-
Notifications
You must be signed in to change notification settings - Fork 5
/
Copy patheval.py
196 lines (156 loc) · 7.65 KB
/
eval.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
import torch
from torch.utils.data import DataLoader, Dataset
import numpy as np
import cv2
import os
import argparse
from tqdm import tqdm
import config
import constants
from models import hmr, SMPL
from utils.pose_utils import reconstruction_error
from torchvision.transforms import Normalize
import pickle
import scipy.misc
# Define command-line arguments
parser = argparse.ArgumentParser()
parser.add_argument('--checkpoint', required=True, type=str, help='Path to network checkpoint')
parser.add_argument('--batch_size', default=16, type=int, help='Batch size for testing')
parser.add_argument('--num_workers', default=0, type=int, help='Number of processes for data loading')
parser.add_argument('--eval_size', default=32, type=int, help='Image resolution for evaluation')
class PW3D(Dataset):
def __init__(self, data_name, pkl_name, img_size):
self.normalize_img = Normalize(mean=constants.IMG_NORM_MEAN, std=constants.IMG_NORM_STD)
self.data = np.load(data_name)
self.imgname = self.data['imgname']
self.pose = self.data['pose'].squeeze().astype(np.float)
self.betas = self.data['shape'].squeeze().astype(np.float)
# Get gender data, if available
try:
gender = self.data['gender']
self.gender = np.array([0 if str(g) == 'm' else 1 for g in gender]).astype(np.int32)
except KeyError:
self.gender = -1 * np.ones(len(self.imgname)).astype(np.int32)
with open(pkl_name, 'rb') as f:
self.img_pkl = pickle.load(f)
self.img_size = img_size
def __len__(self):
return len(self.imgname)
def rgb_processing(self, rgb_img, img_size):
"""Process rgb image and do augmentation."""
if img_size == 224:
rgb_img_up = rgb_img.copy()
rgb_img_up = rgb_img_up.clip(0, 255)
else:
shape = rgb_img.shape
rgb_img_lr = scipy.misc.imresize(rgb_img, (img_size, img_size), interp='bicubic')
rgb_img_lr = rgb_img_lr.clip(0, 255)
rgb_img_up = scipy.misc.imresize(rgb_img_lr, (shape[0], shape[1]), interp='bicubic') # naive upsampling
rgb_img_up = np.transpose(rgb_img_up.astype('float32'), (2, 0, 1)) / 255.0
return rgb_img_up
def __getitem__(self, index):
item = {}
img_str = self.img_pkl[index]
img_encode = np.asarray(bytearray(img_str), dtype=np.uint8)
img = cv2.imdecode(img_encode, cv2.IMREAD_COLOR)
img = img[:, :, ::-1].astype(np.float32)
img_up = self.rgb_processing(img, self.img_size)
img_up = self.normalize_img(torch.from_numpy(img_up).float())
pose = self.pose[index].copy()
betas = self.betas[index].copy()
item['gender'] = self.gender[index]
item['pose'] = torch.from_numpy(pose).float()
item['betas'] = torch.from_numpy(betas).float()
item['img_up'] = img_up
return item
def size_to_scale(size):
if size >= 224:
scale = 0
elif 128 <= size < 224:
scale = 1
elif 64 <= size < 128:
scale = 2
elif 40 <= size < 64:
scale = 3
else:
scale = 4
return scale
def run_evaluation(hmr_model, dataset, eval_size, batch_size=32, num_workers=32, log_freq=50):
"""Run evaluation on the datasets and metrics we report in the paper. """
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
# focal length
focal_length = constants.FOCAL_LENGTH
# Transfer hmr_model to the GPU
hmr_model.to(device)
# Load SMPL hmr_model
smpl_neutral = SMPL(config.SMPL_MODEL_DIR,
create_transl=False).to(device)
