HUAWEI-FWD-RES-TRAP-MIB Alarm ID 0x00f1fff9 Alarm Name hwSessUnrecEntryNumberThresholdExceed Alarm Type processingErrorAlarm Raise or Clear Raise Match trap - Trap Buffer信息参数 参数名称参数含义 hwSessionCurEntryNum 当前表项数目 hwSessionLeakEntryNum 泄漏表项数目 hwSessionUnrecEntry...
HUAWEI-FWD-RES-TRAP-MIB Alarm ID 0x00f1fff9 Alarm Name hwSessUnrecEntryNumberThresholdExceed Alarm Type processingErrorAlarm Raise or Clear Raise Match trap - Trap Buffer信息参数 参数名称参数含义 hwSessionCurEntryNum 当前表项数目 hwSessionLeakEntryNum 泄漏表项数目 hwSessionUnrecEntry...
AI检测代码解析 [test_coco_instance.yml] metric: COCO num_classes: 80 TrainDataset: !COCODataSet image_dir: images anno_path: instances_train_battery.json # annotations/instances_train2017.json dataset_dir: dataset/coco/battery/train data_fields: ['image', 'gt_bbox', 'gt_class', 'gt_poly...
python3 main.py train --model=NARRE --num_fea=2 --output=lfm Note that thenum_fea (1,2,3)corresponds how many features used in the methods, (ID feature, Review-level and Doc-level denoted above). Test the model using the saved pth file incheckpointsin the test datase: for example...
api_attr : { int res; while ((res= psop->nextResult(true)) = GOT_ROW) { printf(" %2d %2d %2d\n", recAttrAttr1->u_32_value(), recAttrAttr2->u_32_value(), recAttrAttr3->u_32_value()); } if (res != NO_MORE_ROWS) APIERROR(psop->getNdbError()); psop->close()...
rec_batch_num=6, rec_char_dict_path='C:\\Anaconda3\\envs\\pocr\\lib\\site-packages\\paddleocr\\ppocr\\utils\\ppocr_keys_v1.txt', rec_image_shape='3, 32, 320', rec_model_dir='C:\\Users\\flash/.paddleocr/whl\\rec\\ch\\ch_PP-OCRv2_rec_infer', save_crop_res=False, sav...
R3D pretrain model is from3D-Resnet-Pytorch C3D C3D C3D pretrain model is fromC3D-Pytorch 3.run demo pretrained I3d on HMDB51 bash scripts/demo.sh c3d bash scripts/c3d_unsupervised_demo.sh r3d bash scripts/r3d_unsupervised_demo.sh ...
out = topk_metrics(y_true=ground_truth, y_pred=match_res, topKs=[topk])returnout user_embedding = trainer.inference_embedding(model=model, mode="user", data_loader=test_dl, model_path=save_dir) item_embedding = trainer.inference_embedding(model=model, mode="item", data_loader=item_dl,...
self.W2 = self.create_parameter(shape=[self.d, self.interest_num]) def forward(self, seq_emb, mask = None): ''' seq_emb : batch,seq,emb mask : batch,seq,1 ''' H = paddle.einsum('bse, ed -> bsd', seq_emb, self.W1).tanh() ...
os.makedirs(os.path.dirname(save_res_path)) model.eval() withopen(save_res_path,"w")asfout: #添加列头 fout.write('new_name'+"\t"+'value'+'\n') # 将图片先进行排序之后再进行预测 infer_list = get_image_file_list(config['Global']['infer_img']) ...