127 lines
5.3 KiB
Python
127 lines
5.3 KiB
Python
# *****************************************************************************
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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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 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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# * Neither the name of the NVIDIA CORPORATION nor the
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# names of its contributors may be used to endorse or promote products
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# derived from this software without specific prior written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
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# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
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# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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# DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
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# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
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# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
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# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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# (INCLUDING 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 models
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import torch
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import argparse
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import numpy as np
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import json
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import time
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from inference import checkpoint_from_distributed, unwrap_distributed, load_and_setup_model, MeasureTime
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import dllogger as DLLogger
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from dllogger import StdOutBackend, JSONStreamBackend, Verbosity
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from apex import amp
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def parse_args(parser):
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"""
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Parse commandline arguments.
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"""
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parser.add_argument('-m', '--model-name', type=str, default='', required=True,
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help='Model to train')
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parser.add_argument('-sr', '--sampling-rate', default=22050, type=int,
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help='Sampling rate')
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parser.add_argument('--amp-run', action='store_true',
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help='inference with AMP')
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parser.add_argument('-bs', '--batch-size', type=int, default=1)
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parser.add_argument('-o', '--output', type=str, required=True,
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help='Directory to save results')
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parser.add_argument('--log-file', type=str, default='nvlog.json',
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help='Filename for logging')
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return parser
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def main():
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"""
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Launches inference benchmark.
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Inference is executed on a single GPU.
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"""
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parser = argparse.ArgumentParser(
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description='PyTorch Tacotron 2 Inference')
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parser = parse_args(parser)
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args, _ = parser.parse_known_args()
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log_file = args.log_file
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DLLogger.init(backends=[JSONStreamBackend(Verbosity.DEFAULT,
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args.output+'/'+args.log_file),
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StdOutBackend(Verbosity.VERBOSE)])
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for k,v in vars(args).items():
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DLLogger.log(step="PARAMETER", data={k:v})
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DLLogger.log(step="PARAMETER", data={'model_name':'Tacotron2_PyT'})
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model = load_and_setup_model(args.model_name, parser, None, args.amp_run,
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forward_is_infer=True)
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if args.model_name == "Tacotron2":
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model = torch.jit.script(model)
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warmup_iters = 3
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num_iters = 1+warmup_iters
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for i in range(num_iters):
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measurements = {}
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if args.model_name == 'Tacotron2':
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text_padded = torch.randint(low=0, high=148, size=(args.batch_size, 140),
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dtype=torch.long).cuda()
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input_lengths = torch.IntTensor([text_padded.size(1)]*args.batch_size).cuda().long()
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with torch.no_grad(), MeasureTime(measurements, "inference_time"):
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mels, _, _ = model(text_padded, input_lengths)
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num_items = mels.size(0)*mels.size(2)
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if args.model_name == 'WaveGlow':
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n_mel_channels = model.upsample.in_channels
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num_mels = 895
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mel_padded = torch.zeros(args.batch_size, n_mel_channels,
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num_mels).normal_(-5.62, 1.98).cuda()
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if args.amp_run:
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mel_padded = mel_padded.half()
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with torch.no_grad(), MeasureTime(measurements, "inference_time"):
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audios = model(mel_padded)
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audios = audios.float()
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num_items = audios.size(0)*audios.size(1)
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if i >= warmup_iters:
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DLLogger.log(step=(i-warmup_iters,), data={"latency": measurements['inference_time']})
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DLLogger.log(step=(i-warmup_iters,), data={"items_per_sec": num_items/measurements['inference_time']})
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DLLogger.log(step=tuple(),
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data={'infer_latency': measurements['inference_time']})
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DLLogger.log(step=tuple(),
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data={'infer_items_per_sec': num_items/measurements['inference_time']})
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DLLogger.flush()
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if __name__ == '__main__':
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main()
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