import gradio as gr from PIL import Image import torch import numpy as np from models.network_swinir import SwinIR as net # model load param_key_g = 'params_ema' device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') super_res_model = net(upscale=4, 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') super_res_pretrained_model = torch.load("model_zoo/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_PSNR.pth") super_res_model.load_state_dict(super_res_pretrained_model[param_key_g] if param_key_g in super_res_pretrained_model.keys() else super_res_pretrained_model, strict=True) super_res_model.eval() def predict(input_img): out = None # preprocess input if(input_img is not None): # model predict img_lq = input_img.astype(np.float32) / 255 img_lq = np.transpose(img_lq if img_lq.shape[2] == 1 else img_lq[:, :, [2, 1, 0]], (2, 0, 1)) # HCW-BGR to CHW-RGB img_lq = torch.from_numpy(img_lq).float().unsqueeze(0).to(device) # CHW-RGB to NCHW-RGB # inference window_size = 8 model = super_res_model.to(device) with torch.no_grad(): # pad input image to be a multiple of window_size _, _, h_old, w_old = img_lq.size() h_pad = (h_old // window_size + 1) * window_size - h_old w_pad = (w_old // window_size + 1) * window_size - w_old img_lq = torch.cat([img_lq, torch.flip(img_lq, [2])], 2)[:, :, :h_old + h_pad, :] img_lq = torch.cat([img_lq, torch.flip(img_lq, [3])], 3)[:, :, :, :w_old + w_pad] output = test(model, img_lq) output = output[..., :h_old * 4, :w_old * 4] # process image 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 # convert to pil image out = Image.fromarray(output) return out def test(model, img_lq): # test the image tile by tile b, c, h, w = img_lq.size() tile = min(800, h, w) tile_overlap = 32 sf = 4 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).type_as(img_lq) W = torch.zeros_like(E) for h_idx in h_idx_list: for w_idx in w_idx_list: in_patch = img_lq[..., 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 gr.Interface( fn=predict, inputs=[ gr.inputs.Image() ], outputs=[ gr.inputs.Image() ], title="SwinIR moon super resolution", description="Description of the app", examples=[ "render0001.png", "render1546.png", "render1682.png" ] ).launch()