分组卷积和深度可分离卷积实现 tensorflow 之tf.nn.depthwise_conv2d and separable_conv2d实现及原理卷积操作详解

- 这里暂时看各种框架api实现，相比于普通卷积（卷积操作详解）的高效实现，分组卷积怎么高效实现待研究！

tensorflow实现：

pytorch实现：

https://pytorch.org/docs/stable/nn.html#conv2d

- torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros')

- groups controls the connections between inputs and outputs. in_channels and out_channels must both be divisible by groups. For example,

At groups=1, all inputs are convolved to all outputs.

At groups=2, the operation becomes equivalent to having two conv layers side by side, each seeing half the input channels, and producing half the output channels, and both subsequently concatenated.

At groups= in_channels, each input channel is convolved with its own set of filters, of size: $\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor⌊in_channelsout_channels⌋.$

caffe实现：

http://caffe.berkeleyvision.org/tutorial/layers/convolution.html

  layer {
    name: "conv1"
    type: "Convolution"
    bottom: "data"
    top: "conv1"
    # learning rate and decay multipliers for the filters
    param { lr_mult: 1 decay_mult: 1 }
    # learning rate and decay multipliers for the biases
    param { lr_mult: 2 decay_mult: 0 }
    convolution_param {
      num_output: 96     # learn 96 filters
      kernel_size: 11    # each filter is 11x11
      stride: 4          # step 4 pixels between each filter application
      weight_filler {
        type: "gaussian" # initialize the filters from a Gaussian
        std: 0.01        # distribution with stdev 0.01 (default mean: 0)
      }
      bias_filler {
        type: "constant" # initialize the biases to zero (0)
        value: 0
      }
    }
  }

Optional

bias_term [default true]: specifies whether to learn and apply a set of additive biases to the filter outputs
pad (or pad_h and pad_w) [default 0]: specifies the number of pixels to (implicitly) add to each side of the input
stride (or stride_h and stride_w) [default 1]: specifies the intervals at which to apply the filters to the input
group (g) [default 1]: If g > 1, we restrict the connectivity of each filter to a subset of the input. Specifically, the input and output channels are separated into g groups, and the $i$

分组卷积和深度可分离卷积实现 tensorflow 之tf.nn.depthwise_conv2d and separable_conv2d实现及原理 卷积操作详解

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分组卷积和深度可分离卷积实现 tensorflow 之tf.nn.depthwise_conv2d and separable_conv2d实现及原理卷积操作详解