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import sys |
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import cv2 |
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import numpy as np |
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import torch |
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import ESRGAN.architecture as esrgan |
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import ESRGAN_plus.architecture as esrgan_plus |
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from run_cmd import run_cmd |
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from ESRGANer import ESRGANer |
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def is_cuda(): |
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if torch.cuda.is_available(): |
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return True |
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else: |
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return False |
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model_type = sys.argv[2] |
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if model_type == "Anime": |
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model_path = "models/4x-AnimeSharp.pth" |
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if model_type == "Photo": |
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model_path = "models/4x_Valar_v1.pth" |
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else: |
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model_path = "models/4x_NMKD-Siax_200k.pth" |
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OUTPUT_PATH = sys.argv[1] |
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device = torch.device('cuda' if is_cuda() else 'cpu') |
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if model_type != "Photo": |
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model = esrgan.RRDB_Net(3, 3, 64, 23, gc=32, upscale=4, norm_type=None, act_type='leakyrelu', mode='CNA', res_scale=1, upsample_mode='upconv') |
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else: |
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model = esrgan_plus.RRDB_Net(3, 3, 64, 23, gc=32, upscale=4, norm_type=None, act_type='leakyrelu', mode='CNA', res_scale=1, upsample_mode='upconv') |
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if is_cuda(): |
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print("Using GPU 🥶") |
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model.load_state_dict(torch.load(model_path), strict=True) |
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else: |
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print("Using CPU 😒") |
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model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')), strict=True) |
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model.eval() |
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for k, v in model.named_parameters(): |
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v.requires_grad = False |
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model = model.to(device) |
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img = cv2.imread(OUTPUT_PATH, cv2.IMREAD_COLOR) |
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img = img * 1.0 / 255 |
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img = torch.from_numpy(np.transpose(img[:, :, [2, 1, 0]], (2, 0, 1))).float() |
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img_LR = img.unsqueeze(0) |
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img_LR = img_LR.to(device) |
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upsampler = ESRGANer(model=model) |
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output = upsampler.enhance(img_LR) |
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output = output.squeeze().float().cpu().clamp_(0, 1).numpy() |
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output = np.transpose(output[[2, 1, 0], :, :], (1, 2, 0)) |
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output = (output * 255.0).round() |
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cv2.imwrite(OUTPUT_PATH, output, [int(cv2.IMWRITE_PNG_COMPRESSION), 5]) |
