# 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.metrics import compute_roc_auc from monai.transforms import AddChanneld, Compose, LoadNiftid, RandRotate90d, Resized, ScaleIntensityd, ToTensord 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) train_files = [{"img": img, "label": label} for img, label in zip(images[:10], labels[:10])] val_files = [{"img": img, "label": label} for img, label in zip(images[-10:], labels[-10:])] # Define transforms for image train_transforms = Compose( [ LoadNiftid(keys=["img"]), AddChanneld(keys=["img"]), ScaleIntensityd(keys=["img"]), Resized(keys=["img"], spatial_size=(96, 96, 96)), RandRotate90d(keys=["img"], prob=0.8, spatial_axes=[0, 2]), ToTensord(keys=["img"]), ] ) val_transforms = Compose( [ LoadNiftid(keys=["img"]), AddChanneld(keys=["img"]), ScaleIntensityd(keys=["img"]), Resized(keys=["img"], spatial_size=(96, 96, 96)), ToTensord(keys=["img"]), ] ) # Define dataset, data loader check_ds = monai.data.Dataset(data=train_files, transform=train_transforms) check_loader = DataLoader(check_ds, batch_size=2, num_workers=4, pin_memory=torch.cuda.is_available()) check_data = monai.utils.misc.first(check_loader) print(check_data["img"].shape, check_data["label"]) # create a training data loader train_ds = monai.data.Dataset(data=train_files, transform=train_transforms) train_loader = DataLoader(train_ds, batch_size=2, shuffle=True, num_workers=4, pin_memory=torch.cuda.is_available()) # create a validation data loader val_ds = monai.data.Dataset(data=val_files, transform=val_transforms) val_loader = DataLoader(val_ds, batch_size=2, num_workers=4, 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 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["img"].to(device), batch_data["label"].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 print(f"epoch {epoch + 1} average loss: {epoch_loss:.4f}") if (epoch + 1) % val_interval == 0: model.eval() with torch.no_grad(): y_pred = torch.tensor([], dtype=torch.float32, device=device) y = torch.tensor([], dtype=torch.long, device=device) for val_data in val_loader: val_images, val_labels = val_data["img"].to(device), val_data["label"].to(device) y_pred = torch.cat([y_pred, model(val_images)], dim=0) y = torch.cat([y, val_labels], dim=0) acc_value = torch.eq(y_pred.argmax(dim=1), y) acc_metric = acc_value.sum().item() / len(acc_value) auc_metric = compute_roc_auc(y_pred, y, to_onehot_y=True, softmax=True) if acc_metric > best_metric: best_metric = acc_metric best_metric_epoch = epoch + 1 torch.save(model.state_dict(), "best_metric_model_classification3d_dict.pth") print("saved new best metric model") print( "current epoch: {} current accuracy: {:.4f} current AUC: {:.4f} best accuracy: {:.4f} at epoch {}".format( epoch + 1, acc_metric, auc_metric, best_metric, best_metric_epoch ) ) writer.add_scalar("val_accuracy", acc_metric, epoch + 1) print(f"train completed, best_metric: {best_metric:.4f} at epoch: {best_metric_epoch}") writer.close() if __name__ == "__main__": main()