Python ops.MaskedConv2d方法代碼示例

本文整理匯總了Python中mmdet.ops.MaskedConv2d方法的典型用法代碼示例。如果您正苦於以下問題：Python ops.MaskedConv2d方法的具體用法？Python ops.MaskedConv2d怎麽用？Python ops.MaskedConv2d使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類mmdet.ops的用法示例。

在下文中一共展示了ops.MaskedConv2d方法的5個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: _init_layers

# 需要導入模塊: from mmdet import ops [as 別名]
# 或者: from mmdet.ops import MaskedConv2d [as 別名]
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)
        self.feature_adaption = FeatureAdaption(
            self.feat_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)

開發者ID:dingjiansw101，項目名稱:AerialDetection，代碼行數:17，代碼來源:guided_anchor_head.py

示例2: _init_layers

# 需要導入模塊: from mmdet import ops [as 別名]
# 或者: from mmdet.ops import MaskedConv2d [as 別名]
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)

開發者ID:open-mmlab，項目名稱:mmdetection，代碼行數:16，代碼來源:guided_anchor_head.py

示例3: _init_layers

# 需要導入模塊: from mmdet import ops [as 別名]
# 或者: from mmdet.ops import MaskedConv2d [as 別名]
def _init_layers(self):
        """Initialize layers of the head."""
        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)
        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)

開發者ID:open-mmlab，項目名稱:mmdetection，代碼行數:48，代碼來源:ga_retina_head.py

示例4: _init_layers

# 需要導入模塊: from mmdet import ops [as 別名]
# 或者: from mmdet.ops import MaskedConv2d [as 別名]
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)
        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)

開發者ID:dingjiansw101，項目名稱:AerialDetection，代碼行數:47，代碼來源:ga_retina_head.py

示例5: _init_layers

# 需要導入模塊: from mmdet import ops [as 別名]
# 或者: from mmdet.ops import MaskedConv2d [as 別名]
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)
        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)

開發者ID:xieenze，項目名稱:PolarMask，代碼行數:47，代碼來源:ga_retina_head.py

注：本文中的mmdet.ops.MaskedConv2d方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。