| 147 | class S2DFsim(nn.Module): |
| 148 | |
| 149 | def __init__(self, block, num_blocks,dense = True,dilation=True): |
| 150 | self.inplanes = 64 |
| 151 | super(S2DFsim, self).__init__() |
| 152 | self.dense = dense |
| 153 | self.num_block = num_blocks |
| 154 | assert(num_blocks>=1 and num_blocks<=4) |
| 155 | self.block1 = nn.Sequential(*[ |
| 156 | nn.Conv2d(3, 64, kernel_size=7, stride=1, padding=3, bias=False), |
| 157 | nn.ReLU(inplace=True), |
| 158 | nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False), |
| 159 | ]) |
| 160 | |
| 161 | self.dilation = dilation |
| 162 | # for i in range(1, num_blocks): |
| 163 | self.block2 = nn.Sequential(*[ |
| 164 | nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False), |
| 165 | nn.ReLU(inplace=True), |
| 166 | nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False), |
| 167 | ]) if num_blocks >= 2 else None |
| 168 | self.block3 = nn.Sequential(*[ |
| 169 | nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False), |
| 170 | nn.ReLU(inplace=True), |
| 171 | nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False), |
| 172 | ]) if num_blocks >= 3 else None |
| 173 | self.block4 = nn.Sequential(*[ |
| 174 | nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False), |
| 175 | nn.ReLU(inplace=True), |
| 176 | nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False), |
| 177 | ]) if num_blocks >= 4 else None |
| 178 | |
| 179 | # for m in self.modules(): |
| 180 | # if isinstance(m, nn.Conv2d): |
| 181 | # n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels |
| 182 | # m.weight.data.normal_(0, math.sqrt(2. / n)) |
| 183 | # elif isinstance(m, nn.BatchNorm2d): |
| 184 | # m.weight.data.fill_(1) |
| 185 | # m.bias.data.zero_() |
| 186 | |
| 187 | def forward(self, x): |
| 188 | y = [] |