def main(device=torch.device('cuda:0')):
# CLI arguments
parser = arg.ArgumentParser(
description='We all know what we are doing. Fighting!')
parser.add_argument("--datasize",
"-d",
default="small",
type=str,
help="data size you want to use, small, medium, total")
# Parsing
args = parser.parse_args()
# Data loaders
datasize = args.datasize
pathname = "data/nyu.zip"
tr_loader, va_loader, te_loader = getTrainingValidationTestingData(
datasize, pathname, batch_size=config("unet.batch_size"))
# Model
model = Net()
# define loss function
# criterion = torch.nn.L1Loss()
# Attempts to restore the latest checkpoint if exists
print("Loading unet...")
model, start_epoch, stats = utils.restore_checkpoint(
model, utils.config("unet.checkpoint"))
acc, loss = utils.evaluate_model(model, te_loader, device)
# axes = util.make_training_plot()
print(f'Test Accuracy:{acc}')
print(f'Test Loss:{loss}')
def main(device=torch.device('cuda:0')):
# CLI arguments
parser = arg.ArgumentParser(
description='We all know what we are doing. Fighting!')
parser.add_argument("--datasize",
"-d",
default="small",
type=str,
help="data size you want to use, small, medium, total")
# Parsing
args = parser.parse_args()
# Data loaders
datasize = args.datasize
pathname = "data/nyu.zip"
tr_loader, va_loader, te_loader = getTrainingValidationTestingData(
datasize, pathname, batch_size=config("unet.batch_size"))
# Model
#model = Net()
#model = Dense121()
model = Dense169()
model = model.to(device)
# define loss function
# criterion = torch.nn.L1Loss()
# Attempts to restore the latest checkpoint if exists
print("Loading unet...")
model, start_epoch, stats = utils.restore_checkpoint(
model, utils.config("unet.checkpoint"))
acc, loss = utils.evaluate_model(model, te_loader, device)
# axes = util.make_training_plot()
print(f'Test Error:{acc}')
print(f'Test Loss:{loss}')
# Get Test Images
img_list = glob("examples/" + "*.png")
# Set model to eval mode
model.eval()
model = model.to(device)
# Begin testing loop
print("Begin Test Loop ...")
for idx, img_name in enumerate(img_list):
img = load_images([img_name])
img = torch.Tensor(img).float().to(device)
print("Processing {}, Tensor Shape: {}".format(img_name, img.shape))
with torch.no_grad():
preds = model(img).squeeze(0)
output = colorize(preds.data)
output = output.transpose((1, 2, 0))
cv2.imwrite(img_name.split(".")[0] + "_result.png", output)
print("Processing {} done.".format(img_name))
def main(device=torch.device('cuda:0')):
"""Print performance metrics for model at specified epoch."""
# Data loaders
pathname = "data/nyu.zip"
tr_loader, va_loader, te_loader = getTrainingValidationTestingData(pathname,
batch_size=util.config("unet.batch_size"))
# Model
model = Net()
# define loss function
# criterion = torch.nn.L1Loss()
# Attempts to restore the latest checkpoint if exists
print("Loading unet...")
model, start_epoch, stats = util.restore_checkpoint(model, util.config("unet.checkpoint"))
acc, loss = util.evaluate_model(model, te_loader, device)
# axes = util.make_training_plot()
print(f'Test Accuracy:{acc}')
print(f'Test Loss:{loss}')
def main(device=torch.device('cuda:0')):
# CLI arguments
parser = arg.ArgumentParser(
description='We all know what we are doing. Fighting!')
parser.add_argument("--datasize",
"-d",
default="small",
type=str,
help="data size you want to use, small, medium, total")
# Parsing
args = parser.parse_args()
# Data loaders
datasize = args.datasize
pathname = "data/nyu.zip"
tr_loader, va_loader, te_loader = getTrainingValidationTestingData(
datasize, pathname, batch_size=config("unet.batch_size"))
# Model
model = Net()
# TODO: define loss function, and optimizer
learning_rate = utils.config("unet.learning_rate")
criterion = DepthLoss(0.1)
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
number_of_epoches = 10
#
# print("Number of float-valued parameters:", util.count_parameters(model))
# Attempts to restore the latest checkpoint if exists
print("Loading unet...")
