From 010c971e0e5faa365529dd3a5a26b4b02ede89b1 Mon Sep 17 00:00:00 2001 From: XiangqianMa Date: Tue, 15 Oct 2019 22:45:39 +0800 Subject: [PATCH] back to the milstone --- README.md | 6 +- choose_thre_area.py | 84 +++++++++++----------- config.py | 24 +++---- create_submission.py | 16 ++--- datasets/steel_dataset.py | 8 +-- models/EfficientNet-PyTorch.tar.gz | Bin 28633 -> 0 bytes models/model.py | 14 ++-- models/segmentation_models.pytorch.tar.gz | Bin 18695 -> 1320810 bytes run.sh | 2 +- train_classify.py | 15 ++-- train_segment.py | 37 ++++------ utils/cal_dice_iou.py | 24 +++---- utils/data_augmentation.py | 2 +- utils/easy_stopping.py | 51 ------------- utils/loss.py | 75 ++++++++++--------- 15 files changed, 144 insertions(+), 214 deletions(-) delete mode 100644 models/EfficientNet-PyTorch.tar.gz delete mode 100644 utils/easy_stopping.py diff --git a/README.md b/README.md index 22f9822..ea094fa 100644 --- a/README.md +++ b/README.md @@ -81,9 +81,7 @@ When run `SUBMISSION=/path/to/csv/file.csv make release-csv`, If you encounter t - [x] finish choose_threshold - [x] finish data augmentation - [ ] EfficientB4( w/ ASPP) -- [x] ResNet50 -- [x] code review(validation dice, threshold dice) +- [ ] code review(validation dice, threshold dice) - [ ] choose fold - [ ] ensemble -- [x] early stopping automaticly -- [ ] GN \ No newline at end of file +- [ ] early stopping automaticly \ No newline at end of file diff --git a/choose_thre_area.py b/choose_thre_area.py index 111d449..4674a88 100644 --- a/choose_thre_area.py +++ b/choose_thre_area.py @@ -19,9 +19,9 @@ class ChooseThresholdMinArea(): ''' 选择每一类的像素阈值和最小连通域 ''' - def __init__(self, model, model_name, valid_loader, fold, save_path, class_num=4): ''' 模型初始化 + Args: model: 使用的模型 model_name: 当前模型的名称 @@ -42,7 +42,7 @@ def __init__(self, model, model_name, valid_loader, fold, save_path, class_num=4 def choose_threshold_minarea(self): ''' 采用网格法搜索各个类别最优像素阈值和最优最小连通域,并画出各个类别搜索过程中的热力图 - + Return: best_thresholds_little: 每一个类别的最优阈值 best_minareas_little: 每一个类别的最优最小连通取余 @@ -51,36 +51,28 @@ def choose_threshold_minarea(self): init_thresholds_range, init_minarea_range = np.arange(0.50, 0.71, 0.03), np.arange(768, 2305, 256) # 阈值列表和最小连通域列表,大小为 Nx4 - thresholds_table_big = np.array([init_thresholds_range, init_thresholds_range, + thresholds_table_big = np.array([init_thresholds_range, init_thresholds_range, \ init_thresholds_range, init_thresholds_range]) # 阈值列表 - minareas_table_big = np.array([init_minarea_range, init_minarea_range, + minareas_table_big = np.array([init_minarea_range, init_minarea_range, \ init_minarea_range, init_minarea_range]) # 最小连通域列表 f, axes = plt.subplots(figsize=(28.8, 18.4), nrows=2, ncols=self.class_num) cmap = sns.cubehelix_palette(start=1.5, rot=3, gamma=0.8, as_cmap=True) - best_thresholds_big, best_minareas_big, max_dices_big = self.grid_search(thresholds_table_big, - minareas_table_big, axes[0, :], cmap) - print('best_thresholds_big:{}, best_minareas_big:{}, max_dices_big:{}'.format(best_thresholds_big, - best_minareas_big, max_dices_big)) + best_thresholds_big, best_minareas_big, max_dices_big = self.grid_search(thresholds_table_big, minareas_table_big, axes[0,:], cmap) + print('best_thresholds_big:{}, best_minareas_big:{}, max_dices_big:{}'.format(best_thresholds_big, best_minareas_big, max_dices_big)) # 开始细分类 thresholds_table_little, minareas_table_little = list(), list() for best_threshold_big, best_minarea_big in zip(best_thresholds_big, best_minareas_big): - thresholds_table_little.append( - np.arange(best_threshold_big - 0.03, best_threshold_big + 0.03, 0.015)) # 阈值列表 - minareas_table_little.append(np.arange(best_minarea_big - 256, best_minarea_big + 257, 128)) # 像素阈值列表 - thresholds_table_little, minareas_table_little = np.array(thresholds_table_little), np.array( - minareas_table_little) - - best_thresholds_little, best_minareas_little, max_dices_little = self.grid_search(thresholds_table_little, - minareas_table_little, - axes[1, :], cmap) - print('best_thresholds_little:{}, best_minareas_little:{}, max_dices_little:{}'.format(best_thresholds_little, - best_minareas_little, - max_dices_little)) - - f.savefig(os.path.join(self.save_path, self.model_name + '_fold' + str(self.fold)), bbox_inches='tight') + thresholds_table_little.append(np.arange(best_threshold_big-0.03, best_threshold_big+0.03, 0.015)) # 阈值列表 + minareas_table_little.append(np.arange(best_minarea_big-256, best_minarea_big+257, 128)) # 像素阈值列表 + thresholds_table_little, minareas_table_little = np.array(thresholds_table_little), np.array(minareas_table_little) + + best_thresholds_little, best_minareas_little, max_dices_little = self.grid_search(thresholds_table_little, minareas_table_little, axes[1,:], cmap) + print('best_thresholds_little:{}, best_minareas_little:{}, max_dices_little:{}'.format(best_thresholds_little, best_minareas_little, max_dices_little)) + + f.savefig(os.path.join(self.save_path, self.model_name + '_fold'+str(self.fold)), bbox_inches='tight') # plt.show() plt.close() @@ -89,11 +81,13 @@ def choose_threshold_minarea(self): def grid_search(self, thresholds_table, minareas_table, axes, cmap): ''' 给定包含各个类别搜索区间的thresholds_table和minareas_table,求的各个类别的最优像素阈值,最优最小连通域,最高dice; 并画出各个类别搜索过程中的热力图 + Args: thresholds_table: 待搜索的阈值范围,维度为[4, N],numpy类型 minareas_table: 待搜索的最小连通域范围,维度为[4, N],numpy类型 axes: 画各个类别搜索热力图时所需要的画柄,尺寸为[class_num] cmap: 画图时所需要的cmap + return: best_thresholds: 各个类别的最优像素阈值,尺寸为[class_num] best_minareas: 各个类别的最优最小连通域,尺寸为[class_num] @@ -102,12 +96,15 @@ def grid_search(self, thresholds_table, minareas_table, axes, cmap): dices_table = np.zeros((self.class_num, np.shape(thresholds_table)[1], np.shape(minareas_table)[1])) tbar = tqdm.tqdm(self.valid_loader) with torch.no_grad(): - for i, (images, masks) in enumerate(tbar): + for i, samples in enumerate(tbar): + if len(samples) == 0: + continue + images, masks = samples[0], samples[1] # 完成网络的前向传播 masks_predict_allclasses = self.solver.forward(images) dices_table += self.grid_search_batch(thresholds_table, minareas_table, masks_predict_allclasses, masks) - dices_table = dices_table / len(tbar) + dices_table = dices_table/len(tbar) best_thresholds, best_minareas, max_dices = list(), list(), list() # 处理每一类的预测结果 for each_class, dices_oneclass_table in enumerate(dices_table): @@ -118,21 +115,21 @@ def grid_search(self, thresholds_table, minareas_table, axes, cmap): best_minareas.append(minareas_table[each_class, max_location[1]]) max_dices.append(max_dice) - data = pd.DataFrame(data=dices_oneclass_table, index=np.around(thresholds_table[each_class, :], 3), - columns=minareas_table[each_class, :]) - sns.heatmap(data, linewidths=0.05, ax=axes[each_class], vmax=np.max(dices_oneclass_table), - vmin=np.min(dices_oneclass_table), cmap=cmap, + data = pd.DataFrame(data=dices_oneclass_table, index=np.around(thresholds_table[each_class,:], 3), columns=minareas_table[each_class,:]) + sns.heatmap(data, linewidths=0.05, ax=axes[each_class], vmax=np.max(dices_oneclass_table), vmin=np.min(dices_oneclass_table), cmap=cmap, annot=True, fmt='.4f') axes[each_class].set_title('search result') return best_thresholds, best_minareas, max_dices def grid_search_batch(self, thresholds_table, minareas_table, masks_predict_allclasses, masks_allclasses): '''给定thresholds、minareas矩阵、一个batch的预测结果和真实标签,遍历每个类的每一个组合得到对应的dice值 + Args: thresholds_table: 待搜索的阈值范围,维度为[4, N],numpy类型 minareas_table: 待搜索的最小连通域范围,维度为[4, N],numpy类型 masks_predict_allclasses: 所有类别的某个batch的预测结果且未经过sigmoid,维度为[batch_size, class_num, height, width] masks_allclasses: 所有类别的某个batch的真实类标,维度为[batch_size, class_num, height, width] + Return: dices_table: 