feat: TensorRT model & inference

This commit is contained in:
Jetson 2023-01-18 16:01:11 +08:00
parent 514d061e7e
commit 3e418bb2dc
3 changed files with 299 additions and 0 deletions

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import multiprocessing
keras_path = '../model_without_preprocess_finetuned.h5'
onnx_path = 'model.onnx'
# convert to onnx
def keras2onnx():
import os
import onnx
import onnxmltools
import tensorflow as tf
from tensorflow import keras
print('[*] Converting Keras Model to onnx')
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
tf.get_logger().setLevel('ERROR')
keras_model = keras.models.load_model(keras_path)
onnx_model = onnxmltools.convert_keras(keras_model)
# one data at a time
onnx_model.graph.input[0].type.tensor_type.shape.dim[0].dim_value = 1
onnx_model.graph.input[1].type.tensor_type.shape.dim[0].dim_value = 1
onnx_model.graph.output[0].type.tensor_type.shape.dim[0].dim_value = 1
onnx.checker.check_model(onnx_model)
onnx.save(onnx_model, onnx_path)
print(f'[*] onnx file saved as {onnx_path}')
def onnx2rt():
# convert to tensorrt
import tensorrt as trt
TRT_LOGGER = trt.Logger(trt.Logger.ERROR)
EXPLICIT_BATCH = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
trt_file = '../model.trt'
print('[*] Converting onnx to tensorrt')
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(network, TRT_LOGGER) as parser:
with open(onnx_path, 'rb') as f:
if not parser.parse(f.read()):
print('ERROR: Failed to parse the ONNX file.')
for error in range(parser.num_errors):
print (parser.get_error(error))
config = builder.create_builder_config()
profile = builder.create_optimization_profile()
config.add_optimization_profile(profile)
# config.flags = 1 << (int)(trt.BuilderFlag.DEBUG)
with open(trt_file, "wb") as f:
f.write(builder.build_serialized_network(network, config))
print(f'[*] tensorrt file saved as {trt_file}')
if __name__ == "__main__":
onnx = multiprocessing.Process(target=keras2onnx)
rt = multiprocessing.Process(target=onnx2rt)
onnx.start()
onnx.join()
rt.start()
rt.join()

