# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
"""GoogleNet"""
import mindspore.nn as nn
from mindspore.common.initializer import TruncatedNormal
from mindspore.ops import operations as P


def weight_variable():
    """Weight variable."""
    return TruncatedNormal(0.02)


class Conv2dBlock(nn.Cell):
    """
     Basic convolutional block
     Args:
         in_channles (int): Input channel.
         out_channels (int): Output channel.
         kernel_size (int): Input kernel size. Default: 1
         stride (int): Stride size for the first convolutional layer. Default: 1.
         padding (int): Implicit paddings on both sides of the input. Default: 0.
         pad_mode (str): Padding mode. Optional values are "same", "valid", "pad". Default: "same".
      Returns:
          Tensor, output tensor.
    """

    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding=0, pad_mode="same"):
        super(Conv2dBlock, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride,
                              padding=padding, pad_mode=pad_mode, weight_init=weight_variable())
        self.bn = nn.BatchNorm2d(out_channels, eps=0.001)
        self.relu = nn.ReLU()

    def construct(self, x):
        x = self.conv(x)
        x = self.bn(x)
        x = self.relu(x)
        return x


class Inception(nn.Cell):
    """
    Inception Block
    """

    def __init__(self, in_channels, n1x1, n3x3red, n3x3, n5x5red, n5x5, pool_planes):
        super(Inception, self).__init__()
        self.b1 = Conv2dBlock(in_channels, n1x1, kernel_size=1)
        self.b2 = nn.SequentialCell([Conv2dBlock(in_channels, n3x3red, kernel_size=1),
                                     Conv2dBlock(n3x3red, n3x3, kernel_size=3, padding=0)])
        self.b3 = nn.SequentialCell([Conv2dBlock(in_channels, n5x5red, kernel_size=1),
                                     Conv2dBlock(n5x5red, n5x5, kernel_size=3, padding=0)])
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=1, pad_mode="same")
        self.b4 = Conv2dBlock(in_channels, pool_planes, kernel_size=1)
        self.concat = P.Concat(axis=1)

    def construct(self, x):
        branch1 = self.b1(x)
        branch2 = self.b2(x)
        branch3 = self.b3(x)
        cell = self.maxpool(x)
        branch4 = self.b4(cell)
        return self.concat((branch1, branch2, branch3, branch4))


class GoogleNet(nn.Cell):
    """
    Googlenet architecture
    """

    def __init__(self, num_classes, include_top=True):
        super(GoogleNet, self).__init__()
        self.conv1 = Conv2dBlock(3, 64, kernel_size=7, stride=2, padding=0)
        self.maxpool1 = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode="same")

        self.conv2 = Conv2dBlock(64, 64, kernel_size=1)
        self.conv3 = Conv2dBlock(64, 192, kernel_size=3, padding=0)
        self.maxpool2 = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode="same")

        self.block3a = Inception(192, 64, 96, 128, 16, 32, 32)
        self.block3b = Inception(256, 128, 128, 192, 32, 96, 64)
        self.maxpool3 = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode="same")

        self.block4a = Inception(480, 192, 96, 208, 16, 48, 64)
        self.block4b = Inception(512, 160, 112, 224, 24, 64, 64)
        self.block4c = Inception(512, 128, 128, 256, 24, 64, 64)
        self.block4d = Inception(512, 112, 144, 288, 32, 64, 64)
        self.block4e = Inception(528, 256, 160, 320, 32, 128, 128)
        self.maxpool4 = nn.MaxPool2d(kernel_size=2, stride=2, pad_mode="same")

        self.block5a = Inception(832, 256, 160, 320, 32, 128, 128)
        self.block5b = Inception(832, 384, 192, 384, 48, 128, 128)

        self.dropout = nn.Dropout(keep_prob=0.8)
        self.include_top = include_top
        if self.include_top:
            self.mean = P.ReduceMean(keep_dims=True)
            self.flatten = nn.Flatten()
            self.classifier = nn.Dense(1024, num_classes, weight_init=weight_variable(),
                                       bias_init=weight_variable())


    def construct(self, x):
        """construct"""
        x = self.conv1(x)
        x = self.maxpool1(x)

        x = self.conv2(x)
        x = self.conv3(x)
        x = self.maxpool2(x)

        x = self.block3a(x)
        x = self.block3b(x)
        x = self.maxpool3(x)

        x = self.block4a(x)
        x = self.block4b(x)
        x = self.block4c(x)
        x = self.block4d(x)
        x = self.block4e(x)
        x = self.maxpool4(x)

        x = self.block5a(x)
        x = self.block5b(x)
        if not self.include_top:
            return x

        x = self.mean(x, (2, 3))
        x = self.flatten(x)
        x = self.classifier(x)

        return x