def train(arguments):
# Parse input arguments
json_filename = arguments.config
network_debug = arguments.debug
# Load options
json_opts = json_file_to_pyobj(json_filename)
train_opts = json_opts.training
# Architecture type
arch_type = train_opts.arch_type
# Setup Dataset and Augmentation
ds_class = get_dataset(arch_type)
ds_path = get_dataset_path(arch_type, json_opts.data_path)
ds_transform = get_dataset_transformation(arch_type, opts=json_opts.augmentation)
# Setup channels
channels = json_opts.data_opts.channels
if len(channels) != json_opts.model.input_nc \
or len(channels) != getattr(json_opts.augmentation, arch_type).scale_size[-1]:
raise Exception('Number of data channels must match number of model channels, and patch and scale size dimensions')
# Setup the NN Model
model = get_model(json_opts.model)
if network_debug:
print('# of pars: ', model.get_number_parameters())
print('fp time: {0:.3f} sec\tbp time: {1:.3f} sec per sample'.format(*model.get_fp_bp_time()))
exit()
# Setup Data Loader
split_opts = json_opts.data_split
train_dataset = ds_class(ds_path, split='train', transform=ds_transform['train'], preload_data=train_opts.preloadData,
train_size=split_opts.train_size, test_size=split_opts.test_size,
valid_size=split_opts.validation_size, split_seed=split_opts.seed, channels=channels)
valid_dataset = ds_class(ds_path, split='validation', transform=ds_transform['valid'], preload_data=train_opts.preloadData,
train_size=split_opts.train_size, test_size=split_opts.test_size,
valid_size=split_opts.validation_size, split_seed=split_opts.seed, channels=channels)
test_dataset = ds_class(ds_path, split='test', transform=ds_transform['valid'], preload_data=train_opts.preloadData,
train_size=split_opts.train_size, test_size=split_opts.test_size,
valid_size=split_opts.validation_size, split_seed=split_opts.seed, channels=channels)
train_loader = DataLoader(dataset=train_dataset, num_workers=16, batch_size=train_opts.batchSize, shuffle=True)
valid_loader = DataLoader(dataset=valid_dataset, num_workers=16, batch_size=train_opts.batchSize, shuffle=False)
test_loader = DataLoader(dataset=test_dataset, num_workers=16, batch_size=train_opts.batchSize, shuffle=False)
# Visualisation Parameters
visualizer = Visualiser(json_opts.visualisation, save_dir=model.save_dir)
error_logger = ErrorLogger()
# Training Function
model.set_scheduler(train_opts)
for epoch in range(model.which_epoch, train_opts.n_epochs):
print('(epoch: %d, total # iters: %d)' % (epoch, len(train_loader)))
train_volumes = []
validation_volumes = []
# Training Iterations
for epoch_iter, (images, labels, indices) in tqdm(enumerate(train_loader, 1), total=len(train_loader)):
# Make a training update
model.set_input(images, labels)
model.optimize_parameters()
#model.optimize_parameters_accumulate_grd(epoch_iter)
# Error visualisation
errors = model.get_current_errors()
error_logger.update(errors, split='train')
ids = train_dataset.get_ids(indices)
volumes = model.get_current_volumes()
visualizer.display_current_volumes(volumes, ids, 'train', epoch)
train_volumes.append(volumes)
# Validation and Testing Iterations
for loader, split, dataset in zip([valid_loader, test_loader], ['validation', 'test'], [valid_dataset, test_dataset]):
for epoch_iter, (images, labels, indices) in tqdm(enumerate(loader, 1), total=len(loader)):
ids = dataset.get_ids(indices)
# Make a forward pass with the model
model.set_input(images, labels)
model.validate()
# Error visualisation
errors = model.get_current_errors()
stats = model.get_segmentation_stats()
error_logger.update({**errors, **stats}, split=split)
# Visualise predictions
if split == 'validation': # do not look at testing
volumes = model.get_current_volumes()
visualizer.display_current_volumes(volumes, ids, split, epoch)
validation_volumes.append(volumes)
# Update the plots
for split in ['train', 'validation', 'test']:
visualizer.plot_current_errors(epoch, error_logger.get_errors(split), split_name=split)
visualizer.print_current_errors(epoch, error_logger.get_errors(split), split_name=split)
visualizer.save_plots(epoch, save_frequency=5)
