# Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
#
# 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 warnings
import collections
from itertools import repeat

import paddle
from paddle import nn


def _ntuple(n):
    def parse(x):
        if isinstance(x, collections.abc.Iterable):
            return tuple(x)
        return tuple(repeat(x, n))

    return parse


_triple = _ntuple(3)


class ConvBNLayer(nn.Layer):
    """A conv block that bundles conv/norm/activation layers.

        This block simplifies the usage of convolution layers, which are commonly
        used with a norm layer (e.g., BatchNorm) and activation layer (e.g., ReLU).
        It is based upon three build methods: `build_conv_layer()`,
        `build_norm_layer()` and `build_activation_layer()`.

        Besides, we add some additional features in this module.
        1. Automatically set `bias` of the conv layer.
        2. Spectral norm is supported.
        3. More padding modes are supported. Before PyTorch 1.5, nn.Conv2d only
        supports zero and circular padding, and we add "reflect" padding mode.

        Args:
            in_channels (int): Number of channels in the input feature map.
                Same as that in ``nn._ConvNd``.
            out_channels (int): Number of channels produced by the convolution.
                Same as that in ``nn._ConvNd``.
            kernel_size (int | tuple[int]): Size of the convolving kernel.
                Same as that in ``nn._ConvNd``.
            stride (int | tuple[int]): Stride of the convolution.
                Same as that in ``nn._ConvNd``.
            padding (int | tuple[int]): Zero-padding added to both sides of
                the input. Same as that in ``nn._ConvNd``.
            dilation (int | tuple[int]): Spacing between kernel elements.
                Same as that in ``nn._ConvNd``.
            groups (int): Number of blocked connections from input channels to
                output channels. Same as that in ``nn._ConvNd``.
        """

    def __init__(
            self,
            in_channels,
            out_channels,
            kernel_size,
            padding=0,
            stride=1,
            dilation=1,
            groups=1,
            act=None,
            bias=None,
    ):
        super(ConvBNLayer, self).__init__()

        self._conv = nn.Conv3D(
            in_channels=in_channels,
            out_channels=out_channels,
            kernel_size=kernel_size,
            stride=stride,
            padding=padding,
            dilation=dilation,
            groups=groups,
            bias_attr=bias)

        self._batch_norm = nn.BatchNorm3D(out_channels, momentum=0.1)
        self.act = act
        if act is not None:
            self._act_op = nn.ReLU()

    def forward(self, inputs):
        y = self._conv(inputs)
        y = self._batch_norm(y)
        if self.act is not None:
            y = self._act_op(y)

        return y


class Bottleneck3d(nn.Layer):
    """Bottleneck 3d block for ResNet3D.

    Args:
        inplanes (int): Number of channels for the input in first conv3d layer.
        planes (int): Number of channels produced by some norm/conv3d layers.
        spatial_stride (int): Spatial stride in the conv3d layer. Default: 1.
        temporal_stride (int): Temporal stride in the conv3d layer. Default: 1.
        dilation (int): Spacing between kernel elements. Default: 1.
        downsample (nn.Module | None): Downsample layer. Default: None.
        inflate (bool): Whether to inflate kernel. Default: True.
        inflate_style (str): ``3x1x1`` or ``3x3x3``. which determines the
            kernel sizes and padding strides for conv1 and conv2 in each block.
            Default: '3x1x1'.
        non_local (bool): Determine whether to apply non-local module in this
            block. Default: False.
        non_local_cfg (dict): Config for non-local module. Default: ``dict()``.
        conv_cfg (dict): Config dict for convolution layer.
            Default: ``dict(type='Conv3d')``.
        norm_cfg (dict): Config for norm layers. required keys are ``type``,
            Default: ``dict(type='BN3d')``.
        act_cfg (dict): Config dict for activation layer.
            Default: ``dict(type='ReLU')``.
        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
            memory while slowing down the training speed. Default: False.
    """
    expansion = 4

    def __init__(self,
                 inplanes,
                 planes,
                 spatial_stride=1,
                 temporal_stride=1,
                 dilation=1,
                 downsample=None,
                 inflate=True,
                 inflate_style='3x1x1',
                 non_local=False,
                 non_local_cfg=dict(),
                 conv_cfg=dict(type='Conv3d'),
                 norm_cfg=dict(type='BN3d'),
                 act_cfg=dict(type='ReLU'),
                 with_cp=False):
        super().__init__()
        assert inflate_style in ['3x1x1', '3x3x3']

