温州哪里有网站,专业做包包的网站,网站采集到wordpress,系统优化有何作用Swin Transformer简介
《Swin Transformer: Hierarchical Vision Transformer using Shifted Windows》作为2021 ICCV最佳论文#xff0c;屠榜了各大CV任务#xff0c;性能优于DeiT、ViT和EfficientNet等主干网络#xff0c;已经替代经典的CNN架构#xff0c;成为了计算机…Swin Transformer简介
《Swin Transformer: Hierarchical Vision Transformer using Shifted Windows》作为2021 ICCV最佳论文屠榜了各大CV任务性能优于DeiT、ViT和EfficientNet等主干网络已经替代经典的CNN架构成为了计算机视觉领域通用的backbone。它基于了ViT模型的思想创新性的引入了滑动窗口机制让模型能够学习到跨窗口的信息同时也。同时通过下采样层使得模型能够处理超分辨率的图片节省计算量以及能够关注全局和局部的信息。而本文将从原理和代码角度详细解析Swin Transformer的架构。
目前将 Transformer 从自然语言处理领域应用到计算机视觉领域主要有两大挑战 1视觉实体的方差较大例如同一个物体拍摄角度不同转化为二进制后的图片就会具有很大的差异。同时在不同场景下视觉 Transformer 性能未必很好。 2图像分辨率高像素点多如果采用ViT模型自注意力的计算量会与像素的平方成正比。针对上述两个问题论文中提出了一种基于滑动窗口机制具有层级设计下采样层 的 Swin Transformer。
其中滑窗操作包括不重叠的 local window和重叠的 cross-window。将注意力计算限制在一个窗口window size固定中一方面能引入 CNN 卷积操作的局部性另一方面能大幅度节省计算量它只和窗口数量成线性关系。通过下采样的层级设计能够逐渐增大感受野从而使得注意力机制也能够注意到全局的特征。
Swin Transformer代码实现
# --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu, Yutong Lin, Yixuan Wei
# --------------------------------------------------------import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
import numpy as np
from timm.models.layers import DropPath, to_2tuple, trunc_normal___all__ [SwinTransformer_Tiny]class Mlp(nn.Module): Multilayer perceptron.def __init__(self, in_features, hidden_featuresNone, out_featuresNone, act_layernn.GELU, drop0.):super().__init__()out_features out_features or in_featureshidden_features hidden_features or in_featuresself.fc1 nn.Linear(in_features, hidden_features)self.act act_layer()self.fc2 nn.Linear(hidden_features, out_features)self.drop nn.Dropout(drop)def forward(self, x):x self.fc1(x)x self.act(x)x self.drop(x)x self.fc2(x)x self.drop(x)return xdef window_partition(x, window_size):Args:x: (B, H, W, C)window_size (int): window sizeReturns:windows: (num_windows*B, window_size, window_size, C)B, H, W, C x.shapex x.view(B, H // window_size, window_size, W // window_size, window_size, C)windows x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)return windowsdef window_reverse(windows, window_size, H, W):Args:windows: (num_windows*B, window_size, window_size, C)window_size (int): Window sizeH (int): Height of imageW (int): Width of imageReturns:x: (B, H, W, C)B int(windows.shape[0] / (H * W / window_size / window_size))x windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)x x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)return xclass WindowAttention(nn.Module): Window based multi-head self attention (W-MSA) module with relative position bias.It supports both of shifted and non-shifted window.Args:dim (int): Number of input channels.window_size (tuple[int]): The height and width of the window.num_heads (int): Number of attention heads.qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: Trueqk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if setattn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0proj_drop (float, optional): Dropout ratio of output. Default: 0.0def __init__(self, dim, window_size, num_heads, qkv_biasTrue, qk_scaleNone, attn_drop0., proj_drop0.):super().__init__()self.dim dimself.window_size window_size # Wh, Wwself.num_heads num_headshead_dim dim // num_headsself.scale qk_scale or head_dim ** -0.5# define a parameter table of relative position biasself.relative_position_bias_table nn.Parameter(torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH# get pair-wise relative position index for each token inside the windowcoords_h torch.arange(self.window_size[0])coords_w torch.arange(self.window_size[1])coords torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Wwcoords_flatten torch.flatten(coords, 1) # 2, Wh*Wwrelative_coords coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Wwrelative_coords relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2relative_coords[:, :, 0] self.window_size[0] - 1 # shift to start from 0relative_coords[:, :, 1] self.window_size[1] - 1relative_coords[:, :, 0] * 2 * self.window_size[1] - 1relative_position_index relative_coords.sum(-1) # Wh*Ww, Wh*Wwself.register_buffer(relative_position_index, relative_position_index)self.qkv nn.Linear(dim, dim * 3, biasqkv_bias)self.attn_drop nn.Dropout(attn_drop)self.proj nn.Linear(dim, dim)self.proj_drop nn.Dropout(proj_drop)trunc_normal_(self.relative_position_bias_table, std.02)self.softmax nn.Softmax(dim-1)def forward(self, x, maskNone): Forward function.Args:x: input features with shape of (num_windows*B, N, C)mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or