From 5e53bcba64bd5b792d0776ad9c2b299d44b5005a Mon Sep 17 00:00:00 2001 From: am Date: Sun, 31 Oct 2021 23:01:43 -0700 Subject: [PATCH 01/18] MIL example Signed-off-by: myron --- .../panda_mil_train_evaluate_pytorch_gpu.py | 678 ++++++++++++++++++ 1 file changed, 678 insertions(+) create mode 100644 pathology/multiple_instance_learning/panda_mil_train_evaluate_pytorch_gpu.py diff --git a/pathology/multiple_instance_learning/panda_mil_train_evaluate_pytorch_gpu.py b/pathology/multiple_instance_learning/panda_mil_train_evaluate_pytorch_gpu.py new file mode 100644 index 0000000000..0ceed035e4 --- /dev/null +++ b/pathology/multiple_instance_learning/panda_mil_train_evaluate_pytorch_gpu.py @@ -0,0 +1,678 @@ + +#dependencies to load .tiff WSI image +#pip install tifffile imagecodecs + +import collections.abc +import os +import json +import time +import shutil +import argparse + +import numpy as np +from numpy.lib.stride_tricks import as_strided +from sklearn.metrics import cohen_kappa_score + +import torch +import torch.nn as nn +# import torch.nn.functional as F + +from torch.utils.tensorboard import SummaryWriter +from torch.cuda.amp import GradScaler, autocast +from torch.utils.data.distributed import DistributedSampler + +import torch.nn.parallel +import torch.distributed as dist +import torch.multiprocessing as mp + +import tifffile #pip install it +from monai import transforms, data +from monai.data.image_reader import WSIReader + +from monai.data.image_reader import WSIReader +from monai.networks.nets import MilMode, MILModel #to be added +from monai.apps.pathology.transforms import TileOnGridd #to be added + +def parse_args(): + + parser = argparse.ArgumentParser(description='MIL example') + + parser.add_argument('--num_classes', default=5, type=int) + parser.add_argument('--max_tiles', default=44, type=int) + parser.add_argument('--tile_size', default=256, type=int) + parser.add_argument('--data_dir', default='/PandaChallenge2020/train_images/') + + parser.add_argument('--checkpoint', default=None) + parser.add_argument('--logdir', default=None) + parser.add_argument('--epochs', default=50, type=int) + parser.add_argument('--batch_size', default=1, type=int) + parser.add_argument('--fold', default=0, type=int) + # parser.add_argument('--checkpoint_fromscratch', action='store_true') + + parser.add_argument('--optim_lr', default=3e-5, type=float) + parser.add_argument('--optim_trans_lr', default=None, type=float) + + parser.add_argument('--quick', action='store_true') #distributed multi gpu + parser.add_argument('--amp', action='store_true') #experimental + parser.add_argument('--val_every', default=1, type=int) + + parser.add_argument('--mil_mode', default='att') + parser.add_argument('--reg_weight', default=0, type=float) + parser.add_argument('--optim_trans_reg_weight', default=0, type=float) + parser.add_argument('--validation_only', action='store_true') + + ###for multigpu + parser.add_argument('--distributed', action='store_true') #distributed multi gpu + parser.add_argument('--world_size', default=1, type=int, help='number of nodes for distributed training') + parser.add_argument('--rank', default=0, type=int, help='node rank for distributed training') + parser.add_argument('--dist-url', default='tcp://127.0.0.1:23456', type=str, help='url used to set up distributed training') + parser.add_argument('--dist-backend', default='nccl', type=str, help='distributed backend') + parser.add_argument('--workers', default=2, type=int) + + parser.add_argument('--tf', action='store_true') + parser.add_argument('--normback', action='store_true') + parser.add_argument('--collate', action='store_true') + + + args = parser.parse_args() + + print("Argument values:") + for k, v in vars(args).items(): + print(k, '=>', v) + print('-----------------') + + return args + + + +#can be removed +class NormalizeBackground(): + + def __init__( self, keys=['image'], tile_background_val=255): + super().__init__() + self.keys = keys + self.tile_background_val=tile_background_val + + def __call__(self, data): + + if self.tile_background_val >0: + d = dict(data) + + for key in self.keys: + img = d[key] + # print('NormalizeBackground', img.shape, img.dtype) + d[key] = (self.tile_background_val - img).astype(np.float32) / float(self.tile_background_val) + return d + +class AverageMeter(): + """Computes and stores the average and current value""" + def __init__(self): + self.reset() + + def reset(self): + self.val = 0 + self.avg = 0 + self.sum = 0 + self.count = 0 + + def update(self, val, n=1): + + if not np.isfinite(np.sum(val)): + val = 0 + + if n > 0: + self.val = val + self.sum += val * n + + self.count += n + self.avg = np.where(self.count > 0, self.sum / self.count, self.sum) + + +def distributed_all_gather(tensor_list, out_numpy=False): + """gathers values in tensor_list from all gpus""" + world_size = torch.distributed.get_world_size() + + tensor_list_out = [] + for tensor in tensor_list: + gather_list = [torch.zeros_like(tensor) for _ in range(world_size)] + torch.distributed.all_gather(gather_list, tensor) + + if out_numpy: + gather_list = [t.cpu().numpy() for t in gather_list] #convert to numpy + + tensor_list_out.append(gather_list) + + return tensor_list_out + + +def train_epoch(model, loader, optimizer, scaler, epoch, args): + '''One train epoch over the dataset''' + + model.train() + criterion = nn.BCEWithLogitsLoss() + + run_loss = AverageMeter() + run_acc = AverageMeter() + + start_time = time.time() + + for idx, batch_data in enumerate(loader): + + data, target = batch_data['image'].cuda(args.rank), batch_data['label'].cuda(args.rank) + + for param in model.parameters(): + param.grad = None + + with autocast(enabled=args.amp): + logits = model(data) + loss = criterion(logits, target) + + if args.amp: + scaler.scale(loss).backward() + scaler.step(optimizer) + scaler.update() + else: + loss.backward() + optimizer.step() + + + acc = (logits.sigmoid().sum(1).detach().round() == target.sum(1).round()).float().mean() + + if args.distributed: + loss_list, acc_list = distributed_all_gather([loss, acc], out_numpy=True) + loss = np.mean(np.stack(loss_list, axis=0)) + acc = np.mean(np.stack(acc_list, axis=0), axis=0) + else: + loss = loss.item() + acc = acc.cpu().numpy() + + + run_loss.update(loss, n=args.batch_size * args.world_size) + run_acc.update(acc, n=args.batch_size * args.world_size) + + if args.rank==0: + print('Epoch {}/{} {}/{}'.format(epoch, args.epochs, idx, len(loader)), + 'loss: {:.4f}'.format(run_loss.avg), + 'acc: {:.4f}'.format(run_acc.avg), + 'time {:.2f}s'.format(time.time() - start_time)) + start_time = time.time() + + return run_loss.avg, run_acc.avg + + +def val_epoch(model, loader, epoch, args, max_tiles=None): + '''One validation epoch over the dataset''' + + model.eval() + + model2 = model if not args.distributed else model.module + has_extra_outputs = (model2.mil_mode=='att_trans_pyramid') + extra_outputs = model2.extra_outputs + calc_head = model2.calc_head + + + criterion = nn.BCEWithLogitsLoss() + + run_loss = AverageMeter() + run_acc = AverageMeter() + start_time = time.time() + + + PREDS = [] + TARGETS = [] + + with torch.no_grad(): + + for idx, batch_data in enumerate(loader): + + data, target = batch_data['image'].cuda(args.rank), batch_data['label'].cuda(args.rank) + + with autocast(enabled=args.amp): + + if max_tiles is not None and data.shape[1] > max_tiles: + + # print('data is too large', data.shape, 'max_tiles', max_tiles) + logits = [] + logits2 = [] + sh = data.shape + + for i in range(int(np.ceil(data.shape[1]/float(max_tiles)))): + data_slize = data[:,i*max_tiles:(i+1)*max_tiles] + # print('taking slice0', i, data_slize.shape) + logits_slize = model(data_slize, no_head=True) + # print('taking slice', i, data_slize.shape, logits_slize.shape) + logits.append(logits_slize) + + if has_extra_outputs: + logits2.append([extra_outputs['layer1'], extra_outputs['layer2'], extra_outputs['layer3'], extra_outputs['layer4']]) + + + logits = torch.cat(logits, dim=1) + # print('combined slice logits', logits.shape) + if has_extra_outputs: + extra_outputs['layer1'] = torch.cat([l[0] for l in logits2], dim=1) + extra_outputs['layer2'] = torch.cat([l[1] for l in logits2], dim=1) + extra_outputs['layer3'] = torch.cat([l[2] for l in logits2], dim=1) + extra_outputs['layer4'] = torch.cat([l[3] for l in logits2], dim=1) + + logits = calc_head(logits) + # print('final slice logits', logits.shape) + + else: #normal + logits = model(data) + + + loss = criterion(logits, target) + + + pred = logits.sigmoid().sum(1).detach().round() + target = target.sum(1).round() + acc = (pred == target).float().mean() + + if args.distributed: + loss_list, acc_list, pred_list, target_list = distributed_all_gather([loss, acc, pred, target], out_numpy=True) + loss = np.mean(np.stack(loss_list, axis=0)) + acc = np.mean(np.stack(acc_list, axis=0), axis=0) + + PREDS.extend(pred_list) + TARGETS.extend(target_list) + + else: + loss = loss.item() + acc = acc.cpu().numpy() + + PREDS.append(pred.cpu().numpy()) + TARGETS.append(target.cpu().numpy()) + + + run_loss.update(loss, n=args.batch_size * args.world_size) + run_acc.update(acc, n=args.batch_size * args.world_size) + + if args.rank==0: + print('Val epoch {}/{} {}/{}'.format(epoch, args.epochs, idx, len(loader)), + 'loss: {:.4f}'.format(run_loss.avg), + 'acc: {:.4f}'.format(run_acc.avg), + 'time {:.2f}s'.format(time.time() - start_time)) + start_time = time.time() + + + #Calculate QWK metric (Quadratic Weigted Kappa) + #https://en.wikipedia.org/wiki/Cohen%27s_kappa + + PREDS = np.concatenate(PREDS) + TARGETS = np.concatenate(TARGETS) + qwk = cohen_kappa_score(PREDS.astype(np.float64), TARGETS.astype(np.float64), weights='quadratic') + + + return run_loss.avg, run_acc.avg, qwk + + +def save_checkpoint(model, epoch, args, filename='model.tar', best_acc=0): + '''Save checkpoint''' + + state_dict = model.state_dict() if not args.distributed else model.module.state_dict() + + save_dict = { + 'epoch': epoch, + 'best_acc': best_acc, + 'state_dict': state_dict + } + + filename=os.path.join(args.logdir, filename) + torch.save(save_dict, filename) + print('Saving checkpoint', filename) + + + + +### can be removed when WSIReader supports tiffile +class LoadTiffd(): + """ + Load TIFF image + + Args: + keys: keys of the corresponding items to be transformed + Defaults to ``['image']``. + level: page index to read from multi-page TIFF file, where 0 correspond to highest resolution + Defaults to ``1``. + + """ + def __init__( self, keys=['image'], level=1) : + super().__init__() + self.keys = keys + self.level=level + + + def __call__(self, data): + + if isinstance(data, str): + data = json.loads(data) + + d = dict(data) + for key in self.keys: + image_path = d[key] + + image = tifffile.imread(image_path, key=self.level) + image = image.transpose(2,0,1) #convert to 3xWxH + + d[key] = image + + return d + +class LabelEncodeIntegerGraded(transforms.Transform): + """ + Convert to integer label to encoded array representation of length num_classes, + with 1 filled in up to label index, and 0 otherwise. For example for num_classes=5, + embedding of 2 -> (1,1,0,0,0) + + Args: + num_classes: the number of classes to convert to encoded format. + keys: keys of the corresponding items to be transformed + Defaults to ``['label']``. + + """ + def __init__( self, num_classes, keys=['label']) : + super().