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from typing import Dict, List
import numpy as np
import torch
from torch import Tensor
import mGPT.utils.geometry_conver as geometry_conver
def lengths_to_mask(lengths: List[int],
device: torch.device,
max_len: int = None) -> Tensor:
lengths = torch.tensor(lengths, device=device)
max_len = max_len if max_len else max(lengths)
mask = torch.arange(max_len, device=device).expand(
len(lengths), max_len) < lengths.unsqueeze(1)
return mask
def detach_to_numpy(tensor):
return tensor.detach().cpu().numpy()
def remove_padding(tensors, lengths):
return [
tensor[:tensor_length]
for tensor, tensor_length in zip(tensors, lengths)
]
def nfeats_of(rottype):
if rottype in ["rotvec", "axisangle"]:
return 3
elif rottype in ["rotquat", "quaternion"]:
return 4
elif rottype in ["rot6d", "6drot", "rotation6d"]:
return 6
elif rottype in ["rotmat"]:
return 9
else:
return TypeError("This rotation type doesn't have features.")
def axis_angle_to(newtype, rotations):
if newtype in ["matrix"]:
rotations = geometry_conver.axis_angle_to_matrix(rotations)
return rotations
elif newtype in ["rotmat"]:
rotations = geometry_conver.axis_angle_to_matrix(rotations)
rotations = matrix_to("rotmat", rotations)
return rotations
elif newtype in ["rot6d", "6drot", "rotation6d"]:
rotations = geometry_conver.axis_angle_to_matrix(rotations)
rotations = matrix_to("rot6d", rotations)
return rotations
elif newtype in ["rotquat", "quaternion"]:
rotations = geometry_conver.axis_angle_to_quaternion(rotations)
return rotations
elif newtype in ["rotvec", "axisangle"]:
return rotations
else:
raise NotImplementedError
def matrix_to(newtype, rotations):
if newtype in ["matrix"]:
return rotations
if newtype in ["rotmat"]:
rotations = rotations.reshape((*rotations.shape[:-2], 9))
return rotations
elif newtype in ["rot6d", "6drot", "rotation6d"]:
rotations = geometry_conver.matrix_to_rotation_6d(rotations)
return rotations
elif newtype in ["rotquat", "quaternion"]:
rotations = geometry_conver.matrix_to_quaternion(rotations)
return rotations
elif newtype in ["rotvec", "axisangle"]:
rotations = geometry_conver.matrix_to_axis_angle(rotations)
return rotations
else:
raise NotImplementedError
def to_matrix(oldtype, rotations):
if oldtype in ["matrix"]:
return rotations
if oldtype in ["rotmat"]:
rotations = rotations.reshape((*rotations.shape[:-2], 3, 3))
return rotations
elif oldtype in ["rot6d", "6drot", "rotation6d"]:
rotations = geometry_conver.rotation_6d_to_matrix(rotations)
return rotations
elif oldtype in ["rotquat", "quaternion"]:
rotations = geometry_conver.quaternion_to_matrix(rotations)
return rotations
elif oldtype in ["rotvec", "axisangle"]:
rotations = geometry_conver.axis_angle_to_matrix(rotations)
return rotations
else:
raise NotImplementedError
# TODO: use a real subsampler..
def subsample(num_frames, last_framerate, new_framerate):
step = int(last_framerate / new_framerate)
assert step >= 1
frames = np.arange(0, num_frames, step)
return frames
# TODO: use a real upsampler..
def upsample(motion, last_framerate, new_framerate):
step = int(new_framerate / last_framerate)
assert step >= 1
# Alpha blending => interpolation
alpha = np.linspace(0, 1, step + 1)
last = np.einsum("l,...->l...", 1 - alpha, motion[:-1])
new = np.einsum("l,...->l...", alpha, motion[1:])
chuncks = (last + new)[:-1]
output = np.concatenate(chuncks.swapaxes(1, 0))
# Don't forget the last one
output = np.concatenate((output, motion[[-1]]))
return output
if __name__ == "__main__":
motion = np.arange(105)
submotion = motion[subsample(len(motion), 100.0, 12.5)]
newmotion = upsample(submotion, 12.5, 100)
print(newmotion)
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