mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-07-07 05:22:06 -07:00
7386a9d6fd
SAEHD: added new option GAN power 0.0 .. 10.0 Train the network in Generative Adversarial manner. Forces the neural network to learn small details of the face. You can enable/disable this option at any time, but better to enable it when the network is trained enough. Typical value is 1.0 GAN power with pretrain mode will not work. Example of enabling GAN on 81k iters +5k iters https://i.imgur.com/OdXHLhU.jpg https://i.imgur.com/CYAJmJx.jpg dfhd: default Decoder dimensions are now 48 the preview for 256 res is now correctly displayed fixed model naming/renaming/removing Improvements for those involved in post-processing in AfterEffects: Codec is reverted back to x264 in order to properly use in AfterEffects and video players. Merger now always outputs the mask to workspace\data_dst\merged_mask removed raw modes except raw-rgb raw-rgb mode now outputs selected face mask_mode (before square mask) 'export alpha mask' button is replaced by 'show alpha mask'. You can view the alpha mask without recompute the frames. 8) 'merged *.bat' now also output 'result_mask.' video file. 8) 'merged lossless' now uses x264 lossless codec (before PNG codec) result_mask video file is always lossless. Thus you can use result_mask video file as mask layer in the AfterEffects.
636 lines
No EOL
23 KiB
Python
636 lines
No EOL
23 KiB
Python
import pickle
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import types
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from pathlib import Path
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from core import pathex
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from core.interact import interact as io
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import numpy as np
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def initialize_layers(nn):
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tf = nn.tf
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class Saveable():
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def __init__(self, name=None):
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self.name = name
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#override
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def get_weights(self):
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#return tf tensors that should be initialized/loaded/saved
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pass
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def save_weights(self, filename, force_dtype=None):
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d = {}
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weights = self.get_weights()
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if self.name is None:
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raise Exception("name must be defined.")
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name = self.name
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for w, w_val in zip(weights, nn.tf_sess.run (weights)):
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w_name_split = w.name.split('/', 1)
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if name != w_name_split[0]:
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raise Exception("weight first name != Saveable.name")
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if force_dtype is not None:
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w_val = w_val.astype(force_dtype)
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d[ w_name_split[1] ] = w_val
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d_dumped = pickle.dumps (d, 4)
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pathex.write_bytes_safe ( Path(filename), d_dumped )
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def load_weights(self, filename):
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"""
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returns True if file exists
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"""
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filepath = Path(filename)
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if filepath.exists():
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result = True
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d_dumped = filepath.read_bytes()
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d = pickle.loads(d_dumped)
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else:
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return False
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weights = self.get_weights()
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if self.name is None:
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raise Exception("name must be defined.")
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tuples = []
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for w in weights:
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w_name_split = w.name.split('/')
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if self.name != w_name_split[0]:
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raise Exception("weight first name != Saveable.name")
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sub_w_name = "/".join(w_name_split[1:])
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w_val = d.get(sub_w_name, None)
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w_val = np.reshape( w_val, w.shape.as_list() )
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if w_val is None:
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io.log_err(f"Weight {w.name} was not loaded from file {filename}")
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tuples.append ( (w, w.initializer) )
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else:
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tuples.append ( (w, w_val) )
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nn.tf_batch_set_value(tuples)
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return True
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def init_weights(self):
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nn.tf_init_weights(self.get_weights())
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nn.Saveable = Saveable
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class LayerBase():
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def __init__(self, name=None, **kwargs):
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self.name = name
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#override
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def build_weights(self):
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pass
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#override
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def get_weights(self):
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return []
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def set_weights(self, new_weights):
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weights = self.get_weights()
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if len(weights) != len(new_weights):
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raise ValueError ('len of lists mismatch')
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tuples = []
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for w, new_w in zip(weights, new_weights):
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if len(w.shape) != new_w.shape:
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new_w = new_w.reshape(w.shape)
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tuples.append ( (w, new_w) )
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nn.tf_batch_set_value (tuples)
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nn.LayerBase = LayerBase
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class ModelBase(Saveable):
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def __init__(self, *args, name=None, **kwargs):
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super().__init__(name=name)
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self.layers = []
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self.built = False
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self.args = args
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self.kwargs = kwargs
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self.run_placeholders = None
