目标检测算法改进系列之Backbone替换为EfficientViT

EfficientViT

Vision Transformer (ViT) 在许多视觉任务中都取得了卓越的性能。然而，在针对高分辨率移动视觉应用时，ViT 不如卷积神经网络 (CNN)。 ViT 的关键计算瓶颈是 softmax 注意力模块，其计算复杂度与输入分辨率成二次方。降低 ViT 的成本以将其部署在边缘设备上至关重要。现有方法（例如 Swin、PVT）将 softmax 注意力限制在局部窗口内或降低键/值张量的分辨率以降低成本，从而牺牲了 ViT 在全局特征提取方面的核心优势。在这项工作中，我们提出了 EfficientViT，这是一种用于高分辨率低计算视觉识别的高效 ViT 架构。我们建议用线性注意力代替softmax注意力，而不是限制softmax注意力，同时通过深度卷积增强其局部特征提取能力。 EfficientViT 保持全局和局部特征提取能力，同时享受线性计算复杂度。 COCO 目标检测和 Cityscapes 语义分割的大量实验证明了我们方法的有效性。在 COCO 数据集上，EfficientViT 使用 4.4G MAC 达到 42.6 AP，超过 EfficientDet-D1 2.4 AP，同时 MAC 减少 27.9%。在 Cityscapes 上，EfficientViT 使用 19.1G MAC 达到 78.7 mIoU，比 SegFormer 高 2.5 mIoU，同时需要不到 1/3 的计算成本。在高通骁龙 855 CPU 上，EfficientViT 比 EfficientNet 快 3 倍，同时实现更高的 ImageNet 精度。

原文地址：EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction

EfficientViT代码实现

# --------------------------------------------------------
# EfficientViT Model Architecture for Downstream Tasks
# Copyright (c) 2022 Microsoft
# Written by: Xinyu Liu
# --------------------------------------------------------
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
import itertools

from timm.models.layers import SqueezeExcite

import numpy as np
import itertools

__all__ = ['EfficientViT_M0', 'EfficientViT_M1', 'EfficientViT_M2', 'EfficientViT_M3', 'EfficientViT_M4', 'EfficientViT_M5']

class Conv2d_BN(torch.nn.Sequential):
    def __init__(self, a, b, ks=1, stride=1, pad=0, dilation=1,
                 groups=1, bn_weight_init=1, resolution=-10000):
        super().__init__()
        self.add_module('c', torch.nn.Conv2d(
            a, b, ks, stride, pad, dilation, groups, bias=False))
        self.add_module('bn', torch.nn.BatchNorm2d(b))
        torch.nn.init.constant_(self.bn.weight, bn_weight_init)
        torch.nn.init.constant_(self.bn.bias, 0)

    @torch.no_grad()
    def fuse(self):
        c, bn = self._modules.values()
        w = bn.weight / (bn.running_var + bn.eps)**0.5
        w = c.weight * w[:, None, None, None]
        b = bn.bias - bn.running_mean * bn.weight / \
            (bn.running_var + bn.eps)**0.5
        m = torch.nn.Conv2d(w.size(1) * self.c.groups, w.size(
            0), w.shape[2:], stride=self.c.stride, padding=self.c.padding, dilation=self.c.dilation, groups=self.c.groups)
        m.weight.data.copy_(w)
        m.bias.data.copy_(b)
        return m

def replace_batchnorm(net):
    for child_name, child in net.named_children():
        if hasattr(child, 'fuse'):
            setattr(net, child_name, child.fuse())
        elif isinstance(child, torch.nn.BatchNorm2d):
            setattr(net, child_name, torch.nn.Identity())
        else:
            replace_batchnorm(child)
            

class PatchMerging(torch.nn.Module):
    def __init__(self, dim, out_dim, input_resolution):
        super().__init__()
        hid_dim = int(dim * 4)
        self.conv1 = Conv2d_BN(dim, hid_dim, 1, 1, 0, resolution=input_resolution)
        self.act = torch.nn.ReLU()
        self.conv2 = Conv2d_BN(hid_dim, hid_dim, 3, 2, 1, groups=hid_dim, resolution=input_resolution)
        self.se = SqueezeExcite(hid_dim, .25)
        self.conv3 = Conv2d_BN(hid_dim, out_dim, 1, 1, 0, resolution=input_resolution // 2)

