def create_session(self):
sess_opt = ort.SessionOptions()
sess_opt.enable_profiling = self.profiling
sess = ort.InferenceSession(self.model, sess_options=sess_opt, providers=[self.provider])
return sess
def testSessionOptionsAddConfigEntry(self):
so = onnxrt.SessionOptions()
key = "CONFIG_KEY"
val = "CONFIG_VAL"
so.add_session_config_entry(key, val)
self.assertEqual(so.get_session_config_entry(key), val)
def _create_ort_training_session(self):
# Validating frozen_weights names
unused_frozen_weights = [n for n in self.options.utils.frozen_weights\
if n not in [i.name for i in self._onnx_model.graph.initializer]]
if unused_frozen_weights:
raise RuntimeError(
"{} params from 'frozen_weights' not found in the ONNX model.".
format(unused_frozen_weights))
# Get loss name from model description
loss_name = [
item.name for item in self.model_desc.outputs if item.is_loss
]
assert len(
loss_name
) == 1, f"Only one loss output is supported ({len(loss_name)} were specified)"
loss_name = loss_name[0]
# Parse optimizer parameters
optimizer_attributes_map = {}
optimizer_int_attributes_map = {}
trainable_params = set()
for initializer in self._onnx_model.graph.initializer:
if initializer.name in self.options.utils.frozen_weights:
continue # only trainable parameters are passed to the backend
trainable_params.add(initializer.name)
optimizer_attributes_map[initializer.name] = {}
optimizer_int_attributes_map[initializer.name] = {}
not_in_param_groups = True
for param_group in self.optim_config.params:
if initializer.name not in param_group['params']:
continue # keep looking for a matching param_group
not_in_param_groups = False
for k, v in param_group.items():
# 'params' is not a hyper parameter, skip it. 'lr' per weight is not supported
if k == 'params' or k == 'lr':
continue
if isinstance(v, float):
optimizer_attributes_map[initializer.name][k] = v
elif isinstance(v, int):
optimizer_int_attributes_map[initializer.name][k] = v
else:
raise ValueError(
"Optimizer attributes must be either float or int."
)
# set default values for params not found in groups
if not_in_param_groups:
for k, v in self.optim_config.defaults.items():
if k == 'lr':
continue
if isinstance(v, float):
optimizer_attributes_map[initializer.name][k] = v
elif isinstance(v, int):
optimizer_int_attributes_map[initializer.name][k] = v
else:
raise ValueError(
"Optimizer attributes must be either float or int."
)
# TrainingParameters
ort_parameters = ort.TrainingParameters()
ort_parameters.loss_output_name = loss_name
ort_parameters.use_mixed_precision = self.options.mixed_precision.enabled
ort_parameters.world_rank = self.options.distributed.world_rank
ort_parameters.world_size = self.options.distributed.world_size
ort_parameters.gradient_accumulation_steps = self.options.batch.gradient_accumulation_steps
ort_parameters.allreduce_post_accumulation = self.options.distributed.allreduce_post_accumulation
ort_parameters.deepspeed_zero_stage = self.options.distributed.deepspeed_zero_optimization.stage
ort_parameters.enable_grad_norm_clip = self.options.utils.grad_norm_clip
ort_parameters.set_gradients_as_graph_outputs = False
ort_parameters.use_invertible_layernorm_grad = self.options.utils.invertible_layer_norm_gradient
ort_parameters.training_optimizer_name = self.optim_config.name
ort_parameters.lr_params_feed_name = self.model_desc.learning_rate.name
ort_parameters.weights_to_train = trainable_params
ort_parameters.optimizer_attributes_map = optimizer_attributes_map
ort_parameters.optimizer_int_attributes_map = optimizer_int_attributes_map
ort_parameters.attn_dropout_recompute = self.options.graph_transformer.attn_dropout_recompute
ort_parameters.gelu_recompute = self.options.graph_transformer.gelu_recompute
ort_parameters.transformer_layer_recompute = self.options.graph_transformer.transformer_layer_recompute
ort_parameters.number_recompute_layers = self.options.graph_transformer.number_recompute_layers
ort_parameters.model_with_training_graph_path = self.options.debug.model_with_training_graph_path
# SessionOptions
session_options = ort.SessionOptions()
session_options.use_deterministic_compute = self.options.debug.deterministic_compute
if (self.options.graph_transformer.attn_dropout_recompute
or self.options.graph_transformer.gelu_recompute
or self.options.graph_transformer.transformer_layer_recompute):
session_options.execution_order = ort.ExecutionOrder.PRIORITY_BASED
# old ort session may already exists and occupies GPU memory when creating new session, this may cause OOM error.
# for example, load_state_dict will be called before returing the function, and it calls _init_session again
del self._training_session
# TrainingSession
self._training_session = ort.TrainingSession(
self._onnx_model.SerializeToString(), ort_parameters,
session_options)
# I/O bindings
self._train_io_binding = self._training_session.io_binding()
self._eval_io_binding = self._training_session.io_binding()
cv2.namedWindow(WINDOW)
if len(sys.argv) > 1:
capture = cv2.VideoCapture(sys.argv[1])
mirror_img = False
else:
capture = cv2.VideoCapture(2)
mirror_img = True
if capture.isOpened():
hasFrame, frame = capture.read()
frame_ct = 0
else:
hasFrame = False
onnx_file_name = 'Preprocess1x256x256xBGRxByte.onnx'
sess_options = onnxruntime.SessionOptions()
sess_options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
sess_options.enable_profiling = True
ort_session = onnxruntime.InferenceSession(onnx_file_name, sess_options)
input_name = ort_session.get_inputs()[0].name
while hasFrame:
img1, img2, scale, pad = resize_pad(frame)
img_in = np.expand_dims(img1, axis=0).astype(np.uint8)
ort_inputs = {input_name: img_in}
ort_outs = ort_session.run(None, ort_inputs)
imgDisp = ort_outs[0][0]
def getSingleSessionProfilingStartTime():
so = onnxrt.SessionOptions()
so.enable_profiling = True
sess = onnxrt.InferenceSession(get_name("mul_1.onnx"),
sess_options=so)
return sess.get_profiling_start_time_ns()
def inference(onnx_model, model_dir, examples):
quantized_str = ''
if 'quantized' in onnx_model:
quantized_str = 'quantized'
onnx_inference = []
# pytorch_inference = []
# onnx session
options = ort.SessionOptions()
options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
options.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL
options.intra_op_num_threads = 16 # does not seem to make a difference, always parallelized
# options.inter_op_num_threads = multiprocessing.cpu_count()
# logger.info(onnx_model)
ort_session = ort.InferenceSession(onnx_model, options)
# pytorch pretrained model and tokenizer
if 'bertweet' in onnx_model:
tokenizer = AutoTokenizer.from_pretrained(model_dir,
normalization=True)
else:
tokenizer = AutoTokenizer.from_pretrained(model_dir)
tokenizer_str = "TokenizerFast"
# logger.info("**************** {} ONNX inference with batch tokenization and with {} tokenizer****************".format(
# quantized_str, tokenizer_str))
start_batch_tokenization = time.time()
tokens_dict = tokenizer.batch_encode_plus(examples, max_length=128)
token0 = get_tokens(tokens_dict, 0)
examples_chunks_list = chunkIt(examples, NUM_BATCHES)
tokens_dict_list = [
tokenizer.batch_encode_plus(chunk, padding='longest')
for chunk in examples_chunks_list
]
# tokens_dict_list = [tokenizer.batch_encode_plus(chunk, max_length=128) for chunk in examples_chunks_list]
minibatches_list = []
for i, token_batch in enumerate(tokens_dict_list):
minibatch = {}
number_examples_in_this_batch = len(token_batch['input_ids'])
minibatch['input_ids'] = np.stack(([
get_tokens(token_batch, i)['input_ids'][0]
for i in range(number_examples_in_this_batch)
]),
axis=0)
minibatch['token_type_ids'] = np.stack(([
get_tokens(token_batch, i)['token_type_ids'][0]
for i in range(number_examples_in_this_batch)
]),
axis=0)
minibatch['attention_mask'] = np.stack(([
get_tokens(token_batch, i)['attention_mask'][0]
for i in range(number_examples_in_this_batch)
]),
axis=0)
# logger.info('y')
minibatches_list.append(minibatch)
# tokens_dict = tokenizer.batch_encode_plus(examples, padding='longest')
total_batch_tokenization_time = time.time() - start_batch_tokenization
total_inference_time = 0
total_build_label_time = 0
start_onnx_inference_batch = time.time()
# for i in range(len(examples)):
for i, minibatch in enumerate(minibatches_list):
"""
Onnx inference with batch tokenization
"""
# if i % 100 == 0:
# logger.info(i, '/', NUM_BATCHES)
tokens = get_tokens(tokens_dict, i)
# inference
start_inference = time.time()
# ort_outs = ort_session.run(None, tokens)
ort_outs = ort_session.run(None, minibatch)
total_inference_time = total_inference_time + (time.time() -
start_inference)
# build label
start_build_label = time.time()
torch_onnx_output = torch.tensor(ort_outs[0], dtype=torch.float32)
onnx_logits = F.softmax(torch_onnx_output, dim=1)
logits_label = torch.argmax(onnx_logits, dim=1)
label = logits_label.detach().cpu().numpy()
# onnx_inference.append(label[0])
# onnx_inference.append(onnx_logits.detach().cpu().numpy().tolist())
# TODO might be able to make this faster by using arrays with pre-defined size isntead of mutating lists like this
onnx_inference = onnx_inference + onnx_logits.detach().cpu().numpy(
).tolist()
# onnx_inference.append(onnx_logits.detach().cpu().numpy()[0].tolist())
# total_build_label_time = total_build_label_time + (time.time() - start_build_label)
# logger.info(i, label[0], onnx_logits.detach().cpu().numpy()[0].tolist(), type(onnx_logits.detach().cpu().numpy()[0]) )
end_onnx_inference_batch = time.time()
