Function resnet_backbone

examples/ResNet/resnet_model.py:124–138

(image, num_blocks, group_func, block_func)

def resnet_backbone(image, num_blocks, group_func, block_func):
    with argscope(Conv2D, use_bias=False,
                  kernel_initializer=tf.variance_scaling_initializer(scale=2.0, mode='fan_out')):
        # Note that TF pads the image by [2, 3] instead of [3, 2].
        # Similar things happen in later stride=2 layers as well.
        l = Conv2D('conv0', image, 64, 7, strides=2, activation=BNReLU)
        l = MaxPooling('pool0', l, pool_size=3, strides=2, padding='SAME')
        l = group_func('group0', l, block_func, 64, num_blocks[0], 1)
        l = group_func('group1', l, block_func, 128, num_blocks[1], 2)
        l = group_func('group2', l, block_func, 256, num_blocks[2], 2)
        l = group_func('group3', l, block_func, 512, num_blocks[3], 2)
        l = GlobalAvgPooling('gap', l)
        logits = FullyConnected('linear', l, 1000,
                                kernel_initializer=tf.random_normal_initializer(stddev=0.01))
    return logits