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import argparse
import torch
import numpy as np
from tokenizer.tokenizer_image.vq_model import VQ_models
from torchvision.utils import save_image
def main(args):
# Setup PyTorch:
torch.manual_seed(args.seed)
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.vq_ckpt, map_location="cpu")
vq_model.load_state_dict(checkpoint["model"])
del checkpoint
# load image code
latent_dim = args.codebook_embed_dim
latent_size = args.image_size // args.downsample_size
codes = torch.from_numpy(np.load(args.code_path)).to(device)
if codes.ndim == 3: # flip augmentation
qzshape = (codes.shape[1], latent_dim, latent_size, latent_size)
else:
qzshape = (1, latent_dim, latent_size, latent_size)
index_sample = codes.reshape(-1)
samples = vq_model.decode_code(index_sample, qzshape) # output value is between [-1, 1]
# save
out_path = "sample_image_code.png"
nrow = max(4, int(codes.shape[1]//2))
save_image(samples, out_path, nrow=nrow, normalize=True, value_range=(-1, 1))
print("Reconstructed image is saved to {}".format(out_path))
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--code-path", type=str, required=True)
parser.add_argument("--vq-model", type=str, choices=list(VQ_models.keys()), default="VQ-16")
parser.add_argument("--vq-ckpt", type=str, default=None, help="ckpt path for vq model")
parser.add_argument("--codebook-size", type=int, default=16384, help="codebook size for vector quantization")
parser.add_argument("--codebook-embed-dim", type=int, default=8, help="codebook dimension for vector quantization")
parser.add_argument("--image-size", type=int, choices=[256, 384, 448, 512], default=256)
parser.add_argument("--downsample-size", type=int, choices=[8, 16], default=16)
parser.add_argument("--seed", type=int, default=0)
args = parser.parse_args()
main(args) |