def main():
# Load the shared object file containing the Clip plugin implementation.
# By doing this, you will also register the Clip plugin with the TensorRT
# PluginRegistry through use of the macro REGISTER_TENSORRT_PLUGIN present
# in the plugin implementation. Refer to plugin/clipPlugin.cpp for more details.
if not os.path.isfile(CLIP_PLUGIN_LIBRARY):
raise IOError("\n{}\n{}\n{}\n".format(
"Failed to load library ({}).".format(CLIP_PLUGIN_LIBRARY),
"Please build the Clip sample plugin.",
"For more information, see the included README.md"))
ctypes.CDLL(CLIP_PLUGIN_LIBRARY)
# Load pretrained model
if not os.path.isfile(MODEL_PATH):
raise IOError("\n{}\n{}\n{}\n".format(
"Failed to load model file ({}).".format(MODEL_PATH),
"Please use 'python lenet5.py' to train and save the model.",
"For more information, see the included README.md"))
# Build an engine and retrieve the image mean from the model.
with build_engine(MODEL_PATH) as engine:
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
trt_rok = gs.create_plugin_node(name="trt_rok",
op="RegionOfKeypoints_TRT",
region_shape=5)
def main():
# Get data files for the model.
data_paths, [
deploy_file, model_file, mean_proto
] = common.find_sample_data(
description="Runs an MNIST network using a Caffe model file",
subfolder="mnist",
find_files=[
"mnist.prototxt", "mnist.caffemodel", "mnist_mean.binaryproto"
])
# Cache the engine in a temporary directory.
engine_path = os.path.join(tempfile.gettempdir(), "mnist.engine")
with get_engine(deploy_file, model_file, engine_path
) as engine, engine.create_execution_context() as context:
# Build an engine, allocate buffers and create a stream.
# For more information on buffer allocation, refer to the introductory samples.
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
mean = retrieve_mean(mean_proto)
# For more information on performing inference, refer to the introductory samples.
inputs[0].host, case_num = load_normalized_test_case(data_paths, mean)
# The common.do_inference function will return a list of outputs - we only have one in this case.
[output] = common.do_inference(context,
bindings=bindings,
inputs=inputs,
outputs=outputs,
stream=stream)
pred = np.argmax(output)
print("Test Case: " + str(case_num))
print("Prediction: " + str(pred))
def get_buffer(engine, img_np):
# allocate buffers
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
# load data
inputs[0].host = img_np
return inputs, outputs, bindings, stream
def main():
data_path = common.find_sample_data(
description="Runs an MNIST network using a UFF model file",
subfolder="mnist")
model_file = ModelData.MODEL_FILE
t1 = time.clock()
with build_engine(model_file) as engine:
# Build an engine, allocate buffers and create a stream.
# For more information on buffer allocation, refer to the introductory samples.
#with open('/home/nvidia/procedure/lenet5.engine','wb') as f:
#f.write(engine.serialize())
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
with engine.create_execution_context() as context:
case_num = load_normalized_test_case(
data_path, pagelocked_buffer=inputs[0].host)
