network_output_downscale = 4
else:
validation_set = 0
output_downscale = 8
path = '../../dataset/ST_partA_' + args.dataset.replace('parta_',
'') + '/'
model_save_dir = './models_BSDR'
batch_size = args.batch_size
count_density_threshold = str(
args.count_thresh).split(',') if args.count_thresh != '' else []
count_density_threshold = list(
map(lambda x: float(x), count_density_threshold))
num_density_categories = len(count_density_threshold) + 1
dataset = CrowdDataset(path,
name=args.dataset,
valid_set_size=validation_set,
gt_downscale_factor=output_downscale,
density_map_sigma=density_map_sigma,
image_size_multiple=output_downscale *
network_output_downscale,
image_size_min=image_size_min,
image_crop_size=image_crop_size)
#print(dataset.data_files['test_valid'], len(dataset.data_files['test_valid']))
print(dataset.data_files['train'], len(dataset.data_files['train']))
# -- Train the model
train_network()
from model import Generator, JointCNN, load_trainer, save_trainer
train_transform = torchvision.transforms.Compose([
transforms.RandomlySelectPatchAndRescale(),
transforms.RandomHorizontalFlip(),
transforms.NegativeOneToOneNormalizeImage(),
transforms.NumpyArraysToTorchTensors()
])
validation_transform = torchvision.transforms.Compose([
transforms.RandomlySelectPatchAndRescale(),
transforms.NegativeOneToOneNormalizeImage(),
transforms.NumpyArraysToTorchTensors()
])
train_dataset = CrowdDataset(settings.train_dataset_path,
'train',
transform=train_transform)
train_dataset_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=settings.batch_size,
shuffle=True,
num_workers=settings.number_of_data_loader_workers)
validation_dataset = CrowdDataset(settings.validation_dataset_path,
'validation',
transform=validation_transform)
validation_dataset_loader = torch.utils.data.DataLoader(
validation_dataset,
batch_size=settings.batch_size,
shuffle=False,
num_workers=settings.number_of_data_loader_workers)
torch.manual_seed(11)
torch.backends.cudnn.enabled = False
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
torch.cuda.manual_seed(11)
torch.cuda.manual_seed_all(11)
# -- Dataset paths
if args.dataset == "parta":
validation_set = 30
path = '../dataset/ST_partA/'
output_downscale = 4
elif args.dataset == "ucfqnrf":
validation_set = 240
output_downscale = 4
path = '../dataset/UCF-QNRF_ECCV18/'
model_save_dir = './models'
batch_size = args.batch_size
dataset = CrowdDataset(path,
name=args.dataset,
valid_set_size=validation_set,
gt_downscale_factor=output_downscale)
print(dataset.data_files['test_valid'],
len(dataset.data_files['test_valid']))
print(dataset.data_files['train'], len(dataset.data_files['train']))
# -- Train the model
train_network()
def train(settings=None):
"""Main script for training the semi-supervised GAN."""
if not settings:
settings = Settings()
train_transform = torchvision.transforms.Compose([transforms.RandomlySelectPatchAndRescale(),
transforms.RandomHorizontalFlip(),
transforms.NegativeOneToOneNormalizeImage(),
transforms.NumpyArraysToTorchTensors()])
validation_transform = torchvision.transforms.Compose([transforms.RandomlySelectPatchAndRescale(),
transforms.NegativeOneToOneNormalizeImage(),
transforms.NumpyArraysToTorchTensors()])
train_dataset = CrowdDataset(settings.train_dataset_path, 'train', transform=train_transform)
train_dataset_loader = torch.utils.data.DataLoader(train_dataset, batch_size=settings.batch_size, shuffle=True,
num_workers=settings.number_of_data_loader_workers)
validation_dataset = CrowdDataset(settings.validation_dataset_path, 'validation', transform=validation_transform)
validation_dataset_loader = torch.utils.data.DataLoader(validation_dataset, batch_size=settings.batch_size,
shuffle=False,
num_workers=settings.number_of_data_loader_workers)
gan = GAN()
gpu(gan)
D = gan.D
G = gan.G
P = gan.P
gpu(P)
discriminator_optimizer = Adam(D.parameters())
generator_optimizer = Adam(G.parameters())
predictor_optimizer = Adam(P.parameters())
step = 0
epoch = 0
if settings.load_model_path:
d_model_state_dict, d_optimizer_state_dict, epoch, step = load_trainer(prefix='discriminator')
D.load_state_dict(d_model_state_dict)
discriminator_optimizer.load_state_dict(d_optimizer_state_dict)
discriminator_optimizer.param_groups[0].update({'lr': 1e-4, 'weight_decay': settings.weight_decay})
if settings.load_model_path:
g_model_state_dict, g_optimizer_state_dict, _, _ = load_trainer(prefix='generator')
G.load_state_dict(g_model_state_dict)
generator_optimizer.load_state_dict(g_optimizer_state_dict)
generator_optimizer.param_groups[0].update({'lr': 1e-4})
running_scalars = defaultdict(float)
validation_running_scalars = defaultdict(float)
running_example_count = 0
datetime_string = datetime.datetime.now().strftime("y%Ym%md%dh%Hm%Ms%S")
trial_directory = os.path.join(settings.log_directory, settings.trial_name + ' ' + datetime_string)
os.makedirs(trial_directory, exist_ok=True)
summary_writer = SummaryWriter(os.path.join(trial_directory, 'train'))
validation_summary_writer = SummaryWriter(os.path.join(trial_directory, 'validation'))
print('Starting training...')
