#copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
#
#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.

# from https://github.com/PaddlePaddle/models/blob/release/1.7/PaddleCV/image_classification/models/resnet_vc.py.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import math

import paddle
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr

__all__ = ["ResNet", "ResNet50_vc", "ResNet101_vc", "ResNet152_vc"]

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]
    }
}


class ResNet():
    def __init__(self, layers=50, is_test=False, global_name=''):
        self.params = train_parameters
        self.layers = layers
        self.is_test = is_test
        self.features = {}
        self.global_name = global_name

    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=32,
            filter_size=3,
            stride=2,
            act='relu',
            name='conv1_1')
        conv = self.conv_bn_layer(
            input=conv,
            num_filters=32,
            filter_size=3,
            stride=1,
            act='relu',
            name='conv1_2')
        conv = self.conv_bn_layer(
            input=conv,
            num_filters=64,
            filter_size=3,
            stride=1,
            act='relu',
            name='conv1_3')

        conv = fluid.layers.pool2d(
            input=conv,
            pool_size=3,
            pool_stride=2,
            pool_padding=1,
            pool_type='max',
            name=self.global_name + 'poo1')

        self.features[conv.name] = conv

        for block in range(len(depth)):
            for i in range(depth[block]):
                if layers in [101, 152] and block == 2:
                    if i == 0:
                        conv_name = "res" + str(block + 2) + "a"
                    else:
                        conv_name = "res" + str(block + 2) + "b" + str(i)
                else:
                    conv_name = "res" + str(block + 2) + chr(97 + i)
                conv = self.bottleneck_block(
                    input=conv,
                    num_filters=num_filters[block],
                    stride=2 if i == 0 and block != 0 else 1,
                    name=conv_name)
                self.features[conv.name] = conv

        pool = fluid.layers.pool2d(
            input=conv,
            pool_type='avg',
            global_pooling=True,
            name=self.global_name + 'global_pooling')

        self.features[pool.name] = pool

        stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
        out = fluid.layers.fc(
            input=pool,
            size=class_dim,
            bias_attr=fluid.param_attr.ParamAttr(
                name=self.global_name + 'fc_0.b_0'),
            param_attr=fluid.param_attr.ParamAttr(
                name=self.global_name + 'fc_0.w_0',
                initializer=fluid.initializer.Uniform(-stdv, stdv)))
        return self.features, out

    def conv_bn_layer(self,
                      input,
                      num_filters,
                      filter_size,
                      stride=1,
                      groups=1,
                      act=None,
                      name=None):
        conv = fluid.layers.conv2d(
            input=input,
            num_filters=num_filters,
            filter_size=filter_size,
            stride=stride,
            padding=(filter_size - 1) // 2,
            groups=groups,
            act=None,
            param_attr=ParamAttr(name=self.global_name + name + "_weights"),
            bias_attr=False,
            name=self.global_name + name + '.conv2d.output.1')
        if name == "conv1":
            bn_name = "bn_" + name
        else:
            bn_name = "bn" + name[3:]
        return fluid.layers.batch_norm(
            input=conv,
            act=act,
            name=self.global_name + bn_name + '.output.1',
            param_attr=ParamAttr(self.global_name + bn_name + '_scale'),
            bias_attr=ParamAttr(self.global_name + bn_name + '_offset'),
            moving_mean_name=self.global_name + bn_name + '_mean',
            moving_variance_name=self.global_name + bn_name + '_variance',
            use_global_stats=self.is_test)

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

    def bottleneck_block(self, input, num_filters, stride, name):
        conv0 = self.conv_bn_layer(
            input=input,
            num_filters=num_filters,
            filter_size=1,
            act='relu',
            name=name + "_branch2a")
        conv1 = self.conv_bn_layer(
            input=conv0,
            num_filters=num_filters,
            filter_size=3,
            stride=stride,
            act='relu',
            name=name + "_branch2b")
        conv2 = self.conv_bn_layer(
            input=conv1,
            num_filters=num_filters * 4,
            filter_size=1,
            act=None,
            name=name + "_branch2c")

        short = self.shortcut(
            input, num_filters * 4, stride, name=name + "_branch1")

        return fluid.layers.elementwise_add(
            x=short,
            y=conv2,
            act='relu',
            name=self.global_name + name + ".add.output.5")


def ResNet50_vc(is_test=True, global_name=''):
    model = ResNet(layers=50, is_test=is_test, global_name=global_name)
    return model


def ResNet101_vc(is_test=True, global_name=''):
    model = ResNet(layers=101, is_test=is_test, global_name=global_name)
    return model


def ResNet152_vc(is_test=True, global_name=''):
    model = ResNet(layers=152, is_test=is_test, global_name=global_name)
    return model