__init__() + self.keys = keys + self.num_classes=num_classes + + def __call__(self, data): + + d = dict(data) + + for key in self.keys: + label = int(d[key]) + + lz = np.zeros(self.num_classes, dtype=np.float32) + lz[:label] = 1. + # lz=(np.arange(self.num_classes) self.p: img = np.flip(img, axis=1) + if np.random.random() > self.p: img = np.flip(img, axis=2) + if np.random.random() > self.p: img = np.transpose(img, axes=(0,2,1)) + images2.append(img) + + d[key] = np.stack(images2, axis=0) + + return d + + +def main(): + + args = parse_args() + args.mil_mode=MilMode(args.mil_mode) + + if args.distributed: + ngpus_per_node = torch.cuda.device_count() + args.optim_lr = ngpus_per_node * args.optim_lr / 2 #heuristic to scale up learning rate in multigpu setup + args.world_size = ngpus_per_node * args.world_size + + print('Multi-gpu', ngpus_per_node, 'rescaled lr', args.optim_lr) + mp.spawn(main_worker, nprocs=ngpus_per_node, args=(args,)) + else: + # Simply call main_worker function + main_worker(0, args) + +from torch.utils.data.dataloader import default_collate +def list_data_collate(batch: collections.abc.Sequence): + for i, item in enumerate(batch): + data = item[0] + data["image"] = torch.stack([ix["image"] for ix in item], dim=0) + batch[i] = data + return default_collate(batch) + + +def main_worker(gpu, args): + + args.gpu = gpu + + + if args.distributed: + args.rank = args.rank * torch.cuda.device_count() + gpu + dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url, world_size=args.world_size, rank=args.rank) + + print(args.rank, ' gpu', args.gpu) + + torch.cuda.set_device(args.gpu) #use this default device (same as args.device if not distributed) + torch.backends.cudnn.benchmark = True + + if args.rank==0: + print('Batch size is:', args.batch_size, 'epochs', args.epochs) + + ############# + + with open('panda_training_fold0.json') as json_file: + training_list = json.load(json_file) + for d in training_list: + d['image'] = os.path.join(args.data_dir, d['image']) + + with open('panda_validation_fold0.json') as json_file: + validation_list = json.load(json_file) + for d in validation_list: + d['image'] = os.path.join(args.data_dir, d['image']) + + + load_image_transform = LoadTiffd(keys=["image"]) if args.tf else transforms.LoadImageD(keys=["image"], reader=WSIReader, backend="cuCIM", dtype=np.uint8, level=1) + normalize_image_transform = NormalizeBackground() if args.normback else transforms.ScaleIntensityRangeD(keys=["image"], a_min=np.float32(255), a_max=np.float32(0), b_min=np.float32(0), b_max=np.float32(1), clip=False) + + + if args.collate: + train_transform = transforms.Compose( + [ + load_image_transform, + LabelEncodeIntegerGraded(keys=["label"], num_classes=args.num_classes), #encode label + # transforms.Lambdad(keys=["label"], func=lambda x: (np.arange(num_classes) loaded checkpoint '{}' (epoch {}) (bestacc {})".format(args.checkpoint, start_epoch, best_acc)) + + + model.cuda(args.gpu) + + if args.distributed: + model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model) + model.cuda(args.gpu) + model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], output_device=args.gpu) + + params = model.parameters() + + if args.mil_mode in [MilMode.ATT_TRANS, MilMode.ATT_TRANS_PYRAMID]: + m = model if not args.distributed else model.module + params = [{'params': list(m.attention.parameters()) + list(m.myfc.parameters()) + list(m.net.parameters())}, + {'params': list(m.transformer.parameters()), 'lr': 3e-6, 'weight_decay': 0.1 }] + + + optimizer = torch.optim.AdamW(params, lr=args.optim_lr, weight_decay = args.reg_weight) + scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=args.epochs, eta_min=0) + + + + if args.logdir is not None and args.rank == 0: + writer = SummaryWriter(log_dir=args.logdir) + if args.rank==0: print('Writing Tensorboard logs to ', writer.log_dir) + else: + writer = None + + if args.validation_only: + epoch_time = time.time() + val_loss, val_acc, qwk = val_epoch(model, valid_loader, epoch=0, args=args, max_tiles=args.max_tiles) + if args.rank == 0: + print('Final validation loss: {:.4f}'.format(val_loss), 'acc: {:.4f}'.format(val_acc), 'qwk: {:.4f}'.format(qwk), 'time {:.2f}s'.format(time.time() - epoch_time)) + + exit(0) + + + ###RUN TRAINING + n_epochs = args.epochs + val_acc_max = 0. + + scaler = None + if args.amp: # new native amp + scaler = GradScaler() + + + for epoch in range(start_epoch, n_epochs): + + if args.distributed: + train_sampler.set_epoch(epoch) + torch.distributed.barrier() #synch processess + + print(args.rank, time.ctime(), 'Epoch:', epoch) + + epoch_time = time.time() + train_loss, train_acc = train_epoch(model, train_loader, optimizer, scaler=scaler, epoch=epoch, args=args) + + if args.rank == 0: + print('Final training {}/{}'.format(epoch, n_epochs - 1), 'loss: {:.4f}'.format(train_loss), + 'acc: {:.4f}'.format(train_acc), 'time {:.2f}s'.format(time.time() - epoch_time)) + + if args.rank==0 and writer is not None: + writer.add_scalar('train_loss', train_loss, epoch) + writer.add_scalar('train_acc', train_acc, epoch) + + + if args.distributed: + torch.distributed.barrier() #synch processess + + + b_new_best=False + val_acc = 0 + if ( (epoch+1) % args.val_every == 0): + + epoch_time = time.time() + val_loss, val_acc, qwk = val_epoch(model, valid_loader, epoch=epoch, args=args, max_tiles=args.max_tiles) + if args.rank == 0: + print('Final validation {}/{}'.format(epoch, n_epochs - 1), 'loss: {:.4f}'.format(val_loss), + 'acc: {:.4f}'.format(val_acc),'qwk: {:.4f}'.format(qwk), 'time {:.2f}s'.format(time.time() - epoch_time)) + if writer is not None: + writer.add_scalar('val_loss', val_loss, epoch) + writer.add_scalar('val_acc', val_acc, epoch) + writer.add_scalar('val_qwk', qwk, epoch) + + val_acc = qwk + + if val_acc > val_acc_max: + print('score2 ({:.6f} --> {:.6f}). Saving model ...'.format(val_acc_max, val_acc)) + val_acc_max = val_acc + b_new_best = True + + + if args.rank == 0 and args.logdir is not None: + save_checkpoint(model, epoch, args, best_acc=val_acc, filename='model_final.tar') + if b_new_best: + print('Copying to model.tar new best model!!!!') + shutil.copyfile(os.path.join(args.logdir, 'model_final.tar'), os.path.join(args.logdir, 'model.tar')) + + scheduler.step() + + print('ALL DONE') + +if __name__ == '__main__': + main() \ No newline at end of file From 87346f11098f747ee2604bb6d7fc4659d2c8bf56 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 8 Nov 2021 07:13:04 +0000 Subject: [PATCH 02/18] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- .../panda_mil_train_evaluate_pytorch_gpu.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pathology/multiple_instance_learning/panda_mil_train_evaluate_pytorch_gpu.py b/pathology/multiple_instance_learning/panda_mil_train_evaluate_pytorch_gpu.py index 0ceed035e4..88a9384df8 100644 --- a/pathology/multiple_instance_learning/panda_mil_train_evaluate_pytorch_gpu.py +++ b/pathology/multiple_instance_learning/panda_mil_train_evaluate_pytorch_gpu.py @@ -675,4 +675,4 @@ def main_worker(gpu, args): print('ALL DONE') if __name__ == '__main__': - main() \ No newline at end of file + main() From 4e91b288ee80f2a4d583e520b2c238bd0c86514f Mon Sep 17 00:00:00 2001 From: myron Date: Sat, 13 Nov 2021 17:17:30 -0800 Subject: [PATCH 03/18] mil tutorial update Signed-off-by: myron --- .../panda_mil_train_evaluate_pytorch_gpu.py | 458 +++++------------- 1 file changed, 124 insertions(+), 334 deletions(-) diff --git a/pathology/multiple_instance_learning/panda_mil_train_evaluate_pytorch_gpu.py b/pathology/multiple_instance_learning/panda_mil_train_evaluate_pytorch_gpu.py index 88a9384df8..1da4ac56e8 100644 --- a/pathology/multiple_instance_learning/panda_mil_train_evaluate_pytorch_gpu.py +++ b/pathology/multiple_instance_learning/panda_mil_train_evaluate_pytorch_gpu.py @@ -1,13 +1,10 @@ - -#dependencies to load .tiff WSI image -#pip install tifffile imagecodecs - -import collections.abc import os -import json import time import shutil import argparse +import copy +import collections.abc +from typing import Dict, Hashable, Mapping, Sequence, Any, Union, Optional, List, cast import numpy as np from numpy.lib.stride_tricks import as_strided @@ -15,63 +12,55 @@ import torch import torch.nn as nn -# import torch.nn.functional as F - from torch.utils.tensorboard import SummaryWriter from torch.cuda.amp import GradScaler, autocast from torch.utils.data.distributed import DistributedSampler +from torch.utils.data.dataloader import default_collate import torch.nn.parallel import torch.distributed as dist import torch.multiprocessing as mp -import tifffile #pip install it + from monai import transforms, data from monai.data.image_reader import WSIReader +from monai.metrics import CumulativeAverage, Cumulative +from monai.data import load_decathlon_datalist -from monai.data.image_reader import WSIReader -from monai.networks.nets import MilMode, MILModel #to be added -from monai.apps.pathology.transforms import TileOnGridd #to be added def parse_args(): - parser = argparse.ArgumentParser(description='MIL example') - - parser.add_argument('--num_classes', default=5, type=int) - parser.add_argument('--max_tiles', default=44, type=int) - parser.add_argument('--tile_size', default=256, type=int) - parser.add_argument('--data_dir', default='/PandaChallenge2020/train_images/') + parser = argparse.ArgumentParser(description='Multiple Instance Learning (MIL) example on WSI images.') + parser.add_argument('--data_root', default='/PandaChallenge2020/train_images/', help="path to root folder of images") + parser.add_argument('--dataset_json', default='./datalist_panda_0.json', help="path to dataset json file") - parser.add_argument('--checkpoint', default=None) - parser.add_argument('--logdir', default=None) - parser.add_argument('--epochs', default=50, type=int) - parser.add_argument('--batch_size', default=1, type=int) - parser.add_argument('--fold', default=0, type=int) - # parser.add_argument('--checkpoint_fromscratch', action='store_true') + parser.add_argument('--num_classes', default=5, type=int, help="number of output classes") + parser.add_argument('--mil_mode', default='att_trans', help="MIL algorithm") + parser.add_argument('--tile_count', default=44, type=int, help="number of patches (instances) to extract from WSI image") + parser.add_argument('--tile_size', default=256, type=int, help="size of square patch (instance) in pixels") - parser.add_argument('--optim_lr', default=3e-5, type=float) - parser.add_argument('--optim_trans_lr', default=None, type=float) + parser.add_argument('--checkpoint', default=None, help="load existing checkpoint, otherwise random intialization") + parser.add_argument('--validate', action='store_true', help="run only inference on the validation set, must specify the checkpoint argument") - parser.add_argument('--quick', action='store_true') #distributed multi gpu - parser.add_argument('--amp', action='store_true') #experimental - parser.add_argument('--val_every', default=1, type=int) + parser.add_argument('--logdir', default=None, help="path to log directory to store Tensorboard logs") - parser.add_argument('--mil_mode', default='att') - parser.add_argument('--reg_weight', default=0, type=float) - parser.add_argument('--optim_trans_reg_weight', default=0, type=float) - parser.add_argument('--validation_only', action='store_true') + parser.add_argument('--epochs', default=50, type=int, help="number of training epochs") + parser.add_argument('--batch_size', default=4, type=int, help="batch size, the number of WSI images per gpu") + parser.add_argument('--optim_lr', default=3e-5, type=float, help="initial learning rate") + parser.add_argument('--weight_decay', default=0, type=float, help="optimizer weight decay") + parser.add_argument('--amp', action='store_true', help="use AMP, recommended") + parser.add_argument('--val_every', default=1, type=int, help="run validation after this number of epochs, default 1 to run every epoch") + parser.add_argument('--workers', default=2, type=int, help="number of workers for data loading") ###for multigpu - parser.add_argument('--distributed', action='store_true') #distributed multi gpu - parser.add_argument('--world_size', default=1, type=int, help='number of nodes for distributed training') - parser.add_argument('--rank', default=0, type=int, help='node rank for distributed training') - parser.add_argument('--dist-url', default='tcp://127.0.0.1:23456', type=str, help='url used to set up distributed training') - parser.add_argument('--dist-backend', default='nccl', type=str, help='distributed backend') - parser.add_argument('--workers', default=2, type=int) + parser.add_argument('--distributed', action='store_true', help="use multigpu training, recommended") + parser.add_argument('--world_size', default=1, type=int, help="number of nodes for distributed training") + parser.add_argument('--rank', default=0, type=int, help="node rank for distributed training") + parser.add_argument('--dist-url', default='tcp://127.0.0.1:23456', type=str, help="url used to set up distributed training") + parser.add_argument('--dist-backend', default='nccl', type=str, help="distributed backend") - parser.add_argument('--tf', action='store_true') - parser.add_argument('--normback', action='store_true') - parser.add_argument('--collate', action='store_true') + + parser.add_argument('--quick', action='store_true', help="use a small subset of data for debugging") # for debugging args = parser.parse_args() @@ -84,77 +73,17 @@ def parse_args(): return args - -#can be removed -class NormalizeBackground(): - - def __init__( self, keys=['image'], tile_background_val=255): - super().