def test_non_user_provided_inputs_never_shape_tensors(self):
# If the user didn't provide metadata, then the value can never be a shape tensor.
input_meta = TensorMetadata().add("X", dtype=np.int32, shape=(3, ))
data_loader = DataLoader()
data_loader.input_metadata = input_meta
feed_dict = data_loader[0]
assert feed_dict["X"].shape == (3, ) # Treat as a normal tensor
def test_shape_tensor_detected(self):
INPUT_DATA = (1, 2, 3)
input_meta = TensorMetadata().add("X", dtype=np.int32, shape=(3, ))
# This contains the shape values
overriden_meta = TensorMetadata().add("X", dtype=np.int32, shape=INPUT_DATA)
data_loader = DataLoader(input_metadata=overriden_meta)
data_loader.input_metadata = input_meta
feed_dict = data_loader[0]
assert np.all(feed_dict["X"] == INPUT_DATA) # values become INPUT_DATA
def test_can_override_shape(self):
model = ONNX_MODELS["dynamic_identity"]
shape = (1, 1, 4, 5)
custom_input_metadata = TensorMetadata().add("X", dtype=None, shape=shape)
data_loader = DataLoader(input_metadata=custom_input_metadata)
# Simulate what the comparator does
data_loader.input_metadata = model.input_metadata
feed_dict = data_loader[0]
assert tuple(feed_dict["X"].shape) == shape
def test_no_shape_tensor_false_positive_float(self):
INPUT_DATA = (-100, -50, 0)
# Float cannot be a shape tensor
input_meta = TensorMetadata().add("X", dtype=np.float32, shape=(3, ))
overriden_meta = TensorMetadata().add("X", dtype=np.float32, shape=INPUT_DATA)
data_loader = DataLoader(input_metadata=overriden_meta)
data_loader.input_metadata = input_meta
feed_dict = data_loader[0]
assert feed_dict["X"].shape == (3, ) # Values are NOT (3, )
assert np.any(feed_dict["X"] != INPUT_DATA) # Values are NOT (3, )
def test_no_shape_tensor_false_positive_negative_dims(self):
INPUT_DATA = (-100, 2, 4)
# This should NOT be detected as a shape tensor
input_meta = TensorMetadata().add("X", dtype=np.int32, shape=(3, ))
overriden_meta = TensorMetadata().add("X", dtype=np.int32, shape=INPUT_DATA)
data_loader = DataLoader(input_metadata=overriden_meta)
data_loader.input_metadata = input_meta
feed_dict = data_loader[0]
assert feed_dict["X"].shape == (3, ) # Shape IS (3, ), because this is NOT a shape tensor
assert np.any(feed_dict["X"] != INPUT_DATA) # Contents are not INPUT_DATA, since it's not treated as a shape value
