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File to run Slow fast architecture for int 8 calibration
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# | |
# This sample uses an ONNX ResNet50 Model to create a TensorRT Inference Engine | |
import random | |
from PIL import Image | |
import numpy as np | |
import pycuda.driver as cuda | |
# This import causes pycuda to automatically manage CUDA context creation and cleanup. | |
import pycuda.autoinit | |
import tensorrt as trt | |
from onnx_tensorrt.tensorrt_engine import Engine | |
import onnx | |
import onnx_tensorrt.backend as backend | |
import calibrator | |
import sys, os | |
sys.path.insert(1, os.path.join(sys.path[0], "..")) | |
import common | |
from slowfast.config.defaults import get_cfg | |
from slowfast.datasets import loader | |
import os | |
cfg = get_cfg() | |
cfg.merge_from_file("/workspace/tensorrt/samples/python/introductory_parser_samples/SlowFast/configs/SLOWFAST_8x8_R50-UCF101.yaml") | |
cfg.NUM_GPUS = 1 | |
cfg.TRAIN.BATCH_SIZE = 1 | |
class ModelData(object): | |
MODEL_PATH = "slow_fast_ucf101_batch1.onnx" | |
DTYPE = trt.float32 | |
TRT_LOGGER = trt.Logger(trt.Logger.WARNING) | |
def main(): | |
onnx_model_file = ModelData.MODEL_PATH | |
data_path = "/workspace/Data/UCF-101" | |
stream1 = calibrator.ImageBatchStream() | |
calib = calibrator.PythonEntropyCalibrator(stream1) | |
model = onnx.load('slow_fast_ucf101_batch1.onnx') | |
engine = backend.prepare(model,calib=calib, device='CUDA:0') | |
test_loader = loader.construct_loader(cfg, 'val') | |
for cur_iter, (inputs_b, labels, _, meta) in enumerate(test_loader): | |
inputs_b[0] = inputs_b[0].numpy() | |
inputs_b[1] = inputs_b[1].numpy() | |
import time | |
outputs = engine.run(inputs_b) | |
if(cur_iter==20): | |
break | |
if __name__ == '__main__': | |
main() |
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