pfcfuse/net_me.py

import torch
import torch.nn as nn
import math
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from einops import rearrange


def drop_path(x, drop_prob: float = 0., training: bool = False):
    if drop_prob == 0. or not training:
        return x
    keep_prob = 1 - drop_prob
    # work with diff dim tensors, not just 2D ConvNets
    shape = (x.shape[0],) + (1,) * (x.ndim - 1)
    random_tensor = keep_prob + \
                    torch.rand(shape, dtype=x.dtype, device=x.device)
    random_tensor.floor_()  # binarize
    output = x.div(keep_prob) * random_tensor
    return output


class DropPath(nn.Module):
    """
    Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
    """

    def __init__(self, drop_prob=None):
        super(DropPath, self).__init__()
        self.drop_prob = drop_prob

    def forward(self, x):
        return drop_path(x, self.drop_prob, self.training)


# 改点，使用Pooling替换AttentionBase
class Pooling(nn.Module):
    def __init__(self, kernel_size=3):
        super().__init__()
        self.pool = nn.AvgPool2d(
            kernel_size, stride=1, padding=kernel_size // 2)

    def forward(self, x):
        return self.pool(x) - x


class PoolMlp(nn.Module):
    """
    实现基于1x1卷积的MLP模块。
    输入：形状为[B, C, H, W]的张量。
    """

    def __init__(self,
                 in_features,
                 hidden_features=None,
                 out_features=None,
                 act_layer=nn.GELU,
                 bias=False,
                 drop=0.):
        """
        初始化PoolMlp模块。

        参数:
            in_features (int): 输入特征的数量。
            hidden_features (int, 可选): 隐藏层特征的数量。默认为None，设置为与in_features相同。
            out_features (int, 可选): 输出特征的数量。默认为None，设置为与in_features相同。
            act_layer (nn.Module, 可选): 使用的激活层。默认为nn.GELU。
            bias (bool, 可选): 是否在卷积层中包含偏置项。默认为False。
            drop (float, 可选): Dropout比率。默认为0。
        """
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Conv2d(in_features, hidden_features, 1, bias=bias)
        self.act = act_layer()
        self.fc2 = nn.Conv2d(hidden_features, out_features, 1, bias=bias)
        self.drop = nn.Dropout(drop)

    def forward(self, x):
        """
        通过PoolMlp模块的前向传播。

        参数:
            x (torch.Tensor): 形状为[B, C, H, W]的输入张量。

        返回:
            torch.Tensor: 形状为[B, C, H, W]的输出张量。
        """
        x = self.fc1(x)  # (B, C, H, W) --> (B, C, H, W)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)  # (B, C, H, W) --> (B, C, H, W)
        x = self.drop(x)
        return x


class BaseFeatureExtraction(nn.Module):
    def __init__(self, dim, pool_size=3, mlp_ratio=4.,
                 act_layer=nn.GELU,
                 # norm_layer=nn.LayerNorm,
                 drop=0., drop_path=0.,
                 use_layer_scale=True, layer_scale_init_value=1e-5):

        super().__init__()

        self.norm1 = LayerNorm(dim, 'WithBias')
        self.token_mixer = Pooling(kernel_size=pool_size)  # vits是msa，MLPs是mlp，这个用pool来替代
        self.norm2 = LayerNorm(dim, 'WithBias')
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.poolmlp = PoolMlp(in_features=dim, hidden_features=mlp_hidden_dim,
                               act_layer=act_layer, drop=drop)