step_time_start = datetime.datetime.now()
while epoch < settings.number_of_epochs:
for examples in train_dataset_loader:
# Real image discriminator processing.
discriminator_optimizer.zero_grad()
images, labels, _ = examples
images, labels = Variable(gpu(images)), Variable(gpu(labels))
predicted_labels, predicted_counts = D(images)
real_feature_layer = D.feature_layer
density_loss = torch.abs(predicted_labels - labels).pow(settings.loss_order).sum(1).sum(1).mean()
count_loss = torch.abs(predicted_counts - labels.sum(1).sum(1)).pow(settings.loss_order).mean()
loss = count_loss + (density_loss * 10)
loss.backward()
running_scalars['Labeled/Loss'] += loss.data[0]
running_scalars['Labeled/Count Loss'] += count_loss.data[0]
running_scalars['Labeled/Density Loss'] += density_loss.data[0]
running_scalars['Labeled/Count ME'] += (predicted_counts - labels.sum(1).sum(1)).mean().data[0]
# Predictor.
predictor_optimizer.zero_grad()
predictor_predicted_counts = P(predicted_counts.detach())
predictor_count_loss = torch.abs(predictor_predicted_counts - labels.sum(1).sum(1)
).pow(settings.loss_order).mean()
predictor_count_loss.backward()
predictor_optimizer.step()
running_scalars['Predictor/Count Loss'] += predictor_count_loss.data[0]
running_scalars['Predictor/Count MAE'] += torch.abs(predictor_predicted_counts - labels.sum(1).sum(1)
).mean().data[0]
running_scalars['Predictor/Count ME'] += (predictor_predicted_counts - labels.sum(1).sum(1)).mean().data[0]
running_scalars['Predictor/Exponent'] += P.exponent.data[0]
# Discriminator update.
discriminator_optimizer.step()
running_example_count += images.size()[0]
if step % settings.summary_step_period == 0 and step != 0:
comparison_image = viewer.create_crowd_images_comparison_grid(cpu(images), cpu(labels),
cpu(predicted_labels))
summary_writer.add_image('Comparison', comparison_image, global_step=step)
print('\rStep {}, {}...'.format(step, datetime.datetime.now() - step_time_start), end='')
step_time_start = datetime.datetime.now()
for name, running_scalar in running_scalars.items():
mean_scalar = running_scalar / running_example_count
summary_writer.add_scalar(name, mean_scalar, global_step=step)
running_scalars[name] = 0
running_example_count = 0
for validation_examples in validation_dataset_loader:
images, labels, _ = validation_examples
images, labels = Variable(gpu(images)), Variable(gpu(labels))
predicted_labels, predicted_counts = D(images)
density_loss = torch.abs(predicted_labels - labels).pow(settings.loss_order).sum(1).sum(1).mean()
count_loss = torch.abs(predicted_counts - labels.sum(1).sum(1)).pow(settings.loss_order).mean()
count_mae = torch.abs(predicted_counts - labels.sum(1).sum(1)).mean()
count_me = (predicted_counts - labels.sum(1).sum(1)).mean()
validation_running_scalars['Labeled/Density Loss'] += density_loss.data[0]
validation_running_scalars['Labeled/Count Loss'] += count_loss.data[0]
validation_running_scalars['Labeled/Count MAE'] += count_mae.data[0]
validation_running_scalars['Labeled/Count ME'] += count_me.data[0]
predictor_predicted_counts = P(predicted_counts.detach())
validation_running_scalars['Predictor/Count MAE'] += torch.abs(predictor_predicted_counts -
labels.sum(1).sum(1)).mean().data[0]
validation_running_scalars['Predictor/Count ME'] += (predictor_predicted_counts -
labels.sum(1).sum(1)).mean().data[0]
comparison_image = viewer.create_crowd_images_comparison_grid(cpu(images), cpu(labels),
cpu(predicted_labels))
validation_summary_writer.add_image('Comparison', comparison_image, global_step=step)
for name, running_scalar in validation_running_scalars.items():
mean_scalar = running_scalar / len(validation_dataset)
validation_summary_writer.add_scalar(name, mean_scalar, global_step=step)
validation_running_scalars[name] = 0
step += 1
epoch += 1
if epoch != 0 and epoch % settings.save_epoch_period == 0:
save_trainer(trial_directory, D, discriminator_optimizer, epoch, step, prefix='discriminator')
save_trainer(trial_directory, G, generator_optimizer, epoch, step, prefix='generator')
save_trainer(trial_directory, D, discriminator_optimizer, epoch, step, prefix='discriminator')
save_trainer(trial_directory, G, generator_optimizer, epoch, step, prefix='generator')
print('Finished Training')
return trial_directory
def train(settings=None):
"""Main script for training the semi-supervised GAN."""