# logger.info("Total batch tokenization time (in seconds): ", total_batch_tokenization_time)
# logger.info("Total inference time (in seconds): ", total_inference_time)
# logger.info("Total build label time (in seconds): ", total_build_label_time)
# logger.info("Duration ONNX inference (in seconds) with {} and batch tokenization: ".format(tokenizer_str),
# logger.info("Duration ONNX inference (in seconds): ",
# end_onnx_inference_batch - start_onnx_inference_batch,
# (end_onnx_inference_batch - start_onnx_inference_batch) / len(examples))
# logger.info(onnx_inference)
return onnx_inference
def main():
args = parse_arguments()
setup_logger(args.verbose)
dump_environment()
enable_past_input = args.enable_past_input
cache_dir = args.cache_dir
if not os.path.exists(cache_dir):
os.makedirs(cache_dir)
output_dir = args.output_dir
if not os.path.exists(output_dir):
os.makedirs(output_dir)
(model_class, tokenizer_class,
model_name_or_path) = MODEL_CLASSES[args.model_type]
tokenizer = tokenizer_class.from_pretrained(model_name_or_path,
cache_dir=cache_dir)
model = model_class.from_pretrained(model_name_or_path,
cache_dir=cache_dir)
model.eval().cpu()
inputs = tokenizer.encode_plus("Here is an example input for GPT2 model",
add_special_tokens=True,
return_tensors='pt')
input_ids = inputs['input_ids']
outputs = model(input_ids=input_ids, past=None)
num_layer = model.config.n_layer
present_names = [f'present_{i}' for i in range(num_layer)]
output_names = ["last_state"] + present_names
input_names = ['input_ids']
dynamic_axes = {
'input_ids': {
0: 'batch_size',
1: 'seq_len'
},
'last_state': {
0: 'batch_size',
1: 'seq_len'
}
}
for name in present_names:
dynamic_axes[name] = {1: 'batch_size', 3: 'seq_len'}
if enable_past_input:
past_names = [f'past_{i}' for i in range(num_layer)]
input_names = ['input_ids'] + past_names
dummy_past = [
torch.zeros(list(outputs[1][0].shape)) for _ in range(num_layer)
]
for name in past_names:
dynamic_axes[name] = {1: 'batch_size', 3: 'seq_len'}
export_inputs = (inputs['input_ids'], tuple(dummy_past))
else:
export_inputs = (inputs['input_ids'])
export_model_path = os.path.join(
output_dir, 'gpt2_past{}.onnx'.format(int(enable_past_input)))
torch.onnx.export(model,
args=export_inputs,
f=export_model_path,
input_names=input_names,
output_names=output_names,
dynamic_axes=dynamic_axes,
opset_version=11,
do_constant_folding=True,
verbose=False)
# Let's run performance test on PyTorch before updating environment variable.
past = dummy_past if enable_past_input else None
outputs = pytorch_inference(model,
input_ids,
past,
total_runs=args.total_runs)
# setup environment variables before importing onnxruntime.
setup_environment(args.use_openmp)
import onnxruntime
if enable_past_input:
onnx_model_path = export_model_path
else:
onnx_model_path = os.path.join(
output_dir, 'gpt2_past{}_out1.onnx'.format(int(enable_past_input)))
remove_past_outputs(export_model_path, onnx_model_path)
if args.enable_optimization:
from optimizer import optimize_model
m = optimize_model(onnx_model_path,
model_type='gpt2',
num_heads=12,
hidden_size=768,
opt_level=0,
optimization_options=None)
onnx_model_path = os.path.join(
output_dir,
'gpt2_past{}_optimized.onnx'.format(int(enable_past_input)))
m.save_model_to_file(onnx_model_path)
if 'CUDAExecutionProvider' in onnxruntime.get_available_providers():
logger.warning(
"onnxruntime-gpu is not built with OpenMP. You might try onnxruntime package to test CPU inference."
)
sess_options = onnxruntime.SessionOptions()
if args.use_openmp:
sess_options.intra_op_num_threads = 1
else:
sess_options.intra_op_num_threads = psutil.cpu_count(logical=True)
logger.info(
f"session option: intra_op_num_threads={sess_options.intra_op_num_threads}"
)
logger.info(f"Start inferencing onnx model: {onnx_model_path}")
session = onnxruntime.InferenceSession(onnx_model_path,
sess_options,
providers=['CPUExecutionProvider'])
ort_outputs = onnxruntime_inference(session, input_ids, past,
args.total_runs)
if args.verify_outputs:
logger.info(
'PyTorch and OnnxRuntime output 0 (last_state) are close:'.format(
0),
numpy.allclose(ort_outputs[0],
outputs[0].cpu(),
rtol=1e-05,
atol=1e-04))
for layer in range(model.config.n_layer):
logger.info(
'PyTorch and OnnxRuntime layer {} state (present_{}) are close:'
.format(layer, layer),
numpy.allclose(ort_outputs[1 + layer],
outputs[1][layer].cpu(),
rtol=1e-05,
atol=1e-04))
def main():
model = GoogleModel(64, 5, 2, 32, 8, True, None, False)
batch_size = 1000
input = torch.randn(batch_size, 5, 9, 9)
outputs = model(input)
model.eval()
# model(input)[0][0, 0].item()
# print("torch CPU:", timeit.timeit(lambda: model(input)[0][0, 0].item(), number=20))
# model.cuda()
# input = input.cuda()
# model(input)[0][0, 0].item()
# print("torch CUDA:", timeit.timeit(lambda: model(input)[0][0, 0].item(), number=20))
print("Exporting to onnx")
torch.onnx.export(
model,
input,
"../../data/onnx/small.onnx",
example_outputs=outputs,
opset_version=12,
input_names=["input"],
output_names=["value", "policy"],
dynamic_axes={
"input": {
0: "batch_size"
},
"value": {
0: "batch_size"
},
"policy": {
0: "batch_size"
}
},
training=TrainingMode.EVAL,
)
# print("Quantizing")
# quantize_dynamic("../../data/onnx/small.onnx", "../../data/onnx/small_quant.onnx", weight_type=QuantType.QUInt8)
print("Loading onnx")
model = onnx.load("../../data/onnx/small.onnx")
onnx.checker.check_model(model)
model = onnx.load("../../data/onnx/small_quant.onnx")
onnx.checker.check_model(model)
print("Building profile")
sess_options = onnxruntime.SessionOptions()
sess_options.enable_profiling = True
# sess_options.log_severity_level = 0
session = onnxruntime.InferenceSession("../../data/onnx/small.onnx",
sess_options=sess_options)
print("Running model")
onnx_input = input.cpu().numpy()
_ = session.run(None, {"input": onnx_input})
_ = session.run(None, {"input": onnx_input})
rounds = 100
delta = timeit.timeit(lambda: session.run(None, {"input": onnx_input}),
number=rounds)
throughput = batch_size * rounds / delta
print(f"onnx cuda (?): {throughput} boards/s")
def load_a_batch(batch_filenames):
unconcatenated_batch_data = []
for image_filename in batch_filenames:
image_filepath = imagenet_path + '/' + image_filename
nchw_data = load_and_resize_image(image_filepath, height, width)
unconcatenated_batch_data.append(nchw_data)
batch_data = np.concatenate(unconcatenated_batch_data, axis=0)
return batch_data
#print("Device: " + rt.get_device())
sess_options = rt.SessionOptions()
if CPU_THREADS > 0:
sess_options.enable_sequential_execution = False
sess_options.session_thread_pool_size = CPU_THREADS
sess = rt.InferenceSession(model_path, sess_options)
input_layer_names = [
x.name for x in sess.get_inputs()
] # FIXME: check that input_layer_name belongs to this list
input_layer_name = input_layer_name or input_layer_names[0]
output_layer_names = [
x.name for x in sess.get_outputs()
] # FIXME: check that output_layer_name belongs to this list
output_layer_name = output_layer_name or output_layer_names[0]
def test_modulated_deform_conv2d():
try:
from mmcv.ops import ModulatedDeformConv2d, get_onnxruntime_op_path
except (ImportError, ModuleNotFoundError):
pytest.skip('modulated_deform_conv op is not successfully compiled')
ort_custom_op_path = get_onnxruntime_op_path()
# modulated deform conv config
in_channels = 3
out_channels = 64
stride = 1
padding = 0
dilation = 1
groups = 1
deform_groups = 1
kernel_size = 3
input = torch.rand(1, in_channels, 28, 28).cuda() # (n, c, h, w)
conv_offset = nn.Conv2d(in_channels=3,
out_channels=deform_groups * 3 * kernel_size *
kernel_size,
kernel_size=kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
bias=True).cuda()
conv_offset.cuda()
out = conv_offset(input)
o1, o2, mask = torch.chunk(out, 3, dim=1)
offset = torch.cat((o1, o2), dim=1)
mask = torch.sigmoid(mask)
model_with_bias = ModulatedDeformConv2d(in_channels,
out_channels,
kernel_size,
stride,
padding,
dilation,
groups,
deform_groups,
bias=True)
model_without_bias = ModulatedDeformConv2d(in_channels,
out_channels,
kernel_size,
stride,
padding,
dilation,
groups,
deform_groups,
bias=False)
models = [model_with_bias.cuda(), model_without_bias.cuda()]
for model in models:
# export and load onnx model
with torch.no_grad():
torch.onnx.export(model, (input, offset, mask),
onnx_file,
export_params=True,
keep_initializers_as_inputs=True,
input_names=['input', 'offset', 'mask'],
opset_version=11)
session_options = rt.SessionOptions()
if os.path.exists(ort_custom_op_path):
session_options.register_custom_ops_library(ort_custom_op_path)
# compute onnx_output
sess = rt.InferenceSession(onnx_file, session_options)
onnx_output = sess.run(
None, {
'input': input.cpu().detach().numpy(),
'offset': offset.cpu().detach().numpy(),
'mask': mask.cpu().detach().numpy()
})[0]
# compute pytorch_output
with torch.no_grad():
pytorch_output = model(input, offset, mask).cpu()
# allclose
assert np.allclose(pytorch_output, onnx_output, atol=1e-3)
def test_deform_conv2d(threshold=1e-3):
try:
from mmcv.ops import DeformConv2d, get_onnxruntime_op_path
except (ImportError, ModuleNotFoundError):
pytest.skip('deform_conv op is not successfully compiled')
ort_custom_op_path = get_onnxruntime_op_path()
if not os.path.exists(ort_custom_op_path):
pytest.skip('custom ops for onnxruntime are not compiled.')
