removing all references to Blob.num property (that assumes Blob is 4D). Replacing it with accessing Blob.shape[0] - for Blobs with num_axes() != 4

python/caffe/pycaffe.py

@@ -98,7 +98,7 @@ def _Net_forward(self, blobs=None, start=None, end=None, **kwargs):
         # Set input according to defined shapes and make arrays single and
         # C-contiguous as Caffe expects.
         for in_, blob in kwargs.iteritems():
-            if blob.shape[0] != self.blobs[in_].num:
+            if blob.shape[0] != self.blobs[in_].shape[0]:
                 raise Exception('Input is not batch sized')
             self.blobs[in_].data[...] = blob
 
@@ -146,7 +146,7 @@ def _Net_backward(self, diffs=None, start=None, end=None, **kwargs):
         # Set top diffs according to defined shapes and make arrays single and
         # C-contiguous as Caffe expects.
         for top, diff in kwargs.iteritems():
-            if diff.shape[0] != self.blobs[top].num:
+            if diff.shape[0] != self.blobs[top].shape[0]:
                 raise Exception('Diff is not batch sized')
             self.blobs[top].diff[...] = diff
 
@@ -257,7 +257,7 @@ def _Net_batch(self, blobs):
     batch: {blob name: list of blobs} dict for a single batch.
     """
     num = len(blobs.itervalues().next())
-    batch_size = self.blobs.itervalues().next().num
+    batch_size = self.blobs.itervalues().next().shape[0]
     remainder = num % batch_size
     num_batches = num / batch_size