__init__() - self.keys = keys - self.tile_background_val=tile_background_val - - def __call__(self, data): - - if self.tile_background_val >0: - d = dict(data) - - for key in self.keys: - img = d[key] - # print('NormalizeBackground', img.shape, img.dtype) - d[key] = (self.tile_background_val - img).astype(np.float32) / float(self.tile_background_val) - return d - -class AverageMeter(): - """Computes and stores the average and current value""" - def __init__(self): - self.reset() - - def reset(self): - self.val = 0 - self.avg = 0 - self.sum = 0 - self.count = 0 - - def update(self, val, n=1): - - if not np.isfinite(np.sum(val)): - val = 0 - - if n > 0: - self.val = val - self.sum += val * n - - self.count += n - self.avg = np.where(self.count > 0, self.sum / self.count, self.sum) - - -def distributed_all_gather(tensor_list, out_numpy=False): - """gathers values in tensor_list from all gpus""" - world_size = torch.distributed.get_world_size() - - tensor_list_out = [] - for tensor in tensor_list: - gather_list = [torch.zeros_like(tensor) for _ in range(world_size)] - torch.distributed.all_gather(gather_list, tensor) - - if out_numpy: - gather_list = [t.cpu().numpy() for t in gather_list] #convert to numpy - - tensor_list_out.append(gather_list) - - return tensor_list_out - - def train_epoch(model, loader, optimizer, scaler, epoch, args): '''One train epoch over the dataset''' model.train() criterion = nn.BCEWithLogitsLoss() - run_loss = AverageMeter() - run_acc = AverageMeter() + run_loss = CumulativeAverage() + run_acc = CumulativeAverage() start_time = time.time() + loss, acc = 0., 0. for idx, batch_data in enumerate(loader): @@ -178,26 +107,22 @@ def train_epoch(model, loader, optimizer, scaler, epoch, args): acc = (logits.sigmoid().sum(1).detach().round() == target.sum(1).round()).float().mean() - if args.distributed: - loss_list, acc_list = distributed_all_gather([loss, acc], out_numpy=True) - loss = np.mean(np.stack(loss_list, axis=0)) - acc = np.mean(np.stack(acc_list, axis=0), axis=0) - else: - loss = loss.item() - acc = acc.cpu().numpy() + run_loss.append(loss) + run_acc.append(acc) + + loss = run_loss.aggregate() + acc = run_acc.aggregate() - run_loss.update(loss, n=args.batch_size * args.world_size) - run_acc.update(acc, n=args.batch_size * args.world_size) if args.rank==0: print('Epoch {}/{} {}/{}'.format(epoch, args.epochs, idx, len(loader)), - 'loss: {:.4f}'.format(run_loss.avg), - 'acc: {:.4f}'.format(run_acc.avg), + 'loss: {:.4f}'.format(loss), + 'acc: {:.4f}'.format(acc), 'time {:.2f}s'.format(time.time() - start_time)) start_time = time.time() - return run_loss.avg, run_acc.avg + return loss, acc def val_epoch(model, loader, epoch, args, max_tiles=None): @@ -206,20 +131,20 @@ def val_epoch(model, loader, epoch, args, max_tiles=None): model.eval() model2 = model if not args.distributed else model.module - has_extra_outputs = (model2.mil_mode=='att_trans_pyramid') + has_extra_outputs = (model2.mil_mode=="att_trans_pyramid") extra_outputs = model2.extra_outputs calc_head = model2.calc_head - criterion = nn.BCEWithLogitsLoss() - run_loss = AverageMeter() - run_acc = AverageMeter() - start_time = time.time() + run_loss = CumulativeAverage() + run_acc = CumulativeAverage() + start_time = time.time() + PREDS = Cumulative() + TARGETS = Cumulative() - PREDS = [] - TARGETS = [] + loss, acc = 0., 0. with torch.no_grad(): @@ -231,16 +156,12 @@ def val_epoch(model, loader, epoch, args, max_tiles=None): if max_tiles is not None and data.shape[1] > max_tiles: - # print('data is too large', data.shape, 'max_tiles', max_tiles) logits = [] logits2 = [] - sh = data.shape for i in range(int(np.ceil(data.shape[1]/float(max_tiles)))): data_slize = data[:,i*max_tiles:(i+1)*max_tiles] - # print('taking slice0', i, data_slize.shape) logits_slize = model(data_slize, no_head=True) - # print('taking slice', i, data_slize.shape, logits_slize.shape) logits.append(logits_slize) if has_extra_outputs: @@ -248,17 +169,15 @@ def val_epoch(model, loader, epoch, args, max_tiles=None): logits = torch.cat(logits, dim=1) - # print('combined slice logits', logits.shape) if has_extra_outputs: - extra_outputs['layer1'] = torch.cat([l[0] for l in logits2], dim=1) - extra_outputs['layer2'] = torch.cat([l[1] for l in logits2], dim=1) - extra_outputs['layer3'] = torch.cat([l[2] for l in logits2], dim=1) - extra_outputs['layer4'] = torch.cat([l[3] for l in logits2], dim=1) + extra_outputs['layer1'] = torch.cat([l[0] for l in logits2], dim=0) + extra_outputs['layer2'] = torch.cat([l[1] for l in logits2], dim=0) + extra_outputs['layer3'] = torch.cat([l[2] for l in logits2], dim=0) + extra_outputs['layer4'] = torch.cat([l[3] for l in logits2], dim=0) logits = calc_head(logits) - # print('final slice logits', logits.shape) - else: #normal + else: # logits = model(data) @@ -269,42 +188,29 @@ def val_epoch(model, loader, epoch, args, max_tiles=None): target = target.sum(1).round() acc = (pred == target).float().mean() - if args.distributed: - loss_list, acc_list, pred_list, target_list = distributed_all_gather([loss, acc, pred, target], out_numpy=True) - loss = np.mean(np.stack(loss_list, axis=0)) - acc = np.mean(np.stack(acc_list, axis=0), axis=0) - - PREDS.extend(pred_list) - TARGETS.extend(target_list) + run_loss.append(loss) + run_acc.append(acc) + loss = run_loss.aggregate() + acc = run_acc.aggregate() - else: - loss = loss.item() - acc = acc.cpu().numpy() - - PREDS.append(pred.cpu().numpy()) - TARGETS.append(target.cpu().numpy()) - - - run_loss.update(loss, n=args.batch_size * args.world_size) - run_acc.update(acc, n=args.batch_size * args.world_size) + PREDS.extend(pred) + TARGETS.extend(target) if args.rank==0: print('Val epoch {}/{} {}/{}'.format(epoch, args.epochs, idx, len(loader)), - 'loss: {:.4f}'.format(run_loss.avg), - 'acc: {:.4f}'.format(run_acc.avg), + 'loss: {:.4f}'.format(loss), + 'acc: {:.4f}'.format(acc), 'time {:.2f}s'.format(time.time() - start_time)) start_time = time.time() - #Calculate QWK metric (Quadratic Weigted Kappa) - #https://en.wikipedia.org/wiki/Cohen%27s_kappa - - PREDS = np.concatenate(PREDS) - TARGETS = np.concatenate(TARGETS) + #Calculate QWK metric (Quadratic Weigted Kappa) https://en.wikipedia.org/wiki/Cohen%27s_kappa + PREDS = PREDS.get_buffer().cpu().numpy() + TARGETS = TARGETS.get_buffer().cpu().numpy() qwk = cohen_kappa_score(PREDS.astype(np.float64), TARGETS.astype(np.float64), weights='quadratic') - return run_loss.avg, run_acc.avg, qwk + return loss, acc, qwk def save_checkpoint(model, epoch, args, filename='model.tar', best_acc=0): @@ -323,47 +229,11 @@ def save_checkpoint(model, epoch, args, filename='model.tar', best_acc=0): print('Saving checkpoint', filename) - - -### can be removed when WSIReader supports tiffile -class LoadTiffd(): - """ - Load TIFF image - - Args: - keys: keys of the corresponding items to be transformed - Defaults to ``['image']``. - level: page index to read from multi-page TIFF file, where 0 correspond to highest resolution - Defaults to ``1``. - - """ - def __init__( self, keys=['image'], level=1) : - super().__init__() - self.keys = keys - self.level=level - - - def __call__(self, data): - - if isinstance(data, str): - data = json.loads(data) - - d = dict(data) - for key in self.keys: - image_path = d[key] - - image = tifffile.imread(image_path, key=self.level) - image = image.transpose(2,0,1) #convert to 3xWxH - - d[key] = image - - return d - class LabelEncodeIntegerGraded(transforms.Transform): """ Convert to integer label to encoded array representation of length num_classes, - with 1 filled in up to label index, and 0 otherwise. For example for num_classes=5, - embedding of 2 -> (1,1,0,0,0) + with 1 filled in up to label index, and 0 otherwise. For example for num_classes=5, + embedding of 2 -> (1,1,0,0,0) Args: num_classes: the number of classes to convert to encoded format. @@ -379,75 +249,34 @@ def __init__( self, num_classes, keys=['label']) : def __call__(self, data): d = dict(data) - for key in self.keys: label = int(d[key]) lz = np.zeros(self.num_classes, dtype=np.float32) lz[:label] = 1. - # lz=(np.arange(self.num_classes) self.p: img = np.flip(img, axis=1) - if np.random.random() > self.p: img = np.flip(img, axis=2) - if np.random.random() > self.p: img = np.transpose(img, axes=(0,2,1)) - images2.append(img) - - d[key] = np.stack(images2, axis=0) - - return d - - def main(): args = parse_args() - args.mil_mode=MilMode(args.mil_mode) if args.distributed: ngpus_per_node = torch.cuda.device_count() args.optim_lr = ngpus_per_node * args.optim_lr / 2 #heuristic to scale up learning rate in multigpu setup args.world_size = ngpus_per_node * args.world_size - print('Multi-gpu', ngpus_per_node, 'rescaled lr', args.optim_lr) + print('Multigpu', ngpus_per_node, 'rescaled lr', args.optim_lr) mp.spawn(main_worker, nprocs=ngpus_per_node, args=(args,)) else: - # Simply call main_worker function main_worker(0, args) -from torch.utils.data.dataloader import default_collate +#combine instances from a list of dicts into a single dict, by stacking them along first dim +# [{'image' : 3xHxW}, {'image' : 3xHxW}, {'image' : 3xHxW}...] - > {'image' : Nx3xHxW} +# followed by the default collate which will form a batch BxNx3xHxW def list_data_collate(batch: collections.abc.Sequence): for i, item in enumerate(batch): data = item[0] @@ -455,12 +284,10 @@ def list_data_collate(batch: collections.abc.Sequence): batch[i] = data return default_collate(batch) - def main_worker(gpu, args): args.gpu = gpu - if args.distributed: args.rank = args.rank * torch.cuda.device_count() + gpu dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url, world_size=args.world_size, rank=args.rank) @@ -474,92 +301,57 @@ def main_worker(gpu, args): print('Batch size is:', args.batch_size, 'epochs', args.epochs) ############# - - with open('panda_training_fold0.json') as json_file: - training_list = json.load(json_file) - for d in training_list: - d['image'] = os.path.join(args.data_dir, d['image']) - - with open('panda_validation_fold0.json') as json_file: - validation_list = json.load(json_file) - for d in validation_list: - d['image'] = os.path.join(args.data_dir, d['image']) - - - load_image_transform = LoadTiffd(keys=["image"]) if args.tf else transforms.LoadImageD(keys=["image"], reader=WSIReader, backend="cuCIM", dtype=np.uint8, level=1) - normalize_image_transform = NormalizeBackground() if args.normback else transforms.ScaleIntensityRangeD(keys=["image"], a_min=np.float32(255), a_max=np.float32(0), b_min=np.float32(0), b_max=np.float32(1), clip=False) - - - if args.collate: - train_transform = transforms.Compose( - [ - load_image_transform, - LabelEncodeIntegerGraded(keys=["label"], num_classes=args.num_classes), #encode label - # transforms.Lambdad(keys=["label"], func=lambda x: (np.arange(num_classes) Date: Sun, 14 Nov 2021 01:20:52 +0000 Subject: [PATCH 04/18] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- .../panda_mil_train_evaluate_pytorch_gpu.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/pathology/multiple_instance_learning/panda_mil_train_evaluate_pytorch_gpu.py b/pathology/multiple_instance_learning/panda_mil_train_evaluate_pytorch_gpu.py index 1da4ac56e8..adf6d7ce32 100644 --- a/pathology/multiple_instance_learning/panda_mil_train_evaluate_pytorch_gpu.py +++ b/pathology/multiple_instance_learning/panda_mil_train_evaluate_pytorch_gpu.py @@ -232,8 +232,8 @@ def save_checkpoint(model, epoch, args, filename='model.tar', best_acc=0): class LabelEncodeIntegerGraded(transforms.Transform): """ Convert to integer label to encoded array representation of length num_classes, - with 1 filled in up to label index, and 0 otherwise. For example for num_classes=5, - embedding of 2 -> (1,1,0,0,0) + with 1 filled in up to label index, and 0 otherwise. For example for num_classes=5, + embedding of 2 -> (1,1,0,0,0) Args: num_classes: the number of classes to convert to encoded format. @@ -465,4 +465,4 @@ def main_worker(gpu, args): print('ALL DONE') if __name__ == '__main__': - main() \ No newline at end of file + main() From 7ba5a3cae8853fba391d10ebc3042ad27152672a Mon Sep 17 00:00:00 2001 From: myron Date: Sun, 14 Nov 2021 21:31:49 -0800 Subject: [PATCH 05/18] mil tutorial update Signed-off-by: myron --- .../multiple_instance_learning/README.md | 110 + .../datalist_panda_0.json | 42462 ++++++++++++++++ .../mil_network.png | Bin 0 -> 53975 bytes .../mil_patches.png | Bin 0 -> 827343 bytes .../panda_mil_train_evaluate_pytorch_gpu.py | 3 + 5 files changed, 42575 insertions(+) create mode 100644 pathology/multiple_instance_learning/README.md create mode 100644 pathology/multiple_instance_learning/datalist_panda_0.json create mode 100644 pathology/multiple_instance_learning/mil_network.png create mode 100644 pathology/multiple_instance_learning/mil_patches.png diff --git a/pathology/multiple_instance_learning/README.md b/pathology/multiple_instance_learning/README.md new file mode 100644 index 0000000000..e7ff1660e0 --- /dev/null +++ b/pathology/multiple_instance_learning/README.md @@ -0,0 +1,110 @@ +# Example of Multiple Instance Learning (MIL) for cancer classification from Whole Slide Images (WSI) + +## Description + +This tutorial contains a baseline method using [MONAI](https://monai.io) for Multiple Instance Learning (MIL) classification from Whole Slide Images (WSI). +The dataset is from [Prostate cANcer graDe Assessment (PANDA) Challenge - 2020](https://www.kaggle.com/c/prostate-cancer-grade-assessment/) for cancer grade classification from prostate hystology WSIs. +The implementation is based on: + +Andriy Myronenko, Ziyue Xu, Dong Yang, Holger Roth, Daguang Xu: "Accounting for Dependencies in Deep Learning Based Multiple Instance Learning for Whole Slide Imaging". In MICCAI (2021) https://arxiv.org/abs/2111.01556 + + +

+ mil_patches +

+

+ mil_network +

