forked from GuyTevet/motion-diffusion-model
/
sample.py
175 lines (146 loc) · 7.28 KB
/
sample.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
# This code is based on https://github.com/openai/guided-diffusion
"""
Generate a large batch of image samples from a model and save them as a large
numpy array. This can be used to produce samples for FID evaluation.
"""
from utils.fixseed import fixseed
import os
import numpy as np
import torch
from utils.parser_util import sample_args
from utils.model_util import create_model_and_diffusion, load_model_wo_clip
from utils import dist_util
from model.cfg_sampler import ClassifierFreeSampleModel
from data_loaders.get_data import get_dataset_loader
from data_loaders.humanml.scripts.motion_process import recover_from_ric
import data_loaders.humanml.utils.paramUtil as paramUtil
from data_loaders.humanml.utils.plot_script import plot_3d_motion
import shutil
from data_loaders.tensors import collate
def main():
args = sample_args()
fixseed(args.seed)
out_path = args.output_dir
name = os.path.basename(os.path.dirname(args.model_path))
niter = os.path.basename(args.model_path).replace('model', '').replace('.pt', '')
max_frames = 196 if args.dataset in ['kit', 'humanml'] else 60
fps = 12.5 if args.dataset == 'kit' else 20
n_frames = min(max_frames, int(args.motion_length*fps))
is_using_data = args.input_text == '' and args.text_prompt == ''
dist_util.setup_dist(args.device)
if out_path == '':
out_path = os.path.join(os.path.dirname(args.model_path),
'samples_{}_{}_seed{}'.format(name, niter, args.seed))
if args.text_prompt != '':
out_path += '_' + args.text_prompt.replace(' ', '_').replace('.', '')
elif args.input_text != '':
out_path += '_' + os.path.basename(args.input_text).replace('.txt', '').replace(' ', '_').replace('.', '')
print("Creating model and diffusion...")
model, diffusion = create_model_and_diffusion(args)
print(f"Loading checkpoints from [{args.model_path}]...")
state_dict = torch.load(args.model_path, map_location='cpu')
load_model_wo_clip(model, state_dict)
if args.guidance_param != 1:
model = ClassifierFreeSampleModel(model) # wrapping model with the classifier-free sampler
model.to(dist_util.dev())
model.eval() # disable random masking
print('Loading dataset...')
texts = []
if args.text_prompt != '':
texts = [args.text_prompt]
args.num_samples = 1
elif args.input_text != '':
assert os.path.exists(args.input_text)
with open(args.input_text, 'r') as fr:
texts = fr.readlines()
texts = [s.replace('\n', '') for s in texts]
args.num_samples = len(texts)
assert args.num_samples <= args.batch_size, \
f'Please either increase batch_size({args.batch_size}) or reduce num_samples({args.num_samples})'
# So why do we need this check? In order to protect GPU from a memory overload in the following line.
# If your GPU can handle batch size larger then default, you can specify it through --batch_size flag.
# If it doesn't, and you still want to sample more prompts, run this script with different seeds
# (specify through the --seed flag)
args.batch_size = args.num_samples # Sampling a single batch from the testset, with exactly args.num_samples
data = get_dataset_loader(name=args.dataset,
batch_size=args.batch_size,
num_frames=max_frames,
split='test',
hml_mode='text_only')
data.fixed_length = n_frames
total_num_samples = args.num_samples * args.num_repetitions
if is_using_data:
iterator = iter(data)
_, model_kwargs = next(iterator)
else:
_, model_kwargs = collate(
[{'inp': torch.tensor([[0.]]), 'target': 0, 'text': txt, 'tokens': None, 'lengths': n_frames}
for txt in texts]
)
all_motions = []
all_lengths = []
all_text = []
for rep_i in range(args.num_repetitions):
print(f'### Start sampling [repetitions #{rep_i}]')
# add CFG scale to batch
if args.guidance_param != 1:
model_kwargs['y']['scale'] = torch.ones(args.batch_size, device=dist_util.dev()) * args.guidance_param
sample_fn = diffusion.p_sample_loop
sample = sample_fn(
model,
(args.batch_size, model.njoints, model.nfeats, n_frames),
clip_denoised=False,
model_kwargs=model_kwargs,
skip_timesteps=0, # 0 is the default value - i.e. don't skip any step
init_image=None,
progress=True,
dump_steps=None,
noise=None,
const_noise=False,
)
# Recover XYZ *positions* from HumanML3D vector representation
if model.data_rep == 'hml_vec':
n_joints = 22 if sample.shape[1] == 263 else 21
sample = data.dataset.t2m_dataset.inv_transform(sample.cpu().permute(0, 2, 3, 1)).float()
sample = recover_from_ric(sample, n_joints)
sample = sample.view(-1, *sample.shape[2:]).permute(0, 2, 3, 1)
all_text += model_kwargs['y']['text']
all_motions.append(sample.cpu().numpy())
all_lengths.append(model_kwargs['y']['lengths'].cpu().numpy())
print(f"created {len(all_motions) * args.batch_size} samples")
all_motions = np.concatenate(all_motions, axis=0)
all_motions = all_motions[:total_num_samples] # [bs, njoints, 6, seqlen]
all_text = all_text[:total_num_samples]
all_lengths = np.concatenate(all_lengths, axis=0)[:total_num_samples]
if os.path.exists(out_path):
shutil.rmtree(out_path)
os.makedirs(out_path)
npy_path = os.path.join(out_path, 'results.npy')
print(f"saving results file to [{npy_path}]")
np.save(npy_path,
{'motion': all_motions, 'text': all_text, 'lengths': all_lengths,
'num_samples': args.num_samples, 'num_repetitions': args.num_repetitions})
with open(npy_path.replace('.npy', '.txt'), 'w') as fw:
fw.write('\n'.join(all_text))
with open(npy_path.replace('.npy', '_len.txt'), 'w') as fw:
fw.write('\n'.join([str(l) for l in all_lengths]))
print(f"saving visualizations to [{out_path}]...")
skeleton = paramUtil.kit_kinematic_chain if args.dataset == 'kit' else paramUtil.t2m_kinematic_chain
for sample_i in range(args.num_samples):
for rep_i in range(args.num_repetitions):
caption = all_text[rep_i*args.batch_size + sample_i]
length = all_lengths[rep_i*args.batch_size + sample_i]
motion = all_motions[rep_i*args.batch_size + sample_i].transpose(2, 0, 1)[:length]
save_file = 'sample{:02d}_rep{:02d}.mp4'.format(sample_i, rep_i)
animation_save_path = os.path.join(out_path, save_file)
print(f'[({sample_i}) "{caption}" | Rep #{rep_i} | -> {save_file}]')
if args.dataset == 'kit':
motion *= 0.003 # scale for visualization
elif args.dataset == 'humanml':
motion *= 1.3 # scale for visualization
plot_3d_motion(animation_save_path, skeleton, motion, title=caption, fps=fps)
# Credit for visualization: https://github.com/EricGuo5513/text-to-motion
abs_path = os.path.abspath(out_path)
print(f'[Done] Results are at [{abs_path}]')
if __name__ == "__main__":
main()