if not settings:
settings = settings_
train_transform = torchvision.transforms.Compose([
transforms.RandomlySelectPatchAndRescale(),
transforms.RandomHorizontalFlip(),
transforms.NegativeOneToOneNormalizeImage(),
transforms.NumpyArraysToTorchTensors()
])
validation_transform = torchvision.transforms.Compose([
transforms.RandomlySelectPatchAndRescale(),
transforms.NegativeOneToOneNormalizeImage(),
transforms.NumpyArraysToTorchTensors()
])
train_dataset = CrowdDatasetWithUnlabeled(settings.train_dataset_path,
'train',
transform=train_transform)
train_dataset_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=settings.batch_size,
shuffle=True,
num_workers=settings.number_of_data_loader_workers)
validation_dataset = CrowdDataset(settings.validation_dataset_path,
'validation',
transform=validation_transform)
validation_dataset_loader = torch.utils.data.DataLoader(
validation_dataset,
batch_size=settings.batch_size,
shuffle=False,
num_workers=settings.number_of_data_loader_workers)
gan = GAN()
gpu(gan)
D = gan.D
G = gan.G
P = gan.P
gpu(P)
discriminator_optimizer = Adam(D.parameters())
generator_optimizer = Adam(G.parameters())
predictor_optimizer = Adam(P.parameters())
step = 0
epoch = 0
if settings.load_model_path:
d_model_state_dict, d_optimizer_state_dict, epoch, step = load_trainer(
prefix='discriminator')
D.load_state_dict(d_model_state_dict)
discriminator_optimizer.load_state_dict(d_optimizer_state_dict)
discriminator_optimizer.param_groups[0].update({
'lr':
1e-3,
'weight_decay':
settings.weight_decay
})
if settings.load_model_path:
g_model_state_dict, g_optimizer_state_dict, _, _ = load_trainer(
prefix='generator')
G.load_state_dict(g_model_state_dict)
generator_optimizer.load_state_dict(g_optimizer_state_dict)
generator_optimizer.param_groups[0].update({'lr': 1e-3})
running_scalars = defaultdict(float)
validation_running_scalars = defaultdict(float)
running_example_count = 0
datetime_string = datetime.datetime.now().strftime("y%Ym%md%dh%Hm%Ms%S")
trial_directory = os.path.join(settings.log_directory,
settings.trial_name + ' ' + datetime_string)
os.makedirs(trial_directory, exist_ok=True)
summary_writer = SummaryWriter(os.path.join(trial_directory, 'train'))
validation_summary_writer = SummaryWriter(
os.path.join(trial_directory, 'validation'))
print('Starting training...')
while epoch < settings.number_of_epochs:
for examples, unlabeled_examples in train_dataset_loader:
# Real image discriminator processing.
discriminator_optimizer.zero_grad()
images, labels, _ = examples
images, labels = Variable(gpu(images)), Variable(gpu(labels))
predicted_labels, predicted_counts = D(images)
real_feature_layer = D.feature_layer
density_loss = torch.abs(predicted_labels - labels).pow(
settings.loss_order).sum(1).sum(1).mean()
count_loss = torch.abs(predicted_counts -
labels.sum(1).sum(1)).pow(
settings.loss_order).mean()
loss = count_loss + (density_loss * 10)
loss.backward()
running_scalars['Labeled/Loss'] += loss.data[0]
running_scalars['Labeled/Count Loss'] += count_loss.data[0]
running_scalars['Labeled/Density Loss'] += density_loss.data[0]
running_scalars['Labeled/Count ME'] += (
predicted_counts - labels.sum(1).sum(1)).mean().data[0]
# Predictor.
predictor_optimizer.zero_grad()
predictor_predicted_counts = P(predicted_counts.detach())
predictor_count_loss = torch.abs(predictor_predicted_counts -
labels.sum(1).sum(1)).pow(
settings.loss_order).mean()
predictor_count_loss.backward()
predictor_optimizer.step()
running_scalars[
'Predictor/Count Loss'] += predictor_count_loss.data[0]
running_scalars['Predictor/Count MAE'] += torch.abs(
predictor_predicted_counts -
labels.sum(1).sum(1)).mean().data[0]
running_scalars['Predictor/Count ME'] += (
predictor_predicted_counts -
labels.sum(1).sum(1)).mean().data[0]
running_scalars['Predictor/Exponent'] += P.exponent.data[0]
# Unlabeled image discriminator processing.
unlabeled_images, _, _ = unlabeled_examples
unlabeled_images = Variable(gpu(unlabeled_images))
unlabeled_predicted_labels, unlabeled_predicted_counts = D(
unlabeled_images)
label_count_mean = labels.sum(1).sum(1).mean()
count_mean = labels.sum(1).sum(1).mean()
unlabeled_predicted_count_mean = unlabeled_predicted_counts.mean()
unlabeled_predicted_label_count_mean = unlabeled_predicted_labels.sum(
1).sum(1).mean()
beta = 2.0
# noinspection PyArgumentList
zero = Variable(gpu(torch.FloatTensor([0])))
unlabeled_count_loss_min = torch.max(
zero, count_mean / beta - unlabeled_predicted_count_mean)
unlabeled_count_loss_max = torch.max(
zero, unlabeled_predicted_count_mean - count_mean * beta)
unlabeled_label_loss_min = torch.max(
zero,
label_count_mean / beta - unlabeled_predicted_label_count_mean)
unlabeled_label_loss_max = torch.max(
zero,
unlabeled_predicted_label_count_mean - label_count_mean * beta)
unlabeled_density_loss = unlabeled_label_loss_max + unlabeled_label_loss_min
unlabeled_count_loss = unlabeled_count_loss_max + unlabeled_count_loss_min
unlabeled_loss = unlabeled_count_loss + (unlabeled_density_loss *
10)
running_scalars['Unlabeled/Count ME'] += (
unlabeled_predicted_count_mean - count_mean).data[0]
running_scalars[
'Unlabeled/Count'] += unlabeled_predicted_count_mean.data[0]
running_scalars['Unlabeled/Loss'] += unlabeled_loss.data[0]
unlabeled_loss.backward()