error_logger.reset()
# Save the model parameters
if epoch % train_opts.save_epoch_freq == 0:
model.save(epoch)
# Update the model learning rate
model.update_learning_rate()
def train(arguments):
# Parse input arguments
json_filename = arguments.config
network_debug = arguments.debug
# Load options
json_opts = json_file_to_pyobj(json_filename)
train_opts = json_opts.training
# Architecture type
arch_type = train_opts.arch_type
# Setup Dataset and Augmentation
ds_class = get_dataset(train_opts.arch_type)
ds_path = get_dataset_path(arch_type, json_opts.data_path)
ds_transform = get_dataset_transformation(arch_type,
opts=json_opts.augmentation)
# Setup Data Loader - to RAM
train_dataset = ds_class(ds_path,
split='train',
transform=ds_transform['train'],
preload_data=train_opts.preloadData)
valid_dataset = ds_class(ds_path,
split='validation',
transform=ds_transform['valid'],
preload_data=train_opts.preloadData)
test_dataset = ds_class(ds_path,
split='test',
transform=ds_transform['valid'],
preload_data=train_opts.preloadData)
train_loader = DataLoader(dataset=train_dataset,
num_workers=0,
batch_size=train_opts.batchSize,
shuffle=False)
valid_loader = DataLoader(dataset=valid_dataset,
num_workers=0,
batch_size=train_opts.batchSize,
shuffle=False) # num_workers=16
test_loader = DataLoader(dataset=test_dataset,
num_workers=0,
batch_size=train_opts.batchSize,
shuffle=False)
# Setup the NN Model
dataDims = [
json_opts.training.batchSize, 1, train_dataset.image_dims[0],
train_dataset.image_dims[1], train_dataset.image_dims[2]
] # This is required only for the STN based network
model = get_model(json_opts.model, dataDims)
if network_debug:
print('# of pars: ', model.get_number_parameters())
print('fp time: {0:.3f} sec\tbp time: {1:.3f} sec per sample'.format(
*model.get_fp_bp_time()))
exit()
# Visualisation Parameters
visualizer = Visualiser(json_opts.visualisation, save_dir=model.save_dir)
error_logger = ErrorLogger()
# Save the json configuration file to the checkpoints directory
Ind = json_filename.rfind('/')
copyfile(
json_filename,
os.path.join(json_opts.model.checkpoints_dir,
json_opts.model.experiment_name, json_filename[Ind + 1:]))
# Training Function
model.set_scheduler(train_opts)
for epoch in range(model.which_epoch, train_opts.n_epochs):
print('(epoch: %d, total # iters: %d)' % (epoch, len(train_loader)))
# Training Iterations
for epoch_iter, (images, labels, _) in tqdm(enumerate(train_loader, 1),
total=len(train_loader)):
#for epoch_iter, (images, labels) in enumerate(train_loader, 1):
# Make a training update
model.set_input(images, labels) # Load data to GPU memory
model.optimize_parameters()
#model.optimize_parameters_accumulate_grd(epoch_iter)
# So we won't increase lambda inside the epoch except for the first time
model.haus_flag = False
# Error visualisation
errors = model.get_current_errors()
error_logger.update(errors, split='train')
# Validation and Testing Iterations
for loader, split in zip([valid_loader, test_loader],
['validation', 'test']):
for epoch_iter, (images, labels, _) in tqdm(enumerate(loader, 1),
total=len(loader)):
# Make a forward pass with the model
model.set_input(images, labels)
model.validate()
# Error visualisation
errors = model.get_current_errors()
stats = model.get_segmentation_stats()
error_logger.update({**errors, **stats}, split=split)
# Visualise predictions
visuals = model.get_current_visuals()
#visualizer.display_current_results(visuals, epoch=epoch, save_result=False)
# Update the plots
for split in ['train', 'validation', 'test']:
visualizer.plot_current_errors(epoch,
error_logger.get_errors(split),
split_name=split)
visualizer.print_current_errors(epoch,
error_logger.get_errors(split),
split_name=split)
error_logger.reset()
# Save the model parameters
if epoch % train_opts.save_epoch_freq == 0:
model.save(epoch)
# Update the model learning rate
model.update_learning_rate()
# Update the Hausdorff distance lambda
if (epoch + 1) % json_opts.model.haus_update_rate == 0:
model.haus_flag = True
print("Hausdorff distance lambda has been updated.")
