mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-08-19 21:13:20 -07:00
Merge remote-tracking branch 'iperov/master'
This commit is contained in:
Vincent Riemer
commit
73c21eb335
13 changed files with 77 additions and 88 deletions
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@ -281,6 +281,13 @@ class DFLJPG(object):
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def has_xseg_mask(self):
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return self.dfl_dict.get('xseg_mask',None) is not None
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def get_xseg_mask_compressed(self):
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mask_buf = self.dfl_dict.get('xseg_mask',None)
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if mask_buf is None:
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return None
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return mask_buf
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def get_xseg_mask(self):
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mask_buf = self.dfl_dict.get('xseg_mask',None)
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if mask_buf is None:
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@ -301,7 +308,7 @@ class DFLJPG(object):
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mask_a = imagelib.normalize_channels(mask_a, 1)
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img_data = np.clip( mask_a*255, 0, 255 ).astype(np.uint8)
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data_max_len = 4096
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data_max_len = 8192
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ret, buf = cv2.imencode('.png', img_data)
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@ -29,11 +29,11 @@ More than 95% of deepfake videos are created with DeepFaceLab.
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DeepFaceLab is used by such popular youtube channels as
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| [Ctrl Shift Face](https://www.youtube.com/channel/UCKpH0CKltc73e4wh0_pgL3g)| [VFXChris Ume](https://www.youtube.com/channel/UCGf4OlX_aTt8DlrgiH3jN3g/videos)|
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| [VFXChris Ume](https://www.youtube.com/channel/UCGf4OlX_aTt8DlrgiH3jN3g/videos)| [Sham00k](https://www.youtube.com/channel/UCZXbWcv7fSZFTAZV4beckyw/videos)|
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|---|---|
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| [Sham00k](https://www.youtube.com/channel/UCZXbWcv7fSZFTAZV4beckyw/videos)| [Collider videos](https://www.youtube.com/watch?v=A91P2qtPT54&list=PLayt6616lBclvOprvrC8qKGCO-mAhPRux)| [iFake](https://www.youtube.com/channel/UCC0lK2Zo2BMXX-k1Ks0r7dg/videos)|
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|---|---|---|
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| [Collider videos](https://www.youtube.com/watch?v=A91P2qtPT54&list=PLayt6616lBclvOprvrC8qKGCO-mAhPRux)| [iFake](https://www.youtube.com/channel/UCC0lK2Zo2BMXX-k1Ks0r7dg/videos)|
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|---|---|
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| [NextFace](https://www.youtube.com/channel/UCFh3gL0a8BS21g-DHvXZEeQ/videos)| [Futuring Machine](https://www.youtube.com/channel/UCC5BbFxqLQgfnWPhprmQLVg)| [RepresentUS](https://www.youtube.com/channel/UCRzgK52MmetD9aG8pDOID3g)|
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@ -317,7 +317,7 @@ QQ 951138799
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</td></tr>
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<tr><td colspan=2 align="center">
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<a href="https://www.paypal.com/cgi-bin/webscr?cmd=_donations&business=lepersorium@gmail.com&lc=US&no_note=0&item_name=Support+DeepFaceLab&cn=&curency_code=USD&bn=PP-DonationsBF:btn_donateCC_LG.gif:NonHosted">Donate via Paypal</a>
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<a href="https://www.paypal.com/paypalme/DeepFaceLab">Donate via Paypal</a>
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</td></tr>
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<tr><td colspan=2 align="center">
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@ -70,19 +70,23 @@ class nn():
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first_run = True
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os.environ['CUDA_CACHE_PATH'] = str(compute_cache_path)
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os.environ['CUDA_CACHE_MAXSIZE'] = '536870912' #512Mb (32mb default)
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#nvcuda.dll ignores this param : os.environ['CUDA_CACHE_MAXSIZE'] = '536870912' #512Mb (32mb default)
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os.environ['TF_MIN_GPU_MULTIPROCESSOR_COUNT'] = '2'
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # tf log errors only
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # tf log errors only
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if first_run:
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io.log_info("Caching GPU kernels...")
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import tensorflow as tf
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nn.tf = tf
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#import tensorflow as tf
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import tensorflow.compat.v1 as tf
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import logging
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# Disable tensorflow warnings
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logging.getLogger('tensorflow').setLevel(logging.ERROR)
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tf_logger = logging.getLogger('tensorflow')
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tf_logger.setLevel(logging.ERROR)
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tf.disable_v2_behavior()
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nn.tf = tf
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# Initialize framework
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import core.leras.ops
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@ -333,7 +333,17 @@ def depth_to_space(x, size):
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x = tf.reshape(x, (-1, oh, ow, oc, ))
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return x
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else:
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b,c,h,w = x.shape.as_list()
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oh, ow = h * size, w * size
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oc = c // (size * size)
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x = tf.reshape(x, (-1, size, size, oc, h, w, ) )
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x = tf.transpose(x, (0, 3, 4, 1, 5, 2))
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x = tf.reshape(x, (-1, oc, oh, ow))
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return x
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return tf.depth_to_space(x, size, data_format=nn.data_format)
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nn.depth_to_space = depth_to_space
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def rgb_to_lab(srgb):
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@ -10,20 +10,22 @@ class RMSprop(nn.OptimizerBase):
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raise ValueError('name must be defined.')