# Fake image discriminator processing.
current_batch_size = images.data.shape[0]
z = torch.randn(current_batch_size, 100)
fake_images = G(Variable(gpu(z)))
fake_predicted_labels, fake_predicted_counts = D(fake_images)
fake_density_loss = torch.abs(fake_predicted_labels).pow(
settings.loss_order).sum(1).sum(1).mean()
fake_count_loss = torch.abs(fake_predicted_counts).pow(
settings.loss_order).mean()
fake_mean_count = fake_predicted_counts.mean()
fake_discriminator_loss = fake_count_loss + (fake_density_loss *
10)
running_scalars['Fake/Count'] += fake_mean_count.data[0]
running_scalars['Fake/Loss'] += fake_discriminator_loss.data[0]
fake_discriminator_loss.backward(retain_graph=True)
# Gradient penalty.
alpha = Variable(gpu(torch.rand(3, current_batch_size, 1, 1, 1)))
alpha = alpha / alpha.sum(0)
interpolates = alpha[0] * images + alpha[
1] * unlabeled_images + alpha[2] * fake_images
interpolates_labels, interpolates_counts = D(interpolates)
density_gradients = torch.autograd.grad(
outputs=interpolates_labels,
inputs=interpolates,
grad_outputs=gpu(torch.ones(interpolates_labels.size())),
create_graph=True,
retain_graph=True,
only_inputs=True)[0]
density_gradients = density_gradients.view(current_batch_size, -1)
density_gradient_penalty = (
(density_gradients.norm(2, dim=1) - 1)**2).mean() * 10
count_gradients = torch.autograd.grad(
outputs=interpolates_counts,
inputs=interpolates,
grad_outputs=gpu(torch.ones(interpolates_counts.size())),
create_graph=True,
retain_graph=True,
only_inputs=True)[0]
count_gradients = count_gradients.view(current_batch_size, -1)
count_gradients_penalty = (
(count_gradients.norm(2, dim=1) - 1)**2).mean() * 10
gradient_penalty = count_gradients_penalty + density_gradient_penalty * 10
gradient_penalty.backward()
# Discriminator update.
discriminator_optimizer.step()
# Generator image processing.
generator_optimizer.zero_grad()
z = torch.randn(current_batch_size, 100)
fake_images = G(Variable(gpu(z)))
_, _ = D(fake_images) # Produces feature layer for next line.
fake_feature_layer = D.feature_layer
detached_predicted_counts = predicted_counts.detach()
detached_predicted_labels = predicted_labels.detach()
detached_real_feature_layer = real_feature_layer.detach()
# noinspection PyArgumentList
epsilon = Variable(gpu(torch.FloatTensor([1e-10])))
count_weights = (detached_predicted_counts / torch.max(
detached_predicted_counts.sum(), epsilon)).view(-1, 1, 1, 1)
labels_weights = (
detached_predicted_labels.sum(1).sum(1) /
torch.max(detached_predicted_labels.sum(), epsilon)).view(
-1, 1, 1, 1)
feature_weights = (count_weights + (labels_weights * 10)) / 11
weighted_real_feature_layer = feature_weights * detached_real_feature_layer
generator_loss = (weighted_real_feature_layer.mean(0) -
fake_feature_layer.mean(0)).abs().sum()
running_scalars['Generator/Loss'] += generator_loss.data[0]
# Generator update.
if step % 5 == 0:
generator_loss.backward()
generator_optimizer.step()
running_example_count += images.size()[0]
if step % settings.summary_step_period == 0 and step != 0:
comparison_image = viewer.create_crowd_images_comparison_grid(
cpu(images), cpu(labels), cpu(predicted_labels))
summary_writer.add_image('Comparison',
comparison_image,
global_step=step)
fake_images_image = torchvision.utils.make_grid(
fake_images.data[:9], nrow=3)
summary_writer.add_image('Fake',
fake_images_image,
global_step=step)
mean_loss = running_scalars[
'Labeled/Loss'] / running_example_count
print('[Epoch: {}, Step: {}] Loss: {:g}'.format(
epoch, step, mean_loss))
for name, running_scalar in running_scalars.items():
mean_scalar = running_scalar / running_example_count
summary_writer.add_scalar(name,
mean_scalar,
global_step=step)
running_scalars[name] = 0
running_example_count = 0
for validation_examples in validation_dataset_loader:
images, labels, _ = validation_examples
images, labels = Variable(gpu(images)), Variable(
gpu(labels))
predicted_labels, predicted_counts = D(images)
density_loss = torch.abs(predicted_labels - labels).pow(
settings.loss_order).sum(1).sum(1).mean()
count_loss = torch.abs(predicted_counts -
labels.sum(1).sum(1)).pow(
settings.loss_order).mean()
count_mae = torch.abs(predicted_counts -
labels.sum(1).sum(1)).mean()
count_me = (predicted_counts - labels.sum(1).sum(1)).mean()
validation_running_scalars[
'Labeled/Density Loss'] += density_loss.data[0]
validation_running_scalars[
'Labeled/Count Loss'] += count_loss.data[0]
validation_running_scalars[
'Labeled/Count MAE'] += count_mae.data[0]
validation_running_scalars[
'Labeled/Count ME'] += count_me.data[0]
predictor_predicted_counts = P(predicted_counts.detach())
validation_running_scalars[
'Predictor/Count MAE'] += torch.abs(
predictor_predicted_counts -
labels.sum(1).sum(1)).mean().data[0]
validation_running_scalars['Predictor/Count ME'] += (
predictor_predicted_counts -
labels.sum(1).sum(1)).mean().data[0]
comparison_image = viewer.create_crowd_images_comparison_grid(
cpu(images), cpu(labels), cpu(predicted_labels))
validation_summary_writer.add_image('Comparison',
comparison_image,
global_step=step)
for name, running_scalar in validation_running_scalars.items():
mean_scalar = running_scalar / len(validation_dataset)
validation_summary_writer.add_scalar(name,
mean_scalar,
global_step=step)
validation_running_scalars[name] = 0
step += 1
epoch += 1
if epoch != 0 and epoch % settings.save_epoch_period == 0:
save_trainer(trial_directory,
D,
discriminator_optimizer,
epoch,
step,
prefix='discriminator')
save_trainer(trial_directory,
G,
generator_optimizer,
epoch,
step,
prefix='generator')
save_trainer(trial_directory,
D,
discriminator_optimizer,
epoch,
step,
prefix='discriminator')
save_trainer(trial_directory,
G,
generator_optimizer,
epoch,
step,
prefix='generator')
print('Finished Training')
return trial_directory
def train(settings=None):
"""Main script for training the semi-supervised GAN."""