# deform conv config
# modulated deform conv config
in_channels = 1
out_channels = 64
stride = 1
padding = 0
dilation = 1
groups = 1
deform_groups = 1
kernel_size = 2
input = [[[[1., 2., 3.], [0., 1., 2.], [3., 5., 2.]]]]
offset_weight = [[[0.1, 0.4, 0.6, 0.1]], [[0.3, 0.2, 0.1, 0.3]],
[[0.5, 0.5, 0.2, 0.8]], [[0.8, 0.3, 0.9, 0.1]],
[[0.3, 0.1, 0.2, 0.5]], [[0.3, 0.7, 0.5, 0.3]],
[[0.6, 0.2, 0.5, 0.3]], [[0.4, 0.1, 0.8, 0.4]]]
offset_bias = [0.7, 0.1, 0.8, 0.5, 0.6, 0.5, 0.4, 0.7]
deform_weight = [[[0.4, 0.2, 0.1, 0.9]]]
x = torch.tensor(input)
conv_offset = nn.Conv2d(in_channels=in_channels,
out_channels=deform_groups * 2 * kernel_size *
kernel_size,
kernel_size=kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
bias=True)
conv_offset.weight.data = torch.nn.Parameter(
torch.Tensor(offset_weight).reshape(8, 1, 2, 2))
conv_offset.bias.data = torch.nn.Parameter(
torch.Tensor(offset_bias).reshape(8))
offset = conv_offset(x)
model = DeformConv2d(in_channels, out_channels, kernel_size, stride,
padding, dilation, groups, deform_groups)
model.weight.data = torch.nn.Parameter(
torch.Tensor(deform_weight).reshape(1, 1, 2, 2))
with torch.no_grad():
torch.onnx.export(model, (x, offset),
onnx_file,
export_params=True,
keep_initializers_as_inputs=True,
input_names=['input', 'offset'],
opset_version=11)
session_options = rt.SessionOptions()
if os.path.exists(ort_custom_op_path):
session_options.register_custom_ops_library(ort_custom_op_path)
# compute onnx_output
sess = rt.InferenceSession(onnx_file, session_options)
onnx_output = sess.run(
None, {
'input': x.cpu().detach().numpy(),
'offset': offset.cpu().detach().numpy(),
})[0]
# compute pytorch_output
with torch.no_grad():
pytorch_output = model(x, offset).cpu()
# allclose
assert np.allclose(pytorch_output, onnx_output, atol=1e-3)
def test_cummax_cummin(key, opset=11):
if torch.__version__ == 'parrots':
pytest.skip('onnx is not supported in parrots directly')
# Note generally `cummax` or `cummin` is exportable to ONNX
# as long as the pytorch version >= 1.5.0, since `torch.cummax`
# is only supported with torch >= 1.5.0.
# But when `cummax` or `cummin` serves as an intermediate component
# whose outputs is used as inputs for another modules, it's expected
# that pytorch version must be >= 1.7.0. Otherwise error appears like:
# `RuntimeError: tuple appears in op that does not forward tuples,
# unsupported 'kind: prim::PythonOp`.
if version.parse(torch.__version__) < version.parse('1.7.0'):
pytest.skip('test_cummax_cummin should be ran with pytorch >= 1.7.0')
# register custom op `mmcv::cummax` and `mmcv::cummin`
from mmcv.onnx.symbolic import register_extra_symbolics
register_extra_symbolics(opset)
from mmcv.ops import get_onnxruntime_op_path
ort_custom_op_path = get_onnxruntime_op_path()
if not os.path.exists(ort_custom_op_path):
pytest.skip('custom ops for onnxruntime are not compiled.')
input_list = [
# arbitrary shape, e.g. 1-D, 2-D, 3-D, ...
torch.rand((2, 3, 4, 1, 5)),
torch.rand((1)),
torch.rand((2, 0, 1)), # tensor.numel() is 0
torch.FloatTensor(), # empty tensor
]
cummax_cummin_funcs = {'cummax': torch.cummax, 'cummin': torch.cummin}
for input in input_list:
ndims = input.dim()
# valid dim range is [-ndims, ndims-1]
# test for all `dim` value which is valid
for dim in range(-ndims, ndims):
cummax_func = partial(cummax_cummin_funcs[key], dim=dim)
wrapped_model = WrapFunction(cummax_func).eval()
with torch.no_grad():
torch.onnx.export(wrapped_model,
input,
onnx_file,
export_params=True,
keep_initializers_as_inputs=True,
input_names=['input'],
output_names=['output', 'indices'],
opset_version=opset)
onnx_model = onnx.load(onnx_file)
input_all = [node.name for node in onnx_model.graph.input]
input_initializer = [
node.name for node in onnx_model.graph.initializer
]
net_feed_input = list(set(input_all) - set(input_initializer))
assert (len(net_feed_input) == 1)
session_options = rt.SessionOptions()
session_options.register_custom_ops_library(ort_custom_op_path)
sess = rt.InferenceSession(onnx_file, session_options)
ort_output, ort_inds = sess.run(None,
{'input': input.detach().numpy()})
pytorch_output, pytorch_inds = wrapped_model(input.clone())
pytorch_output = pytorch_output.detach().numpy()
pytorch_inds = pytorch_inds.detach().numpy()
assert np.allclose(pytorch_output, ort_output, atol=1e-5)
assert np.all(pytorch_inds == ort_inds)
def test_roialign_rotated():
if torch.__version__ == 'parrots':
pytest.skip('onnx is not supported in parrots directly')
try:
from mmcv.ops import get_onnxruntime_op_path, roi_align_rotated
except (ImportError, ModuleNotFoundError):
pytest.skip('roi_align_aligned op is not successfully compiled')
ort_custom_op_path = get_onnxruntime_op_path()
if not os.path.exists(ort_custom_op_path):
pytest.skip('custom ops for onnxruntime are not compiled.')
# roi align config
pool_h = 2
pool_w = 2
spatial_scale = 1.0
sampling_ratio = 2
inputs = [([[[[1., 2.], [3., 4.]]]], [[0., 0.5, 0.5, 1., 1., 0]]),
([[[[1., 2.], [3., 4.]]]], [[0., 0.5, 0.5, 1., 1., np.pi / 2]]),
([[[[1., 2.], [3., 4.]],
[[4., 3.], [2., 1.]]]], [[0., 0.5, 0.5, 1., 1., 0]]),
([[[[1., 2., 5., 6.], [3., 4., 7., 8.], [9., 10., 13., 14.],
[11., 12., 15., 16.]]]], [[0., 1.5, 1.5, 3., 3., 0]]),
([[[[1., 2., 5., 6.], [3., 4., 7., 8.], [9., 10., 13., 14.],
[11., 12., 15., 16.]]]], [[0., 1.5, 1.5, 3., 3.,
np.pi / 2]])]
def warpped_function(torch_input, torch_rois):
return roi_align_rotated(torch_input, torch_rois, (pool_w, pool_h),
spatial_scale, sampling_ratio, True, False)
for case in inputs:
np_input = np.array(case[0], dtype=np.float32)
np_rois = np.array(case[1], dtype=np.float32)
input = torch.from_numpy(np_input)
rois = torch.from_numpy(np_rois)
# compute pytorch_output
with torch.no_grad():
pytorch_output = roi_align_rotated(input, rois, (pool_w, pool_h),
spatial_scale, sampling_ratio,
True, False)
# export and load onnx model
wrapped_model = WrapFunction(warpped_function)
with torch.no_grad():
torch.onnx.export(wrapped_model, (input, rois),
onnx_file,
export_params=True,
keep_initializers_as_inputs=True,
input_names=['features', 'rois'],
opset_version=11)
onnx_model = onnx.load(onnx_file)
session_options = rt.SessionOptions()
if os.path.exists(ort_custom_op_path):
session_options.register_custom_ops_library(ort_custom_op_path)
# compute onnx_output
input_all = [node.name for node in onnx_model.graph.input]
input_initializer = [
node.name for node in onnx_model.graph.initializer
]
net_feed_input = list(set(input_all) - set(input_initializer))
assert (len(net_feed_input) == 2)
sess = rt.InferenceSession(onnx_file, session_options)
onnx_output = sess.run(None, {
'features': input.detach().numpy(),
'rois': rois.detach().numpy()
})
onnx_output = onnx_output[0]
# allclose
assert np.allclose(pytorch_output, onnx_output, atol=1e-3)
def test_softnms():
if torch.__version__ == 'parrots':
pytest.skip('onnx is not supported in parrots directly')
from mmcv.ops import get_onnxruntime_op_path, soft_nms
# only support pytorch >= 1.7.0
if version.parse(torch.__version__) < version.parse('1.7.0'):
warnings.warn('test_softnms should be ran with pytorch >= 1.7.0')
return
# only support onnxruntime >= 1.5.1
assert version.parse(rt.__version__) >= version.parse(
'1.5.1'), 'test_softnms should be ran with onnxruntime >= 1.5.1'
ort_custom_op_path = get_onnxruntime_op_path()
if not os.path.exists(ort_custom_op_path):
pytest.skip('softnms for onnxruntime is not compiled.')
