def train(args):
set_random_seed(42)
model = get_model(args.network)
print('Loading model')
model.encoder.conv1 = nn.Conv2d(
count_channels(args.channels), 64, kernel_size=(7, 7),
stride=(2, 2), padding=(3, 3), bias=False)
model, device = UtilsFactory.prepare_model(model)
train_df = pd.read_csv(args.train_df).to_dict('records')
val_df = pd.read_csv(args.val_df).to_dict('records')
ds = Dataset(args.channels, args.dataset_path, args.image_size, args.batch_size, args.num_workers)
loaders = ds.create_loaders(train_df, val_df)
print(loaders['train'].dataset.data)
criterion = BCE_Dice_Loss(bce_weight=0.2)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer, milestones=[10, 20, 40], gamma=0.3
)
save_path = os.path.join(
args.logdir,
'_'.join([args.network, *args.channels])
)
# model runner
runner = SupervisedRunner()
# model training
runner.train(
model=model,
criterion=criterion,
optimizer=optimizer,
scheduler=scheduler,
loaders=loaders,
callbacks=[
DiceCallback()
],
logdir=save_path,
num_epochs=args.epochs,
verbose=True
)
infer_loader = collections.OrderedDict([('infer', loaders['valid'])])
runner.infer(
model=model,
loaders=infer_loader,
callbacks=[
CheckpointCallback(resume=f'{save_path}/checkpoints/best.pth'),
InferCallback()
],
)
def main():
images_dir = 'c:\\datasets\\ILSVRC2013_DET_val'
canny_cnn = maybe_cuda(CannyModel())
optimizer = Adam(canny_cnn.parameters(), lr=1e-4)
images = find_images_in_dir(images_dir)
train_images, valid_images = train_test_split(images, test_size=0.1, random_state=1234)
num_workers = 6
num_epochs = 100
batch_size = 16
if False:
train_images = train_images[:batch_size * 4]
valid_images = valid_images[:batch_size * 4]
train_loader = DataLoader(EdgesDataset(train_images), batch_size=batch_size, num_workers=num_workers, shuffle=True,
drop_last=True, pin_memory=True)
valid_loader = DataLoader(EdgesDataset(valid_images), batch_size=batch_size, num_workers=num_workers,
pin_memory=True)
loaders = collections.OrderedDict()
loaders["train"] = train_loader
loaders["valid"] = valid_loader
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[10, 20, 40], gamma=0.3)
# model runner
runner = SupervisedRunner()
# checkpoint = UtilsFactory.load_checkpoint("logs/checkpoints//best.pth")
# UtilsFactory.unpack_checkpoint(checkpoint, model=canny_cnn)
# model training
runner.train(
model=canny_cnn,
criterion=FocalLoss(),
optimizer=optimizer,
scheduler=scheduler,
callbacks=[
JaccardCallback(),
ShowPolarBatchesCallback(visualize_canny_predictions, metric='jaccard', minimize=False),
EarlyStoppingCallback(patience=5, min_delta=0.01, metric='jaccard', minimize=False),
],
loaders=loaders,
logdir='logs',
num_epochs=num_epochs,
verbose=True,
main_metric='jaccard',
minimize_metric=False
# check=True
)
def train(args):
set_random_seed(42)
for fold in range(args.folds):
model = get_model(args.network)
print("Loading model")
model, device = UtilsFactory.prepare_model(model)
train_df = pd.read_csv(
os.path.join(args.dataset_path,
f'train{fold}.csv')).to_dict('records')
val_df = pd.read_csv(os.path.join(args.dataset_path,
f'val{fold}.csv')).to_dict('records')
ds = Dataset(args.channels, args.dataset_path, args.image_size,
args.batch_size, args.num_workers)
loaders = ds.create_loaders(train_df, val_df)
criterion = BCE_Dice_Loss(bce_weight=0.2)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer, milestones=[10, 20, 40], gamma=0.3)