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def _build_sub(self, layer, name):
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if isinstance (layer, list):
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for i,sublayer in enumerate(layer):
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self._build_sub(sublayer, f"{name}_{i}")
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elif isinstance (layer, LayerBase) or \
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isinstance (layer, ModelBase):
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if layer.name is None:
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layer.name = name
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if isinstance (layer, LayerBase):
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with tf.variable_scope(layer.name):
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layer.build_weights()
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elif isinstance (layer, ModelBase):
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layer.build()
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self.layers.append (layer)
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def xor_list(self, lst1, lst2):
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return [value for value in lst1+lst2 if (value not in lst1) or (value not in lst2) ]
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def build(self):
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with tf.variable_scope(self.name):
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current_vars = []
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generator = None
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while True:
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if generator is None:
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generator = self.on_build(*self.args, **self.kwargs)
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if not isinstance(generator, types.GeneratorType):
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generator = None
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if generator is not None:
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try:
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next(generator)
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except StopIteration:
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generator = None
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v = vars(self)
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new_vars = self.xor_list (current_vars, list(v.keys()) )
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for name in new_vars:
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self._build_sub(v[name],name)
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current_vars += new_vars
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if generator is None:
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break
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self.built = True
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#override
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def get_weights(self):
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if not self.built:
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self.build()
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weights = []
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for layer in self.layers:
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weights += layer.get_weights()
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return weights
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def get_layers(self):
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if not self.built:
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self.build()
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layers = []
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for layer in self.layers:
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if isinstance (layer, LayerBase):
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layers.append(layer)
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else:
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layers += layer.get_layers()
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return layers
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#override
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def on_build(self, *args, **kwargs):
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"""
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init model layers here
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return 'yield' if build is not finished
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therefore dependency models will be initialized
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"""
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pass
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#override
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def forward(self, *args, **kwargs):
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#flow layers/models/tensors here
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pass
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def __call__(self, *args, **kwargs):
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if not self.built:
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self.build()
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return self.forward(*args, **kwargs)
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def compute_output_shape(self, shapes):
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if not self.built:
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self.build()
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not_list = False
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if not isinstance(shapes, list):
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not_list = True
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shapes = [shapes]
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with tf.device('/CPU:0'):
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# CPU tensors will not impact any performance, only slightly RAM "leakage"
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phs = []
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for dtype,sh in shapes:
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phs += [ tf.placeholder(dtype, sh) ]
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result = self.__call__(phs[0] if not_list else phs)
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if not isinstance(result, list):
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result = [result]
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result_shapes = []
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for t in result:
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result_shapes += [ t.shape.as_list() ]
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return result_shapes[0] if not_list else result_shapes
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def compute_output_channels(self, shapes):
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shape = self.compute_output_shape(shapes)
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shape_len = len(shape)
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if shape_len == 4:
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if nn.data_format == "NCHW":
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return shape[1]
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return shape[-1]
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def build_for_run(self, shapes_list):
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if not isinstance(shapes_list, list):
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raise ValueError("shapes_list must be a list.")
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self.run_placeholders = []
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for dtype,sh in shapes_list:
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self.run_placeholders.append ( tf.placeholder(dtype, sh) )
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self.run_output = self.__call__(self.run_placeholders)
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def run (self, inputs):
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if self.run_placeholders is None:
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raise Exception ("Model didn't build for run.")
