IInt8Calibrator

tensorrt.CalibrationAlgoType

Version of calibration algorithm to use.

Members:

LEGACY_CALIBRATION

ENTROPY_CALIBRATION

ENTROPY_CALIBRATION_2

MINMAX_CALIBRATION

class tensorrt.IInt8Calibrator(self: tensorrt.tensorrt.IInt8Calibrator)

[DEPRECATED] Deprecated in TensorRT 10.1. Superseded by explicit quantization.

Application-implemented interface for calibration. Calibration is a step performed by the builder when deciding suitable scale factors for 8-bit inference. It must also provide a method for retrieving representative images which the calibration process can use to examine the distribution of activations. It may optionally implement a method for caching the calibration result for reuse on subsequent runs.

To implement a custom calibrator, ensure that you explicitly instantiate the base class in __init__() :

class MyCalibrator(trt.IInt8Calibrator):
    def __init__(self):
        trt.IInt8Calibrator.__init__(self)

Variables:

• batch_size – int The batch size used for calibration batches.

• algorithm – CalibrationAlgoType The algorithm used by this calibrator.

get_algorithm(self: tensorrt.tensorrt.IInt8Calibrator) → tensorrt.tensorrt.CalibrationAlgoType

Get the algorithm used by this calibrator.

Returns:

The algorithm used by this calibrator.

get_batch(self: tensorrt.tensorrt.IInt8Calibrator, names: List[str]) → List[int]

Get a batch of input for calibration. The batch size of the input must match the batch size returned by get_batch_size() .

A possible implementation may look like this:

def get_batch(names):
    try:
        # Assume self.batches is a generator that provides batch data.
        data = next(self.batches)
        # Assume that self.device_input is a device buffer allocated by the constructor.
        cuda.memcpy_htod(self.device_input, data)
        return [int(self.device_input)]
    except StopIteration:
        # When we're out of batches, we return either [] or None.
        # This signals to TensorRT that there is no calibration data remaining.
        return None

Parameters:

names – The names of the network inputs for each object in the bindings array.

Returns:

A list of device memory pointers set to the memory containing each network input data, or an empty list if there are no more batches for calibration. You can allocate these device buffers with pycuda, for example, and then cast them to int to retrieve the pointer.

get_batch_size(self: tensorrt.tensorrt.IInt8Calibrator) → int

Get the batch size used for calibration batches.

Returns:

The batch size.

read_calibration_cache(self: tensorrt.tensorrt.IInt8Calibrator) → buffer

Load a calibration cache.

Calibration is potentially expensive, so it can be useful to generate the calibration data once, then use it on subsequent builds of the network. The cache includes the regression cutoff and quantile values used to generate it, and will not be used if these do not match the settings of the current calibrator. However, the network should also be recalibrated if its structure changes, or the input data set changes, and it is the responsibility of the application to ensure this.

Reading a cache is just like reading any other file in Python. For example, one possible implementation is:

def read_calibration_cache(self):
    # If there is a cache, use it instead of calibrating again. Otherwise, implicitly return None.
    if os.path.exists(self.cache_file):
        with open(self.cache_file, "rb") as f:
            return f.read()

Returns:

A cache object or None if there is no data.

write_calibration_cache(self: tensorrt.tensorrt.IInt8Calibrator, cache: buffer) → None

Save a calibration cache.

Writing a cache is just like writing any other buffer in Python. For example, one possible implementation is:

def write_calibration_cache(self, cache):
    with open(self.cache_file, "wb") as f:
        f.write(cache)

Parameters:

cache – The calibration cache to write.