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