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DiMTAIC 2024 数字医学技术及应用创新大赛-甲状腺B超静态及动态影像算法赛-参赛项目

news 来源：原创 2025/6/9 9:34:12

参赛成绩

项目介绍

去年参加完这个比赛之后，整理了项目文件和代码，虽然比赛没有获奖，但是参赛过程中自己也很有收获，自己一个人搭建了完整的pipeline并基于此提交了多次提高成绩，现在把这个项目梳理成博客，便于以后学习借鉴。

本项目涉及对医学图像的分类和分割两种视觉任务，分类任务基于论文：HIFUSE: HIERARCHICAL MULTI-SCALE FEATURE FUSION NETWORK FOR MEDICAL IMAGE CLASSIFICATION 提出的方案

论文源码：https://github.com/huoxiangzuo/HiFuse

分割任务基于论文：SAM2-UNet: Segment Anything 2 Makes Strong Encoder for Natural and Medical Image Segmentation 提出的方案

论文源码：https://github.com/WZH0120/SAM2-UNet

项目结构

/app
├── submit
│ └── label
│ ├── img_0030_0000.png
│ └── ...
├── label.csv
├── run.sh
└── submit.zip

/model_weight
├── cls
│ ├── checkpoint
│ └── best_model.pth
└── seg
└── SAM2-UNet-epoch.pth

/pretrained_model
└── sam2_hiera_large.pt

/sam2

/sam2_configs

/cls_model.py

/cls_test.py

/cls_train.py

/cls_utils.py

/dataset.py

/Dockerfile

/requirements.txt

/SAM2UNet.py

/seg_eval.py

/seg_eval.sh

/seg_test.py

/seg_test.sh

/seg_train.py

/seg_train.sh

/tcdata

重要代码

/app/run.sh

CUDA_VISIBLE_DEVICES="0" \
# seg task train 
python /mnt/home/maojianzeng/2024dimtaic/seg_train.py \
--hiera_path "/mnt/home/maojianzeng/2024dimtaic/pretrained_model/sam2_hiera_large.pt" \
--train_image_path "/mnt/home/maojianzeng/2024dimtaic/tcdata/train/img/" \
--train_mask_path "/mnt/home/maojianzeng/2024dimtaic/tcdata/train/label/" \
--save_path "/mnt/home/maojianzeng/2024dimtaic/model_weight/seg" \
--epoch 20 \
--lr 0.001 \
--batch_size 4 \
--weight_decay 5e-4# seg task test
python /mnt/home/maojianzeng/2024dimtaic/seg_test.py \
--checkpoint "/mnt/home/maojianzeng/2024dimtaic/model_weight/seg/SAM2-UNet-20.pth" \
--test_image_path "/mnt/home/maojianzeng/2024dimtaic/tcdata/train/img/" \
--test_gt_path "/mnt/home/maojianzeng/2024dimtaic/tcdata/train/label/" \
--save_path "submit/label"# cls task train
python /mnt/home/maojianzeng/2024dimtaic/cls_train.py \
--num_classes 2 \
--epochs 20 \
--batch-size 4 \
--lr 0.001 \
--wd 5e-4 \
--train_data_path "/mnt/home/maojianzeng/2024dimtaic/tcdata/train/img/" \
--train_label_path "/mnt/home/maojianzeng/2024dimtaic/tcdata/train/label.csv" \
--val_data_path "/mnt/home/maojianzeng/2024dimtaic/tcdata/train/img" \
--val_label_path "/mnt/home/maojianzeng/2024dimtaic/tcdata/train/label.csv" \
--save_dir "/mnt/home/maojianzeng/2024dimtaic/model_weight/cls" \
--save_best_model_path "/mnt/home/maojianzeng/2024dimtaic/model_weight/cls/best_model.pth" \
--checkpoint_path "/mnt/home/maojianzeng/2024dimtaic/model_weight/cls/checkpoint" # cls task test
python /mnt/home/maojianzeng/2024dimtaic/cls_test.py \
--test_data_path "/mnt/home/maojianzeng/2024dimtaic/tcdata/train/img" \
--test_label_path "/mnt/home/maojianzeng/2024dimtaic/tcdata/train/label.csv" \
--model_weight_path "/mnt/home/maojianzeng/2024dimtaic/model_weight/cls/best_model.pth" \
--save_path "submit/label.csv"zip -r submit.zip submit

