def main():
if len(sys.argv) < 5:
print('Please pass <TensorFlow record path> <Number of classes> <"cpu"/"gpu"> <batch_size>')
exit(0)
imagePath = sys.argv[1]
numClasses = int(sys.argv[2])
if(sys.argv[3] == "cpu"):
raliCPU = True
else:
raliCPU = False
bs = int(sys.argv[4])
nt = 1
di = 0
cropSize = 320
TFRecordReaderType = 1
featureKeyMap = {
'image/encoded': 'image/encoded',
'image/class/label': 'image/object/class/label',
'image/class/text': 'image/object/class/text',
'image/object/bbox/xmin': 'image/object/bbox/xmin',
'image/object/bbox/ymin': 'image/object/bbox/ymin',
'image/object/bbox/xmax': 'image/object/bbox/xmax',
'image/object/bbox/ymax': 'image/object/bbox/ymax',
'image/filename': 'image/filename'
}
pipe = HybridPipe(feature_key_map=featureKeyMap, tfrecordreader_type=TFRecordReaderType,
batch_size=bs, num_threads=nt, device_id=di, data_dir=imagePath, crop=cropSize, rali_cpu=raliCPU)
pipe.build()
imageIterator = RALIIterator(pipe)
for i, (images_array, bboxes_array, labels_array, num_bboxes_array) in enumerate(imageIterator, 0):
images_array = np.transpose(images_array, [0, 2, 3, 1])
print("RALI augmentation pipeline - Processing batch %d....." % i)
for element in list(range(bs)):
print("Processing image %d....." % element)
features_dict = {
"image": images_array[element],
"true_image_shape": np.array([len(images_array[element]), len(images_array[element, 0]), len(images_array[element, 0, 0])])
}
labels_dict = {
"num_groundtruth_boxes": num_bboxes_array[element],
"groundtruth_boxes": bboxes_array[element],
"groundtruth_classes": get_onehot(labels_array[element], numClasses),
"groundtruth_weights": get_weights(num_bboxes_array[element])
}
processed_tensors = (features_dict, labels_dict)
print("\nPROCESSED_TENSORS:\n", processed_tensors)
print("\n\nPrinted first batch with", (bs), "images!")
break
imageIterator.reset()
def main():
if len(sys.argv) < 5:
print(
'Please pass the <TensorFlowrecord> <cpu/gpu> <batch_size> <oneHotLabels=0/1>'
)
exit(0)
imagePath = sys.argv[1]
if (sys.argv[2] == "cpu"):
raliCPU = True
else:
raliCPU = False
bs = int(sys.argv[3])
oneHotLabel = int(sys.argv[4])
nt = 1
di = 0
cropSize = 224
TFRecordReaderType = 0
featureKeyMap = {
'image/encoded': 'image/encoded',
'image/class/label': 'image/class/label',
'image/filename': 'image/filename'
}
pipe = HybridPipe(feature_key_map=featureKeyMap,
tfrecordreader_type=TFRecordReaderType,
batch_size=bs,
num_threads=nt,
device_id=di,
data_dir=imagePath,
crop=cropSize,
oneHotLabels=oneHotLabel,
rali_cpu=raliCPU)
pipe.build()
imageIterator = RALIIterator(pipe)
for i, (images_array, labels_array) in enumerate(imageIterator, 0):
print("\n\n", i)
print("\nIMAGES ARRAY:\n", images_array)
print("\nLABELS ARRAY:\n", labels_array)
imageIterator.reset()