-
Notifications
You must be signed in to change notification settings - Fork 5
/
main.py
469 lines (431 loc) · 17.1 KB
/
main.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
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
import argparse
import logging
import os
import numpy as np
import imageio
from torchvision.utils import make_grid
import torch
from dpsda.logging import setup_logging
from dpsda.data_loader import load_data
from dpsda.feature_extractor import extract_features
from dpsda.metrics import make_fid_stats
from dpsda.metrics import compute_fid
from dpsda.dp_counter import dp_nn_histogram
from dpsda.arg_utils import str2bool
from apis import get_api_class_from_name
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument(
'--api',
type=str,
required=True,
choices=['DALLE', 'stable_diffusion', 'improved_diffusion'],
help='Which foundation model API to use')
parser.add_argument(
'--plot_images',
type=str2bool,
default=True,
help='Whether to save generated images in PNG files')
parser.add_argument(
'--data_checkpoint_path',
type=str,
default="",
help='Path to the data checkpoint')
parser.add_argument(
'--data_checkpoint_step',
type=int,
default=-1,
help='Iteration of the data checkpoint')
parser.add_argument(
'--num_samples_schedule',
type=str,
default='50000,'*9 + '50000',
help='Number of samples to generate at each iteration')
parser.add_argument(
'--variation_degree_schedule',
type=str,
default='0,'*9 + '0',
help='Variation degree at each iteration')
parser.add_argument(
'--num_fid_samples',
type=int,
default=50000,
help='Number of generated samples to compute FID')
parser.add_argument(
'--num_private_samples',
type=int,
default=50000,
help='Number of private samples to load')
parser.add_argument(
'--noise_multiplier',
type=float,
default=0.0,
help='Noise multiplier for DP NN histogram')
parser.add_argument(
'--lookahead_degree',
type=int,
default=0,
help=('Lookahead degree for computing distances between private and '
'generated images'))
parser.add_argument(
'--feature_extractor',
type=str,
default='clip_vit_b_32',
choices=['inception_v3', 'clip_vit_b_32', 'original'],
help='Which image feature extractor to use')
parser.add_argument(
'--num_nearest_neighbor',
type=int,
default=1,
help='Number of nearest neighbors to find in DP NN histogram')
parser.add_argument(
'--nn_mode',
type=str,
default='L2',
choices=['L2', 'IP'],
help='Which distance metric to use in DP NN histogram')
parser.add_argument(
'--private_image_size',
type=int,
default=1024,
help='Size of private images')
parser.add_argument(
'--tmp_folder',
type=str,
default='result/tmp',
help='Temporary folder for storing intermediate results')
parser.add_argument(
'--result_folder',
type=str,
default='result',
help='Folder for storing results')
parser.add_argument(
'--data_folder',
type=str,
required=True,
help='Folder that contains the private images')
parser.add_argument(
'--count_threshold',
type=float,
default=0.0,
help='Threshold for DP NN histogram')
parser.add_argument(
'--compute_fid',
type=str2bool,
default=True,
help='Whether to compute FID')
parser.add_argument(
'--fid_dataset_name',
type=str,
default='customized_dataset',
help=('Name of the dataset for computing FID against. If '
'fid_dataset_name and fid_dataset_split in combination are one '
'of the precomputed datasets in '
'https://github.com/GaParmar/clean-fid and make_fid_stats=False,'
' then the precomputed statistics will be used. Otherwise, the '
'statistics will be computed using the private samples and saved'
' with fid_dataset_name and fid_dataset_split for future use.'))
parser.add_argument(
'--fid_dataset_split',
type=str,
default='train',
help=('Split of the dataset for computing FID against. If '
'fid_dataset_name and fid_dataset_split in combination are one '
'of the precomputed datasets in '
'https://github.com/GaParmar/clean-fid and make_fid_stats=False,'
' then the precomputed statistics will be used. Otherwise, the '
'statistics will be computed using the private samples and saved'
' with fid_dataset_name and fid_dataset_split for future use.'))
