-
Notifications
You must be signed in to change notification settings - Fork 2
/
main_multi.py
663 lines (561 loc) · 28.7 KB
/
main_multi.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
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
#!/usr/bin/env python
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
from torch.utils.data import DataLoader
from pipeline import StableDiffusionPipeline
from ddim_scheduling import DDIMScheduler
import copy
import numpy as np
import argparse
import logging
import os
import time
import torch
import torch.utils.checkpoint
from torchvision import transforms, utils
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import ProjectConfiguration
from tqdm.auto import tqdm
from transformers import AutoTokenizer
from diffusers.optimization import get_scheduler
import bitsandbytes as bnb
from dataset.dataset_multiclass import MVTecDataset
from creat_model import model
from sklearn.metrics import roc_auc_score, precision_recall_curve, average_precision_score
from utilize.utilize import normalize, fix_seeds, compute_pro, reconstruction
from creat_model import get_vit_encoder
import torch.nn.functional as F
from kornia.filters import gaussian_blur2d
import warnings
warnings.filterwarnings("ignore")
logger = get_logger(__name__)
def parse_args(input_args=None):
parser = argparse.ArgumentParser(description="Simple example of a training script.")
parser.add_argument("--train", type=bool)
parser.add_argument("--pretrained_model_name_or_path", type=str,
default="CompVis/stable-diffusion-v1-4",
help="Path to pretrained model or model identifier.", )
parser.add_argument("--seed", type=int, default=0, help="A seed for reproducible training.")
parser.add_argument("--mixed_precision", type=str, default="no", choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and Nvidia Ampere GPU or Intel Gen 4 Xeon (and later) ."),
)
# dataset setting
parser.add_argument("--instance_data_dir", type=str, default="/hdd/Datasets/MVTec-AD")
parser.add_argument("--anomaly_data_dir", type=str, default="/hdd/Datasets/dtd")
parser.add_argument("--output_dir", type=str)
parser.add_argument("--class_name", default="", type=str)
parser.add_argument("--text", type=str, default="")
parser.add_argument("--denoise_step", type=int, default=500)
parser.add_argument("--min_step", type=int, default=350)
parser.add_argument("--instance_prompt", type=str, default="a photo of sks")
parser.add_argument("--resolution", type=int, default=512, )
parser.add_argument("--dino_resolution", type=int, default=512, )
parser.add_argument("--v", type=int, default=0, )
parser.add_argument("--input_threshold", type=float, default=0.0, )
parser.add_argument("--dino_save_path", default=None, type=str)
parser.add_argument("--inference_step", type=int, default=25, )
parser.add_argument("--train_batch_size", type=int, default=32)
parser.add_argument("--test_batch_size", type=int, default=2, help="Batch size (per device) for sampling images.")
# train setting
parser.add_argument("--checkpointing_steps", type=int, default=200, )
parser.add_argument("--gradient_accumulation_steps", type=int, default=1, )
parser.add_argument("--max_train_steps", type=int, default=None,
help="Total number of training steps to perform.", )
parser.add_argument("--learning_rate", type=float, default=5e-6)
parser.add_argument("--lr_scheduler", type=str, default="constant", help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial","constant", "constant_with_warmup"]'), )
parser.add_argument("--lr_warmup_steps", type=int, default=500,
help="Number of steps for the warmup in the lr scheduler.")
parser.add_argument("--use_8bit_adam", action="store_true",
help="Whether or not to use 8-bit Adam from bitsandbytes.")
parser.add_argument("--dataloader_num_workers", type=int, default=8, help=(
"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."), )
parser.add_argument(
"--scale_lr",
action="store_true",
default=False,
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
)
parser.add_argument(
"--gradient_checkpointing",
action="store_true",
help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
)
parser.add_argument("--lr_num_cycles", type=int, default=1,
help="Number of hard resets of the lr in cosine_with_restarts scheduler.", )
parser.add_argument("--lr_power", type=float, default=1.0, help="Power factor of the polynomial scheduler.")
