def cli_main():
parser = ArgumentParser()
parser.add_argument("--DATA_PATH",
type=str,
help="path to folders with images")
parser.add_argument("--MODEL_PATH",
default=None,
type=str,
help="path to model checkpoint")
parser.add_argument("--batch_size",
default=128,
type=int,
help="batch size for SSL")
parser.add_argument("--image_size",
default=256,
type=int,
help="image size for SSL")
parser.add_argument(
"--image_type",
default="tif",
type=str,
help=
"extension of image for PIL to open and parse - i.e. jpeg, gif, tif, etc. Only put the extension name, not the dot (.)"
)
parser.add_argument("--num_workers",
default=1,
type=int,
help="number of CPU cores to use for data processing")
parser.add_argument("--image_embedding_size",
default=128,
type=int,
help="size of image representation of SIMCLR")
parser.add_argument(
"--hidden_dims",
default=128,
type=int,
help=
"hidden dimensions in classification layer added onto model for finetuning"
)
parser.add_argument("--epochs",
default=200,
type=int,
help="number of epochs to train model")
parser.add_argument("--lr",
default=1e-3,
type=float,
help="learning rate for training model")
parser.add_argument(
"--patience",
default=-1,
type=int,
help=
"automatically cuts off training if validation does not drop for (patience) epochs. Leave blank to have no validation based early stopping."
)
parser.add_argument("--val_split",
default=0.2,
type=float,
help="percent in validation data")
parser.add_argument(
"--withold_train_percent",
default=0,
type=float,
help=
"decimal from 0-1 representing how much of the training data to withold during finetuning"
)
parser.add_argument("--gpus",
default=1,
type=int,
help="number of gpus to use for training")
parser.add_argument(
"--eval",
default=True,
type=bool,
help=
"Eval Mode will train and evaluate the finetuned model's performance")
parser.add_argument("--imagenet_weights",
default=False,
type=bool,
help="Use weights from a non-SSL")
parser.add_argument("--version",
default="0",
type=str,
help="version to name checkpoint for saving")
args = parser.parse_args()
DATA_PATH = args.DATA_PATH
batch_size = args.batch_size
image_size = args.image_size
image_type = args.image_type
num_workers = args.num_workers
embedding_size = args.image_embedding_size
hidden_dims = args.hidden_dims
epochs = args.epochs
lr = args.lr
patience = args.patience
val_split = args.val_split
withold_train_percent = args.withold_train_percent
version = args.version
model_checkpoint = args.MODEL_PATH
gpus = args.gpus
eval_model = args.eval
version = args.version
imagenet_weights = args.imagenet_weights
# #testing
# batch_size = 128
# image_type = 'tif'
# image_size = 256
# num_workers = 4
# DATA_PATH ='/content/UCMerced_LandUse/Images'
# embedding_size = 128
# epochs = 15
# hidden_dims = 128
# lr = 1e-3
# patience = 1
# val_split = 0.2
# withold_train_percent = 0.2
# model_checkpoint = '/content/models/SSL/SIMCLR_SSL_0/SIMCLR_SSL_0.pt'
# gpus = 1
# eval_model = True
# version = "0"
#gets dataset. We can't combine since validation data has different transform needed
finetune_dataset = FolderDataset(
DATA_PATH,
validation=False,
val_split=val_split,
withold_train_percent=withold_train_percent,
transform=SimCLRFinetuneTransform(image_size),
image_type=image_type)
finetune_loader = torch.utils.data.DataLoader(finetune_dataset,
batch_size=batch_size,
num_workers=num_workers,
drop_last=True)
print('Training Data Loaded...')
finetune_val_dataset = FolderDataset(
DATA_PATH,
validation=True,
val_split=val_split,
transform=SimCLRFinetuneTransform(image_size),
image_type=image_type)
finetune_val_loader = torch.utils.data.DataLoader(finetune_val_dataset,
batch_size=batch_size,
num_workers=num_workers,
drop_last=True)
print('Validation Data Loaded...')
