From d1f098540ad1dbc2abb8d04322634efba650b631 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 30 Sep 2022 11:42:40 +0300 Subject: remove unwanted formatting/functionality from the PR --- modules/esrgan_model.py | 123 ++++++++++++++++++++++++------------------------ 1 file changed, 62 insertions(+), 61 deletions(-) (limited to 'modules/esrgan_model.py') diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py index ce841aa4..ea91abfe 100644 --- a/modules/esrgan_model.py +++ b/modules/esrgan_model.py @@ -13,6 +13,63 @@ from modules.upscaler import Upscaler, UpscalerData from modules.shared import opts +def fix_model_layers(crt_model, pretrained_net): + # this code is adapted from https://github.com/xinntao/ESRGAN + if 'conv_first.weight' in pretrained_net: + return pretrained_net + + if 'model.0.weight' not in pretrained_net: + is_realesrgan = "params_ema" in pretrained_net and 'body.0.rdb1.conv1.weight' in pretrained_net["params_ema"] + if is_realesrgan: + raise Exception("The file is a RealESRGAN model, it can't be used as a ESRGAN model.") + else: + raise Exception("The file is not a ESRGAN model.") + + crt_net = crt_model.state_dict() + load_net_clean = {} + for k, v in pretrained_net.items(): + if k.startswith('module.'): + load_net_clean[k[7:]] = v + else: + load_net_clean[k] = v + pretrained_net = load_net_clean + + tbd = [] + for k, v in crt_net.items(): + tbd.append(k) + + # directly copy + for k, v in crt_net.items(): + if k in pretrained_net and pretrained_net[k].size() == v.size(): + crt_net[k] = pretrained_net[k] + tbd.remove(k) + + crt_net['conv_first.weight'] = pretrained_net['model.0.weight'] + crt_net['conv_first.bias'] = pretrained_net['model.0.bias'] + + for k in tbd.copy(): + if 'RDB' in k: + ori_k = k.replace('RRDB_trunk.', 'model.1.sub.') + if '.weight' in k: + ori_k = ori_k.replace('.weight', '.0.weight') + elif '.bias' in k: + ori_k = ori_k.replace('.bias', '.0.bias') + crt_net[k] = pretrained_net[ori_k] + tbd.remove(k) + + crt_net['trunk_conv.weight'] = pretrained_net['model.1.sub.23.weight'] + crt_net['trunk_conv.bias'] = pretrained_net['model.1.sub.23.bias'] + crt_net['upconv1.weight'] = pretrained_net['model.3.weight'] + crt_net['upconv1.bias'] = pretrained_net['model.3.bias'] + crt_net['upconv2.weight'] = pretrained_net['model.6.weight'] + crt_net['upconv2.bias'] = pretrained_net['model.6.bias'] + crt_net['HRconv.weight'] = pretrained_net['model.8.weight'] + crt_net['HRconv.bias'] = pretrained_net['model.8.bias'] + crt_net['conv_last.weight'] = pretrained_net['model.10.weight'] + crt_net['conv_last.bias'] = pretrained_net['model.10.bias'] + + return crt_net + class UpscalerESRGAN(Upscaler): def __init__(self, dirname): self.name = "ESRGAN" @@ -28,14 +85,12 @@ class UpscalerESRGAN(Upscaler): scaler_data = UpscalerData(self.model_name, self.model_url, self, 4) scalers.append(scaler_data) for file in model_paths: - print(f"File: {file}") if "http" in file: name = self.model_name else: name = modelloader.friendly_name(file) scaler_data = UpscalerData(name, file, self, 4) - print(f"ESRGAN: Adding scaler {name}") self.scalers.append(scaler_data) def do_upscale(self, img, selected_model): @@ -56,67 +111,14 @@ class UpscalerESRGAN(Upscaler): if not os.path.exists(filename) or filename is None: print("Unable to load %s from %s" % (self.model_path, filename)) return None - # this code is adapted from https://github.com/xinntao/ESRGAN + pretrained_net = torch.load(filename, map_location='cpu' if has_mps else None) crt_model = arch.RRDBNet(3, 3, 64, 23, gc=32) - if 'conv_first.weight' in pretrained_net: - crt_model.load_state_dict(pretrained_net) - return crt_model - - if 'model.0.weight' not in pretrained_net: - is_realesrgan = "params_ema" in pretrained_net and 'body.0.rdb1.conv1.weight' in pretrained_net[ - "params_ema"] - if is_realesrgan: - raise Exception("The file is a RealESRGAN model, it can't be used as a ESRGAN model.") - else: - raise Exception("The file is not a ESRGAN model.") - - crt_net = crt_model.state_dict() - load_net_clean = {} - for k, v in pretrained_net.items(): - if k.startswith('module.'): - load_net_clean[k[7:]] = v - else: - load_net_clean[k] = v - pretrained_net = load_net_clean - - tbd = [] - for k, v in crt_net.items(): - tbd.append(k) - - # directly copy - for k, v in crt_net.items(): - if k in pretrained_net and pretrained_net[k].size() == v.size(): - crt_net[k] = pretrained_net[k] - tbd.remove(k) - - crt_net['conv_first.weight'] = pretrained_net['model.0.weight'] - crt_net['conv_first.bias'] = pretrained_net['model.0.bias'] - - for k in tbd.copy(): - if 'RDB' in k: - ori_k = k.replace('RRDB_trunk.', 'model.1.sub.') - if '.weight' in k: - ori_k = ori_k.replace('.weight', '.0.weight') - elif '.bias' in k: - ori_k = ori_k.replace('.bias', '.0.bias') - crt_net[k] = pretrained_net[ori_k] - tbd.remove(k) - - crt_net['trunk_conv.weight'] = pretrained_net['model.1.sub.23.weight'] - crt_net['trunk_conv.bias'] = pretrained_net['model.1.sub.23.bias'] - crt_net['upconv1.weight'] = pretrained_net['model.3.weight'] - crt_net['upconv1.bias'] = pretrained_net['model.3.bias'] - crt_net['upconv2.weight'] = pretrained_net['model.6.weight'] - crt_net['upconv2.bias'] = pretrained_net['model.6.bias'] - crt_net['HRconv.weight'] = pretrained_net['model.8.weight'] - crt_net['HRconv.bias'] = pretrained_net['model.8.bias'] - crt_net['conv_last.weight'] = pretrained_net['model.10.weight'] - crt_net['conv_last.bias'] = pretrained_net['model.10.bias'] - - crt_model.load_state_dict(crt_net) + pretrained_net = fix_model_layers(crt_model, pretrained_net) + crt_model.load_state_dict(pretrained_net) crt_model.eval() + return crt_model @@ -154,7 +156,6 @@ def esrgan_upscale(model, img): newrow.append([x * scale_factor, w * scale_factor, output]) newtiles.append([y * scale_factor, h * scale_factor, newrow]) - newgrid = images.Grid(newtiles, grid.tile_w * scale_factor, grid.tile_h * scale_factor, - grid.image_w * scale_factor, grid.image_h * scale_factor, grid.overlap * scale_factor) + newgrid = images.Grid(newtiles, grid.tile_w * scale_factor, grid.tile_h * scale_factor, grid.image_w * scale_factor, grid.image_h * scale_factor, grid.overlap * scale_factor) output = images.combine_grid(newgrid) return output -- cgit v1.2.1