def train(arguments):
# Parse input arguments
json_filename = arguments.config
network_debug = arguments.debug
# Load options
json_opts = json_file_to_pyobj(json_filename)
train_opts = json_opts.training
# Architecture type
arch_type = train_opts.arch_type
# Setup Dataset and Augmentation
ds_class = get_dataset(arch_type)
ds_path = get_dataset_path(arch_type, json_opts.data_path)
ds_transform = get_dataset_transformation(arch_type, opts=json_opts.augmentation)
# Setup the NN Model
model = get_model(json_opts.model, im_dim = train_opts.im_dim)
if network_debug:
print('# of pars: ', model.get_number_parameters())
print('fp time: {0:.3f} sec\tbp time: {1:.3f} sec per sample'.format(*model.get_fp_bp_time()))
exit()
# Setup Data Loader
train_dataset = ds_class(ds_path, split='train', im_dim=train_opts.im_dim, transform=ds_transform['train'], preload_data=train_opts.preloadData)
valid_dataset = ds_class(ds_path, split='validation', im_dim=train_opts.im_dim, transform=ds_transform['valid'], preload_data=train_opts.preloadData)
test_dataset = ds_class(ds_path, split='test', im_dim=train_opts.im_dim, transform=ds_transform['valid'], preload_data=train_opts.preloadData)
train_loader = DataLoader(dataset=train_dataset, num_workers=2, batch_size=train_opts.batchSize, shuffle=True)
valid_loader = DataLoader(dataset=valid_dataset, num_workers=2, batch_size=train_opts.batchSize, shuffle=False)
test_loader = DataLoader(dataset=test_dataset, num_workers=2, batch_size=train_opts.batchSize, shuffle=False)
# Visualisation Parameters
visualizer = Visualiser(json_opts.visualisation, save_dir=model.save_dir)
error_logger = ErrorLogger()
# Training Function
model.set_scheduler(train_opts)
for epoch in range(model.which_epoch, train_opts.n_epochs):
print('(epoch: %d, total # iters: %d)' % (epoch, len(train_loader)))
# Training Iterations
for epoch_iter, (images, labels) in tqdm(enumerate(train_loader, 1), total=len(train_loader)):
# Make a training update
model.set_input(images, labels)
model.optimize_parameters()
#model.optimize_parameters_accumulate_grd(epoch_iter)
# Error visualisation
errors = model.get_current_errors()
error_logger.update(errors, split='train')
del images, labels
# Validation and Testing Iterations
for loader, split in zip([valid_loader, test_loader], ['validation', 'test']):
for epoch_iter, (images, labels) in tqdm(enumerate(loader, 1), total=len(loader)):
# Make a forward pass with the model
model.set_input(images, labels)
model.validate()
# Error visualisation
errors = model.get_current_errors()
stats = model.get_segmentation_stats()
error_logger.update({**errors, **stats}, split=split)
# Visualise predictions
visuals = model.get_current_visuals()
visualizer.display_current_results(visuals, epoch=epoch, save_result=False)
del images, labels
# Update the plots
for split in ['train', 'validation', 'test']:
visualizer.plot_current_errors(epoch, error_logger.get_errors(split), split_name=split)
visualizer.print_current_errors(epoch, error_logger.get_errors(split), split_name=split)
error_logger.reset()
print("Memory Usage :", convert_bytes(torch.cuda.max_memory_allocated()))
print("Number of parameters :", model.get_number_parameters())
# Save the model parameters
if epoch % train_opts.save_epoch_freq == 0:
model.save(epoch)
# Update the model learning rate
model.update_learning_rate()
def test(arguments):
# Parse input arguments
json_filename = arguments.config