if not settings:
settings = Settings()
train_transform = torchvision.transforms.Compose([transforms.RandomlySelectPatchAndRescale(),
transforms.RandomHorizontalFlip(),
transforms.NegativeOneToOneNormalizeImage(),
transforms.NumpyArraysToTorchTensors()])
validation_transform = torchvision.transforms.Compose([transforms.RandomlySelectPatchAndRescale(),
transforms.NegativeOneToOneNormalizeImage(),
transforms.NumpyArraysToTorchTensors()])
train_dataset = CrowdDatasetWithUnlabeled(settings.train_dataset_path, 'train', transform=train_transform)
train_dataset_loader = torch.utils.data.DataLoader(train_dataset, batch_size=settings.batch_size, shuffle=True,
num_workers=settings.number_of_data_loader_workers)
validation_dataset = CrowdDataset(settings.validation_dataset_path, 'validation', transform=validation_transform)
validation_dataset_loader = torch.utils.data.DataLoader(validation_dataset, batch_size=settings.batch_size,
shuffle=False,
num_workers=settings.number_of_data_loader_workers)
gan = GAN()
gpu(gan)
D = gan.D
G = gan.G
discriminator_optimizer = Adam(D.parameters())
generator_optimizer = Adam(G.parameters())
step = 0
epoch = 0
if settings.load_model_path:
d_model_state_dict, d_optimizer_state_dict, epoch, step = load_trainer(prefix='discriminator',
settings=settings)
D.load_state_dict(d_model_state_dict)
discriminator_optimizer.load_state_dict(d_optimizer_state_dict)
discriminator_optimizer.param_groups[0].update({'lr': settings.learning_rate, 'weight_decay': settings.weight_decay})
if settings.load_model_path:
g_model_state_dict, g_optimizer_state_dict, _, _ = load_trainer(prefix='generator',
settings=settings)
G.load_state_dict(g_model_state_dict)
generator_optimizer.load_state_dict(g_optimizer_state_dict)
generator_optimizer.param_groups[0].update({'lr': settings.learning_rate})
running_scalars = defaultdict(float)
validation_running_scalars = defaultdict(float)
running_example_count = 0
datetime_string = datetime.datetime.now().strftime("y%Ym%md%dh%Hm%Ms%S")
trial_directory = os.path.join(settings.log_directory, settings.trial_name + ' ' + datetime_string)
os.makedirs(trial_directory, exist_ok=True)
summary_writer = SummaryWriter(os.path.join(trial_directory, 'train'))
validation_summary_writer = SummaryWriter(os.path.join(trial_directory, 'validation'))
print('Starting training...')
step_time_start = datetime.datetime.now()
while epoch < settings.number_of_epochs:
for examples, unlabeled_examples in train_dataset_loader:
unlabeled_images = unlabeled_examples[0]
# Real image discriminator processing.
discriminator_optimizer.zero_grad()
images, labels, _ = examples
images, labels = Variable(gpu(images)), Variable(gpu(labels))
current_batch_size = images.data.shape[0]
predicted_labels, predicted_counts = D(images)
real_feature_layer = D.feature_layer
density_loss = torch.abs(predicted_labels - labels).pow(settings.loss_order).sum(1).sum(1).mean()
count_loss = torch.abs(predicted_counts - labels.sum(1).sum(1)).pow(settings.loss_order).mean()
loss = count_loss + (density_loss * 10)
loss.backward()
running_scalars['Labeled/Loss'] += loss.data[0]
running_scalars['Labeled/Count Loss'] += count_loss.data[0]
running_scalars['Labeled/Density Loss'] += density_loss.data[0]
running_scalars['Labeled/Count ME'] += (predicted_counts - labels.sum(1).sum(1)).mean().data[0]
# Unlabeled.
_ = D(gpu(images))
labeled_feature_layer = D.feature_layer
_ = D(gpu(Variable(unlabeled_images)))
unlabeled_feature_layer = D.feature_layer
unlabeled_loss = feature_distance_loss(unlabeled_feature_layer, labeled_feature_layer,
scale=False) * settings.unlabeled_loss_multiplier
unlabeled_loss.backward()
