def __init__(self, args):
self.args = args
self.det_algorithm = args.det_algorithm
self.use_zero_copy_run = args.use_zero_copy_run
pre_process_list = [{
'DetResizeForTest': {
'limit_side_len': args.det_limit_side_len,
'limit_type': args.det_limit_type
}
}, {
'NormalizeImage': {
'std': [0.229, 0.224, 0.225],
'mean': [0.485, 0.456, 0.406],
'scale': '1./255.',
'order': 'hwc'
}
}, {
'ToCHWImage': None
}, {
'KeepKeys': {
'keep_keys': ['image', 'shape']
}
}]
postprocess_params = {}
if self.det_algorithm == "DB":
postprocess_params['name'] = 'DBPostProcess'
postprocess_params["thresh"] = args.det_db_thresh
postprocess_params["box_thresh"] = args.det_db_box_thresh
postprocess_params["max_candidates"] = 1000
postprocess_params["unclip_ratio"] = args.det_db_unclip_ratio
postprocess_params["use_dilation"] = True
elif self.det_algorithm == "EAST":
postprocess_params['name'] = 'EASTPostProcess'
postprocess_params["score_thresh"] = args.det_east_score_thresh
postprocess_params["cover_thresh"] = args.det_east_cover_thresh
postprocess_params["nms_thresh"] = args.det_east_nms_thresh
elif self.det_algorithm == "SAST":
pre_process_list[0] = {
'DetResizeForTest': {'resize_long': args.det_limit_side_len}
}
postprocess_params['name'] = 'SASTPostProcess'
postprocess_params["score_thresh"] = args.det_sast_score_thresh
postprocess_params["nms_thresh"] = args.det_sast_nms_thresh
self.det_sast_polygon = args.det_sast_polygon
if self.det_sast_polygon:
postprocess_params["sample_pts_num"] = 6
postprocess_params["expand_scale"] = 1.2
postprocess_params["shrink_ratio_of_width"] = 0.2
else:
postprocess_params["sample_pts_num"] = 2
postprocess_params["expand_scale"] = 1.0
postprocess_params["shrink_ratio_of_width"] = 0.3
else:
logger.info("unknown det_algorithm:{}".format(self.det_algorithm))
sys.exit(0)
self.preprocess_op = create_operators(pre_process_list)
self.postprocess_op = build_post_process(postprocess_params)
self.predictor, self.input_tensor, self.output_tensors = utility.create_predictor(
args, 'det', logger) # paddle.jit.load(args.det_model_dir)
def __init__(self, args):
self.rec_image_shape = [int(v) for v in args.rec_image_shape.split(",")]
self.character_type = args.rec_char_type
self.rec_batch_num = args.rec_batch_num
self.rec_algorithm = args.rec_algorithm
self.max_text_length = args.max_text_length
postprocess_params = {
'name': 'CTCLabelDecode',
"character_type": args.rec_char_type,
"character_dict_path": args.rec_char_dict_path,
"use_space_char": args.use_space_char
}
if self.rec_algorithm == "SRN":
postprocess_params = {
'name': 'SRNLabelDecode',
"character_type": args.rec_char_type,
"character_dict_path": args.rec_char_dict_path,
"use_space_char": args.use_space_char
}
elif self.rec_algorithm == "RARE":
postprocess_params = {
'name': 'AttnLabelDecode',
"character_type": args.rec_char_type,
"character_dict_path": args.rec_char_dict_path,
"use_space_char": args.use_space_char
}
self.postprocess_op = build_post_process(postprocess_params)
self.predictor, self.input_tensor, self.output_tensors = \
utility.create_predictor(args, 'rec', logger)
def __init__(self, args):
if args.use_pdserving is False:
self.predictor, self.input_tensor, self.output_tensors =\
utility.create_predictor(args, mode="rec")
self.use_zero_copy_run = args.use_zero_copy_run
self.rec_image_shape = [
int(v) for v in args.rec_image_shape.split(",")
]
self.character_type = args.rec_char_type
self.rec_batch_num = args.rec_batch_num
self.rec_algorithm = args.rec_algorithm
self.text_len = args.max_text_length
char_ops_params = {
"character_type": args.rec_char_type,
"character_dict_path": args.rec_char_dict_path,
"use_space_char": args.use_space_char,
"max_text_length": args.max_text_length
}
if self.rec_algorithm in ["CRNN", "Rosetta", "STAR-Net"]:
char_ops_params['loss_type'] = 'ctc'
self.loss_type = 'ctc'
elif self.rec_algorithm == "RARE":
