example_training.ipynb

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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "If you use TorchIO for your research, please cite the following paper:\nPérez-García et al., TorchIO: a Python library for efficient loading,\npreprocessing, augmentation and patch-based sampling of medical images\nin deep learning. Link: https://arxiv.org/abs/2003.04696\n\n"
    }
   ],
   "source": [
    "# ------------------------------------------------------------------------------\n",
    "# An example of training and evaluating a model for the prostate dataset in the\n",
    "# Medical Segmentation Decathlon. To execute, download the data for task 5 from\n",
    "# http://medicaldecathlon.com/ and specify the path in mp.paths.py. \n",
    "# ------------------------------------------------------------------------------\n",
    "\n",
    "# 1. Imports\n",
    "\n",
    "# Reload changes modules\n",
    "from IPython import get_ipython\n",
    "get_ipython().magic('load_ext autoreload') \n",
    "get_ipython().magic('autoreload 2')\n",
    "\n",
    "# Import modules\n",
    "import torch\n",
    "from torch.utils.data import DataLoader\n",
    "import torch.optim as optim\n",
    "from mp.experiments.experiment import Experiment\n",
    "from mp.data.data import Data\n",
    "from mp.data.datasets.ds_mr_prostate_decathlon import DecathlonProstateT2\n",
    "import mp.visualization.visualize_imgs as vis\n",
    "from mp.data.pytorch.pytorch_seg_dataset import PytorchSeg2DDataset\n",
    "from mp.models.segmentation.unet_fepegar import UNet2D\n",
    "from mp.eval.losses.losses_segmentation import LossClassWeighted, LossDiceBCE\n",
    "from mp.agents.segmentation_agent import SegmentationAgent\n",
    "from mp.eval.result import Result\n",
    "from mp.utils.load_restore import nifty_dump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Device name: GeForce GTX 1050 Ti\n"
    }
   ],
   "source": [
    "# 2. Define configuration\n",
    "\n",
    "# The configuration dictionary boundles training parameters and anything \n",
    "# that can be manually defined. The config file is stored within the directory \n",
    "# created for a given experiment. By specifying arguments in config files, \n",
    "# higher-level modules don't need to be adapted for hyperparameter searches\n",
    "# and experiments are more easily reproducible.\n",
    "\n",
    "config = {'experiment_name':'test_exp', 'device':'cuda:0',\n",
    "    'nr_runs': 1, 'cross_validation': False, 'val_ratio': 0.0, 'test_ratio': 0.3,\n",
    "    'input_shape': (1, 256, 256), 'resize': False, 'augmentation': 'none', \n",
    "    'class_weights': (0.,1.), 'lr': 0.0001, 'batch_size': 8\n",
    "    }\n",
    "device = config['device']\n",
    "device_name = torch.cuda.get_device_name(device)\n",
    "print('Device name: {}'.format(device_name))\n",
    "input_shape = config['input_shape']  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3. Create experiment directories\n",
    "\n",
    "# Initializing an experiment creates a directory exp/<exp name> in the\n",
    "# storage directory defined in mp.paths.py. All files created during that \n",
    "# experiment are stored there.\n",
    "exp = Experiment(config=config, name=config['experiment_name'], notes='', reload_exp=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "\nDATASET: DecathlonProstateT2 with 32 instances\nMean shape: (1, 316, 316, 18), shape std: (0, 22, 22, 2)\nMask labels: [&#39;background&#39;, &#39;prostate&#39;]\n\n"
    }
   ],
   "source": [
    "# 4. Define data\n",
    "\n",
    "# A data object can be initialized with multiple datasets. The idea behind this\n",
    "# is to more easily test with o.o.d data, or simulate continual learning.\n",
    "# In this example, we only add one dataset.\n",
    "data = Data()\n",
    "data.add_dataset(DecathlonProstateT2(merge_labels=True))\n",
    "nr_labels = data.nr_labels\n",
    "label_names = data.label_names\n",
    "train_ds = ('DecathlonProstateT2', 'train')\n",
    "test_ds = ('DecathlonProstateT2', 'test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 5. Create data splits for each repetition\n",
    "\n",
    "# For each dataset, the instance indexes are divided into train, validation\n",
    "# and test sets. The values specified in the config are used to determine\n",
    "# the number of runs and how indexes are divided. For more details, look at the\n",
