def test_color_input(self, rank4_input_model, rank3_input_model):
mlmodel = ct.convert(
rank4_input_model,
inputs=[
ct.ImageType(shape=(1, 3, 10, 20),
color_layout=ct.colorlayout.RGB)
],
minimum_deployment_target=ct.target.macOS13,
)
assert_ops_in_mil_program(mlmodel,
expected_op_list=["cast", "add", "cast"])
assert_spec_input_image_type(
mlmodel._spec, expected_feature_type=ft.ImageFeatureType.RGB)
assert_prog_input_type(mlmodel._mil_program, expected_dtype_str="fp32")
assert_prog_output_type(mlmodel._mil_program,
expected_dtype_str="fp32")
verify_prediction(mlmodel)
with pytest.raises(ValueError, match="must have rank 4"):
mlmodel = ct.convert(
rank3_input_model,
inputs=[
ct.ImageType(shape=(1, 10, 20),
color_layout=ct.colorlayout.RGB)
],
minimum_deployment_target=ct.target.macOS12,
)
def test_color_output(self, rank4_input_model, rank4_input_model_with_channel_first_output):
# check that an error is raised if the output shape is not of form (1, 3, H, W)
with pytest.raises(ValueError, match="Shape of the RGB/BGR image output,"):
mlmodel = ct.convert(rank4_input_model,
inputs=[ct.ImageType(color_layout=ct.colorlayout.RGB)],
outputs=[ct.ImageType(color_layout=ct.colorlayout.RGB)],
minimum_deployment_target=ct.target.macOS13,
)
mlmodel = ct.convert(rank4_input_model_with_channel_first_output,
inputs=[ct.ImageType(color_layout=ct.colorlayout.BGR)],
outputs=[ct.ImageType(color_layout=ct.colorlayout.RGB)],
minimum_deployment_target=ct.target.macOS13,
)
assert_ops_in_mil_program(mlmodel, expected_op_list=["cast", "add", "cast"])
assert_spec_input_image_type(mlmodel._spec, expected_feature_type=ft.ImageFeatureType.BGR)
assert_spec_output_image_type(mlmodel._spec, expected_feature_type=ft.ImageFeatureType.RGB)
assert_prog_input_type(mlmodel._mil_program, expected_dtype_str="fp32")
assert_prog_output_type(mlmodel._mil_program, expected_dtype_str="fp32")
verify_prediction(mlmodel)
# check neural network conversion
mlmodel = ct.convert(rank4_input_model_with_channel_first_output,
inputs=[ct.ImageType(color_layout=ct.colorlayout.RGB)],
outputs=[ct.ImageType(color_layout=ct.colorlayout.BGR)],
)
assert_ops_in_mil_program(mlmodel, expected_op_list=["add"])
assert_spec_input_image_type(mlmodel._spec, expected_feature_type=ft.ImageFeatureType.RGB)
assert_spec_output_image_type(mlmodel._spec, expected_feature_type=ft.ImageFeatureType.BGR)
verify_prediction(mlmodel)
def test_grayscale_output(self, rank4_grayscale_input_model, rank4_grayscale_input_model_with_channel_first_output):
# check that an error is raised if the output shape is not of form (1, 1, H, W)
with pytest.raises(ValueError, match="Shape of the Grayscale image output,"):
mlmodel = ct.convert(rank4_grayscale_input_model,
inputs=[ct.ImageType(color_layout=ct.colorlayout.GRAYSCALE)],
outputs=[ct.ImageType(color_layout=ct.colorlayout.GRAYSCALE)],
)
with pytest.raises(TypeError, match="float16 dtype for outputs is only supported for deployment target >= iOS16/macOS13"):
mlmodel = ct.convert(rank4_grayscale_input_model_with_channel_first_output,
outputs=[ct.ImageType(color_layout=ct.colorlayout.GRAYSCALE_FLOAT16)],
minimum_deployment_target=ct.target.macOS12,
)
mlmodel = ct.convert(rank4_grayscale_input_model_with_channel_first_output,
inputs=[ct.ImageType(color_layout=ct.colorlayout.GRAYSCALE)],
outputs=[ct.ImageType(color_layout=ct.colorlayout.GRAYSCALE)],
)
assert_ops_in_mil_program(mlmodel, expected_op_list=["add"])
assert_spec_input_image_type(mlmodel._spec, expected_feature_type=ft.ImageFeatureType.GRAYSCALE)
assert_spec_output_image_type(mlmodel._spec, expected_feature_type=ft.ImageFeatureType.GRAYSCALE)
verify_prediction(mlmodel)
mlmodel = ct.convert(rank4_grayscale_input_model_with_channel_first_output,