# model runner
runner = SupervisedRunner()
save_path = os.path.join(args.logdir, f'fold{fold}')
# model training
runner.train(model=model,
criterion=criterion,
optimizer=optimizer,
scheduler=scheduler,
loaders=loaders,
callbacks=[DiceCallback()],
logdir=save_path,
num_epochs=args.epochs,
verbose=True)
infer_loader = collections.OrderedDict([("infer", loaders["valid"])])
runner.infer(
model=model,
loaders=infer_loader,
callbacks=[
CheckpointCallback(resume=f'{save_path}/checkpoints/best.pth'),
InferCallback()
],
)
print(f'Fold {fold} ended')
def train(args):
model = Autoencoder_Unet(encoder_name='resnet50')
print("Loading model")
model, device = UtilsFactory.prepare_model(model)
train_df = pd.read_csv(args.train_df).to_dict('records')
val_df = pd.read_csv(args.val_df).to_dict('records')
ds = AutoencoderDataset(args.channels, args.dataset_path, args.image_size,
args.batch_size, args.num_workers)
loaders = ds.create_loaders(train_df, val_df)
criterion = MSELoss()
optimizer = torch.optim.Adam(model.parameters(),
lr=1e-3,
weight_decay=1e-5)
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,
milestones=[10, 20, 40],
gamma=0.3)
# model runner
runner = SupervisedRunner()
# model training
runner.train(model=model,
criterion=criterion,
optimizer=optimizer,
callbacks=[],
scheduler=scheduler,
loaders=loaders,
logdir=args.logdir,
num_epochs=args.epochs,
verbose=True)
infer_loader = collections.OrderedDict([("infer", loaders["valid"])])
runner.infer(
model=model,
loaders=infer_loader,
callbacks=[
CheckpointCallback(resume=f"{args.logdir}/checkpoints/best.pth"),
InferCallback()
],
)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--seed', type=int, default=42, help='Random seed')
parser.add_argument('--fast', action='store_true')
parser.add_argument('--fp16', action='store_true')
parser.add_argument('-dd',
'--data-dir',
type=str,
required=True,
help='Data directory for INRIA sattelite dataset')
parser.add_argument('-m', '--model', type=str, default='unet', help='')
parser.add_argument('-b',
'--batch-size',
type=int,
default=8,
help='Batch Size during training, e.g. -b 64')
parser.add_argument('-e',
'--epochs',
type=int,
default=150,
help='Epoch to run')
parser.add_argument('-es',
'--early-stopping',
type=int,
default=None,
help='Maximum number of epochs without improvement')
# parser.add_argument('-f', '--fold', default=None, required=True, type=int, help='Fold to train')
# # parser.add_argument('-fe', '--freeze-encoder', type=int, default=0, help='Freeze encoder parameters for N epochs')
# # parser.add_argument('-ft', '--fine-tune', action='store_true')
parser.add_argument('-lr',
'--learning-rate',
type=float,
default=1e-3,
help='Initial learning rate')
parser.add_argument('-l',
'--criterion',
type=str,
default='bce',
help='Criterion')
parser.add_argument('-o',
'--optimizer',
default='Adam',
help='Name of the optimizer')
parser.add_argument(
'-c',
'--checkpoint',
type=str,
default=None,
help='Checkpoint filename to use as initial model weights')
parser.add_argument('-w',
'--workers',
default=8,
type=int,
help='Num workers')
args = parser.parse_args()
set_manual_seed(args.seed)
data_dir = args.data_dir
num_workers = args.workers
num_epochs = args.epochs
batch_size = args.batch_size
learning_rate = args.learning_rate
model_name = args.model
optimizer_name = args.optimizer
image_size = (512, 512)
train_loader, valid_loader = get_dataloaders(data_dir=data_dir,
batch_size=batch_size,