num_samples = len(finetune_dataset)
model = SimCLR(arch='resnet18',
batch_size=batch_size,
num_samples=num_samples,
gpus=gpus,
dataset='None',
max_epochs=epochs,
learning_rate=lr) #
model.encoder = resnet18(pretrained=imagenet_weights,
first_conv=model.first_conv,
maxpool1=model.maxpool1,
return_all_feature_maps=False)
model.projection = Projection(input_dim=512,
hidden_dim=256,
output_dim=embedding_size) #overrides
if model_checkpoint is not None:
model.load_state_dict(torch.load(model_checkpoint))
print(
'Successfully loaded your checkpoint. Keep in mind that this does not preserve the previous trainer states, only the model weights'
)
else:
if imagenet_weights:
print('Using imagenet weights instead of a pretrained SSL model')
else:
print('Using random initialization of encoder')
num_classes = len(set(finetune_dataset.labels))
print('Finetuning to classify ', num_classes, ' Classes')
tuner = SSLFineTuner(model,
in_features=512,
num_classes=num_classes,
hidden_dim=hidden_dims,
learning_rate=lr)
if patience > 0:
cb = EarlyStopping('val_loss', patience=patience)
trainer = Trainer(gpus=gpus,
max_epochs=epochs,
callbacks=[cb],
progress_bar_refresh_rate=5)
else:
trainer = Trainer(gpus=gpus,
max_epochs=epochs,
progress_bar_refresh_rate=5)
tuner.cuda()
trainer.fit(tuner,
train_dataloader=finetune_loader,
val_dataloaders=finetune_val_loader)
Path(f"./models/Finetune/SIMCLR_Finetune_{version}").mkdir(parents=True,
exist_ok=True)
if eval_model:
print('Evaluating Model...')
save_path = f"./models/Finetune/SIMCLR_Finetune_{version}/Evaluation/trainingMetrics"
Path(save_path).mkdir(parents=True, exist_ok=True)
eval_finetune(tuner, 'training', finetune_loader, save_path)
save_path = f"./models/Finetune/SIMCLR_Finetune_{version}/Evaluation/validationMetrics"
Path(save_path).mkdir(parents=True, exist_ok=True)
eval_finetune(tuner, 'validation', finetune_val_loader, save_path)
print('Saving model...')
torch.save(
tuner.state_dict(),
f"./models/Finetune/SIMCLR_Finetune_{version}/SIMCLR_FINETUNE_{version}.pt"
)
def cli_main():
parser = ArgumentParser()
parser.add_argument("--DATA_PATH",
type=str,
help="path to folders with images")
parser.add_argument("--MODEL_PATH",
default=None,
type=str,
help="path to model checkpoint")
parser.add_argument("--batch_size",
default=128,
type=int,
help="batch size for SSL")
parser.add_argument("--image_size",
default=256,
type=int,
help="image size for SSL")
parser.add_argument("--image_embedding_size",
default=128,
type=int,
help="size of image representation of SIMCLR")
parser.add_argument(
"--hidden_dims",
default=128,
type=int,
help=
"hidden dimensions in classification layer added onto model for finetuning"
)
parser.add_argument("--epochs",
default=200,
type=int,
help="number of epochs to train model")
parser.add_argument("--lr",
default=0.3,
type=float,
help="learning rate for training model")
parser.add_argument(
"--patience",
default=-1,
type=int,
help=
"automatically cuts off training if validation does not drop for (patience) epochs. Leave blank to have no validation based early stopping."
)
parser.add_argument("--val_split",
default=0.2,
type=float,
help="percent in validation data")
parser.add_argument(
"--withold_train_percent",
default=0,
type=float,
help=
"decimal from 0-1 representing how much of the training data to withold during finetuning"
)
parser.add_argument("--gpus",
default=1,
type=int,
help="number of gpus to use for training")
parser.add_argument(
"--eval",
default=True,
type=bool,
help=
"Eval Mode will train and evaluate the finetuned model's performance")
parser.add_argument(
"--pretrain_encoder",
default=False,
type=bool,
help=
"initialize resnet encoder with pretrained imagenet weights. Ignored if MODEL_PATH is specified."
