resnet.py

import paddle
import paddle.fluid as fluid


def conv_bn_layer(input,
                  ch_out,
                  filter_size,
                  stride,
                  padding,
                  act='relu',
                  bias_attr=False):
    tmp = fluid.layers.conv2d(
        input=input,
        filter_size=filter_size,
        num_filters=ch_out,
        stride=stride,
        padding=padding,
        act=None,
        bias_attr=bias_attr)
    return fluid.layers.batch_norm(input=tmp, act=act)


def shortcut(input, ch_in, ch_out, stride):
    if stride == 2:
        temp = fluid.layers.pool2d(
            input, pool_size=2, pool_type='avg', pool_stride=2)
        temp = fluid.layers.conv2d(
            temp,
            filter_size=1,
            num_filters=ch_out,
            stride=1,
            padding=0,
            act=None,
            bias_attr=None)
        return temp
    elif ch_in != ch_out:
        return conv_bn_layer(input, ch_out, 1, stride, 0, None, None)
    else:
        return input


def basicblock(input, ch_in, ch_out, stride):
    tmp = conv_bn_layer(input, ch_out, 3, stride, 1)
    tmp = conv_bn_layer(tmp, ch_out, 3, 1, 1, act=None, bias_attr=True)
    short = shortcut(input, ch_in, ch_out, stride)
    return fluid.layers.elementwise_add(x=tmp, y=short, act='relu')


def layer_warp(block_func, input, ch_in, ch_out, count, stride):
    tmp = block_func(input, ch_in, ch_out, stride)
    for i in range(1, count):
        tmp = block_func(tmp, ch_out, ch_out, 1)
    return tmp


def resnet_cifar(ipt, depth, class_num):
    # depth should be one of 20, 32, 44, 56, 110, 1202
    assert (depth - 2) % 6 == 0
    n = (depth - 2) // 6
    print('[resnet] depth : {:}, class_num : {:}'.format(depth, class_num))
    conv1 = conv_bn_layer(ipt, ch_out=16, filter_size=3, stride=1, padding=1)
    print('conv-1 : shape = {:}'.format(conv1.shape))
    res1 = layer_warp(basicblock, conv1, 16, 16, n, 1)
    print('res--1 : shape = {:}'.format(res1.shape))
    res2 = layer_warp(basicblock, res1, 16, 32, n, 2)
    print('res--2 : shape = {:}'.format(res2.shape))
    res3 = layer_warp(basicblock, res2, 32, 64, n, 2)
    print('res--3 : shape = {:}'.format(res3.shape))
    pool = fluid.layers.pool2d(
        input=res3, pool_size=8, pool_type='avg', pool_stride=1)
    print('pool   : shape = {:}'.format(pool.shape))
    predict = fluid.layers.fc(input=pool, size=class_num, act='softmax')
    print('predict: shape = {:}'.format(predict.shape))
    return predict