railseek6/openclip_gpu_env/Lib/site-packages/timm/layers/cbam.py

""" CBAM (sort-of) Attention

Experimental impl of CBAM: Convolutional Block Attention Module: https://arxiv.org/abs/1807.06521

WARNING: Results with these attention layers have been mixed. They can significantly reduce performance on
some tasks, especially fine-grained it seems. I may end up removing this impl.

Hacked together by / Copyright 2020 Ross Wightman
"""
from typing import Optional, Tuple, Type, Union

import torch
from torch import nn as nn
import torch.nn.functional as F

from .conv_bn_act import ConvNormAct
from .create_act import create_act_layer, get_act_layer
from .helpers import make_divisible


class ChannelAttn(nn.Module):
    """ Original CBAM channel attention module, currently avg + max pool variant only.
    """
    def __init__(
            self,
            channels: int,
            rd_ratio: float = 1. / 16,
            rd_channels: Optional[int] = None,
            rd_divisor: int = 1,
            act_layer: Type[nn.Module] = nn.ReLU,
            gate_layer: Union[str, Type[nn.Module]] = 'sigmoid',
            mlp_bias=False,
            device=None,
            dtype=None,
    ):
        dd = {'device': device, 'dtype': dtype}
        super().__init__()
        if not rd_channels:
            rd_channels = make_divisible(channels * rd_ratio, rd_divisor, round_limit=0.)
        self.fc1 = nn.Conv2d(channels, rd_channels, 1, bias=mlp_bias, **dd)
        self.act = act_layer(inplace=True)
        self.fc2 = nn.Conv2d(rd_channels, channels, 1, bias=mlp_bias, **dd)
        self.gate = create_act_layer(gate_layer)

    def forward(self, x):
        x_avg = self.fc2(self.act(self.fc1(x.mean((2, 3), keepdim=True))))
        x_max = self.fc2(self.act(self.fc1(x.amax((2, 3), keepdim=True))))
        return x * self.gate(x_avg + x_max)


class LightChannelAttn(ChannelAttn):
    """An experimental 'lightweight' that sums avg + max pool first
    """
    def __init__(
            self,
            channels: int,
            rd_ratio: float = 1./16,
            rd_channels: Optional[int] = None,
            rd_divisor: int = 1,
            act_layer: Type[nn.Module] = nn.ReLU,
            gate_layer: Union[str, Type[nn.Module]] = 'sigmoid',
            mlp_bias: bool = False,
            device=None,
            dtype=None
    ):
        super().__init__(
            channels, rd_ratio, rd_channels, rd_divisor, act_layer, gate_layer, mlp_bias, device=device, dtype=dtype)

    def forward(self, x):
        x_pool = 0.5 * x.mean((2, 3), keepdim=True) + 0.5 * x.amax((2, 3), keepdim=True)
        x_attn = self.fc2(self.act(self.fc1(x_pool)))
        return x * F.sigmoid(x_attn)


class SpatialAttn(nn.Module):
    """ Original CBAM spatial attention module
    """
    def __init__(
            self,
            kernel_size: int = 7,
            gate_layer: Union[str, Type[nn.Module]] = 'sigmoid',
            device=None,
            dtype=None,
    ):
        super().__init__()
        self.conv = ConvNormAct(2, 1, kernel_size, apply_act=False, device=device, dtype=dtype)
        self.gate = create_act_layer(gate_layer)

    def forward(self, x):
        x_attn = torch.cat([x.mean(dim=1, keepdim=True), x.amax(dim=1, keepdim=True)], dim=1)
        x_attn = self.conv(x_attn)
        return x * self.gate(x_attn)


class LightSpatialAttn(nn.Module):
    """An experimental 'lightweight' variant that sums avg_pool and max_pool results.
    """
    def __init__(
            self,
            kernel_size: int = 7,
            gate_layer: Union[str, Type[nn.Module]] = 'sigmoid',
            device=None,
            dtype=None,
    ):
        super().__init__()
        self.conv = ConvNormAct(1, 1, kernel_size, apply_act=False, device=device, dtype=dtype)
        self.gate = create_act_layer(gate_layer)

    def forward(self, x):
        x_attn = 0.5 * x.mean(dim=1, keepdim=True) + 0.5 * x.amax(dim=1, keepdim=True)
        x_attn = self.conv(x_attn)
        return x * self.gate(x_attn)


class CbamModule(nn.Module):
    def __init__(
            self,
            channels: int,
            rd_ratio: float = 1./16,
            rd_channels: Optional[int] = None,
            rd_divisor: int = 1,
            spatial_kernel_size: int = 7,
            act_layer: Type[nn.Module] = nn.ReLU,
            gate_layer: Union[str, Type[nn.Module]] = 'sigmoid',
            mlp_bias: bool = False,
            device=None,
            dtype=None,
    ):
        dd = {'device': device, 'dtype': dtype}
        super().__init__()
        self.channel = ChannelAttn(
            channels,
            rd_ratio=rd_ratio,
            rd_channels=rd_channels,
            rd_divisor=rd_divisor,
            act_layer=act_layer,
            gate_layer=gate_layer,
            mlp_bias=mlp_bias,
            **dd,
        )
        self.spatial = SpatialAttn(spatial_kernel_size, gate_layer=gate_layer, **dd)

    def forward(self, x):
        x = self.channel(x)
        x = self.spatial(x)
        return x


class LightCbamModule(nn.Module):
    def __init__(
            self,
            channels: int,
            rd_ratio: float = 1./16,
            rd_channels: Optional[int] = None,
            rd_divisor: int = 1,
            spatial_kernel_size: int = 7,
            act_layer: Type[nn.Module] = nn.ReLU,
            gate_layer: Union[str, Type[nn.Module]] = 'sigmoid',
            mlp_bias: bool = False,
            device=None,
            dtype=None,
    ):
        dd = {'device': device, 'dtype': dtype}
        super().__init__()
        self.channel = LightChannelAttn(
            channels,
            rd_ratio=rd_ratio,
            rd_channels=rd_channels,
            rd_divisor=rd_divisor,
            act_layer=act_layer,
            gate_layer=gate_layer,
            mlp_bias=mlp_bias,
            **dd,
        )
        self.spatial = LightSpatialAttn(spatial_kernel_size, **dd)

    def forward(self, x):
        x = self.channel(x)
        x = self.spatial(x)
        return x