def test_tb_image_shape(self, shape): with tempfile.TemporaryDirectory() as tempdir: # set up engine def _train_func(engine, batch): return torch.zeros((1, 1, 10, 10)) engine = Engine(_train_func) # set up testing handler stats_handler = TensorBoardImageHandler(log_dir=tempdir) engine.add_event_handler(Events.ITERATION_COMPLETED, stats_handler) data = zip(np.random.normal(size=(10, 4, *shape)), np.random.normal(size=(10, 4, *shape))) engine.run(data, epoch_length=10, max_epochs=1) stats_handler.close() self.assertTrue(len(glob.glob(tempdir)) > 0)
def train_handlers(self, context: Context): handlers = super().train_handlers(context) if context.local_rank == 0: handlers.append( TensorBoardImageHandler( log_dir=context.events_dir, batch_transform=from_engine(["image", "label"]), output_transform=from_engine(["pred"]), interval=20, epoch_level=True, )) return handlers
def test_tb_image_shape(self, shape): default_dir = os.path.join(".", "runs") shutil.rmtree(default_dir, ignore_errors=True) # set up engine def _train_func(engine, batch): return torch.zeros((1, 1, 10, 10)) engine = Engine(_train_func) # set up testing handler stats_handler = TensorBoardImageHandler() engine.add_event_handler(Events.ITERATION_COMPLETED, stats_handler) data = zip(np.random.normal(size=(10, 4, *shape)), np.random.normal(size=(10, 4, *shape))) engine.run(data, epoch_length=10, max_epochs=1) self.assertTrue(os.path.exists(default_dir)) self.assertTrue(len(glob.glob(default_dir)) > 0) shutil.rmtree(default_dir)
def run_training(train_file_list, valid_file_list, config_info): """ Pipeline to train a dynUNet segmentation model in MONAI. It is composed of the following main blocks: * Data Preparation: Extract the filenames and prepare the training/validation processing transforms * Load Data: Load training and validation data to PyTorch DataLoader * Network Preparation: Define the network, loss function, optimiser and learning rate scheduler * MONAI Evaluator: Initialise the dynUNet evaluator, i.e. the class providing utilities to perform validation during training. Attach handlers to save the best model on the validation set. A 2D sliding window approach on the 3D volume is used at evaluation. The mean 3D Dice is used as validation metric. * MONAI Trainer: Initialise the dynUNet trainer, i.e. the class providing utilities to perform the training loop. * Run training: The MONAI trainer is run, performing training and validation during training. Args: train_file_list: .txt or .csv file (with no header) storing two-columns filenames for training: image filename in the first column and segmentation filename in the second column. The two columns should be separated by a comma. See monaifbs/config/mock_train_file_list_for_dynUnet_training.txt for an example of the expected format. valid_file_list: .txt or .csv file (with no header) storing two-columns filenames for validation: image filename in the first column and segmentation filename in the second column. The two columns should be separated by a comma. See monaifbs/config/mock_valid_file_list_for_dynUnet_training.txt for an example of the expected format. config_info: dict, contains configuration parameters for sampling, network and training. See monaifbs/config/monai_dynUnet_training_config.yml for an example of the expected fields. """ """ Read input and configuration parameters """ # print MONAI config information logging.basicConfig(stream=sys.stdout, level=logging.INFO) print_config() # print to log the parameter setups print(yaml.dump(config_info)) # extract network parameters, perform checks/set defaults if not present and print them to log if 'seg_labels' in config_info['training'].keys(): seg_labels = config_info['training']['seg_labels'] else: seg_labels = [1] nr_out_channels = len(seg_labels) print("Considering the following {} labels in the segmentation: {}".format(nr_out_channels, seg_labels)) patch_size = config_info["training"]["inplane_size"] + [1] print("Considering patch size = {}".format(patch_size)) spacing = config_info["training"]["spacing"] print("Bringing all images to spacing = {}".format(spacing)) if 'model_to_load' in config_info['training'].keys() and config_info['training']['model_to_load'] is not None: model_to_load = config_info['training']['model_to_load'] if not os.path.exists(model_to_load): raise FileNotFoundError("Cannot find model: {}".format(model_to_load)) else: print("Loading model from {}".format(model_to_load)) else: model_to_load = None # set up either GPU or CPU usage if torch.cuda.is_available(): print("\n#### GPU INFORMATION ###") print("Using device number: {}, name: {}\n".format(torch.cuda.current_device(), torch.cuda.get_device_name())) current_device = torch.device("cuda:0") else: current_device = torch.device("cpu") print("Using device: {}".format(current_device)) # set determinism if required if 'manual_seed' in config_info['training'].keys() and config_info['training']['manual_seed'] is not None: seed = config_info['training']['manual_seed'] else: seed = None if seed is not None: print("Using determinism with seed = {}\n".format(seed)) set_determinism(seed=seed) """ Setup data output directory """ out_model_dir = os.path.join(config_info['output']['out_dir'], datetime.now().strftime('%Y-%m-%d_%H-%M-%S') + '_' + config_info['output']['out_postfix']) print("Saving to directory {}\n".format(out_model_dir)) # create cache directory to store results for Persistent Dataset if 'cache_dir' in config_info['output'].keys(): out_cache_dir = config_info['output']['cache_dir'] else: out_cache_dir = os.path.join(out_model_dir, 'persistent_cache') persistent_cache: Path = Path(out_cache_dir) persistent_cache.mkdir(parents=True, exist_ok=True) """ Data preparation """ # Read the input files for training and validation print("*** Loading input data for training...") train_files = create_data_list_of_dictionaries(train_file_list) print("Number of inputs for training = {}".format(len(train_files))) val_files = create_data_list_of_dictionaries(valid_file_list) print("Number of inputs for validation = {}".format(len(val_files))) # Define MONAI processing transforms for the training data. This includes: # - Load Nifti files and convert to format Batch x Channel x Dim1 x Dim2 x Dim3 # - CropForegroundd: Reduce the background from the MR image # - InPlaneSpacingd: Perform in-plane resampling to the desired spacing, but preserve the resolution along the # last direction (lowest resolution) to avoid introducing motion artefact resampling errors # - SpatialPadd: Pad the in-plane size to the defined network input patch size [N, M] if needed # - NormalizeIntensityd: Apply whitening # - RandSpatialCropd: Crop a random patch from the input with size [B, C, N, M, 1] # - SqueezeDimd: Convert the 3D patch to a 2D one as input to the network (i.e. bring it to size [B, C, N, M]) # - Apply data augmentation (RandZoomd, RandRotated, RandGaussianNoised, RandGaussianSmoothd, RandScaleIntensityd, # RandFlipd) # - ToTensor: convert to pytorch tensor train_transforms = Compose( [ LoadNiftid(keys=["image", "label"]), AddChanneld(keys=["image", "label"]), CropForegroundd(keys=["image", "label"], source_key="image"), InPlaneSpacingd( keys=["image", "label"], pixdim=spacing, mode=("bilinear", "nearest"), ), SpatialPadd(keys=["image", "label"], spatial_size=patch_size, mode=["constant", "edge"]), NormalizeIntensityd(keys=["image"], nonzero=False, channel_wise=True), RandSpatialCropd(keys=["image", "label"], roi_size=patch_size, random_size=False), SqueezeDimd(keys=["image", "label"], dim=-1), RandZoomd( keys=["image", "label"], min_zoom=0.9, max_zoom=1.2, mode=("bilinear", "nearest"), align_corners=(True, None), prob=0.16, ), RandRotated(keys=["image", "label"], range_x=90, range_y=90, prob=0.2, keep_size=True, mode=["bilinear", "nearest"], padding_mode=["zeros", "border"]), RandGaussianNoised(keys=["image"], std=0.01, prob=0.15), RandGaussianSmoothd( keys=["image"], sigma_x=(0.5, 1.15), sigma_y=(0.5, 1.15), sigma_z=(0.5, 1.15), prob=0.15, ), RandScaleIntensityd(keys=["image"], factors=0.3, prob=0.15), RandFlipd(["image", "label"], spatial_axis=[0, 1], prob=0.5), ToTensord(keys=["image", "label"]), ] ) # Define MONAI processing transforms for the validation data # - Load Nifti files and convert to format Batch x Channel x Dim1 x