    def forward(self, x):
        x = self.conv3(self.se(self.act(self.conv2(self.act(self.conv1(x))))))
        return x


class Residual(torch.nn.Module):
    def __init__(self, m, drop=0.):
        super().__init__()
        self.m = m
        self.drop = drop

    def forward(self, x):
        if self.training and self.drop > 0:
            return x + self.m(x) * torch.rand(x.size(0), 1, 1, 1,
                                              device=x.device).ge_(self.drop).div(1 - self.drop).detach()
        else:
            return x + self.m(x)


class FFN(torch.nn.Module):
    def __init__(self, ed, h, resolution):
        super().__init__()
        self.pw1 = Conv2d_BN(ed, h, resolution=resolution)
        self.act = torch.nn.ReLU()
        self.pw2 = Conv2d_BN(h, ed, bn_weight_init=0, resolution=resolution)

    def forward(self, x):
        x = self.pw2(self.act(self.pw1(x)))
        return x


class CascadedGroupAttention(torch.nn.Module):
    r""" Cascaded Group Attention.

    Args:
        dim (int): Number of input channels.
        key_dim (int): The dimension for query and key.
        num_heads (int): Number of attention heads.
        attn_ratio (int): Multiplier for the query dim for value dimension.
        resolution (int): Input resolution, correspond to the window size.
        kernels (List[int]): The kernel size of the dw conv on query.
    """
    def __init__(self, dim, key_dim, num_heads=8,
                 attn_ratio=4,
                 resolution=14,
                 kernels=[5, 5, 5, 5],):
        super().__init__()
        self.num_heads = num_heads
        self.scale = key_dim ** -0.5
        self.key_dim = key_dim
        self.d = int(attn_ratio * key_dim)
        self.attn_ratio = attn_ratio

        qkvs = []
        dws = []
        for i in range(num_heads):
            qkvs.append(Conv2d_BN(dim // (num_heads), self.key_dim * 2 + self.d, resolution=resolution))
            dws.append(Conv2d_BN(self.key_dim, self.key_dim, kernels[i], 1, kernels[i]//2, groups=self.key_dim, resolution=resolution))
        self.qkvs = torch.nn.ModuleList(qkvs)
        self.dws = torch.nn.ModuleList(dws)
        self.proj = torch.nn.Sequential(torch.nn.ReLU(), Conv2d_BN(
            self.d * num_heads, dim, bn_weight_init=0, resolution=resolution))

        points = list(itertools.product(range(resolution), range(resolution)))
        N = len(points)
        attention_offsets = {}
        idxs = []
        for p1 in points:
            for p2 in points:
                offset = (abs(p1[0] - p2[0]), abs(p1[1] - p2[1]))
                if offset not in attention_offsets:
                    attention_offsets[offset] = len(attention_offsets)
                idxs.append(attention_offsets[offset])
        self.attention_biases = torch.nn.Parameter(
            torch.zeros(num_heads, len(attention_offsets)))
        self.register_buffer('attention_bias_idxs',
                             torch.LongTensor(idxs).view(N, N))

    @torch.no_grad()
    def train(self, mode=True):
        super().train(mode)
        if mode and hasattr(self, 'ab'):
            del self.ab
        else:
            self.ab = self.attention_biases[:, self.attention_bias_idxs]

    def forward(self, x):  # x (B,C,H,W)
        B, C, H, W = x.shape
        trainingab = self.attention_biases[:, self.attention_bias_idxs]
        feats_in = x.chunk(len(self.qkvs), dim=1)
        feats_out = []
        feat = feats_in[0]
        for i, qkv in enumerate(self.qkvs):
            if i > 0: # add the previous output to the input
                feat = feat + feats_in[i]
            feat = qkv(feat)
            q, k, v = feat.view(B, -1, H, W).split([self.key_dim, self.key_dim, self.d], dim=1) # B, C/h, H, W
            q = self.dws[i](q)
            q, k, v = q.flatten(2), k.flatten(2), v.flatten(2) # B, C/h, N
            attn = (
                (q.transpose(-2, -1) @ k) * self.scale
                +
                (trainingab[i] if self.training else self.ab[i])
            )
            attn = attn.softmax(dim=-1) # BNN
            feat = (v @ attn.transpose(-2, -1)).view(B, self.d, H, W) # BCHW
            feats_out.append(feat)
        x = self.proj(torch.cat(feats_out, 1))
        return x


class LocalWindowAttention(torch.nn.Module):
    r""" Local Window Attention.