)
parser.add_argument("--version",
default="0",
type=str,
help="version to name checkpoint for saving")
parser.add_argument("--fix_backbone",
default=True,
type=bool,
help="Fix backbone during finetuning")
parser.add_argument("--num_workers",
default=0,
type=int,
help="number of workers to use to fetch data")
args = parser.parse_args()
URL = args.DATA_PATH
batch_size = args.batch_size
image_size = args.image_size
embedding_size = args.image_embedding_size
hidden_dims = args.hidden_dims
epochs = args.epochs
lr = args.lr
patience = args.patience
val_split = args.val_split
withold_train_percent = args.withold_train_percent
version = args.version
model_checkpoint = args.MODEL_PATH
gpus = args.gpus
eval_model = args.eval
version = args.version
pretrain = args.pretrain_encoder
fix_backbone = args.fix_backbone
num_workers = args.num_workers
dm = FolderDataset2(URL,
val_split=val_split,
train_transform=SimCLRFinetuneTransform(image_size),
val_transform=SimCLRFinetuneTransform(image_size))
dm.setup()
model = SimCLR(arch='resnet18',
batch_size=batch_size,
num_samples=dm.num_samples,
gpus=1,
dataset='None',
max_epochs=100,
learning_rate=lr) #
model.projection = Projection(input_dim=512,
hidden_dim=256,
output_dim=128) #overrides
model.encoder = resnet18(pretrained=pretrain,
first_conv=model.first_conv,
maxpool1=model.maxpool1,
return_all_feature_maps=False)
if model_checkpoint is not None:
model.load_state_dict(torch.load(model_checkpoint))
print(
'Successfully loaded your checkpoint. Keep in mind that this does not preserve the previous trainer states, only the model weights'
)
else:
if pretrain:
print('Using imagenet weights instead of a pretrained SSL model')
else:
print('Using random initialization of encoder')
print('Finetuning to classify ', dm.num_classes, ' Classes')
tuner = SSLFineTuner(model,
in_features=512,
num_classes=dm.num_classes,
epochs=epochs,
hidden_dim=hidden_dims,
dropout=0,
learning_rate=lr,
weight_decay=1e-6,
nesterov=False,
scheduler_type='cosine',
gamma=0.1,
final_lr=0.,
fix_backbone=True)
trainer = pl.Trainer(gpus=gpus,
num_nodes=1,
precision=16,
max_epochs=epochs,
distributed_backend='ddp',
sync_batchnorm=False)
trainer.fit(tuner, dm)
Path(f"./models/Finetune/SIMCLR_Finetune_{version}").mkdir(parents=True,
exist_ok=True)
if eval_model:
print('Evaluating Model...')
save_path = f"./models/Finetune/SIMCLR_Finetune_{version}/Evaluation/validationMetrics"
Path(save_path).mkdir(parents=True, exist_ok=True)
if dm.val_dataloader() is not None:
eval_finetune(tuner, 'validation', dm.val_dataloader(), save_path)
save_path = f"./models/Finetune/SIMCLR_Finetune_{version}/Evaluation/trainingMetrics"
Path(save_path).mkdir(parents=True, exist_ok=True)
eval_finetune(tuner, 'training', dm.train_dataloader(), save_path)
print('Saving model...')
torch.save(
tuner.state_dict(),
f"./models/Finetune/SIMCLR_Finetune_{version}/SIMCLR_FINETUNE_{version}.pt"
)
def cli_main():
parser = ArgumentParser()
parser.add_argument(
"--MODEL_PATH",
type=str,
help="path to .pt file containing SSL-trained SimCLR Resnet18 Model")
parser.add_argument(
"--DATA_PATH",
type=str,
help=
"path to data. If folder already contains validation data only, set val_split to 0"
)
parser.add_argument(
"--val_split",
default=0.2,
type=float,
help="amount of data to use for validation as a decimal")
parser.add_argument("--image_embedding_size",
default=128,
type=int,
help="size of image representation of SIMCLR")
parser.add_argument("--image_size",
default=128,
type=int,
help="height of square image to pass through model")
parser.add_argument("--gpus",
default=1,
type=int,
help="number of gpus to use for training")
parser.add_argument("--rank",
default=50,
type=int,
help="number of neighbors to search for")
parser.add_argument("--batch_size",
default=128,
type=int,
help="batch size for Evaluation")
parser.add_argument(
"--pretrain_encoder",
default=False,
type=bool,
help=
"initialize resnet encoder with pretrained imagenet weights. Will be ignored if MODEL_PATH is specified."
)
args = parser.parse_args()
MODEL_PATH = args.MODEL_PATH
URL = args.DATA_PATH
image_size = args.image_size
embedding_size = args.image_embedding_size
val_split = args.val_split
gpus = args.gpus
rank_to = args.rank
batch_size = args.batch_size
pretrain = args.pretrain_encoder
train_transform = SimCLRTrainDataTransform(256)
val_transform = SimCLREvalDataTransform(256)
dm = ImageDataModule(URL,
train_transform=train_transform,
val_transform=val_transform,
val_split=val_split)
dm.setup()
#init model with batch size, num_samples (len of data), epochs to train, and autofinds learning rate
model = SimCLR(arch='resnet18',
batch_size=batch_size,
num_samples=dm.num_samples,
gpus=gpus,
dataset='None') #
model.encoder = resnet18(pretrained=pretrain,
first_conv=model.first_conv,
maxpool1=model.maxpool1,
return_all_feature_maps=False)
model.projection = Projection(input_dim=512,
hidden_dim=256,
output_dim=embedding_size) #overrides
model.cuda()
print('Successfully loaded your model for evaluation.')
