目标检测算法改进系列之Backbone替换为NextViT

NextViT介绍

由于复杂的注意力机制和模型设计，大多数现有的视觉Transformer（ViTs）在现实的工业部署场景中不能像卷积神经网络（CNNs）那样高效地执行，例如TensorRT 和 CoreML。这带来了一个明显的挑战：视觉神经网络能否设计为与 CNN 一样快的推理和与 ViT 一样强大的性能？最近的工作试图设计 CNN-Transformer 混合架构来解决这个问题，但这些工作的整体性能远不能令人满意。

为了结束这些，我们提出了在现实工业场景中有效部署的下一代视觉Transformer，即 Next-ViT，从延迟/准确性权衡的角度来看，它在 CNN 和 ViT 中均占主导地位。

原文地址：Next-ViT: Next Generation Vision Transformer for Efficient Deployment in Realistic Industrial Scenarios

NextViT代码实现

# Copyright (c) ByteDance Inc. All rights reserved.
from functools import partial
import numpy as np
import torch
import torch.utils.checkpoint as checkpoint
from einops import rearrange
from timm.models.layers import DropPath, trunc_normal_
from torch import nn

__all__ = ['nextvit_small', 'nextvit_base', 'nextvit_large']

NORM_EPS = 1e-5

class ConvBNReLU(nn.Module):
    def __init__(
            self,
            in_channels,
            out_channels,
            kernel_size,
            stride,
            groups=1):
        super(ConvBNReLU, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride,
                              padding=1, groups=groups, bias=False)
        self.norm = nn.BatchNorm2d(out_channels, eps=NORM_EPS)
        self.act = nn.ReLU(inplace=True)

    def forward(self, x):
        x = self.conv(x)
        x = self.norm(x)
        x = self.act(x)
        return x


def _make_divisible(v, divisor, min_value=None):
    if min_value is None:
        min_value = divisor
    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
    # Make sure that round down does not go down by more than 10%.
    if new_v < 0.9 * v:
        new_v += divisor
    return new_v


class PatchEmbed(nn.Module):
    def __init__(self,
                 in_channels,
                 out_channels,
                 stride=1):
        super(PatchEmbed, self).__init__()
        norm_layer = partial(nn.BatchNorm2d, eps=NORM_EPS)
        if stride == 2:
            self.avgpool = nn.AvgPool2d((2, 2), stride=2, ceil_mode=True, count_include_pad=False)
            self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, bias=False)
            self.norm = norm_layer(out_channels)
        elif in_channels != out_channels:
            self.avgpool = nn.Identity()
            self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, bias=False)
            self.norm = norm_layer(out_channels)
        else:
            self.avgpool = nn.Identity()
            self.conv = nn.Identity()
            self.norm = nn.Identity()

    def forward(self, x):
        return self.norm(self.conv(self.avgpool(x)))


class MHCA(nn.Module):
    """
    Multi-Head Convolutional Attention
    """
    def __init__(self, out_channels, head_dim):
        super(MHCA, self).__init__()
        norm_layer = partial(nn.BatchNorm2d, eps=NORM_EPS)
        self.group_conv3x3 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1,
                                       padding=1, groups=out_channels // head_dim, bias=False)
        self.norm = norm_layer(out_channels)
        self.act = nn.ReLU(inplace=True)
        self.projection = nn.Conv2d(out_channels, out_channels, kernel_size=1, bias=False)

    def forward(self, x):
        out = self.group_conv3x3(x)
        out = self.norm(out)
        out = self.act(out)
        out = self.projection(out)
        return out


class Mlp(nn.Module):
    def __init__(self, in_features, out_features=None, mlp_ratio=None, drop=0., bias=True):
        super().__init__()
        out_features = out_features or in_features
        hidden_dim = _make_divisible(in_features * mlp_ratio, 32)
        self.conv1 = nn.Conv2d(in_features, hidden_dim, kernel_size=1, bias=bias)
        self.act = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(hidden_dim, out_features, kernel_size=1, bias=bias)
        self.drop = nn.Dropout(drop)

    def forward(self, x):
        x = self.conv1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.conv2(x)
        x = self.drop(x)
        return x


