commit
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@ -47,8 +47,8 @@ __all__ = [
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]
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SUPPORT_DET_MODEL = ['DB']
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VERSION = '2.5.0.1'
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SUPPORT_REC_MODEL = ['CRNN']
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VERSION = '2.5.0.2'
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SUPPORT_REC_MODEL = ['CRNN', 'SVTR_LCNet']
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BASE_DIR = os.path.expanduser("~/.paddleocr/")
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DEFAULT_OCR_MODEL_VERSION = 'PP-OCRv3'
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@ -79,7 +79,7 @@ def init_args():
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parser.add_argument("--det_fce_box_type", type=str, default='poly')
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# params for text recognizer
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parser.add_argument("--rec_algorithm", type=str, default='CRNN')
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parser.add_argument("--rec_algorithm", type=str, default='SVTR_LCNet')
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parser.add_argument("--rec_model_dir", type=str)
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parser.add_argument("--rec_image_shape", type=str, default="3, 32, 320")
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parser.add_argument("--rec_batch_num", type=int, default=6)
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@ -269,11 +269,11 @@ def create_predictor(args, mode, logger):
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max_input_shape.update(max_pact_shape)
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opt_input_shape.update(opt_pact_shape)
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elif mode == "rec":
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if args.rec_algorithm != "CRNN":
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if args.rec_algorithm not in ["CRNN", "SVTR_LCNet"]:
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use_dynamic_shape = False
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imgH = int(args.rec_image_shape.split(',')[-2])
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min_input_shape = {"x": [1, 3, imgH, 10]}
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max_input_shape = {"x": [args.rec_batch_num, 3, imgH, 1536]}
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max_input_shape = {"x": [args.rec_batch_num, 3, imgH, 2304]}
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opt_input_shape = {"x": [args.rec_batch_num, 3, imgH, 320]}
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elif mode == "cls":
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min_input_shape = {"x": [1, 3, 48, 10]}
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@ -320,7 +320,7 @@ def create_predictor(args, mode, logger):
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def get_output_tensors(args, mode, predictor):
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output_names = predictor.get_output_names()
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output_tensors = []
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if mode == "rec" and args.rec_algorithm == "CRNN":
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if mode == "rec" and args.rec_algorithm in ["CRNN", "SVTR_LCNet"]:
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output_name = 'softmax_0.tmp_0'
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if output_name in output_names:
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return [predictor.get_output_handle(output_name)]
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