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108 lines
3.2 KiB
Python
108 lines
3.2 KiB
Python
"""Tests for the quantized module."""
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import numpy
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import pytest
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import torch
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from torch import nn
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from concrete.quantization import PostTrainingAffineQuantization, QuantizedArray
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from concrete.torch import NumpyModule
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class CNN(nn.Module):
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"""Torch CNN model for the tests."""
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def __init__(self):
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super().__init__()
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self.conv1 = nn.Conv2d(3, 6, 5)
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self.pool = nn.AvgPool2d(2, 2)
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self.conv2 = nn.Conv2d(6, 16, 5)
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self.fc1 = nn.Linear(16 * 5 * 5, 120)
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self.fc2 = nn.Linear(120, 84)
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self.fc3 = nn.Linear(84, 10)
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def forward(self, x):
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"""Forward pass."""
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x = self.pool(torch.relu(self.conv1(x)))
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x = self.pool(torch.relu(self.conv2(x)))
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x = torch.flatten(x, 1)
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x = torch.relu(self.fc1(x))
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x = torch.relu(self.fc2(x))
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x = self.fc3(x)
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return x
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class FC(nn.Module):
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"""Torch model for the tests"""
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def __init__(self):
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super().__init__()
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self.fc1 = nn.Linear(in_features=32 * 32 * 3, out_features=128)
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self.sigmoid1 = nn.Sigmoid()
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self.fc2 = nn.Linear(in_features=128, out_features=64)
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self.sigmoid2 = nn.Sigmoid()
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self.fc3 = nn.Linear(in_features=64, out_features=64)
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self.sigmoid3 = nn.Sigmoid()
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self.fc4 = nn.Linear(in_features=64, out_features=64)
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self.sigmoid4 = nn.Sigmoid()
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self.fc5 = nn.Linear(in_features=64, out_features=10)
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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.sigmoid1(out)
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out = self.fc2(out)
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out = self.sigmoid2(out)
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out = self.fc3(out)
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out = self.sigmoid3(out)
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out = self.fc4(out)
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out = self.sigmoid4(out)
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out = self.fc5(out)
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return out
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N_BITS_ATOL_TUPLE_LIST = [
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(28, 10 ** -2),
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(20, 10 ** -2),
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(16, 10 ** -1),
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(8, 10 ** -0),
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(4, 10 ** -0),
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]
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@pytest.mark.parametrize(
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"n_bits, atol",
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[pytest.param(n_bits, atol) for n_bits, atol in N_BITS_ATOL_TUPLE_LIST],
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)
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@pytest.mark.parametrize(
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"model, input_shape",
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[
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pytest.param(FC, (100, 32 * 32 * 3)),
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],
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)
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def test_quantized_linear(model, input_shape, n_bits, atol, seed_torch):
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"""Test the quantized module with a post-training static quantization.
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With n_bits>>0 we expect the results of the quantized module
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to be the same as the standard module.
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"""
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# Seed torch
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seed_torch()
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# Define the torch model
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torch_fc_model = model()
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# Create random input
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numpy_input = numpy.random.uniform(size=input_shape)
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# Create corresponding numpy model
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numpy_fc_model = NumpyModule(torch_fc_model)
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# Predict with real model
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numpy_prediction = numpy_fc_model(numpy_input)
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# Quantize with post-training static method
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post_training_quant = PostTrainingAffineQuantization(n_bits, numpy_fc_model)
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quantized_model = post_training_quant.quantize_module(numpy_input)
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# Quantize input
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q_input = QuantizedArray(n_bits, numpy_input)
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# Forward and Dequantize to get back to real values
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dequant_prediction = quantized_model.forward_and_dequant(q_input)
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assert numpy.isclose(numpy_prediction, dequant_prediction, atol=atol).all()
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