def predict(self, prefix, target_length):
"""
Predict a sequence using the given prefix.
"""
assert target_length > 0
with torch.no_grad():
prefix = prefix.reshape(prefix.shape[1], -1, 4)
prefix = qeuler_np(prefix, 'zyx')
prefix = qfix(euler_to_quaternion(prefix, 'zyx'))
inputs = torch.from_numpy(
prefix.reshape(1, prefix.shape[0], -1).astype('float32'))
if self.use_cuda:
inputs = inputs.cuda()
predicted, hidden = self.model(inputs)
frames = [predicted]
for i in range(1, target_length):
predicted, hidden = self.model(predicted, hidden)
frames.append(predicted)
result = torch.cat(frames, dim=1)
return result.view(result.shape[0], result.shape[1], -1,
4).cpu().numpy()
def predict(self, prefix, target_length):
"""
Predict a sequence using the given prefix.
"""
assert target_length > 0
with torch.no_grad():
prefix = prefix.reshape(prefix.shape[1], -1, 4)
prefix = qeuler_np(prefix, 'zyx')
prefix = qfix(euler_to_quaternion(prefix, 'zyx'))
inputs = torch.from_numpy(
prefix.reshape(1, prefix.shape[0], -1).astype('float32'))
if self.use_cuda:
inputs = inputs.cuda()
#print("HELLOLLLEOOELLLEOO", inputs.shape, target_length)
predicted, terms = self.model(
inputs, target_length,
0) #teacher forcing ratio = 0 for testing
frames = [predicted]
#result = torch.cat(frames, dim=1)
result = torch.cat(predicted, dim=1)
return result.view(result.shape[0], result.shape[1], -1,
4).cpu().numpy()
def process_file(filename):
# Find class
cls = os.path.splitext(os.path.split(filename)[1])[0][11:-8]
anim, names, frametime = BVH.load(filename)
global_positions = Animation.positions_global(anim)
pos = anim.positions[:, 0] # Root joint trajectory
rot = qfix(
anim.rotations.qs) # Local joint rotations as quaternions
return pos, rot, cls
def _mirror_sequence(self, sequence):
mirrored_rotations = sequence['rotations'].copy()
mirrored_trajectory = sequence['trajectory'].copy()
joints_left = self._skeleton.joints_left()
joints_right = self._skeleton.joints_right()
# Flip left/right joints
mirrored_rotations[:, joints_left] = sequence['rotations'][:, joints_right]
mirrored_rotations[:, joints_right] = sequence['rotations'][:, joints_left]
mirrored_rotations[:, :, [2, 3]] *= -1
mirrored_trajectory[:, 0] *= -1
return {
'rotations': qfix(mirrored_rotations),
'trajectory': mirrored_trajectory
}
out_actions = []
print('Converting dataset...')
subjects = sorted(glob(output_directory + '/h3.6m/dataset/*'))
for subject in subjects:
actions = sorted(glob(subject + '/*'))
result_ = {}
for action_filename in actions:
data = read_file(action_filename)
# Discard the first joint, which represents a corrupted translation
data = data[:, 1:]
# Convert to quaternion and fix antipodal representations
quat = expmap_to_quaternion(-data)
quat = qfix(quat)
out_pos.append(np.zeros(
(quat.shape[0], 3))) # No trajectory for H3.6M
out_rot.append(quat)
tokens = re.split('\/|\.', action_filename.replace('\\', '/'))
subject_name = tokens[-3]
out_subjects.append(subject_name)
action_name = tokens[-2]
out_actions.append(action_name)
print('Saving...')
np.savez_compressed(output_file_path,
trajectories=out_pos,
rotations=out_rot,
subjects=out_subjects,
