# copyright (c) 2020 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 __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import Uniform
import math

__all__ = ["InceptionV3"]


class ConvBNLayer(nn.Layer):
    def __init__(self,
                 num_channels,
                 num_filters,
                 filter_size,
                 stride=1,
                 padding=0,
                 groups=1,
                 act="relu",
                 name=None):
        super(ConvBNLayer, self).__init__()

        self.conv = Conv2D(
            in_channels=num_channels,
            out_channels=num_filters,
            kernel_size=filter_size,
            stride=stride,
            padding=padding,
            groups=groups,
            weight_attr=ParamAttr(name=name + "_weights"),
            bias_attr=False)
        self.batch_norm = BatchNorm(
            num_filters,
            act=act,
            param_attr=ParamAttr(name=name + "_bn_scale"),
            bias_attr=ParamAttr(name=name + "_bn_offset"),
            moving_mean_name=name + "_bn_mean",
            moving_variance_name=name + "_bn_variance")

    def forward(self, inputs):
        y = self.conv(inputs)
        y = self.batch_norm(y)
        return y


class InceptionStem(nn.Layer):
    def __init__(self):
        super(InceptionStem, self).__init__()
        self.conv_1a_3x3 = ConvBNLayer(
            num_channels=3,
            num_filters=32,
            filter_size=3,
            stride=2,
            act="relu",
            name="conv_1a_3x3")
        self.conv_2a_3x3 = ConvBNLayer(
            num_channels=32,
            num_filters=32,
            filter_size=3,
            stride=1,
            act="relu",
            name="conv_2a_3x3")
        self.conv_2b_3x3 = ConvBNLayer(
            num_channels=32,
            num_filters=64,
            filter_size=3,
            padding=1,
            act="relu",
            name="conv_2b_3x3")
        self.maxpool = MaxPool2D(kernel_size=3, stride=2, padding=0)
        self.conv_3b_1x1 = ConvBNLayer(
            num_channels=64,
            num_filters=80,
            filter_size=1,
            act="relu",
            name="conv_3b_1x1")
        self.conv_4a_3x3 = ConvBNLayer(
            num_channels=80,
            num_filters=192,
            filter_size=3,
            act="relu",
            name="conv_4a_3x3")

    def forward(self, x):
        y = self.conv_1a_3x3(x)
        y = self.conv_2a_3x3(y)
        y = self.conv_2b_3x3(y)
        y = self.maxpool(y)
        y = self.conv_3b_1x1(y)
        y = self.conv_4a_3x3(y)
        y = self.maxpool(y)
        return y


class InceptionA(nn.Layer):
    def __init__(self, num_channels, pool_features, name=None):
        super(InceptionA, self).__init__()
        self.branch1x1 = ConvBNLayer(
            num_channels=num_channels,
            num_filters=64,
            filter_size=1,
            act="relu",
            name="inception_a_branch1x1_" + name)
        self.branch5x5_1 = ConvBNLayer(
            num_channels=num_channels,
            num_filters=48,
            filter_size=1,
            act="relu",
            name="inception_a_branch5x5_1_" + name)
        self.branch5x5_2 = ConvBNLayer(
            num_channels=48,
            num_filters=64,
            filter_size=5,
            padding=2,
            act="relu",
            name="inception_a_branch5x5_2_" + name)

        self.branch3x3dbl_1 = ConvBNLayer(
            num_channels=num_channels,
            num_filters=64,
            filter_size=1,
            act="relu",
            name="inception_a_branch3x3dbl_1_" + name)
        self.branch3x3dbl_2 = ConvBNLayer(
            num_channels=64,
            num_filters=96,
            filter_size=3,
            padding=1,
            act="relu",
            name="inception_a_branch3x3dbl_2_" + name)
        self.branch3x3dbl_3 = ConvBNLayer(
            num_channels=96,
            num_filters=96,
            filter_size=3,
            padding=1,
            act="relu",
            name="inception_a_branch3x3dbl_3_" + name)
        self.branch_pool = AvgPool2D(
            kernel_size=3, stride=1, padding=1, exclusive=False)
        self.branch_pool_conv = ConvBNLayer(
            num_channels=num_channels,
            num_filters=pool_features,
            filter_size=1,
            act="relu",
            name="inception_a_branch_pool_" + name)

