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EMO：结合 Attention 重新思考移动端小模型中的基本模块
近年来，由于存储和计算资源的限制，移动应用的需求不断增加，因此，本文的研究对象是端侧轻量级小模型 (参数量一般在 10M 以下)。在众多小模型的设计中，值得注意的是 MobileNetv2[1] 提出了一种基于 Depth-wise Convolution 的高效的倒残差模块 (Inverted Residual Block, IRB)，已成标准的高效轻量级模型的基本模块。此后，很少有设计基于CNN 的端侧轻量级小模型的新思想被引入，也很少有新的轻量级模块跳出 IRB 范式并部分取代它。
近年来，视觉 Transformer 由于其动态建模和对于超大数据集的泛化能力，已经成功地应用在了各种计算机视觉任务中。但是，Transformer 中的 Multi-Head Self-Attention (MHSA) 模块，受限于参数和计算量的对于输入分辨率的平方复杂度，往往会消耗大量的资源，不适合移动端侧的应用。所以说研究人员最近开始结合 Transformer 与 CNN 模型设计高效的混合模型，并在精度，参数量和 FLOPs 方面获得了比基于 CNN 的模型更好的性能，代表性的工作有：MobileViT[2]，MobileViT V2[3]，和 MobileViT V3[4]。但是，这些方案往往引入复杂的结构或者混合模块，这对具体应用的优化非常不利。
所以本文作者简单地结合了 IRB 和 Transformer 的设计思路，希望结合 Attention 重新思考移动端小模型中的基本模块。如下图1所示是本文模型 Efficient MOdel (EMO) 与其他端侧轻量级小模型的精度，FLOPs 和 Params 对比。EMO 实现了新的 SoTA，超越了 MViT，EdgeViT 等模型。
原文地址：Rethinking Mobile Block for Efficient Attention-based Models
EMO代码实现
import math
import numpy as np
import torch.nn as nn
from einops import rearrange, reduce
from timm.models.layers.activations import *
from timm.models.layers import DropPath, trunc_normal_, create_attn
from timm.models.efficientnet_blocks import num_groups, SqueezeExcite as SE
from functools import partial__all__ = ['EMO_1M', 'EMO_2M', 'EMO_5M', 'EMO_6M']inplace = Truedef get_act(act_layer='relu'):act_dict = {'none': nn.Identity,'sigmoid': Sigmoid,'swish': Swish,'mish': Mish,'hsigmoid': HardSigmoid,'hswish': HardSwish,'hmish': HardMish,'tanh': Tanh,'relu': nn.ReLU,'relu6': nn.ReLU6,'prelu': PReLU,'gelu': GELU,'silu': nn.SiLU}return act_dict[act_layer]class LayerNorm2d(nn.Module):def __init__(self, normalized_shape, eps=1e-6, elementwise_affine=True):super().__init__()self.norm = nn.LayerNorm(normalized_shape, eps, elementwise_affine)def forward(self, x):x = rearrange(x, 'b c h w -> b h w c').contiguous()x = self.norm(x)x = rearrange(x, 'b h w c -> b c h w').contiguous()return xdef get_norm(norm_layer='in_1d'):eps = 1e-6norm_dict = {'none': nn.Identity,'in_1d': partial(nn.InstanceNorm1d, eps=eps),'in_2d': partial(nn.InstanceNorm2d, eps=eps),'in_3d': partial(nn.InstanceNorm3d, eps=eps),'bn_1d': partial(nn.BatchNorm1d, eps=eps),'bn_2d': partial(nn.BatchNorm2d, eps=eps),'bn_3d': partial(nn.BatchNorm3d, eps=eps),'gn': partial(nn.GroupNorm, eps=eps),'ln_1d': partial(nn.LayerNorm, eps=eps),'ln_2d': partial(LayerNorm2d, eps=eps),}return norm_dict[norm_layer]class ConvNormAct(nn.Module):def __init__(self, dim_in, dim_out, kernel_size, stride=1, dilation=1, groups=1, bias=False,skip=False, norm_layer='bn_2d', act_layer='relu', inplace=True, drop_path_rate=0.):super(ConvNormAct, self).__init__()self.has_skip = skip and dim_in == dim_outpadding = math.ceil((kernel_size - stride) / 2)self.conv = nn.Conv2d(dim_in, dim_out, kernel_size, stride, padding, dilation, groups, bias)self.norm = get_norm(norm_layer)(dim_out)self.act = get_act(act_layer)(inplace=inplace)self.drop_path = DropPath(drop_path_rate) if drop_path_rate else nn.Identity()def forward(self, x):shortcut = xx = self.conv(x)x = self.norm(x)x = self.act(x)if self.has_skip:x = self.drop_path(x) + shortcutreturn xinplace = True# ========== Multi-Scale Populations, for down-sampling and inductive bias ==========
