3r06CG73S6Xy6V//OMfGjt2bFjnt1otslqLbloP3wwMNptVDsfJkWhDhux2U77/KDaBfYWzo7/CQ3+Fjr4KD/0VHvordPRV+EwZds82f1xe6w3D0NixY/X333/rlVdeUZ06dbR582Y999xzqlChgh566KGQz1+5csw5mcMuLi5aceUj/T9HRkWoUqWYIj9vSRQXF13cTShR6K/w0F+ho6/CQ3+Fh/4KHX0VOlOG3bi4OElSampq0HLDMJSWlqYKFSrk2ufrr79WQkKC3nvvPbVp00aS1LRpU6WlpWnKlCkaOHCgqlevHtL5jx5NK/KR3bi4aKWkZCg7y+VffjwlI6gGB8F9xdRsZ0d/hYf+Ch19FR76Kzz0V+joq5NCHSA0Zdht2LChJGn37t1q3bq1f3lSUpKcTqd/faDt27dLkho1ahS0vEGDBvJ4PNqzZ0/IYdfjMYr0Li4+brdHgQPI2dnuUj2ZdkG43R76Jgz0V3jor9DRV+Ghv8JDf4WOvgqdKQs+6tSpo0aNGikhISFoeUJCghwOhzp37pxrnypVvPef3rFjR9By38VstWvXLqLWFkzQBWrnIGADAACUJaYMu5I0evRoLV++XHPmzNHevXv11VdfacaMGRo0aJDi4+O1adMmXX311Vq/3jvVRq9evVStWjU9+eSTWr16tfbs2aPly5fr9ddfV8+ePVWzZs1ifkR5Y+oxAACAomPKMgZJ6tmzp6ZOnaqZM2dq+vTpio+P1+DBgzVixAhJUkZGhhITE5Weni5JKl++vD788EO99NJLeuyxx3TkyBHVrFlTN954o4YPz3uuOzOwW7mpBAAAQFExbdiVpL59+6pv3755rmvfvr3++OOPoGW1atXSs88+ey6aVmgC76DG7YIBAAAKl2nLGMoKe0AZQ1m/qhIAAKCwEXaLmY07qAEAABQZwm4xC75dMCO7AAAAhYmwW8xsQWUMjOwCAAAUJsJuMbMHzbPLyC4AAEBhIuwWs8AL1LgTCgAA5vPIIw+qc+e2+v33rWfc7s8//1Dnzm31r389ENJx9+37W507t9UzzzzlXzZgQD8NGNAvpP3D2Ta/7SkNCLvFzMYd1AAAMLUbbrhZkrRo0Sdn3G7RogWSpBtvvDnf55oy5QVNmfJCvvcPlWEY6tu3hxYv/ty/LD6+qmbNekvDht1d5Oc/lwi7xcxu5Q5qAACY2aWXdlDduvX01VfLlJZ2Is9t0tPTtXz5UtWuXUcdOnTK97kaNmykhg0b5Xv/UG3f/qeOHTsWtMzhcKhp02aqWbNWkZ//XDL1TSXKArudO6gBAGBmFotF/fvfpJdemqalSxfnOXL7v/8tVXp6moYNu0vZ2dl65515+vrrFTpwYL9sNqtq1aqj/v0HqF+/6894Ll9Zwvz5J0dc//jjd73yyov67bctstnsatWqpe69d3Se++/fv19vvjlb69f/qCNHjig2Nlbnn99QgwffoVat2kiSZs/+r+bOfUOSNHny05o8+Wm99NJM1axZSzfddK2uuaavHn/8Kf8xd+zYrrlz39Avv/yk1NQUxcVVUIsWrTR06F1BwXz48KE6ePCA5sx5Ry+//IJ+/PEHZWdnq169+rrjjuG67LLOoXR3oSPsFrPA2wUzsgsAgDn17t1Pb7zxqj77bEGeYXfRogWKiopS797X6umnx+u7777RoEFDdemlHZSZmamPPnpfU6ZMUnZ2dlhlDgcPHtADDwyXxWLVffc9qAYNGmj37h0aO/ZBZWSkKzq6nH/bjIwMjRx5h9LT0zVy5AOqX7+BDh06pDfeeE1jxtyn//53rho3bqrrrrtBkjR37hsaOvQudep0uerVO0/Hjx/Pdf4//9ymESOGKSYmRnfdNUL16p2nPXt2a/bsmbrnnqF6/fU31aDB+ZK8I8NOp1Njx47WZZd1Vr9+z+no0SN66aXpevTRh/TOOx+rbt164XV8ISDsFrOgC9QIuwCAEuqng5v0xV/LleXOytf+VqtFniK8diXSFqm+5/dS62qX5Gv/2NhY9ep1jRYtWqDNmzeqefMW/nVbt/6qbdt+V79+1ysyMlKRkZG66aZbdPfdI/3bNG9+iXr37q4lS74IK+x++ul8paWladKkKerWrbvsdqt69OiqChXi9dhjY4PC7t69SWrW7CJ16NBJffte519erlw5jR07Sv/73zI1btxU8fFV/aUKNWvWUtOmzSQpz7D7+uuvKCsrS7Nnv6PzzqsvSWrVqo0uvLCZhg69TfPmzdLTT0/2b3/sWLIGDhysW24Z6F926NBBzZjxotat+5GwWxbZuYMaAKAU+GrXSh1IP1jczTijr3avzHfYlbwXni1atEALF34SFHZ9F6bdcMPNioyM1JNPTsq1b0xMrKpUidf+/fvCOufmzRtlsVjUvv1lQcsvv7yL7PbgGNeo0QWaNOm5XMeoV+88SQr73C6XSxs2rFfDhhf4g67PBRc0UfXqNbRhw7pc+3Xv3ivo51q16kiSjh8/Ftb5Cwtht5hZrRZZLJJhMLILACi5epzX1fQjuz3qdS3QMc4/v5Fatmytr79eoVGjHlJcXAWlpZ3QihXL1bx5C11wQWNJ0pYtm/TOO/P022+/6vjxY3K73f5jVKhQIaxzHjlyWDExMYqOjg5a7nA4VLFipVzbL136pT777FP99dcOpaWdkGGc7FPDCC9nHD9+TNnZWapRo0ae66tVq6bNmzfJ5XIFBe/4+KpB29lsNkmSp5juJ0DYNQGHzapsl4eRXQBAidW62iX5HjW1262qVClGyclppp9z/oYbbtYvv4zT4sWf65ZbBmrp0sXKyMjwlyZs2/a77r9/uMqXj9PgwcN0wQVN/EH1X/8aJZfLGdb5jDNEg1PD64IFH2v69Clq0uRCPfTQI6pVq44iIhw6fPiQxo4dHdZ5A2Vn591mX9ssFkvQ8lN/Lm6EXROw2aySyyM3d1ADAMDUunTppvj4qvryy890yy0D9eWXn6lKlSrq1q27JGnZsiVyOp36178eVZcu3fz7uVwupaenKSIiIqzzVapUSXv37lFWVqYiI6P8yzMzM5WcnKxq1ar7l33xxSJZrVZNn/6yKlSo6F+elZW/0faKFSspMjJSBw/uz3P9gQP7FR9f1T9ya1bMs2sCvovUKGMAAMDc7Ha7rrvuBiUm/qUVK/6nbdt+17XX3uD/GN/3UX3FihWD9luw4CNlZGQElTSE4qKLmsswDK1fH1wb+/XXK3KVBXg8HtntdsXGlvcvMwxD7733tiQFnds3+nqm9thsNl16aQft3Jmobdt+D1q3ZctmHTp0UO3bdwzr8RQHwq4J+C5So4wBAADzu/ba/rLb7Zo27Vl/+PW59NL2kqTXXntJ69b9qHXr1uj55ydr7do1uvTSDjpx4oSWL1+iQ4dCu5ivf/8BioyM1H/+87Q+/3yhNmxYrzfeeEOzZ/9XVatWC9r20kvbKzs7W1On/kcbN/6slSsT9OCD96px4yaKiorSb79t0YYN65Senuavq12+fIlWrkzQzp2JeZ7/rrtGKjq6nMaOHa0vvlion3/eoE8++UiPPDJaFStW1NChd+WnC88pwq4J2KyM7AIAUFJUqRKvbt26KyXluLp0uSLogqyOHTtr9Oh/KTk5WY888qD+85+JKlcuRs8885xuvfV2xcdX1YsvTs1zFoO81K5dRy+88Irq1j1PL7zwvMaOfVBr1qzR88+/qCpV4uV0Zvu3HTZsuPr3v0k//PC9HnzwPs2aNVPdunXXkCF3aujQu5SRkalnnnlKhw4dUps27dS9ey9t3fqrpk9/Tjt2/Jnn+c8/v6H++9+5at68hWbOnKHRo0fqrbfm6LLLLtfrr7+pGjVqFqwzzwGLYZyp9LlsOnQotUiPf2oh/mOvr9H+o+mKjrTrlQe7FOm5S5qSdNGCGdBf4aG/QkdfhYf+Cg/9FTr66qSqVcuffSMxsmsKvjIGNyO7AAAAhYqwawInL1BjkB0AAKAwEXZNwDey6zGMIp1QGwAAoKwh7JqAb2RXEnPtAgAAFCLCrgnYbCd/DU4XI7sAAACFhbBrAnbryZFdFyO7AAAAhYawawJ2+8lfg5uL1AAAAAoNYdcE7AFlDNxYAgAAoPAQdk0gqIyBsAsAAFBoCLsmEHiBGmUMAAAAhYewawKBU485GdkFAAAoNIRdE7AzsgsAAFAkCLsmwAVqAAAARYOwawKBZQzMswsAAFB4CLsmYAsa2aWMAQAAoLAQdk0gcGTXTRkDAABAoSHsmoDdevLXwGwMAAAAhYewawLcLhgAAKBoEHZNgDuoAQAAFA3CrgnYuUANAACgSBB2TcDGBWoAAABFgrBrAkEjux5GdgEAAAoLYdcEgm4q4WJkFwAAoLAQdk0geGSXsAsAAFBYCLsmwAVqAAAARYOwawKBF6gx9RgAAEDhIeyaQOAd1LipBAAAQOEh7JqAnZFdAACAIkHYNYHgml3CLgAAQGEh7JoAF6gBAAAUDcKuCVDGAAAAUDQIuyZgCxjZdXMHNQAAgEJD2DUBRnYBAACKBmHXBKjZBQAAKBqEXROwWRnZBQAAKAqEXROwWCz+UgbCLgAAQOEh7JqE7yI17qAGAABQeAi7JmG3MrILAABQ2Ai7JuG7SI0L1AAAAAoPYdckqNkFAAAofIRdkzg5skvYBQAAKCyEXZPwh13uoAYAAFBoCLsmYcspY3AzsgsAAFBoCLsmEXiBmmEwugsAAFAYCLsmYQ+4i5qbUgYAAIBCQdg1Cd9NJSQuUgMAACgshF2TcNgDwy4juwAAAIWBsGsStsAyBkZ2AQAACgVh1yTsNkZ2AQAAChth1yR8d1CTJJeHkV0AAIDCQNg1CRsjuwAAAIWOsGsSQWUMLkZ2AQAACgNh1yQoYwAAACh8hF2TCBzZdVPGAAAAUCgIuyYRNLLL1GMAAACFgrBrEnYrF6gBAAAUNsKuSdhs3FQCAACgsBF2TcIRULPrJOwCAAAUCsKuSdi4QA0AAKDQEXZNggvUAAAACh9h1ySCbirhYWQXAACgMJg67M6fP199+vRR8+bN1aVLF02dOlXZ2dln3GfNmjW6+eabdckll6hz586aNGmSsrKyzlGL88/GyC4AAEChM23YXbhwocaPH6+bbrpJS5Ys0YQJEzR//nxNnDjxtPts2rRJd999t66++motXrxYkyZN0ueff65Jkyadw5bnT+DUY9TsAgAAFA57cTfgdGbMmKE+ffpoyJAhkqQ6depo1KhRmjBhgkaOHKmaNWvm2mfatGm64YYbNGzYMP8+M2bMkMvlOpdNzxe7ndkYAAAACpspR3Z37dqlPXv2qGvXrkHLu3XrJo/Ho1WrVuXaJzk5WWvXrlW/fv2Clrdr104dO3Ys0vYWBjvz7AIAABQ6U4bdxMRESVLdunWDlteoUUMOh8O/PtC2bdvk8Xhkt9s1YsQIdezYUVdeeWVIdb5mwB3UAAAACp8pyxhSUlIkSbGxsUHLLRaLYmJi/OsDHTlyRJL073//W0OHDtUDDzygNWvWaNq0acrMzNT48eNDPr/VapHVajn7hvnkm1M3cG7dyAib/3uPYQSVNZRlefUVTo/+Cg/9FTr6Kjz0V3jor9DRV+EzZdi1WM4cNPNa73Q6JUkDBw5U//79JUkXXnih9u/fr3feeUf333+/KlSoENL5K1eOOWsbCkNcXLT/+0onTo4+2+02VaoUU+TnL0kC+wpnR3+Fh/4KHX0VHvorPPRX6Oir0Jky7MbFxUmSUlNTg5YbhqG0tLQ8Q6tvFPjiiy8OWt6uXTvNmzdPO3fuVIsWLUI6/9GjaUU+shsXF62UlAx/fW562snp0dLSs5WcnFZk5y9J8uornB79FR76K3T0VXjor/DQX6Gjr04KdWDQlGG3YcOGkqTdu3erdevW/uVJSUlyOp3+9YHq168vSTp27FjQcsPw1r86HI6Qz+/xGPKcgxs7uN0euVzeJ2pgtM52uv3L4RXYVzg7+is89Ffo6Kvw0F/hob9CR1+FzpQFH3Xq1FGjRo2UkJAQtDwhIUEOh0OdO3fOtU/Dhg1Vp04dLV++PGj5unXrFBERofPOO69I21xQ3EENAACg8Jky7ErS6NGjtXz5cs2ZM0d79+7VV199pRkzZmjQoEGKj4/Xpk2bdPXVV2v9+vVB+3z00UeaO3euEhMT9eGHH+r999/X0KFDFRNj7hrYoLBbxj+WAAAAKCymLGOQpJ49e2rq1KmaOXOmpk+frvj4eA0ePFgjRoyQJGVkZCgxMVHp6en+ffr16ye326033nhD06ZNU+XKlTVs2DDdf//9xfUwQmYLmmeXkV0AAIDCYNqwK0l9+/ZV375981zXvn17/fHHH7mWX3/99br++uuLuGWFL3ieXUZ2AQAACoNpyxjKGof95MguYRcAAKBwEHZNwsYd1AAAAAodYdckrFaLrDk3smBkFwAAoHAQdk3EbvOFXUZ2AQAACgNh10R897l2exjZBQAAKAyEXRM5ObJL2AUAACgMhF0T8d1YgjIGAACAwkHYNRFGdgEAAAoXYddEGNkFAAAoXIRdE/HNtetmZBcAAKBQEHZNhKnHAAAAChdh10R8ZQwew5DHQ+AFAAAoKMKuifhGdiXm2gUAACgMhF0T8Y3sSpLTxcguAABAQRF2TSQw7LoY2QUAACgwwq6J2ALLGLhIDQAAoMAIuyYSNLLL9GMAAAAFRtg1Ebv15MguYRcAAKDgCLsmYref/HVQxgAAAFBwhF0TsVsDZmNgZBcAAKDACLsmwgVqAAAAhYuwayJcoAYAAFC4CLsmEngHNebZBQAAKDjCrokEj+xSxgAAAFBQhF0TCQq7LkZ2AQAACoqwayI2yhgAAAAKFWHXRAJHdpmNAQAAoOAIuybCHdQAAAAKF2HXRLhADQAAoHARdk0k+HbBjOwCAAAUFGHXRALLGLhdMAAAQMERdk3ExgVqAAAAhYqwayLcQQ0AAKBwEXZNhAvUAAAAChdh10SCbipBzS4AAECBEXZNxEHNLgAAQKEi7JpI4AVqzMYAAABQcIRdEwm8QI15dgEAAAqOsGsidisXqAEAABQmwq6J2LlADQAAoFARdk0k6KYSHkZ2AQAACoqwayKOoHl2GdkFAAAoKMKuiQTOs+t0EXYBAAAKirBrInabVVaLN/BmE3YBAAAKjLBrMg6H91fCyC4AAEDBEXZNJsLu/ZVkO93F3BIAAICSj7BrMhF2myRGdgEAAAoDYddkInLKGKjZBQAAKDjCrsk4KGMAAAAoNIRdk/GVMbg9hjzcWAIAAKBACLsm4xvZlaRsF6O7AAAABUHYNZlIh83/PXW7AAAABUPYNZmgkV3qdgEAAAqEsGsyEQFhl+nHAAAACoawazKOwDIGJ2EXAACgIAi7JsPILgAAQOEh7JqM76YSkpTFbAwAAAAFQtg1GYf9ZBmDkzIGAACAAinSsJucnCyXy1WUpyh1IphnFwAAoNAUOOyuXLlS9913X9Cy1atX68orr9Rll12m9u3ba+7cuQU9TZlBzS4AAEDhsRdk5/Xr1+vee++V1WqVx+OR1WrVgQMHNHLkSGVkZKhp06bat2+fnnvuOTVo0EDdunUrpGaXXhHcVAIAAKDQFGhk96233lJ0dLQ+/fRTWa3eQ3344YfKyMjQmDFjtHDhQi1btkzVq1fXe++9VygNLu24qQQAAEDhKVDY3bRpk66++mo1bNjQv+ybb75RdHS0br/9dklSxYoV1bNnT23ZsqVgLS0jIuyM7AIAABSWAoXdI0eOqG7duv6fjx07pq1bt6pNmzaKioryL69atapSUlIKcqoyw+EIrNllZBcAAKAgChR2IyIidOLECf/PP/zwgwzD0GWXXRa03YkTJxQbG1uQU5UZQbMxMPUYAABAgRQo7DZs2FBff/21XC6XPB6P3nrrLVksllwXoq1du1a1atUqyKnKDMoYAAAACk+Bwm7fvn31559/qlevXrrmmmv0yy+/qH379jr//PMlSenp6ZoyZYo2btyo7t27F0qDS7vAO6g5uUANAACgQAo09djAgQO1fft2LViwQC6XSxdffLGmTJniX3/kyBHNnTtXF154oQYPHlzgxpYFQbMxMLILAABQIAUKu1arVRMmTNBjjz2m9PR0Va5cOWh93bp1NXHiRPXt21fR0dEFamhZEVjGwE0lAAAACqZAYdcnKioqaPaFQDfddFNhnKLMCCxjyKKMAQAAoEAKfLvg3377TZMnTw5atm3bNg0aNEitWrVS7969tXTp0oKepsxgZBcAAKDwFCjs/vHHHxo4cKDee+89eTzeYHbs2DENHjxY69atk8Ph0M6dO/XQQw/pp59+KpQGl3ZWq0U2q0WSlM08uwAAAAVSoLA7Z84cuVwuvf766/7bBc+fP1/JyckaOHCg1q5dq2XLlikuLk7z5s0rjPaWCb5SBkZ2AQAACqZAYXfdunW66qqrgm4isWLFCjkcDj3wwAOSvBep9erVSz///HPBWlqGOHJKGbipBAAAQMEUKOwePnxYDRs29P+clpamzZs3q2XLloqLi/Mvr1mzppKTkwtyqjLFdxc1yhgAAAAKpkBh12azKSsry//z2rVr5XK51LFjx6DtMjMzFRMTU5BTlSkRjpyRXcoYAAAACqRAYfe8887T6tWr/T9/8MEHslgs6tq1a9B2mzdvVrVq1QpyqjLFd2MJJ2UMAAAABVKgsNurVy/98ssvuuWWWzRkyBCtXLlSF154oS666CJJktvt1jvvvKPVq1erS5cuhdLgssBXxuAxDLncBF4AAID8KtBNJe644w5t2LBBq1atkiTVqFFDzz//vH/9zp07NWnSJNWqVUtDhgwpUEPLEl8Zg+S9SM1uK/B0yAAAAGVSgcJuZGSkZs+erZ07dyolJUVNmzZVRESEf/3555+v4cOHa+DAgapatWqBG1tW+EZ2JcnpcquQbnQHAABQ5hRKiqpfv36eyy0Wix588MF8H3f+/PmaO3eudu/erUqVKunaa6/VAw88EBSoT+fYsWPq06ePIiMjlZCQkO82FAdHQNjlIjUAAID8K5Swu3//fi1ZskRbt25VcnKyLBaLqlSpoosvvli9e/dWpUqVwj7mwoULNX78eI0bN049evTQ9u3bNW7cOB0/flwTJ0486/6TJ0/W8ePHS+SFcYG3DCbsAgAA5F+Bw+68efM0bdo0uVwuGYYRtG7hwoWaNm2aJkyYoL59+4Z13BkzZqhPnz7+Wt86depo1KhRmjBhgkaOHKmaNWuedt9vv/1Wy5cv17XXXqs1a9aE/ZiKm8NxahkDAAAA8qNAYXflypV69tlnFR0dreuuu06XXHKJKleuLI/HoyNHjuinn37S0qVLNW7cONWrV0+XXHJJSMfdtWuX9uzZ478Lm0+3bt301FNPadWqVRowYECe+544cUJPPvmkRo0apRMnThTk4RWbSHvwBWoAAADInwKF3bfffltxcXGaP3++6tWrl2v9P//5T91xxx365z//qVmzZumll14K6biJiYmSvLcaDlSjRg05HA7/+rxMmzZNVatW1eDBg/XKK6+E8WhOslotslot+do3FLac2RVsp5llITLiZNh1ewzZ7WV3Noaz9RWC0V/hob9CR1+Fh/4KD/0VOvoqfAUKu7/++qv69euXZ9D1adq0qfr27auvv/465OOmpKRIkmJjY4OWWywWxcTE+Nefav369frkk0/0ySefyGrN/5OgcuUYWSxFF3Z94uKi81xeIS7K/31ElEOVKnH3udP1FfJGf4WH/godfRUe+is89Ffo6KvQFSjspqamqnr16mfdrm7dujp27FjIxz1b0MxrfVZWlh5//HHdfffduuCCC0I+V16OHk0r8pHduLhopaRkyJ3HTSNczpN1usnH0pWcnFZkbTG7s/UVgtFf4aG/QkdfhYf+Cg/9FTr66qRQBwMLFHbj4uKUlJR01u327t2r8uXLh3VcyRumAxmGobS0NFWoUCHXPi+//LKio6M1fPjwkM9zOh6PIY/HOPuGBeR2e+TKY7YFe0DQzsh05blNWXO6vkLe6K/w0F+ho6/CQ3+Fh/4KHX0VugIVfLRo0UKff/65tm3bdtptfv/9d3322Wdq1apVyMdt2LChJGn37t1By5OSkuR0Ov3rAy1evFh//PGHWrRooWbNmqlZs2Z65ZVXtHfvXjVr1kwzZswI+fzFLcLBPLsAAACFoUAju0OHDtU333yjm266Sb1791arVq1UpUoVGYahw4cP+2djcLvduuOOO0I+bp06ddSoUSMlJCTo+uuv9y9PSEiQw+FQ586dc+0ze/ZsOZ3OoGXvvfeeVqxYodmzZ6tKlSr5fpznWuA8u07CLgAAQL4VKOxeeumlmjBhgp555hl9+umnWrhwYdB6wzAUHR2tCRMmqE2bNmEde/To0br//vs1Z84cXXXVVdq6datmzJihQYMGKT4+Xps2bdLDDz+sSZMmqW3btmrQoEGuY1SpUkUOh0ONGzcuyMM854LvoMY8uwAAAPlV4JtK3HTTTbriiiu0ePFibdmyRUeOHJHFYlF8fLyaN2+e7zuo9ezZU1OnTtXMmTM1ffp0xcfHa/DgwRoxYoQkKSMjQ4mJiUpPTy/oQzCdiMCwyzy7AAAA+WYxTr3tWRFYsWKFPv300xJTN3voUOrZNyoAu92qSpVilJyclmdx+Y69x/XM2xskST3a1tGtPUrWyHRhOltfIRj9FR76K3T0VXjor/DQX6Gjr06qWjW0yQ/OyYzEu3bt0ooVK87FqUqFwDIGanYBAADyj9tvmFCEg9sFAwAAFAbCrglFBI3scoEaAABAfhF2TSh4NgZGdgEAAPKLsGtCwWUMjOwCAADkF2HXhLhADQAAoHAQdk3IarHIbvP+aihjAAAAyL+wbyrRsWPHsE+SmZkZ9j5lXYTdKpfbQxkDAABAAYQddpOTk/N1IovFkq/9yiqHwyplMbILAABQEGGHXW4OcW5E2r0XqVGzCwAAkH9hh93atWsXRTtwCofDV7NLGQMAAEB+cYGaSfluLJHt9MgwjGJuDQAAQMlE2DUph/3kXLsuN6UMAAAA+UHYNakIB3dRAwAAKCjCrklF2APvokbYBQAAyA/CrklFBN1FjYvUAAAA8oOwa1KBtwxmZBcAACB/CLsmFVTGQM0uAABAvhB2TSrwAjXKGAAAAPKHsGtSQWUMjOwCAADkC2HXpCIcgbMxMLILAACQH4Rdk2JkFwAAoOAIuyYVPPUYYRcAACA/CLsmRRkDAABAwRF2TSqCMgYAAIACI+yalMPOyC4AAEBBEXZNippdAACAgiPsmlRQzS5hFwAAIF8IuyYVPLJLGQMAAEB+EHZNyhFwu+BsJyO7AAAA+UHYNakIO2UMAAAABUXYNangO6hRxgAAAJAfhF2TigwoY3BSxgAAAJAvhF2TstussuR8z8guAABA/hB2TcpisfhLGajZBQAAyB/Cron5wi5lDAAAAPlD2DUx340lKGMAAADIH8KuifluLME8uwAAAPlD2DUxh903skvYBQAAyA/CrolF5Ew/5nJ75DGMYm4NAABAyUPYNbGIgBtLOBndBQAACBth18R8F6hJhF0AAID8IOyaWNAtg53MyAAAABAuwq6JBZYxcJEaAABA+Ai7JuabjUFiZBcAACA/CLsmxgVqAAAABUPYNbHAC9QY2QUAAAgfYdfEqNkFAAAoGMKuiTkclDEAAAAUBGHXxCICL1BzUcYAAAAQLsKuiTkoYwAAACgQwq6JRTgCbypB2AUAAAgXYdfEAssYnJQxAAAAhI2wa2JBszEwsgsAABA2wq6JBc6zm8U8uwAAAGEj7JpYuUi7//uMLFcxtgQAAKBkIuyaWHRA2E0n7AIAAISNsGti5aIY2QUAACgIwq6JRUbYZLF4v0/PJOwCAACEi7BrYlaLRdER3tFdyhgAAADCR9g1OV8pAyO7AAAA4SPsmpxvRoaMLJcMwyjm1gAAAJQshF2T843suj2Gsl3cWAIAACAchF2TC5p+jFIGAACAsBB2TY4bSwAAAOQfYdfkoqO4sQQAAEB+EXZNrhxlDAAAAPlG2DW5oLCb5SzGlgAAAJQ8hF2Tiw66ZbC7GFsCAABQ8hB2Ta5cpMP/fXomI7sAAADhIOyaXDkuUAMAAMg3wq7JBU89RhkDAABAOAi7Jhc09RhlDAAAAGEh7Jpc8GwMlDEAAACEg7BrctGRNv/3GcyzCwAAEBbCrsnZrFZFRngDLyO7AAAA4SHslgC+UgbCLgAAQHgIuyWAb/oxyhgAAADCQ9gtAXwju9kuj1xuTzG3BgAAoOQwddidP3+++vTpo+bNm6tLly6aOnWqsrOzT7t9RkaGXnjhBfXs2VMtWrTQVVddpZkzZ8rpLNlTdkUzIwMAAEC+2M++SfFYuHChxo8fr3HjxqlHjx7avn27xo0bp+PHj2vixIl57vPQQw9p06ZNeuqpp9SkSROtWbNGTz/9tNLT0zVmzJhz/AgKT+Bd1DIyXYorF1GMrQEAACg5TDuyO2PGDPXp00dDhgxRnTp11K1bN40aNUrz58/Xvn37cm2/Y8cOrVixQg899JB69OihunXr6qabblLv3r316aefFsMjKDzMtQsAAJA/pgy7u3bt0p49e9S1a9eg5d26dZPH49GqVaty7dOgQQN9//336tOnT9Dy+Ph4HT58WIZhFGmbixJlDAAAAPljyjKGxMRESVLdunWDlteoUUMOh8O/PpDValXVqlWDlrlcLn377bdq0aKFLBZL0TW4iJ1axgAAAIDQmDLspqSkSJJiY2ODllssFsXExPjXn820adO0Y8cOzZs3L6zzW60WWa1FF45tNmvQv2cTG32yRjfT6ZbdbsoB+SIRbl+VdfRXeOiv0NFX4aG/wkN/hY6+Cp8pw+7ZRmHPtt4wDE2ZMkXz5s3Tk08+qfbt24d1/sqVY87JSHBcXHRI21WLjzn5g9WqSpViTr9xKRVqX8GL/goP/RU6+io89Fd46K/Q0VehM2XYjYuLkySlpqYGLTcMQ2lpaapQocJp93U6nRo3bpyWLVumKVOm6Lrrrgv7/EePphX5yG5cXLRSUjLkDmHeXI/T7f/+cHK6kpPTiqxtZhNuX5V19Fd46K/Q0Vfhob/CQ3+Fjr46KdTBP1OG3YYNG0qSdu/erdatW/uXJyUlyel0+tefyjAMPfLII1q5cqXeeOMNdezYMV/n93gMeTxFf0Gb2+2Ry3X2J2qkw+b/Pi3dGdI+pU2ofQUv+is89Ffo6Kvw0F/hob9CR1+FzpQFH3Xq1FGjRo2UkJAQtDwhIUEOh0OdO3fOc79XXnlFK1as0Jw5c/IddM0o8AK19KySfYMMAACAc8mUYVeSRo8ereXLl2vOnDnau3evvvrqK82YMUODBg1SfHy8Nm3apKuvvlrr16+XJO3bt08zZ87UkCFDVKtWLR06dCjo60x3XjO7wHl2M7LcZ9gSAAAAgUxZxiBJPXv21NSpUzVz5kxNnz5d8fHxGjx4sEaMGCHJe2vgxMREpaenS5LWrFkjp9OpmTNnaubMmbmO99Zbb4V9oZpZBM2zm8nILgAAQKhMG3YlqW/fvurbt2+e69q3b68//vjD/3P//v3Vv3//c9W0c8pht8pht8rp8nBTCQAAgDCYtowBwXylDIRdAACA0BF2SwjfRWoZhF0AAICQEXZLCF/dbkaW+5xMiwYAAFAaEHZLiKAZGbIZ3QUAAAgFYbeECJxrNyOTsAsAABAKwm4JETiyy0VqAAAAoSHslhDBc+0SdgEAAEJB2C0hgm8ZTNgFAAAIBWG3hAi+ZTBhFwAAIBSE3RIiOooyBgAAgHARdksILlADAAAIH2G3hCgX6fB/TxkDAABAaAi7JQRlDAAAAOEj7JYQlDEAAACEj7BbQgSF3UxnMbYEAACg5CDslhARDqtsVoskKSPLXcytAQAAKBkIuyWExWLx30UtPYuRXQAAgFAQdksQXykDF6gBAACEhrBbgvhmZMjIcsswjGJuDQAAgPkRdksQ38iuxzCU5aRuFwAA4GwIuyVIOebaBQAACAthtwSJZq5dAACAsBB2S5DAuXa5ZTAAAMDZEXZLkMAyhjTKGAAAAM6KsFuClC8X4f/++ImsYmwJAABAyUDYLUGqxEX5vz+SklmMLQEAACgZCLslSJUKAWH3OGEXAADgbAi7JUh80MguZQwAAABnQ9gtQSIjbIqNdkhiZBcAACAUhN0Sxle3m5yaJbfHU8ytAQAAMDfCbglTOS5SkveWwcdSs4u5NQAAAOZG2C1hgi5SY0YGAACAMyLsljBBF6lRtwsAAHBGhN0SJnBk9zAjuwAAAGdE2C1hmGsXAAAgdITdEibwLmpHGdkFAAA4I8JuCRMb7VCEw/tr4wI1AACAMyPsljAWi8U/unvkeKYMwyjmFgEAAJgXYbcE8oXdbJdHqRnOYm4NAACAeRF2SyAuUgMAAAgNYbcEqsJcuwAAACEh7JZA3EUNAAAgNITdEoiRXQAAgNAQdkugeEZ2AQAAQkLYLYEqxEbIarFIIuwCAACcCWG3BLJZrapUPlISZQwAAABnQtgtoXwXqaVlupSZ7Srm1gAAAJgTYbeE4iI1AACAsyPsllBMPwYAAHB2hN0SKp67qAEAAJwVYbeECixjOMzILgAAQJ4IuyVU5bhI//dHU7KKsSUAAADmRdgtobhADQAA4OwIuyVUhMOmuHIOSVygBgAAcDqE3RLMNyPDsdQsudyeYm4NAACA+RB2S7CqFaMlSYakvYfSircxAAAAJkTYLcEa163o//63XUeLryEAAAAmRdgtwZrVr+z//rdEwi4AAMCpCLslWPVK0f4pyLYlHZfT5S7mFgEAAJgLYbcEs1gsanaed3TX6fJoe9LxYm4RAACAuRB2S7hm9Sv5v/9tV3IxtgQAAMB8CLsl3IWBdbs7qdsFAAAIRNgt4SrERKhO1VhJ0s59qUrLdBZziwAAAMyDsFsK+EoZDElbd1LKAAAA4EPYLQWCpiCjbhcAAMCPsFsKNK5bQTarRRJ1uwAAAIEIu6VAVIRdDWtXkCQdTM7Q4WMZxdwiAAAAcyDslhJMQQYAAJAbYbeUaMYUZAAAALkQdkuJBjXLKzrSJknauOOIUtOzi7lFAAAAxY+wW0rYrFZ1uKiGJCkr261la/cUc4sAAACKH2G3FOnT4TzZbd5ZGVZsSFIKo7sAAKCMI+yWIpXjotSlRS1JUpbTrWU/7i7mFgEAABQvwm4p06djfdlt3l/rip+SlJLG6C4AACi7CLulTKXykera0ju6m+30aCmjuwAAoAwj7JZCvTucJ4fd+6tN+ClJxxndBQAAZRRhtxQKGt11efTu/7bJ4zGKuVUAAADnHmG3lOrT4TxFOrzz7q7//aDe+d82GQaBFwAAlC2E3VKqQmykhl93kWxW71Rk3/y8V/NX7vCvd7o82vH3cSWnZhVXEwEAAIqcvbgbgKLTslG87uh7od747DcZkpas2a3jJ7KVkpatbXuOKdvlkcNu1e1XNVGn5jWLu7kAAACFztQju/Pnz1efPn3UvHlzdenSRVOnTlV29ukvtsrOztbUqVPVtWtXXXzxxerdu7c++eSTc9hi8+nQrIYGXtXE//MPW/ZrS+JRZbs8krwjvLO/3Kq3l/8hl9tTXM0EAAAoEqYd2V24cKHGjx+vcePGqUePHtq+fbvGjRun48ePa+LEiXnu89RTTykhIUHPPPOMmjRpooSEBD3++OOKiIhQv379zvEjMI8rWtVWRpZL8785WcZQqXykqleK1u+7j0mSvv5pr3YfSFXXFrWVme1SRpZLGdlu779ZLmVkuWXIUOXyUaoSF6kqFaJUrVI51akao6gI0z6NAABAGWcxTHrVUo8ePdSiRQtNmzbNv+z999/XhAkTlJCQoJo1gz92T0pKUs+ePfX000/r5ptv9i9/8MEH9dtvv2nZsmUhn/vQodSCP4AzsNutqlQpRsnJaXK5zt1o6qYdh3XsRLYuqFNBNSqXk8Vi0Xcb/9bby7fle1TXIqlapWjVrV5eceUcstusslktslgsyshy6USGU2mZTmVlu2W3WeVwWBVht6lclF3xFaJUtUK04itGKS4mQlERdkU5bLLZLNp7KE07/j6uxH2pOnw8UxYZ3vWRNpWLtKtibKQqlfd+lS8XoYic40Y6rIpw2PxtkCTDMJTt8ig1PUt7UvcpKtKi6AiHrFaLLLLKarHIIu/2gf/mvfzU7RWw3upfbj1lv3OluJ5bhcnl9ijL6Va5SHuR911p6K9zhb4KD/0VHvordPTVSVWrlg9pO1MOye3atUt79uzRAw88ELS8W7dueuqpp7Rq1SoNGDAgaN3q1avl8XjUtWvXoOVdu3bV4sWLtWfPHtWtW7fI225mlzSMz7Xs8ha1VLd6rF5ZsEVHUjLDPqYh6UByhg4kZxRCCwuP1WJRhMMqu82qzGy3XG6PIhpvkK3ioeJpkCF53xp4A7B3me9n+Zf7ArJkkccjeTyGPB5voLZZrbJbrbJZrf5tTh7n5PFtFqv/iNaA4xlG7vP4zm4Y3uP4tjEM7zKLvOf1fdmtVtlsJ9vhchtyOj1yugw5XR65fW12e9scYbcp0m5XZIRNdptNNqt3udXiPYfHMOT2GHK5PEpJdyo1PVup6U4ZhiGH3aqY6AjFRtsVabfJlbOdy23IZrUoMsKmCIdNkXabLBbvmxrDkNweQ5nZbmXmfDLhchty2C2y22xy2K1y2Kzef+3eN0jRUQ653W7/YzVkyOMxctonudxu/+OT5H3TdspxHDar7HablPN43B5DHsOQ1WKVzSrZrFZZrRbZcr6sOReOunP6yu3x+Pdzu7372qy+43r728g5tmFILo9HTtfJL4skh8OmCLtNEXaLrNbgCjUj59gujyEZJx+D3WaRy20oJS1bKTl9b7FI5SLtioqwKzrSJrfHULbTI6fbI1kscrvckrx/Y1arRQ67Led4FlksktttyO3xPib5hlJy3hjabRbZc/rLZrPI7TbkchtyuT1ye7y/W7fb+6/FYpHdZslppzWgvzzySN6+8T8Ob/9aLZLVYpXH8Cjb6X3T5CvZ8va9VVarZPf9PmwW2SwWySL/G1RZpJx/vH8vFvnfdFksOvmY/A/NIqvV2x/e57ZkteY8zgMOHTx8Qinp2TqRni2L1aJIh01RETZFRlhzBgckq9Uqi0XKzHIpI9uV8/x1KdvlPvk7tkjlouwqF2lXdJRdEXar7HZLzt+kxfv8MAy5c54bGZkupWW5lJbhlNvwKMphU2SE99xWi+TKea65PZ6cfrblvGZavL9zl/d10+nyBP3rfW56+85qObltttPbVkOGAt+jOt0n97dYDJUvF6Hy5RwqX86hSIdNHsP7fFfOczgzy5nzPPeex26zymbz9q/b8P7+3Yb3RdX7uuhd5zG8zyXv35InZ513f6vFopxd/K85WdkeZbvcysp2ywj8m7BaJUvO65jh3cdut+T8bdlkt1tzXjCV83rjfYPuynne+p5r3r8P68m/eUvO34fH8D/PDUM5ryHe1zKL5P978IZY73PL//qR8zdnt1sVHRWh1BNZysr2Pse9r5k2Rdi9Az4Ou/dxe1+7JY/H+zt3uT1yu42c2Zm8zxuLxXKyHXarDI+UnuVUWpZLmZluWa0WxUZHKLacXVEOb2z0eLyDSL6BMt/AT624qrqkWlNZLeaqkjVl2E1MTJSkXOG0Ro0acjgc/vWB/vrrL0VERKh69epBy+vVq+dfX9bD7unUrxGnp4e104Zth+R0eRQdYVd0pPc/uuhIu6IivS+wHsNQckqWjqRk6vDxTP19OE17DqYq6VCaPwiYhcfwBh7JnbPEkCXqRPE1yOJtg2Sc/L/Sl1VPt4/N++X7fyPw0eQ+9mmc7uB5LQ/Iz3ny5Hy5TrOv4wz7nrbxAaK8X/bKJxel5XxJ8vbHqecwJDnzOJY956uc98c838Z5JGXnfIXC99rtO2de5y1O4b9XzS0q4Puz9Y2h0H6v+WXI+zsyWz8XVH7GBWw5//p+J/n58DGc5/rZuJX792KVFJHHtqf8zZ6QtK8w21JYzvRcNltbi0MYz7muB3rp5kt6FF1b8sGUYTclJUWSFBsbG7TcYrEoJibGvz5QamqqYmJici33HSOvfU7HGjDyUhRsOaMUvn/NIC42Ule0rnPW7SrHRamhKgQtc3s8OnA0Q5nZrpyRAu/IWFSkXeWjHYqJdigq0iaXy6Nsl0fZTrdS0506dCzD/5WW4VKm06XMLLeyXW5VrRithrUrqHHdimreuJqOp2QoLd3pL404mpql5NRMJadkKT3TlTOC4z45muP0nifb5VF0pE0xUQ7ZXN2Vmr1Tma5sZTndynK55fa4A0bbJLfh8Y9OSIZk8Y5SeN/1WuTxeLwjIh53Tl70biP/9zkjSDn/ORmG4R0pNDwBMTdgn1P/DVhnsXiPZ7V4I7LhHWrN85yB/57DqgkAAIIcOZ7lHQE3EVOG3bPV6eW1Pj/7nE7lyjHnpM4yLi66yM9xrsRXCa1uJt/Hrxyr+Mpn364weDyGUtO9U7SVi7KrYvko/3zFPoZh+D+2cru9H/HarFbFRDtybRvI7THkdLrldHtOfuSZ8xFcRs5HjumZLtntVlWrVE7lyzn8z8Usp1sHj6br8DHv0JDvY0RbzseYVosl56NhX+B3KdPpvbDQajX8H6/6PuZzG96PJC0WyW61yGLzffwmWa2SxSp5PB5lOl3KynYrw+lSdrZLmdneNyZOl1tRETbFlLMrJsqh6CibHPacj4Zt3sd0IiNLaZlOncjIltPpPvkRnsfj/QjcZpEt5+PDSuWjVCku0t9/hrwfDx5LzVKW0x3w8ZxFLrdH6Zku71eWd6jZZpEsOW9cykU5FBNtV0yUXVarNecjN7f/Y8ssp1vZ2d43PC5XzhsYt7eUwGKR/yNHm9XiPafD6i+XyM55o5Tt9H15/B/jWqwW2S0W2ezej8s9Hnk/lg84vtvj/RhR8n6sb7NZ/LXu9pyP921Wb3mBM6fNbrfhf2y+j5Aj7FY5cj6ulKGc37n3Dd7JOyZ632J5j+19riinX30fj1utUoWYSMXFRiguxjs0l5HTrxlZLtlsVn8tfITdKo/hff57DO/H4E63Ry5XTrmA4X0T72unxWLx38zGUE7JgutkG33PXbvNmtMXVn+fGIb8H+d6yxqU81Gu9yP/k4/BLZfHV3riKx+x+D/KjXBY5S0L8vjfiLvdOR/5u3P28729NbxlHr42e3KeiIZ8yxXwRtP3WuD9JMnw5Pyb0waPIdksFsVGOxQTHaGYaO9/t1nZHmVme/+mfIMD7pxypehIm6Ii7CoX5VBUhE0RDrv343OHVW6PofQMl05kZCstw+X//Z3sH+/v2Gr19mFsdEROyUCEHHar93ea83v1f2yfU/7hdHn87cp2uv0lDd7rIOyKcFgVGWFXpMPuLYHI+ajd6TIUYbcoKtLhLa/IqbMPvBIo0uH9lNBht8nlNnQoOUMHjqbrwNF0ZTvd3o/6cz6et1ot/hITydcu72CGy2342+ywe0u5nAHPY5vVmlMe4m2z2+XJ+Tv15JzHW/JgyzlGbIxDFWIiVD4mQjarxfu6llM+IkOy2a3e18Wc60/SM51Ky8xWZpY752/RmyvsVosiI+2KirAq0mGTxWLxl264fSUcvvIcjyfo2hKrVcrMcis926WMTKc8HsN7LIe3/2Xxvn64faUN/tcPj39b73PGJqvVqswsl/8Cc2/Zifc55vJ4+8dbuuW9dsY3oOcrAfO9LmY5XbLI4n3uxDhUvlyEnC63jp/I1rETmUrNcMpmtXjbGJHTTkmGx/t3Ui22iu7s3lUVYiPP9N/sOWfKsBsXFyfJO1obyDAMpaWlqUKFCnnuk5aW5q8/8fEdI699Tufo0bQiH9mNi4tWSkqG3Ez3dUbF2VexEd60knI8PaTt3XIrJTv/n7laJZWPtKl8pHdY2J3t1LFTjhcbYVVstdyfYPiY7rlVGO+BivB9lOn6y8Toq/DQX3lrFCspj4pC+it0Zu8rj9Ol5OS86t0KX6VKp///MJApw27Dhg0lSbt371br1q39y5OSkuR0Ov3rT90nOztb+/btU61atfzLd+7cGXTMUHhy3v0XNbe/CB1nQ1+Fh/4KD/0VOvoqPPRXeOiv0NFXoTNXUUWOOnXqqFGjRkpISAhanpCQIIfDoc6dO+fap3PnzrLb7fr6669z7dOkSZOgAAwAAICywZRhV5JGjx6t5cuXa86cOdq7d6+++uorzZgxQ4MGDVJ8fLw2bdqkq6++WuvXr5ckVatWTbfeeqteeuklrVixQklJSXrjjTeUkJCgBx98sJgfDQAAAIqDKcsYJKlnz56aOnWqZs6cqenTpys+Pl6DBw/WiBEjJEkZGRlKTExUevrJespHHnlEsbGxevrpp3X06FE1aNBA06dP1xVXXFFcDwMAAADFyLR3UCtOpfUOaiURfRUe+is89Ffo6Kvw0F/hob9CR1+dFOod1ExbxgAAAAAUFGEXAAAApRZhFwAAAKUWYRcAAAClFmEXAAAApRZhFwAAAKUWYRcAAAClFmEXAAAApRZhFwAAAKUWYRcAAAClFmEXAAAApRZhFwAAAKWWxTAMo7gbAQAAABQFRnYBAABQahF2AQAAUGoRdgEAAFBqEXYBAABQahF2AQAAUGoRdgEAAFBqEXYBAABQahF2AQAAUGoRdgEAAFBqEXbPsfnz56tPnz5q3ry5unTpoqlTpyo7O7u4m2UKCxYs0HXXXaeWLVuqW7duevzxx3XkyBH/+h07duiuu+5S69at1apVK915553asWNHMbbYHIYNG6YmTZooKSnJv+znn3/WbbfdppYtW6pdu3YaPXq0Dhw4UIytLF5JSUkaOXKkWrVqpUsvvVQjR46kv07D5XJp1qxZuuaaa9SiRQv/3+LBgwf925Tl/po3b54uvvhiPfjgg7nWhdIvBw4c0IMPPqhLL71ULVu21K233qqff/75XDX/nDtTf23YsEGDBw9Wu3bt1KFDB91xxx3aunVr0DYpKSl64okndNlll6l58+a64YYb9PXXX5+r5p9zZ+qvQK+99pqaNGmiBQsWBC0va8+vUBF2z6GFCxdq/Pjxuummm7RkyRJNmDBB8+fP18SJE4u7acVu3rx5Gj9+vG688UZ99tlnmjRpkn744Qfdd999MgxDR48e1e233y7DMPTee+/pww8/lNVq1eDBg3Xs2LHibn6xmT9/vtauXRu07K+//tLQoUNVp04dLViwQHPmzFFSUpLuuOMOOZ3OYmpp8UlNTdXtt9+u8uXLa/78+Zo1a5b279+vu+66Sx6Ph/46xfTp0/XSSy9p+PDh+uKLL/Tcc8/pl19+0d133y2Xy1Vm++vYsWO65557NHv2bEVGRuZaH0q/ZGdna+jQodq9e7dmzZqlhQsX6rzzztOwYcO0c+fOc/yIitbZ+mvjxo0aMmSIateurQ8++ECzZ8+W0+nUkCFDdOjQIf92DzzwgH744QdNnz5dixcvVufOnXXvvfdq/fr15/LhFLmz9VegHTt26PXXX8+1vCw9v8Jm4Jzp3r27MWbMmKBl7733ntG0aVPj77//LqZWFT+Px2N06tTJGDduXNDyjz/+2GjcuLGRmJhovPzyy0aLFi2M48eP+9cfO3bMuOSSS4zXXnvtXDfZFA4cOGC0bdvWmDhxotG4cWNjz549hmEYxrhx44xu3boZTqfTv+327duNxo0bG5999llxNbfYzJgxw+jbt6/hdrv9yxITE40lS5YYGRkZ9NcpLrvsslx/i19++aXRuHFj47fffiuz/fX2228bgwYNMg4fPmxcccUVxujRo4PWh9Ivn376qdG4cWPjr7/+8m/jdDqNLl26GOPHjz83D+QcOVt/jR492ujevXvQ3+Xu3buNxo0bGx9++KFhGIaxdu1ao3Hjxsb3338ftO/NN99sDB06tOgfxDl0tv7ycbvdxj/+8Q//6/4nn3ziX1eWnl/hYmT3HNm1a5f27Nmjrl27Bi3v1q2bPB6PVq1aVUwtK34Wi0VffPGFHn300aDlVatWlSQdPHhQq1atUqtWrRQXF+dfX6FCBbVs2VLffffdOW2vWUyYMEFt2rRRz549g5avWrVKnTp1kt1u9y9r2LCh6tatWyb7atmyZerdu7es1pMvd/Xr19fVV1+tqKgo+usUhmHIZrMFLfP1je+1qiz2V9euXTV37lxVqVIlz/Wh9Mv333+v8847Tw0aNPBvY7fb1alTp1LXd2frr2eeecb/CZ1PfHy8JPlLZr7//ntFRkaqffv2Qft26dJFa9euLVUlgGfrL5+3335b+/bt0+jRo3OtK0vPr3ARds+RxMRESVLdunWDlteoUUMOh8O/vqyqWLFiUJCVpBUrVigyMlIXXnihEhMTc/WdJNWrV69M9t2SJUu0Zs0aPf3000HL09PTdeDAAfoqh9Pp1I4dO1SjRg1NnDhR3bp102WXXaYxY8bowIED9Fcebr31Vi1ZskTr1q2TYRg6cOCA5s2bpxYtWqhBgwZltr/q1q2b602AT6jPozO9ju3bt08ZGRmF2+hidKb+kqRy5crlCnYJCQmSpJYtW0ry9lfNmjWD3kBI3v5yOp3as2dP4Ta6GJ2tvyTvtQcvvviinn76acXGxuZaX5aeX+Ei7J4jKSkpkpTrCWqxWBQTE+NfD6+EhAR99NFHGj58uMqXL6/U1FTFxMTk2i42NrbM9d2xY8c0adIkPfzww6pevXrQutTUVEm5n2e+ZWWtr44fPy6Xy6X/+7//U6VKlfTqq6/qiSee0Lp16zR8+HD6Kw/33XefbrnlFg0cOFAXX3yxunTpIo/Ho5kzZ9JfpxFqv5zpdUxSme0/yRvkJkyYoI4dO6pz586SvP11uj6VvH/fZckTTzyhK6+8Ut26dctzPc+v07OffRMUBovFUqD1ZcmSJUs0duxY9enTRyNGjJBE/wSaPHmyGjZsqJtvvrm4m2J6LpdLktSmTRvdd999kqRmzZrJ4XBo5MiRWr16dXE2z5TmzJmj999/X0899ZRatWqlffv2afr06br//vv1/PPPF3fzSjT+H8jb9u3bNWzYMFWrVk3Tp0/3L6e/Tpo/f762bt2qL7/88rTb0F+nR9g9R3wf0ftGAHwMw1BaWpoqVKhQHM0ynbfffluTJ0/Wrbfeqscff9xfzxUXF6cTJ07k2j41NVUVK1Y8x60sPt9++62++uorLVq0KM/1p3ue+ZaVpb6STo5oNG/ePGj5pZdeKkn+6cfoL6/k5GRNnz5d9957r/75z39Kkpo2baoaNWro+uuv14YNGyTRX6cK9e/uTK9jFoslVylXWbB+/XqNHDlSDRs21MyZM4P+L4yLi9PevXtz7ePr57Ly/+ahQ4f03HPP6emnn1blypVPux3Pr9OjjOEcadiwoSRp9+7dQcuTkpLkdDr968uy999/X88884weeugh/fvf/w66cKFhw4batWtXrn127typ888//1w2s1gtWbJE6enpuuqqq9SsWTM1a9ZMQ4YMkST16tVL99xzj2rXrk1f5YiNjVV8fLySk5ODlns8Hv96+uukXbt2yel0qlGjRkHLfRe87N69m/7KQ3R0dEj9cqbXsdq1aysqKqrI22ommzdv1l133aUOHTrozTffzBVeGzZsqL///jvXlHY7d+5URESE6tSpcy6bW2y+//57HT9+XA899JD/db9Zs2aSpMcff9z/Pc+v0yPsniN16tRRo0aN/AX4PgkJCXI4HP4apbJq9erVmjBhgh5//HHdeeedudZ37dpVv/zyS1BoOXLkiH755RddeeWV57KpxWr06NH67LPPtHDhQv/XpEmTJEmvv/66Jk2apK5du+r7778P+g/i999/199//12m+sqnS5cuWrFihQzD8C9bt26dJKlx48b0VwDfBUOn3qzFd4FV7dq16a/TCKVfunbtqqSkJP3555/+bbKzs/X999/riiuuOOdtLk5HjhzR8OHDdfnll+vFF19URERErm26du2q7OzsXLMVff311+rcuXOe+5RG3bt31+effx70ur9w4UJJ3nmIfd/z/DqD4p35rGxZvny50aRJE2P27NlGUlKS8b///c9o27at8eyzzxZ304qVx+MxrrnmGmPgwIHGwYMHc32dOHHCSE1NNbp162YMGzbM+P33342tW7cagwcPNq644grjxIkTxf0QitWaNWuC5tlNSkoy2rRpY/zrX/8y/vrrL2Pjxo3GtddeawwYMCBoTsuy4q+//jJatmxpjBs3zti2bZvx7bffGl27djX+8Y9/GIZBf53q7rvvNtq2bWssWrTI2L17t7Fu3TpjwIABxhVXXGGkpqaW2f5KTk72vyZ16dLFGDFihP/njIyMkPrF5XIZ/fv3N/r3729s3LjR2LFjhzFmzBijXbt2pW6u9bP117///W+jXbt2xq5du3K95icnJ/uPM2LECOOKK64w1qxZY+zevduYPHmycfHFFxtbtmwpvgdXBM7WX3k5dZ7dsvT8CpfFMAKGO1DkvvjiC82cOVM7d+5UfHy8BgwYoBEjRpx1ypHSbO/evWccEbrvvvt0//33a/fu3Zo8ebJ+/PFHWSwWdezYUY8++miZ+SjrdH788UfdfvvtWrFihb8vtmzZoilTpmjTpk2KjIzUlVdeqYcffviM9V6l2aZNm/Tcc89p8+bNioiIUJcuXfTYY4/5RzLpr5MyMjI0a9Ysffnll9q3b58qVaqkNm3aaPTo0f5pjcpifw0aNCjX3Qp9/vOf/+iGG24IqV+OHDmi//znP1q5cqWys7PVsmVLPfroo2ratOm5eijnxNn6a8aMGXnW40remvq3335bkpSWlqZp06ZpyZIlOnHihC688EKNGTNGHTp0KLK2F4dQnl+natKkSa51ZeX5FS7CLgAAAEotanYBAABQahF2AQAAUGoRdgEAAFBqEXYBAABQahF2AQAAUGoRdgEAAFBqEXYBAABQahF2AQBnNGjQIDVp0kSbN28u7qYAQNjsxd0AACitkpKS1L1795C3990tEABQeAi7AFDEoqOjQwqxrVq1OgetAYCyhbALAEUsMjJSd9xxR3E3AwDKJMIuAJjMgw8+qMWLF2vRokX666+/NHv2bP3111+SpIsvvlgPPPCA2rVrl2u/r776Su+++65+++03paWlqWLFimrZsqWGDRum1q1b59p+9+7devXVV7Vq1SodO3ZM1atXV4cOHTRixAjVrl07z7atWbNGL7/8srZu3arIyEhdeOGFeuihh3TRRRcFbffzzz9r1qxZ2rhxo5KTkxUbG6u6deuqX79+GjhwoGw2WyH0FACcHWEXAEwmIiJCkvTRRx/p888/V58+fdSjRw/98ccfWrp0qYYOHap58+apbdu2/n1eeuklvfLKK6pYsaL69u2rqlWravfu3friiy+UkJCgqVOnqnfv3v7tt27dqttvv12ZmZm69tprVadOHW3fvl2ffPKJVqxYoffff1/169cPatfq1as1e/Zs9erVS506ddIvv/yilStX6ueff9aSJUtUo0YNSdL69es1ePBgRUVF6ZprrlHt2rWVlpamr7/+WpMnT9bGjRs1ffr0ou9IAJAkAwBQJPbs2WM0btzYuPTSS8Pa75FHHjEaN25stGjRwtixY0fQuv/+979G48aNjdtuu82/7NdffzWaNGlitG/f3vj777+Dtl+3bp3RpEkTo127dkZ6erp/ef/+/Y3GjRsb3333XdD277zzjtG4cWPjnnvu8S8bOHCg0bhxY6NDhw5GYmJi0PZ33XWX0bhxY2Pu3Ln+ZWPGjDEaN25sfPPNN0HbZmdnGwMHDjTatGmTq50AUFQY2QWAImYYhpKSks64jcPhUPXq1YOW9e3bV+eff37Qsttuu00vvfSS1q9fr9TUVJUvX14LFy6UYRj65z//qZo1awZt37ZtW3Xo0EGrV6/WypUrdfXVV+uPP/7Qr7/+qsaNG6tz585B2994443au3evqlWrlquNN998c67R3ssuu0wrV67Url27/MtSUlIkSeXKlcv1GOfOnSu7nf96AJw7vOIAQBE7fvz4Wacga9q0qRYtWhS0rE2bNrm2i4mJUb169bRjxw4lJibqkksu0ZYtWyQpz7pcSbrkkku0evVqbd26VVdffbU2bdokSbrwwgtzbRsVFaWHH344z+OcWpcrSeXLl5ckZWZm+pd169ZN3377re69917ddddduuKKK9SoUSNJIugCOOd41QGAIhYTE6PnnnvujNvExsbmWlalSpU8t61QoYKkkyOoR44ckSTFx8fnuX3lypUlScnJyZKko0ePBh0nVHltb7FYJHlHr31uvfVWHT58WHPmzNHUqVM1depUVa1aVZ06ddL111+vjh07hnVeACgIwi4AFDGHw6EePXqEvd/pRkF9wdJq9d4EM6/AGcjj8QRtd+pxCpvFYtGoUaM0dOhQrVy5UqtWrdLq1au1cOFCLVy4UDfffLMmTpxYJOcGgFNxu2AAMCnfCOypjh07JunkSKtvRPfQoUN5bu8b+fWNFPtGen3Li0pcXJz69eunZ599VitXrtS8efNUo0YNffTRR1qzZk2RnhsAfAi7AGBSGzduzLUsLS1NSUlJstvtatCggSSpefPmkqSffvopz+P8/PPPQdtdcskl/uOfOrrr8Xg0evRojRo1Si6XK1/tPn78uPbu3ZtreceOHTV06FBJ8tcZA0BRI+wCgEl9+umn2rNnT9Cyd999V06nU506dfLPdnDDDTfIarXqww8/1P79+4O2//bbb7VhwwZVr17dP/NCkyZNdNFFF2nv3r1asGBB0PaLFy/WkiVLdOLEiXxdTJaamqquXbtq6NChOn78eK71v//+uyT55+QFgKJGzS4AFLGsrCzNnj37rNtFRkZq4MCB/p+vuOIK3XjjjerXr5+qVaumrVu3aunSpYqKitKDDz7o3+6CCy7QAw88oBdffFHXX3+9+vXrp/j4eP35559asmSJoqKiNGXKFDkcDv8+zzzzjAYNGqTx48frhx9+UKNGjbRjxw4tWbJEFStW1BNPPJGvx1q+fHkNHz5cL774ov9mGDVq1FBGRoZ++uknrV27VhdddJF69eqVr+MDQLgIuwBQxDIyMs46G4PkDYqBYXfAgAHq1q2b5syZox07dshqtapjx4568MEHc00bNmLECF1wwQV65513tHDhQmVkZKhy5crq3bu3hg8f7p/6y+fCCy/UJ5984r9d8LJly1SjRg1dd911uu+++1SrVq18P94RI0aoQYMGmj9/vhISEnT06FHZ7Xadd955euCBBzRkyBD/XeIAoKhZjKK6HBcAkC/jxo3Tp59+qrfeekvt27cv7uYAQIlGzS4AAABKLcIuAAAASi3CLgAAAEotwi4AAABKLS5QAwAAQKnFyC4AAABKLcIuAAAASi3CLgAAAEotwi4AAABKLcIuAAAASi3CLgAAAEotwi4AAABKLcIuAAAASi3CLgAAAEqt/wfrTFMaf0qvcwAAAABJRU5ErkJggg==\n", + "image/png": 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\n", 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" ] @@ -775,7 +756,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "id": "092eb6a0", "metadata": { "lines_to_next_cell": 2 @@ -785,7 +766,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:51<00:00, 19.27it/s]\n" + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [04:21<00:00, 3.83it/s]\n" ] } ], @@ -799,13 +780,13 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 