# -*- coding: utf-8 -*-
# MIT License
#
# Copyright (c) 2019 Megvii Technology
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:

# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.

# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
#
# ------------------------------------------------------------------------------
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#
# This file has been modified by Megvii ("Megvii Modifications").
# All Megvii Modifications are Copyright (C) 2014-2019 Megvii Inc. All rights reserved.
# ------------------------------------------------------------------------------
import megengine.functional as F
import megengine.hub as hub
import megengine.module as M


class ShuffleV2Block(M.Module):
    def __init__(self, inp, oup, mid_channels, *, ksize, stride):
        super(ShuffleV2Block, self).__init__()
        self.stride = stride
        assert stride in [1, 2]

        self.mid_channels = mid_channels
        self.ksize = ksize
        pad = ksize // 2
        self.pad = pad
        self.inp = inp

        outputs = oup - inp

        branch_main = [
            # pw
            M.Conv2d(inp, mid_channels, 1, 1, 0, bias=False),
            M.BatchNorm2d(mid_channels),
            M.ReLU(),
            # dw
            M.Conv2d(
                mid_channels,
                mid_channels,
                ksize,
                stride,
                pad,
                groups=mid_channels,
                bias=False,
            ),
            M.BatchNorm2d(mid_channels),
            # pw-linear
            M.Conv2d(mid_channels, outputs, 1, 1, 0, bias=False),
            M.BatchNorm2d(outputs),
            M.ReLU(),
        ]
        self.branch_main = M.Sequential(*branch_main)

        if stride == 2:
            branch_proj = [
                # dw
                M.Conv2d(inp, inp, ksize, stride, pad, groups=inp, bias=False),
                M.BatchNorm2d(inp),
                # pw-linear
                M.Conv2d(inp, inp, 1, 1, 0, bias=False),
                M.BatchNorm2d(inp),
                M.ReLU(),
            ]
            self.branch_proj = M.Sequential(*branch_proj)
        else:
            self.branch_proj = None

    def forward(self, old_x):
        if self.stride == 1:
            x_proj, x = self.channel_shuffle(old_x)
            return F.concat((x_proj, self.branch_main(x)), 1)
        elif self.stride == 2:
            x_proj = old_x
            x = old_x
            return F.concat((self.branch_proj(x_proj), self.branch_main(x)), 1)
        else:
            raise ValueError("use stride 1 or 2, current stride {}".format(self.stride))

    def channel_shuffle(self, x):
        batchsize, num_channels, height, width = x.shape
        # assert (num_channels % 4 == 0)
        x = x.reshape(batchsize * num_channels // 2, 2, height * width)
        x = x.dimshuffle(1, 0, 2)
        x = x.reshape(2, -1, num_channels // 2, height, width)
        return x[0], x[1]


class ShuffleNetV2(M.Module):
    def __init__(self, input_size=224, num_classes=1000, model_size="1.5x"):
        super(ShuffleNetV2, self).__init__()

        self.stage_repeats = [4, 8, 4]
        self.model_size = model_size
        if model_size == "0.5x":
            self.stage_out_channels = [-1, 24, 48, 96, 192, 1024]
        elif model_size == "1.0x":
            self.stage_out_channels = [-1, 24, 116, 232, 464, 1024]
        elif model_size == "1.5x":
            self.stage_out_channels = [-1, 24, 176, 352, 704, 1024]
        elif model_size == "2.0x":
            self.stage_out_channels = [-1, 24, 244, 488, 976, 2048]
        else:
            raise NotImplementedError

        # building first layer
        input_channel = self.stage_out_channels[1]
        self.first_conv = M.Sequential(
            M.Conv2d(3, input_channel, 3, 2, 1, bias=False),
            M.BatchNorm2d(input_channel),
            M.ReLU(),
        )

        self.maxpool = M.MaxPool2d(kernel_size=3, stride=2, padding=1)

        self.features = []
        for idxstage in range(len(self.stage_repeats)):
            numrepeat = self.stage_repeats[idxstage]
            output_channel = self.stage_out_channels[idxstage + 2]

            for i in range(numrepeat):
                if i == 0:
                    self.features.append(
                        ShuffleV2Block(
                            input_channel,
                            output_channel,
                            mid_channels=output_channel // 2,
                            ksize=3,
                            stride=2,
                        )
                    )
                else:
                    self.features.append(
                        ShuffleV2Block(
                            input_channel // 2,
                            output_channel,
                            mid_channels=output_channel // 2,
                            ksize=3,
                            stride=1,
                        )
                    )

                input_channel = output_channel

        self.features = M.Sequential(*self.features)

        self.conv_last = M.Sequential(
            M.Conv2d(input_channel, self.stage_out_channels[-1], 1, 1, 0, bias=False),
            M.BatchNorm2d(self.stage_out_channels[-1]),
            M.ReLU(),
        )
        self.globalpool = M.AvgPool2d(7)
        if self.model_size == "2.0x":
            self.dropout = M.Dropout(0.2)
        self.classifier = M.Sequential(
            M.Linear(self.stage_out_channels[-1], num_classes, bias=False)
        )
        self._initialize_weights()

    def forward(self, x):
        x = self.first_conv(x)
        x = self.maxpool(x)
        x = self.features(x)
        x = self.conv_last(x)

        x = self.globalpool(x)
        if self.model_size == "2.0x":
            x = self.dropout(x)
        x = x.reshape(-1, self.stage_out_channels[-1])
        x = self.classifier(x)
        return x

    def _initialize_weights(self):
        for name, m in self.named_modules():
            if isinstance(m, M.Conv2d):
                if "first" in name:
                    M.init.normal_(m.weight, 0, 0.01)
                else:
                    M.init.normal_(m.weight, 0, 1.0 / m.weight.shape[1])
                if m.bias is not None:
                    M.init.fill_(m.bias, 0)
            elif isinstance(m, M.BatchNorm2d):
                M.init.fill_(m.weight, 1)
                if m.bias is not None:
                    M.init.fill_(m.bias, 0.0001)
                M.init.fill_(m.running_mean, 0)
            elif isinstance(m, M.BatchNorm1d):
                M.init.fill_(m.weight, 1)
                if m.bias is not None:
                    M.init.fill_(m.bias, 0.0001)
                M.init.fill_(m.running_mean, 0)
            elif isinstance(m, M.Linear):
                M.init.normal_(m.weight, 0, 0.01)
                if m.bias is not None:
                    M.init.fill_(m.bias, 0)


@hub.pretrained(
    "https://data.megengine.org.cn/models/weights/snetv2_x2_0_75115_497d4601.pkl"
)
def shufflenet_v2_x2_0(num_classes=1000):
    return ShuffleNetV2(num_classes=num_classes, model_size="2.0x")


@hub.pretrained(
    "https://data.megengine.org.cn/models/weights/snetv2_x1_5_72775_38ac4273.pkl"
)
def shufflenet_v2_x1_5(num_classes=1000):
    return ShuffleNetV2(num_classes=num_classes, model_size="1.5x")


@hub.pretrained(
    "https://data.megengine.org.cn/models/weights/snetv2_x1_0_69369_daf9dba0.pkl"
)
def shufflenet_v2_x1_0(num_classes=1000):
    return ShuffleNetV2(num_classes=num_classes, model_size="1.0x")


@hub.pretrained(
    "https://data.megengine.org.cn/models/weights/snetv2_x0_5_60750_c28db1a2.pkl"
)
def shufflenet_v2_x0_5(num_classes=1000):
    return ShuffleNetV2(num_classes=num_classes, model_size="0.5x")