各个类别在其各自的所有搜索组合中所得到的dice值,维度为[4, M, N] ''' @@ -151,13 +148,13 @@ def grid_search_batch(self, thresholds_table, minareas_table, masks_predict_allc def post_process(self, thresholds_range, minareas_range, masks_predict_oneclass, masks_oneclasses): '''给定某个类别的某个batch的数据,遍历所有搜索组合,得到每个组合的dice值 - + Args: thresholds_range: 具体某个类别的像素阈值搜索区间,尺寸为[M] minareas_range: 具体某个类别的最小连通域搜索区间,尺寸为[N] masks_predict_oneclass: 预测出的某个类别的该batch的tensor向量且未经过sigmoid,维度为[batch_size, height, width] masks_oneclasses: 某个类别的该batch的真实类标,维度为[batch_size, height, width] - + Return: dices_range: 某个类别的该batch的所有搜索组合得到dice矩阵,维度为[M, N] ''' @@ -190,10 +187,11 @@ def post_process(self, thresholds_range, minareas_range, masks_predict_oneclass, def get_model(model_name, load_path): ''' 加载网络模型并加载对应的权重 - Args: + + Args: model_name: 当前模型的名称 load_path: 当前模型的权重路径 - + Return: model: 加载出来的模型 ''' @@ -204,15 +202,16 @@ def get_model(model_name, load_path): if __name__ == "__main__": config = get_seg_config() - mean = (0.485, 0.456, 0.406) - std = (0.229, 0.224, 0.225) + mean=(0.485, 0.456, 0.406) + std=(0.229, 0.224, 0.225) mask_only = True - dataloaders = provider(config.dataset_root, os.path.join(config.dataset_root, 'train.csv'), mean, std, - config.batch_size, config.num_workers, config.n_splits, mask_only) + dataloaders = provider(config.dataset_root, os.path.join(config.dataset_root, 'train.csv'), mean, std, config.batch_size, config.num_workers, config.n_splits, mask_only) results = {} # 存放权重的路径 model_path = os.path.join(config.save_path, config.model_name) + best_thresholds_sum, best_minareas_sum, max_dices_sum = [0 for x in range(len(dataloaders))], \ + [0 for x in range(len(dataloaders))], [0 for x in range(len(dataloaders))] for fold_index, [train_loader, valid_loader] in enumerate(dataloaders): if fold_index != 1: continue @@ -221,13 +220,18 @@ def get_model(model_name, load_path): load_path = os.path.join(model_path, '%s_fold%d_best.pth' % (config.model_name, fold_index)) # 加载模型 model = get_model(config.model_name, load_path) - mychoose_threshold_minarea = ChooseThresholdMinArea(model, config.model_name, valid_loader, fold_index, - model_path) + mychoose_threshold_minarea = ChooseThresholdMinArea(model, config.model_name, valid_loader, fold_index, model_path) best_thresholds, best_minareas, max_dices = mychoose_threshold_minarea.choose_threshold_minarea() result = {'best_thresholds': best_thresholds, 'best_minareas': best_minareas, 'max_dices': max_dices} - results[str(fold_index)] = result + + best_thresholds_sum = [x+y for x,y in zip(best_thresholds_sum, best_thresholds)] + best_minareas_sum = [x+y for x,y in zip(best_minareas_sum, best_minareas)] + max_dices_sum = [x+y for x,y in zip(max_dices_sum, max_dices)] + best_thresholds_average, best_minareas_average, max_dices_average = [x/len(dataloaders) for x in best_thresholds_sum], \ + [x/len(dataloaders) for x in best_minareas_sum], [x/len(dataloaders) for x in max_dices_sum] + results['mean'] = {'best_thresholds': best_thresholds_average, 'best_minareas': best_minareas_average, 'max_dices': max_dices_average} with codecs.open(model_path + '/result.json', 'w', "utf-8") as json_file: json.dump(results, json_file, ensure_ascii=False) - print('save the result') \ No newline at end of file + print('save the result') diff --git a/config.py b/config.py index 1ecce34..4ae8975 100755 --- a/config.py +++ b/config.py @@ -14,19 +14,13 @@ def get_seg_config(): parser = argparse.ArgumentParser() ''' unet_resnet34时各个电脑可以设置的最大batch size - zdaiot:12 - z840:16 - mxq:24 + zdaiot:12 z840:16 mxq:24 unet_se_renext50 - hwp: 6 - MXQ: 12 - unet_efficientnet_b4 - MXQ: 6 - + hwp: 6 MXQ: 12 ''' # parser.add_argument('--image_size', type=int, default=768, help='image size') - parser.add_argument('--batch_size', type=int, default=6, help='batch