【 Semantic segmentation 】2021-PVT2 CVMJ

Thesis title ：PVT v2: Improved Baselines with Pyramid Vision Transformer

Thesis link : https://arxiv.org/abs/2106.13797

Paper code ：https://github.com/whai362/PVT

Thesis translation ：PVT,PVTv2 - Simple books (jianshu.com)

1. brief introduction

In computer vision Transformer Encouraging progress has been made recently . In this work , The author added 3 An improved design to improve the original pyramid vision Transformer（PVTv1）, These include ：

• Locally continuous characteristic with convolution ;
• have zero paddings The location code of ,
• With average collection .

With these simple modifications ,PVTv2 In the classification 、 Detection and segmentation are significantly better than PVTv1. Besides ,PVTv2 stay ImageNet-1K Under the pre training, I have achieved more recent works （ Include Swin Transformer） Better performance .

2. The Internet

PVTv1[33] The main limitations of are the following three aspects :

(1) And ViT similar , When processing high-resolution input ( If the short side is 800 Pixels ),PVTv1 The computational complexity of is relatively large .

(2) PVTv1 Treat an image as a set of non overlapping patch Sequence , To some extent, the local continuity of the image is lost ;

(3) PVTv1 The location code in is fixed , It is not flexible for processing images of any size . These problems limit PVTv1 Performance in visual tasks .

2.1 Overall framework

2.2 Linear Spatial Reduction Attention

use LinearSRA replace SRA. Here is a problem that needs to be explained , The author in PVTv1 I didn't use convolution , But it's compressing K、V When using Conv2D（ See github In the code ）. stay PVTv2 Use average pooling instead Conv2D.

2.3 Overlapping Patch Embedding( Overlapping patch embedding )

2.4 Convolutional FeedForward

3. Code pvt2-upernet

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


class DropPath(nn.Module):
    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). Copied from timm This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the argument. """

    def __init__(self, p: float = None):
        super().__init__()
        self.p = p

    def forward(self, x: Tensor) -> Tensor:
        if self.p == 0. or not self.training:
            return x
        kp = 1 - self.p
        shape = (x.shape[0],) + (1,) * (x.ndim - 1)
        random_tensor = kp + torch.rand(shape, dtype=x.dtype, device=x.device)
        random_tensor.floor_()  # binarize
        return x.div(kp) * random_tensor


class DWConv(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, groups=dim)

    def forward(self, x: Tensor, H: int, W: int) -> Tensor:
        B, _, C = x.shape
        x = x.transpose(1, 2).view(B, C, H, W)
        x = self.dwconv(x)
        return x.flatten(2).transpose(1, 2)


class MLP(nn.Module):
    def __init__(self, dim, hidden_dim, out_dim=None) -> None:
        super().__init__()
        out_dim = out_dim or dim
        self.fc1 = nn.Linear(dim, hidden_dim)
        self.fc2 = nn.Linear(hidden_dim, out_dim)
        self.dwconv = DWConv(hidden_dim)

    def forward(self, x: Tensor, H: int, W: int) -> Tensor:
        return self.fc2(F.gelu(self.dwconv(self.fc1(x), H, W)))


class Attention(nn.Module):
    def __init__(self, dim, head, sr_ratio):
        super().__init__()
        self.head = head
        self.sr_ratio = sr_ratio
        self.scale = (dim // head) ** -0.5
        self.q = nn.Linear(dim, dim, bias=True)
        self.kv = nn.Linear(dim, dim * 2, bias=True)
        self.proj = nn.Linear(dim, dim)

        if sr_ratio > 1:
            self.sr = nn.Conv2d(dim, dim, sr_ratio, sr_ratio)
            self.norm = nn.LayerNorm(dim)

    def forward(self, x: Tensor, H, W) -> Tensor:
        B, N, C = x.shape
        q = self.q(x).reshape(B, N, self.head, C // self.head).permute(0, 2, 1, 3)

        if self.sr_ratio > 1:
            x = x.permute(0, 2, 1).reshape(B, C, H, W)
            x = self.sr(x).reshape(B, C, -1).permute(0, 2, 1)
            x = self.norm(x)
        k, v = self.kv(x).reshape(B, -1, 2, self.head, C // self.head).permute(2, 0, 3, 1, 4)

