Caffe 파이썬 테스트를 위해 수정할 것

caffe_test.diff

diff --git a/python/caffe/io.py b/python/caffe/io.py
index fc96266..02b2ffb 100644
--- a/python/caffe/io.py
+++ b/python/caffe/io.py
@@ -251,9 +251,13 @@ class Transformer:
                 ms = (1,) + ms
             if len(ms) != 3:
                 raise ValueError('Mean shape invalid')
-            if ms != self.inputs[in_][1:]:
-                raise ValueError('Mean shape incompatible with input shape.')
-        self.mean[in_] = mean
+        if ms != self.inputs[in_] :
+            print(self.inputs[in_])
+            in_shape = self.inputs[in_][1:]
+            m_min, m_max = mean.min(), mean.max()
+            normal_mean = (mean - m_min) / (m_max - m_min)
+            mean = resize_image(normal_mean.transpose((1,2,0)), in_shape[1:]).transpose((2,0,1)) * (m_max - m_min) + m_min  
+            self.mean[in_] = mean
 
     def set_input_scale(self, in_, scale):
         """
diff --git a/python/classify.py b/python/classify.py
index 4544c51..a5b1540 100755
--- a/python/classify.py
+++ b/python/classify.py
@@ -12,6 +12,7 @@ import glob
 import time
 
 import caffe
+import pandas as pd
 
 
 def main(argv):
@@ -86,6 +87,17 @@ def main(argv):
         help="Image file extension to take as input when a directory " +
              "is given as the input file."
     )
+    parser.add_argument(
+        "--print_results",
+        action='store_true',
+        help="Write output text to stdout rather than serializing to a file."
+    )
+    parser.add_argument(
+        "--labels_file",
+        default=os.path.join(pycaffe_dir,"../data/ilsvrc12/synset_words.txt"),
+        help="Readable label definition file."
+    )
+ 
     args = parser.parse_args()
 
     image_dims = [int(s) for s in args.images_dim.split(',')]
@@ -126,13 +138,25 @@ def main(argv):
 
     # Classify.
     start = time.time()
+    scores = classifier.predict(inputs, not args.center_only).flatten()
     predictions = classifier.predict(inputs, not args.center_only)
     print("Done in %.2f s." % (time.time() - start))
 
+    if args.print_results:
+      with open(args.labels_file) as f:
+        labels_df = pd.DataFrame([{'synset_id':l.strip().split(' ')[0], 'name': ' '.join(l.strip().split(' ')[1:]).split(',')[0]} for l in f.readlines()])
+        labels = labels_df.sort('synset_id')['name'].values
+        
+        indices =(-scores).argsort()[:5]
+        predictions = labels[indices]
+        
+        meta = [(p, '%.5f' % scores[i]) for i,p in zip(indices, predictions)]
+        print meta
+
     # Save
     print("Saving results into %s" % args.output_file)
     np.save(args.output_file, predictions)
-
+ 
 
 if __name__ == '__main__':
     main(sys.argv)