inputs=[ct.ImageType(color_layout=ct.colorlayout.GRAYSCALE_FLOAT16)],
outputs=[ct.ImageType(color_layout=ct.colorlayout.GRAYSCALE_FLOAT16)],
minimum_deployment_target=ct.target.macOS13,
)
assert_cast_ops_count(mlmodel, expected_count=0)
assert_spec_input_image_type(mlmodel._spec, expected_feature_type=ft.ImageFeatureType.GRAYSCALE_FLOAT16)
assert_spec_output_image_type(mlmodel._spec, expected_feature_type=ft.ImageFeatureType.GRAYSCALE_FLOAT16)
assert_prog_input_type(mlmodel._mil_program, expected_dtype_str="fp16")
assert_prog_output_type(mlmodel._mil_program, expected_dtype_str="fp16")
verify_prediction(mlmodel)
mlmodel = ct.convert(rank4_grayscale_input_model_with_channel_first_output,
inputs=[ct.ImageType(color_layout=ct.colorlayout.GRAYSCALE)],
outputs=[ct.ImageType(color_layout=ct.colorlayout.GRAYSCALE_FLOAT16)],
minimum_deployment_target=ct.target.macOS13,
)
assert_ops_in_mil_program(mlmodel, expected_op_list=["cast", "add"])
assert_spec_input_image_type(mlmodel._spec, expected_feature_type=ft.ImageFeatureType.GRAYSCALE)
assert_spec_output_image_type(mlmodel._spec, expected_feature_type=ft.ImageFeatureType.GRAYSCALE_FLOAT16)
assert_prog_input_type(mlmodel._mil_program, expected_dtype_str="fp32")
assert_prog_output_type(mlmodel._mil_program, expected_dtype_str="fp16")
verify_prediction(mlmodel)
def test_grayscale_input(self, rank4_input_model, rank3_input_model, rank4_grayscale_input_model):
with pytest.raises(ValueError, match="must have rank 4"):
mlmodel = ct.convert(rank3_input_model,
inputs=[ct.ImageType(color_layout=ct.colorlayout.GRAYSCALE)],
minimum_deployment_target=ct.target.macOS13,
)
# invalid shape
with pytest.raises(ValueError):
mlmodel = ct.convert(rank4_input_model,
inputs=[ct.ImageType(color_layout=ct.colorlayout.GRAYSCALE)],
minimum_deployment_target=ct.target.macOS13,
)
mlmodel = ct.convert(rank4_grayscale_input_model,
inputs=[ct.ImageType(color_layout=ct.colorlayout.GRAYSCALE)],
minimum_deployment_target=ct.target.macOS13,
)
assert_ops_in_mil_program(mlmodel, expected_op_list=["cast", "transpose", "add", "cast"])
assert_spec_input_image_type(mlmodel._spec, expected_feature_type=ft.ImageFeatureType.GRAYSCALE)
assert_prog_input_type(mlmodel._mil_program, expected_dtype_str="fp32")
assert_prog_output_type(mlmodel._mil_program, expected_dtype_str="fp32")
verify_prediction(mlmodel)
with pytest.raises(TypeError, match="float16 dtype for inputs is only supported for deployment target >= iOS16/macOS13"):
mlmodel = ct.convert(rank4_grayscale_input_model,
inputs=[ct.ImageType(color_layout=ct.colorlayout.GRAYSCALE_FLOAT16)],
minimum_deployment_target=ct.target.macOS12,
)
# test that grayscale_16 raises error when used with neural network
with pytest.raises(TypeError, match="float16 dtype for inputs is only supported for deployment target >= iOS16/macOS13"):
mlmodel = ct.convert(rank4_grayscale_input_model,
inputs=[ct.ImageType(color_layout=ct.colorlayout.GRAYSCALE_FLOAT16)],
)
mlmodel = ct.convert(rank4_grayscale_input_model,
inputs=[ct.ImageType(color_layout=ct.colorlayout.GRAYSCALE_FLOAT16)],
outputs=[ct.TensorType(dtype=np.float16)],
minimum_deployment_target=ct.target.macOS13,
)
assert_ops_in_mil_program(mlmodel, expected_op_list=["transpose", "add"])
assert_spec_input_image_type(mlmodel._spec, expected_feature_type=ft.ImageFeatureType.GRAYSCALE_FLOAT16)
assert_prog_input_type(mlmodel._mil_program, expected_dtype_str="fp16")
assert_output_dtype(mlmodel, expected_type_str="fp16")
verify_prediction(mlmodel)
def test_color_output(self, rank4_input_model, float32_input_model_add_op):
# check that an error is raised if the output shape is not of form (1, 3, H, W)
with pytest.raises(ValueError,