+ + +## Requirements + +The script is tested with: + +- `Ubuntu 18.04` | `Python 3.6` | `CUDA 11.0` | `Pytorch 1.10` + +- the default pipeline requires about 16GB memory per gpu + +- it is tested on 4x16gb multigpu machine + +## Dependencies and installation + +### MONAI + +```bash +pip install "git+https://github.com/Project-MONAI/MONAI#egg=monai[tifffile,imagecodecs]" +``` +For more information please check out [the installation guide](https://docs.monai.io/en/latest/installation.html). + + +### Data + +Prostate biopsy WSI dataset can be downloaded from Prostate cANcer graDe Assessment (PANDA) Challenge on [Kaggle](https://www.kaggle.com/c/prostate-cancer-grade-assessment/data). +In this tutorial, we assume it is downloaded in the /PandaChallenge2020 folder + +### Examples +Check all possible options +``` +python ./panda_mil_train_evaluate_pytorch_gpu.py -h +``` + +#### Train + +Train in multi-gpu mode with AMP using all available gpus, +assuming the training images in /PandaChallenge2020/train_images folder, +it will use the pre-defined 80/20 datasplit in [datalist_panda_0.json ](./datalist_panda_0.json) +``` +python -u panda_mil_train_evaluate_pytorch_gpu.py + --data_root=/PandaChallenge2020/train_images \ + --amp \ + --distributed \ + --mil_mode=att_trans \ + --batch_size=4 \ + --epochs=50 \ + --logdir=./logs +``` + +If you need to use only specific gpus, simply add CUDA_VISIBLE_DEVICES=... prefix +``` +CUDA_VISIBLE_DEVICES= 0,1,2,3 python -u panda_mil_train_evaluate_pytorch_gpu.py + --data_root=/PandaChallenge2020/train_images \ + --amp \ + --distributed \ + --mil_mode=att_trans \ + --batch_size=4 \ + --epochs=50 \ + --logdir=./logs +``` + + +#### Validation +Run inference of the best checkpoint over the validation set +``` +# Validate checkpoint on a single gpu +python -u panda_mil_train_evaluate_pytorch_gpu.py + --data_root=/PandaChallenge2020/train_images \ + --amp \ + --mil_mode=att_trans \ + --checkpoint=./logs/model.pt \ + --validate +``` + +#### Inference +Run inference on a different dataset. It's the same script as for validation, +we just specify a different data_root and json list files +``` +python -u panda_mil_train_evaluate_pytorch_gpu.py + --data_root=/PandaChallenge2020/some_other_files \ + --dataset_json=some_other_files.json + --amp \ + --mil_mode=att_trans \ + --checkpoint=./logs/model.pt \ + --validate +``` + + +### Questions and bugs + +- For questions relating to the use of MONAI, please us our [Discussions tab](https://github.com/Project-MONAI/MONAI/discussions) on the main repository of MONAI. +- For bugs relating to MONAI functionality, please create an issue on the [main repository](https://github.com/Project-MONAI/MONAI/issues). +- For bugs relating to the running of a tutorial, please create an issue in [this repository](https://github.com/Project-MONAI/Tutorials/issues). diff --git a/pathology/multiple_instance_learning/datalist_panda_0.json b/pathology/multiple_instance_learning/datalist_panda_0.json new file mode 100644 index 0000000000..96d82cca4c 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pathology/multiple_instance_learning/README.md | 16 ++++++++-------- .../datalist_panda_0.json | 2 +- 2 files changed, 9 insertions(+), 9 deletions(-) diff --git a/pathology/multiple_instance_learning/README.md b/pathology/multiple_instance_learning/README.md index e7ff1660e0..92a20ef90b 100644 --- a/pathology/multiple_instance_learning/README.md +++ b/pathology/multiple_instance_learning/README.md @@ -2,7 +2,7 @@ ## Description -This tutorial contains a baseline method using [MONAI](https://monai.io) for Multiple Instance Learning (MIL) classification from Whole Slide Images (WSI). +This tutorial contains a baseline method using [MONAI](https://monai.io) for Multiple Instance Learning (MIL) classification from Whole Slide Images (WSI). The dataset is from [Prostate cANcer graDe Assessment (PANDA) Challenge - 2020](https://www.kaggle.com/c/prostate-cancer-grade-assessment/) for cancer grade classification from prostate hystology WSIs. The implementation is based on: @@ -40,7 +40,7 @@ For more information please check out [the installation guide](https://docs.mona ### Data Prostate biopsy WSI dataset can be downloaded from Prostate cANcer graDe Assessment (PANDA) Challenge on [Kaggle](https://www.kaggle.com/c/prostate-cancer-grade-assessment/data). -In this tutorial, we assume it is downloaded in the /PandaChallenge2020 folder +In this tutorial, we assume it is downloaded in the /PandaChallenge2020 folder ### Examples Check all possible options @@ -54,26 +54,26 @@ Train in multi-gpu mode with AMP using all available gpus, assuming the training images in /PandaChallenge2020/train_images folder, it will use the pre-defined 80/20 datasplit in [datalist_panda_0.json ](./datalist_panda_0.json) ``` -python -u panda_mil_train_evaluate_pytorch_gpu.py +python -u panda_mil_train_evaluate_pytorch_gpu.py --data_root=/PandaChallenge2020/train_images \ --amp \ --distributed \ --mil_mode=att_trans \ --batch_size=4 \ --epochs=50 \ - --logdir=./logs + --logdir=./logs ``` If you need to use only specific gpus, simply add CUDA_VISIBLE_DEVICES=... prefix ``` -CUDA_VISIBLE_DEVICES= 0,1,2,3 python -u panda_mil_train_evaluate_pytorch_gpu.py +CUDA_VISIBLE_DEVICES= 0,1,2,3 python -u panda_mil_train_evaluate_pytorch_gpu.py --data_root=/PandaChallenge2020/train_images \ --amp \ --distributed \ --mil_mode=att_trans \ --batch_size=4 \ --epochs=50 \ - --logdir=./logs + --logdir=./logs ``` @@ -81,7 +81,7 @@ CUDA_VISIBLE_DEVICES= 0,1,2,3 python -u panda_mil_train_evaluate_pytorch_gpu.py Run inference of the best checkpoint over the validation set ``` # Validate checkpoint on a single gpu -python -u panda_mil_train_evaluate_pytorch_gpu.py +python -u panda_mil_train_evaluate_pytorch_gpu.py --data_root=/PandaChallenge2020/train_images \ --amp \ --mil_mode=att_trans \ @@ -93,7 +93,7 @@ python -u panda_mil_train_evaluate_pytorch_gpu.py Run inference on a different dataset. It's the same script as for validation, we just specify a different data_root and json list files ``` -python -u panda_mil_train_evaluate_pytorch_gpu.py +python -u panda_mil_train_evaluate_pytorch_gpu.py --data_root=/PandaChallenge2020/some_other_files \ --dataset_json=some_other_files.json --amp \ diff --git a/pathology/multiple_instance_learning/datalist_panda_0.json b/pathology/multiple_instance_learning/datalist_panda_0.json index 96d82cca4c..fde6dcdc18 100644 --- a/pathology/multiple_instance_learning/datalist_panda_0.json +++ b/pathology/multiple_instance_learning/datalist_panda_0.json @@ -42459,4 +42459,4 @@ "label": 5 } ] -} \ No newline at end of file +} From f344b99d5c8bc4427d413a503efd0ad09b0715cb Mon Sep 17 00:00:00 2001 From: myron Date: Wed, 17 Nov 2021 14:53:04 -0800 Subject: [PATCH 07/18] small updates Signed-off-by: myron --- .../panda_mil_train_evaluate_pytorch_gpu.py | 126 +++++++++--------- 1 file changed, 65 insertions(+), 61 deletions(-) diff --git a/pathology/multiple_instance_learning/panda_mil_train_evaluate_pytorch_gpu.py b/pathology/multiple_instance_learning/panda_mil_train_evaluate_pytorch_gpu.py index 7da922335c..40ee7480e1 100644 --- a/pathology/multiple_instance_learning/panda_mil_train_evaluate_pytorch_gpu.py +++ b/pathology/multiple_instance_learning/panda_mil_train_evaluate_pytorch_gpu.py @@ -2,38 +2,33 @@ import time import shutil import argparse -import copy import collections.abc -from typing import Dict, Hashable, Mapping, Sequence, Any, Union, Optional, List, cast import numpy as np -from numpy.lib.stride_tricks import as_strided from sklearn.metrics import cohen_kappa_score import torch import torch.nn as nn -from torch.utils.tensorboard import SummaryWriter from torch.cuda.amp import GradScaler, autocast + +from torch.utils.tensorboard import SummaryWriter from torch.utils.data.distributed import DistributedSampler from torch.utils.data.dataloader import default_collate -import torch.nn.parallel import torch.distributed as dist import torch.multiprocessing as mp - -from monai import transforms, data +from monai.data import Dataset, load_decathlon_datalist from monai.data.image_reader import WSIReader -from monai.metrics import CumulativeAverage, Cumulative -from monai.data import load_decathlon_datalist +from monai.metrics import Cumulative, CumulativeAverage +from monai.transforms import Transform, Compose, LoadImageD, RandFlipd, RandRotate90d, ScaleIntensityRangeD, ToTensord +from monai.apps.pathology.transforms import TileOnGridd +from monai.networks.nets import milmodel -from monai.data.image_reader import WSIReader -from monai.networks.nets import MilMode, MILModel #to be added -from monai.apps.pathology.transforms import TileOnGridd #to be added def parse_args(): - parser = argparse.ArgumentParser(description='Multiple Instance Learning (MIL) example on WSI images.') + parser = argparse.ArgumentParser(description='Multiple Instance Learning (MIL) example of classification from WSI.') parser.add_argument('--data_root', default='/PandaChallenge2020/train_images/', help="path to root folder of images") parser.add_argument('--dataset_json', default='./datalist_panda_0.json', help="path to dataset json file") @@ -42,7 +37,7 @@ def parse_args(): parser.add_argument('--tile_count', default=44, type=int, help="number of patches (instances) to extract from WSI image") parser.add_argument('--tile_size', default=256, type=int, help="size of square patch (instance) in pixels") - parser.add_argument('--checkpoint', default=None, help="load existing checkpoint, otherwise random intialization") + parser.add_argument('--checkpoint', default=None, help="load existing checkpoint") parser.add_argument('--validate', action='store_true', help="run only inference on the validation set, must specify the checkpoint argument") parser.add_argument('--logdir', default=None, help="path to log directory to store Tensorboard logs") @@ -50,6 +45,7 @@ def parse_args(): parser.add_argument('--epochs', default=50, type=int, help="number of training epochs") parser.add_argument('--batch_size', default=4, type=int, help="batch size, the number of WSI images per gpu") parser.add_argument('--optim_lr', default=3e-5, type=float, help="initial learning rate") + parser.add_argument('--weight_decay', default=0, type=float, help="optimizer weight decay") parser.add_argument('--amp', action='store_true', help="use AMP, recommended") parser.add_argument('--val_every', default=1, type=int, help="run validation after this number of epochs, default 1 to run every epoch") @@ -62,9 +58,10 @@ def parse_args(): parser.add_argument('--dist-url', default='tcp://127.0.0.1:23456', type=str, help="url used to set up distributed training") parser.add_argument('--dist-backend', default='nccl', type=str, help="distributed backend") - parser.add_argument('--quick', action='store_true', help="use a small subset of data for debugging") # for debugging + parser.add_argument('--optim_lr_trans', default=3e-6, type=float) + args = parser.parse_args() @@ -92,8 +89,9 @@ def train_epoch(model, loader, optimizer, scaler, epoch, args): data, target = batch_data['image'].cuda(args.rank), batch_data['label'].cuda(args.rank) - for param in model.parameters(): - param.grad = None + # for param in model.parameters(): + # param.grad = None + optimizer.zero_grad(set_to_none=True) with autocast(enabled=args.amp): logits = model(data) @@ -142,11 +140,10 @@ def val_epoch(model, loader, epoch, args, max_tiles=None): run_loss = CumulativeAverage() run_acc = CumulativeAverage() - - start_time = time.time() PREDS = Cumulative() TARGETS = Cumulative() + start_time = time.time() loss, acc = 0., 0. with torch.no_grad(): @@ -158,6 +155,10 @@ def val_epoch(model, loader, epoch, args, max_tiles=None): with autocast(enabled=args.amp): if max_tiles is not None and data.shape[1] > max_tiles: + # During validation, we want to use all instances/patches + # and if its number is very big, we may run out of GPU memory + # in this case, we first iteratively go over subsets of patches to calculate backbone features + # and at the very end calculate the classification output logits = [] logits2 = [] @@ -180,7 +181,9 @@ def val_epoch(model, loader, epoch, args, max_tiles=None): logits = calc_head(logits) - else: # + else: + # if number of instances is not big + # we can run inference directly logits = model(data) @@ -216,27 +219,27 @@ def val_epoch(model, loader, epoch, args, max_tiles=None): return loss, acc, qwk -def save_checkpoint(model, epoch, args, filename='model.tar', best_acc=0): +def save_checkpoint(model, epoch, args, filename='model.pt', best_acc=0): '''Save checkpoint''' state_dict = model.state_dict() if not args.distributed else model.module.state_dict() save_dict = { - 'epoch': epoch, - 'best_acc': best_acc, - 'state_dict': state_dict - } + 'epoch': epoch, + 'best_acc': best_acc, + 'state_dict': state_dict + } filename=os.path.join(args.logdir, filename) torch.save(save_dict, filename) print('Saving