num_workers=num_workers,
image_size=image_size,
fast=args.fast)
model = maybe_cuda(get_model(model_name, image_size=image_size))
criterion = get_loss(args.criterion)
optimizer = get_optimizer(optimizer_name, model.parameters(),
learning_rate)
loaders = collections.OrderedDict()
loaders["train"] = train_loader
loaders["valid"] = valid_loader
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,
milestones=[10, 20, 40],
gamma=0.3)
# model runner
runner = SupervisedRunner()
if args.checkpoint:
checkpoint = UtilsFactory.load_checkpoint(auto_file(args.checkpoint))
UtilsFactory.unpack_checkpoint(checkpoint, model=model)
checkpoint_epoch = checkpoint['epoch']
print('Loaded model weights from', args.checkpoint)
print('Epoch :', checkpoint_epoch)
print('Metrics:', checkpoint['epoch_metrics'])
# try:
# UtilsFactory.unpack_checkpoint(checkpoint, optimizer=optimizer)
# except Exception as e:
# print('Failed to restore optimizer state', e)
# try:
# UtilsFactory.unpack_checkpoint(checkpoint, scheduler=scheduler)
# except Exception as e:
# print('Failed to restore scheduler state', e)
print('Loaded model weights from', args.checkpoint)
current_time = datetime.now().strftime('%b%d_%H_%M')
prefix = f'{current_time}_{args.model}_{args.criterion}'
log_dir = os.path.join('runs', prefix)
os.makedirs(log_dir, exist_ok=False)
print('Train session:', prefix)
print('\tFast mode :', args.fast)
print('\tEpochs :', num_epochs)
print('\tWorkers :', num_workers)
print('\tData dir :', data_dir)
print('\tLog dir :', log_dir)
print('\tTrain size :', len(train_loader), len(train_loader.dataset))
print('\tValid size :', len(valid_loader), len(valid_loader.dataset))
print('Model:', model_name)
print('\tParameters:', count_parameters(model))
print('\tImage size:', image_size)
print('Optimizer:', optimizer_name)
print('\tLearning rate:', learning_rate)
print('\tBatch size :', batch_size)
print('\tCriterion :', args.criterion)
# model training
runner.train(
model=model,
criterion=criterion,
optimizer=optimizer,
scheduler=scheduler,
callbacks=[
# OneCycleLR(
# cycle_len=num_epochs,
# div_factor=10,
# increase_fraction=0.3,
# momentum_range=(0.95, 0.85)),
PixelAccuracyMetric(),
EpochJaccardMetric(),
ShowPolarBatchesCallback(visualize_inria_predictions,
metric='accuracy',
minimize=False),
# EarlyStoppingCallback(patience=5, min_delta=0.01, metric='jaccard', minimize=False),
],
loaders=loaders,
logdir=log_dir,
num_epochs=num_epochs,
verbose=True,
main_metric='jaccard',
minimize_metric=False,
state_kwargs={"cmd_args": vars(args)})
logdir = "./logs/cifar_simple_notebook_1"
# model, criterion, optimizer
model = Net()
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters())
# model runner
runner = SupervisedRunner()
# model training
runner.train(
model=model,
criterion=criterion,
optimizer=optimizer,
loaders=loaders,
logdir=logdir,
num_epochs=num_epochs,
check=True
)
# In[ ]:
# you can use plotly and tensorboard to plot metrics inside jupyter
# by default it only plots loss
# logs_plot = UtilsFactory.plot_metrics(logdir=logdir)
# # Setup 2 - training with scheduler
# In[ ]:
def train(args):
set_random_seed(42)
if(args.model=='lstm_diff'):
model = ULSTMNet(count_channels(args.channels), 1, args.image_size)
elif(args.model=='lstm_decoder'):
model = Unet_LstmDecoder(count_channels(args.channels), all_masks=args.allmasks)
else:
print('Unknown LSTM model. Return to the default model.')