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if len(inputs) != len(self.run_placeholders):
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raise ValueError("len(inputs) != self.run_placeholders")
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feed_dict = {}
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for ph, inp in zip(self.run_placeholders, inputs):
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feed_dict[ph] = inp
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return nn.tf_sess.run ( self.run_output, feed_dict=feed_dict)
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nn.ModelBase = ModelBase
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class Conv2D(LayerBase):
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"""
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use_wscale bool enables equalized learning rate, kernel_initializer will be forced to random_normal
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"""
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def __init__(self, in_ch, out_ch, kernel_size, strides=1, padding='SAME', dilations=1, use_bias=True, use_wscale=False, kernel_initializer=None, bias_initializer=None, trainable=True, dtype=None, **kwargs ):
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if not isinstance(strides, int):
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raise ValueError ("strides must be an int type")
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if not isinstance(dilations, int):
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raise ValueError ("dilations must be an int type")
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kernel_size = int(kernel_size)
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if dtype is None:
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dtype = nn.tf_floatx
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if isinstance(padding, str):
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if padding == "SAME":
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padding = ( (kernel_size - 1) * dilations + 1 ) // 2
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elif padding == "VALID":
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padding = 0
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else:
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raise ValueError ("Wrong padding type. Should be VALID SAME or INT or 4x INTs")
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if isinstance(padding, int):
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if padding != 0:
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if nn.data_format == "NHWC":
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padding = [ [0,0], [padding,padding], [padding,padding], [0,0] ]
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else:
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padding = [ [0,0], [0,0], [padding,padding], [padding,padding] ]
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else:
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padding = None
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if nn.data_format == "NHWC":
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strides = [1,strides,strides,1]
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else:
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strides = [1,1,strides,strides]
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if nn.data_format == "NHWC":
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dilations = [1,dilations,dilations,1]
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else:
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dilations = [1,1,dilations,dilations]
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self.in_ch = in_ch
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self.out_ch = out_ch
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self.kernel_size = kernel_size
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self.strides = strides
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self.padding = padding
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self.dilations = dilations
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self.use_bias = use_bias
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self.use_wscale = use_wscale
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self.kernel_initializer = kernel_initializer
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self.bias_initializer = bias_initializer
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self.trainable = trainable
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self.dtype = dtype
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super().__init__(**kwargs)
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def build_weights(self):
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kernel_initializer = self.kernel_initializer
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if self.use_wscale:
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gain = 1.0 if self.kernel_size == 1 else np.sqrt(2)
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fan_in = self.kernel_size*self.kernel_size*self.in_ch
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he_std = gain / np.sqrt(fan_in) # He init
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self.wscale = tf.constant(he_std, dtype=self.dtype )
