def main(args):
print('==> Using settings {}'.format(args))
convm = torch.zeros(3, 17, 17, dtype=torch.float)
print('==> Loading dataset...')
dataset_path = path.join('data', 'data_3d_' + args.dataset + '.npz')
if args.dataset == 'h36m':
from common.h36m_dataset import Human36mDataset
dataset = Human36mDataset(dataset_path)
else:
raise KeyError('Invalid dataset')
print('==> Preparing data...')
dataset = read_3d_data(dataset)
print('==> Loading 2D detections...')
keypoints = create_2d_data(
path.join('data',
'data_2d_' + args.dataset + '_' + args.keypoints + '.npz'),
dataset)
cudnn.benchmark = True
device = torch.device("cuda")
# Create model
print("==> Creating model...")
if args.architecture == 'linear':
from models.linear_model import LinearModel, init_weights
num_joints = dataset.skeleton().num_joints()
model_pos = LinearModel(num_joints * 2,
(num_joints - 1) * 3).to(device)
model_pos.apply(init_weights)
elif args.architecture == 'gcn':
from models.sem_gcn import SemGCN
from common.graph_utils import adj_mx_from_skeleton
p_dropout = (None if args.dropout == 0.0 else args.dropout)
adj = adj_mx_from_skeleton(dataset.skeleton())
model_pos = SemGCN(convm,
adj,
args.hid_dim,
num_layers=args.num_layers,
p_dropout=p_dropout,
nodes_group=dataset.skeleton().joints_group()
if args.non_local else None).to(device)
else:
raise KeyError('Invalid model architecture')
print("==> Total parameters: {:.2f}M".format(
sum(p.numel() for p in model_pos.parameters()) / 1000000.0))
# Resume from a checkpoint
ckpt_path = args.evaluate
if path.isfile(ckpt_path):
print("==> Loading checkpoint '{}'".format(ckpt_path))
ckpt = torch.load(ckpt_path)
start_epoch = ckpt['epoch']
error_best = ckpt['error']
model_pos.load_state_dict(ckpt['state_dict'])
print("==> Loaded checkpoint (Epoch: {} | Error: {})".format(
start_epoch, error_best))
else:
raise RuntimeError("==> No checkpoint found at '{}'".format(ckpt_path))
print('==> Rendering...')
poses_2d = keypoints[args.viz_subject][args.viz_action]
out_poses_2d = poses_2d[args.viz_camera]
out_actions = [args.viz_camera] * out_poses_2d.shape[0]
poses_3d = dataset[args.viz_subject][args.viz_action]['positions_3d']
assert len(poses_3d) == len(poses_2d), 'Camera count mismatch'
out_poses_3d = poses_3d[args.viz_camera]
ground_truth = dataset[args.viz_subject][args.viz_action]['positions_3d'][
args.viz_camera].copy()
input_keypoints = out_poses_2d.copy()
render_loader = DataLoader(PoseGenerator([out_poses_3d], [out_poses_2d],
[out_actions]),
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
pin_memory=True)
prediction = evaluate(render_loader, model_pos, device,
args.architecture)[0]
# Invert camera transformation
cam = dataset.cameras()[args.viz_subject][args.viz_camera]
prediction = camera_to_world(prediction, R=cam['orientation'], t=0)
prediction[:, :, 2] -= np.min(prediction[:, :, 2])
ground_truth = camera_to_world(ground_truth, R=cam['orientation'], t=0)
ground_truth[:, :, 2] -= np.min(ground_truth[:, :, 2])
anim_output = {'Regression': prediction, 'Ground truth': ground_truth}
input_keypoints = image_coordinates(input_keypoints[..., :2],
w=cam['res_w'],
h=cam['res_h'])
render_animation(input_keypoints,
anim_output,
dataset.skeleton(),
dataset.fps(),
args.viz_bitrate,
cam['azimuth'],
args.viz_output,
limit=args.viz_limit,
downsample=args.viz_downsample,
size=args.viz_size,
input_video_path=args.viz_video,
viewport=(cam['res_w'], cam['res_h']),
input_video_skip=args.viz_skip)
def main(args):
print('==> Using settings {}'.format(args))
print('==> Loading dataset...')
dataset_path = path.join('data', 'data_3d_' + args.dataset + '.npz')
if args.dataset == 'h36m':
from common.h36m_dataset import Human36mDataset, TRAIN_SUBJECTS, TEST_SUBJECTS
dataset = Human36mDataset(dataset_path)
subjects_train = TRAIN_SUBJECTS
subjects_test = TEST_SUBJECTS
else:
raise KeyError('Invalid dataset')
print('==> Preparing data...')
dataset = read_3d_data(dataset)
print('==> Loading 2D detections...')
keypoints = create_2d_data(path.join('data', 'data_2d_' + args.dataset + '_' + args.keypoints + '.npz'), dataset)
action_filter = None if args.actions == '*' else args.actions.split(',')
if action_filter is not None:
action_filter = map(lambda x: dataset.define_actions(x)[0], action_filter)
print('==> Selected actions: {}'.format(action_filter))
stride = args.downsample
cudnn.benchmark = True
device = torch.device("cpu") ############################# cuda!!!!!!!!!!!!!!!!!!!!!!!!!!!!