class NCB(nn.Module):
    """
    Next Convolution Block
    """
    def __init__(self, in_channels, out_channels, stride=1, path_dropout=0,
                 drop=0, head_dim=32, mlp_ratio=3):
        super(NCB, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        norm_layer = partial(nn.BatchNorm2d, eps=NORM_EPS)
        assert out_channels % head_dim == 0

        self.patch_embed = PatchEmbed(in_channels, out_channels, stride)
        self.mhca = MHCA(out_channels, head_dim)
        self.attention_path_dropout = DropPath(path_dropout)

        self.norm = norm_layer(out_channels)
        self.mlp = Mlp(out_channels, mlp_ratio=mlp_ratio, drop=drop, bias=True)
        self.mlp_path_dropout = DropPath(path_dropout)
        self.is_bn_merged = False

    def forward(self, x):
        x = self.patch_embed(x)
        x = x + self.attention_path_dropout(self.mhca(x))
        if not torch.onnx.is_in_onnx_export() and not self.is_bn_merged:
            out = self.norm(x)
        else:
            out = x
        x = x + self.mlp_path_dropout(self.mlp(out))
        return x


class E_MHSA(nn.Module):
    """
    Efficient Multi-Head Self Attention
    """
    def __init__(self, dim, out_dim=None, head_dim=32, qkv_bias=True, qk_scale=None,
                 attn_drop=0, proj_drop=0., sr_ratio=1):
        super().__init__()
        self.dim = dim
        self.out_dim = out_dim if out_dim is not None else dim
        self.num_heads = self.dim // head_dim
        self.scale = qk_scale or head_dim ** -0.5
        self.q = nn.Linear(dim, self.dim, bias=qkv_bias)
        self.k = nn.Linear(dim, self.dim, bias=qkv_bias)
        self.v = nn.Linear(dim, self.dim, bias=qkv_bias)
        self.proj = nn.Linear(self.dim, self.out_dim)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj_drop = nn.Dropout(proj_drop)

        self.sr_ratio = sr_ratio
        self.N_ratio = sr_ratio ** 2
        if sr_ratio > 1:
            self.sr = nn.AvgPool1d(kernel_size=self.N_ratio, stride=self.N_ratio)
            self.norm = nn.BatchNorm1d(dim, eps=NORM_EPS)
        self.is_bn_merged = False

    def forward(self, x):
        B, N, C = x.shape
        q = self.q(x)
        q = q.reshape(B, N, self.num_heads, int(C // self.num_heads)).permute(0, 2, 1, 3)

        if self.sr_ratio > 1:
            x_ = x.transpose(1, 2)
            x_ = self.sr(x_)
            if not torch.onnx.is_in_onnx_export() and not self.is_bn_merged:
                x_ = self.norm(x_)
            x_ = x_.transpose(1, 2)
            k = self.k(x_)
            k = k.reshape(B, -1, self.num_heads, int(C // self.num_heads)).permute(0, 2, 3, 1)
            v = self.v(x_)
            v = v.reshape(B, -1, self.num_heads, int(C // self.num_heads)).permute(0, 2, 1, 3)
        else:
            k = self.k(x)
            k = k.reshape(B, -1, self.num_heads, int(C // self.num_heads)).permute(0, 2, 3, 1)
            v = self.v(x)
            v = v.reshape(B, -1, self.num_heads, int(C // self.num_heads)).permute(0, 2, 1, 3)
        attn = (q @ k) * self.scale

        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)

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


class NTB(nn.Module):
    """
    Next Transformer Block
    """
    def __init__(
            self, in_channels, out_channels, path_dropout, stride=1, sr_ratio=1,
            mlp_ratio=2, head_dim=32, mix_block_ratio=0.75, attn_drop=0, drop=0,
    ):
        super(NTB, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.mix_block_ratio = mix_block_ratio
        norm_func = partial(nn.BatchNorm2d, eps=NORM_EPS)

        self.mhsa_out_channels = _make_divisible(int(out_channels * mix_block_ratio), 32)
        self.mhca_out_channels = out_channels - self.mhsa_out_channels

        self.patch_embed = PatchEmbed(in_channels, self.mhsa_out_channels, stride)
        self.norm1 = norm_func(self.mhsa_out_channels)
        self.e_mhsa = E_MHSA(self.mhsa_out_channels, head_dim=head_dim, sr_ratio=sr_ratio,
                             attn_drop=attn_drop, proj_drop=drop)
        self.mhsa_path_dropout = DropPath(path_dropout * mix_block_ratio)

