fd32b990ac
- support for PyTorch 1.7 and TensorRT 7.2 - limit sample audio file length
55 lines
2.4 KiB
Python
55 lines
2.4 KiB
Python
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
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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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# 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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import time
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import torch
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from fastspeech.utils.logging import tprint
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class TimeElapsed(object):
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def __init__(self, name, device='cuda', cuda_sync=False, format=""):
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self.name = name
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self.device = device
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self.cuda_sync = cuda_sync
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self.format = format
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def __enter__(self):
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self.start()
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def __exit__(self, *exc_info):
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self.end()
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def start(self):
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if self.device == 'cuda' and self.cuda_sync:
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torch.cuda.synchronize()
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self.start_time = time.time()
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def end(self):
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if not hasattr(self, "start_time"):
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return
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if self.device == 'cuda' and self.cuda_sync:
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torch.cuda.synchronize()
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self.end_time = time.time()
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self.time_elapsed = self.end_time - self.start_time
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tprint(("[{}] Time elapsed: {" + self.format + "}").format(self.name, self.time_elapsed)) |