def main():
parser = U.get_argparser()
args = parser.parse_args()
U.set_manual_seed(args.seed)
train_session_args = vars(args)
train_session = U.get_random_name()
current_time = datetime.now().strftime('%b%d_%H_%M')
prefix = f'{current_time}_{args.model}_{args.prepare}_{args.augmentation}_{train_session}'
if args.fold is not None:
prefix += f'_fold_{args.stratify}_{args.fold}'
log_dir = os.path.join('runs', prefix)
exp_dir = os.path.join('experiments', args.model, args.prepare,
args.augmentation, prefix)
os.makedirs(exp_dir, exist_ok=True)
train_ids = D.all_train_ids()
depths = D.read_depths(train_ids)
images = D.read_train_images(train_ids)
masks = D.read_train_masks(train_ids)
if args.fix_masks:
masks, changed_ids = D.fix_masks(masks, train_ids)
with open(os.path.join(exp_dir, 'fixed_masks.txt'), 'w') as f:
for sample_id in changed_ids:
f.write(sample_id)
f.write('\n')
print(f'Fixed {len(changed_ids)} masks')
if args.fold is not None:
train_indexes, test_indexes = D.get_train_test_split_for_fold(
args.stratify, args.fold, train_ids)
else:
train_indexes, test_indexes = train_test_split(
np.arange(len(train_ids)),
shuffle=False,
random_state=args.split_seed,
test_size=0.2)
ids_train, ids_test = train_ids[train_indexes], train_ids[test_indexes]
img_train, img_test = images[train_indexes], images[test_indexes]
mask_train, mask_test = masks[train_indexes], masks[test_indexes]
depth_train, depth_test = depths[train_indexes], depths[test_indexes]
# Here we can exclude some images from training, but keep in validation
train_mask = D.drop_some(img_train,
mask_train,
drop_black=True,
drop_vstrips=args.drop_vstrips,
drop_few=args.drop_few)
ids_train = ids_train[train_mask]
img_train = img_train[train_mask]
mask_train = mask_train[train_mask]
depth_train = depth_train[train_mask]
if not is_sorted(ids_train):
raise RuntimeError("ids_train is not sorted")
if not is_sorted(ids_test):
raise RuntimeError("ids_test_sorted is not sorted")
prepare_fn = D.get_prepare_fn(args.prepare, **train_session_args)
# This line valid if we apply prepare_fn first and then do augmentation
target_size = prepare_fn.target_size if prepare_fn is not None else D.ORIGINAL_SIZE
# target_size = D.ORIGINAL_SIZE
build_augmentation_fn = D.AUGMENTATION_MODES[args.augmentation]
aug = build_augmentation_fn(target_size, border_mode=args.border_mode)
train_transform_list = []
valid_transform_list = []
if prepare_fn is not None:
train_transform_list.append(prepare_fn.t_forward)
valid_transform_list.append(prepare_fn.t_forward)
train_transform_list.append(aug)
trainset = D.ImageAndMaskDataset(ids_train,
img_train,
mask_train,
depth_train,
augment=A.Compose(train_transform_list))
validset = D.ImageAndMaskDataset(ids_test,
img_test,
mask_test,
depth_test,
augment=A.Compose(valid_transform_list))
trainloader = DataLoader(trainset,
batch_size=args.batch_size,
num_workers=args.workers,
pin_memory=True,
drop_last=True,
shuffle=True)
validloader = DataLoader(validset,
batch_size=args.batch_size,
pin_memory=True,
drop_last=False,
shuffle=False)
# Save train/val split for future use
train_session_args.update({
'train_set': list(ids_train),
'valid_set': list(ids_test)
})
# Declare variables we will use during training
start_epoch = 0
train_history = pd.DataFrame()
target_metric = args.target_metric
target_metric_mode = 'max'
best_metric_val = 0
best_lb_checkpoint = os.path.join(exp_dir, f'{prefix}_{target_metric}.pth')
model = U.get_model(args.model,
num_classes=args.num_classes,
num_channels=trainset.channels(),
abn=args.abn,
use_dropout=not args.no_dropout,
pretrained=not args.no_pretrain).cuda()
print('Train set size :', len(ids_train), 'batch size',
trainloader.batch_size)
print('Valid set size :', len(ids_test), 'batch size',
validloader.batch_size)
print('Tile transform :', prepare_fn if prepare_fn is not None else "None")
print('Model :', args.model, count_parameters(model))
