import contextlib import os import numpy as np import torch from PIL import Image from basicsr.utils.download_util import load_file_from_url from tqdm import tqdm from modules import modelloader, devices from modules.shared import cmd_opts, opts from modules.swinir_model_arch import SwinIR as net from modules.swinir_model_arch_v2 import Swin2SR as net2 from modules.upscaler import Upscaler, UpscalerData class UpscalerSwinIR(Upscaler): def __init__(self, dirname): self.name = "SwinIR" self.model_url = "https://github.com/JingyunLiang/SwinIR/releases/download/v0.0" \ "/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR" \ "-L_x4_GAN.pth " self.model_name = "SwinIR 4x" self.user_path = dirname super().__init__() scalers = [] model_files = self.find_models(ext_filter=[".pt", ".pth"]) for model in model_files: if "http" in model: name = self.model_name else: name = modelloader.friendly_name(model) model_data = UpscalerData(name, model, self) scalers.append(model_data) self.scalers = scalers def do_upscale(self, img, model_file): model = self.load_model(model_file) if model is None: return img model = model.to(devices.device_swinir) img = upscale(img, model) try: torch.cuda.empty_cache() except: pass return img def load_model(self, path, scale=4): if "http" in path: dl_name = "%s%s" % (self.model_name.replace(" ", "_"), ".pth") filename = load_file_from_url(url=path, model_dir=self.model_path, file_name=dl_name, progress=True) else: filename = path if filename is None or not os.path.exists(filename): return None if filename.endswith(".v2.pth"): model = net2( upscale=scale, in_chans=3, img_size=64, window_size=8, img_range=1.0, depths=[6, 6, 6, 6, 6, 6], embed_dim=180, num_heads=[6, 6, 6, 6, 6, 6], mlp_ratio=2, upsampler="nearest+conv", resi_connection="1conv", ) params = None else: model = net( upscale=scale, in_chans=3, img_size=64, window_size=8, img_range=1.0, depths=[6, 6, 6, 6, 6, 6, 6, 6, 6], embed_dim=240, num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8], mlp_ratio=2, upsampler="nearest+conv", resi_connection="3conv", ) params = "params_ema" pretrained_model = torch.load(filename) if params is not None: model.load_state_dict(pretrained_model[params], strict=True) else: model.load_state_dict(pretrained_model, strict=True) if not cmd_opts.no_half: model = model.half() return model def upscale( img, model, tile=opts.SWIN_tile, tile_overlap=opts.SWIN_tile_overlap, window_size=8, scale=4, ): img = np.array(img) img = img[:, :, ::-1] img = np.moveaxis(img, 2, 0) / 255 img = torch.from_numpy(img).float() img = img.unsqueeze(0).to(devices.device_swinir) with torch.no_grad(), devices.autocast(): _, _, h_old, w_old = img.size() h_pad = (h_old // window_size + 1) * window_size - h_old w_pad = (w_old // window_size + 1) * window_size - w_old img = torch.cat([img, torch.flip(img, [2])], 2)[:, :, : h_old + h_pad, :] img = torch.cat([img, torch.flip(img, [3])], 3)[:, :, :, : w_old + w_pad] output = inference(img, model, tile, tile_overlap, window_size, scale) output = output[..., : h_old * scale, : w_old * scale] output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy() if output.ndim == 3: output = np.transpose( output[[2, 1, 0], :, :], (1, 2, 0) ) # CHW-RGB to HCW-BGR output = (output * 255.0).round().astype(np.uint8) # float32 to uint8 return Image.fromarray(output, "RGB") def inference(img, model, tile, tile_overlap, window_size, scale): # test the image tile by tile b, c, h, w = img.size() tile = min(tile, h, w) assert tile % window_size == 0, "tile size should be a multiple of window_size" sf = scale stride = tile - tile_overlap h_idx_list = list(range(0, h - tile, stride)) + [h - tile] w_idx_list = list(range(0, w - tile, stride)) + [w - tile] E = torch.zeros(b, c, h * sf, w * sf, dtype=torch.half, device=devices.device_swinir).type_as(img) W = torch.zeros_like(E, dtype=torch.half, device=devices.device_swinir) with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="SwinIR tiles") as pbar: for h_idx in h_idx_list: for w_idx in w_idx_list: in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile] out_patch = model(in_patch) out_patch_mask = torch.ones_like(out_patch) E[ ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf ].add_(out_patch) W[ ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf ].add_(out_patch_mask) pbar.update(1) output = E.div_(W) return output