class MSPatchEmb(nn.Module):def __init__(self, dim_in, emb_dim, kernel_size=2, c_group=-1, stride=1, dilations=[1, 2, 3],norm_layer='bn_2d', act_layer='silu'):super().__init__()self.dilation_num = len(dilations)assert dim_in % c_group == 0c_group = math.gcd(dim_in, emb_dim) if c_group == -1 else c_groupself.convs = nn.ModuleList()for i in range(len(dilations)):padding = math.ceil(((kernel_size - 1) * dilations[i] + 1 - stride) / 2)self.convs.append(nn.Sequential(nn.Conv2d(dim_in, emb_dim, kernel_size, stride, padding, dilations[i], groups=c_group),get_norm(norm_layer)(emb_dim),get_act(act_layer)(emb_dim)))def forward(self, x):if self.dilation_num == 1:x = self.convs[0](x)else:x = torch.cat([self.convs[i](x).unsqueeze(dim=-1) for i in range(self.dilation_num)], dim=-1)x = reduce(x, 'b c h w n -> b c h w', 'mean').contiguous()return xclass iRMB(nn.Module):def __init__(self, dim_in, dim_out, norm_in=True, has_skip=True, exp_ratio=1.0, norm_layer='bn_2d',act_layer='relu', v_proj=True, dw_ks=3, stride=1, dilation=1, se_ratio=0.0, dim_head=64, window_size=7,attn_s=True, qkv_bias=False, attn_drop=0., drop=0., drop_path=0., v_group=False, attn_pre=False):super().__init__()self.norm = get_norm(norm_layer)(dim_in) if norm_in else nn.Identity()dim_mid = int(dim_in * exp_ratio)self.has_skip = (dim_in == dim_out and stride == 1) and has_skipself.attn_s = attn_sif self.attn_s:assert dim_in % dim_head == 0, 'dim should be divisible by num_heads'self.dim_head = dim_headself.window_size = window_sizeself.num_head = dim_in // dim_headself.scale = self.dim_head ** -0.5self.attn_pre = attn_preself.qk = ConvNormAct(dim_in, int(dim_in * 2), kernel_size=1, bias=qkv_bias, norm_layer='none', act_layer='none')self.v = ConvNormAct(dim_in, dim_mid, kernel_size=1, groups=self.num_head if v_group else 1, bias=qkv_bias, norm_layer='none', act_layer=act_layer, inplace=inplace)self.attn_drop = nn.Dropout(attn_drop)else:if v_proj:self.v = ConvNormAct(dim_in, dim_mid, kernel_size=1, bias=qkv_bias, norm_layer='none', act_layer=act_layer, inplace=inplace)else:self.v = nn.Identity()self.conv_local = ConvNormAct(dim_mid, dim_mid, kernel_size=dw_ks, stride=stride, dilation=dilation, groups=dim_mid, norm_layer='bn_2d', act_layer='silu', inplace=inplace)self.se = SE(dim_mid, rd_ratio=se_ratio, act_layer=get_act(act_layer)) if se_ratio > 0.0 else nn.Identity()self.proj_drop = nn.Dropout(drop)self.proj = ConvNormAct(dim_mid, dim_out, kernel_size=1, norm_layer='none', act_layer='none', inplace=inplace)self.drop_path = DropPath(drop_path) if drop_path else nn.Identity()def forward(self, x):shortcut = xx = self.norm(x)B, C, H, W = x.shapeif self.attn_s:# paddingif self.window_size <= 0:window_size_W, window_size_H = W, Helse:window_size_W, window_size_H = self.window_size, self.window_sizepad_l, pad_t = 0, 0pad_r = (window_size_W - W % window_size_W) % window_size_Wpad_b = (window_size_H - H % window_size_H) % window_size_Hx = F.pad(x, (pad_l, pad_r, pad_t, pad_b, 0, 0,))n1, n2 = (H + pad_b) // window_size_H, (W + pad_r) // window_size_Wx = rearrange(x, 'b c (h1 n1) (w1 n2) -> (b n1 n2) c h1 w1', n1=n1, n2=n2).contiguous()# attentionb, c, h, w = x.shapeqk = self.qk(x)qk = rearrange(qk, 'b (qk heads dim_head) h w -> qk b heads (h w) dim_head', qk=2, heads=self.num_head, dim_head=self.dim_head).contiguous()q, k = qk[0], qk[1]attn_spa = (q @ k.transpose(-2, -1)) * self.scaleattn_spa = attn_spa.softmax(dim=-1)attn_spa = self.attn_drop(attn_spa)if self.attn_pre:x = rearrange(x, 'b (heads dim_head) h w -> b heads (h w) dim_head', heads=self.num_head).contiguous()x_spa = attn_spa @ xx_spa = rearrange(x_spa, 'b heads (h w) dim_head -> b (heads dim_head) h w', heads=self.num_head, h=h, w=w).contiguous()x_spa = self.v(x_spa)else:v = self.v(x)v = rearrange(v, 'b (heads dim_head) h w -> b heads (h w) dim_head', heads=self.num_head).contiguous()x_spa = attn_spa @ vx_spa = rearrange(x_spa, 'b heads (h w) dim_head -> b (heads dim_head) h w', heads=self.num_head, h=h, w=w).contiguous()# unpaddingx = rearrange(x_spa, '(b n1 n2) c h1 w1 -> b c (h1 n1) (w1 n2)', n1=n1, n2=n2).contiguous()if pad_r > 0 or pad_b > 0:x = x[:, :, :H, :W].contiguous()else:x = self.v(x)x = x + self.se(self.conv_local(x)) if self.has_skip else self.se(self.conv_local(x))x = self.proj_drop(x)x = self.proj(x)x = (shortcut + self.drop_path(x)) if self.has_skip else xreturn xclass EMO(nn.Module):def __init__(self, dim_in=3, num_classes=1000, img_size=224,depths=[1, 2, 4, 2], stem_dim=16, embed_dims=[64, 128, 256, 512], exp_ratios=[4., 4., 4., 4.],norm_layers=['bn_2d', 'bn_2d', 'bn_2d', 'bn_2d'], act_layers=['relu', 'relu', 'relu', 'relu'],dw_kss=[3, 3, 5, 5], se_ratios=[0.0, 0.0, 0.0, 0.0], dim_heads=[32, 32, 32, 32],window_sizes=[7, 7, 7, 7], attn_ss=[False, False, True, True], qkv_bias=True,attn_drop=0., drop=0., drop_path=0., v_group=False, attn_pre=False, pre_dim=0):super().__init__()self.num_classes = num_classesassert num_classes > 0dprs = [x.item() for x in torch.linspace(0, drop_path, sum(depths))]self.stage0 = nn.ModuleList([MSPatchEmb( # down to 112dim_in, stem_dim, kernel_size=dw_kss[0], c_group=1, stride=2, dilations=[1],norm_layer=norm_layers[0], act_layer='none'),iRMB( # dsstem_dim, stem_dim, norm_in=False, has_skip=False, exp_ratio=1,norm_layer=norm_layers[0], act_layer=act_layers[0], v_proj=False, dw_ks=dw_kss[0],stride=1, dilation=1, se_ratio=1,dim_head=dim_heads[0], window_size=window_sizes[0], attn_s=False,qkv_bias=qkv_bias, attn_drop=attn_drop, drop=drop, drop_path=0.,attn_pre=attn_pre)])emb_dim_pre = stem_dimfor i in range(len(depths)):layers = []dpr = dprs[sum(depths[:i]):sum(depths[:i + 1])]for j in range(depths[i]):if j == 0:stride, has_skip, attn_s, exp_ratio = 2, False, False, exp_ratios[i] * 2else:stride, has_skip, attn_s, exp_ratio = 1, True, attn_ss[i], exp_ratios[i]layers.append(iRMB(emb_dim_pre, embed_dims[i], norm_in=True, has_skip=has_skip, exp_ratio=exp_ratio,norm_layer=norm_layers[i], act_layer=act_layers[i], v_proj=True, dw_ks=dw_kss[i],stride=stride, dilation=1, se_ratio=se_ratios[i],dim_head=dim_heads[i], window_size=window_sizes[i], attn_s=attn_s,qkv_bias=qkv_bias, attn_drop=attn_drop, drop=drop, drop_path=dpr[j], v_group=v_group,attn_pre=attn_pre))emb_dim_pre = embed_dims[i]self.