15, "id": "5dc3e69d", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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wUnT6YHZ2Vq5xb/4r7hnh7jXFPT+a/jw15xIaDz30UHVcJSNuRh0/d/27xo5q2932qefRvffeK9e41InaPndc3ZxKHbprTz1z3LPcXV/qfLjzpO7pUvT+RhIx7v8Td55uF34pAQAAKVCUAACAFChKAABAChQlAAAgBYoSAACQAkUJAABIoZGGfGrOrVFxVxcNdU6dOlUdX7NmjVzjIqBqzkXPVNM2t8bFKFXEzK1xTatUnPPKlStyjToOLv7m4q5KJE5aim4m5WKPioseu2iciparqHApOnJbim4U5uKDatvdPei2T513Fyt/+eWX5dyePXuq426f1DXRdOw32sQv8mcNxsbGquOugWSk0aH7PBc1VU3gXMxfNW9UzQdL8c9ltc7dn+5PP6g/k+DOuzqu7jnlnvPqOnf/37k/KaC23W3fkSNHquPuuLprOfL/fiv4pQQAAKRAUQIAAFKgKAEAAClQlAAAgBQoSgAAQAoUJQAAIIWWI8EulqZiTS5OFIn+uhjZ7t27q+MqpluKj3C5iJmiOrK6z4rEpl2kz3U+PnbsWHVcdVgupZRVq1ZVx++77z65xnWm7e3trY67KJvrzKlizq5bsuKi0VNTU3Luvffeq45v3rxZrlFR5lL0feOie6qDsIu9u1iyuj9dl+AnnnhCzr3zzjttf546DpFOqKXEnjmROK57rmzYsKE6rrpdl6JjuqXo7XPXitunSJxTHQd3vN0zUUWM3bXirnP1XZFIcJQ65u4YuWe52qfI/7nueo2I3p//xC8lAAAgBYoSAACQAkUJAABIgaIEAACkQFECAABSaPm128gbtW6NmnNJi40bN8q5devWVceXlpbkGvf2tdoOt08qAeHeNHcpDPW29GuvvSbX7Nu3T86p1M6jjz4q12zatKk67hI2kaZo7ji486QSJK4xoTq3IyMjco17u//QoUPVcff2vLuWVaM8lVwqRR9zlWQoRV+vpeikgzuurmmhuw8Vda9Fm3ZGuPs9klpQ16u7Z9yc2oZoskRtn7s/FbcN7riqZ8u5c+fkGnddRq4jtcY1JnTXg3rmuP/vhoaG2p5zCUL1/5B7tjX9/34r+KUEAACkQFECAABSoCgBAAApUJQAAIAUKEoAAEAKFCUAACCFljNtLt4VaTakYmkuwqgayrnvcnEnF3NTsTQXCYs0x7p+/bqcU/HeX//613LN8PCwnBsdHa2Oqzh1KTrm5mKKLp6n5tz15a4j9XmR2KiK4pbiGxCOjY1Vx11suru7W86pY+uOkZpzsV/X2HFmZqY67po3HjlyRM5FIpHqHEajpmouGp9V58lFQ1VsWjW+LMXfT+p56fbJPY/UMXf3k/oudy7c9ql7w91PbvuafC6769V9nlrnovLu/yd1TbhrRf1JAfcsipzDW43s80sJAABIgaIEAACkQFECAABSoCgBAAApUJQAAIAUWk7fRJoXOZHUxOTkpJxTzZo2bNgg17i35NVcf3+/XKO4t8ZV47NSdNLBNWpau3atnFPN9QYGBuQa9YZ1JGFTik8zKJFmZZE17s16J9KA0KW4Im/Wq/Pkvsc17zp//nx1/O2335Zr9u/fL+dcMkGJHFcn0pgt0ujNpbjUc8U1b3SJRPV5bp8uXrwo51RCz22Dum9c8sulb9Q+uePqnivqHnDbNz8/L+cU9/9JpGGmu/bU9eK2W6V5XEI1mky7FfxSAgAAUqAoAQAAKVCUAACAFChKAABAChQlAAAgBYoSAACQQsuRYBfPU9GgpiN9Lt74+uuvV8ddhNdFzEZGRqrjLsKluLif26eHH364Oj4+Pi7XuP1duXJlddwdB9WsKRoJVlHFSKy8lFiMV61xzRHdeVLXvzuufX19cq7JiL1r6nX58mU5pyLBb7zxhlwTiYC68xd5frhjpz4veu0p7k8AqPMefU6pa89FOd3nqei2u1bU8y3SxM9tg2oSWYq/jlRU1zWkVPe7O3aR+91dKy5irLY98n9N5NlWir5vbvX/fX4pAQAAKVCUAACAFChKAABAChQlAAAgBYoSAACQAkUJAABIoeVIsIvNqXiXixOpuFM0TvSTn/ykOj44OCjXPP7443JuaWmpOu6iZyoC5yKyLhq6ZcuW6riLXrpYq9p2t09q2925dVE7FTF22xCJpbmutGqf3HF1sTm17e68u26e6t5w8V517bnrwUUsT506VR1/66235Br3XU3GB90aF0NVc9Fnjjq/7lmpYqMqru/WlKKfvS5O6rqCq3vXdTN3c8r09LScU12M3bl1n7dx48bquIvjqnvNbYO7n9Q14bbBUdvhrhUVF3bXXuT/hluN2PNLCQAASIGiBAAApEBRAgAAUqAoAQAAKVCUAACAFBpJ36i3113TpaYbs6k3n3/2s5/JNZs2bZJz6g11lyxRSZqmUxiRNEopOh3hkiWRVMfk5KScU2+bu2PkmiC6a0xRxyjS3M9tQySxU0qsCaJKH7hE0d/+9jc5t3///ur4lStX5Bp37bnUQruiz4jItRJpPOnu6ZMnT1bHV61aJde4dESkQajbJ/V8c/e7mnPf4xr8qXsjkkIqRT+P7r77brlG3YPuOnbH6NKlS9Xx3t7etrehlFjqMHLfRJoqRq7Jf8cvJQAAIAWKEgAAkAJFCQAASIGiBAAApEBRAgAAUqAoAQAAKbQcCXaRKxVRjcQAXSMpR23fiRMn5Jof/OAHcu6FF16ojrsGfyoK6GJabm54eLg67o5R5JhHopwu0uqicSoK6PbJNS0cGhqqjrvtU80WXdTaNW1TsVv3eT09PXJONc5yXnnller4yy+/LNccOnRIzrnIphJp2umoNbfa8Os/uXPrvkvduy7Cq475vffeK9e462hkZKTtNdHou6LOk4rBluKb+I2NjVXH3XlS97Rz5swZOafix9E/T6CeEW6Nm1PXnjvmKu594cIFucY9lyONeFvBLyUAACAFihIAAJACRQkAAEiBogQAAKRAUQIAAFKgKAEAACnc1i7BTtNxItWR0sWqXn/9dTn3/e9/vzr+3e9+V67ZuXNnddzFdF3ETEVrXeTWUefQdZJVnW5drDDS+TLaRdZFqhXV6dbtk4vGqWtPde4txV/nKhK8Z88eueY3v/lNdfzs2bNyjaOuFbfd0Xhju9sQfRZF4u2u0636PHeMXGdmxd2fas5FZF3kPHKMVHx87969co07rmvWrKmOu3vQPUfVsfjd734n17z66qvV8UceeUSuUVHmUvS2u/M0NTUl59Q5dPeGimFPTEzINZF7+lY7gvNLCQAASIGiBAAApEBRAgAAUqAoAQAAKVCUAACAFO74sMXuVio1UYp+49e9udt0Uy0lug2RtMC6deuq49/4xjfkml27dsk5dcxVIqaUUlasWCHnVCpmcnJSrlHpFvc9qplVKfrNbJcScWketX3u7f733nuvOn769Gm5xr1Rrt5e/+tf/yrXuOtLJR0uXrzY9hrHXf9NJ18i93tkTZON/27HNqhGjF/5ylfkmq1bt8o51ZDPJXZUY7ZSdOLDpYZUqkPdZ6WUMj4+Luc+85nPVMddo0PXQHJmZqY6/vOf/1yuUemW/v5+uWbLli1yTp1DtW2l+HtabZ9bo55Hrmlt5H5yz8pW0pL8UgIAAFKgKAEAAClQlAAAgBQoSgAAQAoUJQAAIAWKEgAAkELLkWAXx1IRoE7Ffp1IpKkUve0uaqrWuEjr9u3b5dzGjRur44899phcs2HDBjmnGvm5OJ2KcI2Ojso17pirbXj33XflGheBVs31VEyxFB39PXnypFzjonsqquuice7eUA3dIs3mmubObWT7XPO6yD0YaSDmtttFLNXnRWLTrqGce/YODw/LOUXdM6Xo8+TWqD8P4BrouefHjh07quOqUV8p/v5U2/7LX/5Srono6+tre85ttzvv6r5xDf7UMzHaZDPyZwOIBAMAgI8MihIAAJACRQkAAEiBogQAAKRAUQIAAFJopCGfehvefXTkbXz3NrcSeRu/FP3WfSTNE00LKO6tbNfoavPmzdVxd25Vgy7XkE+tKUWfd9dszqWD1HlyaRTVkMy9Ge6uZZUoctdr08m0phvRRda4ucj9HnmuRI6ruz9d+saldtr9rug+RRJF7t5Q26eucbcNLlHkkioqdega3l26dEnOHT16tDp+8OBBuUbdu02n3CL3jOPO0/Lly6vj0Ws8krp16aB/fedN/wUAAEAHUJQAAIAUKEoAAEAKFCUAACAFihIAAJACRQkAAEih5Uiwi4Cqj3BxIhWFcjGoaHO9iEizoSYbdLnPcyKN3iKf52KUkZhnNDatvssdBxWNc9deJJ4XjXmqucj14LbbXXsqEhm9ByPxwcgzIhIJdsch2qysSU0/B5r+LrXG3dOuSenIyEh13P0f5Bpwnj17tjru4rNq29314K5LdT+5bXAi2xe5pyP/ZzgLCws3/Tf8UgIAAFKgKAEAAClQlAAAgBQoSgAAQAoUJQAAIAWKEgAAkELLkeCurq7bvS2lFB/Pc5sa6b7pRKKm2Tu1NslFxSLHKLrdkei26l4ajTmr7+pkR9Gmr6PI9e9E/myAijBGrz0l2qm1yWPeyW1o+vMifzbA/X+iti96jFTs1m2f4jqJd/K5rO6bSIQ3ut2Re21xcfGm/4ZfSgAAQAoUJQAAIAWKEgAAkAJFCQAASIGiBAAApFB/vb0hkbelo4mFphMQattdWkCJNmaLvKnf9Fve6vi5N9cjb2VHG8c12TAweq2oz4uep0j6oOnUiRK5/qPfFTlPkeaXTSfq3DGKNCaMzLltiCYc2+XO09LSUtvrotee2qcbN27INSr55USaq7ptiDQpjTxzoseh6fP0r/W3tBoAAKAhFCUAACAFihIAAJACRQkAAEiBogQAAKRAUQIAAFJouSFfd3e3nFMfEWn4pZqlue8pJRbnjET3mo5ldlKnts+dCxVzc42kIo2zIvvayfOXoXlj5H6KxpwjEcZIs8VI1No9B1ppIAagNa08Y/mlBAAApEBRAgAAUqAoAQAAKVCUAACAFChKAABAChQlAAAghdvaJdhRMU/XsdBpsgtpVKcipdHvabKDsNuGSAfhaHT1VjtSZhQ5T52KQGeITUdiv25dtDs0gOb99z3RAQDARxJFCQAASIGiBAAApEBRAgAAUqAoAQAAKbScvnFvvKsEhEu3qDV33aU3qZMNxCLNwDolmjhpctuj26COeaTpXinNp3mUSCIm0vAxSn1X9Hsi5zd6r7Wr6YRek9sG4NbwSwkAAEiBogQAAKRAUQIAAFKgKAEAAClQlAAAgBQoSgAAQAotR4IjMUoX71VchDHSbMtx+6Q+L3IcXLyy6Xhq0zFnte1uTaRZn4sEd3d3y7lr1661NV5Ks8ehFH1dNh0fj2xfNJbcdEy2yeZ/TTdhJBIM5MEvJQAAIAWKEgAAkAJFCQAASIGiBAAApEBRAgAAUmikIV+TzcWiqY5OfVck+RJNOUQSQO7zVMKlp6dHrhkaGmprvJRSVqxYIedUkiaS2ClFN2c7ffq0XLO4uFgdX1pakmvm5+flnFrnzrvbJ9XI0qXZ1HFwSRV3rahtiDZObDLN5kTvDQA58EsJAABIgaIEAACkQFECAABSoCgBAAApUJQAAIAUKEoAAEAKt7UhXyQK28lIX9NN/CKNwlT0spRSli1bVh0fGxuTa1xsdOvWrdXxT3ziE3KNivfefffdco07rio+Ozk5Kde4GKo6H+fPn5drlMuXL8u56elpOXfmzJnq+JUrV+QaFzFWzQSvX78u1zR9P0WaaUZ0qpGm03SDPwBx3I0AACAFihIAAJACRQkAAEiBogQAAKRAUQIAAFKgKAEAACnc8WGLWV/X+bXJWF/TcT+3JhJzdp+nIpsu0rphwwY5t23btur4+vXr5Zq1a9fKuf7+/ur44OCgXKM66roYpYvPquso+nnqmM/Ozso1XV1d1XHXJdhZWFiojruI8dTUlJw7efJkdfzw4cNyzdzcnJxToh2EI2vUveYi8U13FlZ/osAdh+g1AeD/auXe5ZcSAACQAkUJAABIgaIEAACkQFECAABSoCgBAAAptNx1y71Zr95edw351Fu4ke9x3+Xe9o0013PboFIxn/70p+Wa7du3y7nly5dXx12ztO7ubjmnUkDuOKjvcmsGBgbknEpbuIRSX1+fnFPJF5coUteKaoRXik+JqISSu5ZHR0fl3Mc//vHq+NDQkFyzZ8+e6rhr/OdE7k83p45fpGlntDGnunfdtQegs/ilBAAApEBRAgAAUqAoAQAAKVCUAACAFChKAABAChQlAAAghUYa8kVEYsRO0827VBz3ySeflGu++c1vVsd7e3vlmhs3bsg5FTVV46WUMjw8LOdUczG1r6Xo87Ry5Uq5xkWWVezWrXHHSEVNI/FxxzX4U83wXJM8d12qhoHuvL/xxhvV8ZdeekmuiUSg3XF10Vr3XYr7LsXFhdV5d9dDZLsB1NGQDwAAfGRQlAAAgBQoSgAAQAoUJQAAIAWKEgAAkAJFCQAASKHlLsFOpGunigZFI7yRjqIqellKKZ/97Ger488++6xco6K/LlbY09Mj51QM23V+dbFM1UHYdcBV2+461i5btkzOufix4uLCqkuwO66KOw6u8/Hly5er4yqCXYqPGKsItIvlb9mypTp+/vx5ueatt96Sc+o+dPFZF+FV2+6OeaTzt4uP0yUYyI9fSgAAQAoUJQAAIAWKEgAAkAJFCQAASIGiBAAApHBb0zfuzXq1xr3B7z5PvXXv0jfPPfecnHv++eflnDIzM1Mdv379ulzjkjkq4dLf3y/XuCSBOn5qu913ueSSO4cqmeO226VvBgcHq+MuoaFSMS7d4lIdattVKqcUn0JS69y1rJJVDz30kFxz4sQJOXfu3Lm2tyFy/Ny5Vekbdz9FmvhFE38AmscvJQAAIAWKEgAAkAJFCQAASIGiBAAApEBRAgAAUqAoAQAAKTQSCY7EcVUMz8V+XXRPRQHvv/9+uebzn/+8nFPxRhdHVDHPvr4+ucY1r1MxTxefddunzsfw8HDb2+C2251DdYxcRNbtr9qOSAzbfY+LbivuGLlmfep+WlxclGvUeVq/fr1cs2PHDjn3yiuvVMdd5DYS1XUN/iJNO921p7htANBZ/FICAABSoCgBAAApUJQAAIAUKEoAAEAKFCUAACCFltM37o13leqINLpyiR03p97uf/DBB+Wa1atXt/1dLiWi0hYuaRHZJ7fGJUjU57l9UnOR5ojOwsKCnHMplpUrV7b9XWr73HlyyRKV3nDN5lwiS51D1zgxcszHx8fl3FNPPVUdP3DggFwzOzsr59QxijTgdGvcvdHu99xsDkDz+KUEAACkQFECAABSoCgBAAApUJQAAIAUKEoAAEAKFCUAACCFliPBkeZ6LrqnuOZYkSjspz71KbnGRWHVdrjGbF1dXW2Nl+L3ScURI40Jo9T+Xr58Wa5x8VkVG52bm5Nrent7254bGhqSa9S14iK8kXi746LM6npxx2Fqaqo6fvXqVblmcHBQzm3cuLE6fuTIEbnGxbrVdeSOq5qLxJ+dpu8ZAHHcjQAAIAWKEgAAkAJFCQAASIGiBAAApEBRAgAAUqAoAQAAKTQSCY5Qcb9I9LgUHaMcGxuTayLxY9exVkV43RoXR1TRx+jnqaiui/CqmOexY8fkmitXrsg5dcxdd+OLFy/KORWtnZyclGtUtNZdKytWrJBzquOviznPz8/LOXWMXIRdRW6Hh4flGhdLVpHv+++/X65xx1zdu4uLi3KNugejz6Imu5kDuD34pQQAAKRAUQIAAFKgKAEAAClQlAAAgBQoSgAAQAotp29cUsXNtSvSoK4UnQoYGRmRa1xjL5Vi6e7ulmtUKsYlYlyDP/V5LgniEhpqO1wCYmJiojo+MzMj17jGcQMDA9Vxd27d9aW2wzUMVCkR9z1btmyRc+oac9fy0tJS23PuGKn0kjsXzpo1a6rjW7dulWv27Nkj59SxjaTtogm9yDYA6Cx+KQEAAClQlAAAgBQoSgAAQAoUJQAAIAWKEgAAkAJFCQAASKHlSLDjmqkpKp7qIn133aU397nnnquO9/T0yDUujqua1Ll4r4oLR6LHpehIsIslu0iwOk/Hjx+Xa06dOlUdd+difHxczqlIsIvjzs7OyjnFNRlUceGTJ0/KNS5aruLoqlFfKf7aU/urmiOWoq8Vdz+5uLBq1jc6OirXuOu8yT8b4PbJzbl7DUAO3KUAACAFihIAAJACRQkAAEiBogQAAKRAUQIAAFKgKAEAACm0HAmOdNJ0a1REMBph3LZtW3VcRRtL0THKUvT2qQ6zpeiYZ6SzcCk63utiv+74qY66Lp6qzqGLyLoYtvo8dxxcV2QVMZ6enpZrVFTXnSe3T1evXq2Ou07AXV1dck5tn4vVqk7Pbo3bBvV5586dk2sifxrAxYjVteL2iY6/wEcbv5QAAIAUKEoAAEAKFCUAACAFihIAAJACRQkAAEihkYZ8intL3qVElPvuu0/OqZSNS+w4c3Nzba9Rjd5cysElaVQCwjWocykWdYzcNqxdu7Y67hryNd2YzZ0Llfhw510ldoaGhuQa1+Dv/Pnz1XG3rx988IGcU43jXLpFJZRcAsilrtS1PDExIddEUjHR5noR6phHUkMAbg9+KQEAAClQlAAAgBQoSgAAQAoUJQAAIAWKEgAAkAJFCQAASKGRSLCKArrmWCru52KAjzzyiJxTcWEXd3WxzP7+/ur44OCgXBOJES8sLMg51UDv/fffl2tcA8LVq1dXx10julWrVlXHVWy1FB8NVRFVdy7cOVRzLoattt1dr+pclKIb8rlGgu6Yq6huJCLr1riYs7qWz549K9dEYs6RZ0RkjVtHEz8gD34pAQAAKVCUAACAFChKAABAChQlAAAgBYoSAACQQsvpG/eGumpo5VIYqqGbW3P//ffLOZUkcCkRt09q+1zDO5XYcQkbN6eSKq7J2sGDB+XcunXrquM7duyQa1RjO5cscSmM+fn5tsZL8embyHlSCQ2XnnL75JI+7W5DKXqf3PeoJohun9y9tnfv3ur4m2++Kde4fYo0YlT3rvueSCqM9A2QB7+UAACAFChKAABAChQlAAAgBYoSAACQAkUJAABIgaIEAACk0EhDvkijMBWxdNFB18RMRUoXFxflmp6eHjmnts9FQ1Us2UV4r1y5Iuempqaq4y4+65qs/eUvf6mOj4yMyDUqRuwityoiXoo+v6oJXSm+GZ46H+64qoix224XG3XXhOKuS7W/Lhqt7g0XCX7xxRfl3J49e6rj7v6MNspT1He52K+jti+ybQBuD34pAQAAKVCUAACAFChKAABAChQlAAAgBYoSAACQAkUJAABIoeVIcCTm6aKSKtbnIq3vvfeenPvCF74g5xQXQ1Xb4SKWk5OT1fHp6Wm5xnXbVd1ix8fH5RrXSfb06dPVcdVhthR9HFz8MxKfdWsmJibk3DvvvFMdP3funFzz97//vTo+MDAg14yOjso51b3aRc5V3LsUHVFVXahL0THi/fv3yzWHDx+Wc+o8uThu09Fa9V3Ray/SdRhAZ/FLCQAASIGiBAAApEBRAgAAUqAoAQAAKVCUAACAFFpO37hGXErkLXmVOCmllDfffFPOqfSBS1S4pM/CwkJ13KVvrl69Wh13zddcWqa3t7c63tfX1/YaN+cavamEUuR6iK5zzf/efffd6vjx48flGnWeIsmNUvw5VJpOiai0jEuYNc3tU0QkFePWqO2LNvgD0DzuRgAAkAJFCQAASIGiBAAApEBRAgAAUqAoAQAAKVCUAACAFFqOBLvmekqkMZtr/KcaypVSyquvvlodf/LJJ+UaFzVV0dVITLG7u1vOuQi0mnPb4PZJNf9bWlqSa1R81m135Li68z40NCTn1LFw16vaPndc3ZyKfLuoadON7SJrIhHeaPO6yHdFotE01wM+2vilBAAApEBRAgAAUqAoAQAAKVCUAACAFChKAABAChQlAAAghZYjwS7Sp2KeriOs+jwX6Zufn5dzv/jFL9r6nlJKuffee+Wc2g4Xn125cmV13EVaXRQ2Eol0nY9v3LhRHXf75OYUd8zVtrvtdvFZdcybjoZGus+6NdEuy+1qOnoc7ajb5P5Gj6uKsbt7EEBn8UsJAABIgaIEAACkQFECAABSoCgBAAApUJQAAIAUWk7fuLfa1dvrkcZZbo178//MmTPV8R/96Edyzc6dO+XcyMhIdXzVqlVyzebNm6vjrkGd2yfVVO7atWtyzdzcnJxTzfWmp6flGpUsiTabU9eRS/lcunRJzikuUaFSSE4kURRNqjSZHIqkhtycew64OXUs3HmKbEPkPLn7CUBn8UsJAABIgaIEAACkQFECAABSoCgBAAApUJQAAIAUKEoAAEAKLUeCVTOrUpptthWNUartUzHYUkr54x//KOd2795dHXeN42ZmZqrj3d3dcs3y5cvlnIowun2anZ1te85Fgnt6eqrjbp9cDFXFcS9evCjXqLh3KTpuHYnCRq/jpuPtal0k7hppUFdKrBmk2ye17S6eHX0WtLsN7rgC6Cx+KQEAAClQlAAAgBQoSgAAQAoUJQAAIAWKEgAAkELL6Zsmm4SV0vwb7+ot/kjDu1L0/rq0jGrsNT8/L9csLCzIOZWcWFxcbHsb3Of19/fLNSqh4VJIbp/UsXBN91SqqZRSzp49Wx1351YdB3dNRhpSum2IJGki92B0G5pOqqh1kYZ8TqRhIOkbIA9+KQEAAClQlAAAgBQoSgAAQAoUJQAAIAWKEgAAkAJFCQAASKGRSHCTceFIUy+3DS4S6eaOHj1aHe/q6pJrVqxYUR132+3ivWr71Pfc7LvUnPs8dVxdzHlubk7OqaaArpHg3r175dzp06er4+7cKi5OGokLR5ruublIdDUaiVci11cpsWdEJBodacQIIA9+KQEAAClQlAAAgBQoSgAAQAoUJQAAIAWKEgAAkAJFCQAASOG2RoIjkcimo8duG1Rn4VJKOXToUHXcdcBV0dqRkRG5xsVnVSfewcFBucZ1MVYRUNfxV3UJdpaWluTc5cuXq+Mqgn2zObVPnYrcunUuYhz5vGgX4ya3oenIrfu8SNS66W0A0Fn8UgIAAFKgKAEAAClQlAAAgBQoSgAAQAoUJQAAIIVG0jfqbfgMb+rfeeedba8pRac6JiYm5Jrh4eHq+NjYmFzjkioqSeMSNi6ZoJr/uTTPsmXL2vqsUvy5VYkn1VivlFKuXbvW9ndFGsdFG/IpkXsm+nmK2+7INrj7KZJ4cskvdZ2780RDPuCjjV9KAABAChQlAAAgBYoSAACQAkUJAABIgaIEAACkQFECAABSuONDulEBAIAE+KUEAACkQFECAABSoCgBAAApUJQAAIAUKEoAAEAKFCUAACAFihIAAJACRQkAAEiBogQAAKTwP9q315pjwfN0AAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] @@ -823,76 +804,6 @@ "plt.axis(\"off\")\n", "plt.show()" ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "9329ec38", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([[[[[-0.0118, -0.0161, -0.0204, ..., -0.0177, -0.0213, -0.0121],\n", - " [-0.0221, -0.0141, -0.0138, ..., -0.0157, -0.0150, -0.0172],\n", - " [-0.0207, -0.0173, -0.0138, ..., -0.0152, -0.0128, -0.0035],\n", - " ...,\n", - " [-0.0197, -0.0170, -0.0155, ..., -0.0220, -0.0165, -0.0158],\n", - " [-0.0122, -0.0134, -0.0122, ..., -0.0125, -0.0143, -0.0174],\n", - " [-0.0097, -0.0158, -0.0132, ..., -0.0090, -0.0218, -0.0054]],\n", - "\n", - " [[-0.0135, -0.0153, -0.0159, ..., -0.0154, -0.0136, -0.0122],\n", - " [-0.0202, -0.0044, -0.0154, ..., -0.0104, -0.0127, -0.0237],\n", - " [-0.0146, -0.0061, -0.0209, ..., -0.0099, -0.0035, -0.0175],\n", - " ...,\n", - " [-0.0141, -0.0155, -0.0132, ..., -0.0117, -0.0034, -0.0124],\n", - " [-0.0151, -0.0135, -0.0084, ..., -0.0099, -0.0103, -0.0147],\n", - " [-0.0154, -0.0138, -0.0188, ..., -0.0135, -0.0183, -0.0186]],\n", - "\n", - " [[-0.0140, -0.0153, -0.0128, ..., -0.0140, -0.0157, -0.0100],\n", - " [-0.0080, -0.0141, -0.0195, ..., -0.0190, -0.0130, -0.0143],\n", - " [-0.0109, -0.0158, -0.0169, ..., -0.0086, -0.0183, -0.0083],\n", - " ...,\n", - " [-0.0169, -0.0170, -0.0210, ..., -0.0166, -0.0115, -0.0055],\n", - " [-0.0139, -0.0102, -0.0239, ..., -0.0054, -0.0090, -0.0142],\n", - " [-0.0142, -0.0163, -0.0141, ..., -0.0292, -0.0181, -0.0136]],\n", - "\n", - " ...,\n", - "\n", - " [[-0.0163, -0.0123, -0.0178, ..., -0.0183, -0.0132, -0.0167],\n", - " [-0.0114, -0.0064, -0.0112, ..., -0.0125, -0.0078, -0.0170],\n", - " [-0.0226, -0.0162, -0.0194, ..., -0.0125, -0.0053, -0.0162],\n", - " ...,\n", - " [-0.0061, -0.0163, -0.0128, ..., -0.0163, -0.0257, -0.0110],\n", - " [-0.0109, -0.0073, -0.0129, ..., -0.0172, -0.0035, -0.0134],\n", - " [-0.0187, -0.0208, -0.0168, ..., -0.0154, -0.0192, -0.0203]],\n", - "\n", - " [[-0.0140, -0.0088, -0.0152, ..., -0.0193, -0.0113, -0.0151],\n", - " [-0.0126, -0.0144, -0.0108, ..., -0.0057, -0.0083, -0.0140],\n", - " [-0.0204, -0.0089, -0.0156, ..., -0.0183, -0.0147, -0.0155],\n", - " ...,\n", - " [-0.0176, -0.0158, -0.0191, ..., -0.0175, -0.0069, -0.0122],\n", - " [-0.0103, -0.0158, -0.0040, ..., -0.0104, -0.0180, -0.0171],\n", - " [-0.0099, -0.0032, -0.0117, ..., -0.0122, -0.0135, -0.0179]],\n", - "\n", - " [[ 0.0144, -0.0134, -0.0216, ..., -0.0157, -0.0157, -0.0201],\n", - " [-0.0065, -0.0194, -0.0155, ..., -0.0205, -0.0168, -0.0207],\n", - " [-0.0139, -0.0150, -0.0128, ..., -0.0134, -0.0197, -0.0205],\n", - " ...,\n", - " [-0.0083, -0.0041, -0.0108, ..., -0.0177, -0.0169, -0.0201],\n", - " [-0.0153, -0.0141, -0.0211, ..., -0.0135, -0.0171, -0.0232],\n", - " [-0.0652, -0.0062, -0.0201, ..., -0.0215, -0.0135, -0.0569]]]]],\n", - " device='cuda:0')" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "image" - ] } ], "metadata": { diff --git a/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.py b/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.py index 06a19b0b..09e99ee6 100644 --- a/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.py +++ b/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.py @@ -44,7 +44,6 @@ # limitations under the License. import os -import tempfile import time import matplotlib.pyplot as plt @@ -78,7 +77,8 @@ # %% directory = os.environ.get("MONAI_DATA_DIRECTORY") -root_dir = tempfile.mkdtemp() if directory is None else directory +root_dir = "/tmp/tmpk32kv7za" +# root_dir = tempfile.mkdtemp() if directory is None else directory print(root_dir) # %% [markdown] @@ -118,13 +118,13 @@ root_dir=root_dir, task="Task01_BrainTumour", transform=train_transform, section="training", download=True ) -train_loader = DataLoader(train_ds, batch_size=16, shuffle=True, num_workers=8) +train_loader = DataLoader(train_ds, batch_size=8, shuffle=True, num_workers=8) val_ds = DecathlonDataset( root_dir=root_dir, task="Task01_BrainTumour", transform=val_transform, section="validation", download=True ) -val_loader = DataLoader(val_ds, batch_size=16, shuffle=False, num_workers=8) +val_loader = DataLoader(val_ds, batch_size=8, shuffle=False, num_workers=8) # %% [markdown] @@ -149,10 +149,10 @@ spatial_dims=3, in_channels=1, out_channels=1, - num_channels=[128, 128, 256, 256], - attention_levels=[False, False, False, True], - num_head_channels=[128, 128, 256, 256], - num_res_blocks=1, + num_channels=[256, 256, 512], + attention_levels=[False, False, True], + num_head_channels=[256, 256, 512], + num_res_blocks=2, ) model.to(device) @@ -282,6 +282,3 @@ plt.tight_layout() plt.axis("off") plt.show() - -# %% -image From 472d52cdd4e9af6762844c09a4f351fdccf95bf6 Mon Sep 17 00:00:00 2001 From: Walter Hugo Lopez Pinaya Date: Mon, 27 Feb 2023 22:14:36 +0000 Subject: [PATCH 3/4] Address comments Signed-off-by: Walter Hugo Lopez Pinaya --- tutorials/generative/3d_ddpm/3d_ddpm_tutorial.ipynb | 6 +++--- tutorials/generative/3d_ddpm/3d_ddpm_tutorial.py | 7 ++++--- 2 files changed, 7 insertions(+), 6 deletions(-) diff --git a/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.ipynb b/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.ipynb index dd5a86f5..1a57071a 100644 --- a/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.ipynb +++ b/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.ipynb @@ -23,6 +23,7 @@ "metadata": {}, "outputs": [], "source": [ + "!python -c \"import monai\" || pip install -q \"monai-weekly[nibabel, tqdm]\"\n", "!python -c \"import matplotlib\" || pip install -q matplotlib\n", "%matplotlib inline" ] @@ -151,8 +152,7 @@ ], "source": [ "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n", - "root_dir = \"/tmp/tmpk32kv7za\"\n", - "# root_dir = tempfile.mkdtemp() if directory is None else directory\n", + "root_dir = tempfile.mkdtemp() if directory is None else directory\n", "print(root_dir)" ] }, @@ -799,7 +799,7 @@ "plt.style.use(\"default\")\n", "plotting_image_0 = np.concatenate([image[0, 0, :, :, 15].cpu(), np.flipud(image[0, 0, :, 24, :].cpu().T)], axis=1)\n", "plotting_image_1 = np.concatenate([np.flipud(image[0, 0, 15, :, :].cpu().T), np.zeros((32, 32))], axis=1)\n", - "plt.imshow(np.concatenate([plotting_image_0, plotting_image_1], axis=0), cmap=\"gray\")\n", + "plt.imshow(np.concatenate([plotting_image_0, plotting_image_1], axis=0), vmin=0, vmax=1, cmap=\"gray\")\n", "plt.tight_layout()\n", "plt.axis(\"off\")\n", "plt.show()" diff --git a/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.py b/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.py index 09e99ee6..f31b0ae1 100644 --- a/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.py +++ b/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.py @@ -25,6 +25,7 @@ # ## Setup environment # %% +# !python -c "import monai" || pip install -q "monai-weekly[nibabel, tqdm]" # !python -c "import matplotlib" || pip install -q matplotlib # %matplotlib inline @@ -44,6 +45,7 @@ # limitations under the License. import os +import tempfile import time import matplotlib.pyplot as plt @@ -77,8 +79,7 @@ # %% directory = os.environ.get("MONAI_DATA_DIRECTORY") -root_dir = "/tmp/tmpk32kv7za" -# root_dir = tempfile.mkdtemp() if directory is None else directory +root_dir = tempfile.mkdtemp() if directory is None else directory print(root_dir) # %% [markdown] @@ -278,7 +279,7 @@ plt.style.use("default") plotting_image_0 = np.concatenate([image[0, 0, :, :, 15].cpu(), np.flipud(image[0, 0, :, 24, :].cpu().T)], axis=1) plotting_image_1 = np.concatenate([np.flipud(image[0, 0, 15, :, :].cpu().T), np.zeros((32, 32))], axis=1) -plt.imshow(np.concatenate([plotting_image_0, plotting_image_1], axis=0), cmap="gray") +plt.imshow(np.concatenate([plotting_image_0, plotting_image_1], axis=0), vmin=0, vmax=1, cmap="gray") plt.tight_layout() plt.axis("off") plt.show() From 7e531f9cc92e9d50f9279b032a6ca9f42518e820 Mon Sep 17 00:00:00 2001 From: Walter Hugo Lopez Pinaya Date: Tue, 28 Feb 2023 07:30:26 +0000 Subject: [PATCH 4/4] Address comments Signed-off-by: Walter Hugo Lopez Pinaya --- .../generative/3d_ddpm/3d_ddpm_tutorial.ipynb | 388 ++++++++++-------- .../generative/3d_ddpm/3d_ddpm_tutorial.py | 8 +- 2 files changed, 214 insertions(+), 182 deletions(-) diff --git a/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.ipynb b/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.ipynb index 1a57071a..70c23960 100644 --- a/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.ipynb +++ b/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.ipynb @@ -54,6 +54,8 @@ "name": "stdout", "output_type": "stream", "text": [ + "2023-02-28 00:16:00,289 - 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", @@ -146,7 +148,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "/tmp/tmpk32kv7za\n" + "/tmp/tmp4dbx0xcm\n" ] } ], @@ -228,36 +230,56 @@ "lines_to_next_cell": 2 }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Task01_BrainTumour.tar: 7.09GB [06:55, 18.3MB/s] " + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-02-26 15:37:24,411 - INFO - Verified 'Task01_BrainTumour.tar', md5: 240a19d752f0d9e9101544901065d872.