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ddd5669e79
Removing one of the 2d conv layers for pitch estimation reduces complexity without noticeable degradation. FARGAN model has more adversarial training. Also, no need for the double precision in the low-pass filter.
109 lines
3.4 KiB
Python
109 lines
3.4 KiB
Python
"""
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/* Copyright (c) 2022 Amazon
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Written by Jan Buethe */
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/*
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Redistribution and use in source and binary forms, with or without
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modification, are permitted provided that the following conditions
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are met:
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- Redistributions of source code must retain the above copyright
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notice, this list of conditions and the following disclaimer.
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- Redistributions in binary form must reproduce the above copyright
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notice, this list of conditions and the following disclaimer in the
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documentation and/or other materials provided with the distribution.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
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A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER
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OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
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LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
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NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*/
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"""
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import os
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import argparse
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import sys
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sys.path.append(os.path.join(os.path.dirname(__file__), '../weight-exchange'))
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parser = argparse.ArgumentParser()
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parser.add_argument('checkpoint', type=str, help='model checkpoint')
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parser.add_argument('output_dir', type=str, help='output folder')
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args = parser.parse_args()
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import torch
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import numpy as np
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from models import PitchDNN
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from wexchange.torch import dump_torch_weights
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from wexchange.c_export import CWriter, print_vector
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def c_export(args, model):
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message = f"Auto generated from checkpoint {os.path.basename(args.checkpoint)}"
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writer = CWriter(os.path.join(args.output_dir, "pitchdnn_data"), message=message, model_struct_name='PitchDNN')
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writer.header.write(
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f"""
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#include "opus_types.h"
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"""
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)
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dense_layers = [
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('if_upsample.0', "dense_if_upsampler_1"),
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('if_upsample.2', "dense_if_upsampler_2"),
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('downsample.0', "dense_downsampler"),
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("upsample.0", "dense_final_upsampler")
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]
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for name, export_name in dense_layers:
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layer = model.get_submodule(name)
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dump_torch_weights(writer, layer, name=export_name, verbose=True, quantize=True, scale=None)
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conv_layers = [
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('conv.1', "conv2d_1"),
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('conv.4', "conv2d_2")
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]
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for name, export_name in conv_layers:
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layer = model.get_submodule(name)
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dump_torch_weights(writer, layer, name=export_name, verbose=True)
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gru_layers = [
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("GRU", "gru_1"),
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]
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max_rnn_units = max([dump_torch_weights(writer, model.get_submodule(name), export_name, verbose=True, input_sparse=False, quantize=True, scale=None, recurrent_scale=None)
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for name, export_name in gru_layers])
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writer.header.write(
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f"""
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#define PITCH_DNN_MAX_RNN_UNITS {max_rnn_units}
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"""
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)
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writer.close()
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if __name__ == "__main__":
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os.makedirs(args.output_dir, exist_ok=True)
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model = PitchDNN()
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checkpoint = torch.load(args.checkpoint, map_location='cpu')
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model.load_state_dict(checkpoint['state_dict'])
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c_export(args, model)
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