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kernel_initializer = tf.initializers.random_normal(0, 1.0, dtype=self.dtype)
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if kernel_initializer is None:
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kernel_initializer = tf.initializers.glorot_uniform(dtype=self.dtype)
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self.weight = tf.get_variable("weight", (self.kernel_size,self.kernel_size,self.in_ch,self.out_ch), dtype=self.dtype, initializer=kernel_initializer, trainable=self.trainable )
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if self.use_bias:
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bias_initializer = self.bias_initializer
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if bias_initializer is None:
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bias_initializer = tf.initializers.zeros(dtype=self.dtype)
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self.bias = tf.get_variable("bias", (self.out_ch,), dtype=self.dtype, initializer=bias_initializer, trainable=self.trainable )
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def get_weights(self):
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weights = [self.weight]
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if self.use_bias:
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weights += [self.bias]
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return weights
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def __call__(self, x):
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weight = self.weight
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if self.use_wscale:
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weight = weight * self.wscale
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if self.padding is not None:
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x = tf.pad (x, self.padding, mode='CONSTANT')
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x = tf.nn.conv2d(x, weight, self.strides, 'VALID', dilations=self.dilations, data_format=nn.data_format)
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if self.use_bias:
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if nn.data_format == "NHWC":
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bias = tf.reshape (self.bias, (1,1,1,self.out_ch) )
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else:
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bias = tf.reshape (self.bias, (1,self.out_ch,1,1) )
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x = tf.add(x, bias)
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return x
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def __str__(self):
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r = f"{self.__class__.__name__} : in_ch:{self.in_ch} out_ch:{self.out_ch} "
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return r
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nn.Conv2D = Conv2D
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class Conv2DTranspose(LayerBase):
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"""
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use_wscale enables weight scale (equalized learning rate)
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kernel_initializer will be forced to random_normal
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"""
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def __init__(self, in_ch, out_ch, kernel_size, strides=2, padding='SAME', use_bias=True, use_wscale=False, kernel_initializer=None, bias_initializer=None, trainable=True, dtype=None, **kwargs ):
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if not isinstance(strides, int):
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raise ValueError ("strides must be an int type")
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kernel_size = int(kernel_size)
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if dtype is None:
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dtype = nn.tf_floatx
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self.in_ch = in_ch
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self.out_ch = out_ch
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self.kernel_size = kernel_size
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self.strides = strides
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self.padding = padding
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self.use_bias = use_bias
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self.use_wscale = use_wscale
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self.kernel_initializer = kernel_initializer
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self.bias_initializer = bias_initializer
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self.trainable = trainable
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self.dtype = dtype
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super().__init__(**kwargs)
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def build_weights(self):
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kernel_initializer = self.kernel_initializer
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if self.use_wscale:
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gain = 1.0 if self.kernel_size == 1 else np.sqrt(2)
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fan_in = self.kernel_size*self.kernel_size*self.in_ch