        # The following two techniques are useful to train deep PoolFormers.
        self.drop_path = DropPath(drop_path) if drop_path > 0. \
            else nn.Identity()
        self.use_layer_scale = use_layer_scale

        if use_layer_scale:
            self.layer_scale_1 = nn.Parameter(
                torch.ones(dim, dtype=torch.float32) * layer_scale_init_value)

            self.layer_scale_2 = nn.Parameter(
                torch.ones(dim, dtype=torch.float32) * layer_scale_init_value)

    def forward(self, x):
        if self.use_layer_scale:
            x = x + self.drop_path(
                self.layer_scale_1.unsqueeze(-1).unsqueeze(-1)
                * self.token_mixer(self.norm1(x)))
            x = x + self.drop_path(
                self.layer_scale_2.unsqueeze(-1).unsqueeze(-1)
                * self.poolmlp(self.norm2(x)))
        else:
            x = x + self.drop_path(self.token_mixer(self.norm1(x)))  # 匹配cddfuse
            x = x + self.drop_path(self.poolmlp(self.norm2(x)))
        return x


class InvertedResidualBlock(nn.Module):
    def __init__(self, inp, oup, expand_ratio):
        super(InvertedResidualBlock, self).__init__()
        hidden_dim = int(inp * expand_ratio)
        self.bottleneckBlock = nn.Sequential(
            # pw
            nn.Conv2d(inp, hidden_dim, 1, bias=False),
            # nn.BatchNorm2d(hidden_dim),
            nn.ReLU6(inplace=True),
            # dw
            nn.ReflectionPad2d(1),
            nn.Conv2d(hidden_dim, hidden_dim, 3, groups=hidden_dim, bias=False),
            # nn.BatchNorm2d(hidden_dim),
            nn.ReLU6(inplace=True),
            # pw-linear
            nn.Conv2d(hidden_dim, oup, 1, bias=False),
            # nn.BatchNorm2d(oup),
        )

    def forward(self, x):
        return self.bottleneckBlock(x)


class DetailNode(nn.Module):

    # <img src = "http://42.192.130.83:9000/picgo/imgs/小绿鲸英文文献阅读器_ELTITYqm5G.png" / > '
    def __init__(self):
        super(DetailNode, self).__init__()

        self.theta_phi = InvertedResidualBlock(inp=32, oup=32, expand_ratio=2)
        self.theta_rho = InvertedResidualBlock(inp=32, oup=32, expand_ratio=2)
        self.theta_eta = InvertedResidualBlock(inp=32, oup=32, expand_ratio=2)
        self.shffleconv = nn.Conv2d(64, 64, kernel_size=1,
                                    stride=1, padding=0, bias=True)

    def separateFeature(self, x):
        z1, z2 = x[:, :x.shape[1] // 2], x[:, x.shape[1] // 2:x.shape[1]]
        return z1, z2

    def forward(self, z1, z2):
        z1, z2 = self.separateFeature(
            self.shffleconv(torch.cat((z1, z2), dim=1)))
        z2 = z2 + self.theta_phi(z1)
        z1 = z1 * torch.exp(self.theta_rho(z2)) + self.theta_eta(z2)
        return z1, z2


class DetailFeatureExtraction(nn.Module):
    def __init__(self, num_layers=3):
        super(DetailFeatureExtraction, self).__init__()
        INNmodules = [DetailNode() for _ in range(num_layers)]
        self.net = nn.Sequential(*INNmodules)

    def forward(self, x):
        z1, z2 = x[:, :x.shape[1] // 2], x[:, x.shape[1] // 2:x.shape[1]]
        for layer in self.net:
            z1, z2 = layer(z1, z2)
        return torch.cat((z1, z2), dim=1)


# =============================================================================

# =============================================================================
import numbers


##########################################################################
## Layer Norm
def to_3d(x):
    return rearrange(x, 'b c h w -> b (h w) c')


def to_4d(x, h, w):
    return rearrange(x, 'b (h w) c -> b c h w', h=h, w=w)


class BiasFree_LayerNorm(nn.Module):
    def __init__(self, normalized_shape):
        super(BiasFree_LayerNorm, self).__init__()
        if isinstance(normalized_shape, numbers.Integral):
            normalized_shape = (normalized_shape,)
        normalized_shape = torch.Size(normalized_shape)

        assert len(normalized_shape) == 1

        self.weight = nn.Parameter(torch.ones(normalized_shape))
        self.normalized_shape = normalized_shape

