"""
FBNet model builder
"""

from __future__ import absolute_import, division, print_function, unicode_literals

import copy
import logging
import math
from collections import OrderedDict

import torch
import torch.nn as nn
from maskrcnn_benchmark.layers import (
    BatchNorm2d,
    Conv2d,
    FrozenBatchNorm2d,
    interpolate,
)
from maskrcnn_benchmark.layers.misc import _NewEmptyTensorOp


logger = logging.getLogger(__name__)


def _py2_round(x):
    return math.floor(x + 0.5) if x >= 0.0 else math.ceil(x - 0.5)


def _get_divisible_by(num, divisible_by, min_val):
    ret = int(num)
    if divisible_by > 0 and num % divisible_by != 0:
        ret = int((_py2_round(num / divisible_by) or min_val) * divisible_by)
    return ret


PRIMITIVES = {
    "skip": lambda C_in, C_out, expansion, stride, **kwargs: Identity(
        C_in, C_out, stride
    ),
    "ir_k3": lambda C_in, C_out, expansion, stride, **kwargs: IRFBlock(
        C_in, C_out, expansion, stride, **kwargs
    ),
    "ir_k5": lambda C_in, C_out, expansion, stride, **kwargs: IRFBlock(
        C_in, C_out, expansion, stride, kernel=5, **kwargs
    ),
    "ir_k7": lambda C_in, C_out, expansion, stride, **kwargs: IRFBlock(
        C_in, C_out, expansion, stride, kernel=7, **kwargs
    ),
    "ir_k1": lambda C_in, C_out, expansion, stride, **kwargs: IRFBlock(
        C_in, C_out, expansion, stride, kernel=1, **kwargs
    ),
    "shuffle": lambda C_in, C_out, expansion, stride, **kwargs: IRFBlock(
        C_in, C_out, expansion, stride, shuffle_type="mid", pw_group=4, **kwargs
    ),
    "basic_block": lambda C_in, C_out, expansion, stride, **kwargs: CascadeConv3x3(
        C_in, C_out, stride
    ),
    "shift_5x5": lambda C_in, C_out, expansion, stride, **kwargs: ShiftBlock5x5(
        C_in, C_out, expansion, stride
    ),
    # layer search 2
    "ir_k3_e1": lambda C_in, C_out, expansion, stride, **kwargs: IRFBlock(
        C_in, C_out, 1, stride, kernel=3, **kwargs
    ),
    "ir_k3_e3": lambda C_in, C_out, expansion, stride, **kwargs: IRFBlock(
        C_in, C_out, 3, stride, kernel=3, **kwargs
    ),
    "ir_k3_e6": lambda C_in, C_out, expansion, stride, **kwargs: IRFBlock(
        C_in, C_out, 6, stride, kernel=3, **kwargs
    ),
    "ir_k3_s4": lambda C_in, C_out, expansion, stride, **kwargs: IRFBlock(
        C_in, C_out, 4, stride, kernel=3, shuffle_type="mid", pw_group=4, **kwargs
    ),
    "ir_k5_e1": lambda C_in, C_out, expansion, stride, **kwargs: IRFBlock(
        C_in, C_out, 1, stride, kernel=5, **kwargs
    ),
    "ir_k5_e3": lambda C_in, C_out, expansion, stride, **kwargs: IRFBlock(
        C_in, C_out, 3, stride, kernel=5, **kwargs
    ),
    "ir_k5_e6": lambda C_in, C_out, expansion, stride, **kwargs: IRFBlock(
        C_in, C_out, 6, stride, kernel=5, **kwargs
    ),
    "ir_k5_s4": lambda C_in, C_out, expansion, stride, **kwargs: IRFBlock(
        C_in, C_out, 4, stride, kernel=5, shuffle_type="mid", pw_group=4, **kwargs
    ),
    # layer search se
    "ir_k3_e1_se": lambda C_in, C_out, expansion, stride, **kwargs: IRFBlock(
        C_in, C_out, 1, stride, kernel=3, se=True, **kwargs
    ),
    "ir_k3_e3_se": lambda C_in, C_out, expansion, stride, **kwargs: IRFBlock(
        C_in, C_out, 3, stride, kernel=3, se=True, **kwargs
    ),
    "ir_k3_e6_se": lambda C_in, C_out, expansion, stride, **kwargs: IRFBlock(
        C_in, C_out, 6, stride, kernel=3, se=True, **kwargs
    ),