\n", - "2023-02-26 15:37:24,412 - INFO - File exists: /tmp/tmpk32kv7za/Task01_BrainTumour.tar, skipped downloading.\n", - "2023-02-26 15:37:24,412 - INFO - Non-empty folder exists in /tmp/tmpk32kv7za/Task01_BrainTumour, skipped extracting.\n" + "2023-02-28 00:22:56,253 - INFO - Downloaded: /tmp/tmp4dbx0xcm/Task01_BrainTumour.tar\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Loading dataset: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 388/388 [04:04<00:00, 1.59it/s]\n" + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2023-02-26 15:41:37,641 - INFO - Verified 'Task01_BrainTumour.tar', md5: 240a19d752f0d9e9101544901065d872.\n", - "2023-02-26 15:41:37,641 - INFO - File exists: /tmp/tmpk32kv7za/Task01_BrainTumour.tar, skipped downloading.\n", - "2023-02-26 15:41:37,642 - INFO - Non-empty folder exists in /tmp/tmpk32kv7za/Task01_BrainTumour, skipped extracting.\n" + "2023-02-28 00:23:04,765 - INFO - Verified 'Task01_BrainTumour.tar', md5: 240a19d752f0d9e9101544901065d872.\n", + "2023-02-28 00:23:04,766 - INFO - Writing into directory: /tmp/tmp4dbx0xcm.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Loading dataset: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 96/96 [01:00<00:00, 1.58it/s]\n" + "Loading dataset: 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task=\"Task01_BrainTumour\", transform=train_transform, section=\"training\", download=True\n", ")\n", "\n", - "train_loader = DataLoader(train_ds, batch_size=8, shuffle=True, num_workers=8)\n", + "train_loader = DataLoader(train_ds, batch_size=4, shuffle=True, num_workers=8)\n", "\n", "val_ds = DecathlonDataset(\n", " root_dir=root_dir, task=\"Task01_BrainTumour\", transform=val_transform, section=\"validation\", download=True\n", ")\n", "\n", - "val_loader = DataLoader(val_ds, batch_size=8, shuffle=False, num_workers=8)" + "val_loader = DataLoader(val_ds, batch_size=4, shuffle=False, num_workers=8)" ] }, { @@ -340,7 +362,7 @@ ")\n", "model.to(device)\n", "\n", - "scheduler = DDPMScheduler(num_train_timesteps=1000, beta_schedule=\"scaled_linear\", beta_start=0.0015, beta_end=0.0195)\n", + "scheduler = DDPMScheduler(num_train_timesteps=1000)\n", "\n", "inferer = DiffusionInferer(scheduler)\n", "\n", @@ -367,37 +389,37 @@ "name": "stderr", "output_type": "stream", "text": [ - "Epoch 0: 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"Epoch 10: 100%|█████████| 97/97 [01:15<00:00, 1.29it/s, loss=0.00719]\n", + "Epoch 11: 100%|███████████| 97/97 [01:14<00:00, 1.29it/s, loss=0.006]\n", + "Epoch 12: 100%|█████████| 97/97 [01:15<00:00, 1.29it/s, loss=0.00456]\n", + "Epoch 13: 100%|█████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00512]\n", + "Epoch 14: 100%|█████████| 97/97 [01:14<00:00, 1.30it/s, loss=0.00733]\n", + "Epoch 15: 100%|█████████| 97/97 [01:13<00:00, 1.31it/s, loss=0.00684]\n", + "Epoch 16: 100%|█████████| 97/97 [01:15<00:00, 1.29it/s, loss=0.00879]\n", + "Epoch 17: 100%|█████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00484]\n", + "Epoch 18: 100%|█████████| 97/97 [01:13<00:00, 1.31it/s, loss=0.00766]\n", + "Epoch 19: 100%|█████████| 97/97 [01:13<00:00, 1.31it/s, loss=0.00575]\n", + "Epoch 20: 100%|█████████| 97/97 [01:13<00:00, 1.31it/s, loss=0.00678]\n", + "Epoch 21: 100%|█████████| 97/97 [01:13<00:00, 1.31it/s, loss=0.00489]\n", + "Epoch 22: 100%|█████████| 97/97 [01:13<00:00, 1.31it/s, loss=0.00636]\n", + "Epoch 23: 100%|█████████| 97/97 [01:14<00:00, 1.30it/s, loss=0.00934]\n", + "Epoch 24: 100%|██████████| 97/97 [01:13<00:00, 1.31it/s, loss=0.0084]\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [01:12<00:00, 13.80it/s]\n" ] }, { "data": { - "image/png": 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\n", 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" ] @@ -409,37 +431,37 @@ "name": "stderr", "output_type": "stream", "text": [ - "Epoch 25: 100%|███████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.002]\n", - "Epoch 26: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00265]\n", - "Epoch 27: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00256]\n", - "Epoch 28: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00223]\n", - "Epoch 29: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00263]\n", - "Epoch 30: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00259]\n", - "Epoch 31: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00268]\n", - "Epoch 32: 100%|██████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.0024]\n", - "Epoch 33: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00203]\n", - "Epoch 34: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00245]\n", - "Epoch 35: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00163]\n", - "Epoch 36: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00214]\n", - "Epoch 37: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00204]\n", - "Epoch 38: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00298]\n", - "Epoch 39: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00194]\n", - "Epoch 40: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00208]\n", - "Epoch 41: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00216]\n", - "Epoch 42: 100%|██████████| 49/49 [01:06<00:00, 1.37s/it, loss=0.0022]\n", - "Epoch 43: 100%|██████████| 49/49 [01:06<00:00, 1.37s/it, loss=0.0017]\n", - "Epoch 44: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00186]\n", - "Epoch 45: 100%|██████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.0021]\n", - "Epoch 46: 100%|█████████| 49/49 [01:06<00:00, 1.37s/it, loss=0.00274]\n", - "Epoch 47: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00186]\n", - "Epoch 48: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00191]\n", - "Epoch 49: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00198]\n", - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [01:12<00:00, 13.84it/s]\n" + "Epoch 25: 100%|█████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00832]\n", + "Epoch 26: 100%|█████████| 97/97 [01:13<00:00, 1.31it/s, loss=0.00654]\n", + "Epoch 27: 100%|██████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.0067]\n", + "Epoch 28: 100%|█████████| 97/97 [01:13<00:00, 1.31it/s, loss=0.00384]\n", + "Epoch 29: 100%|█████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00468]\n", + "Epoch 30: 100%|█████████| 97/97 [01:13<00:00, 1.31it/s, loss=0.00522]\n", + "Epoch 31: 100%|█████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00428]\n", + "Epoch 32: 100%|█████████| 97/97 [01:13<00:00, 1.31it/s, loss=0.00462]\n", + "Epoch 33: 100%|█████████| 97/97 [01:13<00:00, 1.31it/s, loss=0.00528]\n", + "Epoch 34: 100%|█████████| 97/97 [01:13<00:00, 1.31it/s, loss=0.00514]\n", + "Epoch 35: 100%|█████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00571]\n", + "Epoch 36: 100%|█████████| 97/97 [01:13<00:00, 1.31it/s, loss=0.00401]\n", + "Epoch 37: 100%|█████████| 97/97 [01:13<00:00, 1.31it/s, loss=0.00459]\n", + "Epoch 38: 100%|█████████| 97/97 [01:13<00:00, 1.31it/s, loss=0.00446]\n", + "Epoch 39: 100%|█████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00445]\n", + "Epoch 40: 100%|██████████| 97/97 [01:13<00:00, 1.31it/s, loss=0.0051]\n", + "Epoch 41: 100%|█████████| 97/97 [01:13<00:00, 1.31it/s, loss=0.00364]\n", + "Epoch 42: 100%|██████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.0041]\n", + "Epoch 43: 100%|█████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00536]\n", + "Epoch 44: 100%|██████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.0047]\n", + "Epoch 45: 100%|█████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00469]\n", + "Epoch 46: 100%|█████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00476]\n", + "Epoch 47: 100%|█████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00426]\n", + "Epoch 48: 100%|█████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00367]\n", + "Epoch 49: 100%|█████████| 97/97 [01:13<00:00, 1.31it/s, loss=0.00532]\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [01:12<00:00, 13.81it/s]\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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loss=0.00214]\n", - "Epoch 62: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00208]\n", - "Epoch 63: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00145]\n", - "Epoch 64: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00141]\n", - "Epoch 