def _init_engines(self) -> Tuple[Engine, Engine]:
self.train_metrics = {
'total_loss':
metrics.RunningAverage(output_transform=lambda x: x['loss']),
'segment_loss':
metrics.RunningAverage(
output_transform=lambda x: x['segment_loss']),
'kl_div':
metrics.RunningAverage(output_transform=lambda x: x['kl_div'])
}
self.val_metrics = {
'vae_metrics':
VAEMetrics(loss_fn=self.criterion,
mse_factor=0,
kld_factor=self.starting_kld_factor),
'segment_metrics':
SegmentationMetrics(num_classes=self.data_loaders.num_classes,
threshold=self.config.binarize_threshold),
}
trainer = self._init_trainer_engine()
evaluator = self._init_evaluator_engine()
return trainer, evaluator
def _init_engines(self) -> Tuple[Engine, Engine]:
if self.use_ensemble:
trainer = self._init_trainer_engine_ensemble()
evaluator = self._init_evaluator_engine_ensemble()
else:
trainer = self._init_trainer_engine()
evaluator = self._init_evaluator_engine()
metrics.RunningAverage(output_transform=lambda x: x).attach(trainer, 'train_loss')
return trainer, evaluator
def _init_engines(self):
trainer = self._init_bayesian_trainer_engine(self.model,
self.optimizer, self.vi,
self.device)
evaluator = self._init_bayesian_evaluator_engine(
self.model, self.metrics, self.device)
metrics.RunningAverage(output_transform=lambda x: x).attach(
trainer, 'train_loss')
return trainer, evaluator
def get_engines(learning_rate=0.01):
# We remove 1 because of the target column
model = get_model(n_features=len(useful_columns) - 1)
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
loss = nn.MSELoss()
trainer = engine.create_supervised_trainer(model, optimizer, loss)
evaluator = engine.create_supervised_evaluator(model, metrics={
'mae': metrics.MeanAbsoluteError(),
'loss': metrics.Loss(loss),
'avg': metrics.RunningAverage(metrics.Loss(loss))
})
# workaround for using the ProgressBar with training loss
Identity().attach(trainer, 'loss')
Average().attach(trainer, 'avg_loss')
return trainer, evaluator
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 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
class Metrics(enum.Enum):
train_class_metrics: t.Dict[str, im.Metric] = {
'acc_1':
im.RunningAverage(im.Accuracy(output_transform=lambda x: x[1:3])),
'acc_5':
im.RunningAverage(
im.TopKCategoricalAccuracy(k=5,
output_transform=lambda x: x[1:3])),
'ce_loss':
im.RunningAverage(output_transform=lambda x: x[0]),
'total_loss':
im.RunningAverage(output_transform=lambda x: x[0])
}
train_ae_metrics: t.Dict[str, im.Metric] = {
'acc_1':
im.RunningAverage(
im.Accuracy(output_transform=lambda x: (x[1], x[5]))),
'acc_5':
im.RunningAverage(
im.TopKCategoricalAccuracy(k=5,
output_transform=lambda x:
(x[1], x[5]))),
'ce_loss':
train_ae_ce_loss,
'l1_loss':
train_ae_l1_loss,
'total_loss':
train_ae_total_loss
}
train_gsnn_metrics: t.Dict[str, im.Metric] = {
'acc_1':
im.RunningAverage(
im.Accuracy(
output_transform=lambda x: (x[0].squeeze(dim=1), x[6]))),
'acc_5':
im.RunningAverage(
im.TopKCategoricalAccuracy(k=5,
output_transform=lambda x:
(x[0].squeeze(dim=1), x[6]))),
'ce_loss':
train_gsnn_ce_loss,
'kld_loss':
train_gsnn_kld_loss,
'total_loss':
train_gsnn_total_loss,
'kld_factor':
train_gsnn_kld_factor,
}
train_vae_metrics: t.Dict[str, im.Metric] = {
'acc_1':
im.RunningAverage(
im.Accuracy(
output_transform=lambda x: (x[1].squeeze(dim=1), x[7]))),
'acc_5':
im.RunningAverage(
im.TopKCategoricalAccuracy(k=5,
output_transform=lambda x:
(x[1].squeeze(dim=1), x[7]))),
'ce_loss':
train_vae_ce_loss,
'l1_loss':
train_vae_l1_loss,
'kld_loss':
train_vae_kld_loss,
'total_loss':
train_vae_total_loss,
'kld_factor':
train_vae_kld_factor,
}
eval_class_metrics = {
'acc_1':