    def forward(self, x):
        branch1x1 = self.branch1x1(x)
        branch5x5 = self.branch5x5_1(x)
        branch5x5 = self.branch5x5_2(branch5x5)

        branch3x3dbl = self.branch3x3dbl_1(x)
        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
        branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)

        branch_pool = self.branch_pool(x)

        branch_pool = self.branch_pool_conv(branch_pool)
        outputs = paddle.concat(
            [branch1x1, branch5x5, branch3x3dbl, branch_pool], axis=1)
        return outputs


class InceptionB(nn.Layer):
    def __init__(self, num_channels, name=None):
        super(InceptionB, self).__init__()
        self.branch3x3 = ConvBNLayer(
            num_channels=num_channels,
            num_filters=384,
            filter_size=3,
            stride=2,
            act="relu",
            name="inception_b_branch3x3_" + name)
        self.branch3x3dbl_1 = ConvBNLayer(
            num_channels=num_channels,
            num_filters=64,
            filter_size=1,
            act="relu",
            name="inception_b_branch3x3dbl_1_" + name)
        self.branch3x3dbl_2 = ConvBNLayer(
            num_channels=64,
            num_filters=96,
            filter_size=3,
            padding=1,
            act="relu",
            name="inception_b_branch3x3dbl_2_" + name)
        self.branch3x3dbl_3 = ConvBNLayer(
            num_channels=96,
            num_filters=96,
            filter_size=3,
            stride=2,
            act="relu",
            name="inception_b_branch3x3dbl_3_" + name)
        self.branch_pool = MaxPool2D(kernel_size=3, stride=2)

    def forward(self, x):
        branch3x3 = self.branch3x3(x)

        branch3x3dbl = self.branch3x3dbl_1(x)
        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
        branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)

        branch_pool = self.branch_pool(x)

        outputs = paddle.concat([branch3x3, branch3x3dbl, branch_pool], axis=1)

        return outputs


class InceptionC(nn.Layer):
    def __init__(self, num_channels, channels_7x7, name=None):
        super(InceptionC, self).__init__()
        self.branch1x1 = ConvBNLayer(
            num_channels=num_channels,
            num_filters=192,
            filter_size=1,
            act="relu",
            name="inception_c_branch1x1_" + name)
        self.branch7x7_1 = ConvBNLayer(
            num_channels=num_channels,
            num_filters=channels_7x7,
            filter_size=1,
            stride=1,
            act="relu",
            name="inception_c_branch7x7_1_" + name)
        self.branch7x7_2 = ConvBNLayer(
            num_channels=channels_7x7,
            num_filters=channels_7x7,
            filter_size=(1, 7),
            stride=1,
            padding=(0, 3),
            act="relu",
            name="inception_c_branch7x7_2_" + name)
        self.branch7x7_3 = ConvBNLayer(
            num_channels=channels_7x7,
            num_filters=192,
            filter_size=(7, 1),
            stride=1,
            padding=(3, 0),
            act="relu",
            name="inception_c_branch7x7_3_" + name)

        self.branch7x7dbl_1 = ConvBNLayer(
            num_channels=num_channels,
            num_filters=channels_7x7,
            filter_size=1,
            act="relu",
            name="inception_c_branch7x7dbl_1_" + name)
        self.branch7x7dbl_2 = ConvBNLayer(
            num_channels=channels_7x7,
            num_filters=channels_7x7,
            filter_size=(7, 1),
            padding=(3, 0),
            act="relu",
            name="inception_c_branch7x7dbl_2_" + name)
        self.branch7x7dbl_3 = ConvBNLayer(
            num_channels=channels_7x7,
            num_filters=channels_7x7,
            filter_size=(1, 7),
            padding=(0, 3),
            act="relu",
            name="inception_c_branch7x7dbl_3_" + name)
        self.branch7x7dbl_4 = ConvBNLayer(
            num_channels=channels_7x7,
            num_filters=channels_7x7,
            filter_size=(7, 1),
            padding=(3, 0),
            act="relu",
            name="inception_c_branch7x7dbl_4_" + name)
        self.branch7x7dbl_5 = ConvBNLayer(
            num_channels=channels_7x7,
            num_filters=192,
            filter_size=(1, 7),
            padding=(0, 3),
            act="relu",
            name="inception_c_branch7x7dbl_5_" + name)

