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Upgraded to TF version 1.13.2

Browse source 
Removed the wait at first launch for most graphics cards.

Increased speed of training by 10-20%, but you have to retrain all models from scratch.

SAEHD:

added option 'use float16'
	Experimental option. Reduces the model size by half.
	Increases the speed of training.
	Decreases the accuracy of the model.
	The model may collapse or not train.
	Model may not learn the mask in large resolutions.

true_face_training option is replaced by
"True face power". 0.0000 .. 1.0
Experimental option. Discriminates the result face to be more like the src face. Higher value - stronger discrimination.
Comparison - https://i.imgur.com/czScS9q.png
This commit is contained in:
Colombo 2020-01-25 21:58:19 +04:00
parent a3dfcb91b9
commit 76ca79216e
49 changed files with 1320 additions and 1297 deletions
Download patch file Download diff file Expand all files Collapse all files

15
facelib/FANExtractor.py
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@ -18,7 +18,7 @@ class FANExtractor(object):
if not model_path.exists():
raise Exception("Unable to load FANExtractor model")
nn.initialize()
nn.initialize(data_format="NHWC")
tf = nn.tf
class ConvBlock(nn.ModelBase):
@ -29,10 +29,10 @@ class FANExtractor(object):
self.bn1 = nn.BatchNorm2D(in_planes)
self.conv1 = nn.Conv2D (in_planes, out_planes/2, kernel_size=3, strides=1, padding='SAME', use_bias=False )
self.bn2 = nn.BatchNorm2D(out_planes/2)
self.bn2 = nn.BatchNorm2D(out_planes//2)
self.conv2 = nn.Conv2D (out_planes/2, out_planes/4, kernel_size=3, strides=1, padding='SAME', use_bias=False )
self.bn3 = nn.BatchNorm2D(out_planes/4)
self.bn3 = nn.BatchNorm2D(out_planes//4)
self.conv3 = nn.Conv2D (out_planes/4, out_planes/4, kernel_size=3, strides=1, padding='SAME', use_bias=False )
if self.in_planes != self.out_planes:
@ -55,6 +55,7 @@ class FANExtractor(object):
x = self.bn3(x)
x = tf.nn.relu(x)
x = out3 = self.conv3(x)
x = tf.concat ([out1, out2, out3], axis=-1)
if self.in_planes != self.out_planes:
@ -148,7 +149,9 @@ class FANExtractor(object):
if i < 4 - 1:
ll = self.bl[i](ll)
previous = previous + ll + self.al[i](tmp_out)
return outputs[-1]
x = outputs[-1]
x = tf.transpose(x, (0,3,1,2) )
return x
e = None
if place_model_on_cpu:
@ -159,7 +162,7 @@ class FANExtractor(object):
self.model.load_weights(str(model_path))
if e is not None: e.__exit__(None,None,None)
self.model.build_for_run ([ ( tf.float32, (256,256,3) ) ])
self.model.build_for_run ([ ( tf.float32, (None,256,256,3) ) ])
def extract (self, input_image, rects, second_pass_extractor=None, is_bgr=True, multi_sample=False):
if len(rects) == 0:
@ -197,7 +200,7 @@ class FANExtractor(object):
predicted = []
for i in range( len(images) ):
predicted += [ self.model.run ( [ images[i][None,...] ] ).transpose (0,3,1,2)[0] ]
predicted += [ self.model.run ( [ images[i][None,...] ] )[0] ]
predicted = np.stack(predicted)