print('Augmentations :', args.augmentation, args.border_mode)
print('Input channels :', trainset.channels())
print('Output classes :', args.num_classes)
print('Optimizer :', args.optimizer, 'wd', args.weight_decay)
print('Use of dropout :', not args.no_dropout)
print('Train session :', train_session)
print('Freeze encoder :', args.freeze_encoder)
print('Seed :', args.seed, args.split_seed)
print('Restart every :', args.restart_every)
print('Fold :', args.fold, args.stratify)
print('Fine-tune :', args.fine_tune)
print('ABN Mode :', args.abn)
print('Fix masks :', args.fix_masks)
if args.resume:
fname = U.auto_file(args.resume)
start_epoch, train_history, best_score = U.restore_checkpoint(
fname, model)
print(train_history)
print('Resuming training from epoch', start_epoch, ' and score',
best_score, args.resume)
if args.fine_tune and args.freeze_encoder > 0:
raise ValueError(
'Incompatible options --fune-tune and --freeze-encoder')
writer = SummaryWriter(log_dir)
writer.add_text('train/params',
'```' + json.dumps(train_session_args, indent=2) + '```',
0)
config_fname = os.path.join(exp_dir, f'{train_session}.json')
with open(config_fname, 'w') as f:
f.write(json.dumps(train_session_args, indent=2))
weights = {
'mask': 1.0,
'class': 0.05,
'dsv': 0.1,
}
bce = U.get_loss('bce')
bce_lovasz = U.get_loss('bce_lovasz')
bce_jaccard = U.get_loss('bce_jaccard')
losses = {
'warmup': {
'mask': bce,
'class': bce,
'dsv': bce,
},
'main': {
'mask': bce_jaccard,
'class': bce,
'dsv': bce,
},
'annealing': {
'mask': bce_lovasz,
'class': bce,
'dsv': bce,
}
}
epochs = {'warmup': 50, 'main': 250, 'annealing': 50}
if args.fast:
for key in epochs.keys():
epochs[key] = 1
learning_rates = {
'warmup': args.learning_rate,
'main': 1e-3,
'annealing': 1e-2
}
# Warmup phase
if epochs['warmup']:
print(torch.cuda.max_memory_allocated(),
torch.cuda.max_memory_cached())
trainable_parameters = filter(lambda p: p.requires_grad,
model.parameters())
optimizer = U.get_optimizer(args.optimizer,
trainable_parameters,
learning_rates['warmup'],
weight_decay=args.weight_decay)
scheduler = None # StepLR(optimizer, gamma=0.5, step_size=50)
train_history, best_metric_val, start_epoch = train(
model,
losses['warmup'],
weights,
optimizer,
scheduler,
trainloader,
validloader,
writer,
start_epoch,
epochs=epochs['warmup'],
early_stopping=args.early_stopping,
train_history=train_history,
experiment_dir=exp_dir,
target_metric=target_metric,
best_metric_val=best_metric_val,
target_metric_mode=target_metric_mode,
checkpoint_filename=best_lb_checkpoint)
U.save_checkpoint(os.path.join(exp_dir, f'{prefix}_warmup.pth'),
model,
start_epoch,
train_history,
metric_name=target_metric,
metric_score=best_metric_val)
del trainable_parameters, optimizer, scheduler
torch.cuda.empty_cache()
torch.cuda.synchronize()
print('Finished warmup phase. Main train loop.')
# Main training phase
print(torch.cuda.max_memory_allocated(), torch.cuda.max_memory_cached())
trainable_parameters = filter(lambda p: p.requires_grad,
model.parameters())
optimizer = U.get_optimizer(args.optimizer,
trainable_parameters,
learning_rates['main'],
weight_decay=args.weight_decay)
scheduler = ReduceLROnPlateau(optimizer,
mode='max',
patience=50,
factor=0.5,
min_lr=1e-5)
train_history, best_metric_val, start_epoch = train(
model,
losses['main'],
weights,
optimizer,
scheduler,
trainloader,
validloader,
writer,
start_epoch,
epochs=epochs['main'],
early_stopping=args.early_stopping,
train_history=train_history,
experiment_dir=exp_dir,
target_metric=target_metric,
best_metric_val=best_metric_val,
target_metric_mode=target_metric_mode,
checkpoint_filename=best_lb_checkpoint)
del trainable_parameters, optimizer, scheduler
torch.cuda.empty_cache()
torch.cuda.synchronize()
snapshots = [best_lb_checkpoint]
U.save_checkpoint(os.path.join(exp_dir, f'{prefix}_main.pth'),
model,
start_epoch,
train_history,
metric_name=target_metric,
metric_score=best_metric_val)
print('Finished train phase.')