        self.inplanes = inplanes
        self.planes = planes
        self.spatial_stride = spatial_stride
        self.temporal_stride = temporal_stride
        self.dilation = dilation
        self.inflate = inflate
        self.inflate_style = inflate_style
        self.norm_cfg = norm_cfg
        self.conv_cfg = conv_cfg
        self.act_cfg = act_cfg
        self.with_cp = with_cp
        self.non_local = non_local
        self.non_local_cfg = non_local_cfg

        self.conv1_stride_s = 1
        self.conv2_stride_s = spatial_stride
        self.conv1_stride_t = 1
        self.conv2_stride_t = temporal_stride

        if self.inflate:
            if inflate_style == '3x1x1':
                conv1_kernel_size = (3, 1, 1)
                conv1_padding = (1, 0, 0)
                conv2_kernel_size = (1, 3, 3)
                conv2_padding = (0, dilation, dilation)
            else:
                conv1_kernel_size = (1, 1, 1)
                conv1_padding = (0, 0, 0)
                conv2_kernel_size = (3, 3, 3)
                conv2_padding = (1, dilation, dilation)
        else:
            conv1_kernel_size = (1, 1, 1)
            conv1_padding = (0, 0, 0)
            conv2_kernel_size = (1, 3, 3)
            conv2_padding = (0, dilation, dilation)
        self.conv1 = ConvBNLayer(
            in_channels=inplanes,
            out_channels=planes,
            kernel_size=conv1_kernel_size,
            stride=(self.conv1_stride_t, self.conv1_stride_s,
                    self.conv1_stride_s),
            padding=conv1_padding,
            bias=False,
            act='relu')

        self.conv2 = ConvBNLayer(
            in_channels=planes,
            out_channels=planes,
            kernel_size=conv2_kernel_size,
            stride=(self.conv2_stride_t, self.conv2_stride_s,
                    self.conv2_stride_s),
            padding=conv2_padding,
            dilation=(1, dilation, dilation),
            bias=False,
            act='relu')

        self.conv3 = ConvBNLayer(
            in_channels=planes,
            out_channels=planes * self.expansion,
            kernel_size=1,
            bias=False,
            act=None,
        )

        self.downsample = downsample
        self.relu = nn.ReLU()

    def forward(self, x):
        """Defines the computation performed at every call."""

        def _inner_forward(x):
            """Forward wrapper for utilizing checkpoint."""
            identity = x

            out = self.conv1(x)
            out = self.conv2(out)
            out = self.conv3(out)

            if self.downsample is not None:
                identity = self.downsample(x)

            out = out + identity
            return out

        out = _inner_forward(x)
        out = self.relu(out)

        if self.non_local:
            out = self.non_local_block(out)

        return out


class ResNet3d(nn.Layer):
    """ResNet 3d backbone.

    Args:
        depth (int): Depth of resnet, from {18, 34, 50, 101, 152}.
        pretrained (str | None): Name of pretrained model.
        stage_blocks (tuple | None): Set number of stages for each res layer.
            Default: None.
        pretrained2d (bool): Whether to load pretrained 2D model.
            Default: True.
        in_channels (int): Channel num of input features. Default: 3.
        base_channels (int): Channel num of stem output features. Default: 64.
        out_indices (Sequence[int]): Indices of output feature. Default: (3, ).
        num_stages (int): Resnet stages. Default: 4.
        spatial_strides (Sequence[int]):
            Spatial strides of residual blocks of each stage.
            Default: ``(1, 2, 2, 2)``.
        temporal_strides (Sequence[int]):
            Temporal strides of residual blocks of each stage.
            Default: ``(1, 1, 1, 1)``.
        dilations (Sequence[int]): Dilation of each stage.
            Default: ``(1, 1, 1, 1)``.
        conv1_kernel (Sequence[int]): Kernel size of the first conv layer.
            Default: ``(3, 7, 7)``.
        conv1_stride_s (int): Spatial stride of the first conv layer.
            Default: 2.
        conv1_stride_t (int): Temporal stride of the first conv layer.
            Default: 1.
        pool1_stride_s (int): Spatial stride of the first pooling layer.
            Default: 2.
        pool1_stride_t (int): Temporal stride of the first pooling layer.
            Default: 1.
        with_pool2 (bool): Whether to use pool2. Default: True.
        inflate (Sequence[int]): Inflate Dims of each block.
            Default: (1, 1, 1, 1).
        inflate_style (str): ``3x1x1`` or ``3x3x3``. which determines the
            kernel sizes and padding strides for conv1 and conv2 in each block.
            Default: '3x1x1'.
        conv_cfg (dict): Config for conv layers. required keys are ``type``
            Default: ``dict(type='Conv3d')``.
        norm_cfg (dict): Config for norm layers. required keys are ``type`` and
            ``requires_grad``.
            Default: ``dict(type='BN3d', requires_grad=True)``.
        act_cfg (dict): Config dict for activation layer.
            Default: ``dict(type='ReLU', inplace=True)``.
        norm_eval (bool): Whether to set BN layers to eval mode, namely, freeze
            running stats (mean and var). Default: False.
        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
            memory while slowing down the training speed. Default: False.
        non_local (Sequence[int]): Determine whether to apply non-local module
            in the corresponding block of each stages. Default: (0, 0, 0, 0).
        non_local_cfg (dict): Config for non-local module. Default: ``dict()``.
        zero_init_residual (bool):
            Whether to use zero initialization for residual block,
            Default: True.
        kwargs (dict, optional): Key arguments for "make_res_layer".
    """