/cls_model.py

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
import numpy as np
from typing import Optionaldef drop_path_f(x, drop_prob: float = 0., training: bool = False):"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).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 forchanging the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use'survival rate' as the argument."""if drop_prob == 0. or not training:return xkeep_prob = 1 - drop_probshape = (x.shape[0],) + (1,) * (x.ndim - 1)  # work with diff dim tensors, not just 2D ConvNetsrandom_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)random_tensor.floor_()  # binarizeoutput = x.div(keep_prob) * random_tensorreturn outputclass DropPath(nn.Module):"""Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks)."""def __init__(self, drop_prob=None):super(DropPath, self).__init__()self.drop_prob = drop_probdef forward(self, x):return drop_path_f(x, self.drop_prob, self.training)class main_model(nn.Module):def __init__(self, num_classes, patch_size=4, in_chans=3, embed_dim=96, depths=(2, 2, 2, 2),num_heads=(3, 6, 12, 24), window_size=7, qkv_bias=True, drop_rate=0,attn_drop_rate=0, drop_path_rate=0., norm_layer=nn.LayerNorm, patch_norm=True,use_checkpoint=False, HFF_dp=0.,conv_depths=(2, 2, 2, 2), conv_dims=(96, 192, 384, 768), conv_drop_path_rate=0.,conv_head_init_scale: float = 1., **kwargs):super().__init__()###### Local Branch Setting #######self.downsample_layers = nn.ModuleList()   # stem + 3 stage downsamplestem = nn.Sequential(nn.Conv2d(in_chans, conv_dims[0], kernel_size=4, stride=4),LayerNorm(conv_dims[0], eps=1e-6, data_format="channels_first"))self.downsample_layers.append(stem)# stage2-4 downsamplefor i in range(3):downsample_layer = nn.Sequential(LayerNorm(conv_dims[i], eps=1e-6, data_format="channels_first"),nn.Conv2d(conv_dims[i], conv_dims[i+1], kernel_size=2, stride=2))self.downsample_layers.append(downsample_layer)self.stages = nn.ModuleList()  # 4 feature resolution stages, each consisting of multiple blocksdp_rates = [x.item() for x in torch.linspace(0, conv_drop_path_rate, sum(conv_depths))]cur = 0# Build stacks of blocks in each stagefor i in range(4):stage = nn.Sequential(*[Local_block(dim=conv_dims[i], drop_rate=dp_rates[cur + j])for j in range(conv_depths[i])])self.stages.append((stage))cur += conv_depths[i]self.conv_norm = nn.LayerNorm(conv_dims[-1], eps=1e-6)   # final norm layerself.conv_head = nn.Linear(conv_dims[-1], num_classes)self.conv_head.weight.data.mul_(conv_head_init_scale)self.conv_head.bias.data.mul_(conv_head_init_scale)###### Global Branch Setting ######self.num_classes = num_classesself.num_layers = len(depths)self.embed_dim = embed_dimself.patch_norm = patch_norm# The channels of stage4 output feature matrixself.num_features = int(embed_dim * 2 ** (self.num_layers - 1))# split image into non-overlapping patchesself.patch_embed = PatchEmbed(patch_size=patch_size, in_c=in_chans, embed_dim=embed_dim,norm_layer=norm_layer if self.patch_norm else None)self.pos_drop = nn.Dropout(p=drop_rate)# stochastic depthdpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rulei_layer = 0self.layers1 = BasicLayer(dim=int(embed_dim * 2 ** i_layer),depth=depths[i_layer],num_heads=num_heads[i_layer],window_size=window_size,qkv_bias=qkv_bias,drop=drop_rate,attn_drop=attn_drop_rate,drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],norm_layer=norm_layer,downsample=PatchMerging if (i_layer > 0) else None,use_checkpoint=use_checkpoint)i_layer = 1self.layers2 = BasicLayer(dim=int(embed_dim * 2 ** i_layer),depth=depths[i_layer],num_heads=num_heads[i_layer],window_size=window_size,qkv_bias=qkv_bias,drop=drop_rate,attn_drop=attn_drop_rate,drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],norm_layer=norm_layer,downsample=PatchMerging if (i_layer > 0) else None,use_checkpoint=use_checkpoint)i_layer = 2self.layers3 = BasicLayer(dim=int(embed_dim * 2 ** i_layer),depth=depths[i_layer],num_heads=num_heads[i_layer],window_size=window_size,qkv_bias=qkv_bias,drop=drop_rate,attn_drop=attn_drop_rate,drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],norm_layer=norm_layer,downsample=PatchMerging if (i_layer > 0) else None,use_checkpoint=use_checkpoint)i_layer = 3self.layers4 = BasicLayer(dim=int(embed_dim * 2 ** i_layer),depth=depths[i_layer],num_heads=num_heads[i_layer],window_size=window_size,qkv_bias=qkv_bias,drop=drop_rate,attn_drop=attn_drop_rate,drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],norm_layer=norm_layer,downsample=PatchMerging if (i_layer > 0) else None,use_checkpoint=use_checkpoint)self.norm = norm_layer(self.num_features)self.avgpool = nn.AdaptiveAvgPool1d(1)self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()self.apply(self._init_weights)###### Hierachical Feature Fusion Block Setting #######self.fu1 = HFF_block(ch_1=96, ch_2=96, r_2=16, ch_int=96, ch_out=96, drop_rate=HFF_dp)self.fu2 = HFF_block(ch_1=192, ch_2=192, r_2=16, ch_int=192, ch_out=192, drop_rate=HFF_dp)self.fu3 = HFF_block(ch_1=384, ch_2=384, r_2=16, ch_int=384, ch_out=384, drop_rate=HFF_dp)self.fu4 = HFF_block(ch_1=768, ch_2=768, r_2=16, ch_int=768, ch_out=768, drop_rate=HFF_dp)def _init_weights(self, m):if isinstance(m, nn.Linear):nn.init.trunc_normal_(m.weight, std=.02)if isinstance(m, nn.Linear) and m.bias is not None:nn.init.constant_(m.bias, 0)elif isinstance(m, nn.LayerNorm):nn.init.constant_(m.bias, 0)nn.init.constant_(m.weight, 1.0)elif isinstance(m, (nn.Conv2d, nn.Linear)):nn.init.trunc_normal_(m.weight, std=0.2)nn.init.constant_(m.bias, 0)def forward(self, imgs):######  Global Branch ######x_s, H, W = self.patch_embed(imgs)x_s = self.pos_drop(x_s)x_s_1, H, W = self.layers1(x_s, H, W)x_s_2, H, W = self.layers2(x_s_1, H, W)x_s_3, H, W = self.layers3(x_s_2, H, W)x_s_4, H, W = self.layers4(x_s_3, H, W)# [B,L,C] ---> [B,C,H,W]x_s_1 = torch.transpose(x_s_1, 1, 2)x_s_1 = x_s_1.view(x_s_1.shape[0], -1, 56, 56)x_s_2 = torch.transpose(x_s_2, 1, 2)x_s_2 = x_s_2.view(x_s_2.shape[0], -1, 28, 28)x_s_3 = torch.transpose(x_s_3, 1, 2)x_s_3 = x_s_3.view(x_s_3.shape[0], -1, 14, 14)x_s_4 = torch.transpose(x_s_4, 1, 2)x_s_4 = x_s_4.view(x_s_4.shape[0], -1, 7, 7)######  Local Branch ######x_c = self.downsample_layers[0](imgs)x_c_1 = self.stages[0](x_c)x_c = self.downsample_layers[1](x_c_1)x_c_2 = self.stages[1](x_c)x_c = self.downsample_layers[2](x_c_2)x_c_3 = self.stages[2](x_c)x_c = self.downsample_layers[3](x_c_3)x_c_4 = self.stages[3](x_c)###### Hierachical Feature Fusion Path ######x_f_1 = self.fu1(x_c_1, x_s_1, None)x_f_2 = self.fu2(x_c_2, x_s_2, x_f_1)x_f_3 = self.fu3(x_c_3, x_s_3, x_f_2)x_f_4 = self.fu4(x_c_4, x_s_4, x_f_3)x_fu = self.conv_norm(x_f_4.mean([-2, -1]))  # global average pooling, (N, C, H, W) -> (N, C)x_fu = self.conv_head(x_fu)return x_fu##### Local Feature Block Component #####class LayerNorm(nn.Module):r""" LayerNorm that supports two data formats: channels_last (default) or channels_first.The ordering of the dimensions in the inputs. channels_last corresponds to inputs withshape (batch_size, height, width, channels) while channels_first corresponds to inputswith shape (batch_size, channels, height, width)."""def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"):super().__init__()self.weight = nn.Parameter(torch.ones(normalized_shape), requires_grad=True)self.bias = nn.Parameter(torch.zeros(normalized_shape), requires_grad=True)self.eps = epsself.data_format = data_formatif self.data_format not in ["channels_last", "channels_first"]:raise ValueError(f"not support data format '{self.data_format}'")self.normalized_shape = (normalized_shape,)def forward(self, x: torch.Tensor) -> torch.Tensor:if self.data_format == "channels_last":return F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)elif self.data_format == "channels_first":# [batch_size, channels, height, width]mean = x.mean(1, keepdim=True)var = (x - mean).pow(2).mean(1, keepdim=True)x = (x - mean) / torch.sqrt(var + self.eps)x = self.weight[:, None, None] * x + self.bias[:, None, None]return xclass Local_block(nn.Module):r""" Local Feature Block. There are two equivalent implementations:(1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W)(2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute backWe use (2) as we find it slightly faster in PyTorchArgs:dim (int): Number of input channels.drop_rate (float): Stochastic depth rate. Default: 0.0"""def __init__(self, dim, drop_rate=0.):super().__init__()self.dwconv = nn.Conv2d(dim, dim, kernel_size=3, padding=1, groups=dim)  # depthwise convself.norm = LayerNorm(dim, eps=1e-6, data_format="channels_last")self.pwconv = nn.Linear(dim, dim)  # pointwise/1x1 convs, implemented with linear layersself.act = nn.GELU()self.drop_path = DropPath(drop_rate) if drop_rate > 0. else nn.Identity()def forward(self, x: torch.Tensor) -> torch.Tensor:shortcut = xx = self.dwconv(x)x = x.permute(0, 2, 3, 1)  # [N, C, H, W] -> [N, H, W, C]x = self.norm(x)x = self.pwconv(x)x = self.act(x)x = x.permute(0, 3, 1, 2)  # [N, H, W, C] -> [N, C, H, W]x = shortcut + self.drop_path(x)return x# Hierachical Feature Fusion Block
class HFF_block(nn.Module):def __init__(self, ch_1, ch_2, r_2, ch_int, ch_out, drop_rate=0.):super(HFF_block, self).__init__()self.maxpool=nn.AdaptiveMaxPool2d(1)self.avgpool=nn.AdaptiveAvgPool2d(1)self.se=nn.Sequential(nn.Conv2d(ch_2, ch_2 // r_2, 1,bias=False),nn.ReLU(),nn.Conv2d(ch_2 // r_2, ch_2, 1,bias=False))self.sigmoid = nn.Sigmoid()self.spatial = Conv(2, 1, 7, bn=True, relu=False, bias=False)self.W_l = Conv(ch_1, ch_int, 1, bn=True, relu=False)self.W_g = Conv(ch_2, ch_int, 1, bn=True, relu=False)self.Avg = nn.AvgPool2d(2, stride=2)self.Updim = Conv(ch_int//2, ch_int, 1, bn=True, relu=True)self.norm1 = LayerNorm(ch_int * 3, eps=1e-6, data_format="channels_first")self.norm2 = LayerNorm(ch_int * 2, eps=1e-6, data_format="channels_first")self.norm3 = LayerNorm(ch_1 + ch_2 + ch_int, eps=1e-6, data_format="channels_first")self.W3 = Conv(ch_int * 3, ch_int, 1, bn=True, relu=False)self.W = Conv(ch_int * 2, ch_int, 1, bn=True, relu=False)self.gelu = nn.GELU()self.residual = IRMLP(ch_1 + ch_2 + ch_int, ch_out)self.drop_path = DropPath(drop_rate) if drop_rate > 0. else nn.Identity()def forward(self, l, g, f):W_local = self.W_l(l)   # local feature from Local Feature BlockW_global = self.W_g(g)   # global feature from Global Feature Blockif f is not None:W_f = self.Updim(f)W_f = self.Avg(W_f)shortcut = W_fX_f = torch.cat([W_f, W_local, W_global], 1)X_f = self.norm1(X_f)X_f = self.W3(X_f)X_f = self.gelu(X_f)else:shortcut = 0X_f = torch.cat([W_local, W_global], 1)X_f = self.norm2(X_f)X_f = self.W(X_f)X_f = self.gelu(X_f)# spatial attention for ConvNeXt branchl_jump = lmax_result, _ = torch.max(l, dim=1, keepdim=True)avg_result = torch.mean(l, dim=1, keepdim=True)result = torch.cat([max_result, avg_result], 1)l = self.spatial(result)l = self.sigmoid(l) * l_jump# channel attetion for transformer branchg_jump = gmax_result=self.maxpool(g)avg_result=self.avgpool(g)max_out=self.se(max_result)avg_out=self.se(avg_result)g = self.sigmoid(max_out+avg_out) * g_jumpfuse = torch.cat([g, l, X_f], 1)fuse = self.norm3(fuse)fuse = self.residual(fuse)fuse = shortcut + self.drop_path(fuse)return fuseclass Conv(nn.Module):def __init__(self, inp_dim, out_dim, kernel_size=3, stride=1, bn=False, relu=True, bias=True, group=1):super(Conv, self).__init__()self.inp_dim = inp_dimself.conv = nn.Conv2d(inp_dim, out_dim, kernel_size, stride, padding=(kernel_size-1)//2, bias=bias)self.relu = Noneself.bn = Noneif relu:self.relu = nn.ReLU(inplace=True)if bn:self.bn = nn.BatchNorm2d(out_dim)def forward(self, x):assert x.size()[1] == self.inp_dim, "{} {}".format(x.size()[1], self.inp_dim)x = self.conv(x)if self.bn is not None:x = self.bn(x)if self.relu is not None:x = self.relu(x)return x#### Inverted Residual MLP