network_debug = arguments.debug
# Load options
json_opts = json_file_to_pyobj(json_filename)
train_opts = json_opts.training
# Architecture type
arch_type = train_opts.arch_type
# Setup Dataset and Augmentation
ds_class = get_dataset(arch_type)
ds_path = get_dataset_path(arch_type, json_opts.data_path)
ds_transform = get_dataset_transformation(arch_type,
opts=json_opts.augmentation)
# Setup the NN Model
with HiddenPrints():
model = get_model(json_opts.model)
if network_debug:
print('# of pars: ', model.get_number_parameters())
print('fp time: {0:.8f} sec\tbp time: {1:.8f} sec per sample'.format(
*model.get_fp_bp_time2((1, 1, 224, 288))))
exit()
# Setup Data Loader
num_workers = train_opts.num_workers if hasattr(train_opts,
'num_workers') else 16
valid_dataset = ds_class(ds_path,
split='val',
transform=ds_transform['valid'],
preload_data=train_opts.preloadData)
test_dataset = ds_class(ds_path,
split='test',
transform=ds_transform['valid'],
preload_data=train_opts.preloadData)
# loader
batch_size = train_opts.batchSize
valid_loader = DataLoader(dataset=valid_dataset,
num_workers=num_workers,
batch_size=train_opts.batchSize,
shuffle=False)
test_loader = DataLoader(dataset=test_dataset,
num_workers=0,
batch_size=train_opts.batchSize,
shuffle=False)
# Visualisation Parameters
filename = 'test_loss_log.txt'
visualizer = Visualiser(json_opts.visualisation,
save_dir=model.save_dir,
filename=filename)
error_logger = ErrorLogger()
# Training Function
track_labels = np.arange(len(valid_dataset.label_names))
model.set_labels(track_labels)
model.set_scheduler(train_opts)
if hasattr(model.net, 'deep_supervised'):
model.net.deep_supervised = False
# Validation and Testing Iterations
pr_lbls = []
gt_lbls = []
for loader, split in zip([test_loader], ['test']):
#for loader, split in zip([valid_loader, test_loader], ['validation', 'test']):
model.reset_results()
for epoch_iter, (images, labels) in tqdm(enumerate(loader, 1),
total=len(loader)):
# Make a forward pass with the model
model.set_input(images, labels)
model.validate()
# Error visualisation
errors = model.get_accumulated_errors()
stats = model.get_classification_stats()
error_logger.update({**errors, **stats}, split=split)
# Update the plots
# for split in ['train', 'validation', 'test']:
for split in ['test']:
# exclude bckground
#track_labels = np.delete(track_labels, 3)
#show_labels = train_dataset.label_names[:3] + train_dataset.label_names[4:]
show_labels = valid_dataset.label_names
visualizer.plot_current_errors(300,
error_logger.get_errors(split),
split_name=split,
labels=show_labels)
visualizer.print_current_errors(300,
error_logger.get_errors(split),
split_name=split)
import pickle as pkl
dst_file = os.path.join(model.save_dir, 'test_result.pkl')
with open(dst_file, 'wb') as f:
d = error_logger.get_errors(split)
d['labels'] = valid_dataset.label_names
d['pr_lbls'] = np.hstack(model.pr_lbls)
d['gt_lbls'] = np.hstack(model.gt_lbls)
pkl.dump(d, f)
error_logger.reset()
if arguments.time:
print('# of pars: ', model.get_number_parameters())
print('fp time: {0:.8f} sec\tbp time: {1:.8f} sec per sample'.format(
*model.get_fp_bp_time2((1, 1, 224, 288))))
def train(arguments):
# Parse input arguments
json_filename = arguments.config
network_debug = arguments.debug
# Load options
json_opts = json_file_to_pyobj(json_filename)
train_opts = json_opts.training
# Architecture type
arch_type = train_opts.arch_type