184
common.py Normal file
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#
# SPDX-FileCopyrightText: Copyright (c) 1993-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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 argparse
import os
import numpy as np
import pycuda.autoinit
import pycuda.driver as cuda
import tensorrt as trt
try:
# Sometimes python does not understand FileNotFoundError
FileNotFoundError
except NameError:
FileNotFoundError = IOError
EXPLICIT_BATCH = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
def GiB(val):
return val * 1 << 30
def add_help(description):
parser = argparse.ArgumentParser(description=description, formatter_class=argparse.ArgumentDefaultsHelpFormatter)
args, _ = parser.parse_known_args()
def find_sample_data(description="Runs a TensorRT Python sample", subfolder="", find_files=[], err_msg=""):
"""
Parses sample arguments.
Args:
description (str): Description of the sample.
subfolder (str): The subfolder containing data relevant to this sample
find_files (str): A list of filenames to find. Each filename will be replaced with an absolute path.
Returns:
str: Path of data directory.
"""
# Standard command-line arguments for all samples.
kDEFAULT_DATA_ROOT = os.path.join(os.sep, "usr", "src", "tensorrt", "data")
parser = argparse.ArgumentParser(description=description, formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument(
"-d",
"--datadir",
help="Location of the TensorRT sample data directory, and any additional data directories.",
action="append",
default=[kDEFAULT_DATA_ROOT],
)
args, _ = parser.parse_known_args()
def get_data_path(data_dir):
# If the subfolder exists, append it to the path, otherwise use the provided path as-is.
data_path = os.path.join(data_dir, subfolder)
if not os.path.exists(data_path):
if data_dir != kDEFAULT_DATA_ROOT:
print("WARNING: " + data_path + " does not exist. Trying " + data_dir + " instead.")
data_path = data_dir
# Make sure data directory exists.
if not (os.path.exists(data_path)) and data_dir != kDEFAULT_DATA_ROOT:
print(
"WARNING: {:} does not exist. Please provide the correct data path with the -d option.".format(
data_path
)
)
return data_path
data_paths = [get_data_path(data_dir) for data_dir in args.datadir]
return data_paths, locate_files(data_paths, find_files, err_msg)
def locate_files(data_paths, filenames, err_msg=""):
"""
Locates the specified files in the specified data directories.
If a file exists in multiple data directories, the first directory is used.
Args:
data_paths (List[str]): The data directories.
filename (List[str]): The names of the files to find.
Returns:
List[str]: The absolute paths of the files.
Raises:
FileNotFoundError if a file could not be located.
"""
found_files = [None] * len(filenames)
for data_path in data_paths:
# Find all requested files.
for index, (found, filename) in enumerate(zip(found_files, filenames)):
if not found:
file_path = os.path.abspath(os.path.join(data_path, filename))
if os.path.exists(file_path):
found_files[index] = file_path
# Check that all files were found
for f, filename in zip(found_files, filenames):
if not f or not os.path.exists(f):
raise FileNotFoundError(
"Could not find {:}. Searched in data paths: {:}\n{:}".format(filename, data_paths, err_msg)
)
return found_files
# Simple helper data class that's a little nicer to use than a 2-tuple.
class HostDeviceMem(object):
def __init__(self, host_mem, device_mem):
self.host = host_mem
self.device = device_mem
def __str__(self):
return "Host:\n" + str(self.host) + "\nDevice:\n" + str(self.device)
def __repr__(self):
return self.__str__()
# Allocates all buffers required for an engine, i.e. host/device inputs/outputs.
def allocate_buffers(engine):
inputs = []
outputs = []
bindings = []
stream = cuda.Stream()
for binding in engine:
size = trt.volume(engine.get_binding_shape(binding)) * engine.max_batch_size
dtype = trt.nptype(engine.get_binding_dtype(binding))
# Allocate host and device buffers
host_mem = cuda.pagelocked_empty(size, dtype)
device_mem = cuda.mem_alloc(host_mem.nbytes)
# Append the device buffer to device bindings.
bindings.append(int(device_mem))
# Append to the appropriate list.
if engine.binding_is_input(binding):
inputs.append(HostDeviceMem(host_mem, device_mem))
else:
outputs.append(HostDeviceMem(host_mem, device_mem))
return inputs, outputs, bindings, stream
# This function is generalized for multiple inputs/outputs.
# inputs and outputs are expected to be lists of HostDeviceMem objects.
def do_inference(context, bindings, inputs, outputs, stream, batch_size=1):
# Transfer input data to the GPU.
[cuda.memcpy_htod_async(inp.device, inp.host, stream) for inp in inputs]
# Run inference.
context.execute_async(batch_size=batch_size, bindings=bindings, stream_handle=stream.handle)
# Transfer predictions back from the GPU.
[cuda.memcpy_dtoh_async(out.host, out.device, stream) for out in outputs]
# Synchronize the stream
stream.synchronize()
# Return only the host outputs.
return [out.host for out in outputs]
# This function is generalized for multiple inputs/outputs for full dimension networks.
# inputs and outputs are expected to be lists of HostDeviceMem objects.
def do_inference_v2(context, bindings, inputs, outputs, stream):
# Transfer input data to the GPU.
[cuda.memcpy_htod_async(inp.device, inp.host, stream) for inp in inputs]
# Run inference.
context.execute_async_v2(bindings=bindings, stream_handle=stream.handle)
# Transfer predictions back from the GPU.
[cuda.memcpy_dtoh_async(out.host, out.device, stream) for out in outputs]
# Synchronize the stream
stream.synchronize()
# Return only the host outputs.
return [out.host for out in outputs]

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inference_rt.py Normal file
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labels = ['can', 'paper_cup', 'paper_box', 'paper_milkbox', 'plastic']
print("[*] Importing packages...")
import common
import tensorrt as trt
import os
import cv2
import pandas as pd
import numpy as np
TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
print("[*] Loading model...")
# load trt engine
trt_path = 'model.trt'
with open(trt_path, "rb") as f, trt.Runtime(TRT_LOGGER) as runtime:
engine = runtime.deserialize_cuda_engine(f.read())
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
if __name__ == '__main__':
def pred(f, dirpath):
img = cv2.imread(os.path.join(dirpath, f))
weight = df.loc[f]['weight']
inputs[0].host = np.expand_dims(img, 0).astype('float32')
inputs[1].host = np.expand_dims(weight, 0).astype('float32')
# inference
with engine.create_execution_context() as context:
trt_outputs = common.do_inference_v2(context, bindings=bindings, inputs=inputs, outputs=outputs,stream=stream)
result = trt_outputs[0].argmax(-1)
return labels[result]
df = pd.read_csv('test_data/weights_test.csv')
df = df.set_index('name')
for dirpath, dirnames, filenames in os.walk('test_data'):
for f in filenames:
if f.endswith('.jpg'):
print(f'{f}: {pred(f, dirpath)}')