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| import numpy as np | |
| import torch.nn as nn | |
| from annotator.uniformer.mmcv.cnn import ConvModule | |
| from annotator.uniformer.mmseg.ops import resize | |
| from ..builder import HEADS | |
| from .decode_head import BaseDecodeHead | |
| class FPNHead(BaseDecodeHead): | |
| """Panoptic Feature Pyramid Networks. | |
| This head is the implementation of `Semantic FPN | |
| <https://arxiv.org/abs/1901.02446>`_. | |
| Args: | |
| feature_strides (tuple[int]): The strides for input feature maps. | |
| stack_lateral. All strides suppose to be power of 2. The first | |
| one is of largest resolution. | |
| """ | |
| def __init__(self, feature_strides, **kwargs): | |
| super(FPNHead, self).__init__( | |
| input_transform='multiple_select', **kwargs) | |
| assert len(feature_strides) == len(self.in_channels) | |
| assert min(feature_strides) == feature_strides[0] | |
| self.feature_strides = feature_strides | |
| self.scale_heads = nn.ModuleList() | |
| for i in range(len(feature_strides)): | |
| head_length = max( | |
| 1, | |
| int(np.log2(feature_strides[i]) - np.log2(feature_strides[0]))) | |
| scale_head = [] | |
| for k in range(head_length): | |
| scale_head.append( | |
| ConvModule( | |
| self.in_channels[i] if k == 0 else self.channels, | |
| self.channels, | |
| 3, | |
| padding=1, | |
| conv_cfg=self.conv_cfg, | |
| norm_cfg=self.norm_cfg, | |
| act_cfg=self.act_cfg)) | |
| if feature_strides[i] != feature_strides[0]: | |
| scale_head.append( | |
| nn.Upsample( | |
| scale_factor=2, | |
| mode='bilinear', | |
| align_corners=self.align_corners)) | |
| self.scale_heads.append(nn.Sequential(*scale_head)) | |
| def forward(self, inputs): | |
| x = self._transform_inputs(inputs) | |
| output = self.scale_heads[0](x[0]) | |
| for i in range(1, len(self.feature_strides)): | |
| # non inplace | |
| output = output + resize( | |
| self.scale_heads[i](x[i]), | |
| size=output.shape[2:], | |
| mode='bilinear', | |
| align_corners=self.align_corners) | |
| output = self.cls_seg(output) | |
| return output | |