Dim2 x Dim3 # - CropForegroundd: Reduce the background from the MR image # - InPlaneSpacingd: Perform in-plane resampling to the desired spacing, but preserve the resolution along the # last direction (lowest resolution) to avoid introducing motion artefact resampling errors # - SpatialPadd: Pad the in-plane size to the defined network input patch size [N, M] if needed # - NormalizeIntensityd: Apply whitening # - ToTensor: convert to pytorch tensor # NOTE: The validation data is kept 3D as a 2D sliding window approach is used throughout the volume at inference val_transforms = Compose( [ LoadNiftid(keys=["image", "label"]), AddChanneld(keys=["image", "label"]), CropForegroundd(keys=["image", "label"], source_key="image"), InPlaneSpacingd( keys=["image", "label"], pixdim=spacing, mode=("bilinear", "nearest"), ), SpatialPadd(keys=["image", "label"], spatial_size=patch_size, mode=["constant", "edge"]), NormalizeIntensityd(keys=["image"], nonzero=False, channel_wise=True), ToTensord(keys=["image", "label"]), ] ) """ Load data """ # create training data loader train_ds = PersistentDataset(data=train_files, transform=train_transforms, cache_dir=persistent_cache) train_loader = DataLoader(train_ds, batch_size=config_info['training']['batch_size_train'], shuffle=True, num_workers=config_info['device']['num_workers']) check_train_data = misc.first(train_loader) print("Training data tensor shapes:") print("Image = {}; Label = {}".format(check_train_data["image"].shape, check_train_data["label"].shape)) # create validation data loader if config_info['training']['batch_size_valid'] != 1: raise Exception("Batch size different from 1 at validation ar currently not supported") val_ds = PersistentDataset(data=val_files, transform=val_transforms, cache_dir=persistent_cache) val_loader = DataLoader(val_ds, batch_size=1, shuffle=False, num_workers=config_info['device']['num_workers']) check_valid_data = misc.first(val_loader) print("Validation data tensor shapes (Example):") print("Image = {}; Label = {}\n".format(check_valid_data["image"].shape, check_valid_data["label"].shape)) """ Network preparation """ print("*** Preparing the network ...") # automatically extracts the strides and kernels based on nnU-Net empirical rules spacings = spacing[:2] sizes = patch_size[:2] strides, kernels = [], [] while True: spacing_ratio = [sp / min(spacings) for sp in spacings] stride = [2 if ratio <= 2 and size >= 8 else 1 for (ratio, size) in zip(spacing_ratio, sizes)] kernel = [3 if ratio <= 2 else 1 for ratio in spacing_ratio] if all(s == 1 for s in stride): break sizes = [i / j for i, j in zip(sizes, stride)] spacings = [i * j for i, j in zip(spacings, stride)] kernels.append(kernel) strides.append(stride) strides.insert(0, len(spacings) * [1]) kernels.append(len(spacings) * [3]) # initialise the network net = DynUNet( spatial_dims=2, in_channels=1, out_channels=nr_out_channels, kernel_size=kernels, strides=strides, upsample_kernel_size=strides[1:], norm_name="instance", deep_supervision=True, deep_supr_num=2, res_block=False, ).to(current_device) print(net) # define the loss function loss_function = choose_loss_function(nr_out_channels, config_info) # define the optimiser and the learning rate scheduler opt = torch.optim.SGD(net.parameters(), lr=float(config_info['training']['lr']), momentum=0.95) scheduler = torch.optim.lr_scheduler.LambdaLR( opt, lr_lambda=lambda epoch: (1 - epoch / config_info['training']['nr_train_epochs']) ** 0.9 ) """ MONAI evaluator """ print("*** Preparing the dynUNet evaluator engine...\n") # val_post_transforms = Compose( # [ # Activationsd(keys="pred", sigmoid=True), # ] # ) val_handlers = [ StatsHandler(output_transform=lambda x: None), TensorBoardStatsHandler(log_dir=os.path.join(out_model_dir, "valid"), output_transform=lambda x: None, global_epoch_transform=lambda x: trainer.state.iteration), CheckpointSaver(save_dir=out_model_dir, save_dict={"net": net, "opt": opt}, save_key_metric=True, file_prefix='best_valid'), ] if config_info['output']['val_image_to_tensorboad']: val_handlers.append(TensorBoardImageHandler(log_dir=os.path.join(out_model_dir, "valid"), batch_transform=lambda x: (x["image"], x["label"]), output_transform=lambda x: x["pred"], interval=2)) # Define customized evaluator class DynUNetEvaluator(SupervisedEvaluator): def _iteration(self, engine, batchdata): inputs, targets = self.prepare_batch(batchdata) inputs, targets = inputs.to(engine.state.device), targets.to(engine.state.device) flip_inputs_1 = torch.flip(inputs, dims=(2,)) flip_inputs_2 = torch.flip(inputs, dims=(3,)) flip_inputs_3 = torch.flip(inputs, dims=(2, 3)) def _compute_pred(): pred = self.inferer(inputs, self.network) # use random flipping as data augmentation at inference flip_pred_1 = torch.flip(self.inferer(flip_inputs_1, self.network), dims=(2,)) flip_pred_2 = torch.flip(self.inferer(flip_inputs_2, self.network), dims=(3,)) flip_pred_3 = torch.flip(self.inferer(flip_inputs_3, self.network), dims=(2, 3)) return (pred + flip_pred_1 + flip_pred_2 + flip_pred_3) / 4 # execute forward computation self.network.eval() with torch.no_grad(): if self.amp: with torch.cuda.amp.autocast(): predictions = _compute_pred() else: predictions = _compute_pred() return {"image": inputs, "label": targets, "pred": predictions} evaluator = DynUNetEvaluator( device=current_device, val_data_loader=val_loader, network=net, inferer=SlidingWindowInferer2D(roi_size=patch_size, sw_batch_size=4, overlap=0.0), post_transform=None, key_val_metric={ "Mean_dice": MeanDice( include_background=False, to_onehot_y=True, mutually_exclusive=True, output_transform=lambda x: (x["pred"], x["label"]), ) }, val_handlers=val_handlers, amp=False, ) """ MONAI trainer """ print("*** Preparing the dynUNet trainer engine...\n") # train_post_transforms = Compose( # [ # Activationsd(keys="pred", sigmoid=True), # ] # ) validation_every_n_epochs = config_info['training']['validation_every_n_epochs'] epoch_len = len(train_ds) // train_loader.batch_size validation_every_n_iters = validation_every_n_epochs * epoch_len # define event handlers for the trainer writer_train = SummaryWriter(log_dir=os.path.join(out_model_dir, "train")) train_handlers = [ LrScheduleHandler(lr_scheduler=scheduler, print_lr=True), ValidationHandler(validator=evaluator, interval=validation_every_n_iters, epoch_level=False), StatsHandler(tag_name="train_loss", output_transform=lambda x: x["loss"]), TensorBoardStatsHandler(summary_writer=writer_train, log_dir=os.path.join(out_model_dir, "train"), tag_name="Loss", output_transform=lambda x: x["loss"], global_epoch_transform=lambda x: trainer.state.iteration), CheckpointSaver(save_dir=out_model_dir, save_dict={"net": net, "opt": opt}, save_final=True, save_interval=2, epoch_level=True, n_saved=config_info['output']['max_nr_models_saved']), ] if model_to_load is not None: train_handlers.append(CheckpointLoader(load_path=model_to_load, load_dict={"net": net, "opt": opt})) # define customized trainer class DynUNetTrainer(SupervisedTrainer): def _iteration(self, engine, batchdata): inputs, targets = self.prepare_batch(batchdata) inputs, targets = inputs.to(engine.state.device), targets.to(engine.state.device) def _compute_loss(preds, label): labels = [label] + [interpolate(label, pred.shape[2:]) for pred in preds[1:]] return sum([0.5 ** i * self.loss_function(p, l) for i, (p, l) in enumerate(zip(preds, labels))]) self.network.train() self.optimizer.zero_grad() if self.amp and self.scaler is not None: with torch.cuda.amp.autocast(): predictions = self.inferer(inputs, self.network) loss = _compute_loss(predictions, targets) self.scaler.scale(loss).backward() self.scaler.step(self.optimizer) self.scaler.update() else: predictions = self.inferer(inputs, self.network) loss = _compute_loss(predictions, targets).mean() loss.backward() self.optimizer.step() return {"image": inputs, "label": targets, "pred": predictions, "loss": loss.item()} trainer = DynUNetTrainer( device=current_device, max_epochs=config_info['training']['nr_train_epochs'], train_data_loader=train_loader, network=net, optimizer=opt, loss_function=loss_function, inferer=SimpleInferer(), post_transform=None, key_train_metric=None, train_handlers=train_handlers, amp=False, ) """ Run training """ print("*** Run training...") trainer.run() print("Done!")