# Cosine annealing
if epochs['annealing']:
for snapshot in range(5):
print(f'Starting annealing phase {snapshot}')
print(torch.cuda.max_memory_allocated(),
torch.cuda.max_memory_cached())
# model.set_fine_tune(True)
trainable_parameters = filter(lambda p: p.requires_grad,
model.parameters())
optimizer = U.get_optimizer('sgd',
trainable_parameters,
learning_rates['annealing'],
weight_decay=args.weight_decay)
scheduler = CosineAnnealingLR(optimizer,
epochs['annealing'],
eta_min=1e-7)
snapshot_name = os.path.join(
exp_dir, f'{prefix}_{target_metric}_snapshot_{snapshot}.pth')
snapshots.append(snapshot_name)
train_history, best_metric_val, start_epoch = train(
model,
losses['annealing'],
weights,
optimizer,
scheduler,
trainloader,
validloader,
writer,
start_epoch,
epochs=epochs['annealing'],
early_stopping=args.early_stopping,
train_history=train_history,
experiment_dir=exp_dir,
target_metric=target_metric,
best_metric_val=0,
target_metric_mode=target_metric_mode,
checkpoint_filename=snapshot_name)
del trainable_parameters, optimizer, scheduler
torch.cuda.empty_cache()
torch.cuda.synchronize()
print('Training finished')
train_history.to_csv(os.path.join(exp_dir, 'train_history.csv'),
index=False)
for snapshot_file in snapshots:
generate_model_submission(snapshot_file,
config_fname,
mine_on_val=True)
def main():
parser = U.get_argparser()
args = parser.parse_args()
U.set_manual_seed(args.seed)
train_session_args = vars(args)
train_session = U.get_random_name()
current_time = datetime.now().strftime('%b%d_%H_%M')
prefix = f'{current_time}_{args.model}_{args.prepare}_{args.augmentation}_{train_session}'
if args.fold is not None:
prefix += f'_fold_{args.stratify}_{args.fold}'
log_dir = os.path.join('runs', prefix)
exp_dir = os.path.join('experiments', args.model, args.prepare,
args.augmentation, prefix)
os.makedirs(exp_dir, exist_ok=True)
train_ids = D.get_train_ids(drop_black=True,
drop_vstrips=args.drop_vstrips,
drop_empty=args.drop_empty,
drop_few=args.drop_few,
fast=args.fast)
depths = D.read_depths(train_ids)
images = D.read_train_images(train_ids)
masks = D.read_train_masks(train_ids)
if args.fix_masks:
masks, changed_ids = D.fix_masks(masks, train_ids)
with open(os.path.join(exp_dir, 'fixed_masks.txt'), 'w') as f:
for sample_id in changed_ids:
f.write(sample_id)
f.write('\n')
print(f'Fixed {len(changed_ids)} masks')
if args.fold is not None:
train_indexes, test_indexes = D.get_train_test_split_for_fold(
args.stratify, args.fold, train_ids)
else:
train_indexes, test_indexes = train_test_split(
np.arange(len(train_ids)),
shuffle=False,
random_state=args.split_seed,
test_size=0.2)
ids_train, ids_test = train_ids[train_indexes], train_ids[test_indexes]
if not is_sorted(ids_train):
raise RuntimeError("ids_train is not sorted")
if not is_sorted(ids_test):
raise RuntimeError("ids_test_sorted is not sorted")
img_train, img_test = images[train_indexes], images[test_indexes]
mask_train, mask_test = masks[train_indexes], masks[test_indexes]
depth_train, depth_test = depths[train_indexes], depths[test_indexes]
prepare_fn = D.get_prepare_fn(args.prepare, **train_session_args)
# This line valid if we apply prepare_fn first and then do augmentation
target_size = prepare_fn.target_size if prepare_fn is not None else D.ORIGINAL_SIZE
# target_size = D.ORIGINAL_SIZE
build_augmentation_fn = D.AUGMENTATION_MODES[args.augmentation]
aug = build_augmentation_fn(target_size, border_mode=args.border_mode)
train_transform_list = []
valid_transform_list = []
if prepare_fn is not None:
train_transform_list.append(prepare_fn.t_forward)
