├── LICENSE ├── README.md ├── config.py ├── data ├── hf_round1_testA │ └── testA │ │ └── 1.txt └── hf_round1_train │ └── train │ └── 2.txt ├── data_process.py ├── dataset.py ├── main.py ├── models ├── __init__.py └── resnet.py └── utils.py /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. Definitions. 8 | 9 | "License" shall mean the terms and conditions for use, reproduction, 10 | and distribution as defined by Sections 1 through 9 of this document. 11 | 12 | "Licensor" shall mean the copyright owner or entity authorized by 13 | the copyright owner that is granting the License. 14 | 15 | "Legal Entity" shall mean the union of the acting entity and all 16 | other entities that control, are controlled by, or are under common 17 | control with that entity. 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-------------------------------------------------------------------------------- 1 | 来源：["合肥高新杯"心电人机智能大赛](https://tianchi.aliyun.com/competition/entrance/231754/introduction) 2 | 3 | **A版单模型20190916在线F1-Score=0.801** 4 | 5 | **B版单模型20191010在线F1-Score=0.837** 6 | 7 | 系统环境：*centos7 python3.6 pytorch1.0* 8 | 9 | **大致思路：每个导联作为一个通道，使用1维卷积进行训练** 10 | 11 | # 数据预处理 12 | 数据解压放在data目录下，使用8个导联的数据，简单进行train_val数据集划分 13 | ```shell 14 | python data_process.py 15 | ``` 16 | 17 | # 模型训练 18 | ```shell 19 | python main.py train #从零开始训练 20 | ``` 21 | 22 | # 模型测试 23 | 模型测试，在submit文件夹下生成提交结果 24 | ```shell 25 | python main.py test --ckpt=..model_path #加载预训练权重进行测试 26 | ``` 27 | 28 | **一些细节** 29 | 30 | 1. 本次测试模型为1dconv_resnet34，直接修改于torchvision 31 | 2. 训练数据只进行了简单的数据增强，最终无normalize 32 | 3. 由于设备问题，数据进行了重采样，推荐使用原数据 33 | 34 | 35 | 参考论文： 36 | https://www.nature.com/articles/s41591-018-0268-3 37 | -------------------------------------------------------------------------------- /config.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | ''' 3 | @time: 2019/9/8 18:45 4 | 5 | @ author: javis 6 | ''' 7 | import os 8 | 9 | 10 | class Config: 11 | # for data_process.py 12 | #root = r'D:\ECG' 13 | root = r'data' 14 | train_dir = os.path.join(root, 'hf_round1_train/train') 15 | test_dir = os.path.join(root, 'hf_round1_testA/testA') 16 | train_label = os.path.join(root, 'hf_round1_label.txt') 17 | test_label = os.path.join(root, 'hf_round1_subA.txt') 18 | arrythmia = os.path.join(root, 'hf_round1_arrythmia.txt') 19 | train_data = os.path.join(root, 'train.pth') 20 | 21 | # for train 22 | #训练的模型名称 23 | model_name = 'resnet34' 24 | #在第几个epoch进行到下一个state,调整lr 25 | stage_epoch = [32,64,128] 26 | #训练时的batch大小 27 | batch_size = 64 28 | #label的类别数 29 | num_classes = 55 30 | #最大训练多少个epoch 31 | max_epoch = 256 32 | #目标的采样长度 33 | target_point_num = 2048 34 | #保存模型的文件夹 35 | ckpt = 'ckpt' 36 | #保存提交文件的文件夹 37 | sub_dir = 'submit' 38 | #初始的学习率 39 | lr = 1e-3 40 | #保存模型当前epoch的权重 41 | current_w = 'current_w.pth' 42 | #保存最佳的权重 43 | best_w = 'best_w.pth' 44 | # 学习率衰减 lr/=lr_decay 45 | lr_decay = 10 46 | 47 | #for test 48 | temp_dir=os.path.join(root,'temp') 49 | 50 | 51 | config = Config() 52 | -------------------------------------------------------------------------------- /data/hf_round1_train/train/2.txt: -------------------------------------------------------------------------------- 1 | I II V1 V2 V3 V4 V5 V6 2 | -5 1 9 4 6 5 3 -3 3 | -4 1 9 5 7 6 4 -3 4 | -3 1 9 4 6 6 3 -3 5 | -3 2 8 5 6 6 3 -3 6 | -3 3 