forked from DataXujing/wenet_trt8
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathonnx2trt.py
executable file
·145 lines (130 loc) · 5.84 KB
/
onnx2trt.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
#
# 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)