model, start_epoch, stats = utils.restore_checkpoint(
model, utils.config("unet.checkpoint"))
# axes = utils.make_training_plot()
# Evaluate the randomly initialized model
# evaluate_epoch(
# axes, tr_loader, va_loader, te_loader, model, criterion, start_epoch, stats
# )
# loss = criterion()
# initial val loss for early stopping
# prev_val_loss = stats[0][1]
running_va_loss = []
running_va_acc = []
running_tr_loss = []
running_tr_acc = []
# TODO: define patience for early stopping
# patience = 1
# curr_patience = 0
#
tr_acc, tr_loss = utils.evaluate_model(model, tr_loader, device)
acc, loss = utils.evaluate_model(model, va_loader, device)
running_va_acc.append(acc)
running_va_loss.append(loss)
running_tr_acc.append(tr_acc)
running_tr_loss.append(tr_loss)
# Loop over the entire dataset multiple times
# for epoch in range(start_epoch, config('cnn.num_epochs')):
epoch = start_epoch
# while curr_patience < patience:
while epoch < number_of_epoches:
# Train model
utils.train_epoch(tr_loader, model, criterion, optimizer)
tr_acc, tr_loss = utils.evaluate_model(model, tr_loader, device)
va_acc, va_loss = utils.evaluate_model(model, va_loader, device)
running_va_acc.append(va_acc)
running_va_loss.append(va_loss)
running_tr_acc.append(tr_acc)
running_tr_loss.append(tr_loss)
# Evaluate model
# evaluate_epoch(
# axes, tr_loader, va_loader, te_loader, model, criterion, epoch + 1, stats
# )
# Save model parameters
utils.save_checkpoint(model, epoch + 1,
utils.config("unet.checkpoint"), stats)
# update early stopping parameters
"""
curr_patience, prev_val_loss = early_stopping(
stats, curr_patience, prev_val_loss
)
"""
epoch += 1
print("Finished Training")
# Save figure and keep plot open
# utils.save_training_plot()
# utils.hold_training_plot()
utils.make_plot(running_tr_loss, running_tr_acc, running_va_loss,
running_va_acc)
)
datasize = input('Please input the size you want to use(small/medium/total): ')
normal = input(
'Please input whether you want to do normalization(1 for yes/0 for no): ')
normalization = (int(normal) == 1)
grey = input(
'Please input whether you want to do greyscale as an augmentation(1 for yes/0 for no): '
)
greyscale = (int(grey) == 1)
filename = "data/nyu_new.zip"
pathname = f"drive/MyDrive/Dense-Depth/{filename}"
csv = "data/nyu_csv.zip"
tr_loader, va_loader, te_loader = getTrainingValidationTestingData(
datasize,
csv,
filename,
batch_size=config(modelSelection + ".batch_size"),
normalization=normalization,
greyscale=greyscale)
def main(device, tr_loader, va_loader, te_loader, modelSelection):
"""Train CNN and show training plots."""
# Model
if modelSelection.lower() == 'res50':
model = Res50()
elif modelSelection.lower() == 'dense121':
model = Dense121()
elif modelSelection.lower() == 'dense161':
model = Dense161()
elif modelSelection.lower() == 'mobv2':
modelSelection = input(
'Please input the type of model to be used(res50,dense121,dense169,mob_v2,mob,squeeze):'
)
datasize = input('Please input the size you want to use(small/medium/total): ')
normal = input(
'Please input whether you want to do normalization(1 for yes/0 for no): ')
normalization = (int(normal) == 1)
grey = input(
'Please input whether you want to do greyscale as an augmentation(1 for yes/0 for no): '
)
greyscale = (int(grey) == 1)
filename = "nyu_new.zip"
pathname = f"drive/MyDrive/Dense-Depth/data/{filename}"
csv = "data/nyu_csv.zip"
tr_loader, va_loader, te_loader = getTrainingValidationTestingData(
datasize, csv, pathname, batch_size=config(modelSelection + ".batch_size"))
def main(device, tr_loader, va_loader, te_loader, modelSelection):
"""Train CNN and show training plots."""