65: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00188]\n", - "Epoch 66: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00154]\n", - "Epoch 67: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00163]\n", - "Epoch 68: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00176]\n", - "Epoch 69: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00232]\n", - "Epoch 70: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00178]\n", - "Epoch 71: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00187]\n", - "Epoch 72: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00134]\n", - "Epoch 73: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00161]\n", - "Epoch 74: 100%|██████████| 49/49 [01:06<00:00, 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1.31it/s, loss=0.00408]\n", + "Epoch 63: 100%|█████████| 97/97 [01:13<00:00, 1.31it/s, loss=0.00397]\n", + "Epoch 64: 100%|█████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00409]\n", + "Epoch 65: 100%|█████████| 97/97 [01:13<00:00, 1.31it/s, loss=0.00431]\n", + "Epoch 66: 100%|█████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00276]\n", + "Epoch 67: 100%|█████████| 97/97 [01:13<00:00, 1.31it/s, loss=0.00339]\n", + "Epoch 68: 100%|█████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00321]\n", + "Epoch 69: 100%|█████████| 97/97 [01:13<00:00, 1.31it/s, loss=0.00337]\n", + "Epoch 70: 100%|█████████| 97/97 [01:13<00:00, 1.31it/s, loss=0.00343]\n", + "Epoch 71: 100%|█████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00496]\n", + "Epoch 72: 100%|█████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00263]\n", + "Epoch 73: 100%|█████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00483]\n", + "Epoch 74: 100%|█████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00724]\n", "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [01:12<00:00, 13.85it/s]\n" ] }, { "data": { - "image/png": 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IEARuznMURaV0Y11wUBuR0oXZlCRJMBgMcHp6ijRNkaapq8CxkgzYXrLPP43P8ZhAeR1ES0KqZqopPZ71rBnno0TUEIyN5/H4arNqCRbHYj13n5q39rMljrUbVdKrJGY4iXHJOq4aBnwHEXd1+3nBWQwbxzEuLy/x7t07nJ+fl5af05gi4K+U5v9AmaA2iG2JyPZWYnG7Bqv1xqr3rXE/9mkzNb7z94VslFC+0JOOmbBteB5Kdp1kpjFNLtg0m83cXGfa3XXCUQpjGQJRiagz9BjC8KmxKmiWglIK2KwXJKnYnnaRT33Zceu+wGYu28bwrJesdpvvuz32tjEoETl2NVvsJDOeJ6MBKg3rRkLgwCk+qj+qYS0DU9urKArEcVxp5LMvDXtYR8VHAB9peDwfAW0oyZJHj+WT1r4xqwbxtfcFpHWc/K7OEKF2rZKMUo+lX5PJBOPx2EnFFyMR9YJ2Oh28fv0ap6enePXqlbMLVSLSgCbReLH1QvuKJQD/DDoNi1hSEkVRbFWvPpuMn9t+U2dF+66KC2pM0I5PzQk9lko9Xh9VufP53KUIP378iLu7O4zHY9zc3GA8HpeyWofCviGig6lmtRFZoJkkSaly2DdRiAThU25jYg/BGv+WiKpCd8kyWCmm0tBKOhs68RHIfrfH8J2HL7RlSa/Bf7ULJ5MJ7u/v3WphdVpVVnG0CfY2IW9tHt+fTetZh0L7t9uAslSxUtbua8dLWNL4tluJqNuq2muuvOqa+X6zmSeqZGZQuKos327AFWZt8cgxsI85d9RFmOh1qnMBbBbD+r5rVYsvvuZTl5wkZdODdly2D2sTqoOjnrudAahtLEntamDbbLRtklDTdTrPhtJutVq5BTwXiwVubm5wf3/vSurqioOn+PR/qw6rHJKHiPkQCRUkoeZauY/anL4+fFLMRyAlYtV23zlyfAqaKDombasVSUpIXdJ4Pp+7Yof5fO6WMq6bg6I4uESk2qCNQqMaKHulSjafx2vzwDoTb5uq5xg036xBZi3V55hsPz516+tf2/hIXNWnEsReGx0THyrfH68riUoJSPVd56mkwBGIuFwucXd3hzzP0W633TvvbOZDiaKSk9mPPM9LCXubhQiCb3OQqU59703R/dRu1HhkFYn5G1CeU7KNZPxdP3U86vnaYLuNAqjk4wPO/fVNA9PpFJPJxL2+jcuSvGiJyCroVqtVWojcJ53U/tol2Mvtquo1FslAucLaXZrb5bYqc4H/6/7A5gxEH+EsEfgAqrRS9alOneaitSROPWVeW5WE/KtrWk9x8PANvTgAbgV8vq6C4Z0qz1SJqI6BJaiqU1+wm+1se5v9UOeGsMTyqV6VmFZ6VjkoqgmsRN1m89qAvT7IPhL6kgN1xMGLHubzuQukdjodfP78GUVRIE1TbwzP2n5anqWq2Xez1NYriqK0or+GcXx2GElCdavtVXX6pCDPVUnE9r55yuzfJ0GryKjaQvPKQRA48jFUw/cHqsR80UQEvr0tablcugrt6XTqbB5Nt/lQlRnxST39f1ug2tqj3IfbrarXEJJta1W2dcCU4BrH0wfGOli7SEQbl+UDpg6Lncj1oonIG6jSkTYOZ5hpOs5Hql2hNmfVWKpUt4ZD9HcN/fiItY2Idl/fK9Z856AOim1PAtPbV6Lp9Fy1DetOQuBI4Rs6DTc3N/jzzz+RpqlbEWwwGLgXgVsybpMSauf56vas2lVngvM8tH9d9NJKOnWANLf70NxgnQClElFNDhsh8M2nVuiiUUpyhnCYY+ZkqbrHD4mjLl08n89xd3eH2WyGNE0xm83cW5c6nc4G8XxPsrXpfBUzhK/oQFWaVbtq3Nu51EDZvmTabJukUYdBsztabKHvglH7WCWiNSX0XPUBs+pZnaW646grxjLMsF6vMR6PcXt766QRJSJQDipb58H3vUr1qNpVqaa/+/6Asg1n+7OeuB5H21rnRFVtVS2kjsPGKfWYWZa5XLJ+aplX3dWx4qhE5JRSFst++PABaZri6uoKg8GgtEQIU3C+WKD1ptneEtISkd99ZWYan/PZcCp5aGtqxRD7tJOnCDv7zyfBdR89b+2TYE757u4O8/kcX79+xc3NjSPosQsc/l8c/V18VG3T6RR3d3dYLBYYDAYbIQardrhNoY4B4JdwwKaNSNi2vqocWwltx6jw9aP9Va0Hab9b0qt9yTa0UW2ljZoPzwlP9nZSSscsy/Dlyxf8/fffuL+/R6/Xw9nZWWmaqfWEfWqPErSKiLqvvdG0v3wpRO5PIqlk5bg0FcmguB4LqH47gRKHsx15LHVMlOR0TG5vb3F3d+eKG56jSiaC9Y6j3hbb+h6EYehmxw0GA1xeXqLb7eLq6gq//fabW2GWKrsqw2GreaqyIESV1FE1bcv3VX37igeU3HZRAA1o232KonASjWYIVbI6LkRRfCv3Wi6XuLm5wWg0KuWafcd6auxCsYOvfVMFXlTg22JLzIRcXFwA2Hwfiaprn+SzUk7HquO3ZNQ2Gk7h2DSNxrHbih/dbmfT6ditdObkJgb+uUyeBq31OJPJxEk/TgGoe0HDLngy1aygNFiv1xiNRvjnn39wf3+P4XCILMtcbloXQyKqPE6f+uanqnptoxOs1Ea0NqpPzdpCB5+0JWEoNfM8d6+15YPJ2KQNQwVB4ApIdHLUc1XFFgeb17wPuHRdEARYLBb4+vUroijCxcUF3r59iyRJcH5+jqurK7deDhe35JiA8s33pdTU9gqCoBRrUyloc9b2/PWTx+WnJSDVtJbyj8djjMdjZFmG0WjkpJpdLMmCEtRXrVNH7GPO1UIiAt9uOKc/Av8FjcMwRJqmCIIAw+GwRBBVmdbLtqEW9q/znfW4QDm95iOctdm2pSR1LCQkbcDZbFYi4mg0KqXmtB/b54+K2hDRB87LzbLMrR7GV+r2+323QBIXFlJ17AtEq51GQuk62748tA3v+AoXdO6ITmafTqeYTqcoigLT6dRVxIzHY7dUH6WkBt0VL4GEwB5es3qmx0Kr1XLLkiRJ4t7SPhwOnZoeDAZuCRNVuzZnTIIxh2sD4BouUVJoKk+9WiWtesqcEpHnuQuvrFYrp46LonAervXErV37nGG9/YdQa4mY5zkmkwmCIHDqjJXeKgl7vR7W63Up9mg9YnVOKBE1XrjLpC6bldH51ySiLvExmUwwGo2wXC5xf3+P8Xjs2vqC5z+61NuGWjgrD8HG+WazGW5vb516K4rCSTouOWw9X37nu+nYRosgVCKSKCSVert6/lTHlIKsQl+tVs4jtiq4Sv3+SKhyuKqws2q2S/M+BVS90ntOkgS9Xg9RFKHb7TrbkW1IMEpLvheQ2RB9C5auucgHTtWuVtPoDLrxeIzRaFTygpWU3LfuM+kOhV3O+VkR0QcSsNVquUwMHZskSRwJGYPk4qCs/eN2X/iGIRK1ETUWSKeExQfL5bJESo0LvmQ8amalrtBY3WKxwGQyQavVcg6BFpySiPqWeMYjbSaDRFRnQotRtQpa44KcFvEjZDuOib29ZqB+No2Sh+VWvipoBrS1jU0jApsLY2pIiN/V3tNXYzyneSLHwqOq5joTcR/4ilKryssUlli2tOw5X5ND40Wo5n2hAW0f4YBNIlZlORqp93h4kUR8LOI8RSjrR8Vx3oX1g6Ih4eNhZyJWqbEGDR4DexfGAo0kaLAbDlIG9lD5U4MGin21586qufEQG+yDfXmy93tWbFnV9x74UHhplSxVkqcO57/PGB7thT+Nuj4eqmKfhFasPxc04ZsGtcDOKb4GDQ6JRiI2qAUaIjaoBRoiNqgFGiI2qAUaIjaoBRoiNqgFGiI2qAUaIjaoBRoiNqgF/geRtGivcY6RgwAAAABJRU5ErkJggg==\n", 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" ] @@ -493,37 +515,37 @@ "name": "stderr", "output_type": "stream", "text": [ - "Epoch 75: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00176]\n", - "Epoch 76: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00215]\n", - "Epoch 77: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00179]\n", - "Epoch 78: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00136]\n", - "Epoch 79: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00153]\n", - "Epoch 80: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00163]\n", - "Epoch 81: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00164]\n", - "Epoch 82: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00139]\n", - "Epoch 83: 100%|██████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.0014]\n", - "Epoch 84: 100%|█████████| 49/49 [01:06<00:00, 1.37s/it, loss=0.00131]\n", - "Epoch 85: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00233]\n", - "Epoch 86: 100%|█████████| 49/49 [01:06<00:00, 1.37s/it, loss=0.00221]\n", - "Epoch 87: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00219]\n", - "Epoch 88: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00285]\n", - "Epoch 89: 100%|█████████| 49/49 [01:07<00:00, 1.37s/it, loss=0.00185]\n", - "Epoch 90: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00162]\n", - "Epoch 91: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00149]\n", - "Epoch 92: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00151]\n", - "Epoch 93: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00176]\n", - "Epoch 94: 100%|█████████| 49/49 [01:06<00:00, 1.37s/it, loss=0.00218]\n", - "Epoch 95: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00161]\n", - "Epoch 96: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00128]\n", - "Epoch 97: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00179]\n", - "Epoch 98: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00178]\n", - "Epoch 99: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00222]\n", - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [01:12<00:00, 13.85it/s]\n" + "Epoch 75: 100%|█████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00703]\n", + "Epoch 76: 100%|█████████| 97/97 [01:13<00:00, 1.31it/s, loss=0.00351]\n", + "Epoch 77: 100%|█████████| 97/97 [01:13<00:00, 1.31it/s, loss=0.00445]\n", + "Epoch 78: 100%|█████████| 97/97 [01:13<00:00, 1.31it/s, loss=0.00264]\n", + "Epoch 79: 100%|█████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00324]\n", + "Epoch 80: 100%|█████████| 97/97 [01:13<00:00, 1.31it/s, loss=0.00307]\n", + "Epoch 81: 100%|█████████| 97/97 [01:13<00:00, 1.31it/s, loss=0.00522]\n", + "Epoch 82: 100%|█████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00569]\n", + "Epoch 83: 100%|█████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00378]\n", + "Epoch 84: 100%|█████████| 97/97 [01:15<00:00, 1.29it/s, loss=0.00442]\n", + "Epoch 85: 100%|█████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00387]\n", + "Epoch 86: 100%|█████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00523]\n", + "Epoch 87: 100%|██████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.0028]\n", + "Epoch 88: 100%|█████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00541]\n", + "Epoch 89: 100%|█████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00382]\n", + "Epoch 90: 100%|█████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00329]\n", + "Epoch 91: 100%|█████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00379]\n", + "Epoch 92: 100%|█████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00409]\n", + "Epoch 93: 100%|███████████| 97/97 [01:13<00:00, 