    Args:
        dim (int): Number of input channels.
        key_dim (int): The dimension for query and key.
        num_heads (int): Number of attention heads.
        attn_ratio (int): Multiplier for the query dim for value dimension.
        resolution (int): Input resolution.
        window_resolution (int): Local window resolution.
        kernels (List[int]): The kernel size of the dw conv on query.
    """
    def __init__(self, dim, key_dim, num_heads=8,
                 attn_ratio=4,
                 resolution=14,
                 window_resolution=7,
                 kernels=[5, 5, 5, 5],):
        super().__init__()
        self.dim = dim
        self.num_heads = num_heads
        self.resolution = resolution
        assert window_resolution > 0, 'window_size must be greater than 0'
        self.window_resolution = window_resolution
        
        self.attn = CascadedGroupAttention(dim, key_dim, num_heads,
                                attn_ratio=attn_ratio, 
                                resolution=window_resolution,
                                kernels=kernels,)

    def forward(self, x):
        B, C, H, W = x.shape
               
        if H <= self.window_resolution and W <= self.window_resolution:
            x = self.attn(x)
        else:
            x = x.permute(0, 2, 3, 1)
            pad_b = (self.window_resolution - H %
                     self.window_resolution) % self.window_resolution
            pad_r = (self.window_resolution - W %
                     self.window_resolution) % self.window_resolution
            padding = pad_b > 0 or pad_r > 0

            if padding:
                x = torch.nn.functional.pad(x, (0, 0, 0, pad_r, 0, pad_b))

            pH, pW = H + pad_b, W + pad_r
            nH = pH // self.window_resolution
            nW = pW // self.window_resolution
            # window partition, BHWC -> B(nHh)(nWw)C -> BnHnWhwC -> (BnHnW)hwC -> (BnHnW)Chw
            x = x.view(B, nH, self.window_resolution, nW, self.window_resolution, C).transpose(2, 3).reshape(
                B * nH * nW, self.window_resolution, self.window_resolution, C
            ).permute(0, 3, 1, 2)
            x = self.attn(x)
            # window reverse, (BnHnW)Chw -> (BnHnW)hwC -> BnHnWhwC -> B(nHh)(nWw)C -> BHWC
            x = x.permute(0, 2, 3, 1).view(B, nH, nW, self.window_resolution, self.window_resolution,
                       C).transpose(2, 3).reshape(B, pH, pW, C)

            if padding:
                x = x[:, :H, :W].contiguous()

            x = x.permute(0, 3, 1, 2)

        return x


class EfficientViTBlock(torch.nn.Module):
    """ A basic EfficientViT building block.

    Args:
        type (str): Type for token mixer. Default: 's' for self-attention.
        ed (int): Number of input channels.
        kd (int): Dimension for query and key in the token mixer.
        nh (int): Number of attention heads.
        ar (int): Multiplier for the query dim for value dimension.
        resolution (int): Input resolution.
        window_resolution (int): Local window resolution.
        kernels (List[int]): The kernel size of the dw conv on query.
    """
    def __init__(self, type,
                 ed, kd, nh=8,
                 ar=4,
                 resolution=14,
                 window_resolution=7,
                 kernels=[5, 5, 5, 5],):
        super().__init__()
            
        self.dw0 = Residual(Conv2d_BN(ed, ed, 3, 1, 1, groups=ed, bn_weight_init=0., resolution=resolution))
        self.ffn0 = Residual(FFN(ed, int(ed * 2), resolution))

        if type == 's':
            self.mixer = Residual(LocalWindowAttention(ed, kd, nh, attn_ratio=ar, \
                    resolution=resolution, window_resolution=window_resolution, kernels=kernels))
                
        self.dw1 = Residual(Conv2d_BN(ed, ed, 3, 1, 1, groups=ed, bn_weight_init=0., resolution=resolution))
        self.ffn1 = Residual(FFN(ed, int(ed * 2), resolution))

    def forward(self, x):
        return self.ffn1(self.dw1(self.mixer(self.ffn0(self.dw0(x)))))


