test_mean_scale_hyperprior_joint_training.py

# Copyright (c) 2021-2022, InterDigital Communications, Inc
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from collections import defaultdict
import argparse
import math
import random
import sys
import torch
from torch.utils.data import DataLoader
from torchvision import transforms
from compressai.zoo import image_models
from dataset import Kodak24Dataset
from load_model import load_model
from edsr import EDSR
import torch.nn.functional as F

def parse_args(argv):
    parser = argparse.ArgumentParser(description="Example training script.")
    '''
    "bmshj2018-factorized": bmshj2018_factorized,
    "bmshj2018-hyperprior": bmshj2018_hyperprior,
    "mbt2018-mean": mbt2018_mean,
    "mbt2018": mbt2018,
    "cheng2020-anchor": cheng2020_anchor,
    "cheng2020-attn": cheng2020_attn,
    '''
    parser.add_argument(
        "-m",
        "--model",
        default="mbt2018-mean",
        choices=image_models.keys(),
        help="Model architecture (default: %(default)s)",
    )
    parser.add_argument(
        "-q", "--quality", type=int, default=0, help="quality of the model"
    )

    parser.add_argument(
        "-d", "--dataset", type=str, default='../data', help="Training dataset"
    )
    parser.add_argument(
        "-save_dir", "--save_dir", type=str, default='save/', help="save_dir"
    )
    parser.add_argument(
        "-log_dir", "--log_dir", type=str, default='log/', help="log_dir"
    )
    parser.add_argument(
        "-total_step",
        "--total_step",
        default=5000000,    # infinite (we can use early stop)
        type=int,
        help="total_step (default: %(default)s)",
    )
    parser.add_argument(
        "-test_step",
        "--test_step",
        default=5000,
        type=int,
        help="test_step (default: %(default)s)",
    )
    parser.add_argument(
        "-save_step",
        "--save_step",
        default=100000,
        type=int,
        help="save_step (default: %(default)s)",
    )
    parser.add_argument(
        "-lr",
        "--learning-rate",
        default=1e-4,
        type=float,
        help="Learning rate (default: %(default)s)",
    )
    parser.add_argument(
        "-n",
        "--num-workers",
        type=int,
        default=4,
        help="Dataloaders threads (default: %(default)s)",
    )
    # parser.add_argument(
    #     "--lambda",
    #     dest="lmbda",
    #     type=float,
    #     default=1e-2,
    #     # quality   :     1        2        3        4        5        6       7       8
    #     # lambda    :  0.0018   0.0035   0.0067   0.0130   0.0250   0.0483  0.0932   0.1800
    #     help="Bit-rate distortion parameter (default: %(default)s)",
    # )
    parser.add_argument(
        "--batch-size", type=int, default=16, help="Batch size (default: %(default)s)"
    )
    parser.add_argument(
        "--test-batch-size",
        type=int,
        default=1,
        help="Test batch size (default: %(default)s)",
    )
    parser.add_argument(
        "--aux-learning-rate",
        default=1e-3,
        help="Auxiliary loss learning rate (default: %(default)s)",
    )
    parser.add_argument(
        "--patch-size",
        type=int,
        nargs=2,
        default=(256, 256),
        help="Size of the patches to be cropped (default: %(default)s)",
    )
    parser.add_argument("--cuda", action="store_true", default=True, help="Use cuda")
    parser.add_argument(
        "--save", action="store_true", default=True, help="Save model to disk"
    )
    parser.add_argument(
        "--seed", type=float, default=123, help="Set random seed for reproducibility"
    )
    parser.add_argument(
        "--clip_max_norm",
        default=1.0,
        type=float,
        help="gradient clipping max norm (default: %(default)s",
    )
    parser.add_argument("--checkpoint", type=str, help="Path to a checkpoint")
    args = parser.parse_args(argv)
    return args


def build_dataset(args):
    # Warning, the order of the transform composition should be kept.
    kodak_transform = transforms.Compose(
        [transforms.ToTensor()]
    )

    kodak_dataset = Kodak24Dataset(
        args.dataset,
        transform=kodak_transform,
    )

    kodak_dataloader = DataLoader(
        kodak_dataset,
        batch_size=args.test_batch_size,
        num_workers=args.num_workers,
        shuffle=False,
        pin_memory=True,
    )

    return kodak_dataloader


class AverageMeter:
    """Compute running average."""

    def __init__(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count

#test 하는 코드
def comrpess_and_decompress(model, edsr, test_dataloader, device):
    psnr = AverageMeter()
    bpp = AverageMeter()
    with torch.no_grad():
        for i_, x in enumerate(test_dataloader):
            x = x.to(device)

            # Downsampling
            x_lr = F.interpolate(x, scale_factor=0.5, mode='bicubic').clamp_(0, 1)

            # compress
            compressed = model.compress(x_lr)  # {"strings": [y_strings, z_strings], "shape": z.size()[-2:]}
            strings = compressed['strings']
            shape = compressed['shape']

            # decompress
            decompressed = model.decompress(strings, shape)

            # Upsampling
            x_hat = edsr(decompressed['x_hat']).clamp_(0, 1)

            bpp_y = (len(strings[0][0])) * 8 / (x.shape[2] * x.shape[3]) #string 이 8비트라 8곱함
            bpp_z = (len(strings[1][0])) * 8 / (x.shape[2] * x.shape[3])
            bpp_ = bpp_y + bpp_z
            mse_ = (x_hat - x).pow(2).mean()
    
            psnr_ = 10 * (torch.log(1 * 1 / mse_) / math.log(10))
            bpp.update(bpp_)
            psnr.update(psnr_)
            print(
                f"\tPSNR: {psnr_:.3f} |"
                f"\tBPP: {bpp_:.3f} |"
            )

    print(
        f"\tTest PSNR: {psnr.avg:.3f} |"
        f"\tTest BPP: {bpp.avg:.3f} |"
    )

def main(argv):
    args = parse_args(argv)

    if args.seed is not None:
        torch.manual_seed(args.seed)
        random.seed(args.seed)

    # device = "cpu"
    device = "cuda"
    model = load_model(args.model, metric="mse", quality=args.quality, pretrained=False).to(device).eval()
    edsr = EDSR().to(device).eval()
    # edsr.load_state_dict(torch.load('./save/EDSR/edsr_baseline_x2-1bc95232.pt'), strict=False)

    if args.checkpoint:  # load from previous checkpoint
        print("Loading", args.checkpoint)
        checkpoint = torch.load(args.checkpoint, map_location=device)
        model.load_state_dict(checkpoint["state_dict"])
        edsr.load_state_dict(checkpoint["state_dict_edsr"])

    test_dataloader = build_dataset(args)
    comrpess_and_decompress(model, edsr, test_dataloader, device)


if __name__ == "__main__":
    main(sys.argv[1:])