8 5 7 6 4 -2 7 | -3 4 6 5 7 7 3 -2 8 | -3 3 6 5 7 8 4 0 9 | -3 1 6 4 6 7 3 -2 10 | -3 0 7 6 8 8 3 -2 11 | -2 2 9 7 9 9 4 0 12 | 0 3 10 7 9 9 5 0 13 | -3 1 9 6 7 8 4 -3 14 | -5 0 7 5 7 7 3 -3 15 | -4 2 6 6 7 8 4 -2 16 | -3 3 7 7 9 10 5 1 17 | -3 3 7 6 10 10 5 0 18 | -3 4 6 6 9 9 4 0 19 | -2 5 7 7 9 10 5 1 20 | -2 5 7 8 10 11 5 1 21 | -2 4 8 7 10 11 4 0 22 | -3 2 9 8 11 11 4 0 23 | -3 2 8 8 11 11 5 0 24 | -3 3 8 8 11 10 5 0 25 | -2 3 6 7 10 9 4 -2 26 | -2 3 6 7 10 9 4 -2 27 | -2 4 6 7 10 9 4 0 28 | -3 4 6 7 10 9 4 0 29 | -3 3 7 9 11 10 5 2 30 | -3 3 7 9 11 10 5 1 31 | -4 2 7 8 10 10 5 0 32 | -4 1 8 9 11 11 6 0 33 | -4 0 7 8 11 10 5 -2 34 | -3 0 6 8 11 10 4 -2 35 | -3 1 6 7 11 9 4 -3 36 | -3 2 6 7 11 11 5 0 37 | -3 2 6 7 10 10 5 0 38 | -4 2 6 6 9 9 4 -2 39 | -2 2 6 5 9 10 4 0 40 | 0 3 5 5 8 10 4 -2 41 | -3 2 5 5 9 10 4 -3 42 | -4 1 5 5 9 10 4 -3 43 | -3 0 6 5 9 9 4 -3 44 | -2 1 6 4 9 9 4 -3 45 | -2 2 5 3 8 8 4 -3 46 | -4 2 4 2 6 7 3 -4 47 | -5 1 4 3 7 8 4 -3 48 | -5 2 4 3 7 8 3 -2 49 | -4 3 4 2 7 8 2 -3 50 | -3 3 5 3 7 7 2 -2 51 | -3 2 5 2 7 7 2 -3 52 | -4 1 5 3 7 8 3 -2 53 | -4 0 5 2 6 7 2 -4 54 | -4 -2 4 0 5 6 1 -5 55 | -2 -2 4 -2 5 5 0 -5 56 | 1 0 3 0 5 6 0 -4 57 | 0 1 3 -2 4 5 0 -5 58 | -4 -2 3 -3 2 3 -2 -6 59 | -4 -2 2 -3 2 3 -2 -4 60 | -3 0 4 -2 4 5 0 -3 61 | -4 0 5 -3 3 5 0 -4 62 | -5 -2 5 -3 2 4 0 -5 63 | -5 -2 5 -2 3 4 0 -5 64 | -3 -2 3 -3 2 4 0 -5 65 | 0 -2 1 -4 1 3 -2 -6 66 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-------------------------------------------------------------------------------- /data_process.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | ''' 3 | @time: 2019/9/8 18:44 4 | 数据预处理： 5 | 1.构建label2index和index2label 6 | 2.划分数据集 7 | @ author: javis 8 | ''' 9 | import os, torch 10 | import numpy as np 11 | from config import config 12 | 13 | # 保证每次划分数据一致 14 | np.random.seed(41) 15 | 16 | 17 | def name2index(path): 18 | ''' 19 | 把类别名称转换为index索引 20 | :param path: 文件路径 21 | :return: 字典 22 | ''' 23 | list_name = [] 24 | for line in open(path, encoding='utf-8'): 25 | list_name.append(line.strip()) 26 | name2indx = {name: i for i, name in enumerate(list_name)} 27 | return name2indx 28 | 29 | 30 | def split_data(file2idx, val_ratio=0.1): 31 | ''' 32 | 划分数据集,val需保证每类至少有1个样本 33 | :param file2idx: 34 | :param val_ratio:验证集占总数据的比例 35 | :return:训练集，验证集路径 36 | ''' 37 | data = set(os.listdir(config.train_dir)) 38 | val = set() 39 | idx2file = [[] for _ in range(config.num_classes)] 40 | for file, list_idx in file2idx.items(): 41 | for idx in list_idx: 42 | idx2file[idx].append(file) 43 | for item in idx2file: 44 | # print(len(item), item) 45 | num = int(len(item) * val_ratio) 46 | val = val.union(item[:num]) 47 | train = data.difference(val) 48 | return list(train), list(val) 49 | 50 | 51 | def file2index(path, name2idx): 52 | ''' 53 | 获取文件id对应的标签类别 54 | :param path:文件路径 55 | :return:文件id对应label列表的字段 56 | ''' 57 | file2index = dict() 58 | for line in open(path, encoding='utf-8'): 59 | arr = line.strip().split('\t') 60 | id = arr[0] 61 | labels = [name2idx[name] for name in arr[3:]] 62 | # print(id, labels) 63 | file2index[id] = labels 64 | return file2index 65 | 66 | 67 | def count_labels(data, file2idx): 68 | ''' 69 | 统计每个类别的样本数 70 | :param data: 71 | :param file2idx: 72 | :return: 73 | ''' 74 | cc = [0] * config.num_classes 75 | for fp in data: 