        self.projection = PatchEmbed(self.mhsa_out_channels, self.mhca_out_channels, stride=1)
        self.mhca = MHCA(self.mhca_out_channels, head_dim=head_dim)
        self.mhca_path_dropout = DropPath(path_dropout * (1 - mix_block_ratio))

        self.norm2 = norm_func(out_channels)
        self.mlp = Mlp(out_channels, mlp_ratio=mlp_ratio, drop=drop)
        self.mlp_path_dropout = DropPath(path_dropout)

        self.is_bn_merged = False

    def forward(self, x):
        x = self.patch_embed(x)
        B, C, H, W = x.shape
        if not torch.onnx.is_in_onnx_export() and not self.is_bn_merged:
            out = self.norm1(x)
        else:
            out = x
        out = rearrange(out, "b c h w -> b (h w) c")  # b n c
        out = self.mhsa_path_dropout(self.e_mhsa(out))
        x = x + rearrange(out, "b (h w) c -> b c h w", h=H)

        out = self.projection(x)
        out = out + self.mhca_path_dropout(self.mhca(out))
        x = torch.cat([x, out], dim=1)

        if not torch.onnx.is_in_onnx_export() and not self.is_bn_merged:
            out = self.norm2(x)
        else:
            out = x
        x = x + self.mlp_path_dropout(self.mlp(out))
        return x


class NextViT(nn.Module):
    def __init__(self, stem_chs, depths, path_dropout, attn_drop=0, drop=0, num_classes=1000,
                 strides=[1, 2, 2, 2], sr_ratios=[8, 4, 2, 1], head_dim=32, mix_block_ratio=0.75,
                 use_checkpoint=False):
        super(NextViT, self).__init__()
        self.use_checkpoint = use_checkpoint

        self.stage_out_channels = [[96] * (depths[0]),
                                   [192] * (depths[1] - 1) + [256],
                                   [384, 384, 384, 384, 512] * (depths[2] // 5),
                                   [768] * (depths[3] - 1) + [1024]]

        # Next Hybrid Strategy
        self.stage_block_types = [[NCB] * depths[0],
                                  [NCB] * (depths[1] - 1) + [NTB],
                                  [NCB, NCB, NCB, NCB, NTB] * (depths[2] // 5),
                                  [NCB] * (depths[3] - 1) + [NTB]]

        self.stem = nn.Sequential(
            ConvBNReLU(3, stem_chs[0], kernel_size=3, stride=2),
            ConvBNReLU(stem_chs[0], stem_chs[1], kernel_size=3, stride=1),
            ConvBNReLU(stem_chs[1], stem_chs[2], kernel_size=3, stride=1),
            ConvBNReLU(stem_chs[2], stem_chs[2], kernel_size=3, stride=2),
        )
        input_channel = stem_chs[-1]
        features = []
        idx = 0
        dpr = [x.item() for x in torch.linspace(0, path_dropout, sum(depths))]  # stochastic depth decay rule
        for stage_id in range(len(depths)):
            numrepeat = depths[stage_id]
            output_channels = self.stage_out_channels[stage_id]
            block_types = self.stage_block_types[stage_id]
            for block_id in range(numrepeat):
                if strides[stage_id] == 2 and block_id == 0:
                    stride = 2
                else:
                    stride = 1
                output_channel = output_channels[block_id]
                block_type = block_types[block_id]
                if block_type is NCB:
                    layer = NCB(input_channel, output_channel, stride=stride, path_dropout=dpr[idx + block_id],
                                drop=drop, head_dim=head_dim)
                    features.append(layer)
                elif block_type is NTB:
                    layer = NTB(input_channel, output_channel, path_dropout=dpr[idx + block_id], stride=stride,
                                sr_ratio=sr_ratios[stage_id], head_dim=head_dim, mix_block_ratio=mix_block_ratio,
                                attn_drop=attn_drop, drop=drop)
                    features.append(layer)
                input_channel = output_channel
            idx += numrepeat
        self.features = nn.Sequential(*features)