class EfficientViT(torch.nn.Module):
    def __init__(self, img_size=400,
                 patch_size=16,
                 frozen_stages=0,
                 in_chans=3,
                 stages=['s', 's', 's'],
                 embed_dim=[64, 128, 192],
                 key_dim=[16, 16, 16],
                 depth=[1, 2, 3],
                 num_heads=[4, 4, 4],
                 window_size=[7, 7, 7],
                 kernels=[5, 5, 5, 5],
                 down_ops=[['subsample', 2], ['subsample', 2], ['']],
                 pretrained=None,
                 distillation=False,):
        super().__init__()

        resolution = img_size
        self.patch_embed = torch.nn.Sequential(Conv2d_BN(in_chans, embed_dim[0] // 8, 3, 2, 1, resolution=resolution), torch.nn.ReLU(),
                           Conv2d_BN(embed_dim[0] // 8, embed_dim[0] // 4, 3, 2, 1, resolution=resolution // 2), torch.nn.ReLU(),
                           Conv2d_BN(embed_dim[0] // 4, embed_dim[0] // 2, 3, 2, 1, resolution=resolution // 4), torch.nn.ReLU(),
                           Conv2d_BN(embed_dim[0] // 2, embed_dim[0], 3, 1, 1, resolution=resolution // 8))

        resolution = img_size // patch_size
        attn_ratio = [embed_dim[i] / (key_dim[i] * num_heads[i]) for i in range(len(embed_dim))]
        self.blocks1 = []
        self.blocks2 = []
        self.blocks3 = []
        for i, (stg, ed, kd, dpth, nh, ar, wd, do) in enumerate(
                zip(stages, embed_dim, key_dim, depth, num_heads, attn_ratio, window_size, down_ops)):
            for d in range(dpth):
                eval('self.blocks' + str(i+1)).append(EfficientViTBlock(stg, ed, kd, nh, ar, resolution, wd, kernels))
            if do[0] == 'subsample':
                #('Subsample' stride)
                blk = eval('self.blocks' + str(i+2))
                resolution_ = (resolution - 1) // do[1] + 1
                blk.append(torch.nn.Sequential(Residual(Conv2d_BN(embed_dim[i], embed_dim[i], 3, 1, 1, groups=embed_dim[i], resolution=resolution)),
                                    Residual(FFN(embed_dim[i], int(embed_dim[i] * 2), resolution)),))
                blk.append(PatchMerging(*embed_dim[i:i + 2], resolution))
                resolution = resolution_
                blk.append(torch.nn.Sequential(Residual(Conv2d_BN(embed_dim[i + 1], embed_dim[i + 1], 3, 1, 1, groups=embed_dim[i + 1], resolution=resolution)),
                                    Residual(FFN(embed_dim[i + 1], int(embed_dim[i + 1] * 2), resolution)),))
        self.blocks1 = torch.nn.Sequential(*self.blocks1)
        self.blocks2 = torch.nn.Sequential(*self.blocks2)
        self.blocks3 = torch.nn.Sequential(*self.blocks3)
        
        self.channel = [i.size(1) for i in self.forward(torch.randn(1, 3, 640, 640))]

    def forward(self, x):
        outs = []
        x = self.patch_embed(x)
        x = self.blocks1(x)
        outs.append(x)
        x = self.blocks2(x)
        outs.append(x)
        x = self.blocks3(x)
        outs.append(x)
        return outs

EfficientViT_m0 = {
        'img_size': 224,
        'patch_size': 16,
        'embed_dim': [64, 128, 192],
        'depth': [1, 2, 3],
        'num_heads': [4, 4, 4],
        'window_size': [7, 7, 7],
        'kernels': [7, 5, 3, 3],
    }

EfficientViT_m1 = {
        'img_size': 224,
        'patch_size': 16,
        'embed_dim': [128, 144, 192],
        'depth': [1, 2, 3],
        'num_heads': [2, 3, 3],
        'window_size': [7, 7, 7],
        'kernels': [7, 5, 3, 3],
    }

EfficientViT_m2 = {
        'img_size': 224,
        'patch_size': 16,
        'embed_dim': [128, 192, 224],
        'depth': [1, 2, 3],
        'num_heads': [4, 3, 2],
        'window_size': [7, 7, 7],
        'kernels': [7, 5, 3, 3],
    }