    "# method definition for 'set_data_splits'. The index splitting is also stored \n",
    "# within the experiment directory. For each repetition, a subdirectory is \n",
    "# created named after the repetition index.\n",
    "exp.set_data_splits(data)\n",
    "\n",
    "# Get the experiment run respective to the first data split. This would usually \n",
    "# be repeated for all 'nr_runs' runs\n",
    "exp_run = exp.get_run(run_ix=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 6. Bring data to Pytorch format\n",
    "\n",
    "# Transform data to PyTorch format\n",
    "datasets = dict()\n",
    "for ds_name, ds in data.datasets.items():\n",
    "    for split, data_ixs in exp.splits[ds_name][exp_run.run_ix].items():\n",
    "        if len(data_ixs) > 0: # Sometimes val indexes may be an empty list\n",
    "            aug = config['augmentation'] if not('test' in split) else 'none'\n",
    "            datasets[(ds_name, split)] = PytorchSeg2DDataset(ds, \n",
    "                ix_lst=data_ixs, size=input_shape, aug_key=aug, \n",
    "                resize=config['resize'])\n",
    "# Visualize the first instance from the training data\n",
    "subject_ix = 0\n",
    "subject = datasets[train_ds].instances[subject_ix].get_subject()\n",
    "vis.plot_3d_subject_gt(subject)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 7. Build train dataloader, and visualize\n",
    "\n",
    "dl = DataLoader(datasets[(train_ds)], \n",
    "    batch_size=config['batch_size'], shuffle=True)\n",
    "vis.visualize_dataloader_with_masks(dl, img_size=(128, 128))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
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      "text/plain": "UNet2D(\n  (encoder): Encoder(\n    (encoding_blocks): ModuleList(\n      (0): EncodingBlock(\n        (conv1): ConvolutionalBlock(\n          (conv_layer): Conv2d(1, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n          (activation_layer): ReLU()\n          (block): Sequential(\n            (0): Conv2d(1, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n            (1): ReLU()\n          )\n        )\n        (conv2): ConvolutionalBlock(\n          (conv_layer): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n          (norm_layer): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n          (activation_layer): ReLU()\n          (block): Sequential(\n            (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n            (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n            (2): ReLU()\n          )\n        )\n        (downsample): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n      )\n      (1): EncodingBlock(\n        (conv1): ConvolutionalBlock(\n          (conv_layer): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n          (norm_layer): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n          (activation_layer): ReLU()\n          (block): Sequential(\n            (0): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n            (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n            (2): ReLU()\n          )\n        )\n        (conv2): ConvolutionalBlock(\n          (conv_layer): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n          (norm_layer): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n          (activation_layer): ReLU()\n          (block): Sequential(\n            (0): Conv2d(32, 32, 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track_running_stats=True)\n            (2): ReLU()\n          )\n        )\n        (conv2): ConvolutionalBlock(\n          (conv_layer): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n          (norm_layer): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n          (activation_layer): ReLU()\n          (block): Sequential(\n            (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n            (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n            (2): ReLU()\n          )\n        )\n        (downsample): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n      )\n    )\n  )\n  (bottom_block): EncodingBlock(\n    (conv1): ConvolutionalBlock(\n      (conv_layer): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n      (norm_layer): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (activation_layer): ReLU()\n      (block): Sequential(\n        (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (2): ReLU()\n      )\n    )\n    (conv2): ConvolutionalBlock(\n      (conv_layer): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n      (norm_layer): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (activation_layer): ReLU()\n      (block): Sequential(\n        (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (2): ReLU()\n      )\n    )\n  )\n  (decoder): Decoder(\n    (decoding_blocks): ModuleList(\n      (0): DecodingBlock(\n        (upsample): ConvTranspose2d(256, 256, kernel_size=(2, 2), stride=(2, 2))\n        (conv1): ConvolutionalBlock(\n          (conv_layer): Conv2d(384, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n          (norm_layer): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n          (activation_layer): ReLU()\n          (block): Sequential(\n            (0): Conv2d(384, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n            (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n            (2): ReLU()\n          )\n        )\n        (conv2): ConvolutionalBlock(\n          (conv_layer): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n          (norm_layer): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n          (activation_layer): ReLU()\n          (block): Sequential(\n            (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n            (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n            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1))\n          (norm_layer): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n          (activation_layer): ReLU()\n          (block): Sequential(\n            (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n            (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n            (2): ReLU()\n          )\n        )\n      )\n      (3): DecodingBlock(\n        (upsample): ConvTranspose2d(32, 32, kernel_size=(2, 2), stride=(2, 2))\n        (conv1): ConvolutionalBlock(\n          (conv_layer): Conv2d(48, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n          (norm_layer): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n          (activation_layer): ReLU()\n          (block): Sequential(\n            (0): Conv2d(48, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n            (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n            (2): ReLU()\n          )\n        )\n        (conv2): ConvolutionalBlock(\n          (conv_layer): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n          (norm_layer): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n          (activation_layer): ReLU()\n          (block): Sequential(\n            (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n            (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n            (2): ReLU()\n          )\n        )\n      )\n    )\n  )\n  (classifier): ConvolutionalBlock(\n    (conv_layer): Conv2d(16, 2, kernel_size=(1, 1), stride=(1, 1))\n    (block): Sequential(\n      (0): Conv2d(16, 2, kernel_size=(1, 1), stride=(1, 1))\n    )\n  )\n)"
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "# 8. Initialize model\n",
    "\n",
    "model = UNet2D(input_shape, nr_labels)\n",
    "model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 9. Define loss and optimizer\n",
    "\n",
    "# Define loss and optimizer. In this case, the loss is a combination of Dice\n",
    "# and binary cross-entropy, weighted so that only the dice on the 'prostate'\n",
    "# class is considered and not that for 'background'\n",
    "loss_g = LossDiceBCE(bce_weight=1., smooth=1., device=device)\n",
    "loss_f = LossClassWeighted(loss=loss_g, weights=config['class_weights'], \n",
    "    device=device)\n",
    "optimizer = optim.Adam(model.parameters(), lr=config['lr'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Epoch 0 dataset (&#39;DecathlonProstateT2&#39;, &#39;train&#39;)\nLoss_LossClassWeighted[loss=LossCombined[1.0xLossDice[smooth=1.0]+1.0xLossBCE]; weights=(0.0, 1.0)]: 1.5473218692242912\nLoss_LossCombined[1.0xLossDice[smooth=1.0]+1.0xLossBCE][0]: 0.9132229618862113\nLoss_LossCombined[1.0xLossDice[smooth=1.0]+1.0xLossBCE][1]: 1.5473218692242912\nScoreDice: 0.46513506322135806\nScoreIoU: 0.39571227202250175\nScoreDice[background]: 0.8585674187014544\nScoreIoU[background]: 0.7533623757627844\nScoreDice[prostate]: 0.07170270774126192\nScoreIoU[prostate]: 0.0380621682822191\nEpoch 0 dataset (&#39;DecathlonProstateT2&#39;, &#39;test&#39;)\nLoss_LossClassWeighted[loss=LossCombined[1.0xLossDice[smooth=1.0]+1.0xLossBCE]; weights=(0.0, 