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import os
import numpy as np
import torch
from torch.utils.data import DataLoader
N_POSES = 21
class AMASSDataset(torch.utils.data.Dataset):
def __init__(self, root_path, version='version0', subset='train', basis_path='base_amass.npy',
sample_interval=None, num_coeffs=100, return_shape=False,
normalize=True, min_max=False):
self.root_path = root_path
self.version = version
assert subset in ['train', 'valid', 'test']
self.subset = subset
self.sample_interval = sample_interval
self.return_shape = return_shape
self.normalize = normalize
self.min_max = min_max
self.num_coeffs = num_coeffs
self.poses, self.shapes = self.read_data()
if self.sample_interval:
self._sample(sample_interval)
if self.normalize:
if self.min_max:
self.min_poses, self.max_poses, self.min_shapes, self.max_shapes = self.Normalize()
else:
self.mean_poses, self.std_poses, self.mean_shapes, self.std_shapes = self.Normalize()
self.real_data_len = len(self.poses)
def __getitem__(self, idx):
"""
Return:
[21, 3] or [21, 6] for poses including body and root orient
[10] for shapes (betas) [Optimal]
"""
data_poses = self.poses[idx % self.real_data_len]
#coeffs = data_poses}
if self.return_shape:
return data_poses, self.shapes[idx % self.real_data_len]
return data_poses
def __len__(self, ):
return len(self.poses)
def _sample(self, sample_interval):
print(f'Class AMASSDataset({self.subset}): sample dataset every {sample_interval} frame')
self.poses = self.poses[::sample_interval]
def read_data(self):
data_path = os.path.join(self.root_path, self.subset)
# root_orient = torch.load(os.path.join(data_path, 'root_orient.pt'))
coeffs = torch.load(os.path.join(data_path, 'train_coeffs.pt'))
shapes = torch.load(os.path.join(data_path, 'betas.pt')) if self.return_shape else None
# poses = torch.cat([root_orient, pose_body], dim=1)
data_len = len(coeffs)
if self.num_coeffs < 300:
coeffs = coeffs[:, -self.num_coeffs:]
return coeffs, shapes
def Normalize(self):
# Use train dataset for normalize computing, Z_score or min-max Normalize
if self.min_max:
normalize_path = os.path.join(self.root_path, 'train', 'coeffs_' + str(self.num_coeffs) + '_normalize1.pt')
else:
normalize_path = os.path.join(self.root_path, 'train', 'coeffs_' + str(self.num_coeffs) + '_normalize2.pt')
if os.path.exists(normalize_path):
normalize_params = torch.load(normalize_path)
if self.min_max:
min_poses, max_poses, min_shapes, max_shapes = (
normalize_params['min_poses'],
normalize_params['max_poses'],
normalize_params['min_shapes'],
normalize_params['max_shapes']
)
else:
mean_poses, std_poses, mean_shapes, std_shapes = (
normalize_params['mean_poses'],
normalize_params['std_poses'],
normalize_params['mean_shapes'],
normalize_params['std_shapes']
)
else:
if self.min_max:
min_poses = torch.min(self.poses, dim=0)[0]
max_poses = torch.max(self.poses, dim=0)[0]
min_shapes = torch.min(self.shapes, dim=0)[0] if self.return_shape else None
max_shapes = torch.max(self.shapes, dim=0)[0] if self.return_shape else None
torch.save({
'min_poses': min_poses,
'max_poses': max_poses,
'min_shapes': min_shapes,
'max_shapes': max_shapes
}, normalize_path)
else:
mean_poses = torch.mean(self.poses, dim=0)
std_poses = torch.std(self.poses, dim=0)
mean_shapes = torch.mean(self.shapes, dim=0) if self.return_shape else None