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self.lr_dropout = lr_dropout
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self.lr = lr
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self.rho = rho
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self.epsilon = epsilon
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self.clipnorm = clipnorm
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with tf.device('/CPU:0') :
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with tf.variable_scope(self.name):
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self.lr = tf.Variable (lr, name="lr")
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self.rho = tf.Variable (rho, name="rho")
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self.epsilon = tf.Variable (epsilon, name="epsilon")
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self.iterations = tf.Variable(0, dtype=tf.int64, name='iters')
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self.accumulators_dict = {}
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self.lr_rnds_dict = {}
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def get_weights(self):
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return [self.lr, self.rho, self.epsilon, self.iterations] + list(self.accumulators_dict.values())
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return [self.iterations] + list(self.accumulators_dict.values())
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def initialize_variables(self, trainable_weights, vars_on_cpu=True, lr_dropout_on_cpu=False):
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# Initialize here all trainable variables used in training
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@ -53,13 +55,11 @@ class RMSprop(nn.OptimizerBase):
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a = self.accumulators_dict[ v.name ]
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rho = tf.cast(self.rho, a.dtype)
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new_a = rho * a + (1. - rho) * tf.square(g)
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new_a = self.rho * a + (1. - self.rho) * tf.square(g)
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lr = tf.cast(self.lr, a.dtype)
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epsilon = tf.cast(self.epsilon, a.dtype)
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lr = tf.constant(self.lr, g.dtype)
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v_diff = - lr * g / (tf.sqrt(new_a) + epsilon)
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v_diff = - lr * g / (tf.sqrt(new_a) + self.epsilon)
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if self.lr_dropout != 1.0:
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lr_rnd = self.lr_rnds_dict[v.name]
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v_diff *= lr_rnd
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@ -114,7 +114,7 @@ class ArrayFillerSubprocessor(Subprocessor):
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def __init__(self, sh_b, data_list ):
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self.sh_b = sh_b
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self.data_list = data_list
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super().__init__('ArrayFillerSubprocessor', ArrayFillerSubprocessor.Cli, 60)
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super().__init__('ArrayFillerSubprocessor', ArrayFillerSubprocessor.Cli, 60, io_loop_sleep_time=0.001)
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#override
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def process_info_generator(self):
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@ -124,7 +124,7 @@ class ArrayFillerSubprocessor(Subprocessor):
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#override
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def get_data(self, host_dict):
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if len(self.data_list) > 0:
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return self.data_list.pop(0)
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return self.data_list.pop(-1)
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return None
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Binary file not shown.
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Before Width: | Height: | Size: 1.2 MiB After Width: | Height: | Size: 1.3 MiB |
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@ -1,50 +0,0 @@
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{
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"folders": [
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{
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"path": "."
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}
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],
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"settings": {
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"workbench.colorTheme": "Visual Studio Light",
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"diffEditor.ignoreTrimWhitespace": true,
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"workbench.sideBar.location": "right",
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"breadcrumbs.enabled": false,
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"editor.renderWhitespace": "none",
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"editor.minimap.enabled": false,
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"workbench.activityBar.visible": true,
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"window.menuBarVisibility": "default",
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"editor.fastScrollSensitivity": 10,
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"editor.mouseWheelScrollSensitivity": 2,
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"window.zoomLevel": 0,
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"extensions.ignoreRecommendations": true,
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"python.linting.pylintEnabled": false,
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"python.linting.enabled": false,
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"python.linting.pylamaEnabled": false,
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"python.linting.pydocstyleEnabled": false,
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"python.pythonPath": "${env:PYTHON_EXECUTABLE}",
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"workbench.editor.tabCloseButton": "off",
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"workbench.editor.tabSizing": "shrink",
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"workbench.editor.highlightModifiedTabs": true,
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"editor.mouseWheelScrollSensitivity": 3,
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"editor.folding": false,
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"editor.glyphMargin": false,
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"files.exclude": {
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"**/__pycache__": true,
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"**/.github": true,
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"**/.vscode": true,
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"**/*.dat": true,
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"**/*.h5": true,
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"**/*.npy": true
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},
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"editor.quickSuggestions": {
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"other": false,
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"comments": false,
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"strings": false
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},
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"editor.trimAutoWhitespace": false,
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"python.linting.pylintArgs": [
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"--disable=import-error"
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]
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}
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}
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@ -1,9 +1,9 @@
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tqdm
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numpy==1.17.0
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numpy==1.19.3
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h5py==2.9.0
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opencv-python==4.1.0.25
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ffmpeg-python==0.1.17
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scikit-image==0.14.2
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scipy==1.4.1
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colorama
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tensorflow-gpu==1.13.2
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tensorflow-gpu==2.3.1
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@ -1,11 +1,10 @@
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tqdm
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numpy==1.17.0
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numpy==1.19.3
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h5py==2.9.0
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opencv-python==4.1.0.25
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ffmpeg-python==0.1.17
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scikit-image==0.14.2
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scipy==1.4.1
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colorama
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labelme==4.2.9
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tensorflow-gpu==1.13.2
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tensorflow-gpu==2.4.0rc1
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pyqt5
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@ -79,6 +79,9 @@ class Sample(object):
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self._filename_offset_size = None
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def has_xseg_mask(self):
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return self.xseg_mask is not None or self.xseg_mask_compressed is not None
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def get_xseg_mask(self):
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if self.xseg_mask_compressed is not None:
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xseg_mask = cv2.imdecode(self.xseg_mask_compressed, cv2.IMREAD_UNCHANGED)
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@ -26,11 +26,14 @@ class SampleGeneratorFaceXSeg(SampleGeneratorBase):
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samples = sum([ SampleLoader.load (SampleType.FACE, path) for path in paths ] )
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seg_sample_idxs = SegmentedSampleFilterSubprocessor(samples).run()
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seg_samples_len = len(seg_sample_idxs)
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if seg_samples_len == 0:
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raise Exception(f"No segmented faces found.")
