def tb_log_valid_epoch_vars(engine, logger, event_name):
log_tag = 'valid_iter'
# log monitored epoch metrics
epoch_metrics = engine.state.epoch_metrics
confusion_matrix = epoch_metrics['confusion_matrix'] # [1:, 1:]
ious = calculate_iou(confusion_matrix)
dices = calculate_dice(confusion_matrix)
mean_ious = np.mean(list(ious.values()))
mean_dices = np.mean(list(dices.values()))
logger.writer.add_scalar('mIoU', mean_ious, engine.state.epoch)
logger.writer.add_scalar('mIoU', mean_dices, engine.state.epoch)
def validator_epoch_comp_callback(engine):
# log ignite metrics
# logging_logger.info(engine.state.metrics)
# ious = engine.state.metrics['iou']
# msg = 'IoU: '
# for ins_id, iou in enumerate(ious):
# msg += '{:d}: {:.3f}, '.format(ins_id + 1, iou)
# logging_logger.info(msg)
# logging_logger.info('nonzero mean IoU for all data: {:.3f}'.format(ious[ious > 0].mean()))
# log monitored epoch metrics
epoch_metrics = engine.state.epoch_metrics
######### NOTICE: Two metrics are available but different ##########
### 1. mean metrics for all data calculated by confusion matrix ####
'''
compared with using confusion_matrix[1:, 1:] in original code,
we use the full confusion matrix and only present non-background result
'''
confusion_matrix = epoch_metrics['confusion_matrix'] # [1:, 1:]
ious = calculate_iou(confusion_matrix)
dices = calculate_dice(confusion_matrix)
mean_ious = np.mean(list(ious.values()))
mean_dices = np.mean(list(dices.values()))
std_ious = np.std(list(ious.values()))
std_dices = np.std(list(dices.values()))
logging_logger.info('mean IoU: %.3f, std: %.3f, for each class: %s' %
(mean_ious, std_ious, ious))
logging_logger.info('mean Dice: %.3f, std: %.3f, for each class: %s' %
(mean_dices, std_dices, dices))
### 2. mean metrics for all data calculated by definition ###
iou_data_mean = epoch_metrics['iou'].data_mean()
dice_data_mean = epoch_metrics['dice'].data_mean()
logging_logger.info('data (%d) mean IoU: %.3f, std: %.3f' %
(len(iou_data_mean['items']),
iou_data_mean['mean'], iou_data_mean['std']))
logging_logger.info('data (%d) mean Dice: %.3f, std: %.3f' %
(len(dice_data_mean['items']),
dice_data_mean['mean'], dice_data_mean['std']))
# record metrics in trainer every epoch
# trainer.state.metrics_records[trainer.state.epoch] = \
# {'miou': mean_ious, 'std_miou': std_ious,
# 'mdice': mean_dices, 'std_mdice': std_dices}
trainer.state.metrics_records[trainer.state.epoch] = \
{'miou': iou_data_mean['mean'], 'std_miou': iou_data_mean['std'],
'mdice': dice_data_mean['mean'], 'std_mdice': dice_data_mean['std']}
def test_model(test_loader, net):
net.eval()
device = params['device']
batch_size = params['batch_size']
test_loss = 0
test_acc = 0
test_iou = {}
with torch.no_grad():
for batch_index, (img, target) in enumerate(test_loader):
img, target = img.to(device), target.to(device)
if model_version == 'deeplab':
output = net(img)['out']
else:
output = net(img)
target = target.long()
loss = criterion(output, target).item()
test_loss += loss
pred = aux.get_predicted_image(output)
output, target, pred = output.detach().cpu(), target.detach(
).cpu(), pred.detach().cpu()
# compute number of correct predictions in the batch
test_accuracy = metrics.calculate_accuracy(output, target)
test_acc += test_accuracy
iou_inds = metrics.calculate_iou(pred, target)
for key in iou_inds:
if key not in test_iou:
test_iou[key] = iou_inds[key]
else:
test_iou[key] += iou_inds[key]
test_loss = test_loss / (len(test_loader.dataset) / batch_size)
test_acc = 100 * (test_acc / (len(test_loader.dataset) / batch_size))
test_iou = metrics.convert_batched_iou(
test_iou, (len(test_loader.dataset) / batch_size))
mIoU = metrics.get_mIoU(test_iou)
mIoU_desc = metrics.miou_to_string(test_iou)
return test_loss, test_acc, mIoU, mIoU_desc
def val_one_epoch(val_loader, net):
net.eval()
device = params['device']
batch_size = params['batch_size']
val_loss = 0
val_acc = 0
val_iou = {}
pred = 0
with torch.no_grad():
for batch_index, (img, target) in enumerate(val_loader):
img, target = img.to(device), target.to(device)
output = net(img)
target = target.long()
loss = criterion(output, target).item()
val_loss += loss
pred = aux.get_predicted_image(output)
# Desvinculamos el valor de los nuevos targets y los pasamos a CPU para calcular las métricas
output, target, pred = output.detach().cpu(), target.detach(