im.Accuracy(output_transform=lambda x: x[0:2]),
'acc_5':
im.TopKCategoricalAccuracy(k=5, output_transform=lambda x: x[0:2]),
'ce_loss':
im.Loss(nn.CrossEntropyLoss(), output_transform=lambda x: x[0:2]),
'total_loss':
im.Loss(nn.CrossEntropyLoss(), output_transform=lambda x: x[0:2])
}
eval_ae_metrics = {
'acc_1':
im.Accuracy(output_transform=lambda x: (x[1], x[5])),
'acc_5':
im.TopKCategoricalAccuracy(k=5,
output_transform=lambda x: (x[1], x[5])),
'ce_loss':
eval_ae_loss_metric[0],
'l1_loss':
eval_ae_loss_metric[1],
'total_loss':
eval_ae_total_loss,
}
eval_gsnn_metrics = {
'acc_1':
im.Accuracy(output_transform=lambda x: (x[-1], x[-2])),
'acc_5':
im.TopKCategoricalAccuracy(k=5,
output_transform=lambda x: (x[-1], x[-2])),
'ce_loss':
eval_gsnn_loss_metric[0],
'kld_loss':
eval_gsnn_loss_metric[1],
'total_loss':
eval_gsnn_total_loss,
}
eval_vae_metrics = {
'acc_1':
im.Accuracy(output_transform=lambda x: (x[-1], x[-2])),
'acc_5':
im.TopKCategoricalAccuracy(k=5,
output_transform=lambda x: (x[-1], x[-2])),
'ce_loss':
eval_vae_loss_metric[0],
'l1_loss':
eval_vae_loss_metric[1],
'kld_loss':
eval_vae_loss_metric[2],
'total_loss':
eval_vae_total_loss,
}
import enum
import typing as t
import ignite.metrics as im
import torch.nn as nn
import criterion.custom as cc
import metrics.custom as cm
import models as mo
########################################################################################################################
# AE HELPERS
########################################################################################################################
train_ae_ce_loss = im.RunningAverage(output_transform=lambda x: x[-2])
train_ae_l1_loss = im.RunningAverage(output_transform=lambda x: x[-1])
train_ae_total_loss = im.RunningAverage(
output_transform=lambda x: sum([x[-2], x[-1]]))
eval_ae_loss_metric = cm.AELoss(cc.AECriterion())
eval_ae_total_loss = im.MetricsLambda(lambda x: sum(x), eval_ae_loss_metric)
########################################################################################################################
# GSNN HELPERS
########################################################################################################################
train_gsnn_ce_loss = im.RunningAverage(output_transform=lambda x: x[-3])
train_gsnn_kld_loss = im.RunningAverage(output_transform=lambda x: x[-2])
train_gsnn_kld_factor = im.RunningAverage(output_transform=lambda x: x[-1])
train_gsnn_total_loss = im.RunningAverage(
output_transform=lambda x: sum([x[-3], x[-2]]))
eval_gsnn_loss_metric = cm.GSNNLoss(cc.GSNNCriterion())
eval_gsnn_total_loss = im.MetricsLambda(lambda x: sum(x),
eval_gsnn_loss_metric)
def run(experiment_name: str,
visdom_host: str,
visdom_port: int,
visdom_env_path: str,
model_class: str,
model_args: Dict[str, Any],
optimizer_class: str,
optimizer_args: Dict[str, Any],
dataset_class: str,
dataset_args: Dict[str, Any],
batch_train: int,
batch_test: int,
workers_train: int,
workers_test: int,
transforms: List[Dict[str, Union[str, Dict[str, Any]]]],
epochs: int,
log_interval: int,
saved_models_path: str,
performance_metrics: Optional = None,
scheduler_class: Optional[str] = None,
scheduler_args: Optional[Dict[str, Any]] = None,
model_suffix: Optional[str] = None,
setup_suffix: Optional[str] = None,
orig_stdout: Optional[io.TextIOBase] = None):
with _utils.tqdm_stdout(orig_stdout) as orig_stdout:
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
transforms_train = list()
transforms_test = list()
for idx, transform in enumerate(transforms):
use_train = transform.get('train', True)
use_test = transform.get('test', True)
transform = _utils.load_class(
