def refit_engine(self, net):
with trt.Refitter(self.engine, self.logger) as refitter:
state_dict = net.state_dict()
variables = []
# Why use a variable list?
# we know that in c++ functions, a python array may be deleted
# after ref count of a var decrease to zero.
# TensorRT 5.1.5.0 refitter ONLY EXECUTED in refit_cuda_engine,
# so we must keep variable alive before refit_cuda_engine call.
for k, v in self.refit_weight_dict.items():
if v["type"] == "Linear":
weight = state_dict[v["weight"]].detach().cpu().numpy()
refitter.set_weights(k, trt.WeightsRole.KERNEL, weight)
variables.append(weight)
if "bias" in v:
bias = state_dict[v["bias"]].detach().cpu().numpy()
refitter.set_weights(k, trt.WeightsRole.BIAS, bias)
variables.append(bias)
elif v["type"] == "Convolution":
weight = state_dict[
v["weight"]].detach().float().cpu().numpy()
refitter.set_weights(k, trt.WeightsRole.KERNEL, weight)
variables.append(weight)
if "bias" in v:
bias = state_dict[v["bias"]].detach().cpu().numpy()
refitter.set_weights(k, trt.WeightsRole.BIAS, bias)
variables.append(bias)
elif v["type"] == "BatchNorm":
running_var = state_dict[v["running_var"]]
running_mean = state_dict[v["running_mean"]]
weight = state_dict[v["weight"]]
bias = state_dict[v["bias"]]
eps = v["eps"]
running_mean = running_mean.detach().cpu().numpy()
running_var = running_var.detach().cpu().numpy()
weight = weight.detach().cpu().numpy()
bias = bias.detach().cpu().numpy()
shift = (-running_mean /
np.sqrt(running_var + eps)) * weight + bias
scale = weight / np.sqrt(running_var + eps)
refitter.set_weights(k, trt.WeightsRole.SCALE, scale)
refitter.set_weights(k, trt.WeightsRole.SHIFT, shift)
variables.append(scale)
variables.append(shift)
else:
raise NotImplementedError
# Get description of missing weights. This should return empty
# lists in this case.
[missingLayers, weightRoles] = refitter.get_missing()
assert len(
missingLayers
) == 0, "Refitter found missing weights. Call set_weights() for all missing weights"
# Refit the engine with the new weights. This will return True if
# the refit operation succeeded.
assert refitter.refit_cuda_engine()
def main():
onnx_file_path = 'bidaf-modified.onnx'
engine_file_path = "bidaf.trt"
# input
context = 'A quick brown fox jumps over the lazy dog.'
query = 'What color is the fox?'
cw_str, _ = preprocess(context)
# get ravelled data
cw, cc, qw, qc = get_inputs(context, query)
# Do inference with TensorRT
refit_weights = np.load("Parameter576_B_0.npy")
fake_weights = np.ones_like(refit_weights)
engine = get_engine(onnx_file_path, engine_file_path)
refitter = trt.Refitter(engine, TRT_LOGGER)
context = engine.create_execution_context()
for weights, answer_correct in [(fake_weights, False), (refit_weights, True)]:
print("Refitting engine...")
# To get a list of all refittable weights' names
# in the network, use refitter.get_all_weights().
# Refit named weights via set_named_weights
refitter.set_named_weights('Parameter576_B_0', weights)
# Get missing weights names. This should return empty
# lists in this case.
missing_weights = refitter.get_missing_weights()
assert len(
missing_weights) == 0, "Refitter found missing weights. Call set_named_weights() or set_weights() for all missing weights"
# Refit the engine with the new weights. This will return True if
# the refit operation succeeded.
assert refitter.refit_cuda_engine()
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
print("Doing inference...")