).cpu(), pred.detach().cpu()
# compute number of correct predictions in the batch
val_accuracy = metrics.calculate_accuracy(output, target)
val_acc += val_accuracy
iou_inds = metrics.calculate_iou(pred, target)
for key in iou_inds:
if key not in val_iou:
val_iou[key] = iou_inds[key]
else:
val_iou[key] += iou_inds[key]
#print('Batch index: {}, loss: {}, accuracy: {:.2f}%'.format(batch_index, loss, val_accuracy * 100))
# Average acc across all correct predictions batches now
val_loss = val_loss / (len(val_loader.dataset) / batch_size)
val_acc = 100 * (val_acc / (len(val_loader.dataset) / batch_size))
val_iou = metrics.convert_batched_iou(
val_iou, (len(val_loader.dataset) / batch_size))
mIoU = metrics.get_mIoU(val_iou)
#print('\nValidation set: Average loss: {:.4f}, Accuracy: {:.0f}%, mIoU: {:.4f}\n'.format(val_loss, val_acc, mIoU))
mIoU_desc = metrics.miou_to_string(val_iou)
return val_loss, val_acc, mIoU, mIoU_desc
def evaluator_epoch_comp_callback(engine):
# save masks for each batch
batch_output = engine.state.output
input_filenames = batch_output['input_filename']
masks = batch_output['mask']
for i, input_filename in enumerate(input_filenames):
mask = cv2.resize(masks[i],
dsize=(utils.cropped_width, utils.cropped_height),
interpolation=cv2.INTER_AREA)
# if pad:
# h_start, w_start = utils.h_start, utils.w_start
# h, w = mask.shape
# # recover to original shape
# full_mask = np.zeros((original_height, original_width))
# full_mask[h_start:h_start + h, w_start:w_start + w] = t_mask
# mask = full_mask
#print("Input Filename-->", input_filename)
#instrument_folder_name = input_filename.parent.parent.name
instrument_folder_name = os.path.basename(
os.path.dirname(os.path.dirname(input_filename)))
#print("instrument_folder_name-->", instrument_folder_name)
# mask_folder/instrument_dataset_x/problem_type_masks/framexxx.png
mask_folder = mask_save_dir / instrument_folder_name / utils.mask_folder[
args.problem_type]
mask_folder.mkdir(exist_ok=True, parents=True)
mask_filename = mask_folder / os.path.basename(input_filename)
#print("mask_filename-->", mask_filename)
cv2.imwrite(str(mask_filename), mask)
if 'TAPNet' in args.model:
attmap = batch_output['attmap'][i]
attmap_folder = mask_save_dir / instrument_folder_name / '_'.join(
args.problem_type, 'attmaps')
attmap_folder.mkdir(exist_ok=True, parents=True)
attmap_filename = attmap_folder / input_filename.name
cv2.imwrite(str(attmap_filename), attmap)
evaluator.run(eval_loader)
# validator engine
validator = engine.Engine(valid_step)
# monitor loss
valid_ra_loss = imetrics.RunningAverage(
output_transform=lambda x: x['loss'], alpha=0.98)
valid_ra_loss.attach(validator, 'valid_ra_loss')
# monitor validation loss over epoch
valid_loss = imetrics.Loss(loss_func,
output_transform=lambda x:
(x['output'], x['target']))
valid_loss.attach(validator, 'valid_loss')
# monitor <data> mean metrics
valid_data_miou = imetrics.RunningAverage(
output_transform=lambda x: x['iou'].data_mean()['mean'], alpha=0.98)
valid_data_miou.attach(validator, 'mIoU')
valid_data_mdice = imetrics.RunningAverage(
output_transform=lambda x: x['dice'].data_mean()['mean'], alpha=0.98)
valid_data_mdice.attach(validator, 'mDice')
# show metrics on progress bar (after every iteration)
valid_pbar = c_handlers.ProgressBar(persist=True, dynamic_ncols=True)
valid_metric_names = ['valid_ra_loss', 'mIoU', 'mDice']
valid_pbar.attach(validator, metric_names=valid_metric_names)
# ## monitor ignite IoU (the same as iou we are using) ###
# cm = imetrics.ConfusionMatrix(num_classes,
# output_transform=lambda x: (x['output'], x['target']))
# imetrics.IoU(cm,
# ignore_index=0
# ).attach(validator, 'iou')
# # monitor ignite mean iou (over all classes even not exist in gt)
# mean_iou = imetrics.mIoU(cm,
# ignore_index=0
# ).attach(validator, 'mean_iou')
@validator.on(engine.Events.STARTED)
def validator_start_callback(engine):
pass
@validator.on(engine.Events.EPOCH_STARTED)
def validator_epoch_start_callback(engine):
engine.state.epoch_metrics = {
# directly use definition to calculate
'iou':
MetricRecord(),
'dice':
MetricRecord(),
'confusion_matrix':
np.zeros((num_classes, num_classes), dtype=np.uint32),
}
# evaluate after iter finish
@validator.on(engine.Events.ITERATION_COMPLETED)
def validator_iter_comp_callback(engine):
pass
# evaluate after epoch finish
@validator.on(engine.Events.EPOCH_COMPLETED)