        self.branch_pool = AvgPool2D(
            kernel_size=3, stride=1, padding=1, exclusive=False)
        self.branch_pool_conv = ConvBNLayer(
            num_channels=num_channels,
            num_filters=192,
            filter_size=1,
            act="relu",
            name="inception_c_branch_pool_" + name)

    def forward(self, x):
        branch1x1 = self.branch1x1(x)

        branch7x7 = self.branch7x7_1(x)
        branch7x7 = self.branch7x7_2(branch7x7)
        branch7x7 = self.branch7x7_3(branch7x7)

        branch7x7dbl = self.branch7x7dbl_1(x)
        branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl)
        branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl)
        branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl)
        branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl)

        branch_pool = self.branch_pool(x)
        branch_pool = self.branch_pool_conv(branch_pool)

        outputs = paddle.concat(
            [branch1x1, branch7x7, branch7x7dbl, branch_pool], axis=1)

        return outputs


class InceptionD(nn.Layer):
    def __init__(self, num_channels, name=None):
        super(InceptionD, self).__init__()
        self.branch3x3_1 = ConvBNLayer(
            num_channels=num_channels,
            num_filters=192,
            filter_size=1,
            act="relu",
            name="inception_d_branch3x3_1_" + name)
        self.branch3x3_2 = ConvBNLayer(
            num_channels=192,
            num_filters=320,
            filter_size=3,
            stride=2,
            act="relu",
            name="inception_d_branch3x3_2_" + name)
        self.branch7x7x3_1 = ConvBNLayer(
            num_channels=num_channels,
            num_filters=192,
            filter_size=1,
            act="relu",
            name="inception_d_branch7x7x3_1_" + name)
        self.branch7x7x3_2 = ConvBNLayer(
            num_channels=192,
            num_filters=192,
            filter_size=(1, 7),
            padding=(0, 3),
            act="relu",
            name="inception_d_branch7x7x3_2_" + name)
        self.branch7x7x3_3 = ConvBNLayer(
            num_channels=192,
            num_filters=192,
            filter_size=(7, 1),
            padding=(3, 0),
            act="relu",
            name="inception_d_branch7x7x3_3_" + name)
        self.branch7x7x3_4 = ConvBNLayer(
            num_channels=192,
            num_filters=192,
            filter_size=3,
            stride=2,
            act="relu",
            name="inception_d_branch7x7x3_4_" + name)
        self.branch_pool = MaxPool2D(kernel_size=3, stride=2)

    def forward(self, x):
        branch3x3 = self.branch3x3_1(x)
        branch3x3 = self.branch3x3_2(branch3x3)

        branch7x7x3 = self.branch7x7x3_1(x)
        branch7x7x3 = self.branch7x7x3_2(branch7x7x3)
        branch7x7x3 = self.branch7x7x3_3(branch7x7x3)
        branch7x7x3 = self.branch7x7x3_4(branch7x7x3)

        branch_pool = self.branch_pool(x)

        outputs = paddle.concat([branch3x3, branch7x7x3, branch_pool], axis=1)
        return outputs


class InceptionE(nn.Layer):
    def __init__(self, num_channels, name=None):
        super(InceptionE, self).__init__()
        self.branch1x1 = ConvBNLayer(
            num_channels=num_channels,
            num_filters=320,
            filter_size=1,
            act="relu",
            name="inception_e_branch1x1_" + name)
        self.branch3x3_1 = ConvBNLayer(
            num_channels=num_channels,
            num_filters=384,
            filter_size=1,
            act="relu",
            name="inception_e_branch3x3_1_" + name)
        self.branch3x3_2a = ConvBNLayer(
            num_channels=384,
            num_filters=384,
            filter_size=(1, 3),
            padding=(0, 1),
            act="relu",
            name="inception_e_branch3x3_2a_" + name)
        self.branch3x3_2b = ConvBNLayer(
            num_channels=384,
            num_filters=384,
            filter_size=(3, 1),
            padding=(1, 0),
            act="relu",
            name="inception_e_branch3x3_2b_" + name)