    arch_settings = {
        50: (Bottleneck3d, (3, 4, 6, 3)),
        101: (Bottleneck3d, (3, 4, 23, 3)),
        152: (Bottleneck3d, (3, 8, 36, 3))
    }

    def __init__(self,
                 depth,
                 stage_blocks=None,
                 pretrained2d=True,
                 in_channels=3,
                 num_stages=4,
                 base_channels=64,
                 out_indices=(3, ),
                 spatial_strides=(1, 2, 2, 2),
                 temporal_strides=(1, 1, 1, 1),
                 dilations=(1, 1, 1, 1),
                 conv1_kernel=(3, 7, 7),
                 conv1_stride_s=2,
                 conv1_stride_t=1,
                 pool1_stride_s=2,
                 pool1_stride_t=1,
                 with_pool1=True,
                 with_pool2=True,
                 inflate=(1, 1, 1, 1),
                 inflate_style='3x1x1',
                 conv_cfg=dict(type='Conv3d'),
                 norm_cfg=dict(type='BN3d', requires_grad=True),
                 act_cfg=dict(type='ReLU', inplace=True),
                 norm_eval=False,
                 with_cp=False,
                 non_local=(0, 0, 0, 0),
                 non_local_cfg=dict(),
                 zero_init_residual=True,
                 **kwargs):
        super().__init__()
        if depth not in self.arch_settings:
            raise KeyError(f'invalid depth {depth} for resnet')
        self.depth = depth
        self.pretrained2d = pretrained2d
        self.in_channels = in_channels
        self.base_channels = base_channels
        self.num_stages = num_stages
        assert 1 <= num_stages <= 4
        self.stage_blocks = stage_blocks
        self.out_indices = out_indices
        assert max(out_indices) < num_stages
        self.spatial_strides = spatial_strides
        self.temporal_strides = temporal_strides
        self.dilations = dilations
        assert len(spatial_strides) == len(temporal_strides) == len(
            dilations) == num_stages
        if self.stage_blocks is not None:
            assert len(self.stage_blocks) == num_stages

        self.conv1_kernel = conv1_kernel
        self.conv1_stride_s = conv1_stride_s
        self.conv1_stride_t = conv1_stride_t
        self.pool1_stride_s = pool1_stride_s
        self.pool1_stride_t = pool1_stride_t
        self.with_pool1 = with_pool1
        self.with_pool2 = with_pool2
        self.stage_inflations = _ntuple(num_stages)(inflate)
        self.non_local_stages = _ntuple(num_stages)(non_local)
        self.inflate_style = inflate_style
        self.conv_cfg = conv_cfg
        self.norm_cfg = norm_cfg
        self.act_cfg = act_cfg
        self.norm_eval = norm_eval
        self.with_cp = with_cp
        self.zero_init_residual = zero_init_residual

        self.block, stage_blocks = self.arch_settings[depth]

        if self.stage_blocks is None:
            self.stage_blocks = stage_blocks[:num_stages]

        self.inplanes = self.base_channels

        self.non_local_cfg = non_local_cfg

        self._make_stem_layer()