np_boxes = np.array([[6.0, 3.0, 8.0, 7.0], [3.0, 6.0, 9.0, 11.0],
[3.0, 7.0, 10.0, 12.0], [1.0, 4.0, 13.0, 7.0]],
dtype=np.float32)
np_scores = np.array([0.6, 0.9, 0.7, 0.2], dtype=np.float32)
boxes = torch.from_numpy(np_boxes)
scores = torch.from_numpy(np_scores)
configs = [[0.3, 0.5, 0.01, 'linear'], [0.3, 0.5, 0.01, 'gaussian'],
[0.3, 0.5, 0.01, 'naive']]
session_options = rt.SessionOptions()
session_options.register_custom_ops_library(ort_custom_op_path)
for _iou_threshold, _sigma, _min_score, _method in configs:
pytorch_dets, pytorch_inds = soft_nms(boxes,
scores,
iou_threshold=_iou_threshold,
sigma=_sigma,
min_score=_min_score,
method=_method)
nms = partial(soft_nms,
iou_threshold=_iou_threshold,
sigma=_sigma,
min_score=_min_score,
method=_method)
wrapped_model = WrapFunction(nms)
wrapped_model.cpu().eval()
with torch.no_grad():
torch.onnx.export(wrapped_model, (boxes, scores),
onnx_file,
export_params=True,
keep_initializers_as_inputs=True,
input_names=['boxes', 'scores'],
opset_version=11)
onnx_model = onnx.load(onnx_file)
# get onnx output
input_all = [node.name for node in onnx_model.graph.input]
input_initializer = [
node.name for node in onnx_model.graph.initializer
]
net_feed_input = list(set(input_all) - set(input_initializer))
assert (len(net_feed_input) == 2)
sess = rt.InferenceSession(onnx_file, session_options)
onnx_dets, onnx_inds = sess.run(None, {
'scores': scores.detach().numpy(),
'boxes': boxes.detach().numpy()
})
assert np.allclose(pytorch_dets, onnx_dets, atol=1e-3)
assert np.allclose(onnx_inds, onnx_inds, atol=1e-3)
def evaluate(opt):
# set config
config = load_config(opt)
if opt.num_threads > 0: torch.set_num_threads(opt.num_threads)
config['opt'] = opt
logger.info("%s", config)
# set path
set_path(config)
# prepare test dataset
test_loader = prepare_datasets(config)
# load pytorch model checkpoint
checkpoint = load_checkpoint(config)
# prepare model and load parameters
model = load_model(config, checkpoint)
model.eval()
# convert to onnx format
if opt.convert_onnx:
(x, y) = next(iter(test_loader))
x = to_device(x, opt.device)
y = to_device(y, opt.device)
convert_onnx(config, model, x)
check_onnx(config)
logger.info("[ONNX model saved at {}".format(opt.onnx_path))
return
# load onnx model for using onnxruntime
if opt.enable_ort:
import onnxruntime as ort
sess_options = ort.SessionOptions()
sess_options.inter_op_num_threads = opt.num_threads
sess_options.intra_op_num_threads = opt.num_threads
ort_session = ort.InferenceSession(opt.onnx_path,
sess_options=sess_options)
# convert to tvm format
if opt.convert_tvm:
(x, y) = next(iter(test_loader))
x = to_device(x, opt.device)
y = to_device(y, opt.device)
convert_tvm(config, model, x)
logger.info("[TVM model saved at {}".format(opt.tvm_path))
return
# enable to use dynamic quantized model (pytorch>=1.3.0)
if opt.enable_dqm and opt.device == 'cpu':
model = torch.quantization.quantize_dynamic(model, {torch.nn.Linear},
dtype=torch.qint8)
print(model)
# evaluation
preds = None
ys = None
correct = 0
n_batches = len(test_loader)
total_examples = 0
whole_st_time = time.time()
first_time = time.time()
first_examples = 0
total_duration_time = 0.0
with torch.no_grad():
for i, (x, y) in enumerate(tqdm(test_loader, total=n_batches)):
start_time = time.time()
x = to_device(x, opt.device)
y = to_device(y, opt.device)
if opt.enable_ort:
x = to_numpy(x)
if config['emb_class'] == 'glove':
ort_inputs = {ort_session.get_inputs()[0].name: x}
if config['emb_class'] in [
'bert', 'distilbert', 'albert', 'roberta', 'bart',
'electra'
]:
if config['emb_class'] in ['distilbert', 'bart']:
ort_inputs = {
ort_session.get_inputs()[0].name: x[0],
ort_session.get_inputs()[1].name: x[1]
}
else:
ort_inputs = {
ort_session.get_inputs()[0].name: x[0],
ort_session.get_inputs()[1].name: x[1],
ort_session.get_inputs()[2].name: x[2]
}
logits = ort_session.run(None, ort_inputs)[0]
logits = to_device(torch.tensor(logits), opt.device)
else:
logits = model(x)
if preds is None:
preds = to_numpy(logits)
ys = to_numpy(y)
else:
preds = np.append(preds, to_numpy(logits), axis=0)
ys = np.append(ys, to_numpy(y), axis=0)
predicted = logits.argmax(1)
correct += (predicted == y).sum().item()
cur_examples = y.size(0)
total_examples += cur_examples
if i == 0: # first one may take longer time, so ignore in computing duration.
first_time = float((time.time() - first_time) * 1000)
first_examples = cur_examples
if opt.num_examples != 0 and total_examples >= opt.num_examples:
logger.info("[Stop Evaluation] : up to the {} examples".format(
total_examples))
break
duration_time = float((time.time() - start_time) * 1000)
if i != 0: total_duration_time += duration_time
'''
logger.info("[Elapsed Time] : {}ms".format(duration_time))
'''
# generate report
labels = model.labels
label_names = [v for k, v in sorted(labels.items(), key=lambda x: x[0])]
preds_ids = np.argmax(preds, axis=1)
try:
print(
classification_report(ys,
preds_ids,
target_names=label_names,
digits=4))
print(labels)
print(confusion_matrix(ys, preds_ids))
except Exception as e:
logger.warn(str(e))
acc = correct / total_examples
whole_time = float((time.time() - whole_st_time) * 1000)
avg_time = (whole_time - first_time) / (total_examples - first_examples)
# write predictions to file
write_prediction(opt, preds, labels)
logger.info("[Accuracy] : {:.4f}, {:5d}/{:5d}".format(
acc, correct, total_examples))
logger.info("[Elapsed Time] : {}ms, {}ms on average".format(
whole_time, avg_time))
logger.info(
"[Elapsed Time(total_duration_time, average)] : {}ms, {}ms".format(
total_duration_time, total_duration_time / (total_examples - 1)))
def _load_model(args) -> Any:
# validation
if args.device not in [None, "cpu"] and args.engine != TORCH_ENGINE:
raise ValueError(
f"device {args.device} is not supported for {args.engine}")
if args.fp16 and args.engine != TORCH_ENGINE:
raise ValueError(f"half precision is not supported for {args.engine}")
if args.quantized_inputs and args.engine == TORCH_ENGINE:
raise ValueError(f"quantized inputs not supported for {args.engine}")
if args.num_cores is not None and args.engine == TORCH_ENGINE:
raise ValueError(
f"overriding default num_cores not supported for {args.engine}")
if (args.num_cores is not None and args.engine == ORT_ENGINE
and onnxruntime.__version__ < "1.7"):
raise ValueError(
"overriding default num_cores not supported for onnxruntime < 1.7.0. "
"If using an older build with OpenMP, try setting the OMP_NUM_THREADS "
"environment variable")
# scale static ONNX graph to desired image shape
if args.engine in [DEEPSPARSE_ENGINE, ORT_ENGINE]:
args.model_filepath, _ = modify_yolo_onnx_input_shape(
args.model_filepath, args.image_shape)
has_postprocessing = yolo_onnx_has_postprocessing(args.model_filepath)
# load model
if args.engine == DEEPSPARSE_ENGINE:
_LOGGER.info(f"Compiling DeepSparse model for {args.model_filepath}")
model = compile_model(args.model_filepath, 1, args.num_cores)
if args.quantized_inputs and not model.cpu_vnni:
_LOGGER.warning("WARNING: VNNI instructions not detected, "
"quantization speedup not well supported")
elif args.engine == ORT_ENGINE:
_LOGGER.info(f"Loading onnxruntime model for {args.model_filepath}")
sess_options = onnxruntime.SessionOptions()
if args.num_cores is not None:
sess_options.intra_op_num_threads = args.num_cores
sess_options.log_severity_level = 3
sess_options.graph_optimization_level = (
onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL)
onnx_model = onnx.load(args.model_filepath)
override_model_batch_size(onnx_model, 1)
model = onnxruntime.InferenceSession(onnx_model.SerializeToString(),
sess_options=sess_options)
elif args.engine == TORCH_ENGINE:
_LOGGER.info(f"Loading torch model for {args.model_filepath}")
model = torch.load(args.model_filepath)
if isinstance(model, dict):
model = model["model"]
model.to(args.device)
model.eval()
if args.fp16:
_LOGGER.info("Using half precision")
model.half()
else:
_LOGGER.info("Using full precision")
model.float()
has_postprocessing = True
return model, has_postprocessing
def inference(opt):
# set config
config = load_config(opt)
if opt.num_threads > 0: torch.set_num_threads(opt.num_threads)
config['opt'] = opt
# set path: opt.embedding_path, opt.vocab_path, opt.label_path
set_path(config)
# load pytorch model checkpoint
checkpoint = load_checkpoint(config)
# prepare model and load parameters
model = load_model(config, checkpoint)
model.eval()
# load onnx model for using onnxruntime
if opt.enable_ort:
import onnxruntime as ort
