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https://github.com/iperov/DeepFaceLab.git
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52 lines
No EOL
2.1 KiB
Python
52 lines
No EOL
2.1 KiB
Python
import numpy as np
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def initialize_initializers(nn):
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tf = nn.tf
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from tensorflow.python.ops import init_ops
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class initializers():
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class ca (init_ops.Initializer):
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def __init__(self, dtype=None):
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pass
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def __call__(self, shape, dtype=None, partition_info=None):
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return tf.zeros( shape, name="_cai_")
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@staticmethod
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def generate(shape, eps_std=0.05, dtype=np.float32):
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"""
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Super fast implementation of Convolution Aware Initialization for 4D shapes
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Convolution Aware Initialization https://arxiv.org/abs/1702.06295
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"""
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if len(shape) != 4:
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raise ValueError("only shape with rank 4 supported.")
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row, column, stack_size, filters_size = shape
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fan_in = stack_size * (row * column)
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kernel_shape = (row, column)
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kernel_fft_shape = np.fft.rfft2(np.zeros(kernel_shape)).shape
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basis_size = np.prod(kernel_fft_shape)
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if basis_size == 1:
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x = np.random.normal( 0.0, eps_std, (filters_size, stack_size, basis_size) )
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else:
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nbb = stack_size // basis_size + 1
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x = np.random.normal(0.0, 1.0, (filters_size, nbb, basis_size, basis_size))
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x = x + np.transpose(x, (0,1,3,2) ) * (1-np.eye(basis_size))
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u, _, v = np.linalg.svd(x)
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x = np.transpose(u, (0,1,3,2) )
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x = np.reshape(x, (filters_size, -1, basis_size) )
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x = x[:,:stack_size,:]
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x = np.reshape(x, ( (filters_size,stack_size,) + kernel_fft_shape ) )
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x = np.fft.irfft2( x, kernel_shape ) \
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+ np.random.normal(0, eps_std, (filters_size,stack_size,)+kernel_shape)
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x = x * np.sqrt( (2/fan_in) / np.var(x) )
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x = np.transpose( x, (2, 3, 1, 0) )
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return x.astype(dtype)
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nn.initializers = initializers |