-
Notifications
You must be signed in to change notification settings - Fork 368
/
test_mvdiffusion_seq.py
325 lines (250 loc) · 11 KB
/
test_mvdiffusion_seq.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
import argparse
import datetime
import logging
import inspect
import math
import os
from typing import Dict, Optional, Tuple, List
from omegaconf import OmegaConf
from PIL import Image
import cv2
import numpy as np
from dataclasses import dataclass
from packaging import version
import shutil
from collections import defaultdict
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import torchvision.transforms.functional as TF
from torchvision.utils import make_grid, save_image
import transformers
import accelerate
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import ProjectConfiguration, set_seed
import diffusers
from diffusers import AutoencoderKL, DDPMScheduler, DDIMScheduler, StableDiffusionPipeline, UNet2DConditionModel
from diffusers.optimization import get_scheduler
from diffusers.training_utils import EMAModel
from diffusers.utils import check_min_version, deprecate, is_wandb_available
from diffusers.utils.import_utils import is_xformers_available
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from mvdiffusion.models.unet_mv2d_condition import UNetMV2DConditionModel
from mvdiffusion.data.single_image_dataset import SingleImageDataset as MVDiffusionDataset
from mvdiffusion.pipelines.pipeline_mvdiffusion_image import MVDiffusionImagePipeline
from einops import rearrange
from rembg import remove
import pdb
weight_dtype = torch.float16
@dataclass
class TestConfig:
pretrained_model_name_or_path: str
pretrained_unet_path:str
revision: Optional[str]
validation_dataset: Dict
save_dir: str
seed: Optional[int]
validation_batch_size: int
dataloader_num_workers: int
local_rank: int
pipe_kwargs: Dict
pipe_validation_kwargs: Dict
unet_from_pretrained_kwargs: Dict
validation_guidance_scales: List[float]
validation_grid_nrow: int
camera_embedding_lr_mult: float
num_views: int
camera_embedding_type: str
pred_type: str # joint, or ablation
enable_xformers_memory_efficient_attention: bool
cond_on_normals: bool
cond_on_colors: bool
def log_validation(dataloader, pipeline, cfg: TestConfig, weight_dtype, name, save_dir):
pipeline.set_progress_bar_config(disable=True)
if cfg.seed is None:
generator = None
else:
generator = torch.Generator(device=pipeline.device).manual_seed(cfg.seed)
images_cond, images_pred = [], defaultdict(list)
for i, batch in tqdm(enumerate(dataloader)):
# (B, Nv, 3, H, W)
imgs_in = batch['imgs_in']
alphas = batch['alphas']
# (B, Nv, Nce)
camera_embeddings = batch['camera_embeddings']
filename = batch['filename']
bsz, num_views = imgs_in.shape[0], imgs_in.shape[1]
# (B*Nv, 3, H, W)
imgs_in = rearrange(imgs_in, "B Nv C H W -> (B Nv) C H W")
alphas = rearrange(alphas, "B Nv C H W -> (B Nv) C H W")
# (B*Nv, Nce)
camera_embeddings = rearrange(camera_embeddings, "B Nv Nce -> (B Nv) Nce")
images_cond.append(imgs_in)
with torch.autocast("cuda"):
# B*Nv images
for guidance_scale in cfg.validation_guidance_scales:
out = pipeline(
imgs_in, camera_embeddings, generator=generator, guidance_scale=guidance_scale, output_type='pt', num_images_per_prompt=1, **cfg.pipe_validation_kwargs
).images
images_pred[f"{name}-sample_cfg{guidance_scale:.1f}"].append(out)
cur_dir = os.path.join(save_dir, f"cropsize-{cfg.validation_dataset.crop_size}-cfg{guidance_scale:.1f}")
# pdb.set_trace()
for i in range(bsz):
scene = os.path.basename(filename[i])
print(scene)
scene_dir = os.path.join(cur_dir, scene)
outs_dir = os.path.join(scene_dir, "outs")
masked_outs_dir = os.path.join(scene_dir, "masked_outs")
os.makedirs(outs_dir, exist_ok=True)
os.makedirs(masked_outs_dir, exist_ok=True)
img_in = imgs_in[i*num_views]
alpha = alphas[i*num_views]
img_in = torch.cat([img_in, alpha], dim=0)
save_image(img_in, os.path.join(scene_dir, scene+".png"))
for j in range(num_views):
view = VIEWS[j]
idx = i*num_views + j
pred = out[idx]
# pdb.set_trace()
out_filename = f"{cfg.pred_type}_000_{view}.png"
pred = save_image(pred, os.path.join(outs_dir, out_filename))
rm_pred = remove(pred)
save_image_numpy(rm_pred, os.path.join(scene_dir, out_filename))
torch.cuda.empty_cache()
def save_image(tensor, fp):
ndarr = tensor.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to("cpu", torch.uint8).numpy()
# pdb.set_trace()
im = Image.fromarray(ndarr)