transform['class'])(**transform['args'])
if use_train:
transforms_train.append(transform)
if use_test:
transforms_test.append(transform)
transforms[idx]['train'] = use_train
transforms[idx]['test'] = use_test
transforms_train = tv.transforms.Compose(transforms_train)
transforms_test = tv.transforms.Compose(transforms_test)
Dataset: Type = _utils.load_class(dataset_class)
train_loader, eval_loader = _utils.get_data_loaders(
Dataset, dataset_args, batch_train, batch_test, workers_train,
workers_test, transforms_train, transforms_test)
Network: Type = _utils.load_class(model_class)
model: _interfaces.AbstractNet = Network(**model_args)
model = model.to(device)
Optimizer: Type = _utils.load_class(optimizer_class)
optimizer: torch.optim.Optimizer = Optimizer(model.parameters(),
**optimizer_args)
if scheduler_class is not None:
Scheduler: Type = _utils.load_class(scheduler_class)
if scheduler_args is None:
scheduler_args = dict()
scheduler: Optional[
torch.optim.lr_scheduler._LRScheduler] = Scheduler(
optimizer, **scheduler_args)
else:
scheduler = None
model_short_name = ''.join(
[c for c in Network.__name__ if c == c.upper()])
model_name = '{}{}'.format(
model_short_name,
'-{}'.format(model_suffix) if model_suffix is not None else '')
visdom_env_name = '{}_{}_{}{}'.format(
Dataset.__name__, experiment_name, model_name,
'-{}'.format(setup_suffix) if setup_suffix is not None else '')
vis, vis_pid = _visdom.get_visdom_instance(visdom_host, visdom_port,
visdom_env_name,
visdom_env_path)
prog_bar_epochs = tqdm.tqdm(total=epochs,
desc='Epochs',
file=orig_stdout,
dynamic_ncols=True,
unit='epoch')
prog_bar_iters = tqdm.tqdm(desc='Batches',
file=orig_stdout,
dynamic_ncols=True)
tqdm.tqdm.write(f'\n{repr(model)}\n')
tqdm.tqdm.write('Total number of parameters: {:.2f}M'.format(
sum(p.numel() for p in model.parameters()) / 1e6))
def training_step(_: ieng.Engine,
batch: _interfaces.TensorPair) -> torch.Tensor:
model.train()
optimizer.zero_grad()
x, y = batch
x = x.to(device)
y = y.to(device)
_, loss = model(x, y)
loss.backward(retain_graph=False)
optimizer.step(None)
return loss.item()
def eval_step(_: ieng.Engine,
batch: _interfaces.TensorPair) -> _interfaces.TensorPair:
model.eval()
with torch.no_grad():
x, y = batch
x = x.to(device)
y = y.to(device)
y_pred = model(x)
return y_pred, y
trainer = ieng.Engine(training_step)
validator_train = ieng.Engine(eval_step)
validator_eval = ieng.Engine(eval_step)
# placeholder for summary window
vis.text(text='',
win=experiment_name,
env=visdom_env_name,
opts={
'title': 'Summary',
'width': 940,
'height': 416
},
append=vis.win_exists(experiment_name, visdom_env_name))
default_metrics = {
"Loss": {
"window_name":
None,
"x_label":
"#Epochs",
"y_label":
model.loss_fn_name,
"width":
940,
"height":
416,
"lines": [{
"line_label":
"SMA",
"object":
imet.RunningAverage(output_transform=lambda x: x),
"test":
False,
"update_rate":
"iteration"
}, {
"line_label": "Val.",
"object": imet.Loss(model.loss_fn)
}]
}
}
performance_metrics = {**default_metrics, **performance_metrics}
checkpoint_metrics = list()
for scope_name, scope in performance_metrics.items():
scope['window_name'] = scope.get('window_name',
scope_name) or scope_name
for line in scope['lines']:
if 'object' not in line:
line['object']: imet.Metric = _utils.load_class(
line['class'])(**line['args'])
line['metric_label'] = '{}: {}'.format(scope['window_name'],
line['line_label'])
line['update_rate'] = line.get('update_rate', 'epoch')
line_suffixes = list()
if line['update_rate'] == 'iteration':
line['object'].attach(trainer, line['metric_label'])
line['train'] = False
line['test'] = False
line_suffixes.append(' Train.')