        self.norm = nn.BatchNorm2d(output_channel, eps=NORM_EPS)
        self.stage_out_idx = [sum(depths[:idx + 1]) - 1 for idx in range(len(depths))]
        self.channel = [i.size(1) for i in self.forward(torch.randn(1, 3, 640, 640))]
        self._initialize_weights()

    def _initialize_weights(self):
        for n, m in self.named_modules():
            if isinstance(m, (nn.BatchNorm2d, nn.GroupNorm, nn.LayerNorm, nn.BatchNorm1d)):
                nn.init.constant_(m.weight, 1.0)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                trunc_normal_(m.weight, std=.02)
                if hasattr(m, 'bias') and m.bias is not None:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Conv2d):
                trunc_normal_(m.weight, std=.02)
                if hasattr(m, 'bias') and m.bias is not None:
                    nn.init.constant_(m.bias, 0)

    def forward(self, x):
        res = []
        x = self.stem(x)
        for idx, layer in enumerate(self.features):
            if self.use_checkpoint:
                x = checkpoint.checkpoint(layer, x)
            else:
                x = layer(x)
            if idx in self.stage_out_idx:
                res.append(x)
        res[-1] = self.norm(res[-1])
        return res

def update_weight(model_dict, weight_dict):
    idx, temp_dict = 0, {}
    for k, v in weight_dict.items():
        if k in model_dict.keys() and np.shape(model_dict[k]) == np.shape(v):
            temp_dict[k] = v
            idx += 1
    model_dict.update(temp_dict)
    print(f'loading weights... {idx}/{len(model_dict)} items')
    return model_dict

def nextvit_small(weights=''):
    model = NextViT(stem_chs=[64, 32, 64], depths=[3, 4, 10, 3], path_dropout=0.1)
    if weights:
        pretrained_weight = torch.load(weights)['model']
        model.load_state_dict(update_weight(model.state_dict(), pretrained_weight))
    return model


def nextvit_base(weights=''):
    model = NextViT(stem_chs=[64, 32, 64], depths=[3, 4, 20, 3], path_dropout=0.2)
    if weights:
        pretrained_weight = torch.load(weights)['model']
        model.load_state_dict(update_weight(model.state_dict(), pretrained_weight))
    return model


def nextvit_large(weights=''):
    model = NextViT(stem_chs=[64, 32, 64], depths=[3, 4, 30, 3], path_dropout=0.2)
    if weights:
        pretrained_weight = torch.load(weights)['model']
        model.load_state_dict(update_weight(model.state_dict(), pretrained_weight))
    return model
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Backbone替换

yolo.py修改

def parse_model函数

def parse_model(d, ch):  # model_dict, input_channels(3)
    # Parse a YOLOv5 model.yaml dictionary
    LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10}  {'module':<40}{'arguments':<30}")
    anchors, nc, gd, gw, act = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'], d.get('activation')
    if act:
        Conv.default_act = eval(act)  # redefine default activation, i.e. Conv.default_act = nn.SiLU()
        LOGGER.info(f"{colorstr('activation:')} {act}")  # print
    na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors  # number of anchors
    no = na * (nc + 5)  # number of outputs = anchors * (classes + 5)

    is_backbone = False
    layers, save, c2 = [], [], ch[-1]  # layers, savelist, ch out
    for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']):  # from, number, module, args
        try:
            t = m
            m = eval(m) if isinstance(m, str) else m  # eval strings
        except:
            pass
        for j, a in enumerate(args):
            with contextlib.suppress(NameError):
                try:
                    args[j] = eval(a) if isinstance(a, str) else a  # eval strings
                except:
                    args[j] = a

        n = n_ = max(round(n * gd), 1) if n > 1 else n  # depth gain
        if m in {
                Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv,
                BottleneckCSP, C3, C3TR, C3SPP, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x}:
            c1, c2 = ch[f], args[0]
            if c2 != no:  # if not output
                c2 = make_divisible(c2 * gw, 8)