valid_transform_list.append(prepare_fn.t_forward)
train_transform_list.append(aug)
trainset = D.ImageAndMaskDataset(ids_train,
img_train,
mask_train,
depth_train,
augment=A.Compose(train_transform_list))
validset = D.ImageAndMaskDataset(ids_test,
img_test,
mask_test,
depth_test,
augment=A.Compose(valid_transform_list))
trainloader = DataLoader(trainset,
batch_size=args.batch_size,
num_workers=args.workers,
pin_memory=True,
drop_last=True,
shuffle=True)
validloader = DataLoader(validset,
batch_size=args.batch_size,
pin_memory=True,
drop_last=False,
shuffle=False)
# Save train/val split for future use
train_session_args.update({
'train_set': list(ids_train),
'valid_set': list(ids_test)
})
# Declare variables we will use during training
start_epoch = 0
train_history = pd.DataFrame()
scheduler = None
optimizer = None
target_metric = args.target_metric
target_metric_mode = 'max'
best_metric_val = 0
best_lb_checkpoint = os.path.join(exp_dir, f'{prefix}_{target_metric}.pth')
model = U.get_model(args.model,
num_classes=args.num_classes,
num_channels=trainset.channels(),
abn=args.abn,
use_dropout=not args.no_dropout,
pretrained=not args.no_pretrain).cuda()
print('Train set size :', len(trainloader), 'batch size',
trainloader.batch_size)
print('Valid set size :', len(validloader), 'batch size',
validloader.batch_size)
print('Tile transform :', prepare_fn if prepare_fn is not None else "None")
print('Model :', args.model, count_parameters(model))
print('Augmentations :', args.augmentation, args.border_mode)
print('Input channels :', trainset.channels())
print('Output classes :', args.num_classes)
print('Criterion :', args.loss),
print('Optimizer :', args.optimizer, args.learning_rate,
args.weight_decay)
print('Use of dropout :', not args.no_dropout)
print('Train session :', train_session)
print('Freeze encoder :', args.freeze_encoder)
print('Seed :', args.seed, args.split_seed)
print('Restart every :', args.restart_every)
print('Fold :', args.fold, args.stratify)
print('Fine-tune :', args.fine_tune)
print('ABN Mode :', args.abn)
print('Fix masks :', args.fix_masks)
if args.resume:
fname = U.auto_file(args.resume)
start_epoch, train_history, best_score = U.restore_checkpoint(
fname, model)
print(train_history)
print('Resuming training from epoch', start_epoch, ' and score',
best_score, args.resume)
segmentation_loss = U.get_loss(args.loss)
if args.fine_tune and args.freeze_encoder > 0:
raise ValueError(
'Incompatible options --fune-tune and --freeze-encoder')
writer = SummaryWriter(log_dir)
writer.add_text('train/params',
'```' + json.dumps(train_session_args, indent=2) + '```',
0)
config_fname = os.path.join(exp_dir, f'{train_session}.json')
with open(config_fname, 'w') as f:
f.write(json.dumps(train_session_args, indent=2))
# Start training loop
no_improvement_epochs = 0
for epoch in range(start_epoch, start_epoch + args.epochs):
# On Epoch begin
if U.should_quit(exp_dir) or (
args.early_stopping is not None
and no_improvement_epochs > args.early_stopping):
break
epochs_trained = epoch - start_epoch
should_restart_optimizer = (
args.restart_every > 0 and epochs_trained % args.restart_every
== 0) or (epochs_trained
== args.freeze_encoder) or optimizer is None
if should_restart_optimizer:
del optimizer
if args.fine_tune:
model.set_fine_tune(args.fine_tune)
else:
model.set_encoder_training_enabled(
epochs_trained >= args.freeze_encoder)
trainable_parameters = filter(lambda p: p.requires_grad,
model.parameters())
optimizer = U.get_optimizer(args.optimizer,
trainable_parameters,
args.learning_rate,
weight_decay=args.weight_decay)
print('Restarting optimizer state', epoch, count_parameters(model))
