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import tensorrt as trt

import sys, os
sys.path.insert(1, os.path.join(sys.path[0], ".."))

# You can set the logger severity higher to suppress messages (or lower to display more messages).
TRT_LOGGER = trt.Logger(trt.Logger.WARNING)

class ModelData(object):
    INPUT_NAME ="conv2d_input"
    INPUT_SHAPE = (1, 28, 28)
    OUTPUT_NAME = "dense_2/Softmax"

def build_engine(model_file):
    # For more information on TRT basics, refer to the introductory samples.
    with trt.Builder(TRT_LOGGER) as builder, builder.create_network() as network, trt.UffParser() as parser:
        builder.max_workspace_size = 4 << 30    # 4 GiB
        builder.max_batch_size = 1
        # Parse the Uff Network
        parser.register_input(ModelData.INPUT_NAME, ModelData.INPUT_SHAPE)
        parser.register_output(ModelData.OUTPUT_NAME)
        parser.parse(model_file, network)
        # Build and return an engine.
        return builder.build_cuda_engine(network)

def main():
    if len(sys.argv) != 3:
        print(f'{sys.argv[0]} model_file.uff engine_file.engine')
        return

    model_file = sys.argv[1]
    engine_file = sys.argv[2]
    print(f'model file: {model_file}')

    with build_engine(model_file) as engine, open(engine_file, 'wb') as f:
        f.write(engine.serialize())
        print(f'written engine to {engine_file}')


if __name__ == '__main__':
    main()