checkpoint', filename) -class LabelEncodeIntegerGraded(transforms.Transform): +class LabelEncodeIntegerGraded(Transform): """ - Convert to integer label to encoded array representation of length num_classes, - with 1 filled in up to label index, and 0 otherwise. For example for num_classes=5, - embedding of 2 -> (1,1,0,0,0) + Convert an integer label to encoded array representation of length num_classes, + with 1 filled in up to label index, and 0 otherwise. For example for num_classes=5, + embedding of 2 -> (1,1,0,0,0) Args: num_classes: the number of classes to convert to encoded format. @@ -321,43 +324,43 @@ def main_worker(gpu, args): validation_list = validation_list[:16] - train_transform = transforms.Compose( + train_transform = Compose( [ - transforms.LoadImageD(keys=["image"], reader=WSIReader, backend="TiffFile", dtype=np.uint8, level=1, image_only=True), + LoadImageD(keys=["image"], reader=WSIReader, backend="TiffFile", dtype=np.uint8, level=1, image_only=True), LabelEncodeIntegerGraded(keys=["label"], num_classes=args.num_classes), TileOnGridd(keys=["image"], tile_count=args.tile_count, tile_size=args.tile_size, random_offset=True, background_val=255, return_list_of_dicts=True), - transforms.RandFlipd(keys=["image"], spatial_axis=0, prob=0.5), - transforms.RandFlipd(keys=["image"], spatial_axis=1, prob=0.5), - transforms.RandRotate90d(keys=["image"], prob=0.5), - transforms.ScaleIntensityRangeD(keys=["image"], a_min=np.float32(255), a_max=np.float32(0)), - transforms.ToTensord(keys=["image", "label"]), + RandFlipd(keys=["image"], spatial_axis=0, prob=0.5), + RandFlipd(keys=["image"], spatial_axis=1, prob=0.5), + RandRotate90d(keys=["image"], prob=0.5), + ScaleIntensityRangeD(keys=["image"], a_min=np.float32(255), a_max=np.float32(0)), + ToTensord(keys=["image", "label"]), ] ) - valid_transform = transforms.Compose( + valid_transform = Compose( [ - transforms.LoadImageD(keys=["image"], reader=WSIReader, backend="TiffFile", dtype=np.uint8, level=1, image_only=True), + LoadImageD(keys=["image"], reader=WSIReader, backend="TiffFile", dtype=np.uint8, level=1, image_only=True), LabelEncodeIntegerGraded(keys=["label"], num_classes=args.num_classes), TileOnGridd(keys=["image"], tile_count=None, tile_size=args.tile_size, random_offset=False, background_val=255, return_list_of_dicts=True), - transforms.ScaleIntensityRangeD(keys=["image"], a_min=np.float32(255), a_max=np.float32(0)), - transforms.ToTensord(keys=["image", "label"]), + ScaleIntensityRangeD(keys=["image"], a_min=np.float32(255), a_max=np.float32(0)), + ToTensord(keys=["image", "label"]), ] ) - dataset_train = data.Dataset(data=training_list, transform=train_transform) - dataset_valid = data.Dataset(data=validation_list, transform=valid_transform) + dataset_train = Dataset(data=training_list, transform=train_transform) + dataset_valid = Dataset(data=validation_list, transform=valid_transform) train_sampler = DistributedSampler(dataset_train) if args.distributed else None val_sampler = DistributedSampler(dataset_valid, shuffle=False) if args.distributed else None - train_loader = torch.utils.data.DataLoader(dataset_train, batch_size=args.batch_size, shuffle=(train_sampler is None), num_workers=args.workers, pin_memory=False, multiprocessing_context='spawn', sampler=train_sampler, collate_fn=list_data_collate) - valid_loader = torch.utils.data.DataLoader(dataset_valid, batch_size=1, shuffle=False, num_workers=args.workers, pin_memory=False, multiprocessing_context='spawn', sampler=val_sampler, collate_fn=list_data_collate) + train_loader = torch.utils.data.DataLoader(dataset_train, batch_size=args.batch_size, shuffle=(train_sampler is None), num_workers=args.workers, pin_memory=True, multiprocessing_context='spawn', sampler=train_sampler, collate_fn=list_data_collate) + valid_loader = torch.utils.data.DataLoader(dataset_valid, batch_size=1, shuffle=False, num_workers=args.workers, pin_memory=True, multiprocessing_context='spawn', sampler=val_sampler, collate_fn=list_data_collate) if args.rank==0: print('Dataset training:', len(dataset_train), 'validation:', len(dataset_valid)) - os.environ["TORCH_HOME"] = "../../torchhome" #TODO, remove later - model = MILModel(num_classes=args.num_classes, pretrained=True, mil_mode=args.mil_mode) + # os.environ["TORCH_HOME"] = "../../torchhome" #TODO, remove later + model = milmodel.MILModel(num_classes=args.num_classes, pretrained=True, mil_mode=args.mil_mode) best_acc = 0 start_epoch = 0 @@ -368,19 +371,28 @@ def main_worker(gpu, args): if 'best_acc' in checkpoint: best_acc = checkpoint['best_acc'] print("=> loaded checkpoint '{}' (epoch {}) (bestacc {})".format(args.checkpoint, start_epoch, best_acc)) - model.cuda(args.gpu) if args.distributed: model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model) model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], output_device=args.gpu) + if args.validate: + # if we only want to validate existing checkpoint + epoch_time = time.time() + val_loss, val_acc, qwk = val_epoch(model, valid_loader, epoch=0, args=args, max_tiles=args.tile_count) + if args.rank == 0: + print('Final validation loss: {:.4f}'.format(val_loss), 'acc: {:.4f}'.format(val_acc), 'qwk: {:.4f}'.format(qwk), 'time {:.2f}s'.format(time.time() - epoch_time)) + + exit(0) + + params = model.parameters() if args.mil_mode in ["att_trans", "att_trans_pyramid"]: m = model if not args.distributed else model.module params = [{'params': list(m.attention.parameters()) + list(m.myfc.parameters()) + list(m.net.parameters())}, - {'params': list(m.transformer.parameters()), 'lr': 3e-6, 'weight_decay': 0.1 }] + {'params': list(m.transformer.parameters()), 'lr': args.optim_lr_trans, 'weight_decay': 0.1 }] optimizer = torch.optim.AdamW(params, lr=args.optim_lr, weight_decay=args.weight_decay) @@ -393,14 +405,6 @@ def main_worker(gpu, args): else: writer = None - if args.validate: - epoch_time = time.time() - val_loss, val_acc, qwk = val_epoch(model, valid_loader, epoch=0, args=args, max_tiles=args.tile_count) - if args.rank == 0: - print('Final validation loss: {:.4f}'.format(val_loss), 'acc: {:.4f}'.format(val_acc), 'qwk: {:.4f}'.format(qwk), 'time {:.2f}s'.format(time.time() - epoch_time)) - - exit(0) - ###RUN TRAINING n_epochs = args.epochs @@ -452,20 +456,20 @@ def main_worker(gpu, args): val_acc = qwk if val_acc > val_acc_max: - print('score2 ({:.6f} --> {:.6f}). Saving model ...'.format(val_acc_max, val_acc)) + print('qwk ({:.6f} --> {:.6f})'.format(val_acc_max, val_acc)) val_acc_max = val_acc b_new_best = True if args.rank == 0 and args.logdir is not None: - save_checkpoint(model, epoch, args, best_acc=val_acc, filename='model_final.tar') + save_checkpoint(model, epoch, args, best_acc=val_acc, filename='model_final.pt') if b_new_best: - print('Copying to model.tar new best model!!!!') - shutil.copyfile(os.path.join(args.logdir, 'model_final.tar'), os.path.join(args.logdir, 'model.tar')) + print('Copying to model.pt new best model!!!!') + shutil.copyfile(os.path.join(args.logdir, 'model_final.pt'), os.path.join(args.logdir, 'model.pt')) scheduler.step() print('ALL DONE') if __name__ == '__main__': - main() + main() \ No newline at end of file From 247dc0b51721d51fd2bbb527e5a72f32af00eff9 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Wed, 17 Nov 2021 22:53:21 +0000 Subject: [PATCH 08/18] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- .../panda_mil_train_evaluate_pytorch_gpu.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/pathology/multiple_instance_learning/panda_mil_train_evaluate_pytorch_gpu.py b/pathology/multiple_instance_learning/panda_mil_train_evaluate_pytorch_gpu.py index 40ee7480e1..fbda9b0be5 100644 --- a/pathology/multiple_instance_learning/panda_mil_train_evaluate_pytorch_gpu.py +++ b/pathology/multiple_instance_learning/panda_mil_train_evaluate_pytorch_gpu.py @@ -238,8 +238,8 @@ def save_checkpoint(model, epoch, args, filename='model.pt', best_acc=0): class LabelEncodeIntegerGraded(Transform): """ Convert an integer label to encoded array representation of length num_classes, - with 1 filled in up to label index, and 0 otherwise. For example for num_classes=5, - embedding of 2 -> (1,1,0,0,0) + with 1 filled in up to label index, and 0 otherwise. For example for num_classes=5, + embedding of 2 -> (1,1,0,0,0) Args: num_classes: the number of classes to convert to encoded format. @@ -472,4 +472,4 @@ def main_worker(gpu, args): print('ALL DONE') if __name__ == '__main__': - main() \ No newline at end of file + main() From a9961e248a5528605d76b45a995f7d448835d73a Mon Sep 17 00:00:00 2001 From: myron Date: Wed, 17 Nov 2021 15:39:40 -0800 Subject: [PATCH 09/18] updated images Signed-off-by: myron --- pathology/multiple_instance_learning/README.md | 14 ++++++-------- .../multiple_instance_learning/mil_network.jpg | Bin 0 -> 90370 bytes .../multiple_instance_learning/mil_network.png | Bin 53975 -> 0 bytes .../multiple_instance_learning/mil_patches.jpg | Bin 0 -> 228436 bytes .../multiple_instance_learning/mil_patches.png | Bin 827343 -> 0 bytes 5 files changed, 6 insertions(+), 8 deletions(-) create mode 100644 pathology/multiple_instance_learning/mil_network.jpg delete mode 100644 pathology/multiple_instance_learning/mil_network.png create mode 100644 pathology/multiple_instance_learning/mil_patches.jpg delete mode 100644 pathology/multiple_instance_learning/mil_patches.png diff --git a/pathology/multiple_instance_learning/README.md b/pathology/multiple_instance_learning/README.md index 92a20ef90b..0dadb56b06 100644 --- a/pathology/multiple_instance_learning/README.md +++ b/pathology/multiple_instance_learning/README.md @@ -1,8 +1,6 @@ -# Example of Multiple Instance Learning (MIL) for cancer classification from Whole Slide Images (WSI) +# Multiple Instance Learning (MIL) Examples -## Description - -This tutorial contains a baseline method using [MONAI](https://monai.io) for Multiple Instance Learning (MIL) classification from Whole Slide Images (WSI). +This tutorial contains a baseline method of Multiple Instance Learning (MIL) classification from Whole Slide Images (WSI). The dataset is from [Prostate cANcer graDe Assessment (PANDA) Challenge - 2020](https://www.kaggle.com/c/prostate-cancer-grade-assessment/) for cancer grade classification from prostate hystology WSIs. The implementation is based on: @@ -10,10 +8,10 @@ Andriy Myronenko, Ziyue Xu, Dong Yang, Holger Roth, Daguang Xu: "Accounting for

    - mil_patches + mil_patches

    - mil_network + mil_network

    @@ -64,9 +62,9 @@ python -u panda_mil_train_evaluate_pytorch_gpu.py --logdir=./logs ``` -If you need to use only specific gpus, simply add CUDA_VISIBLE_DEVICES=... prefix +If you need to use only specific gpus, simply add the prefix CUDA_VISIBLE_DEVICES=... prefi ``` -CUDA_VISIBLE_DEVICES= 0,1,2,3 python -u panda_mil_train_evaluate_pytorch_gpu.py +CUDA_VISIBLE_DEVICES=0,1,2,3 python -u panda_mil_train_evaluate_pytorch_gpu.py --data_root=/PandaChallenge2020/train_images \ --amp \ --distributed \ diff --git a/pathology/multiple_instance_learning/mil_network.jpg b/pathology/multiple_instance_learning/mil_network.jpg new file mode 100644 index 0000000000000000000000000000000000000000..01c31c0434eee304aa6dbb239bc9cc946f1296af GIT binary patch literal 90370 zcmeFYcU)7?);GH8T{;MeLAoHI(rZK%M1)8e5F?$?rK_0O=pqUV3Q812K$=Lep`#+w zqzFn#5RfK`NC>3et-qt^Jm;Rz`#$%%@BQokCQN2!_RQ>AYub0MH83U_vw*-!b4zo8 zi3tEsfFFQ?0|Ji6B76bB+8R&+0Du!p0NC;nGAmxG_^Ogv;snhMyyAe#v%2C8h{ZrHq65-&?i#H)5i}HVl2DfjFFW=cpJ+)Yg-?*4m0!dN1V76 z?qh$+#=+}SpqIY4EYw8CDApi0I4syF(nBUTI4C5-Al6v+SK|gC{WDubR_0fc$UtM+ zGuEeN%tFI`WOURIsvndEYlnOL8rU5(|3_W$m$B?WIvEoaqaLHF9vbeaaY$cZU*q6m zjl+l4z!GW^7egXFV%0(-*`kMMXjG)JtgHt3291Bb;wSm1 zuO>2ohrcE8w*>x{z~2)1TLOPe;QxOT_$S)&2>~Ha42Wd_#u}h^8iY!rAYKZS(NsSO z=pD7RX8j4l!0?M!_(ckQkiPXX9eBmMbd~GDD)}gqf#-|0G&6&rwu6~lo;v;u;qm!< zgoOn$3jjcHNMyL3#Zj3vF0L{h^8gRP2k?VXPuIgMBFyyk=~F-3{GI-4|IZg`{3i?q zfKj!dZT-pq?-4@YAS?or9eCEtPhaxsx%KWPF;i-dz6 z1b|=siunCS@BW$Z`4@fQR~ZL8GqB7N5UcWfc!c`_0PoNK%S3zmf$i}A?Bl@@Z$t=4 zKLTkJFMkhjkp2YH2ZDn`L7I;fq?N()VIX}Fr1d=irA@zop*=i2|FrMn;rECBi!ZPy z*ok9^h_E1!i@$#SpZp3AiUMW#EBu7sy#C=Q?7&nDl;LDxsMQ}j#M1`$J0AgZ_nY>Q zI^p=6_KGkA<@#4$Z;xYEzv+-5o1e1#)uu1P{3l-jMI$0D9e&e35y$O+)8V0Zf8OOA zZf5#B-y})-AI22P`UYw7cY;0_TMMO^q*};ocYr?Z=Ykwf0y?O zas0z?Sme<^?+Oe0wN8G?$0x|*PkuzS{hzv#;SPWD!y-@rkzs(x$;$fXv~8hYtb3&$a!t3;=9Q{#vt4Vx0fX3poIaqm#j4y!&UKHyZ#np#VT| z^q+b1&j3IG0{|Vjy`sXS|7wr*XTk<>gL{SuAOY+Jo0EkFm*3-kj+z!)$M%mVYk3P1oz z0ELN(iGzucNtj833Bshnq{?)dNsq~d={VCVCOak3QHDC zK1&5l6H6b@iTdX;(#jJI#-K?XmI938H zosExe51T5R0h=Y;88$z*7`6nqEVk!twQN0XV{G$mBzAUoF?J<(UG@{~F6;s9m)UQz zKW49FZ)YE7pJU(T;NXzpP~|Y@fN^+mL~Jme_jz;KLk%yW=Ac{m}QhdIqTT{%NI zuXAQ`zTj-*9N}E#q;LsvDR3EZ!MMD+E^?)FJ>zQP8swViqHqgxD{&ihJ8%bZU*mqj zUBTVM{fT>nhlfX&N1w-*2f=fd=K;@4p4U7$o^4(sUKL(5-m|<>ytjFac-whD@^0|) z@hS2h;dAAS$&!$;mFyi0wTUNFn`o_=2ufT80e~$kWe-?i= z{~-UW0GEKGfSG`Y08$`Vpiy8#U{g?7@Q@%(Fj(-G;0wVwf?tI=gcOC23waCO6eJCC1WJ>Bzq)Rr9`9*rM#q4q+Ur)?P1=dvd3=Euh`xrdjs~~+uOEx2_g(Jfgm9FAQ;H9jHryMOpwe2nQj@vzCHU+?2Fp>bl>}Z z6j>!%N7?JLm9n4ZxaIWZeC6)Rb;=RsA@bJpaq^||Qwm%PdJ6su*$RCMTZ&4G&WeeO z^@?AWb}LybT~aDl`lQUK3{}3MoUi<0Kl^^Y{ek-*?;lcOR?$}RSIJcwRApAxQAMae zRvkXTc0m6?$btL=6KcF_rfSh@rD`~JG4)gG*VXIQziG&8xN6+h=+mGd)IJz=@Y%uX zLqdm69=d+0>CpGXDu=xe=N|r`$){7O==5kTWcq3cWTpgbaldY zDs+}~<#o^NKGL1g6VW@ZcT4ZJKAZkgeUyHS{1A1B zNjRx{^2*6>D_$#ms~ju*DV0;=@