    "ir_k3_s4_se": lambda C_in, C_out, expansion, stride, **kwargs: IRFBlock(
        C_in,
        C_out,
        4,
        stride,
        kernel=3,
        shuffle_type="mid",
        pw_group=4,
        se=True,
        **kwargs
    ),
    "ir_k5_e1_se": lambda C_in, C_out, expansion, stride, **kwargs: IRFBlock(
        C_in, C_out, 1, stride, kernel=5, se=True, **kwargs
    ),
    "ir_k5_e3_se": lambda C_in, C_out, expansion, stride, **kwargs: IRFBlock(
        C_in, C_out, 3, stride, kernel=5, se=True, **kwargs
    ),
    "ir_k5_e6_se": lambda C_in, C_out, expansion, stride, **kwargs: IRFBlock(
        C_in, C_out, 6, stride, kernel=5, se=True, **kwargs
    ),
    "ir_k5_s4_se": lambda C_in, C_out, expansion, stride, **kwargs: IRFBlock(
        C_in,
        C_out,
        4,
        stride,
        kernel=5,
        shuffle_type="mid",
        pw_group=4,
        se=True,
        **kwargs
    ),
    # layer search 3 (in addition to layer search 2)
    "ir_k3_s2": lambda C_in, C_out, expansion, stride, **kwargs: IRFBlock(
        C_in, C_out, 1, stride, kernel=3, shuffle_type="mid", pw_group=2, **kwargs
    ),
    "ir_k5_s2": lambda C_in, C_out, expansion, stride, **kwargs: IRFBlock(
        C_in, C_out, 1, stride, kernel=5, shuffle_type="mid", pw_group=2, **kwargs
    ),
    "ir_k3_s2_se": lambda C_in, C_out, expansion, stride, **kwargs: IRFBlock(
        C_in,
        C_out,
        1,
        stride,
        kernel=3,
        shuffle_type="mid",
        pw_group=2,
        se=True,
        **kwargs
    ),
    "ir_k5_s2_se": lambda C_in, C_out, expansion, stride, **kwargs: IRFBlock(
        C_in,
        C_out,
        1,
        stride,
        kernel=5,
        shuffle_type="mid",
        pw_group=2,
        se=True,
        **kwargs
    ),
    # layer search 4 (in addition to layer search 3)
    "ir_k3_sep": lambda C_in, C_out, expansion, stride, **kwargs: IRFBlock(
        C_in, C_out, expansion, stride, kernel=3, cdw=True, **kwargs
    ),
    "ir_k33_e1": lambda C_in, C_out, expansion, stride, **kwargs: IRFBlock(
        C_in, C_out, 1, stride, kernel=3, cdw=True, **kwargs
    ),
    "ir_k33_e3": lambda C_in, C_out, expansion, stride, **kwargs: IRFBlock(
        C_in, C_out, 3, stride, kernel=3, cdw=True, **kwargs
    ),
    "ir_k33_e6": lambda C_in, C_out, expansion, stride, **kwargs: IRFBlock(
        C_in, C_out, 6, stride, kernel=3, cdw=True, **kwargs
    ),
    # layer search 5 (in addition to layer search 4)
    "ir_k7_e1": lambda C_in, C_out, expansion, stride, **kwargs: IRFBlock(
        C_in, C_out, 1, stride, kernel=7, **kwargs
    ),
    "ir_k7_e3": lambda C_in, C_out, expansion, stride, **kwargs: IRFBlock(
        C_in, C_out, 3, stride, kernel=7, **kwargs
    ),
    "ir_k7_e6": lambda C_in, C_out, expansion, stride, **kwargs: IRFBlock(
        C_in, C_out, 6, stride, kernel=7, **kwargs
    ),
    "ir_k7_sep": lambda C_in, C_out, expansion, stride, **kwargs: IRFBlock(
        C_in, C_out, expansion, stride, kernel=7, cdw=True, **kwargs
    ),
    "ir_k7_sep_e1": lambda C_in, C_out, expansion, stride, **kwargs: IRFBlock(
        C_in, C_out, 1, stride, kernel=7, cdw=True, **kwargs
    ),
    "ir_k7_sep_e3": lambda C_in, C_out, expansion, stride, **kwargs: IRFBlock(
        C_in, C_out, 3, stride, kernel=7, cdw=True, **kwargs
    ),
    "ir_k7_sep_e6": lambda C_in, C_out, expansion, stride, **kwargs: IRFBlock(
        C_in, C_out, 6, stride, kernel=7, cdw=True, **kwargs
    ),
}