#running eval on validation data
save_path = f"{MODEL_PATH[:-3]}/Evaluation/validationMetrics"
Path(save_path).mkdir(parents=True, exist_ok=True)
if dm.val_dataloader() is not None:
eval_embeddings(model, dm.val_dataloader(), save_path, rank_to)
print('Validation Data Evaluation Complete.')
#running eval on training data
save_path = f"{MODEL_PATH[:-3]}/Evaluation/trainingMetrics"
Path(save_path).mkdir(parents=True, exist_ok=True)
eval_embeddings(model, dm.train_dataloader(), save_path, rank_to)
print('Training Data Evaluation Complete.')
print(f'Please check {MODEL_PATH[:-3]}/Evaluation/ for your results')
def cli_main():
parser = ArgumentParser()
parser.add_argument("--DATA_PATH",
type=str,
help="path to folders with images")
parser.add_argument("--MODEL_PATH",
default=None,
type=str,
help="path to model checkpoint")
parser.add_argument("--batch_size",
default=128,
type=int,
help="batch size for SSL")
parser.add_argument("--image_size",
default=256,
type=int,
help="image size for SSL")
parser.add_argument("--num_workers",
default=1,
type=int,
help="number of CPU cores to use for data processing")
parser.add_argument("--image_embedding_size",
default=128,
type=int,
help="size of image representation of SIMCLR")
parser.add_argument("--epochs",
default=200,
type=int,
help="number of epochs to train model")
parser.add_argument("--lr",
default=1e-3,
type=float,
help="learning rate for training model")
parser.add_argument(
"--patience",
default=-1,
type=int,
help=
"automatically cuts off training if validation does not drop for (patience) epochs. Leave blank to have no validation based early stopping."
)
parser.add_argument("--val_split",
default=0.2,
type=float,
help="percent in validation data")
parser.add_argument(
"--pretrain_encoder",
default=False,
type=bool,
help=
"initialize resnet encoder with pretrained imagenet weights. Cannot be true if passing previous SSL model checkpoint."
)
parser.add_argument(
"--withold_train_percent",
default=0,
type=float,
help=
"decimal from 0-1 representing how much of the training data to withold during SSL training"
)
parser.add_argument("--version",
default="0",
type=str,
help="version to name checkpoint for saving")
parser.add_argument("--gpus",
default=1,
type=int,
help="number of gpus to use for training")
parser.add_argument("--num_workers",
default=0,
type=int,
help="number of workers to use to fetch data")
args = parser.parse_args()
URL = args.DATA_PATH
batch_size = args.batch_size
image_size = args.image_size
num_workers = args.num_workers
embedding_size = args.image_embedding_size
epochs = args.epochs
lr = args.lr
patience = args.patience
val_split = args.val_split
pretrain = args.pretrain_encoder
withold_train_percent = args.withold_train_percent
version = args.version
model_checkpoint = args.MODEL_PATH
gpus = args.gpus
num_workers = args.num_workers
train_transform = SimCLRTrainDataTransform(256)
val_transform = SimCLREvalDataTransform(256)
dm = ImageDataModule(URL,
train_transform=train_transform,
val_transform=val_transform,
val_split=val_split,
num_workers=num_workers)
dm.setup()
#init model with batch size, num_samples (len of data), epochs to train, and autofinds learning rate
model = SimCLR(arch='resnet18',
batch_size=batch_size,
num_samples=dm.num_samples,
gpus=gpus,
dataset='None',
max_epochs=epochs,
learning_rate=lr) #
model.encoder = resnet18(pretrained=pretrain,
first_conv=model.first_conv,
maxpool1=model.maxpool1,
return_all_feature_maps=False)
model.projection = Projection(input_dim=512,
hidden_dim=256,
output_dim=embedding_size) #overrides
if patience > 0:
cb = EarlyStopping('val_loss', patience=patience)
trainer = Trainer(gpus=gpus,
max_epochs=epochs,
callbacks=[cb],
progress_bar_refresh_rate=5)
else:
trainer = Trainer(gpus=gpus,
max_epochs=epochs,
progress_bar_refresh_rate=5)