# Setup Dataset and Augmentation
ds_class = get_dataset(arch_type)
ds_path = get_dataset_path(arch_type, json_opts.data_path)
ds_transform = get_dataset_transformation(arch_type,
opts=json_opts.augmentation)
# Setup the NN Model
model = get_model(json_opts.model)
if network_debug:
print('# of pars: ', model.get_number_parameters())
print('fp time: {0:.3f} sec\tbp time: {1:.3f} sec per sample'.format(
*model.get_fp_bp_time()))
exit()
# Setup Data Loader
num_workers = train_opts.num_workers if hasattr(train_opts,
'num_workers') else 16
train_dataset = ds_class(ds_path,
split='train',
transform=ds_transform['train'],
preload_data=train_opts.preloadData)
valid_dataset = ds_class(ds_path,
split='val',
transform=ds_transform['valid'],
preload_data=train_opts.preloadData)
test_dataset = ds_class(ds_path,
split='test',
transform=ds_transform['valid'],
preload_data=train_opts.preloadData)
# create sampler
if train_opts.sampler == 'stratified':
print('stratified sampler')
train_sampler = StratifiedSampler(train_dataset.labels,
train_opts.batchSize)
batch_size = 52
elif train_opts.sampler == 'weighted2':
print('weighted sampler with background weight={}x'.format(
train_opts.bgd_weight_multiplier))
# modify and increase background weight
weight = train_dataset.weight
bgd_weight = np.min(weight)
weight[abs(weight - bgd_weight) <
1e-8] = bgd_weight * train_opts.bgd_weight_multiplier
train_sampler = sampler.WeightedRandomSampler(
weight, len(train_dataset.weight))
batch_size = train_opts.batchSize
else:
print('weighted sampler')
train_sampler = sampler.WeightedRandomSampler(
train_dataset.weight, len(train_dataset.weight))
batch_size = train_opts.batchSize
# loader
train_loader = DataLoader(dataset=train_dataset,
num_workers=num_workers,
batch_size=batch_size,
sampler=train_sampler)
valid_loader = DataLoader(dataset=valid_dataset,
num_workers=num_workers,
batch_size=train_opts.batchSize,
shuffle=True)
test_loader = DataLoader(dataset=test_dataset,
num_workers=num_workers,
batch_size=train_opts.batchSize,
shuffle=True)
# Visualisation Parameters
visualizer = Visualiser(json_opts.visualisation, save_dir=model.save_dir)
error_logger = ErrorLogger()
# Training Function
track_labels = np.arange(len(train_dataset.label_names))
model.set_labels(track_labels)
model.set_scheduler(train_opts)
if hasattr(model, 'update_state'):
model.update_state(0)
for epoch in range(model.which_epoch, train_opts.n_epochs):
print('(epoch: %d, total # iters: %d)' % (epoch, len(train_loader)))
# # # --- Start ---
# import matplotlib.pyplot as plt
# plt.ion()
# plt.figure()
# target_arr = np.zeros(14)
# # # --- End ---
# Training Iterations
for epoch_iter, (images, labels) in tqdm(enumerate(train_loader, 1),
total=len(train_loader)):
# Make a training update
model.set_input(images, labels)
model.optimize_parameters()
if epoch == (train_opts.n_epochs - 1):
import time
time.sleep(36000)
if train_opts.max_it == epoch_iter:
break
# # # --- visualise distribution ---
# for lab in labels.numpy():
# target_arr[lab] += 1
# plt.clf(); plt.bar(train_dataset.label_names, target_arr); plt.pause(0.01)
# # # --- End ---
# Visualise predictions
if epoch_iter <= 100:
visuals = model.get_current_visuals()
visualizer.display_current_results(visuals,
epoch=epoch,
save_result=False)
# Error visualisation
errors = model.get_current_errors()
error_logger.update(errors, split='train')
# Validation and Testing Iterations
pr_lbls = []
gt_lbls = []
