Spaces:
Runtime error
Runtime error
File size: 8,134 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 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 |
import torch
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
import torch.nn.functional as F
import torch.distributed as dist
from torch.utils.data import DataLoader
from torch.utils.data.distributed import DistributedSampler
from torchvision import transforms
from tqdm import tqdm
import os
from PIL import Image
import numpy as np
import argparse
import itertools
from skimage.metrics import peak_signal_noise_ratio as psnr_loss
from skimage.metrics import structural_similarity as ssim_loss
from dataset.augmentation import center_crop_arr
from dataset.build import build_dataset
from tokenizer.tokenizer_image.vq_model import VQ_models
def create_npz_from_sample_folder(sample_dir, num=50000):
"""
Builds a single .npz file from a folder of .png samples.
"""
samples = []
for i in tqdm(range(num), desc="Building .npz file from samples"):
sample_pil = Image.open(f"{sample_dir}/{i:06d}.png")
sample_np = np.asarray(sample_pil).astype(np.uint8)
samples.append(sample_np)
samples = np.stack(samples)
assert samples.shape == (num, samples.shape[1], samples.shape[2], 3)
npz_path = f"{sample_dir}.npz"
np.savez(npz_path, arr_0=samples)
print(f"Saved .npz file to {npz_path} [shape={samples.shape}].")
return npz_path
def main(args):
# Setup PyTorch:
assert torch.cuda.is_available(), "Sampling with DDP requires at least one GPU. sample.py supports CPU-only usage"
torch.set_grad_enabled(False)
# Setup DDP:
dist.init_process_group("nccl")
rank = dist.get_rank()
device = rank % torch.cuda.device_count()
seed = args.global_seed * dist.get_world_size() + rank
torch.manual_seed(seed)
torch.cuda.set_device(device)
print(f"Starting rank={rank}, seed={seed}, world_size={dist.get_world_size()}.")
# 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")
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")
vq_model.load_state_dict(model_weight)
del checkpoint
# Create folder to save samples:
folder_name = (f"{args.vq_model}-{args.dataset}-size-{args.image_size}-size-{args.image_size_eval}"
f"-codebook-size-{args.codebook_size}-dim-{args.codebook_embed_dim}-seed-{args.global_seed}")
sample_folder_dir = f"{args.sample_dir}/{folder_name}"
if rank == 0:
os.makedirs(sample_folder_dir, exist_ok=True)
print(f"Saving .png samples at {sample_folder_dir}")
dist.barrier()
# Setup data:
transform = transforms.Compose([
transforms.Lambda(lambda pil_image: center_crop_arr(pil_image, args.image_size)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True)
])
if args.dataset == 'imagenet':
dataset = build_dataset(args, transform=transform)
num_fid_samples = 50000
elif args.dataset == 'coco':
dataset = build_dataset(args, transform=transform)
num_fid_samples = 5000
else:
raise Exception("please check dataset")
sampler = DistributedSampler(
dataset,
num_replicas=dist.get_world_size(),
rank=rank,
shuffle=False,
seed=args.global_seed
)
loader = DataLoader(
dataset,
batch_size=args.per_proc_batch_size,
shuffle=False,
sampler=sampler,
num_workers=args.num_workers,
pin_memory=True,
drop_last=False
)
# Figure out how many samples we need to generate on each GPU and how many iterations we need to run:
n = args.per_proc_batch_size
global_batch_size = n * dist.get_world_size()
psnr_val_rgb = []
ssim_val_rgb = []
loader = tqdm(loader) if rank == 0 else loader
total = 0
for x, _ in loader:
if args.image_size_eval != args.image_size:
rgb_gts = F.interpolate(x, size=(args.image_size_eval, args.image_size_eval), mode='bicubic')
else:
rgb_gts = x
rgb_gts = (rgb_gts.permute(0, 2, 3, 1).to("cpu").numpy() + 1.0) / 2.0 # rgb_gt value is between [0, 1]
x = x.to(device, non_blocking=True)
with torch.no_grad():
latent, _, [_, _, indices] = vq_model.encode(x)
samples = vq_model.decode_code(indices, latent.shape) # output value is between [-1, 1]
if args.image_size_eval != args.image_size:
samples = F.interpolate(samples, size=(args.image_size_eval, args.image_size_eval), mode='bicubic')
samples = torch.clamp(127.5 * samples + 128.0, 0, 255).permute(0, 2, 3, 1).to("cpu", dtype=torch.uint8).numpy()
# Save samples to disk as individual .png files
for i, (sample, rgb_gt) in enumerate(zip(samples, rgb_gts)):
index = i * dist.get_world_size() + rank + total
Image.fromarray(sample).save(f"{sample_folder_dir}/{index:06d}.png")
# metric
rgb_restored = sample.astype(np.float32) / 255. # rgb_restored value is between [0, 1]
psnr = psnr_loss(rgb_restored, rgb_gt)
ssim = ssim_loss(rgb_restored, rgb_gt, multichannel=True, data_range=2.0, channel_axis=-1)
psnr_val_rgb.append(psnr)
ssim_val_rgb.append(ssim)
total += global_batch_size
# ------------------------------------
# Summary
# ------------------------------------
# Make sure all processes have finished saving their samples
dist.barrier()
world_size = dist.get_world_size()
gather_psnr_val = [None for _ in range(world_size)]
gather_ssim_val = [None for _ in range(world_size)]
dist.all_gather_object(gather_psnr_val, psnr_val_rgb)
dist.all_gather_object(gather_ssim_val, ssim_val_rgb)
if rank == 0:
gather_psnr_val = list(itertools.chain(*gather_psnr_val))
gather_ssim_val = list(itertools.chain(*gather_ssim_val))
psnr_val_rgb = sum(gather_psnr_val) / len(gather_psnr_val)
ssim_val_rgb = sum(gather_ssim_val) / len(gather_ssim_val)
print("PSNR: %f, SSIM: %f " % (psnr_val_rgb, ssim_val_rgb))
result_file = f"{sample_folder_dir}_results.txt"
print("writing results to {}".format(result_file))
with open(result_file, 'w') as f:
print("PSNR: %f, SSIM: %f " % (psnr_val_rgb, ssim_val_rgb), file=f)
create_npz_from_sample_folder(sample_folder_dir, num_fid_samples)
print("Done.")
dist.barrier()
dist.destroy_process_group()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--data-path", type=str, required=True)
parser.add_argument("--dataset", type=str, choices=['imagenet', 'coco'], default='imagenet')
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, 512], default=256)
parser.add_argument("--image-size-eval", type=int, choices=[256, 384, 512], default=256)
parser.add_argument("--sample-dir", type=str, default="reconstructions")
parser.add_argument("--per-proc-batch-size", type=int, default=32)
parser.add_argument("--global-seed", type=int, default=0)
parser.add_argument("--num-workers", type=int, default=4)
args = parser.parse_args()
main(args) |