    def forward(self, x):
        sigma = x.var(-1, keepdim=True, unbiased=False)
        return x / torch.sqrt(sigma + 1e-5) * self.weight


class WithBias_LayerNorm(nn.Module):
    def __init__(self, normalized_shape):
        super(WithBias_LayerNorm, self).__init__()
        if isinstance(normalized_shape, numbers.Integral):
            normalized_shape = (normalized_shape,)
        normalized_shape = torch.Size(normalized_shape)

        assert len(normalized_shape) == 1

        self.weight = nn.Parameter(torch.ones(normalized_shape))
        self.bias = nn.Parameter(torch.zeros(normalized_shape))
        self.normalized_shape = normalized_shape

    def forward(self, x):
        mu = x.mean(-1, keepdim=True)
        sigma = x.var(-1, keepdim=True, unbiased=False)
        return (x - mu) / torch.sqrt(sigma + 1e-5) * self.weight + self.bias


class LayerNorm(nn.Module):
    def __init__(self, dim, LayerNorm_type):
        super(LayerNorm, self).__init__()
        if LayerNorm_type == 'BiasFree':
            self.body = BiasFree_LayerNorm(dim)
        else:
            self.body = WithBias_LayerNorm(dim)

    def forward(self, x):
        h, w = x.shape[-2:]
        return to_4d(self.body(to_3d(x)), h, w)


##########################################################################
## Gated-Dconv Feed-Forward Network (GDFN)
class FeedForward(nn.Module):
    def __init__(self, dim, ffn_expansion_factor, bias):
        super(FeedForward, self).__init__()

        hidden_features = int(dim * ffn_expansion_factor)

        self.project_in = nn.Conv2d(
            dim, hidden_features * 2, kernel_size=1, bias=bias)

        self.dwconv = nn.Conv2d(hidden_features * 2, hidden_features * 2, kernel_size=3,
                                stride=1, padding=1, groups=hidden_features * 2, bias=bias)

        self.project_out = nn.Conv2d(
            hidden_features, dim, kernel_size=1, bias=bias)

    def forward(self, x):
        x = self.project_in(x)
        x1, x2 = self.dwconv(x).chunk(2, dim=1)
        x = F.gelu(x1) * x2
        x = self.project_out(x)
        return x


##########################################################################
## Multi-DConv Head Transposed Self-Attention (MDTA)
class Attention(nn.Module):
    def __init__(self, dim, num_heads, bias):
        super(Attention, self).__init__()
        self.num_heads = num_heads
        self.temperature = nn.Parameter(torch.ones(num_heads, 1, 1))

        self.qkv = nn.Conv2d(dim, dim * 3, kernel_size=1, bias=bias)
        self.qkv_dwconv = nn.Conv2d(
            dim * 3, dim * 3, kernel_size=3, stride=1, padding=1, groups=dim * 3, bias=bias)
        self.project_out = nn.Conv2d(dim, dim, kernel_size=1, bias=bias)

    def forward(self, x):
        b, c, h, w = x.shape

        qkv = self.qkv_dwconv(self.qkv(x))
        q, k, v = qkv.chunk(3, dim=1)

        q = rearrange(q, 'b (head c) h w -> b head c (h w)',
                      head=self.num_heads)
        k = rearrange(k, 'b (head c) h w -> b head c (h w)',
                      head=self.num_heads)
        v = rearrange(v, 'b (head c) h w -> b head c (h w)',
                      head=self.num_heads)

        q = torch.nn.functional.normalize(q, dim=-1)
        k = torch.nn.functional.normalize(k, dim=-1)

        attn = (q @ k.transpose(-2, -1)) * self.temperature
        attn = attn.softmax(dim=-1)

        out = (attn @ v)

        out = rearrange(out, 'b head c (h w) -> b (head c) h w',
                        head=self.num_heads, h=h, w=w)

