make places demo compatible with pytorch0.4

run_placesCNN_basic.py

@@ -15,28 +15,18 @@
 arch = 'resnet18'
 
 # load the pre-trained weights
-model_file = 'whole_%s_places365_python36.pth.tar' % arch
+model_file = '%s_places365.pth.tar' % arch
 if not os.access(model_file, os.W_OK):
     weight_url = 'http://places2.csail.mit.edu/models_places365/' + model_file
     os.system('wget ' + weight_url)
 
-useGPU = 1
-if useGPU == 1:
-    model = torch.load(model_file)
-else:
-    model = torch.load(model_file, map_location=lambda storage, loc: storage) # model trained in GPU could be deployed in CPU machine like this!
-
-## assume all the script in python36, so the following is not necessary
-## if you encounter the UnicodeDecodeError when use python3 to load the model, add the following line will fix it. Thanks to @soravux
-#from functools import partial
-#import pickle
-#pickle.load = partial(pickle.load, encoding="latin1")
-#pickle.Unpickler = partial(pickle.Unpickler, encoding="latin1")
-#model = torch.load(model_file, map_location=lambda storage, loc: storage, pickle_module=pickle)
-#torch.save(model, 'whole_%s_places365_python36.pth.tar'%arch)
-
+model = models.__dict__[arch](num_classes=365)
+checkpoint = torch.load(model_file, map_location=lambda storage, loc: storage)
+state_dict = {str.replace(k,'module.',''): v for k,v in checkpoint['state_dict'].items()}
+model.load_state_dict(state_dict)
 model.eval()
 
+
 # load the image transformer
 centre_crop = trn.Compose([
         trn.Resize((256,256)),
@@ -63,7 +53,7 @@
     os.system('wget ' + img_url)
 
 img = Image.open(img_name)
-input_img = V(centre_crop(img).unsqueeze(0), volatile=True)
+input_img = V(centre_crop(img).unsqueeze(0))
 
 # forward pass
 logit = model.forward(input_img)

run_placesCNN_unified.py

@@ -86,15 +86,19 @@ def returnTF():
 def load_model():
     # this model has a last conv feature map as 14x14
 
-    model_file = 'whole_wideresnet18_places365_python36.pth.tar'
+    model_file = 'wideresnet18_places365.pth.tar'
     if not os.access(model_file, os.W_OK):
         os.system('wget http://places2.csail.mit.edu/models_places365/' + model_file)
         os.system('wget https://raw.githubusercontent.com/csailvision/places365/master/wideresnet.py')
-    useGPU = 0
-    if useGPU == 1:
-        model = torch.load(model_file)
-    else:
-        model = torch.load(model_file, map_location=lambda storage, loc: storage) # allow cpu
+
+    import wideresnet
+    model = wideresnet.resnet18(num_classes=365)
+    checkpoint = torch.load(model_file, map_location=lambda storage, loc: storage)
+    state_dict = {str.replace(k,'module.',''): v for k,v in checkpoint['state_dict'].items()}
+    model.load_state_dict(state_dict)
+    model.eval()
+
+
 
     # the following is deprecated, everything is migrated to python36
 
@@ -132,17 +136,19 @@ def load_model():
 img_url = 'http://places2.csail.mit.edu/imgs/12.jpg'
 os.system('wget %s -q -O test.jpg' % img_url)
 img = Image.open('test.jpg')
-input_img = V(tf(img).unsqueeze(0), volatile=True)
+input_img = V(tf(img).unsqueeze(0))
 
 # forward pass
 logit = model.forward(input_img)
 h_x = F.softmax(logit, 1).data.squeeze()
 probs, idx = h_x.sort(0, True)
+probs = probs.numpy()
+idx = idx.numpy()
 
 print('RESULT ON ' + img_url)
 
 # output the IO prediction
-io_image = np.mean(labels_IO[idx[:10].numpy()]) # vote for the indoor or outdoor
+io_image = np.mean(labels_IO[idx[:10]]) # vote for the indoor or outdoor
 if io_image < 0.5:
     print('--TYPE OF ENVIRONMENT: indoor')
 else:

wideresnet.py

@@ -115,8 +115,10 @@ def __init__(self, block, layers, num_classes=1000):
                 n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                 m.weight.data.normal_(0, math.sqrt(2. / n))
             elif isinstance(m, nn.BatchNorm2d):
-                m.weight.data.fill_(1)
-                m.bias.data.zero_()
+                #m.weight.data.fill_(1)
+                #m.bias.data.zero_()
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
 
     def _make_layer(self, block, planes, blocks, stride=1):
         downsample = None