# CLI arguments
# parser = arg.ArgumentParser(description='We all know what we are doing. Fighting!')
# parser.add_argument("--datasize", "-d", default="small", type=str,
# help="data size you want to use, small, medium, total")
# Parsing
# args = parser.parse_args()
# Data loaders
# datasize = args.datasize
# Model
if modelSelection.lower() == 'res50':
model = Res50()
def main(device=torch.device('cuda:0')):
"""Train CNN and show training plots."""
# Data loaders
"""
if check_for_augmented_data("./data"):
tr_loader, va_loader, te_loader, _ = get_train_val_test_loaders(
task="target", batch_size=config("cnn.batch_size"), augment=True
)
else:
tr_loader, va_loader, te_loader, _ = get_train_val_test_loaders(
task="target",
batch_size=config("cnn.batch_size"),
)
"""
# pathname = "data/nyu_depth.zip"
pathname = "data/nyu_small.zip"
tr_loader, va_loader, te_loader = getTrainingValidationTestingData(pathname,
batch_size=util.config("unet.batch_size"))
# Model
model = Net()
# TODO: define loss function, and optimizer
learning_rate = util.config("unet.learning_rate")
criterion = DepthLoss(0.1)
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
number_of_epoches = 10
#
# print("Number of float-valued parameters:", util.count_parameters(model))
# Attempts to restore the latest checkpoint if exists
print("Loading unet...")
model, start_epoch, stats = util.restore_checkpoint(model, util.config("unet.checkpoint"))
# axes = utils.make_training_plot()
# Evaluate the randomly initialized model
# evaluate_epoch(
# axes, tr_loader, va_loader, te_loader, model, criterion, start_epoch, stats
# )
# loss = criterion()
# initial val loss for early stopping
# prev_val_loss = stats[0][1]
running_va_loss = []
running_va_acc = []
running_tr_loss = []
running_tr_acc = []
# TODO: define patience for early stopping
# patience = 1
# curr_patience = 0
#
tr_acc, tr_loss = util.evaluate_model(model, tr_loader, device)
acc, loss = util.evaluate_model(model, va_loader, device)
running_va_acc.append(acc)
running_va_loss.append(loss)
running_tr_acc.append(tr_acc)
running_tr_loss.append(tr_loss)
# Loop over the entire dataset multiple times
# for epoch in range(start_epoch, config('cnn.num_epochs')):
epoch = start_epoch
# while curr_patience < patience:
while epoch < number_of_epoches:
# Train model
util.train_epoch(tr_loader, model, criterion, optimizer)
tr_acc, tr_loss = util.evaluate_model(model, tr_loader, device)
va_acc, va_loss = util.evaluate_model(model, va_loader, device)
running_va_acc.append(va_acc)
running_va_loss.append(va_loss)
running_tr_acc.append(tr_acc)
running_tr_loss.append(tr_loss)
# Evaluate model
# evaluate_epoch(
# axes, tr_loader, va_loader, te_loader, model, criterion, epoch + 1, stats
# )
# Save model parameters
util.save_checkpoint(model, epoch + 1, util.config("unet.checkpoint"), stats)
# update early stopping parameters
"""
curr_patience, prev_val_loss = early_stopping(
stats, curr_patience, prev_val_loss
)
"""
epoch += 1
print("Finished Training")
# Save figure and keep plot open
# utils.save_training_plot()
# utils.hold_training_plot()
util.make_plot(running_tr_loss, running_tr_acc, running_va_loss, running_va_acc)