class IRMLP(nn.Module):def __init__(self, inp_dim, out_dim):super(IRMLP, self).__init__()self.conv1 = Conv(inp_dim, inp_dim, 3, relu=False, bias=False, group=inp_dim)self.conv2 = Conv(inp_dim, inp_dim * 4, 1, relu=False, bias=False)self.conv3 = Conv(inp_dim * 4, out_dim, 1, relu=False, bias=False, bn=True)self.gelu = nn.GELU()self.bn1 = nn.BatchNorm2d(inp_dim)def forward(self, x):residual = xout = self.conv1(x)out = self.gelu(out)out += residualout = self.bn1(out)out = self.conv2(out)out = self.gelu(out)out = self.conv3(out)return out####### Shift Window MSA #############class WindowAttention(nn.Module):r""" 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: Trueattn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0proj_drop (float, optional): Dropout ratio of output. Default: 0.0"""def __init__(self, dim, window_size, num_heads, qkv_bias=True, attn_drop=0., proj_drop=0.):super().__init__()self.dim = dimself.window_size = window_size  # [Mh, Mw]self.num_heads = num_headshead_dim = dim // num_headsself.scale = 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*Mh-1 * 2*Mw-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], indexing="ij"))  # [2, Mh, Mw]coords_flatten = torch.flatten(coords, 1)  # [2, Mh*Mw]# [2, Mh*Mw, 1] - [2, 1, Mh*Mw]relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # [2, Mh*Mw, Mh*Mw]relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # [Mh*Mw, Mh*Mw, 2]relative_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)  # [Mh*Mw, Mh*Mw]self.register_buffer("relative_position_index", relative_position_index)self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)self.attn_drop = nn.Dropout(attn_drop)self.proj = nn.Linear(dim, dim)self.proj_drop = nn.Dropout(proj_drop)nn.init.trunc_normal_(self.relative_position_bias_table, std=.02)self.softmax = nn.Softmax(dim=-1)def forward(self, x, mask: Optional[torch.Tensor] = None):"""Args:x: input features with shape of (num_windows*B, Mh*Mw, C)mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None"""# [batch_size*num_windows, Mh*Mw, total_embed_dim]B_, N, C = x.shape# qkv(): -> [batch_size*num_windows, Mh*Mw, 3 * total_embed_dim]# reshape: -> [batch_size*num_windows, Mh*Mw, 3, num_heads, embed_dim_per_head]# permute: -> [3, batch_size*num_windows, num_heads, Mh*Mw, embed_dim_per_head]qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)# [batch_size*num_windows, num_heads, Mh*Mw, embed_dim_per_head]q, k, v = qkv.unbind(0)  # make torchscript happy (cannot use tensor as tuple)# transpose: -> [batch_size*num_windows, num_heads, embed_dim_per_head, Mh*Mw]# @: multiply -> [batch_size*num_windows, num_heads, Mh*Mw, Mh*Mw]q = q * self.scaleattn = (q @ k.transpose(-2, -1))# relative_position_bias_table.view: [Mh*Mw*Mh*Mw,nH] -> [Mh*Mw,Mh*Mw,nH]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)relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # [nH, Mh*Mw, Mh*Mw]attn = attn + relative_position_bias.unsqueeze(0)if mask is not None:# mask: [nW, Mh*Mw, Mh*Mw]nW = mask.shape[0]  # num_windows# attn.view: [batch_size, num_windows, num_heads, Mh*Mw, Mh*Mw]# mask.unsqueeze: [1, nW, 1, Mh*Mw, Mh*Mw]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)# @: multiply -> [batch_size*num_windows, num_heads, Mh*Mw, embed_dim_per_head]# transpose: -> [batch_size*num_windows, Mh*Mw, num_heads, embed_dim_per_head]# reshape: -> [batch_size*num_windows, Mh*Mw, total_embed_dim]x = (attn @ v).transpose(1, 2).reshape(B_, N, C)x = self.proj(x)x = self.proj_drop(x)return x### Global Feature Block
class Global_block(nn.Module):r""" Global Feature Block from modified 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.qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: Truedrop (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.LayerNorm"""def __init__(self, dim, num_heads, window_size=7, shift_size=0,qkv_bias=True, drop=0., attn_drop=0., drop_path=0.,act_layer=nn.GELU, norm_layer=nn.LayerNorm):super().__init__()self.dim = dimself.num_heads = num_headsself.window_size = window_sizeself.shift_size = shift_sizeassert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"self.norm1 = norm_layer(dim)self.attn = WindowAttention(dim, window_size=(self.window_size, self.window_size), num_heads=num_heads, qkv_bias=qkv_bias,attn_drop=attn_drop, proj_drop=drop)self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()self.norm2 = norm_layer(dim)self.fc1 = nn.Linear(dim, dim)self.act = act_layer()def forward(self, x, attn_mask):H, W = self.H, self.WB, L, C = x.shapeassert L == H * W, "input feature has wrong size"shortcut = xx = self.norm1(x)x = x.view(B, H, W, C)pad_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))else:shifted_x = xattn_mask = None# partition windowsx_windows = window_partition(shifted_x, self.window_size)  # [nW*B, Mh, Mw, C]x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # [nW*B, Mh*Mw, C]# W-MSA/SW-MSAattn_windows = self.attn(x_windows, mask=attn_mask)  # [nW*B, Mh*Mw, C]# merge windowsattn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)  # [nW*B, Mh, Mw, 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)x = self.fc1(x)x = self.act(x)x = shortcut + self.drop_path(x)return xclass BasicLayer(nn.Module):"""Downsampling and Global Feature Block for one stage.Args:dim (int): Number of input channels.depth (int): Number of blocks.num_heads (int): Number of attention heads.window_size (int): Local window size.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: Truedrop (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_size,mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0.,drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):super().__init__()self.dim = dimself.depth = depthself.window_size = window_sizeself.use_checkpoint = use_checkpointself.shift_size = window_size // 2# build blocksself.blocks = nn.ModuleList([Global_block(dim=dim,num_heads=num_heads,window_size=window_size,shift_size=0 if (i % 2 == 0) else self.shift_size,qkv_bias=qkv_bias,drop=drop,attn_drop=attn_drop,drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,norm_layer=norm_layer)for i in range(depth)])# patch merging layerif downsample is not None:self.downsample = downsample(dim=dim, norm_layer=norm_layer)else:self.downsample = Nonedef create_mask(self, x, H, W):# 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), device=x.device)  # [1, Hp, Wp, 1]h_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, Mh, Mw, 1]mask_windows = mask_windows.view(-1, self.window_size * self.window_size)  # [nW, Mh*Mw]attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)  # [nW, 1, Mh*Mw] - [nW, Mh*Mw, 1]# [nW, Mh*Mw, Mh*Mw]attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))return attn_maskdef forward(self, x, H, W):if self.downsample is not None:x = self.downsample(x, H, W)         #patch merging stage2 in [6,3136,96] out [6,784,192]H, W = (H + 1) // 2, (W + 1) // 2attn_mask = self.create_mask(x, H, W)  # [nW, Mh*Mw, Mh*Mw]for blk in self.blocks:                  # global blockblk.H, blk.W = H, Wif not torch.jit.is_scripting() and self.use_checkpoint:x = checkpoint.checkpoint(blk, x, attn_mask)else:x = blk(x, attn_mask)return x, H, Wdef window_partition(x, window_size: int):"""Args:x: (B, H, W, C)window_size (int): window size(M)Returns: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)# permute: [B, H//Mh, Mh, W//Mw, Mw, C] -> [B, H//Mh, W//Mh, Mw, Mw, C]# view: [B, H//Mh, W//Mw, Mh, Mw, C] -> [B*num_windows, Mh, Mw, 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: int, H: int, W: int):"""Args:windows: (num_windows*B, window_size, window_size, C)window_size (int): Window size(M)H (int): Height of imageW (int): Width of imageReturns:x: (B, H, W, C)"""B = int(windows.shape[0] / (H * W / window_size / window_size))# view: [B*num_windows, Mh, Mw, C] -> [B, H//Mh, W//Mw, Mh, Mw, C]x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)# permute: [B, H//Mh, W//Mw, Mh, Mw, C] -> [B, H//Mh, Mh, W//Mw, Mw, C]# view: [B, H//Mh, Mh, W//Mw, Mw, C] -> [B, H, W, C]x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)return xclass PatchEmbed(nn.Module):"""2D Image to Patch Embedding"""def __init__(self, patch_size=4, in_c=3, embed_dim=96, norm_layer=None):super().__init__()patch_size = (patch_size, patch_size)self.patch_size = patch_sizeself.in_chans = in_cself.embed_dim = embed_dimself.proj = nn.Conv2d(in_c, embed_dim, kernel_size=patch_size, stride=patch_size)self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()def forward(self, x):_, _, H, W = x.shape# paddingpad_input = (H % self.patch_size[0] != 0) or (W % self.patch_size[1] != 0)if pad_input:# to pad the last 3 dimensions,# (W_left, W_right, H_top,H_bottom, C_front, C_back)x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1],0, self.patch_size[0] - H % self.patch_size[0],0, 0))# downsample patch_size timesx = self.proj(x)_, _, H, W = x.shape# flatten: [B, C, H, W] -> [B, C, HW]# transpose: [B, C, HW] -> [B, HW, C]x = x.flatten(2).transpose(1, 2)x = self.norm(x)return x, H, Wclass PatchMerging(nn.Module):r""" Patch Merging Layer.Args:dim (int): Number of input channels.norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm"""def __init__(self, dim, norm_layer=nn.LayerNorm):super().__init__()dim = dim//2self.dim = dimself.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)self.norm = norm_layer(4 * dim)def forward(self, x, H, W):"""x: B, H*W, C"""B, L, C = x.shapeassert L == H * W, "input feature has wrong size"x = x.view(B, H, W, C)# paddingpad_input = (H % 2 == 1) or (W % 2 == 1)if pad_input:# to pad the last 3 dimensions, starting from the last dimension and moving forward.# (C_front, C_back, W_left, W_right, H_top, H_bottom)x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))x0 = x[:, 0::2, 0::2, :]  # [B, H/2, W/2, C]x1 = x[:, 1::2, 0::2, :]  # [B, H/2, W/2, C]x2 = x[:, 0::2, 1::2, :]  # [B, H/2, W/2, C]x3 = x[:, 1::2, 1::2, :]  # [B, H/2, W/2, C]x = torch.cat([x0, x1, x2, x3], -1)  # [B, H/2, W/2, 4*C]x = x.view(B, -1, 4 * C)  # [B, H/2*W/2, 4*C]x = self.norm(x)x = self.reduction(x)  # [B, H/2*W/2, 2*C]return xdef HiFuse_Tiny(num_classes: int):model = main_model(depths=(2, 2, 2, 2),conv_depths=(2, 2, 2, 2),num_classes=num_classes)return modeldef HiFuse_Small(num_classes: int):model = main_model(depths=(2, 2, 6, 2),conv_depths=(2, 2, 6, 2),num_classes=num_classes)return modeldef HiFuse_Base(num_classes: int):model = main_model(depths=(2, 2, 18, 2),conv_depths=(2, 2, 18, 2),num_classes=num_classes)return model

