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2nd pass of extractor : speed significantly increased

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iperov 2019-03-08 16:05:24 +04:00
parent 8f7b268376
commit 0cc251a9ab
3 changed files with 9 additions and 3 deletions
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10
__dev/port.py
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@ -12,7 +12,7 @@ import os
import time import time
import code import code
class TorchBatchNorm2D(keras.engine.topology.Layer): class TorchBatchNorm2D(keras.engine.Layer):
def __init__(self, axis=-1, momentum=0.99, epsilon=1e-3, **kwargs): def __init__(self, axis=-1, momentum=0.99, epsilon=1e-3, **kwargs):
super(TorchBatchNorm2D, self).__init__(**kwargs) super(TorchBatchNorm2D, self).__init__(**kwargs)
self.supports_masking = True self.supports_masking = True
@ -71,6 +71,7 @@ fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D,enable_cuda=F
fa.eval() fa.eval()
def KerasConvBlock(in_planes, out_planes, input, srctorch): def KerasConvBlock(in_planes, out_planes, input, srctorch):
out1 = TorchBatchNorm2D(axis=1, momentum=0.1, epsilon=1e-05, weights=t2kw_bn2d(srctorch.bn1) )(input) out1 = TorchBatchNorm2D(axis=1, momentum=0.1, epsilon=1e-05, weights=t2kw_bn2d(srctorch.bn1) )(input)
out1 = KL.Activation( keras.backend.relu ) (out1) out1 = KL.Activation( keras.backend.relu ) (out1)
@ -156,6 +157,11 @@ else:
model.save (model_path) model.save (model_path)
model.save_weights ( os.path.join( os.path.dirname(__file__) , 'weights.h5') ) model.save_weights ( os.path.join( os.path.dirname(__file__) , 'weights.h5') )
model_short = keras.models.Model (_input, outputs[0])
model_short.compile ( loss='mse', optimizer='adam' )
model_short.save ( os.path.join( os.path.dirname(__file__) , "2DFAN-4_light.h5" ) )
model_short.save_weights ( os.path.join( os.path.dirname(__file__) , '_light_weights.h5') )
def transform(point, center, scale, resolution, invert=False): def transform(point, center, scale, resolution, invert=False):
_pt = torch.ones(3) _pt = torch.ones(3)
_pt[0] = point[0] _pt[0] = point[0]
@ -268,7 +274,7 @@ pts_img = pts_img.view(68, 2).numpy()
m_pts_img = get_preds_fromhm2 (m_out[0], center, scale) m_pts_img = get_preds_fromhm2 (m_out[0], center, scale)
print ('pts1 == pts2 == %s' % ( np.array_equal(pts_img, m_pts_img) ) ) print ('pts1 == pts2 == %s' % ( np.array_equal(pts_img, m_pts_img) ) )
import code
code.interact(local=dict(globals(), **locals())) code.interact(local=dict(globals(), **locals()))
#print ( np.array_equal (fa_out, m_out) ) #>>> False #print ( np.array_equal (fa_out, m_out) ) #>>> False

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facelib/2DFAN-4.h5
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2
facelib/LandmarksExtractor.py
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@ -121,7 +121,7 @@ class LandmarksExtractor(object):
predicted = self.keras_model.predict (image) predicted = self.keras_model.predict (image)
pts_img = get_pts_from_predict ( predicted[-1][0], center, scale) pts_img = get_pts_from_predict ( predicted[-1], center, scale)
pts_img = [ ( int(pt[0]), int(pt[1]) ) for pt in pts_img ] pts_img = [ ( int(pt[0]), int(pt[1]) ) for pt in pts_img ]
landmarks.append ( ( (left, top, right, bottom),pts_img ) ) landmarks.append ( ( (left, top, right, bottom),pts_img ) )