# For more information on performing inference, refer to the introductory samples.
# The common.do_inference function will return a list of outputs - we only have one in this case.
[output] = common.do_inference(context,
bindings=bindings,
inputs=inputs,
outputs=outputs,
stream=stream)
pred = np.argmax(output)
print("Test Case: " + str(case_num))
print("Prediction: " + str(pred))
t2 = time.clock()
print("use_time:" + str(t2 - t1))
def predict(self, preprocessed_image):
np_image = preprocessed_image
assert (
1,
self.channels,
self.model_height,
self.model_width,
) == np_image.shape, "Image must be resized to model shape"
if self.is_fp16:
np_image = np_image.astype(np.float16)
self.cfx.push()
try:
inputs, outputs, bindings, stream = common.allocate_buffers(self.engine)
# Do inference
inputs[0].host = np_image
trt_outputs = do_inference(
self.context,
bindings=bindings,
inputs=inputs,
outputs=outputs,
stream=stream,
)
finally:
self.cfx.pop() # very important
# logger.debug('Len of outputs: ', len(trt_outputs))
num_classes = len(self.labels)
trt_outputs[0] = trt_outputs[0].reshape(1, -1, 1, 4)
trt_outputs[1] = trt_outputs[1].reshape(1, -1, num_classes)
return trt_outputs
def main():
data_paths, _ = common.find_sample_data(
description="Runs an MNIST network using a UFF model file",
subfolder="mnist")
model_path = os.environ.get("MODEL_PATH") or os.path.join(
os.path.dirname(__file__), "models")
model_file = os.path.join(model_path, ModelData.MODEL_FILE)
with build_engine(model_file) as engine:
# Build an engine, allocate buffers and create a stream.
# For more information on buffer allocation, refer to the introductory samples.
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
with engine.create_execution_context() as context:
case_num = load_normalized_test_case(
data_paths, pagelocked_buffer=inputs[0].host)
# For more information on performing inference, refer to the introductory samples.
# The common.do_inference function will return a list of outputs - we only have one in this case.
[output] = common.do_inference(context,
bindings=bindings,
inputs=inputs,
outputs=outputs,
stream=stream)
pred = np.argmax(output)
print("Test Case: " + str(case_num))
print("Prediction: " + str(pred))
def main():
# Load the shared object file containing the Clip plugin implementation.
# By doing this, you will also register the Clip plugin with the TensorRT
# PluginRegistry through use of the macro REGISTER_TENSORRT_PLUGIN present
# in the plugin implementation. Refer to plugin/clipPlugin.cpp for more details.
if not os.path.isfile(CLIP_PLUGIN_LIBRARY):
raise IOError("\n{}\n{}\n{}\n".format(
"Failed to load library ({}).".format(CLIP_PLUGIN_LIBRARY),
"Please build the Clip sample plugin.",
"For more information, see the included README.md"))
ctypes.CDLL(CLIP_PLUGIN_LIBRARY)
# Load pretrained model
if not os.path.isfile(MODEL_PATH):
raise IOError("\n{}\n{}\n{}\n".format(
"Failed to load model file ({}).".format(MODEL_PATH),
"Please use 'python lenet5.py' to train and save the model.",
"For more information, see the included README.md"))
# Build an engine and retrieve the image mean from the model.
with build_engine(MODEL_PATH) as engine:
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
with engine.create_execution_context() as context:
print("\n=== Testing ===")
test_case = load_normalized_test_case(inputs[0].host)
print("Loading Test Case: " + str(test_case))
# The common do_inference function will return a list of outputs - we only have one in this case.
[pred] = common.do_inference(context,
bindings=bindings,
inputs=inputs,
outputs=outputs,
stream=stream)
print("Prediction: " + str(np.argmax(pred)))
def main():
# By doing this, you will also register the Clip plugin with the TensorRT
# PluginRegistry through use of the macro REGISTER_TENSORRT_PLUGIN present
# in the plugin implementation. Refer to plugin/clipPlugin.cpp for more details.
if not os.path.isfile(CLIP_PLUGIN_LIBRARY):
raise IOError("\n{}\n{}\n{}\n".format(
"Failed to load library ({}).".format(CLIP_PLUGIN_LIBRARY),
"Please build the Clip sample plugin.",
"For more information, see the included README.md"))
# Train MNIST data and get weights.
mnist_model = MnistModel()
mnist_model.learn()
weights = mnist_model.get_weights()
# Do inference with TensorRT.
with build_engine(weights) as engine:
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
with engine.create_execution_context() as context:
case_num = load_random_test_case(mnist_model,
pagelocked_buffer=inputs[0].host)
