import argparse
import sys
import os
from typing import Dict, Optional, Tuple, List
from omegaconf import OmegaConf
from PIL import Image
from dataclasses import dataclass
from collections import defaultdict
import torch
import torch.utils.checkpoint
from torchvision.utils import make_grid, save_image
from accelerate.utils import set_seed
from tqdm.auto import tqdm
import torch.nn.functional as F
from einops import rearrange
from rembg import remove, new_session
import pdb
from mvdiffusion.pipelines.pipeline_mvdiffusion_unclip import StableUnCLIPImg2ImgPipeline
from econdataset import SMPLDataset
from reconstruct import ReMesh
providers = [
('CUDAExecutionProvider', {
'device_id': 0,
'arena_extend_strategy': 'kSameAsRequested',
'gpu_mem_limit': 8 * 1024 * 1024 * 1024,
'cudnn_conv_algo_search': 'HEURISTIC',
})
]
session = new_session(providers=providers)
weight_dtype = torch.float16
def tensor_to_numpy(tensor):
return tensor.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to("cpu", torch.uint8).numpy()
@dataclass
class TestConfig:
pretrained_model_name_or_path: str
revision: Optional[str]
validation_dataset: Dict
save_dir: str
seed: Optional[int]
validation_batch_size: int
dataloader_num_workers: int
# save_single_views: bool
save_mode: str
local_rank: int
pipe_kwargs: Dict
pipe_validation_kwargs: Dict
unet_from_pretrained_kwargs: Dict
validation_guidance_scales: float
validation_grid_nrow: int
num_views: int
enable_xformers_memory_efficient_attention: bool
with_smpl: Optional[bool]
recon_opt: Dict
def convert_to_numpy(tensor):
return tensor.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to("cpu", torch.uint8).numpy()
def convert_to_pil(tensor):
return Image.fromarray(convert_to_numpy(tensor))
def save_image(tensor, fp):
ndarr = convert_to_numpy(tensor)
# pdb.set_trace()
save_image_numpy(ndarr, fp)
return ndarr
def save_image_numpy(ndarr, fp):
im = Image.fromarray(ndarr)
im.save(fp)
def run_inference(dataloader, econdata, pipeline, carving, cfg: TestConfig, save_dir):
pipeline.set_progress_bar_config(disable=True)
if cfg.seed is None:
generator = None
else:
generator = torch.Generator(device=pipeline.unet.device).manual_seed(cfg.seed)
images_cond, pred_cat = [], defaultdict(list)
for case_id, batch in tqdm(enumerate(dataloader)):
images_cond.append(batch['imgs_in'][:, 0])
imgs_in = torch.cat([batch['imgs_in']]*2, dim=0)
num_views = imgs_in.shape[1]
imgs_in = rearrange(imgs_in, "B Nv C H W -> (B Nv) C H W")# (B*Nv, 3, H, W)
if cfg.with_smpl:
smpl_in = torch.cat([batch['smpl_imgs_in']]*2, dim=0)
smpl_in = rearrange(smpl_in, "B Nv C H W -> (B Nv) C H W")
else:
smpl_in = None
normal_prompt_embeddings, clr_prompt_embeddings = batch['normal_prompt_embeddings'], batch['color_prompt_embeddings']
prompt_embeddings = torch.cat([normal_prompt_embeddings, clr_prompt_embeddings], dim=0)
prompt_embeddings = rearrange(prompt_embeddings, "B Nv N C -> (B Nv) N C")
with torch.autocast("cuda"):
# B*Nv images
guidance_scale = cfg.validation_guidance_scales
unet_out = pipeline(
imgs_in, None, prompt_embeds=prompt_embeddings,
dino_feature=None, smpl_in=smpl_in,
generator=generator, guidance_scale=guidance_scale, output_type='pt', num_images_per_prompt=1,
**cfg.pipe_validation_kwargs
)
out = unet_out.images