/cls_test.py

import os
import json
import argparse
import torch
from PIL import Image
from torchvision import transforms
import matplotlib.pyplot as plt
from cls_model import main_model as create_modeldef main(args):device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")print(f"using {device} device.")test_data_path = args.test_data_pathtest_label_path = args.test_label_pathnum_classes = 2img_size = 224data_transform = transforms.Compose([transforms.Resize(int(256)),transforms.CenterCrop(img_size),transforms.ToTensor(),transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])test_img_path = []test_img_label = []with open(test_label_path, 'r', encoding='utf-8') as file:for line in file:if line.strip().split(',')[0] == 'case':continuetest_img_path.append(line.strip().split(',')[0]+'.png')test_img_label.append(line.strip().split(',')[1])# create modelmodel = create_model(num_classes=num_classes).to(device)# load model weightsmodel_weight_path = args.model_weight_pathmodel.load_state_dict(torch.load(model_weight_path, map_location=device, weights_only=True), strict=False)model.eval()infer_data = []# load imagefor i in range(0,len(test_img_path)):img_path = os.path.join(test_data_path, test_img_path[i])label = torch.tensor(int(test_img_label[i]), dtype=torch.long)assert os.path.exists(img_path), "file: '{}' dose not exist.".format(img_path)img = Image.open(img_path).convert('RGB')plt.imshow(img)# [N, C, H, W]img = data_transform(img)# expand batch dimensionimg = torch.unsqueeze(img, dim=0)with torch.no_grad():# predict classoutput = torch.squeeze(model(img.to(device))).cpu()predict = torch.softmax(output, dim=0)predict_cla = torch.argmax(predict).numpy()print_infer = "img: {}  predict_class: {}  prob: {:.3}".format([test_img_path[i]], [str(predict_cla)], predict[predict_cla].numpy())print(print_infer)infer_data.append((test_img_path[i].split('.')[0], str(predict_cla)))total = sum([param.nelement() for param in model.parameters()])print("Number of parameters: %.2fM" % (total/1e6))if not os.path.exists(args.save_path):with open(args.save_path, 'w', encoding='utf-8') as f:f.write('case,prob\n')else:with open(args.save_path, 'w', encoding='utf-8') as f:f.write('case,prob\n')  # 追加数据到文件with open(args.save_path, 'a', encoding='utf-8') as f:for case, prob in infer_data:f.write(f'{case},{prob}\n')print("Successfully saved {} !".format(args.save_path))print('====> cls task test finished !')print('=' * 100)if __name__ == '__main__':parser = argparse.ArgumentParser()parser.add_argument('--test_data_path', type=str, default="/home/maojianzeng/2024dimtaic/tcdata/train/img")parser.add_argument('--test_label_path', type=str, default="/home/maojianzeng/2024dimtaic/tcdata/train/label.csv")parser.add_argument('--model_weight_path', type=str, default="model_weight/cls/best_model.pth")parser.add_argument('--save_path', type=str, default="submit/label.csv")opt = parser.parse_args()main(opt)