def validator_epoch_comp_callback(engine):
# log ignite metrics
# logging_logger.info(engine.state.metrics)
# ious = engine.state.metrics['iou']
# msg = 'IoU: '
# for ins_id, iou in enumerate(ious):
# msg += '{:d}: {:.3f}, '.format(ins_id + 1, iou)
# logging_logger.info(msg)
# logging_logger.info('nonzero mean IoU for all data: {:.3f}'.format(ious[ious > 0].mean()))
# log monitored epoch metrics
epoch_metrics = engine.state.epoch_metrics
######### NOTICE: Two metrics are available but different ##########
### 1. mean metrics for all data calculated by confusion matrix ####
'''
compared with using confusion_matrix[1:, 1:] in original code,
we use the full confusion matrix and only present non-background result
'''
confusion_matrix = epoch_metrics['confusion_matrix'] # [1:, 1:]
ious = calculate_iou(confusion_matrix)
dices = calculate_dice(confusion_matrix)
mean_ious = np.mean(list(ious.values()))
mean_dices = np.mean(list(dices.values()))
std_ious = np.std(list(ious.values()))
std_dices = np.std(list(dices.values()))
logging_logger.info('mean IoU: %.3f, std: %.3f, for each class: %s' %
(mean_ious, std_ious, ious))
logging_logger.info('mean Dice: %.3f, std: %.3f, for each class: %s' %
(mean_dices, std_dices, dices))
### 2. mean metrics for all data calculated by definition ###
iou_data_mean = epoch_metrics['iou'].data_mean()
dice_data_mean = epoch_metrics['dice'].data_mean()
logging_logger.info('data (%d) mean IoU: %.3f, std: %.3f' %
(len(iou_data_mean['items']),
iou_data_mean['mean'], iou_data_mean['std']))
logging_logger.info('data (%d) mean Dice: %.3f, std: %.3f' %
(len(dice_data_mean['items']),
dice_data_mean['mean'], dice_data_mean['std']))
# record metrics in trainer every epoch
# trainer.state.metrics_records[trainer.state.epoch] = \
# {'miou': mean_ious, 'std_miou': std_ious,
# 'mdice': mean_dices, 'std_mdice': std_dices}
trainer.state.metrics_records[trainer.state.epoch] = \
{'miou': iou_data_mean['mean'], 'std_miou': iou_data_mean['std'],
'mdice': dice_data_mean['mean'], 'std_mdice': dice_data_mean['std']}
# log interal variables(attention maps, outputs, etc.) on validation
def tb_log_valid_iter_vars(engine, logger, event_name):
log_tag = 'valid_iter'
output = engine.state.output
batch_size = output['output'].shape[0]
res_grid = tvutils.make_grid(
torch.cat([
output['output_argmax'].unsqueeze(1),
output['target'].unsqueeze(1),
]),
padding=2,
normalize=False, # show origin image
nrow=batch_size).cpu()
logger.writer.add_image(tag='%s (outputs, targets)' % (log_tag),
img_tensor=res_grid)
if 'TAPNet' in args.model:
# log attention maps and other internal values
inter_vals_grid = tvutils.make_grid(torch.cat([
output['attmap'],
]),
padding=2,
normalize=True,
nrow=batch_size).cpu()
logger.writer.add_image(tag='%s internal vals' % (log_tag),
img_tensor=inter_vals_grid)
def tb_log_valid_epoch_vars(engine, logger, event_name):
log_tag = 'valid_iter'
# log monitored epoch metrics
epoch_metrics = engine.state.epoch_metrics
confusion_matrix = epoch_metrics['confusion_matrix'] # [1:, 1:]
ious = calculate_iou(confusion_matrix)
dices = calculate_dice(confusion_matrix)
mean_ious = np.mean(list(ious.values()))
mean_dices = np.mean(list(dices.values()))
logger.writer.add_scalar('mIoU', mean_ious, engine.state.epoch)
logger.writer.add_scalar('mIoU', mean_dices, engine.state.epoch)
if args.tb_log:
# log internal values
tb_logger.attach(validator,
log_handler=tb_log_valid_iter_vars,
event_name=engine.Events.ITERATION_COMPLETED)
tb_logger.attach(validator,
log_handler=tb_log_valid_epoch_vars,
event_name=engine.Events.EPOCH_COMPLETED)
# tb_logger.attach(validator, log_handler=OutputHandler('valid_iter', valid_metric_names),
# event_name=engine.Events.ITERATION_COMPLETED)
tb_logger.attach(validator,
log_handler=OutputHandler('valid_epoch',
['valid_loss']),
event_name=engine.Events.EPOCH_COMPLETED)
# score function for model saving
ckpt_score_function = lambda engine: \
np.mean(list(calculate_iou(engine.state.epoch_metrics['confusion_matrix']).values()))
# ckpt_score_function = lambda engine: engine.state.epoch_metrics['iou'].data_mean()['mean']
ckpt_filename_prefix = 'fold_%d' % fold
# model saving handler
model_ckpt_handler = handlers.ModelCheckpoint(
dirname=args.model_save_dir,
filename_prefix=ckpt_filename_prefix,
score_function=ckpt_score_function,
create_dir=True,
