CalvinBackup/iperov-DeepFaceLab
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Upgraded to TF version 1.13.2

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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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facelib/LandmarksProcessor.py
+25 -25
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@@ -263,29 +263,29 @@ def get_transform_mat (image_landmarks, output_size, face_type, scale=1.0, full_
tb_diag_vec /= npla.norm(tb_diag_vec)
bt_diag_vec = (l_p[1]-l_p[3]).astype(np.float32)
bt_diag_vec /= npla.norm(bt_diag_vec)
mod = (1.0 / scale)* ( npla.norm(l_p[0]-l_p[2])*(padding*np.sqrt(2.0) + 0.5) )
if not remove_align:
l_t = np.array( [ np.round( l_c - tb_diag_vec*mod ),
np.round( l_c + bt_diag_vec*mod ),
np.round( l_c + tb_diag_vec*mod ) ] )
l_t = np.array( [ np.round( l_c - tb_diag_vec*mod ),
np.round( l_c + bt_diag_vec*mod ),
np.round( l_c + tb_diag_vec*mod ) ] )
else:
l_t = np.array( [ np.round( l_c - tb_diag_vec*mod ),
np.round( l_c + bt_diag_vec*mod ),
l_t = np.array( [ np.round( l_c - tb_diag_vec*mod ),
np.round( l_c + bt_diag_vec*mod ),
np.round( l_c + tb_diag_vec*mod ),
np.round( l_c - bt_diag_vec*mod ),
np.round( l_c - bt_diag_vec*mod ),
] )
area = mathlib.polygon_area(l_t[:,0], l_t[:,1] )
side = np.float32(math.sqrt(area) / 2)
l_t = np.array( [ np.round( l_c + [-side,-side] ),
np.round( l_c + [ side,-side] ),
np.round( l_c + [ side, side] ) ] )
l_t = np.array( [ np.round( l_c + [-side,-side] ),
np.round( l_c + [ side,-side] ),
np.round( l_c + [ side, side] ) ] )
pts2 = np.float32(( (0,0),(output_size,0),(output_size,output_size) ))
mat = cv2.getAffineTransform(l_t,pts2)
#if remove_align:
# bbox = transform_points ( [ (0,0), (0,output_size), (output_size, output_size), (output_size,0) ], mat, True)
@@ -301,24 +301,24 @@ def get_transform_mat (image_landmarks, output_size, face_type, scale=1.0, full_
return mat
#if full_face_align_top and (face_type == FaceType.FULL or face_type == FaceType.FULL_NO_ALIGN):
# #lmrks2 = expand_eyebrows(image_landmarks)
# #lmrks2_ = transform_points( [ lmrks2[19], lmrks2[24] ], mat, False )
# #y_diff = np.float32( (0,np.min(lmrks2_[:,1])) )
# #lmrks2 = expand_eyebrows(image_landmarks)
# #lmrks2_ = transform_points( [ lmrks2[19], lmrks2[24] ], mat, False )
# #y_diff = np.float32( (0,np.min(lmrks2_[:,1])) )
# #y_diff = transform_points( [ np.float32( (0,0) ), y_diff], mat, True)
# #y_diff = y_diff[1]-y_diff[0]
#
#
# x_diff = np.float32((0,0))
#
# lmrks2_ = transform_points( [ image_landmarks[0], image_landmarks[16] ], mat, False )
#
# lmrks2_ = transform_points( [ image_landmarks[0], image_landmarks[16] ], mat, False )
# if lmrks2_[0,0] < 0:
# x_diff = lmrks2_[0,0]
# x_diff = lmrks2_[0,0]
# x_diff = transform_points( [ np.float32( (0,0) ), np.float32((x_diff,0)) ], mat, True)
# x_diff = x_diff[1]-x_diff[0]
# x_diff = x_diff[1]-x_diff[0]
# elif lmrks2_[1,0] >= output_size:
# x_diff = lmrks2_[1,0]-(output_size-1)
# x_diff = transform_points( [ np.float32( (0,0) ), np.float32((x_diff,0)) ], mat, True)
# x_diff = x_diff[1]-x_diff[0]
#
# x_diff = x_diff[1]-x_diff[0]
#
# mat = cv2.getAffineTransform( l_t+y_diff+x_diff ,pts2)
def expand_eyebrows(lmrks, eyebrows_expand_mod=1.0):
if len(lmrks) != 68:
@@ -687,5 +687,5 @@ def estimate_pitch_yaw_roll(aligned_256px_landmarks):
pitch = np.clip ( pitch, -math.pi, math.pi )
yaw = np.clip ( yaw , -math.pi, math.pi )
roll = np.clip ( roll, -math.pi, math.pi )
return -pitch, yaw, roll