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https://github.com/iperov/DeepFaceLab.git
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101 lines
No EOL
4.3 KiB
Python
101 lines
No EOL
4.3 KiB
Python
import numpy as np
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import os
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import cv2
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from pathlib import Path
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from nnlib import nnlib
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class FANSegmentator(object):
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def __init__ (self, resolution, face_type_str, load_weights=True, weights_file_root=None):
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exec( nnlib.import_all(), locals(), globals() )
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self.model = FANSegmentator.BuildModel(resolution, ngf=32)
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if weights_file_root:
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weights_file_root = Path(weights_file_root)
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else:
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weights_file_root = Path(__file__).parent
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self.weights_path = weights_file_root / ('FANSeg_%d_%s.h5' % (resolution, face_type_str) )
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if load_weights:
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self.model.load_weights (str(self.weights_path))
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def __enter__(self):
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return self
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def __exit__(self, exc_type=None, exc_value=None, traceback=None):
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return False #pass exception between __enter__ and __exit__ to outter level
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def save_weights(self):
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self.model.save_weights (str(self.weights_path))
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def train_on_batch(self, inp, outp):
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return self.model.train_on_batch(inp, outp)
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def extract_from_bgr (self, input_image):
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#return np.clip ( self.model.predict(input_image), 0, 1.0 )
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return np.clip ( (self.model.predict(input_image) + 1) / 2.0, 0, 1.0 )
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@staticmethod
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def BuildModel ( resolution, ngf=64):
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exec( nnlib.import_all(), locals(), globals() )
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inp = Input ( (resolution,resolution,3) )
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x = inp
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x = FANSegmentator.EncFlow(ngf=ngf)(x)
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x = FANSegmentator.DecFlow(ngf=ngf)(x)
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model = Model(inp,x)
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model.compile (loss='mse', optimizer=Padam(tf_cpu_mode=2) )
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#model.compile (loss='mse', optimizer=Adam(tf_cpu_mode=2) )
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return model
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@staticmethod
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def EncFlow(ngf=64, num_downs=4):
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exec( nnlib.import_all(), locals(), globals() )
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use_bias = True
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def XNormalization(x):
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return InstanceNormalization (axis=3, gamma_initializer=RandomNormal(1., 0.02))(x)
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def downscale (dim):
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def func(x):
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return LeakyReLU(0.1)(XNormalization(Conv2D(dim, kernel_size=5, strides=2, padding='same', kernel_initializer=RandomNormal(0, 0.02))(x)))
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return func
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def func(input):
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x = input
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result = []
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for i in range(num_downs):
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x = downscale ( min(ngf*(2**i), ngf*8) )(x)
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result += [x]
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return result
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return func
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@staticmethod
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def DecFlow(output_nc=1, ngf=64, activation='tanh'):
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exec (nnlib.import_all(), locals(), globals())
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use_bias = True
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def XNormalization(x):
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return InstanceNormalization (axis=3, gamma_initializer=RandomNormal(1., 0.02))(x)
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def Conv2D (filters, kernel_size, strides=(1, 1), padding='valid', data_format=None, dilation_rate=(1, 1), activation=None, use_bias=use_bias, kernel_initializer=RandomNormal(0, 0.02), bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None):
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return keras.layers.Conv2D( filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, data_format=data_format, dilation_rate=dilation_rate, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, kernel_constraint=kernel_constraint, bias_constraint=bias_constraint )
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def upscale (dim):
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def func(x):
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return SubpixelUpscaler()( LeakyReLU(0.1)(XNormalization(Conv2D(dim, kernel_size=3, strides=1, padding='same', kernel_initializer=RandomNormal(0, 0.02))(x))))
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return func
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def func(input):
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input_len = len(input)
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x = input[input_len-1]
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for i in range(input_len-1, -1, -1):
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x = upscale( min(ngf* (2**i) *4, ngf*8 *4 ) )(x)
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if i != 0:
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x = Concatenate(axis=3)([ input[i-1] , x])
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return Conv2D(output_nc, 3, 1, 'same', activation=activation)(x)
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return func |