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he_std = gain / np.sqrt(fan_in) # He init
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self.wscale = tf.constant(he_std, dtype=self.dtype )
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kernel_initializer = tf.initializers.random_normal(0, 1.0, dtype=self.dtype)
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if kernel_initializer is None:
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kernel_initializer = tf.initializers.glorot_uniform(dtype=self.dtype)
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self.weight = tf.get_variable("weight", (self.kernel_size,self.kernel_size,self.out_ch,self.in_ch), dtype=self.dtype, initializer=kernel_initializer, trainable=self.trainable )
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if self.use_bias:
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bias_initializer = self.bias_initializer
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if bias_initializer is None:
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bias_initializer = tf.initializers.zeros(dtype=self.dtype)
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self.bias = tf.get_variable("bias", (self.out_ch,), dtype=self.dtype, initializer=bias_initializer, trainable=self.trainable )
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def get_weights(self):
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weights = [self.weight]
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if self.use_bias:
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weights += [self.bias]
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return weights
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def __call__(self, x):
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shape = x.shape
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if nn.data_format == "NHWC":
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h,w,c = shape[1], shape[2], shape[3]
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output_shape = tf.stack ( (tf.shape(x)[0],
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self.deconv_length(w, self.strides, self.kernel_size, self.padding),
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self.deconv_length(h, self.strides, self.kernel_size, self.padding),
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self.out_ch) )
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strides = [1,self.strides,self.strides,1]
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else:
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c,h,w = shape[1], shape[2], shape[3]
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output_shape = tf.stack ( (tf.shape(x)[0],
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self.out_ch,
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self.deconv_length(w, self.strides, self.kernel_size, self.padding),
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self.deconv_length(h, self.strides, self.kernel_size, self.padding),
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) )
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strides = [1,1,self.strides,self.strides]
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weight = self.weight
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if self.use_wscale:
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weight = weight * self.wscale
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x = tf.nn.conv2d_transpose(x, weight, output_shape, strides, padding=self.padding, data_format=nn.data_format)
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if self.use_bias:
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if nn.data_format == "NHWC":
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bias = tf.reshape (self.bias, (1,1,1,self.out_ch) )
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else:
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bias = tf.reshape (self.bias, (1,self.out_ch,1,1) )
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x = tf.add(x, bias)
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return x
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def __str__(self):
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r = f"{self.__class__.__name__} : in_ch:{self.in_ch} out_ch:{self.out_ch} "
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return r
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def deconv_length(self, dim_size, stride_size, kernel_size, padding):
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assert padding in {'SAME', 'VALID', 'FULL'}
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if dim_size is None:
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return None
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if padding == 'VALID':
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dim_size = dim_size * stride_size + max(kernel_size - stride_size, 0)
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elif padding == 'FULL':
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dim_size = dim_size * stride_size - (stride_size + kernel_size - 2)
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elif padding == 'SAME':
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dim_size = dim_size * stride_size
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return dim_size
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nn.Conv2DTranspose = Conv2DTranspose