im.save(fp)
return ndarr
def save_image_numpy(ndarr, fp):
im = Image.fromarray(ndarr)
im.save(fp)
def log_validation_joint(dataloader, pipeline, cfg: TestConfig, weight_dtype, name, save_dir):
pipeline.set_progress_bar_config(disable=True)
if cfg.seed is None:
generator = None
else:
generator = torch.Generator(device=pipeline.device).manual_seed(cfg.seed)
images_cond, normals_pred, images_pred = [], defaultdict(list), defaultdict(list)
for i, batch in tqdm(enumerate(dataloader)):
# repeat (2B, Nv, 3, H, W)
imgs_in = torch.cat([batch['imgs_in']]*2, dim=0)
filename = batch['filename']
# (2B, Nv, Nce)
camera_embeddings = torch.cat([batch['camera_embeddings']]*2, dim=0)
task_embeddings = torch.cat([batch['normal_task_embeddings'], batch['color_task_embeddings']], dim=0)
camera_embeddings = torch.cat([camera_embeddings, task_embeddings], dim=-1)
# (B*Nv, 3, H, W)
imgs_in = rearrange(imgs_in, "B Nv C H W -> (B Nv) C H W")
# (B*Nv, Nce)
camera_embeddings = rearrange(camera_embeddings, "B Nv Nce -> (B Nv) Nce")
images_cond.append(imgs_in)
num_views = len(VIEWS)
with torch.autocast("cuda"):
# B*Nv images
for guidance_scale in cfg.validation_guidance_scales:
out = pipeline(
imgs_in, camera_embeddings, generator=generator, guidance_scale=guidance_scale, output_type='pt', num_images_per_prompt=1, **cfg.pipe_validation_kwargs
).images
bsz = out.shape[0] // 2
normals_pred = out[:bsz]
images_pred = out[bsz:]
cur_dir = os.path.join(save_dir, f"cropsize-{cfg.validation_dataset.crop_size}-cfg{guidance_scale:.1f}")
for i in range(bsz//num_views):
scene = filename[i]
scene_dir = os.path.join(cur_dir, scene)
normal_dir = os.path.join(scene_dir, "normals")
masked_colors_dir = os.path.join(scene_dir, "masked_colors")
os.makedirs(normal_dir, exist_ok=True)
os.makedirs(masked_colors_dir, exist_ok=True)
for j in range(num_views):
view = VIEWS[j]
idx = i*num_views + j
normal = normals_pred[idx]
color = images_pred[idx]
normal_filename = f"normals_000_{view}.png"
rgb_filename = f"rgb_000_{view}.png"
normal = save_image(normal, os.path.join(normal_dir, normal_filename))
color = save_image(color, os.path.join(scene_dir, rgb_filename))
rm_normal = remove(normal)
rm_color = remove(color)
save_image_numpy(rm_normal, os.path.join(scene_dir, normal_filename))
save_image_numpy(rm_color, os.path.join(masked_colors_dir, rgb_filename))
torch.cuda.empty_cache()
def load_wonder3d_pipeline(cfg):
pipeline = MVDiffusionImagePipeline.from_pretrained(
cfg.pretrained_model_name_or_path,
torch_dtype=weight_dtype
)
# pipeline.to('cuda:0')
pipeline.unet.enable_xformers_memory_efficient_attention()
if torch.cuda.is_available():
pipeline.to('cuda:0')
# sys.main_lock = threading.Lock()
return pipeline
def main(
cfg: TestConfig
):
# If passed along, set the training seed now.
if cfg.seed is not None:
set_seed(cfg.seed)
pipeline = load_wonder3d_pipeline(cfg)
if cfg.enable_xformers_memory_efficient_attention:
if is_xformers_available():
import xformers
xformers_version = version.parse(xformers.__version__)
if xformers_version == version.parse("0.0.16"):
print(
"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
)
pipeline.unet.enable_xformers_memory_efficient_attention()
print("use xformers.")
else:
raise ValueError("xformers is not available. Make sure it is installed correctly")
# Get the dataset
validation_dataset = MVDiffusionDataset(
**cfg.validation_dataset
)
# DataLoaders creation:
validation_dataloader = torch.utils.data.DataLoader(
validation_dataset, batch_size=cfg.validation_batch_size, shuffle=False, num_workers=cfg.dataloader_num_workers
)
os.makedirs(cfg.save_dir, exist_ok=True)
if cfg.pred_type == 'joint':
log_validation_joint(
validation_dataloader,
pipeline,
cfg,
weight_dtype,
'validation',
cfg.save_dir
)
else:
log_validation(
validation_dataloader,
pipeline,
cfg,
weight_dtype,
'validation',
cfg.save_dir
)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--config', type=str, required=True)
args, extras = parser.parse_known_args()
from utils.misc import load_config
# parse YAML config to OmegaConf
cfg = load_config(args.config, cli_args=extras)
print(cfg)
schema = OmegaConf.structured(TestConfig)
# cfg = OmegaConf.load(args.config)
cfg = OmegaConf.merge(schema, cfg)
if cfg.num_views == 6:
VIEWS = ['front', 'front_right', 'right', 'back', 'left', 'front_left']
elif cfg.num_views == 4:
VIEWS = ['front', 'right', 'back', 'left']
main(cfg)