sess_options = ort.SessionOptions()
sess_options.inter_op_num_threads = opt.num_threads
sess_options.intra_op_num_threads = opt.num_threads
ort_session = ort.InferenceSession(opt.onnx_path,
sess_options=sess_options)
# enable to use dynamic quantized model (pytorch>=1.3.0)
if opt.enable_dqm and opt.device == 'cpu':
model = torch.quantization.quantize_dynamic(model, {torch.nn.Linear},
dtype=torch.qint8)
print(model)
# prepare tokenizer
tokenizer = prepare_tokenizer(config, model)
# prepare labels
labels = model.labels
# inference
f_out = open(opt.test_path + '.inference', 'w', encoding='utf-8')
total_examples = 0
total_duration_time = 0.0
with torch.no_grad(), open(opt.test_path, 'r', encoding='utf-8') as f:
for i, line in enumerate(f):
start_time = time.time()
sent, label = line.strip().split('\t')
x_raw = sent.split()
y_raw = label
text = ' '.join(x_raw)
x = encode_text(config, tokenizer, text)
x = to_device(x, opt.device)
if opt.enable_ort:
x = to_numpy(x)
if config['emb_class'] == 'glove':
ort_inputs = {ort_session.get_inputs()[0].name: x}
if config['emb_class'] in [
'bert', 'distilbert', 'albert', 'roberta', 'bart',
'electra'
]:
if config['emb_class'] in ['distilbert', 'bart']:
ort_inputs = {
ort_session.get_inputs()[0].name: x[0],
ort_session.get_inputs()[1].name: x[1]
}
else:
ort_inputs = {
ort_session.get_inputs()[0].name: x[0],
ort_session.get_inputs()[1].name: x[1],
ort_session.get_inputs()[2].name: x[2]
}
logits = ort_session.run(None, ort_inputs)[0]
logits = to_device(torch.tensor(logits), opt.device)
else:
logits = model(x)
predicted = logits.argmax(1)
predicted = to_numpy(predicted)[0]
predicted_raw = labels[predicted]
f_out.write(text + '\t' + y_raw + '\t' + predicted_raw + '\n')
total_examples += 1
if opt.num_examples != 0 and total_examples >= opt.num_examples:
logger.info("[Stop Inference] : up to the {} examples".format(
total_examples))
break
duration_time = float((time.time() - start_time) * 1000)
if i != 0: total_duration_time += duration_time
logger.info("[Elapsed Time] : {}ms".format(duration_time))
f_out.close()
logger.info(
"[Elapsed Time(total_duration_time, average)] : {}ms, {}ms".format(
total_duration_time, total_duration_time / (total_examples - 1)))
def inference(onnx_model, model_dir, examples, fast_tokenizer, num_threads):
quantized_str = ''
if 'quantized' in onnx_model:
quantized_str = 'quantized'
onnx_inference = []
# pytorch_inference = []
# onnx session
options = ort.SessionOptions()
options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
options.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL
options.intra_op_num_threads = 1
print(onnx_model)
ort_session = ort.InferenceSession(onnx_model, options)
# pytorch pretrained model and tokenizer
tokenizer = BertTokenizerFast.from_pretrained(model_dir)
tokenizer_str = "BertTokenizerFast"
print(
"**************** {} ONNX inference with batch tokenization and with {} tokenizer****************"
.format(quantized_str, tokenizer_str))
start_onnx_inference_batch = time.time()
start_batch_tokenization = time.time()
tokens_dict = tokenizer.batch_encode_plus(examples, max_length=128)
total_batch_tokenization_time = time.time() - start_batch_tokenization
total_inference_time = 0
total_build_label_time = 0
for i in range(len(examples)):
"""
Onnx inference with batch tokenization
"""
if i % 100 == 0:
print('[inference... ]', i, 'out of ', len(examples))
tokens = get_tokens(tokens_dict, i)
#inference
start_inference = time.time()
ort_outs = ort_session.run(None, tokens)
total_inference_time = total_inference_time + (time.time() -
start_inference)
#build label
start_build_label = time.time()
torch_onnx_output = torch.tensor(ort_outs[0], dtype=torch.float32)
onnx_logits = F.softmax(torch_onnx_output, dim=1)
logits_label = torch.argmax(onnx_logits, dim=1)
label = logits_label.detach().cpu().numpy()
# onnx_inference.append(label[0])
onnx_inference.append(onnx_logits.detach().cpu().numpy()[0].tolist())
total_build_label_time = total_build_label_time + (time.time() -
start_build_label)
# print(i, label[0], onnx_logits.detach().cpu().numpy()[0].tolist(), type(onnx_logits.detach().cpu().numpy()[0]) )
end_onnx_inference_batch = time.time()
print("Total batch tokenization time (in seconds): ",
total_batch_tokenization_time)
print("Total inference time (in seconds): ", total_inference_time)
print("Total build label time (in seconds): ", total_build_label_time)
print(
"Duration ONNX inference (in seconds) with {} and batch tokenization: "
.format(tokenizer_str),
end_onnx_inference_batch - start_onnx_inference_batch,
(end_onnx_inference_batch - start_onnx_inference_batch) /
len(examples))
return onnx_inference
def generate_test_data(onnx_file,
output_path,
batch_size,
sequence_length,
use_cpu=True,
input_tensor_only=False,
dictionary_size=DICT_SIZE,
test_cases=3):
input_data_type = np.int32
for test_case in range(test_cases):
input_1 = np.random.randint(dictionary_size,
size=(batch_size, sequence_length),
dtype=input_data_type)
tensor_1 = numpy_helper.from_array(input_1, 'input_ids')
actual_seq_len = random.randint(sequence_length - 3, sequence_length)
input_2 = np.zeros((batch_size, sequence_length),
dtype=input_data_type)
temp = np.ones((batch_size, actual_seq_len), dtype=input_data_type)
input_2[:temp.shape[0], :temp.shape[1]] = temp
tensor_2 = numpy_helper.from_array(input_2, 'attention_mask')
input_3 = np.zeros((batch_size, sequence_length),
dtype=input_data_type)
tensor_3 = numpy_helper.from_array(input_3, 'token_type_ids')
path = os.path.join(output_path, 'test_data_set_' + str(test_case))
try:
os.mkdir(path)
except OSError:
print("Creation of the directory %s failed" % path)
else:
print("Successfully created the directory %s " % path)
if input_tensor_only:
return
sess_options = onnxruntime.SessionOptions()
sess_options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_DISABLE_ALL
sess = onnxruntime.InferenceSession(onnx_file,
sess_options,
providers=['CPUExecutionProvider'])
input1_name = sess.get_inputs()[0].name
output_names = [output.name for output in sess.get_outputs()]
inputs = {
'input_ids': input_1,
'attention_mask': input_2,
'token_type_ids': input_3
}
print("inputs", inputs)
result = sess.run(output_names, inputs)
with open(os.path.join(path, 'input_{}.pb'.format(0)), 'wb') as f:
f.write(tensor_1.SerializeToString())
with open(os.path.join(path, 'input_{}.pb'.format(1)), 'wb') as f:
f.write(tensor_2.SerializeToString())
with open(os.path.join(path, 'input_{}.pb'.format(2)), 'wb') as f:
f.write(tensor_3.SerializeToString())
for i, output_name in enumerate(output_names):
tensor_result = numpy_helper.from_array(
np.asarray(result[i]).reshape((batch_size, sequence_length)),
output_names[i])
with open(os.path.join(path, 'output_{}.pb'.format(i)), 'wb') as f:
f.write(tensor_result.SerializeToString())
start_time = timeit.default_timer()
sess_options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_EXTENDED
path_prefix = onnx_file[:-5] #remove .onnx suffix
if use_cpu:
sess_options.optimized_model_filepath = path_prefix + "_optimized_cpu.onnx"
else:
sess_options.optimized_model_filepath = path_prefix + "_optimized_gpu.onnx"
session = onnxruntime.InferenceSession(onnx_file, sess_options)
if use_cpu:
session.set_providers(['CPUExecutionProvider']) # use cpu
else:
if 'CUDAExecutionProvider' not in session.get_providers():
print("Warning: GPU not found")
continue
outputs = session.run(None, inputs)
evalTime = timeit.default_timer() - start_time
if outputs[0].tolist() != result[0].tolist():
print(
"Error: not same result after optimization. use_cpu={}, no_opt_output={}, opt_output={}"
.format(use_cpu, result[0].tolist(), outputs[1].tolist()))
print("** Evaluation done in total {} secs".format(evalTime))
num_gpus = torch.cuda.device_count()
print(target, model_name)
model = model_fn(pretrained=True)
# pytorch --> onnx model conversion
if (target != "pytorch"):
# write model out to onnx
ref_input = torch.tensor(vidl.get_random(0, batch_len * num_gpus))
torch.onnx.export(model, (ref_input),
"bench_out.onnx",
keep_initializers_as_inputs=True,
verbose=True,
opset_version=10)
so = ort.SessionOptions()
so.optimized_model_filepath = "bench_out.onnx.opt"
session = ort.InferenceSession("bench_out.onnx", so)
del session
del so
res = None
if (num_gpus > 0):
scaled_batch_len = batch_len * num_gpus
else:
scaled_batch_len = batch_len
latency = []
# PYTORCH BENCH
if target == "pytorch":
def main():
parser = argparse.ArgumentParser(description='Simple ONNX Runtime Test Tool.')