size') - parser.add_argument('--epoch', type=int, default=50, help='epoch') + parser.add_argument('--batch_size', type=int, default=8, help='batch size') + parser.add_argument('--epoch', type=int, default=65, help='epoch') parser.add_argument('--augmentation_flag', type=bool, default=True, help='if true, use augmentation method in train set') parser.add_argument('--n_splits', type=int, default=5, help='n_splits_fold') @@ -37,7 +31,7 @@ def get_seg_config(): # model set parser.add_argument('--model_name', type=str, default='unet_efficientnet_b4', \ - help='unet_resnet34/unet_se_resnext50_32x4d/unet_efficientnet_b4') + help='unet_resnet34/unet_se_resnext50_32x4d/unet_efficientnet_b4/unet_resnet50') # model hyper-parameters parser.add_argument('--class_num', type=int, default=4) @@ -70,11 +64,9 @@ def get_classify_config(): zdaiot:12 z840:16 mxq:48 unet_se_renext50 hwp: 8 - unet_efficientnet_b4 - MXQ: 8 ''' # parser.add_argument('--image_size', type=int, default=768, help='image size') - parser.add_argument('--batch_size', type=int, default=8, help='batch size') + parser.add_argument('--batch_size', type=int, default=48, help='batch size') parser.add_argument('--epoch', type=int, default=30, help='epoch') parser.add_argument('--augmentation_flag', type=bool, default=True, help='if true, use augmentation method in train set') @@ -84,8 +76,8 @@ def get_classify_config(): parser.add_argument('--width', type=int, default=512, help='the width of cropped image') # model set - parser.add_argument('--model_name', type=str, default='unet_efficientnet_b4', \ - help='unet_resnet34/unet_se_resnext50_32x4d/unet_efficientnet_b4') + parser.add_argument('--model_name', type=str, default='unet_resnet34', \ + help='unet_resnet34/unet_se_resnext50_32x4d/unet_efficientnet_b4/unet_resnet50') # model hyper-parameters parser.add_argument('--class_num', type=int, default=4) diff --git a/create_submission.py b/create_submission.py index d61c78f..e5a29f3 100644 --- a/create_submission.py +++ b/create_submission.py @@ -10,9 +10,8 @@ kaggle = 0 if kaggle: os.system('pip install /kaggle/input/segmentation_models/pretrainedmodels-0.7.4/ > /dev/null') - os.system('pip install /kaggle/input/segmentation_models/EfficientNet-PyTorch/ > /dev/null') os.system('pip install /kaggle/input/segmentation_models/segmentation_models.pytorch/ > /dev/null') - package_path = '/kaggle/input/sources' # add unet script dataset + package_path = '/kaggle/input/sources' # add unet script dataset import sys sys.path.append(package_path) from classify_segment import Classify_Segment_Folds, Classify_Segment_Fold, Classify_Segment_Folds_Split @@ -92,10 +91,7 @@ def create_submission(classify_splits, seg_splits, model_name, batch_size, num_w # start prediction predictions = [] for i, (fnames, images) in enumerate(tqdm(test_loader)): - if len(classify_splits) != len(seg_splits): - results = classify_segment(images, average_strategy=average_strategy).detach().cpu().numpy() - else: - results = classify_segment(images).detach().cpu().numpy() + results = classify_segment(images, average_strategy=average_strategy).detach().cpu().numpy() for fname, preds in zip(fnames, results): for cls, pred in enumerate(preds): @@ -110,13 +106,13 @@ def create_submission(classify_splits, seg_splits, model_name, batch_size, num_w if __name__ == "__main__": # 设置超参数 - model_name = 'unet_efficientnet_b4' + model_name = 'unet_resnet34' num_workers = 12 - batch_size = 1 + batch_size = 4 mean = (0.485, 0.456, 0.406) std = (0.229, 0.224, 0.225) - classify_splits = [1]# [0, 1, 2, 3, 4] - segment_splits = [1] + classify_splits = [1] # [0, 1, 2, 3, 4] + segment_splits = [0, 1, 2, 3, 4] tta_flag = True average_strategy = False diff --git a/datasets/steel_dataset.py b/datasets/steel_dataset.py index 08c13a7..ba83ac5 100644 --- a/datasets/steel_dataset.py +++ b/datasets/steel_dataset.py @@ -318,15 +318,15 @@ def classify_provider( if __name__ == "__main__": - data_folder = "datasets/Steel_data" - df_path = "datasets/Steel_data/train.csv" + data_folder = "./Steel_data" + df_path = "./Steel_data/train.csv" mean = (0.485, 0.456, 0.406) std = (0.229, 0.224, 0.225) batch_size = 12 num_workers = 4 n_splits = 1 - mask_only = True - crop = False + mask_only = False + crop = True height = 256 width = 512 # 测试分割数据集 diff --git a/models/EfficientNet-PyTorch.tar.gz b/models/EfficientNet-PyTorch.tar.gz deleted file mode 100644 index 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z?v+q9oC2I%>DVW6=uj1EVUC8ji>OS4wcUTAw{x0(Q5WHC1d*MDvoCYqgm*04Y*<#K zZNjS1x%+P=h43hDFKQr7@ioqxwVK=5&ir() z+b?)FwLR4>xEMoIQ#ML_&1o(D!}31FMFR*URca$-ZK^hL*%3sJVRC0Z7*^sw9E#dF q5dS8^y!(HvC}ZjSKi2MUXLob|XA?GI6E@*F!~X{*DKtX><^cd=Od3f5 diff --git a/run.sh b/run.sh index cda5bb9..8351016 100755 --- a/run.sh +++ b/run.sh @@ -1,4 +1,4 @@ #!/bin/bash -python train_classify.py +# python train_classify.py python train_segment.py python choose_thre_area.py \ No newline at end of file diff --git a/train_classify.py b/train_classify.py index bafd184..63cc3a8 100755 --- a/train_classify.py +++ b/train_classify.py @@ -1,21 +1,21 @@ from torch import optim import torch import tqdm +from config import get_classify_config +from solver import Solver from torch.utils.tensorboard import SummaryWriter import datetime import os import codecs, json import time -import pickle -from config import get_classify_config -from solver import Solver from models.model import ClassifyResNet from utils.loss import ClassifyLoss from datasets.steel_dataset import classify_provider from utils.cal_classify_accuracy import Meter from utils.set_seed import seed_torch -from utils.easy_stopping import EarlyStopping +import pickle +import random class TrainVal(): @@ -94,8 +94,7 @@ def train(self, train_loader, valid_loader): global_step += len(train_loader) # Print the log info - average_loss = epoch_loss / len(tbar) - print('Finish Epoch [%d/%d], Average Loss: %.7f' % (epoch, self.epoch, average_loss)) + print('Finish Epoch [%d/%d], Average Loss: %.7f' % (epoch, self.epoch, epoch_loss/len(tbar))) # 验证模型 class_neg_accuracy, class_pos_accuracy, class_accuracy, neg_accuracy, pos_accuracy, accuracy, loss_valid = \ @@ -155,8 +154,8 @@ def validation(self, valid_loader): if __name__ == "__main__": config = get_classify_config() - mean = (0.485, 0.456, 0.406) - std = (0.229, 0.224, 0.225) + mean=(0.485, 0.456, 0.406) + std=(0.229, 0.224, 0.225) dataloaders = classify_provider( config.dataset_root, os.path.join(config.dataset_root, 'train.csv'), diff --git a/train_segment.py b/train_segment.py index 2c60091..73b44a9 100644 --- a/train_segment.py +++ b/train_segment.py @@ -33,7 +33,6 @@ def __init__(self, config, fold): self.weight_decay = config.weight_decay self.epoch = config.epoch self.fold = fold - self.class_num = config.class_num # 创建保存权重的路径 self.model_path = os.path.join(config.save_path, config.model_name) @@ -48,7 +47,8 @@ def __init__(self, config, fold): self.solver = Solver(self.model) # 加载损失函数 - self.criterion = torch.nn.BCEWithLogitsLoss() + # self.criterion = torch.nn.BCEWithLogitsLoss() + self.criterion = MultiClassesSoftBCEDiceLoss(classes_num=4, size_average=True, weight=[1.0, 1.0]) # 保存json文件和初始化tensorboard TIMESTAMP = "{0:%Y-%m-%dT%H-%M-%S-%d}".format(datetime.datetime.now(), fold) @@ -105,25 +105,22 @@ def train(self, train_loader, valid_loader): global_step += len(train_loader) # Print the log info - average_loss = epoch_loss/len(tbar) - print('Finish Epoch [%d/%d], Average Loss: %.7f' % (epoch, self.epoch, average_loss)) + print('Finish Epoch [%d/%d], Average Loss: %.7f' % (epoch, self.epoch, epoch_loss/len(tbar))) # 验证模型 loss_valid, dice_valid, iou_valid = self.validation(valid_loader) - if