# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import sys __dir__ = os.path.dirname(os.path.abspath(__file__)) sys.path.append(__dir__) sys.path.insert(0, os.path.abspath(os.path.join(__dir__, ".."))) import argparse import paddle from paddle.jit import to_static from ppocr.modeling.architectures import build_model from ppocr.postprocess import build_post_process from ppocr.utils.save_load import load_model from ppocr.utils.logging import get_logger from tools.program import load_config, merge_config, ArgsParser def export_single_model(model, arch_config, save_path, logger, input_shape=None, quanter=None): if arch_config["algorithm"] == "SRN": max_text_length = arch_config["Head"]["max_text_length"] other_shape = [ paddle.static.InputSpec( shape=[None, 1, 64, 256], dtype="float32"), [ paddle.static.InputSpec( shape=[None, 256, 1], dtype="int64"), paddle.static.InputSpec( shape=[None, max_text_length, 1], dtype="int64"), paddle.static.InputSpec( shape=[None, 8, max_text_length, max_text_length], dtype="int64"), paddle.static.InputSpec( shape=[None, 8, max_text_length, max_text_length], dtype="int64") ] ] model = to_static(model, input_spec=other_shape) elif arch_config["algorithm"] == "SAR": other_shape = [ paddle.static.InputSpec( shape=[None, 3, 48, 160], dtype="float32"), ] model = to_static(model, input_spec=other_shape) elif arch_config["algorithm"] == "SVTR": if arch_config["Head"]["name"] == 'MultiHead': other_shape = [ paddle.static.InputSpec( shape=[None, 3, 48, -1], dtype="float32"), ] else: other_shape = [ paddle.static.InputSpec( shape=[None] + input_shape, dtype="float32"), ] model = to_static(model, input_spec=other_shape) elif arch_config["algorithm"] == "PREN": other_shape = [ paddle.static.InputSpec( shape=[None, 3, 64, 512], dtype="float32"), ] model = to_static(model, input_spec=other_shape) elif arch_config["model_type"] == "sr": other_shape = [ paddle.static.InputSpec( shape=[None, 3, 16, 64], dtype="float32") ] model = to_static(model, input_spec=other_shape) elif arch_config["algorithm"] == "ViTSTR": other_shape = [ paddle.static.InputSpec( shape=[None, 1, 224, 224], dtype="float32"), ] model = to_static(model, input_spec=other_shape) elif arch_config["algorithm"] == "ABINet": other_shape = [ paddle.static.InputSpec( shape=[None, 3, 32, 128], dtype="float32"), ] # print([None, 3, 32, 128]) model = to_static(model, input_spec=other_shape) elif arch_config["algorithm"] in ["NRTR", "SPIN"]: other_shape = [ paddle.static.InputSpec( shape=[None, 1, 32, 100], dtype="float32"), ] model = to_static(model, input_spec=other_shape) elif arch_config["algorithm"] == "VisionLAN": other_shape = [ paddle.static.InputSpec( shape=[None, 3, 64, 256], dtype="float32"), ] model = to_static(model, input_spec=other_shape) elif arch_config["algorithm"] in ["LayoutLM", "LayoutLMv2", "LayoutXLM"]: input_spec = [ paddle.static.InputSpec( shape=[None, 512], dtype="int64"), # input_ids paddle.static.InputSpec( shape=[None, 512, 4], dtype="int64"), # bbox paddle.static.InputSpec( shape=[None, 512], dtype="int64"), # attention_mask paddle.static.InputSpec( shape=[None, 512], dtype="int64"), # token_type_ids paddle.static.InputSpec( shape=[None, 3, 224, 224], dtype="int64"), # image ] if arch_config["algorithm"] == "LayoutLM": input_spec.pop(4) model = to_static(model, input_spec=[input_spec]) else: infer_shape = [3, -1, -1] if arch_config["model_type"] == "rec": infer_shape = [3, 48, -1] # for rec model, H must be 32 if "Transform" in arch_config and arch_config[ "Transform"] is not None and arch_config["Transform"][ "name"] == "TPS": logger.info( "When there is tps in the network, variable length input is not supported, and the input size needs to be the same as during training" ) infer_shape[-1] = 100 elif arch_config["model_type"] == "table": infer_shape = [3, 488, 488] if arch_config["algorithm"] == "TableMaster": infer_shape = [3, 480, 480] if arch_config["algorithm"] == "SLANet": infer_shape = [3, -1, -1] model = to_static( model, input_spec=[ paddle.static.InputSpec( shape=[None] + infer_shape, dtype="float32") ]) if quanter is None: paddle.jit.save(model, save_path) else: quanter.save_quantized_model(model, save_path) logger.info("inference model is saved to {}".format(save_path)) return def main(): FLAGS = ArgsParser().parse_args() config = load_config(FLAGS.config) config = merge_config(config, FLAGS.opt) logger = get_logger() # build post process post_process_class = build_post_process(config["PostProcess"], config["Global"]) # build model # for rec algorithm if hasattr(post_process_class, "character"): char_num = len(getattr(post_process_class, "character")) if config["Architecture"]["algorithm"] in ["Distillation", ]: # distillation model for key in config["Architecture"]["Models"]: if config["Architecture"]["Models"][key]["Head"][ "name"] == 'MultiHead': # multi head out_channels_list = {} if config['PostProcess'][ 'name'] == 'DistillationSARLabelDecode': char_num = char_num - 2 out_channels_list['CTCLabelDecode'] = char_num out_channels_list['SARLabelDecode'] = char_num + 2 config['Architecture']['Models'][key]['Head'][ 'out_channels_list'] = out_channels_list else: config["Architecture"]["Models"][key]["Head"][ "out_channels"] = char_num # just one final tensor needs to exported for inference config["Architecture"]["Models"][key][ "return_all_feats"] = False elif config['Architecture']['Head'][ 'name'] == 'MultiHead': # multi head out_channels_list = {} char_num = len(getattr(post_process_class, 'character')) if config['PostProcess']['name'] == 'SARLabelDecode': char_num = char_num - 2 out_channels_list['CTCLabelDecode'] = char_num out_channels_list['SARLabelDecode'] = char_num + 2 config['Architecture']['Head'][ 'out_channels_list'] = out_channels_list else: # base rec model config["Architecture"]["Head"]["out_channels"] = char_num # for sr algorithm if config["Architecture"]["model_type"] == "sr": config['Architecture']["Transform"]['infer_mode'] = True model = build_model(config["Architecture"]) load_model(config, model, model_type=config['Architecture']["model_type"]) model.eval() save_path = config["Global"]["save_inference_dir"] arch_config = config["Architecture"] if arch_config["algorithm"] == "SVTR" and arch_config["Head"][ "name"] != 'MultiHead': input_shape = config["Eval"]["dataset"]["transforms"][-2][ 'SVTRRecResizeImg']['image_shape'] else: input_shape = None if arch_config["algorithm"] in ["Distillation", ]: # distillation model archs = list(arch_config["Models"].values()) for idx, name in enumerate(model.model_name_list): sub_model_save_path = os.path.join(save_path, name, "inference") export_single_model(model.model_list[idx], archs[idx], sub_model_save_path, logger) else: save_path = os.path.join(save_path, "inference") export_single_model( model, arch_config, save_path, logger, input_shape=input_shape) if __name__ == "__main__": main()