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

        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
        x = self.proj(x)
        return x


class Block(nn.Module):
    def __init__(self, dim, head, sr_ratio=1, mlp_ratio=4, dpr=0.):
        super().__init__()
        self.norm1 = nn.LayerNorm(dim)
        self.attn = Attention(dim, head, sr_ratio)
        self.drop_path = DropPath(dpr) if dpr > 0. else nn.Identity()
        self.norm2 = nn.LayerNorm(dim)
        self.mlp = MLP(dim, int(dim * mlp_ratio))

    def forward(self, x: Tensor, H, W) -> Tensor:
        x = x + self.drop_path(self.attn(self.norm1(x), H, W))
        x = x + self.drop_path(self.mlp(self.norm2(x), H, W))
        return x


class PatchEmbed(nn.Module):
    def __init__(self, c1=3, c2=64, patch_size=7, stride=4):
        super().__init__()
        self.proj = nn.Conv2d(c1, c2, patch_size, stride, patch_size // 2)
        self.norm = nn.LayerNorm(c2)

    def forward(self, x: Tensor) -> Tensor:
        x = self.proj(x)
        _, _, H, W = x.shape
        x = x.flatten(2).transpose(1, 2)
        x = self.norm(x)
        return x, H, W


pvtv2_settings = {
    
    'B1': [2, 2, 2, 2],  # depths
    'B2': [3, 4, 6, 3],
    'B3': [3, 4, 18, 3],
    'B4': [3, 8, 27, 3],
    'B5': [3, 6, 40, 3]
}


class PVTv2(nn.Module):
    def __init__(self, model_name: str = 'B1') -> None:
        super().__init__()
        assert model_name in pvtv2_settings.keys(), f"PVTv2 model name should be in {
      list(pvtv2_settings.keys())}"
        depths = pvtv2_settings[model_name]
        embed_dims = [64, 128, 320, 512]
        drop_path_rate = 0.1
        self.embed_dims = embed_dims
        # patch_embed
        self.patch_embed1 = PatchEmbed(3, embed_dims[0], 7, 4)
        self.patch_embed2 = PatchEmbed(embed_dims[0], embed_dims[1], 3, 2)
        self.patch_embed3 = PatchEmbed(embed_dims[1], embed_dims[2], 3, 2)
        self.patch_embed4 = PatchEmbed(embed_dims[2], embed_dims[3], 3, 2)

        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]
        # transformer encoder
        cur = 0
        self.block1 = nn.ModuleList([Block(embed_dims[0], 1, 8, 8, dpr[cur + i]) for i in range(depths[0])])
        self.norm1 = nn.LayerNorm(embed_dims[0])

        cur += depths[0]
        self.block2 = nn.ModuleList([Block(embed_dims[1], 2, 4, 8, dpr[cur + i]) for i in range(depths[1])])
        self.norm2 = nn.LayerNorm(embed_dims[1])

        cur += depths[1]
        self.block3 = nn.ModuleList([Block(embed_dims[2], 5, 2, 4, dpr[cur + i]) for i in range(depths[2])])
        self.norm3 = nn.LayerNorm(embed_dims[2])

        cur += depths[2]
        self.block4 = nn.ModuleList([Block(embed_dims[3], 8, 1, 4, dpr[cur + i]) for i in range(depths[3])])
        self.norm4 = nn.LayerNorm(embed_dims[3])

    def forward(self, x: Tensor) -> Tensor:
        B = x.shape[0]
        # stage 1
        x, H, W = self.patch_embed1(x)
        for blk in self.block1:
            x = blk(x, H, W)
        x1 = self.norm1(x).reshape(B, H, W, -1).permute(0, 3, 1, 2)

        # stage 2
        x, H, W = self.patch_embed2(x1)
        for blk in self.block2:
            x = blk(x, H, W)
        x2 = self.norm2(x).reshape(B, H, W, -1).permute(0, 3, 1, 2)

        # stage 3
        x, H, W = self.patch_embed3(x2)
        for blk in self.block3:
            x = blk(x, H, W)
        x3 = self.norm3(x).reshape(B, H, W, -1).permute(0, 3, 1, 2)