76 | for i in file2idx[fp]: 77 | cc[i] += 1 78 | return np.array(cc) 79 | 80 | 81 | def train(name2idx, idx2name): 82 | file2idx = file2index(config.train_label, name2idx) 83 | train, val = split_data(file2idx) 84 | wc=count_labels(train,file2idx) 85 | print(wc) 86 | dd = {'train': train, 'val': val, "idx2name": idx2name, 'file2idx': file2idx,'wc':wc} 87 | torch.save(dd, config.train_data) 88 | 89 | 90 | if __name__ == '__main__': 91 | pass 92 | name2idx = name2index(config.arrythmia) 93 | idx2name = {idx: name for name, idx in name2idx.items()} 94 | train(name2idx, idx2name) 95 | -------------------------------------------------------------------------------- /dataset.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | ''' 3 | @time: 2019/9/8 19:47 4 | 5 | @ author: javis 6 | ''' 7 | import pywt, os, copy 8 | import torch 9 | import numpy as np 10 | import pandas as pd 11 | from config import config 12 | from torch.utils.data import Dataset 13 | from sklearn.preprocessing import scale 14 | from scipy import signal 15 | 16 | 17 | def resample(sig, target_point_num=None): 18 | ''' 19 | 对原始信号进行重采样 20 | :param sig: 原始信号 21 | :param target_point_num:目标型号点数 22 | :return: 重采样的信号 23 | ''' 24 | sig = signal.resample(sig, target_point_num) if target_point_num else sig 25 | return sig 26 | 27 | def scaling(X, sigma=0.1): 28 | scalingFactor = np.random.normal(loc=1.0, scale=sigma, size=(1, X.shape[1])) 29 | myNoise = np.matmul(np.ones((X.shape[0], 1)), scalingFactor) 30 | return X * myNoise 31 | 32 | def verflip(sig): 33 | ''' 34 | 信号竖直翻转 35 | :param sig: 36 | :return: 37 | ''' 38 | return sig[::-1, :] 39 | 40 | def shift(sig, interval=20): 41 | ''' 42 | 上下平移 43 | :param sig: 44 | :return: 45 | ''' 46 | for col in range(sig.shape[1]): 47 | offset = np.random.choice(range(-interval, interval)) 48 | sig[:, col] += offset 49 | return sig 50 | 51 | 52 | def transform(sig, train=False): 53 | # 前置不可或缺的步骤 54 | sig = resample(sig, config.target_point_num) 55 | # # 数据增强 56 | if train: 57 | if np.random.randn() > 0.5: sig = scaling(sig) 58 | if np.random.randn() > 0.5: sig = verflip(sig) 59 | if np.random.randn() > 0.5: sig = shift(sig) 60 | # 后置不可或缺的步骤 61 | sig = sig.transpose() 62 | sig = torch.tensor(sig.copy(), dtype=torch.float) 63 | return sig 64 | 65 | 66 | class ECGDataset(Dataset): 67 | """ 68 | A generic data loader where the samples are arranged in this way: 69 | dd = {'train': train, 'val': val, "idx2name": idx2name, 'file2idx': file2idx} 70 | """ 71 | 72 | def __init__(self, data_path, train=True): 73 | super(ECGDataset, self).__init__() 74 | dd = torch.load(config.train_data) 75 | self.train = train 76 | self.data = dd['train'] if train else dd['val'] 77 | self.idx2name = dd['idx2name'] 78 | self.file2idx = dd['file2idx'] 79 | self.wc = 1. / np.log(dd['wc']) 80 | 81 | def __getitem__(self, index): 82 | fid = self.data[index] 83 | file_path = os.path.join(config.train_dir, fid) 84 | df = pd.read_csv(file_path, sep=' ').values 85 | x = transform(df, self.train) 86 | target = np.zeros(config.num_classes) 87 | target[self.file2idx[fid]] = 1 88 | target = torch.tensor(target, dtype=torch.float32) 89 | return x, target 90 | 91 | def __len__(self): 92 | return len(self.data) 93 | 94 | 95 | if __name__ == '__main__': 96 | d = ECGDataset(config.train_data) 97 | print(d[0]) 98 | -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | ''' 3 | @time: 2019/7/23 19:42 4 | 5 | @ author: javis 6 | ''' 7 | import torch, time, os, shutil 8 | import models, utils 9 | import numpy as np 10 | import pandas as pd 11 | from tensorboard_logger import Logger 12 | from torch import nn, optim 13 | from torch.utils.data import DataLoader 14 | from dataset import ECGDataset 15 | from config import config 16 | 17 | device = torch.device("cuda" if torch.cuda.is_available() else "cpu") 18 | 19 | torch.manual_seed(41) 20 | torch.cuda.manual_seed(41) 21 | 22 | 23 | # 保存当前模型的权重，并且更新最佳的模型权重 24 | def save_ckpt(state, is_best, model_save_dir): 25 | current_w = os.path.join(model_save_dir, config.current_w) 26 | best_w = os.path.join(model_save_dir, config.best_w) 27 | torch.save(state, current_w) 28 | if is_best: shutil.copyfile(current_w, best_w) 29 | 30 | 31 | def train_epoch(model, optimizer, criterion, train_dataloader, show_interval=10): 32 | model.train() 33 | f1_meter, loss_meter, it_count = 0, 0, 0 34 | for inputs, target in train_dataloader: 35 | inputs = inputs.to(device) 36 | target = target.to(device) 37 | # zero the parameter gradients 38 | optimizer.zero_grad() 39 | # forward 40 | output = model(inputs) 41 | loss = criterion(output, target) 42 | loss.backward() 43 | optimizer.step() 44 | loss_meter += loss.item() 45 | it_count += 1 46 | f1 = utils.calc_f1(target, torch.sigmoid(output)) 47 | f1_meter += f1 48 | if it_count != 0 and it_count % show_interval == 0: 49 | print("%d,loss:%.3e f1:%.3f" % (it_count, loss.item(), f1)) 50 | return loss_meter / it_count, f1_meter / it_count 51 | 52 | 53 | def val_epoch(model, criterion, val_dataloader, threshold=0.5): 54 | model.eval() 55 | f1_meter, loss_meter, it_count = 0, 0, 0 56 | with torch.no_grad(): 57 | for inputs, target in val_dataloader: 58 | inputs = inputs.to(device) 59 | target = target.to(device) 60 | output = model(inputs) 61 | loss = criterion(output, target) 62 | loss_meter += loss.item() 63 | it_count += 1 64 | output = torch.sigmoid(output) 65 | f1 = utils.calc_f1(target, output, threshold) 66 | f1_meter += f1 67 | return loss_meter / it_count, f1_meter / it_count 68 | 69 | 70 | def train(args): 71 | # model 72 | model = getattr(models, config.model_name)() 73 | if args.ckpt and not args.resume: 74 | state = torch.load(args.ckpt, map_location='cpu') 75 | model.load_state_dict(state['state_dict']) 76 | print('train with pretrained weight val_f1', state['f1']) 77 | model = model.to(device) 78 | # data 79 | train_dataset = ECGDataset(data_path=config.train_data, train=True) 80 | train_dataloader = DataLoader(train_dataset, batch_size=config.batch_size, shuffle=True, num_workers=6) 81 | val_dataset = ECGDataset(data_path=config.train_data, train=False) 82 | val_dataloader = DataLoader(val_dataset, batch_size=config.batch_size, num_workers=4) 83 | print("train_datasize", len(train_dataset), "val_datasize", len(val_dataset)) 84 | # optimizer and loss 85 | optimizer = optim.Adam(model.parameters(), lr=config.lr) 86 | w = torch.tensor(train_dataset.wc, dtype=torch.float).to(device) 87 | criterion = utils.WeightedMultilabel(w) 88 | # 模型保存文件夹 89 | model_save_dir = '%s/%s_%s' % (config.ckpt, config.model_name, time.strftime("%Y%m%d%H%M")) 90 | if args.ex: model_save_dir += args.ex 91 | best_f1 = -1 92 | lr = config.lr 93 | start_epoch = 1 94 | stage = 1 95 | # 从上一个断点，继续训练 96 | if args.resume: 97 | if os.path.exists(args.ckpt): # 这里是存放权重的目录 98 | model_save_dir = args.ckpt 99 | current_w = torch.load(os.path.join(args.ckpt, config.current_w)) 100 | best_w = torch.load(os.path.join(model_save_dir, config.best_w)) 101 | best_f1 = best_w['loss'] 102 | start_epoch = current_w['epoch'] + 1 103 | lr = current_w['lr'] 104 | stage = current_w['stage'] 105 | model.load_state_dict(current_w['state_dict']) 106 | # 如果中断点恰好为转换stage的点 107 | if start_epoch - 1 in config.stage_epoch: 108 | stage += 1 109 | lr /= config.lr_decay 110 | utils.adjust_learning_rate(optimizer, lr) 111 | model.load_state_dict(best_w['state_dict']) 112 | print("=> loaded checkpoint (epoch {})".format(start_epoch - 1)) 113 | logger = Logger(logdir=model_save_dir, flush_secs=2) 114 | # =========>开始训练<========= 115 | for epoch in range(start_epoch, config.max_epoch + 1): 116 | since = time.time() 117 | train_loss, train_f1 = train_epoch(model, optimizer, criterion, train_dataloader, show_interval=100) 118 | val_loss, val_f1 = val_epoch(model, criterion, val_dataloader) 119 | print('#epoch:%02d stage:%d train_loss:%.3e train_f1:%.3f val_loss:%0.3e val_f1:%.3f time:%s\n' 120 | % (epoch, stage, train_loss, train_f1, val_loss, val_f1, utils.print_time_cost(since))) 121 | logger.log_value('train_loss', train_loss, step=epoch) 122 | logger.log_value('train_f1', train_f1, step=epoch) 123 | logger.log_value('val_loss', val_loss, step=epoch) 124 | logger.log_value('val_f1', val_f1, step=epoch) 125 | state = {"state_dict": model.state_dict(), "epoch": epoch, "loss": val_loss, 'f1': val_f1, 'lr': lr, 126 | 'stage': stage} 127 | save_ckpt(state, best_f1 < val_f1, model_save_dir) 128 | best_f1 = max(best_f1, val_f1) 129 | if epoch in config.stage_epoch: 130 | stage += 1 131 | lr /= config.lr_decay 132 | best_w = os.path.join(model_save_dir, config.best_w) 133 | model.load_state_dict(torch.load(best_w)['state_dict']) 134 | print("*" * 10, "step into stage%02d lr %.3ef" % (stage, lr)) 135 | utils.adjust_learning_rate(optimizer, lr) 136 | 137 | #用于测试加载模型 138 | def val(args): 139 | list_threhold = [0.5] 140 | model = getattr(models, config.model_name)() 141 | if args.ckpt: model.load_state_dict(torch.load(args.ckpt, map_location='cpu')['state_dict']) 142 | model = model.to(device) 143 | criterion = nn.BCEWithLogitsLoss() 144 | val_dataset = ECGDataset(data_path=config.train_data, train=False) 145 | val_dataloader = DataLoader(val_dataset, batch_size=config.batch_size, num_workers=4) 146 | for threshold in list_threhold: 147 | val_loss, val_f1 = val_epoch(model, criterion, val_dataloader, threshold) 148 | print('threshold %.2f val_loss:%0.3e val_f1:%.3f\n' % (threshold, val_loss, val_f1)) 149 | 150 | #提交结果使用 151 | def test(args): 152 | from dataset import transform 153 | from data_process import name2index 154 | name2idx = name2index(config.arrythmia) 155 | idx2name = {idx: name for name, idx in name2idx.items()} 156 | utils.mkdirs(config.sub_dir) 157 | # model 158 | model = getattr(models, config.model_name)() 159 | model.load_state_dict(torch.load(args.ckpt, map_location='cpu')['state_dict']) 160 | model = model.to(device) 161 | model.eval() 162 | sub_file = '%s/subA_%s.txt' % (config.sub_dir, time.strftime("%Y%m%d%H%M")) 163 | fout = open(sub_file, 'w', encoding='utf-8') 164 | with torch.no_grad(): 165 | for line in open(config.test_label, encoding='utf-8'): 166 | fout.write(line.strip('\n')) 167 | id = line.split('\t')[0] 168 | file_path = os.path.join(config.test_dir, id) 169 | df = pd.read_csv(file_path, sep=' ').values 170 | x = transform(df).unsqueeze(0).to(device) 171 | output = torch.sigmoid(model(x)).squeeze().cpu().numpy() 172 | ixs = [i for i, out in enumerate(output) if out > 0.5] 173 | for i in ixs: 174 | fout.write("\t" + idx2name[i]) 175 | fout.write('\n') 176 | fout.close() 177 | 178 | 179 | 180 | if __name__ == '__main__': 181 | 182 | import argparse 183 | 184 | parser = argparse.ArgumentParser() 185 | parser.add_argument("command", metavar="", help="train or infer") 186 | parser.add_argument("--ckpt", type=str, help="the path of model weight file") 187 | parser.add_argument("--ex", type=str, help="experience name") 188 | parser.add_argument("--resume", action='store_true', default=False) 189 | args = parser.parse_args() 190 | if (args.command == "train"): 191 | train(args) 192 | if (args.command == "test"): 193 | test(args) 194 | if (args.command == "val"): 195 | val(args) -------------------------------------------------------------------------------- /models/__init__.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | ''' 3 | @time: 2019/9/8 20:13 4 | 5 | @ author: javis 6 | ''' 7 | 8 | from .resnet import resnet34,resnet101 -------------------------------------------------------------------------------- /models/resnet.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | ''' 3 | @time: 2019/9/8 20:14 4 | 直接修改torch的resnet 5 | @ author: javis 6 | ''' 7 | 8 | import torch.nn as nn 9 | import math 10 | import torch.utils.model_zoo as model_zoo 11 | 12 | __all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 13 | 'resnet152'] 14 | 15 | model_urls = { 16 | 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', 17 | 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth', 18 | 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth', 19 | 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth', 20 | 'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed1d.pth', 21 | } 22 | 23 | 24 | def conv3x3(in_planes, out_planes, stride=1): 25 | """3x3 convolution with padding""" 26 | return nn.Conv1d(in_planes, out_planes, kernel_size=7, stride=stride, 27 | padding=3, bias=False) 28 | 29 | 30 | class BasicBlock(nn.Module): 31 | expansion = 1 32 | 33 | def __init__(self, inplanes, planes, stride=1, downsample=None): 34 | super(BasicBlock, self).__init__() 35 | self.conv1 = conv3x3(inplanes, planes, stride) 36 | self.bn1 = nn.BatchNorm1d(planes) 37 | self.relu = nn.ReLU(inplace=True) 38 | self.conv2 = conv3x3(planes, planes) 39 | self.bn2 = nn.BatchNorm1d(planes) 40 | self.downsample = downsample 41 | self.stride = stride 42 | self.dropout = nn.Dropout(.2) 43 | 44 | def forward(self, x): 45 | residual = x 46 | 47 | out = self.conv1(x) 48 | out = self.bn1(out) 49 | out = self.relu(out) 50 | out = self.dropout(out) 51 | out = self.conv2(out) 52 | out = self.bn2(out) 53 | 54 | if self.downsample is not None: 55 | residual = self.downsample(x) 56 | 57 | out += residual 58 | out = self.relu(out) 59 | 60 | return out 61 | 62 | 63 | class Bottleneck(nn.Module): 64 | expansion = 4 65 | 66 | def __init__(self, inplanes, planes, stride=1, downsample=None): 67 | super(Bottleneck, self).