dice_valid > self.max_dice_valid: + if dice_valid > self.max_dice_valid: is_best = True self.max_dice_valid = dice_valid - else: - is_best = False - + else: is_best = False + state = { 'epoch': epoch, 'state_dict': self.model.module.state_dict(), 'max_dice_valid': self.max_dice_valid, } - self.solver.save_checkpoint(os.path.join(self.model_path, '%s_fold%d.pth' % (self.model_name, self.fold)), - state, is_best) + self.solver.save_checkpoint(os.path.join(self.model_path, '%s_fold%d.pth' % (self.model_name, self.fold)), state, is_best) self.writer.add_scalar('valid_loss', loss_valid, epoch) self.writer.add_scalar('valid_dice', dice_valid, epoch) @@ -132,16 +129,12 @@ def validation(self, valid_loader): Args: valid_loader: 验证数据的Dataloader - - :return loss_mean: 验证集上的loss平均值 - :return dice_mean: 验证集上的各类dice平均值 - :return dice_classes: 验证集上各类的dice值 ''' self.model.eval() meter = Meter() tbar = tqdm.tqdm(valid_loader) loss_sum = 0 - + with torch.no_grad(): for i, samples in enumerate(tbar): if len(samples) == 0: @@ -151,20 +144,20 @@ def validation(self, valid_loader): masks_predict = self.solver.forward(images) loss = self.solver.cal_loss(masks, masks_predict, self.criterion) loss_sum += loss.item() - + # 注意,损失函数中包含sigmoid函数,meter.update中也包含了sigmoid函数 # masks_predict_binary = torch.sigmoid(masks_predict) > 0.5 meter.update(masks, masks_predict.detach().cpu()) descript = "Val Loss: {:.7f}".format(loss.item()) tbar.set_description(desc=descript) - loss_mean = loss_sum / len(tbar) - - dices, iou = meter.get_metrics() - dice, dice_neg, dice_pos = dices - print("IoU: %0.4f | dice: %0.4f | dice_neg: %0.4f | dice_pos: %0.4f" % (iou, dice, dice_neg, dice_pos)) - return loss_mean, dice, iou + loss_mean = loss_sum/len(tbar) + dices, iou = meter.get_metrics() + dice, dice_neg, dice_pos = dices + print("IoU: %0.4f | dice: %0.4f | dice_neg: %0.4f | dice_pos: %0.4f" % (iou, dice, dice_neg, dice_pos)) + return loss_mean, dice, iou + def load_weight(self, weight_path): """加载权重 """ diff --git a/utils/cal_dice_iou.py b/utils/cal_dice_iou.py index 1d09f54..fbd1e0a 100644 --- a/utils/cal_dice_iou.py +++ b/utils/cal_dice_iou.py @@ -16,7 +16,7 @@ def metric(probability, truth, threshold=0.5, reduction='none'): with torch.no_grad(): probability = probability.view(batch_size, -1) truth = truth.view(batch_size, -1) - assert (probability.shape == truth.shape) + assert(probability.shape == truth.shape) p = (probability > threshold).float() t = (truth > 0.5).float() @@ -27,7 +27,7 @@ def metric(probability, truth, threshold=0.5, reduction='none'): pos_index = torch.nonzero(t_sum >= 1) dice_neg = (p_sum == 0).float() - dice_pos = 2 * (p * t).sum(-1) / ((p + t).sum(-1)) + dice_pos = 2 * (p*t).sum(-1)/((p+t).sum(-1)) dice_neg = dice_neg[neg_index] dice_pos = dice_pos[pos_index] @@ -45,9 +45,8 @@ def metric(probability, truth, threshold=0.5, reduction='none'): class Meter: '''A meter to keep track of iou and dice scores throughout an epoch''' - def __init__(self): - self.base_threshold = 0.5 # <<<<<<<<<<< here's the threshold + self.base_threshold = 0.5 # <<<<<<<<<<< here's the threshold self.base_dice_scores = [] self.dice_neg_scores = [] self.dice_pos_scores = [] @@ -76,8 +75,7 @@ def epoch_log(epoch, epoch_loss, meter, start): '''logging the metrics at the end of an epoch''' dices, iou = meter.get_metrics() dice, dice_neg, dice_pos = dices - print("Loss: %0.4f | IoU: %0.4f | dice: %0.4f | dice_neg: %0.4f | dice_pos: %0.4f" % ( - epoch_loss, iou, dice, dice_neg, dice_pos)) + print("Loss: %0.4f | IoU: %0.4f | dice: %0.4f | dice_neg: %0.4f | dice_pos: %0.4f" % (epoch_loss, iou, dice, dice_neg, dice_pos)) return dice, iou @@ -101,8 +99,8 @@ def compute_ious(pred, label, classes, ignore_index=255, only_present=True): def compute_iou_batch(outputs, labels, classes=None): '''computes