# The common.do_inference function will return a list of outputs - we only have one in this case.
[output] = common.do_inference(context,
bindings=bindings,
inputs=inputs,
outputs=outputs,
stream=stream)
pred = np.argmax(output)
print("Test Case: " + str(case_num))
print("Prediction: " + str(pred))
def main():
engine = init_construct_network()
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
with engine.create_execution_context() as context:
print(np.ones((x1 * y1 * z1), np.float32).reshape(-1))
np.copyto(inputs[0].host,
np.ones((x1 * y1 * z1), np.float32).reshape(-1))
np.copyto(inputs[1].host,
np.ones((x2 * y2 * z2), np.float32).reshape(-1))
time_start = time.time()
output = common.do_inference(context,
bindings=bindings,
inputs=inputs,
outputs=outputs,
stream=stream)
time_end = time.time()
print("time ", time_end - time_start)
print(output[0].reshape((x1 * y1 * z1)))
data = output[0].reshape((x1 * y1 * z1))
print(data[0])
print(output[1].reshape((x1 * y1 * z1)))
print("ok")
def main():
# Get data files for the model.
data_path, [deploy_file, model_file, mean_proto] = common.find_sample_data(
description="Runs an MNIST network using a Caffe model file",
subfolder="mnist",
find_files=[
"mnist.prototxt", "mnist.caffemodel", "mnist_mean.binaryproto"
])
with build_engine(deploy_file, model_file) as engine:
# Build an engine, allocate buffers and create a stream.
# For more information on buffer allocation, refer to the introductory samples.
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
mean = retrieve_mean(mean_proto)
with engine.create_execution_context() as context:
case_num = load_normalized_test_case(data_path, inputs[0].host,
mean)
# For more information on performing inference, refer to the introductory samples.
# The common.do_inference function will return a list of outputs - we only have one in this case.
[output] = common.do_inference(context,
bindings=bindings,
inputs=inputs,
outputs=outputs,
stream=stream)
pred = np.argmax(output)
print("Test Case: " + str(case_num))
print("Prediction: " + str(pred))
# After the engine is destroyed, we destroy the plugin. This function is exposed through the binding code in plugin/pyFullyConnected.cpp.
fc_factory.destroy_plugin()
def check_accuracy(context, batch_size, test_set, test_labels):
inputs, outputs, bindings, stream = common.allocate_buffers(context.engine)
num_correct = 0
num_total = 0
batch_num = 0
for start_idx in range(0, test_set.shape[0], batch_size):
batch_num += 1
if batch_num % 10 == 0:
print("Validating batch {:}".format(batch_num))
# If the number of images in the test set is not divisible by the batch size, the last batch will be smaller.
# This logic is used for handling that case.
end_idx = min(start_idx + batch_size, test_set.shape[0])
effective_batch_size = end_idx - start_idx
# Do inference for every batch.
inputs[0].host = test_set[start_idx:start_idx + effective_batch_size]
[output] = common.do_inference(context,
bindings=bindings,
inputs=inputs,
outputs=outputs,
stream=stream,
batch_size=effective_batch_size)
# Use argmax to get predictions and then check accuracy
preds = np.argmax(output.reshape(32, 10)[0:effective_batch_size],
axis=1)
labels = test_labels[start_idx:start_idx + effective_batch_size]
num_total += effective_batch_size
num_correct += np.count_nonzero(np.equal(preds, labels))
percent_correct = 100 * num_correct / float(num_total)
print("Total Accuracy: {:}%".format(percent_correct))
def main():
# Set the data path to the directory that contains the trained models and test images for inference.
_, data_files = common.find_sample_data(description="Runs a ResNet50 network with a TensorRT inference engine.", subfolder="resnet50", find_files=["binoculars.jpeg", "reflex_camera.jpeg", "tabby_tiger_cat.jpg", ModelData.MODEL_PATH, "class_labels.txt"])
# Get test images, models and labels.
test_images = data_files[0:3]
onnx_model_file, labels_file = data_files[3:]
labels = open(labels_file, 'r').read().split('\n')
# Build a TensorRT engine.
with build_engine_onnx(onnx_model_file) as engine:
# 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.
with engine.create_execution_context() as context:
# Load a normalized test case into the host input page-locked buffer.
test_image = random.choice(test_images)
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
trt_outputs = common.do_inference_v2(context, bindings=bindings, inputs=inputs, outputs=outputs, stream=stream)
# 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]:
print("Correctly recognized " + test_case + " as " + pred)
else:
print("Incorrectly recognized " + test_case + " as " + pred)
def main():
data_path, data_files = common.find_sample_data(
description="Runs a Caffe MNIST network in Int8 mode",
subfolder="mnist",
find_files=["batches", ModelData.DEPLOY_PATH, ModelData.MODEL_PATH])
[batch_data_dir, deploy_file, model_file] = data_files
# Now we create a calibrator and give it the location of our calibration data.
# We also allow it to cache calibration data for faster engine building.
calibration_cache = "mnist_calibration.cache"
calib = calibrator.MNISTEntropyCalibrator(batch_data_dir,
cache_file=calibration_cache)
# We will use the calibrator batch size across the board.
# This is not a requirement, but in this case it is convenient.
batch_size = calib.get_batch_size()
with build_int8_engine(
deploy_file, model_file,
calib) as engine, engine.create_execution_context() as context:
# Allocate engine buffers.
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
# Do inference for the whole batch. We have to specify batch size here, as the common.do_inference uses a default
inputs[0].host, labels = load_random_batch(calib)
[output] = common.do_inference(context,
bindings=bindings,
inputs=inputs,
outputs=outputs,
stream=stream,
batch_size=batch_size)
# Next we need to reshape the output to Nx10 (10 probabilities, one per digit), where N is batch size.
output = output.reshape(batch_size, 10)
validate_output(output, labels)
def main():
"""Create a TensorRT engine for ONNX-based YOLOv3-608 and run inference."""
global INPUTS, OUTPUTS, BINDINGS, STREAM, CONTEXT
# Try to load a previously generated YOLOv3-608 network graph in ONNX format:
onnx_file_path = os.path.join(
sys.path[0],
'/home/wlz/catkin_ws/src/krrt-planner/opnet/models/no_surf_80_32.onnx')
engine_file_path = os.path.join(
sys.path[0],
'/home/wlz/catkin_ws/src/krrt-planner/opnet/models/no_surf_80_32.trt')
input = np.random.randn(1, SHAPE[1], SHAPE[2], SHAPE[3]).astype(np.float32)
# Output shapes expected by the post-processor
output_shapes = [SHAPE]
# Do inference with TensorRT
trt_outputs = []
with get_engine(onnx_file_path, engine_file_path
) as engine, engine.create_execution_context() as context:
print("GET ENGINE SUCCEED")
CONTEXT = context
INPUTS, OUTPUTS, BINDINGS, STREAM = common.allocate_buffers(engine)
# inputs, outputs, bindings, stream = common.allocate_buffers(engine)
# Do inference
time0 = time.time()
for i in range(10):
# print('Running inference')
output = trt_inference(input)
print("prepocess time: %fs" % (time.time() - time0))
print('done')
def main():
# Set the data path to the directory that contains the trained models and test images for inference.
# data_path, data_files = common.find_sample_data(description="Runs a ResNet50 network with a TensorRT inference engine.", subfolder="resnet50", find_files=["binoculars.jpeg", "reflex_camera.jpeg", "tabby_tiger_cat.jpg", ModelData.MODEL_PATH, ModelData.DEPLOY_PATH, "class_labels.txt"])
# Get test images, models and labels.
# test_images = data_files[0:3]
# test_image = "0.jpg"
test_image_list = os.listdir('car_rec_test')
print(test_image_list)
engine_file_path = "car_rec.trt"
caffe_model_file, caffe_deploy_file, labels_file = [ModelData.MODEL_PATH, ModelData.DEPLOY_PATH, ModelData.LABEL_PATH]
labels = open(labels_file, 'r').read().split('\n')
# Build a TensorRT engine.
with build_engine_caffe(caffe_model_file, caffe_deploy_file, engine_file_path) as engine:
