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* FaceSwap 3 (#1515) * Update extract pipeline * Update requirements + setup for nvidia * Remove allow-growth option * tf.keras to keras updates * lib.model.losses - Port + fix all loss functions for Keras3 * lib.model - port initializers, layers. normalization to Keras3 * lib.model.autoclip to Keras 3 * Update mixed precision layer storage * model file to .keras format * Restructure nn_blocks to initialize layers in __init__ * Tensorboard - Trainer: Add Torch compatible Tensorboard callbacks - GUI event reader remove TF dependency * Loss logging - Flush TB logs on save - Replace TB live iterator for GUI * Backup models on total loss drop rather than per side * Update all models to Keras3 Compat * Remove lib.model.session * Update clip ViT to Keras 3 * plugins.extract.mask.unet-dfl - Fix for Keras3/Torch backend * Port AdaBelief to Keras 3 * setup.py: - Add --dev flag for dev tool install * Fix Keras 3 syntax * Fix LR Finder for Keras 3 * Fix mixed precision switching for Keras 3 * Add more optimizers + open up config setting * train: Remove updating FS1 weights to FS2 models * Alignments: Remove support for legacy .json files * tools.model: - Remove TF Saved Format saving - Fix Backup/Restore + Nan-Scan * Fix inference model creation for Keras 3 * Preview tool: Fix for Keras3 * setup.py: Configure keras backend * train: Migration of FS2 models to FS3 * Training: Default coverage to 100% * Remove DirectML backend * Update setup for MacOS * GUI: Force line reading to UTF-8 * Remove redundant Tensorflow references * Remove redundant code * Legacy model loading: Fix TFLamdaOp scalar ops and DepthwiseConv2D * Add vertical offset option for training * Github actions: Add more python versions * Add python version to workflow names * Github workflow: Exclude Python 3.12 for macOS * Implement custom training loop * Fs3 - Add RTX5xxx and ROCm 6.1-6.4 support (#1511) * setup.py: Add Cuda/ROCm version select options * bump minimum python version to 3.11 * Switch from setup.cgf to pyproject.toml * Documentation: Update all docs to use automodapi * Allow sysinfo to run with missing packages + correctly install tk under Linux * Bugfix: dot naming convention in clip models * lib.config: Centralise globally rather than passing as object - Add torch DataParallel for multi-gpu training - GUI: Group switches together when generating cli args - CLI: Remove deprecated multi-character argparse args - Refactor: - Centralise tensorboard reading/writing + unit tests - Create trainer plugin interfaces + add original + distributed * Update installers
106 lines
4.1 KiB
Python
106 lines
4.1 KiB
Python
#!/usr/bin/env python3
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""" Tests for Faceswap Normalization.
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Adapted from Keras tests.
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"""
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from itertools import product
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import numpy as np
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import pytest
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from keras import device, regularizers, models, layers
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from lib.model import normalization
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from lib.utils import get_backend
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from tests.lib.model.layers_test import layer_test
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@pytest.mark.parametrize('dummy', [None], ids=[get_backend().upper()])
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def test_instance_normalization(dummy): # pylint:disable=unused-argument
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""" Basic test for instance normalization. """
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layer_test(normalization.InstanceNormalization,
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kwargs={'epsilon': 0.1,
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'gamma_regularizer': regularizers.l2(0.01),
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'beta_regularizer': regularizers.l2(0.01)},
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input_shape=(3, 4, 2))
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layer_test(normalization.InstanceNormalization,
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kwargs={'epsilon': 0.1,
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'axis': 1},
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input_shape=(1, 4, 1))
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layer_test(normalization.InstanceNormalization,
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kwargs={'gamma_initializer': 'ones',
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'beta_initializer': 'ones'},
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input_shape=(3, 4, 2, 4))
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layer_test(normalization.InstanceNormalization,
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kwargs={'epsilon': 0.1,
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'axis': 1,
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'scale': False,
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'center': False},
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input_shape=(3, 4, 2, 4))
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@pytest.mark.parametrize('dummy', [None], ids=[get_backend().upper()])
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def test_group_normalization(dummy): # pylint:disable=unused-argument
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""" Basic test for instance normalization. """
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layer_test(normalization.GroupNormalization,
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kwargs={'epsilon': 0.1,
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'gamma_regularizer': regularizers.l2(0.01),
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'beta_regularizer': regularizers.l2(0.01)},
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input_shape=(4, 3, 4, 128))
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layer_test(normalization.GroupNormalization,
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kwargs={'epsilon': 0.1,
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'axis': 1},
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input_shape=(4, 1, 4, 256))
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layer_test(normalization.GroupNormalization,
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kwargs={'gamma_init': 'ones',
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'beta_init': 'ones'},
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input_shape=(4, 64))
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layer_test(normalization.GroupNormalization,
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kwargs={'epsilon': 0.1,
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'axis': 1,
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'group': 16},
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input_shape=(3, 64))
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_PARAMS_NORM = ["center", "scale"]
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_VALUES_NORM = list(product([True, False], repeat=len(_PARAMS_NORM)))
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_IDS = [f"{'|'.join([_PARAMS_NORM[idx] for idx, b in enumerate(v) if b])}[{get_backend().upper()}]"
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for v in _VALUES_NORM]
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@pytest.mark.parametrize(_PARAMS_NORM, _VALUES_NORM, ids=_IDS)
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def test_adain_normalization(center, scale):
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""" Basic test for Ada Instance Normalization. """
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with device("cpu"):
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norm = normalization.AdaInstanceNormalization(center=center, scale=scale)
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shapes = [(4, 8, 8, 1280), (4, 1, 1, 1280), (4, 1, 1, 1280)]
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norm.build(shapes)
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expected_output_shape = norm.compute_output_shape(shapes)
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inputs = [layers.Input(shape=shapes[0][1:]),
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layers.Input(shape=shapes[1][1:]),
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layers.Input(shape=shapes[2][1:])]
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model = models.Model(inputs, norm(inputs))
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data = [10 * np.random.random(shape) for shape in shapes]
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actual_output = model.predict(data, verbose=0)
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actual_output_shape = actual_output.shape
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for expected_dim, actual_dim in zip(expected_output_shape,
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actual_output_shape):
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if expected_dim is not None:
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assert expected_dim == actual_dim
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_PARAMS = ["partial", "bias"]
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_VALUES = [(0.0, False), (0.25, False), (0.5, True), (0.75, False), (1.0, True)] # type:ignore
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_IDS = [f"partial={v[0]}|bias={v[1]}[{get_backend().upper()}]" for v in _VALUES]
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@pytest.mark.parametrize(_PARAMS, _VALUES, ids=_IDS)
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def test_rms_normalization(partial, bias): # pylint:disable=unused-argument
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""" Basic test for RMS Layer normalization. """
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layer_test(normalization.RMSNormalization,
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kwargs={"partial": partial, "bias": bias},
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input_shape=(4, 512))
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