make swinir actually useful

1 files changed, 0 insertions, 74 deletions

diff --git a/swinir.py b/swinir.py
deleted file mode 100644
index cb2bbe3d..00000000
--- a/swinir.py
+++ /dev/null
@@ -1,74 +0,0 @@
-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
\ No newline at end of file