import os
import paddle.fluid as fluid


def inception_v4(img, class_dim):

    tmp = stem(input=img)
    for i in range(1):
        tmp = inception_A(input=tmp, depth=i)
    tmp = reduction_A(input=tmp)

    for i in range(7):
        tmp = inception_B(input=tmp, depth=i)
    reduction_B(input=tmp)

    for i in range(3):
        tmp = inception_C(input=tmp, depth=i)

    pool = fluid.layers.pool2d(
        pool_type='avg', input=tmp, pool_size=7, pool_stride=1)
    dropout = fluid.layers.dropout(x=pool, dropout_prob=0.2)
    fc = fluid.layers.fc(input=dropout, size=class_dim, act='softmax')
    out = fluid.layers.softmax(input=fc)
    return out


def conv_bn_layer(name,
                  input,
                  num_filters,
                  filter_size,
                  padding=0,
                  stride=1,
                  groups=1,
                  act=None):
    conv = fluid.layers.conv2d(
        name=name,
        input=input,
        num_filters=num_filters,
        filter_size=filter_size,
        stride=stride,
        padding=padding,
        groups=groups,
        act=None,
        bias_attr=False)
    return fluid.layers.batch_norm(name=name + '_norm', input=conv, act=act)


def stem(input):
    conv0 = conv_bn_layer(
        name='stem_conv_0',
        input=input,
        num_filters=32,
        filter_size=3,
        padding=1,
        stride=2)
    conv1 = conv_bn_layer(
        name='stem_conv_1',
        input=conv0,
        num_filters=32,
        filter_size=3,
        padding=1)
    conv2 = conv_bn_layer(
        name='stem_conv_2',
        input=conv1,
        num_filters=64,
        filter_size=3,
        padding=1)

    def block0(input):
        pool0 = fluid.layers.pool2d(
            input=input,
            pool_size=3,
            pool_stride=2,
            pool_type='max',
            pool_padding=1)
        conv0 = conv_bn_layer(
            name='stem_block0_conv',
            input=input,
            num_filters=96,
            filter_size=3,
            stride=2,
            padding=1)
        return fluid.layers.concat(input=[pool0, conv0], axis=1)

    def block1(input):
        l_conv0 = conv_bn_layer(
            name='stem_block1_l_conv0',
            input=input,
            num_filters=64,
            filter_size=1,
            stride=1,
            padding=0)
        l_conv1 = conv_bn_layer(
            name='stem_block1_l_conv1',
            input=l_conv0,
            num_filters=96,
            filter_size=3,
            stride=1,
            padding=1)
        r_conv0 = conv_bn_layer(
            name='stem_block1_r_conv0',
            input=input,
            num_filters=64,
            filter_size=1,
            stride=1,
            padding=0)
        r_conv1 = conv_bn_layer(
            name='stem_block1_r_conv1',
            input=r_conv0,
            num_filters=64,
            filter_size=(7, 1),
            stride=1,
            padding=(3, 0))
        r_conv2 = conv_bn_layer(
            name='stem_block1_r_conv2',
            input=r_conv1,
            num_filters=64,
            filter_size=(1, 7),
            stride=1,
            padding=(0, 3))
        r_conv3 = conv_bn_layer(
            name='stem_block1_r_conv3',
            input=r_conv2,
            num_filters=96,
            filter_size=3,
            stride=1,
            padding=1)
        return fluid.layers.concat(input=[l_conv1, r_conv3], axis=1)

    def block2(input):
        conv0 = conv_bn_layer(
            name='stem_block2_conv',
            input=input,
            num_filters=192,
            filter_size=3,
            stride=2,
            padding=1)
        pool0 = fluid.layers.pool2d(
            input=input,
            pool_size=3,
            pool_stride=2,
            pool_padding=1,
            pool_type='max')
        return fluid.layers.concat(input=[conv0, pool0], axis=1)

    conv3 = block0(conv2)
    conv4 = block1(conv3)
    conv5 = block2(conv4)
    return conv5


def inception_A(input, depth):
    b0_pool0 = fluid.layers.pool2d(
        name='inceptA{0}_branch0_pool0'.format(depth),
        input=input,
        pool_size=3,
        pool_stride=1,
        pool_padding=1,
        pool_type='avg')
    b0_conv0 = conv_bn_layer(
        name='inceptA{0}_branch0_conv0'.format(depth),
        input=b0_pool0,
        num_filters=96,
        filter_size=1,
        stride=1,
        padding=0)
    b1_conv0 = conv_bn_layer(
        name='inceptA{0}_branch1_conv0'.format(depth),
        input=input,
        num_filters=96,
        filter_size=1,
        stride=1,
        padding=0)
    b2_conv0 = conv_bn_layer(
        name='inceptA{0}_branch2_conv0'.format(depth),
        input=input,
        num_filters=64,
        filter_size=1,
        stride=1,
        padding=0)
    b2_conv1 = conv_bn_layer(
        name='inceptA{0}_branch2_conv1'.format(depth),
        input=b2_conv0,
        num_filters=96,
        filter_size=3,
        stride=1,
        padding=1)
    b3_conv0 = conv_bn_layer(
        name='inceptA{0}_branch3_conv0'.format(depth),
        input=input,
        num_filters=64,
        filter_size=1,
        stride=1,
        padding=0)
    b3_conv1 = conv_bn_layer(
        name='inceptA{0}_branch3_conv1'.format(depth),
        input=b3_conv0,
        num_filters=96,
        filter_size=3,
        stride=1,
        padding=1)
    b3_conv2 = conv_bn_layer(
        name='inceptA{0}_branch3_conv2'.format(depth),
        input=b3_conv1,
        num_filters=96,
        filter_size=3,
        stride=1,
        padding=1)
    return fluid.layers.concat(
        input=[b0_conv0, b1_conv0, b2_conv1, b3_conv2], axis=1)


def reduction_A(input):
    b0_pool0 = fluid.layers.pool2d(
        name='ReductA_branch0_pool0',
        input=input,
        pool_size=3,
        pool_stride=2,
        pool_padding=1,
        pool_type='max')
    b1_conv0 = conv_bn_layer(
        name='ReductA_branch1_conv0',
        input=input,
        num_filters=384,
        filter_size=3,
        stride=2,
        padding=1)
    b2_conv0 = conv_bn_layer(
        name='ReductA_branch2_conv0',
        input=input,
        num_filters=192,
        filter_size=1,
        stride=1,
        padding=0)
    b2_conv1 = conv_bn_layer(
        name='ReductA_branch2_conv1',
        input=b2_conv0,
        num_filters=224,
        filter_size=3,
        stride=1,
        padding=1)
    b2_conv2 = conv_bn_layer(
        name='ReductA_branch2_conv2',
        input=b2_conv1,
        num_filters=256,
        filter_size=3,
        stride=2,
        padding=1)
    return fluid.layers.concat(input=[b0_pool0, b1_conv0, b2_conv2], axis=1)


def inception_B(input, depth):
    b0_pool0 = fluid.layers.pool2d(
        name='inceptB{0}_branch0_pool0'.format(depth),
        input=input,
        pool_size=3,
        pool_stride=1,
        pool_padding=1,
        pool_type='avg')
    b0_conv0 = conv_bn_layer(
        name='inceptB{0}_branch0_conv0'.format(depth),
        input=b0_pool0,
        num_filters=128,
        filter_size=1,
        stride=1,
        padding=0)
    b1_conv0 = conv_bn_layer(
        name='inceptB{0}_branch1_conv0'.format(depth),
        input=input,
        num_filters=384,
        filter_size=1,
        stride=1,
        padding=0)
    b2_conv0 = conv_bn_layer(
        name='inceptB{0}_branch2_conv0'.format(depth),
        input=input,
        num_filters=192,
        filter_size=1,
        stride=1,
        padding=0)
    b2_conv1 = conv_bn_layer(
        name='inceptB{0}_branch2_conv1'.format(depth),
        input=b2_conv0,
        num_filters=224,
        filter_size=(1, 7),
        stride=1,
        padding=(0, 3))
    b2_conv2 = conv_bn_layer(
        name='inceptB{0}_branch2_conv2'.format(depth),
        input=b2_conv1,
        num_filters=256,
        filter_size=(7, 1),
        stride=1,
        padding=(3, 0))
    b3_conv0 = conv_bn_layer(
        name='inceptB{0}_branch3_conv0'.format(depth),
        input=input,
        num_filters=192,
        filter_size=1,
        stride=1,
        padding=0)
    b3_conv1 = conv_bn_layer(
        name='inceptB{0}_branch3_conv1'.format(depth),
        input=b3_conv0,
        num_filters=192,
        filter_size=(1, 7),
        stride=1,
        padding=(0, 3))
    b3_conv2 = conv_bn_layer(
        name='inceptB{0}_branch3_conv2'.format(depth),
        input=b3_conv1,
        num_filters=224,
        filter_size=(7, 1),
        stride=1,
        padding=(3, 0))
    b3_conv3 = conv_bn_layer(
        name='inceptB{0}_branch3_conv3'.format(depth),
        input=b3_conv2,
        num_filters=224,
        filter_size=(1, 7),
        stride=1,
        padding=(0, 3))
    b3_conv4 = conv_bn_layer(
        name='inceptB{0}_branch3_conv4'.format(depth),
        input=b3_conv3,
        num_filters=256,
        filter_size=(7, 1),
        stride=1,
        padding=(3, 0))
    return fluid.layers.concat(
        input=[b0_conv0, b1_conv0, b2_conv2, b3_conv4], axis=1)


