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# ------------------------------------------------------------------------------
# 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.module as M

__all__ = ['MobileNetV2', 'mobilenet_v2']


def _make_divisible(v, divisor, min_value=None):
    """
    This function is taken from the original tf repo.
    It ensures that all layers have a channel number that is divisible by 8
    It can be seen here:
    https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
    :param v:
    :param divisor:
    :param min_value:
    :return:
    """
    if min_value is None:
        min_value = divisor
    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
    # Make sure that round down does not go down by more than 10%.
    if new_v < 0.9 * v:
        new_v += divisor
    return new_v


class InvertedResidual(M.Module):
    def __init__(self, inp, oup, stride, expand_ratio):
        super(InvertedResidual, self).__init__()
        self.stride = stride
        assert stride in [1, 2]

        hidden_dim = int(round(inp * expand_ratio))
        self.use_res_connect = self.stride == 1 and inp == oup

        layers = []
        if expand_ratio != 1:
            # pw
            layers.append(M.ConvBnRelu2d(inp, hidden_dim, kernel_size=1, bias=False))
        layers.extend([
            # dw
            M.ConvBnRelu2d(hidden_dim, hidden_dim, kernel_size=3, padding=1,
                           stride=stride, groups=hidden_dim, bias=False),
            # pw-linear
            M.ConvBn2d(hidden_dim, oup, kernel_size=1, bias=False)
        ])
        self.conv = M.Sequential(*layers)
        self.add = M.Elemwise("ADD")

    def forward(self, x):
        if self.use_res_connect:
            return self.add(x, self.conv(x))
        else:
            return self.conv(x)


class MobileNetV2(M.Module):
    def __init__(self, num_classes=1000, width_mult=1.0, inverted_residual_setting=None, round_nearest=8):
        """
        MobileNet V2 main class

        Args:
            num_classes (int): Number of classes
            width_mult (float): Width multiplier - adjusts number of channels in each layer by this amount
            inverted_residual_setting: Network structure
            round_nearest (int): Round the number of channels in each layer to be a multiple of this number
            Set to 1 to turn off rounding
        """
        super(MobileNetV2, self).__init__()
        block = InvertedResidual
        input_channel = 32
        last_channel = 1280

        if inverted_residual_setting is None:
            inverted_residual_setting = [
                # t, c, n, s
                [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],
            ]

        # only check the first element, assuming user knows t,c,n,s are required
        if len(inverted_residual_setting) == 0 or len(inverted_residual_setting[0]) != 4:
            raise ValueError("inverted_residual_setting should be non-empty "
                             "or a 4-element list, got {}".format(inverted_residual_setting))

        # building first layer
        input_channel = _make_divisible(input_channel * width_mult, round_nearest)
        self.last_channel = _make_divisible(last_channel * max(1.0, width_mult), round_nearest)
        features = [M.ConvBnRelu2d(3, input_channel, kernel_size=3, padding=1, stride=2, bias=False)]
        # building inverted residual blocks
        for t, c, n, s in inverted_residual_setting:
            output_channel = _make_divisible(c * width_mult, round_nearest)
            for i in range(n):
                stride = s if i == 0 else 1
                features.append(block(input_channel, output_channel, stride, expand_ratio=t))
                input_channel = output_channel
        # building last several layers
        features.append(M.ConvBnRelu2d(input_channel, self.last_channel, kernel_size=1, bias=False))
        # make it M.Sequential
        self.features = M.Sequential(*features)

        # building classifier
        self.classifier = M.Sequential(
            M.Dropout(0.2),
            M.Linear(self.last_channel, num_classes),
        )

        self.quant = M.QuantStub()
        self.dequant = M.DequantStub()

        # weight initialization
        for m in self.modules():
            if isinstance(m, M.Conv2d):
                M.init.msra_normal_(m.weight, mode='fan_out')
                if m.bias is not None:
                    M.init.zeros_(m.bias)
            elif isinstance(m, M.BatchNorm2d):
                M.init.ones_(m.weight)
                
M.init.zeros_(m.bias)