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument("--logging_dir", type=str, default="logs", help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."),
)
parser.add_argument(
"--pre_compute_text_embeddings",
action="store_true",
help="Whether or not to pre-compute text embeddings. If text embeddings are pre-computed, the text encoder will not be kept in memory during training and will leave more GPU memory available for training the rest of the model. This is not compatible with `--train_text_encoder`.",
)
if input_args is not None:
args = parser.parse_args(input_args)
else:
args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
return args
def compute_text_embeddings(prompt, tokenizer, text_encoder):
with torch.no_grad():
text_inputs = tokenizer(prompt,
truncation=True,
padding="max_length",
max_length=tokenizer.model_max_length,
return_tensors="pt",
)
prompt_embeds = encode_prompt(text_encoder, text_inputs.input_ids, text_encoder.device)
return prompt_embeds
def encode_prompt(text_encoder, text_inputs_ids, device):
with torch.no_grad():
text_encoder.to(device)
prompt_embeds = text_encoder(
text_inputs_ids.to(device),
attention_mask=None,
)[0]
return prompt_embeds
def predict_eps(
alphas_cumprod,
x_0_anomaly,
x_0_normal,
timesteps,
noise
):
x_0_anomaly = x_0_anomaly.to(torch.double)
noise = noise.to(torch.double)
x_0_normal = x_0_normal.to(torch.double)
# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
alphas_cumprod = alphas_cumprod.to(device=x_0_anomaly.device, dtype=x_0_anomaly.dtype)
sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5
sqrt_alpha_prod = sqrt_alpha_prod.flatten()
while len(sqrt_alpha_prod.shape) < len(x_0_anomaly.shape):
sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5
sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
while len(sqrt_one_minus_alpha_prod.shape) < len(x_0_anomaly.shape):
sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
eps = (sqrt_alpha_prod * x_0_anomaly + sqrt_one_minus_alpha_prod * noise - sqrt_alpha_prod * x_0_normal) / sqrt_one_minus_alpha_prod
return eps.to(torch.float32)
def train_one_epoch(accelerator,
vae, text_encoder, unet, noise_scheduler,
train_dataloader, pre_encoder_hidden_states,
optimizer, lr_scheduler,
weight_dtype,
global_step, progress_bar,
args,
):
unet.train()
while (True):
for step, batch in enumerate(train_dataloader):
with accelerator.accumulate(unet):
with torch.no_grad():
instance_images = batch["anomaly_images"].to(dtype=weight_dtype)
supervise_images = batch["instance_images"].to(dtype=weight_dtype)
instance_images_512 = torch.nn.functional.interpolate(instance_images, size=512, mode='bilinear', align_corners=True)
supervise_images_512 = torch.nn.functional.interpolate(supervise_images, size=512, mode='bilinear', align_corners=True)
anomaly_input = vae.encode(instance_images_512).latent_dist.sample(torch.Generator(args.seed)) * vae.config.scaling_factor
supervise_input = vae.encode(supervise_images_512).latent_dist.sample(torch.Generator(args.seed)) * vae.config.scaling_factor
if pre_encoder_hidden_states is not None:
encoder_hidden_states = pre_encoder_hidden_states.to(accelerator.device)
encoder_hidden_states = encoder_hidden_states.repeat(anomaly_input.shape[0], 1, 1)
else:
encoder_hidden_states = encode_prompt(text_encoder, batch["instance_prompt_ids"].squeeze(),
accelerator.device)
timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (anomaly_input.shape[0],),
device=accelerator.device).long()
alpha = (timesteps / noise_scheduler.config.num_train_timesteps).reshape(timesteps.shape[0], 1, 1, 1)
synthesis_features = alpha * anomaly_input + (1 - alpha) * supervise_input
noise = torch.randn_like(anomaly_input)
noisy_model_input = noise_scheduler.add_noise(synthesis_features, noise, timesteps)
model_pred = unet(noisy_model_input, timesteps, encoder_hidden_states).sample
eps = predict_eps(noise_scheduler.alphas_cumprod, synthesis_features, supervise_input, timesteps, noise)
loss = F.mse_loss(model_pred.float(), eps.float(), reduction="mean")
