def main(args):
# Select the hardware device to use for inference.
if torch.cuda.is_available():
device = torch.device('cuda', torch.cuda.current_device())
torch.backends.cudnn.benchmark = True
else:
device = torch.device('cpu')
# Disable gradient calculations.
torch.set_grad_enabled(False)
pretrained = not args.model_file
if pretrained:
print(
'No model weights file specified, using pretrained weights instead.'
)
# Create the model, downloading pretrained weights if necessary.
if args.arch == 'hg1':
model = hg1(pretrained=pretrained)
elif args.arch == 'hg2':
model = hg2(pretrained=pretrained)
elif args.arch == 'hg8':
model = hg8(pretrained=pretrained)
else:
raise Exception('unrecognised model architecture: ' + args.model)
model = model.to(device)
if not pretrained:
assert os.path.isfile(args.model_file)
print('Loading model weights from file: {}'.format(args.model_file))
checkpoint = torch.load(args.model_file)
state_dict = checkpoint['state_dict']
if sorted(state_dict.keys())[0].startswith('module.'):
model = DataParallel(model)
model.load_state_dict(state_dict)
# Initialise the MPII validation set dataloader.
val_dataset = Mpii(args.image_path, is_train=False)
val_loader = DataLoader(val_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
# Generate predictions for the validation set.
_, _, predictions = do_validation_epoch(val_loader, model, device,
Mpii.DATA_INFO, args.flip)
# Report PCKh for the predictions.
print('\nFinal validation PCKh scores:\n')
print_mpii_validation_accuracy(predictions)
def test_do_validation_epoch(mpii_image_dir):
if not torch.cuda.is_available():
pytest.skip('requires CUDA device')
device = torch.device('cuda', torch.cuda.current_device())
model = hg1(pretrained=True)
model = model.to(device)
val_dataset = Mpii(mpii_image_dir, is_train=False)
val_dataset.valid_list = val_dataset.valid_list[:32]
val_loader = DataLoader(val_dataset,
batch_size=8,
shuffle=False,
num_workers=2,
pin_memory=True)
avg_loss, avg_acc, predictions = do_validation_epoch(val_loader,
model,
device,
flip=False)
assert avg_loss == pytest.approx(0.00014652813479187898, abs=1e-6)
assert avg_acc == pytest.approx(0.8879464417695999, abs=1e-6)
def test_do_validation_epoch(cuda_device, mpii_image_dir):
model = hg1(pretrained=True)
model = model.to(cuda_device)
val_dataset = Mpii(mpii_image_dir, is_train=False)
val_dataset.valid_list = val_dataset.valid_list[:32]
val_loader = DataLoader(val_dataset,
batch_size=8,
shuffle=False,
num_workers=2,
pin_memory=True)
avg_loss, avg_acc, predictions = do_validation_epoch(
val_loader,
model,
cuda_device,
Mpii.DATA_INFO,
flip=False,
quiet=True,
acc_joints=Mpii.ACC_JOINTS)
assert avg_loss == pytest.approx(0.00014652813479187898, abs=1e-6)
assert avg_acc == pytest.approx(0.8879464417695999, abs=1e-6)
assert predictions.shape == (32, 16, 2)
def main(args):
"""Train/ Cross validate for data source = YogiDB."""
# Create data loader
"""Generic(data.Dataset)(image_set, annotations,
is_train=True, inp_res=256, out_res=64, sigma=1,
scale_factor=0, rot_factor=0, label_type='Gaussian',
rgb_mean=RGB_MEAN, rgb_stddev=RGB_STDDEV)."""
annotations_source = 'basic-thresholder'
# Get the data from yogi
db_obj = YogiDB(config.db_url)
imageset = db_obj.get_filtered(ImageSet,
name=args.image_set_name)
annotations = db_obj.get_annotations(image_set_name=args.image_set_name,
annotation_source=annotations_source)
pts = torch.Tensor(annotations[0]['joint_self'])
num_classes = pts.size(0)
crop_size = 512
if args.crop:
crop_size = args.crop
crop = True
else:
crop = False
# Using the default RGB mean and std dev as 0
RGB_MEAN = torch.as_tensor([0.0, 0.0, 0.0])
RGB_STDDEV = torch.as_tensor([0.0, 0.0, 0.0])
dataset = Generic(image_set=imageset,
inp_res=args.inp_res,
out_res=args.out_res,
annotations=annotations,
mode=args.mode,
crop=crop, crop_size=crop_size,
rgb_mean=RGB_MEAN, rgb_stddev=RGB_STDDEV)
train_dataset = dataset
train_dataset.is_train = True
train_loader = DataLoader(train_dataset,
batch_size=args.train_batch, shuffle=True,
num_workers=args.workers, pin_memory=True)
val_dataset = dataset
val_dataset.is_train = False
val_loader = DataLoader(val_dataset,
batch_size=args.test_batch, shuffle=False,
num_workers=args.workers, pin_memory=True)
