train baseline models

ajing · Jun 15, 2017 · 706d5c7 · 706d5c7
1 parent 04e69ab
commit 706d5c7
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Showing 7 changed files with 18,009 additions and 7 deletions.
diff --git a/probs/densenet121.txt b/probs/densenet121.txt
diff --git a/probs/densenet161.txt b/probs/densenet161.txt
diff --git a/probs/densenet169.txt b/probs/densenet169.txt
diff --git a/probs/resnet18_planet.txt b/probs/resnet18_planet.txt
diff --git a/probs/resnet34_planet.txt b/probs/resnet34_planet.txt
diff --git a/probs/resnet50_planet.txt b/probs/resnet50_planet.txt
diff --git a/util.py b/util.py
@@ -16,20 +16,22 @@ def save_results(models, dataloader):
     """Given model/models, this function saves the result of F.sigmoid(model(x))"""
     for model in models:
         name = str(model).split()[1]
-
         # create
-        model = model()
-        model = nn.DataParallel(model.cuda())
+        # net = model()
+        # net = nn.DataParallel(net.cuda())# nn.DataParallel(densenet169())
+        # net.load_state_dict(torch.load('models/%s.pth' % name)['state_dic'
+        net = torch.load('models/%s.pth' % name)
+        net.eval()
+        # model = nn.DataParallel(model.cuda())
 
         # load
-        model.load_state_dict(torch.load('models/{}.pth'.format(name)))
 
         # forward
         result = []
-        for i, (image, index) in enumerate(dataloader):
+        for i, (image, target, index) in enumerate(dataloader):
             image = Variable(image.cuda(), volatile=True)
             # N * 17
-            probs = F.sigmoid(model(image))
+            probs = F.sigmoid(net(image))
             result.append(probs.data.cpu().numpy())
 
         # concatenate the probabilities
@@ -239,4 +241,4 @@ def save_time(self, start_time, end_time):
     )
     dataloader = DataLoader(validation)
     save_results([resnet18_planet, resnet34_planet, resnet50_planet,
-        densenet121, densenet169, densenet161,], dataloader)
+        densenet121, densenet169, densenet161,], dataloader)