        out = self.project_out(out)
        return out


##########################################################################
class TransformerBlock(nn.Module):
    def __init__(self, dim, num_heads, ffn_expansion_factor, bias, LayerNorm_type):
        super(TransformerBlock, self).__init__()

        self.norm1 = LayerNorm(dim, LayerNorm_type)
        self.attn = Attention(dim, num_heads, bias)
        self.norm2 = LayerNorm(dim, LayerNorm_type)
        self.ffn = FeedForward(dim, ffn_expansion_factor, bias)

    def forward(self, x):
        x = x + self.attn(self.norm1(x))
        x = x + self.ffn(self.norm2(x))

        return x


##########################################################################
## Overlapped image patch embedding with 3x3 Conv
class OverlapPatchEmbed(nn.Module):
    def __init__(self, in_c=3, embed_dim=48, bias=False):
        super(OverlapPatchEmbed, self).__init__()

        self.proj = nn.Conv2d(in_c, embed_dim, kernel_size=3,
                              stride=1, padding=1, bias=bias)

    def forward(self, x):
        x = self.proj(x)
        return x


class Restormer_Encoder(nn.Module):
    def __init__(self,
                 inp_channels=1,
                 out_channels=1,
                 dim=64,
                 num_blocks=[4, 4],
                 heads=[8, 8, 8],
                 ffn_expansion_factor=2,
                 bias=False,
                 LayerNorm_type='WithBias',
                 ):
        super(Restormer_Encoder, self).__init__()

        self.patch_embed = OverlapPatchEmbed(inp_channels, dim)

        self.encoder_level1 = nn.Sequential(
            *[TransformerBlock(dim=dim, num_heads=heads[0], ffn_expansion_factor=ffn_expansion_factor,
                               bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[0])])
        self.baseFeature = BaseFeatureExtraction(dim=dim)

        self.detailFeature = DetailFeatureExtraction()

    def forward(self, inp_img):
        inp_enc_level1 = self.patch_embed(inp_img)
        out_enc_level1 = self.encoder_level1(inp_enc_level1)
        base_feature = self.baseFeature(out_enc_level1)
        detail_feature = self.detailFeature(out_enc_level1)
        return base_feature, detail_feature, out_enc_level1


class Restormer_Decoder(nn.Module):
    def __init__(self,
                 inp_channels=1,
                 out_channels=1,
                 dim=64,
                 num_blocks=[4, 4],
                 heads=[8, 8, 8],
                 ffn_expansion_factor=2,
                 bias=False,
                 LayerNorm_type='WithBias',
                 ):

        super(Restormer_Decoder, self).__init__()
        self.reduce_channel = nn.Conv2d(int(dim * 2), int(dim), kernel_size=1, bias=bias)
        self.encoder_level2 = nn.Sequential(
            *[TransformerBlock(dim=dim, num_heads=heads[1], ffn_expansion_factor=ffn_expansion_factor,
                               bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[1])])
        self.output = nn.Sequential(
            nn.Conv2d(int(dim), int(dim) // 2, kernel_size=3,
                      stride=1, padding=1, bias=bias),
            nn.LeakyReLU(),
            nn.Conv2d(int(dim) // 2, out_channels, kernel_size=3,
                      stride=1, padding=1, bias=bias), )
        self.sigmoid = nn.Sigmoid()

    def forward(self, inp_img, base_feature, detail_feature):
        out_enc_level0 = torch.cat((base_feature, detail_feature), dim=1)
        out_enc_level0 = self.reduce_channel(out_enc_level0)
        out_enc_level1 = self.encoder_level2(out_enc_level0)
        if inp_img is not None:
            out_enc_level1 = self.output(out_enc_level1) + inp_img
        else:
            out_enc_level1 = self.output(out_enc_level1)
        return self.sigmoid(out_enc_level1), out_enc_level0


if __name__ == '__main__':
    height = 128
    width = 128
    window_size = 8
    modelE = Restormer_Encoder().cuda()
    modelD = Restormer_Decoder().cuda()
    print(modelE)
    print(modelD)