parser.add_argument('model_path', help='model path')
parser.add_argument('num_iters', nargs='?', type=int, default=1000, help='model run iterations. default=1000')
parser.add_argument('--debug', action='store_true', help='pause execution to allow attaching a debugger.')
parser.add_argument('--profile', action='store_true', help='enable chrome timeline trace profiling.')
args = parser.parse_args()
iters = args.num_iters
if args.debug:
print("Pausing execution ready for debugger to attach to pid: {}".format(os.getpid()))
print("Press key to continue.")
sys.stdin.read(1)
sess_options = None
if args.profile:
sess_options = onnxrt.SessionOptions()
sess_options.enable_profiling = True
sess_options.profile_file_prefix = os.path.basename(args.model_path)
sess = onnxrt.InferenceSession(args.model_path, sess_options)
meta = sess.get_modelmeta()
feeds = {}
for input_meta in sess.get_inputs():
# replace any symbolic dimensions (value is None) with 1
shape = [dim if dim else 1 for dim in input_meta.shape]
if input_meta.type in float_dict:
feeds[input_meta.name] = np.random.rand(*shape).astype(float_dict[input_meta.type])
elif input_meta.type in integer_dict:
feeds[input_meta.name] = np.random.uniform(high=1000,
size=tuple(shape)).astype(integer_dict[input_meta.type])
elif input_meta.type == 'tensor(bool)':
feeds[input_meta.name] = np.random.randint(2, size=tuple(shape)).astype('bool')
else:
print("unsupported input type {} for input {}".format(input_meta.type, input_meta.name))
sys.exit(-1)
# Starting with IR4 some initializers provide default values
# and can be overridden (available in IR4). For IR < 4 models
# the list would be empty
for initializer in sess.get_overridable_initializers():
shape = [dim if dim else 1 for dim in initializer.shape]
if initializer.type in float_dict:
feeds[initializer.name] = np.random.rand(*shape).astype(float_dict[initializer.type])
elif initializer.type in integer_dict:
feeds[initializer.name] = np.random.uniform(high=1000,
size=tuple(shape)).astype(integer_dict[initializer.type])
elif initializer.type == 'tensor(bool)':
feeds[initializer.name] = np.random.randint(2, size=tuple(shape)).astype('bool')
else:
print("unsupported initializer type {} for initializer {}".format(initializer.type, initializer.name))
sys.exit(-1)
start = timer()
for i in range(iters):
sess.run([], feeds) # fetch all outputs
end = timer()
print("model: {}".format(meta.graph_name))
print("version: {}".format(meta.version))
print("iterations: {}".format(iters))
print("avg latency: {} ms".format(((end - start) * 1000) / iters))
if args.profile:
trace_file = sess.end_profiling()
print("trace file written to: {}".format(trace_file))
return 0
def predict(self, data):
import onnxruntime as ort
assert self.model is not None
remainder_sess = None
sess_options = ort.SessionOptions()
sess_options.intra_op_num_threads = self.params["nthread"]
sess_options.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL
sess = ort.InferenceSession(self.model.SerializeToString(),
sess_options=sess_options)
if self.remainder_model is not None:
remainder_sess = ort.InferenceSession(
self.remainder_model.SerializeToString(),
sess_options=sess_options)
batch_size = 1 if self.params["operator"] == "xgb" else self.params[
"batch_size"]
input_name = sess.get_inputs()[0].name
is_regression = data.learning_task == LearningTask.REGRESSION
if is_regression:
output_name_index = 0
else:
output_name_index = 1
output_name = sess.get_outputs()[output_name_index].name
with Timer() as t:
predict_data = ScoreBackend.get_data(data.X_test)
total_size = len(predict_data)
iterations = total_size // batch_size
iterations += 1 if total_size % batch_size > 0 else 0
iterations = max(1, iterations)
self.predictions = np.empty([total_size, self.params["n_classes"]],
dtype="f4")
for i in range(0, iterations):
start = i * batch_size
end = min(start + batch_size, total_size)
if self.params["operator"] == "xgb":
self.predictions[start:end, :] = sess.run(
[output_name],
{input_name: predict_data[start:end, :]})
elif self.params["operator"] == "lgbm" or "rf":
if i == iterations - 1 and self.remainder_model is not None:
pred = remainder_sess.run(
[output_name],
{input_name: predict_data[start:end, :]})
else:
pred = sess.run(
[output_name],
{input_name: predict_data[start:end, :]})
if is_regression:
self.predictions[start:end, :] = pred[0]
else:
self.predictions[start:end, :] = list(
map(lambda x: list(x.values()), pred[0]))
if is_regression:
self.predictions = self.predictions.flatten()
del sess
if remainder_sess is not None:
del remainder_sess
return t.interval
for shp in ishapes:
ts = np.product(shp)
#print("reshaping %s with offset %d" % (str(shp), offset), file=sys.stderr)
inputs.append(read(ts).reshape(shp))
ret = m.run(None, dict(zip(keys, inputs)))
#print(ret, file=sys.stderr)
for r in ret:
write(r)
if __name__ == "__main__":
print(ort.get_available_providers(), file=sys.stderr)
if 'OpenVINOExecutionProvider' in ort.get_available_providers(
) and 'ONNXCPU' not in os.environ:
print("OnnxJit is using openvino", file=sys.stderr)
options = ort.SessionOptions()
options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_DISABLE_ALL
provider = 'OpenVINOExecutionProvider'
elif 'CUDAExecutionProvider' in ort.get_available_providers(
) and 'ONNXCPU' not in os.environ:
print("OnnxJit is using CUDA")
options = ort.SessionOptions()
options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_DISABLE_ALL
provider = 'CUDAExecutionProvider'
else:
print("OnnxJit is using CPU", file=sys.stderr)
options = ort.SessionOptions()
options.intra_op_num_threads = 4
options.inter_op_num_threads = 8
options.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL
options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
def onnx2tensorrt(onnx_file,
trt_file,
input_config,
verify=False,
show=False,
dataset='coco',
workspace_size=1,
verbose=False):
import tensorrt as trt
onnx_model = onnx.load(onnx_file)
input_shape = input_config['input_shape']
# create trt engine and wraper
opt_shape_dict = {'input': [input_shape, input_shape, input_shape]}
max_workspace_size = get_GiB(workspace_size)
trt_engine = onnx2trt(
onnx_model,
opt_shape_dict,
log_level=trt.Logger.VERBOSE if verbose else trt.Logger.ERROR,
fp16_mode=False,
max_workspace_size=max_workspace_size)
save_dir, _ = osp.split(trt_file)
if save_dir:
os.makedirs(save_dir, exist_ok=True)
save_trt_engine(trt_engine, trt_file)
print(f'Successfully created TensorRT engine: {trt_file}')
if verify:
one_img, one_meta = preprocess_example_input(input_config)
input_img_cpu = one_img.detach().cpu().numpy()
input_img_cuda = one_img.cuda()
img = one_meta['show_img']
# Get results from ONNXRuntime
ort_custom_op_path = get_onnxruntime_op_path()
session_options = ort.SessionOptions()
if osp.exists(ort_custom_op_path):
session_options.register_custom_ops_library(ort_custom_op_path)
sess = ort.InferenceSession(onnx_file, session_options)
output_names = [_.name for _ in sess.get_outputs()]
ort_outputs = sess.run(None, {
'input': input_img_cpu,
})
with_mask = len(output_names) == 3
ort_outputs = [_.squeeze(0) for _ in ort_outputs]
ort_dets, ort_labels = ort_outputs[:2]
ort_masks = ort_outputs[2] if with_mask else None
ort_shapes = [_.shape for _ in ort_outputs]
print(f'ONNX Runtime output names: {output_names}, \
output shapes: {ort_shapes}')
# Get results from TensorRT
trt_model = TRTWraper(trt_file, ['input'], output_names)
with torch.no_grad():
trt_outputs = trt_model({'input': input_img_cuda})
trt_outputs = [
trt_outputs[_].detach().cpu().numpy().squeeze(0)
for _ in output_names
]
trt_dets, trt_labels = trt_outputs[:2]
trt_shapes = [_.shape for _ in trt_outputs]
print(f'TensorRT output names: {output_names}, \
output shapes: {trt_shapes}')
trt_masks = trt_outputs[2] if with_mask else None
# Show detection outputs
if show:
CLASSES = get_classes(dataset)
score_thr = 0.35
imshow_det_bboxes(img.copy(),
trt_dets,
trt_labels,
segms=trt_masks,
class_names=CLASSES,
score_thr=score_thr,
win_name='TensorRT')
imshow_det_bboxes(img.copy(),
ort_dets,
ort_labels,
segms=ort_masks,
class_names=CLASSES,
score_thr=score_thr,
win_name='ONNXRuntime')
# Compare results
np.testing.assert_allclose(ort_dets, trt_dets, rtol=1e-03, atol=1e-05)
np.testing.assert_allclose(ort_labels, trt_labels)
if with_mask:
np.testing.assert_allclose(ort_masks,
trt_masks,
rtol=1e-03,
atol=1e-05)
print('The numerical values are the same ' +
'between ONNXRuntime and TensorRT')
