parsed['trt_engine_path'], ssd_model_uff_path,
parsed['trt_engine_datatype'], parsed['max_batch_size'])
# Outputs from TensorRT are handled differently than
# outputs from Tensorflow, that's why we use another
# function to produce the detections from them
produce_tensorrt_detections(detection_files,
trt_inference_wrapper, parsed['max_batch_size'],
voc_image_numbers, voc_image_path)
elif parsed['inference_backend'] == 'tensorflow':
# In case of Tensorflow all we need to
# initialize inference is frozen model...
tf_inference_wrapper = \
inference_utils.TensorflowInference(ssd_model_pb_path)
# ...and after initializing it, we can
# proceed to producing detections
produce_tensorflow_detections(detection_files,
tf_inference_wrapper, parsed['max_batch_size'],
voc_image_numbers, voc_image_path)
# Flush detection to files to make sure evaluation is correct
for key in detection_files:
detection_files[key].flush()
# Do mAP computation based on saved detections
voc_mAP_utils.do_python_eval(parsed['results_dir'])
# Close detection files, they are not needed anymore
for key in detection_files:
detection_files[key].close()
def main():
# Parse command line arguments
parsed = parse_commandline_arguments()
# Check if inference should be skipped (if model inference
# results are already computed, we don't need to recompute
# them for VOC mAP computation)
skip_inference = should_skip_inference(parsed)
# And if inference will not be skipped, then we
# create files to store its results in
detection_files = {}
if not skip_inference:
for voc_class in VOC_CLASSES:
detection_files[voc_class] = open(
os.path.join(parsed['results_dir'],
'det_test_{}.txt'.format(voc_class)), 'w')
# Fetch frozen model .pb path...
ssd_model_pb_path = PATHS.get_model_pb_path(MODEL_NAME)
# ...and .uff path, if needed (converting .pb to .uff if not already done)
if parsed['inference_backend'] == 'tensorrt':
ssd_model_uff_path = PATHS.get_model_uff_path(MODEL_NAME)
# This block of code sets up and performs inference, if needed
if not skip_inference:
# Preprocess VOC dataset if necessary by resizing images
preprocess_voc()
# Fetch image list and input .ppm files path
with open(PATHS.get_voc_image_set_path(), 'r') as f:
voc_image_numbers = f.readlines()
voc_image_numbers = [line.strip() for line in voc_image_numbers]
voc_image_path = PATHS.get_voc_ppm_img_path()
# Tensorflow and TensorRT paths are a little bit different,
# so we must treat each one individually
if parsed['inference_backend'] == 'tensorrt':
# TRTInference initialization initializes
# all TensorRT structures, creates engine if it doesn't
# already exist and finally saves it to file for future uses
from utils.inference_trt import TRTInference
trt_inference_wrapper = TRTInference(parsed['trt_engine_path'],
ssd_model_uff_path,
parsed['trt_engine_datatype'],
parsed['max_batch_size'])
# Outputs from TensorRT are handled differently than
# outputs from Tensorflow, that's why we use another
# function to produce the detections from them
produce_tensorrt_detections(detection_files, trt_inference_wrapper,
parsed['max_batch_size'],
voc_image_numbers, voc_image_path)
elif parsed['inference_backend'] == 'tensorflow':
# In case of Tensorflow all we need to
# initialize inference is frozen model...
from utils.inference_tf import TensorflowInference
tf_inference_wrapper = TensorflowInference(ssd_model_pb_path)
# ...and after initializing it, we can
# proceed to producing detections
produce_tensorflow_detections(detection_files,
tf_inference_wrapper,
parsed['max_batch_size'],
voc_image_numbers, voc_image_path)
# Flush detection to files to make sure evaluation is correct
for key in detection_files:
detection_files[key].flush()
# Do mAP computation based on saved detections
voc_mAP_utils.do_python_eval(parsed['results_dir'])
# Close detection files, they are not needed anymore
for key in detection_files:
detection_files[key].close()