def main(): monai.config.print_config() logging.basicConfig(stream=sys.stdout, level=logging.INFO) # create a temporary directory and 40 random image, mask paris tempdir = tempfile.mkdtemp() print('generating synthetic data to {} (this may take a while)'.format(tempdir)) 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, 'im%i.nii.gz' % i)) n = nib.Nifti1Image(seg, np.eye(4)) nib.save(n, os.path.join(tempdir, 'seg%i.nii.gz' % i)) 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), ToTensor() ]) train_segtrans = Compose([ AddChannel(), RandSpatialCrop((96, 96, 96), random_size=False), ToTensor() ]) val_imtrans = Compose([ ScaleIntensity(), AddChannel(), Resize((96, 96, 96)), ToTensor() ]) val_segtrans = Compose([ AddChannel(), Resize((96, 96, 96)), 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=5, 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=5, num_workers=8, pin_memory=torch.cuda.is_available()) # create UNet, DiceLoss and Adam optimizer net = 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, ) loss = monai.losses.DiceLoss(do_sigmoid=True) lr = 1e-3 opt = torch.optim.Adam(net.parameters(), lr) device = torch.device('cuda:0') # ignite trainer expects batch=(img, seg) and returns output=loss at every iteration, # user can add output_transform to return other values, like: y_pred, y, etc. trainer = create_supervised_trainer(net, opt, loss, device, False) # adding checkpoint handler to save models (network params and optimizer stats) during training checkpoint_handler = ModelCheckpoint('./runs/', 'net', n_saved=10, require_empty=False) trainer.add_event_handler(event_name=Events.EPOCH_COMPLETED, handler=checkpoint_handler, to_save={'net': net, 'opt': opt}) # StatsHandler prints loss at every iteration and print metrics at every epoch, # we don't set metrics for trainer here, so just print loss, user can also customize print functions # and can use output_transform to convert engine.state.output if it's not a loss value train_stats_handler = StatsHandler(name='trainer') train_stats_handler.attach(trainer) # TensorBoardStatsHandler plots loss at every iteration and plots metrics at every epoch, same as StatsHandler train_tensorboard_stats_handler = TensorBoardStatsHandler() train_tensorboard_stats_handler.attach(trainer) validation_every_n_epochs = 1 # Set parameters for validation metric_name = 'Mean_Dice' # add evaluation metric to the evaluator engine val_metrics = {metric_name: MeanDice(add_sigmoid=True, to_onehot_y=False)} # ignite evaluator expects batch=(img, seg) and returns output=(y_pred, y) at every iteration, # user can add output_transform to return other values evaluator = create_supervised_evaluator(net, val_metrics, device, True) @trainer.on(Events.EPOCH_COMPLETED(every=validation_every_n_epochs)) def run_validation(engine): evaluator.run(val_loader) # add early stopping handler to evaluator early_stopper = EarlyStopping(patience=4, score_function=stopping_fn_from_metric(metric_name), trainer=trainer) evaluator.add_event_handler(event_name=Events.EPOCH_COMPLETED, handler=early_stopper) # add stats event handler to print validation stats via evaluator val_stats_handler = StatsHandler( name='evaluator', output_transform=lambda x: None, # no need to print loss value, so disable per iteration output global_epoch_transform=lambda x: trainer.state.epoch) # fetch global epoch number from trainer val_stats_handler.attach(evaluator) # add handler to record metrics to TensorBoard at every validation epoch val_tensorboard_stats_handler = TensorBoardStatsHandler( output_transform=lambda x: None, # no need to plot loss value, so disable per iteration output global_epoch_transform=lambda x: trainer.state.epoch) # fetch global epoch number from trainer val_tensorboard_stats_handler.attach(evaluator) # add handler to draw the first image and the corresponding label and model output in the last batch # here we draw the 3D output as GIF format along Depth axis, at every validation epoch val_tensorboard_image_handler = TensorBoardImageHandler( batch_transform=lambda batch: (batch[0], batch[1]), output_transform=lambda output: predict_segmentation(output[0]), global_iter_transform=lambda x: trainer.state.epoch ) evaluator.add_event_handler(event_name=Events.EPOCH_COMPLETED, handler=val_tensorboard_image_handler) train_epochs = 30 state = trainer.run(train_loader, train_epochs) shutil.rmtree(tempdir)
def main(tempdir): monai.config.print_config() logging.basicConfig(stream=sys.stdout, level=logging.INFO) ################################ DATASET ################################ # 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, channel_dim=-1) n = nib.Nifti1Image(im, np.eye(4)) nib.save(n, os.path.join(tempdir, f"img{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, "img*.nii.gz"))) segs = sorted(glob(os.path.join(tempdir, "seg*.nii.gz"))) train_files = [{"image": img, "label": seg} for img, seg in zip(images[:20], segs[:20])] val_files = [{"image": img, "label": seg} for img, seg in zip(images[-20:], segs[-20:])] # define transforms for image and segmentation train_transforms = Compose( [ LoadImaged(keys=["image", "label"]), AsChannelFirstd(keys=["image", "label"], channel_dim=-1), ScaleIntensityd(keys="image"), RandCropByPosNegLabeld( keys=["image", "label"], label_key="label", spatial_size=[96, 96, 96], pos=1, neg=1, num_samples=4 ), RandRotate90d(keys=["image", "label"], prob=0.5, spatial_axes=[0, 2]), ToTensord(keys=["image", "label"]), ] ) val_transforms = Compose( [ LoadImaged(keys=["image", "label"]), AsChannelFirstd(keys=["image", "label"], channel_dim=-1), ScaleIntensityd(keys="image"), ToTensord(keys=["image", "label"]), ] ) # create a training data loader train_ds = monai.data.CacheDataset(data=train_files, transform=train_transforms, cache_rate=0.5) # use batch_size=2 to load images and use RandCropByPosNegLabeld to generate 2 x 4 images for network training train_loader = monai.data.DataLoader(train_ds, batch_size=2, shuffle=True, num_workers=4) # create a validation data loader val_ds = monai.data.CacheDataset(data=val_files, transform=val_transforms, cache_rate=1.0) val_loader = monai.data.DataLoader(val_ds, batch_size=1, num_workers=4) ################################ DATASET ################################ ################################ NETWORK ################################ # create UNet, DiceLoss and Adam optimizer device = torch.device("cuda" if torch.cuda.is_available() else "cpu") net = 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) ################################ NETWORK ################################ ################################ LOSS ################################ loss = monai.losses.DiceLoss(sigmoid=True) ################################ LOSS ################################ ################################ OPT ################################ opt = torch.optim.Adam(net.parameters(), 1e-3) ################################ OPT ################################ ################################ LR ################################ lr_scheduler = torch.optim.lr_scheduler.StepLR(opt, step_size=2, gamma=0.1) ################################ LR ################################ val_post_transforms = Compose( [ Activationsd(keys="pred", sigmoid=True), AsDiscreted(keys="pred", threshold_values=True), KeepLargestConnectedComponentd(keys="pred", applied_labels=[1]), ] ) val_handlers = [ StatsHandler(output_transform=lambda x: None), TensorBoardStatsHandler(log_dir="./runs/", output_transform=lambda x: None), TensorBoardImageHandler( log_dir="./runs/", batch_transform=lambda x: (x["image"], x["label"]), output_transform=lambda x: x["pred"], ), CheckpointSaver(save_dir="./runs/", save_dict={"net": net}, save_key_metric=True), ] evaluator = SupervisedEvaluator( device=device, val_data_loader=val_loader, network=net, inferer=SlidingWindowInferer(roi_size=(96, 96, 96), sw_batch_size=4, overlap=0.5), post_transform=val_post_transforms, key_val_metric={ "val_mean_dice": MeanDice(include_background=True, output_transform=lambda x: (x["pred"], x["label"])) }, additional_metrics={"val_acc": Accuracy(output_transform=lambda x: (x["pred"], x["label"]))}, val_handlers=val_handlers, # if no FP16 support in GPU or PyTorch version < 1.6, will not enable AMP evaluation amp=True if monai.utils.get_torch_version_tuple() >= (1, 6) else False, ) train_post_transforms = Compose( [ Activationsd(keys="pred", sigmoid=True), AsDiscreted(keys="pred", threshold_values=True), KeepLargestConnectedComponentd(keys="pred", applied_labels=[1]), ] ) train_handlers = [ LrScheduleHandler(lr_scheduler=lr_scheduler, print_lr=True), ValidationHandler(validator=evaluator, interval=2, epoch_level=True), StatsHandler(tag_name="train_loss", output_transform=lambda x: x["loss"]), TensorBoardStatsHandler(log_dir="./runs/", tag_name="train_loss", output_transform=lambda x: x["loss"]), CheckpointSaver(save_dir="./runs/", save_dict={"net": net, "opt": opt}, save_interval=2, epoch_level=True), ] trainer = SupervisedTrainer( device=device, max_epochs=5, train_data_loader=train_loader, network=net, optimizer=opt, loss_function=loss, inferer=SimpleInferer(), post_transform=train_post_transforms, key_train_metric={"train_acc": Accuracy(output_transform=lambda x: (x["pred"], x["label"]))}, train_handlers=train_handlers, # if no FP16 support in GPU or PyTorch version < 1.6, will not enable AMP training amp=True if monai.utils.get_torch_version_tuple() >= (1, 6) else False, ) trainer.run()
def run_training_test(root_dir, device="cuda:0", amp=False): images = sorted(glob(os.path.join(root_dir, "img*.nii.gz"))) segs = sorted(glob(os.path.join(root_dir, "seg*.nii.gz"))) train_files = [{ "image": img, "label": seg } for img, seg in zip(images[:20], segs[:20])] val_files = [{ "image": img, "label": seg } for img, seg in zip(images[-20:], segs[-20:])] # define transforms for image and segmentation train_transforms = Compose([ LoadNiftid(keys=["image", "label"]), AsChannelFirstd(keys=["image", "label"], channel_dim=-1), ScaleIntensityd(keys=["image", "label"]), RandCropByPosNegLabeld(keys=["image", "label"], label_key="label", spatial_size=[96, 96, 96], pos=1, neg=1, num_samples=4), RandRotate90d(keys=["image", "label"], prob=0.5, spatial_axes=[0, 2]), ToTensord(keys=["image", "label"]), ]) val_transforms = Compose([ LoadNiftid(keys=["image", "label"]), AsChannelFirstd(keys=["image", "label"], channel_dim=-1), ScaleIntensityd(keys=["image", "label"]), ToTensord(keys=["image", "label"]), ]) # create a training data loader train_ds = monai.data.CacheDataset(data=train_files, transform=train_transforms, cache_rate=0.5) # use batch_size=2 to load images and use RandCropByPosNegLabeld to generate 2 x 4 images for network training train_loader = monai.data.DataLoader(train_ds, batch_size=2, shuffle=True, num_workers=4) # create a validation data loader val_ds = monai.data.CacheDataset(data=val_files, transform=val_transforms, cache_rate=1.0) val_loader = monai.data.DataLoader(val_ds, batch_size=1, num_workers=4) # create UNet, DiceLoss and Adam optimizer net = 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 = monai.losses.DiceLoss(sigmoid=True) opt = torch.optim.Adam(net.parameters(), 1e-3) lr_scheduler = torch.optim.lr_scheduler.StepLR(opt, step_size=2, gamma=0.1) val_post_transforms = Compose([ Activationsd(keys="pred", sigmoid=True), AsDiscreted(keys="pred", threshold_values=True), KeepLargestConnectedComponentd(keys="pred", applied_labels=[1]), ]) val_handlers = [ StatsHandler(output_transform=lambda x: None), TensorBoardStatsHandler(log_dir=root_dir, output_transform=lambda x: None), TensorBoardImageHandler(log_dir=root_dir, batch_transform=lambda x: (x["image"], x["label"]), output_transform=lambda x: x["pred"]), CheckpointSaver(save_dir=root_dir, save_dict={"net": net}, save_key_metric=True), ] evaluator = SupervisedEvaluator( device=device, val_data_loader=val_loader, network=net, inferer=SlidingWindowInferer(roi_size=(96, 96, 96), sw_batch_size=4, overlap=0.5), post_transform=val_post_transforms, key_val_metric={ "val_mean_dice": MeanDice(include_background=True, output_transform=lambda x: (x["pred"], x["label"])) }, additional_metrics={ "val_acc": Accuracy(output_transform=lambda x: (x["pred"], x["label"])) }, val_handlers=val_handlers, amp=True if amp else False, ) train_post_transforms = Compose([ Activationsd(keys="pred", sigmoid=True), AsDiscreted(keys="pred", threshold_values=True), KeepLargestConnectedComponentd(keys="pred", applied_labels=[1]), ]) train_handlers = [ LrScheduleHandler(lr_scheduler=lr_scheduler, print_lr=True), ValidationHandler(validator=evaluator, interval=2, epoch_level=True), StatsHandler(tag_name="train_loss", output_transform=lambda x: x["loss"]), TensorBoardStatsHandler(log_dir=root_dir, tag_name="train_loss", output_transform=lambda x: x["loss"]), CheckpointSaver(save_dir=root_dir, save_dict={ "net": net, "opt": opt }, save_interval=2, epoch_level=True), ] trainer = SupervisedTrainer( device=device, max_epochs=5, train_data_loader=train_loader, network=net, optimizer=opt, loss_function=loss, inferer=SimpleInferer(), post_transform=train_post_transforms, key_train_metric={ "train_acc": Accuracy(output_transform=lambda x: (x["pred"], x["label"])) }, train_handlers=train_handlers, amp=True if amp else False, ) trainer.run() return evaluator.state.best_metric
