74 lines
3.4 KiB
Python
74 lines
3.4 KiB
Python
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import sys
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import traceback
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import cv2
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from collections import OrderedDict
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import os
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import requests
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from collections import namedtuple
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import numpy as np
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from PIL import Image
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import torch
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import modules.images
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from modules.shared import cmd_opts, opts, device
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from modules.swinir_arch import SwinIR as net
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precision_scope = torch.autocast if cmd_opts.precision == "autocast" else contextlib.nullcontext
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def load_model(task = "realsr", large_model = True, model_path=next(os.listdir(cmd_opts.esrgan_models_path))):
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if not large_model:
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# use 'nearest+conv' to avoid block artifacts
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model = net(upscale=scale, in_chans=3, img_size=64, window_size=8,
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img_range=1., depths=[6, 6, 6, 6, 6, 6], embed_dim=180, num_heads=[6, 6, 6, 6, 6, 6],
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mlp_ratio=2, upsampler='nearest+conv', resi_connection='1conv')
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else:
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# larger model size; use '3conv' to save parameters and memory; use ema for GAN training
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model = net(upscale=scale, in_chans=3, img_size=64, window_size=8,
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img_range=1., depths=[6, 6, 6, 6, 6, 6, 6, 6, 6], 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, upsampler='nearest+conv', resi_connection='3conv')
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pretrained_model = torch.load(model_path)
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model.load_state_dict(pretrained_model, strict=True)
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return model.half().to(device)
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def upscale(img, tile=opts.ESRGAN_tile, tile_overlap=opts.ESRGAN_tile_overlap, window_size = 8, scale = 4):
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img = cv2.imread(img, cv2.IMREAD_COLOR).astype(np.float16) / 255.
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model = load_model()
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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(output[[2, 1, 0], :, :], (1, 2, 0)) # 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 output
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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=device).type_as(img)
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W = torch.zeros_like(E, dtype=torch.half, device=device)
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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[..., h_idx*sf:(h_idx+tile)*sf, w_idx*sf:(w_idx+tile)*sf].add_(out_patch)
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W[..., h_idx*sf:(h_idx+tile)*sf, w_idx*sf:(w_idx+tile)*sf].add_(out_patch_mask)
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output = E.div_(W)
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return output
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