extremely basic and incomplete swinir implementation

swinir.py

+import sys
+import traceback
+import cv2
+from collections import OrderedDict
+import os
+import requests
+from collections import namedtuple
+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(task = "realsr", large_model = True, model_path=next(os.listdir(cmd_opts.esrgan_models_path))):
+    if not large_model:
+    # use 'nearest+conv' to avoid block artifacts
+        model = net(upscale=scale, in_chans=3, img_size=64, window_size=8,
+                    img_range=1., 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')
+    else:
+        # larger model size; use '3conv' to save parameters and memory; use ema for GAN training
+        model = net(upscale=scale, in_chans=3, img_size=64, window_size=8,
+                    img_range=1., 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(model_path)
+    model.load_state_dict(pretrained_model, strict=True)
+
+    return model.half().to(device)
+    
+def upscale(img, tile=opts.ESRGAN_tile, tile_overlap=opts.ESRGAN_tile_overlap, window_size = 8, scale = 4):
+    img = cv2.imread(img, cv2.IMREAD_COLOR).astype(np.float16) / 255.
+    model = load_model()
+    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 output
+    
+    
+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
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