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torzdf
837bc2d51d
* 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
67 lines
1.9 KiB
Python
67 lines
1.9 KiB
Python
#!/usr/bin/env python3
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""" Tests for Faceswap Initializers.
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Adapted from Keras tests.
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"""
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import pytest
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import numpy as np
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from keras import device, initializers as k_initializers, Variable
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from lib.model import initializers
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from lib.utils import get_backend
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CONV_SHAPE = (3, 3, 256, 2048)
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CONV_ID = get_backend().upper()
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def _runner(init, shape, target_mean=None, target_std=None,
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target_max=None, target_min=None):
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with device("cpu"):
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variable = Variable(init(shape))
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output = variable.numpy()
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lim = 3e-2
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if target_std is not None:
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assert abs(output.std() - target_std) < lim
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if target_mean is not None:
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assert abs(output.mean() - target_mean) < lim
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if target_max is not None:
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assert abs(output.max() - target_max) < lim
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if target_min is not None:
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assert abs(output.min() - target_min) < lim
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@pytest.mark.parametrize('tensor_shape', [CONV_SHAPE], ids=[CONV_ID])
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def test_icnr(tensor_shape):
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""" ICNR Initialization Test
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Parameters
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----------
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tensor_shape: tuple
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The shape of the tensor to feed to the initializer
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"""
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with device("cpu"):
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fan_in, _ = initializers.compute_fans(tensor_shape)
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std = np.sqrt(2. / fan_in)
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_runner(initializers.ICNR(initializer=k_initializers.he_uniform(), scale=2),
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tensor_shape,
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target_mean=0,
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target_std=std)
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@pytest.mark.parametrize('tensor_shape', [CONV_SHAPE], ids=[CONV_ID])
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def test_convolution_aware(tensor_shape):
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""" Convolution Aware Initialization Test
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Parameters
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----------
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tensor_shape: tuple
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The shape of the tensor to feed to the initializer
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"""
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with device("cpu"):
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fan_in, _ = initializers.compute_fans(tensor_shape)
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std = np.sqrt(2. / fan_in)
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_runner(initializers.ConvolutionAware(seed=123), tensor_shape,
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target_mean=0, target_std=std)
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