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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)
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