Asfiya Baig
107 lines
3.6 KiB
Python
107 lines
3.6 KiB
Python
#
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# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import os
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import sys
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import argparse
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import numpy as np
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from infer import TensorRTInfer
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from image_batcher import ImageBatcher
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def main(args):
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automl_path = os.path.realpath(args.automl_path)
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sys.path.insert(1, os.path.join(automl_path, "efficientdet"))
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try:
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import coco_metric
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except ImportError:
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print(
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"Could not import the 'coco_metric' module from AutoML. Searching in: {}".format(
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automl_path
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)
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)
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print(
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"Please clone the repository https://github.com/google/automl and provide its path with --automl_path."
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)
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sys.exit(1)
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trt_infer = TensorRTInfer(args.engine)
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batcher = ImageBatcher(args.input, *trt_infer.input_spec())
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evaluator = coco_metric.EvaluationMetric(filename=args.annotations)
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for batch, images, scales in batcher.get_batch():
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print(
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"Processing Image {} / {}".format(batcher.image_index, batcher.num_images),
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end="\r",
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)
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detections = trt_infer.process(batch, scales, args.nms_threshold)
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coco_det = np.zeros((len(images), max([len(d) for d in detections]), 7))
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coco_det[:, :, -1] = -1
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for i in range(len(images)):
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for n in range(len(detections[i])):
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source_id = int(os.path.splitext(os.path.basename(images[i]))[0])
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det = detections[i][n]
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coco_det[i][n] = [
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source_id,
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det["xmin"],
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det["ymin"],
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det["xmax"] - det["xmin"],
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det["ymax"] - det["ymin"],
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det["score"],
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det["class"]
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+ 1, # The COCO evaluator expects class 0 to be background, so offset by 1
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]
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evaluator.update_state(None, coco_det)
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print()
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evaluator.result(100)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("-e", "--engine", help="The TensorRT engine to infer with")
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parser.add_argument(
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"-i",
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"--input",
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help="The input to infer, either a single image path, or a directory of images",
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)
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parser.add_argument(
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"-a",
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"--annotations",
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help="Set the path to the COCO 'instances_val2017.json' file",
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)
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parser.add_argument(
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"-p",
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"--automl_path",
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default="./automl",
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help="Set the path where to find the AutoML repository, from "
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"https://github.com/google/automl. Default: ./automl",
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)
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parser.add_argument(
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"-t",
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"--nms_threshold",
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type=float,
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help="Override the score threshold for the NMS operation, "
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"if higher than the threshold in the engine.",
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)
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args = parser.parse_args()
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if not all([args.engine, args.input, args.annotations]):
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parser.print_help()
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print("\nThese arguments are required: --engine --input and --annotations")
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sys.exit(1)
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main(args)
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