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onnx2trt.py
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#
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import os
import sys
import numpy as np
from glob import glob
from datetime import datetime as dt
# import torch as t
# #import torchvision as tv # 使用 pyTorch 默认的 MNIST 数据（含下载）
# from torch.utils import data
# import torch.nn.functional as F
# from torch.autograd import Variable
from cuda import cudart
import tensorrt as trt
# from trt_qant import calibrator
import ctypes
cacheFile = "./int8.cache"
calibrationCount = 10
np.set_printoptions(precision=4, linewidth=200, suppress=True)
cudart.cudaDeviceSynchronize()
planFilePath = "./"
soFileList = ["./FasterTransformer_wenet/build/lib/libwenet_plugin.so"]
#-------------------------------------------------------------------------------
logger = trt.Logger(trt.Logger.VERBOSE)
trt.init_libnvinfer_plugins(logger, '')
if len(soFileList) > 0:
print("Find Plugin %s!"%soFileList)
else:
print("No Plugin!")
for soFile in soFileList:
ctypes.cdll.LoadLibrary(soFile)
# calibration.npz files
# 'speech-16', 'speech-64', 'speech-256',
# 'speech_lengths-16', 'speech_lengths-64', 'speech_lengths-256',
# 'encoder_out-16', 'encoder_out-64', 'encoder_out-256',
# 'encoder_out_lens-16', 'encoder_out_lens-64', 'encoder_out_lens-256',
# 'hyps_pad_sos_eos-16', 'hyps_pad_sos_eos-64', 'hyps_pad_sos_eos-256',
# 'hyps_lens_sos-16', 'hyps_lens_sos-64', 'hyps_lens_sos-256',
# 'ctc_score-16', 'ctc_score-64', 'ctc_score-256'
ckey = sys.argv[1] # encoder or decoder
assert ckey in ["encoder", "decoder"]
onnxFile = sys.argv[2]
trtFile = sys.argv[3]
# calibData = np.load("data/calibration.npz")
# if ckey == "encoder":
# npDataList = [
# {
# "speech": calibData['speech-16'],
# "speech_lengths": calibData['speech_lengths-16']
# },
# # {
# # "speech": calibData['speech-64'],
# # "speech_lengths": calibData['speech_lengths-64']
# # },
# # {
# # "speech": calibData['speech-256'],
# # "speech_lengths": calibData['speech_lengths-256']
# # },
# ]
# inputShapes = {"speech": (1,16,80), "speech_lengths": (1,)}
# TensorRT 中加载 .onnx 创建 engine ----------------------------------------------
logger = trt.Logger(trt.Logger.INFO)
logger.min_severity = trt.Logger.Severity.VERBOSE
if 1:
builder = trt.Builder(logger)
network = builder.create_network(1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
profile = builder.create_optimization_profile()
config = builder.create_builder_config()
if 1 or ckey == "encoder":
config.flags = 1 << int(trt.BuilderFlag.INT8)
# config.int8_calibrator = calibrator.MyCalibrator(npDataList, calibrationCount, inputShapes, cacheFile)
# config.max_workspace_size = 1 << 50
parser = trt.OnnxParser(network, logger)
if not os.path.exists(onnxFile):
print("Failed finding onnx file!")
exit()
print("Succeeded finding onnx file!")
with open(onnxFile, 'rb') as model:
if not parser.parse(model.read()):
print("Failed parsing .onnx file!")
for error in range(parser.num_errors):
print(parser.get_error(error))
exit()
print("Succeeded parsing .onnx file!")
if ckey == "decoder":
profile.set_shape("encoder_out", (1, 40, 256), (4, 165, 256), (16, 370, 256))
profile.set_shape("encoder_out_lens", (1,), (4,), (16,))
profile.set_shape("hyps_pad_sos_eos", (1, 10, 64), (4, 10, 64), (16, 10, 64))
profile.set_shape("hyps_lens_sos", (1, 10), (4, 10), (16, 10))
profile.set_shape("ctc_score", (1, 10), (4, 10), (16, 10))
profile.set_shape("self_attn_mask", (10, 63, 63), (40, 63, 63), (160, 63, 63))
profile.set_shape("cross_attn_mask", (10, 63, 40), (40, 63, 165), (160, 63, 370))
elif ckey == "encoder":
profile.set_shape("speech", (1, 1, 80), (4, 750, 80), (16, 1500, 80))
profile.set_shape("speech_lengths", (1,), (4,), (16,))
profile.set_shape("speech_lengths_mask", (1, 40, 40), (4, 220, 220), (16, 400, 400))
# if ckey == "decoder":
# profile.set_shape("encoder_out", (1,16,256), (4,64,256), (16,256,256))
# profile.set_shape("encoder_out_lens", (1,), (4,), (16,))
# profile.set_shape("hyps_pad_sos_eos", (1,10,64), (4,10,64), (16,10,64))
# profile.set_shape("hyps_lens_sos", (1,10), (4,10), (16,10))
# profile.set_shape("ctc_score", (1,10), (4,10), (16,10))
# profile.set_shape("self_attn_mask", (10,63,63), (40,63,63), (160,63,63))
# profile.set_shape("cross_attn_mask", (10,63,16), (40,63,64), (160,63,256))
# elif ckey == "encoder":
# profile.set_shape("speech", (1,16,80), (4,64,80), (16,256,80))
# profile.set_shape("speech_lengths", (1,), (4,), (16,))
# profile.set_shape("speech_lengths_mask", (1,3,3), (4,15,15), (16,63,63))
config.add_optimization_profile(profile)
engineString = builder.build_serialized_network(network, config)
if engineString == None:
print("Failed building engine!")
exit()
print("Succeeded building engine!")
with open(trtFile, 'wb') as f:
f.write(engineString)
engine = trt.Runtime(logger).deserialize_cuda_engine(engineString)