Spaces:
Runtime error
Runtime error
File size: 3,312 Bytes
4d20c2f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 |
import torch
import torch.nn.functional as F
import os
import argparse
import numpy as np
from PIL import Image
from tokenizer.tokenizer_image.vq_model import VQ_models
from dataset.augmentation import center_crop_arr
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
model = VQ_models[args.vq_model](
codebook_size=args.codebook_size,
codebook_embed_dim=args.codebook_embed_dim)
model.to(device)
model.eval()
checkpoint = torch.load(args.vq_ckpt, map_location="cpu")
if "ema" in checkpoint: # ema
model_weight = checkpoint["ema"]
elif "model" in checkpoint: # ddp
model_weight = checkpoint["model"]
elif "state_dict" in checkpoint:
model_weight = checkpoint["state_dict"]
else:
raise Exception("please check model weight")
model.load_state_dict(model_weight)
del checkpoint
# output dir
os.makedirs(args.output_dir, exist_ok=True)
out_path = args.image_path.replace('.jpg', '_{}.jpg'.format(args.suffix))
out_path = out_path.replace('.jpeg', '_{}.jpeg'.format(args.suffix))
out_path = out_path.replace('.png', '_{}.png'.format(args.suffix))
out_filename = out_path.split('/')[-1]
out_path = os.path.join(args.output_dir, out_filename)
# load image
pil_image = Image.open(args.image_path).convert("RGB")
img = center_crop_arr(pil_image, args.image_size)
# # preprocess
# size_org = img.size
# img = img.resize((input_size, input_size))
img = np.array(img) / 255.
x = 2.0 * img - 1.0 # x value is between [-1, 1]
x = torch.tensor(x)
x = x.unsqueeze(dim=0)
x = torch.einsum('nhwc->nchw', x)
x_input = x.float().to("cuda")
# inference
with torch.no_grad():
latent, _, [_, _, indices] = model.encode(x_input)
output = model.decode_code(indices, latent.shape) # output value is between [-1, 1]
# postprocess
output = F.interpolate(output, size=[args.image_size, args.image_size], mode='bicubic').permute(0, 2, 3, 1)[0]
sample = torch.clamp(127.5 * output + 128.0, 0, 255).to("cpu", dtype=torch.uint8).numpy()
# save
Image.fromarray(sample).save(out_path)
print("Reconstructed image is saved to {}".format(out_path))
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--image-path", type=str, default="assets/example.jpg")
parser.add_argument("--output-dir", type=str, default="output_vq_demo")
parser.add_argument("--suffix", type=str, default="tokenizer_image")
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, 1024], default=512)
parser.add_argument("--seed", type=int, default=0)
args = parser.parse_args()
main(args) |