1.0)]: 1.5391011298003823\nLoss_LossCombined[1.0xLossDice[smooth=1.0]+1.0xLossBCE][0]: 0.910014008139119\nLoss_LossCombined[1.0xLossDice[smooth=1.0]+1.0xLossBCE][1]: 1.5391011298003823\nScoreDice: 0.4732915197648023\nScoreIoU: 0.4039635342939699\nScoreDice[background]: 0.8668687877512342\nScoreIoU[background]: 0.7661925449737959\nScoreDice[prostate]: 0.07971425177837045\nScoreIoU[prostate]: 0.041734523614143995\n\nRunning loss: 1.0535691970910868\nEpoch 5 dataset (&#39;DecathlonProstateT2&#39;, &#39;train&#39;)\nLoss_LossClassWeighted[loss=LossCombined[1.0xLossDice[smooth=1.0]+1.0xLossBCE]; weights=(0.0, 1.0)]: 1.0898576528601287\nLoss_LossCombined[1.0xLossDice[smooth=1.0]+1.0xLossBCE][0]: 0.4336691182399845\nLoss_LossCombined[1.0xLossDice[smooth=1.0]+1.0xLossBCE][1]: 1.0898576528601287\nScoreDice: 0.8363267995326528\nScoreIoU: 0.7606523880692659\nScoreDice[background]: 0.9905078487508604\nScoreIoU[background]: 0.9812143005377961\nScoreDice[prostate]: 0.6821457503144448\nScoreIoU[prostate]: 0.540090475600736\nEpoch 5 dataset (&#39;DecathlonProstateT2&#39;, &#39;test&#39;)\nLoss_LossClassWeighted[loss=LossCombined[1.0xLossDice[smooth=1.0]+1.0xLossBCE]; weights=(0.0, 1.0)]: 1.0685035134144505\nLoss_LossCombined[1.0xLossDice[smooth=1.0]+1.0xLossBCE][0]: 0.4286183509155356\nLoss_LossCombined[1.0xLossDice[smooth=1.0]+1.0xLossBCE][1]: 1.0685035134144505\nScoreDice: 0.8598781483031347\nScoreIoU: 0.7832989851902311\nScoreDice[background]: 0.9911923616845872\nScoreIoU[background]: 0.9825528858359129\nScoreDice[prostate]: 0.7285639349216825\nScoreIoU[prostate]: 0.5840450845445495\n\nRunning loss: 0.8917752033711607\nEpoch 10 dataset (&#39;DecathlonProstateT2&#39;, &#39;train&#39;)\nLoss_LossClassWeighted[loss=LossCombined[1.0xLossDice[smooth=1.0]+1.0xLossBCE]; weights=(0.0, 1.0)]: 0.9337032272510452\nLoss_LossCombined[1.0xLossDice[smooth=1.0]+1.0xLossBCE][0]: 0.29872093587499116\nLoss_LossCombined[1.0xLossDice[smooth=1.0]+1.0xLossBCE][1]: 0.9337032272510452\nScoreDice: 0.8468520481000751\nScoreIoU: 0.7761653398325823\nScoreDice[background]: 0.991326599392128\nScoreIoU[background]: 0.9828275990458573\nScoreDice[prostate]: 0.7023774968080218\nScoreIoU[prostate]: 0.569503080619307\nEpoch 10 dataset (&#39;DecathlonProstateT2&#39;, &#39;test&#39;)\nLoss_LossClassWeighted[loss=LossCombined[1.0xLossDice[smooth=1.0]+1.0xLossBCE]; weights=(0.0, 1.0)]: 0.9124713282422585\nLoss_LossCombined[1.0xLossDice[smooth=1.0]+1.0xLossBCE][0]: 0.2998429857804017\nLoss_LossCombined[1.0xLossDice[smooth=1.0]+1.0xLossBCE][1]: 0.9124713282422585\nScoreDice: 0.8638489268142397\nScoreIoU: 0.7889604336195764\nScoreDice[background]: 0.9912150453774086\nScoreIoU[background]: 0.9825979870327834\nScoreDice[prostate]: 0.7364828082510707\nScoreIoU[prostate]: 0.5953228802063691\n"
    }
   ],
   "source": [
    "# 10. Train model\n",
    "\n",
    "# We train here only for 10 epochs for testing purposes, altough in practice\n",
    "# many more are necessary to reach convergence\n",
    "results = Result(name='training_trajectory')   \n",
    "agent = SegmentationAgent(model=model, label_names=label_names, device=device)\n",
    "agent.train(results, optimizer, loss_f, train_dataloader=dl,\n",
    "    init_epoch=0, nr_epochs=10, run_loss_print_interval=5,\n",
    "    eval_datasets=datasets, eval_interval=5, \n",
    "    save_path=exp_run.paths['states'], save_interval=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Last Dice score for prostate class: 0.7364828082510707\n"
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# 11. Save and print results for this experiment run\n",
    "\n",
    "exp_run.finish(results=results, plot_metrics=['Mean_ScoreDice', 'Mean_ScoreDice[prostate]'])\n",
    "test_ds_key = '_'.join(test_ds)\n",
    "metric = 'Mean_ScoreDice[prostate]'\n",
    "last_dice = results.get_epoch_metric(\n",
    "    results.get_max_epoch(metric, data=test_ds_key), metric, data=test_ds_key)\n",
    "print('Last Dice score for prostate class: {}'.format(last_dice))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 12. Visualize results qualitatively\n",
    "\n",
    "# Visualize result for the first subject in the test dataset\n",
    "subject_ix = 0\n",
    "subject = datasets[test_ds].instances[subject_ix].get_subject()\n",
    "pred = datasets[test_ds].predictor.get_subject_prediction(agent, subject_ix)\n",
    "vis.plot_3d_subject_pred(subject, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}