accelerator.backward(loss)
if accelerator.sync_gradients:
params_to_clip = (unet.parameters())
max_grad_norm = 1.0
accelerator.clip_grad_norm_(params_to_clip, max_grad_norm)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad(set_to_none=True)
if accelerator.sync_gradients:
progress_bar.update(1)
global_step += 1
if accelerator.is_main_process:
if global_step % args.checkpointing_steps == 0:
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
accelerator.save_state(save_path)
# os.remove(f"{save_path}/optimizer.bin")
# os.remove(f"{save_path}/random_states_0.pkl")
# os.remove(f"{save_path}/scheduler.bin")
logger.info(f"Saved state to {save_path}")
logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
accelerator.log(logs, step=global_step)
if global_step >= args.max_train_steps:
return loss, global_step
def test(dino_model, dino_frozen, val_pipe, weight_dtype, val_dataloader, args, device, class_name, checkpoint_step):
print(f"test:{class_name}, checkpoint_step:{checkpoint_step}")
with torch.no_grad():
val_pipe.to(device)
val_pipe.set_progress_bar_config(disable=True)
val_pipe.unet.eval()
val_pipe.vae.eval()
val_pipe.text_encoder.eval()
transform = transforms.Compose([
transforms.Lambda(lambda t: (t + 1) / (2)),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
preds = []
masks = []
scores = []
labels = []
for batch in val_dataloader:
image_input = batch["instance_images"].to(device)
anomaly_mask = batch["instance_masks"].to(device)
object_mask = batch["object_mask"].to(device)
image_input_512 = torch.nn.functional.interpolate(image_input, size=512, mode='bilinear', align_corners=True)
reconstruct_images, step = reconstruction(val_pipe, weight_dtype, args, image_input_512, dino_frozen)
reconstruct_images = torch.nn.functional.interpolate(reconstruct_images, size=args.dino_resolution, mode='bilinear', align_corners=True)
image_input = transform(image_input)
reconstruct_images = transform(reconstruct_images)
_, patch_tokens_i = dino_model(image_input.to(dtype=weight_dtype))
_, patch_tokens_r = dino_model(reconstruct_images.to(dtype=weight_dtype))
sigma = 6
kernel_size = 2 * int(4 * sigma + 0.5) + 1
b, n, c = patch_tokens_i[0][:, 1:, :].shape
h = int(n ** 0.5)
anomaly_maps1 = torch.zeros((b, 1, args.dino_resolution, args.dino_resolution)).to(device)
anomaly_maps2 = torch.zeros((b, 1, args.dino_resolution, args.dino_resolution)).to(device)
for idx in range(len(patch_tokens_i)):
pi = patch_tokens_i[idx][:, 1:, :]
pr = patch_tokens_r[idx][:, 1:, :]
pi = pi / torch.norm(pi, p=2, dim=-1, keepdim=True)
pr = pr / torch.norm(pr, p=2, dim=-1, keepdim=True)
cos0 = torch.bmm(pi, pr.permute(0, 2, 1))
anomaly_map1, _ = torch.min(1 - cos0, dim=-1)
anomaly_map1 = F.interpolate(anomaly_map1.reshape(-1, 1, h, h), size=args.dino_resolution, mode='bilinear', align_corners=True)
anomaly_maps1 += anomaly_map1
if class_name in ["transistor", "pcb1", "pcb4"]:
anomaly_map2, _ = torch.min(1 - cos0, dim=-2)
anomaly_map2 = F.interpolate(anomaly_map2.reshape(-1, 1, h, h), size=args.dino_resolution, mode='bilinear', align_corners=True)
anomaly_maps2 += anomaly_map2
if class_name in ["transistor", "pcb1", "pcb4"]:
anomaly_maps1 = (anomaly_maps1 + anomaly_maps2) / 2
distance_map = torch.mean(torch.abs(image_input - reconstruct_images), dim=1, keepdim=True)
anomaly_maps1 = anomaly_maps1 + args.v * torch.max(anomaly_maps1) / torch.max(distance_map) * distance_map
anomaly_maps1 = gaussian_blur2d(anomaly_maps1, kernel_size=(kernel_size, kernel_size), sigma=(sigma, sigma))[:, 0]
anomaly_maps = anomaly_maps1 * object_mask.to(device)
score = torch.topk(torch.flatten(anomaly_maps, start_dim=1), 250)[0].mean(dim=1)
masks.extend([m for m in anomaly_mask[:, 0, :, :].cpu().numpy()])
preds.extend([a for a in anomaly_maps.cpu().numpy()])
scores.extend([s for s in score.cpu().numpy()])
labels.extend([l for l in batch["instance_label"].cpu().numpy()])
scores = normalize(np.array(scores))
labels = np.array(labels)
preds = np.array(preds)