require_empty=False,
save_as_state_dict=True,
atomic=True)
validator.add_event_handler(event_name=engine.Events.EPOCH_COMPLETED,
handler=model_ckpt_handler,
to_save={
'model': model,
})
# early stop
# trainer=trainer, but should be handled by validator
early_stopping = handlers.EarlyStopping(patience=args.es_patience,
score_function=ckpt_score_function,
trainer=trainer)
validator.add_event_handler(event_name=engine.Events.EPOCH_COMPLETED,
handler=early_stopping)
# evaluate after epoch finish
@trainer.on(engine.Events.EPOCH_COMPLETED)
def trainer_epoch_comp_callback(engine):
validator.run(valid_loader)
trainer.run(train_loader, max_epochs=args.max_epochs)
if args.tb_log:
# close tb_logger
tb_logger.close()
return trainer.state.metrics_records
def valid_step(engine, batch):
with torch.no_grad():
model.eval()
inputs = batch['input'].cuda(non_blocking=True)
targets = batch['target'].cuda(non_blocking=True)
# additional arguments
add_params = {}
# for TAPNet, add attention maps
if 'TAPNet' in args.model:
add_params['attmap'] = batch['attmap'].cuda(non_blocking=True)
# output logits
outputs = model(inputs, **add_params)
# loss
loss = loss_func(outputs, targets)
output_softmaxs = torch.softmax(outputs, dim=1)
output_argmaxs = output_softmaxs.argmax(dim=1)
# output_classes and target_classes: <b, h, w>
output_classes = output_argmaxs.cpu().numpy()
target_classes = targets.cpu().numpy()
# record current batch metrics
iou_mRecords = MetricRecord()
dice_mRecords = MetricRecord()
cm_b = np.zeros((num_classes, num_classes), dtype=np.uint32)
for output_class, target_class in zip(output_classes,
target_classes):
# calculate metrics for each frame
# calculate using confusion matrix or dirctly using definition
cm = calculate_confusion_matrix_from_arrays(
output_class, target_class, num_classes)
iou_mRecords.update_record(calculate_iou(cm))
dice_mRecords.update_record(calculate_dice(cm))
cm_b += cm
######## calculate directly using definition ##########
# iou_mRecords.update_record(iou_multi_np(target_class, output_class))
# dice_mRecords.update_record(dice_multi_np(target_class, output_class))
# accumulate batch metrics to engine state
engine.state.epoch_metrics['confusion_matrix'] += cm_b
engine.state.epoch_metrics['iou'].merge(iou_mRecords)
engine.state.epoch_metrics['dice'].merge(dice_mRecords)
return_dict = {
'loss': loss.item(),
'output': outputs,
'output_argmax': output_argmaxs,
'target': targets,
# for monitoring
'iou': iou_mRecords,
'dice': dice_mRecords,
}
if 'TAPNet' in args.model:
# for TAPNet, update attention maps after each iteration
valid_loader.dataset.update_attmaps(
output_softmaxs.cpu().numpy(), batch['abs_idx'].numpy())
# for TAPNet, return extra internal values
return_dict['attmap'] = add_params['attmap']
# TODO: for TAPNet, return internal self-learned attention maps
return return_dict
def train_fold(fold, args):
# loggers
logging_logger = args.logging_logger
if args.tb_log:
tb_logger = args.tb_logger
num_classes = utils.problem_class[args.problem_type]
# init model
model = eval(args.model)(in_channels=3, num_classes=num_classes, bn=False)
model = nn.DataParallel(model, device_ids=args.device_ids).cuda()
# transform for train/valid data
train_transform, valid_transform = get_transform(args.model)
# loss function
loss_func = LossMulti(num_classes, args.jaccard_weight)
if args.semi:
loss_func_semi = LossMultiSemi(num_classes, args.jaccard_weight, args.semi_loss_alpha, args.semi_method)
# train/valid filenames
train_filenames, valid_filenames = utils.trainval_split(args.train_dir, fold)
# DataLoader and Dataset args
train_shuffle = True
train_ds_kwargs = {
'filenames': train_filenames,
'problem_type': args.problem_type,
'transform': train_transform,
'model': args.model,
'mode': 'train',
'semi': args.semi,
}
valid_num_workers = args.num_workers
valid_batch_size = args.batch_size
if 'TAPNet' in args.model:
# for TAPNet, cancel default shuffle, use self-defined shuffle in torch.Dataset instead
train_shuffle = False
train_ds_kwargs['batch_size'] = args.batch_size
train_ds_kwargs['mf'] = args.mf
if args.semi == True:
train_ds_kwargs['semi_method'] = args.semi_method
train_ds_kwargs['semi_percentage'] = args.semi_percentage
# additional valid dataset kws
valid_ds_kwargs = {
'filenames': valid_filenames,
'problem_type': args.problem_type,
'transform': valid_transform,