__setattr__(f'stage{i + 1}', nn.ModuleList(layers))self.norm = get_norm(norm_layers[-1])(embed_dims[-1])self.apply(self._init_weights)self.channel = [i.size(1) for i in self.forward(torch.randn(1, 3, 640, 640))]def _init_weights(self, m):if isinstance(m, nn.Linear):trunc_normal_(m.weight, std=.02)if m.bias is not None:nn.init.zeros_(m.bias)elif isinstance(m, (nn.LayerNorm, nn.GroupNorm,nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d,nn.InstanceNorm1d, nn.InstanceNorm2d, nn.InstanceNorm3d)):nn.init.zeros_(m.bias)nn.init.ones_(m.weight)@torch.jit.ignoredef no_weight_decay(self):return {'token'}@torch.jit.ignoredef no_weight_decay_keywords(self):return {'alpha', 'gamma', 'beta'}@torch.jit.ignoredef no_ft_keywords(self):# return {'head.weight', 'head.bias'}return {}@torch.jit.ignoredef ft_head_keywords(self):return {'head.weight', 'head.bias'}, self.num_classesdef get_classifier(self):return self.headdef reset_classifier(self, num_classes):self.num_classes = num_classesself.head = nn.Linear(self.pre_dim, num_classes) if num_classes > 0 else nn.Identity()def check_bn(self):for name, m in self.named_modules():if isinstance(m, nn.modules.batchnorm._NormBase):m.running_mean = torch.nan_to_num(m.running_mean, nan=0, posinf=1, neginf=-1)m.running_var = torch.nan_to_num(m.running_var, nan=0, posinf=1, neginf=-1)def forward_features(self, x):for blk in self.stage0:x = blk(x)x1 = xfor blk in self.stage1:x = blk(x)x2 = xfor blk in self.stage2:x = blk(x)x3 = xfor blk in self.stage3:x = blk(x)x4 = xfor blk in self.stage4:x = blk(x)x5 = xreturn [x1, x2, x3, x4, x5]def forward(self, x):x = self.forward_features(x)x[-1] = self.norm(x[-1])return xdef 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] = vidx += 1model_dict.update(temp_dict)print(f'loading weights... {idx}/{len(model_dict)} items')return model_dictdef EMO_1M(weights='', **kwargs):model = EMO(# dim_in=3, num_classes=1000, img_size=224,depths=[2, 2, 8, 3], stem_dim=24, embed_dims=[32, 48, 80, 168], exp_ratios=[2., 2.5, 3.0, 3.5],norm_layers=['bn_2d', 'bn_2d', 'ln_2d', 'ln_2d'], act_layers=['silu', 'silu', 'gelu', 'gelu'],dw_kss=[3, 3, 5, 5], dim_heads=[16, 16, 20, 21], window_sizes=[7, 7, 7, 7], attn_ss=[False, False, True, True],qkv_bias=True, attn_drop=0., drop=0., drop_path=0.04036, v_group=False, attn_pre=True, pre_dim=0,**kwargs)if weights:pretrained_weight = torch.load(weights)model.load_state_dict(update_weight(model.state_dict(), pretrained_weight))return modeldef EMO_2M(weights='', **kwargs):model = EMO(# dim_in=3, num_classes=1000, img_size=224,depths=[3, 3, 9, 3], stem_dim=24, embed_dims=[32, 48, 120, 200], exp_ratios=[2., 2.5, 3.0, 3.5],norm_layers=['bn_2d', 'bn_2d', 'ln_2d', 'ln_2d'], act_layers=['silu', 'silu', 'gelu', 'gelu'],dw_kss=[3, 3, 5, 5], dim_heads=[16, 16, 20, 20], window_sizes=[7, 7, 7, 7], attn_ss=[False, False, True, True],qkv_bias=True, attn_drop=0., drop=0., drop_path=0.05, v_group=False, attn_pre=True, pre_dim=0,**kwargs)if weights:pretrained_weight = torch.load(weights)model.load_state_dict(update_weight(model.state_dict(), pretrained_weight))return modeldef EMO_5M(weights='', **kwargs):model = EMO(# dim_in=3, num_classes=1000, img_size=224,depths=[3, 3, 9, 3], stem_dim=24, embed_dims=[48, 72, 160, 288], exp_ratios=[2., 3., 4., 4.],norm_layers=['bn_2d', 'bn_2d', 'ln_2d', 'ln_2d'], act_layers=['silu', 'silu', 'gelu', 'gelu'],dw_kss=[3, 3, 5, 5], dim_heads=[24, 24, 32, 32], window_sizes=[7, 7, 7, 7], attn_ss=[False, False, True, True],qkv_bias=True, attn_drop=0., drop=0., drop_path=0.05, v_group=False, attn_pre=True, pre_dim=0,**kwargs)if weights:pretrained_weight = torch.load(weights)model.load_state_dict(update_weight(model.state_dict(), pretrained_weight))return modeldef EMO_6M(weights='', **kwargs):model = EMO(# dim_in=3, num_classes=1000, img_size=224,depths=[3, 3, 9, 3], stem_dim=24, embed_dims=[48, 72, 160, 320], exp_ratios=[2., 3., 4., 5.],norm_layers=['bn_2d', 'bn_2d', 'ln_2d', 'ln_2d'], act_layers=['silu', 'silu', 'gelu', 'gelu'],dw_kss=[3, 3, 5, 5], dim_heads=[16, 24, 20, 32], window_sizes=[7, 7, 7, 7], attn_ss=[False, False, True, True],qkv_bias=True, attn_drop=0., drop=0., drop_path=0.05, v_group=False, attn_pre=True, pre_dim=0,**kwargs)if weights:pretrained_weight = torch.load(weights)model.load_state_dict(update_weight(model.state_dict(), pretrained_weight))return modelif __name__ == '__main__':model = EMO_1M('EMO_1M/net.pth')model = EMO_2M('EMO_2M/net.pth')model = EMO_5M('EMO_5M/net.pth')model = EMO_6M('EMO_6M/net.pth')
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 dictionaryLOGGER.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}") # printna = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchorsno = na * (nc + 5) # number of outputs = anchors * (classes + 5)is_backbone = Falselayers, save, c2 = [], [], ch[-1] # layers, savelist, ch outfor i, (f, n, m, args) in enumerate(d['backbone'] + d['head']): # from, number, module, argstry:t = mm = eval(m) if isinstance(m, str) else m # eval stringsexcept:passfor j, a in enumerate(args):with contextlib.suppress(NameError):try:args[j] = eval(a) if isinstance(a, str) else a # eval stringsexcept:args[j] = an = n_ = max(round(n * gd), 1) if n > 1 else n # depth gainif 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 outputc2 = make_divisible(c2 * gw, 8)args = [c1, c2, *args[1:]]if m in {BottleneckCSP, C3, C3TR, C3Ghost, C3x}:args.insert(2, n) # number of repeatsn = 1elif m is nn.BatchNorm2d:args = [ch[f]]elif m is Concat:c2 = sum(ch[x] for x in f)# TODO: channel, gw, gdelif m in {Detect, Segment}:args.append([ch[x] for x in f])if isinstance(args[1], int): # number of anchorsargs[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] ** 2elif m is Expand:c2 = ch[f] // args[0] ** 2elif isinstance(m, str):t = mm = timm.create_model(m, pretrained=args[0], features_only=True)c2 = m.feature_info.channels()elif m in {EMO_1M'}: #可添加更多Backbonem = m(*args)c2 = m.channelelse:c2 = ch[f]if isinstance(c2, list):is_backbone = Truem_ = mm_.backbone = Trueelse:m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # modulet = str(m)[8:-2].replace('__main__.', '') # module typenp = sum(x.numel() for x in m_.parameters()) # number paramsm_.i, m_.f, m_.type, m_.np = i + 4 if is_backbone else i, f, t, np # attach index, 'from' index, type, number paramsLOGGER.info(f'{i:>3}{str(f):>18}{n_:>3}{np:10.0f} {t:<40}{str(args):<30}') # printsave.extend(x % (i + 4 if is_backbone else i) for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelistlayers.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)
def _forward_once函数
def _forward_once(self, x, profile=False, visualize=False):y, dt = [], [] # outputsfor m in self.model:if m.f != -1: # if not from previous layerx = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layersif 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) # runy.append(x if m.i in self.save else None) # save outputif visualize:feature_visualization(x, m.type, m.i, save_dir=visualize)return x
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, EMO_1M, [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)]