            args = [c1, c2, *args[1:]]
            if m in {BottleneckCSP, C3, C3TR, C3Ghost, C3x}:
                args.insert(2, n)  # number of repeats
                n = 1
        elif m is nn.BatchNorm2d:
            args = [ch[f]]
        elif m is Concat:
            c2 = sum(ch[x] for x in f)
        # TODO: channel, gw, gd
        elif m in {Detect, Segment}:
            args.append([ch[x] for x in f])
            if isinstance(args[1], int):  # number of anchors
                args[1] = [list(range(args[1] * 2))] * len(f)
            if m is Segment:
                args[3] = make_divisible(args[3] * gw, 8)
        elif m is Contract:
            c2 = ch[f] * args[0] ** 2
        elif m is Expand:
            c2 = ch[f] // args[0] ** 2
        elif isinstance(m, str):
            t = m
            m = timm.create_model(m, pretrained=args[0], features_only=True)
            c2 = m.feature_info.channels()
        elif m in {nextvit_small}: #可添加更多Backbone
            m = m(*args)
            c2 = m.channel
        else:
            c2 = ch[f]
        if isinstance(c2, list):
            is_backbone = True
            m_ = m
            m_.backbone = True
        else:
            m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args)  # module
            t = str(m)[8:-2].replace('__main__.', '')  # module type
        np = sum(x.numel() for x in m_.parameters())  # number params
        m_.i, m_.f, m_.type, m_.np = i + 4 if is_backbone else i, f, t, np  # attach index, 'from' index, type, number params
        LOGGER.info(f'{i:>3}{str(f):>18}{n_:>3}{np:10.0f}  {t:<40}{str(args):<30}')  # print
        save.extend(x % (i + 4 if is_backbone else i) for x in ([f] if isinstance(f, int) else f) if x != -1)  # append to savelist
        layers.append(m_)
        if i == 0:
            ch = []
        if isinstance(c2, list):
            ch.extend(c2)
            for _ in range(5 - len(ch)):
                ch.insert(0, 0)
        else:
            ch.append(c2)
    return nn.Sequential(*layers), sorted(save)
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def _forward_once函数

def _forward_once(self, x, profile=False, visualize=False):
    y, dt = [], []  # outputs
    for m in self.model:
        if m.f != -1:  # if not from previous layer
            x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers
        if profile:
            self._profile_one_layer(m, x, dt)
        if hasattr(m, 'backbone'):
            x = m(x)
            for _ in range(5 - len(x)):
                x.insert(0, None)
            for i_idx, i in enumerate(x):
                if i_idx in self.save:
                    y.append(i)
                else:
                    y.append(None)
            x = x[-1]
        else:
            x = m(x)  # run
            y.append(x if m.i in self.save else None)  # save output
        if visualize:
            feature_visualization(x, m.type, m.i, save_dir=visualize)
    return x
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创建.yaml配置文件

# YOLOv5 🚀 by Ultralytics, GPL-3.0 license

# Parameters
nc: 80  # number of classes
depth_multiple: 0.33  # model depth multiple
width_multiple: 0.25  # layer channel multiple
anchors:
  - [10,13, 16,30, 33,23]  # P3/8
  - [30,61, 62,45, 59,119]  # P4/16
  - [116,90, 156,198, 373,326]  # P5/32

# 0-P1/2
# 1-P2/4
# 2-P3/8
# 3-P4/16
# 4-P5/32

# YOLOv5 v6.0 backbone
backbone:
  # [from, number, module, args]
  [[-1, 1, nextvit_small, [False]], # 4
   [-1, 1, SPPF, [1024, 5]],  # 5
  ]

# YOLOv5 v6.0 head
head:
  [[-1, 1, Conv, [512, 1, 1]], # 6
   [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 7
   [[-1, 3], 1, Concat, [1]],  # cat backbone P4 8
   [-1, 3, C3, [512, False]],  # 9

   [-1, 1, Conv, [256, 1, 1]], # 10
   [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 11
   [[-1, 2], 1, Concat, [1]],  # cat backbone P3 12
   [-1, 3, C3, [256, False]],  # 13 (P3/8-small)

   [-1, 1, Conv, [256, 3, 2]], # 14
   [[-1, 10], 1, Concat, [1]],  # cat head P4 15
   [-1, 3, C3, [512, False]],  # 16 (P4/16-medium)

   [-1, 1, Conv, [512, 3, 2]], # 17
   [[-1, 5], 1, Concat, [1]],  # cat head P5 18
   [-1, 3, C3, [1024, False]],  # 19 (P5/32-large)

   [[13, 16, 19], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
  ]
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