mean iou for a batch of ground truth masks and predicted masks''' ious = [] - preds = np.copy(outputs) # copy is imp - labels = np.array(labels) # tensor to np + preds = np.copy(outputs) # copy is imp + labels = np.array(labels) # tensor to np for pred, label in zip(preds, labels): ious.append(np.nanmean(compute_ious(pred, label, classes))) iou = np.nanmean(ious) @@ -111,11 +109,12 @@ def compute_iou_batch(outputs, labels, classes=None): def compute_dice_class(preds, targs): ''' 计算某一类的dice值,注意 preds 要在外部经过sigmoid函数 + Args: preds: 维度为[batch_size, 1, height, width],每一个值表示预测出的属于该类的概率 targs: 维度为[batch_size, 1, height, width],每一个值表示真实是否属于该类 - - Return: + + Return: 计算出的dice值 ''' n = preds.shape[0] # batch size为多少 @@ -130,11 +129,12 @@ def compute_dice_class(preds, targs): union = (preds + targs).sum(-1).float() ''' 输入图片真实类标与预测类标无并集有两种情况:第一种为预测与真实均没有类标,此时并集之和为0;第二种为真实有类标,但是预测完全错误,此时并集之和不为0; + 寻找输入图片真实类标与预测类标并集之和为0的情况,将其交集置为1,并集置为2,最后还有一个2*交集/并集,值为1; 其余情况,直接按照2*交集/并集计算,因为上面的并集并没有减去交集,所以需要拿2*交集,其最大值为1 ''' u0 = union == 0 intersect[u0] = 1 union[u0] = 2 - - return (2. * intersect / union).mean() + + return (2. * intersect / union).mean() \ No newline at end of file diff --git a/utils/data_augmentation.py b/utils/data_augmentation.py index f600d8c..475d316 100644 --- a/utils/data_augmentation.py +++ b/utils/data_augmentation.py @@ -110,7 +110,7 @@ def data_augmentation(original_image, original_mask, crop=False, height=None, wi image_id, mask = make_mask(index, df) image_path = os.path.join(data_folder, 'train_images', image_id) image = cv2.imread(image_path) - image_aug, mask_aug = data_augmentation(image, mask, crop=False, height=256, width=400) + image_aug, mask_aug = data_augmentation(image, mask, crop=True, height=256, width=400) normalize = Normalize(mean=mean, std=std) image = normalize(image=image)['image'] image_aug = normalize(image=image_aug)['image'] diff --git a/utils/easy_stopping.py b/utils/easy_stopping.py deleted file mode 100644 index 2ebd5fd..0000000 --- a/utils/easy_stopping.py +++ /dev/null @@ -1,51 +0,0 @@ -import numpy as np - - -class EarlyStopping(object): - def __init__(self, mode='min', min_delta=0, patience=10, percentage=False): - self.mode = mode - self.min_delta = min_delta - self.patience = patience - self.best = None - self.num_bad_epochs = 0 - self.is_better = None - self._init_is_better(mode, min_delta, percentage) - - if patience == 0: - self.is_better = lambda a, b: True - self.step = lambda a: False - - def step(self, metrics): - if self.best is None: - self.best = metrics - return False - - if np.isnan(metrics): - return True - - if self.is_better(metrics, self.best): - self.num_bad_epochs = 0 - self.best = metrics - else: - self.num_bad_epochs += 1 - - if self.num_bad_epochs >= self.patience: - return True - - return False - - def _init_is_better(self, mode, min_delta, percentage): - if mode not in {'min', 'max'}: - raise ValueError('mode ' + mode + ' is unknown!') - if not percentage: - if mode == 'min': - self.is_better = lambda a, best: a < best - min_delta - if mode == 'max': - self.is_better = lambda a, best: a > best + min_delta - else: - if mode == 'min': - self.is_better = lambda a, best: a < best - ( - best * min_delta / 100) - if mode == 'max': - self.is_better = lambda a, best: a > best + ( - best * min_delta / 100) diff --git a/utils/loss.py b/utils/loss.py index 7a2ad6f..25b1aa7 100644 --- a/utils/loss.py +++ b/utils/loss.py @@ -80,46 +80,61 @@ def soft_dice_criterion(self, logit, truth): return loss +# reference https://www.kaggle.com/c/siim-acr-pneumothorax-segmentation/discussion/101429#latest-588288 +class SoftBceLoss(nn.Module): + """二分类交叉熵加权损失 + """ + def __init__(self, weight=[0.25, 0.75]): + super(SoftBceLoss, self).