class Identity(nn.Module):
    def __init__(self, C_in, C_out, stride):
        super(Identity, self).__init__()
        self.conv = (
            ConvBNRelu(
                C_in,
                C_out,
                kernel=1,
                stride=stride,
                pad=0,
                no_bias=1,
                use_relu="relu",
                bn_type="bn",
            )
            if C_in != C_out or stride != 1
            else None
        )

    def forward(self, x):
        if self.conv:
            out = self.conv(x)
        else:
            out = x
        return out


class CascadeConv3x3(nn.Sequential):
    def __init__(self, C_in, C_out, stride):
        assert stride in [1, 2]
        ops = [
            Conv2d(C_in, C_in, 3, stride, 1, bias=False),
            BatchNorm2d(C_in),
            nn.ReLU(inplace=True),
            Conv2d(C_in, C_out, 3, 1, 1, bias=False),
            BatchNorm2d(C_out),
        ]
        super(CascadeConv3x3, self).__init__(*ops)
        self.res_connect = (stride == 1) and (C_in == C_out)

    def forward(self, x):
        y = super(CascadeConv3x3, self).forward(x)
        if self.res_connect:
            y += x
        return y


class Shift(nn.Module):
    def __init__(self, C, kernel_size, stride, padding):
        super(Shift, self).__init__()
        self.C = C
        kernel = torch.zeros((C, 1, kernel_size, kernel_size), dtype=torch.float32)
        ch_idx = 0

        assert stride in [1, 2]
        self.stride = stride
        self.padding = padding
        self.kernel_size = kernel_size
        self.dilation = 1

        hks = kernel_size // 2
        ksq = kernel_size ** 2

        for i in range(kernel_size):
            for j in range(kernel_size):
                if i == hks and j == hks:
                    num_ch = C // ksq + C % ksq
                else:
                    num_ch = C // ksq
                kernel[ch_idx : ch_idx + num_ch, 0, i, j] = 1
                ch_idx += num_ch

        self.register_parameter("bias", None)
        self.kernel = nn.Parameter(kernel, requires_grad=False)

    def forward(self, x):
        if x.numel() > 0:
            return nn.functional.conv2d(
                x,
                self.kernel,
                self.bias,
                (self.stride, self.stride),
                (self.padding, self.padding),
                self.dilation,
                self.C,  # groups
            )

        output_shape = [
            (i + 2 * p - (di * (k - 1) + 1)) // d + 1
            for i, p, di, k, d in zip(
                x.shape[-2:],
                (self.padding, self.dilation),
                (self.dilation, self.dilation),
                (self.kernel_size, self.kernel_size),
                (self.stride, self.stride),
            )
        ]
        output_shape = [x.shape[0], self.C] + output_shape
        return _NewEmptyTensorOp.apply(x, output_shape)


class ShiftBlock5x5(nn.Sequential):
    def __init__(self, C_in, C_out, expansion, stride):
        assert stride in [1, 2]
        self.res_connect = (stride == 1) and (C_in == C_out)

        C_mid = _get_divisible_by(C_in * expansion, 8, 8)

        ops = [
            # pw
            Conv2d(C_in, C_mid, 1, 1, 0, bias=False),
            BatchNorm2d(C_mid),
            nn.ReLU(inplace=True),
            # shift
            Shift(C_mid, 5, stride, 2),
            # pw-linear
            Conv2d(C_mid, C_out, 1, 1, 0, bias=False),
            BatchNorm2d(C_out),
        ]
        super(ShiftBlock5x5, self).__init__(*ops)

    def forward(self, x):
        y = super(ShiftBlock5x5, self).forward(x)
        if self.res_connect:
            y += x
        return y


class ChannelShuffle(nn.Module):
    def __init__(self, groups):
        super(ChannelShuffle, self).__init__()
        self.groups = groups

    def forward(self, x):
        """Channel shuffle: [N,C,H,W] -> [N,g,C/g,H,W] -> [N,C/g,g,H,w] -> [N,C,H,W]"""
        N, C, H, W = x.size()
        g = self.groups
        assert C % g == 0, "Incompatible group size {} for input channel {}".format(
            g, C
        )
        return (
            x.view(N, g, int(C / g), H, W)
            .permute(0, 2, 1, 3, 4)
            .contiguous()
            .view(N, C, H, W)
        )


class ConvBNRelu(nn.Sequential):
    def __init__(
        self,
        input_depth,
        output_depth,
        kernel,
        stride,
        pad,
        no_bias,
        use_relu,
        bn_type,
        group=1,
        *args,
        **kwargs
    ):
        super(ConvBNRelu, self).__init__()

