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@author: xingyu liao
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RexNeXt bottleneck type C
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inplanes: input channel dimensionality
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planes: output channel dimensionality
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baseWidth: base width.
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cardinality: num of convolution groups.
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stride: conv stride. Replaces pooling layer.
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ResNext optimized for the ImageNet dataset, as specified in
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https://arxiv.org/pdf/1611.05431.pdf
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Args:
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baseWidth: baseWidth for ResNeXt.
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cardinality: number of convolution groups.
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layers: config of layers, e.g., [3, 4, 6, 3]
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num_classes: number of classes
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r$ é i N)r r6 r r& r% r" Ú output_sizer r r r r r r Ú MaxPool2dÚmaxpool1Ú _make_layerÚlayer1Úlayer2Úlayer3Úlayer4Ú random_init)r! Ú last_strider$ ÚblockÚlayersr% r& )r) r* r+ r Q s zResNeXt.__init__r Fc C sÆ d}|dks | j ||j krLt tj| j ||j d|ddt ||j ¡¡}g }|dkr\d}| || j ||| j| j||¡ ||j | _ x2t d|D ]$}| || j ||| j| jdd¡ qW tj| S )aÊ Stack n bottleneck modules where n is inferred from the depth of the network.
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Args:
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block: block type used to construct ResNext
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planes: number of output channels (need to multiply by block.expansion)
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blocks: number of blocks to be built
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stride: factor to reduce the spatial dimensionality in the first bottleneck of the block.
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Returns: a Module consisting of n sequential bottlenecks.
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Create a ResNeXt instance from config.
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ResNeXt: a :class:`ResNeXt` instance.
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