| 273 | |
| 274 | class FeatureNet(nn.Module): |
| 275 | def __init__(self, base_channels, num_stage=3, stride=4, mode="fpn",layernorm=False): |
| 276 | super(FeatureNet, self).__init__() |
| 277 | assert mode in ["unet", "fpn"], print("mode must be in 'unet', 'fpn', but get:{}".format(mode)) |
| 278 | self.mode = mode |
| 279 | self.stride = stride |
| 280 | self.base_channels = base_channels |
| 281 | self.num_stage = num_stage |
| 282 | self.layernorm=layernorm |
| 283 | self.conv0 = nn.Sequential( |
| 284 | Conv2d(3, base_channels, 3, 1, padding=1), |
| 285 | Conv2d(base_channels, base_channels, 3, 1, padding=1), |
| 286 | ) |
| 287 | |
| 288 | self.conv1 = nn.Sequential( |
| 289 | Conv2d(base_channels, base_channels * 2, 5, stride=2, padding=2), |
| 290 | Conv2d(base_channels * 2, base_channels * 2, 3, 1, padding=1), |
| 291 | Conv2d(base_channels * 2, base_channels * 2, 3, 1, padding=1), |
| 292 | ) |
| 293 | |
| 294 | self.conv2 = nn.Sequential( |
| 295 | Conv2d(base_channels * 2, base_channels * 4, 5, stride=2, padding=2), |
| 296 | Conv2d(base_channels * 4, base_channels * 4, 3, 1, padding=1), |
| 297 | Conv2d(base_channels * 4, base_channels * 4, 3, 1, padding=1), |
| 298 | ) |
| 299 | |
| 300 | |
| 301 | self.out1 = nn.Conv2d(base_channels * 4, base_channels * 4 *2, 1, bias=False) |
| 302 | self.out_channels = [4 * base_channels] |
| 303 | final_chs = base_channels * 4 |
| 304 | |
| 305 | self.inner1 = nn.Conv2d(base_channels * 2, final_chs, 1, bias=True) |
| 306 | self.inner2 = nn.Conv2d(base_channels * 1, final_chs, 1, bias=True) |
| 307 | |
| 308 | self.out2 = nn.Conv2d(final_chs, base_channels * 2 *2, 3, padding=1, bias=False) |
| 309 | self.out3 = nn.Conv2d(final_chs, base_channels *2, 3, padding=1, bias=False) |
| 310 | self.out_channels.append(base_channels * 2) |
| 311 | self.out_channels.append(base_channels) |
| 312 | |
| 313 | |
| 314 | |