/cls_train.py

import os
import argparse
import torch
import torch.optim as optim
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
from torch.utils.data import DataLoader
from cls_utils import MyDataset
from cls_model import HiFuse_Small as create_model
from cls_utils import create_lr_scheduler, get_params_groups, train_one_epoch, evaluatedef main(args):device = torch.device(args.device if torch.cuda.is_available() else "cpu")print(f"using {device} device.")print(args)print('Start Tensorboard with "tensorboard --logdir=runs", view at http://localhost:6006/')tb_writer = SummaryWriter()train_dataset = MyDataset(args.train_label_path,"train", args.train_data_path)val_dataset = MyDataset(args.val_label_path,"val", args.val_data_path)batch_size = args.batch_sizenw = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8])  # number of workersprint('Using {} dataloader workers every process'.format(nw))train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)model = create_model(num_classes=args.num_classes).to(device)if args.RESUME == False:if args.weights != "":assert os.path.exists(args.weights), "weights file: '{}' not exist.".format(args.weights)weights_dict = torch.load(args.weights, map_location=device)['state_dict']# Delete the weight of the relevant categoryfor k in list(weights_dict.keys()):if "head" in k:del weights_dict[k]model.load_state_dict(weights_dict, strict=False)if args.freeze_layers:for name, para in model.named_parameters():# All weights except head are frozenif "head" not in name:para.requires_grad_(False)else:print("training {}".format(name))# pg = [p for p in model.parameters() if p.requires_grad]pg = get_params_groups(model, weight_decay=args.wd)optimizer = optim.AdamW(pg, lr=args.lr, weight_decay=args.wd)lr_scheduler = create_lr_scheduler(optimizer, len(train_loader), args.epochs,warmup=True, warmup_epochs=2)best_acc = 0.start_epoch = 0# if args.RESUME:#     path_checkpoint = "./model_weight/checkpoint/ckpt_best_100.pth"#     print("model continue train")#     checkpoint = torch.load(path_checkpoint)#     model.load_state_dict(checkpoint['net'])#     optimizer.load_state_dict(checkpoint['optimizer'])#     start_epoch = checkpoint['epoch']#     lr_scheduler.load_state_dict(checkpoint['lr_schedule'])for epoch in range(start_epoch + 1, args.epochs + 1):# traintrain_loss, train_acc = train_one_epoch(model=model,optimizer=optimizer,data_loader=train_loader,device=device,epoch=epoch,lr_scheduler=lr_scheduler)# validateval_loss, val_acc = evaluate(model=model,data_loader=val_loader,device=device,epoch=epoch)tags = ["train_loss", "train_acc", "val_loss", "val_acc", "learning_rate"]tb_writer.add_scalar(tags[0], train_loss, epoch)tb_writer.add_scalar(tags[1], train_acc, epoch)tb_writer.add_scalar(tags[2], val_loss, epoch)tb_writer.add_scalar(tags[3], val_acc, epoch)tb_writer.add_scalar(tags[4], optimizer.param_groups[0]["lr"], epoch)if best_acc < val_acc:if not os.path.isdir(args.save_dir):os.mkdir(args.save_dir)torch.save(model.state_dict(), args.save_best_model_path)print("Saved epoch{} as new best model".format(epoch))best_acc = val_accif epoch % 10 == 0:print('epoch:', epoch)print('learning rate:', optimizer.state_dict()['param_groups'][0]['lr'])checkpoint = {"net": model.state_dict(),'optimizer': optimizer.state_dict(),"epoch": epoch,'lr_schedule': lr_scheduler.state_dict()}if not os.path.isdir(args.checkpoint_path):os.mkdir(args.checkpoint_path)torch.save(checkpoint, args.checkpoint_path +'/ckpt_best_%s.pth' % (str(epoch)))#add loss, acc and lr into tensorboardprint("[epoch {}] accuracy: {}".format(epoch, round(val_acc, 3)))total = sum([param.nelement() for param in model.parameters()])print("Number of parameters: %.2fM" % (total/1e6))print('====> cls task train finished !')print('=' * 100)if __name__ == '__main__':parser = argparse.ArgumentParser()parser.add_argument('--num_classes', type=int, default=2)parser.add_argument('--epochs', type=int, default=20)parser.add_argument('--batch-size', type=int, default=4)parser.add_argument('--lr', type=float, default=1e-4)parser.add_argument('--wd', type=float, default=1e-2)parser.add_argument('--RESUME', type=bool, default=True)parser.add_argument('--train_data_path', type=str, default="/home/maojianzeng/2024dimtaic/tcdata/train/img")parser.add_argument('--val_data_path', type=str, default="/home/maojianzeng/2024dimtaic/tcdata/train/img")parser.add_argument('--train_label_path', type=str, default="/home/maojianzeng/2024dimtaic/tcdata/train/label.csv")parser.add_argument('--val_label_path', type=str, default="/home/maojianzeng/2024dimtaic/tcdata/train/label.csv")parser.add_argument('--save_dir', type=str, default="/mnt/home/maojianzeng/2024dimtaic/SAM2-UNet/model_weight/cls")parser.add_argument('--save_best_model_path', type=str, default="/mnt/home/maojianzeng/2024dimtaic/SAM2-UNet/model_weight/cls/best_model.pth")parser.add_argument('--checkpoint_path', type=str, default="/mnt/home/maojianzeng/2024dimtaic/SAM2-UNet/model_weight/cls/checkpoint")parser.add_argument('--weights', type=str, default='',help='initial weights path')parser.add_argument('--freeze-layers', type=bool, default=False)parser.add_argument('--device', default='cuda:0', help='device id (i.e. 0 or 0,1 or cpu)')opt = parser.parse_args()main(opt)

/Dockerfile

## 获取基础镜像
FROM registry.cn-shanghai.aliyuncs.com/tcc-public/pytorch:2.0.0-py3.9.12-cuda11.8.0-u22.04
## 把当前文件夹里的文件构建到镜像的根目录下,并设置为默认工作目录
ADD . /
WORKDIR /
## 复制文件，确保可以找到requirements.txt 
COPY requirements.txt /requirements.txt 
## 安装python环境的依赖包
RUN pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
## 添加可执行权限
RUN chmod +x app/run.sh
## 执行命令
CMD [ "sh", "app/run.sh" ]

