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import os |
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import sys |
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__dir__ = os.path.dirname(os.path.abspath(__file__)) |
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sys.path.append(__dir__) |
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sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '..'))) |
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import torch |
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from openrec.modeling import build_model |
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from openrec.postprocess import build_post_process |
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from tools.engine.config import Config |
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from tools.infer_rec import build_rec_process |
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from tools.utility import ArgsParser |
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from tools.utils.ckpt import load_ckpt |
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from tools.utils.logging import get_logger |
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def to_onnx(model, dummy_input, dynamic_axes, sava_path='model.onnx'): |
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input_axis_name = ['batch_size', 'channel', 'in_width', 'int_height'] |
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output_axis_name = ['batch_size', 'channel', 'out_width', 'out_height'] |
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torch.onnx.export( |
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model.to('cpu'), |
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dummy_input, |
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sava_path, |
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input_names=['input'], |
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output_names=['output'], |
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dynamic_axes={ |
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'input': {axis: input_axis_name[axis] |
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for axis in dynamic_axes}, |
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'output': {axis: output_axis_name[axis] |
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for axis in dynamic_axes}, |
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}, |
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) |
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def export_single_model(model: torch.nn.Module, _cfg, export_dir, |
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export_config, logger, type): |
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for layer in model.modules(): |
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if hasattr(layer, 'rep') and not getattr(layer, 'is_repped'): |
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layer.rep() |
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os.makedirs(export_dir, exist_ok=True) |
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export_cfg = {'PostProcess': _cfg['PostProcess']} |
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export_cfg['Transforms'] = build_rec_process(_cfg) |
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cfg.save(os.path.join(export_dir, 'config.yaml'), export_cfg) |
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dummy_input = torch.randn(*export_config['export_shape'], device='cpu') |
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if type == 'script': |
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save_path = os.path.join(export_dir, 'model.pt') |
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trace_model = torch.jit.trace(model, dummy_input, strict=False) |
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torch.jit.save(trace_model, save_path) |
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elif type == 'onnx': |
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save_path = os.path.join(export_dir, 'model.onnx') |
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to_onnx(model, dummy_input, export_config.get('dynamic_axes', []), |
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save_path) |
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else: |
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raise NotImplementedError |
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logger.info(f'finish export model to {save_path}') |
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def main(cfg, type): |
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_cfg = cfg.cfg |
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logger = get_logger() |
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global_config = _cfg['Global'] |
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export_config = _cfg['Export'] |
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post_process_class = build_post_process(_cfg['PostProcess']) |
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char_num = len(getattr(post_process_class, 'character')) |
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cfg['Architecture']['Decoder']['out_channels'] = char_num |
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model = build_model(_cfg['Architecture']) |
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load_ckpt(model, _cfg) |
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model.eval() |
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export_dir = export_config.get('export_dir', '') |
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if not export_dir: |
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export_dir = os.path.join(global_config.get('output_dir', 'output'), |
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'export') |
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if _cfg['Architecture']['algorithm'] in ['Distillation' |
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]: |
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_cfg['PostProcess'][ |
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'name'] = post_process_class.__class__.__base__.__name__ |
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for model_name in model.model_list: |
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sub_model_save_path = os.path.join(export_dir, model_name) |
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export_single_model( |
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model.model_list[model_name], |
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_cfg, |
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sub_model_save_path, |
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export_config, |
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logger, |
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type, |
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) |
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else: |
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export_single_model(model, _cfg, export_dir, export_config, logger, |
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type) |
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def parse_args(): |
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parser = ArgsParser() |
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parser.add_argument('--type', |
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type=str, |
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default='onnx', |
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help='type of export') |
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args = parser.parse_args() |
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return args |
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if __name__ == '__main__': |
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FLAGS = parse_args() |
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cfg = Config(FLAGS.config) |
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FLAGS = vars(FLAGS) |
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opt = FLAGS.pop('opt') |
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cfg.merge_dict(FLAGS) |
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cfg.merge_dict(opt) |
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main(cfg, FLAGS['type']) |
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