parser.add_argument(
'--fid_model_name',
type=str,
default='inception_v3',
choices=['inception_v3', 'clip_vit_b_32'],
help='Which embedding network to use for computing FID')
parser.add_argument(
'--make_fid_stats',
type=str2bool,
default=True,
help='Whether to compute FID stats for the private samples')
parser.add_argument(
'--data_loading_batch_size',
type=int,
default=100,
help='Batch size for loading private samples')
parser.add_argument(
'--feature_extractor_batch_size',
type=int,
default=500,
help='Batch size for feature extraction')
parser.add_argument(
'--fid_batch_size',
type=int,
default=500,
help='Batch size for computing FID')
parser.add_argument(
'--gen_class_cond',
type=str2bool,
default=False,
help='Whether to generate class labels')
parser.add_argument(
'--initial_prompt',
action='append',
type=str,
help='Initial prompt for image generation. It can be specified '
'multiple times to provide a list of prompts. If the API accepts '
'prompts, the initial samples will be generated with these '
'prompts')
parser.add_argument(
'--image_size',
type=str,
default='1024x1024',
help='Size of generated images in the format of HxW')
args, api_args = parser.parse_known_args()
args.num_samples_schedule = list(map(
int, args.num_samples_schedule.split(',')))
variation_degree_type = (float if '.' in args.variation_degree_schedule
else int)
args.variation_degree_schedule = list(map(
variation_degree_type, args.variation_degree_schedule.split(',')))
if len(args.num_samples_schedule) != len(args.variation_degree_schedule):
raise ValueError('The length of num_samples_schedule and '
'variation_degree_schedule should be the same')
api_class = get_api_class_from_name(args.api)
api = api_class.from_command_line_args(api_args)
return args, api
def log_samples(samples, additional_info, folder, plot_images):
if not os.path.exists(folder):
os.makedirs(folder)
np.savez(
os.path.join(folder, 'samples.npz'),
samples=samples,
additional_info=additional_info)
if plot_images:
for i in range(samples.shape[0]):
imageio.imwrite(os.path.join(folder, f'{i}.png'), samples[i])
def load_samples(path):
data = np.load(path)
samples = data['samples']
additional_info = data['additional_info']
return samples, additional_info
def log_count(count, clean_count, path):
dirname = os.path.dirname(path)
if not os.path.exists(dirname):
os.makedirs(dirname)
np.savez(path, count=count, clean_count=clean_count)
def round_to_uint8(image):
return np.around(np.clip(image, a_min=0, a_max=255)).astype(np.uint8)
def visualize(samples, packed_samples, count, folder, suffix=''):
if not os.path.exists(folder):
os.makedirs(folder)
samples = samples.transpose((0, 3, 1, 2))
packed_samples = packed_samples.transpose((0, 1, 4, 2, 3))
ids = np.argsort(count)[::-1][:5]
print(count[ids])
vis_samples = []
for i in range(len(ids)):
vis_samples.append(samples[ids[i]])
for j in range(packed_samples.shape[1]):
vis_samples.append(packed_samples[ids[i]][j])
vis_samples = np.stack(vis_samples)
vis_samples = make_grid(
torch.Tensor(vis_samples),
nrow=packed_samples.shape[1] + 1).numpy().transpose((1, 2, 0))
vis_samples = round_to_uint8(vis_samples)
imageio.imsave(
os.path.join(folder, f'visualize_top_{suffix}.png'), vis_samples)
ids = np.argsort(count)[:5]
print(count[ids])
vis_samples = []
for i in range(len(ids)):
vis_samples.append(samples[ids[i]])
for j in range(packed_samples.shape[1]):
vis_samples.append(packed_samples[ids[i]][j])
vis_samples = np.stack(vis_samples)
vis_samples = make_grid(
torch.Tensor(vis_samples),
nrow=packed_samples.shape[1] + 1).numpy().transpose((1, 2, 0))
vis_samples = round_to_uint8(vis_samples)
imageio.imsave(
os.path.join(folder, f'visualize_bottom_{suffix}.png'), vis_samples)
def log_fid(folder, fid, t):
with open(os.path.join(folder, 'fid.csv'), 'a') as f:
f.write(f'{t} {fid}\n')
def main():
args, api = parse_args()
if os.path.exists(args.result_folder):
raise RuntimeError(f'{args.result_folder} exists')
os.makedirs(args.result_folder)
setup_logging(os.path.join(args.result_folder, 'log.log'))
logging.info(f'config: {args}')
logging.info(f'API config: {api.args}')
all_private_samples, all_private_labels = load_data(
data_dir=args.data_folder,
batch_size=args.data_loading_batch_size,
image_size=args.private_image_size,
class_cond=args.gen_class_cond,
num_private_samples=args.num_private_samples)
private_classes = list(sorted(set(list(all_private_labels))))
private_num_classes = len(private_classes)
logging.info(f'Private_num_classes: {private_num_classes}')
logging.info('Extracting features')
all_private_features = extract_features(
data=all_private_samples,
tmp_folder=args.tmp_folder,
model_name=args.feature_extractor,
res=args.private_image_size,
batch_size=args.feature_extractor_batch_size)
logging.info(f'all_private_features.shape: {all_private_features.shape}')
if args.make_fid_stats:
logging.info('Computing FID stats')
make_fid_stats(
samples=all_private_samples,
dataset=args.fid_dataset_name,
dataset_res=args.private_image_size,
dataset_split=args.fid_dataset_split,
tmp_folder=args.tmp_folder,
model_name=args.fid_model_name,
batch_size=args.fid_batch_size)
