Delete testing_utils.py

testing_utils.py

@@ -1,210 +0,0 @@
-import argparse
-import json
-from PIL import Image
-from torchvision import transforms
-import torch.nn.functional as F
-from glob import glob
-
-import cv2
-import math
-import numpy as np
-import os
-import os.path as osp
-import random
-import time
-import torch
-from pathlib import Path
-from torch.utils import data as data
-
-from basicsr.utils import DiffJPEG, USMSharp
-from basicsr.utils.img_process_util import filter2D
-from basicsr.data.transforms import paired_random_crop, triplet_random_crop
-from basicsr.data.degradations import random_add_gaussian_noise_pt, random_add_poisson_noise_pt, random_add_speckle_noise_pt, random_add_saltpepper_noise_pt, bivariate_Gaussian
-
-from basicsr.data.degradations import circular_lowpass_kernel, random_mixed_kernels
-from basicsr.data.transforms import augment
-from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor
-from basicsr.utils.registry import DATASET_REGISTRY
-
-
-def parse_args_paired_testing(input_args=None):
-    """
-    Parses command-line arguments used for configuring an paired session (pix2pix-Turbo).
-    This function sets up an argument parser to handle various training options.
-
-    Returns:
-    argparse.Namespace: The parsed command-line arguments.
-   """
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--ref_path", type=str, default=None,)
-    parser.add_argument("--base_config", default="./configs/sr_test.yaml", type=str)
-    parser.add_argument("--tracker_project_name", type=str, default="train_pix2pix_turbo", help="The name of the wandb project to log to.")
-
-    # details about the model architecture
-    parser.add_argument("--sd_path")
-    parser.add_argument("--de_net_path")
-    parser.add_argument("--pretrained_path", type=str, default=None,)
-    parser.add_argument("--revision", type=str, default=None,)
-    parser.add_argument("--variant", type=str, default=None,)
-    parser.add_argument("--tokenizer_name", type=str, default=None)
-    parser.add_argument("--lora_rank_unet", default=32, type=int)
-    parser.add_argument("--lora_rank_vae", default=16, type=int)
-
-    parser.add_argument("--scale", type=int, default=4, help="Scale factor for SR.")
-    parser.add_argument("--chop_size", type=int, default=128, choices=[512, 256, 128], help="Chopping forward.")
-    parser.add_argument("--chop_stride", type=int, default=96, help="Chopping stride.")
-    parser.add_argument("--padding_offset", type=int, default=32, help="padding offset.")
-
-    parser.add_argument("--vae_decoder_tiled_size", type=int, default=224)
-    parser.add_argument("--vae_encoder_tiled_size", type=int, default=1024) 
-    parser.add_argument("--latent_tiled_size", type=int, default=96) 
-    parser.add_argument("--latent_tiled_overlap", type=int, default=32)
-
-    parser.add_argument("--align_method", type=str, default="wavelet")
-    
-    parser.add_argument("--pos_prompt", type=str, default="A high-resolution, 8K, ultra-realistic image with sharp focus, vibrant colors, and natural lighting.")
-    parser.add_argument("--neg_prompt", type=str, default="oil painting, cartoon, blur, dirty, messy, low quality, deformation, low resolution, oversmooth")
-
-    # training details
-    parser.add_argument("--output_dir", required=True)
-    parser.add_argument("--cache_dir", default=None,)
-    parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
-    parser.add_argument("--resolution", type=int, default=512,)
-    parser.add_argument("--checkpointing_steps", type=int, default=500,)
-    parser.add_argument("--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.",)
-    parser.add_argument("--gradient_checkpointing", action="store_true",)
-
-    parser.add_argument("--dataloader_num_workers", type=int, default=0,)
-    parser.add_argument("--allow_tf32", action="store_true",
-        help=(
-            "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
-            " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
-        ),
-    )
-    parser.add_argument("--report_to", type=str, default="wandb",
-        help=(
-            'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
-            ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
-        ),
-    )
-    parser.add_argument("--mixed_precision", type=str, default=None, choices=["no", "fp16", "bf16"],)
-    parser.add_argument("--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers.")
-    parser.add_argument("--set_grads_to_none", action="store_true",)
-
-    parser.add_argument('--world_size', default=1, type=int,
-                        help='number of distributed processes')
-    parser.add_argument('--local_rank', default=-1, type=int)
-    parser.add_argument('--dist_url', default='env://',
-                        help='url used to set up distributed training')
-
-    if input_args is not None:
-        args = parser.parse_args(input_args)
-    else:
-        args = parser.parse_args()
-
-    return args
-
-
-class PlainDataset(data.Dataset):
-    """Modified dataset based on the dataset used for Real-ESRGAN model:
-    Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data.