if model_checkpoint is not None:
model.load_state_dict(torch.load(model_checkpoint))
print(
'Successfully loaded your checkpoint. Keep in mind that this does not preserve the previous trainer states, only the model weights'
)
model.cuda()
print('Model Initialized')
trainer.fit(model, dm)
Path(f"./models/SSL/SIMCLR_SSL_{version}").mkdir(parents=True,
exist_ok=True)
torch.save(model.state_dict(),
f"./models/SSL/SIMCLR_SSL_{version}/SIMCLR_SSL_{version}.pt")
def cli_main():
parser = ArgumentParser()
parser.add_argument("--DATA_PATH",
type=str,
help="path to folders with images")
parser.add_argument("--MODEL_PATH",
default=None,
type=str,
help="path to model checkpoint")
parser.add_argument("--batch_size",
default=128,
type=int,
help="batch size for SSL")
parser.add_argument("--image_size",
default=256,
type=int,
help="image size for SSL")
parser.add_argument(
"--image_type",
default="tif",
type=str,
help=
"extension of image for PIL to open and parse - i.e. jpeg, gif, tif, etc. Only put the extension name, not the dot (.)"
)
parser.add_argument("--num_workers",
default=1,
type=int,
help="number of CPU cores to use for data processing")
parser.add_argument("--image_embedding_size",
default=128,
type=int,
help="size of image representation of SIMCLR")
parser.add_argument("--epochs",
default=200,
type=int,
help="number of epochs to train model")
parser.add_argument("--lr",
default=1e-3,
type=float,
help="learning rate for training model")
parser.add_argument(
"--patience",
default=-1,
type=int,
help=
"automatically cuts off training if validation does not drop for (patience) epochs. Leave blank to have no validation based early stopping."
)
parser.add_argument("--val_split",
default=0.2,
type=float,
help="percent in validation data")
parser.add_argument(
"--pretrain_encoder",
default=False,
type=bool,
help=
"initialize resnet encoder with pretrained imagenet weights. Cannot be true if passing previous SSL model checkpoint."
)
parser.add_argument(
"--withold_train_percent",
default=0,
type=float,
help=
"decimal from 0-1 representing how much of the training data to withold during SSL training"
)
parser.add_argument("--version",
default="0",
type=str,
help="version to name checkpoint for saving")
parser.add_argument("--gpus",
default=1,
type=int,
help="number of gpus to use for training")
args = parser.parse_args()
URL = args.DATA_PATH
batch_size = args.batch_size
image_size = args.image_size
image_type = args.image_type
num_workers = args.num_workers
embedding_size = args.image_embedding_size
epochs = args.epochs
lr = args.lr
patience = args.patience
val_split = args.val_split
pretrain = args.pretrain_encoder
withold_train_percent = args.withold_train_percent
version = args.version
model_checkpoint = args.MODEL_PATH
gpus = args.gpus
# #testing
# batch_size = 128
# image_type = 'tif'
# image_size = 256
# num_workers = 4
# URL ='/content/UCMerced_LandUse/Images'
# embedding_size = 128
# epochs = 2
# lr = 1e-3
# patience = 1
# val_split = 0.2
# pretrain = False
# withold_train_percent = 0.2
# version = "1"
# model_checkpoint = '/content/models/SSL/SIMCLR_SSL_0.pt'
# gpus = 1
# #gets dataset. We can't combine since validation data has different transform needed
train_dataset = FolderDataset(
URL,
validation=False,
val_split=val_split,
withold_train_percent=withold_train_percent,
transform=SimCLRTrainDataTransform(image_size),
image_type=image_type)
data_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=batch_size,
num_workers=num_workers,
drop_last=True)
print('Training Data Loaded...')
val_dataset = FolderDataset(URL,
validation=True,
val_split=val_split,
transform=SimCLREvalDataTransform(image_size),
image_type=image_type)
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=batch_size,
num_workers=num_workers,
drop_last=True)
print('Validation Data Loaded...')
