Refitter¶

class tensorrt.Refitter(self: tensorrt.tensorrt.Refitter, engine: tensorrt.tensorrt.ICudaEngine, logger: tensorrt.tensorrt.ILogger) → None¶

Updates weights in an ICudaEngine .

Parameters:	engine – The engine to refit. logger – The logger to use.

__del__(self: tensorrt.tensorrt.Refitter) → None¶

__exit__(exc_type, exc_value, traceback)¶: Destroy this object, freeing all memory associated with it. This should be called to ensure that the object is cleaned up properly. Equivalent to invoking __del__()

__init__(self: tensorrt.tensorrt.Refitter, engine: tensorrt.tensorrt.ICudaEngine, logger: tensorrt.tensorrt.ILogger) → None¶

Parameters:	engine – The engine to refit. logger – The logger to use.

get_all(self: tensorrt.tensorrt.Refitter) → Tuple[List[str], List[tensorrt.tensorrt.WeightsRole]]¶

Get description of all weights that could be refit.

Returns:	The names of layers with refittable weights, and the roles of those weights.

get_dynamic_range(self: tensorrt.tensorrt.Refitter, tensor_name: str) → tuple¶

Gets the dynamic range of a tensor. If the dynamic range was never set, returns the range computed during calibration.

Parameters:	tensor_name – The name of the tensor whose dynamic range to retrieve.
Returns:	`Tuple[float, float]` A tuple containing the [minimum, maximum] of the dynamic range.

get_missing(self: tensorrt.tensorrt.Refitter) → Tuple[List[str], List[tensorrt.tensorrt.WeightsRole]]¶

Get description of missing weights.

For example, if some Weights have been set, but the engine was optimized in a way that combines weights, any unsupplied Weights in the combination are considered missing.

Returns:	The names of layers with missing weights, and the roles of those weights.

get_tensors_with_dynamic_range(self: tensorrt.tensorrt.Refitter) → List[str]¶

Get names of all tensors that have refittable dynamic ranges.

Returns:	The names of tensors with refittable dynamic ranges.

refit_cuda_engine(self: tensorrt.tensorrt.Refitter) → bool¶

Updates associated engine. Return True if successful.

Failure occurs if get_missing() != 0 before the call.

set_dynamic_range(self: tensorrt.tensorrt.Refitter, tensor_name: str, range: List[float]) → bool¶

Update dynamic range for a tensor.

Parameters:	tensor_name – The name of the tensor whose dynamic range to update. range – The new range.
Returns:	`True` if successful, `False` otherwise.

Returns false if there is no Int8 engine tensor derived from a network tensor of that name. If successful, then get_missing() may report that some weights need to be supplied.

set_weights(self: tensorrt.tensorrt.Refitter, layer_name: str, role: tensorrt.tensorrt.WeightsRole, weights: tensorrt.tensorrt.Weights) → bool¶

Specify new weights for a layer of given name. Possible reasons for rejection are:

There is no such layer by that name.
The layer does not have weights with the specified role.
The number of weights is inconsistent with the layer’s original specification.

Modifying the weights before refit_cuda_engine() completes will result in undefined behavior.

Parameters:	layer_name – The name of the layer. role – The role of the weights. See `WeightsRole` for more information. weights – The weights to refit with.
Returns:	`True` on success, or `False` if new weights are rejected.