__init__() 68 | self.conv1 = nn.Conv1d(inplanes, planes, kernel_size=7, bias=False, padding=3) 69 | self.bn1 = nn.BatchNorm1d(planes) 70 | self.conv2 = nn.Conv1d(planes, planes, kernel_size=11, stride=stride, 71 | padding=5, bias=False) 72 | self.bn2 = nn.BatchNorm1d(planes) 73 | self.conv3 = nn.Conv1d(planes, planes * 4, kernel_size=7, bias=False, padding=3) 74 | self.bn3 = nn.BatchNorm1d(planes * 4) 75 | self.relu = nn.ReLU(inplace=True) 76 | self.downsample = downsample 77 | self.stride = stride 78 | self.dropout = nn.Dropout(.2) 79 | 80 | def forward(self, x): 81 | residual = x 82 | 83 | out = self.conv1(x) 84 | out = self.bn1(out) 85 | out = self.relu(out) 86 | 87 | out = self.conv2(out) 88 | out = self.bn2(out) 89 | out = self.relu(out) 90 | out = self.dropout(out) 91 | 92 | out = self.conv3(out) 93 | out = self.bn3(out) 94 | 95 | if self.downsample is not None: 96 | residual = self.downsample(x) 97 | 98 | out += residual 99 | out = self.relu(out) 100 | 101 | return out 102 | 103 | 104 | class ResNet(nn.Module): 105 | 106 | def __init__(self, block, layers, num_classes=55): 107 | self.inplanes = 64 108 | super(ResNet, self).__init__() 109 | self.conv1 = nn.Conv1d(8, 64, kernel_size=15, stride=2, padding=7, 110 | bias=False) 111 | self.bn1 = nn.BatchNorm1d(64) 112 | self.relu = nn.ReLU(inplace=True) 113 | self.maxpool = nn.MaxPool1d(kernel_size=3, stride=2, padding=1) 114 | self.layer1 = self._make_layer(block, 64, layers[0]) 115 | self.layer2 = self._make_layer(block, 128, layers[1], stride=2) 116 | self.layer3 = self._make_layer(block, 256, layers[2], stride=2) 117 | self.layer4 = self._make_layer(block, 512, layers[3], stride=2) 118 | self.avgpool = nn.AdaptiveAvgPool1d(1) 119 | self.fc = nn.Linear(512 * block.expansion, num_classes) 120 | 121 | for m in self.modules(): 122 | if isinstance(m, nn.Conv1d): 123 | n = m.kernel_size[0] * m.kernel_size[0] * m.out_channels 124 | m.weight.data.normal_(0, math.sqrt(2. / n)) 125 | elif isinstance(m, nn.BatchNorm1d): 126 | m.weight.data.fill_(1) 127 | m.bias.data.zero_() 128 | 129 | def _make_layer(self, block, planes, blocks, stride=1): 130 | downsample = None 131 | if stride != 1 or self.inplanes != planes * block.expansion: 132 | downsample = nn.Sequential( 133 | nn.Conv1d(self.inplanes, planes * block.expansion, 134 | kernel_size=1, stride=stride, bias=False), 135 | nn.BatchNorm1d(planes * block.expansion), 136 | ) 137 | 138 | layers = [] 139 | layers.append(block(self.inplanes, planes, stride, downsample)) 140 | self.inplanes = planes * block.expansion 141 | for i in range(1, blocks): 142 | layers.append(block(self.inplanes, planes)) 143 | 144 | return nn.Sequential(*layers) 145 | 146 | def forward(self, x): 147 | x = self.conv1(x) 148 | x = self.bn1(x) 149 | x = self.relu(x) 150 | x = self.maxpool(x) 151 | 152 | x = self.layer1(x) 153 | x = self.layer2(x) 154 | x = self.layer3(x) 155 | x = self.layer4(x) 156 | x = self.avgpool(x) 157 | x = x.view(x.size(0), -1) 158 | x = self.fc(x) 159 | 160 | return x 161 | 162 | 163 | def resnet18(pretrained=False, **kwargs): 164 | """Constructs a ResNet-18 model. 