for loader, split in zip([valid_loader, test_loader],
['validation', 'test']):
model.reset_results()
for epoch_iter, (images, labels) in tqdm(enumerate(loader, 1),
total=len(loader)):
# Make a forward pass with the model
model.set_input(images, labels)
model.validate()
# Visualise predictions
visuals = model.get_current_visuals()
visualizer.display_current_results(visuals,
epoch=epoch,
save_result=False)
if train_opts.max_it == epoch_iter:
break
# Error visualisation
errors = model.get_accumulated_errors()
stats = model.get_classification_stats()
error_logger.update({**errors, **stats}, split=split)
# HACK save validation error
if split == 'validation':
valid_err = errors['CE']
# Update the plots
for split in ['train', 'validation', 'test']:
# exclude bckground
#track_labels = np.delete(track_labels, 3)
#show_labels = train_dataset.label_names[:3] + train_dataset.label_names[4:]
show_labels = train_dataset.label_names
visualizer.plot_current_errors(epoch,
error_logger.get_errors(split),
split_name=split,
labels=show_labels)
visualizer.print_current_errors(epoch,
error_logger.get_errors(split),
split_name=split)
error_logger.reset()
# Save the model parameters
if epoch % train_opts.save_epoch_freq == 0:
model.save(epoch)
if hasattr(model, 'update_state'):
model.update_state(epoch)
# Update the model learning rate
model.update_learning_rate(metric=valid_err, epoch=epoch)
def train(arguments):
# Parse input arguments
json_filename = arguments.config
network_debug = arguments.debug
# Load options
json_opts = json_file_to_pyobj(json_filename)
train_opts = json_opts.training
# Architecture type
arch_type = train_opts.arch_type
# Create train-test-validation splits
np.random.seed(41)
root_dir = json_opts.data_path.ct_82
num_files = len(get_dicom_dirs(os.path.join(root_dir, "image")))
train_idx, test_idx, val_idx = get_train_test_val_indices(num_files,
test_frac=0.25,
val_frac=0.0)
ds_transform = get_dataset_transformation(arch_type,
opts=json_opts.augmentation)
train_dataset = CT82Dataset(root_dir,
"train",
train_idx,
transform=ds_transform['train'],
resample=True,
preload_data=train_opts.preloadData)
test_dataset = CT82Dataset(root_dir,
"test",
test_idx,
transform=ds_transform['valid'],
resample=True,
preload_data=train_opts.preloadData)
# val_dataset = CT82Dataset(root_dir, "validation", val_idx, transform=ds_transform['valid'], resample=True)
# Setup the NN Model
model = get_model(json_opts.model)
if network_debug:
print('# of pars: ', model.get_number_parameters())
print('fp time: {0:.3f} sec\tbp time: {1:.3f} sec per sample'.format(
*model.get_fp_bp_time()))
exit()
# Setup Data Loaders
train_loader = DataLoader(dataset=train_dataset,
num_workers=train_opts.num_workers,
batch_size=train_opts.batchSize,
shuffle=True)
# val_loader = DataLoader(dataset=val_dataset, num_workers=train_opts.num_workers, batch_size=train_opts.batchSize, shuffle=False)
test_loader = DataLoader(dataset=test_dataset,
num_workers=train_opts.num_workers,
batch_size=train_opts.batchSize,
shuffle=False)
# Visualisation Parameters
visualizer = Visualiser(json_opts.visualisation, save_dir=model.save_dir)
error_logger = ErrorLogger()
# Training Function
model.set_scheduler(train_opts)
for epoch in range(model.which_epoch, train_opts.n_epochs):
print('(epoch: %d, total # iters: %d)' % (epoch, len(train_loader)))
# Training Iterations
for epoch_iter, (images, labels) in tqdm(enumerate(train_loader, 1),