        assert use_relu in ["relu", None]
        if isinstance(bn_type, (list, tuple)):
            assert len(bn_type) == 2
            assert bn_type[0] == "gn"
            gn_group = bn_type[1]
            bn_type = bn_type[0]
        assert bn_type in ["bn", "af", "gn", None]
        assert stride in [1, 2, 4]

        op = Conv2d(
            input_depth,
            output_depth,
            kernel_size=kernel,
            stride=stride,
            padding=pad,
            bias=not no_bias,
            groups=group,
            *args,
            **kwargs
        )
        nn.init.kaiming_normal_(op.weight, mode="fan_out", nonlinearity="relu")
        if op.bias is not None:
            nn.init.constant_(op.bias, 0.0)
        self.add_module("conv", op)

        if bn_type == "bn":
            bn_op = BatchNorm2d(output_depth)
        elif bn_type == "gn":
            bn_op = nn.GroupNorm(num_groups=gn_group, num_channels=output_depth)
        elif bn_type == "af":
            bn_op = FrozenBatchNorm2d(output_depth)
        if bn_type is not None:
            self.add_module("bn", bn_op)

        if use_relu == "relu":
            self.add_module("relu", nn.ReLU(inplace=True))


class SEModule(nn.Module):
    reduction = 4

    def __init__(self, C):
        super(SEModule, self).__init__()
        mid = max(C // self.reduction, 8)
        conv1 = Conv2d(C, mid, 1, 1, 0)
        conv2 = Conv2d(mid, C, 1, 1, 0)

        self.op = nn.Sequential(
            nn.AdaptiveAvgPool2d(1), conv1, nn.ReLU(inplace=True), conv2, nn.Sigmoid()
        )

    def forward(self, x):
        return x * self.op(x)


class Upsample(nn.Module):
    def __init__(self, scale_factor, mode, align_corners=None):
        super(Upsample, self).__init__()
        self.scale = scale_factor
        self.mode = mode
        self.align_corners = align_corners

    def forward(self, x):
        return interpolate(
            x, scale_factor=self.scale, mode=self.mode,
            align_corners=self.align_corners
        )


def _get_upsample_op(stride):
    assert (
        stride in [1, 2, 4]
        or stride in [-1, -2, -4]
        or (isinstance(stride, tuple) and all(x in [-1, -2, -4] for x in stride))
    )

    scales = stride
    ret = None
    if isinstance(stride, tuple) or stride < 0:
        scales = [-x for x in stride] if isinstance(stride, tuple) else -stride
        stride = 1
        ret = Upsample(scale_factor=scales, mode="nearest", align_corners=None)

    return ret, stride


class IRFBlock(nn.Module):
    def __init__(
        self,
        input_depth,
        output_depth,
        expansion,
        stride,
        bn_type="bn",
        kernel=3,
        width_divisor=1,
        shuffle_type=None,
        pw_group=1,
        se=False,
        cdw=False,
        dw_skip_bn=False,
        dw_skip_relu=False,
    ):
        super(IRFBlock, self).__init__()

        assert kernel in [1, 3, 5, 7], kernel

        self.use_res_connect = stride == 1 and input_depth == output_depth
        self.output_depth = output_depth

        mid_depth = int(input_depth * expansion)
        mid_depth = _get_divisible_by(mid_depth, width_divisor, width_divisor)

        # pw
        self.pw = ConvBNRelu(
            input_depth,
            mid_depth,
            kernel=1,
            stride=1,
            pad=0,
            no_bias=1,
            use_relu="relu",
            bn_type=bn_type,
            group=pw_group,
        )

        # negative stride to do upsampling
        self.upscale, stride = _get_upsample_op(stride)

        # dw
        if kernel == 1:
            self.dw = nn.Sequential()
        elif cdw:
            dw1 = ConvBNRelu(
                mid_depth,
                mid_depth,
                kernel=kernel,
                stride=stride,
                pad=(kernel // 2),
                group=mid_depth,
                no_bias=1,
                use_relu="relu",
                bn_type=bn_type,
            )
            dw2 = ConvBNRelu(
                mid_depth,
                mid_depth,
                kernel=kernel,
                stride=1,
                pad=(kernel // 2),
                group=mid_depth,
                no_bias=1,
                use_relu="relu" if not dw_skip_relu else None,
                bn_type=bn_type if not dw_skip_bn else None,
            )
            self.dw = nn.Sequential(OrderedDict([("dw1", dw1), ("dw2", dw2)]))
        else:
            self.dw = ConvBNRelu(
                mid_depth,
                mid_depth,
                kernel=kernel,
                stride=stride,
                pad=(kernel // 2),
                group=mid_depth,
                no_bias=1,
                use_relu="relu" if not dw_skip_relu else None,
                bn_type=bn_type if not dw_skip_bn else None,
            )