# Fake.
_ = D(gpu(Variable(unlabeled_images)))
unlabeled_feature_layer = D.feature_layer
z = torch.from_numpy(MixtureModel([norm(-settings.mean_offset, 1), norm(settings.mean_offset, 1)]).rvs(
size=[current_batch_size, 100]).astype(np.float32))
# z = torch.randn(settings.batch_size, noise_size)
fake_examples = G(gpu(Variable(z)))
_ = D(fake_examples.detach())
fake_feature_layer = D.feature_layer
fake_loss = feature_distance_loss(unlabeled_feature_layer, fake_feature_layer,
order=1).neg() * settings.fake_loss_multiplier
fake_loss.backward()
# Feature norm loss.
_ = D(gpu(Variable(unlabeled_images)))
unlabeled_feature_layer = D.feature_layer
feature_norm_loss = (unlabeled_feature_layer.norm(dim=1).mean() - 1).pow(2)
feature_norm_loss.backward()
# Gradient penalty.
if settings.gradient_penalty_on:
alpha = gpu(Variable(torch.rand(2)))
alpha = alpha / alpha.sum(0)
interpolates = (alpha[0] * gpu(Variable(unlabeled_images, requires_grad=True)) +
alpha[1] * gpu(Variable(fake_examples.detach().data, requires_grad=True)))
_ = D(interpolates)
interpolates_predictions = D.feature_layer
gradients = torch.autograd.grad(outputs=interpolates_predictions, inputs=interpolates,
grad_outputs=gpu(torch.ones(interpolates_predictions.size())),
create_graph=True, only_inputs=True)[0]
gradient_penalty = ((gradients.norm(dim=1) - 1) ** 2).mean() * settings.gradient_penalty_multiplier
gradient_penalty.backward()
# Discriminator update.
discriminator_optimizer.step()
# Generator.
if step % 1 == 0:
generator_optimizer.zero_grad()
_ = D(gpu(Variable(unlabeled_images)))
unlabeled_feature_layer = D.feature_layer.detach()
z = torch.randn(current_batch_size, 100)
fake_examples = G(gpu(Variable(z)))
_ = D(fake_examples)
fake_feature_layer = D.feature_layer
generator_loss = feature_distance_loss(unlabeled_feature_layer, fake_feature_layer)
generator_loss.backward()
generator_optimizer.step()
running_example_count += images.size()[0]
if step % settings.summary_step_period == 0 and step != 0:
comparison_image = viewer.create_crowd_images_comparison_grid(cpu(images), cpu(labels),
cpu(predicted_labels))
summary_writer.add_image('Comparison', comparison_image, global_step=step)
fake_images_image = torchvision.utils.make_grid(fake_examples.data[:9], nrow=3)
summary_writer.add_image('Fake', fake_images_image, global_step=step)
print('\rStep {}, {}...'.format(step, datetime.datetime.now() - step_time_start), end='')
step_time_start = datetime.datetime.now()
for name, running_scalar in running_scalars.items():
mean_scalar = running_scalar / running_example_count
summary_writer.add_scalar(name, mean_scalar, global_step=step)
running_scalars[name] = 0
running_example_count = 0
for validation_examples in validation_dataset_loader:
images, labels, _ = validation_examples
images, labels = Variable(gpu(images)), Variable(gpu(labels))
predicted_labels, predicted_counts = D(images)
density_loss = torch.abs(predicted_labels - labels).pow(settings.loss_order).sum(1).sum(1).mean()
count_loss = torch.abs(predicted_counts - labels.sum(1).sum(1)).pow(settings.loss_order).mean()
count_mae = torch.abs(predicted_counts - labels.sum(1).sum(1)).mean()
count_me = (predicted_counts - labels.sum(1).sum(1)).mean()
validation_running_scalars['Labeled/Density Loss'] += density_loss.data[0]
validation_running_scalars['Labeled/Count Loss'] += count_loss.data[0]
validation_running_scalars['Test/Count MAE'] += count_mae.data[0]
validation_running_scalars['Labeled/Count ME'] += count_me.data[0]
comparison_image = viewer.create_crowd_images_comparison_grid(cpu(images), cpu(labels),
cpu(predicted_labels))
validation_summary_writer.add_image('Comparison', comparison_image, global_step=step)
for name, running_scalar in validation_running_scalars.items():
mean_scalar = running_scalar / len(validation_dataset)
validation_summary_writer.add_scalar(name, mean_scalar, global_step=step)
validation_running_scalars[name] = 0
step += 1
epoch += 1
if epoch != 0 and epoch % settings.save_epoch_period == 0:
save_trainer(trial_directory, D, discriminator_optimizer, epoch, step, prefix='discriminator')
save_trainer(trial_directory, G, generator_optimizer, epoch, step, prefix='generator')
save_trainer(trial_directory, D, discriminator_optimizer, epoch, step, prefix='discriminator')
save_trainer(trial_directory, G, generator_optimizer, epoch, step, prefix='generator')
print('Finished Training')
return trial_directory
# -- Setting seeds for reproducability
np.random.seed(11)
random.seed(11)
torch.manual_seed(11)
torch.backends.cudnn.enabled = False
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
torch.cuda.manual_seed(11)
torch.cuda.manual_seed_all(11)
# -- Dataset paths
if args.dataset == "VisDrone":
validation_set = 30
path = "./VisDrone2020-CC"
output_downscale = 4
dataset = CrowdDataset(path, name=args.dataset, valid_set_size=validation_set,
gt_downscale_factor=output_downscale, stage_1=True)
elif args.dataset == "ucfqnrf":
validation_set = 240
output_downscale = 4
path = "../../dataset/UCF-QNRF_ECCV18"
dataset = CrowdDataset(path, name=args.dataset, valid_set_size=validation_set,
gt_downscale_factor=output_downscale, stage_1=True, image_size_max=768)
model_save_dir = './models'
batch_size = args.batch_size
print(dataset.data_files['test_valid'],
len(dataset.data_files['test_valid']))
print(dataset.data_files['train'], len(dataset.data_files['train']))
# -- Train the model
def test(settings=None):
"""Main script for testing a model."""