'model': args.model,
'mode': 'valid',
}
if 'TAPNet' in args.model:
# in validation, num_workers should be set to 0 for sequences
valid_num_workers = 0
# in validation, batch_size should be set to 1 for sequences
valid_batch_size = 1
valid_ds_kwargs['mf'] = args.mf
# train dataloader
train_loader = DataLoader(
dataset=RobotSegDataset(**train_ds_kwargs),
shuffle=train_shuffle, # set to False to disable pytorch dataset shuffle
num_workers=args.num_workers,
batch_size=args.batch_size,
pin_memory=True
)
# valid dataloader
valid_loader = DataLoader(
dataset=RobotSegDataset(**valid_ds_kwargs),
shuffle=False, # in validation, no need to shuffle
num_workers=valid_num_workers,
batch_size=valid_batch_size, # in valid time. have to use one image by one
pin_memory=True
)
# optimizer
optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
# optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=0.9,
# weight_decay=args.weight_decay, nesterov=True)
# ignite trainer process function
def train_step(engine, batch):
# set model to train
model.train()
# clear gradients
optimizer.zero_grad()
# additional params to feed into model
add_params = {}
inputs = batch['input'].cuda(non_blocking=True)
with torch.no_grad():
targets = batch['target'].cuda(non_blocking=True)
# for TAPNet, add attention maps
if 'TAPNet' in args.model:
add_params['attmap'] = batch['attmap'].cuda(non_blocking=True)
outputs = model(inputs, **add_params)
loss_kwargs = {}
if args.semi:
loss_kwargs['labeled'] = batch['labeled']
if args.semi_method == 'rev_flow':
loss_kwargs['optflow'] = batch['optflow']
loss = loss_func_semi(outputs, targets, **loss_kwargs)
else:
loss = loss_func(outputs, targets, **loss_kwargs)
loss.backward()
optimizer.step()
return_dict = {
'output': outputs,
'target': targets,
'loss_kwargs': loss_kwargs,
'loss': loss.item(),
}
# for TAPNet, update attention maps after each iteration
if 'TAPNet' in args.model:
# output_classes and target_classes: <b, h, w>
output_softmax_np = torch.softmax(outputs, dim=1).detach().cpu().numpy()
# update attention maps
train_loader.dataset.update_attmaps(output_softmax_np, batch['abs_idx'].numpy())
return_dict['attmap'] = add_params['attmap']
return return_dict
# init trainer
trainer = engine.Engine(train_step)
# lr scheduler and handler
# cyc_scheduler = optim.lr_scheduler.CyclicLR(optimizer, args.lr / 100, args.lr)
# lr_scheduler = c_handlers.param_scheduler.LRScheduler(cyc_scheduler)
# trainer.add_event_handler(engine.Events.ITERATION_COMPLETED, lr_scheduler)
step_scheduler = optim.lr_scheduler.StepLR(optimizer,
step_size=args.lr_decay_epochs, gamma=args.lr_decay)
lr_scheduler = c_handlers.param_scheduler.LRScheduler(step_scheduler)
trainer.add_event_handler(engine.Events.EPOCH_STARTED, lr_scheduler)
@trainer.on(engine.Events.STARTED)
def trainer_start_callback(engine):
logging_logger.info('training fold {}, {} train / {} valid files'. \
format(fold, len(train_filenames), len(valid_filenames)))
# resume training
if args.resume:
# ckpt for current fold fold_<fold>_model_<epoch>.pth
ckpt_dir = Path(args.ckpt_dir)
ckpt_filename = ckpt_dir.glob('fold_%d_model_[0-9]*.pth' % fold)[0]
res = re.match(r'fold_%d_model_(\d+).pth' % fold, ckpt_filename)
# restore epoch
engine.state.epoch = int(res.groups()[0])
# load model state dict
model.load_state_dict(torch.load(str(ckpt_filename)))
logging_logger.info('restore model [{}] from epoch {}.'.format(args.model, engine.state.epoch))
else:
logging_logger.info('train model [{}] from scratch'.format(args.model))
# record metrics history every epoch
engine.state.metrics_records = {}
@trainer.on(engine.Events.EPOCH_STARTED)
def trainer_epoch_start_callback(engine):
# log learning rate on pbar
train_pbar.log_message('model: %s, problem type: %s, fold: %d, lr: %.5f, batch size: %d' % \
(args.model, args.problem_type, fold, lr_scheduler.get_param(), args.batch_size))
# for TAPNet, change dataset schedule to random after the first epoch
if 'TAPNet' in args.model and engine.state.epoch > 1:
train_loader.dataset.set_dataset_schedule("shuffle")
@trainer.on(engine.Events.ITERATION_COMPLETED)
def trainer_iter_comp_callback(engine):
# logging_logger.info(engine.state.metrics)
pass
# monitor loss
# running average loss
train_ra_loss = imetrics.RunningAverage(output_transform=
lambda x: x['loss'], alpha=0.98)
train_ra_loss.attach(trainer, 'train_ra_loss')