char_ops_params['loss_type'] = 'attention'
self.loss_type = 'attention'
elif self.rec_algorithm == "SRN":
char_ops_params['loss_type'] = 'srn'
self.loss_type = 'srn'
self.char_ops = CharacterOps(char_ops_params)
def __init__(self, args):
pre_process_list = [{
'ResizeTableImage': {
'max_len': args.table_max_len
}
}, {
'NormalizeImage': {
'std': [0.229, 0.224, 0.225],
'mean': [0.485, 0.456, 0.406],
'scale': '1./255.',
'order': 'hwc'
}
}, {
'PaddingTableImage': None
}, {
'ToCHWImage': None
}, {
'KeepKeys': {
'keep_keys': ['image']
}
}]
postprocess_params = {
'name': 'TableLabelDecode',
"character_type": args.table_char_type,
"character_dict_path": args.table_char_dict_path,
}
self.preprocess_op = create_operators(pre_process_list)
self.postprocess_op = build_post_process(postprocess_params)
self.predictor, self.input_tensor, self.output_tensors, self.config = \
utility.create_predictor(args, 'table', logger)
def __init__(self, args):
max_side_len = args.det_max_side_len
self.det_algorithm = args.det_algorithm
preprocess_params = {'max_side_len': max_side_len}
postprocess_params = {}
if self.det_algorithm == "DB":
self.preprocess_op = DBProcessTest(preprocess_params)
postprocess_params["thresh"] = args.det_db_thresh
postprocess_params["box_thresh"] = args.det_db_box_thresh
postprocess_params["max_candidates"] = 1000
postprocess_params["unclip_ratio"] = args.det_db_unclip_ratio
self.postprocess_op = DBPostProcess(postprocess_params)
elif self.det_algorithm == "EAST":
self.preprocess_op = EASTProcessTest(preprocess_params)
postprocess_params["score_thresh"] = args.det_east_score_thresh
postprocess_params["cover_thresh"] = args.det_east_cover_thresh
postprocess_params["nms_thresh"] = args.det_east_nms_thresh
self.postprocess_op = EASTPostPocess(postprocess_params)
elif self.det_algorithm == "SAST":
self.preprocess_op = SASTProcessTest(preprocess_params)
postprocess_params["score_thresh"] = args.det_sast_score_thresh
postprocess_params["nms_thresh"] = args.det_sast_nms_thresh
postprocess_params["sample_pts_num"] = args.det_sast_sample_pts_num
postprocess_params["expand_scale"] = args.det_sast_expand_scale
postprocess_params["shrink_ratio_of_width"] = args.det_sast_shrink_ratio_of_width
self.postprocess_op = SASTPostProcess(postprocess_params)
else:
logger.info("unknown det_algorithm:{}".format(self.det_algorithm))
sys.exit(0)
self.predictor, self.input_tensor, self.output_tensors =\
utility.create_predictor(args, mode="det")
def __init__(self, args):
if args.use_pdserving is False:
self.predictor, self.input_tensor, self.output_tensors = \
utility.create_predictor(args, mode="cls")
self.use_zero_copy_run = args.use_zero_copy_run
self.cls_image_shape = [int(v) for v in args.cls_image_shape.split(",")]
self.cls_batch_num = args.rec_batch_num
self.label_list = args.label_list
self.cls_thresh = args.cls_thresh
def __init__(self, args):
self.rec_image_shape = [int(v) for v in args.rec_image_shape.split(",")]
self.rec_batch_num = args.rec_batch_num
self.rec_algorithm = args.rec_algorithm
postprocess_params = {
'name': 'CTCLabelDecode',
"character_dict_path": args.rec_char_dict_path,
"use_space_char": args.use_space_char
}
if self.rec_algorithm == "SRN":
postprocess_params = {
'name': 'SRNLabelDecode',
"character_dict_path": args.rec_char_dict_path,
"use_space_char": args.use_space_char
}
elif self.rec_algorithm == "RARE":
postprocess_params = {
'name': 'AttnLabelDecode',
"character_dict_path": args.rec_char_dict_path,
"use_space_char": args.use_space_char
}
elif self.rec_algorithm == 'NRTR':
postprocess_params = {
'name': 'NRTRLabelDecode',
"character_dict_path": args.rec_char_dict_path,
"use_space_char": args.use_space_char
}
elif self.rec_algorithm == "SAR":
postprocess_params = {
'name': 'SARLabelDecode',
"character_dict_path": args.rec_char_dict_path,
"use_space_char": args.use_space_char
}
self.postprocess_op = build_post_process(postprocess_params)
