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@ -188,6 +188,44 @@ class FastSCNN(nn.Module):
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norm_cfg=dict(type='BN'),
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act_cfg=dict(type='ReLU'),
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align_corners=False):
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"""Fast-SCNN Backbone.
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Args:
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in_channels(int): Number of input image channels. Default=3 (RGB)
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downsample_dw_channels1(int): Number of output channels after
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the first conv layer in Learning-To-Downsample (LTD) module.
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downsample_dw_channels2(int): Number of output channels after the second conv layer in LTD.
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global_in_channels(int): Number of input channels of Global Feature Extractor(GFE).
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Equal to number of output channels of LTD.
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global_block_channels(tuple): Tuple of integers that describe the output channels for
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each of the MobileNet-v2 bottleneck residual blocks in GFE.
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global_out_channels(int): Number of output channels of GFE.
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higher_in_channels(int): Number of input channels of the higher resolution branch in FFM.
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Equal to global_in_channels.
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lower_in_channels(int): Number of input channels of the lower resolution branch in FFM.
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Equal to global_out_channels.
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fusion_out_channels(int): Number of output channels of FFM.
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scale_factor(int): The upsampling factor of the higher resolution branch in FFM.
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Equal to the downsampling factor in GFE.
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out_indices(tuple): Tuple of indices of list [higher_res_features, lower_res_features, fusion_output].
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Often set to (0,1,2) to enable aux. heads.
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conv_cfg (dict|None): Config of conv layers.
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norm_cfg (dict|None): Config of norm layers.
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act_cfg (dict): Config of activation layers.
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align_corners (bool): align_corners argument of F.interpolate.
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"""
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super(FastSCNN, self).__init__()
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self.in_channels = in_channels
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self.downsample_dw_channels1 = downsample_dw_channels1
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@ -5,6 +5,27 @@ from .fcn_head import FCNHead
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@HEADS.register_module()
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class SepFCNHead(FCNHead):
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"""Depthwise-Separable Fully Convolutional Network for Semantic Segmentation
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This head is implemented according to Fast-SCNN.
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Args:
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in_channels(int): Number of output channels of FFM.
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channels(int): Number of middle-stage channels in the decode head.
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concat_input(bool): Whether to concatenate original decode input into
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the result of consecutive convolution layers.
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num_classes(int): Used to determine the dimension of final prediction tensor.
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in_index(int): Correspond with 'out_indices' in FastSCNN backbone.
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norm_cfg (dict|None): Config of norm layers.
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align_corners (bool): align_corners argument of F.interpolate.
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loss_decode(dict): Config of loss type and some relevant additional options.
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"""
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def __init__(self, **kwargs):
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super(SepFCNHead, self).__init__(**kwargs)
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