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class BlurPool(LayerBase):
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def __init__(self, filt_size=3, stride=2, **kwargs ):
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self.strides = [1,stride,stride,1]
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self.filt_size = filt_size
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pad = [ int(1.*(filt_size-1)/2), int(np.ceil(1.*(filt_size-1)/2)) ]
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if nn.data_format == "NHWC":
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self.padding = [ [0,0], pad, pad, [0,0] ]
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|
else:
|
|
self.padding = [ [0,0], [0,0], pad, pad ]
|
|
|
|
if(self.filt_size==1):
|
|
a = np.array([1.,])
|
|
elif(self.filt_size==2):
|
|
a = np.array([1., 1.])
|
|
elif(self.filt_size==3):
|
|
a = np.array([1., 2., 1.])
|
|
elif(self.filt_size==4):
|
|
a = np.array([1., 3., 3., 1.])
|
|
elif(self.filt_size==5):
|
|
a = np.array([1., 4., 6., 4., 1.])
|
|
elif(self.filt_size==6):
|
|
a = np.array([1., 5., 10., 10., 5., 1.])
|
|
elif(self.filt_size==7):
|
|
a = np.array([1., 6., 15., 20., 15., 6., 1.])
|
|
|
|
a = a[:,None]*a[None,:]
|
|
a = a / np.sum(a)
|
|
a = a[:,:,None,None]
|
|
self.a = a
|
|
super().__init__(**kwargs)
|
|
|
|
def build_weights(self):
|
|
self.k = tf.constant (self.a, dtype=nn.tf_floatx )
|
|
|
|
def __call__(self, x):
|
|
k = tf.tile (self.k, (1,1,x.shape[-1],1) )
|
|
x = tf.pad(x, self.padding )
|
|
x = tf.nn.depthwise_conv2d(x, k, self.strides, 'VALID')
|
|
return x
|
|
nn.BlurPool = BlurPool
|
|
|
|
class Dense(LayerBase):
|
|
def __init__(self, in_ch, out_ch, use_bias=True, use_wscale=False, maxout_ch=0, kernel_initializer=None, bias_initializer=None, trainable=True, dtype=None, **kwargs ):
|
|
"""
|
|
use_wscale enables weight scale (equalized learning rate)
|
|
kernel_initializer will be forced to random_normal
|
|
|
|
maxout_ch https://link.springer.com/article/10.1186/s40537-019-0233-0
|
|
typical 2-4 if you want to enable DenseMaxout behaviour
|
|
"""
|
|
self.in_ch = in_ch
|
|
self.out_ch = out_ch
|
|
self.use_bias = use_bias
|
|
self.use_wscale = use_wscale
|
|
self.maxout_ch = maxout_ch
|
|
self.kernel_initializer = kernel_initializer
|
|
self.bias_initializer = bias_initializer
|
|
self.trainable = trainable
|
|
if dtype is None:
|
|
dtype = nn.tf_floatx
|
|
|
|
self.dtype = dtype
|
|
super().__init__(**kwargs)
|
|
|
|
def build_weights(self):
|
|
if self.maxout_ch > 1:
|
|
weight_shape = (self.in_ch,self.out_ch*self.maxout_ch)
|
|
else:
|
|
weight_shape = (self.in_ch,self.out_ch)
|
|
|
|
kernel_initializer = self.kernel_initializer
|
|
|
|
if self.use_wscale:
|
|
gain = 1.0
|
|
fan_in = np.prod( weight_shape[:-1] )
|
|
he_std = gain / np.sqrt(fan_in) # He init
|
|
self.wscale = tf.constant(he_std, dtype=self.dtype )
|
|
kernel_initializer = tf.initializers.random_normal(0, 1.0, dtype=self.dtype)
|
|
|
|
if kernel_initializer is None:
|
|
kernel_initializer = tf.initializers.glorot_uniform(dtype=self.dtype)
|
|
|
|
self.weight = tf.get_variable("weight", weight_shape, dtype=self.dtype, initializer=kernel_initializer, trainable=self.trainable )
|
|
|
|
if self.use_bias:
|
|
bias_initializer = self.bias_initializer
|
|
if bias_initializer is None:
|
|
bias_initializer = tf.initializers.zeros(dtype=self.dtype)
|
|
self.bias = tf.get_variable("bias", (self.out_ch,), dtype=self.dtype, initializer=bias_initializer, trainable=self.trainable )
|
|
|
|
def get_weights(self):
|
|
weights = [self.weight]
|
|
if self.use_bias:
|
|
weights += [self.bias]
|
|
return weights
|
|
|
|
def __call__(self, x):
|
|
weight = self.weight
|
|
if self.use_wscale:
|
|
weight = weight * self.wscale
|
|
|
|
x = tf.matmul(x, weight)
|
|
|
|
if self.maxout_ch > 1:
|
|
x = tf.reshape (x, (-1, self.out_ch, self.maxout_ch) )
|
|
x = tf.reduce_max(x, axis=-1)
|
|
|
|
if self.use_bias:
|
|
x = tf.add(x, tf.reshape(self.bias, (1,self.out_ch) ) )
|
|
|
|
return x
|
|
nn.Dense = Dense
|
|
|
|
class BatchNorm2D(LayerBase):
|
|
"""
|
|
currently not for training
|
|
"""
|
|
def __init__(self, dim, eps=1e-05, momentum=0.1, dtype=None, **kwargs):
|
|
self.dim = dim
|
|
self.eps = eps
|
|
self.momentum = momentum
|
|
if dtype is None:
|
|
dtype = nn.tf_floatx
|
|
self.dtype = dtype
|
|
super().__init__(**kwargs)
|
|
|
|
def build_weights(self):
|
|
self.weight = tf.get_variable("weight", (self.dim,), dtype=self.dtype, initializer=tf.initializers.ones() )
|
|
self.bias = tf.get_variable("bias", (self.dim,), dtype=self.dtype, initializer=tf.initializers.zeros() )
|
|
self.running_mean = tf.get_variable("running_mean", (self.dim,), dtype=self.dtype, initializer=tf.initializers.zeros(), trainable=False )
|
|
self.running_var = tf.get_variable("running_var", (self.dim,), dtype=self.dtype, initializer=tf.initializers.zeros(), trainable=False )
|
|
|
|
def get_weights(self):
|
|
return [self.weight, self.bias, self.running_mean, self.running_var]
|
|
|
|
def __call__(self, x):
|
|
if nn.data_format == "NHWC":
|
|
shape = (1,1,1,self.dim)
|
|
else:
|
|
shape = (1,self.dim,1,1)
|
|
|
|
weight = tf.reshape ( self.weight , shape )
|
|
bias = tf.reshape ( self.bias , shape )
|
|
running_mean = tf.reshape ( self.running_mean, shape )
|
|
running_var = tf.reshape ( self.running_var , shape )
|
|
|
|
x = (x - running_mean) / tf.sqrt( running_var + self.eps )
|
|
x *= weight
|
|
x += bias
|
|
return x
|
|
|
|
nn.BatchNorm2D = BatchNorm2D |