if line.get('train', True):
line['object'].attach(validator_train,
line['metric_label'])
line_suffixes.append(' Train.')
if line.get('test', True):
line['object'].attach(validator_eval, line['metric_label'])
line_suffixes.append(' Eval.')
if line.get('is_checkpoint', False):
checkpoint_metrics.append(line['metric_label'])
for line_suffix in line_suffixes:
_visdom.plot_line(
vis=vis,
window_name=scope['window_name'],
env=visdom_env_name,
line_label=line['line_label'] + line_suffix,
x_label=scope['x_label'],
y_label=scope['y_label'],
width=scope['width'],
height=scope['height'],
draw_marker=(line['update_rate'] == 'epoch'))
if checkpoint_metrics:
score_name = 'performance'
def get_score(engine: ieng.Engine) -> float:
current_mode = getattr(
engine.state.dataloader.iterable.dataset,
dataset_args['training']['key'])
val_mode = dataset_args['training']['no']
score = 0.0
if current_mode == val_mode:
for metric_name in checkpoint_metrics:
try:
score += engine.state.metrics[metric_name]
except KeyError:
pass
return score
model_saver = ihan.ModelCheckpoint(os.path.join(
saved_models_path, visdom_env_name),
filename_prefix=visdom_env_name,
score_name=score_name,
score_function=get_score,
n_saved=3,
save_as_state_dict=True,
require_empty=False,
create_dir=True)
validator_eval.add_event_handler(ieng.Events.EPOCH_COMPLETED,
model_saver, {model_name: model})
@trainer.on(ieng.Events.EPOCH_STARTED)
def reset_progress_iterations(engine: ieng.Engine):
prog_bar_iters.clear()
prog_bar_iters.n = 0
prog_bar_iters.last_print_n = 0
prog_bar_iters.start_t = time.time()
prog_bar_iters.last_print_t = time.time()
prog_bar_iters.total = len(engine.state.dataloader)
@trainer.on(ieng.Events.ITERATION_COMPLETED)
def log_training(engine: ieng.Engine):
prog_bar_iters.update(1)
num_iter = (engine.state.iteration - 1) % len(train_loader) + 1
early_stop = np.isnan(engine.state.output) or np.isinf(
engine.state.output)
if num_iter % log_interval == 0 or num_iter == len(
train_loader) or early_stop:
tqdm.tqdm.write(
'Epoch[{}] Iteration[{}/{}] Loss: {:.4f}'.format(
engine.state.epoch, num_iter, len(train_loader),
engine.state.output))
x_pos = engine.state.epoch + num_iter / len(train_loader) - 1
for scope_name, scope in performance_metrics.items():
for line in scope['lines']:
if line['update_rate'] == 'iteration':
line_label = '{} Train.'.format(line['line_label'])
line_value = engine.state.metrics[
line['metric_label']]
if engine.state.epoch > 1:
_visdom.plot_line(
vis=vis,
window_name=scope['window_name'],
env=visdom_env_name,
line_label=line_label,
x_label=scope['x_label'],
y_label=scope['y_label'],
x=np.full(1, x_pos),
y=np.full(1, line_value))
if early_stop:
tqdm.tqdm.write(
colored('Early stopping due to invalid loss value.',
'red'))
trainer.terminate()
def log_validation(engine: ieng.Engine, train: bool = True):
if train:
run_type = 'Train.'
data_loader = train_loader
validator = validator_train
else:
run_type = 'Eval.'
