feat: TensorRT model & inference
This commit is contained in:
parent
514d061e7e
commit
3e418bb2dc
70
TensorRT_Model/convert_model.py
Normal file
70
TensorRT_Model/convert_model.py
Normal file
@ -0,0 +1,70 @@
|
||||
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
184
common.py
Normal file
@ -0,0 +1,184 @@
|
||||
#
|
||||
# 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]
|
45
inference_rt.py
Normal file
45
inference_rt.py
Normal file
@ -0,0 +1,45 @@
|
||||
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)}')
|
||||
|
||||
|
Loading…
x
Reference in New Issue
Block a user