165 | 166 | Args: 167 | pretrained (bool): If True, returns a model pre-trained on ImageNet 168 | """ 169 | model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) 170 | if pretrained: 171 | model.load_state_dict(model_zoo.load_url(model_urls['resnet18'])) 172 | return model 173 | 174 | 175 | def resnet34(pretrained=False, **kwargs): 176 | """Constructs a ResNet-34 model. 177 | 178 | Args: 179 | pretrained (bool): If True, returns a model pre-trained on ImageNet 180 | """ 181 | model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs) 182 | if pretrained: 183 | model.load_state_dict(model_zoo.load_url(model_urls['resnet34'])) 184 | return model 185 | 186 | 187 | def resnet50(pretrained=False, **kwargs): 188 | """Constructs a ResNet-50 model. 189 | 190 | Args: 191 | pretrained (bool): If True, returns a model pre-trained on ImageNet 192 | """ 193 | model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs) 194 | if pretrained: 195 | model.load_state_dict(model_zoo.load_url(model_urls['resnet50'])) 196 | return model 197 | 198 | 199 | def resnet101(pretrained=False, **kwargs): 200 | """Constructs a ResNet-101 model. 201 | 202 | Args: 203 | pretrained (bool): If True, returns a model pre-trained on ImageNet 204 | """ 205 | model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs) 206 | if pretrained: 207 | model.load_state_dict(model_zoo.load_url(model_urls['resnet101'])) 208 | return model 209 | 210 | 211 | def resnet152(pretrained=False, **kwargs): 212 | """Constructs a ResNet-152 model. 213 | 214 | Args: 215 | pretrained (bool): If True, returns a model pre-trained on ImageNet 216 | """ 217 | model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs) 218 | if pretrained: 219 | model.load_state_dict(model_zoo.load_url(model_urls['resnet152'])) 220 | return model 221 | 222 | 223 | if __name__ == '__main__': 224 | import torch 225 | 226 | x = torch.randn(1, 8, 2048) 227 | m = resnet34() 228 | m(x) 229 | from torchvision.models import resnet 230 | -------------------------------------------------------------------------------- /utils.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | ''' 3 | @time: 2019/9/12 15:16 4 | 5 | @ author: javis 6 | ''' 7 | import torch 8 | import numpy as np 9 | import time,os 10 | from sklearn.metrics import f1_score 11 | from torch import nn 12 | 13 | 14 | def mkdirs(path): 15 | if not os.path.exists(path): 16 | os.makedirs(path) 17 | 18 | #计算F1score 19 | def calc_f1(y_true, y_pre, threshold=0.5): 20 | y_true = y_true.view(-1).cpu().detach().numpy().astype(np.int) 21 | y_pre = y_pre.view(-1).cpu().detach().numpy() > threshold 22 | return f1_score(y_true, y_pre) 23 | 24 | #打印时间 25 | def print_time_cost(since): 26 | time_elapsed = time.time() - since 27 | return '{:.0f}m{:.0f}s\n'.format(time_elapsed // 60, time_elapsed % 60) 28 | 29 | 30 | # 调整学习率 31 | def adjust_learning_rate(optimizer, lr): 32 | for param_group in optimizer.param_groups: 33 | param_group['lr'] = lr 34 | return lr 35 | 36 | #多标签使用类别权重 37 | class WeightedMultilabel(nn.Module): 38 | def __init__(self, weights: torch.Tensor): 39 | super(WeightedMultilabel, self).__init__() 40 | self.cerition = nn.BCEWithLogitsLoss(reduction='none') 41 | self.weights = weights 42 | 43 | def forward(self, outputs, targets): 44 | loss = self.cerition(outputs, targets) 45 | return (loss * self.weights).mean() 46 | --------------------------------------------------------------------------------