# monitor train loss over epoch
if args.semi:
train_loss = imetrics.Loss(loss_func_semi, output_transform=lambda x: (x['output'], x['target'], x['loss_kwargs']))
else:
train_loss = imetrics.Loss(loss_func, output_transform=lambda x: (x['output'], x['target']))
train_loss.attach(trainer, 'train_loss')
# progress bar
train_pbar = c_handlers.ProgressBar(persist=True, dynamic_ncols=True)
train_metric_names = ['train_ra_loss']
train_pbar.attach(trainer, metric_names=train_metric_names)
# tensorboardX: log train info
if args.tb_log:
tb_logger.attach(trainer, log_handler=OptimizerParamsHandler(optimizer, 'lr'),
event_name=engine.Events.EPOCH_STARTED)
tb_logger.attach(trainer, log_handler=OutputHandler('train_iter', train_metric_names),
event_name=engine.Events.ITERATION_COMPLETED)
tb_logger.attach(trainer, log_handler=OutputHandler('train_epoch', ['train_loss']),
event_name=engine.Events.EPOCH_COMPLETED)
tb_logger.attach(trainer,
log_handler=WeightsScalarHandler(model, reduction=torch.norm),
event_name=engine.Events.ITERATION_COMPLETED)
# tb_logger.attach(trainer, log_handler=tb_log_train_vars,
# event_name=engine.Events.ITERATION_COMPLETED)
# ignite validator process function
def valid_step(engine, batch):
with torch.no_grad():
model.eval()
inputs = batch['input'].cuda(non_blocking=True)
targets = batch['target'].cuda(non_blocking=True)
# additional arguments
add_params = {}
# for TAPNet, add attention maps
if 'TAPNet' in args.model:
add_params['attmap'] = batch['attmap'].cuda(non_blocking=True)
# output logits
outputs = model(inputs, **add_params)
# loss
loss = loss_func(outputs, targets)
output_softmaxs = torch.softmax(outputs, dim=1)
output_argmaxs = output_softmaxs.argmax(dim=1)
# output_classes and target_classes: <b, h, w>
output_classes = output_argmaxs.cpu().numpy()
target_classes = targets.cpu().numpy()
# record current batch metrics
iou_mRecords = MetricRecord()
dice_mRecords = MetricRecord()
cm_b = np.zeros((num_classes, num_classes), dtype=np.uint32)
for output_class, target_class in zip(output_classes, target_classes):
# calculate metrics for each frame
# calculate using confusion matrix or dirctly using definition
cm = calculate_confusion_matrix_from_arrays(output_class, target_class, num_classes)
iou_mRecords.update_record(calculate_iou(cm))
dice_mRecords.update_record(calculate_dice(cm))
cm_b += cm
######## calculate directly using definition ##########
# iou_mRecords.update_record(iou_multi_np(target_class, output_class))
# dice_mRecords.update_record(dice_multi_np(target_class, output_class))
# accumulate batch metrics to engine state
engine.state.epoch_metrics['confusion_matrix'] += cm_b
engine.state.epoch_metrics['iou'].merge(iou_mRecords)
engine.state.epoch_metrics['dice'].merge(dice_mRecords)
return_dict = {
'loss': loss.item(),
'output': outputs,
'output_argmax': output_argmaxs,
'target': targets,
# for monitoring
'iou': iou_mRecords,
'dice': dice_mRecords,
}
if 'TAPNet' in args.model:
# for TAPNet, update attention maps after each iteration
valid_loader.dataset.update_attmaps(output_softmaxs.cpu().numpy(), batch['abs_idx'].numpy())
# for TAPNet, return extra internal values
return_dict['attmap'] = add_params['attmap']
# TODO: for TAPNet, return internal self-learned attention maps
return return_dict
# validator engine
validator = engine.Engine(valid_step)
# monitor loss
valid_ra_loss = imetrics.RunningAverage(output_transform=
lambda x: x['loss'], alpha=0.98)
valid_ra_loss.attach(validator, 'valid_ra_loss')
# monitor validation loss over epoch
valid_loss = imetrics.Loss(loss_func, output_transform=lambda x: (x['output'], x['target']))
valid_loss.attach(validator, 'valid_loss')
# monitor <data> mean metrics
valid_data_miou = imetrics.RunningAverage(output_transform=
lambda x: x['iou'].data_mean()['mean'], alpha=0.98)
valid_data_miou.attach(validator, 'mIoU')
valid_data_mdice = imetrics.RunningAverage(output_transform=
lambda x: x['dice'].data_mean()['mean'], alpha=0.98)
valid_data_mdice.attach(validator, 'mDice')
# show metrics on progress bar (after every iteration)
valid_pbar = c_handlers.ProgressBar(persist=True, dynamic_ncols=True)
valid_metric_names = ['valid_ra_loss', 'mIoU', 'mDice']
valid_pbar.attach(validator, metric_names=valid_metric_names)