# 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.
# h_input, d_input, h_output, d_output, stream = allocate_buffers(engine)
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
# Contexts are used to perform inference.
with engine.create_execution_context() as context:
# Load a normalized test case into the host input page-locked buffer.
# test_image = random.choice(test_images)
# test_case = load_normalized_test_case(test_image, h_input)
test_cases = load_normalized_test_cases(test_image_list, inputs)
# 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
# do_inference(context, h_input, d_input, h_output, d_output, stream)
trt_outputs = common.do_inference(context, bindings, inputs, outputs, stream, 16)
outs = trt_outputs[0].reshape(16,427)
print(outs)
for x in range(0,len(outs)):
pred = labels[np.argmax(outs[x])]
print(pred)
pass
def main(args):
with get_engine(args.engine_path, args.model_dir) as engine:
with engine.create_execution_context() as context:
origin_img = cv2.imread(args.image_path)
t1 = time.time()
img, (ratio_h, ratio_w) = preprocess(origin_img)
cv2.imwrite("processed.jpg", img)
h, w, _ = img.shape
# hwc to chw
img = img.transpose((2, 0, 1))
# flatten the image into a 1D array
img = img.ravel()
context.set_binding_shape(0, (1, 3, h, w))
# allocate buffers and create a stream.
inputs, outputs, bindings, stream = common.allocate_buffers(
engine, context)
# copy to pagelocked memory
np.copyto(inputs[0].host, img)
# The common.do_inference function will return a list of outputs - we only have one in this case.
[output] = common.do_inference_v2(context,
bindings=bindings,
inputs=inputs,
outputs=outputs,
stream=stream)
# reshape 1D array to chw
output = np.reshape(output, (6, h // 4, w // 4))
# transpose chw to hwc
output = output.transpose(1, 2, 0)
boxes = postprocess(origin_img, output, ratio_h, ratio_w)
t2 = time.time()
print("total cost %fms" % ((t2 - t1) * 1000))
draw_result(origin_img, boxes)
def main():
#data_path, _ = common.find_sample_data(description="Runs an MNIST network using a UFF model file", subfolder="mnist")
data_path = '/home/ai/tensorrt_tar/TensorRT-5.1.5.0/data/mnist'
model_path = os.environ.get("MODEL_PATH") or os.path.join(
os.path.dirname(__file__), "models")
model_file = os.path.join(model_path, ModelData.MODEL_FILE)
with build_engine(model_file) as engine:
# Build an engine, allocate buffers and create a stream.
# For more information on buffer allocation, refer to the introductory samples.
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
with engine.create_execution_context() as context:
case_num = load_normalized_test_case(
data_path, pagelocked_buffer=inputs[0].host)
# For more information on performing inference, refer to the introductory samples.
# The common.do_inference function will return a list of outputs - we only have one in this case.
start1 = time.time()
[output] = common.do_inference(context,
bindings=bindings,
inputs=inputs,
outputs=outputs,
stream=stream)
pred = np.argmax(output)
print("Prediction: " + str(pred), 'time is :',
time.time() - start1)
model1 = model.create_model()
model1.load_weights("models/lenet5.pb")
start2 = time.time()
output = model1.predict(inputs[0])
pred = np.argmax(output)
print("Prediction: " + str(pred), 'time is :',
time.time() - start2)
def predict(inp: Image, metadata):
image_raw, image = preprocessor.process(inp)
shape_orig_WH = image_raw.size
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
# Do inference
print('Running inference on image')
