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import sys
import os
# spaces/ZeroGPU sets PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True by default, which
# hits an NVML assertion (CUDACachingAllocator.cpp:1131) on MIG-sliced GPUs. Disable it
# before torch initializes its CUDA allocator.
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:False"
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
if torch.cuda.is_available():
_props = torch.cuda.get_device_properties(0)
print(f"GPU_DIAG: {_props.name} total_memory={_props.total_memory/1e9:.1f}GB", flush=True)
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():
# A blanket .to('cuda') OOMs on this ZeroGPU MIG slice's effective VRAM.
# MMGP dynamically offloads unused submodules to CPU RAM and quantizes the
# main model, instead of keeping the whole pipeline resident in VRAM at once.
from mmgp import offload, profile_type
# image_normalizer uses a custom .scale() method rather than .forward(), so
# mmgp's just-in-time weight-loading hook never fires for it, leaving its
# mean/std buffers stranded on CPU. It's two tiny tensors, so just pin it on
# GPU directly instead of routing it through mmgp's offload management.
mmgp_components = {k: v for k, v in pipeline.components.items() if k != "image_normalizer"}
offload.profile(mmgp_components, profile_type.VerylowRAM_LowVRAM, verboseLevel=1)
if getattr(pipeline, "image_normalizer", None) is not None:
pipeline.image_normalizer.to("cuda")
# offload.profile() leaves torch'''s global default device set to '''cuda'''.
# mmgp'''s own hooks move their tracked models''' weights explicitly regardless of
# this setting, but plenty of *other* code later in this script (econdataset,
# pymaf, smplx) constructs plain tensors with no explicit device, assuming the
# normal CPU default. Reset it so that code behaves as originally written.
torch.set_default_device('''cpu''')
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)
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