def testOrtExecutionMode(self):
opt = onnxrt.SessionOptions()
self.assertEqual(opt.execution_mode,
onnxrt.ExecutionMode.ORT_SEQUENTIAL)
opt.execution_mode = onnxrt.ExecutionMode.ORT_PARALLEL
self.assertEqual(opt.execution_mode, onnxrt.ExecutionMode.ORT_PARALLEL)
## conda create -n cv2020 python=3.8
## conda activate cv2020
## conda install onnx protobuf numpy pip six fastapi uvicorn python-multipart -c conda-forge
## pip install opencv-python # need to install from pip due to QT dependencies on arm64
## ONNXRuntime https://elinux.org/Jetson_Zoo#ONNX_Runtime
## wget https://nvidia.box.com/shared/static/8xgbee5ghhb92i9rrcr04yymg0n3x3t0.whl -O onnxruntime_gpu-1.7.0-cp38-cp38-linux_aarch64.whl
## pip install onnxruntime_gpu-1.7.0-cp38-cp38-linux_aarch64.whl
import cv2
import onnxruntime as rt
import numpy as np
####
sessOptions = rt.SessionOptions()
sessOptions.graph_optimization_level = rt.GraphOptimizationLevel.ORT_ENABLE_ALL
raccoonModel = rt.InferenceSession('raccoon_sim.onnx', sessOptions)
####
inputStream = cv2.VideoCapture(0)
while True:
isImageValid, inputImage = inputStream.read()
if isImageValid:
### Pre-processing ###
inputTensor = cv2.resize(inputImage, (320, 320))
inputTensor = (inputTensor -
[103.53, 116.28, 123.675]) / [57.375, 57.12, 58.395]
inputTensor = inputTensor.transpose(2, 0, 1)[np.newaxis].astype(
np.float32) #NCHW
### Inference ###
def create_ort_training_session_with_optimizer(model, device, training_optimizer_name, lr_params_feed_name,
map_optimizer_attributes, world_rank=-1, world_size=1,
gradient_accumulation_steps=1, bind_parameters=False,
use_mixed_precision=False, allreduce_post_accumulation=False,
deepspeed_zero_stage=0,
enable_grad_norm_clip=True,
frozen_weights=[], opset_version=DEFAULT_OPSET_VERSION,
use_deterministic_compute=False,
use_invertible_layernorm_grad=False,
enable_adasum=False):
output_name = model.graph.output[0].name
ort_parameters = ort.TrainingParameters()
ort_parameters.loss_output_name = output_name
ort_parameters.use_mixed_precision = use_mixed_precision
ort_parameters.world_rank = world_rank
ort_parameters.world_size = world_size
ort_parameters.gradient_accumulation_steps = gradient_accumulation_steps
ort_parameters.allreduce_post_accumulation = allreduce_post_accumulation
ort_parameters.deepspeed_zero_stage = deepspeed_zero_stage
ort_parameters.enable_grad_norm_clip = enable_grad_norm_clip
ort_parameters.set_gradients_as_graph_outputs = False
ort_parameters.use_invertible_layernorm_grad = use_invertible_layernorm_grad
ort_parameters.enable_adasum = enable_adasum
output_types = {}
for output in model.graph.output:
output_types[output.name] = output.type.tensor_type
# pybind does not allow to add directly to ort_parameters.weights_to_train.
# Have to work around by using a temporary weights_to_train.
torch_params = {}
optimizer_attributes_map = {}
optimizer_int_attributes_map = {}
unused_frozen_weights = [n for n in frozen_weights if n not in [i.name for i in model.graph.initializer]]
if unused_frozen_weights:
raise RuntimeError("{} in frozen_weights not found in model weights.".format(unused_frozen_weights))
weights_to_train = set()
for initializer in model.graph.initializer:
if initializer.name in frozen_weights:
continue
weights_to_train.add(initializer.name)
if map_optimizer_attributes is not None:
attributes = map_optimizer_attributes(initializer.name)
optimizer_attributes_map[initializer.name] = {}
optimizer_int_attributes_map[initializer.name] = {}
for k, v in attributes.items():
if isinstance(v, float):
optimizer_attributes_map[initializer.name][k] = v
elif isinstance(v, int):
optimizer_int_attributes_map[initializer.name][k] = v
else:
raise ValueError("Optimizer attributes must be either float or int.")
else:
optimizer_attributes_map[initializer.name] = {}
optimizer_int_attributes_map[initializer.name] = {}
if bind_parameters:
for initializer in model.graph.initializer:
torch_tensor = torch.nn.Parameter(torch.as_tensor(numpy_helper.to_array(initializer), device=device))
delete_input_with_name(model.graph.input, initializer.name)
model.graph.input.extend(
[helper.make_tensor_value_info(initializer.name, initializer.data_type, initializer.dims)])
torch_params[initializer.name] = torch_tensor
del model.graph.initializer[:]
ort_parameters.weights_to_train = weights_to_train
ort_parameters.training_optimizer_name = training_optimizer_name
ort_parameters.lr_params_feed_name = lr_params_feed_name
ort_parameters.optimizer_attributes_map = optimizer_attributes_map
ort_parameters.optimizer_int_attributes_map = optimizer_int_attributes_map
sessionOptions = ort.SessionOptions()
sessionOptions.use_deterministic_compute = use_deterministic_compute
session = ort.TrainingSession(model.SerializeToString(), ort_parameters, sessionOptions)
train_io_binding = session.io_binding()
eval_io_binding = session.io_binding()
if bind_parameters:
for param in torch_params.keys():
torch_tensor = torch_params[param]
train_io_binding.bind_input(param, torch_tensor.device.type, get_device_index(torch_tensor.device),
dtype_torch_to_numpy(torch_params[param].dtype), list(torch_tensor.size()),
torch_tensor.data_ptr())
eval_io_binding.bind_input(param, torch_tensor.device.type, get_device_index(torch_tensor.device),
dtype_torch_to_numpy(torch_params[param].dtype), list(torch_tensor.size()),
torch_tensor.data_ptr())
return session, train_io_binding, eval_io_binding, output_name, torch_params, output_types
def convert_to_onnx_and_check(
job_func,
print_outlier=False,
explicit_init=True,
external_data=False,
ort_optimize=True,
opset=None,
):
check_point = flow.train.CheckPoint()
if explicit_init:
# it is a trick to keep check_point.save() from hanging when there is no variable
@flow.global_function(flow.FunctionConfig())
def add_var():
return flow.get_variable(
name="trick",
shape=(1, ),
dtype=flow.float,
initializer=flow.random_uniform_initializer(),
)
check_point.init()
flow_weight_dir = tempfile.TemporaryDirectory()
check_point.save(flow_weight_dir.name)
# TODO(daquexian): a more elegant way?
while not os.path.exists(
os.path.join(flow_weight_dir.name, "snapshot_done")):
pass
onnx_model_dir = tempfile.TemporaryDirectory()
onnx_model_path = os.path.join(onnx_model_dir.name, "model.onnx")
flow.onnx.export(
job_func,
flow_weight_dir.name,
onnx_model_path,
opset=opset,
external_data=external_data,
)
flow_weight_dir.cleanup()
ort_sess_opt = ort.SessionOptions()
ort_sess_opt.graph_optimization_level = (
ort.GraphOptimizationLevel.ORT_ENABLE_EXTENDED
if ort_optimize else ort.GraphOptimizationLevel.ORT_DISABLE_ALL)
sess = ort.InferenceSession(onnx_model_path, sess_options=ort_sess_opt)
onnx_model_dir.cleanup()
assert len(sess.get_outputs()) == 1
assert len(sess.get_inputs()) <= 1
ipt_dict = OrderedDict()
for ipt in sess.get_inputs():
ipt_data = np.random.uniform(low=-10, high=10,
size=ipt.shape).astype(np.float32)
ipt_dict[ipt.name] = ipt_data
onnx_res = sess.run([], ipt_dict)[0]
oneflow_res = job_func(*ipt_dict.values()).get().numpy()
rtol, atol = 1e-2, 1e-5
if print_outlier:
a = onnx_res.flatten()
b = oneflow_res.flatten()
for i in range(len(a)):
if np.abs(a[i] - b[i]) > atol + rtol * np.abs(b[i]):
print("a[{}]={}, b[{}]={}".format(i, a[i], i, b[i]))
assert np.allclose(onnx_res, oneflow_res, rtol=rtol, atol=atol)
def __test_export_route(self, module, out_name, mode, input_example=None):
# select correct extension based on the output format
ext = {
DF.ONNX: ".onnx",
DF.TRTONNX: ".trt.onnx",
DF.PYTORCH: ".pt",
DF.TORCHSCRIPT: ".ts"
}.get(mode, ".onnx")
out = Path(f"{out_name}{ext}")
out_name = str(out)
if out.exists():
os.remove(out)
module.eval()
outputs_fwd = (module.forward(*tuple(input_example.values()))
if isinstance(input_example, OrderedDict) else
(module.forward(
*input_example) if isinstance(input_example, tuple)
else module.forward(input_example)
if input_example is not None else None))
deploy_input_example = (tuple(input_example.values()) if isinstance(
input_example, OrderedDict) else input_example)
self.nf.deployment_export(
module=module,
output=out_name,
input_example=deploy_input_example,
d_format=mode,
output_example=outputs_fwd,
)
tol = 5.0e-3
assert out.exists() == True
if mode == DF.TRTONNX:
data_loader = DefaultDataLoader()
loader_cache = DataLoaderCache(data_loader)
profile_shapes = OrderedDict()
names = list(module.input_ports) + list(module.output_ports)
names = list(
filter(
lambda x: x not in
(module._disabled_deployment_input_ports | module.
