# Copyright © 2020 Arm Ltd. All rights reserved. # SPDX-License-Identifier: MIT import pytest import numpy as np import pyarmnn as ann def _get_const_tensor_info(dt): tensor_info = ann.TensorInfo(ann.TensorShape((2, 3)), dt, 0.0, 0, True) return tensor_info @pytest.mark.parametrize("dt, data", [ (ann.DataType_Float32, np.random.randint(1, size=(2, 4)).astype(np.float32)), (ann.DataType_Float16, np.random.randint(1, size=(2, 4)).astype(np.float16)), (ann.DataType_QAsymmU8, np.random.randint(1, size=(2, 4)).astype(np.uint8)), (ann.DataType_QAsymmS8, np.random.randint(1, size=(2, 4)).astype(np.int8)), (ann.DataType_QSymmS8, np.random.randint(1, size=(2, 4)).astype(np.int8)), (ann.DataType_Signed32, np.random.randint(1, size=(2, 4)).astype(np.int32)), (ann.DataType_QSymmS16, np.random.randint(1, size=(2, 4)).astype(np.int16)) ], ids=['float32', 'float16', 'unsigned int8', 'signed int8', 'signed int8', 'int32', 'int16']) def test_const_tensor_too_many_elements(dt, data): tensor_info = _get_const_tensor_info(dt) num_bytes = tensor_info.GetNumBytes() with pytest.raises(ValueError) as err: ann.ConstTensor(tensor_info, data) assert 'ConstTensor requires {} bytes, {} provided.'.format(num_bytes, data.nbytes) in str(err.value) @pytest.mark.parametrize("dt, data", [ (ann.DataType_Float32, np.random.randint(1, size=(2, 2)).astype(np.float32)), (ann.DataType_Float16, np.random.randint(1, size=(2, 2)).astype(np.float16)), (ann.DataType_QAsymmU8, np.random.randint(1, size=(2, 2)).astype(np.uint8)), (ann.DataType_QAsymmS8, np.random.randint(1, size=(2, 2)).astype(np.int8)), (ann.DataType_QSymmS8, np.random.randint(1, size=(2, 2)).astype(np.int8)), (ann.DataType_Signed32, np.random.randint(1, size=(2, 2)).astype(np.int32)), (ann.DataType_QSymmS16, np.random.randint(1, size=(2, 2)).astype(np.int16)) ], ids=['float32', 'float16', 'unsigned int8', 'signed int8', 'signed int8', 'int32', 'int16']) def test_const_tensor_too_little_elements(dt, data): tensor_info = _get_const_tensor_info(dt) num_bytes = tensor_info.GetNumBytes() with pytest.raises(ValueError) as err: ann.ConstTensor(tensor_info, data) assert 'ConstTensor requires {} bytes, {} provided.'.format(num_bytes, data.nbytes) in str(err.value) @pytest.mark.parametrize("dt, data", [ (ann.DataType_Float32, np.random.randint(1, size=(2, 2, 3, 3)).astype(np.float32)), (ann.DataType_Float16, np.random.randint(1, size=(2, 2, 3, 3)).astype(np.float16)), (ann.DataType_QAsymmU8, np.random.randint(1, size=(2, 2, 3, 3)).astype(np.uint8)), (ann.DataType_QAsymmS8, np.random.randint(1, size=(2, 2, 3, 3)).astype(np.int8)), (ann.DataType_QSymmS8, np.random.randint(1, size=(2, 2, 3, 3)).astype(np.int8)), (ann.DataType_Signed32, np.random.randint(1, size=(2, 2, 3, 3)).astype(np.int32)), (ann.DataType_QSymmS16, np.random.randint(1, size=(2, 2, 3, 3)).astype(np.int16)) ], ids=['float32', 'float16', 'unsigned int8', 'signed int8', 'signed int8', 'int32', 'int16']) def test_const_tensor_multi_dimensional_input(dt, data): tensor = ann.ConstTensor(ann.TensorInfo(ann.TensorShape((2, 2, 3, 3)), dt, 0.0, 0, True), data) assert data.size == tensor.GetNumElements() assert data.nbytes == tensor.GetNumBytes() assert dt == tensor.GetDataType() assert tensor.get_memory_area().data def test_create_const_tensor_from_tensor(): tensor_info = ann.TensorInfo(ann.TensorShape((2, 3)), ann.DataType_Float32, 0.0, 0, True) tensor = ann.Tensor(tensor_info) copied_tensor = ann.ConstTensor(tensor) assert copied_tensor != tensor, "Different objects" assert copied_tensor.GetInfo() != tensor.GetInfo(), "Different objects" assert copied_tensor.get_memory_area().ctypes.data == tensor.get_memory_area().ctypes.data, "Same memory area" assert copied_tensor.GetNumElements() == tensor.GetNumElements() assert copied_tensor.GetNumBytes() == tensor.GetNumBytes() assert copied_tensor.GetDataType() == tensor.GetDataType() def