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b/pathology/multiple_instance_learning/panda_mil_train_evaluate_pytorch_gpu.py index fbda9b0be5..ccfa37fa4f 100644 --- a/pathology/multiple_instance_learning/panda_mil_train_evaluate_pytorch_gpu.py +++ b/pathology/multiple_instance_learning/panda_mil_train_evaluate_pytorch_gpu.py @@ -3,6 +3,7 @@ import shutil import argparse import collections.abc +import gdown import numpy as np from sklearn.metrics import cohen_kappa_score @@ -30,7 +31,8 @@ def parse_args(): parser = argparse.ArgumentParser(description='Multiple Instance Learning (MIL) example of classification from WSI.') parser.add_argument('--data_root', default='/PandaChallenge2020/train_images/', help="path to root folder of images") - parser.add_argument('--dataset_json', default='./datalist_panda_0.json', help="path to dataset json file") + # parser.add_argument('--dataset_json', default='./datalist_panda_0.json', help="path to dataset json file") + parser.add_argument('--dataset_json', default=None, type=str, help="path to dataset json file") parser.add_argument('--num_classes', default=5, type=int, help="number of output classes") parser.add_argument('--mil_mode', default='att_trans', help="MIL algorithm") @@ -89,8 +91,6 @@ def train_epoch(model, loader, optimizer, scaler, epoch, args): data, target = batch_data['image'].cuda(args.rank), batch_data['label'].cuda(args.rank) - # for param in model.parameters(): - # param.grad = None optimizer.zero_grad(set_to_none=True) with autocast(enabled=args.amp): @@ -238,8 +238,8 @@ def save_checkpoint(model, epoch, args, filename='model.pt', best_acc=0): class LabelEncodeIntegerGraded(Transform): """ Convert an integer label to encoded array representation of length num_classes, - with 1 filled in up to label index, and 0 otherwise. For example for num_classes=5, - embedding of 2 -> (1,1,0,0,0) + with 1 filled in up to label index, and 0 otherwise. For example for num_classes=5, + embedding of 2 -> (1,1,0,0,0) Args: num_classes: the number of classes to convert to encoded format. @@ -270,6 +270,15 @@ def main(): args = parse_args() + if args.dataset_json is None: + #download default json datalist + resource = "https://drive.google.com/uc?id=1L6PtKBlHHyUgTE4rVhRuOLTQKgD4tBRK" + dst = "./datalist_panda_0.json" + if not os.path.exists(dst): + gdown.download(resource, dst, quiet=False) + args.dataset_json=dst + + if args.distributed: ngpus_per_node = torch.cuda.device_count() args.optim_lr = ngpus_per_node * args.optim_lr / 2 #heuristic to scale up learning rate in multigpu setup @@ -473,3 +482,4 @@ def main_worker(gpu, args): if __name__ == '__main__': main() + \ No newline at end of file From abd71cec89352bd7858d27c65874048141f121d1 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 18 Nov 2021 20:25:13 +0000 Subject: [PATCH 11/18] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- .../panda_mil_train_evaluate_pytorch_gpu.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/pathology/multiple_instance_learning/panda_mil_train_evaluate_pytorch_gpu.py b/pathology/multiple_instance_learning/panda_mil_train_evaluate_pytorch_gpu.py index ccfa37fa4f..80db6c257c 100644 --- a/pathology/multiple_instance_learning/panda_mil_train_evaluate_pytorch_gpu.py +++ b/pathology/multiple_instance_learning/panda_mil_train_evaluate_pytorch_gpu.py @@ -238,8 +238,8 @@ def save_checkpoint(model, epoch, args, filename='model.pt', best_acc=0): class LabelEncodeIntegerGraded(Transform): """ Convert an integer label to encoded array representation of length num_classes, - with 1 filled in up to label index, and 0 otherwise. For example for num_classes=5, - embedding of 2 -> (1,1,0,0,0) + with 1 filled in up to label index, and 0 otherwise. For example for num_classes=5, + embedding of 2 -> (1,1,0,0,0) Args: num_classes: the number of classes to convert to encoded format. @@ -482,4 +482,4 @@ def main_worker(gpu, args): if __name__ == '__main__': main() - \ No newline at end of file + From 3fb11b9c27c598e4e082b5c2d2a8302a6ffb31ec Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 19 Nov 2021 00:17:56 +0000 Subject: [PATCH 12/18] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- .../panda_mil_train_evaluate_pytorch_gpu.py | 1 - 1 file changed, 1 deletion(-) diff --git a/pathology/multiple_instance_learning/panda_mil_train_evaluate_pytorch_gpu.py b/pathology/multiple_instance_learning/panda_mil_train_evaluate_pytorch_gpu.py index 80db6c257c..0be2396397 100644 --- a/pathology/multiple_instance_learning/panda_mil_train_evaluate_pytorch_gpu.py +++ b/pathology/multiple_instance_learning/panda_mil_train_evaluate_pytorch_gpu.py @@ -482,4 +482,3 @@ def main_worker(gpu, args): if __name__ == '__main__': main() - From 6df772a637fda43775d70cd79d8ba0aeb8bf04b8 Mon Sep 17 00:00:00 2001 From: Behrooz <3968947+drbeh@users.noreply.github.com> Date: Fri, 19 Nov 2021 01:36:47 +0000 Subject: [PATCH 13/18] Update README Signed-off-by: Behrooz <3968947+drbeh@users.noreply.github.com> --- .../multiple_instance_learning/README.md | 52 +++++++++---------- 1 file changed, 26 insertions(+), 26 deletions(-) diff --git a/pathology/multiple_instance_learning/README.md b/pathology/multiple_instance_learning/README.md index 0dadb56b06..40eaeb3d80 100644 --- a/pathology/multiple_instance_learning/README.md +++ b/pathology/multiple_instance_learning/README.md @@ -1,19 +1,13 @@ # Multiple Instance Learning (MIL) Examples This tutorial contains a baseline method of Multiple Instance Learning (MIL) classification from Whole Slide Images (WSI). -The dataset is from [Prostate cANcer graDe Assessment (PANDA) Challenge - 2020](https://www.kaggle.com/c/prostate-cancer-grade-assessment/) for cancer grade classification from prostate hystology WSIs. +The dataset is from [Prostate cANcer graDe Assessment (PANDA) Challenge - 2020](https://www.kaggle.com/c/prostate-cancer-grade-assessment/) for cancer grade classification from prostate histology WSIs. The implementation is based on: -Andriy Myronenko, Ziyue Xu, Dong Yang, Holger Roth, Daguang Xu: "Accounting for Dependencies in Deep Learning Based Multiple Instance Learning for Whole Slide Imaging". In MICCAI (2021) https://arxiv.org/abs/2111.01556 - - -

    - mil_patches -

    -

    - mil_network -

    +Andriy Myronenko, Ziyue Xu, Dong Yang, Holger Roth, Daguang Xu: "Accounting for Dependencies in Deep Learning Based Multiple Instance Learning for Whole Slide Imaging". In MICCAI (2021). [arXiv](https://arxiv.org/abs/2111.01556) +![mil_patches](./mil_patches.jpg) +![mil_network](./mil_network.jpg) ## Requirements @@ -23,7 +17,7 @@ The script is tested with: - the default pipeline requires about 16GB memory per gpu -- it is tested on 4x16gb multigpu machine +- it is tested on 4x16gb multi-gpu machine ## Dependencies and installation @@ -32,26 +26,29 @@ The script is tested with: ```bash pip install "git+https://github.com/Project-MONAI/MONAI#egg=monai[tifffile,imagecodecs]" ``` -For more information please check out [the installation guide](https://docs.monai.io/en/latest/installation.html). +For more information please check out [the installation guide](https://docs.monai.io/en/latest/installation.html). ### Data Prostate biopsy WSI dataset can be downloaded from Prostate cANcer graDe Assessment (PANDA) Challenge on [Kaggle](https://www.kaggle.com/c/prostate-cancer-grade-assessment/data). -In this tutorial, we assume it is downloaded in the /PandaChallenge2020 folder +In this tutorial, we assume it is downloaded in the `/PandaChallenge2020` folder + +## Examples -### Examples Check all possible options -``` + +```bash python ./panda_mil_train_evaluate_pytorch_gpu.py -h ``` -#### Train +### Train Train in multi-gpu mode with AMP using all available gpus, assuming the training images in /PandaChallenge2020/train_images folder, -it will use the pre-defined 80/20 datasplit in [datalist_panda_0.json ](./datalist_panda_0.json) -``` +it will use the pre-defined 80/20 data split in [datalist_panda_0.json](https://drive.google.com/drive/u/0/folders/1CAHXDZqiIn5QUfg5A7XsK1BncRu6Ftbh) + +```bash python -u panda_mil_train_evaluate_pytorch_gpu.py --data_root=/PandaChallenge2020/train_images \ --amp \ @@ -62,8 +59,9 @@ python -u panda_mil_train_evaluate_pytorch_gpu.py --logdir=./logs ``` -If you need to use only specific gpus, simply add the prefix CUDA_VISIBLE_DEVICES=... prefi -``` +If you need to use only specific gpus, simply add the prefix `CUDA_VISIBLE_DEVICES=...` + +```bash CUDA_VISIBLE_DEVICES=0,1,2,3 python -u panda_mil_train_evaluate_pytorch_gpu.py --data_root=/PandaChallenge2020/train_images \ --amp \ @@ -74,10 +72,11 @@ CUDA_VISIBLE_DEVICES=0,1,2,3 python -u panda_mil_train_evaluate_pytorch_gpu.py --logdir=./logs ``` +### Validation -#### Validation Run inference of the best checkpoint over the validation set -``` + +```bash # Validate checkpoint on a single gpu python -u panda_mil_train_evaluate_pytorch_gpu.py --data_root=/PandaChallenge2020/train_images \ @@ -87,10 +86,12 @@ python -u panda_mil_train_evaluate_pytorch_gpu.py --validate ``` -#### Inference +### Inference + Run inference on a different dataset. It's the same script as for validation, we just specify a different data_root and json list files -``` + +```bash python -u panda_mil_train_evaluate_pytorch_gpu.py --data_root=/PandaChallenge2020/some_other_files \ --dataset_json=some_other_files.json @@ -100,8 +101,7 @@ python -u panda_mil_train_evaluate_pytorch_gpu.py --validate ``` - -### Questions and bugs +## Questions and bugs - For questions relating to the use of MONAI, please us our [Discussions tab](https://github.com/Project-MONAI/MONAI/discussions) on the main repository of MONAI. - For bugs relating to MONAI functionality, please create an issue on the [main repository](https://github.com/Project-MONAI/MONAI/issues). From 1294b8696ff32899c044f8a20c9cd63be61cf73a Mon Sep 17 00:00:00 2001 From: Behrooz <3968947+drbeh@users.noreply.github.com> Date: Fri, 19 Nov 2021 01:44:54 +0000 Subject: [PATCH 14/18] Fix formatting and typos Signed-off-by: Behrooz <3968947+drbeh@users.noreply.github.com> --- .../panda_mil_train_evaluate_pytorch_gpu.py | 326 +++++++++++------- 1 file changed, 194 insertions(+), 132 deletions(-) diff --git a/pathology/multiple_instance_learning/panda_mil_train_evaluate_pytorch_gpu.py b/pathology/multiple_instance_learning/panda_mil_train_evaluate_pytorch_gpu.py index 0be2396397..9a3af9926f 100644 --- a/pathology/multiple_instance_learning/panda_mil_train_evaluate_pytorch_gpu.py +++ b/pathology/multiple_instance_learning/panda_mil_train_evaluate_pytorch_gpu.py @@ -29,54 +29,70 @@ def parse_args(): - parser = argparse.ArgumentParser(description='Multiple Instance Learning (MIL) example of classification from WSI.') - parser.add_argument('--data_root', default='/PandaChallenge2020/train_images/', help="path to root folder of images") + parser = argparse.ArgumentParser(description="Multiple Instance Learning (MIL) example of classification from WSI.") + parser.add_argument( + "--data_root", default="/PandaChallenge2020/train_images/", help="path to root folder of images" + ) # parser.add_argument('--dataset_json', default='./datalist_panda_0.json', help="path to dataset json file") - parser.add_argument('--dataset_json', default=None, type=str, help="path to dataset json file") + parser.add_argument("--dataset_json", default=None, type=str, help="path to dataset json file") - parser.add_argument('--num_classes', default=5, type=int, help="number of output classes") - parser.add_argument('--mil_mode', default='att_trans', help="MIL algorithm") - parser.add_argument('--tile_count', default=44, type=int, help="number of patches (instances) to extract from WSI image") - parser.add_argument('--tile_size', default=256, type=int, help="size of square patch (instance) in pixels") + parser.add_argument("--num_classes", default=5, type=int, help="number of output classes") + parser.add_argument("--mil_mode", default="att_trans", help="MIL algorithm") + parser.add_argument( + "--tile_count", default=44, type=int, help="number of patches (instances) to extract from WSI image" + ) + parser.add_argument("--tile_size", default=256, type=int, help="size of square patch (instance) in pixels") - parser.add_argument('--checkpoint', default=None, help="load existing checkpoint") - parser.add_argument('--validate', action='store_true', help="run only inference on the validation set, must specify the checkpoint argument") + parser.add_argument("--checkpoint", default=None, help="load existing checkpoint") + parser.add_argument( + "--validate", + action="store_true", + help="run only inference on the validation set, must specify the