Add dropout

Author: hassony2

i3d_pt_demo.py

@@ -24,8 +24,8 @@ def get_scores(sample, model):
         print(
             'Top {} classes and associated probabilities: '.format(args.top_k))
         for i in range(args.top_k):
-            print('[{}]: {}'.format(kinetics_classes[top_idx[0, i]], top_val[
-                0, i]))
+            print('[{}]: {:.6E}'.format(kinetics_classes[top_idx[0, i]],
+                                        top_val[0, i]))
         return out_logit
 
     # Rung RGB model
@@ -55,11 +55,11 @@ def get_scores(sample, model):
         top_val, top_idx = torch.sort(out_softmax, 1, descending=True)
 
         print('===== Final predictions ====')
-        print('proba logits class '.format(args.top_k))
+        print('logits proba class '.format(args.top_k))
         for i in range(args.top_k):
             logit_score = out_logit[0, top_idx[0, i]].data[0]
-            print('{} {} {}'.format(logit_score, top_val[0, i],
-                                    kinetics_classes[top_idx[0, i]]))
+            print('{:.6e} {:.6e} {}'.format(logit_score, top_val[0, i],
+                                            kinetics_classes[top_idx[0, i]]))
 
 
 if __name__ == "__main__":

src/i3dpt.py

@@ -159,7 +159,11 @@ def forward(self, inp):
 
 
 class I3D(torch.nn.Module):
-    def __init__(self, num_classes, modality='rgb', name='inception'):
+    def __init__(self,
+                 num_classes,
+                 modality='rgb',
+                 dropout_keep_prob=1,
+                 name='inception'):
         super(I3D, self).__init__()
 
         self.name = name
@@ -223,6 +227,7 @@ def __init__(self, num_classes, modality='rgb', name='inception'):
         self.mixed_5c = Mixed(832, [384, 192, 384, 48, 128, 128])
 
         self.avg_pool = torch.nn.AvgPool3d((2, 7, 7), (1, 1, 1))
+        self.dropout = torch.nn.Dropout(dropout_keep_prob)
         self.conv3d_0c_1x1 = Unit3Dpy(
             in_channels=1024,
             out_channels=self.num_classes,
@@ -252,6 +257,7 @@ def forward(self, inp):
         out = self.mixed_5b(out)
         out = self.mixed_5c(out)
         out = self.avg_pool(out)
+        out = self.dropout(out)
         out = self.conv3d_0c_1x1(out)
         out = out.squeeze(3)
         out = out.squeeze(3)