masks = np.array(masks, dtype=np.int_)
precisions_image, recalls_image, _ = precision_recall_curve(labels, scores)
f1_scores_image = (2 * precisions_image * recalls_image) / (precisions_image + recalls_image)
best_f1_scores_image = np.max(f1_scores_image[np.isfinite(f1_scores_image)])
auroc_image = roc_auc_score(labels, scores)
AP_image = average_precision_score(labels, scores)
precisions_pixel, recalls_pixel, _ = precision_recall_curve(masks.ravel(), preds.ravel())
f1_scores_pixel = (2 * precisions_pixel * recalls_pixel) / (precisions_pixel + recalls_pixel)
best_f1_scores_pixel = np.max(f1_scores_pixel[np.isfinite(f1_scores_pixel)])
auroc_pixel = roc_auc_score(masks.ravel(), preds.ravel())
AP_pixel = average_precision_score(masks.ravel(), preds.ravel())
pro = compute_pro(masks, preds)
print(f"test-------- I-AUROC/I-AP/I-F1-max/P-AUROC/P-AP/P-F1-max/PRO:{round(auroc_image, 4)}/{round(AP_image, 4)}/{round(best_f1_scores_image, 4)}/"
f"{round(auroc_pixel, 4)}/{round(AP_pixel, 4)}/{round(best_f1_scores_pixel, 4)}/{round(pro, 4)}-----")
return round(auroc_image, 4) * 100, round(AP_image, 4) * 100, round(best_f1_scores_image, 4) * 100, round(
auroc_pixel, 4) * 100, round(AP_pixel, 4) * 100, round(best_f1_scores_pixel, 4) * 100, round(pro, 4) * 100
def load_vae(vae):
print(args.instance_data_dir)
if "VisA" in args.instance_data_dir:
vae_path = 'model/vae/visa_diad_epoch=118-step=64498.ckpt'
elif 'PCBBank' in args.instance_data_dir:
vae_path = 'model/vae/pacbank_epoch=245-step=64944.ckpt'
else:
vae_path = None
if vae_path:
sd = torch.load(vae_path)["state_dict"]
print(f"load vae in test :{vae_path}")
keys = list(sd.keys())
for k in keys:
if "loss" in k:
del sd[k]
vae.load_state_dict(sd)
return vae
def load_test_model(args, weight_dtype):
dino_model = get_vit_encoder(vit_arch="vit_base", vit_model="dino", vit_patch_size=8, enc_type_feats=None).to(device, dtype=weight_dtype)
dino_model.eval()
val_pipe = StableDiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
scheduler=DDIMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler"),
torch_dtype=weight_dtype,
)
return dino_model, val_pipe
def main(args, class_name):
logging_dir = os.path.join(args.output_dir, args.logging_dir)
accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
accelerator = Accelerator(
mixed_precision=args.mixed_precision,
gradient_accumulation_steps=args.gradient_accumulation_steps,
project_config=accelerator_project_config,
)
if accelerator.is_main_process:
tracker_config = vars(copy.deepcopy(args))
accelerator.init_trackers("GLAD", config=tracker_config)
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
tokenizer = AutoTokenizer.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="tokenizer",
use_fast=False,
)
text_encoder, vae, unet = model(args)
vae.to(accelerator.device, dtype=weight_dtype)
vae = load_vae(vae)
text_encoder.to(accelerator.device, dtype=weight_dtype)
noise_scheduler = DDIMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
vae.eval()
text_encoder.eval()
if args.pre_compute_text_embeddings:
pre_encoder_hidden_states = compute_text_embeddings(args.instance_prompt, tokenizer, text_encoder)
else:
pre_encoder_hidden_states = None
optimizer_class = bnb.optim.AdamW8bit
params_to_optimize = (unet.parameters())
optimizer = optimizer_class(
params_to_optimize,
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
lr_scheduler = get_scheduler(
args.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
num_training_steps=args.max_train_steps * accelerator.num_processes,
num_cycles=args.lr_num_cycles,
power=args.lr_power,
)
train_dataset = MVTecDataset(
instance_data_root=args.instance_data_dir,
instance_prompt=args.instance_prompt,
class_name=class_name,
tokenizer=tokenizer,
resize=args.resolution,
img_size=args.resolution,
anomaly_path=args.anomaly_data_dir,
train=True
)
train_dataloader = DataLoader(
train_dataset,
batch_size=args.train_batch_size,
shuffle=True,
num_workers=args.dataloader_num_workers,
)
unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