# ## monitor ignite IoU (the same as iou we are using) ###
# cm = imetrics.ConfusionMatrix(num_classes,
# output_transform=lambda x: (x['output'], x['target']))
# imetrics.IoU(cm,
# ignore_index=0
# ).attach(validator, 'iou')
# # monitor ignite mean iou (over all classes even not exist in gt)
# mean_iou = imetrics.mIoU(cm,
# ignore_index=0
# ).attach(validator, 'mean_iou')
@validator.on(engine.Events.STARTED)
def validator_start_callback(engine):
pass
@validator.on(engine.Events.EPOCH_STARTED)
def validator_epoch_start_callback(engine):
engine.state.epoch_metrics = {
# directly use definition to calculate
'iou': MetricRecord(),
'dice': MetricRecord(),
'confusion_matrix': np.zeros((num_classes, num_classes), dtype=np.uint32),
}
# evaluate after iter finish
@validator.on(engine.Events.ITERATION_COMPLETED)
def validator_iter_comp_callback(engine):
pass
# evaluate after epoch finish
@validator.on(engine.Events.EPOCH_COMPLETED)
def validator_epoch_comp_callback(engine):
# log ignite metrics
# logging_logger.info(engine.state.metrics)
# ious = engine.state.metrics['iou']
# msg = 'IoU: '
# for ins_id, iou in enumerate(ious):
# msg += '{:d}: {:.3f}, '.format(ins_id + 1, iou)
# logging_logger.info(msg)
# logging_logger.info('nonzero mean IoU for all data: {:.3f}'.format(ious[ious > 0].mean()))
# log monitored epoch metrics
epoch_metrics = engine.state.epoch_metrics
######### NOTICE: Two metrics are available but different ##########
### 1. mean metrics for all data calculated by confusion matrix ####
'''
compared with using confusion_matrix[1:, 1:] in original code,
we use the full confusion matrix and only present non-background result
'''
confusion_matrix = epoch_metrics['confusion_matrix']# [1:, 1:]
ious = calculate_iou(confusion_matrix)
dices = calculate_dice(confusion_matrix)
mean_ious = np.mean(list(ious.values()))
mean_dices = np.mean(list(dices.values()))
std_ious = np.std(list(ious.values()))
std_dices = np.std(list(dices.values()))
logging_logger.info('mean IoU: %.3f, std: %.3f, for each class: %s' %
(mean_ious, std_ious, ious))
logging_logger.info('mean Dice: %.3f, std: %.3f, for each class: %s' %
(mean_dices, std_dices, dices))
### 2. mean metrics for all data calculated by definition ###
iou_data_mean = epoch_metrics['iou'].data_mean()
dice_data_mean = epoch_metrics['dice'].data_mean()
logging_logger.info('data (%d) mean IoU: %.3f, std: %.3f' %
(len(iou_data_mean['items']), iou_data_mean['mean'], iou_data_mean['std']))
logging_logger.info('data (%d) mean Dice: %.3f, std: %.3f' %
(len(dice_data_mean['items']), dice_data_mean['mean'], dice_data_mean['std']))
# record metrics in trainer every epoch
# trainer.state.metrics_records[trainer.state.epoch] = \
# {'miou': mean_ious, 'std_miou': std_ious,
# 'mdice': mean_dices, 'std_mdice': std_dices}
trainer.state.metrics_records[trainer.state.epoch] = \
{'miou': iou_data_mean['mean'], 'std_miou': iou_data_mean['std'],
'mdice': dice_data_mean['mean'], 'std_mdice': dice_data_mean['std']}
# log interal variables(attention maps, outputs, etc.) on validation
def tb_log_valid_iter_vars(engine, logger, event_name):
log_tag = 'valid_iter'
output = engine.state.output
batch_size = output['output'].shape[0]
res_grid = tvutils.make_grid(torch.cat([
output['output_argmax'].unsqueeze(1),
output['target'].unsqueeze(1),
]), padding=2,
normalize=False, # show origin image
nrow=batch_size).cpu()
logger.writer.add_image(tag='%s (outputs, targets)' % (log_tag), img_tensor=res_grid)
if 'TAPNet' in args.model:
# log attention maps and other internal values
inter_vals_grid = tvutils.make_grid(torch.cat([
output['attmap'],
]), padding=2, normalize=True, nrow=batch_size).cpu()
logger.writer.add_image(tag='%s internal vals' % (log_tag), img_tensor=inter_vals_grid)
def tb_log_valid_epoch_vars(engine, logger, event_name):
log_tag = 'valid_iter'
# log monitored epoch metrics
epoch_metrics = engine.state.epoch_metrics
confusion_matrix = epoch_metrics['confusion_matrix']# [1:, 1:]
ious = calculate_iou(confusion_matrix)
dices = calculate_dice(confusion_matrix)
mean_ious = np.mean(list(ious.values()))
mean_dices = np.mean(list(dices.values()))
logger.writer.add_scalar('mIoU', mean_ious, engine.state.epoch)
logger.writer.add_scalar('mIoU', mean_dices, engine.state.epoch)
if args.tb_log:
# log internal values
tb_logger.attach(validator, log_handler=tb_log_valid_iter_vars,
event_name=engine.Events.ITERATION_COMPLETED)