# Set host input to the image. The common.do_inference function will copy the input to the GPU before executing.
inputs[0].host = image
a = perf_counter()
trt_outputs = common.do_inference(context,
bindings=bindings,
inputs=inputs,
outputs=outputs,
stream=stream)
b = perf_counter()
metadata['TensorRT Inference Latency (s)'] = (b - a)
trt_outputs = [
output.reshape(shape)
for output, shape in zip(trt_outputs, output_shapes)
]
postprocessor = PostprocessYOLO(**postprocessor_args)
# Run the post-processing algorithms on the TensorRT outputs and get the bounding box details of detected objects
boxes, classes, scores = postprocessor.process(trt_outputs,
(shape_orig_WH))
# Draw the bounding boxes onto the original input image and save it as a PNG file
obj_detected_img = draw_bboxes(image_raw, boxes, scores, classes,
ALL_CATEGORIES)
return obj_detected_img
def main():
#命令行参数解析器
common.add_help(description="Runs an MNIST network using a PyTorch model")
# Train the PyTorch model
#创建一个模型类实例
mnist_model = model.MnistModel()
#进行训练
mnist_model.learn()
#获取相应的权重
weights = mnist_model.get_weights()
# Do inference with TensorRT.
#进行推理
#build_engine具体参考本文件的实现
with build_engine(weights) as engine:
# Build an engine, allocate buffers and create a stream.
# For more information on buffer allocation, refer to the introductory samples.
#分配相应的缓冲区内存
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
with engine.create_execution_context() as context:
#load_random_test_case参考本文件下的实现
#随即加载测试数据，复制到主机内存
case_num = load_random_test_case(mnist_model,
pagelocked_buffer=inputs[0].host)
# For more information on performing inference, refer to the introductory samples.
# The common.do_inference function will return a list of outputs - we only have one in this case.
#进行推理
[output] = common.do_inference(context,
bindings=bindings,
inputs=inputs,
outputs=outputs,
stream=stream)
#获取最终结果
pred = np.argmax(output)
print("Test Case: " + str(case_num))
print("Prediction: " + str(pred))
def main3():
device = torch.device('cuda:0')
path = '04.jpg'
onnx_file_path = './models/ResNet50.onnx'
engine_file_path = './models/ResNet50.trt'
with get_engine(
onnx_file_path,
engine_file_path,
) as engine:
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
with engine.create_execution_context() as context:
load_normalized_test_case(path, inputs[0].host)
start1 = time.time()
trt_outputs = common.do_inference(context,
bindings=bindings,
inputs=inputs,
outputs=outputs,
stream=stream)
output = trt_outputs
print('processing time3 is', time.time() - start1, np.argmax(output))
return output
def main(engine_path, image_path, image_size):
with get_engine(engine_path) as engine, engine.create_execution_context(
) as context:
buffers = common.allocate_buffers(engine)
image_src = cv2.imread(image_path)
detect(engine, context, buffers, image_src, image_size)
def main():
engine_file_path = 'plate_detection.trt'
input_image_path = '../cat.jpg'
input_resolution_plate_detection_HW = (325, 325)
preprocessor = PreprocessYOLO(input_resolution_plate_detection_HW)
image_raw, image = preprocessor.process(input_image_path)
print(image.shape)
trt_outputs = []
with get_engine_from_bin(engine_file_path) as engine, engine.create_execution_context() as context:
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
# Do inference
print('Running inference on image {}...'.format(input_image_path))
# Set host input to the image. The common.do_inference function will copy the input to the GPU before executing.
inputs[0].host = image
trt_outputs = common.do_inference(context, bindings=bindings, inputs=inputs, outputs=outputs, stream=stream, batch_size=1)
# in this case, it demonstrates to perform inference for 50 times
total_time = 0; n_time_inference = 10000
for i in range(n_time_inference):
t1 = time.time()
trt_outputs = common.do_inference(context, bindings=bindings, inputs=inputs, outputs=outputs, stream=stream, batch_size=1)
t2 = time.time()
delta_time = t2 - t1
total_time += delta_time
print('inference-{} cost: {}ms'.format(str(i+1), delta_time*1000))
avg_time_original_model = total_time / n_time_inference
print("average inference time: {}ms".format(avg_time_original_model*1000))
print(trt_outputs[0].shape)
print(trt_outputs[1].shape)
def main():
"""Create a TensorRT engine for ONNX-based model and run inference."""