checkpoint argument", + ) - parser.add_argument('--logdir', default=None, help="path to log directory to store Tensorboard logs") + parser.add_argument("--logdir", default=None, help="path to log directory to store Tensorboard logs") - parser.add_argument('--epochs', default=50, type=int, help="number of training epochs") - parser.add_argument('--batch_size', default=4, type=int, help="batch size, the number of WSI images per gpu") - parser.add_argument('--optim_lr', default=3e-5, type=float, help="initial learning rate") + parser.add_argument("--epochs", default=50, type=int, help="number of training epochs") + parser.add_argument("--batch_size", default=4, type=int, help="batch size, the number of WSI images per gpu") + parser.add_argument("--optim_lr", default=3e-5, type=float, help="initial learning rate") - parser.add_argument('--weight_decay', default=0, type=float, help="optimizer weight decay") - parser.add_argument('--amp', action='store_true', help="use AMP, recommended") - parser.add_argument('--val_every', default=1, type=int, help="run validation after this number of epochs, default 1 to run every epoch") - parser.add_argument('--workers', default=2, type=int, help="number of workers for data loading") + parser.add_argument("--weight_decay", default=0, type=float, help="optimizer weight decay") + parser.add_argument("--amp", action="store_true", help="use AMP, recommended") + parser.add_argument( + "--val_every", + default=1, + type=int, + help="run validation after this number of epochs, default 1 to run every epoch", + ) + parser.add_argument("--workers", default=2, type=int, help="number of workers for data loading") ###for multigpu - parser.add_argument('--distributed', action='store_true', help="use multigpu training, recommended") - parser.add_argument('--world_size', default=1, type=int, help="number of nodes for distributed training") - parser.add_argument('--rank', default=0, type=int, help="node rank for distributed training") - parser.add_argument('--dist-url', default='tcp://127.0.0.1:23456', type=str, help="url used to set up distributed training") - parser.add_argument('--dist-backend', default='nccl', type=str, help="distributed backend") - - parser.add_argument('--quick', action='store_true', help="use a small subset of data for debugging") # for debugging + parser.add_argument("--distributed", action="store_true", help="use multigpu training, recommended") + parser.add_argument("--world_size", default=1, type=int, help="number of nodes for distributed training") + parser.add_argument("--rank", default=0, type=int, help="node rank for distributed training") + parser.add_argument( + "--dist-url", default="tcp://127.0.0.1:23456", type=str, help="url used to set up distributed training" + ) + parser.add_argument("--dist-backend", default="nccl", type=str, help="distributed backend") - parser.add_argument('--optim_lr_trans', default=3e-6, type=float) + parser.add_argument( + "--quick", action="store_true", help="use a small subset of data for debugging" + ) # for debugging + parser.add_argument("--optim_lr_trans", default=3e-6, type=float) args = parser.parse_args() print("Argument values:") for k, v in vars(args).items(): - print(k, '=>', v) - print('-----------------') + print(k, "=>", v) + print("-----------------") return args def train_epoch(model, loader, optimizer, scaler, epoch, args): - '''One train epoch over the dataset''' + """One train epoch over the dataset""" model.train() criterion = nn.BCEWithLogitsLoss() @@ -85,11 +101,11 @@ def train_epoch(model, loader, optimizer, scaler, epoch, args): run_acc = CumulativeAverage() start_time = time.time() - loss, acc = 0., 0. + loss, acc = 0.0, 0.0 for idx, batch_data in enumerate(loader): - data, target = batch_data['image'].cuda(args.rank), batch_data['label'].cuda(args.rank) + data, target = batch_data["image"].cuda(args.rank), batch_data["label"].cuda(args.rank) optimizer.zero_grad(set_to_none=True) @@ -105,7 +121,6 @@ def train_epoch(model, loader, optimizer, scaler, epoch, args): loss.backward() optimizer.step() - acc = (logits.sigmoid().sum(1).detach().round() == target.sum(1).round()).float().mean() run_loss.append(loss) @@ -114,25 +129,25 @@ def train_epoch(model, loader, optimizer, scaler, epoch, args): loss = run_loss.aggregate() acc = run_acc.aggregate() - - - if args.rank==0: - print('Epoch {}/{} {}/{}'.format(epoch, args.epochs, idx, len(loader)), - 'loss: {:.4f}'.format(loss), - 'acc: {:.4f}'.format(acc), - 'time {:.2f}s'.format(time.time() - start_time)) + if args.rank == 0: + print( + "Epoch {}/{} {}/{}".format(epoch, args.epochs, idx, len(loader)), + "loss: {:.4f}".format(loss), + "acc: {:.4f}".format(acc), + "time {:.2f}s".format(time.time() - start_time), + ) start_time = time.time() return loss, acc def val_epoch(model, loader, epoch, args, max_tiles=None): - '''One validation epoch over the dataset''' + """One validation epoch over the dataset""" model.eval() model2 = model if not args.distributed else model.module - has_extra_outputs = (model2.mil_mode=="att_trans_pyramid") + has_extra_outputs = model2.mil_mode == "att_trans_pyramid" extra_outputs = model2.extra_outputs calc_head = model2.calc_head @@ -144,13 +159,13 @@ def val_epoch(model, loader, epoch, args, max_tiles=None): TARGETS = Cumulative() start_time = time.time() - loss, acc = 0., 0. + loss, acc = 0.0, 0.0 with torch.no_grad(): for idx, batch_data in enumerate(loader): - data, target = batch_data['image'].cuda(args.rank), batch_data['label'].cuda(args.rank) + data, target = batch_data["image"].cuda(args.rank), batch_data["label"].cuda(args.rank) with autocast(enabled=args.amp): @@ -161,23 +176,29 @@ def val_epoch(model, loader, epoch, args, max_tiles=None): # and at the very end calculate the classification output logits = [] - logits2 = [] + logits2 = [] - for i in range(int(np.ceil(data.shape[1]/float(max_tiles)))): - data_slize = data[:,i*max_tiles:(i+1)*max_tiles] - logits_slize = model(data_slize, no_head=True) - logits.append(logits_slize) + for i in range(int(np.ceil(data.shape[1] / float(max_tiles)))): + data_slice = data[:, i * max_tiles : (i + 1) * max_tiles] + logits_slice = model(data_slice, no_head=True) + logits.append(logits_slice) if has_extra_outputs: - logits2.append([extra_outputs['layer1'], extra_outputs['layer2'], extra_outputs['layer3'], extra_outputs['layer4']]) - + logits2.append( + [ + extra_outputs["layer1"], + extra_outputs["layer2"], + extra_outputs["layer3"], + extra_outputs["layer4"], + ] + ) logits = torch.cat(logits, dim=1) if has_extra_outputs: - extra_outputs['layer1'] = torch.cat([l[0] for l in logits2], dim=0) - extra_outputs['layer2'] = torch.cat([l[1] for l in logits2], dim=0) - extra_outputs['layer3'] = torch.cat([l[2] for l in logits2], dim=0) - extra_outputs['layer4'] = torch.cat([l[3] for l in logits2], dim=0) + extra_outputs["layer1"] = torch.cat([l[0] for l in logits2], dim=0) + extra_outputs["layer2"] = torch.cat([l[1] for l in logits2], dim=0) + extra_outputs["layer3"] = torch.cat([l[2] for l in logits2], dim=0) + extra_outputs["layer4"] = torch.cat([l[3] for l in logits2], dim=0) logits = calc_head(logits) @@ -186,10 +207,8 @@ def val_epoch(model, loader, epoch, args, max_tiles=None): # we can run inference directly logits = model(data) - loss = criterion(logits, target) - pred = logits.sigmoid().sum(1).detach().round() target = target.sum(1).round() acc = (pred == target).float().mean() @@ -202,37 +221,33 @@ def val_epoch(model, loader, epoch, args, max_tiles=None): PREDS.extend(pred) TARGETS.extend(target) - if args.rank==0: - print('Val epoch {}/{} {}/{}'.format(epoch, args.epochs, idx, len(loader)), - 'loss: {:.4f}'.format(loss), - 'acc: {:.4f}'.format(acc), - 'time {:.2f}s'.format(time.time() - start_time)) + if args.rank == 0: + print( + "Val epoch {}/{} {}/{}".format(epoch, args.epochs, idx, len(loader)), + "loss: {:.4f}".format(loss), + "acc: {:.4f}".format(acc), + "time {:.2f}s".format(time.time() - start_time), + ) start_time = time.time() - - #Calculate QWK metric (Quadratic Weigted Kappa) https://en.wikipedia.org/wiki/Cohen%27s_kappa + # Calculate QWK metric (Quadratic Weigted Kappa) https://en.wikipedia.org/wiki/Cohen%27s_kappa PREDS = PREDS.get_buffer().cpu().numpy() TARGETS = TARGETS.get_buffer().cpu().numpy() - qwk = cohen_kappa_score(PREDS.astype(np.float64), TARGETS.astype(np.float64), weights='quadratic') - + qwk = cohen_kappa_score(PREDS.astype(np.float64), TARGETS.astype(np.float64), weights="quadratic") return loss, acc, qwk -def save_checkpoint(model, epoch, args, filename='model.pt', best_acc=0): - '''Save checkpoint''' +def save_checkpoint(model, epoch, args, filename="model.pt", best_acc=0): + """Save checkpoint""" - state_dict = model.state_dict() if not args.distributed else model.module.state_dict() + state_dict = model.state_dict() if not args.distributed else model.module.state_dict() - save_dict = { - 'epoch': epoch, - 'best_acc': best_acc, - 'state_dict': state_dict - } + save_dict = {"epoch": epoch, "best_acc": best_acc, "state_dict": state_dict} - filename=os.path.join(args.logdir, filename) + filename = os.path.join(args.logdir, filename) torch.save(save_dict, filename) - print('Saving checkpoint', filename) + print("Saving checkpoint", filename) class LabelEncodeIntegerGraded(Transform): @@ -247,10 +262,11 @@ class LabelEncodeIntegerGraded(Transform): Defaults to ``['label']``. """ - def __init__( self, num_classes, keys=['label']) : + + def __init__(self, num_classes, keys=["label"]): super().__init__() self.keys = keys - self.num_classes=num_classes + self.num_classes = num_classes def __call__(self, data): @@ -259,7 +275,7 @@ def __call__(self, data): label = int(d[key]) lz = np.zeros(self.num_classes, dtype=np.float32) - lz[:label] = 1. + lz[:label] = 1.0 # alternative oneliner lz=(np.arange(self.num_classes) {'image' : Nx3xHxW} # followed by the default collate which will form a batch BxNx3xHxW def list_data_collate(batch: collections.abc.Sequence): @@ -299,21 +315,24 @@ def list_data_collate(batch: collections.abc.Sequence): batch[i] = data return default_collate(batch) + def main_worker(gpu, args): args.gpu = gpu if args.distributed: args.rank = args.rank * torch.cuda.device_count() + gpu - dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url, world_size=args.world_size, rank=args.rank) + dist.init_process_group( + backend=args.dist_backend, init_method=args.dist_url, world_size=args.world_size, rank=args.rank + ) - print(args.rank, ' gpu', args.gpu) + print(args.rank, " gpu", args.gpu) - torch.cuda.set_device(args.gpu) #use this default device (same as args.device if not distributed) + torch.cuda.set_device(args.gpu) # use this default device (same as args.device if not distributed) torch.backends.cudnn.benchmark = True - if args.rank==0: - print('Batch size is:', args.batch_size, 'epochs', args.epochs) + if args.rank == 0: + print("Batch size is:", args.batch_size, "epochs", args.epochs) ############# # Create MONAI dataset @@ -328,16 +347,22 @@ def main_worker(gpu, args): base_dir=args.data_root, ) - if args.quick: #for debugging on a small subset + if args.quick: # for debugging on a small subset training_list = training_list[:16] validation_list = validation_list[:16] - train_transform = Compose( [ LoadImageD(keys=["image"], reader=WSIReader, backend="TiffFile", dtype=np.uint8, level=1, image_only=True), LabelEncodeIntegerGraded(keys=["label"], num_classes=args.num_classes), - TileOnGridd(keys=["image"], tile_count=args.tile_count, tile_size=args.tile_size, random_offset=True, background_val=255, return_list_of_dicts=True), + TileOnGridd( + keys=["image"], + tile_count=args.tile_count, + tile_size=args.tile_size, + random_offset=True, + background_val=255, + return_list_of_dicts=True, + ), RandFlipd(keys=["image"], spatial_axis=0, prob=0.5), RandFlipd(keys=["image"], spatial_axis=1, prob=0.5), RandRotate90d(keys=["image"], prob=0.5), @@ -350,7 +375,14 @@ def main_worker(gpu, args): [ LoadImageD(keys=["image"], reader=WSIReader, backend="TiffFile", dtype=np.uint8, level=1, image_only=True), LabelEncodeIntegerGraded(keys=["label"], num_classes=args.num_classes), - TileOnGridd(keys=["image"], tile_count=None, tile_size=args.tile_size, random_offset=False, background_val=255, return_list_of_dicts=True), + TileOnGridd( + keys=["image"], + tile_count=None, + tile_size=args.tile_size, + random_offset=False, + background_val=255, + return_list_of_dicts=True, + ), ScaleIntensityRangeD(keys=["image"], a_min=np.float32(255), a_max=np.float32(0)), ToTensord(keys=["image", "label"]), ] @@ -362,11 +394,29 @@ def main_worker(gpu, args): train_sampler = DistributedSampler(dataset_train) if args.distributed else None val_sampler = DistributedSampler(dataset_valid, shuffle=False) if args.distributed else None - train_loader = torch.utils.data.DataLoader(dataset_train, batch_size=args.batch_size, shuffle=(train_sampler is