num_samples = len(train_dataset)
#init model with batch size, num_samples (len of data), epochs to train, and autofinds learning rate
model = SimCLR(arch='resnet18',
batch_size=batch_size,
num_samples=num_samples,
gpus=gpus,
dataset='None',
max_epochs=epochs,
learning_rate=lr) #
model.encoder = resnet18(pretrained=pretrain,
first_conv=model.first_conv,
maxpool1=model.maxpool1,
return_all_feature_maps=False)
model.projection = Projection(input_dim=512,
hidden_dim=256,
output_dim=embedding_size) #overrides
if patience > 0:
cb = EarlyStopping('val_loss', patience=patience)
trainer = Trainer(gpus=gpus,
max_epochs=epochs,
callbacks=[cb],
progress_bar_refresh_rate=5)
else:
trainer = Trainer(gpus=gpus,
max_epochs=epochs,
progress_bar_refresh_rate=5)
if model_checkpoint is not None:
model.load_state_dict(torch.load(model_checkpoint))
print(
'Successfully loaded your checkpoint. Keep in mind that this does not preserve the previous trainer states, only the model weights'
)
model.cuda()
print('Model Initialized')
trainer.fit(model, data_loader, val_loader)
Path(f"./models/SSL/SIMCLR_SSL_{version}").mkdir(parents=True,
exist_ok=True)
torch.save(model.state_dict(),
f"./models/SSL/SIMCLR_SSL_{version}/SIMCLR_SSL_{version}.pt")
def cli_main():
parser = ArgumentParser()
parser.add_argument("--DATA_PATH", type=str, help="path to folders with images")
parser.add_argument("--MODEL_PATH", default=None , type=str, help="path to model checkpoint")
parser.add_argument("--batch_size", default=128, type=int, help="batch size for SSL")
parser.add_argument("--image_size", default=256, type=int, help="image size for SSL")
parser.add_argument("--num_workers", default=1, type=int, help="number of CPU cores to use for data processing")
parser.add_argument("--image_embedding_size", default=128, type=int, help="size of image representation of SIMCLR")
parser.add_argument("--hidden_dims", default=128, type=int, help="hidden dimensions in classification layer added onto model for finetuning")
parser.add_argument("--epochs", default=200, type=int, help="number of epochs to train model")
parser.add_argument("--lr", default=1e-3, type=float, help="learning rate for training model")
parser.add_argument("--patience", default=-1, type=int, help="automatically cuts off training if validation does not drop for (patience) epochs. Leave blank to have no validation based early stopping.")
parser.add_argument("--val_split", default=0.2, type=float, help="percent in validation data")
parser.add_argument("--withold_train_percent", default=0, type=float, help="decimal from 0-1 representing how much of the training data to withold during finetuning")
parser.add_argument("--gpus", default=1, type=int, help="number of gpus to use for training")
parser.add_argument("--eval", default=True, type=bool, help="Eval Mode will train and evaluate the finetuned model's performance")
parser.add_argument("--pretrain_encoder", default=False, type=bool, help="initialize resnet encoder with pretrained imagenet weights. Ignored if MODEL_PATH is specified.")
parser.add_argument("--version", default="0", type=str, help="version to name checkpoint for saving")
parser.add_argument("--fix_backbone", default=True, type=bool, help="Fix backbone during finetuning")
args = parser.parse_args()
URL = args.DATA_PATH
batch_size = args.batch_size
image_size = args.image_size
num_workers = args.num_workers
embedding_size = args.image_embedding_size
hidden_dims = args.hidden_dims
epochs = args.epochs
lr = args.lr
patience = args.patience
val_split = args.val_split
withold_train_percent = args.withold_train_percent
version = args.version
model_checkpoint = args.MODEL_PATH
gpus = args.gpus
eval_model = args.eval
version = args.version
pretrain= args.pretrain_encoder
fix_backbone = args.fix_backbone
train_transform = SimCLRFinetuneTransform(256, eval_transform=False)
val_transform = SimCLRFinetuneTransform(256, eval_transform=True)
dm = ImageDataModule(URL, train_transform = train_transform, val_transform = val_transform, val_split = val_split)
dm.setup()
#init model with batch size, num_samples (len of data), epochs to train, and autofinds learning rate
model = SimCLR(arch = 'resnet18', batch_size = batch_size, num_samples = dm.num_samples, gpus = gpus, dataset = 'None', max_epochs = epochs, learning_rate = lr) #