# Try to load a previously generated network graph in ONNX format:
onnx_file_path = '/models/run09/jetracer.onnx'
engine_file_path = '/models/run09/jetracer.trt'
ino = 378
# Do inference with TensorRT
trt_outputs = []
with get_engine(onnx_file_path, engine_file_path
) as engine, engine.create_execution_context() as context:
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
# Do inference
print('Running inference on image')
# Set host input to the image. The common.do_inference function will copy the input to the GPU before executing.
image = cv2.imread(
f'/models/train_data/Images/{ino:03d}.jpg').transpose(
2, 0, 1).reshape(1, 3, 320, 640)
inputs[0].host = np.array(image, dtype=np.float16, order='C')
trt_outputs = common.do_inference_v2(context,
bindings=bindings,
inputs=inputs,
outputs=outputs,
stream=stream)
# Before doing post-processing, we need to reshape the outputs as the common.do_inference will give us flat arrays.
#mask = trt_outputs.reshape(320,640).numpy()[0][0]>0.4
print(trt_outputs[0].shape)
def main():
x = "/home/dgxuser125/rt-kennan/Swish3/build/libswish.so"
ctypes.CDLL(x)
data_paths, _ = common.find_sample_data(
description="Runs an MNIST network using a UFF model file",
subfolder="mnist")
model_path = os.environ.get("MODEL_PATH") or os.path.join(
os.path.dirname(__file__), "models")
model_file = os.path.join(model_path, ModelData.MODEL_FILE)
with build_engine(model_file) as engine:
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
with engine.create_execution_context() as context:
# # Start measuring time
inference_start_time = time.time()
for i in range(1000):
case_num = load_normalized_test_case(
data_paths, pagelocked_buffer=inputs[0].host)
[output] = common.do_inference(context,
bindings=bindings,
inputs=inputs,
outputs=outputs,
stream=stream)
pred = np.argmax(output)
# print("Test Case: " + str(case_num))
# print("Prediction: " + str(pred))
end_time = time.time()
print("time taken for one input with tenosrrt: ",
(end_time - inference_start_time) / 1000)
def main():
common.add_help(description="Runs an MNIST network using a PyTorch model")
# Train the PyTorch model
mnist_model = model.MnistModel()
mnist_model.learn()
weights = mnist_model.get_weights()
# Do inference with TensorRT.
engine = build_engine(weights)
# Build an engine, allocate buffers and create a stream.
# For more information on buffer allocation, refer to the introductory samples.
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
context = engine.create_execution_context()
case_num = load_random_test_case(mnist_model,
pagelocked_buffer=inputs[0].host)
# For more information on performing inference, refer to the introductory samples.
# The common.do_inference function will return a list of outputs - we only have one in this case.
[output] = common.do_inference(context,
bindings=bindings,
inputs=inputs,
outputs=outputs,
stream=stream)
pred = np.argmax(output)
print("Test Case: " + str(case_num))
print("Prediction: " + str(pred))
def main():
# load label
labels = [line.rstrip('\n') for line in open('class_labels.txt')]
# load engine
trt_engine = './vgg16_32.trt'
Cifar10_engine = load_engine(trt_engine)
dispW = 1280
dispH = 720
flip = 0
fpsReport = 0
camSet = 'nvarguscamerasrc ! video/x-raw(memory:NVMM), width=1280, height=720, format=NV12, framerate=60/1 \
! nvvidconv flip-method=' + str(
flip) + ' ! video/x-raw, width=' + str(dispW) + ', height=' + str(
dispH) + ',\
format=BGRx ! videoconvert !video/x-raw, format=BGR ! appsink'
cap = cv2.VideoCapture(camSet, cv2.CAP_GSTREAMER)