if args.lr_scheduler:
scheduler = U.get_lr_scheduler(args.lr_scheduler, optimizer,
args.epochs)
if scheduler is not None and not isinstance(scheduler,
ReduceLROnPlateau):
scheduler.step(epochs_trained)
U.log_learning_rate(writer, optimizer, epoch)
# Epoch
train_metrics = process_epoch(model,
segmentation_loss,
optimizer,
trainloader,
epoch,
True,
writer,
mask_postprocess=prepare_fn.backward)
valid_metrics = process_epoch(model,
segmentation_loss,
None,
validloader,
epoch,
False,
writer,
mask_postprocess=prepare_fn.backward)
all_metrics = {}
all_metrics.update(train_metrics)
all_metrics.update(valid_metrics)
# On Epoch End
summary = {
'epoch': [int(epoch)],
'lr': [float(optimizer.param_groups[0]['lr'])]
}
for k, v in all_metrics.items():
summary[k] = [v]
train_history = train_history.append(pd.DataFrame.from_dict(summary),
ignore_index=True)
print(epoch, summary)
if isinstance(scheduler, ReduceLROnPlateau):
scheduler.step(all_metrics[target_metric], epochs_trained)
if U.is_better(all_metrics[target_metric], best_metric_val,
target_metric_mode):
best_metric_val = all_metrics[target_metric]
U.save_checkpoint(best_lb_checkpoint,
model,
epoch,
train_history,
metric_name=target_metric,
metric_score=best_metric_val)
print('Checkpoint saved', epoch, best_metric_val,
best_lb_checkpoint)
no_improvement_epochs = 0
else:
no_improvement_epochs += 1
print('Training finished')
generate_model_submission(best_lb_checkpoint,
config_fname,
mine_on_val=True)
net_em = models.environment_model.EnvironmentModel(obs_shape, act_n,
config)
# net_em.load_state_dict(torch.load(config.EM_FILE_NAME, map_location=lambda storage, loc: storage))
net_em = net_em.to(device)
config.EM_NET = str(net_em)
net_i2a = i2a_model_no_LSTM.I2A_FC(obs_shape, act_n, net_em, net_policy,
config).to(device)
config.I2A_NET = str(net_i2a)
config.ROLLOUT_ENCODER = str(net_i2a.encoder)
# net_i2a.load_state_dict(torch.load("saves/03_i2a_test/best_pong_-018.667_1300.dat", map_location=lambda storage, loc: storage))
# print(net_policy)
# print(net_em)
print(net_i2a)
print("em param count: ", common.count_parameters(net_em))
print("net_policy param count: ", common.count_parameters(net_policy))
print("ia policy param count: ", common.count_parameters(net_i2a))
obs = envs[0].reset()
obs_v = ptan.agent.default_states_preprocessor([obs]).to(device)
res = net_i2a(obs_v)
optimizer = optim.RMSprop(net_i2a.parameters(),
lr=config.LEARNING_RATE,
eps=1e-5)
policy_opt = optim.Adam(net_policy.parameters(), lr=config.POLICY_LR)
trainer = lib.trainer.A2CTrainer(envs, test_env, net_i2a, optimizer,
device, config)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-g',
'--grayscale',
action='store_true',
help='Whether to use grayscale image instead of RGB')
parser.add_argument('-m',
'--model',
required=True,
type=str,
help='Name of the model')
parser.add_argument('-p', '--patch-size', type=int, default=224)
parser.add_argument('-b',
'--batch-size',
type=int,
default=1,
help='Batch Size during training, e.g. -b 64')
parser.add_argument('-lr',
'--learning-rate',
type=float,
default=1e-3,
help='Initial learning rate')
parser.add_argument('-l',
'--loss',
type=str,
default='bce',
help='Target loss')
parser.add_argument('-o',
'--optimizer',
default='SGD',
help='Name of the optimizer')
parser.add_argument('-e',
'--epochs',
type=int,
default=100,
help='Epoch to run')
parser.add_argument('-d',
'--dataset',
type=str,
help='Name of the dataset to use for training.')
parser.add_argument('-dd',
'--data-dir',
type=str,
default='data',
help='Root directory where datasets are located.')