__init__() + self.weight = weight + + def forward(self, logit_pixel, truth_pixel): + logit = logit_pixel.view(-1) + truth = truth_pixel.view(-1) + assert(logit.shape==truth.shape) + + loss = F.binary_cross_entropy_with_logits(logit, truth, reduction='none') + if self.weight: + pos = (truth>0.5).float() + neg = (truth<0.5).float() + # pos_weight = pos.sum().item() + 1e-12 + # neg_weight = neg.sum().item() + 1e-12 + # loss = (self.weight[0]*pos*loss/pos_weight + self.weight[1]*neg*loss/neg_weight).sum() + loss = (self.weight[1]*pos*loss + self.weight[0]*neg*loss).mean() + else: + loss = loss.mean() + return loss + + class SoftBCEDiceLoss(nn.Module): """加权BCE+DiceLoss """ - def __init__(self, size_average=True, weight=[1.0, 1.0]): + def __init__(self, size_average=True, weight=[0.2, 0.8]): """ weight: weight[0]为负类的权重,weight[1]为正类的权重 """ super(SoftBCEDiceLoss, self).__init__() self.size_average = size_average self.weight = weight - self.bce_loss = nn.BCEWithLogitsLoss(size_average=self.size_average, pos_weight=torch.tensor(weight[0])) + self.bce_loss = nn.BCEWithLogitsLoss(size_average=self.size_average, pos_weight=torch.tensor(self.weight[1])) # self.bce_loss = SoftBceLoss(weight=weight) self.softdiceloss = SoftDiceLoss(size_average=self.size_average, weight=weight) def forward(self, input, target): soft_bce_loss = self.bce_loss(input, target) soft_dice_loss = self.softdiceloss(input, target) - loss = 0.85 * soft_bce_loss + 0.15 * soft_dice_loss + loss = 0.7 * soft_bce_loss + 0.3 * soft_dice_loss return loss class MultiClassesSoftBCEDiceLoss(nn.Module): - def __init__(self, classes_num=4, size_average=True, weight=[[1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0]], class_weight=[0.3, 0.2, 0.3, 0.2]): - """ - - Args: - weight: 正负样本权重 - class_weight: 类别间权重 - """ + def __init__(self, classes_num=4, size_average=True, weight=[0.2, 0.8]): super(MultiClassesSoftBCEDiceLoss, self).__init__() self.classes_num = classes_num self.size_average = size_average - self.class_weight = class_weight - self.soft_bce_dice_loss = [ - SoftBCEDiceLoss(size_average=self.size_average, weight=weight[0]), - SoftBCEDiceLoss(size_average=self.size_average, weight=weight[1]), - SoftBCEDiceLoss(size_average=self.size_average, weight=weight[2]), - SoftBCEDiceLoss(size_average=self.size_average, weight=weight[3]), - ] + self.weight = weight + self.soft_bce_dice_loss = SoftBCEDiceLoss(size_average=self.size_average, weight=self.weight) def forward(self, input, target): """ @@ -131,26 +146,10 @@ def forward(self, input, target): for class_index in range(self.classes_num): input_single_class = input[:, class_index, :, :] target_singlt_class = target[:, class_index, :, :] - single_class_loss = self.soft_bce_dice_loss[class_index](input_single_class, target_singlt_class) - loss += self.class_weight[class_index] * single_class_loss - - return loss - - -class MultiClassBCELoss(nn.Module): - def __init__(self, class_num=4, class_weight=[0.3, 0.2, 0.3, 0.2]): - super(MultiClassBCELoss, self).__init__() - self.bce_loss = nn.BCEWithLogitsLoss() - self.class_num = class_num - self.class_weight = class_weight - - def forward(self, input, target): - loss = 0 - for class_index in range(self.class_num): - input_single_class = input[:, class_index, :, :] - target_singlt_class = target[:, class_index, :, :] - single_class_loss = self.bce_loss(input_single_class, target_singlt_class) - loss += self.class_weight[class_index] * single_class_loss + single_class_loss = self.soft_bce_dice_loss(input_single_class, target_singlt_class) + loss += single_class_loss + + loss /= self.classes_num return loss @@ -158,6 +157,6 @@ def forward(self, input, target): if __name__ == "__main__": input = torch.Tensor(4, 4, 256, 1600) target = torch.Tensor(4, 4, 256, 1600) - criterion = MultiClassBCELoss(4) + criterion = MultiClassesSoftBCEDiceLoss(4, True) loss = criterion(input, target)