# Do inference
# Set host input. The common.do_inference_v2 function will copy the input to the GPU before executing.
inputs[0].host = cw
inputs[1].host = cc
inputs[2].host = qw
inputs[3].host = qc
trt_outputs = common.do_inference_v2(context, bindings=bindings, inputs=inputs, outputs=outputs, stream=stream)
start = np.asscalar(trt_outputs[0])
end = np.asscalar(trt_outputs[1])
answer = [w.encode() for w in cw_str[start:end + 1].reshape(-1)]
assert answer_correct == (answer == [b'brown'])
print("Passed")
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.
with build_engine_with_some_missing_weights(weights) 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)
print("Accuracy Before Engine Refit")
get_trt_test_accuracy(engine, inputs, outputs, bindings, stream,
mnist_model)
# Refit the engine with the actual trained weights for the conv_1 layer.
with trt.Refitter(engine, TRT_LOGGER) as refitter:
# To get a list of all refittable layers and associated weightRoles
# in the network, use refitter.get_all()
# Set the actual weights for the conv_1 layer. Since it consists of
# kernel weights and bias weights, set each of them by specifying
# the WeightsRole.
refitter.set_weights("conv_1", trt.WeightsRole.KERNEL,
weights['conv1.weight'].numpy())
refitter.set_weights("conv_1", trt.WeightsRole.BIAS,
weights['conv1.bias'].numpy())
# Get description of missing weights. This should return empty
# lists in this case.
[missingLayers, weightRoles] = refitter.get_missing()
assert len(
missingLayers
) == 0, "Refitter found missing weights. Call set_weights() for all missing weights"
# Refit the engine with the new weights. This will return True if
# the refit operation succeeded.
assert refitter.refit_cuda_engine()
expected_correct_predictions = mnist_model.get_latest_test_set_accuracy(
)
print(
"Accuracy After Engine Refit (expecting {:.1f}% correct predictions)"
.format(100 * expected_correct_predictions))
assert get_trt_test_accuracy(
engine, inputs, outputs, bindings, stream,
mnist_model) >= expected_correct_predictions
def run():
logger = trt.Logger(trt.Logger.ERROR)
if os.path.isfile(trtFile):
with open(trtFile, 'rb') as f:
engine = trt.Runtime(logger).deserialize_cuda_engine(f.read())
if engine == None:
print("Failed loading engine!")
exit()
print("Succeeded loading engine!")
onnxFile = onnxFile1 # 已经有 model.plan，读进 model1.onnx 做 Refit
else:
onnxFile = onnxFile0 # 还没有 model.plan，先用 model0.onnx 构建 model.plan
builder = trt.Builder(logger)
network = builder.create_network(1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
config = builder.create_builder_config()
config.flags = 1 << int(trt.BuilderFlag.REFIT)
config.max_workspace_size = 3 << 30
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 os.path.isfile(trtFile): # 进行 Refit
refitter = trt.Refitter(engine, logger)
layerNameList, weightRoleList = refitter.get_all()
for name, role in zip(layerNameList, weightRoleList):
print("LayerName:%s,WeightRolw:%s"%(name, role))
for i in range(network.num_layers):
layer = network.get_layer(i)
if layer.name in layerNameList:
# 据实际网络情况，可能需要添加更多 Layer
if layer.type == trt.LayerType.CONVOLUTION:
layer.__class__ = trt.IConvolutionLayer
refitter.set_weights(layer.name, trt.WeightsRole.KERNEL, layer.kernel)
refitter.set_weights(layer.name, trt.WeightsRole.BIAS, layer.bias)
layerNameList.remove
if layer.type == trt.LayerType.FULLY_CONNECTED:
layer.__class__ = trt.IFullyConnectedLayer
refitter.set_weights(layer.name, trt.WeightsRole.KERNEL, layer.kernel)
if layer.type == trt.LayerType.CONSTANT:
layer.__class__ = trt.IConstantLayer
refitter.set_weights(layer.name, trt.WeightsRole.CONSTANT, layer.weights)
if refitter.refit_cuda_engine() == False:
print("Failed refitting engine, missing weight:")
[missingLayer, weightRole] = refitter.get_missing()
for layer, role in zip(missingLayer, weightRole):
print("\tLayerName:%s,WeightRolw:%s"%(name, role))
return
print("Succeeded refitting engine!")
