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run_sample_camera_image.py
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import torch
import torch.nn.functional as F
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
torch.set_float32_matmul_precision('high')
setattr(torch.nn.Linear, 'reset_parameters', lambda self: None)
setattr(torch.nn.LayerNorm, 'reset_parameters', lambda self: None)
from torchvision.utils import save_image
from torchvision import transforms
import numpy as np
import os
import cv2
import time
import argparse
from tokenizer.tokenizer_image.vq_model import VQ_models
from tokenizer.tokenizer_camera.vq_model import CAM_models
from autoregressive.models.gpt import GPT_models
from autoregressive.models.generate import generate
from einops import rearrange, repeat
from PIL import Image
os.environ["TOKENIZERS_PARALLELISM"] = "false"
from utils.ray_utils import plucker_to_3d, create_ref_ray, plucker_to_camera, create_ref_camera
from utils.o3d_utils import save_images_and_cameras_from_cameras
from utils.image_utils import read_image_local
SPECIAL_TOKEN_IDX = {
'<GEN>': 18432,
'<EST>': 18433,
}
def empty_folder(folder_path):
if not os.path.exists(folder_path):
try:
os.mkdir(folder_path)
except:
pass
else:
for filename in os.listdir(folder_path):
file_path = os.path.join(folder_path, filename)
if os.path.isfile(file_path):
os.remove(file_path)
def main(args):
# Setup PyTorch:
torch.manual_seed(args.seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
torch.set_grad_enabled(False)
device = "cuda" if torch.cuda.is_available() else "cpu"
# create and load model
vq_model = VQ_models[args.vq_model](
codebook_size=args.codebook_size,
codebook_embed_dim=args.codebook_embed_dim
)
vq_model.to(device)
vq_model.eval()
checkpoint = torch.load(args.image_ckpt, map_location="cpu")
vq_model.load_state_dict(checkpoint["model"])
del checkpoint
print(f"image tokenizer is loaded")
# create and load camera model
camera_model = CAM_models[args.cam_model](
codebook_size=args.camera_codebook_size,
codebook_embed_dim=args.camera_embed_dim,
)
camera_model.to(device)
camera_model.eval()
checkpoint = torch.load(args.camera_ckpt, map_location="cpu")
camera_model.load_state_dict(checkpoint["model"])
del checkpoint
print(f"camera tokenizer is loaded")
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True)
])
# create and load gpt model
precision = {'none': torch.float32, 'bf16': torch.bfloat16, 'fp16': torch.float16}[args.precision]
latent_size = args.image_size // args.downsample_size
gpt_model = GPT_models[args.gpt_model](
vocab_size=args.vocab_size,
block_size=latent_size ** 2,
cls_token_num=args.cls_token_num,
model_type=args.gpt_type,
qk_norm=args.qk_norm,
special_token_num=args.special_token_num,
).to(device=device, dtype=precision)
checkpoint = torch.load(args.gpt_ckpt, map_location="cpu")
model_weight = checkpoint["model"]
gpt_model.load_state_dict(model_weight, strict=True)
gpt_model.eval()
del checkpoint
print(f"gpt model is loaded")
# load image
cond_img = read_image_local(args.image_path, args.image_size, white_bg=True, transform=transform)
cond_img = repeat(cond_img, "c h w -> b c h w", b=args.num_sample)
cond_img = cond_img.to(device, non_blocking=True)
_, _, [_, _, cond_indices] = vq_model.encode(cond_img)
cond_x = cond_indices.reshape(cond_img.shape[0], -1)
c_indices = cond_x # [B, 256]
if args.special_token_num > 0:
special_token = torch.ones((cond_x.shape[0], 1)).to(cond_x.dtype).to(cond_x.device) * SPECIAL_TOKEN_IDX['<GEN>']
c_indices = torch.cat([c_indices, special_token], dim=-1)
qzshape = [int(cond_x.shape[0]), args.codebook_embed_dim, latent_size, latent_size]
ref_qzshape = [ 1, args.codebook_embed_dim, latent_size, latent_size]
camera_qzshape = [int(cond_x.shape[0]), args.camera_embed_dim, latent_size, latent_size]
# begin sample
t1 = time.time()
index_sample = generate(
gpt_model, c_indices, (latent_size ** 2) * 2,
cfg_scale=args.cfg_scale,
temperature=args.temperature, top_k=args.top_k,
top_p=args.top_p, sample_logits=True,
)
sampling_time = time.time() - t1
print(f"Full sampling takes about {sampling_time:.2f} seconds.")
# decoder
cameras = camera_model.decode_code(
torch.clamp(index_sample[:, :(latent_size ** 2)]- args.codebook_size, 0, args.camera_codebook_size-1),
camera_qzshape
)
samples = vq_model.decode_code(
torch.clamp(index_sample[:, -(latent_size ** 2):], 0, args.codebook_size-1),
qzshape
)
empty_folder(args.sample_path)
sample_paths = []
for i in range(args.num_sample):
sample_path = os.path.join(args.sample_path, f"sample_{i}.png")
save_image(
samples[i:i+1], sample_path, nrow=4, normalize=True, value_range=(-1, 1)
)
sample_paths.append(sample_path)
# save GT
samples = vq_model.decode_code(cond_x[:1], ref_qzshape) # output value is between [-1, 1]
ref_sample_path = os.path.join(args.sample_path, f"sample_gt.png")
save_image(
samples, ref_sample_path, nrow=4, normalize=True, value_range=(-1, 1)
)
sample_paths.append(ref_sample_path)
# save ply
B, _, H, W = cameras.shape
c2ws = plucker_to_camera(cameras.permute(0, 2, 3, 1).reshape(B, -1, 6)) # [B, 3] [B, 4096, 3]
c2w_ref = create_ref_camera()
c2ws = np.concatenate([c2ws, c2w_ref], axis=0)
save_images_and_cameras_from_cameras(
c2ws,
cameras.permute(0, 2, 3, 1).reshape(B, -1, 6).shape,
sample_paths,
args.sample_path,
camera_scale=args.ply_scale,
)
print('\nDONE !')
if __name__ == "__main__":
from args import get_args
args = get_args()
with torch.no_grad():
main(args)