# Select the hardware device to use for inference.
if torch.cuda.is_available():
device = torch.device('cuda', torch.cuda.current_device())
torch.backends.cudnn.benchmark = True
else:
device = torch.device('cpu')
# Disable gradient calculations by default.
torch.set_grad_enabled(False)
# create checkpoint dir
os.makedirs(args.checkpoint, exist_ok=True)
if args.arch == 'hg1':
model = hg1(pretrained=False, num_classes=num_classes)
elif args.arch == 'hg2':
model = hg2(pretrained=False, num_classes=num_classes)
elif args.arch == 'hg8':
model = hg8(pretrained=False, num_classes=num_classes)
else:
raise Exception('unrecognised model architecture: ' + args.model)
model = DataParallel(model).to(device)
if args.optimizer == "Adam":
optimizer = Adam(model.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
else:
optimizer = RMSprop(model.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
best_acc = 0
# optionally resume from a checkpoint
title = args.data_identifier + ' ' + args.arch
if args.resume:
assert os.path.isfile(args.resume)
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_acc = checkpoint['best_acc']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch']))
logger = Logger(os.path.join(args.checkpoint, 'log.txt'), title=title, resume=True)
else:
logger = Logger(os.path.join(args.checkpoint, 'log.txt'), title=title)
logger.set_names(['Epoch', 'LR', 'Train Loss', 'Val Loss', 'Train Acc', 'Val Acc'])
# train and eval
lr = args.lr
for epoch in range(args.start_epoch, args.epochs):
lr = adjust_learning_rate(optimizer, epoch, lr, args.schedule, args.gamma)
print('\nEpoch: %d | LR: %.8f' % (epoch + 1, lr))
# train for one epoch
train_loss, train_acc = do_training_epoch(train_loader, model, device, optimizer)
# evaluate on validation set
if args.debug == 1:
valid_loss, valid_acc, predictions, validation_log = do_validation_epoch(val_loader, model, device, False, True, os.path.join(args.checkpoint, 'debug.csv'), epoch + 1)
else:
valid_loss, valid_acc, predictions, _ = do_validation_epoch(val_loader, model, device, False)
# append logger file
logger.append([epoch + 1, lr, train_loss, valid_loss, train_acc, valid_acc])
# remember best acc and save checkpoint
is_best = valid_acc > best_acc
best_acc = max(valid_acc, best_acc)
save_checkpoint({
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_acc': best_acc,
'optimizer': optimizer.state_dict(),
}, predictions, is_best, checkpoint=args.checkpoint, snapshot=args.snapshot)
logger.close()
logger.plot(['Train Acc', 'Val Acc'])
savefig(os.path.join(args.checkpoint, 'log.eps'))
def main(args):
# Select the hardware device to use for inference.
if torch.cuda.is_available():
device = torch.device('cuda', torch.cuda.current_device())
torch.backends.cudnn.benchmark = True
else:
device = torch.device('cpu')