timeStamp = time.time()
font = cv2.FONT_HERSHEY_SIMPLEX
while True:
_, frame = cap.read()
frame = frame.astype('float32')
frameRGB = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
img = cv2.resize(frameRGB, (32, 32)) / 255
img = img.transpose((2, 0, 1)).flatten()
# 分配buffers給inputs和outputs
inputs, outputs, bindings, stream = common.allocate_buffers(
Cifar10_engine)
inputs[0].host = img
# inference
with Cifar10_engine.create_execution_context() as context:
trt_outputs = common.do_inference(context,
bindings=bindings,
inputs=inputs,
outputs=outputs,
stream=stream)
pred = trt_outputs[0].argmax(-1)
# fps
dt = time.time() - timeStamp
fps = 1 / dt
fpsReport = .9 * fpsReport + .1 * fps
timeStamp = time.time()
cv2.rectangle(frame, (0, 0), (350 + len(labels[pred]) * 30, 80),
(0, 0, 255), -1)
cv2.putText(frame,
str(round(fpsReport, 1)) + 'fps' + ', ' + labels[pred],
(0, 60), font, 2, (0, 255, 255), 3)
cv2.imshow('stream', frame / 255)
if cv2.waitKey(1) == 27:
break
cap.release()
cv2.destroyAllWindows()
def init():
global inputs, outputs, bindings, stream, engine, TRT_LOGGER, context
TRT_LOGGER = trt.Logger()
onnx_file_path = 'yolov3.onnx'
engine_file_path = "yolov3.trt"
engine = get_engine(TRT_LOGGER, onnx_file_path, engine_file_path)
context = engine.create_execution_context()
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
def main(win_title):
# load trt engine
print('load trt engine')
trt_path = 'engine.trt'
engine = load_engine(trt_runtime, trt_path)
print('load labels')
label = get_label('keras_models/labels.txt')
# allocate buffers
print('allocate buffers')
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
print('create execution context')
context = engine.create_execution_context()
print('start stream')
fps = -1
GSTREAMER_PIPELINE = 'nvarguscamerasrc ! video/x-raw(memory:NVMM), width=1920, height=1080, format=(string)NV12, framerate=60/1 ! nvvidconv flip-method=0 ! video/x-raw, width=640, height=480, format=(string)BGRx ! videoconvert ! video/x-raw, format=(string)BGR ! appsink'
cap = cv2.VideoCapture(GSTREAMER_PIPELINE, cv2.CAP_GSTREAMER)
while (1):
t_start = time.time()
ret, frame = cap.read()
size = (224, 224)
inputs[0].host = preprocess(frame)
# with engine.create_execution_context() as context:
trt_outputs = common.do_inference(context,
bindings=bindings,
inputs=inputs,
outputs=outputs,
stream=stream)
preds = trt_outputs[0]
idx = np.argmax(preds)
result = label[idx]
info = '{} : {:.3f} , FPS {}'.format(result, preds[idx], fps)
cv2.putText(frame, info, (10, 40), cv2.FONT_HERSHEY_SIMPLEX, 1,
(0, 0, 255), 4)
cv2.imshow(win_title, frame)
if cv2.waitKey(1) == ord('q'):
break
fps = int(1 / (time.time() - t_start))
cap.release()
cv2.destroyAllWindows()
print('Quit')
def __init__(self, onnx_path, engine_path, dimx=80, dimy=80, dimz=48):
self.output_shape = [1, dimx, dimy, dimz]
self.trt_logger = trt.Logger(trt.Logger.INFO)
self.cuda_ctx = cuda.Device(0).make_context()
self.engine = self.get_engine(onnx_path, engine_path)
self.context = self.engine.create_execution_context()
self.inputs, self.outputs, self.bindings, self.stream = common.allocate_buffers(
self.engine)
print("BUIND ENGINE SUCCEED")
def _infer(self, input_data):
inputs, outputs, bindings, stream = common.allocate_buffers(
self.engine)
# Set host input to the image. The common.do_inference function will copy the input to the GPU before executing.
inputs[0].host = input_data
return common.do_inference(self.context,
bindings=bindings,
inputs=inputs,
outputs=outputs,
stream=stream)