40-validation-early-stop

examples/unet_segmentation_3d.ipynb

@@ -2,19 +2,13 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
-     "text": [
-      "MONAI version: 0.0.1\n",
-      "Python version: 3.7.3 (default, Mar 27 2019, 22:11:17)  [GCC 7.3.0]\n",
-      "Numpy version: 1.16.4\n",
-      "Pytorch version: 1.3.1\n",
-      "Ignite version: 0.2.1\n"
-     ]
+     "text": "MONAI version: 0.0.1\nPython version: 3.8.1 (default, Jan  8 2020, 22:29:32)  [GCC 7.3.0]\nNumpy version: 1.18.1\nPytorch version: 1.4.0\nIgnite version: 0.3.0\n"
     }
    ],
    "source": [
@@ -28,29 +22,32 @@
     "import torch\n",
     "import torch.nn as nn\n",
     "from torch.utils.data import DataLoader\n",
-    "import monai.data.transforms.compose as transforms\n",
     "\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "import nibabel as nib\n",
     "\n",
-    "from ignite.engine import Events, create_supervised_trainer\n",
-    "from ignite.handlers import ModelCheckpoint\n",
+    "from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator\n",
+    "from ignite.handlers import ModelCheckpoint, EarlyStopping\n",
     "\n",
     "# assumes the framework is found here, change as necessary\n",
     "sys.path.append(\"..\")\n",
     "\n",
+    "\n",
+    "import monai.data.transforms.compose as transforms\n",
     "from monai import application, data, networks, utils\n",
     "from monai.data.readers import NiftiDataset\n",
     "from monai.data.transforms import AddChannel, Transpose, Rescale, ToTensor, UniformRandomPatch, GridPatchDataset\n",
+    "from monai.networks.metrics.mean_dice import MeanDice\n",
+    "from monai.utils.stopperutils import stopping_fn_from_metric\n",
     "\n",
     "\n",
     "application.config.print_config()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -81,7 +78,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -99,15 +96,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
-     "text": [
-      "torch.Size([10, 1, 64, 64, 64]) torch.Size([10, 1, 64, 64, 64])\n"
-     ]
+     "text": "torch.Size([10, 1, 64, 64, 64]) torch.Size([10, 1, 64, 64, 64])\n"
     }
    ],
    "source": [
@@ -136,7 +131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -157,46 +152,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1 Loss: 0.8619852662086487\n",
-      "Epoch 2 Loss: 0.8307779431343079\n",
-      "Epoch 3 Loss: 0.8064168691635132\n",
-      "Epoch 4 Loss: 0.7981672883033752\n",
-      "Epoch 5 Loss: 0.7950631976127625\n",
-      "Epoch 6 Loss: 0.7949732542037964\n",
-      "Epoch 7 Loss: 0.7963427901268005\n",
-      "Epoch 8 Loss: 0.7939450144767761\n",
-      "Epoch 9 Loss: 0.7926643490791321\n",
-      "Epoch 10 Loss: 0.7911991477012634\n",
-      "Epoch 11 Loss: 0.7886414527893066\n",
-      "Epoch 12 Loss: 0.7867528796195984\n",
-      "Epoch 13 Loss: 0.7857398390769958\n",
-      "Epoch 14 Loss: 0.7833380699157715\n",
-      "Epoch 15 Loss: 0.7791398763656616\n",
-      "Epoch 16 Loss: 0.7720394730567932\n",
-      "Epoch 17 Loss: 0.7671006917953491\n",
-      "Epoch 18 Loss: 0.7646064758300781\n",
-      "Epoch 19 Loss: 0.7672612071037292\n",
-      "Epoch 20 Loss: 0.7600041627883911\n",
-      "Epoch 21 Loss: 0.7583478689193726\n",
-      "Epoch 22 Loss: 0.7571365833282471\n",
-      "Epoch 23 Loss: 0.7545363306999207\n",
-      "Epoch 24 Loss: 0.7499511241912842\n",
-      "Epoch 25 Loss: 0.7481640577316284\n",
-      "Epoch 26 Loss: 0.7469437122344971\n",
-      "Epoch 27 Loss: 0.7460543513298035\n",
-      "Epoch 28 Loss: 0.74577796459198\n",
-      "Epoch 29 Loss: 0.7429620027542114\n",
-      "Epoch 30 Loss: 0.7424858808517456\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "trainEpochs = 30\n",
     "\n",
@@ -218,16 +176,60 @@
     "\n",
     "\n",
     "loader = DataLoader(ds, batch_size=20, num_workers=8, pin_memory=torch.cuda.is_available())\n",
-    "    \n",
+    "val_loader = DataLoader(ds, batch_size=20, num_workers=8, pin_memory=torch.cuda.is_available())\n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "validation_every_n_epochs = 1\n",