parser.add_argument('-s',
'--steps',
type=int,
default=128,
help='Steps per epoch')
parser.add_argument('-x',
'--experiment',
type=str,
help='Name of the experiment')
parser.add_argument('-w',
'--workers',
default=0,
type=int,
help='Num workers')
parser.add_argument('-r', '--resume', action='store_true')
parser.add_argument('-mem', '--memory', action='store_true')
parser.add_argument('-sgdr', action='store_true')
args = parser.parse_args()
cudnn.benchmark = True
if args.experiment is None:
args.experiment = '%s_%s_%d_%s_%s' % (
args.dataset, args.model, args.patch_size,
'gray' if args.grayscale else 'rgb', args.loss)
experiment_dir = os.path.join('experiments', args.dataset, args.loss,
args.experiment)
os.makedirs(experiment_dir, exist_ok=True)
writer = SummaryWriter(comment='_' + args.experiment)
with open(os.path.join(experiment_dir, 'arguments.txt'), 'w') as f:
f.write(' '.join(sys.argv[1:]))
model = get_model(args.model,
patch_size=args.patch_size,
num_channels=1 if args.grayscale else 3)
# Write model graph
dummy_input = torch.autograd.Variable(
torch.rand((args.batch_size, 1 if args.grayscale else 3,
args.patch_size, args.patch_size)))
writer.add_graph(model, dummy_input)
model = model.cuda()
loss = get_loss(args.loss).cuda()
optimizer = get_optimizer(args.optimizer, model.parameters(),
args.learning_rate)
metrics = {
'iou': JaccardScore().cuda(),
'accuracy': PixelAccuracy().cuda()
}
trainset, validset, num_classes = get_dataset(args.dataset,
args.data_dir,
grayscale=args.grayscale,
patch_size=args.patch_size,
keep_in_mem=args.memory)
print('Train set size', len(trainset))
print('Valid set size', len(validset))
print('Model ', args.model)
print('Parameters ', count_parameters(model))
trainloader = DataLoader(trainset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True,
drop_last=True)
validloader = DataLoader(validset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True,
drop_last=True)
start_epoch = 0
best_loss = np.inf
train_history = pd.DataFrame()
# Checkpoint is train result of epoch with best loss
checkpoint_filename = os.path.join(experiment_dir,
f'{args.model}_checkpoint.pth')
# Snapshot is train result of last epoch
snapshot_filename = os.path.join(experiment_dir,
f'{args.model}_snapshot.pth')
if args.resume:
start_epoch, train_history, best_loss = restore_snapshot(
model, optimizer, checkpoint_filename)
print('Resuming training from epoch', start_epoch, ' and loss',
best_loss)
print(train_history)
scheduler = None
if args.sgdr:
scheduler = CosineAnnealingLR(optimizer, T_max=10, eta_min=1e-8)
for epoch in range(start_epoch, args.epochs):
if scheduler is not None:
scheduler.step(epoch)
lrs = scheduler.get_lr()
if len(lrs) > 1:
writer.add_scalars('train/lr',
dict(enumerate(lrs)),
global_step=epoch)
else:
writer.add_scalar('train/lr', lrs[0], global_step=epoch)
train_loss, train_scores = train(model,
loss,
optimizer,
trainloader,
epoch,
metrics,
summary_writer=writer)
valid_loss, valid_scores = validate(model,
loss,
validloader,
epoch,
metrics,
summary_writer=writer)
summary = {
'epoch': [epoch],
'loss': [train_loss.avg],
'val_loss': [valid_loss.avg]
}
for key, value in train_scores.items():
summary[key] = [value.avg]
for key, value in valid_scores.items():
summary['val_' + key] = [value.avg]
train_history = train_history.append(pd.DataFrame.from_dict(summary),
ignore_index=True)
print(epoch, summary)
if valid_loss.avg < best_loss:
save_snapshot(model, optimizer, valid_loss.avg, epoch,
train_history, checkpoint_filename)
best_loss = valid_loss.avg
print('Checkpoint saved', epoch, best_loss)
save_snapshot(model, optimizer, valid_loss.avg, epoch, train_history,
snapshot_filename)
print('Training is finished...')
train_history.to_csv(os.path.join(experiment_dir,
args.experiment + '.csv'),
index=False,
mode='a' if args.resume else 'w',
header=not args.resume)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-g',
'--grayscale',
action='store_true',
help='Whether to use grayscale image instead of RGB')
parser.add_argument('-m',
'--model',
required=True,
type=str,
help='Name of the model')
parser.add_argument('-p', '--patch-size', type=int, default=224)
parser.add_argument('-b',
'--batch-size',
type=int,
default=1,
help='Batch Size during training, e.g. -b 64')
parser.add_argument('-lr',
'--learning-rate',
type=float,
default=1e-3,
help='Initial learning rate')
parser.add_argument('-l',
'--loss',
type=str,
default='bce',
help='Target loss')
parser.add_argument('-o',
'--optimizer',
default='SGD',
help='Name of the optimizer')
parser.add_argument('-e',
'--epochs',
type=int,
default=100,
help='Epoch to run')
parser.add_argument('-d',
'--dataset',
type=str,
help='Name of the dataset to use for training.')
