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Made further fixes to model.py.

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Emiliana 2020-09-24 18:46:56 +02:00 • committed by GitHub
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commit cfe2e2f12e
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1 changed files with 18 additions and 14 deletions
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model.py
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@ -121,6 +121,10 @@ class OpenSeeFaceDetect(geffnet.mobilenetv3.MobileNetV3):
def forward(self, x):
return self._forward_impl(x)
def logit_arr(p, factor=16.0):
p = p.clamp(0.0000001, 0.9999999)
return torch.log(p / (1 - p)) / factor
# Landmark detection model
# Models:
# 0: "small", 0.5
@ -162,16 +166,16 @@ class OpenSeeFaceLandmarks(geffnet.mobilenetv3.MobileNetV3):
if self.inference:
t_main = x[:, 0:66].reshape((-1, 66, 28*28))
t_m = t_main.argmax(dim=1)
indices = t_m.unsqueeze(1)
t_conf = t_main.gather(1, indices).squeeze(1)
t_off_x = x[:, 66:132].reshape((-1, 66, 28*28)).gather(1, indices).squeeze(1)
t_off_y = x[:, 132:198].reshape((-1, 66, 28*28)).gather(1, indices).squeeze(1)
t_m = t_main.argmax(dim=2)
indices = t_m.unsqueeze(2)
t_conf = t_main.gather(2, indices).squeeze(2)
t_off_x = x[:, 66:132].reshape((-1, 66, 28*28)).gather(2, indices).squeeze(2)
t_off_y = x[:, 132:198].reshape((-1, 66, 28*28)).gather(2, indices).squeeze(2)
t_off_x = (223. * logit_arr(t_off_x) + 0.5).floor()
t_off_y = (223. * logit_arr(t_off_y) + 0.5).floor()
t_x = 223. * (t_m / 28.).floor() / 27. + t_off_x
t_y = 223. * t_m.remainder(28.).float() / 27. + t_off_y
x = (t_conf.mean(), torch.stack([t_x, t_y, t_conf], 1))
x = (t_conf.mean(1), torch.stack([t_x, t_y, t_conf], 2))
return x
def forward(self, x):
@ -204,16 +208,16 @@ class OpenSeeFaceLandmarks30Pt(geffnet.mobilenetv3.MobileNetV3):
if self.inference:
t_main = x[:, 0:30].reshape((-1, 30, 7*7))
t_m = t_main.argmax(dim=1)
indices = t_m.unsqueeze(1)
t_conf = t_main.gather(1, indices).squeeze(1)
t_off_x = x[:, 30:60].reshape((-1, 30, 7*7)).gather(1, indices).squeeze(1)
t_off_y = x[:, 60:90].reshape((-1, 30, 7*7)).gather(1, indices).squeeze(1)
t_off_x = 55. * logit_arr(t_off_x)
t_off_y = 55. * logit_arr(t_off_y)
t_m = t_main.argmax(dim=2)
indices = t_m.unsqueeze(2)
t_conf = t_main.gather(2, indices).squeeze(2)
t_off_x = x[:, 30:60].reshape((-1, 30, 7*7)).gather(2, indices).squeeze(2)
t_off_y = x[:, 60:90].reshape((-1, 30, 7*7)).gather(2, indices).squeeze(2)
t_off_x = 55. * logit_arr(t_off_x, factor=8.0)
t_off_y = 55. * logit_arr(t_off_y, factor=8.0)
t_x = 55. * (t_m / 7.).floor() / 6. + t_off_x
t_y = 55. * t_m.remainder(7.).float() / 6. + t_off_y
x = (t_conf.mean(), torch.stack([t_x, t_y, t_conf], 1))
x = (t_conf.mean(1), torch.stack([t_x, t_y, t_conf], 2))
return x
def forward(self, x):