-
Notifications
You must be signed in to change notification settings - Fork 2
/
image_sample.py
164 lines (134 loc) · 5.12 KB
/
image_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
"""
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.
"""
import argparse
import os
import torch as th
import torch.distributed as dist
import torchvision as tv
from guided_diffusion.image_datasets import load_data
from guided_diffusion import dist_util, logger
from guided_diffusion.script_util import (
model_and_diffusion_defaults,
create_model_and_diffusion,
add_dict_to_argparser,
args_to_dict,
)
def main():
args = create_argparser().parse_args()
dist_util.init_distributed_mode(args)
logger.configure()
logger.log("creating model and diffusion...")
model, diffusion = create_model_and_diffusion(
**args_to_dict(args, model_and_diffusion_defaults().keys())
)
model.load_state_dict(
th.load(args.model_path, map_location="cpu")
)
model.to(dist_util.dev())
logger.log("creating data loader...")
data = load_data(
dataset_mode=args.dataset_mode,
data_dir=args.data_dir,
batch_size=args.batch_size,
image_size=args.image_size,
class_cond=args.class_cond,
deterministic=True,
random_crop=False,
random_flip=False,
is_train=False,
rank=dist_util.get_rank(),
world_size=dist_util.get_world_size(),
)
if args.use_fp16:
model.convert_to_fp16()
model.eval()
image_path = os.path.join(args.results_path, 'images')
os.makedirs(image_path, exist_ok=True)
label_path = os.path.join(args.results_path, 'labels')
os.makedirs(label_path, exist_ok=True)
sample_path = os.path.join(args.results_path, 'samples')
os.makedirs(sample_path, exist_ok=True)
logger.log("sampling...")
all_samples = []
for i, (batch, cond) in enumerate(data):
# import pdb; pdb.set_trace()
image = None
if batch is not None:
image = ((batch + 1.0) / 2.0).cuda()
label = (cond['label_ori'].float() / 255.0).cuda()
model_kwargs = preprocess_input(cond, num_classes=args.num_classes)
# set hyperparameter
model_kwargs['s'] = args.guidance
sample_fn = (
diffusion.p_sample_loop if not args.use_ddim else diffusion.ddim_sample_loop
)
sample = sample_fn(
model,
(args.batch_size, 3, label.shape[1], label.shape[2]),
clip_denoised=args.clip_denoised,
model_kwargs=model_kwargs,
progress=True
)
sample = (sample + 1) / 2.0
gathered_samples = [th.zeros_like(sample) for _ in range(dist.get_world_size())]
dist.all_gather(gathered_samples, sample) # gather not supported with NCCL
all_samples.extend([sample.cpu().numpy() for sample in gathered_samples])
for j in range(sample.shape[0]):
# tv.utils.save_image(image[j], os.path.join(image_path, cond['path'][j].split('/')[-1].split('.')[0] + '.png'))
tv.utils.save_image(sample[j], os.path.join(sample_path, cond['path'][j].split('/')[-1].split('.')[0] + '.png'))
tv.utils.save_image(label[j], os.path.join(label_path, cond['path'][j].split('/')[-1].split('.')[0] + '.png'))
logger.log(f"created {len(all_samples) * args.batch_size} samples")
dist.barrier()
logger.log("sampling complete")
def preprocess_input(data, num_classes):
inst_map = data['label'].clone()
# move to GPU and change data types
data['label'] = data['label'].long()
# create one-hot label map
label_map = data['label']
bs, _, h, w = label_map.size()
input_label = th.FloatTensor(bs, num_classes, h, w).zero_()
input_semantics = input_label.scatter_(1, label_map, 1.0)
# concatenate instance map if it exists
if 'instance' in data:
inst_map = data['instance']
instance_edge_map = get_edges(inst_map)
input_semantics = th.cat((input_semantics, instance_edge_map), dim=1)
else:
instance_edge_map = get_edges(inst_map)
input_semantics = th.cat((input_semantics, instance_edge_map), dim=1)
return {'y': input_semantics}
def get_edges(t):
edge = th.ByteTensor(t.size()).zero_()
edge[:, :, :, 1:] = edge[:, :, :, 1:] | (t[:, :, :, 1:] != t[:, :, :, :-1])
edge[:, :, :, :-1] = edge[:, :, :, :-1] | (t[:, :, :, 1:] != t[:, :, :, :-1])
edge[:, :, 1:, :] = edge[:, :, 1:, :] | (t[:, :, 1:, :] != t[:, :, :-1, :])
edge[:, :, :-1, :] = edge[:, :, :-1, :] | (t[:, :, 1:, :] != t[:, :, :-1, :])
return edge.float()
def create_argparser():
defaults = dict(
data_dir="",
dataset_mode="",
clip_denoised=True,
num_samples=100000,
batch_size=1,
use_ddim=False,
model_path="",
results_path="",
is_train=False,
guidance=1.5,
device='cuda',
seed=42,
world_size=4,
dist_url='env://',
distributed=True,
timestep_respacing=250,
)
defaults.update(model_and_diffusion_defaults())
parser = argparse.ArgumentParser()
add_dict_to_argparser(parser, defaults)
return parser
if __name__ == "__main__":
main()