# Create model
print("==> Creating model...")
num_joints = dataset.skeleton().num_joints()
model_pos = LinearModel(num_joints * 2, (num_joints - 1) * 3).to(device)
model_pos.apply(init_weights)
print("==> Total parameters: {:.2f}M".format(sum(p.numel() for p in model_pos.parameters()) / 1000000.0))
criterion = nn.MSELoss(reduction='mean').to(device)
optimizer = torch.optim.Adam(model_pos.parameters(), lr=args.lr)
# Optionally resume from a checkpoint
if args.resume or args.evaluate:
ckpt_path = (args.resume if args.resume else args.evaluate)
if path.isfile(ckpt_path):
print("==> Loading checkpoint '{}'".format(ckpt_path))
ckpt = torch.load(ckpt_path, map_location='cpu')
start_epoch = ckpt['epoch']
error_best = ckpt['error']
glob_step = ckpt['step']
lr_now = ckpt['lr']
model_pos.load_state_dict(ckpt['state_dict'])
optimizer.load_state_dict(ckpt['optimizer'])
print("==> Loaded checkpoint (Epoch: {} | Error: {})".format(start_epoch, error_best))
if args.resume:
ckpt_dir_path = path.dirname(ckpt_path)
logger = Logger(path.join(ckpt_dir_path, 'log.txt'), resume=True)
else:
raise RuntimeError("==> No checkpoint found at '{}'".format(ckpt_path))
else:
start_epoch = 0
error_best = None
glob_step = 0
lr_now = args.lr
ckpt_dir_path = path.join(args.checkpoint, datetime.datetime.now().isoformat())
if not path.exists(ckpt_dir_path):
os.makedirs(ckpt_dir_path)
print('==> Making checkpoint dir: {}'.format(ckpt_dir_path))
logger = Logger(os.path.join(ckpt_dir_path, 'log.txt'))
logger.set_names(['epoch', 'lr', 'loss_train', 'error_eval_p1', 'error_eval_p2'])
if args.evaluate:
print('==> Evaluating...')
if action_filter is None:
action_filter = dataset.define_actions()
errors_p1 = np.zeros(len(action_filter))
errors_p2 = np.zeros(len(action_filter))
for i, action in enumerate(action_filter):
poses_valid, poses_valid_2d, actions_valid = fetch(subjects_test, dataset, keypoints, [action], stride)
valid_loader = DataLoader(PoseGenerator(poses_valid, poses_valid_2d, actions_valid),
batch_size=args.batch_size, shuffle=False,
num_workers=args.num_workers, pin_memory=True)
errors_p1[i], errors_p2[i] = evaluate(valid_loader, model_pos, device)
print('Protocol #1 (MPJPE) action-wise average: {:.2f} (mm)'.format(np.mean(errors_p1).item()))
print('Protocol #2 (P-MPJPE) action-wise average: {:.2f} (mm)'.format(np.mean(errors_p2).item()))
exit(0)
poses_train, poses_train_2d, actions_train = fetch(subjects_train, dataset, keypoints, action_filter, stride)
train_loader = DataLoader(PoseGenerator(poses_train, poses_train_2d, actions_train), batch_size=args.batch_size,
shuffle=True, num_workers=args.num_workers, pin_memory=True)
poses_valid, poses_valid_2d, actions_valid = fetch(subjects_test, dataset, keypoints, action_filter, stride)
valid_loader = DataLoader(PoseGenerator(poses_valid, poses_valid_2d, actions_valid), batch_size=args.batch_size,
shuffle=False, num_workers=args.num_workers, pin_memory=True)
for epoch in range(start_epoch, args.epochs):
print('\nEpoch: %d | LR: %.8f' % (epoch + 1, lr_now))
# Train for one epoch
epoch_loss, lr_now, glob_step = train(train_loader, model_pos, criterion, optimizer, device, args.lr, lr_now,
glob_step, args.lr_decay, args.lr_gamma, max_norm=args.max_norm)
# Evaluate
error_eval_p1, error_eval_p2 = evaluate(valid_loader, model_pos, device)
# Update log file
logger.append([epoch + 1, lr_now, epoch_loss, error_eval_p1, error_eval_p2])
# Save checkpoint
if error_best is None or error_best > error_eval_p1:
error_best = error_eval_p1
save_ckpt({'epoch': epoch + 1, 'lr': lr_now, 'step': glob_step, 'state_dict': model_pos.state_dict(),
'optimizer': optimizer.state_dict(), 'error': error_eval_p1}, ckpt_dir_path, suffix='best')
if (epoch + 1) % args.snapshot == 0:
save_ckpt({'epoch': epoch + 1, 'lr': lr_now, 'step': glob_step, 'state_dict': model_pos.state_dict(),
'optimizer': optimizer.state_dict(), 'error': error_eval_p1}, ckpt_dir_path)
logger.close()
logger.plot(['loss_train', 'error_eval_p1'])
savefig(path.join(ckpt_dir_path, 'log.eps'))
return