# 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 numpy as np import torch from torch.utils.data import DataLoader from torch.utils.tensorboard import SummaryWriter import monai from monai.data import NiftiDataset from monai.transforms import AddChannel, Compose, RandRotate90, Resize, ScaleIntensity, ToTensor def main(): monai.config.print_config() logging.basicConfig(stream=sys.stdout, level=logging.INFO) # IXI dataset as a demo, downloadable from https://brain-development.org/ixi-dataset/ images = [ os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI314-IOP-0889-T1.nii.gz"]), os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI249-Guys-1072-T1.nii.gz"]), os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI609-HH-2600-T1.nii.gz"]), os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI173-HH-1590-T1.nii.gz"]), os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI020-Guys-0700-T1.nii.gz"]), os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI342-Guys-0909-T1.nii.gz"]), os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI134-Guys-0780-T1.nii.gz"]), os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI577-HH-2661-T1.nii.gz"]), os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI066-Guys-0731-T1.nii.gz"]), os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI130-HH-1528-T1.nii.gz"]), os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI607-Guys-1097-T1.nii.gz"]), os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI175-HH-1570-T1.nii.gz"]), os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI385-HH-2078-T1.nii.gz"]), os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI344-Guys-0905-T1.nii.gz"]), os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI409-Guys-0960-T1.nii.gz"]), os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI584-Guys-1129-T1.nii.gz"]), os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI253-HH-1694-T1.nii.gz"]), os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI092-HH-1436-T1.nii.gz"]), os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI574-IOP-1156-T1.nii.gz"]), os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI585-Guys-1130-T1.nii.gz"]), ] # 2 binary labels for gender classification: man and woman labels = np.array([0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0], dtype=np.int64) # Define transforms train_transforms = Compose([ScaleIntensity(), AddChannel(), Resize((96, 96, 96)), RandRotate90(), ToTensor()]) val_transforms = Compose([ScaleIntensity(), AddChannel(), Resize((96, 96, 96)), ToTensor()]) # Define nifti dataset, data loader check_ds = NiftiDataset(image_files=images, labels=labels, transform=train_transforms) check_loader = DataLoader(check_ds, batch_size=2, num_workers=2, pin_memory=torch.cuda.is_available()) im, label = monai.utils.misc.first(check_loader) print(type(im), im.shape, label) # create a training data loader train_ds = NiftiDataset(image_files=images[:10], labels=labels[:10], transform=train_transforms) train_loader = DataLoader(train_ds, batch_size=2, shuffle=True, num_workers=2, pin_memory=torch.cuda.is_available()) # create a validation data loader val_ds = NiftiDataset(image_files=images[-10:], labels=labels[-10:], transform=val_transforms) val_loader = DataLoader(val_ds, batch_size=2, num_workers=2, pin_memory=torch.cuda.is_available()) # Create DenseNet121, CrossEntropyLoss and Adam optimizer device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = monai.networks.nets.densenet.densenet121(spatial_dims=3, in_channels=1, out_channels=2).to(device) loss_function = torch.nn.CrossEntropyLoss() optimizer = torch.optim.Adam(model.parameters(), 1e-5) # 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(): num_correct = 0.0 metric_count = 0 for val_data in val_loader: val_images, val_labels = val_data[0].to(device), val_data[1].to(device) val_outputs = model(val_images) value = torch.eq(val_outputs.argmax(dim=1), val_labels) metric_count += len(value) num_correct += value.sum().item() metric = num_correct / 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_classification3d_array.pth") print("saved new best metric model") print( "current epoch: {} current accuracy: {:.4f} best accuracy: {:.4f} at epoch {}".format( epoch + 1, metric, best_metric, best_metric_epoch ) ) writer.add_scalar("val_accuracy", metric, epoch + 1) print(f"train completed, best_metric: {best_metric:.4f} at epoch: {best_metric_epoch}") writer.close() if __name__ == "__main__": main()