data_loader = eval_loader
validator = validator_eval
prog_bar_validation = tqdm.tqdm(data_loader,
desc=f'Validation {run_type}',
file=orig_stdout,
dynamic_ncols=True,
leave=False)
validator.run(prog_bar_validation)
prog_bar_validation.clear()
prog_bar_validation.close()
tqdm_info = ['Epoch: {}'.format(engine.state.epoch)]
for scope_name, scope in performance_metrics.items():
for line in scope['lines']:
if line['update_rate'] == 'epoch':
try:
line_label = '{} {}'.format(
line['line_label'], run_type)
line_value = validator.state.metrics[
line['metric_label']]
_visdom.plot_line(vis=vis,
window_name=scope['window_name'],
env=visdom_env_name,
line_label=line_label,
x_label=scope['x_label'],
y_label=scope['y_label'],
x=np.full(1, engine.state.epoch),
y=np.full(1, line_value),
draw_marker=True)
tqdm_info.append('{}: {:.4f}'.format(
line_label, line_value))
except KeyError:
pass
tqdm.tqdm.write('{} results - {}'.format(run_type,
'; '.join(tqdm_info)))
@trainer.on(ieng.Events.EPOCH_COMPLETED)
def log_validation_train(engine: ieng.Engine):
log_validation(engine, True)
@trainer.on(ieng.Events.EPOCH_COMPLETED)
def log_validation_eval(engine: ieng.Engine):
log_validation(engine, False)
if engine.state.epoch == 1:
summary = _utils.build_summary_str(
experiment_name=experiment_name,
model_short_name=model_name,
model_class=model_class,
model_args=model_args,
optimizer_class=optimizer_class,
optimizer_args=optimizer_args,
dataset_class=dataset_class,
dataset_args=dataset_args,
transforms=transforms,
epochs=epochs,
batch_train=batch_train,
log_interval=log_interval,
saved_models_path=saved_models_path,
scheduler_class=scheduler_class,
scheduler_args=scheduler_args)
_visdom.create_summary_window(vis=vis,
visdom_env_name=visdom_env_name,
experiment_name=experiment_name,
summary=summary)
vis.save([visdom_env_name])
prog_bar_epochs.update(1)
if scheduler is not None:
scheduler.step(engine.state.epoch)
trainer.run(train_loader, max_epochs=epochs)
if vis_pid is not None:
tqdm.tqdm.write('Stopping visdom')
os.kill(vis_pid, signal.SIGTERM)
del vis
del train_loader
del eval_loader
prog_bar_iters.clear()
prog_bar_iters.close()
prog_bar_epochs.clear()
prog_bar_epochs.close()
tqdm.tqdm.write('\n')
def _stacked_ae_fit(stacked_ae,
train_data,
val_data,
batch_size,
optimizer,
max_epochs,
early_stopping=False,
check_every=100,
patience=10,
writer=None):
"""
Function used to fit a stacked autoencoder on data.
MSELoss used as lossfunction.
### Parameters
- *ae*: Autoencoder, required
+ The autoencoder to fit.
- *train_data*: torch.Tensor, required
+ Dataset with the data to train on where samples are in rows
- *val_data*: torch.Tensor, required
+ Dataset with the data to validate on where samples are in rows
- *optimizer*: torch.optim.Optimizer, required
- *batch_size*: int, required
- *max_epochs*: int, required
+ Training is at most run for this number of epochs
- *early_stopping*: bool, required
+ Whether to use early stopping or not
- *check_every*: int, optional, default=1500
+ Scores are calculated every *check_every* iteration.
- *patience*: int, optional, default=10
+ If the validation score has not increased after *patience* checks
and early stopping is True, training is stopped.