test_const_tensor_from_tensor_has_memory_area_access_after_deletion_of_original_tensor(): tensor_info = ann.TensorInfo(ann.TensorShape((2, 3)), ann.DataType_Float32, 0.0, 0, True) tensor = ann.Tensor(tensor_info) tensor.get_memory_area()[0] = 100 copied_mem = tensor.get_memory_area().copy() assert 100 == copied_mem[0], "Memory was copied correctly" copied_tensor = ann.ConstTensor(tensor) tensor.get_memory_area()[0] = 200 assert 200 == tensor.get_memory_area()[0], "Tensor and copied Tensor point to the same memory" assert 200 == copied_tensor.get_memory_area()[0], "Tensor and copied Tensor point to the same memory" assert 100 == copied_mem[0], "Copied test memory not affected" copied_mem[0] = 200 # modify test memory to equal copied Tensor del tensor np.testing.assert_array_equal(copied_tensor.get_memory_area(), copied_mem), "After initial tensor was deleted, " \ "copied Tensor still has " \ "its memory as expected" def test_create_const_tensor_incorrect_args(): with pytest.raises(ValueError) as err: ann.ConstTensor('something', 'something') expected_error_message = "Incorrect number of arguments or type of arguments provided to create Const Tensor." assert expected_error_message in str(err.value) @pytest.mark.parametrize("dt, data", [ # -1 not in data type enum (-1, np.random.randint(1, size=(2, 3)).astype(np.float32)), ], ids=['unknown']) def test_const_tensor_unsupported_datatype(dt, data): tensor_info = _get_const_tensor_info(dt) with pytest.raises(ValueError) as err: ann.ConstTensor(tensor_info, data) assert 'The data type provided for this Tensor is not supported: -1' in str(err.value) @pytest.mark.parametrize("dt, data", [ (ann.DataType_Float32, [[1, 1, 1], [1, 1, 1]]), (ann.DataType_Float16, [[1, 1, 1], [1, 1, 1]]), (ann.DataType_QAsymmU8, [[1, 1, 1], [1, 1, 1]]), (ann.DataType_QAsymmS8, [[1, 1, 1], [1, 1, 1]]), (ann.DataType_QSymmS8, [[1, 1, 1], [1, 1, 1]]) ], ids=['float32', 'float16', 'unsigned int8', 'signed int8', 'signed int8']) def test_const_tensor_incorrect_input_datatype(dt, data): tensor_info = _get_const_tensor_info(dt) with pytest.raises(TypeError) as err: ann.ConstTensor(tensor_info, data) assert 'Data must be provided as a numpy array.' in str(err.value) @pytest.mark.parametrize("dt, data", [ (ann.DataType_Float32, np.random.randint(1, size=(2, 3)).astype(np.float32)), (ann.DataType_Float16, np.random.randint(1, size=(2, 3)).astype(np.float16)), (ann.DataType_QAsymmU8, np.random.randint(1, size=(2, 3)).astype(np.uint8)), (ann.DataType_QAsymmS8, np.random.randint(1, size=(2, 3)).astype(np.int8)), (ann.DataType_QSymmS8, np.random.randint(1, size=(2, 3)).astype(np.int8)), (ann.DataType_Signed32, np.random.randint(1, size=(2, 3)).astype(np.int32)), (ann.DataType_QSymmS16, np.random.randint(1, size=(2, 3)).astype(np.int16)) ], ids=['float32', 'float16', 'unsigned int8', 'signed int8', 'signed int8', 'int32', 'int16']) class TestNumpyDataTypes: def test_copy_const_tensor(self, dt, data): tensor_info = _get_const_tensor_info(dt) tensor = ann.ConstTensor(tensor_info, data) copied_tensor = ann.ConstTensor(tensor) assert copied_tensor != tensor, "Different objects" assert copied_tensor.GetInfo() != tensor.GetInfo(), "Different objects" assert copied_tensor.get_memory_area().ctypes.data == tensor.get_memory_area().ctypes.data, "Same memory area" assert copied_tensor.GetNumElements() == tensor.GetNumElements() assert copied_tensor.GetNumBytes() == tensor.GetNumBytes() assert copied_tensor.GetDataType() == tensor.GetDataType() def test_const_tensor__str__(self, dt, data): tensor_info = _get_const_tensor_info(dt) d_type = tensor_info.GetDataType() num_dimensions = tensor_info.GetNumDimensions() num_bytes = tensor_info.GetNumBytes() num_elements = tensor_info.GetNumElements() tensor = ann.ConstTensor(tensor_info, data) assert str(tensor) == "ConstTensor{{DataType: {}, NumBytes: {}, NumDimensions: " \ "{}, NumElements: {}}}".format(d_type, num_bytes, num_dimensions, num_elements) def test_const_tensor_with_info(self, dt, data): tensor_info = _get_const_tensor_info(dt) elements = tensor_info.GetNumElements() num_bytes = tensor_info.GetNumBytes() d_type = dt tensor = ann.ConstTensor(tensor_info, data) assert tensor_info != tensor.GetInfo(), "Different objects" assert elements == tensor.GetNumElements() assert num_bytes == tensor.GetNumBytes() assert d_type == tensor.GetDataType() def test_immutable_memory(self, dt, data): tensor_info = _get_const_tensor_info(dt) tensor = ann.ConstTensor(tensor_info, data) with pytest.raises(ValueError) as err: tensor.get_memory_area()[0] = 0 assert 'is read-only' in str(err.value) def test_numpy_dtype_matches_ann_dtype(self, dt, data): np_data_type_mapping = {ann.DataType_QAsymmU8: np.uint8, ann.DataType_QAsymmS8: np.int8, ann.DataType_QSymmS8: np.int8, ann.DataType_Float32: np.float32, ann.DataType_QSymmS16: np.int16, ann.DataType_Signed32: np.int32, ann.DataType_Float16: np.float16} tensor_info = _get_const_tensor_info(dt) tensor = ann.ConstTensor(tensor_info, data) assert np_data_type_mapping[tensor.GetDataType()] == data.dtype # This test checks that mismatched numpy and PyArmNN datatypes with same number of bits raises correct error. @pytest.mark.parametrize("dt, data", [ (ann.DataType_Float32, np.random.randint(1, size=(2, 3)).astype(np.int32)), (ann.DataType_Float16, np.random.randint(1, size=(2, 3)).astype(np.int16)), (ann.DataType_QAsymmU8, np.random.randint(1, size=(2, 3)).astype(np.int8)), (ann.DataType_QAsymmS8, np.random.randint(1, size=(2, 3)).astype(np.uint8)), (ann.DataType_QSymmS8, np.random.randint(1, size=(2, 3)).astype(np.uint8)), (ann.DataType_Signed32, np.random.randint(1, size=(2, 3)).astype(np.float32)), (ann.DataType_QSymmS16, np.random.randint(1, size=(2, 3)).astype(np.float16)) ], ids=['float32', 'float16', 'unsigned int8', 'signed int8', 'signed int8', 'int32', 'int16']) def test_numpy_dtype_mismatch_ann_dtype(dt, data): np_data_type_mapping = {ann.DataType_QAsymmU8: np.uint8, ann.DataType_QAsymmS8: np.int8, ann.DataType_QSymmS8: np.int8, ann.DataType_Float32: np.float32, ann.DataType_QSymmS16: np.int16, ann.DataType_Signed32: np.int32, ann.DataType_Float16: np.float16} tensor_info = _get_const_tensor_info(dt) with pytest.raises(TypeError) as err: ann.ConstTensor(tensor_info, data) assert str(err.value) == "Expected data to have type {} for type {} but instead got numpy.{}".format( np_data_type_mapping[dt], dt, data.dtype) @pytest.mark.parametrize("dt, data", [ (ann.DataType_Float32, np.random.randint(1, size=(2, 3)).astype(np.float32)), (ann.DataType_Float16, np.random.randint(1, size=(2, 3)).astype(np.float16)), (ann.DataType_QAsymmU8, np.random.randint(1, size=(2, 3)).astype(np.uint8)), (ann.DataType_QAsymmS8, np.random.randint(1, size=(2, 3)).astype(np.int8)), (ann.DataType_QSymmS8, np.random.randint(1, size=(2, 3)).astype(np.int8)), (ann.DataType_Signed32, np.random.randint(1, size=(2, 3)).astype(np.int32)), (ann.DataType_QSymmS16, np.random.randint(1, size=(2, 3)).astype(np.int16)) ], ids=['float32', 'float16', 'unsigned int8', 'signed int8', 'signed int8', 'int32', 'int16']) class TestConstTensorConstructorErrors: def test_tensorinfo_isconstant_not_set(self, dt, data): with pytest.raises(ValueError) as err: ann.ConstTensor(ann.TensorInfo(ann.TensorShape((2, 2, 3, 3)), dt, 0.0, 0, False), data) assert str(err.value) == "TensorInfo when initializing ConstTensor must be set to constant." def test_tensor_tensorinfo_isconstant_not_set(self, dt, data): with pytest.raises(ValueError) as err: ann.ConstTensor(ann.Tensor(ann.TensorInfo(ann.TensorShape((2, 2, 3, 3)), dt, 0.0, 0, False), data)) assert str(err.value) == "TensorInfo of Tensor when initializing ConstTensor must be set to constant."