def fill_defaults(self, network, default_shape_value=None):
"""
Fill this profile with sane default values for any bindings whose
shapes have not been set explicitly.
Args:
network (trt.INetworkDefinition):
The TensorRT network this profile is meant for.
This will be used to determine model inputs and their shapes.
default_shape_value (int):
The value to use to override dynamic dimensions.
Returns:
Profile: Self
"""
default_shape_value = util.default(default_shape_value,
constants.DEFAULT_SHAPE_VALUE)
for idx in range(network.num_inputs):
inp = network.get_input(idx)
if inp.name in self:
continue
with G_LOGGER.verbosity(
G_LOGGER.CRITICAL): # WAR for spam from TRT
is_shape_tensor = inp.is_shape_tensor
if is_shape_tensor:
rank = inp.shape[0]
shape = (default_shape_value, ) * rank
G_LOGGER.warning(
"{:} | No values provided; Will use input values: {:} for min/opt/max in profile.\n"
.format(trt_util.str_from_tensor(inp, is_shape_tensor),
shape, rank),
mode=LogMode.ONCE,
)
G_LOGGER.warning(
"This will cause the shape-tensor to have static values. If this is incorrect, please "
"set the range of values for this input shape-tensor.",
mode=LogMode.ONCE,
)
else:
shape = util.override_dynamic_shape(inp.shape,
default_shape_value)
if shape != inp.shape:
G_LOGGER.warning(
"{:} | No shapes provided; Will use shape: {:} for min/opt/max in profile.\n"
.format(trt_util.str_from_tensor(inp, is_shape_tensor),
shape),
mode=LogMode.ONCE,
)
G_LOGGER.warning(
"This will cause the tensor to have a static shape. If this is incorrect, please "
"set the range of shapes for this input tensor.",
mode=LogMode.ONCE,
)
self.add(inp.name, shape, shape, shape)
return self
def to_trt(self, builder, network):
"""
Creates a TensorRT IOptimizationProfile based on the values set in this Profile.
Args:
builder (trt.Builder):
A TensorRT builder. This will be used to construct the IOptimizationProfile.
network (trt.INetworkDefinition):
The TensorRT network the profile applies to.
Returns:
trt.IOptimizationProfile: A TensorRT optimization profile.
"""
trt_profile = builder.create_optimization_profile()
unused_keys = set(self.keys())
available_inputs = set()
for idx in range(network.num_inputs):
inp = network.get_input(idx)
if inp.name in unused_keys:
unused_keys.remove(inp.name)
available_inputs.add(inp.name)
with G_LOGGER.verbosity(): # WAR for spam from TRT
is_shape_tensor = inp.is_shape_tensor
if is_shape_tensor:
if inp.name in self:
shapes = self[inp.name]
trt_profile.set_shape_input(inp.name, shapes.min,
shapes.opt, shapes.max)
G_LOGGER.verbose(
"{:} | Setting input shape-tensor value range to: {:}".
format(trt_util.str_from_tensor(inp, is_shape_tensor),
shapes))
else:
G_LOGGER.warning(
"{:} | No values provided. "
"Assuming this is not a dynamic shape-tensor.".format(
trt_util.str_from_tensor(inp, is_shape_tensor)),
mode=LogMode.ONCE,
)
else:
shapes = self[inp.name]
trt_profile.set_shape(inp.name, shapes.min, shapes.opt,
shapes.max)
G_LOGGER.verbose(
"{:} | Setting input tensor shapes to: {:}".format(
trt_util.str_from_tensor(inp, is_shape_tensor),
shapes))
if unused_keys:
G_LOGGER.error(
"Invalid inputs were provided to the optimization profile: {:}\n"
"Note: Inputs available in the TensorRT network are: {:}".
format(unused_keys, available_inputs))
return trt_util.check_profile(trt_profile)