        # pw-linear
        self.pwl = ConvBNRelu(
            mid_depth,
            output_depth,
            kernel=1,
            stride=1,
            pad=0,
            no_bias=1,
            use_relu=None,
            bn_type=bn_type,
            group=pw_group,
        )

        self.shuffle_type = shuffle_type
        if shuffle_type is not None:
            self.shuffle = ChannelShuffle(pw_group)

        self.se4 = SEModule(output_depth) if se else nn.Sequential()

        self.output_depth = output_depth

    def forward(self, x):
        y = self.pw(x)
        if self.shuffle_type == "mid":
            y = self.shuffle(y)
        if self.upscale is not None:
            y = self.upscale(y)
        y = self.dw(y)
        y = self.pwl(y)
        if self.use_res_connect:
            y += x
        y = self.se4(y)
        return y


def _expand_block_cfg(block_cfg):
    assert isinstance(block_cfg, list)
    ret = []
    for idx in range(block_cfg[2]):
        cur = copy.deepcopy(block_cfg)
        cur[2] = 1
        cur[3] = 1 if idx >= 1 else cur[3]
        ret.append(cur)
    return ret


def expand_stage_cfg(stage_cfg):
    """ For a single stage """
    assert isinstance(stage_cfg, list)
    ret = []
    for x in stage_cfg:
        ret += _expand_block_cfg(x)
    return ret


def expand_stages_cfg(stage_cfgs):
    """ For a list of stages """
    assert isinstance(stage_cfgs, list)
    ret = []
    for x in stage_cfgs:
        ret.append(expand_stage_cfg(x))
    return ret


def _block_cfgs_to_list(block_cfgs):
    assert isinstance(block_cfgs, list)
    ret = []
    for stage_idx, stage in enumerate(block_cfgs):
        stage = expand_stage_cfg(stage)
        for block_idx, block in enumerate(stage):
            cur = {"stage_idx": stage_idx, "block_idx": block_idx, "block": block}
            ret.append(cur)
    return ret


def _add_to_arch(arch, info, name):
    """ arch = [{block_0}, {block_1}, ...]
        info = [
            # stage 0
            [
                block0_info,
                block1_info,
                ...
            ], ...
        ]
        convert to:
        arch = [
            {
                block_0,
                name: block0_info,
            },
            {
                block_1,
                name: block1_info,
            }, ...
        ]
    """
    assert isinstance(arch, list) and all(isinstance(x, dict) for x in arch)
    assert isinstance(info, list) and all(isinstance(x, list) for x in info)
    idx = 0
    for stage_idx, stage in enumerate(info):
        for block_idx, block in enumerate(stage):
            assert (
                arch[idx]["stage_idx"] == stage_idx
                and arch[idx]["block_idx"] == block_idx
            ), "Index ({}, {}) does not match for block {}".format(
                stage_idx, block_idx, arch[idx]
            )
            assert name not in arch[idx]
            arch[idx][name] = block
            idx += 1


def unify_arch_def(arch_def):
    """ unify the arch_def to:
        {
            ...,
            "arch": [
                {
                    "stage_idx": idx,
                    "block_idx": idx,
                    ...
                },
                {}, ...
            ]
        }
    """
    ret = copy.deepcopy(arch_def)

    assert "block_cfg" in arch_def and "stages" in arch_def["block_cfg"]
    assert "stages" not in ret
    # copy 'first', 'last' etc. inside arch_def['block_cfg'] to ret
    ret.update({x: arch_def["block_cfg"][x] for x in arch_def["block_cfg"]})
    ret["stages"] = _block_cfgs_to_list(arch_def["block_cfg"]["stages"])
    del ret["block_cfg"]

    assert "block_op_type" in arch_def
    _add_to_arch(ret["stages"], arch_def["block_op_type"], "block_op_type")
    del ret["block_op_type"]

    return ret


def get_num_stages(arch_def):
    ret = 0
    for x in arch_def["stages"]:
        ret = max(x["stage_idx"], ret)
    ret = ret + 1
    return ret


