def create_onnx_modelconfig(
models_dir, model_version, max_batch, dtype,
input_shapes, input_model_shapes, output_shapes, output_model_shapes):
assert len(input_shapes) == len(input_model_shapes)
assert len(output_shapes) == len(output_model_shapes)
assert len(input_shapes) == len(output_shapes)
if not tu.validate_for_onnx_model(dtype, dtype, dtype,
input_shapes[0], input_shapes[0], input_shapes[0]):
return
io_cnt = len(input_shapes)
# Use a different model name for the non-batching variant
model_name = tu.get_zero_model_name("onnx_nobatch" if max_batch == 0 else "onnx",
io_cnt, dtype)
config_dir = models_dir + "/" + model_name
config = emu.create_general_modelconfig(model_name, "onnxruntime_onnx", max_batch,
emu.repeat(dtype, io_cnt), input_shapes, input_model_shapes,
emu.repeat(dtype, io_cnt), output_shapes, output_model_shapes,
emu.repeat(None, io_cnt), force_tensor_number_suffix=True)
try:
os.makedirs(config_dir)
except OSError as ex:
pass # ignore existing dir
with open(config_dir + "/config.pbtxt", "w") as cfile:
cfile.write(config)
def create_onnx_modelconfig(models_dir, model_version, max_batch, dtype,
input_shapes, input_model_shapes, output_shapes,
output_model_shapes):
assert len(input_shapes) == len(input_model_shapes)
assert len(output_shapes) == len(output_model_shapes)
assert len(input_shapes) == len(output_shapes)
if not tu.validate_for_onnx_model(dtype, dtype, dtype, input_shapes[0],
input_shapes[0], input_shapes[0]):
return
io_cnt = len(input_shapes)
# Use a different model name for the non-batching variant
model_name = tu.get_zero_model_name(
"onnx_nobatch" if max_batch == 0 else "onnx", io_cnt, dtype)
config_dir = models_dir + "/" + model_name
# Must make sure all Onnx models will be loaded to the same GPU if they are
# run on GPU. This is due to the current limitation of Onnx Runtime
# https://github.com/microsoft/onnxruntime/issues/1034
instance_group_string = '''
instance_group [
{
count: 1
kind: KIND_GPU
gpus: [ 0 ]
}
]
'''
config = emu.create_general_modelconfig(
model_name,
"onnxruntime_onnx",
max_batch,
emu.repeat(dtype, io_cnt),
input_shapes,
input_model_shapes,
emu.repeat(dtype, io_cnt),
output_shapes,
output_model_shapes,
emu.repeat(None, io_cnt),
force_tensor_number_suffix=True,
instance_group_str=instance_group_string)
try:
os.makedirs(config_dir)
except OSError as ex:
pass # ignore existing dir
with open(config_dir + "/config.pbtxt", "w") as cfile:
cfile.write(config)
def create_onnx_modelconfig(
models_dir, max_batch, model_version,
input_shape, output0_shape, output1_shape,
input_dtype, output0_dtype, output1_dtype,
output0_label_cnt, version_policy):
if not tu.validate_for_onnx_model(input_dtype, output0_dtype, output1_dtype,
input_shape, output0_shape, output1_shape):
return
# Use a different model name for the non-batching variant
model_name = tu.get_model_name("onnx_nobatch" if max_batch == 0 else "onnx",
input_dtype, output0_dtype, output1_dtype)
config_dir = models_dir + "/" + model_name
# Must make sure all Onnx models will be loaded to the same GPU if they are
# run on GPU. This is due to the current limitation of Onnx Runtime
# https://github.com/microsoft/onnxruntime/issues/1034
instance_group_string = '''
instance_group [
{
count: 1
kind: KIND_GPU
gpus: [ 0 ]
}
]
'''
# [TODO] move create_general_modelconfig() out of emu as it is general
# enough for all backends to use
config = emu.create_general_modelconfig(model_name, "onnxruntime_onnx", max_batch,
emu.repeat(input_dtype, 2), emu.repeat(input_shape, 2), emu.repeat(None, 2),
[output0_dtype, output1_dtype], [output0_shape, output1_shape], emu.repeat(None, 2),
["output0_labels.txt", None],
version_policy=version_policy, force_tensor_number_suffix=True,
instance_group_str=instance_group_string)
try:
os.makedirs(config_dir)
except OSError as ex:
pass # ignore existing dir
with open(config_dir + "/config.pbtxt", "w") as cfile:
cfile.write(config)
with open(config_dir + "/output0_labels.txt", "w") as lfile:
for l in range(output0_label_cnt):
lfile.write("label" + str(l) + "\n")