/cls_utils.py

import os
import sys
import json
import pickle
import random
import math
from PIL import Image
import torch
from tqdm import tqdm
import matplotlib.pyplot as plt
from torch.utils.data import Dataset
from torch.utils.data import Dataset
import torchvision.transforms as transformsdef plot_data_loader_image(data_loader):batch_size = data_loader.batch_sizeplot_num = min(batch_size, 4)json_path = './class_indices.json'assert os.path.exists(json_path), json_path + " does not exist."json_file = open(json_path, 'r')class_indices = json.load(json_file)for data in data_loader:images, labels = datafor i in range(plot_num):# [C, H, W] -> [H, W, C]img = images[i].numpy().transpose(1, 2, 0)img = (img * [0.5, 0.5, 0.5] + [0.5, 0.5, 0.5]) * 255label = labels[i].item()plt.subplot(1, plot_num, i+1)plt.xlabel(class_indices[str(label)])plt.xticks([])plt.yticks([])plt.imshow(img.astype('uint8'))plt.show()def write_pickle(list_info: list, file_name: str):with open(file_name, 'wb') as f:pickle.dump(list_info, f)def read_pickle(file_name: str) -> list:with open(file_name, 'rb') as f:info_list = pickle.load(f)return info_listdef train_one_epoch(model, optimizer, data_loader, device, epoch, lr_scheduler):model.train()loss_function = torch.nn.CrossEntropyLoss()accu_loss = torch.zeros(1).to(device)accu_num = torch.zeros(1).to(device)optimizer.zero_grad()sample_num = 0data_loader = tqdm(data_loader, file=sys.stdout)for step, data in enumerate(data_loader):images, labels = datasample_num += images.shape[0]pred = model(images.to(device))pred_classes = torch.max(pred, dim=1)[1]accu_num += torch.eq(pred_classes, labels.to(device)).sum()loss = loss_function(pred, labels.to(device))loss.backward()accu_loss += loss.detach()data_loader.desc = "[train epoch {}] loss: {:.3f}, acc: {:.3f}, lr: {:.5f}".format(epoch,accu_loss.item() / (step + 1),accu_num.item() / sample_num,optimizer.param_groups[0]["lr"])if not torch.isfinite(loss):print('WARNING: non-finite loss, ending training ', loss)sys.exit(1)optimizer.step()optimizer.zero_grad()# update lrlr_scheduler.step()return accu_loss.item() / (step + 1), accu_num.item() / sample_numclass MyDataset(Dataset):def __init__(self, label_path, mode, data_path):self.img_path = []self.label = []self.mode = data_pathwith open(label_path, 'r', encoding='utf-8') as file:for line in file:if line.strip().split(',')[0] == 'case':continueself.img_path.append(line.strip().split(',')[0]+'.png')self.label.append(line.strip().split(',')[1])img_size = 224if mode == "train":self.transform = transforms.Compose([transforms.Resize((img_size, img_size)),transforms.RandomHorizontalFlip(),transforms.RandomVerticalFlip(),transforms.ToTensor(),transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])else:self.transform = transforms.Compose([transforms.Resize((img_size, img_size)),transforms.ToTensor(),transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])# 测试
#         print(len(self.img_path))self.img_path = self.img_path[:]self.label = self.label[:]def __len__(self):return len(self.label)def __getitem__(self, i):image_path = os.path.join(self.mode, self.img_path[i])image = Image.open(image_path).convert('RGB')image = self.transform(image)label = torch.tensor(int(self.label[i]), dtype=torch.long)# for some augmentation code, waiting...return image, label@torch.no_grad()
def evaluate(model, data_loader, device, epoch):loss_function = torch.nn.CrossEntropyLoss()model.eval()accu_num = torch.zeros(1).to(device)accu_loss = torch.zeros(1).to(device)sample_num = 0data_loader = tqdm(data_loader, file=sys.stdout)for step, data in enumerate(data_loader):images, labels = datasample_num += images.shape[0]pred = model(images.to(device))pred_classes = torch.max(pred, dim=1)[1]accu_num += torch.eq(pred_classes, labels.to(device)).sum()loss = loss_function(pred, labels.to(device))accu_loss += lossdata_loader.desc = "[valid epoch {}] loss: {:.3f}, acc: {:.3f}".format(epoch,accu_loss.item() / (step + 1),accu_num.item() / sample_num)return accu_loss.item() / (step + 1), accu_num.item() / sample_numdef create_lr_scheduler(optimizer,num_step: int,epochs: int,warmup=True,warmup_epochs=1,warmup_factor=1e-3,end_factor=1e-2):assert num_step > 0 and epochs > 0if warmup is False:warmup_epochs = 0def f(x):if warmup is True and x <= (warmup_epochs * num_step):alpha = float(x) / (warmup_epochs * num_step)return warmup_factor * (1 - alpha) + alphaelse:current_step = (x - warmup_epochs * num_step)cosine_steps = (epochs - warmup_epochs) * num_stepreturn ((1 + math.cos(current_step * math.pi / cosine_steps)) / 2) * (1 - end_factor) + end_factorreturn torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=f)def get_params_groups(model: torch.nn.Module, weight_decay: float = 1e-5):parameter_group_vars = {"decay": {"params": [], "weight_decay": weight_decay},"no_decay": {"params": [], "weight_decay": 0.}}parameter_group_names = {"decay": {"params": [], "weight_decay": weight_decay},"no_decay": {"params": [], "weight_decay": 0.}}for name, param in model.named_parameters():if not param.requires_grad:continue  # frozen weightsif len(param.shape) == 1 or name.endswith(".bias"):group_name = "no_decay"else:group_name = "decay"parameter_group_vars[group_name]["params"].append(param)parameter_group_names[group_name]["params"].append(name)# print("Param groups = %s" % json.dumps(parameter_group_names, indent=2))return list(parameter_group_vars.values())

/dataset.py

import torchvision.transforms.functional as F
import numpy as np
import random
import os
from PIL import Image
from torchvision.transforms import InterpolationMode
from torch.utils.data import Dataset
from torchvision import transforms
from PIL import Imageclass ToTensor(object):def __call__(self, data):image, label = data['image'], data['label']return {'image': F.to_tensor(image), 'label': F.to_tensor(label)}class Resize(object):def __init__(self, size):self.size = sizedef __call__(self, data):image, label = data['image'], data['label']return {'image': F.resize(image, self.size), 'label': F.resize(label, self.size, interpolation=InterpolationMode.BICUBIC)}class RandomHorizontalFlip(object):def __init__(self, p=0.5):self.p = pdef __call__(self, data):image, label = data['image'], data['label']if random.random() < self.p:return {'image': F.hflip(image), 'label': F.hflip(label)}return {'image': image, 'label': label}class RandomVerticalFlip(object):def __init__(self, p=0.5):self.p = pdef __call__(self, data):image, label = data['image'], data['label']if random.random() < self.p:return {'image': F.vflip(image), 'label': F.vflip(label)}return {'image': image, 'label': label}class Normalize(object):def __init__(self, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]):self.mean = meanself.std = stddef __call__(self, sample):image, label = sample['image'], sample['label']image = F.normalize(image, self.mean, self.std)return {'image': image, 'label': label}class FullDataset(Dataset):def __init__(self, image_root, gt_root, size, mode):self.images = [image_root + f for f in os.listdir(image_root) if f.endswith('.jpg') or f.endswith('.png')]self.gts = [gt_root + f for f in os.listdir(gt_root) if f.endswith('.png')]self.images = sorted(self.images)self.gts = sorted(self.gts)if mode == 'train':self.transform = transforms.Compose([Resize((size, size)),RandomHorizontalFlip(p=0.5),RandomVerticalFlip(p=0.5),ToTensor(),Normalize()])else:self.transform = transforms.Compose([Resize((size, size)),ToTensor(),Normalize()])def __getitem__(self, idx):image = self.rgb_loader(self.images[idx])label = self.binary_loader(self.gts[idx])data = {'image': image, 'label': label}data = self.transform(data)return datadef __len__(self):return len(self.images)def rgb_loader(self, path):with open(path, 'rb') as f:img = Image.open(f)return img.convert('RGB')def binary_loader(self, path):with open(path, 'rb') as f:img = Image.open(f)return img.convert('L')class TestDataset:def __init__(self, image_root, gt_root, size):self.images = [image_root + f for f in os.listdir(image_root) if f.endswith('.jpg') or f.endswith('.png')]self.gts = [gt_root + f for f in os.listdir(gt_root) if f.endswith('.png')]self.images = sorted(self.images)self.gts = sorted(self.gts)self.transform = transforms.Compose([transforms.Resize((size, size)),transforms.ToTensor(),transforms.Normalize([0.485, 0.456, 0.406],[0.229, 0.224, 0.225])])self.gt_transform = transforms.ToTensor()self.size = len(self.images)self.index = 0def load_data(self):image = self.rgb_loader(self.images[self.index])image = self.transform(image).unsqueeze(0)gt = self.binary_loader(self.gts[self.index])gt = np.array(gt)name = self.images[self.index].split('/')[-1]self.index += 1return image, gt, namedef rgb_loader(self, path):with open(path, 'rb') as f:img = Image.open(f)return img.convert('RGB')def binary_loader(self, path):with open(path, 'rb') as f:img = Image.open(f)return img.convert('L')

/requirements.txt

torch==2.4.0
torchaudio
torchvision
-f https://download.pytorch.org/whl/cu118
hydra-core==1.3.2
tqdm==4.65.2
urllib==3.12
imageio
pysodmetrics
matplotlib
numpy
tensorboard

