def test(net, epoch, dataLoader, testF, config): net.eval() total_mask_loss = 0.0 dataprocess = tqdm(dataLoader) result = {"TP": {i: 0 for i in range(8)}, "TA": {i: 0 for i in range(8)}} for batch_item in dataprocess: image, mask = batch_item['image'], batch_item['mask'] if torch.cuda.is_available(): image, mask = image.cuda(device=device_list[0]), mask.cuda( device=device_list[0]) out = net(image) mask_loss = MySoftmaxCrossEntropyLoss(nbclasses=config.NUM_CLASSES)( out, mask) total_mask_loss += mask_loss.detach().item() pred = torch.argmax(F.softmax(out, dim=1), dim=1) result = compute_iou(pred, mask, result) dataprocess.set_description_str("epoch:{}".format(epoch)) dataprocess.set_postfix_str("mask_loss:{:.4f}".format(mask_loss)) testF.write("Epoch:{} \n".format(epoch)) for i in range(8): result_string = "{}: {:.4f} \n".format( i, result["TP"][i] / result["TA"][i]) print(result_string) testF.write(result_string) testF.write("Epoch:{}, mask loss is {:.4f} \n".format( epoch, total_mask_loss / len(dataLoader))) testF.flush()
def test(net, epoch, dataLoader, testF, config): net.eval() total_mask_loss = 0.0 dataprocess = tqdm(dataLoader) result = {"TP": {i:0 for i in range(8)}, "TA":{i:0 for i in range(8)}} for batch_item in dataprocess: image, mask = batch_item['image'], batch_item['mask'] if torch.cuda.is_available(): image, mask = image.cuda(device=device_list[0]), mask.cuda(device=device_list[0]) out = net(image) mask_loss = MySoftmaxCrossEntropyLoss(nbclasses=config.NUM_CLASSES)(out, mask) # detach()截断梯度的作用,截断后如果tensor中的data发生了变化,反向传播时会报错(和正向传播时候的值不一样了) total_mask_loss += mask_loss.detach().item() # dim表示对channle进行处理得到N,H,W pred = torch.argmax(F.softmax(out, dim=1), dim=1) # 计算iou result = compute_iou(pred, mask, result) dataprocess.set_description_str("epoch:{}".format(epoch)) dataprocess.set_postfix_str("mask_loss:{:.4f}".format(mask_loss)) testF.write("Epoch:{} \n".format(epoch)) # 求出每一个类别的iou for i in range(8): # 计算每一类的交并比 result_string = "{}: {:.4f} \n".format(i, result["TP"][i]/result["TA"][i]) print(result_string) # 写入测试log文件 testF.write(result_string) testF.write("Epoch:{}, mask loss is {:.4f} \n".format(epoch, total_mask_loss / len(dataLoader))) testF.flush()
def val_epoch(net, epoch, dataLoader, valF, args): logger.info("======start val epoch step=======") net.eval() total_mask_loss = 0.0 dataprocess = tqdm(dataLoader) result = {"TP": {i: 0 for i in range(8)}, "TA": {i: 0 for i in range(8)}} evaluator = Evaluator(args.number_class) for batch_item in dataprocess: image, mask = batch_item['image'], batch_item['mask'] out = net(image) mask_loss = MySoftmaxCrossEntropyLoss(nbclasses=args.number_class)( out, mask) total_mask_loss += mask_loss.detach().item() pred = torch.argmax(F.softmax(out, dim=1), dim=1) result = compute_iou(pred, mask, result) dataprocess.set_description_str("epoch:{}".format(epoch)) dataprocess.set_postfix_str("mask_loss:{:.4f}".format(mask_loss)) Acc = evaluator.Pixel_Accuracy() Acc_class = evaluator.Pixel_Accuracy_Class() mIoU = evaluator.Mean_Intersection_over_Union() FWIoU = evaluator.Frequency_Weighted_Intersection_over_Union() valF.write("Epoch:{}, val/mIoU is {:.4f} \n".format(epoch, mIoU)) valF.write("Epoch:{}, val/Acc is {:.4f} \n".format(epoch, Acc)) valF.write("Epoch:{}, val/Acc_class is {:.4f} \n".format(epoch, Acc_class)) valF.write("Epoch:{}, val/FWIoU is {:.4f} \n".format(epoch, FWIoU)) for i in range(8): result_string = "{}: {:.4f} \n".format( i, result["TP"][i] / result["TA"][i]) logger.info("val class result {}".format(result_string)) valF.write(result_string) valF.write("Epoch:{}, mask loss is {:.4f} \n".format( epoch, total_mask_loss / len(dataLoader))) logger.info("Acc:{}, Acc_class:{}, mIoU:{}, fwIoU: {}".format( Acc, Acc_class, mIoU, FWIoU)) valF.flush()
def train_epoch(net, epoch, dataLoader, optimizer, trainF, config): net.train() total_mask_loss = 0.0 dataprocess = tqdm(dataLoader) for batch_item in dataprocess: image, mask = batch_item['image'], batch_item['mask'] if torch.cuda.is_available(): image, mask = image.cuda(device=device_list[0]), mask.cuda(device=device_list[0]).long() # optimizer.zero将每个parameter的梯度清0 optimizer.zero_grad() # 输出预测的mask out = net(image) # 计算交叉熵loss mask_loss = MySoftmaxCrossEntropyLoss(nbclasses=config.NUM_CLASSES)(out, mask) total_mask_loss += mask_loss.detach().item() mask_loss.backward() optimizer.step() dataprocess.set_description_str("epoch:{}".format(epoch)) dataprocess.set_postfix_str("mask_loss:{:.4f}".format(mask_loss.detach().item())) # 记录数据迭代了多少次 trainF.write("Epoch:{}, mask loss is {:.4f} \n".format(epoch, total_mask_loss / len(dataLoader))) trainF.flush()
def test_epoch(net, epoch, dataloader, writer, logger, config): net.eval() total_mask_loss = 0.0 dataprocess = tqdm(dataloader) confusion_matrix = np.zeros((config.NUM_CLASS, config.NUM_CLASS)) logger.info("Val EPOCH {}: ".format(epoch)) with torch.no_grad(): for batch_item in dataprocess: image, mask = batch_item['image'], batch_item['mask'] if torch.cuda.is_available(): image, mask = image.cuda(), mask.cuda() out = net(image) mask_loss = MySoftmaxCrossEntropyLoss(nbclasses=config.NUM_CLASS)( out, mask) total_mask_loss += mask_loss.detach().item() confusion_matrix += get_confusion_matrix(mask, out, mask.size(), config.NUM_CLASS) dataprocess.set_description_str('epoch{}:'.format(epoch)) dataprocess.set_postfix_str('mask loss is {:.4f}'.format( mask_loss.item())) logger.info("\taverage loss is {:.4f}".format(total_mask_loss / len(dataloader))) pos = confusion_matrix.sum(0) res = confusion_matrix.sum(1) tp = np.diag(confusion_matrix) IoU_array = (tp / np.maximum(1.0, pos + res - tp)) for i in range(8): print('{} IoU is : {}'.format(i, IoU_array[i])) logger.info('\t{} Iou is : {}'.format(i, IoU_array[i])) miou = IoU_array[1:].mean() logger.info('Val miou is : {:.4f}'.format(miou)) with writer as w: w.add_scalar('EPOCH Loss', total_mask_loss / len(dataloader), epoch) w.add_scalar('EPOCH mIoU', miou, epoch) print('epoch{}: miou is {}'.format(epoch, miou))