total=len(train_loader)):
# Make a training update
model.set_input(images, labels)
model.optimize_parameters()
#model.optimize_parameters_accumulate_grd(epoch_iter)
# Error visualisation
errors = model.get_current_errors()
error_logger.update(errors, split='train')
# Validation and Testing Iterations
with torch.no_grad():
for epoch_iter, (images, labels) in tqdm(enumerate(test_loader, 1),
total=len(test_loader)):
# Make a forward pass with the model
model.set_input(images, labels)
model.validate()
# Error visualisation
errors = model.get_current_errors()
stats = model.get_segmentation_stats()
error_logger.update({**errors, **stats}, split='test')
# Visualise predictions
visuals = model.get_current_visuals()
visualizer.display_current_results(visuals,
epoch=epoch,
save_result=False)
# Update the plots
for split in ['train', 'test']:
visualizer.plot_current_errors(epoch,
error_logger.get_errors(split),
split_name=split)
visualizer.print_current_errors(epoch,
error_logger.get_errors(split),
split_name=split)
error_logger.reset()
# Save the model parameters
if epoch % train_opts.save_epoch_freq == 0:
model.save(epoch)
# Update the model learning rate
model.update_learning_rate()
def train(arguments, data_splits, n_split = 0):
# Parse input arguments
json_filename = arguments.config
network_debug = arguments.debug
predict_path = arguments.predict_path
# Load options
json_opts = json_file_to_pyobj(json_filename)
train_opts = json_opts.training
# Architecture type
arch_type = train_opts.arch_type
# Setup Dataset and Augmentation
ds_class = get_dataset(arch_type)
ds_path = get_dataset_path(arch_type, json_opts.data_path)
ds_transform = get_dataset_transformation(arch_type, opts=json_opts.augmentation)
# Setup the NN Model
model = get_model(json_opts.model, im_dim = train_opts.im_dim, split=n_split)
if network_debug:
print('# of pars: ', model.get_number_parameters())
print('fp time: {0:.3f} sec\tbp time: {1:.3f} sec per sample'.format(*model.get_fp_bp_time()))
exit()
# Setup Data Loader
test_dataset = ds_class(ds_path, split='test', data_splits = data_splits['test'], im_dim=train_opts.im_dim, transform=ds_transform['valid'], preload_data=train_opts.preloadData)
test_loader = DataLoader(dataset=test_dataset, num_workers=2, batch_size=train_opts.batchSize, shuffle=False)
# Visualisation Parameters
visualizer = Visualiser(json_opts.visualisation, save_dir=model.save_dir)
error_logger = ErrorLogger()
# Training Function
model.set_scheduler(train_opts)
# Validation and Testing Iterations
loader, split = [test_loader, 'test']
for epoch_iter, (images, labels) in tqdm(enumerate(loader, 1), total=len(loader)):
# Make a forward pass with the model
model.set_input(images, labels)
model.predict(predict_path)
# Error visualisation
errors = model.get_current_errors()
stats = model.get_segmentation_stats()
error_logger.update({**errors, **stats}, split=split)
# Visualise predictions
visuals = model.get_current_visuals()
visualizer.display_current_results(visuals, epoch=1, save_result=False)
del images, labels
# Update the plots
for split in ['test']:
visualizer.plot_current_errors(1, error_logger.get_errors(split), split_name=split)
visualizer.print_current_errors(1, error_logger.get_errors(split), split_name=split)
error_logger.reset()
# print("Memory Usage :", convert_bytes(torch.cuda.max_memory_allocated()))
print("Number of parameters :", model.get_number_parameters())