def setUpClass(cls) -> None:
cls.current_path = os.path.dirname(os.path.abspath(__file__))
cls.input_path = os.path.join(cls.current_path, "input")
cls.image_path = os.path.join(cls.input_path, "images")
cls.onnx_path = os.path.join(cls.current_path, "onnx")
cls.onnx_file = os.path.join(cls.onnx_path, "tiny_yolo_2.onnx")
cls.out_c_path = os.path.join(cls.current_path, "out_c")
cls.out_c_file = os.path.join(cls.out_c_path, "qumico.so")
cls.ckpt_file = os.path.join(cls.current_path, "input",
"tiny_yolo2.ckpt")
cls.pb_file = os.path.join(cls.current_path, "input", "tiny_yolo2.pb")
cls.classes = tiny_yolo2_infer.voc2007_classes
cls.num_classes = len(cls.classes)
cls.classes = tiny_yolo2_infer.voc2007_classes
cls.num_classes = len(cls.classes)
cls.batch_size = 1
pic_num = 0
data_list_path = os.path.join(cls.input_path, "images")
cls.label_list_path = os.path.join(cls.input_path, "annotations")
data_list = numpy.asarray(
pre_process_tool.get_data_path_list(data_list_path)
[pic_num:pic_num + 1])
label_list = numpy.asarray(
pre_process_tool.get_data_path_list(
cls.label_list_path)[pic_num:pic_num + 1])
cls.annotation_dataset_tool = AnnotationDatasetTool(
training_flag=True,
data_list=data_list,
label_list=label_list,
category_class=cls.classes,
one_hot_classes=cls.num_classes,
resize_flag=True,
target_h=416,
target_w=416,
label_file_type="voc_xml",
format="NCHW",
data_rescale=True,
label_resclar=True)
cls.model = tiny_yolo2_model.TINY_YOLO_v2(output_op_name="output",
num_classes=cls.num_classes,
is_train=False,
width=416,
height=416)
cls._infer_prepare()
def test_get_data_path_list_depth_2(self):
depth = 2
result = pre_process_tool.get_data_path_list(self.input_path, depth=depth)
for c in self.dir_test_str:
for i in range(depth):
self.assertTrue([s for s in result if f"{c}{i}" in s], msg=f"Directory {c}{i} not found")
for c in self.dir_test_str:
for i in range(depth-1, depth):
self.assertTrue([s for s in result if f"{c}{i}.txt" in s], msg=f"File {c}{i}.txt not found")
def setUpClass(cls) -> None:
cls.current_path = os.path.dirname(os.path.realpath(__file__))
cls.input_path = os.path.join(cls.current_path, "input")
cls.model_path = os.path.join(cls.current_path, "model")
cls.data_list_path = os.path.join(cls.input_path, "images")
cls.label_list_path = os.path.join(cls.input_path, "annotations")
cls.batch_size = 1
cls.classes = tiny_yolo2_infer.voc2007_classes
cls.data_list = numpy.asarray(
pre_process_tool.get_data_path_list(cls.data_list_path)[:])
cls.label_list = numpy.asarray(
pre_process_tool.get_data_path_list(cls.label_list_path)[:])
cls.transformer = None
cls.dataset = annotation_dataset_tool.AnnotationDatasetTool(
training_flag=True,
data_list=cls.data_list,
label_list=cls.label_list,
category_class=cls.classes,
one_hot_classes=len(cls.classes),
resize_flag=True,
target_h=416,
target_w=416,
label_file_type="voc_xml",
format="NCHW",
data_rescale=True,
label_resclar=True,
transformer=cls.transformer)
cls.model = tiny_yolo2_model.TINY_YOLO_v2(height=416,
width=416,
output_op_name="output",
num_classes=len(cls.classes),
is_train=True,
batch_size=cls.batch_size)
cls.epochs = 999
cls._prepare()
def setUpClass(cls) -> None:
cls.current_path = os.path.dirname(os.path.abspath(__file__))
cls.input_path = os.path.join(cls.current_path, "input")
cls.image_path = os.path.join(cls.input_path, "images")
cls.boxes_test = numpy.asarray([[0, 1, 2, 3], [-4, -5, -6, -7], [8, 9, -10, 11], [12, -13, -14, 15]])
cls.feature_test = numpy.asarray([[0, 1, 2, 3, 0],
[-4, -5, -6, -7, 0],
[8, 9, -10, 11, 0],
[12, -13, -14, 15, 0],
[16, -17, 18, 19, 0]])
cls.classes = tiny_yolo2_infer.voc2007_classes
cls.num_classes = len(cls.classes)
cls.batch_size = 1
cls.data_list_path = os.path.join(cls.input_path, "images")
cls.label_list_path = os.path.join(cls.input_path, "annotations")
pic_num = 0
data_list = numpy.asarray(pre_process_tool.get_data_path_list(cls.data_list_path)[pic_num:pic_num + 1])
label_list = numpy.asarray(pre_process_tool.get_data_path_list(cls.label_list_path)[pic_num:pic_num + 1])
cls.annotation_dataset_tool = AnnotationDatasetTool(training_flag=True, data_list=data_list,
label_list=label_list, category_class=cls.classes,
one_hot_classes=cls.num_classes, resize_flag=True,
target_h=416, target_w=416, label_file_type="voc_xml",
format="NCHW", data_rescale=True, label_resclar=True)
cls.model = tiny_yolo2_model.TINY_YOLO_v2(output_op_name="output",
num_classes=cls.num_classes,
is_train=False,
width=416,
height=416)
cls.ckpt_file = os.path.join(cls.current_path, "input", "tiny_yolo2.ckpt")
if __name__ == '__main__':
voc2007_classes = [
'chair', 'bird', 'sofa', 'bicycle', 'cat', 'motorbike', 'bus', 'boat',
'sheep', 'bottle', 'cow', 'person', 'horse', 'diningtable',
'pottedplant', 'aeroplane', 'car', 'train', 'dog', 'tvmonitor'
]
num_classes = len(voc2007_classes)
root_path = "train_data_mini/"
data_list_path = root_path + "images"
label_list_path = root_path + "annotations"
data_list = np.asarray(list_reader.get_data_path_list(data_list_path)[:50])
label_list = np.asarray(
list_reader.get_data_path_list(label_list_path)[:50])
annotation_dataset_tool = AnnotationDatasetTool(
training_flag=True,
data_list=data_list,
label_list=label_list,
category_class=voc2007_classes,
one_hot_classes=num_classes,
resize_flag=True,
target_h=416,
target_w=416,
label_file_type="voc_xml",
format="NCHW")
def test_get_data_path_list_depth_other_depth(self):
self.assertFalse(pre_process_tool.get_data_path_list(self.input_path, depth=0))
self.assertFalse(pre_process_tool.get_data_path_list(self.input_path, depth=3))
self.assertFalse(pre_process_tool.get_data_path_list(self.input_path, depth=-1))
def test_get_data_path_list_no_data_root_path(self):
self.assertRaises(TypeError, lambda: pre_process_tool.get_data_path_list(None))