NoneB_, N, C x.shapeqkv self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)q, k, v qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)q q * self.scaleattn (q k.transpose(-2, -1))relative_position_bias self.relative_position_bias_table[self.relative_position_index.view(-1)].view(self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nHrelative_position_bias relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Wwattn attn relative_position_bias.unsqueeze(0)if mask is not None:nW mask.shape[0]attn attn.view(B_ // nW, nW, self.num_heads, N, N) mask.unsqueeze(1).unsqueeze(0)attn attn.view(-1, self.num_heads, N, N)attn self.softmax(attn)else:attn self.softmax(attn)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 xclass SwinTransformerBlock(nn.Module): Swin Transformer Block.Args:dim (int): Number of input channels.num_heads (int): Number of attention heads.window_size (int): Window size.shift_size (int): Shift size for SW-MSA.mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: Trueqk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.drop (float, optional): Dropout rate. Default: 0.0attn_drop (float, optional): Attention dropout rate. Default: 0.0drop_path (float, optional): Stochastic depth rate. Default: 0.0act_layer (nn.Module, optional): Activation layer. Default: nn.GELUnorm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNormdef __init__(self, dim, num_heads, window_size7, shift_size0,mlp_ratio4., qkv_biasTrue, qk_scaleNone, drop0., attn_drop0., drop_path0.,act_layernn.GELU, norm_layernn.LayerNorm):super().__init__()self.dim dimself.num_heads num_headsself.window_size window_sizeself.shift_size shift_sizeself.mlp_ratio mlp_ratioassert 0 self.shift_size self.window_size, shift_size must in 0-window_sizeself.norm1 norm_layer(dim)self.attn WindowAttention(dim, window_sizeto_2tuple(self.window_size), num_headsnum_heads,qkv_biasqkv_bias, qk_scaleqk_scale, attn_dropattn_drop, proj_dropdrop)self.drop_path DropPath(drop_path) if drop_path 0. else nn.Identity()self.norm2 norm_layer(dim)mlp_hidden_dim int(dim * mlp_ratio)self.mlp Mlp(in_featuresdim, hidden_featuresmlp_hidden_dim, act_layeract_layer, dropdrop)self.H Noneself.W Nonedef forward(self, x, mask_matrix): Forward function.Args:x: Input feature, tensor size (B, H*W, C).H, W: Spatial resolution of the input feature.mask_matrix: Attention mask for cyclic shift.B, L, C x.shapeH, W self.H, self.Wassert L H * W, input feature has wrong sizeshortcut xx self.norm1(x)x x.view(B, H, W, C)# pad feature maps to multiples of window sizepad_l pad_t 0pad_r (self.window_size - W % self.window_size) % self.window_sizepad_b (self.window_size - H % self.window_size) % self.window_sizex F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))_, Hp, Wp, _ x.shape# cyclic shiftif self.shift_size 0:shifted_x torch.roll(x, shifts(-self.shift_size, -self.shift_size), dims(1, 2))attn_mask mask_matrix.type(x.dtype)else:shifted_x xattn_mask None# partition windowsx_windows window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, Cx_windows x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C# W-MSA/SW-MSAattn_windows self.attn(x_windows, maskattn_mask) # nW*B, window_size*window_size, C# merge windowsattn_windows attn_windows.view(-1, self.window_size, self.window_size, C)shifted_x window_reverse(attn_windows, self.window_size, Hp, Wp) # B H W C# reverse cyclic shiftif self.shift_size 0:x torch.roll(shifted_x, shifts(self.shift_size, self.shift_size), dims(1, 2))else:x shifted_xif pad_r 0 or pad_b 0:x x[:, :H, :W, :].contiguous()x x.view(B, H * W, C)# FFNx shortcut self.drop_path(x)x x self.drop_path(self.mlp(self.norm2(x)))return xclass PatchMerging(nn.Module): Patch Merging LayerArgs:dim (int): Number of input channels.norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNormdef __init__(self, dim, norm_layernn.LayerNorm):super().