open_face.py 文件源码

def __init__(self, inputSize, kernelSize, kernelStride, outputSize, reduceSize, pool, useBatchNorm, reduceStride=None, padding=True):
        super(Inception, self).__init__()
        #
        self.seq_list = []
        self.outputSize = outputSize

        #
        # 1x1 conv (reduce) -> 3x3 conv
        # 1x1 conv (reduce) -> 5x5 conv
        # ...
        for i in range(len(kernelSize)):
            od = OrderedDict()
            # 1x1 conv
            od['1_conv'] = Conv2d(inputSize, reduceSize[i], (1, 1),
                                  reduceStride[i] if reduceStride is not None else 1, (0, 0))
            if useBatchNorm:
                od['2_bn'] = BatchNorm(reduceSize[i])
            od['3_relu'] = nn.ReLU()
            # nxn conv
            pad = int(numpy.floor(kernelSize[i] / 2)) if padding else 0
            od['4_conv'] = Conv2d(
                reduceSize[i], outputSize[i], kernelSize[i], kernelStride[i], pad)
            if useBatchNorm:
                od['5_bn'] = BatchNorm(outputSize[i])
            od['6_relu'] = nn.ReLU()
            #
            self.seq_list.append(nn.Sequential(od))

        ii = len(kernelSize)
        # pool -> 1x1 conv
        od = OrderedDict()
        od['1_pool'] = pool
        if ii < len(reduceSize) and reduceSize[ii] is not None:
            i = ii
            od['2_conv'] = Conv2d(inputSize, reduceSize[i], (1, 1),
                                  reduceStride[i] if reduceStride is not None else 1, (0, 0))
            if useBatchNorm:
                od['3_bn'] = BatchNorm(reduceSize[i])
            od['4_relu'] = nn.ReLU()
        #
        self.seq_list.append(nn.Sequential(od))
        ii += 1

        # reduce: 1x1 conv (channel-wise pooling)
        if ii < len(reduceSize) and reduceSize[ii] is not None:
            i = ii
            od = OrderedDict()
            od['1_conv'] = Conv2d(inputSize, reduceSize[i], (1, 1),
                                  reduceStride[i] if reduceStride is not None else 1, (0, 0))
            if useBatchNorm:
                od['2_bn'] = BatchNorm(reduceSize[i])
            od['3_relu'] = nn.ReLU()
            self.seq_list.append(nn.Sequential(od))

        self.seq_list = nn.ModuleList(self.seq_list)