min_subgraph_size=15, # skip the minmum trt subgraph use_calib_mode=False) predictor = create_predictor(config) self.pool.put(predictor) n += 1 print('已加载{n}个rec模型'.format(n=n)) def acquire(self): try: rec_predictor = self.pool.get(block=False) except queue.Empty: print("...
[2024/12/30 19:59:05] ppocr DEBUG: Namespace(help='==SUPPRESS==', use_gpu=False, use_xpu=False, use_npu=False, use_mlu=False, ir_optim=True, use_tensorrt=False, min_subgraph_size=15, precision='fp32', gpu_mem=500, gpu_id=0, image_dir=None, page_num=0, det_algorithm='...
['0', '180'], lang='ch', layout_path_model='lp://PubLayNet/ppyolov2_r50vd_dcn_365e_publaynet/config', max_batch_size=10, max_text_length=25, min_subgraph_size=15, output='./output/table', precision='fp32', process_id=0, rec=True, rec_algorithm='CRNN', rec_batch_num=...
use_gpu=True, use_xpu=False, use_npu=False, use_mlu=False, ir_optim=True, use_tensorrt=False, min_subgraph_size=15, precision='fp32', gpu_mem=500, gpu_id=0, image_dir='348621372-cdaaf8a5-0373-452d-a618-0c85e2ef2197
问题描述 Issue Description [2024/12/01 20:36:03] ppocr DEBUG: Namespace(help='==SUPPRESS==', use_gpu=True, use_xpu=False, use_npu=False, use_mlu=False, ir_optim=True, use_tensorrt=True, min_subgraph_size=15, precision='fp32', gpu_mem=1000, gpu_id=0, image_dir=None, page_...
['0','180'], lang='ch', layout=True, layout_dict_path=None, layout_model_dir=None, layout_nms_threshold=0.5, layout_score_threshold=0.5, max_batch_size=10, max_text_length=25, merge_no_span_structure=True, min_subgraph_size=15, mode='structure', ocr=True, ocr_order_method=None,...
在使用PaddleOCR进行模型推理时,可以自定义修改参数,来修改模型、数据、预处理、后处理等内容,详细的参数解释如下所示。 全局信息 预测引擎相关 文本检测模型相关 其中,DB算法相关参数如下 EAST算法相关参数如下 SAST算法相关参数如下 PSE算法相关参数如下 文本识别模型相关 ...
parser.add_argument("--min_subgraph_size", type=int, default=15) parser.add_argument("--precision", type=str, default="fp32") parser.add_argument("--gpu_mem", type=int, default=500) parser.add_argument("--gpu_id", type=int, default=0) #...
config.switch_ir_optim() config.enable_memory_optim() config.enable_tensorrt_engine(workspace_size=1 << 30, precision_mode=PrecisionType.Float32,max_batch_size=1, min_subgraph_size=5, use_static=False, use_calib_mode=False) predictor = create_predictor(config) return predictor ...
parser.add_argument("--min_subgraph_size", type=int, default=15) parser.add_argument("--precision", type=str, default="fp32") parser.add_argument("--gpu_mem", type=int, default=500) # params for text detector parser.add_argument("--image_dir", type=str) ...