parser.add_argument('-dd',
'--data-dir',
type=str,
default='data',
help='Root directory where datasets are located.')
parser.add_argument('-s',
'--steps',
type=int,
default=128,
help='Steps per epoch')
parser.add_argument('-x',
'--experiment',
type=str,
help='Name of the experiment')
parser.add_argument('-w',
'--workers',
default=0,
type=int,
help='Num workers')
parser.add_argument('-r', '--resume', action='store_true')
parser.add_argument('-mem', '--memory', action='store_true')
args = parser.parse_args()
cudnn.benchmark = True
if args.experiment is None:
args.experiment = 'torch_%s_%s_afterburn_%d_%s_%s' % (
args.dataset, args.model, args.patch_size,
'gray' if args.grayscale else 'rgb', args.loss)
experiment_dir = os.path.join('experiments', args.dataset, args.loss,
args.experiment)
os.makedirs(experiment_dir, exist_ok=True)
writer = SummaryWriter(comment=args.experiment)
with open(os.path.join(experiment_dir, 'arguments.txt'), 'w') as f:
f.write(' '.join(sys.argv[1:]))
trainset, validset, num_classes = TT.get_dataset(
args.dataset,
args.data_dir,
grayscale=args.grayscale,
patch_size=args.patch_size,
keep_in_mem=args.memory)
print('Train set size', len(trainset))
print('Valid set size', len(validset))
trainloader = DataLoader(trainset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True,
drop_last=True)
validloader = DataLoader(validset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True,
drop_last=True)
head_model = TT.get_model(args.model,
patch_size=args.patch_size,
num_channels=1 if args.grayscale else 3).cuda()
TT.restore_snapshot(head_model, None,
auto_file('linknet34_checkpoint.pth'))
# Freeze model training
for param in head_model.parameters():
param.requires_grad = False
afterburner = Afterburner()
model = nn.Sequential(head_model, nn.Sigmoid(), afterburner).cuda()
optimizer = TT.get_optimizer(args.optimizer, afterburner.parameters(),
args.learning_rate)
loss = TT.get_loss(args.loss).cuda()
metrics = {
'iou': JaccardScore().cuda(),
'accuracy': PixelAccuracy().cuda()
}
start_epoch = 0
best_loss = np.inf
train_history = pd.DataFrame()
checkpoint_filename = os.path.join(experiment_dir,
f'{args.model}_checkpoint.pth')
if args.resume:
start_epoch, train_history, best_loss = restore_snapshot(
model, optimizer, checkpoint_filename)
print('Resuming training from epoch', start_epoch, ' and loss',
best_loss)
print(train_history)
print('Head :', count_parameters(head_model))
print('Afterburner:', count_parameters(afterburner))
for epoch in range(start_epoch, args.epochs):
train_loss, train_scores = train(model,
loss,
optimizer,
trainloader,
epoch,
metrics,
summary_writer=writer)
valid_loss, valid_scores = validate(model,
loss,
validloader,
epoch,
metrics,
summary_writer=writer)
summary = {
'epoch': [epoch],
'loss': [train_loss.avg],
'val_loss': [valid_loss.avg]
}
for key, value in train_scores.items():
summary[key] = [value.avg]
for key, value in valid_scores.items():
summary['val_' + key] = [value.avg]
train_history = train_history.append(pd.DataFrame.from_dict(summary),
ignore_index=True)
print(epoch, summary)
if valid_loss.avg < best_loss:
save_snapshot(model, optimizer, valid_loss.avg, epoch,
train_history, checkpoint_filename)
best_loss = valid_loss.avg
print('Checkpoint saved', epoch, best_loss)
print('Training is finished...')
