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Running
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Zero
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""
Sample new images from a pre-trained DiT.
"""
import os
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import argparse
import yaml
import json
import numpy as np
from pathlib import Path
import gin
import importlib
import logging
import cv2
import matplotlib.pyplot as plt
logging.basicConfig(
format="[%(asctime)s.%(msecs)03d] [%(module)s] [%(levelname)s] | %(message)s",
datefmt="%H:%M:%S",
level=logging.INFO,
)
logger = logging.getLogger(__name__)
import torch
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
from torch.utils.data import DataLoader
from core.diffusion import create_diffusion
from core.models import DiT_models
from core.dataset import ImageParamsDataset
from core.utils.train_utils import load_model
from core.utils.math_utils import unnormalize_params
from scripts.prepare_data import generate
def main(cfg, generator):
# Setup PyTorch:
torch.manual_seed(cfg["seed"])
torch.set_grad_enabled(False)
device = "cuda" if torch.cuda.is_available() else "cpu"
# Load model:
latent_size = cfg["num_params"]
model = DiT_models[cfg["model"]](input_size=latent_size).to(device)
# load a custom DiT checkpoint from train.py:
state_dict = load_model(cfg["ckpt_path"])
model.load_state_dict(state_dict)
model.eval() # important!
diffusion = create_diffusion(str(cfg["num_sampling_steps"]))
# Load dataset
dataset = ImageParamsDataset(cfg["data_root"], cfg["test_file"], cfg["params_dict_file"])
loader = DataLoader(
dataset,
batch_size=cfg["batch_size"],
shuffle=False,
num_workers=cfg["num_workers"],
pin_memory=True,
drop_last=False
)
params_dict = json.load(open(cfg["params_dict_file"]))
idx = 0
total_error = np.zeros(cfg["num_params"])
for x, img_feat, img in loader:
# sample from random noise, conditioned on image features
img_feat = img_feat.to(device)
model_kwargs = dict(y=img_feat)
z = torch.randn(cfg["batch_size"], 1, latent_size, device=device)
# Sample target params:
samples = diffusion.p_sample_loop(
model.forward, z.shape, z, clip_denoised=False, model_kwargs=model_kwargs, progress=True, device=device
)
samples = samples.reshape(cfg["batch_size"], 1, -1)
samples = samples.squeeze(1).cpu().numpy()
x = x.squeeze(1).cpu().numpy()
img = img.cpu().numpy()
if cfg["run_generate"]:
# save GT & sampled params & images
for x_, params, img_ in zip(x, samples, img):
# generate 3D using sampled params
params_original = unnormalize_params(params, params_dict)
save_dir = os.path.join(cfg["save_dir"], "{:05d}".format(idx))
os.makedirs(save_dir, exist_ok=True)
save_name = "sampled"
asset, _ = generate(generator, params_original, seed=cfg["seed"], save_dir=save_dir, save_name=save_name,
save_blend=True, save_img=True, save_gif=False, save_mesh=True,
cam_dists=cfg["r_cam_dists"], cam_elevations=cfg["r_cam_elevations"], cam_azimuths=cfg["r_cam_azimuths"], zoff=cfg["r_zoff"],
resolution='256x256', sample=100)
np.save(os.path.join(save_dir, "params.npy"), params_original)
print("Generating model using sampled parameters. Saved in {}".format(save_dir))
# also save GT image & GT params
x_original = unnormalize_params(x_, params_dict)
np.save(os.path.join(save_dir, "gt_params.npy"), x_original)
cv2.imwrite(os.path.join(save_dir, "gt.png"), img_[:,:,::-1])
idx += 1
# calculate metrics for sampled params & GT params
error = np.abs(x - samples)
total_error += error
# print the average error for each parameter
avg_error = total_error / len(dataset)
param_names = params_dict.keys()
for param_name, error in zip(param_names, avg_error):
print(f"{param_name}: {error:.4f}")
# plot the error for each parameter
fig, ax = plt.subplots()
fig.set_size_inches(20, 15)
ax.barh(param_names, avg_error)
ax.set_xlabel("Average Error")
ax.set_ylabel("Parameters")
ax.set_title("Average Error for Each Parameter")
plt.yticks(fontsize=10)
fig.tight_layout()
fig.savefig(os.path.join(cfg["save_dir"], "avg_error.png"))
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--config", type=str, required=True)
args = parser.parse_args()
with open(args.config) as f:
cfg = yaml.load(f, Loader=yaml.FullLoader)
# load the Blender procedural generator
OBJECTS_PATH = Path(cfg["generator_root"])
assert OBJECTS_PATH.exists(), OBJECTS_PATH
generator = None
for subdir in sorted(list(OBJECTS_PATH.iterdir())):
clsname = subdir.name.split(".")[0].strip()
with gin.unlock_config():
module = importlib.import_module(f"core.assets.{clsname}")
if hasattr(module, cfg["generator"]):
generator = getattr(module, cfg["generator"])
logger.info("Found {} in {}".format(cfg["generator"], subdir))
break
logger.debug("{} not found in {}".format(cfg["generator"], subdir))
if generator is None:
raise ModuleNotFoundError("{} not Found.".format(cfg["generator"]))
gen = generator(cfg["seed"])
# create visualize dir
os.makedirs(cfg["save_dir"], exist_ok=True)
main(cfg, gen)
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