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https://github.com/deepfakes/faceswap.git
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torzdf
40103b9c4a
Standardize inputs for AutoEncoders Name outputs from AutoEncoders Name layers in nn_blocks.py
56 lines
2.3 KiB
Python
56 lines
2.3 KiB
Python
#!/usr/bin/env python3
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""" DFaker Model
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Based on the dfaker model: https://github.com/dfaker """
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from keras.initializers import RandomNormal
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from keras.layers import Input
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from keras.models import Model as KerasModel
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from .original import logger, Model as OriginalModel
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class Model(OriginalModel):
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""" Improved Autoeencoder Model """
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def __init__(self, *args, **kwargs):
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logger.debug("Initializing %s: (args: %s, kwargs: %s",
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self.__class__.__name__, args, kwargs)
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kwargs["input_shape"] = (64, 64, 3)
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kwargs["encoder_dim"] = 1024
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self.kernel_initializer = RandomNormal(0, 0.02)
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super().__init__(*args, **kwargs)
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logger.debug("Initialized %s", self.__class__.__name__)
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def decoder(self):
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""" Decoder Network """
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input_ = Input(shape=(8, 8, 512))
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var_x = input_
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var_x = self.blocks.upscale(var_x, 512, res_block_follows=True)
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var_x = self.blocks.res_block(var_x, 512, kernel_initializer=self.kernel_initializer)
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var_x = self.blocks.upscale(var_x, 256, res_block_follows=True)
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var_x = self.blocks.res_block(var_x, 256, kernel_initializer=self.kernel_initializer)
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var_x = self.blocks.upscale(var_x, 128, res_block_follows=True)
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var_x = self.blocks.res_block(var_x, 128, kernel_initializer=self.kernel_initializer)
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var_x = self.blocks.upscale(var_x, 64)
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var_x = self.blocks.conv2d(var_x, 3,
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kernel_size=5,
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padding="same",
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activation="sigmoid",
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name="face_out")
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outputs = [var_x]
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if self.config.get("mask_type", None):
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var_y = input_
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var_y = self.blocks.upscale(var_y, 512)
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var_y = self.blocks.upscale(var_y, 256)
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var_y = self.blocks.upscale(var_y, 128)
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var_y = self.blocks.upscale(var_y, 64)
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var_y = self.blocks.conv2d(var_y, 1,
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kernel_size=5,
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padding="same",
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activation="sigmoid",
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name="mask_out")
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outputs.append(var_y)
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return KerasModel([input_], outputs=outputs)
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