__init__()self.dim dimself.reduction nn.Linear(4 * dim, 2 * dim, biasFalse)self.norm norm_layer(4 * dim)def forward(self, x, H, W): Forward function.Args:x: Input feature, tensor size (B, H*W, C).H, W: Spatial resolution of the input feature.B, L, C x.shapeassert L H * W, input feature has wrong sizex x.view(B, H, W, C)# paddingpad_input (H % 2 1) or (W % 2 1)if pad_input:x F.pad(x, (0, 0, 0, W % 2, 0, H % 2))x0 x[:, 0::2, 0::2, :] # B H/2 W/2 Cx1 x[:, 1::2, 0::2, :] # B H/2 W/2 Cx2 x[:, 0::2, 1::2, :] # B H/2 W/2 Cx3 x[:, 1::2, 1::2, :] # B H/2 W/2 Cx torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*Cx x.view(B, -1, 4 * C) # B H/2*W/2 4*Cx self.norm(x)x self.reduction(x)return xclass BasicLayer(nn.Module): A basic Swin Transformer layer for one stage.Args:dim (int): Number of feature channelsdepth (int): Depths of this stage.num_heads (int): Number of attention head.window_size (int): Local window size. Default: 7.mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: Trueqk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.drop (float, optional): Dropout rate. Default: 0.0attn_drop (float, optional): Attention dropout rate. Default: 0.0drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNormdownsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: Noneuse_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.def __init__(self,dim,depth,num_heads,window_size7,mlp_ratio4.,qkv_biasTrue,qk_scaleNone,drop0.,attn_drop0.,drop_path0.,norm_layernn.LayerNorm,downsampleNone,use_checkpointFalse):super().__init__()self.window_size window_sizeself.shift_size window_size // 2self.depth depthself.use_checkpoint use_checkpoint# build blocksself.blocks nn.ModuleList([SwinTransformerBlock(dimdim,num_headsnum_heads,window_sizewindow_size,shift_size0 if (i % 2 0) else window_size // 2,mlp_ratiomlp_ratio,qkv_biasqkv_bias,qk_scaleqk_scale,dropdrop,attn_dropattn_drop,drop_pathdrop_path[i] if isinstance(drop_path, list) else drop_path,norm_layernorm_layer)for i in range(depth)])# patch merging layerif downsample is not None:self.downsample downsample(dimdim, norm_layernorm_layer)else:self.downsample Nonedef forward(self, x, H, W): Forward function.Args:x: Input feature, tensor size (B, H*W, C).H, W: Spatial resolution of the input feature.# calculate attention mask for SW-MSAHp int(np.ceil(H / self.window_size)) * self.window_sizeWp int(np.ceil(W / self.window_size)) * self.window_sizeimg_mask torch.zeros((1, Hp, Wp, 1), devicex.device) # 1 Hp Wp 1h_slices (slice(0, -self.window_size),slice(-self.window_size, -self.shift_size),slice(-self.shift_size, None))w_slices (slice(0, -self.window_size),slice(-self.window_size, -self.shift_size),slice(-self.shift_size, None))cnt 0for h in h_slices:for w in w_slices:img_mask[:, h, w, :] cntcnt 1mask_windows window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1mask_windows mask_windows.view(-1, self.window_size * self.window_size)attn_mask mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)attn_mask attn_mask.masked_fill(attn_mask ! 0, float(-100.0)).masked_fill(attn_mask 0, float(0.0))for blk in self.blocks:blk.H, blk.W H, Wif self.use_checkpoint:x checkpoint.checkpoint(blk, x, attn_mask)else:x blk(x, attn_mask)if self.downsample is not None:x_down self.downsample(x, H, W)Wh, Ww (H 1) // 2, (W 1) // 2return x, H, W, x_down, Wh, Wwelse:return x, H, W, x, H, Wclass PatchEmbed(nn.Module): Image to Patch EmbeddingArgs:patch_size (int): Patch token size. Default: 4.in_chans (int): Number of input image channels. Default: 3.embed_dim (int): Number of linear projection output channels. Default: 96.norm_layer (nn.Module, optional): Normalization layer. Default: Nonedef __init__(self, patch_size4, in_chans3, embed_dim96, norm_layerNone):super().__init__()patch_size to_2tuple(patch_size)self.patch_size patch_sizeself.in_chans in_chansself.embed_dim embed_dimself.proj nn.Conv2d(in_chans, embed_dim, kernel_sizepatch_size, stridepatch_size)if norm_layer is not None:self.norm norm_layer(embed_dim)else:self.norm Nonedef forward(self, x):Forward function.# padding_, _, H, W x.size()if W % self.patch_size[1] ! 0:x F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))if H % self.patch_size[0] ! 0:x F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))x self.proj(x) # B C Wh Wwif self.norm is not None:Wh, Ww x.size(2), x.size(3)x x.flatten(2).transpose(1, 2)x self.norm(x)x x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)return xclass SwinTransformer(nn.Module): Swin Transformer backbone.A PyTorch impl of : Swin Transformer: Hierarchical Vision Transformer using Shifted Windows -https://arxiv.org/pdf/2103.14030Args:pretrain_img_size (int): Input image size for training the pretrained model,used in absolute postion embedding. Default 224.patch_size (int | tuple(int)): Patch size. Default: 4.in_chans (int): Number of input image channels. Default: 3.embed_dim (int): Number of linear projection output channels. Default: 96.depths (tuple[int]): Depths of each Swin Transformer stage.num_heads (tuple[int]): Number of attention head of each stage.window_size (int): Window size. Default: 7.mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: Trueqk_scale (float): Override default qk scale of head_dim ** -0.5 if set.drop_rate (float): Dropout rate.attn_drop_rate (float): Attention dropout rate. Default: 0.drop_path_rate (float): Stochastic depth rate. Default: 0.2.norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.ape (bool): If True, add absolute position embedding to the patch embedding. Default: False.patch_norm (bool): If True, add normalization after patch embedding. Default: True.out_indices (Sequence[int]): Output from which stages.frozen_stages (int): Stages to be frozen (stop grad and set eval mode).