def main(): monai.config.print_config() logging.basicConfig(stream=sys.stdout, level=logging.INFO) # create a temporary directory and 40 random image, mask paris tempdir = tempfile.mkdtemp() 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, channel_dim=-1) n = nib.Nifti1Image(im, np.eye(4)) nib.save(n, os.path.join(tempdir, f"img{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, "img*.nii.gz"))) segs = sorted(glob(os.path.join(tempdir, "seg*.nii.gz"))) train_files = [{ "img": img, "seg": seg } for img, seg in zip(images[:20], segs[:20])] val_files = [{ "img": img, "seg": seg } for img, seg in zip(images[-20:], segs[-20:])] # define transforms for image and segmentation train_transforms = Compose([ LoadNiftid(keys=["img", "seg"]), AsChannelFirstd(keys=["img", "seg"], channel_dim=-1), ScaleIntensityd(keys=["img", "seg"]), RandCropByPosNegLabeld(keys=["img", "seg"], label_key="seg", spatial_size=[96, 96, 96], pos=1, neg=1, num_samples=4), RandRotate90d(keys=["img", "seg"], prob=0.5, spatial_axes=[0, 2]), ToTensord(keys=["img", "seg"]), ]) val_transforms = Compose([ LoadNiftid(keys=["img", "seg"]), AsChannelFirstd(keys=["img", "seg"], channel_dim=-1), ScaleIntensityd(keys=["img", "seg"]), ToTensord(keys=["img", "seg"]), ]) # define dataset, data loader check_ds = monai.data.Dataset(data=train_files, transform=train_transforms) # use batch_size=2 to load images and use RandCropByPosNegLabeld to generate 2 x 4 images for network training check_loader = DataLoader(check_ds, batch_size=2, num_workers=4, collate_fn=list_data_collate, pin_memory=torch.cuda.is_available()) check_data = monai.utils.misc.first(check_loader) print(check_data["img"].shape, check_data["seg"].shape) # create a training data loader train_ds = monai.data.Dataset(data=train_files, transform=train_transforms) # use batch_size=2 to load images and use RandCropByPosNegLabeld to generate 2 x 4 images for network training train_loader = DataLoader( train_ds, batch_size=2, shuffle=True, num_workers=4, collate_fn=list_data_collate, 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=5, num_workers=8, collate_fn=list_data_collate, pin_memory=torch.cuda.is_available()) # create UNet, DiceLoss and Adam optimizer net = 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, ) loss = monai.losses.DiceLoss(sigmoid=True) lr = 1e-3 opt = torch.optim.Adam(net.parameters(), lr) device = torch.device("cuda:0") # Ignite trainer expects batch=(img, seg) and returns output=loss at every iteration, # user can add output_transform to return other values, like: y_pred, y, etc. def prepare_batch(batch, device=None, non_blocking=False): return _prepare_batch((batch["img"], batch["seg"]), device, non_blocking) trainer = create_supervised_trainer(net, opt, loss, device, False, prepare_batch=prepare_batch) # adding checkpoint handler to save models (network params and optimizer stats) during training checkpoint_handler = ModelCheckpoint("./runs/", "net", n_saved=10, require_empty=False) trainer.add_event_handler(event_name=Events.EPOCH_COMPLETED, handler=checkpoint_handler, to_save={ "net": net, "opt": opt }) # StatsHandler prints loss at every iteration and print metrics at every epoch, # we don't set metrics for trainer here, so just print loss, user can also customize print functions # and can use output_transform to convert engine.state.output if it's not loss value train_stats_handler = StatsHandler(name="trainer") train_stats_handler.attach(trainer) # TensorBoardStatsHandler plots loss at every iteration and plots metrics at every epoch, same as StatsHandler train_tensorboard_stats_handler = TensorBoardStatsHandler() train_tensorboard_stats_handler.attach(trainer) validation_every_n_iters = 5 # set parameters for validation metric_name = "Mean_Dice" # add evaluation metric to the evaluator engine val_metrics = {metric_name: MeanDice(sigmoid=True, to_onehot_y=False)} # Ignite evaluator expects batch=(img, seg) and returns output=(y_pred, y) at every iteration, # user can add output_transform to return other values evaluator = create_supervised_evaluator(net, val_metrics, device, True, prepare_batch=prepare_batch) @trainer.on(Events.ITERATION_COMPLETED(every=validation_every_n_iters)) def run_validation(engine): evaluator.run(val_loader) # add early stopping handler to evaluator early_stopper = EarlyStopping( patience=4, score_function=stopping_fn_from_metric(metric_name), trainer=trainer) evaluator.add_event_handler(event_name=Events.EPOCH_COMPLETED, handler=early_stopper) # add stats event handler to print validation stats via evaluator val_stats_handler = StatsHandler( name="evaluator", output_transform=lambda x: None, # no need to print loss value, so disable per iteration output global_epoch_transform=lambda x: trainer.state.epoch, ) # fetch global epoch number from trainer val_stats_handler.attach(evaluator) # add handler to record metrics to TensorBoard at every validation epoch val_tensorboard_stats_handler = TensorBoardStatsHandler( output_transform=lambda x: None, # no need to plot loss value, so disable per iteration output global_epoch_transform=lambda x: trainer.state.iteration, ) # fetch global iteration number from trainer val_tensorboard_stats_handler.attach(evaluator) # add handler to draw the first image and the corresponding label and model output in the last batch # here we draw the 3D output as GIF format along the depth axis, every 2 validation iterations. val_tensorboard_image_handler = TensorBoardImageHandler( batch_transform=lambda batch: (batch["img"], batch["seg"]), output_transform=lambda output: predict_segmentation(output[0]), global_iter_transform=lambda x: trainer.state.epoch, ) evaluator.add_event_handler(event_name=Events.ITERATION_COMPLETED(every=2), handler=val_tensorboard_image_handler) train_epochs = 5 state = trainer.run(train_loader, train_epochs) print(state) shutil.rmtree(tempdir)
def main(): """ Basic UNet as implemented in MONAI for Fetal Brain Segmentation, but using ignite to manage training and validation loop and checkpointing :return: """ """ Read input and configuration parameters """ parser = argparse.ArgumentParser( description='Run basic UNet with MONAI - Ignite version.') parser.add_argument('--config', dest='config', metavar='config', type=str, help='config file') args = parser.parse_args() with open(args.config) as f: config_info = yaml.load(f, Loader=yaml.FullLoader) # print to log the parameter setups print(yaml.dump(config_info)) # GPU params cuda_device = config_info['device']['cuda_device'] num_workers = config_info['device']['num_workers'] # training and validation params loss_type = config_info['training']['loss_type'] batch_size_train = config_info['training']['batch_size_train'] batch_size_valid = config_info['training']['batch_size_valid'] lr = float(config_info['training']['lr']) lr_decay = config_info['training']['lr_decay'] if lr_decay is not None: lr_decay = float(lr_decay) nr_train_epochs = config_info['training']['nr_train_epochs'] validation_every_n_epochs = config_info['training'][ 'validation_every_n_epochs'] sliding_window_validation = config_info['training'][ 'sliding_window_validation'] if 'model_to_load' in config_info['training'].keys(): model_to_load = config_info['training']['model_to_load'] if not os.path.exists(model_to_load): raise BlockingIOError( "cannot find model: {}".format(model_to_load)) else: model_to_load = None if 'manual_seed' in config_info['training'].keys(): seed = config_info['training']['manual_seed'] else: seed = None # data params data_root = config_info['data']['data_root'] training_list = config_info['data']['training_list'] validation_list = config_info['data']['validation_list'] # model saving out_model_dir = os.path.join( config_info['output']['out_model_dir'], datetime.now().strftime('%Y-%m-%d_%H-%M-%S') + '_' + config_info['output']['output_subfix']) print("Saving