model.encoder = resnet18(pretrained=pretrain, first_conv=model.first_conv, maxpool1=model.maxpool1, return_all_feature_maps=False)
model.projection = Projection(input_dim = 512, hidden_dim = 256, output_dim = embedding_size) #overrides
if model_checkpoint is not None:
model.load_state_dict(torch.load(model_checkpoint))
print('Successfully loaded your checkpoint. Keep in mind that this does not preserve the previous trainer states, only the model weights')
else:
if pretrain:
print('Using imagenet weights instead of a pretrained SSL model')
else:
print('Using random initialization of encoder')
print('Finetuning to classify ', dm.num_classes, ' Classes')
tuner = SSLFineTuner(
backbone,
in_features=512,
num_classes=dm.num_classes,
epochs=epochs,
hidden_dim=None,
dropout=0,
learning_rate=0.3,
weight_decay=1e-6,
nesterov=False,
scheduler_type='cosine',
gamma=0.1,
final_lr=0.,
fix_backbone = fix_backbone
)
def cli_main():
parser = ArgumentParser()
parser.add_argument(
"--MODEL_PATH",
type=str,
help="path to .pt file containing SSL-trained SimCLR Resnet18 Model")
parser.add_argument(
"--DATA_PATH",
type=str,
help=
"path to data. If folder already contains validation data only, set val_split to 0"
)
parser.add_argument(
"--val_split",
default=0.2,
type=float,
help="amount of data to use for validation as a decimal")
parser.add_argument(
"--image_type",
default="tif",
type=str,
help=
"extension of image for PIL to open and parse - i.e. jpeg, gif, tif, etc. Only put the extension name, not the dot (.)"
)
parser.add_argument("--image_embedding_size",
default=128,
type=int,
help="size of image representation of SIMCLR")
parser.add_argument("--image_size",
default=128,
type=int,
help="height of square image to pass through model")
parser.add_argument("--gpus",
default=1,
type=int,
help="number of gpus to use for training")
parser.add_argument("--rank",
default=50,
type=int,
help="number of neighbors to search for")
parser.add_argument(
"--filter_same_group",
default=False,
type=bool,
help="custom arg for hurricane data to filter same hurricanes out")
args = parser.parse_args()
MODEL_PATH = args.MODEL_PATH
DATA_PATH = args.DATA_PATH
image_size = args.image_size
image_type = args.image_type
embedding_size = args.image_embedding_size
val_split = args.val_split
gpus = args.gpus
rank_to = args.rank
filter_hur = args.filter_same_group
#testing
# MODEL_PATH = '/content/models/SSL/SIMCLR_SSL_0.pt'
# DATA_PATH = '/content/UCMerced_LandUse/Images'
# image_size = 128
# image_type = 'tif'
# embedding_size = 128
# val_split = 0.2
# gpus = 1
# #gets dataset. We can't combine since validation data has different transform needed
train_dataset = FolderDataset(
DATA_PATH,
validation=False,
val_split=val_split,
transform=SimCLRTrainDataTransform(image_size),
image_type=image_type)
print('Training Data Loaded...')
val_dataset = FolderDataset(DATA_PATH,
validation=True,
val_split=val_split,
transform=SimCLREvalDataTransform(image_size),
image_type=image_type)
print('Validation Data Loaded...')
#load model
num_samples = len(train_dataset)
#init model with batch size, num_samples (len of data), epochs to train, and autofinds learning rate
model = SimCLR(arch='resnet18',
batch_size=1,
num_samples=num_samples,
gpus=gpus,
dataset='None') #
model.encoder = resnet18(pretrained=False,
first_conv=model.first_conv,
maxpool1=model.maxpool1,
return_all_feature_maps=False)
model.projection = Projection(input_dim=512,
hidden_dim=256,
output_dim=embedding_size) #overrides
model.load_state_dict(torch.load(MODEL_PATH))
model.cuda()
print('Successfully loaded your model for evaluation.')
#running eval on validation data
save_path = f"{MODEL_PATH[:-3]}/Evaluation/validationMetrics"
Path(save_path).mkdir(parents=True, exist_ok=True)
eval_embeddings(model, val_dataset, save_path, rank_to, filter_hur)
print('Validation Data Evaluation Complete.')
#running eval on training data
save_path = f"{MODEL_PATH[:-3]}/Evaluation/trainingMetrics"
Path(save_path).mkdir(parents=True, exist_ok=True)
eval_embeddings(model, train_dataset, save_path, rank_to, filter_hur)
print('Training Data Evaluation Complete.')
print(f'Please check {MODEL_PATH[:-3]}/Evaluation/ for your results')