-1 means not freezing any parameters.use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.def __init__(self,pretrain_img_size224,patch_size4,in_chans3,embed_dim96,depths[2, 2, 6, 2],num_heads[3, 6, 12, 24],window_size7,mlp_ratio4.,qkv_biasTrue,qk_scaleNone,drop_rate0.,attn_drop_rate0.,drop_path_rate0.2,norm_layernn.LayerNorm,apeFalse,patch_normTrue,out_indices(0, 1, 2, 3),frozen_stages-1,use_checkpointFalse):super().__init__()self.pretrain_img_size pretrain_img_sizeself.num_layers len(depths)self.embed_dim embed_dimself.ape apeself.patch_norm patch_normself.out_indices out_indicesself.frozen_stages frozen_stages# split image into non-overlapping patchesself.patch_embed PatchEmbed(patch_sizepatch_size, in_chansin_chans, embed_dimembed_dim,norm_layernorm_layer if self.patch_norm else None)# absolute position embeddingif self.ape:pretrain_img_size to_2tuple(pretrain_img_size)patch_size to_2tuple(patch_size)patches_resolution [pretrain_img_size[0] // patch_size[0], pretrain_img_size[1] // patch_size[1]]self.absolute_pos_embed nn.Parameter(torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1]))trunc_normal_(self.absolute_pos_embed, std.02)self.pos_drop nn.Dropout(pdrop_rate)# stochastic depthdpr [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule# build layersself.layers nn.ModuleList()for i_layer in range(self.num_layers):layer BasicLayer(dimint(embed_dim * 2 ** i_layer),depthdepths[i_layer],num_headsnum_heads[i_layer],window_sizewindow_size,mlp_ratiomlp_ratio,qkv_biasqkv_bias,qk_scaleqk_scale,dropdrop_rate,attn_dropattn_drop_rate,drop_pathdpr[sum(depths[:i_layer]):sum(depths[:i_layer 1])],norm_layernorm_layer,downsamplePatchMerging if (i_layer self.num_layers - 1) else None,use_checkpointuse_checkpoint)self.layers.append(layer)num_features [int(embed_dim * 2 ** i) for i in range(self.num_layers)]self.num_features num_features# add a norm layer for each outputfor i_layer in out_indices:layer norm_layer(num_features[i_layer])layer_name fnorm{i_layer}self.add_module(layer_name, layer)self.channel [i.size(1) for i in self.forward(torch.randn(1, 3, 640, 640))]def forward(self, x):Forward function.x self.patch_embed(x)Wh, Ww x.size(2), x.size(3)if self.ape:# interpolate the position embedding to the corresponding sizeabsolute_pos_embed F.interpolate(self.absolute_pos_embed, size(Wh, Ww), modebicubic)x (x absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww Celse:x x.flatten(2).transpose(1, 2)x self.pos_drop(x)outs []for i in range(self.num_layers):layer self.layers[i]x_out, H, W, x, Wh, Ww layer(x, Wh, Ww)if i in self.out_indices:norm_layer getattr(self, fnorm{i})x_out norm_layer(x_out)out x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()outs.append(out)return outsdef 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(floading weights... {idx}/{len(model_dict)} items)return model_dictdef SwinTransformer_Tiny(weights):model SwinTransformer(depths[2, 2, 6, 2], num_heads[3, 6, 12, 24])if weights:model.load_state_dict(update_weight(model.state_dict(), torch.load(weights)[model]))return modelif __name__ __main__:device torch.device(cuda:0)model SwinTransformer().to(device)model.half()# model.load_state_dict(update_weight(model.state_dict(), torch.load(swin_tiny_patch4_window7_224_22k.pth)[model]))inputs torch.randn((1, 3, 640, 512)).to(device).half()res model(inputs)for i in res:print(i.size())print(model.channel)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, pretrainedargs[0], features_onlyTrue)c2 m.feature_info.channels()elif m in {SwinTransformer_Tiny}: #添加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, profileFalse, visualizeFalse):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_dirvisualize)return x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, SwinTransformer_Tiny, [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)]