None), num_workers=args.workers, pin_memory=True, multiprocessing_context='spawn', sampler=train_sampler, collate_fn=list_data_collate) - valid_loader = torch.utils.data.DataLoader(dataset_valid, batch_size=1, shuffle=False, num_workers=args.workers, pin_memory=True, multiprocessing_context='spawn', sampler=val_sampler, collate_fn=list_data_collate) + train_loader = torch.utils.data.DataLoader( + dataset_train, + batch_size=args.batch_size, + shuffle=(train_sampler is None), + num_workers=args.workers, + pin_memory=True, + multiprocessing_context="spawn", + sampler=train_sampler, + collate_fn=list_data_collate, + ) + valid_loader = torch.utils.data.DataLoader( + dataset_valid, + batch_size=1, + shuffle=False, + num_workers=args.workers, + pin_memory=True, + multiprocessing_context="spawn", + sampler=val_sampler, + collate_fn=list_data_collate, + ) - if args.rank==0: - print('Dataset training:', len(dataset_train), 'validation:', len(dataset_valid)) + if args.rank == 0: + print("Dataset training:", len(dataset_train), "validation:", len(dataset_valid)) # os.environ["TORCH_HOME"] = "../../torchhome" #TODO, remove later model = milmodel.MILModel(num_classes=args.num_classes, pretrained=True, mil_mode=args.mil_mode) @@ -374,10 +424,12 @@ def main_worker(gpu, args): best_acc = 0 start_epoch = 0 if args.checkpoint is not None: - checkpoint = torch.load(args.checkpoint, map_location='cpu') - model.load_state_dict(checkpoint['state_dict']) - if 'epoch' in checkpoint: start_epoch = checkpoint['epoch'] - if 'best_acc' in checkpoint: best_acc = checkpoint['best_acc'] + checkpoint = torch.load(args.checkpoint, map_location="cpu") + model.load_state_dict(checkpoint["state_dict"]) + if "epoch" in checkpoint: + start_epoch = checkpoint["epoch"] + if "best_acc" in checkpoint: + best_acc = checkpoint["best_acc"] print("=> loaded checkpoint '{}' (epoch {}) (bestacc {})".format(args.checkpoint, start_epoch, best_acc)) model.cuda(args.gpu) @@ -391,94 +443,104 @@ def main_worker(gpu, args): epoch_time = time.time() val_loss, val_acc, qwk = val_epoch(model, valid_loader, epoch=0, args=args, max_tiles=args.tile_count) if args.rank == 0: - print('Final validation loss: {:.4f}'.format(val_loss), 'acc: {:.4f}'.format(val_acc), 'qwk: {:.4f}'.format(qwk), 'time {:.2f}s'.format(time.time() - epoch_time)) + print( + "Final validation loss: {:.4f}".format(val_loss), + "acc: {:.4f}".format(val_acc), + "qwk: {:.4f}".format(qwk), + "time {:.2f}s".format(time.time() - epoch_time), + ) exit(0) - params = model.parameters() if args.mil_mode in ["att_trans", "att_trans_pyramid"]: m = model if not args.distributed else model.module - params = [{'params': list(m.attention.parameters()) + list(m.myfc.parameters()) + list(m.net.parameters())}, - {'params': list(m.transformer.parameters()), 'lr': args.optim_lr_trans, 'weight_decay': 0.1 }] - + params = [ + {"params": list(m.attention.parameters()) + list(m.myfc.parameters()) + list(m.net.parameters())}, + {"params": list(m.transformer.parameters()), "lr": args.optim_lr_trans, "weight_decay": 0.1}, + ] optimizer = torch.optim.AdamW(params, lr=args.optim_lr, weight_decay=args.weight_decay) scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=args.epochs, eta_min=0) if args.logdir is not None and args.rank == 0: writer = SummaryWriter(log_dir=args.logdir) - if args.rank==0: - print('Writing Tensorboard logs to ', writer.log_dir) + if args.rank == 0: + print("Writing Tensorboard logs to ", writer.log_dir) else: writer = None - ###RUN TRAINING n_epochs = args.epochs - val_acc_max = 0. + val_acc_max = 0.0 scaler = None if args.amp: # new native amp scaler = GradScaler() - for epoch in range(start_epoch, n_epochs): if args.distributed: train_sampler.set_epoch(epoch) torch.distributed.barrier() - print(args.rank, time.ctime(), 'Epoch:', epoch) + print(args.rank, time.ctime(), "Epoch:", epoch) epoch_time = time.time() train_loss, train_acc = train_epoch(model, train_loader, optimizer, scaler=scaler, epoch=epoch, args=args) if args.rank == 0: - print('Final training {}/{}'.format(epoch, n_epochs - 1), 'loss: {:.4f}'.format(train_loss), - 'acc: {:.4f}'.format(train_acc), 'time {:.2f}s'.format(time.time() - epoch_time)) - - if args.rank==0 and writer is not None: - writer.add_scalar('train_loss', train_loss, epoch) - writer.add_scalar('train_acc', train_acc, epoch) + print( + "Final training {}/{}".format(epoch, n_epochs - 1), + "loss: {:.4f}".format(train_loss), + "acc: {:.4f}".format(train_acc), + "time {:.2f}s".format(time.time() - epoch_time), + ) + if args.rank == 0 and writer is not None: + writer.add_scalar("train_loss", train_loss, epoch) + writer.add_scalar("train_acc", train_acc, epoch) if args.distributed: torch.distributed.barrier() - - b_new_best=False + b_new_best = False val_acc = 0 - if ( (epoch+1) % args.val_every == 0): + if (epoch + 1) % args.val_every == 0: epoch_time = time.time() val_loss, val_acc, qwk = val_epoch(model, valid_loader, epoch=epoch, args=args, max_tiles=args.tile_count) if args.rank == 0: - print('Final validation {}/{}'.format(epoch, n_epochs - 1), 'loss: {:.4f}'.format(val_loss), - 'acc: {:.4f}'.format(val_acc),'qwk: {:.4f}'.format(qwk), 'time {:.2f}s'.format(time.time() - epoch_time)) + print( + "Final validation {}/{}".format(epoch, n_epochs - 1), + "loss: {:.4f}".format(val_loss), + "acc: {:.4f}".format(val_acc), + "qwk: {:.4f}".format(qwk), + "time {:.2f}s".format(time.time() - epoch_time), + ) if writer is not None: - writer.add_scalar('val_loss', val_loss, epoch) - writer.add_scalar('val_acc', val_acc, epoch) - writer.add_scalar('val_qwk', qwk, epoch) + writer.add_scalar("val_loss", val_loss, epoch) + writer.add_scalar("val_acc", val_acc, epoch) + writer.add_scalar("val_qwk", qwk, epoch) val_acc = qwk if val_acc > val_acc_max: - print('qwk ({:.6f} --> {:.6f})'.format(val_acc_max, val_acc)) + print("qwk ({:.6f} --> {:.6f})".format(val_acc_max, val_acc)) val_acc_max = val_acc b_new_best = True - if args.rank == 0 and args.logdir is not None: - save_checkpoint(model, epoch, args, best_acc=val_acc, filename='model_final.pt') + save_checkpoint(model, epoch, args, best_acc=val_acc, filename="model_final.pt") if b_new_best: - print('Copying to model.pt new best model!!!!') - shutil.copyfile(os.path.join(args.logdir, 'model_final.pt'), os.path.join(args.logdir, 'model.pt')) + print("Copying to model.pt new best model!!!!") + shutil.copyfile(os.path.join(args.logdir, "model_final.pt"), os.path.join(args.logdir, "model.pt")) scheduler.step() - print('ALL DONE') + print("ALL DONE") + -if __name__ == '__main__': +if __name__ == "__main__": main() From dfa955728c129d2eb742ff068eb29df91ad955f6 Mon Sep 17 00:00:00 2001 From: myron Date: Fri, 19 Nov 2021 14:16:32 -0800 Subject: [PATCH 15/18] small fixes Signed-off-by: myron --- .../panda_mil_train_evaluate_pytorch_gpu.py | 18 ++++++++---------- 1 file changed, 8 insertions(+), 10 deletions(-) diff --git a/pathology/multiple_instance_learning/panda_mil_train_evaluate_pytorch_gpu.py b/pathology/multiple_instance_learning/panda_mil_train_evaluate_pytorch_gpu.py index 9a3af9926f..c37bd5196d 100644 --- a/pathology/multiple_instance_learning/panda_mil_train_evaluate_pytorch_gpu.py +++ b/pathology/multiple_instance_learning/panda_mil_train_evaluate_pytorch_gpu.py @@ -33,7 +33,6 @@ def parse_args(): parser.add_argument( "--data_root", default="/PandaChallenge2020/train_images/", help="path to root folder of images" ) - # parser.add_argument('--dataset_json', default='./datalist_panda_0.json', help="path to dataset json file") parser.add_argument("--dataset_json", default=None, type=str, help="path to dataset json file") parser.add_argument("--num_classes", default=5, type=int, help="number of output classes") @@ -79,8 +78,6 @@ def parse_args(): "--quick", action="store_true", help="use a small subset of data for debugging" ) # for debugging - parser.add_argument("--optim_lr_trans", default=3e-6, type=float) - args = parser.parse_args() print("Argument values:") @@ -203,8 +200,7 @@ def val_epoch(model, loader, epoch, args, max_tiles=None): logits = calc_head(logits) else: - # if number of instances is not big - # we can run inference directly + # if number of instances is not big, we can run inference directly logits = model(data) loss = criterion(logits, target) @@ -305,10 +301,13 @@ def main(): main_worker(0, args) -# combine instances from a list of dicts into a single dict, by stacking them along first dim -# [{'image' : 3xHxW}, {'image' : 3xHxW}, {'image' : 3xHxW}...] - > {'image' : Nx3xHxW} -# followed by the default collate which will form a batch BxNx3xHxW def list_data_collate(batch: collections.abc.Sequence): + ''' + Combine instances from a list of dicts into a single dict, by stacking them along first dim + [{'image' : 3xHxW}, {'image' : 3xHxW}, {'image' : 3xHxW}...] - > {'image' : Nx3xHxW} + followed by the default collate which will form a batch BxNx3xHxW + ''' + for i, item in enumerate(batch): data = item[0] data["image"] = torch.stack([ix["image"] for ix in item], dim=0) @@ -418,7 +417,6 @@ def main_worker(gpu, args): if args.rank == 0: print("Dataset training:", len(dataset_train), "validation:", len(dataset_valid)) - # os.environ["TORCH_HOME"] = "../../torchhome" #TODO, remove later model = milmodel.MILModel(num_classes=args.num_classes, pretrained=True, mil_mode=args.mil_mode) best_acc = 0 @@ -458,7 +456,7 @@ def main_worker(gpu, args): m = model if not args.distributed else model.module params = [ {"params": list(m.attention.parameters()) + list(m.myfc.parameters()) + list(m.net.parameters())}, - {"params": list(m.transformer.parameters()), "lr": args.optim_lr_trans, "weight_decay": 0.1}, + {"params": list(m.transformer.parameters()), "lr": 6e-6, "weight_decay": 0.1}, ] optimizer = torch.optim.AdamW(params, lr=args.optim_lr, weight_decay=args.weight_decay) From 0c1192e3d1142f9c5dca28c74dd5de17bc04d2ed Mon Sep 17 00:00:00 2001 From: myron Date: Tue, 23 Nov 2021 10:53:20 -0800 Subject: [PATCH 16/18] stats Signed-off-by: myron --- pathology/multiple_instance_learning/README.md | 11 +++++++++++ .../mil_train_loss.png | Bin 0 -> 31514 bytes .../multiple_instance_learning/mil_val_loss.png | Bin 0 -> 44969 bytes .../multiple_instance_learning/mil_val_qwk.png | Bin 0 -> 36959 bytes 4 files changed, 11 insertions(+) create mode 100644 pathology/multiple_instance_learning/mil_train_loss.png create mode 100644 pathology/multiple_instance_learning/mil_val_loss.png create mode 100644 pathology/multiple_instance_learning/mil_val_qwk.png diff --git a/pathology/multiple_instance_learning/README.md b/pathology/multiple_instance_learning/README.md index 40eaeb3d80..d123976ddc 100644 --- a/pathology/multiple_instance_learning/README.md +++ b/pathology/multiple_instance_learning/README.md @@ -101,6 +101,17 @@ python -u panda_mil_train_evaluate_pytorch_gpu.py --validate ``` +### Stats + +Expected train and validation loss curves + +![mil_train_loss](./mil_train_loss.png) +![mil_val_loss](./mil_val_loss.png) + +Expected validation QWK metric + +![mil_val_qwk](./mil_val_qwk.png) + ## Questions and bugs - For questions relating to the use of MONAI, please us our [Discussions tab](https://github.com/Project-MONAI/MONAI/discussions) on the main repository of MONAI. diff --git a/pathology/multiple_instance_learning/mil_train_loss.png 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b/pathology/multiple_instance_learning/README.md index d123976ddc..f316819ccc 100644 --- a/pathology/multiple_instance_learning/README.md +++ b/pathology/multiple_instance_learning/README.md @@ -22,12 +22,12 @@ The script is tested with: ## Dependencies and installation ### MONAI - -```bash -pip install "git+https://github.com/Project-MONAI/MONAI#egg=monai[tifffile,imagecodecs]" +Please install the required dependencies +``` +pip install tifffile +pip install imagecodecs ``` -For more information please check out [the installation guide](https://docs.monai.io/en/latest/installation.html). ### Data From 95157ed322e73e664d95709a16354c9c62df79c1 Mon Sep 17 00:00:00 2001 From: myron Date: Wed, 24 Nov 2021 15:00:11 -0800 Subject: [PATCH 18/18] README fixes Signed-off-by: myron --- pathology/multiple_instance_learning/README.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/pathology/multiple_instance_learning/README.md b/pathology/multiple_instance_learning/README.md index f316819ccc..bca59bbe2c 100644 --- a/pathology/multiple_instance_learning/README.md +++ b/pathology/multiple_instance_learning/README.md @@ -1,3 +1,4 @@ + # Multiple Instance Learning (MIL) Examples This tutorial contains a baseline method of Multiple Instance Learning (MIL) classification from Whole Slide Images (WSI). @@ -22,12 +23,15 @@ The script is tested with: ## Dependencies and installation ### MONAI + Please install the required dependencies -``` + +```bash pip install tifffile pip install imagecodecs ``` +For more information please check out [the installation guide](https://docs.monai.io/en/latest/installation.html). ### Data

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