/SAM2UNet.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from sam2.build_sam import build_sam2class DoubleConv(nn.Module):"""(convolution => [BN] => ReLU) * 2"""def __init__(self, in_channels, out_channels, mid_channels=None):super().__init__()if not mid_channels:mid_channels = out_channelsself.double_conv = nn.Sequential(nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1, bias=False),nn.BatchNorm2d(mid_channels),nn.ReLU(inplace=True),nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1, bias=False),nn.BatchNorm2d(out_channels),nn.ReLU(inplace=True))def forward(self, x):return self.double_conv(x)class Up(nn.Module):"""Upscaling then double conv"""def __init__(self, in_channels, out_channels):super().__init__()self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)self.conv = DoubleConv(in_channels, out_channels, in_channels // 2)def forward(self, x1, x2):x1 = self.up(x1)# input is CHWdiffY = x2.size()[2] - x1.size()[2]diffX = x2.size()[3] - x1.size()[3]x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2,diffY // 2, diffY - diffY // 2])# if you have padding issues, see# https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a# https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bdx = torch.cat([x2, x1], dim=1)return self.conv(x)class Adapter(nn.Module):def __init__(self, blk) -> None:super(Adapter, self).__init__()self.block = blkdim = blk.attn.qkv.in_featuresself.prompt_learn = nn.Sequential(nn.Linear(dim, 32),nn.GELU(),nn.Linear(32, dim),nn.GELU())def forward(self, x):prompt = self.prompt_learn(x)promped = x + promptnet = self.block(promped)return netclass BasicConv2d(nn.Module):def __init__(self, in_planes, out_planes, kernel_size, stride=1, padding=0, dilation=1):super(BasicConv2d, self).__init__()self.conv = nn.Conv2d(in_planes, out_planes,kernel_size=kernel_size, stride=stride,padding=padding, dilation=dilation, bias=False)self.bn = nn.BatchNorm2d(out_planes)self.relu = nn.ReLU(inplace=True)def forward(self, x):x = self.conv(x)x = self.bn(x)return xclass RFB_modified(nn.Module):def __init__(self, in_channel, out_channel):super(RFB_modified, self).__init__()self.relu = nn.ReLU(True)self.branch0 = nn.Sequential(BasicConv2d(in_channel, out_channel, 1),)self.branch1 = nn.Sequential(BasicConv2d(in_channel, out_channel, 1),BasicConv2d(out_channel, out_channel, kernel_size=(1, 3), padding=(0, 1)),BasicConv2d(out_channel, out_channel, kernel_size=(3, 1), padding=(1, 0)),BasicConv2d(out_channel, out_channel, 3, padding=3, dilation=3))self.branch2 = nn.Sequential(BasicConv2d(in_channel, out_channel, 1),BasicConv2d(out_channel, out_channel, kernel_size=(1, 5), padding=(0, 2)),BasicConv2d(out_channel, out_channel, kernel_size=(5, 1), padding=(2, 0)),BasicConv2d(out_channel, out_channel, 3, padding=5, dilation=5))self.branch3 = nn.Sequential(BasicConv2d(in_channel, out_channel, 1),BasicConv2d(out_channel, out_channel, kernel_size=(1, 7), padding=(0, 3)),BasicConv2d(out_channel, out_channel, kernel_size=(7, 1), padding=(3, 0)),BasicConv2d(out_channel, out_channel, 3, padding=7, dilation=7))self.conv_cat = BasicConv2d(4*out_channel, out_channel, 3, padding=1)self.conv_res = BasicConv2d(in_channel, out_channel, 1)def forward(self, x):x0 = self.branch0(x)x1 = self.branch1(x)x2 = self.branch2(x)x3 = self.branch3(x)x_cat = self.conv_cat(torch.cat((x0, x1, x2, x3), 1))x = self.relu(x_cat + self.conv_res(x))return xclass SAM2UNet(nn.Module):def __init__(self, checkpoint_path=None) -> None:super(SAM2UNet, self).__init__()    model_cfg = "sam2_hiera_l.yaml"if checkpoint_path:print('Useing pretrained sam2 model {}'.format(checkpoint_path))model = build_sam2(model_cfg, checkpoint_path)else:model = build_sam2(model_cfg)del model.sam_mask_decoderdel model.sam_prompt_encoderdel model.memory_encoderdel model.memory_attentiondel model.mask_downsampledel model.obj_ptr_tpos_projdel model.obj_ptr_projdel model.image_encoder.neckself.encoder = model.image_encoder.trunkfor param in self.encoder.parameters():param.requires_grad = Falseblocks = []for block in self.encoder.blocks:blocks.append(Adapter(block))self.encoder.blocks = nn.Sequential(*blocks)self.rfb1 = RFB_modified(144, 64)self.rfb2 = RFB_modified(288, 64)self.rfb3 = RFB_modified(576, 64)self.rfb4 = RFB_modified(1152, 64)self.up1 = (Up(128, 64))self.up2 = (Up(128, 64))self.up3 = (Up(128, 64))self.up4 = (Up(128, 64))self.side1 = nn.Conv2d(64, 1, kernel_size=1)self.side2 = nn.Conv2d(64, 1, kernel_size=1)self.head = nn.Conv2d(64, 1, kernel_size=1)def forward(self, x):x1, x2, x3, x4 = self.encoder(x)x1, x2, x3, x4 = self.rfb1(x1), self.rfb2(x2), self.rfb3(x3), self.rfb4(x4)x = self.up1(x4, x3)out1 = F.interpolate(self.side1(x), scale_factor=16, mode='bilinear')x = self.up2(x, x2)out2 = F.interpolate(self.side2(x), scale_factor=8, mode='bilinear')x = self.up3(x, x1)out = F.interpolate(self.head(x), scale_factor=4, mode='bilinear')return out, out1, out2if __name__ == "__main__":with torch.no_grad():model = SAM2UNet().cuda()x = torch.randn(1, 3, 352, 352).cuda()out, out1, out2 = model(x)print(out.shape, out1.shape, out2.shape)