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if len(seg_sample_idxs) == 0:
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seg_sample_idxs = SegmentedSampleFilterSubprocessor(samples, count_xseg_mask=True).run()
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if len(seg_sample_idxs) == 0:
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raise Exception(f"No segmented faces found.")
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else:
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io.log_info(f"Using {len(seg_sample_idxs)} xseg labeled samples.")
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else:
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io.log_info(f"Using {seg_samples_len} segmented samples.")
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io.log_info(f"Using {len(seg_sample_idxs)} segmented samples.")
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if self.debug:
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self.generators_count = 1
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@ -80,8 +83,16 @@ class SampleGeneratorFaceXSeg(SampleGeneratorBase):
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def gen_img_mask(sample):
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img = sample.load_bgr()
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h,w,c = img.shape
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mask = np.zeros ((h,w,1), dtype=np.float32)
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sample.seg_ie_polys.overlay_mask(mask)
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if sample.seg_ie_polys.has_polys():
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mask = np.zeros ((h,w,1), dtype=np.float32)
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sample.seg_ie_polys.overlay_mask(mask)
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elif sample.has_xseg_mask():
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mask = sample.get_xseg_mask()
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mask[mask < 0.5] = 0.0
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mask[mask >= 0.5] = 1.0
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else:
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raise Exception(f'no mask in sample {sample.filename}')
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if face_type == sample.face_type:
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if w != resolution:
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@ -158,9 +169,10 @@ class SampleGeneratorFaceXSeg(SampleGeneratorBase):
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class SegmentedSampleFilterSubprocessor(Subprocessor):
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#override
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def __init__(self, samples ):
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def __init__(self, samples, count_xseg_mask=False ):
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self.samples = samples
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self.samples_len = len(self.samples)
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self.count_xseg_mask = count_xseg_mask
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self.idxs = [*range(self.samples_len)]
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self.result = []
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@ -169,7 +181,7 @@ class SegmentedSampleFilterSubprocessor(Subprocessor):
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#override
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def process_info_generator(self):
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for i in range(multiprocessing.cpu_count()):
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yield 'CPU%d' % (i), {}, {'samples':self.samples}
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yield 'CPU%d' % (i), {}, {'samples':self.samples, 'count_xseg_mask':self.count_xseg_mask}
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#override
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def on_clients_initialized(self):
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@ -203,6 +215,10 @@ class SegmentedSampleFilterSubprocessor(Subprocessor):
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#overridable optional
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def on_initialize(self, client_dict):
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self.samples = client_dict['samples']
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self.count_xseg_mask = client_dict['count_xseg_mask']
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def process_data(self, idx):
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return idx, self.samples[idx].seg_ie_polys.get_pts_count() != 0
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if self.count_xseg_mask:
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return idx, self.samples[idx].has_xseg_mask()
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else:
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return idx, self.samples[idx].seg_ie_polys.get_pts_count() != 0
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@ -81,7 +81,7 @@ class SampleLoader:
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shape,
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landmarks,
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seg_ie_polys,
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xseg_mask,
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xseg_mask_compressed,
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eyebrows_expand_mod,
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source_filename ) = data
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@ -91,7 +91,7 @@ class SampleLoader:
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shape=shape,
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landmarks=landmarks,
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seg_ie_polys=seg_ie_polys,
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xseg_mask=xseg_mask,
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xseg_mask_compressed=xseg_mask_compressed,
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eyebrows_expand_mod=eyebrows_expand_mod,
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source_filename=source_filename,
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))
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@ -163,7 +163,7 @@ class FaceSamplesLoaderSubprocessor(Subprocessor):
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dflimg.get_shape(),
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dflimg.get_landmarks(),
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dflimg.get_seg_ie_polys(),
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dflimg.get_xseg_mask(),
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dflimg.get_xseg_mask_compressed(),
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dflimg.get_eyebrows_expand_mod(),
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dflimg.get_source_filename() )
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|
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Reference in a new issue