From bfb7f15d46048f27338eeac3a591a5943d03c5f1 Mon Sep 17 00:00:00 2001 From: d8ahazard Date: Mon, 26 Sep 2022 09:29:22 -0500 Subject: [PATCH] Rename swinir -> swinir_model --- modules/{swinir.py => swinir_model.py} | 246 ++++++++++++------------- 1 file changed, 123 insertions(+), 123 deletions(-) rename modules/{swinir.py => swinir_model.py} (96%) diff --git a/modules/swinir.py b/modules/swinir_model.py similarity index 96% rename from modules/swinir.py rename to modules/swinir_model.py index 8c534495..e86d0789 100644 --- a/modules/swinir.py +++ b/modules/swinir_model.py @@ -1,123 +1,123 @@ -import sys -import traceback -import cv2 -import os -import contextlib -import numpy as np -from PIL import Image -import torch -import modules.images -from modules.shared import cmd_opts, opts, device -from modules.swinir_arch import SwinIR as net - -precision_scope = ( - torch.autocast if cmd_opts.precision == "autocast" else contextlib.nullcontext -) - - -def load_model(filename, scale=4): - 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", - ) - - pretrained_model = torch.load(filename) - model.load_state_dict(pretrained_model["params_ema"], strict=True) - if not cmd_opts.no_half: - model = model.half() - return model - - -def load_models(dirname): - for file in os.listdir(dirname): - path = os.path.join(dirname, file) - model_name, extension = os.path.splitext(file) - - if extension != ".pt" and extension != ".pth": - continue - - try: - modules.shared.sd_upscalers.append(UpscalerSwin(path, model_name)) - except Exception: - print(f"Error loading SwinIR model: {path}", file=sys.stderr) - print(traceback.format_exc(), file=sys.stderr) - - -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(device) - with torch.no_grad(), precision_scope("cuda"): - _, _, 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=device).type_as(img) - W = torch.zeros_like(E, dtype=torch.half, device=device) - - 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) - output = E.div_(W) - - return output - - -class UpscalerSwin(modules.images.Upscaler): - def __init__(self, filename, title): - self.name = title - self.model = load_model(filename) - - def do_upscale(self, img): - model = self.model.to(device) - img = upscale(img, model) - return img +import sys +import traceback +import cv2 +import os +import contextlib +import numpy as np +from PIL import Image +import torch +import modules.images +from modules.shared import cmd_opts, opts, device +from modules.swinir_arch import SwinIR as net + +precision_scope = ( + torch.autocast if cmd_opts.precision == "autocast" else contextlib.nullcontext +) + + +def load_model(filename, scale=4): + 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", + ) + + pretrained_model = torch.load(filename) + model.load_state_dict(pretrained_model["params_ema"], strict=True) + if not cmd_opts.no_half: + model = model.half() + return model + + +def load_models(dirname): + for file in os.listdir(dirname): + path = os.path.join(dirname, file) + model_name, extension = os.path.splitext(file) + + if extension != ".pt" and extension != ".pth": + continue + + try: + modules.shared.sd_upscalers.append(UpscalerSwin(path, model_name)) + except Exception: + print(f"Error loading SwinIR model: {path}", file=sys.stderr) + print(traceback.format_exc(), file=sys.stderr) + + +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(device) + with torch.no_grad(), precision_scope("cuda"): + _, _, 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=device).type_as(img) + W = torch.zeros_like(E, dtype=torch.half, device=device) + + 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) + output = E.div_(W) + + return output + + +class UpscalerSwin(modules.images.Upscaler): + def __init__(self, filename, title): + self.name = title + self.model = load_model(filename) + + def do_upscale(self, img): + model = self.model.to(device) + img = upscale(img, model) + return img