unet, optimizer, train_dataloader, lr_scheduler
)
num_update_steps_per_epoch = len(train_dataloader)
num_epochs = round(args.max_train_steps / num_update_steps_per_epoch)
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Class name = {class_name}")
logger.info(f" Num train examples = {len(train_dataset)}")
logger.info(f" Num batches each epoch = {len(train_dataloader)}")
logger.info(f" Num Epochs = {num_epochs}")
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
logger.info(f" save model into {args.output_dir}")
global_step = 0
progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)
progress_bar.set_description("Steps")
loss, global_step = train_one_epoch(accelerator,
vae, text_encoder, unet, noise_scheduler,
train_dataloader, pre_encoder_hidden_states,
optimizer, lr_scheduler,
weight_dtype,
global_step, progress_bar,
args)
logger.info(f"train--------train loss:{loss}-----")
del vae
del text_encoder
del unet
torch.cuda.empty_cache()
if __name__ == "__main__":
args = parse_args()
device = torch.device("cuda")
dino_model, val_pipe = load_test_model(args, torch.float16)
dino_frozen = copy.deepcopy(dino_model)
args.output_dir = os.path.join('model', args.instance_data_dir.split('/')[-1] + '_' + args.output_dir + f"_{args.seed}")
if args.train:
main(args, "")
else:
if 'MVTec-AD' in args.instance_data_dir:
args.input_threshold = 0.45
args.denoise_step = 650
args.min_step = 350
args.resolution = 256
args.dino_resolution = 256
checkpoint_step = 20000
args.v = 0
args.dino_save_path = None
CLSNAMES = [
'carpet',
'grid',
'leather',
'tile',
'wood',
'bottle',
'cable',
"capsule",
'hazelnut',
'metal_nut',
'pill',
'screw',
'toothbrush',
'transistor',
'zipper',
]
elif 'MPDD' in args.instance_data_dir:
args.input_threshold = 0.35
args.denoise_step = 500
args.min_step = 350
args.resolution = 256
args.dino_resolution = 256
checkpoint_step = 3000
args.v = 0
args.dino_save_path = None
CLSNAMES = [
'bracket_black',
'bracket_brown',
'bracket_white',
'connector',
'metal_plate',
'tubes',
]
elif 'VisA' in args.instance_data_dir:
args.input_threshold = 0.15
args.denoise_step = 500
args.min_step = 250
args.resolution = 256
args.dino_resolution = 256
checkpoint_step = 20000
args.v = 2
args.dino_save_path = None
CLSNAMES = [
'candle',
'capsules',
'cashew',
'chewinggum',
'fryum',
'macaroni1',
'macaroni2',
'pcb1',
'pcb2',
'pcb3',
'pcb4',
'pipe_fryum',
]
elif 'PCBBank' in args.instance_data_dir:
args.input_threshold = 0.2
args.denoise_step = 500
args.min_step = 250
args.resolution = 256
args.dino_resolution = 256
checkpoint_step = 20000
args.v = 1
args.dino_save_path = 'model/pcbbank_dino_multi/PCBBank_4mlp_256_200_bs16_0.0003_15_no_grad2_lmd0.01/epoch1.pth'
CLSNAMES = {
'pcb1',
'pcb2',
'pcb3',
'pcb4',
'pcb5',
'pcb6',
'pcb7',
}
if args.dino_save_path:
dino_model.load_state_dict(torch.load(args.dino_save_path))
print(f"Test checkpoint step {checkpoint_step}", time.asctime())
val_pipe.unet.load_state_dict(
torch.load(f"{args.output_dir}/checkpoint-{checkpoint_step}/pytorch_model.bin")
)
val_pipe.unet.to(dtype=torch.float16)
val_pipe.vae = load_vae(val_pipe.vae)
print(args)
performances = [[], [], [], [], [], [], []]
for class_name in CLSNAMES:
args.instance_prompt = 'a photo of sks' + class_name
print(class_name, f"input_threshold is {args.input_threshold} {args.denoise_step} {args.min_step} {args.v}")
fix_seeds(args.seed)
val_dataset = MVTecDataset(
instance_data_root=args.instance_data_dir,
instance_prompt=args.instance_prompt,
class_name=class_name,
tokenizer=None,
resize=args.resolution,
img_size=args.resolution,
train=False
)
val_dataloader = DataLoader(
val_dataset,
batch_size=args.test_batch_size,
num_workers=args.dataloader_num_workers,
shuffle=False,
pin_memory=True,
)
results = test(
dino_model, dino_frozen, val_pipe, torch.float16, val_dataloader, args, device,
class_name,
checkpoint_step
)
for i, result in enumerate(results):
performances[i].append(result)
performances = np.array(performances).T
print("mean:", np.mean(performances, axis=0))