self.predictor, self.input_tensor, self.output_tensors, self.config = \
utility.create_predictor(args, 'rec', logger)
self.benchmark = args.benchmark
self.use_onnx = args.use_onnx
if args.benchmark:
import auto_log
pid = os.getpid()
gpu_id = utility.get_infer_gpuid()
self.autolog = auto_log.AutoLogger(
model_name="rec",
model_precision=args.precision,
batch_size=args.rec_batch_num,
data_shape="dynamic",
save_path=None, #args.save_log_path,
inference_config=self.config,
pids=pid,
process_name=None,
gpu_ids=gpu_id if args.use_gpu else None,
time_keys=[
'preprocess_time', 'inference_time', 'postprocess_time'
],
warmup=0,
logger=logger)
def __init__(self, args):
self.cls_image_shape = [int(v) for v in args.cls_image_shape.split(",")]
self.cls_batch_num = args.cls_batch_num
self.cls_thresh = args.cls_thresh
postprocess_params = {
'name': 'ClsPostProcess',
"label_list": args.label_list,
}
self.postprocess_op = build_post_process(postprocess_params)
self.predictor, self.input_tensor, self.output_tensors = \
utility.create_predictor(args, 'cls', logger)
def __init__(self, args):
self.rec_image_shape = [
int(v) for v in args.rec_image_shape.split(",")
]
self.character_type = args.rec_char_type
self.rec_batch_num = args.rec_batch_num
self.rec_algorithm = args.rec_algorithm
self.use_zero_copy_run = args.use_zero_copy_run
postprocess_params = {
'name': 'CTCLabelDecode',
"character_type": args.rec_char_type,
"character_dict_path": args.rec_char_dict_path,
"use_space_char": args.use_space_char
}
self.postprocess_op = build_post_process(postprocess_params)
self.predictor, self.input_tensor, self.output_tensors = \
utility.create_predictor(args, 'rec', logger)
def __init__(self, args):
self.predictor, self.input_tensor, self.output_tensors =\
utility.create_predictor(args, mode="rec")
image_shape = [int(v) for v in args.rec_image_shape.split(",")]
self.rec_image_shape = image_shape
self.character_type = args.rec_char_type
self.rec_batch_num = args.rec_batch_num
self.rec_algorithm = args.rec_algorithm
char_ops_params = {}
char_ops_params["character_type"] = args.rec_char_type
char_ops_params["character_dict_path"] = args.rec_char_dict_path
if self.rec_algorithm != "RARE":
char_ops_params['loss_type'] = 'ctc'
self.loss_type = 'ctc'
else:
char_ops_params['loss_type'] = 'attention'
self.loss_type = 'attention'
self.char_ops = CharacterOps(char_ops_params)
def __init__(self, args):
max_side_len = args.det_max_side_len
self.det_algorithm = args.det_algorithm
preprocess_params = {
'test_image_shape': [640, 640],
'max_side_len': max_side_len
}
postprocess_params = {}
if self.det_algorithm == "DB":
self.preprocess_op = DBProcessTest(preprocess_params)
postprocess_params["thresh"] = args.det_db_thresh
postprocess_params["box_thresh"] = args.det_db_box_thresh
postprocess_params["max_candidates"] = 1000
postprocess_params["unclip_ratio"] = args.det_db_unclip_ratio
self.postprocess_op = DBPostProcess(postprocess_params)
elif self.det_algorithm == "EAST":
self.preprocess_op = EASTProcessTest(preprocess_params)
postprocess_params["score_thresh"] = args.det_east_score_thresh
postprocess_params["cover_thresh"] = args.det_east_cover_thresh
postprocess_params["nms_thresh"] = args.det_east_nms_thresh
self.postprocess_op = EASTPostPocess(postprocess_params)
elif self.det_algorithm == "SAST":
self.preprocess_op = SASTProcessTest(preprocess_params)
postprocess_params["score_thresh"] = args.det_sast_score_thresh
postprocess_params["nms_thresh"] = args.det_sast_nms_thresh
self.det_sast_polygon = args.det_sast_polygon
if self.det_sast_polygon:
postprocess_params["sample_pts_num"] = 6
postprocess_params["expand_scale"] = 1.2
postprocess_params["shrink_ratio_of_width"] = 0.2
else:
postprocess_params["sample_pts_num"] = 2
postprocess_params["expand_scale"] = 1.0
postprocess_params["shrink_ratio_of_width"] = 0.3
self.postprocess_op = SASTPostProcess(postprocess_params)
