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https://github.com/open-mmlab/mmdeploy.git
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de9498a8f2
* add docstring for apis * add simple docstring for mmdet * add simple docstring for mmseg * add simple docstring for mmcls * add simple docstring for mmedit * add simple docstring for mmocr * add simple docstring for rewriting * update thresh for docstring coverage * update * update docstring * solve comments * remove unrelated symbol
129 lines
3.8 KiB
Python
129 lines
3.8 KiB
Python
from typing import Dict, Sequence, Union
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import h5py
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import numpy as np
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import tensorrt as trt
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import torch
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DEFAULT_CALIBRATION_ALGORITHM = trt.CalibrationAlgoType.ENTROPY_CALIBRATION_2
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class HDF5Calibrator(trt.IInt8Calibrator):
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"""HDF5 calibrator.
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Args:
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calib_file (str | h5py.File): Input calibration file.
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input_shapes (Dict[str, Sequence[int]]): The min/opt/max shape of
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each input.
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model_type (str): Input model type, defaults to 'end2end'.
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device_id (int): Cuda device id, defaults to 0.
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algorithm (trt.CalibrationAlgoType): Calibration algo type, defaults
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to `trt.CalibrationAlgoType.ENTROPY_CALIBRATION_2`.
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"""
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def __init__(
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self,
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calib_file: Union[str, h5py.File],
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input_shapes: Dict[str, Sequence[int]],
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model_type: str = 'end2end',
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device_id: int = 0,
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algorithm: trt.CalibrationAlgoType = DEFAULT_CALIBRATION_ALGORITHM,
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**kwargs):
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super().__init__()
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if isinstance(calib_file, str):
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calib_file = h5py.File(calib_file, mode='r')
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assert 'calib_data' in calib_file
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calib_data = calib_file['calib_data']
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assert model_type in calib_data
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calib_data = calib_data[model_type]
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self.calib_file = calib_file
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self.calib_data = calib_data
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self.device_id = device_id
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self.algorithm = algorithm
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self.input_shapes = input_shapes
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self.kwargs = kwargs
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# create buffers that will hold data batches
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self.buffers = dict()
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self.count = 0
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first_input_group = calib_data[list(calib_data.keys())[0]]
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self.dataset_length = len(first_input_group)
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self.batch_size = first_input_group['0'].shape[0]
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def __del__(self):
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if hasattr(self, 'calib_file'):
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self.calib_file.close()
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def get_batch(self, names: Sequence[str], **kwargs):
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"""Get batch data."""
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if self.count < self.dataset_length:
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ret = []
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for name in names:
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input_group = self.calib_data[name]
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data_np = input_group[str(self.count)][...]
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data_torch = torch.from_numpy(data_np)
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# tile the tensor so we can keep the same distribute
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opt_shape = self.input_shapes[name]['opt_shape']
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data_shape = data_torch.shape
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reps = [
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int(np.ceil(opt_s / data_s))
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for opt_s, data_s in zip(opt_shape, data_shape)
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]
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data_torch = data_torch.tile(reps)
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for dim, opt_s in enumerate(opt_shape):
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if data_torch.shape[dim] != opt_s:
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data_torch = data_torch.narrow(dim, 0, opt_s)
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if name not in self.buffers:
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self.buffers[name] = data_torch.cuda(self.device_id)
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else:
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self.buffers[name].copy_(data_torch.cuda(self.device_id))
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ret.append(int(self.buffers[name].data_ptr()))
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self.count += 1
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return ret
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else:
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return None
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def get_algorithm(self):
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"""Get Calibration algo type.
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Returns:
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trt.CalibrationAlgoType: Calibration algo type.
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"""
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return self.algorithm
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def get_batch_size(self):
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"""Get batch size.
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Returns:
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int: An integer represents batch size.
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"""
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return self.batch_size
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def read_calibration_cache(self, *args, **kwargs):
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"""Read calibration cache.
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Notes:
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No need to implement this function.
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"""
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pass
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def write_calibration_cache(self, cache, *args, **kwargs):
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"""Write calibration cache.
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Notes:
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No need to implement this function.
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"""
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pass
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