自己训练的Resnet部署（python版本）
路线：xx.pth -> xx.onnx -> xx.trt
实验版本：torch 1.10.0；tensorrt 7.2.3.4； onnx 1.8.1
1. pth2onnx.py # 将pytorch训练的模型xx.pth转为xx.onnx。
import torch
from model import resnet50 # 导入模型结构
import onnx
print(torch.__version__)
pth_filename = './resNet50.pth' # 训练好的权重
onnx_filename = './resNet50.onnx'
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
net = resnet50(num_classes=10).to(device) # num_classes 根据你的类别个数调整
net.load_state_dict(torch.load(pth_filename, map_location=device))
net.eval()
dummy_input = torch.randn(1, 3, 224, 224, device = device)
torch.onnx.export(net, dummy_input, onnx_filename,
input_names=['input'], output_names=['ouput'],
export_params=True, verbose=False, opset_version=12,
dynamic_axes={'input':{0:"batch_size"},
'output':{0:"batch_size"}})
# 检查一下生成的onnx
test = onnx.load("resNet50.onnx")
onnx.checker.check_model(test)
print("==> Passed")2.onnx2trt2infer.py # 转xx.trt 并推理
import os
# This sample uses an ONNX ResNet50 Model to create a TensorRT Inference Engine
import random
import sys
import numpy as np
# This import causes pycuda to automatically manage CUDA context creation and cleanup.
import pycuda.autoinit
import tensorrt as trt
from PIL import Image
import common
import json
import time
class ModelData(object):
MODEL_PATH = "resNet50.onnx"
INPUT_SHAPE = (3, 224, 224)
# We can convert TensorRT data types to numpy types with trt.nptype()
DTYPE = trt.float32
# You can set the logger severity higher to suppress messages (or lower to display more messages).
TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
# The Onnx path is used for Onnx models.
def get_engine_onnx(engine_file_path, model_file):
def build_engine_onnx(model_file):
builder = trt.Builder(TRT_LOGGER)
network = builder.create_network(common.EXPLICIT_BATCH)
config = builder.create_builder_config()
parser = trt.OnnxParser(network, TRT_LOGGER)
config.max_workspace_size = common.GiB(1)
# Load the Onnx model and parse it in order to populate the TensorRT network.
with open(model_file, 'rb') as model:
if not parser.parse(model.read()):
print ('ERROR: Failed to parse the ONNX file.')
for error in range(parser.num_errors):
print (parser.get_error(error))
return None
print("num layers:",network.num_layers)
network.get_input(0).shape = [1, 3, 224, 224]
engine = builder.build_cuda_engine(network)
print("Completed creating Engine")
with open(engine_file_path, "wb") as f:
f.write(engine.serialize())
return engine
if os.path.exists(engine_file_path):
# If a serialized engine exists, load it instead of building a new one.
print("Reading engine from file {}".format(engine_file_path))
with open(engine_file_path, "rb") as f, trt.Runtime(TRT_LOGGER) as runtime:
return runtime.deserialize_cuda_engine(f.read())
# return builder.build_engine(network, config)
return build_engine_onnx(model_file)
def load_normalized_test_case(test_image, pagelocked_buffer):
# Converts the input image to a CHW Numpy array
def normalize_image(image):
# Resize, antialias and transpose the image to CHW.
c, h, w = ModelData.INPUT_SHAPE
image_arr = np.asarray(image.resize((w, h), Image.ANTIALIAS)).transpose([2, 0, 1]).astype(trt.nptype(ModelData.DTYPE)).ravel()
# This particular ResNet50 model requires some preprocessing, specifically, mean normalization.
return (image_arr / 255.0 - 0.45) / 0.225
# Normalize the image and copy to pagelocked memory.
np.copyto(pagelocked_buffer, normalize_image(Image.open(test_image)))
return test_image
def main():
test_image = "xx/xx.jpg"
onnx_model_file = "./resNet50.onnx"
engine_file_path = './resNet50.trt'
# read class_indict
json_path = './class_indices.json' # 存放着类别信息
assert os.path.exists(json_path), "file: '{}' dose not exist.".format(json_path)
json_file = open(json_path, "r")
class_indict = json.load(json_file)
# Build a TensorRT engine.
engine = get_engine_onnx(engine_file_path, onnx_model_file)
# Inference is the same regardless of which parser is used to build the engine, since the model architecture is the same.
# Allocate buffers and create a CUDA stream.
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
# Contexts are used to perform inference.
context = engine.create_execution_context()
# Load a normalized test case into the host input page-locked buffer.
test_case = load_normalized_test_case(test_image, inputs[0].host)
# Run the engine. The output will be a 1D tensor of length 1000, where each value represents the
# probability that the image corresponds to that label
t1 = time.time()
trt_outputs = common.do_inference_v2(context, bindings=bindings, inputs=inputs, outputs=outputs, stream=stream)
t2 = time.time()
predict = np.exp(trt_outputs[0]) / sum(np.exp(trt_outputs[0]))
pred = class_indict[str(np.argmax(trt_outputs[0]))]
print(test_image, np.argmax(predict), max(predict), pred)
print("Inference time(fps) with the TensorRT engine: {}".format(60/(t2-t1)))
# We use the highest probability as our prediction. Its index corresponds to the predicted label.
# pred = labels[np.argmax(trt_outputs[0])]
if "_".join(pred.split()) in os.path.splitext(os.path.basename(test_case))[0]: # 图片名字为类的名字
print("Correctly recognized " + test_case + " as " + pred)
else:
print("Incorrectly recognized " + test_case + " as " + pred)
if __name__ == '__main__':
main()2.1 注：其中的class_indices.json为
{
"0": "类1",
"1": "类2",
...
"10": "类10"
}
可由下面代码得到：
from torchvision import datasets
train_dataset = datasets.ImageFolder(root=os.path.join(image_path, "train"),
transform=data_transform["train"])
xx_list = train_dataset.class_to_idx
cla_dict = dict((val, key) for key, val in xx_list.items())
# write dict into json file
json_str = json.dumps(cla_dict, indent=4) # 4 代表格式化好看
with open('class_indices.json', 'w') as json_file:
json_file.write(json_str)2.2 common.py 为TRT官方代码
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]
print(data_paths)
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()
print(engine)
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]