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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
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36
samplelib/SampleProcessor.py
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@ -37,7 +37,7 @@ opts:
'resolution' : N
'motion_blur' : (chance_int, range) - chance 0..100 to apply to face (not mask), and max_size of motion blur
'ct_mode' :
'ct_mode' :
'normalize_tanh' : bool
"""
@ -94,11 +94,11 @@ class SampleProcessor(object):
@staticmethod
def process (samples, sample_process_options, output_sample_types, debug, ct_sample=None):
SPTF = SampleProcessor.Types
sample_rnd_seed = np.random.randint(0x80000000)
outputs = []
for sample in samples:
for sample in samples:
sample_bgr = sample.load_bgr()
ct_sample_bgr = None
ct_sample_mask = None
@ -123,9 +123,11 @@ class SampleProcessor(object):
normalize_vgg = opts.get('normalize_vgg', False)
motion_blur = opts.get('motion_blur', None)
gaussian_blur = opts.get('gaussian_blur', None)
ct_mode = opts.get('ct_mode', 'None')
normalize_tanh = opts.get('normalize_tanh', False)
data_format = opts.get('data_format', 'NHWC')
img_type = SPTF.NONE
target_face_type = SPTF.NONE
@ -149,7 +151,7 @@ class SampleProcessor(object):
img = l
elif img_type == SPTF.IMG_PITCH_YAW_ROLL or img_type == SPTF.IMG_PITCH_YAW_ROLL_SIGMOID:
pitch_yaw_roll = sample.get_pitch_yaw_roll()
if params['flip']:
yaw = -yaw
@ -174,7 +176,7 @@ class SampleProcessor(object):
if len(mask.shape) == 2:
mask = mask[...,np.newaxis]
return img, mask
img = sample_bgr
@ -202,7 +204,7 @@ class SampleProcessor(object):
if gaussian_blur is not None:
chance, kernel_max_size = gaussian_blur
chance = np.clip(chance, 0, 100)
if np.random.randint(100) < chance:
img = cv2.GaussianBlur(img, ( np.random.randint( kernel_max_size )*2+1 ,) *2 , 0)
@ -221,7 +223,7 @@ class SampleProcessor(object):
img = cv2.resize( img, (resolution,resolution), cv2.INTER_CUBIC )
else:
img, mask = do_transform (img, mask)
mat = LandmarksProcessor.get_transform_mat (sample.landmarks, resolution, target_ft)
img = cv2.warpAffine( img, mat, (resolution,resolution), borderMode=(cv2.BORDER_REPLICATE if border_replicate else cv2.BORDER_CONSTANT), flags=cv2.INTER_CUBIC )
mask = cv2.warpAffine( mask, mat, (resolution,resolution), borderMode=cv2.BORDER_CONSTANT, flags=cv2.INTER_CUBIC )
@ -256,7 +258,7 @@ class SampleProcessor(object):
img_bgr = imagelib.reinhard_color_transfer ( np.clip( (img_bgr*255).astype(np.uint8), 0, 255),
np.clip( (ct_sample_bgr_resized*255).astype(np.uint8), 0, 255) )
img_bgr = np.clip( img_bgr.astype(np.float32) / 255.0, 0.0, 1.0)
elif ct_mode == 'mkl':
elif ct_mode == 'mkl':
img_bgr = imagelib.color_transfer_mkl (img_bgr, ct_sample_bgr_resized)
elif ct_mode == 'idt':
img_bgr = imagelib.color_transfer_idt (img_bgr, ct_sample_bgr_resized)
@ -271,21 +273,21 @@ class SampleProcessor(object):
img_bgr[:,:,0] -= 103.939
img_bgr[:,:,1] -= 116.779
img_bgr[:,:,2] -= 123.68
if mode_type == SPTF.MODE_BGR:
img = img_bgr
elif mode_type == SPTF.MODE_BGR_SHUFFLE:
rnd_state = np.random.RandomState (sample_rnd_seed)
img = np.take (img_bgr, rnd_state.permutation(img_bgr.shape[-1]), axis=-1)
elif mode_type == SPTF.MODE_BGR_RANDOM_HSV_SHIFT:
rnd_state = np.random.RandomState (sample_rnd_seed)
hsv = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2HSV)
h, s, v = cv2.split(hsv)
h, s, v = cv2.split(hsv)
h = (h + rnd_state.randint(360) ) % 360
s = np.clip ( s + rnd_state.random()-0.5, 0, 1 )
v = np.clip ( v + rnd_state.random()-0.5, 0, 1 )
hsv = cv2.merge([h, s, v])
hsv = cv2.merge([h, s, v])
img = np.clip( cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR) , 0, 1 )
elif mode_type == SPTF.MODE_G:
img = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY)[...,None]
@ -300,9 +302,13 @@ class SampleProcessor(object):
else:
img = np.clip (img, 0.0, 1.0)
if data_format == "NCHW":
img = np.transpose(img, (2,0,1) )
outputs_sample.append ( img )
outputs += [outputs_sample]
return outputs
"""