+    "\n",
+    "val_metrics = {'Mean Dice': MeanDice(add_sigmoid=True)}\n",
+    "evaluator = create_supervised_evaluator(net, val_metrics, device, True,\n",
+    "                                        output_transform=lambda x, y, y_pred: (y_pred[0], y))\n",
+    "\n",
+    "\n",
+    "early_stopper = EarlyStopping(patience=4, \n",
+    "                              score_function=stopping_fn_from_metric('Mean Dice'),\n",
+    "                              trainer=trainer)\n",
+    "evaluator.add_event_handler(event_name=Events.EPOCH_COMPLETED, handler=early_stopper)\n",
+    "\n",
+    "@evaluator.on(Events.EPOCH_COMPLETED)\n",
+    "def log_validation_metrics(engine):\n",
+    "    for name, value in engine.state.metrics.items():\n",
+    "        print(\"Validation --\", name, \":\", value)\n",
+    "\n",
+    "@trainer.on(Events.EPOCH_COMPLETED(every=validation_every_n_epochs))\n",
+    "def run_validation(engine):\n",
+    "    evaluator.run(val_loader)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": "Epoch 1 Loss: 0.8975554704666138\nValidation -- Mean Dice : 0.11846490800380707\nEpoch 2 Loss: 0.8451039791107178\nValidation -- Mean Dice : 0.12091563045978546\nEpoch 3 Loss: 0.9355515241622925\nValidation -- Mean Dice : 0.12139833569526673\nEpoch 4 Loss: 0.843208909034729\nValidation -- Mean Dice : 0.12108306288719177\nEpoch 5 Loss: 0.8225834965705872\nValidation -- Mean Dice : 0.12179622799158096\nEpoch 6 Loss: 0.957372784614563\nValidation -- Mean Dice : 0.12193384170532226\nEpoch 7 Loss: 0.9011092782020569\nValidation -- Mean Dice : 0.1230143740773201\nEpoch 8 Loss: 0.8651387691497803\nValidation -- Mean Dice : 0.1254110112786293\nEpoch 9 Loss: 0.8767974972724915\nValidation -- Mean Dice : 0.12633273899555206\nEpoch 10 Loss: 0.8193061947822571\nValidation -- Mean Dice : 0.12657881826162337\nEpoch 11 Loss: 0.9466649293899536\nValidation -- Mean Dice : 0.12699378579854964\nEpoch 12 Loss: 0.8258659243583679\nValidation -- Mean Dice : 0.12790720015764237\nEpoch 13 Loss: 0.8661612868309021\nValidation -- Mean Dice : 0.12980296313762665\nEpoch 14 Loss: 0.8039132356643677\nValidation -- Mean Dice : 0.1311295285820961\nEpoch 15 Loss: 0.8050084114074707\nValidation -- Mean Dice : 0.13225494623184203\nEpoch 16 Loss: 0.9048625230789185\nValidation -- Mean Dice : 0.1330576255917549\nEpoch 17 Loss: 0.9179995656013489\nValidation -- Mean Dice : 0.13361359685659407\nEpoch 18 Loss: 0.8956605195999146\nValidation -- Mean Dice : 0.13432369381189346\nEpoch 19 Loss: 0.8029189705848694\nValidation -- Mean Dice : 0.13532216250896453\nEpoch 20 Loss: 0.8359838128089905\nValidation -- Mean Dice : 0.13622953295707702\nEpoch 21 Loss: 0.9225850105285645\nValidation -- Mean Dice : 0.13677610754966735\nEpoch 22 Loss: 0.7023072242736816\nValidation -- Mean Dice : 0.13693425357341765\nEpoch 23 Loss: 0.8776397705078125\nValidation -- Mean Dice : 0.13710424304008484\nEpoch 24 Loss: 0.9571539163589478\nValidation -- Mean Dice : 0.1370883911848068\nEpoch 25 Loss: 0.8877002596855164\nValidation -- Mean Dice : 0.13701471388339997\nEpoch 26 Loss: 0.817417562007904\nValidation -- Mean Dice : 0.13696834743022918\nEpoch 27 Loss: 0.8971314430236816\nValidation -- Mean Dice : 0.1371448516845703\nEpoch 28 Loss: 0.9443905353546143\nValidation -- Mean Dice : 0.13739778995513915\nEpoch 29 Loss: 0.7578094005584717\nValidation -- Mean Dice : 0.137495020031929\nEpoch 30 Loss: 0.7037953734397888\nValidation -- Mean Dice : 0.13759489357471466\n"
+    }
+   ],
+   "source": [
     "state = trainer.run(loader, trainEpochs)"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3.7.5 64-bit ('pytorch': conda)",
    "language": "python",
-   "name": "python3"
+   "name": "python37564bitpytorchconda9e7dd2186ac2430b947ee08d8eff35b4"
   },
   "language_info": {
    "codemirror_mode": {
@@ -239,9 +241,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.8.1-final"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