match="must have rank 4. Instead it has rank 2"):
ct.convert(float32_input_model_add_op,
inputs=[ct.TensorType(shape=(10, 20))],
outputs=[ct.ImageType(color_layout=ct.colorlayout.RGB)],
minimum_deployment_target=ct.target.macOS13)
mlmodel = ct.convert(
rank4_input_model,
inputs=[
ct.ImageType(shape=(1, 3, 10, 20),
color_layout=ct.colorlayout.BGR)
],
outputs=[ct.ImageType(color_layout=ct.colorlayout.RGB)],
minimum_deployment_target=ct.target.macOS13,
)
assert_ops_in_mil_program(mlmodel,
expected_op_list=["cast", "add", "cast"])
assert_spec_input_image_type(
mlmodel._spec, expected_feature_type=ft.ImageFeatureType.BGR)
assert_spec_output_image_type(
mlmodel._spec, expected_feature_type=ft.ImageFeatureType.RGB)
assert_prog_input_type(mlmodel._mil_program, expected_dtype_str="fp32")
assert_prog_output_type(mlmodel._mil_program,
expected_dtype_str="fp32")
verify_prediction(mlmodel)
# check neural network conversion
mlmodel = ct.convert(
rank4_input_model,
inputs=[
ct.ImageType(shape=(1, 3, 10, 20),
color_layout=ct.colorlayout.RGB)
],
outputs=[ct.ImageType(color_layout=ct.colorlayout.BGR)],
)
assert_ops_in_mil_program(mlmodel, expected_op_list=["add"])
assert_spec_input_image_type(
mlmodel._spec, expected_feature_type=ft.ImageFeatureType.RGB)
assert_spec_output_image_type(
mlmodel._spec, expected_feature_type=ft.ImageFeatureType.BGR)
verify_prediction(mlmodel)
def test_grayscale_output(self, rank4_grayscale_input_model):
with pytest.raises(
TypeError,
match=
"float16 dtype for outputs is only supported for deployment target >= iOS16/macOS13"
):
ct.convert(
rank4_grayscale_input_model,
inputs=[ct.TensorType(shape=(1, 1, 10, 20))],
outputs=[
ct.ImageType(color_layout=ct.colorlayout.GRAYSCALE_FLOAT16)
],
minimum_deployment_target=ct.target.macOS12,
)
mlmodel = ct.convert(
rank4_grayscale_input_model,
inputs=[
ct.ImageType(shape=(1, 1, 10, 20),
color_layout=ct.colorlayout.GRAYSCALE)
],
outputs=[ct.ImageType(color_layout=ct.colorlayout.GRAYSCALE)],
)
assert_ops_in_mil_program(mlmodel, expected_op_list=["add"])
assert_spec_input_image_type(
mlmodel._spec, expected_feature_type=ft.ImageFeatureType.GRAYSCALE)
assert_spec_output_image_type(
mlmodel._spec, expected_feature_type=ft.ImageFeatureType.GRAYSCALE)
verify_prediction(mlmodel)
mlmodel = ct.convert(
rank4_grayscale_input_model,
inputs=[
ct.ImageType(shape=(1, 1, 10, 20),
color_layout=ct.colorlayout.GRAYSCALE_FLOAT16)
],
outputs=[
ct.ImageType(color_layout=ct.colorlayout.GRAYSCALE_FLOAT16)
],
minimum_deployment_target=ct.target.macOS13,
)
assert_ops_in_mil_program(mlmodel, expected_op_list=["add"])
assert_spec_input_image_type(
mlmodel._spec,
expected_feature_type=ft.ImageFeatureType.GRAYSCALE_FLOAT16)
assert_spec_output_image_type(
mlmodel._spec,
expected_feature_type=ft.ImageFeatureType.GRAYSCALE_FLOAT16)
assert_prog_input_type(mlmodel._mil_program, expected_dtype_str="fp16")
assert_prog_output_type(mlmodel._mil_program,
expected_dtype_str="fp16")
verify_prediction(mlmodel)
mlmodel = ct.convert(
rank4_grayscale_input_model,
inputs=[
ct.ImageType(shape=(1, 1, 10, 20),
color_layout=ct.colorlayout.GRAYSCALE)
],
outputs=[
ct.ImageType(color_layout=ct.colorlayout.GRAYSCALE_FLOAT16)
],
minimum_deployment_target=ct.target.macOS13,
)
assert_ops_in_mil_program(mlmodel, expected_op_list=["cast", "add"])
assert_spec_input_image_type(
mlmodel._spec, expected_feature_type=ft.ImageFeatureType.GRAYSCALE)
assert_spec_output_image_type(
mlmodel._spec,
expected_feature_type=ft.ImageFeatureType.GRAYSCALE_FLOAT16)
assert_prog_input_type(mlmodel._mil_program, expected_dtype_str="fp32")
assert_prog_output_type(mlmodel._mil_program,
expected_dtype_str="fp16")
verify_prediction(mlmodel)