# Copyright 2020 MONAI Consortium # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # http://www.apache.org/licenses/LICENSE-2.0 # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging import os import sys import tempfile from glob import glob import nibabel as nib import numpy as np import torch from torch.utils.data import DataLoader from torch.utils.tensorboard import SummaryWriter import monai from monai.data import NiftiDataset, create_test_image_3d from monai.inferers import sliding_window_inference from monai.metrics import DiceMetric from monai.transforms import AddChannel, Compose, RandRotate90, RandSpatialCrop, ScaleIntensity, ToTensor from monai.visualize import plot_2d_or_3d_image def main(tempdir): monai.config.print_config() logging.basicConfig(stream=sys.stdout, level=logging.INFO) # create a temporary directory and 40 random image, mask pairs print(f"generating synthetic data to {tempdir} (this may take a while)") for i in range(40): im, seg = create_test_image_3d(128, 128, 128, num_seg_classes=1) n = nib.Nifti1Image(im, np.eye(4)) nib.save(n, os.path.join(tempdir, f"im{i:d}.nii.gz")) n = nib.Nifti1Image(seg, np.eye(4)) nib.save(n, os.path.join(tempdir, f"seg{i:d}.nii.gz")) 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 train_imtrans = Compose( [ ScaleIntensity(), AddChannel(), RandSpatialCrop((96, 96, 96), random_size=False), RandRotate90(prob=0.5, spatial_axes=(0, 2)), ToTensor(), ] ) train_segtrans = Compose( [ AddChannel(), RandSpatialCrop((96, 96, 96), random_size=False), RandRotate90(prob=0.5, spatial_axes=(0, 2)), ToTensor(), ] ) val_imtrans = Compose([ScaleIntensity(), AddChannel(), ToTensor()]) val_segtrans = Compose([AddChannel(), ToTensor()]) # define nifti dataset, data loader check_ds = NiftiDataset(images, segs, transform=train_imtrans, seg_transform=train_segtrans) check_loader = DataLoader(check_ds, batch_size=10, num_workers=2, pin_memory=torch.cuda.is_available()) im, seg = monai.utils.misc.first(check_loader) print(im.shape, seg.shape) # create a training data loader train_ds = NiftiDataset(images[:20], segs[:20], transform=train_imtrans, seg_transform=train_segtrans) train_loader = DataLoader(train_ds, batch_size=4, shuffle=True, num_workers=8, pin_memory=torch.cuda.is_available()) # create a validation data loader val_ds = NiftiDataset(images[-20:], segs[-20:], transform=val_imtrans, seg_transform=val_segtrans) val_loader = DataLoader(val_ds, batch_size=1, num_workers=4, pin_memory=torch.cuda.is_available()) dice_metric = DiceMetric(include_background=True, to_onehot_y=False, sigmoid=True, reduction="mean") # create UNet, DiceLoss and Adam optimizer device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = monai.networks.nets.UNet( dimensions=3, in_channels=1, out_channels=1, channels=(16, 32, 64, 128, 256), strides=(2, 2, 2, 2), num_res_units=2, ).to(device) loss_function = monai.losses.DiceLoss(sigmoid=True) optimizer = torch.optim.Adam(model.parameters(), 1e-3) # start a typical PyTorch training val_interval = 2 best_metric = -1 best_metric_epoch = -1 epoch_loss_values = list() metric_values = list() writer = SummaryWriter() for epoch in range(5): print("-" * 10) print(f"epoch {epoch + 1}/{5}") model.train() epoch_loss = 0 step = 0 for batch_data in train_loader: step += 1 inputs, labels = batch_data[0].to(device), batch_data[1].to(device) optimizer.zero_grad() outputs = model(inputs) loss = loss_function(outputs, labels) loss.backward() optimizer.step() epoch_loss += loss.item() epoch_len = len(train_ds) // train_loader.batch_size print(f"{step}/{epoch_len}, train_loss: {loss.item():.4f}") writer.add_scalar("train_loss", loss.item(), epoch_len * epoch + step) epoch_loss /= step epoch_loss_values.append(epoch_loss) print(f"epoch {epoch + 1} average loss: {epoch_loss:.4f}") if (epoch + 1) % val_interval == 0: model.eval() with torch.no_grad(): metric_sum = 0.0 metric_count = 0 val_images = None val_labels = None val_outputs = None for val_data in val_loader: val_images, val_labels = val_data[0].to(device), val_data[1].to(device) roi_size = (96, 96, 96) sw_batch_size = 4 val_outputs = sliding_window_inference(val_images, roi_size, sw_batch_size, model) value = dice_metric(y_pred=val_outputs, y=val_labels) metric_count += len(value) metric_sum += value.item() * len(value) metric = metric_sum / metric_count metric_values.append(metric) if metric > best_metric: best_metric = metric best_metric_epoch = epoch + 1 torch.save(model.state_dict(), "best_metric_model_segmentation3d_array.pth") print("saved new best metric model") print( "current epoch: {} current mean dice: {:.4f} best mean dice: {:.4f} at epoch {}".format( epoch + 1, metric, best_metric, best_metric_epoch ) ) writer.add_scalar("val_mean_dice", metric, epoch + 1) # plot the last model output as GIF image in TensorBoard with the corresponding image and label plot_2d_or_3d_image(val_images, epoch + 1, writer, index=0, tag="image") plot_2d_or_3d_image(val_labels, epoch + 1, writer, index=0, tag="label") plot_2d_or_3d_image(val_outputs, epoch + 1, writer, index=0, tag="output") print(f"train completed, best_metric: {best_metric:.4f} at epoch: {best_metric_epoch}") writer.close() if __name__ == "__main__": with tempfile.TemporaryDirectory() as tempdir: main(tempdir)