def get_blocks(arch_def, stage_indices=None, block_indices=None):
    ret = copy.deepcopy(arch_def)
    ret["stages"] = []
    for block in arch_def["stages"]:
        keep = True
        if stage_indices not in (None, []) and block["stage_idx"] not in stage_indices:
            keep = False
        if block_indices not in (None, []) and block["block_idx"] not in block_indices:
            keep = False
        if keep:
            ret["stages"].append(block)
    return ret


class FBNetBuilder(object):
    def __init__(
        self,
        width_ratio,
        bn_type="bn",
        width_divisor=1,
        dw_skip_bn=False,
        dw_skip_relu=False,
    ):
        self.width_ratio = width_ratio
        self.last_depth = -1
        self.bn_type = bn_type
        self.width_divisor = width_divisor
        self.dw_skip_bn = dw_skip_bn
        self.dw_skip_relu = dw_skip_relu

    def add_first(self, stage_info, dim_in=3, pad=True):
        # stage_info: [c, s, kernel]
        assert len(stage_info) >= 2
        channel = stage_info[0]
        stride = stage_info[1]
        out_depth = self._get_divisible_width(int(channel * self.width_ratio))
        kernel = 3
        if len(stage_info) > 2:
            kernel = stage_info[2]

        out = ConvBNRelu(
            dim_in,
            out_depth,
            kernel=kernel,
            stride=stride,
            pad=kernel // 2 if pad else 0,
            no_bias=1,
            use_relu="relu",
            bn_type=self.bn_type,
        )
        self.last_depth = out_depth
        return out

    def add_blocks(self, blocks):
        """ blocks: [{}, {}, ...]
        """
        assert isinstance(blocks, list) and all(
            isinstance(x, dict) for x in blocks
        ), blocks

        modules = OrderedDict()
        for block in blocks:
            stage_idx = block["stage_idx"]
            block_idx = block["block_idx"]
            block_op_type = block["block_op_type"]
            tcns = block["block"]
            n = tcns[2]
            assert n == 1
            nnblock = self.add_ir_block(tcns, [block_op_type])
            nn_name = "xif{}_{}".format(stage_idx, block_idx)
            assert nn_name not in modules
            modules[nn_name] = nnblock
        ret = nn.Sequential(modules)
        return ret

    def add_last(self, stage_info):
        """ skip last layer if channel_scale == 0
            use the same output channel if channel_scale < 0
        """
        assert len(stage_info) == 2
        channels = stage_info[0]
        channel_scale = stage_info[1]

        if channel_scale == 0.0:
            return nn.Sequential()

        if channel_scale > 0:
            last_channel = (
                int(channels * self.width_ratio) if self.width_ratio > 1.0 else channels
            )
            last_channel = int(last_channel * channel_scale)
        else:
            last_channel = int(self.last_depth * (-channel_scale))
        last_channel = self._get_divisible_width(last_channel)

        dim_in = self.last_depth
        ret = ConvBNRelu(
            dim_in,
            last_channel,
            kernel=1,
            stride=1,
            pad=0,
            no_bias=1,
            use_relu="relu",
            bn_type=self.bn_type,
        )
        self.last_depth = last_channel
        return ret

    # def add_final_pool(self, model, blob_in, kernel_size):
    #     ret = model.AveragePool(blob_in, "final_avg", kernel=kernel_size, stride=1)
    #     return ret

    def _add_ir_block(
        self, dim_in, dim_out, stride, expand_ratio, block_op_type, **kwargs
    ):
        ret = PRIMITIVES[block_op_type](
            dim_in,
            dim_out,
            expansion=expand_ratio,
            stride=stride,
            bn_type=self.bn_type,
            width_divisor=self.width_divisor,
            dw_skip_bn=self.dw_skip_bn,
            dw_skip_relu=self.dw_skip_relu,
            **kwargs
        )
        return ret, ret.output_depth

    def add_ir_block(self, tcns, block_op_types, **kwargs):
        t, c, n, s = tcns
        assert n == 1
        out_depth = self._get_divisible_width(int(c * self.width_ratio))
        dim_in = self.last_depth
        op, ret_depth = self._add_ir_block(
            dim_in,
            out_depth,
            stride=s,
            expand_ratio=t,
            block_op_type=block_op_types[0],
            **kwargs
        )
        self.last_depth = ret_depth
        return op

    def _get_divisible_width(self, width):
        ret = _get_divisible_by(int(width), self.width_divisor, self.width_divisor)
        return ret