/seg_eval.py

import os
import cv2
import py_sod_metrics
import argparseFM = py_sod_metrics.Fmeasure()
WFM = py_sod_metrics.WeightedFmeasure()
SM = py_sod_metrics.Smeasure()
EM = py_sod_metrics.Emeasure()
MAE = py_sod_metrics.MAE()
MSIOU = py_sod_metrics.MSIoU()parser = argparse.ArgumentParser()
parser.add_argument("--dataset_name", type=str, required=True, help="path to the prediction results")
parser.add_argument("--pred_path", type=str, required=True, help="path to the prediction results")
parser.add_argument("--gt_path", type=str, required=True,help="path to the ground truth masks")
args = parser.parse_args()sample_gray = dict(with_adaptive=True, with_dynamic=True)
sample_bin = dict(with_adaptive=False, with_dynamic=False, with_binary=True, sample_based=True)
overall_bin = dict(with_adaptive=False, with_dynamic=False, with_binary=True, sample_based=False)
FMv2 = py_sod_metrics.FmeasureV2(metric_handlers={"fm": py_sod_metrics.FmeasureHandler(**sample_gray, beta=0.3),"f1": py_sod_metrics.FmeasureHandler(**sample_gray, beta=1),"pre": py_sod_metrics.PrecisionHandler(**sample_gray),"rec": py_sod_metrics.RecallHandler(**sample_gray),"fpr": py_sod_metrics.FPRHandler(**sample_gray),"iou": py_sod_metrics.IOUHandler(**sample_gray),"dice": py_sod_metrics.DICEHandler(**sample_gray),"spec": py_sod_metrics.SpecificityHandler(**sample_gray),"ber": py_sod_metrics.BERHandler(**sample_gray),"oa": py_sod_metrics.OverallAccuracyHandler(**sample_gray),"kappa": py_sod_metrics.KappaHandler(**sample_gray),"sample_bifm": py_sod_metrics.FmeasureHandler(**sample_bin, beta=0.3),"sample_bif1": py_sod_metrics.FmeasureHandler(**sample_bin, beta=1),"sample_bipre": py_sod_metrics.PrecisionHandler(**sample_bin),"sample_birec": py_sod_metrics.RecallHandler(**sample_bin),"sample_bifpr": py_sod_metrics.FPRHandler(**sample_bin),"sample_biiou": py_sod_metrics.IOUHandler(**sample_bin),"sample_bidice": py_sod_metrics.DICEHandler(**sample_bin),"sample_bispec": py_sod_metrics.SpecificityHandler(**sample_bin),"sample_biber": py_sod_metrics.BERHandler(**sample_bin),"sample_bioa": py_sod_metrics.OverallAccuracyHandler(**sample_bin),"sample_bikappa": py_sod_metrics.KappaHandler(**sample_bin),"overall_bifm": py_sod_metrics.FmeasureHandler(**overall_bin, beta=0.3),"overall_bif1": py_sod_metrics.FmeasureHandler(**overall_bin, beta=1),"overall_bipre": py_sod_metrics.PrecisionHandler(**overall_bin),"overall_birec": py_sod_metrics.RecallHandler(**overall_bin),"overall_bifpr": py_sod_metrics.FPRHandler(**overall_bin),"overall_biiou": py_sod_metrics.IOUHandler(**overall_bin),"overall_bidice": py_sod_metrics.DICEHandler(**overall_bin),"overall_bispec": py_sod_metrics.SpecificityHandler(**overall_bin),"overall_biber": py_sod_metrics.BERHandler(**overall_bin),"overall_bioa": py_sod_metrics.OverallAccuracyHandler(**overall_bin),"overall_bikappa": py_sod_metrics.KappaHandler(**overall_bin),}
)pred_root = args.pred_path
mask_root = args.gt_path
mask_name_list = sorted(os.listdir(mask_root))
for i, mask_name in enumerate(mask_name_list):print(f"[{i}] Processing {mask_name}...")mask_path = os.path.join(mask_root, mask_name)pred_path = os.path.join(pred_root, mask_name[:-4] + '.png')mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)pred = cv2.imread(pred_path, cv2.IMREAD_GRAYSCALE)FM.step(pred=pred, gt=mask)WFM.step(pred=pred, gt=mask)SM.step(pred=pred, gt=mask)EM.step(pred=pred, gt=mask)MAE.step(pred=pred, gt=mask)FMv2.step(pred=pred, gt=mask)fm = FM.get_results()["fm"]
wfm = WFM.get_results()["wfm"]
sm = SM.get_results()["sm"]
em = EM.get_results()["em"]
mae = MAE.get_results()["mae"]
fmv2 = FMv2.get_results()curr_results = {"meandice": fmv2["dice"]["dynamic"].mean(),"meaniou": fmv2["iou"]["dynamic"].mean(),'Smeasure': sm,"wFmeasure": wfm,  # For Marine Animal Segmentation"adpFm": fm["adp"], # For Camouflaged Object Detection"meanEm": em["curve"].mean(),"MAE": mae,
}print(args.dataset_name)
print("mDice:       ", format(curr_results['meandice'], '.3f'))
print("mIoU:        ", format(curr_results['meaniou'], '.3f'))
print("S_{alpha}:   ", format(curr_results['Smeasure'], '.3f'))
print("F^{w}_{beta}:", format(curr_results['wFmeasure'], '.3f'))
print("F_{beta}:    ", format(curr_results['adpFm'], '.3f'))
print("E_{phi}:     ", format(curr_results['meanEm'], '.3f'))
print("MAE:         ", format(curr_results['MAE'], '.3f'))

/seg_test.py

import argparse
import os
import torch
import imageio
import numpy as np
import torch.nn.functional as F
from SAM2UNet import SAM2UNet
from dataset import TestDatasetparser = argparse.ArgumentParser()
parser.add_argument("--checkpoint", type=str, required=True,help="path to the checkpoint of sam2-unet")
parser.add_argument("--test_image_path", type=str, required=True, help="path to the image files for testing")
parser.add_argument("--test_gt_path", type=str, required=True,help="path to the mask files for testing")
parser.add_argument("--save_path", type=str, required=True,help="path to save the predicted masks")
args = parser.parse_args()device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
test_loader = TestDataset(args.test_image_path, args.test_gt_path, 352)
model = SAM2UNet().to(device)
model.load_state_dict(torch.load(args.checkpoint), strict=True)
model.eval()
model.cuda()
os.makedirs(args.save_path, exist_ok=True)
for i in range(test_loader.size):with torch.no_grad():image, gt, name = test_loader.load_data()gt = np.asarray(gt, np.float32)image = image.to(device)res, _, _ = model(image)res = torch.sigmoid(res)res = F.upsample(res, size=gt.shape, mode='bilinear', align_corners=False)res = res.sigmoid().data.cpu()res = res.numpy().squeeze()res = (res - res.min()) / (res.max() - res.min() + 1e-8)res = (res * 255).astype(np.uint8)print("Saving " + name)imageio.imsave(os.path.join(args.save_path, name[:-4] + ".png"), res)
print('====> seg task test finished !')
print('=' * 100)

/seg_train.py

import os
import argparse
import random
import numpy as np
import torch
import torch.optim as opt
import torch.nn.functional as F
import urllib.request
from torch.utils.data import DataLoader
from torch.optim.lr_scheduler import CosineAnnealingLR
from dataset import FullDataset
from SAM2UNet import SAM2UNetparser = argparse.ArgumentParser("SAM2-UNet")
parser.add_argument("--hiera_path", type=str, required=True, help="path to the sam2 pretrained hiera")
parser.add_argument("--train_image_path", type=str, required=True, help="path to the image that used to train the model")
parser.add_argument("--train_mask_path", type=str, required=True,help="path to the mask file for training")
parser.add_argument('--save_path', type=str, required=True,help="path to store the checkpoint")
parser.add_argument("--epoch", type=int, default=20, help="training epochs")
parser.add_argument("--lr", type=float, default=0.001, help="learning rate")
parser.add_argument("--batch_size", default=12, type=int)
parser.add_argument("--weight_decay", default=5e-4, type=float)
args = parser.parse_args()def structure_loss(pred, mask):weit = 1 + 5*torch.abs(F.avg_pool2d(mask, kernel_size=31, stride=1, padding=15) - mask)wbce = F.binary_cross_entropy_with_logits(pred, mask, reduce='none')wbce = (weit*wbce).sum(dim=(2, 3)) / weit.sum(dim=(2, 3))pred = torch.sigmoid(pred)inter = ((pred * mask)*weit).sum(dim=(2, 3))union = ((pred + mask)*weit).sum(dim=(2, 3))wiou = 1 - (inter + 1)/(union - inter+1)return (wbce + wiou).mean()def main(args): if not os.path.exists(args.hiera_path):urllib.request.urlretrieve('https://dl.fbaipublicfiles.com/segment_anything_2/072824/sam2_hiera_large.pt', args.hiera_path)dataset = FullDataset(args.train_image_path, args.train_mask_path, 352, mode='train')dataloader = DataLoader(dataset, batch_size=args.batch_size, shuffle=True, num_workers=8)device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")model = SAM2UNet(args.hiera_path)model.to(device)optim = opt.AdamW([{"params":model.parameters(), "initia_lr": args.lr}], lr=args.lr, weight_decay=args.weight_decay)scheduler = CosineAnnealingLR(optim, args.epoch, eta_min=1.0e-7)os.makedirs(args.save_path, exist_ok=True)for epoch in range(args.epoch):for i, batch in enumerate(dataloader):x = batch['image']target = batch['label']x = x.to(device)target = target.to(device)optim.zero_grad()pred0, pred1, pred2 = model(x)loss0 = structure_loss(pred0, target)loss1 = structure_loss(pred1, target)loss2 = structure_loss(pred2, target)loss = loss0 + loss1 + loss2loss.backward()optim.step()if i % 50 == 0:print("epoch:{}-{}: loss:{}".format(epoch + 1, i + 1, loss.item()))scheduler.step()if (epoch+1) % 10 == 0 or (epoch+1) == args.epoch:torch.save(model.state_dict(), os.path.join(args.save_path, 'SAM2-UNet-%d.pth' % (epoch + 1)))print('[Saving Snapshot:]', os.path.join(args.save_path, 'SAM2-UNet-%d.pth'% (epoch + 1)))print('====> seg task train finished!')print('=' * 100)if __name__ == "__main__":main(args)