# 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.
# ============================================================================
import mindspore.nn as nn
import mindspore.ops.operations as P


class ShuffleV2Block(nn.Cell):
    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
            nn.Conv2d(in_channels=inp, out_channels=mid_channels, kernel_size=1, stride=1,
                      pad_mode='pad', padding=0, has_bias=False),
            nn.BatchNorm2d(num_features=mid_channels, momentum=0.9),
            nn.ReLU(),
            # dw
            nn.Conv2d(in_channels=mid_channels, out_channels=mid_channels, kernel_size=ksize, stride=stride,
                      pad_mode='pad', padding=pad, group=mid_channels, has_bias=False),
            nn.BatchNorm2d(num_features=mid_channels, momentum=0.9),
            # pw-linear
            nn.Conv2d(in_channels=mid_channels, out_channels=outputs, kernel_size=1, stride=1,
                      pad_mode='pad', padding=0, has_bias=False),
            nn.BatchNorm2d(num_features=outputs, momentum=0.9),
            nn.ReLU(),
        ]
        self.branch_main = nn.SequentialCell(branch_main)

        if stride == 2:
            branch_proj = [
                # dw
                nn.Conv2d(in_channels=inp, out_channels=inp, kernel_size=ksize, stride=stride,
                          pad_mode='pad', padding=pad, group=inp, has_bias=False),
                nn.BatchNorm2d(num_features=inp, momentum=0.9),
                # pw-linear
                nn.Conv2d(in_channels=inp, out_channels=inp, kernel_size=1, stride=1,
                          pad_mode='pad', padding=0, has_bias=False),
                nn.BatchNorm2d(num_features=inp, momentum=0.9),
                nn.ReLU(),
            ]
            self.branch_proj = nn.SequentialCell(branch_proj)
        else:
            self.branch_proj = None

    def construct(self, old_x):
        if self.stride == 1:
            x_proj, x = self.channel_shuffle(old_x)
            return P.Concat(1)((x_proj, self.branch_main(x)))
        if self.stride == 2:
            x_proj = old_x
            x = old_x
            return P.Concat(1)((self.branch_proj(x_proj), self.branch_main(x)))
        return None

    def channel_shuffle(self, x):
        batchsize, num_channels, height, width = P.Shape()(x)
        ##assert (num_channels % 4 == 0)
        x = P.Reshape()(x, (batchsize * num_channels // 2, 2, height * width,))
        x = P.Transpose()(x, (1, 0, 2,))
        x = P.Reshape()(x, (2, -1, num_channels // 2, height, width,))
        return x[0], x[1]