extremely basic and incomplete swinir implementation

1 files changed, 74 insertions, 0 deletions

diff --git a/swinir.py b/swinir.py
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index 00000000..cb2bbe3d
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+++ b/swinir.py
@@ -0,0 +1,74 @@
+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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