bsz = out.shape[0] // 2
normals_pred = out[:bsz]
images_pred = out[bsz:]
if cfg.save_mode == 'concat': ## save concatenated color and normal---------------------
pred_cat[f"cfg{guidance_scale:.1f}"].append(torch.cat([normals_pred, images_pred], dim=-1)) # b, 3, h, w
cur_dir = os.path.join(save_dir, f"cropsize-{cfg.validation_dataset.crop_size}-cfg{guidance_scale:.1f}-seed{cfg.seed}-smpl-{cfg.with_smpl}")
os.makedirs(cur_dir, exist_ok=True)
for i in range(bsz//num_views):
scene = batch['filename'][i].split('.')[0]
img_in_ = images_cond[-1][i].to(out.device)
vis_ = [img_in_]
for j in range(num_views):
idx = i*num_views + j
normal = normals_pred[idx]
color = images_pred[idx]
vis_.append(color)
vis_.append(normal)
out_filename = f"{cur_dir}/{scene}.png"
vis_ = torch.stack(vis_, dim=0)
vis_ = make_grid(vis_, nrow=len(vis_), padding=0, value_range=(0, 1))
save_image(vis_, out_filename)
elif cfg.save_mode == 'rgb':
for i in range(bsz//num_views):
scene = batch['filename'][i].split('.')[0]
img_in_ = images_cond[-1][i].to(out.device)
normals, colors = [], []
for j in range(num_views):
idx = i*num_views + j
normal = normals_pred[idx]
if j == 0:
color = imgs_in[0].to(out.device)
else:
color = images_pred[idx]
if j in [3, 4]:
normal = torch.flip(normal, dims=[2])
color = torch.flip(color, dims=[2])
colors.append(color)
if j == 6:
normal = F.interpolate(normal.unsqueeze(0), size=(256, 256), mode='bilinear', align_corners=False).squeeze(0)
normals.append(normal)
## save color and normal---------------------
# normal_filename = f"normals_{view}_masked.png"
# rgb_filename = f"color_{view}_masked.png"
# save_image(normal, os.path.join(scene_dir, normal_filename))
# save_image(color, os.path.join(scene_dir, rgb_filename))
normals[0][:, :256, 256:512] = normals[-1]
colors = [remove(convert_to_pil(tensor), session=session) for tensor in colors[:6]]
normals = [remove(convert_to_pil(tensor), session=session) for tensor in normals[:6]]
pose = econdata.__getitem__(case_id)
carving.optimize_case(scene, pose, colors, normals)
torch.cuda.empty_cache()
def load_pshuman_pipeline(cfg):
pipeline = StableUnCLIPImg2ImgPipeline.from_pretrained(cfg.pretrained_model_name_or_path, torch_dtype=weight_dtype)
pipeline.unet.enable_xformers_memory_efficient_attention()
if torch.cuda.is_available():
pipeline.to('cuda')
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_pshuman_pipeline(cfg)
if cfg.with_smpl:
from mvdiffusion.data.testdata_with_smpl import SingleImageDataset
else:
from mvdiffusion.data.single_image_dataset import SingleImageDataset
# Get the dataset
validation_dataset = SingleImageDataset(
**cfg.validation_dataset
)
validation_dataloader = torch.utils.data.DataLoader(
validation_dataset, batch_size=cfg.validation_batch_size, shuffle=False, num_workers=cfg.dataloader_num_workers
)
dataset_param = {'image_dir': validation_dataset.root_dir, 'seg_dir': None, 'colab': False, 'has_det': True, 'hps_type': 'pixie'}
econdata = SMPLDataset(dataset_param, device='cuda')
carving = ReMesh(cfg.recon_opt, econ_dataset=econdata)
run_inference(validation_dataloader, econdata, pipeline, carving, cfg, 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)
schema = OmegaConf.structured(TestConfig)
cfg = OmegaConf.merge(schema, cfg)
main(cfg)