# Disable gradient calculations by default.
torch.set_grad_enabled(False)
# create checkpoint dir
os.makedirs(args.checkpoint, exist_ok=True)
if args.arch == 'hg1':
model = hg1(pretrained=False)
elif args.arch == 'hg2':
model = hg2(pretrained=False)
elif args.arch == 'hg8':
model = hg8(pretrained=False)
else:
raise Exception('unrecognised model architecture: ' + args.arch)
model = DataParallel(model).to(device)
optimizer = RMSprop(model.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
best_acc = 0
# optionally resume from a checkpoint
if args.resume:
assert os.path.isfile(args.resume)
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_acc = checkpoint['best_acc']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
logger = Logger(os.path.join(args.checkpoint, 'log.txt'), resume=True)
else:
logger = Logger(os.path.join(args.checkpoint, 'log.txt'))
logger.set_names(
['Epoch', 'LR', 'Train Loss', 'Val Loss', 'Train Acc', 'Val Acc'])
# create data loader
train_dataset = Mpii(args.image_path, is_train=True)
train_loader = DataLoader(train_dataset,
batch_size=args.train_batch,
shuffle=True,
num_workers=args.workers,
pin_memory=True)
val_dataset = Mpii(args.image_path, is_train=False)
val_loader = DataLoader(val_dataset,
batch_size=args.test_batch,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
# train and eval
lr = args.lr
for epoch in trange(args.start_epoch,
args.epochs,
desc='Overall',
ascii=True):
lr = adjust_learning_rate(optimizer, epoch, lr, args.schedule,
args.gamma)
# train for one epoch
train_loss, train_acc = do_training_epoch(train_loader,
model,
device,
Mpii.DATA_INFO,
optimizer,
acc_joints=Mpii.ACC_JOINTS)
# evaluate on validation set
valid_loss, valid_acc, predictions = do_validation_epoch(
val_loader,
model,
device,
Mpii.DATA_INFO,
False,
acc_joints=Mpii.ACC_JOINTS)
# print metrics
tqdm.write(
f'[{epoch + 1:3d}/{args.epochs:3d}] lr={lr:0.2e} '
f'train_loss={train_loss:0.4f} train_acc={100 * train_acc:0.2f} '
f'valid_loss={valid_loss:0.4f} valid_acc={100 * valid_acc:0.2f}')
# append logger file
logger.append(
[epoch + 1, lr, train_loss, valid_loss, train_acc, valid_acc])
logger.plot_to_file(os.path.join(args.checkpoint, 'log.svg'),
['Train Acc', 'Val Acc'])
# remember best acc and save checkpoint
is_best = valid_acc > best_acc
best_acc = max(valid_acc, best_acc)
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_acc': best_acc,
'optimizer': optimizer.state_dict(),
},
predictions,
is_best,
checkpoint=args.checkpoint,
snapshot=args.snapshot)
logger.close()
def main(args):
"""Predict for data source = YogiDB."""
# Select the hardware device to use for inference.
if torch.cuda.is_available():
device = torch.device('cuda', torch.cuda.current_device())
torch.backends.cudnn.benchmark = True
else:
device = torch.device('cpu')
# Disable gradient calculations.
torch.set_grad_enabled(False)
pretrained = not args.model_file
if pretrained:
print('No model weights file specified, exiting.')
exit()
# Get the data from yogi
db_obj = YogiDB(config.db_url)
imageset = db_obj.get_filtered(ImageSet, name=args.image_set_name)
annotations_source = 'basic-thresholder'
annotations = db_obj.get_annotations(annotation_source=annotations_source)
pts = torch.Tensor(annotations[0]['joint_self'])
num_classes = pts.size(0)
# Initialise the Yogi validation set dataloader.
val_dataset = Generic(image_set=imageset,
inp_res=args.inp_res,
out_res=args.out_res,
annotations=annotations,
mode=args.mode)
val_loader = DataLoader(val_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
# Create the model, downloading pretrained weights if necessary.
if args.arch == 'hg1':
model = hg1(pretrained=False, num_classes=num_classes)
elif args.arch == 'hg2':
model = hg2(pretrained=False, num_classes=num_classes)
elif args.arch == 'hg8':
model = hg8(pretrained=False, num_classes=num_classes)
else:
raise Exception('unrecognised model architecture: ' + args.model)
model = model.to(device)
# create output dir
os.makedirs(args.output_location, exist_ok=True)
title = args.image_set_name + ' ' + args.arch
filename_pre = title + '_preds_' + datetime.now().strftime("%Y%m%dT%H%M%S")
log_filepath = os.path.join(args.output_location,
filename_pre + '_log.txt')
logger = Logger(log_filepath, title=title)
logger.set_names(['Val Loss', 'Val Acc'])
# Generate predictions for the validation set.
valid_loss, valid_acc, predictions = do_validation_epoch(
val_loader, model, device, args.flip)
# append logger file
logger.append([valid_loss, valid_acc])
# TODO: Report PCKh for the predictions.
# print('\nFinal validation PCKh scores:\n')
# print_mpii_validation_accuracy(predictions)
predictions = to_numpy(predictions)
filepath = os.path.join(args.output_location, filename_pre + '.mat')
scipy.io.savemat(filepath, mdict={'preds': predictions})