else:
logger.info("unknown det_algorithm:{}".format(self.det_algorithm))
sys.exit(0)
if args.use_pdserving is False:
self.use_zero_copy_run = args.use_zero_copy_run
self.predictor, self.input_tensor, self.output_tensors =\
utility.create_predictor(args, mode="det")
def __init__(self, args):
self.args = args
self.e2e_algorithm = args.e2e_algorithm
pre_process_list = [{
'E2EResizeForTest': {}
}, {
'NormalizeImage': {
'std': [0.229, 0.224, 0.225],
'mean': [0.485, 0.456, 0.406],
'scale': '1./255.',
'order': 'hwc'
}
}, {
'ToCHWImage': None
}, {
'KeepKeys': {
'keep_keys': ['image', 'shape']
}
}]
postprocess_params = {}
if self.e2e_algorithm == "PGNet":
pre_process_list[0] = {
'E2EResizeForTest': {
'max_side_len': args.e2e_limit_side_len,
'valid_set': 'totaltext'
}
}
postprocess_params['name'] = 'PGPostProcess'
postprocess_params["score_thresh"] = args.e2e_pgnet_score_thresh
postprocess_params["character_dict_path"] = args.e2e_char_dict_path
postprocess_params["valid_set"] = args.e2e_pgnet_valid_set
postprocess_params["mode"] = args.e2e_pgnet_mode
self.e2e_pgnet_polygon = args.e2e_pgnet_polygon
else:
logger.info("unknown e2e_algorithm:{}".format(self.e2e_algorithm))
sys.exit(0)
self.preprocess_op = create_operators(pre_process_list)
self.postprocess_op = build_post_process(postprocess_params)
self.predictor, self.input_tensor, self.output_tensors = utility.create_predictor(
args, 'e2e', logger) # paddle.jit.load(args.det_model_dir)
def __init__(self, args):
self.args = args
self.det_algorithm = args.det_algorithm
pre_process_list = [{
'DetResizeForTest': {
'limit_side_len': args.det_limit_side_len,
'limit_type': args.det_limit_type,
}
}, {
'NormalizeImage': {
'std': [0.229, 0.224, 0.225],
'mean': [0.485, 0.456, 0.406],
'scale': '1./255.',
'order': 'hwc'
}
}, {
'ToCHWImage': None
}, {
'KeepKeys': {
'keep_keys': ['image', 'shape']
}
}]
postprocess_params = {}
if self.det_algorithm == "DB":
postprocess_params['name'] = 'DBPostProcess'
postprocess_params["thresh"] = args.det_db_thresh
postprocess_params["box_thresh"] = args.det_db_box_thresh
postprocess_params["max_candidates"] = 1000
postprocess_params["unclip_ratio"] = args.det_db_unclip_ratio
postprocess_params["use_dilation"] = args.use_dilation
postprocess_params["score_mode"] = args.det_db_score_mode
elif self.det_algorithm == "EAST":
postprocess_params['name'] = 'EASTPostProcess'
postprocess_params["score_thresh"] = args.det_east_score_thresh
postprocess_params["cover_thresh"] = args.det_east_cover_thresh
postprocess_params["nms_thresh"] = args.det_east_nms_thresh
elif self.det_algorithm == "SAST":
pre_process_list[0] = {
'DetResizeForTest': {
'resize_long': args.det_limit_side_len
}
}
postprocess_params['name'] = 'SASTPostProcess'
postprocess_params["score_thresh"] = args.det_sast_score_thresh
postprocess_params["nms_thresh"] = args.det_sast_nms_thresh
self.det_sast_polygon = args.det_sast_polygon
if self.det_sast_polygon:
postprocess_params["sample_pts_num"] = 6
postprocess_params["expand_scale"] = 1.2
postprocess_params["shrink_ratio_of_width"] = 0.2
else:
postprocess_params["sample_pts_num"] = 2
postprocess_params["expand_scale"] = 1.0
postprocess_params["shrink_ratio_of_width"] = 0.3
else:
logger.info("unknown det_algorithm:{}".format(self.det_algorithm))
sys.exit(0)
self.preprocess_op = create_operators(pre_process_list)
self.postprocess_op = build_post_process(postprocess_params)
self.predictor, self.input_tensor, self.output_tensors, self.config = utility.create_predictor(
args, 'det', logger)
if args.benchmark:
import auto_log
pid = os.getpid()
self.autolog = auto_log.AutoLogger(model_name="det",
model_precision=args.precision,
batch_size=1,
data_shape="dynamic",
save_path=args.save_log_path,
inference_config=self.config,
pids=pid,
process_name=None,
gpu_ids=0,
time_keys=[
'preprocess_time',
'inference_time',
'postprocess_time'
],
warmup=10)