        self.res_layers = []
        for i, num_blocks in enumerate(self.stage_blocks):
            spatial_stride = spatial_strides[i]
            temporal_stride = temporal_strides[i]
            dilation = dilations[i]
            planes = self.base_channels * 2**i
            res_layer = self.make_res_layer(
                self.block,
                self.inplanes,
                planes,
                num_blocks,
                spatial_stride=spatial_stride,
                temporal_stride=temporal_stride,
                dilation=dilation,
                norm_cfg=self.norm_cfg,
                conv_cfg=self.conv_cfg,
                act_cfg=self.act_cfg,
                non_local=self.non_local_stages[i],
                non_local_cfg=self.non_local_cfg,
                inflate=self.stage_inflations[i],
                inflate_style=self.inflate_style,
                with_cp=with_cp,
                **kwargs)
            self.inplanes = planes * self.block.expansion
            layer_name = f'layer{i + 1}'
            self.add_sublayer(layer_name, res_layer)
            self.res_layers.append(layer_name)

        self.feat_dim = self.block.expansion * self.base_channels * 2**(
            len(self.stage_blocks) - 1)

    @staticmethod
    def make_res_layer(block,
                       inplanes,
                       planes,
                       blocks,
                       spatial_stride=1,
                       temporal_stride=1,
                       dilation=1,
                       inflate=1,
                       inflate_style='3x1x1',
                       non_local=0,
                       non_local_cfg=dict(),
                       norm_cfg=None,
                       act_cfg=None,
                       conv_cfg=None,
                       with_cp=False,
                       **kwargs):
        """Build residual layer for ResNet3D.

        Args:
            block (nn.Module): Residual module to be built.
            inplanes (int): Number of channels for the input feature
                in each block.
            planes (int): Number of channels for the output feature
                in each block.
            blocks (int): Number of residual blocks.
            spatial_stride (int | Sequence[int]): Spatial strides in
                residual and conv layers. Default: 1.
            temporal_stride (int | Sequence[int]): Temporal strides in
                residual and conv layers. Default: 1.
            dilation (int): Spacing between kernel elements. Default: 1.
            inflate (int | Sequence[int]): Determine whether to inflate
                for each block. Default: 1.
            inflate_style (str): ``3x1x1`` or ``3x3x3``. which determines
                the kernel sizes and padding strides for conv1 and conv2
                in each block. Default: '3x1x1'.
            non_local (int | Sequence[int]): Determine whether to apply
                non-local module in the corresponding block of each stages.
                Default: 0.
            non_local_cfg (dict): Config for non-local module.
                Default: ``dict()``.
            conv_cfg (dict | None): Config for norm layers. Default: None.
            norm_cfg (dict | None): Config for norm layers. Default: None.
            act_cfg (dict | None): Config for activate layers. Default: None.
            with_cp (bool | None): Use checkpoint or not. Using checkpoint
                will save some memory while slowing down the training speed.
                Default: False.

        Returns:
            nn.Module: A residual layer for the given config.
        """
        inflate = inflate if not isinstance(inflate,
                                            int) else (inflate, ) * blocks
        non_local = non_local if not isinstance(non_local,
                                                int) else (non_local, ) * blocks
        assert len(inflate) == blocks and len(non_local) == blocks
        downsample = None
        if spatial_stride != 1 or inplanes != planes * block.expansion:
            downsample = ConvBNLayer(
                in_channels=inplanes,
                out_channels=planes * block.expansion,
                kernel_size=1,
                stride=(temporal_stride, spatial_stride, spatial_stride),
                bias=False,
                act=None)

        layers = []
        layers.append(
            block(
                inplanes,
                planes,
                spatial_stride=spatial_stride,
                temporal_stride=temporal_stride,
                dilation=dilation,
                downsample=downsample,
                inflate=(inflate[0] == 1),
                inflate_style=inflate_style,
                non_local=(non_local[0] == 1),
                non_local_cfg=non_local_cfg,
                norm_cfg=norm_cfg,
                conv_cfg=conv_cfg,
                act_cfg=act_cfg,
                with_cp=with_cp,
                **kwargs))
        inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(
                block(
                    inplanes,
                    planes,
                    spatial_stride=1,
                    temporal_stride=1,
                    dilation=dilation,
                    inflate=(inflate[i] == 1),
                    inflate_style=inflate_style,
                    non_local=(non_local[i] == 1),
                    non_local_cfg=non_local_cfg,
                    norm_cfg=norm_cfg,
                    conv_cfg=conv_cfg,
                    act_cfg=act_cfg,
                    with_cp=with_cp,
                    **kwargs))

        return nn.Sequential(*layers)

    @staticmethod
    def _inflate_conv_params(conv3d, state_dict_2d, module_name_2d,
                             inflated_param_names):
        """Inflate a conv module from 2d to 3d.

