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96 lines
2.7 KiB
Python
96 lines
2.7 KiB
Python
"""Tests for the torch to numpy module."""
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import numpy
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import pytest
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from torch import nn
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from concrete.quantization import QuantizedArray
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from concrete.torch.compile import compile_torch_model
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# INPUT_OUTPUT_FEATURE is the number of input and output of each of the network layers.
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# (as well as the input of the network itself)
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INPUT_OUTPUT_FEATURE = [1, 2, 3]
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class FC(nn.Module):
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"""Torch model for the tests"""
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def __init__(self, input_output, activation_function):
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super().__init__()
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self.fc1 = nn.Linear(in_features=input_output, out_features=input_output)
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self.act_f = activation_function()
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self.fc2 = nn.Linear(in_features=input_output, out_features=input_output)
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def forward(self, x):
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"""Forward pass."""
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out = self.fc1(x)
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out = self.act_f(out)
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out = self.fc2(out)
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return out
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@pytest.mark.parametrize(
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"activation_function",
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[
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pytest.param(nn.Sigmoid, id="sigmoid"),
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pytest.param(nn.ReLU6, id="relu"),
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],
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)
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@pytest.mark.parametrize(
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"model",
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[pytest.param(FC)],
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)
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@pytest.mark.parametrize(
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"input_output_feature",
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[pytest.param(input_output_feature) for input_output_feature in INPUT_OUTPUT_FEATURE],
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)
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def test_compile_torch(
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input_output_feature,
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model,
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activation_function,
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seed_torch,
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default_compilation_configuration,
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check_is_good_execution,
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):
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"""Test the different model architecture from torch numpy."""
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# Seed torch
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seed_torch()
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n_bits = 2
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# Define an input shape (n_examples, n_features)
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n_examples = 50
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# Define the torch model
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torch_fc_model = model(input_output_feature, activation_function)
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# Create random input
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inputset = [
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numpy.random.uniform(-100, 100, size=input_output_feature) for _ in range(n_examples)
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]
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# Compile
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quantized_numpy_module = compile_torch_model(
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torch_fc_model,
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inputset,
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default_compilation_configuration,
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n_bits=n_bits,
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)
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# Quantize inputs all at once to have meaningful scale and zero point
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q_input = QuantizedArray(n_bits, numpy.array(inputset))
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# Compare predictions between FHE and QuantizedModule
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for x_q in q_input.qvalues:
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x_q = numpy.expand_dims(x_q, 0)
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check_is_good_execution(
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fhe_circuit=quantized_numpy_module.forward_fhe,
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function=quantized_numpy_module.forward,
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args=[x_q.astype(numpy.uint8)],
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postprocess_output_func=lambda x: quantized_numpy_module.dequantize_output(
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x.astype(numpy.float32)
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),
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check_function=numpy.isclose,
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verbose=False,
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)
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