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from . import common |
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import torch |
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import torch.nn as nn |
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from models import register |
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import torch.nn.functional as F |
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from argparse import Namespace |
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def make_model(args, parent=False): |
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return VDSR(args) |
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@register('VDSR') |
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def VDSR(scale_ratio, rgb_range=1): |
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args = Namespace() |
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args.scale = [scale_ratio] |
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args.n_colors = 3 |
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args.rgb_range = rgb_range |
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return VDSR(args) |
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class VDSR(nn.Module): |
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def __init__(self, args, conv=common.default_conv): |
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super(VDSR, self).__init__() |
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n_feats = 64 |
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kernel_size = 3 |
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m_head = [common.BasicBlock(conv, args.n_colors, n_feats, kernel_size, bias=True, bn=True)] |
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layer_nums = 18 |
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m_body = [ |
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common.BasicBlock(conv, n_feats, n_feats, kernel_size, bias=True, bn=True) |
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for _ in range(layer_nums) |
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] |
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m_tail = [conv(n_feats, args.n_colors, kernel_size, bias=True)] |
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self.head = nn.Sequential(*m_head) |
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self.body = nn.Sequential(*m_body) |
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self.tail = nn.Sequential(*m_tail) |
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def forward(self, x, out_size): |
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x = F.interpolate(x, size=out_size, mode='bicubic') |
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residual = x |
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x = self.head(x) |
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x = self.body(x) |
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x = self.tail(x) |
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out = x + residual |
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return out |
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def load_state_dict(self, state_dict, strict=False): |
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own_state = self.state_dict() |
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for name, param in state_dict.items(): |
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if name in own_state: |
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if isinstance(param, nn.Parameter): |
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param = param.data |
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try: |
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own_state[name].copy_(param) |
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except Exception: |
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if name.find('tail') >= 0: |
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print('Replace pre-trained upsampler to new one...') |
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else: |
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raise RuntimeError('While copying the parameter named {}, ' |
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'whose dimensions in the model are {} and ' |
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'whose dimensions in the checkpoint are {}.' |
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.format(name, own_state[name].size(), param.size())) |
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elif strict: |
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if name.find('tail') == -1: |
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raise KeyError('unexpected key "{}" in state_dict' |
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.format(name)) |
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if strict: |
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missing = set(own_state.keys()) - set(state_dict.keys()) |
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if len(missing) > 0: |
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raise KeyError('missing keys in state_dict: "{}"'.format(missing)) |
