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https://github.com/zama-ai/concrete.git
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2e831e4415
Also introduce new compilation options for parallel execution bump concrete-compiler to 0.6.0 which support loop parallelization
45 lines
1.5 KiB
Python
45 lines
1.5 KiB
Python
"""Test module for convolution compilation and execution."""
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import numpy as np
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import pytest
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import concrete.numpy as hnp
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from concrete.common.data_types.integers import Integer
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from concrete.common.values.tensors import EncryptedTensor
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from concrete.numpy.compile import compile_numpy_function
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@pytest.mark.parametrize(
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"input_shape, weight_shape",
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[
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pytest.param((1, 1, 4, 4), (1, 1, 2, 2)),
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pytest.param((4, 3, 4, 4), (2, 3, 2, 2)),
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],
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)
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@pytest.mark.parametrize("strides", [(2, 2)])
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@pytest.mark.parametrize("dilations", [(1, 1)])
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@pytest.mark.parametrize("has_bias", [True, False])
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def test_compile_and_run(
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input_shape, weight_shape, strides, dilations, has_bias, default_compilation_configuration
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):
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"""Test function to make sure compilation and execution of conv2d works properly"""
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if has_bias:
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bias = np.random.randint(0, 4, size=(weight_shape[0],))
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else:
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bias = None
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weight = np.random.randint(0, 4, size=weight_shape)
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def conv(x):
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return hnp.conv2d(x, weight, bias, strides=strides, dilations=dilations)
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compiler_engine = compile_numpy_function(
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conv,
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{"x": EncryptedTensor(Integer(64, False), input_shape)},
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[np.random.randint(0, 4, size=input_shape) for i in range(20)],
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default_compilation_configuration,
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)
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x = np.random.randint(0, 4, size=input_shape, dtype=np.uint8)
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expected = conv(x)
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result = compiler_engine.encrypt_run_decrypt(x)
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assert (expected == result).all()
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