std_shapes = torch.std(self.shapes, dim=0) if self.return_shape else None
torch.save({
'mean_poses': mean_poses,
'std_poses': std_poses,
'mean_shapes': mean_shapes,
'std_shapes': std_shapes
}, normalize_path)
if self.min_max:
self.poses = 2 * (self.poses - min_poses) / (max_poses - min_poses) - 1
if self.return_shape:
self.shapes = 2 * (self.shapes - min_shapes) / (max_shapes - min_shapes) - 1
return min_poses, max_poses, min_shapes, max_shapes
else:
self.poses = (self.poses - mean_poses) / std_poses
if self.return_shape:
self.shapes = (self.shapes - mean_shapes) / std_shapes
return mean_poses, std_poses, mean_shapes, std_shapes
def Denormalize(self, poses, shapes=None):
assert len(poses.shape) == 2 or len(poses.shape) == 3 # [b, data_dim] or [t, b, data_dim]
if self.min_max:
min_poses = self.min_poses.view(1, -1).to(poses.device)
max_poses = self.max_poses.view(1, -1).to(poses.device)
if len(poses.shape) == 3: # [t, b, data_dim]
min_poses = min_poses.unsqueeze(0)
max_poses = max_poses.unsqueeze(0)
normalized_poses = 0.5 * ((poses + 1) * (max_poses - min_poses) + 2 * min_poses)
if shapes is not None and self.min_shapes is not None:
min_shapes = self.min_shapes.view(1, -1).to(shapes.device)
max_shapes = self.max_shapes.view(1, -1).to(shapes.device)
if len(shapes.shape) == 3:
min_shapes = min_shapes.unsqueeze(0)
max_shapes = max_shapes.unsqueeze(0)
normalized_shapes = 0.5 * ((shapes + 1) * (max_shapes - min_shapes) + 2 * min_shapes)
return normalized_poses, normalized_shapes
else:
return normalized_poses
else:
mean_poses = self.mean_poses.view(1, -1).to(poses.device)
std_poses = self.std_poses.view(1, -1).to(poses.device)
if len(poses.shape) == 3: # [t, b, data_dim]
mean_poses = mean_poses.unsqueeze(0)
std_poses = std_poses.unsqueeze(0)
normalized_poses = poses * std_poses + mean_poses
if shapes is not None and self.mean_shapes is not None:
mean_shapes = self.mean_shapes.view(1, -1)
std_shapes = self.std_shapes.view(1, -1)
if len(shapes.shape) == 3:
mean_shapes = mean_shapes.unsqueeze(0)
std_shapes = std_shapes.unsqueeze(0)
normalized_shapes = shapes * std_shapes + mean_shapes
return normalized_poses, normalized_shapes
else:
return normalized_poses
def eval(self, preds):
pass
class Posenormalizer:
def __init__(self, data_path, device='cuda:0', normalize=True, min_max=True, rot_rep=None):
assert rot_rep in ['rot6d', 'axis']
self.normalize = normalize
self.min_max = min_max
self.rot_rep = rot_rep
normalize_params = torch.load(os.path.join(data_path, '{}_normalize1.pt'.format(rot_rep)))
self.min_poses, self.max_poses = normalize_params['min_poses'].to(device), normalize_params['max_poses'].to(device)
normalize_params = torch.load(os.path.join(data_path, '{}_normalize2.pt'.format(rot_rep)))
self.mean_poses, self.std_poses = normalize_params['mean_poses'].to(device), normalize_params['std_poses'].to(device)
def offline_normalize(self, poses, from_axis=False):
assert len(poses.shape) == 2 or len(poses.shape) == 3 # [b, data_dim] or [t, b, data_dim]
pose_shape = poses.shape
if not self.normalize:
return poses
if self.min_max:
min_poses = self.min_poses.view(1, -1)
max_poses = self.max_poses.view(1, -1)
if len(poses.shape) == 3: # [t, b, data_dim]
min_poses = min_poses.unsqueeze(0)
max_poses = max_poses.unsqueeze(0)
normalized_poses = 2 * (poses - min_poses) / (max_poses - min_poses) - 1
else:
mean_poses = self.mean_poses.view(1, -1)
std_poses = self.std_poses.view(1, -1)
if len(poses.shape) == 3: # [t, b, data_dim]
mean_poses = mean_poses.unsqueeze(0)
std_poses = std_poses.unsqueeze(0)
normalized_poses = (poses - mean_poses) / std_poses
return normalized_poses
def offline_denormalize(self, poses, to_axis=False):
assert len(poses.shape) == 2 or len(poses.shape) == 3 # [b, data_dim] or [t, b, data_dim]
if not self.normalize:
denormalized_poses = poses
else:
if self.min_max:
min_poses = self.min_poses.view(1, -1)
max_poses = self.max_poses.view(1, -1)
if len(poses.shape) == 3: # [t, b, data_dim]
min_poses = min_poses.unsqueeze(0)
max_poses = max_poses.unsqueeze(0)
denormalized_poses = 0.5 * ((poses + 1) * (max_poses - min_poses) + 2 * min_poses)
else:
mean_poses = self.mean_poses.view(1, -1)
std_poses = self.std_poses.view(1, -1)
if len(poses.shape) == 3: # [t, b, data_dim]
mean_poses = mean_poses.unsqueeze(0)
std_poses = std_poses.unsqueeze(0)
denormalized_poses = poses * std_poses + mean_poses
return denormalized_poses
# a simple eval process for completion task
class Evaler:
def __init__(self, body_model, part=None):
self.body_model = body_model
self.part = part
if self.part is not None:
self.joint_idx = np.array(getattr(BodyPartIndices, self.part)) + 1 # skip pelvis
self.vert_idx = np.array(getattr(BodySegIndices, self.part))
else:
self.joint_idx = slice(None)
self.vert_idx = slice(None)
def eval_bodys(self, outs, gts):
'''
:param outs: [b, j*3] axis-angle results of body poses
:param gts: [b, j*3] axis-angle groundtruth of body poses
:return: result dict for every sample
'''
sample_num = len(outs)
eval_result = {'mpvpe_all': [], 'mpjpe_body': []}
body_gt = self.body_model(pose_body=gts)
body_out = self.body_model(pose_body=outs)
for n in range(sample_num):
# MPVPE from all vertices
mesh_gt = body_gt.v.detach().cpu().numpy()[n, self.vert_idx]
mesh_out = body_out.v.detach().cpu().numpy()[n, self.vert_idx]
eval_result['mpvpe_all'].append(np.sqrt(np.sum((mesh_out - mesh_gt) ** 2, 1)).mean() * 1000)
joint_gt_body = body_gt.Jtr.detach().cpu().numpy()[n, self.joint_idx]
joint_out_body = body_out.Jtr.detach().cpu().numpy()[n, self.joint_idx]
eval_result['mpjpe_body'].append(
np.sqrt(np.sum((joint_out_body - joint_gt_body) ** 2, 1)).mean() * 1000)
return eval_result
def multi_eval_bodys(self, outs, gts):
'''
:param outs: [b, hypo, j*3] axis-angle results of body poses, multiple hypothesis
:param gts: [b, j*3] axis-angle groundtruth of body poses
:return: result dict
'''
hypo_num = outs.shape[1]
eval_result = {f'mpvpe_all': [], f'mpjpe_body': []}
for hypo in range(hypo_num):
result = self.eval_bodys(outs[:, hypo], gts)
eval_result['mpvpe_all'].append(result['mpvpe_all'])
eval_result['mpjpe_body'].append(result['mpjpe_body'])
eval_result['mpvpe_all'] = np.min(eval_result['mpvpe_all'], axis=0)
eval_result['mpjpe_body'] = np.min(eval_result['mpjpe_body'], axis=0)
return eval_result
def print_eval_result(self, eval_result):
print('MPVPE (All): %.2f mm' % np.mean(eval_result['mpvpe_all']))
print('MPJPE (Body): %.2f mm' % np.mean(eval_result['mpjpe_body']))
def print_multi_eval_result(self, eval_result, hypo_num):
print(f'multihypo {hypo_num} MPVPE (All): %.2f mm' % np.mean(eval_result['mpvpe_all']))
print(f'multihypo {hypo_num} MPJPE (Body): %.2f mm' % np.mean(eval_result['mpjpe_body'])) |