examples/unet_segmentation_3d.py

@@ -20,15 +20,16 @@
 import torch
 import monai.data.transforms.compose as transforms
 from torch.utils.tensorboard import SummaryWriter
-from ignite.engine import Events, create_supervised_trainer
-from ignite.handlers import ModelCheckpoint
+from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator
+from ignite.handlers import ModelCheckpoint, EarlyStopping
 from torch.utils.data import DataLoader
 
 from monai import application, networks, utils
 from monai.data.readers import NiftiDataset
 from monai.data.transforms import (AddChannel, Rescale, ToTensor, UniformRandomPatch)
 from monai.application.handlers.stats_handler import StatsHandler
 from monai.networks.metrics.mean_dice import MeanDice
+from monai.utils.stopperutils import stopping_fn_from_metric
 
 # assumes the framework is found here, change as necessary
 sys.path.append("..")
@@ -60,7 +61,7 @@ def create_test_image_3d(height, width, depth, num_objs=12, rad_max=30, noise_ma
 
     return noisyimage, labels
 
-
+# Create a temporary directory and 50 random image, mask paris
 tempdir = tempfile.mkdtemp()
 
 for i in range(50):
@@ -75,18 +76,21 @@ def create_test_image_3d(height, width, depth, num_objs=12, rad_max=30, noise_ma
 images = sorted(glob(os.path.join(tempdir, 'im*.nii.gz')))
 segs = sorted(glob(os.path.join(tempdir, 'seg*.nii.gz')))
 
+# Define transforms for image and segmentation
 imtrans = transforms.Compose([Rescale(), AddChannel(), UniformRandomPatch((64, 64, 64)), ToTensor()])
-
 segtrans = transforms.Compose([AddChannel(), UniformRandomPatch((64, 64, 64)), ToTensor()])
 
+# Define nifti dataset, dataloader.
 ds = NiftiDataset(images, segs, imtrans, segtrans)
-
 loader = DataLoader(ds, batch_size=10, num_workers=2, pin_memory=torch.cuda.is_available())
 im, seg = utils.mathutils.first(loader)
 print(im.shape, seg.shape)
 
+
 lr = 1e-3
+train_epochs = 30
 
+# Create UNet, DiceLoss and Adam optimizer.
 net = networks.nets.UNet(
     dimensions=3,
     in_channels=1,
@@ -101,13 +105,12 @@ def create_test_image_3d(height, width, depth, num_objs=12, rad_max=30, noise_ma
 
 train_epochs = 3
 
-
+# Since network outputs logits and segmentation, we need a custom function.
 def _loss_fn(i, j):
     return loss(i[0], j)
 
-
+# Create trainer
 device = torch.device("cuda:0")
-
 trainer = create_supervised_trainer(net, opt, _loss_fn, device, False,
                                     output_transform=lambda x, y, y_pred, loss: [y_pred, loss.item(), y])
 
@@ -154,6 +157,28 @@ def log_training_loss(engine):
 
 
 loader = DataLoader(ds, batch_size=20, num_workers=8, pin_memory=torch.cuda.is_available())
+val_loader = DataLoader(ds, batch_size=20, num_workers=8, pin_memory=torch.cuda.is_available())
 writer = SummaryWriter()
 
+# Define mean dice metric and Evaluator.
+validation_every_n_epochs = 1
+
+val_metrics = {'Mean Dice': MeanDice(add_sigmoid=True)}
+evaluator = create_supervised_evaluator(net, val_metrics, device, True,
+                                        output_transform=lambda x, y, y_pred: (y_pred[0], y))
+
+val_stats_handler = StatsHandler()
+val_stats_handler.attach(evaluator)
+
+# Add early stopping handler to evaluator.
+early_stopper = EarlyStopping(patience=4, 
+                              score_function=stopping_fn_from_metric('Mean Dice'),
+                              trainer=trainer)
+evaluator.add_event_handler(event_name=Events.EPOCH_COMPLETED, handler=early_stopper)
+
+@trainer.on(Events.EPOCH_COMPLETED(every=validation_every_n_epochs))
+def run_validation(engine):
+    evaluator.run(val_loader)
+
+
 state = trainer.run(loader, train_epochs)

monai/utils/stopperutils.py

@@ -0,0 +1,22 @@
+# Copyright 2020 MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+def stopping_fn_from_metric(metric_name):
+    """Returns a stopping function for ignite.handlers.EarlyStopping using the given metric name."""
+    def stopping_fn(engine):
+        return engine.state.metrics[metric_name]
+    return stopping_fn
+
+def stopping_fn_from_loss():
+    """Returns a stopping function for ignite.handlers.EarlyStopping using the loss value."""
+    def stopping_fn(engine):
+        return -engine.state.output
+    return stopping_fn