        # stage 4
        x, H, W = self.patch_embed4(x3)
        for blk in self.block4:
            x = blk(x, H, W)
        x4 = self.norm4(x).reshape(B, H, W, -1).permute(0, 3, 1, 2)

        return x1, x2, x3, x4


class PPM(nn.ModuleList):
    """  Pyramid pooling model  Pyramid Pooling Module https://arxiv.org/abs/1612.01105 CVPR 2017 year   The job of   Use maximum pooling , obtain  """

    def __init__(self, pool_sizes, in_channels, out_channels):
        super(PPM, self).__init__()
        self.pool_sizes = pool_sizes
        self.in_channels = in_channels
        self.out_channels = out_channels
        for pool_size in pool_sizes:
            self.append(
                nn.Sequential(
                    nn.AdaptiveMaxPool2d(pool_size),
                    nn.Conv2d(self.in_channels, self.out_channels, kernel_size=1),
                )
            )

    def forward(self, x):
        out_puts = []
        for ppm in self:
            ppm_out = nn.functional.interpolate(ppm(x), size=(x.size(2), x.size(3)), mode='bilinear',
                                                align_corners=True)
            out_puts.append(ppm_out)
        return out_puts


class PPMHEAD(nn.Module):
    def __init__(self, in_channels, out_channels, pool_sizes=[1, 2, 3, 6], ):
        super(PPMHEAD, self).__init__()
        self.pool_sizes = pool_sizes
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.psp_modules = PPM(self.pool_sizes, self.in_channels, self.out_channels)
        self.final = nn.Sequential(
            nn.Conv2d(self.in_channels + len(self.pool_sizes) * self.out_channels, 4 * self.out_channels,
                      kernel_size=1),
            nn.BatchNorm2d(4 * self.out_channels),
            nn.ReLU(),
        )

    def forward(self, x):
        out = self.psp_modules(x)

        out.append(x)
        out = torch.cat(out, 1)
        out = self.final(out)
        return out


class FPNHEAD(nn.Module):
    def __init__(self, out_channels=512, num_classes=19, channels=[64, 128, 320, 512]):
        """ Args: out_channels:  The last level of integration   The channel number , Number of channels before classification  num_classes:  Number of final classifications  channels:  four layers backbone The number of channels  """
        super(FPNHEAD, self).__init__()
        self.num_classes = num_classes
        self.PPMHead = PPMHEAD(in_channels=channels[-1], out_channels=channels[-1] // 4)

        self.Conv_fuse1 = nn.Sequential(
            nn.Conv2d(channels[-2], channels[-2], 1),
            nn.BatchNorm2d(channels[-2]),
            nn.ReLU()
        )
        self.Conv_fuse1_ = nn.Sequential(
            nn.Conv2d(channels[-2] + channels[-1], channels[-2], 1),
            nn.BatchNorm2d(channels[-2]),
            nn.ReLU()
        )
        self.Conv_fuse2 = nn.Sequential(
            nn.Conv2d(channels[-3], channels[-3], 1),
            nn.BatchNorm2d(channels[-3]),
            nn.ReLU()
        )
        self.Conv_fuse2_ = nn.Sequential(
            nn.Conv2d(channels[-3] + channels[-2], channels[-3], 1),
            nn.BatchNorm2d(channels[-3]),
            nn.ReLU()
        )

        self.Conv_fuse3 = nn.Sequential(
            nn.Conv2d(channels[-4], channels[-4], 1),
            nn.BatchNorm2d(channels[-4]),
            nn.ReLU()
        )
        self.Conv_fuse3_ = nn.Sequential(
            nn.Conv2d(channels[-4] + channels[-3], channels[-4], 1),
            nn.BatchNorm2d(channels[-4]),
            nn.ReLU()
        )

        self.fuse_all = nn.Sequential(
            nn.Conv2d(sum(channels), out_channels, 1),
            nn.BatchNorm2d(out_channels),
            nn.ReLU()
        )

        self.cls_seg = nn.Sequential(
            nn.Conv2d(out_channels, self.num_classes, kernel_size=3, padding=1),
        )

    def forward(self, input_fpn):
        """ Args: input_fpn:  Four characteristic diagrams  Returns: """
        ##############################
        # x1 = torch.randn(1, 64, 56, 56)
        # x2 = torch.randn(1, 128, 28, 28)
        # x3 = torch.randn(1, 320, 14, 14)
        # x4 = torch.randn(1, 512, 7, 7)

