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| from . import common | |
| from argparse import Namespace | |
| import torch | |
| import torch.nn as nn | |
| from models import register | |
| import torch.nn.functional as F | |
| def make_model(args, parent=False): | |
| return DIM(args) | |
| def DCM(scale_ratio, rgb_range=1): | |
| args = Namespace() | |
| args.scale = [scale_ratio] | |
| args.n_colors = 3 | |
| args.rgb_range = rgb_range | |
| return DIM(args) | |
| class DIM(nn.Module): | |
| def __init__(self, args, conv=common.default_conv): | |
| super(DIM, self).__init__() | |
| self.scale = args.scale[0] | |
| # feature extractor part | |
| self.fe_conv1 = common.BasicBlock(conv, args.n_colors, 196, kernel_size=3, bias=True, act=nn.PReLU()) | |
| self.fe_conv2 = common.BasicBlock(conv, 196, 166, kernel_size=3, bias=True, act=nn.PReLU()) | |
| self.fe_conv3 = common.BasicBlock(conv, 166, 148, kernel_size=3, bias=True, act=nn.PReLU()) | |
| self.fe_conv4 = common.BasicBlock(conv, 148, 133, kernel_size=3, bias=True, act=nn.PReLU()) | |
| self.fe_conv5 = common.BasicBlock(conv, 133, 120, kernel_size=3, bias=True, act=nn.PReLU()) | |
| self.fe_conv6 = common.BasicBlock(conv, 120, 108, kernel_size=3, bias=True, act=nn.PReLU()) | |
| self.fe_conv7 = common.BasicBlock(conv, 108, 97, kernel_size=3, bias=True, act=nn.PReLU()) | |
| self.fe_conv8 = common.BasicBlock(conv, 97, 86, kernel_size=3, bias=True, act=nn.PReLU()) | |
| self.fe_conv9 = common.BasicBlock(conv, 86, 76, kernel_size=3, bias=True, act=nn.PReLU()) | |
| self.fe_conv10 = common.BasicBlock(conv, 76, 66, kernel_size=3, bias=True, act=nn.PReLU()) | |
| self.fe_conv11 = common.BasicBlock(conv, 66, 57, kernel_size=3, bias=True, act=nn.PReLU()) | |
| self.fe_conv12 = common.BasicBlock(conv, 57, 48, kernel_size=3, bias=True, act=nn.PReLU()) | |
| # reconstruction part | |
| self.re_a = common.BasicBlock(conv, 196 + 48, 64, kernel_size=3, bias=True, act=nn.PReLU()) | |
| self.re_b1 = common.BasicBlock(conv, 196 + 48, 32, kernel_size=3, bias=True, act=nn.PReLU()) | |
| self.re_b2 = common.BasicBlock(conv, 32, 32, kernel_size=3, bias=True, act=nn.PReLU()) | |
| self.re_u = common.Upsampler(conv, self.scale, 96, act=False) | |
| self.re_r = conv(96, args.n_colors, kernel_size=1) | |
| def forward(self, x, out_size=None): | |
| residual = F.interpolate(x, scale_factor=self.scale, mode='bicubic') | |
| # feature extractor part | |
| fe_conv1 = self.fe_conv1(x) | |
| fe_conv2 = self.fe_conv2(fe_conv1) | |
| fe_conv3 = self.fe_conv3(fe_conv2) | |
| fe_conv4 = self.fe_conv4(fe_conv3) | |
| fe_conv5 = self.fe_conv5(fe_conv4) | |
| fe_conv6 = self.fe_conv6(fe_conv5) | |
| fe_conv7 = self.fe_conv7(fe_conv6) | |
| fe_conv8 = self.fe_conv8(fe_conv7) | |
| fe_conv9 = self.fe_conv9(fe_conv8) | |
| fe_conv10 = self.fe_conv10(fe_conv9) | |
| fe_conv11 = self.fe_conv11(fe_conv10) | |
| fe_conv12 = self.fe_conv12(fe_conv11) | |
| # reconstruction part | |
| feat = torch.cat((fe_conv1, fe_conv12), dim=1) | |
| re_a = self.re_a(feat) | |
| re_b1 = self.re_b1(feat) | |
| re_b2 = self.re_b2(re_b1) | |
| feat = torch.cat((re_a, re_b2), dim=1) | |
| re_u = self.re_u(feat) | |
| re_r = self.re_r(re_u) | |
| out = re_r + residual | |
| return out | |
| def load_state_dict(self, state_dict, strict=False): | |
| own_state = self.state_dict() | |
| for name, param in state_dict.items(): | |
| if name in own_state: | |
| if isinstance(param, nn.Parameter): | |
| param = param.data | |
| try: | |
| own_state[name].copy_(param) | |
| except Exception: | |
| if name.find('tail') >= 0: | |
| print('Replace pre-trained upsampler to new one...') | |
| else: | |
| raise RuntimeError('While copying the parameter named {}, ' | |
| 'whose dimensions in the model are {} and ' | |
| 'whose dimensions in the checkpoint are {}.' | |
| .format(name, own_state[name].size(), param.size())) | |
| elif strict: | |
| if name.find('tail') == -1: | |
| raise KeyError('unexpected key "{}" in state_dict' | |
| .format(name)) | |
| if strict: | |
| missing = set(own_state.keys()) - set(state_dict.keys()) | |
| if len(missing) > 0: | |
| raise KeyError('missing keys in state_dict: "{}"'.format(missing)) | |