init

finetune/mmseg/models/decode_heads/sep_fcn_head.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+from mmcv.cnn import DepthwiseSeparableConvModule
+
+from mmseg.registry import MODELS
+from .fcn_head import FCNHead
+
+
+@MODELS.register_module()
+class DepthwiseSeparableFCNHead(FCNHead):
+    """Depthwise-Separable Fully Convolutional Network for Semantic
+    Segmentation.
+
+    This head is implemented according to `Fast-SCNN: Fast Semantic
+    Segmentation Network <https://arxiv.org/abs/1902.04502>`_.
+
+    Args:
+        in_channels(int): Number of output channels of FFM.
+        channels(int): Number of middle-stage channels in the decode head.
+        concat_input(bool): Whether to concatenate original decode input into
+            the result of several consecutive convolution layers.
+            Default: True.
+        num_classes(int): Used to determine the dimension of
+            final prediction tensor.
+        in_index(int): Correspond with 'out_indices' in FastSCNN backbone.
+        norm_cfg (dict | None): Config of norm layers.
+        align_corners (bool): align_corners argument of F.interpolate.
+            Default: False.
+        loss_decode(dict): Config of loss type and some
+            relevant additional options.
+        dw_act_cfg (dict):Activation config of depthwise ConvModule. If it is
+            'default', it will be the same as `act_cfg`. Default: None.
+    """
+
+    def __init__(self, dw_act_cfg=None, **kwargs):
+        super().__init__(**kwargs)
+        self.convs[0] = DepthwiseSeparableConvModule(
+            self.in_channels,
+            self.channels,
+            kernel_size=self.kernel_size,
+            padding=self.kernel_size // 2,
+            norm_cfg=self.norm_cfg,
+            dw_act_cfg=dw_act_cfg)
+
+        for i in range(1, self.num_convs):
+            self.convs[i] = DepthwiseSeparableConvModule(
+                self.channels,
+                self.channels,
+                kernel_size=self.kernel_size,
+                padding=self.kernel_size // 2,
+                norm_cfg=self.norm_cfg,
+                dw_act_cfg=dw_act_cfg)
+
+        if self.concat_input:
+            self.conv_cat = DepthwiseSeparableConvModule(
+                self.in_channels + self.channels,
+                self.channels,
+                kernel_size=self.kernel_size,
+                padding=self.kernel_size // 2,
+                norm_cfg=self.norm_cfg,
+                dw_act_cfg=dw_act_cfg)