        # 1/32 Characteristic graph   Use PPMHead torch.Size([1, 2048, 7, 7])
        # x1= [1, 512, 7, 7]
        x1 = self.PPMHead(input_fpn[-1])
        # print(x1.shape)

        # [1, 512, 7, 7]-->[1, 512, 14, 14]
        x = F.interpolate(x1,
                          size=(x1.size(2) * 2, x1.size(3) * 2),
                          mode='bilinear',
                          align_corners=True)

        #  The fusion 1/16 Graph  torch.Size([1, 3072, 14, 14]). Just splice on the channel 
        # [1, 512, 14, 14] + [1,320, 14, 14] =[1, 832, 14, 14]
        x = torch.cat([x, self.Conv_fuse1(input_fpn[-2])], dim=1)

        ##############################
        # [1, 832, 14, 14] -->[1, 320, 14, 14] , Reduce the number of channels 
        x2 = self.Conv_fuse1_(x)
        # [1, 320, 14, 14]->[1, 320, 28,28]
        x = F.interpolate(x2,
                          size=(x2.size(2) * 2, x2.size(3) * 2),
                          mode='bilinear',
                          align_corners=True)

        #  The fusion 1/8 Graph 
        # [1, 320, 28,28] +[1, 128, 28,28] = [1, 448, 28,28]
        x = torch.cat([x, self.Conv_fuse2(input_fpn[-3])], dim=1)
        # print(x.shape)
        ##############################
        # [1, 448, 28,28] -> [1, 128, 28, 28] Perform channel reduction .
        x3 = self.Conv_fuse2_(x)

        #  Yes 1/8---> 1/4
        # [1, 128, 28, 28]-> [1, 128, 56, 56]
        x = F.interpolate(x3,
                          size=(x3.size(2) * 2, x3.size(3) * 2),
                          mode='bilinear',
                          align_corners=True)
        #  The fusion 1/4 Graph 
        # [1, 128, 56, 56]+[1, 64, 56, 56]=[1, 192, 56, 56]
        x = torch.cat([x, self.Conv_fuse3(input_fpn[-4])], dim=1)

        ##############################
        # [1, 192, 56, 56]-> [1, 64, 56, 56]
        x4 = self.Conv_fuse3_(x)

        x1 = F.interpolate(x1, x4.size()[-2:], mode='bilinear', align_corners=True)
        x2 = F.interpolate(x2, x4.size()[-2:], mode='bilinear', align_corners=True)
        x3 = F.interpolate(x3, x4.size()[-2:], mode='bilinear', align_corners=True)

        x = self.fuse_all(torch.cat([x1, x2, x3, x4], 1))
        # print(x.shape)
        x = F.interpolate(x, size=(x.size(2) * 4, x.size(3) * 4), mode='bilinear', align_corners=True)
        # print(x.shape)
        x = self.cls_seg(x)
        return x


class pvt2_upernet(nn.Module):
    def __init__(self, num_classes, channels, size="B1"):
        """  Number of categories  Args: num_classes: """
        super(pvt2_upernet, self).__init__()
        self.backbone = PVTv2(size)
        self.decoder = FPNHEAD(num_classes=num_classes, channels=channels)

    def forward(self, x):
        x = self.backbone(x)
        x = self.decoder(x)
        return x


def pvt2_B1_upernet(num_classes):
    model = pvt2_upernet(num_classes=num_classes, size="B1", channels=[64, 128, 320, 512])
    return model


def pvt2_B2_upernet(num_classes):
    model = pvt2_upernet(num_classes=num_classes, size="B2", channels=[64, 128, 320, 512])
    return model


def pvt2_B3_upernet(num_classes):
    model = pvt2_upernet(num_classes=num_classes, size="B3", channels=[64, 128, 320, 512])
    return model

def pvt2_B4_upernet(num_classes):
    model = pvt2_upernet(num_classes=num_classes, size="B3", channels=[64, 128, 320, 512])
    return model

if __name__ == '__main__':
    x=torch.randn(1,3,224,224)
    model=pvt2_B2_upernet(num_classes=19)
    y=model(x)
    print(y.shape)

Reference material

PVTv2: Improved Baselines with Pyramid Vision Transformer——PVT2 Reading - You know (zhihu.com)

Transformer Backbone network ——PVT_V2 Nanny level analysis _ It's just git clone Programmer's blog -CSDN Blog