Rajeev Rao
80 lines
3.4 KiB
Python
80 lines
3.4 KiB
Python
#
|
|
# 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.
|
|
#
|
|
|
|
import os
|
|
import sys
|
|
import argparse
|
|
|
|
import numpy as np
|
|
|
|
from infer import TensorRTInfer
|
|
from image_batcher import ImageBatcher
|
|
|
|
|
|
def main(args):
|
|
automl_path = os.path.realpath(args.automl_path)
|
|
sys.path.insert(1, os.path.join(automl_path, "efficientdet"))
|
|
try:
|
|
import coco_metric
|
|
except ImportError:
|
|
print("Could not import the 'coco_metric' module from AutoML. Searching in: {}".format(automl_path))
|
|
print("Please clone the repository https://github.com/google/automl and provide its path with --automl_path.")
|
|
sys.exit(1)
|
|
|
|
trt_infer = TensorRTInfer(args.engine)
|
|
batcher = ImageBatcher(args.input, *trt_infer.input_spec())
|
|
evaluator = coco_metric.EvaluationMetric(filename=args.annotations)
|
|
for batch, images, scales in batcher.get_batch():
|
|
print("Processing Image {} / {}".format(batcher.image_index, batcher.num_images), end="\r")
|
|
detections = trt_infer.infer(batch, scales, args.nms_threshold)
|
|
coco_det = np.zeros((len(images), max([len(d) for d in detections]), 7))
|
|
coco_det[:, :, -1] = -1
|
|
for i in range(len(images)):
|
|
for n in range(len(detections[i])):
|
|
source_id = int(os.path.splitext(os.path.basename(images[i]))[0])
|
|
det = detections[i][n]
|
|
coco_det[i][n] = [
|
|
source_id,
|
|
det['xmin'],
|
|
det['ymin'],
|
|
det['xmax'] - det['xmin'],
|
|
det['ymax'] - det['ymin'],
|
|
det['score'],
|
|
det['class'] + 1, # The COCO evaluator expects class 0 to be background, so offset by 1
|
|
]
|
|
evaluator.update_state(None, coco_det)
|
|
print()
|
|
evaluator.result(100)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument("-e", "--engine", help="The TensorRT engine to infer with")
|
|
parser.add_argument("-i", "--input",
|
|
help="The input to infer, either a single image path, or a directory of images")
|
|
parser.add_argument("-a", "--annotations", help="Set the path to the COCO 'instances_val2017.json' file")
|
|
parser.add_argument("-p", "--automl_path", default="./automl",
|
|
help="Set the path where to find the AutoML repository, from "
|
|
"https://github.com/google/automl. Default: ./automl")
|
|
parser.add_argument("-t", "--nms_threshold", type=float, help="Override the score threshold for the NMS operation, "
|
|
"if higher than the threshold in the engine.")
|
|
args = parser.parse_args()
|
|
if not all([args.engine, args.input, args.annotations]):
|
|
parser.print_help()
|
|
print("\nThese arguments are required: --engine --input and --annotations")
|
|
sys.exit(1)
|
|
main(args)
|