# Generating initial samples.
if args.data_checkpoint_path != '':
logging.info(
f'Loading data checkpoint from {args.data_checkpoint_path}')
samples, additional_info = load_samples(args.data_checkpoint_path)
if args.data_checkpoint_step < 0:
raise ValueError('data_checkpoint_step should be >= 0')
start_t = args.data_checkpoint_step + 1
else:
logging.info('Generating initial samples')
samples, additional_info = api.image_random_sampling(
prompts=args.initial_prompt,
num_samples=args.num_samples_schedule[0],
size=args.image_size)
log_samples(
samples=samples,
additional_info=additional_info,
folder=f'{args.result_folder}/{0}',
plot_images=args.plot_images)
if args.data_checkpoint_step >= 0:
logging.info('Ignoring data_checkpoint_step')
start_t = 1
if args.compute_fid:
logging.info('Computing FID')
fid = compute_fid(
samples=samples,
tmp_folder=args.tmp_folder,
num_fid_samples=args.num_fid_samples,
dataset_res=args.private_image_size,
dataset=args.fid_dataset_name,
dataset_split=args.fid_dataset_split,
model_name=args.fid_model_name,
batch_size=args.fid_batch_size)
logging.info(f'fid={fid}')
log_fid(args.result_folder, fid, 0)
for t in range(start_t, len(args.num_samples_schedule)):
logging.info(f't={t}')
assert samples.shape[0] % private_num_classes == 0
num_samples_per_class = samples.shape[0] // private_num_classes
if args.lookahead_degree == 0:
packed_samples = np.expand_dims(samples, axis=1)
else:
logging.info('Running image variation')
packed_samples = api.image_variation(
images=samples,
additional_info=additional_info,
num_variations_per_image=args.lookahead_degree,
size=args.image_size,
variation_degree=args.variation_degree_schedule[t])
packed_features = []
logging.info('Running feature extraction')
for i in range(packed_samples.shape[1]):
sub_packed_features = extract_features(
data=packed_samples[:, i],
tmp_folder=args.tmp_folder,
model_name=args.feature_extractor,
res=args.private_image_size,
batch_size=args.feature_extractor_batch_size)
logging.info(
f'sub_packed_features.shape: {sub_packed_features.shape}')
packed_features.append(sub_packed_features)
packed_features = np.mean(packed_features, axis=0)
logging.info('Computing histogram')
count = []
for class_i, class_ in enumerate(private_classes):
sub_count, sub_clean_count = dp_nn_histogram(
public_features=packed_features[
num_samples_per_class * class_i:
num_samples_per_class * (class_i + 1)],
private_features=all_private_features[
all_private_labels == class_],
noise_multiplier=args.noise_multiplier,
num_nearest_neighbor=args.num_nearest_neighbor,
mode=args.nn_mode,
threshold=args.count_threshold)
log_count(
sub_count,
sub_clean_count,
f'{args.result_folder}/{t}/count_class{class_}.npz')
count.append(sub_count)
count = np.concatenate(count)
for class_i, class_ in enumerate(private_classes):
visualize(
samples=samples[
num_samples_per_class * class_i:
num_samples_per_class * (class_i + 1)],
packed_samples=packed_samples[
num_samples_per_class * class_i:
num_samples_per_class * (class_i + 1)],
count=count[
num_samples_per_class * class_i:
num_samples_per_class * (class_i + 1)],
folder=f'{args.result_folder}/{t}',
suffix=f'class{class_}')
logging.info('Generating new indices')
assert args.num_samples_schedule[t] % private_num_classes == 0
new_num_samples_per_class = (
args.num_samples_schedule[t] // private_num_classes)
new_indices = []
for class_i in private_classes:
sub_count = count[
num_samples_per_class * class_i:
num_samples_per_class * (class_i + 1)]
sub_new_indices = np.random.choice(
np.arange(num_samples_per_class * class_i,
num_samples_per_class * (class_i + 1)),
size=new_num_samples_per_class,
p=sub_count / np.sum(sub_count))
new_indices.append(sub_new_indices)
new_indices = np.concatenate(new_indices)
new_samples = samples[new_indices]
new_additional_info = additional_info[new_indices]
logging.info('Generating new samples')
new_new_samples = api.image_variation(
images=new_samples,
additional_info=new_additional_info,
num_variations_per_image=1,
size=args.image_size,
variation_degree=args.variation_degree_schedule[t])
new_new_samples = np.squeeze(new_new_samples, axis=1)
new_new_additional_info = new_additional_info
if args.compute_fid:
logging.info('Computing FID')
new_new_fid = compute_fid(
new_new_samples,
tmp_folder=args.tmp_folder,
num_fid_samples=args.num_fid_samples,
dataset_res=args.private_image_size,
dataset=args.fid_dataset_name,
dataset_split=args.fid_dataset_split,
model_name=args.fid_model_name,
batch_size=args.fid_batch_size)
logging.info(f'fid={new_new_fid}')
log_fid(args.result_folder, new_new_fid, t)
samples = new_new_samples
additional_info = new_new_additional_info
log_samples(
samples=samples,
additional_info=additional_info,
folder=f'{args.result_folder}/{t}',
plot_images=args.plot_images)
if __name__ == '__main__':
main()