def reduction_B(input):
    b0_pool0 = fluid.layers.pool2d(
        name='ReductB_branch0_pool0',
        input=input,
        pool_size=3,
        pool_stride=2,
        pool_padding=1,
        pool_type='max')
    b1_conv0 = conv_bn_layer(
        name='ReductB_branch1_conv0',
        input=input,
        num_filters=192,
        filter_size=1,
        stride=1,
        padding=0)
    b1_conv1 = conv_bn_layer(
        name='ReductB_branch1_conv1',
        input=b1_conv0,
        num_filters=192,
        filter_size=3,
        stride=2,
        padding=1)
    b2_conv0 = conv_bn_layer(
        name='ReductB_branch2_conv0',
        input=input,
        num_filters=256,
        filter_size=1,
        stride=1,
        padding=0)
    b2_conv1 = conv_bn_layer(
        name='ReductB_branch2_conv1',
        input=b2_conv0,
        num_filters=256,
        filter_size=(1, 7),
        stride=1,
        padding=(0, 3))
    b2_conv2 = conv_bn_layer(
        name='ReductB_branch2_conv2',
        input=b2_conv1,
        num_filters=320,
        filter_size=(7, 1),
        stride=1,
        padding=(3, 0))
    b2_conv3 = conv_bn_layer(
        name='ReductB_branch2_conv3',
        input=b2_conv2,
        num_filters=320,
        filter_size=3,
        stride=2,
        padding=1)
    return fluid.layers.concat(input=[b0_pool0, b1_conv1, b2_conv3], axis=1)


def inception_C(input, depth):
    b0_pool0 = fluid.layers.pool2d(
        name='inceptC{0}_branch0_pool0'.format(depth),
        input=input,
        pool_size=3,
        pool_stride=1,
        pool_padding=1,
        pool_type='avg')
    b0_conv0 = conv_bn_layer(
        name='inceptC{0}_branch0_conv0'.format(depth),
        input=b0_pool0,
        num_filters=256,
        filter_size=1,
        stride=1,
        padding=0)
    b1_conv0 = conv_bn_layer(
        name='inceptC{0}_branch1_conv0'.format(depth),
        input=input,
        num_filters=256,
        filter_size=1,
        stride=1,
        padding=0)
    b2_conv0 = conv_bn_layer(
        name='inceptC{0}_branch2_conv0'.format(depth),
        input=input,
        num_filters=384,
        filter_size=1,
        stride=1,
        padding=0)
    b2_conv1 = conv_bn_layer(
        name='inceptC{0}_branch2_conv1'.format(depth),
        input=b2_conv0,
        num_filters=256,
        filter_size=(1, 3),
        stride=1,
        padding=(0, 1))
    b2_conv2 = conv_bn_layer(
        name='inceptC{0}_branch2_conv2'.format(depth),
        input=b2_conv0,
        num_filters=256,
        filter_size=(3, 1),
        stride=1,
        padding=(1, 0))
    b3_conv0 = conv_bn_layer(
        name='inceptC{0}_branch3_conv0'.format(depth),
        input=input,
        num_filters=384,
        filter_size=1,
        stride=1,
        padding=0)
    b3_conv1 = conv_bn_layer(
        name='inceptC{0}_branch3_conv1'.format(depth),
        input=b3_conv0,
        num_filters=448,
        filter_size=(1, 3),
        stride=1,
        padding=(0, 1))
    b3_conv2 = conv_bn_layer(
        name='inceptC{0}_branch3_conv2'.format(depth),
        input=b3_conv1,
        num_filters=512,
        filter_size=(3, 1),
        stride=1,
        padding=(1, 0))
    b3_conv3 = conv_bn_layer(
        name='inceptC{0}_branch3_conv3'.format(depth),
        input=b3_conv2,
        num_filters=256,
        filter_size=(3, 1),
        stride=1,
        padding=(1, 0))
    b3_conv4 = conv_bn_layer(
        name='inceptC{0}_branch3_conv4'.format(depth),
        input=b3_conv2,
        num_filters=256,
        filter_size=(1, 3),
        stride=1,
        padding=(0, 1))
    return fluid.layers.concat(
        input=[b0_conv0, b1_conv0, b2_conv1, b2_conv2, b3_conv3, b3_conv4],
        axis=1)