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 get_static_shape(name, shape):
static_shape = shape
if util.is_shape_dynamic(shape):
static_shape = util.override_dynamic_shape(shape)
if static_shape != shape and name not in self.user_input_metadata:
if not util.is_valid_shape_override(static_shape, shape):
G_LOGGER.critical("Input tensor: {:} | Cannot override original shape: {:} to {:}".format(name, shape, static_shape))
G_LOGGER.warning("Input tensor: {:} | Will generate data of shape: {:}.\n"
"If this is incorrect, please set input_metadata "
"or provide a custom data loader.".format(name, static_shape), mode=LogMode.ONCE)
return static_shape
def get_shapes(lst, idx):
nonlocal default_shapes
default_shapes = copy.copy(default_shapes)
if idx < len(lst):
default_shapes.update(
args_util.parse_meta(lst[idx], includes_dtype=False))
# Don't care about dtype, and need to override dynamic dimensions
shapes = {
name: util.override_dynamic_shape(shape)
for name, (_, shape) in default_shapes.items()
}
for name, shape in shapes.items():
if tuple(default_shapes[name].shape) != tuple(shape):
G_LOGGER.warning(
"Input tensor: {:} | For TensorRT profile, overriding dynamic shape: {:} to: {:}"
.format(name, default_shapes[name].shape, shape),
mode=LogMode.ONCE,
)
return shapes