#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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import argparse
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import glob
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import os
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import sys
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import cv2
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import numpy as np
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import tqdm
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sys.path.append("/export/home/lxy/runtimelib-tensorrt-tiny/build")
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import pytrt
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def get_parser():
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parser = argparse.ArgumentParser(description="trt model inference")
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parser.add_argument(
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"--model_path",
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default="outputs/trt_model/baseline.engine",
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help="trt model path"
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)
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parser.add_argument(
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"--input",
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nargs="+",
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help="A list of space separated input images; "
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"or a single glob pattern such as 'directory/*.jpg'",
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)
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parser.add_argument(
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"--output",
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default="trt_output",
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help="path to save trt model inference results"
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)
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parser.add_argument(
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"--output-name",
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help="tensorRT model output name"
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)
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parser.add_argument(
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"--height",
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type=int,
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default=256,
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help="height of image"
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)
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parser.add_argument(
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"--width",
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type=int,
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default=128,
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help="width of image"
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)
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return parser
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def preprocess(image_path, image_height, image_width):
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original_image = cv2.imread(image_path)
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# the model expects RGB inputs
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original_image = original_image[:, :, ::-1]
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# Apply pre-processing to image.
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img = cv2.resize(original_image, (image_width, image_height), interpolation=cv2.INTER_CUBIC)
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img = img.astype("float32").transpose(2, 0, 1)[np.newaxis] # (1, 3, h, w)
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return img
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def normalize(nparray, order=2, axis=-1):
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"""Normalize a N-D numpy array along the specified axis."""
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norm = np.linalg.norm(nparray, ord=order, axis=axis, keepdims=True)
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return nparray / (norm + np.finfo(np.float32).eps)
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if __name__ == "__main__":
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args = get_parser().parse_args()
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trt = pytrt.Trt()
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onnxModel = ""
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engineFile = args.model_path
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customOutput = []
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maxBatchSize = 1
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calibratorData = []
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mode = 2
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trt.CreateEngine(onnxModel, engineFile, customOutput, maxBatchSize, mode, calibratorData)
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if not os.path.exists(args.output): os.makedirs(args.output)
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if args.input:
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if os.path.isdir(args.input[0]):
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args.input = glob.glob(os.path.expanduser(args.input[0]))
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assert args.input, "The input path(s) was not found"
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for path in tqdm.tqdm(args.input):
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input_numpy_array = preprocess(path, args.height, args.width)
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trt.DoInference(input_numpy_array)
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feat = trt.GetOutput(args.output_name)
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feat = normalize(feat, axis=1)
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np.save(os.path.join(args.output, path.replace('.jpg', '.npy').split('/')[-1]), feat)
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