else: # 构建 model.plan
inputTensor = network.get_input(0)
inputTensor.shape = [1, 1, 28, 28]
''' # 逐层打印网络信息
for i in range(network.num_layers):
layer = network.get_layer(i)
print(i, "%s,in=%d,out=%d,%s" % (str(layer.type)[10:], layer.num_inputs, layer.num_outputs, layer.name))
for j in range(layer.num_inputs):
tensor = layer.get_input(j)
if tensor == None:
print("\tInput %2d:" % j, "None")
else:
print("\tInput %2d:%s,%s,%s" % (j, tensor.shape, str(tensor.dtype)[9:], tensor.name))
for j in range(layer.num_outputs):
tensor = layer.get_output(j)
if tensor == None:
print("\tOutput %2d:" % j, "None")
else:
print("\tOutput %2d:%s,%s,%s" % (j, tensor.shape, str(tensor.dtype)[9:], tensor.name))
'''
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)
context = engine.create_execution_context()
context.set_binding_shape(0, [1, 1, 28, 28])
_, stream = cudart.cudaStreamCreate()
print("Binding0->", engine.get_binding_shape(0), context.get_binding_shape(0), engine.get_binding_dtype(0))
print("Binding1->", engine.get_binding_shape(1), context.get_binding_shape(1), engine.get_binding_dtype(1))
data = cv2.imread(inputImage, cv2.IMREAD_GRAYSCALE).astype(np.float32)
inputH0 = np.ascontiguousarray(data.reshape(-1))
outputH0 = np.empty(context.get_binding_shape(1), dtype=trt.nptype(engine.get_binding_dtype(1)))
_, inputD0 = cudart.cudaMallocAsync(inputH0.nbytes, stream)
_, outputD0 = cudart.cudaMallocAsync(outputH0.nbytes, stream)
cudart.cudaMemcpyAsync(inputD0, inputH0.ctypes.data, inputH0.nbytes, cudart.cudaMemcpyKind.cudaMemcpyHostToDevice, stream)
context.execute_async_v2([int(inputD0), int(outputD0)], stream)
cudart.cudaMemcpyAsync(outputH0.ctypes.data, outputD0, outputH0.nbytes, cudart.cudaMemcpyKind.cudaMemcpyDeviceToHost, stream)
cudart.cudaStreamSynchronize(stream)
print("inputH0 :", data.shape)
#print(data)
print("outputH0:", outputH0.shape)
print(outputH0)
cudart.cudaStreamDestroy(stream)
cudart.cudaFree(inputD0)
cudart.cudaFree(outputD0)
print("Succeeded running model in TensorRT!")
def run(nRunTime):
logger = trt.Logger(trt.Logger.ERROR)
if os.path.isfile(trtFile):
with open(trtFile, 'rb') as f:
engine = trt.Runtime(logger).deserialize_cuda_engine(f.read())
if engine == None:
print("Failed loading engine!")
return
print("Succeeded loading engine!")
else:
builder = trt.Builder(logger)
network = builder.create_network(
1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
config = builder.create_builder_config()
config.flags = 1 << int(trt.BuilderFlag.REFIT)
inputT0 = network.add_input('inputT0', trt.float32,
(nIn, cIn, hIn, wIn))
fakeWeight = np.zeros([cOut, cIn, wW, wW], dtype=np.float32)
fakeBias = np.zeros([cOut], dtype=np.float32)
convolutionLayer = network.add_convolution_nd(inputT0, cOut, (hW, wW),
fakeWeight, fakeBias)
#convolutionLayer.name = 'conv'
network.set_weights_name(convolutionLayer.kernel, "conv-w")
network.set_weights_name(convolutionLayer.bias, "conv-b")
network.mark_output(convolutionLayer.get_output(0))
engineString = builder.build_serialized_network(network, config)
if engineString == None:
print("Failed building engine!")
return
print("Succeeded building engine!")
with open(trtFile, 'wb') as f:
f.write(engineString)
engine = trt.Runtime(logger).deserialize_cuda_engine(engineString)
if nRunTime == 0:
print("Do not refit!")
else:
print("Refit!")
refitter = trt.Refitter(engine, logger)
refitter.set_named_weights("conv-w", weight)
refitter.set_named_weights("conv-b", bias)
[missingLayer, weightRole] = refitter.get_missing()
for layer, role in zip(missingLayer, weightRole):
print("[", layer, "-", role, "]")
if refitter.refit_cuda_engine() == False:
print("Failed Refitting engine!")
