def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, config_file, pytorch_dump_path, base_model):
    # Initialise PyTorch model
    config = FunnelConfig.from_json_file(config_file)
    print(f"Building PyTorch model from configuration: {config}")
    model = FunnelBaseModel(config) if base_model else FunnelModel(config)

    # Load weights from tf checkpoint
    load_tf_weights_in_funnel(model, config, tf_checkpoint_path)

    # Save pytorch-model
    print(f"Save PyTorch model to {pytorch_dump_path}")
    torch.save(model.state_dict(), pytorch_dump_path)
Esempio n. 2
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def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, config_file,
                                     pytorch_dump_path):
    # Initialise PyTorch model
    config = FunnelConfig.from_json_file(config_file)
    print("Building PyTorch model from configuration: {}".format(str(config)))
    model = FunnelForPreTraining(config)

    # Load weights from tf checkpoint
    load_tf_weights_in_funnel(model, config, tf_checkpoint_path)

    # Save pytorch-model
    print("Save PyTorch model to {}".format(pytorch_dump_path))
    torch.save(model.state_dict(), pytorch_dump_path)