# 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.
import os

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 paddlehub.module.module import moduleinfo
from paddlehub.module.cv_module import ImageClassifierModule


class ConvBNLayer(nn.Layer):
    """Basic conv bn layer."""

    def __init__(self,
                 num_channels: int,
                 filter_size: int,
                 num_filters: int,
                 stride: int,
                 padding: int,
                 num_groups: int = 1,
                 name: str = 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=num_groups,
            weight_attr=ParamAttr(name=name + "_weights"),
            bias_attr=False)

        self._batch_norm = BatchNorm(
            num_filters,
            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: paddle.Tensor, if_act: bool = True):
        y = self._conv(inputs)
        y = self._batch_norm(y)
        if if_act:
            y = F.relu6(y)
        return y


class InvertedResidualUnit(nn.Layer):
    """Inverted Residual unit."""

    def __init__(self, num_channels: int, num_in_filter: int, num_filters: int, stride: int, filter_size: int,
                 padding: int, expansion_factor: int, name: str):
        super(InvertedResidualUnit, self).__init__()

        num_expfilter = int(round(num_in_filter * expansion_factor))
        self._expand_conv = ConvBNLayer(
            num_channels=num_channels,
            num_filters=num_expfilter,
            filter_size=1,
            stride=1,
            padding=0,
            num_groups=1,
            name=name + "_expand")

        self._bottleneck_conv = ConvBNLayer(
            num_channels=num_expfilter,
            num_filters=num_expfilter,
            filter_size=filter_size,
            stride=stride,
            padding=padding,
            num_groups=num_expfilter,
            name=name + "_dwise")

        self._linear_conv = ConvBNLayer(
            num_channels=num_expfilter,
            num_filters=num_filters,
            filter_size=1,
            stride=1,
            padding=0,
            num_groups=1,
            name=name + "_linear")

    def forward(self, inputs: paddle.Tensor, ifshortcut: bool):
        y = self._expand_conv(inputs, if_act=True)
        y = self._bottleneck_conv(y, if_act=True)
        y = self._linear_conv(y, if_act=False)
        if ifshortcut:
            y = paddle.elementwise_add(inputs, y)
        return y


class InversiBlocks(nn.Layer):
    """Inverted residual block composed by inverted residual unit."""

    def __init__(self, in_c: int, t: int, c: int, n: int, s: int, name: str):
        super(InversiBlocks, self).__init__()

        self._first_block = InvertedResidualUnit(
            num_channels=in_c,
            num_in_filter=in_c,
            num_filters=c,
            stride=s,
            filter_size=3,
            padding=1,
            expansion_factor=t,
            name=name + "_1")

        self._block_list = []
        for i in range(1, n):
            block = self.add_sublayer(
                name + "_" + str(i + 1),
                sublayer=InvertedResidualUnit(
                    num_channels=c,
                    num_in_filter=c,
                    num_filters=c,
                    stride=1,
                    filter_size=3,
                    padding=1,
                    expansion_factor=t,
                    name=name + "_" + str(i + 1)))
            self._block_list.append(block)

    def forward(self, inputs: paddle.Tensor):
        y = self._first_block(inputs, ifshortcut=False)
        for block in self._block_list:
            y = block(y, ifshortcut=True)
        return y


@moduleinfo(
    name="mobilenet_v2_imagenet",
    type="cv/classification",
    author="paddlepaddle",
    author_email="",
    summary="mobilenet_v2_imagenet is a classification model, "
    "this module is trained with Imagenet dataset.",
    version="1.1.0",
    meta=ImageClassifierModule)
class MobileNet(nn.Layer):
    """MobileNetV2"""

    def __init__(self, class_dim: int = 1000, load_checkpoint: str = None):
        super(MobileNet, self).__init__()

        self.class_dim = class_dim

        bottleneck_params_list = [(1, 16, 1, 1), (6, 24, 2, 2), (6, 32, 3, 2), (6, 64, 4, 2), (6, 96, 3, 1),
                                  (6, 160, 3, 2), (6, 320, 1, 1)]

        self.conv1 = ConvBNLayer(
            num_channels=3, num_filters=int(32), filter_size=3, stride=2, padding=1, name="conv1_1")

        self.block_list = []
        i = 1
        in_c = int(32)
        for layer_setting in bottleneck_params_list:
            t, c, n, s = layer_setting
            i += 1
            block = self.add_sublayer(
                "conv" + str(i), sublayer=InversiBlocks(in_c=in_c, t=t, c=int(c), n=n, s=s, name="conv" + str(i)))
            self.block_list.append(block)
            in_c = int(c)

        self.out_c = 1280
        self.conv9 = ConvBNLayer(
            num_channels=in_c, num_filters=self.out_c, filter_size=1, stride=1, padding=0, name="conv9")

        self.pool2d_avg = AdaptiveAvgPool2d(1)

        self.out = Linear(
            self.out_c, class_dim, weight_attr=ParamAttr(name="fc10_weights"), bias_attr=ParamAttr(name="fc10_offset"))

        if load_checkpoint is not None:
            model_dict = paddle.load(load_checkpoint)[0]
            self.set_dict(model_dict)
            print("load custom checkpoint success")

        else:
            checkpoint = os.path.join(self.directory, 'mobilenet_v2_imagenet.pdparams')
            if not os.path.exists(checkpoint):
                os.system(
                    'wget https://paddlehub.bj.bcebos.com/dygraph/image_classification/mobilenet_v2_imagenet.pdparams -O '
                    + checkpoint)
            model_dict = paddle.load(checkpoint)[0]
            self.set_dict(model_dict)
            print("load pretrained checkpoint success")

    def forward(self, inputs: paddle.Tensor):
        y = self.conv1(inputs, if_act=True)
        for block in self.block_list:
            y = block(y)
        y = self.conv9(y, if_act=True)
        y = self.pool2d_avg(y)
        y = paddle.reshape(y, shape=[-1, self.out_c])
        y = self.out(y)
        return y
