# 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 numpy as np

from mindspore import Tensor
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)
        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]


class ShuffleNetV2(nn.Cell):
    def __init__(self, input_size=224, n_class=1000, model_size='1.0x'):
        super(ShuffleNetV2, self).__init__()
        print('model size is ', model_size)

        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 = nn.SequentialCell([
            nn.Conv2d(in_channels=3, out_channels=input_channel, kernel_size=3, stride=2,
                      pad_mode='pad', padding=1, has_bias=False),
            nn.BatchNorm2d(num_features=input_channel, momentum=0.9),
            nn.ReLU(),
        ])

        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='same')

        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 = nn.SequentialCell([*self.features])

        self.conv_last = nn.SequentialCell([
            nn.Conv2d(in_channels=input_channel, out_channels=self.stage_out_channels[-1], kernel_size=1, stride=1,
                      pad_mode='pad', padding=0, has_bias=False),
            nn.BatchNorm2d(num_features=self.stage_out_channels[-1], momentum=0.9),
            nn.ReLU()
        ])
        self.globalpool = nn.AvgPool2d(kernel_size=7, stride=7, pad_mode='valid')
        if self.model_size == '2.0x':
            self.dropout = nn.Dropout(keep_prob=0.8)
        self.classifier = nn.SequentialCell([nn.Dense(in_channels=self.stage_out_channels[-1],
                                                      out_channels=n_class, has_bias=False)])
        ##TODO init weights
        self._initialize_weights()

    def construct(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 = P.Reshape()(x, (-1, self.stage_out_channels[-1],))
        x = self.classifier(x)
        return x

    def _initialize_weights(self):
        for name, m in self.cells_and_names():
            if isinstance(m, nn.Conv2d):
                if 'first' in name:
                    m.weight.set_data(Tensor(np.random.normal(0, 0.01,
                                                              m.weight.data.shape).astype("float32")))
                else:
                    m.weight.set_data(Tensor(np.random.normal(0, 1.0/m.weight.data.shape[1],
                                                              m.weight.data.shape).astype("float32")))

            if isinstance(m, nn.Dense):
                m.weight.set_data(Tensor(np.random.normal(0, 0.01, m.weight.data.shape).astype("float32")))