tb_logger.attach(validator, log_handler=tb_log_valid_epoch_vars,
event_name=engine.Events.EPOCH_COMPLETED)
# tb_logger.attach(validator, log_handler=OutputHandler('valid_iter', valid_metric_names),
# event_name=engine.Events.ITERATION_COMPLETED)
tb_logger.attach(validator, log_handler=OutputHandler('valid_epoch', ['valid_loss']),
event_name=engine.Events.EPOCH_COMPLETED)
# score function for model saving
ckpt_score_function = lambda engine: \
np.mean(list(calculate_iou(engine.state.epoch_metrics['confusion_matrix']).values()))
# ckpt_score_function = lambda engine: engine.state.epoch_metrics['iou'].data_mean()['mean']
ckpt_filename_prefix = 'fold_%d' % fold
# model saving handler
model_ckpt_handler = handlers.ModelCheckpoint(
dirname=args.model_save_dir,
filename_prefix=ckpt_filename_prefix,
score_function=ckpt_score_function,
create_dir=True,
require_empty=False,
save_as_state_dict=True,
atomic=True)
validator.add_event_handler(event_name=engine.Events.EPOCH_COMPLETED,
handler=model_ckpt_handler,
to_save={
'model': model,
})
# early stop
# trainer=trainer, but should be handled by validator
early_stopping = handlers.EarlyStopping(patience=args.es_patience,
score_function=ckpt_score_function,
trainer=trainer
)
validator.add_event_handler(event_name=engine.Events.EPOCH_COMPLETED,
handler=early_stopping)
# evaluate after epoch finish
@trainer.on(engine.Events.EPOCH_COMPLETED)
def trainer_epoch_comp_callback(engine):
validator.run(valid_loader)
trainer.run(train_loader, max_epochs=args.max_epochs)
if args.tb_log:
# close tb_logger
tb_logger.close()
return trainer.state.metrics_records
def train_one_epoch(train_loader, net, optimizer, criterion, hparams):
# Activate the train=True flag inside the model
net.train()
device = hparams['device']
batch_size = hparams['batch_size']
train_loss, train_accs = 0, 0
train_iou = {}
times_per_step_iteration = []
times_per_metric_iteration = []
times_per_iteration = []
for batch_index, (img, target) in enumerate(train_loader):
#Arrancamos temporizador general
start_total.record()
img, target = img.to(device), target.to(device)
optimizer.zero_grad()
# Arrancamos temporizador para inferencia
start.record()
output = net(img)
target = target.long()
loss = criterion(output, target)
loss.backward()
optimizer.step()
pred = aux.get_predicted_image(output)
#Paramos temporizador de inferencia
end.record()
torch.cuda.synchronize()
times_per_step_iteration.append(start.elapsed_time(end))
# Accuracy
#Arrancamos temporizador para métricas
start.record()
# Desvinculamos el valor de los nuevos targets y los pasamos a CPU para calcular las métricas
output, target, pred = output.detach().cpu(), target.detach().cpu(
), pred.detach().cpu()
train_loss += loss.item()
# Devuelve values, indices. Los indices son el nº de feature map (clase) en la que se encuentra el valor más alto en el pixel
train_accuracy = metrics.calculate_accuracy(output,
target) #, predicted
train_accs += train_accuracy
iou_inds = metrics.calculate_iou(pred, target)
for key in iou_inds:
if key not in train_iou:
train_iou[key] = iou_inds[key]
else:
train_iou[key] += iou_inds[key]
#Paramos temporizador para métricas
end.record()
torch.cuda.synchronize()
times_per_metric_iteration.append(start.elapsed_time(end))
#Paramos temporizador general
end_total.record()
torch.cuda.synchronize()
times_per_iteration.append(start_total.elapsed_time(end))
avg_time_taken = sum(times_per_iteration) / len(
times_per_iteration)
avg_time_step_taken = sum(times_per_step_iteration) / len(
times_per_step_iteration)
avg_time_metrics_taken = sum(times_per_metric_iteration) / len(
times_per_metric_iteration)
print('Average Time spent total: {:.02f}s'.format(avg_time_taken *
1e-3))
print('Average Time spent by steps: {:.02f}s'.format(
avg_time_step_taken * 1e-3))
print('Average Time spent by metrics: {:.02f}s'.format(
avg_time_metrics_taken * 1e-3))
print('Average Time spent by data load: {:.02f}s'.format(
avg_time_taken * 1e-3 - avg_time_step_taken * 1e-3 -
avg_time_metrics_taken * 1e-3))
train_loss = train_loss / (len(train_loader.dataset) / batch_size)
train_accs = 100 * (train_accs /
(len(train_loader.dataset) / batch_size))
train_iou = metrics.convert_batched_iou(
train_iou, (len(train_loader.dataset) / batch_size))
mIoU = metrics.get_mIoU(train_iou)
mIoU_desc = metrics.miou_to_string(train_iou)
return train_loss, train_accs, mIoU, mIoU_desc