return
context = engine.create_execution_context()
_, stream = cudart.cudaStreamCreate()
inputH0 = np.ascontiguousarray(data.reshape(-1))
outputH0 = np.empty(context.get_binding_shape(1),
dtype=trt.nptype(engine.get_binding_dtype(1)))
_, inputD0 = cudart.cudaMallocAsync(inputH0.nbytes, stream)
_, outputD0 = cudart.cudaMallocAsync(outputH0.nbytes, stream)
cudart.cudaMemcpyAsync(inputD0, inputH0.ctypes.data, inputH0.nbytes,
cudart.cudaMemcpyKind.cudaMemcpyHostToDevice,
stream)
context.execute_async_v2([int(inputD0), int(outputD0)], stream)
cudart.cudaMemcpyAsync(outputH0.ctypes.data, outputD0, outputH0.nbytes,
cudart.cudaMemcpyKind.cudaMemcpyDeviceToHost,
stream)
cudart.cudaStreamSynchronize(stream)
print("data:", data.shape)
print(data)
print("outputH0:", outputH0.shape)
print(outputH0)
cudart.cudaStreamDestroy(stream)
cudart.cudaFree(inputD0)
cudart.cudaFree(outputD0)
def main():
#add_help参考common.py中的实现，实际上是一个命令行参数解析器
common.add_help(description="Runs an MNIST network using a PyTorch model")
# Train the PyTorch model
#训练相应的模型
#创建一个模型
mnist_model = model.MnistModel()
#进行训练
mnist_model.learn()
#提取相应的权重
weights = mnist_model.get_weights()
# Do inference with TensorRT.
#在tensorrt中进行相应的推理
#build_engine_with_some_missing_weights参考本文件中的具体实现
with build_engine_with_some_missing_weights(weights) as engine:
# Build an engine, allocate buffers and create a stream.
# For more information on buffer allocation, refer to the introductory samples.
#allocate_buffers的具体实现参考common.py
#分配相应的缓冲区，返回输入输出数据缓冲区指列表和相应的绑定等列表
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
print("Accuracy Before Engine Refit")
#进行相应的推理并计算准确率
get_trt_test_accuracy(engine, inputs, outputs, bindings, stream,
mnist_model)
# Refit the engine with the actual trained weights for the conv_1 layer.
#用训练过的第一个卷积层的权值重新填充引擎
#Refitter用来更新引擎中的权重，具体参考https://docs.nvidia.com/deeplearning/tensorrt/api/python_api/infer/Core/Refitter.html?highlight=refitter#tensorrt.Refitter
with trt.Refitter(engine, TRT_LOGGER) as refitter:
# To get a list of all refittable layers and associated weightRoles
# in the network, use refitter.get_all()
# Set the actual weights for the conv_1 layer. Since it consists of
# kernel weights and bias weights, set each of them by specifying
# the WeightsRole.
#set_weights用于给指定的层次指定新的权值
#具体参考https://docs.nvidia.com/deeplearning/tensorrt/api/python_api/infer/Core/Refitter.html?highlight=set_weights#tensorrt.Refitter.set_weights
refitter.set_weights("conv_1", trt.WeightsRole.KERNEL,
weights['conv1.weight'].numpy())
refitter.set_weights("conv_1", trt.WeightsRole.BIAS,
weights['conv1.bias'].numpy())
# Get description of missing weights. This should return empty
# lists in this case.
#get_missing用来获取相应丢失权重的描述
[missingLayers, weightRoles] = refitter.get_missing()
#判断是否存在丢失权重的层次
assert len(
missingLayers
) == 0, "Refitter found missing weights. Call set_weights() for all missing weights"
# Refit the engine with the new weights. This will return True if
# the refit operation succeeded.
#refit_cuda_engine用来更新相关的引擎，如果成功返回true
assert refitter.refit_cuda_engine()
#get_latest_test_set_accuracy的具体实现参考model.py中的实现
#用来获取最后一次训练得到的准确率
expected_correct_predictions = mnist_model.get_latest_test_set_accuracy(
)
print(
"Accuracy After Engine Refit (expecting {:.1f}% correct predictions)"
.format(100 * expected_correct_predictions))
#获取相应的tensorrt的推理准确率
assert get_trt_test_accuracy(
engine, inputs, outputs, bindings, stream,
mnist_model) >= expected_correct_predictions