_disabled_deployment_output_ports),
names,
))
if isinstance(input_example, tuple):
si = [
tuple(input_example[i].shape)
for i in range(len(input_example))
]
elif isinstance(input_example, OrderedDict):
si = [
tuple(input_example.values())[i].shape
for i in range(len(input_example))
]
else:
si = [tuple(input_example.shape)]
if isinstance(outputs_fwd, tuple):
fi = [
tuple(outputs_fwd[i].shape)
for i in range(len(outputs_fwd))
]
else:
fi = [tuple(outputs_fwd.shape)]
si = si + fi
i = 0
for name in names:
profile_shapes[name] = [si[i]] * 3
i = i + 1
onnx_loader = OnnxFileLoader(out_name)
network_loader = OnnxNetworkLoader(onnx_loader,
explicit_precision=False)
model_loader = BuildEngineLoader(
network_loader,
max_workspace_size=1 << 30,
fp16_mode=False,
int8_mode=False,
profile_shapes=profile_shapes,
write_engine=None,
calibrator=None,
layerwise=False,
)
with TensorRTRunnerV2(model_loader=model_loader) as active_runner:
input_metadata = active_runner.get_input_metadata()
if input_metadata is None:
logging.critical(
"For {:}, get_input_metadata() returned None!".format(
active_runner.name))
logging.debug("Runner Inputs: {:}".format(input_metadata))
feed_dict = loader_cache.load(iteration=0,
input_metadata=input_metadata,
input_example=input_example)
inputs = dict()
input_names = list(input_metadata.keys())
for i in range(len(input_names)):
input_name = input_names[i]
if input_name in module._disabled_deployment_input_ports:
continue
inputs[input_name] = (
input_example[input_name].cpu().numpy() if isinstance(
input_example, OrderedDict) else
(input_example[i].cpu().numpy() if isinstance(
input_example, tuple) else
input_example.cpu().numpy()))
out_dict = active_runner.infer(feed_dict=feed_dict,
output=outputs_fwd)
for ov in out_dict.values():
outputs_scr = torch.from_numpy(ov).cuda()
break
outputs = []
outputs.append(copy.deepcopy(out_dict))
logging.debug("Received outputs: {:}".format([
"{:}: {:}".format(name, out.shape)
for name, out in out_dict.items()
]))
logging.info("Output Buffers: {:}".format(outputs))
inpex = []
for ie in feed_dict.values(): # loader_cache.cache[0].values():
if ie.dtype.type is np.int32:
inpex.append(torch.from_numpy(ie).long().cuda())
else:
inpex.append(torch.from_numpy(ie).cuda())
if len(inpex) == len(input_example):
break
inpex = tuple(inpex)
outputs_fwd = module.forward(*inpex)
elif mode == DF.ONNX:
# Must recompute because *module* might be different now
outputs_fwd = (
module.forward(*tuple(input_example.values())) if isinstance(
input_example, OrderedDict) else
(module.forward(*input_example) if isinstance(
input_example, tuple) else module.forward(input_example)))
sess_options = ort.SessionOptions()
sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_BASIC
ort_session = ort.InferenceSession(out_name, sess_options,
['CUDAExecutionProvider'])
print('Execution Providers: ', ort_session.get_providers())
inputs = dict()
input_names = list(module.input_ports)
ort_inputs = ort_session.get_inputs()
for i in range(len(input_names)):
input_name = input_names[i]
if input_name in module._disabled_deployment_input_ports:
continue
inputs[input_name] = (input_example[input_name].cpu().numpy()
if isinstance(input_example,
OrderedDict) else
(input_example[i].cpu().numpy()
if isinstance(input_example, tuple) else
input_example.cpu().numpy()))
outputs_scr = ort_session.run(None, inputs)
outputs_scr = torch.from_numpy(outputs_scr[0]).cuda()
elif mode == DF.TORCHSCRIPT:
scr = torch.jit.load(out_name)
if isinstance(module, nemo.backends.pytorch.tutorials.TaylorNet):
input_example = torch.randn(4, 1).cuda()
outputs_fwd = module.forward(input_example)
outputs_scr = (
module.forward(*tuple(input_example.values())) if isinstance(
input_example, OrderedDict) else
(module.forward(*input_example) if isinstance(
input_example, tuple) else module.forward(input_example)))
elif mode == DF.PYTORCH:
module.load_state_dict(torch.load(out_name))
module.eval()
outputs_scr = (
module.forward(*tuple(input_example.values())) if isinstance(
input_example, OrderedDict) else
(module.forward(*input_example) if isinstance(
input_example, tuple) else module.forward(input_example)))
outputs_scr = (outputs_scr[0] if isinstance(outputs_scr, tuple)
or isinstance(outputs_scr, list) else outputs_scr)
outputs_fwd = (outputs_fwd[0] if isinstance(outputs_fwd, tuple)
or isinstance(outputs_fwd, list) else outputs_fwd)
assert (outputs_scr - outputs_fwd).norm(p=2) < tol
if out.exists():
os.remove(out)
def onnx2tensorrt(onnx_file: str,
trt_file: str,
config: dict,
input_config: dict,
model_type: str,
img_path: str,
fp16: bool = False,
verify: bool = False,
show: bool = False,
workspace_size: int = 1,
verbose: bool = False):
"""Convert ONNX model to TensorRT model
Args:
onnx_file (str): the path of the input ONNX file.
trt_file (str): the path to output the TensorRT file.
config (dict): MMCV configuration.
input_config (dict): contains min_shape, max_shape and \
input image path.
fp16 (bool): whether to enable fp16 mode.
verify (bool): whether to verify the outputs of TensorRT \
and ONNX are same.
show (bool): whether to show the outputs of TensorRT and ONNX.
verbose (bool): whether to print the log when generating \
TensorRT model.
"""
import tensorrt as trt
min_shape = input_config['min_shape']
max_shape = input_config['max_shape']
# create trt engine and wrapper
opt_shape_dict = {'input': [min_shape, min_shape, max_shape]}
max_workspace_size = get_GiB(workspace_size)
trt_engine = onnx2trt(
onnx_file,
opt_shape_dict,
log_level=trt.Logger.VERBOSE if verbose else trt.Logger.ERROR,
fp16_mode=fp16,
max_workspace_size=max_workspace_size)
save_dir, _ = osp.split(trt_file)
if save_dir:
os.makedirs(save_dir, exist_ok=True)
save_trt_engine(trt_engine, trt_file)
print(f'Successfully created TensorRT engine: {trt_file}')
if verify:
inputs = _prepare_input_img(
model_type=model_type, img_path=img_path, config=config)
imgs = inputs['imgs']
img_list = [imgs.unsqueeze(0)]
if max_shape[0] > 1:
# concate flip image for batch test
flip_img_list = [_.flip(-1) for _ in img_list]
img_list = [
torch.cat((ori_img, flip_img), 0)
for ori_img, flip_img in zip(img_list, flip_img_list)
]
# Get results from ONNXRuntime
ort_custom_op_path = get_onnxruntime_op_path()
session_options = ort.SessionOptions()
if osp.exists(ort_custom_op_path):
session_options.register_custom_ops_library(ort_custom_op_path)
sess = ort.InferenceSession(onnx_file, session_options)
sess.set_providers(['CPUExecutionProvider'], [{}]) # use cpu mode
onnx_output = sess.run(['output'],
{'input': img_list[0].detach().numpy()})[0][0]
# Get results from TensorRT
trt_model = TRTWrapper(trt_file, ['input'], ['output'])
with torch.no_grad():
trt_outputs = trt_model({'input': img_list[0].contiguous().cuda()})
trt_output = trt_outputs['output'][0].cpu().detach().numpy()
if show:
onnx_visualize = onnx_output.transpose(1, 2, 0)
onnx_visualize = np.clip(onnx_visualize, 0, 1)[:, :, ::-1]
trt_visualize = trt_output.transpose(1, 2, 0)
trt_visualize = np.clip(trt_visualize, 0, 1)[:, :, ::-1]
cv2.imshow('ONNXRuntime', onnx_visualize)
cv2.imshow('TensorRT', trt_visualize)
cv2.waitKey()
np.testing.assert_allclose(
onnx_output, trt_output, rtol=1e-03, atol=1e-05)
print('TensorRT and ONNXRuntime output all close.')
