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Diffstat (limited to 'python/pyarmnn/test/test_const_tensor.py')
| -rw-r--r-- | python/pyarmnn/test/test_const_tensor.py | 199 |
1 files changed, 199 insertions, 0 deletions
diff --git a/python/pyarmnn/test/test_const_tensor.py b/python/pyarmnn/test/test_const_tensor.py new file mode 100644 index 0000000000..b0c42b8b6c --- /dev/null +++ b/python/pyarmnn/test/test_const_tensor.py @@ -0,0 +1,199 @@ +# Copyright © 2019 Arm Ltd. All rights reserved. +# SPDX-License-Identifier: MIT +import pytest +import numpy as np + +import pyarmnn as ann + + +def _get_tensor_info(dt): + tensor_info = ann.TensorInfo(ann.TensorShape((2, 3)), dt) + + 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_QuantisedAsymm8, np.random.randint(1, size=(2, 4)).astype(np.uint8)), + (ann.DataType_Signed32, np.random.randint(1, size=(2, 4)).astype(np.int32)), + (ann.DataType_QuantisedSymm16, np.random.randint(1, size=(2, 4)).astype(np.int16)) + ], ids=['float32', 'float16', 'unsigned int8', 'int32', 'int16']) +def test_const_tensor_too_many_elements(dt, data): + tensor_info = _get_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_QuantisedAsymm8, np.random.randint(1, size=(2, 2)).astype(np.uint8)), + (ann.DataType_Signed32, np.random.randint(1, size=(2, 2)).astype(np.int32)), + (ann.DataType_QuantisedSymm16, np.random.randint(1, size=(2, 2)).astype(np.int16)) + ], ids=['float32', 'float16', 'unsigned int8', 'int32', 'int16']) +def test_const_tensor_too_little_elements(dt, data): + tensor_info = _get_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_QuantisedAsymm8, np.random.randint(1, size=(2, 2, 3, 3)).astype(np.uint8)), + (ann.DataType_Signed32, np.random.randint(1, size=(2, 2, 3, 3)).astype(np.int32)), + (ann.DataType_QuantisedSymm16, np.random.randint(1, size=(2, 2, 3, 3)).astype(np.int16)) + ], ids=['float32', 'float16', 'unsigned int8', 'int32', 'int16']) +def test_const_tensor_multi_dimensional_input(dt, data): + tensor = ann.ConstTensor(ann.TensorInfo(ann.TensorShape((2, 2, 3, 3)), dt), 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) + 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().data == tensor.get_memory_area().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) + 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_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_QuantisedAsymm8, [[1, 1, 1], [1, 1, 1]]) + ], ids=['float32', 'float16', 'unsigned int8']) +def test_const_tensor_incorrect_input_datatype(dt, data): + tensor_info = _get_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_QuantisedAsymm8, np.random.randint(1, size=(2, 3)).astype(np.uint8)), + (ann.DataType_Signed32, np.random.randint(1, size=(2, 3)).astype(np.int32)), + (ann.DataType_QuantisedSymm16, np.random.randint(1, size=(2, 3)).astype(np.int16)) + ], ids=['float32', 'float16', 'unsigned int8', 'int32', 'int16']) +class TestNumpyDataTypes: + + def test_copy_const_tensor(self, dt, data): + tensor_info = _get_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_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_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_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) |