1.31it/s, loss=0.003]\n", + "Epoch 94: 100%|██████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.0042]\n", + "Epoch 95: 100%|█████████| 97/97 [01:13<00:00, 1.31it/s, loss=0.00496]\n", + "Epoch 96: 100%|█████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00296]\n", + "Epoch 97: 100%|█████████| 97/97 [01:13<00:00, 1.31it/s, loss=0.00307]\n", + "Epoch 98: 100%|█████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00362]\n", + "Epoch 99: 100%|█████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00498]\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [01:12<00:00, 13.84it/s]\n" ] }, { "data": { - "image/png": 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loss=0.00203]\n", - "Epoch 112: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00156]\n", - "Epoch 113: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00186]\n", - "Epoch 114: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00139]\n", - "Epoch 115: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00139]\n", - "Epoch 116: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00154]\n", - "Epoch 117: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00216]\n", - "Epoch 118: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00135]\n", - "Epoch 119: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00175]\n", - "Epoch 120: 100%|████████| 49/49 [01:06<00:00, 1.37s/it, loss=0.00199]\n", - "Epoch 121: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00192]\n", - "Epoch 122: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00159]\n", - "Epoch 123: 100%|█████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.0015]\n", - "Epoch 124: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00138]\n", - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [01:12<00:00, 13.87it/s]\n" + "Epoch 100: 100%|████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00304]\n", + "Epoch 101: 100%|████████| 97/97 [01:13<00:00, 1.31it/s, loss=0.00281]\n", + "Epoch 102: 100%|████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00374]\n", + "Epoch 103: 100%|████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00435]\n", + "Epoch 104: 100%|████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00424]\n", + "Epoch 105: 100%|████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00353]\n", + "Epoch 106: 100%|████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00509]\n", + "Epoch 107: 100%|████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00329]\n", + "Epoch 108: 100%|████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00366]\n", + "Epoch 109: 100%|████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00379]\n", + "Epoch 110: 100%|████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00335]\n", + "Epoch 111: 100%|████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00367]\n", + "Epoch 112: 100%|████████| 97/97 [01:13<00:00, 1.31it/s, loss=0.00427]\n", + "Epoch 113: 100%|████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00236]\n", + "Epoch 114: 100%|████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00235]\n", + "Epoch 115: 100%|████████| 97/97 [01:13<00:00, 1.31it/s, loss=0.00248]\n", + "Epoch 116: 100%|████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00408]\n", + "Epoch 117: 100%|████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00376]\n", + "Epoch 118: 100%|████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00507]\n", + "Epoch 119: 100%|████████| 97/97 [01:13<00:00, 1.31it/s, loss=0.00283]\n", + "Epoch 120: 100%|██████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.003]\n", + "Epoch 121: 100%|████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00485]\n", + "Epoch 122: 100%|████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00346]\n", + "Epoch 123: 100%|████████| 97/97 [01:13<00:00, 1.31it/s, loss=0.00338]\n", + "Epoch 124: 100%|████████| 97/97 [01:13<00:00, 1.31it/s, loss=0.00318]\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [01:12<00:00, 13.85it/s]\n" ] }, { "data": { - "image/png": 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loss=0.00131]\n", - "Epoch 137: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00162]\n", - "Epoch 138: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00165]\n", - "Epoch 139: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00159]\n", - "Epoch 140: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00151]\n", - "Epoch 141: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00201]\n", - "Epoch 142: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00159]\n", - "Epoch 143: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00163]\n", - "Epoch 144: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00116]\n", - "Epoch 145: 100%|█████████| 49/49 [01:06<00:00, 1.37s/it, loss=0.0016]\n", - "Epoch 146: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00159]\n", - "Epoch 147: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00157]\n", - "Epoch 148: 100%|████████| 49/49 [01:06<00:00, 1.36s/it, loss=0.00125]\n", - "Epoch 149: 100%|████████| 49/49 [01:06<00:00, 1.37s/it, loss=0.00133]\n", - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [01:12<00:00, 13.86it/s]\n" + "Epoch 125: 100%|████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00231]\n", + "Epoch 126: 100%|████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00388]\n", + "Epoch 127: 100%|████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00416]\n", + "Epoch 128: 100%|████████| 97/97 [01:13<00:00, 1.31it/s, loss=0.00245]\n", + "Epoch 129: 100%|████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00435]\n", + "Epoch 130: 100%|████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00393]\n", + "Epoch 131: 100%|█████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.0031]\n", + "Epoch 132: 100%|████████| 97/97 [01:13<00:00, 1.31it/s, loss=0.00241]\n", + "Epoch 133: 100%|█████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.0032]\n", + "Epoch 134: 100%|████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00255]\n", + "Epoch 135: 100%|████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00315]\n", + "Epoch 136: 100%|████████| 97/97 [01:15<00:00, 1.29it/s, loss=0.00352]\n", + "Epoch 137: 100%|████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00245]\n", + "Epoch 138: 100%|████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00301]\n", + "Epoch 139: 100%|████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00411]\n", + "Epoch 140: 100%|████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00247]\n", + "Epoch 141: 100%|████████| 97/97 [01:14<00:00, 1.29it/s, loss=0.00238]\n", + "Epoch 142: 100%|████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00269]\n", + "Epoch 143: 100%|████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00374]\n", + "Epoch 144: 100%|████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00252]\n", + "Epoch 145: 100%|████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00443]\n", + "Epoch 146: 100%|████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00534]\n", + "Epoch 147: 100%|████████| 97/97 [01:13<00:00, 1.31it/s, loss=0.00356]\n", + "Epoch 148: 100%|████████| 97/97 [01:14<00:00, 1.31it/s, loss=0.00262]\n", + "Epoch 149: 100%|████████| 97/97 [01:13<00:00, 1.31it/s, loss=0.00305]\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [01:12<00:00, 13.85it/s]\n" ] }, { "data": { - "image/png": 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\n", 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" ] @@ -619,7 +641,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "train completed, total time: 10538.769879579544.\n" + "train completed, total time: 11627.830752849579.\n" ] } ], @@ -718,7 +740,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -756,7 +778,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "id": "092eb6a0", "metadata": { "lines_to_next_cell": 2 @@ -766,7 +788,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [04:21<00:00, 3.83it/s]\n" + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [04:18<00:00, 3.87it/s]\n" ] } ], @@ -780,13 +802,13 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "id": "5dc3e69d", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -804,6 +826,14 @@ "plt.axis(\"off\")\n", "plt.show()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e3e43b95", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.py b/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.py index f31b0ae1..400a49af 100644 --- a/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.py +++ b/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.py @@ -119,13 +119,13 @@ root_dir=root_dir, task="Task01_BrainTumour", transform=train_transform, section="training", download=True ) -train_loader = DataLoader(train_ds, batch_size=8, shuffle=True, num_workers=8) +train_loader = DataLoader(train_ds, batch_size=4, shuffle=True, num_workers=8) val_ds = DecathlonDataset( root_dir=root_dir, task="Task01_BrainTumour", transform=val_transform, section="validation", download=True ) -val_loader = DataLoader(val_ds, batch_size=8, shuffle=False, num_workers=8) +val_loader = DataLoader(val_ds, batch_size=4, shuffle=False, num_workers=8) # %% [markdown] @@ -157,7 +157,7 @@ ) model.to(device) -scheduler = DDPMScheduler(num_train_timesteps=1000, beta_schedule="scaled_linear", beta_start=0.0015, beta_end=0.0195) +scheduler = DDPMScheduler(num_train_timesteps=1000) inferer = DiffusionInferer(scheduler) @@ -283,3 +283,5 @@ plt.tight_layout() plt.axis("off") plt.show() + +# %%