def _init_layers(self):
self.relu = nn.ReLU(inplace=True)
self.cls_convs = nn.ModuleList()
self.reg_convs = nn.ModuleList()
for i in range(self.stacked_convs):
chn = self.in_channels if i == 0 else self.feat_channels
self.cls_convs.append(
ConvModule(chn,
self.feat_channels,
3,
stride=1,
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg))
self.reg_convs.append(
ConvModule(chn,
self.feat_channels,
3,
stride=1,
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg))
self.conv_loc = nn.Conv2d(self.feat_channels, 1, 1)
self.conv_shape = nn.Conv2d(self.feat_channels, self.num_anchors * 2,
1)
self.feature_adaption_cls = FeatureAdaption(
self.feat_channels,
self.feat_channels,
kernel_size=3,
deformable_groups=self.deformable_groups)
self.feature_adaption_reg = FeatureAdaption(
self.feat_channels,
self.feat_channels,
kernel_size=3,
deformable_groups=self.deformable_groups)
# adding two more 3*3 conv
self.conv_feat_cls = nn.Conv2d(self.feat_channels,
self.feat_channels,
kernel_size=3,
stride=1,
padding=1)
self.conv_feat_reg = nn.Conv2d(self.feat_channels,
self.feat_channels,
kernel_size=3,
stride=1,
padding=1)
self.retina_cls = MaskedConv2d(self.feat_channels,
self.num_anchors *
self.cls_out_channels,
3,
padding=1)
self.retina_reg = MaskedConv2d(self.feat_channels,
self.num_anchors * 4,
3,
padding=1)
def _init_layers(self):
self.relu = nn.ReLU(inplace=True)
self.conv_loc = nn.Conv2d(self.in_channels, 1, 1)
self.conv_shape = nn.Conv2d(self.in_channels, self.num_anchors * 2, 1)
self.feature_adaption = FeatureAdaption(
self.in_channels,
self.feat_channels,
kernel_size=3,
deformable_groups=self.deformable_groups)
self.conv_cls = MaskedConv2d(self.feat_channels,
self.num_anchors * self.cls_out_channels,
1)
self.conv_reg = MaskedConv2d(self.feat_channels, self.num_anchors * 4,
1)
def _init_layers(self):
self.relu = nn.ReLU(inplace=True) #激活层
self.conv_loc = nn.Conv2d(self.in_channels, 1,
1) #中心预测1*1卷积，输出位置中心，只有1个通道
self.conv_shape = nn.Conv2d(self.in_channels, self.num_anchors * 2,
1) #形状预测，还是有anchor个数，因为不知道是哪一个匹配最好
self.feature_adaption = FeatureAdaption(
self.in_channels,
self.feat_channels,
kernel_size=3,
deformable_groups=self.deformable_groups) #DCN卷积
self.conv_cls = MaskedConv2d(self.feat_channels,
self.num_anchors * self.cls_out_channels,
1) #这个是分类预测层的卷积，如果是推断还需要用到mask
self.conv_reg = MaskedConv2d(self.feat_channels, self.num_anchors * 4,
1) #这个是回归预测层的卷积，如果是推断还需要用到mask
def _init_layers(self):
self.relu = nn.ReLU(inplace=True)
self.cls_convs = nn.ModuleList()
self.reg_convs = nn.ModuleList()
for i in range(self.stacked_convs):
chn = self.in_channels if i == 0 else self.feat_channels
self.cls_convs.append(
nn.Conv2d(chn, self.feat_channels, 3, stride=1, padding=1))
self.reg_convs.append(
nn.Conv2d(chn, self.feat_channels, 3, stride=1, padding=1))
self.conv_loc = nn.Conv2d(self.feat_channels, 1, 1)
self.conv_shape = nn.Conv2d(self.feat_channels, self.num_anchors * 2,
1)
deformable_groups = 4
offset_channels = 3 * 3 * 2
self.conv_offset_cls = nn.Conv2d(self.num_anchors * 2,
deformable_groups * offset_channels,
1,
bias=False)
self.conv_adaption_cls = DeformConv(
self.feat_channels,
self.feat_channels,
kernel_size=3,
padding=1,
deformable_groups=deformable_groups)
self.conv_offset_reg = nn.Conv2d(self.num_anchors * 2,
deformable_groups * offset_channels,
1,
bias=False)
self.conv_adaption_reg = DeformConv(
self.feat_channels,
self.feat_channels,
kernel_size=3,
padding=1,
deformable_groups=deformable_groups)
self.retina_cls = MaskedConv2d(self.feat_channels,
self.num_anchors *
self.cls_out_channels,
3,
padding=1)
self.retina_reg = MaskedConv2d(self.feat_channels,
self.num_anchors * 4,
3,
padding=1)
def _init_layers(self):
self.relu = nn.ReLU(inplace=True)
self.rpn_conv = nn.Conv2d(self.in_channels,
self.feat_channels,
3,
padding=1)
self.conv_loc = nn.Conv2d(self.in_channels, 1, 1)
self.conv_reg_first = nn.Conv2d(self.in_channels, self.num_anchors * 4,
1)
self.conv_cls = MaskedConv2d(self.feat_channels,
self.num_anchors * self.cls_out_channels,
1)
self.conv_reg = MaskedConv2d(self.feat_channels, self.num_anchors * 4,
1)
self.feature_adaptation = nn.ModuleList()
for i in range(self.num_conv):
self.feature_adaptation.append(
ConvModule(self.in_channels, self.in_channels, 3, padding=1))
def _init_layers(self):
self.relu = nn.ReLU(inplace=True)
self.conv_loc = nn.Conv2d(self.feat_channels, 1, 1)
self.conv_shape = nn.Conv2d(self.feat_channels, self.num_anchors * 2,
1)
deformable_groups = 4
offset_channels = 3 * 3 * 2
self.conv_offset = nn.Conv2d(self.num_anchors * 2,
deformable_groups * offset_channels,
1,
bias=False)
self.conv_adaption = DeformConv(self.feat_channels,
self.feat_channels,
kernel_size=3,
padding=1,
deformable_groups=deformable_groups)
self.conv_cls = MaskedConv2d(self.feat_channels,
self.num_anchors * self.cls_out_channels,
1)
self.conv_reg = MaskedConv2d(self.feat_channels, self.num_anchors * 4,
1)
