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.
87 lines
3.5 KiB
Python
87 lines
3.5 KiB
Python
#!/usr/bin/env python3
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""" Original Model
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Based on the original https://www.reddit.com/r/deepfakes/
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code sample + contribs """
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from keras.layers import Dense, Flatten, Input, Reshape
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from keras.models import Model as KerasModel
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from ._base import ModelBase, logger
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class Model(ModelBase):
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""" Original Faceswap 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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self.configfile = kwargs.get("configfile", None)
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if "input_shape" not in kwargs:
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kwargs["input_shape"] = (64, 64, 3)
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if "encoder_dim" not in kwargs:
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kwargs["encoder_dim"] = 512 if self.config["lowmem"] else 1024
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super().__init__(*args, **kwargs)
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logger.debug("Initialized %s", self.__class__.__name__)
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def add_networks(self):
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""" Add the original model weights """
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logger.debug("Adding networks")
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self.add_network("decoder", "a", self.decoder(), is_output=True)
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self.add_network("decoder", "b", self.decoder(), is_output=True)
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self.add_network("encoder", None, self.encoder())
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logger.debug("Added networks")
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def build_autoencoders(self, inputs):
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""" Initialize original model """
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logger.debug("Initializing model")
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for side in ("a", "b"):
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logger.debug("Adding Autoencoder. Side: %s", side)
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decoder = self.networks["decoder_{}".format(side)].network
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output = decoder(self.networks["encoder"].network(inputs[0]))
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autoencoder = KerasModel(inputs, output)
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self.add_predictor(side, autoencoder)
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logger.debug("Initialized model")
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def encoder(self):
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""" Encoder Network """
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input_ = Input(shape=self.input_shape)
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var_x = input_
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var_x = self.blocks.conv(var_x, 128)
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var_x = self.blocks.conv(var_x, 256)
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var_x = self.blocks.conv(var_x, 512)
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if not self.config.get("lowmem", False):
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var_x = self.blocks.conv(var_x, 1024)
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var_x = Dense(self.encoder_dim)(Flatten()(var_x))
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var_x = Dense(4 * 4 * 1024)(var_x)
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var_x = Reshape((4, 4, 1024))(var_x)
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var_x = self.blocks.upscale(var_x, 512)
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return KerasModel(input_, var_x)
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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, 256)
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var_x = self.blocks.upscale(var_x, 128)
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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, 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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