to directory ", out_model_dir) if 'cache_dir' in config_info['output'].keys(): out_cache_dir = config_info['output']['cache_dir'] else: out_cache_dir = os.path.join(out_model_dir, 'persistent_cache') max_nr_models_saved = config_info['output']['max_nr_models_saved'] val_image_to_tensorboad = config_info['output']['val_image_to_tensorboad'] monai.config.print_config() logging.basicConfig(stream=sys.stdout, level=logging.INFO) torch.cuda.set_device(cuda_device) if seed is not None: # set manual seed if required (both numpy and torch) set_determinism(seed=seed) # # set torch only seed # torch.manual_seed(seed) # torch.backends.cudnn.deterministic = True # torch.backends.cudnn.benchmark = False """ Data Preparation """ # create cache directory to store results for Persistent Dataset persistent_cache: Path = Path(out_cache_dir) persistent_cache.mkdir(parents=True, exist_ok=True) # create training and validation data lists train_files = create_data_list(data_folder_list=data_root, subject_list=training_list, img_postfix='_Image', label_postfix='_Label') print(len(train_files)) print(train_files[0]) print(train_files[-1]) val_files = create_data_list(data_folder_list=data_root, subject_list=validation_list, img_postfix='_Image', label_postfix='_Label') print(len(val_files)) print(val_files[0]) print(val_files[-1]) # data preprocessing for training: # - convert data to right format [batch, channel, dim, dim, dim] # - apply whitening # - resize to (96, 96) in-plane (preserve z-direction) # - define 2D patches to be extracted # - add data augmentation (random rotation and random flip) # - squeeze to 2D train_transforms = Compose([ LoadNiftid(keys=['img', 'seg']), AddChanneld(keys=['img', 'seg']), NormalizeIntensityd(keys=['img']), Resized(keys=['img', 'seg'], spatial_size=[96, 96], interp_order=[1, 0], anti_aliasing=[True, False]), RandSpatialCropd(keys=['img', 'seg'], roi_size=[96, 96, 1], random_size=False), RandRotated(keys=['img', 'seg'], degrees=90, prob=0.2, spatial_axes=[0, 1], interp_order=[1, 0], reshape=False), RandFlipd(keys=['img', 'seg'], spatial_axis=[0, 1]), SqueezeDimd(keys=['img', 'seg'], dim=-1), ToTensord(keys=['img', 'seg']) ]) # create a training data loader # train_ds = monai.data.Dataset(data=train_files, transform=train_transforms) # train_ds = monai.data.CacheDataset(data=train_files, transform=train_transforms, cache_rate=1.0, # num_workers=num_workers) train_ds = monai.data.PersistentDataset(data=train_files, transform=train_transforms, cache_dir=persistent_cache) train_loader = DataLoader(train_ds, batch_size=batch_size_train, shuffle=True, num_workers=num_workers, collate_fn=list_data_collate, pin_memory=torch.cuda.is_available()) # check_train_data = monai.utils.misc.first(train_loader) # print("Training data tensor shapes") # print(check_train_data['img'].shape, check_train_data['seg'].shape) # data preprocessing for validation: # - convert data to right format [batch, channel, dim, dim, dim] # - apply whitening # - resize to (96, 96) in-plane (preserve z-direction) if sliding_window_validation: val_transforms = Compose([ LoadNiftid(keys=['img', 'seg']), AddChanneld(keys=['img', 'seg']), NormalizeIntensityd(keys=['img']), Resized(keys=['img', 'seg'], spatial_size=[96, 96], interp_order=[1, 0], anti_aliasing=[True, False]), ToTensord(keys=['img', 'seg']) ]) do_shuffle = False collate_fn_to_use = None else: # - add extraction of 2D slices from validation set to emulate how loss is computed at training val_transforms = Compose([ LoadNiftid(keys=['img', 'seg']), AddChanneld(keys=['img', 'seg']), NormalizeIntensityd(keys=['img']), Resized(keys=['img', 'seg'], spatial_size=[96, 96], interp_order=[1, 0], anti_aliasing=[True, False]), RandSpatialCropd(keys=['img', 'seg'], roi_size=[96, 96, 1], random_size=False), SqueezeDimd(keys=['img', 'seg'], dim=-1), ToTensord(keys=['img', 'seg']) ]) do_shuffle = True collate_fn_to_use = list_data_collate # create a validation data loader # val_ds = monai.data.Dataset(data=val_files, transform=val_transforms) # val_ds = monai.data.CacheDataset(data=val_files, transform=val_transforms, cache_rate=1.0, # num_workers=num_workers) val_ds = monai.data.PersistentDataset(data=val_files, transform=val_transforms, cache_dir=persistent_cache) val_loader = DataLoader(val_ds, batch_size=batch_size_valid, shuffle=do_shuffle, collate_fn=collate_fn_to_use, num_workers=num_workers) # check_valid_data = monai.utils.misc.first(val_loader) # print("Validation data tensor shapes") # print(check_valid_data['img'].shape, check_valid_data['seg'].shape) """ Network preparation """ # Create UNet, DiceLoss and Adam optimizer. net = monai.networks.nets.UNet( dimensions=2, in_channels=1, out_channels=1, channels=(16, 32, 64, 128, 256), strides=(2, 2, 2, 2), num_res_units=2, ) loss_function = monai.losses.DiceLoss(do_sigmoid=True) opt = torch.optim.Adam(net.parameters(), lr) device = torch.cuda.current_device() if lr_decay is not None: lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer=opt, gamma=lr_decay, last_epoch=-1) """ Set ignite trainer """ # function to manage batch at training def prepare_batch(batch, device=None, non_blocking=False): return _prepare_batch((batch['img'], batch['seg']), device, non_blocking) trainer = create_supervised_trainer(model=net, optimizer=opt, loss_fn=loss_function, device=device, non_blocking=False, prepare_batch=prepare_batch) # adding checkpoint handler to save models (network params and optimizer stats) during training if model_to_load is not None: checkpoint_handler = CheckpointLoader(load_path=model_to_load, load_dict={ 'net': net, 'opt': opt, }) checkpoint_handler.attach(trainer) state = trainer.state_dict() else: checkpoint_handler = ModelCheckpoint(out_model_dir, 'net', n_saved=max_nr_models_saved, require_empty=False) # trainer.add_event_handler(event_name=Events.EPOCH_COMPLETED, handler=save_params) trainer.add_event_handler(event_name=Events.EPOCH_COMPLETED, handler=checkpoint_handler, to_save={ 'net': net, 'opt': opt }) # StatsHandler prints loss at every iteration and print metrics at every epoch train_stats_handler = StatsHandler(name='trainer') train_stats_handler.attach(trainer) # TensorBoardStatsHandler plots loss at every iteration and plots metrics at every epoch, same as StatsHandler writer_train = SummaryWriter(log_dir=os.path.join(out_model_dir, "train")) train_tensorboard_stats_handler = TensorBoardStatsHandler( summary_writer=writer_train) train_tensorboard_stats_handler.attach(trainer) if lr_decay is not None: print("Using Exponential LR decay") lr_schedule_handler = LrScheduleHandler(lr_scheduler, print_lr=True, name="lr_scheduler", writer=writer_train) lr_schedule_handler.attach(trainer) """ Set ignite evaluator to perform validation at training """ # set parameters for validation metric_name = 'Mean_Dice' # add evaluation metric to the evaluator engine val_metrics = { "Loss": 1.0 - MeanDice(add_sigmoid=True, to_onehot_y=False), "Mean_Dice": MeanDice(add_sigmoid=True, to_onehot_y=False) } def _sliding_window_processor(engine, batch): net.eval() with torch.no_grad(): val_images, val_labels = batch['img'].to(device), batch['seg'].to( device) roi_size = (96, 96, 1) seg_probs = sliding_window_inference(val_images, roi_size, batch_size_valid, net) return seg_probs, val_labels if sliding_window_validation: # use sliding window inference at validation print("3D evaluator is used") net.to(device) evaluator = Engine(_sliding_window_processor) for name, metric in val_metrics.items(): metric.attach(evaluator, name) else: # ignite evaluator expects batch=(img, seg) and returns output=(y_pred, y) at every iteration, # user can add output_transform to return other values print("2D evaluator is used") evaluator = create_supervised_evaluator(model=net, metrics=val_metrics, device=device, non_blocking=True, prepare_batch=prepare_batch) epoch_len = len(train_ds) // train_loader.batch_size validation_every_n_iters = validation_every_n_epochs * epoch_len @trainer.on(Events.ITERATION_COMPLETED(every=validation_every_n_iters)) def run_validation(engine): evaluator.run(val_loader) # add early stopping handler to evaluator # early_stopper = EarlyStopping(patience=4, # score_function=stopping_fn_from_metric(metric_name), # trainer=trainer) # evaluator.add_event_handler(event_name=Events.EPOCH_COMPLETED, handler=early_stopper) # add stats event handler to print validation stats via evaluator val_stats_handler = StatsHandler( name='evaluator', output_transform=lambda x: None, # no need to print loss value, so disable per iteration output global_epoch_transform=lambda x: trainer.state.epoch ) # fetch global epoch number from trainer val_stats_handler.attach(evaluator) # add handler to record metrics to TensorBoard at every validation epoch writer_valid = SummaryWriter(log_dir=os.path.join(out_model_dir, "valid")) val_tensorboard_stats_handler = TensorBoardStatsHandler( summary_writer=writer_valid, output_transform=lambda x: None, # no need to plot loss value, so disable per iteration output global_epoch_transform=lambda x: trainer.state.iteration ) # fetch global iteration number from trainer val_tensorboard_stats_handler.attach(evaluator) # add handler to draw the first image and the corresponding label and model output in the last batch # here we draw the 3D output as GIF format along the depth axis, every 2 validation iterations. if val_image_to_tensorboad: val_tensorboard_image_handler = TensorBoardImageHandler( summary_writer=writer_valid, batch_transform=lambda batch: (batch['img'], batch['seg']), output_transform=lambda output: predict_segmentation(output[0]), global_iter_transform=lambda x: trainer.state.epoch) evaluator.add_event_handler( event_name=Events.ITERATION_COMPLETED(every=1), handler=val_tensorboard_image_handler) """ Run training """ state = trainer.run(train_loader, nr_train_epochs) print("Done!")
# add stats event handler to print validation stats via evaluator val_stats_handler = StatsHandler( name='evaluator', output_transform=lambda x: None, # no need to print loss value, so disable per iteration output global_epoch_transform=lambda x: trainer.state.epoch ) # fetch global epoch number from trainer val_stats_handler.attach(evaluator) # add handler to record metrics to TensorBoard at every validation epoch val_tensorboard_stats_handler = TensorBoardStatsHandler( output_transform=lambda x: None, # no need to plot loss value, so disable per iteration output global_epoch_transform=lambda x: trainer.state.iteration ) # fetch global iteration number from trainer val_tensorboard_stats_handler.attach(evaluator) # add handler to draw the first image and the corresponding label and model output in the last batch # here we draw the 3D output as GIF format along the depth axis, every 2 validation iterations. val_tensorboard_image_handler = TensorBoardImageHandler( batch_transform=lambda batch: (batch['img'], batch['seg']), output_transform=lambda output: predict_segmentation(output[0]), global_iter_transform=lambda x: trainer.state.epoch) evaluator.add_event_handler(event_name=Events.ITERATION_COMPLETED(every=2), handler=val_tensorboard_image_handler) train_epochs = 5 state = trainer.run(train_loader, train_epochs) shutil.rmtree(tempdir)
def run_training_test(root_dir, device="cuda:0", amp=False, num_workers=4): images = sorted(glob(os.path.join(root_dir, "img*.nii.gz"))) segs = sorted(glob(os.path.join(root_dir, "seg*.nii.gz"))) train_files = [{"image": img, "label": seg} for img, seg in zip(images[:20], segs[:20])] val_files = [{"image": img, "label": seg} for img, seg in zip(images[-20:], segs[-20:])] # define transforms for image and segmentation train_transforms = Compose( [ LoadImaged(keys=["image", "label"]), AsChannelFirstd(keys=["image", "label"], channel_dim=-1), ScaleIntensityd(keys=["image", "label"]), RandCropByPosNegLabeld( keys=["image", "label"], label_key="label", spatial_size=[96, 96, 96], pos=1, neg=1, num_samples=4 ), RandRotate90d(keys=["image", "label"], prob=0.5, spatial_axes=[0, 2]), ToTensord(keys=["image", "label"]), ] ) val_transforms = Compose( [ LoadImaged(keys=["image", "label"]), AsChannelFirstd(keys=["image", "label"], channel_dim=-1), ScaleIntensityd(keys=["image", "label"]), ToTensord(keys=["image", "label"]), ] ) # create a training data loader train_ds = monai.data.CacheDataset(data=train_files, transform=train_transforms, cache_rate=0.5) # use batch_size=2 to load images and use RandCropByPosNegLabeld to generate 2 x 4 images for network training train_loader = monai.data.DataLoader(train_ds, batch_size=2, shuffle=True, num_workers=num_workers) # create a validation data loader val_ds = monai.data.CacheDataset(data=val_files, transform=val_transforms, cache_rate=1.0) val_loader = monai.data.DataLoader(val_ds, batch_size=1, num_workers=num_workers) # create UNet, DiceLoss and Adam optimizer net = monai.networks.nets.UNet( spatial_dims=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 = monai.losses.DiceLoss(sigmoid=True) opt = torch.optim.Adam(net.parameters(), 1e-3) lr_scheduler = torch.optim.lr_scheduler.StepLR(opt, step_size=2, gamma=0.1) summary_writer = SummaryWriter(log_dir=root_dir) val_postprocessing = Compose( [ ToTensord(keys=["pred", "label"]), Activationsd(keys="pred", sigmoid=True), AsDiscreted(keys="pred", threshold=0.5), KeepLargestConnectedComponentd(keys="pred", applied_labels=[1]), ] ) class _TestEvalIterEvents: def attach(self, engine): engine.add_event_handler(IterationEvents.FORWARD_COMPLETED, self._forward_completed) def _forward_completed(self, engine): pass val_handlers = [ StatsHandler(iteration_log=False), TensorBoardStatsHandler(summary_writer=summary_writer, iteration_log=False), TensorBoardImageHandler( log_dir=root_dir, batch_transform=from_engine(["image", "label"]), output_transform=from_engine("pred") ), CheckpointSaver(save_dir=root_dir, save_dict={"net": net}, save_key_metric=True), _TestEvalIterEvents(), ] evaluator = SupervisedEvaluator( device=device, val_data_loader=val_loader, network=net, inferer=SlidingWindowInferer(roi_size=(96, 96, 