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Testing Neural Network Normalization Layer Implementations in Faceswap

This test suite validates normalization implementations in the Faceswap project, focusing on various normalization layers like Instance, Group, Ada Instance, and RMS normalization. The tests ensure proper functionality and compatibility with different initialization parameters and configurations.

Test Coverage Overview

The test suite provides comprehensive coverage of normalization layer implementations:

Instance Normalization testing with different epsilon values and regularizers
Group Normalization validation across various input shapes and configurations
Ada Instance Normalization verification with center and scale parameters
RMS Normalization testing with partial and bias options

Implementation Analysis

The testing approach utilizes pytest’s parametrize feature for comprehensive test variations. The implementation leverages the layer_test utility function to validate layer behaviors, ensuring proper shape handling, initialization, and forward pass operations.

Tests verify normalization behavior across different input dimensions and parameter configurations.

Technical Details

Testing Framework: pytest
Key Dependencies: TensorFlow, Keras, NumPy
Test Utilities: Custom layer_test function
Backend Compatibility: Multiple backend support through get_backend()

Best Practices Demonstrated

The test suite exemplifies several testing best practices:

Parametrized testing for comprehensive coverage
Clear test case isolation and organization
Proper validation of shape transformations
Explicit testing of initialization parameters
Backend-agnostic test implementation

deepfakes/faceswap

tests/lib/model/normalization_test.py

            
#!/usr/bin/env python3
""" Tests for Faceswap Normalization.

Adapted from Keras tests.
"""
from itertools import product

import numpy as np
import pytest

from tensorflow.keras import regularizers, models, layers  # noqa:E501  # pylint:disable=import-error

from lib.model import normalization
from lib.utils import get_backend

from tests.lib.model.layers_test import layer_test


@pytest.mark.parametrize('dummy', [None], ids=[get_backend().upper()])
def test_instance_normalization(dummy):  # pylint:disable=unused-argument
    """ Basic test for instance normalization. """
    layer_test(normalization.InstanceNormalization,
               kwargs={'epsilon': 0.1,
                       'gamma_regularizer': regularizers.l2(0.01),
                       'beta_regularizer': regularizers.l2(0.01)},
               input_shape=(3, 4, 2))
    layer_test(normalization.InstanceNormalization,
               kwargs={'epsilon': 0.1,
                       'axis': 1},
               input_shape=(1, 4, 1))
    layer_test(normalization.InstanceNormalization,
               kwargs={'gamma_initializer': 'ones',
                       'beta_initializer': 'ones'},
               input_shape=(3, 4, 2, 4))
    layer_test(normalization.InstanceNormalization,
               kwargs={'epsilon': 0.1,
                       'axis': 1,
                       'scale': False,
                       'center': False},
               input_shape=(3, 4, 2, 4))


@pytest.mark.parametrize('dummy', [None], ids=[get_backend().upper()])
def test_group_normalization(dummy):  # pylint:disable=unused-argument
    """ Basic test for instance normalization. """
    layer_test(normalization.GroupNormalization,
               kwargs={'epsilon': 0.1,
                       'gamma_regularizer': regularizers.l2(0.01),
                       'beta_regularizer': regularizers.l2(0.01)},
               input_shape=(4, 3, 4, 128))
    layer_test(normalization.GroupNormalization,
               kwargs={'epsilon': 0.1,
                       'axis': 1},
               input_shape=(4, 1, 4, 256))
    layer_test(normalization.GroupNormalization,
               kwargs={'gamma_init': 'ones',
                       'beta_init': 'ones'},
               input_shape=(4, 64))
    layer_test(normalization.GroupNormalization,
               kwargs={'epsilon': 0.1,
                       'axis': 1,
                       'group': 16},
               input_shape=(3, 64))


_PARAMS = ["center", "scale"]
_VALUES = list(product([True, False], repeat=len(_PARAMS)))
_IDS = [f"{'|'.join([_PARAMS[idx] for idx, b in enumerate(v) if b])}[{get_backend().upper()}]"
        for v in _VALUES]


@pytest.mark.parametrize(_PARAMS, _VALUES, ids=_IDS)
def test_adain_normalization(center, scale):
    """ Basic test for Ada Instance Normalization. """
    norm = normalization.AdaInstanceNormalization(center=center, scale=scale)
    shapes = [(4, 8, 8, 1280), (4, 1, 1, 1280), (4, 1, 1, 1280)]
    norm.build(shapes)
    expected_output_shape = norm.compute_output_shape(shapes)
    inputs = [layers.Input(shape=shapes[0][1:]),
              layers.Input(shape=shapes[1][1:]),
              layers.Input(shape=shapes[2][1:])]
    model = models.Model(inputs, norm(inputs))
    data = [10 * np.random.random(shape) for shape in shapes]

    actual_output = model.predict(data, verbose=0)
    actual_output_shape = actual_output.shape

    for expected_dim, actual_dim in zip(expected_output_shape,
                                        actual_output_shape):
        if expected_dim is not None:
            assert expected_dim == actual_dim


_PARAMS = ["partial", "bias"]
_VALUES = [(0.0, False), (0.25, False), (0.5, True), (0.75, False), (1.0, True)]  # type:ignore
_IDS = [f"partial={v[0]}|bias={v[1]}[{get_backend().upper()}]" for v in _VALUES]


@pytest.mark.parametrize(_PARAMS, _VALUES, ids=_IDS)
def test_rms_normalization(partial, bias):  # pylint:disable=unused-argument
    """ Basic test for RMS Layer normalization. """
    layer_test(normalization.RMSNormalization,
               kwargs={"partial": partial, "bias": bias},
               input_shape=(4, 512))