mmdetection/projects/EfficientDet/efficientdet/utils.py

import math
from typing import Tuple, Union

import torch
import torch.nn as nn
from mmcv.cnn.bricks import Swish, build_norm_layer
from torch.nn import functional as F
from torch.nn.init import _calculate_fan_in_and_fan_out, trunc_normal_

from mmdet.registry import MODELS
from mmdet.utils import OptConfigType


def variance_scaling_trunc(tensor, gain=1.):
    fan_in, _ = _calculate_fan_in_and_fan_out(tensor)
    gain /= max(1.0, fan_in)
    std = math.sqrt(gain) / .87962566103423978
    return trunc_normal_(tensor, 0., std)


@MODELS.register_module()
class Conv2dSamePadding(nn.Conv2d):

    def __init__(self,
                 in_channels: int,
                 out_channels: int,
                 kernel_size: Union[int, Tuple[int, int]],
                 stride: Union[int, Tuple[int, int]] = 1,
                 padding: Union[int, Tuple[int, int]] = 0,
                 dilation: Union[int, Tuple[int, int]] = 1,
                 groups: int = 1,
                 bias: bool = True):
        super().__init__(in_channels, out_channels, kernel_size, stride, 0,
                         dilation, groups, bias)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        img_h, img_w = x.size()[-2:]
        kernel_h, kernel_w = self.weight.size()[-2:]
        extra_w = (math.ceil(img_w / self.stride[1]) -
                   1) * self.stride[1] - img_w + kernel_w
        extra_h = (math.ceil(img_h / self.stride[0]) -
                   1) * self.stride[0] - img_h + kernel_h

        left = extra_w // 2
        right = extra_w - left
        top = extra_h // 2
        bottom = extra_h - top
        x = F.pad(x, [left, right, top, bottom])
        return F.conv2d(x, self.weight, self.bias, self.stride, self.padding,
                        self.dilation, self.groups)


class MaxPool2dSamePadding(nn.Module):

    def __init__(self,
                 kernel_size: Union[int, Tuple[int, int]] = 3,
                 stride: Union[int, Tuple[int, int]] = 2,
                 **kwargs):
        super().__init__()
        self.pool = nn.MaxPool2d(kernel_size, stride, **kwargs)
        self.stride = self.pool.stride
        self.kernel_size = self.pool.kernel_size

        if isinstance(self.stride, int):
            self.stride = [self.stride] * 2
        if isinstance(self.kernel_size, int):
            self.kernel_size = [self.kernel_size] * 2

    def forward(self, x):
        h, w = x.shape[-2:]

        extra_h = (math.ceil(w / self.stride[1]) -
                   1) * self.stride[1] - w + self.kernel_size[1]
        extra_v = (math.ceil(h / self.stride[0]) -
                   1) * self.stride[0] - h + self.kernel_size[0]

        left = extra_h // 2
        right = extra_h - left
        top = extra_v // 2
        bottom = extra_v - top

        x = F.pad(x, [left, right, top, bottom])
        x = self.pool(x)

        return x


class DepthWiseConvBlock(nn.Module):

    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        apply_norm: bool = True,
        conv_bn_act_pattern: bool = False,
        norm_cfg: OptConfigType = dict(type='BN', momentum=1e-2, eps=1e-3)
    ) -> None:
        super(DepthWiseConvBlock, self).__init__()
        self.depthwise_conv = Conv2dSamePadding(
            in_channels,
            in_channels,
            kernel_size=3,
            stride=1,
            groups=in_channels,
            bias=False)
        self.pointwise_conv = Conv2dSamePadding(
            in_channels, out_channels, kernel_size=1, stride=1)

        self.apply_norm = apply_norm
        if self.apply_norm:
            self.bn = build_norm_layer(norm_cfg, num_features=out_channels)[1]

        self.apply_activation = conv_bn_act_pattern
        if self.apply_activation:
            self.swish = Swish()

    def forward(self, x):
        x = self.depthwise_conv(x)
        x = self.pointwise_conv(x)
        if self.apply_norm:
            x = self.bn(x)
        if self.apply_activation:
            x = self.swish(x)

        return x


class DownChannelBlock(nn.Module):

    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        apply_norm: bool = True,
        conv_bn_act_pattern: bool = False,
        norm_cfg: OptConfigType = dict(type='BN', momentum=1e-2, eps=1e-3)
    ) -> None:
        super(DownChannelBlock, self).__init__()
        self.down_conv = Conv2dSamePadding(in_channels, out_channels, 1)
        self.apply_norm = apply_norm
        if self.apply_norm:
            self.bn = build_norm_layer(norm_cfg, num_features=out_channels)[1]
        self.apply_activation = conv_bn_act_pattern
        if self.apply_activation:
            self.swish = Swish()

    def forward(self, x):
        x = self.down_conv(x)
        if self.apply_norm:
            x = self.bn(x)
        if self.apply_activation:
            x = self.swish(x)

        return x