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calibrator.py
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#
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# ~~~Medcare AI Lab~~~
import os
import tensorrt as trt
import pycuda.driver as cuda
import pycuda.autoinit
import numpy as np
import ctypes
class Calibrator(trt.IInt8EntropyCalibrator2):
'''calibrator
IInt8EntropyCalibrator2
IInt8LegacyCalibrator
IInt8EntropyCalibrator
IInt8MinMaxCalibrator
'''
def __init__(self, stream, cache_file=""):
trt.IInt8EntropyCalibrator2.__init__(self)
self.stream = stream
self.d_input = cuda.mem_alloc(self.stream.calibration_data.nbytes)
self.cache_file = cache_file
# print(self.cache_file)
stream.reset()
def get_batch_size(self):
return self.stream.batch_size
def get_batch(self, names):
batch = self.stream.next_batch()
if not batch.size:
return None
cuda.memcpy_htod(self.d_input, batch)
return [int(self.d_input)]
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:
print(f"[INFO] Using calibration cache to save time: {self.cache_file}")
return f.read()
def write_calibration_cache(self, cache):
with open(self.cache_file, "wb") as f:
print(f"[INFO] Caching calibration data for future use: {self.cache_file}")
f.write(cache)