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| import torch.nn as nn | |
| from mmcv.cnn import ConvModule, Linear, constant_init, xavier_init | |
| from mmcv.runner import auto_fp16 | |
| from mmdet.models.builder import HEADS | |
| from .fcn_mask_head import FCNMaskHead | |
| class CoarseMaskHead(FCNMaskHead): | |
| """Coarse mask head used in PointRend. | |
| Compared with standard ``FCNMaskHead``, ``CoarseMaskHead`` will downsample | |
| the input feature map instead of upsample it. | |
| Args: | |
| num_convs (int): Number of conv layers in the head. Default: 0. | |
| num_fcs (int): Number of fc layers in the head. Default: 2. | |
| fc_out_channels (int): Number of output channels of fc layer. | |
| Default: 1024. | |
| downsample_factor (int): The factor that feature map is downsampled by. | |
| Default: 2. | |
| """ | |
| def __init__(self, | |
| num_convs=0, | |
| num_fcs=2, | |
| fc_out_channels=1024, | |
| downsample_factor=2, | |
| *arg, | |
| **kwarg): | |
| super(CoarseMaskHead, self).__init__( | |
| *arg, num_convs=num_convs, upsample_cfg=dict(type=None), **kwarg) | |
| self.num_fcs = num_fcs | |
| assert self.num_fcs > 0 | |
| self.fc_out_channels = fc_out_channels | |
| self.downsample_factor = downsample_factor | |
| assert self.downsample_factor >= 1 | |
| # remove conv_logit | |
| delattr(self, 'conv_logits') | |
| if downsample_factor > 1: | |
| downsample_in_channels = ( | |
| self.conv_out_channels | |
| if self.num_convs > 0 else self.in_channels) | |
| self.downsample_conv = ConvModule( | |
| downsample_in_channels, | |
| self.conv_out_channels, | |
| kernel_size=downsample_factor, | |
| stride=downsample_factor, | |
| padding=0, | |
| conv_cfg=self.conv_cfg, | |
| norm_cfg=self.norm_cfg) | |
| else: | |
| self.downsample_conv = None | |
| self.output_size = (self.roi_feat_size[0] // downsample_factor, | |
| self.roi_feat_size[1] // downsample_factor) | |
| self.output_area = self.output_size[0] * self.output_size[1] | |
| last_layer_dim = self.conv_out_channels * self.output_area | |
| self.fcs = nn.ModuleList() | |
| for i in range(num_fcs): | |
| fc_in_channels = ( | |
| last_layer_dim if i == 0 else self.fc_out_channels) | |
| self.fcs.append(Linear(fc_in_channels, self.fc_out_channels)) | |
| last_layer_dim = self.fc_out_channels | |
| output_channels = self.num_classes * self.output_area | |
| self.fc_logits = Linear(last_layer_dim, output_channels) | |
| def init_weights(self): | |
| for m in self.fcs.modules(): | |
| if isinstance(m, nn.Linear): | |
| xavier_init(m) | |
| constant_init(self.fc_logits, 0.001) | |
| def forward(self, x): | |
| for conv in self.convs: | |
| x = conv(x) | |
| if self.downsample_conv is not None: | |
| x = self.downsample_conv(x) | |
| x = x.flatten(1) | |
| for fc in self.fcs: | |
| x = self.relu(fc(x)) | |
| mask_pred = self.fc_logits(x).view( | |
| x.size(0), self.num_classes, *self.output_size) | |
| return mask_pred | |