mirror of
https://github.com/deepfakes/faceswap
synced 2025-06-09 04:36:50 -04:00
43a4d06540
* Smart Masks - Training - Reinstate smart mask training code - Reinstate mask_type back to model.config - change 'replicate_input_mask to 'learn_mask' - Add learn mask option - Add mask loading from alignments to plugins.train.trainer - Add mask_blur and mask threshold options - _base.py - Pass mask options through training_opts dict - plugins.train.model - check for mask_type not None for learn_mask and penalized_mask_loss - Limit alignments loading to just those faces that appear in the training folder - Raise error if not all training images have an alignment, and alignment file is required - lib.training_data - Mask generation code - lib.faces_detect - cv2 dimension stripping bugfix - Remove cv2 linting code * Update mask helptext in cli.py * Fix Warp to Landmarks Remove SHA1 hashing from training data * Update mask training config * Capture missing masks at training init * lib.image.read_image_batch - Return filenames with batch for ordering * scripts.train - Documentation * plugins.train.trainer - documentation * Ensure backward compatibility. Fix convert for new predicted masks * Update removed masks to components for legacy models.
55 lines
2.3 KiB
Python
55 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("learn_mask", False):
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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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