### Returns
- *time*: Time taken for fitting
"""
start_time = time()
num_nets = stacked_ae.num_nets
train_loss = StackedMSELoss(size_average=None,
reduce=None,
reduction='elementwise_mean')
val_loss = StackedMSELoss(size_average=None,
reduce=None,
reduction='stacked_elementwise_mean')
train_dataset = StackedTensorDataset(train_data, train_data, shuffle=True)
train_loader = StackedDataLoader(train_dataset,
batch_size=batch_size,
shuffle=True)
val_dataset = StackedTensorDataset(val_data, val_data, shuffle=False)
val_loader = StackedDataLoader(val_dataset,
batch_size=4 * batch_size,
shuffle=False)
logger = logging.getLogger('stacked_ae_fit')
trainer = create_supervised_trainer(stacked_ae, optimizer, train_loss)
run_av = metrics.RunningAverage(
output_transform=lambda x: x / len(stacked_ae.net_list), alpha=0.9)
run_av.attach(trainer, 'av_loss')
pbar_train = ProgressBar()
pbar_train.attach(trainer, ['av_loss'])
val_loss_metric = StackedLossMetric(val_loss)
evaluator = create_supervised_evaluator(
stacked_ae, metrics={'stacked_loss': val_loss_metric})
def score_function(evaluator):
return -evaluator.state.metrics['stacked_loss']
evaluator.register_events(*EarlyStoppingEvents)
evaluator.add_event_handler(
Events.COMPLETED,
StackedEarlyStopping(patience, score_function, stacked_ae.num_nets))
evaluator.num_converged = 0
@evaluator.on(EarlyStoppingEvents.NET_CONVERGED)
def update_count(evaluator):
evaluator.num_converged += 1
if early_stopping:
evaluator.best_state = {}
for name, tensor in stacked_ae.state_dict().items():
# Create a new instance of the tensor on the cpu
evaluator.best_state[name] = torch.tensor(tensor, device='cpu')
@evaluator.on(EarlyStoppingEvents.HAS_IMPROVED)
def store_states(evaluator):
for name, tensor in stacked_ae.state_dict().items():
# Create a new instance of the tensor on the cpu
evaluator.best_state[name] = torch.tensor(tensor, device='cpu')
@evaluator.on(EarlyStoppingEvents.EARLY_STOPPING_DONE)
def terminate(evaluator):
trainer.terminate()
class CustomEvents(Enum):
N_ITERATIONS_COMPLETED = "n_iterations_completed"
trainer.register_events(CustomEvents['N_ITERATIONS_COMPLETED'])
@trainer.on(Events.ITERATION_COMPLETED, check_every)
def every_n(trainer, check_every):
if trainer.state.iteration % check_every == 0:
trainer.fire_event(CustomEvents.N_ITERATIONS_COMPLETED)
@trainer.on(CustomEvents.N_ITERATIONS_COMPLETED)
def run_validation(trainer):
iteration = trainer.state.iteration
epoch = trainer.state.epoch
evaluator.run(val_loader, max_epochs=1)
msg = (("Iteration {} (Epoch {})" + "training error: {:.5f}, " +
"mean validation error: {:.5f}, " +
"{}/{} nets convered.").format(
iteration, epoch, trainer.state.metrics['av_loss'],
evaluator.state.metrics['stacked_loss'].mean(),
evaluator.num_converged, num_nets))
logger.info(msg)
# clear some memory
evaluator.state.output = None
if writer is not None:
writer.add_scalar('train_error_estimate', trainer.state.output,
iteration)
# Write to tensorboard
for i, loss in enumerate(evaluator.state.metrics['stacked_loss']):
if writer is not None:
writer.add_scalar('val_error_{}'.format(i), loss, iteration)
trainer.run(train_loader, max_epochs=max_epochs)
if early_stopping:
# Load the best states
stacked_ae.load_state_dict(evaluator.best_state)
stacked_ae.update_list()
train_time = time() - start_time
if early_stopping and evaluator.num_converged < num_nets:
msg = ("Only {}/{} nets converged".format(evaluator.num_converged,
num_nets))
logger.warning(msg)
msg = ('Trained for {} epochs, {}/{} nets converged.'