model = ULSTMNet(count_channels(args.channels), 1, args.image_size)
if torch.cuda.is_available(): model.cuda()
print('Loading model')
model, device = UtilsFactory.prepare_model(model)
print(device)
optimizer = get_optimizer(args.optimizer, args.lr, model)
criterion = get_loss(args.loss)
scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer, milestones=[10, 40, 80, 150, 300], gamma=0.2
)
save_path = os.path.join(
args.logdir,
args.name
)
os.system(f"mkdir {save_path}")
train_df = pd.read_csv(args.train_df)
val_df = pd.read_csv(args.val_df)
train_dataset = LstmDataset(args.neighbours, train_df, 'train',args.channels, args.dataset_path, args.image_size, args.batch_size, args.allmasks)
valid_dataset = LstmDataset(args.neighbours, val_df, 'valid',args.channels, args.dataset_path, args.image_size, args.batch_size, args.allmasks)
train_loader = DataLoader(train_dataset, batch_size=args.batch_size,
shuffle=sampler is None, num_workers=args.num_workers, sampler=sampler(train_df))
valid_loader = DataLoader(valid_dataset, batch_size=1,
shuffle=False, num_workers=args.num_workers)
loaders = collections.OrderedDict()
loaders['train'] = train_loader
loaders['valid'] = valid_loader
runner = SupervisedRunner()
if args.model_weights_path:
checkpoint = torch.load(args.model_weights_path, map_location='cpu')
model.load_state_dict(checkpoint['model_state_dict'])
runner.train(
model=model,
criterion=criterion,
optimizer=optimizer,
scheduler=scheduler,
loaders=loaders,
callbacks=[
DiceCallback()
],
logdir=save_path,
num_epochs=args.epochs,
verbose=True
)
infer_loader = collections.OrderedDict([('infer', loaders['valid'])])
runner.infer(
model=model,
loaders=infer_loader,
callbacks=[
CheckpointCallback(resume=f'{save_path}/checkpoints/best.pth'),
InferCallback()
],
)
'''
def main():
DATA_DIR = os.path.join(os.path.dirname(__file__), 'CamVid')
x_train_dir = os.path.join(DATA_DIR, 'train')
y_train_dir = os.path.join(DATA_DIR, 'trainannot')
x_valid_dir = os.path.join(DATA_DIR, 'val')
y_valid_dir = os.path.join(DATA_DIR, 'valannot')
x_test_dir = os.path.join(DATA_DIR, 'test')
y_test_dir = os.path.join(DATA_DIR, 'testannot')
train_ds = CamVidDataset(x_train_dir, y_train_dir,
transform=get_training_augmentation())
valid_ds = CamVidDataset(x_valid_dir, y_valid_dir,
transform=get_validation_augmentation())
test_ds = CamVidDataset(x_test_dir, y_test_dir,
transform=get_validation_augmentation())
data_loaders = OrderedDict()
num_train_samples = len(train_ds)
mul_factor = 10
batch_size = 4 * torch.cuda.device_count()
data_loaders['train'] = DataLoader(train_ds,
batch_size=batch_size,
shuffle=False,
num_workers=8,
pin_memory=True,
sampler=WeightedRandomSampler(np.ones(num_train_samples),
num_train_samples * mul_factor))
data_loaders['valid'] = DataLoader(valid_ds,
batch_size=batch_size,
shuffle=False,
num_workers=0,
pin_memory=True)
data_loaders['test'] = DataLoader(test_ds,
batch_size=batch_size,
shuffle=False,
num_workers=0,
pin_memory=True)
print(len(train_ds), len(valid_ds))
num_classes = len(CLASSES)
model = effnetB7_fpn(num_classes).cuda()
# model runner
runner = SupervisedRunner()
optimizer = Adam(model.parameters(), lr=1e-3)
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,
milestones=[50, 75, 100, 125, 130, 135, 140, 145],
gamma=0.5)
# model training
runner.train(
model=model,
criterion=nn.CrossEntropyLoss(),
optimizer=optimizer,
scheduler=scheduler,
callbacks=[
MulticlassIoUCallback(prefix='iou'),
ShowPolarBatchesCallback(visualize_predictions, metric='iou', minimize=True),
],
logdir='runs/effnetB7_fpn',
loaders=data_loaders,
num_epochs=150,
verbose=True,
main_metric='iou',
minimize_metric=False
)
def train(args):
set_random_seed(42)
model = get_model(args.network, args.classification_head)
print('Loading model')
model.encoder.conv1 = nn.Conv2d(count_channels(args.channels) *
args.neighbours,
64,
kernel_size=(7, 7),
stride=(2, 2),
padding=(3, 3),
bias=False)
model, device = UtilsFactory.prepare_model(model)
train_df = pd.read_csv(args.train_df).to_dict('records')
val_df = pd.read_csv(args.val_df).to_dict('records')
ds = Dataset(args.channels, args.dataset_path, args.image_size,
