Made further fixes to model.py.

model.py

@@ -121,6 +121,10 @@ def _forward_impl(self, x):
     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 @@ def _forward_impl(self, x):
 
         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 @@ def _forward_impl(self, x):
 
         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):