if not settings:
settings = settings_
# test_dataset = CrowdDataset(settings.test_dataset_path, 'test')
net = JointCNN()
model_state_dict, _, _, _ = load_trainer(prefix='discriminator')
net.load_state_dict(model_state_dict)
gpu(net)
net.eval()
count_errors = []
density_errors = []
# print('Starting test...')
# scene_number = 1
# running_count = 0
# running_count_error = 0
# running_density_error = 0
# for full_example_index, full_example in enumerate(test_dataset):
# print('Processing example {}'.format(full_example_index), end='\r')
# sum_density_label = np.zeros_like(full_example.label, dtype=np.float32)
# sum_count_label = np.zeros_like(full_example.label, dtype=np.float32)
# hit_predicted_label = np.zeros_like(full_example.label, dtype=np.int32)
# for batch in batches_of_examples_with_meta(full_example):
# images = torch.stack([example_with_meta.example.image for example_with_meta in batch])
# rois = torch.stack([example_with_meta.example.roi for example_with_meta in batch])
# images = Variable(gpu(images))
# predicted_labels, predicted_counts = net(images)
# predicted_labels = predicted_labels * Variable(gpu(rois))
# predicted_labels = cpu(predicted_labels.data).numpy()
# predicted_counts = cpu(predicted_counts.data).numpy()
# for example_index, example_with_meta in enumerate(batch):
# predicted_label = predicted_labels[example_index]
# predicted_count = predicted_counts[example_index]
# x, y = example_with_meta.x, example_with_meta.y
# half_patch_size = example_with_meta.half_patch_size
# predicted_label_sum = np.sum(predicted_label)
# original_patch_dimensions = ((2 * half_patch_size) + 1, (2 * half_patch_size) + 1)
# predicted_label = scipy.misc.imresize(predicted_label, original_patch_dimensions, mode='F')
# unnormalized_predicted_label_sum = np.sum(predicted_label)
# if unnormalized_predicted_label_sum != 0:
# density_label = predicted_label * predicted_label_sum / unnormalized_predicted_label_sum
# count_label = predicted_label * predicted_count / unnormalized_predicted_label_sum
# else:
# density_label = predicted_label
# count_label = np.full(predicted_label.shape, predicted_count / predicted_label.size)
# y_start_offset = 0
# if y - half_patch_size < 0:
# y_start_offset = half_patch_size - y
# y_end_offset = 0
# if y + half_patch_size >= full_example.label.shape[0]:
# y_end_offset = y + half_patch_size + 1 - full_example.label.shape[0]
# x_start_offset = 0
# if x - half_patch_size < 0:
# x_start_offset = half_patch_size - x
# x_end_offset = 0
# if x + half_patch_size >= full_example.label.shape[1]:
# x_end_offset = x + half_patch_size + 1 - full_example.label.shape[1]
# sum_density_label[y - half_patch_size + y_start_offset:y + half_patch_size + 1 - y_end_offset,
# x - half_patch_size + x_start_offset:x + half_patch_size + 1 - x_end_offset
# ] += density_label[y_start_offset:density_label.shape[0] - y_end_offset,
# x_start_offset:density_label.shape[1] - x_end_offset]
# sum_count_label[y - half_patch_size + y_start_offset:y + half_patch_size + 1 - y_end_offset,
# x - half_patch_size + x_start_offset:x + half_patch_size + 1 - x_end_offset
# ] += count_label[y_start_offset:count_label.shape[0] - y_end_offset,
# x_start_offset:count_label.shape[1] - x_end_offset]
# hit_predicted_label[y - half_patch_size + y_start_offset:y + half_patch_size + 1 - y_end_offset,
# x - half_patch_size + x_start_offset:x + half_patch_size + 1 - x_end_offset
# ] += 1
# sum_density_label *= full_example.roi
# sum_count_label *= full_example.roi
# full_predicted_label = sum_density_label / hit_predicted_label.astype(np.float32)
# full_predicted_count = np.sum(sum_count_label / hit_predicted_label.astype(np.float32))
# label_in_roi = full_example.label * full_example.roi
# density_loss = np.abs(full_predicted_label - label_in_roi).sum()
# count_loss = np.abs(full_predicted_count - label_in_roi.sum())
# running_count += full_example.label.sum()
# running_count_error += count_loss
# running_density_error += density_loss
# if ((full_example_index + 1) % 120) == 0:
# print('Scene {}'.format(scene_number))
# print('Total count: {}'.format(running_count))
# count_error = running_count_error / 120
# print('Mean count error: {}'.format(count_error))
# density_error = running_density_error / 120
# print('Mean density error: {}'.format(density_error))
# count_errors.append(count_error)
# density_errors.append(density_error)
# running_count = 0
# running_count_error = 0
# running_density_error = 0
# scene_number += 1
validation_dataset = CrowdDataset(settings.validation_dataset_path,
'200608 Time Lapse Demo')
print('Starting test...')