        Args:
            conv3d (nn.Module): The destination conv3d module.
            state_dict_2d (OrderedDict): The state dict of pretrained 2d model.
            module_name_2d (str): The name of corresponding conv module in the
                2d model.
            inflated_param_names (list[str]): List of parameters that have been
                inflated.
        """
        weight_2d_name = module_name_2d + '.weight'

        conv2d_weight = state_dict_2d[weight_2d_name]
        kernel_t = conv3d.weight.data.shape[2]

        new_weight = conv2d_weight.data.unsqueeze(2).expand_as(
            conv3d.weight) / kernel_t
        conv3d.weight.data.copy_(new_weight)
        inflated_param_names.append(weight_2d_name)

        if getattr(conv3d, 'bias') is not None:
            bias_2d_name = module_name_2d + '.bias'
            conv3d.bias.data.copy_(state_dict_2d[bias_2d_name])
            inflated_param_names.append(bias_2d_name)

    @staticmethod
    def _inflate_bn_params(bn3d, state_dict_2d, module_name_2d,
                           inflated_param_names):
        """Inflate a norm module from 2d to 3d.

        Args:
            bn3d (nn.Module): The destination bn3d module.
            state_dict_2d (OrderedDict): The state dict of pretrained 2d model.
            module_name_2d (str): The name of corresponding bn module in the
                2d model.
            inflated_param_names (list[str]): List of parameters that have been
                inflated.
        """
        for param_name, param in bn3d.named_parameters():
            param_2d_name = f'{module_name_2d}.{param_name}'
            param_2d = state_dict_2d[param_2d_name]
            if param.data.shape != param_2d.shape:
                warnings.warn(f'The parameter of {module_name_2d} is not'
                              'loaded due to incompatible shapes. ')
                return

            param.data.copy_(param_2d)
            inflated_param_names.append(param_2d_name)

        for param_name, param in bn3d.named_buffers():
            param_2d_name = f'{module_name_2d}.{param_name}'
            # some buffers like num_batches_tracked may not exist in old
            # checkpoints
            if param_2d_name in state_dict_2d:
                param_2d = state_dict_2d[param_2d_name]
                param.data.copy_(param_2d)
                inflated_param_names.append(param_2d_name)

    def _make_stem_layer(self):
        """Construct the stem layers consists of a conv+norm+act module and a
        pooling layer."""

        self.conv1 = ConvBNLayer(
            in_channels=self.in_channels,
            out_channels=self.base_channels,
            kernel_size=self.conv1_kernel,
            stride=(self.conv1_stride_t, self.conv1_stride_s,
                    self.conv1_stride_s),
            padding=tuple([(k - 1) // 2 for k in _triple(self.conv1_kernel)]),
            bias=False,
            act="relu")

        self.maxpool = nn.MaxPool3D(
            kernel_size=(1, 3, 3),
            stride=(self.pool1_stride_t, self.pool1_stride_s,
                    self.pool1_stride_s),
            padding=(0, 1, 1))

        self.pool2 = nn.MaxPool3D(kernel_size=(2, 1, 1), stride=(2, 1, 1))

    @staticmethod
    def _init_weights(self, pretrained=None):
        pass

    def init_weights(self, pretrained=None):
        self._init_weights(self, pretrained)

    def forward(self, x):
        """Defines the computation performed at every call.

        Args:
            x (torch.Tensor): The input data.

        Returns:
            torch.Tensor: The feature of the input
            samples extracted by the backbone.
        """
        x = self.conv1(x)
        if self.with_pool1:
            x = self.maxpool(x)
        outs = []
        for i, layer_name in enumerate(self.res_layers):
            res_layer = getattr(self, layer_name)
            x = res_layer(x)
            if i == 0 and self.with_pool2:
                x = self.pool2(x)
            if i in self.out_indices:
                outs.append(x)
        if len(outs) == 1:
            return outs[0]

        return tuple(outs)

    def train(self, mode=True):
        """Set the optimization status when training."""
        super().train()
        if mode and self.norm_eval:
            for m in self.modules():
                if isinstance(m, paddle.nn._BatchNormBase):
                    m.eval()