#   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
import math
from paddle.fluid.param_attr import ParamAttr
__all__ = [
    "SE_ResNeXt", "SE_ResNeXt50_32x4d_vd_embedding", "SE_ResNeXt101_32x4d_vd_embedding",
    "SE_ResNeXt152_64x4d_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": [10, 16, 20],
        "steps": [0.01, 0.001, 0.0001, 0.00001]
    }
}
class SE_ResNeXt():
    def __init__(self, layers=50):
        self.params = train_parameters
        self.layers = layers
    def net(self, input, embedding_size=256):
        layers = self.layers
        supported_layers = [50, 101, 152]
        assert layers in supported_layers, \
            "supported layers are {} but input layer is {}".format(supported_layers, layers)
        endpoints = {}
        if layers == 50:
            cardinality = 32
            reduction_ratio = 16
            depth = [3, 4, 6, 3]
            num_filters = [128, 256, 512, 1024]
            conv = self.conv_bn_layer(
                input=input, num_filters=64, filter_size=3, stride=2, act='relu', name='conv1_1')
            conv = self.conv_bn_layer(
                input=conv, num_filters=64, filter_size=3, stride=1, act='relu', name='conv1_2')
            conv = self.conv_bn_layer(
                input=conv, num_filters=128, 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')
        elif layers == 101:
            cardinality = 32
            reduction_ratio = 16
            depth = [3, 4, 23, 3]
            num_filters = [128, 256, 512, 1024]
            conv = self.conv_bn_layer(
                input=input, num_filters=64, filter_size=3, stride=2, act='relu', name='conv1_1')
            conv = self.conv_bn_layer(
                input=conv, num_filters=64, filter_size=3, stride=1, act='relu', name='conv1_2')
            conv = self.conv_bn_layer(
                input=conv, num_filters=128, 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')
        elif layers == 152:
            cardinality = 64
            reduction_ratio = 16
            depth = [3, 8, 36, 3]
            num_filters = [256, 512, 1024, 2048]
            conv = self.conv_bn_layer(
                input=input,
                num_filters=64,
                filter_size=3,
                stride=2,
                act='relu',
                name='conv1_1')
            conv = self.conv_bn_layer(
                input=conv, num_filters=64, filter_size=3, stride=1, act='relu',name='conv1_2')
            conv = self.conv_bn_layer(
                input=conv,
                num_filters=128,
                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')
        n = 1 if layers == 50 or layers == 101 else 3
        for block in range(len(depth)):
            n += 1
            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,
                    cardinality=cardinality,
                    reduction_ratio=reduction_ratio,
                    if_first=block==0, 
                    name=str(n)+'_'+str(i+1))
        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 shortcut(self, input, ch_out, stride, name, if_first=False):
        ch_in = input.shape[1]
        if ch_in != ch_out or stride != 1:
            filter_size = 1
            if if_first:
                return self.conv_bn_layer(input, ch_out, filter_size, stride, name='conv'+name+'_prj')
            else:
                return self.conv_bn_layer_new(input, ch_out, filter_size, stride, name='conv'+name+'_prj')
        else:
            return input
    def bottleneck_block(self, input, num_filters, stride, cardinality,
                         reduction_ratio,if_first, name=None):
        conv0 = self.conv_bn_layer(
            input=input, num_filters=num_filters, filter_size=1, act='relu',name='conv'+name+'_x1')
        conv1 = self.conv_bn_layer(
            input=conv0,
            num_filters=num_filters,
            filter_size=3,
            stride=stride,
            groups=cardinality,
            act='relu',
            name='conv'+name+'_x2')
        if cardinality == 64:
            num_filters = num_filters / 2
        conv2 = self.conv_bn_layer(
            input=conv1, num_filters=num_filters * 2, filter_size=1, act=None, name='conv'+name+'_x3')
        scale = self.squeeze_excitation(
            input=conv2,
            num_channels=num_filters * 2,
            reduction_ratio=reduction_ratio,
            name='fc'+name)
        short = self.shortcut(input, num_filters * 2, stride, if_first=if_first, name=name)
        return fluid.layers.elementwise_add(x=short, y=scale, act='relu')
    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,
            bias_attr=False,
            param_attr=ParamAttr(name=name + '_weights'),
            )
        bn_name = name + "_bn"
        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)
        bn_name = name + "_bn"
        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 squeeze_excitation(self, input, num_channels, reduction_ratio, name=None):
        pool = fluid.layers.pool2d(
            input=input, pool_size=0, pool_type='avg', global_pooling=True)
        stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
        squeeze = fluid.layers.fc(input=pool,
                                  size=num_channels / reduction_ratio,
                                  act='relu',
                                  param_attr=fluid.param_attr.ParamAttr(
                                      initializer=fluid.initializer.Uniform(
                                          -stdv, stdv),name=name+'_sqz_weights'),
                                 bias_attr=ParamAttr(name=name+'_sqz_offset'))
        stdv = 1.0 / math.sqrt(squeeze.shape[1] * 1.0)
        excitation = fluid.layers.fc(input=squeeze,
                                     size=num_channels,
                                     act='sigmoid',
                                     param_attr=fluid.param_attr.ParamAttr(
                                         initializer=fluid.initializer.Uniform(
                                             -stdv, stdv),name=name+'_exc_weights'),
                                    bias_attr=ParamAttr(name=name+'_exc_offset'))
        scale = fluid.layers.elementwise_mul(x=input, y=excitation, axis=0)
        return scale
def SE_ResNeXt50_32x4d_vd_embedding():
    model = SE_ResNeXt(layers=50)
    return model
def SE_ResNeXt101_32x4d_vd_embedding():
    model = SE_ResNeXt(layers=101)
    return model
def SE_ResNeXt152_64x4d_vd_embedding():
    model = SE_ResNeXt(layers=152)
    return model
