#coding=utf-8
from __future__ import division
'''
 * Copyright 2017-2018 Baidu Inc.
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
'''
"""
定义ResNet
"""
from builtins import range
from builtins import object
from past.utils import old_div
import paddle.fluid as fluid
import math

__all__ = ["resnet_cifar10","ResNet", "ResNet50", "ResNet101", "ResNet152"]



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 ch_in != ch_out:
        return conv_bn_layer(input, ch_out, 1, stride, 0, 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_cifar10(ipt, depth=32):
    # depth should be one of 20, 32, 44, 56, 110, 1202
    assert (depth - 2) % 6 == 0
    n = old_div((depth - 2), 6)
    nStages = {16, 64, 128}
    conv1 = conv_bn_layer(ipt, ch_out=16, filter_size=3, stride=1, padding=1)
    res1 = layer_warp(basicblock, conv1, 16, 16, n, 1)
    res2 = layer_warp(basicblock, res1, 16, 32, n, 2)
    res3 = layer_warp(basicblock, res2, 32, 64, n, 2)
    pool = fluid.layers.pool2d(
        input=res3, pool_size=8, pool_type='avg', pool_stride=1)
    predict = fluid.layers.fc(input=pool, size=10, act='softmax')
    return predict

#定义训练参数
train_parameters = {
    "input_size": [3, 224, 224],
    "input_mean": [0.485, 0.456, 0.406],
    "input_std": [0.229, 0.224, 0.225],
    "learning_strategy": {
        "name": "piecewise_decay",
        "batch_size": 256,
        "epochs": [30, 60, 90],
        "steps": [0.1, 0.01, 0.001, 0.0001]
    }
}

#定义通用的ResNet类
class ResNet(object):
    def __init__(self, layers=50):
        self.params = train_parameters
        self.layers = layers

    def net(self, input, class_dim=1000):
        layers = self.layers
        supported_layers = [50, 101, 152]
        assert layers in supported_layers, \
            "supported layers are {} but input layer is {}".format(supported_layers, layers)

        if layers == 50:
            depth = [3, 4, 6, 3]
        elif layers == 101:
            depth = [3, 4, 23, 3]
        elif layers == 152:
            depth = [3, 8, 36, 3]
        num_filters = [64, 128, 256, 512]

        conv = self.conv_bn_layer(
            input=input, num_filters=64, filter_size=7, stride=2, act='relu')
        conv = fluid.layers.pool2d(
            input=conv,
            pool_size=3,
            pool_stride=2,
            pool_padding=1,
            pool_type='max')

        for block in range(len(depth)):
            for i in range(depth[block]):
                conv = self.bottleneck_block(
                    input=conv,
                    num_filters=num_filters[block],
                    stride=2 if i == 0 and block != 0 else 1)

        pool = fluid.layers.pool2d(
            input=conv, pool_size=7, pool_type='avg', global_pooling=True)
        stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
        out = fluid.layers.fc(input=pool,
                              size=class_dim,
                              act='softmax',
                              param_attr=fluid.param_attr.ParamAttr(
                                  initializer=fluid.initializer.Uniform(-stdv,
                                                                        stdv)))
        return out

    def conv_bn_layer(self,
                      input,
                      num_filters,
                      filter_size,
                      stride=1,
                      groups=1,
                      act=None):
        conv = fluid.layers.conv2d(
            input=input,
            num_filters=num_filters,
            filter_size=filter_size,
            stride=stride,
            padding=old_div((filter_size - 1), 2),
            groups=groups,
            act=None,
            bias_attr=False)
        return fluid.layers.batch_norm(input=conv, act=act)

    def shortcut(self, input, ch_out, stride):
        ch_in = input.shape[1]
        if ch_in != ch_out or stride != 1:
            return self.conv_bn_layer(input, ch_out, 1, stride)
        else:
            return input

    def bottleneck_block(self, input, num_filters, stride):
        conv0 = self.conv_bn_layer(
            input=input, num_filters=num_filters, filter_size=1, act='relu')
        conv1 = self.conv_bn_layer(
            input=conv0,
            num_filters=num_filters,
            filter_size=3,
            stride=stride,
            act='relu')
        conv2 = self.conv_bn_layer(
            input=conv1, num_filters=num_filters * 4, filter_size=1, act=None)

        short = self.shortcut(input, num_filters * 4, stride)

        return fluid.layers.elementwise_add(x=short, y=conv2, act='relu')


def ResNet50():
    model = ResNet(layers=50)
    return model


def ResNet101():
    model = ResNet(layers=101)
    return model


def ResNet152():
    model = ResNet(layers=152)
    return model