running_count = 0
running_count_error = 0
running_density_error = 0
initial_label = validation_dataset[0].label
full_predicted_labels = np.zeros(shape=(len(validation_dataset),
initial_label.shape[0],
initial_label.shape[1]),
dtype=np.float32)
for full_example_index, full_example in enumerate(validation_dataset):
print('Processing example {}'.format(full_example_index), end='\r')
sum_density_label = np.zeros_like(full_example.label, dtype=np.float32)
sum_count_label = np.zeros_like(full_example.label, dtype=np.float32)
hit_predicted_label = np.zeros_like(full_example.label, dtype=np.int32)
for batch in batches_of_examples_with_meta(full_example):
images = torch.stack([
example_with_meta.example.image for example_with_meta in batch
])
rois = torch.stack(
[example_with_meta.example.roi for example_with_meta in batch])
images = Variable(gpu(images))
predicted_labels, predicted_counts = net(images)
predicted_labels = predicted_labels * Variable(gpu(rois))
predicted_labels = cpu(predicted_labels.data).numpy()
predicted_counts = cpu(predicted_counts.data).numpy()
for example_index, example_with_meta in enumerate(batch):
predicted_label = predicted_labels[example_index]
predicted_count = predicted_counts[example_index]
x, y = example_with_meta.x, example_with_meta.y
half_patch_size = example_with_meta.half_patch_size
predicted_label_sum = np.sum(predicted_label)
original_patch_dimensions = ((2 * half_patch_size) + 1,
(2 * half_patch_size) + 1)
predicted_label = scipy.misc.imresize(
predicted_label, original_patch_dimensions, mode='F')
unnormalized_predicted_label_sum = np.sum(predicted_label)
if unnormalized_predicted_label_sum != 0:
density_label = predicted_label * predicted_label_sum / unnormalized_predicted_label_sum
count_label = predicted_label * predicted_count / unnormalized_predicted_label_sum
else:
density_label = predicted_label
count_label = np.full(
predicted_label.shape,
predicted_count / predicted_label.size)
y_start_offset = 0
if y - half_patch_size < 0:
y_start_offset = half_patch_size - y
y_end_offset = 0
if y + half_patch_size >= full_example.label.shape[0]:
y_end_offset = y + half_patch_size + 1 - full_example.label.shape[
0]
x_start_offset = 0
if x - half_patch_size < 0:
x_start_offset = half_patch_size - x
x_end_offset = 0
if x + half_patch_size >= full_example.label.shape[1]:
x_end_offset = x + half_patch_size + 1 - full_example.label.shape[
1]
sum_density_label[y - half_patch_size + y_start_offset:y +
half_patch_size + 1 - y_end_offset,
x - half_patch_size + x_start_offset:x +
half_patch_size + 1 -
x_end_offset] += density_label[
y_start_offset:density_label.shape[0] -
y_end_offset,
x_start_offset:density_label.shape[1] -
x_end_offset]
sum_count_label[y - half_patch_size + y_start_offset:y +
half_patch_size + 1 - y_end_offset,
x - half_patch_size + x_start_offset:x +
half_patch_size + 1 -
x_end_offset] += count_label[
y_start_offset:count_label.shape[0] -
y_end_offset,
x_start_offset:count_label.shape[1] -
x_end_offset]
hit_predicted_label[y - half_patch_size + y_start_offset:y +
half_patch_size + 1 - y_end_offset,
x - half_patch_size + x_start_offset:x +
half_patch_size + 1 - x_end_offset] += 1
hit_predicted_label[hit_predicted_label == 0] = 1
sum_density_label *= full_example.roi
sum_count_label *= full_example.roi
full_predicted_label = sum_density_label / hit_predicted_label.astype(
np.float32)
full_predicted_count = np.sum(sum_count_label /
hit_predicted_label.astype(np.float32))
label_in_roi = full_example.label * full_example.roi
density_loss = np.abs(full_predicted_label - label_in_roi).sum()
count_loss = np.abs(full_predicted_count - label_in_roi.sum())
running_count += full_example.label.sum()
running_count_error += count_loss
running_density_error += density_loss
full_predicted_labels[full_example_index] = full_predicted_label
validation_count_error = running_count_error / len(validation_dataset)
csv_file_path = os.path.join(settings.log_directory, 'Test Results.csv')
if not os.path.isfile(csv_file_path):
with open(csv_file_path, 'w') as csv_file:
writer = csv.writer(csv_file)
writer.writerow([
'Run Name', 'Scene 1', 'Scene 2', 'Scene 3', 'Scene 4',
'Scene 5', 'Mean', 'Scene 1 Density', 'Scene 2 Density',
'Scene 3 Density', 'Scene 4 Density', 'Scene 5 Density',
'Mean Density', 'Mean Validation'
])
with open(csv_file_path, 'a') as csv_file:
writer = csv.writer(csv_file)
path_list = os.path.normpath(settings.load_model_path).split(os.sep)
model_name = os.path.join(*path_list[-2:])
test_results = [
model_name, *count_errors,
np.mean(count_errors), *density_errors,
np.mean(density_errors), validation_count_error
]
writer.writerow(test_results)
np.save(
os.path.join(settings.log_directory,
'predicted_labels_time_lapse.npy'), full_predicted_labels)
print('Finished test.')
settings.load_model_path = None
return np.mean(count_errors)