-
-    It loads gt (Ground-Truth) images, and augments them.
-    It also generates blur kernels and sinc kernels for generating low-quality images.
-    Note that the low-quality images are processed in tensors on GPUS for faster processing.
-
-    Args:
-        opt (dict): Config for train datasets. It contains the following keys:
-            dataroot_gt (str): Data root path for gt.
-            meta_info (str): Path for meta information file.
-            io_backend (dict): IO backend type and other kwarg.
-            use_hflip (bool): Use horizontal flips.
-            use_rot (bool): Use rotation (use vertical flip and transposing h and w for implementation).
-            Please see more options in the codes.
-    """
-
-    def __init__(self, opt):
-        super(PlainDataset, self).__init__()
-        self.opt = opt
-        self.file_client = None
-        self.io_backend_opt = opt['io_backend']
-
-        if 'image_type' not in opt:
-            opt['image_type'] = 'png'
-
-        # support multiple type of data: file path and meta data, remove support of lmdb
-        self.lr_paths = []
-        if 'lr_path' in opt:
-            if isinstance(opt['lr_path'], str):
-                self.lr_paths.extend(sorted(
-                    [str(x) for x in Path(opt['lr_path']).glob('*.png')] +
-                    [str(x) for x in Path(opt['lr_path']).glob('*.jpg')] +
-                    [str(x) for x in Path(opt['lr_path']).glob('*.jpeg')]
-                ))
-            else:
-                self.lr_paths.extend(sorted([str(x) for x in Path(opt['lr_path'][0]).glob('*.'+opt['image_type'])]))
-                if len(opt['lr_path']) > 1:
-                    for i in range(len(opt['lr_path'])-1):
-                        self.lr_paths.extend(sorted([str(x) for x in Path(opt['lr_path'][i+1]).glob('*.'+opt['image_type'])]))
-
-    def __getitem__(self, index):
-        if self.file_client is None:
-            self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt)
-
-        # -------------------------------- Load gt images -------------------------------- #
-        # Shape: (h, w, c); channel order: BGR; image range: [0, 1], float32.
-        lr_path = self.lr_paths[index]
-
-        # avoid errors caused by high latency in reading files
-        retry = 3
-        while retry > 0:
-            try:
-                lr_img_bytes = self.file_client.get(lr_path, 'gt')
-            except (IOError, OSError) as e:
-                # logger = get_root_logger()
-                # logger.warn(f'File client error: {e}, remaining retry times: {retry - 1}')
-                # change another file to read
-                index = random.randint(0, self.__len__()-1)
-                lr_path = self.lr_paths[index]
-                time.sleep(1)  # sleep 1s for occasional server congestion
-            else:
-                break
-            finally:
-                retry -= 1
-
-        img_lr = imfrombytes(lr_img_bytes, float32=True)
-        
-        # BGR to RGB, HWC to CHW, numpy to tensor
-        img_lr = img2tensor([img_lr], bgr2rgb=True, float32=True)[0]
-
-        return_d = {'lr': img_lr, 'lr_path': lr_path}
-        return return_d
-
-    def __len__(self):
-        return len(self.lr_paths)
-
-
-def lr_proc(config, batch, device):
-    im_lr = batch['lr'].cuda()
-    im_lr = im_lr.to(memory_format=torch.contiguous_format).float()    
-
-    ori_lr = im_lr
-
-    im_lr = F.interpolate(
-            im_lr,
-            size=(im_lr.size(-2) * config.sf,
-                  im_lr.size(-1) * config.sf),
-            mode='bicubic',
-            )
-
-    im_lr = im_lr.contiguous() 
-    im_lr = im_lr * 2 - 1.0
-    im_lr = torch.clamp(im_lr, -1.0, 1.0)
-
-    ori_h, ori_w = im_lr.size(-2), im_lr.size(-1)
-
-    pad_h = (math.ceil(ori_h / 64)) * 64 - ori_h
-    pad_w = (math.ceil(ori_w / 64)) * 64 - ori_w
-    im_lr = F.pad(im_lr, pad=(0, pad_w, 0, pad_h), mode='reflect')
-
-    return im_lr.to(device), ori_lr.to(device), (ori_h, ori_w)