EfficientViT_m3 = {
        'img_size': 224,
        'patch_size': 16,
        'embed_dim': [128, 240, 320],
        'depth': [1, 2, 3],
        'num_heads': [4, 3, 4],
        'window_size': [7, 7, 7],
        'kernels': [5, 5, 5, 5],
    }

EfficientViT_m4 = {
        'img_size': 224,
        'patch_size': 16,
        'embed_dim': [128, 256, 384],
        'depth': [1, 2, 3],
        'num_heads': [4, 4, 4],
        'window_size': [7, 7, 7],
        'kernels': [7, 5, 3, 3],
    }

EfficientViT_m5 = {
        'img_size': 224,
        'patch_size': 16,
        'embed_dim': [192, 288, 384],
        'depth': [1, 3, 4],
        'num_heads': [3, 3, 4],
        'window_size': [7, 7, 7],
        'kernels': [7, 5, 3, 3],
    }

def EfficientViT_M0(pretrained='', frozen_stages=0, distillation=False, fuse=False, pretrained_cfg=None, model_cfg=EfficientViT_m0):
    model = EfficientViT(frozen_stages=frozen_stages, distillation=distillation, pretrained=pretrained, **model_cfg)
    if pretrained:
        model.load_state_dict(update_weight(model.state_dict(), torch.load(pretrained)['model']))
    if fuse:
        replace_batchnorm(model)
    return model

def EfficientViT_M1(pretrained='', frozen_stages=0, distillation=False, fuse=False, pretrained_cfg=None, model_cfg=EfficientViT_m1):
    model = EfficientViT(frozen_stages=frozen_stages, distillation=distillation, pretrained=pretrained, **model_cfg)
    if pretrained:
        model.load_state_dict(update_weight(model.state_dict(), torch.load(pretrained)['model']))
    if fuse:
        replace_batchnorm(model)
    return model

def EfficientViT_M2(pretrained='', frozen_stages=0, distillation=False, fuse=False, pretrained_cfg=None, model_cfg=EfficientViT_m2):
    model = EfficientViT(frozen_stages=frozen_stages, distillation=distillation, pretrained=pretrained, **model_cfg)
    if pretrained:
        model.load_state_dict(update_weight(model.state_dict(), torch.load(pretrained)['model']))
    if fuse:
        replace_batchnorm(model)
    return model

def EfficientViT_M3(pretrained='', frozen_stages=0, distillation=False, fuse=False, pretrained_cfg=None, model_cfg=EfficientViT_m3):
    model = EfficientViT(frozen_stages=frozen_stages, distillation=distillation, pretrained=pretrained, **model_cfg)
    if pretrained:
        model.load_state_dict(update_weight(model.state_dict(), torch.load(pretrained)['model']))
    if fuse:
        replace_batchnorm(model)
    return model
    
def EfficientViT_M4(pretrained='', frozen_stages=0, distillation=False, fuse=False, pretrained_cfg=None, model_cfg=EfficientViT_m4):
    model = EfficientViT(frozen_stages=frozen_stages, distillation=distillation, pretrained=pretrained, **model_cfg)
    if pretrained:
        model.load_state_dict(update_weight(model.state_dict(), torch.load(pretrained)['model']))
    if fuse:
        replace_batchnorm(model)
    return model

def EfficientViT_M5(pretrained='', frozen_stages=0, distillation=False, fuse=False, pretrained_cfg=None, model_cfg=EfficientViT_m5):
    model = EfficientViT(frozen_stages=frozen_stages, distillation=distillation, pretrained=pretrained, **model_cfg)
    if pretrained:
        model.load_state_dict(update_weight(model.state_dict(), torch.load(pretrained)['model']))
    if fuse:
        replace_batchnorm(model)
    return model

def update_weight(model_dict, weight_dict):
    idx, temp_dict = 0, {}
    for k, v in weight_dict.items():
        # k = k[9:]
        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

if __name__ == '__main__':
    model = EfficientViT_M0('efficientvit_m0.pth')
    inputs = torch.randn((1, 3, 640, 640))
    res = model(inputs)
    for i in res:
        print(i.size())
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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 {EfficientViT_M0}: #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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yaml配置文件

# YOLOv5 
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