train_history.to_csv(os.path.join(experiment_dir,
args.experiment + '.csv'),
index=False,
mode='a' if args.resume else 'w',
header=not args.resume)
device = torch.device(config.DEVICE)
print(config.REPLACEMENT)
print(type(config.REPLACEMENT))
writer = SummaryWriter(comment="_a2c_" + config.build_name_for_writer())
saves_path = writer.logdir
#envs used for sampling tuples of experience
envs = [
common.makeCustomizedGridEnv(config) for _ in range(config.NUM_ENVS)
]
#env used to test the avg reward produced by the current best net
test_env = common.makeCustomizedGridEnv(config)
net = common.getNet(device, config)
print(common.count_parameters(net))
config.A2CNET = str(net)
#sets seed on torch operations and on all environments
common.set_seed(seed=config.SEED, envs=envs)
common.set_seed(seed=config.SEED, envs=[test_env])
optimizer = optim.Adam(net.parameters(), lr=config.LEARNING_RATE, eps=1e-5)
trainer = lib.trainer.A2CTrainer(envs, test_env, net, optimizer, device,
config)
epoch = 0
total_steps = 0
best_reward = None
ts_start = time.time()
def maybe_drop(self, x, p=0.5):
if self.use_dropout:
x = F.dropout(x, p, training=self.training)
return x
def set_fine_tune(self, fine_tune_enabled):
layers = [self.conv1, self.conv2, self.conv3, self.conv4, self.conv5]
for layer in layers:
for param in layer.parameters():
param.requires_grad = bool(not fine_tune_enabled)
def set_encoder_training_enabled(self, enabled):
# First layer is trainable since we use 1-channel image instead of 3-channel
layers = [self.conv2, self.conv3, self.conv4, self.conv5]
for layer in layers:
for param in layer.parameters():
param.requires_grad = bool(enabled)
if __name__ == '__main__':
net = TernausNetOC(num_classes=1, num_channels=1)
net = net.eval()
print(count_parameters(net))
x = {'image': torch.rand((4, 1, 128, 128)),
'depth': torch.rand((4))
}
y = net(x)
print(y['mask'].size())
net.load_state_dict(
torch.load(config.A2C_FILE_NAME,
map_location=lambda storage, loc: storage))
net = net.to(device)
config.A2CNET = str(net)
# net = common.AtariA2C(envs[0].observation_space.shape, envs[0].action_space.n)
net_em = models.environment_model.EnvironmentModel(
envs[0].observation_space.shape, envs[0].action_space.n,
config).to(device)
# net_em.load_state_dict(torch.load("/home/valy/OneDrive/experiments/repl/9_22/Jan19_20-40-19_valy_em_22_9_True/best_1.4249e-06_195121.dat", map_location=lambda storage, loc: storage))
config.EM_NET = str(net_em)
print(net)
print(net_em)
print("em param count: " + str(common.count_parameters(net_em)))
# sets seed on torch operations and on all environments
common.set_seed(seed=config.SEED, envs=envs)
optimizer = optim.Adam(net_em.parameters(), lr=config.LEARNING_RATE)
epoch = 0
best_loss = np.inf
desc = ""
pbar = trange(config.EM_STEPS, desc='', leave=True)
progress = iter(pbar)
with ptan.common.utils.TBMeanTracker(
writer, batch_size=config.BATCH_SIZE) as tb_tracker:
#obtain batch transitions from the a2c model free agent (st, at, st+1, r)
encoder_outs.append(before_pool)
for i, module in enumerate(self.up_convs):
before_pool = encoder_outs[-(i + 2)]
x = module(before_pool, x)
# No softmax is used. This means you need to use
# nn.CrossEntropyLoss is your training script,
# as this module includes a softmax already.
x = self.drop_final(x)
x = self.conv_final(x)
return x
def set_fine_tune(self, fine_tune_enabled):
pass
def set_encoder_training_enabled(self, enabled):
pass
if __name__ == "__main__":
"""
testing
"""
model = UNet(num_classes=1, num_channels=1, depth=5,
merge_mode='concat').eval()
x = torch.rand((1, 1, 128, 128))
out = model(x)
print(out.size())
print(count_parameters(model))
def resnext50(**kwargs):
return ResNeXt([3, 4, 6, 3], **kwargs)
def resnext101(pretrained=True, input_3x3=True, abn_block=ABN, **kwargs):
model = ResNeXt([3, 4, 23, 3],
input_3x3=input_3x3,
abn_block=abn_block,
classes=1000,
**kwargs)
if pretrained and input_3x3:
checkpoint = torch.load(
os.path.join('pretrain', 'resnext101_ipabn_lr_512.pth.tar'))
state_dict = checkpoint['state_dict']
new_state_dict = OrderedDict()
for k, v in state_dict.items():
name = k[7:] # remove `module.`
new_state_dict[name] = v
model.load_state_dict(new_state_dict)
return model
def resnext152(**kwargs):
return ResNeXt([3, 8, 36, 3], **kwargs)
if __name__ == '__main__':
print(count_parameters(resnext50()))
print(count_parameters(resnext101()))
print(count_parameters(resnext152()))