        self.branch3x3dbl_1 = ConvBNLayer(
            num_channels=num_channels,
            num_filters=448,
            filter_size=1,
            act="relu",
            name="inception_e_branch3x3dbl_1_" + name)
        self.branch3x3dbl_2 = ConvBNLayer(
            num_channels=448,
            num_filters=384,
            filter_size=3,
            padding=1,
            act="relu",
            name="inception_e_branch3x3dbl_2_" + name)
        self.branch3x3dbl_3a = ConvBNLayer(
            num_channels=384,
            num_filters=384,
            filter_size=(1, 3),
            padding=(0, 1),
            act="relu",
            name="inception_e_branch3x3dbl_3a_" + name)
        self.branch3x3dbl_3b = ConvBNLayer(
            num_channels=384,
            num_filters=384,
            filter_size=(3, 1),
            padding=(1, 0),
            act="relu",
            name="inception_e_branch3x3dbl_3b_" + name)
        self.branch_pool = AvgPool2D(
            kernel_size=3, stride=1, padding=1, exclusive=False)
        self.branch_pool_conv = ConvBNLayer(
            num_channels=num_channels,
            num_filters=192,
            filter_size=1,
            act="relu",
            name="inception_e_branch_pool_" + name)

    def forward(self, x):
        branch1x1 = self.branch1x1(x)

        branch3x3 = self.branch3x3_1(x)
        branch3x3 = [
            self.branch3x3_2a(branch3x3),
            self.branch3x3_2b(branch3x3),
        ]
        branch3x3 = paddle.concat(branch3x3, axis=1)

        branch3x3dbl = self.branch3x3dbl_1(x)
        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
        branch3x3dbl = [
            self.branch3x3dbl_3a(branch3x3dbl),
            self.branch3x3dbl_3b(branch3x3dbl),
        ]
        branch3x3dbl = paddle.concat(branch3x3dbl, axis=1)

        branch_pool = self.branch_pool(x)
        branch_pool = self.branch_pool_conv(branch_pool)

        outputs = paddle.concat(
            [branch1x1, branch3x3, branch3x3dbl, branch_pool], axis=1)
        return outputs


class InceptionV3(nn.Layer):
    def __init__(self, class_dim=1000):
        super(InceptionV3, self).__init__()
        self.inception_a_list = [[192, 256, 288], [32, 64, 64]]
        self.inception_c_list = [[768, 768, 768, 768], [128, 160, 160, 192]]

        self.inception_stem = InceptionStem()
        self.inception_block_list = []
        for i in range(len(self.inception_a_list[0])):
            inception_a = self.add_sublayer(
                "inception_a_" + str(i + 1),
                InceptionA(
                    self.inception_a_list[0][i],
                    self.inception_a_list[1][i],
                    name=str(i + 1)))
            self.inception_block_list.append(inception_a)
        inception_b = self.add_sublayer(
            "nception_b_1", InceptionB(
                288, name="1"))
        self.inception_block_list.append(inception_b)

        for i in range(len(self.inception_c_list[0])):
            inception_c = self.add_sublayer(
                "inception_c_" + str(i + 1),
                InceptionC(
                    self.inception_c_list[0][i],
                    self.inception_c_list[1][i],
                    name=str(i + 1)))
            self.inception_block_list.append(inception_c)
        inception_d = self.add_sublayer(
            "inception_d_1", InceptionD(
                768, name="1"))
        self.inception_block_list.append(inception_d)
        inception_e = self.add_sublayer(
            "inception_e_1", InceptionE(
                1280, name="1"))
        self.inception_block_list.append(inception_e)
        inception_e = self.add_sublayer(
            "inception_e_2", InceptionE(
                2048, name="2"))
        self.inception_block_list.append(inception_e)

        self.gap = AdaptiveAvgPool2D(1)
        self.drop = Dropout(p=0.2, mode="downscale_in_infer")
        stdv = 1.0 / math.sqrt(2048 * 1.0)
        self.out = Linear(
            2048,
            class_dim,
            weight_attr=ParamAttr(
                initializer=Uniform(-stdv, stdv), name="fc_weights"),
            bias_attr=ParamAttr(name="fc_offset"))

    def forward(self, x):
        y = self.inception_stem(x)
        for inception_block in self.inception_block_list:
            y = inception_block(y)
        y = self.gap(y)
        y = paddle.reshape(y, shape=[-1, 2048])
        y = self.drop(y)
        y = self.out(y)
        return y