args.batch_size, args.num_workers, args.neighbours,
args.classification_head)
loaders = ds.create_loaders(train_df, val_df)
save_path = os.path.join(args.logdir, args.name)
optimizer = get_optimizer(args.optimizer, args.lr, model)
if not args.classification_head:
scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer, milestones=[10, 40, 80, 150, 300], gamma=0.1)
criterion = get_loss(args.loss)
runner = SupervisedRunner()
if args.model_weights_path:
checkpoint = torch.load(args.model_weights_path,
map_location='cpu')
model.load_state_dict(checkpoint['model_state_dict'])
runner.train(model=model,
criterion=criterion,
optimizer=optimizer,
scheduler=scheduler,
loaders=loaders,
callbacks=[DiceCallback()],
logdir=save_path,
num_epochs=args.epochs,
verbose=True)
infer_loader = collections.OrderedDict([('infer', loaders['valid'])])
runner.infer(
model=model,
loaders=infer_loader,
callbacks=[
CheckpointCallback(resume=f'{save_path}/checkpoints/best.pth'),
InferCallback()
],
)
else:
criterion = get_loss('multi')
net = Model(model,
optimizer,
criterion,
batch_metrics=[
classification_head_accuracy, segmentation_head_dice
])
net = net.to(device)
net.fit_generator(loaders['train'],
loaders['valid'],
epochs=args.epochs,
callbacks=[
ModelCheckpoint(
f'{save_path}/checkpoints/best.pth', ),
MultiStepLR(milestones=[10, 40, 80, 150, 300],
gamma=0.1)
])
# model, criterion, optimizer
model = Unet(num_classes=1, in_channels=1, num_channels=32, num_blocks=2)
criterion = nn.BCEWithLogitsLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,
milestones=[10, 20, 40],
gamma=0.3)
# model runner
runner = SupervisedRunner()
# model training
runner.train(model=model,
criterion=criterion,
optimizer=optimizer,
loaders=loaders,
logdir=logdir,
num_epochs=num_epochs,
check=True)
# # Inference
# In[ ]:
from catalyst.dl.callbacks import InferCallback, CheckpointCallback
loaders = collections.OrderedDict([("infer", loaders["valid"])])
runner.infer(
model=model,
loaders=loaders,
callbacks=[
CheckpointCallback(resume=f"{logdir}/checkpoints/best.pth"),
def main():
parser = argparse.ArgumentParser()
arg = parser.add_argument
arg('-c', '--config_path', default='configs/dataset.json', type=str)
arg('-f', '--fold_id', default=0, type=int, help='Fold id.')
arg('-l',
'--log_dir',
default='./logdir',
type=str,
help='Path to store logs')
arg('-n', '--num_epochs', default=42, type=int, help='Number of epochs.')
arg('-m',
'--model_name',
default='resnet18',
type=str,
help='Name of the network',
choices=encoders)
arg('-j',
'--num_workers',
default=1,
type=int,
help='Number of CPU threads to use.')
arg('-b', '--batch_size', default=1, type=int, help='Size of the batch')
arg('--lr', default=0.0001, type=float, help='Learning Rate')
arg('--jaccard_weight',
default=0.3,
type=float,
help='Weight for soft Jaccard in loss.')
arg('--num_folds', default=5, type=int, help='Number of folds.')
args = parser.parse_args()
with open(args.config_path) as f:
config = json.load(f)
data_path = Path(config['data_path']).absolute().expanduser()
train_file_names, val_files_names = get_train_val_image_paths(
data_path / 'train', args.fold_id, args.num_folds)
train_transform = augmentations.get_train_transform()
val_transform = augmentations.get_val_transform()
train_loader = get_loader(train_file_names,
shuffle=True,
transform=train_transform,
num_workers=args.num_workers,
batch_size=args.batch_size)
val_loader = get_loader(val_files_names,
shuffle=False,
transform=val_transform,
num_workers=args.num_workers,
batch_size=args.batch_size)
# data
loaders = OrderedDict({"train": train_loader, "valid": val_loader})
# model, criterion, optimizer
model = get_model(args.model_name)
criterion = LossBinary(jaccard_weight=args.jaccard_weight)
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,
milestones=[10, 20, 30],
gamma=0.3)
# model runner
runner = SupervisedRunner()
# model training
runner.train(
model=model,
criterion=criterion,
optimizer=optimizer,
loaders=loaders,
callbacks=[EpochJaccardMetric()],
scheduler=scheduler,
logdir=args.log_dir + '_' + args.model_name + '_' + str(args.fold_id),
num_epochs=args.num_epochs,
main_metric='jaccard',
verbose=True,
)
