#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
import paddle
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
import math
__all__ = ["ResNet", "ResNet50_vd_embedding","ResNet101_vd_embedding", "ResNet152_vd_embedding", "ResNet200_vd_embedding"]
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_3x3 = False):
        self.params = train_parameters
        self.layers = layers
        self.is_3x3 = is_3x3
    def net(self, input, embedding_size=256):
        is_3x3 = self.is_3x3
        layers = self.layers
        supported_layers = [50, 101, 152, 200]
        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]
        elif layers == 200:
            depth = [3, 12, 48, 3]
        num_filters = [64, 128, 256, 512]
        endpoints = {}
        if is_3x3 == False:
            conv = self.conv_bn_layer(
                input=input, num_filters=64, filter_size=7, stride=2, act='relu')
        else:
            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')
        for block in range(len(depth)):
            for i in range(depth[block]):
                if layers in [101, 152, 200] 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,
                    if_first=block==0, 
                    name=conv_name)
        pool = fluid.layers.pool2d(
            input=conv, pool_size=14, pool_type='avg', global_pooling=True)
        if embedding_size > 0:
            embedding = fluid.layers.fc(input=pool, size=embedding_size)
            endpoints['embedding'] = embedding
        else:
            endpoints['embedding'] = pool
        return endpoints
 
    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=name + "_weights"),
            bias_attr=False)
        if name == "conv1":
            bn_name = "bn_" + name
        else:
            bn_name = "bn" + name[3:] 
        return fluid.layers.batch_norm(input=conv, 
                                       act=act,
                                       param_attr=ParamAttr(name=bn_name + '_scale'),
                                       bias_attr=ParamAttr(bn_name + '_offset'),
                                       moving_mean_name=bn_name + '_mean',
                                       moving_variance_name=bn_name + '_variance')
    
    def conv_bn_layer_new(self,
                      input,
                      num_filters,
                      filter_size,
                      stride=1,
                      groups=1,
                      act=None,
                      name=None):
        pool = fluid.layers.pool2d(input=input,
            pool_size=2,
            pool_stride=2,
            pool_padding=0,
            pool_type='avg')
        
        conv = fluid.layers.conv2d(
            input=pool,
            num_filters=num_filters,
            filter_size=filter_size,
            stride=1,
            padding=(filter_size - 1) / 2,
            groups=groups,
            act=None,
            param_attr=ParamAttr(name=name + "_weights"),
            bias_attr=False)
        if name == "conv1":
            bn_name = "bn_" + name
        else:
            bn_name = "bn" + name[3:]
        return fluid.layers.batch_norm(input=conv, 
                                       act=act,
                                       param_attr=ParamAttr(name=bn_name + '_scale'),
                                       bias_attr=ParamAttr(bn_name + '_offset'),
                                       moving_mean_name=bn_name + '_mean',
                                       moving_variance_name=bn_name + '_variance')
    
    def shortcut(self, input, ch_out, stride, name, if_first=False):
        ch_in = input.shape[1]
        if ch_in != ch_out or stride != 1:
            if if_first:
                return self.conv_bn_layer(input, ch_out, 1, stride, name=name)
            else:
                return self.conv_bn_layer_new(input, ch_out, 1, stride, name=name)
        else:
            return input
    def bottleneck_block(self, input, num_filters, stride, name, if_first):
        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, if_first=if_first, name=name + "_branch1")
        return fluid.layers.elementwise_add(x=short, y=conv2, act='relu')
    
def ResNet50_vd_embedding():
    model = ResNet(layers=50, is_3x3 = True)
    return model
def ResNet101_vd_embedding():
    model = ResNet(layers=101, is_3x3 = True)
    return model
def ResNet152_vd_embedding():
    model = ResNet(layers=152, is_3x3 = True)
    return model
def ResNet200_vd_embedding():
    model = ResNet(layers=200, is_3x3 = True)
    return model