def test_gen_C(self):
output = tests.read_from_output(lambda: tests.execute_file(self.gen_c_path))
file_list = ["qumico.c", "qumico.so", "qumico.h", "qumico_type.h"]
folder_list = [QUMICO_LIB, QUMICO_INCLUDE]
self.assertTrue(tests.is_dir_contains(dirs=self.output_path, folder_list=folder_list, file_list=file_list))
self.assertIn("Cソースを生成しました。出力先:", output)
def test_dataset_tool_instance_training_flag_true_data_label_different(
self):
result = tests.read_from_output(lambda: dataset_tool.DatasetTool(
training_flag=True,
data=self.dataset,
label=numpy.array([1, 2, 3, 5, 6])))
self.assertIn("学習データサイズが異なる, 入力データサイズ = 3 ラベルサイズ = 5", result)
def test_conv_test(self):
output = tests.read_from_output(
lambda: conv_test.conv_test(model=self.model,
test_data=self.dataset_test,
ckpt_file=self.ckpt_file,
batch_size=1000))
self.assertIn("Total", output)
self.assertIn("Accuracy", output)
def test_vgg16_infer(self):
vgg16_infer.classes = self.classes_test
count_correct = 0
for x in vgg16_infer.classes:
vgg16_infer.img_file = os.path.join(self.input_path, (x + ".jpg"))
output = tests.read_from_output(lambda: vgg16_infer.main())
if x in output:
count_correct += 1
accuracy = count_correct / len(self.classes_test)
self.assertGreaterEqual(accuracy, 0.7)
def test_gen_c(self):
res = tests.read_from_output(
lambda: tests.execute_file(self.gen_c_path))
file_list = ["numpy.c", "qumico.c", "qumico.so"]
folder_list = ["include", "initializers", "lib"]
self.assertIn("Cソースを生成しました。出力先:", res)
self.assertTrue(
tests.is_dir_contains(self.output_path,
file_list=file_list,
folder_list=folder_list))
def test_conv_onnx(self):
output = tests.read_from_output(
lambda: tests.execute_file(self.module_path))
folder_list = ["model"]
file_list = ["tiny_yolo_v2_yad2k.onnx"]
self.assertTrue(
tests.is_dir_contains(dirs=self.current_path,
folder_list=folder_list))
self.assertTrue(
tests.is_dir_contains(dirs=self.output_path, file_list=file_list))
self.assertIn("tiny_yolo_v2_yad2k.onnxを作成しました。", output)
def test_infer_c(self):
res = tests.read_from_output(lambda: mobilenet_infer_c.infer(image_path=self.image_input,
so_lib_path=self.c_path))
self.assertIn("tiger", res)
def test_infer_c(self):
output = tests.read_from_output(
lambda: tests.execute_file(self.module_file_dir))
self.assertIn("sheep", output)
self.assertIn("person", output)
self.assertIn("cow", output)
def test_vgg16_to_onnx(self):
output = tests.read_from_output(lambda: tests.execute_file(self.vgg16_to_onnx_path))
self.assertTrue(tests.is_dir_contains(dirs=self.output_path, file_list=["vgg16.onnx"]))
self.assertIn("onnx/vgg16.onnxを作成しました。", output)
def test_infer(self):
res = tests.read_from_output(lambda: mobilenet_infer.infer(
tflite_model_path=self.tflite_input, image_path=self.image_input))
self.assertIn("tiger", res)
def test_vgg16_test(self):
output = tests.read_from_output(lambda: self.vgg_test_instance.main())
self.assertIn("image : ", output)
self.assertIn("1", output)
def test_infer(self):
res = tests.read_from_output(lambda: automl_infer.infer(tflite_model_path=self.tflite_input,
image_path=self.image_input))
self.assertIn("dog", res)
def test_infer_c(self):
res = tests.read_from_output(lambda: automl_infer_c.infer(
image_path=self.image_input, so_lib_path=self.c_path))
self.assertIn("dog", res)