'\n Training took {}s'.format(trainer.state.epoch,
evaluator.num_converged, num_nets,
train_time))
logger.info(msg)
return (train_time)
def fit(self,
train_loader: _data.DataLoader,
val_loader: _data.DataLoader,
epochs: int = 1,
batches: int = None,
learning_rate: float = 1e-3) -> None:
if batches is None:
batches = VocalExtractor.get_number_of_batches(train_loader)
loss_fn = nn.BCELoss()
optimizer = _optim.Adam(self.model.parameters(), lr=learning_rate)
trainer = _engine.create_supervised_trainer(self.model,
optimizer,
loss_fn,
device=self.device)
_metrics.RunningAverage(output_transform=lambda x: x,
device=self.device).attach(trainer, 'loss')
progressbar = _chandlers.ProgressBar(
bar_format=
"{desc}[{n_fmt}/{total_fmt}] {percentage:3.0f}%|{bar:20}| "
"[{elapsed}<{remaining}]{postfix}",
persist=True,
ascii=" #")
progressbar.attach(trainer, ['loss'])
def get_metrics_fn() -> Dict[str, _metrics.Metric]:
def rounded_transform(output):
y_pred, y = output
return torch.round(y_pred), y
transform = rounded_transform
accuracy = _metrics.Accuracy(transform, device=self.device)
precision = _metrics.Precision(transform, device=self.device)
recall = _metrics.Recall(transform, device=self.device)
f1 = precision * recall * 2 / (precision + recall + 1e-20)
return {
'loss': _metrics.Loss(loss_fn),
'accuracy': accuracy,
'precision': precision,
'recall': recall,
'f1': f1
}
evaluator = _engine.create_supervised_evaluator(
self.model, metrics=get_metrics_fn(), device=self.device)
score_fn_name = "f1"
def score_function(engine: _engine.Engine):
return engine.state.metrics[score_fn_name]
best_model_saver = _handlers.ModelCheckpoint(
dirname="best_models",
filename_prefix="vocal_extractor",
score_name=score_fn_name,
score_function=score_function,
n_saved=5,
create_dir=True)
evaluator.add_event_handler(_engine.Events.COMPLETED, best_model_saver,
{"model": self.model})
each_model_saver = _handlers.ModelCheckpoint(
dirname="all_models",
filename_prefix="vocal_extractor",
score_name=score_fn_name,
score_function=score_function,
n_saved=None,
create_dir=True)
evaluator.add_event_handler(_engine.Events.COMPLETED, each_model_saver,
{"model": self.model})
@trainer.on(_engine.Events.EPOCH_COMPLETED)
def on_epoch_completed(engine: _engine.Engine) -> None:
metrics = VocalExtractor.compute_metrics(val_loader, evaluator)
string = ", ".join(f"val_{k}: {v:.4f}" for k, v in metrics.items())
progressbar.log_message(string + "\n")
with _tb_logger.TensorboardLogger(log_dir="tb_logs") as tb_logger:
global_step = _tb_logger.global_step_from_engine(trainer)
train_running_loss_log_handler = _tb_logger.OutputHandler(
tag="training", output_transform=lambda x: {'running_loss': x})
tb_logger.attach(trainer,
log_handler=train_running_loss_log_handler,
event_name=_engine.Events.ITERATION_COMPLETED)
val_metrics_log_handler = _tb_logger.OutputHandler(
tag="validation",
metric_names=[name for name, _ in get_metrics_fn().items()],
global_step_transform=global_step)
tb_logger.attach(evaluator,
log_handler=val_metrics_log_handler,
event_name=_engine.Events.EPOCH_COMPLETED)
tb_logger.attach(
trainer,
log_handler=_tb_logger.OptimizerParamsHandler(optimizer),
event_name=_engine.Events.ITERATION_STARTED)
tb_logger.attach(trainer,
log_handler=_tb_logger.WeightsScalarHandler(
self.model),
event_name=_engine.Events.ITERATION_COMPLETED)
tb_logger.attach(trainer,
log_handler=_tb_logger.WeightsHistHandler(
self.model),
event_name=_engine.Events.EPOCH_COMPLETED)
tb_logger.attach(trainer,
log_handler=_tb_logger.GradsScalarHandler(
self.model),
event_name=_engine.Events.ITERATION_COMPLETED)
tb_logger.attach(trainer,
log_handler=_tb_logger.GradsHistHandler(
self.model),
event_name=_engine.Events.EPOCH_COMPLETED)
torchsummary.summary(self.model,
input_size=(1, self.freq_bins, self.time_bins),
batch_size=train_loader.batch_size,
device=self.device)
trainer.run(data=train_loader, epoch_length=batches, max_epochs=epochs)