smpl_male = SMPL(config.SMPL_MODEL_DIR,
gender='male',
create_transl=False).to(device)
smpl_female = SMPL(config.SMPL_MODEL_DIR,
gender='female',
create_transl=False).to(device)
# Regressor for H36m joints
J_regressor = torch.from_numpy(np.load(config.JOINT_REGRESSOR_H36M)).float()
# Create dataloader for the dataset
data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers)
# Pose metrics
# MPJPE and Reconstruction error for the non-parametric and parametric shapes
mpjpe = np.zeros((len(dataset)))
recon_err = np.zeros((len(dataset)))
joint_mapper_h36m = constants.H36M_TO_J14
# Iterate over the entire dataset
for step, batch in enumerate(tqdm(data_loader, desc='Eval', total=len(data_loader))):
# Get ground truth annotations from the batch
gt_pose = batch['pose'].to(device)
gt_betas = batch['betas'].to(device)
gender = batch['gender'].to(device)
images = batch['img_up']
curr_batch_size = images.shape[0]
with torch.no_grad():
images = images.to(device)
pred_rotmat, pred_betas, pred_camera, _ = hmr_model(images, scale=size_to_scale(eval_size))
pred_output = smpl_neutral(betas=pred_betas, body_pose=pred_rotmat[:, 1:],
global_orient=pred_rotmat[:, 0].unsqueeze(1), pose2rot=False)
pred_vertices = pred_output.vertices
J_regressor_batch = J_regressor[None, :].expand(pred_vertices.shape[0], -1, -1).to(device)
gt_vertices = smpl_male(global_orient=gt_pose[:, :3], body_pose=gt_pose[:, 3:], betas=gt_betas).vertices
gt_vertices_female = smpl_female(global_orient=gt_pose[:, :3], body_pose=gt_pose[:, 3:],
betas=gt_betas).vertices
gt_vertices[gender == 1, :, :] = gt_vertices_female[gender == 1, :, :]
gt_keypoints_3d = torch.matmul(J_regressor_batch, gt_vertices)
gt_pelvis = gt_keypoints_3d[:, [0], :].clone()
gt_keypoints_3d = gt_keypoints_3d[:, joint_mapper_h36m, :]
gt_keypoints_3d = gt_keypoints_3d - gt_pelvis
pred_keypoints_3d = torch.matmul(J_regressor_batch, pred_vertices)
pred_pelvis = pred_keypoints_3d[:, [0], :].clone()
pred_keypoints_3d = pred_keypoints_3d[:, joint_mapper_h36m, :]
pred_keypoints_3d = pred_keypoints_3d - pred_pelvis
# Absolute error (MPJPE)
error = torch.sqrt(((pred_keypoints_3d - gt_keypoints_3d) ** 2).sum(dim=-1)).mean(dim=-1).cpu().numpy()
mpjpe[step * batch_size:step * batch_size + curr_batch_size] = error
# Reconstuction_error
r_error = reconstruction_error(pred_keypoints_3d.cpu().numpy(), gt_keypoints_3d.cpu().numpy(), reduction=None)
recon_err[step * batch_size:step * batch_size + curr_batch_size] = r_error
# Print intermediate results during evaluation
if step % log_freq == log_freq - 1:
print('MPJPE: ' + str(1000 * mpjpe[:step * batch_size].mean()))
print('Reconstruction Error: ' + str(1000 * recon_err[:step * batch_size].mean()))
# Print final results during evaluation
print('*** Final Results ***')
print()
print('MPJPE: {}'.format(1000 * mpjpe.mean()))
print('Reconstruction Error: {}'.format(1000 * recon_err.mean()))
print()
if __name__ == '__main__':
args = parser.parse_args()
pkl_path = os.path.join(config.DATASET_PKL_PATH, '3dpw_imgs_test.pkl')
data_path = config.DATASET_FILES[0]['3dpw']
ds = PW3D(data_path, pkl_path, args.eval_size)
hmr_model = hmr(config.SMPL_MEAN_PARAMS)
checkpoint = torch.load(args.checkpoint)
hmr_model.load_state_dict(checkpoint, strict=False)
hmr_model.eval()
# Run evaluation
run_evaluation(hmr_model, ds, eval_size=args.eval_size, batch_size=args.batch_size, num_workers=args.num_workers)