96), sw_batch_size=4, overlap=0.5), postprocessing=val_postprocessing, key_val_metric={ "val_mean_dice": MeanDice(include_background=True, output_transform=from_engine(["pred", "label"])) }, additional_metrics={"val_acc": Accuracy(output_transform=from_engine(["pred", "label"]))}, metric_cmp_fn=lambda cur, prev: cur >= prev, # if greater or equal, treat as new best metric val_handlers=val_handlers, amp=bool(amp), to_kwargs={"memory_format": torch.preserve_format}, amp_kwargs={"dtype": torch.float16 if bool(amp) else torch.float32}, ) train_postprocessing = Compose( [ ToTensord(keys=["pred", "label"]), Activationsd(keys="pred", sigmoid=True), AsDiscreted(keys="pred", threshold=0.5), KeepLargestConnectedComponentd(keys="pred", applied_labels=[1]), ] ) class _TestTrainIterEvents: def attach(self, engine): engine.add_event_handler(IterationEvents.FORWARD_COMPLETED, self._forward_completed) engine.add_event_handler(IterationEvents.LOSS_COMPLETED, self._loss_completed) engine.add_event_handler(IterationEvents.BACKWARD_COMPLETED, self._backward_completed) engine.add_event_handler(IterationEvents.MODEL_COMPLETED, self._model_completed) def _forward_completed(self, engine): pass def _loss_completed(self, engine): pass def _backward_completed(self, engine): pass def _model_completed(self, engine): pass train_handlers = [ LrScheduleHandler(lr_scheduler=lr_scheduler, print_lr=True), ValidationHandler(validator=evaluator, interval=2, epoch_level=True), StatsHandler(tag_name="train_loss", output_transform=from_engine("loss", first=True)), TensorBoardStatsHandler( summary_writer=summary_writer, tag_name="train_loss", output_transform=from_engine("loss", first=True) ), CheckpointSaver(save_dir=root_dir, save_dict={"net": net, "opt": opt}, save_interval=2, epoch_level=True), _TestTrainIterEvents(), ] trainer = SupervisedTrainer( device=device, max_epochs=5, train_data_loader=train_loader, network=net, optimizer=opt, loss_function=loss, inferer=SimpleInferer(), postprocessing=train_postprocessing, key_train_metric={"train_acc": Accuracy(output_transform=from_engine(["pred", "label"]))}, train_handlers=train_handlers, amp=bool(amp), optim_set_to_none=True, to_kwargs={"memory_format": torch.preserve_format}, amp_kwargs={"dtype": torch.float16 if bool(amp) else torch.float32}, ) trainer.run() return evaluator.state.best_metric
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), EnsureType(), ]) train_segtrans = Compose([ AddChannel(), RandSpatialCrop((96, 96, 96), random_size=False), EnsureType() ]) val_imtrans = Compose( [ScaleIntensity(), AddChannel(), Resize((96, 96, 96)), EnsureType()]) val_segtrans = Compose([AddChannel(), Resize((96, 96, 96)), EnsureType()]) # define image dataset, data loader check_ds = ImageDataset(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 = ImageDataset(images[:20], segs[:20], transform=train_imtrans, seg_transform=train_segtrans) train_loader = DataLoader( train_ds, batch_size=5, shuffle=True, num_workers=8, pin_memory=torch.cuda.is_available(), ) # create a validation data loader val_ds = ImageDataset(images[-20:], segs[-20:], transform=val_imtrans, seg_transform=val_segtrans) val_loader = DataLoader(val_ds, batch_size=5, num_workers=8, pin_memory=torch.cuda.is_available()) # create UNet, DiceLoss and Adam optimizer device = torch.device("cuda" if torch.cuda.is_available() else "cpu") net = monai.networks.nets.UNet( spatial_dims=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 = monai.losses.DiceLoss(sigmoid=True) lr = 1e-3 opt = torch.optim.Adam(net.parameters(), lr) # Ignite trainer expects batch=(img, seg) and returns output=loss at every iteration, # user can add output_transform to return other values, like: y_pred, y, etc. trainer = create_supervised_trainer(net, opt, loss, device, False) # adding checkpoint handler to save models (network params and optimizer stats) during training checkpoint_handler = ModelCheckpoint("./runs_array/", "net", n_saved=10, require_empty=False) trainer.add_event_handler( event_name=Events.EPOCH_COMPLETED, handler=checkpoint_handler, to_save={ "net": net, "opt": opt }, ) # StatsHandler prints loss at every iteration and print metrics at every epoch, # we don't set metrics for trainer here, so just print loss, user can also customize print functions # and can use output_transform to convert engine.state.output if it's not a loss value train_stats_handler = StatsHandler(name="trainer", output_transform=lambda x: x) train_stats_handler.attach(trainer) # TensorBoardStatsHandler plots loss at every iteration and plots metrics at every epoch, same as StatsHandler train_tensorboard_stats_handler = TensorBoardStatsHandler( output_transform=lambda x: x) train_tensorboard_stats_handler.attach(trainer) validation_every_n_epochs = 1 # Set parameters for validation metric_name = "Mean_Dice" # add evaluation metric to the evaluator engine val_metrics = {metric_name: MeanDice()} post_pred = Compose( [EnsureType(), Activations(sigmoid=True), AsDiscrete(threshold=0.5)]) post_label = Compose([EnsureType(), AsDiscrete(threshold=0.5)]) # Ignite evaluator expects batch=(img, seg) and returns output=(y_pred, y) at every iteration, # user can add output_transform to return other values evaluator = create_supervised_evaluator( net, val_metrics, device, True, output_transform=lambda x, y, y_pred: ([post_pred(i) for i in decollate_batch(y_pred)], [post_label(i) for i in decollate_batch(y)]), ) @trainer.on(Events.EPOCH_COMPLETED(every=validation_every_n_epochs)) def run_validation(engine): evaluator.run(val_loader) # add early stopping handler to evaluator early_stopper = EarlyStopping( patience=4, score_function=stopping_fn_from_metric(metric_name), trainer=trainer) evaluator.add_event_handler(event_name=Events.EPOCH_COMPLETED, handler=early_stopper) # add stats event handler to print validation stats via evaluator val_stats_handler = StatsHandler( name="evaluator", output_transform=lambda x: None, # no need to print loss value, so disable per iteration output global_epoch_transform=lambda x: trainer.state.epoch, ) # fetch global epoch number from trainer val_stats_handler.attach(evaluator) # add handler to record metrics to TensorBoard at every validation epoch val_tensorboard_stats_handler = TensorBoardStatsHandler( output_transform=lambda x: None, # no need to plot loss value, so disable per iteration output global_epoch_transform=lambda x: trainer.state.epoch, ) # fetch global epoch number from trainer val_tensorboard_stats_handler.attach(evaluator) # add handler to draw the first image and the corresponding label and model output in the last batch # here we draw the 3D output as GIF format along Depth axis, at every validation epoch val_tensorboard_image_handler = TensorBoardImageHandler( batch_transform=lambda batch: (batch[0], batch[1]), output_transform=lambda output: output[0], global_iter_transform=lambda x: trainer.state.epoch, ) evaluator.add_event_handler(event_name=Events.EPOCH_COMPLETED, handler=val_tensorboard_image_handler) train_epochs = 30 state = trainer.run(train_loader, train_epochs) print(state)