[2/2] incubator-singa git commit: SINGA-369 the errors of examples in testing

[wa...@apache.org]

SINGA-369 the errors of examples in testing


Project: http://git-wip-us.apache.org/repos/asf/incubator-singa/repo
Commit: http://git-wip-us.apache.org/repos/asf/incubator-singa/commit/c5b454d1
Tree: http://git-wip-us.apache.org/repos/asf/incubator-singa/tree/c5b454d1
Diff: http://git-wip-us.apache.org/repos/asf/incubator-singa/diff/c5b454d1

Branch: refs/heads/master
Commit: c5b454d1e231c6cf80186b2c99c1b332188e1b98
Parents: 01ed877
Author: sheyujian <sh...@me.com>
Authored: Sun May 20 17:09:45 2018 +0800
Committer: sheyujian <sh...@me.com>
Committed: Sun May 20 17:09:45 2018 +0800

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 examples/imagenet/googlenet/serve.py | 2 +-
 python/singa/net.py                  | 8 +++++++-
 2 files changed, 8 insertions(+), 2 deletions(-)
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http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/c5b454d1/examples/imagenet/googlenet/serve.py
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diff --git a/examples/imagenet/googlenet/serve.py b/examples/imagenet/googlenet/serve.py
index 35b0c86..809e6cb 100644
--- a/examples/imagenet/googlenet/serve.py
+++ b/examples/imagenet/googlenet/serve.py
@@ -148,7 +148,7 @@ def create_net(shape, weight_path='bvlc_googlenet.pickle'):
     # layer
     for key, val in zip(net.param_names(), net.param_values()):
         # print key
-        if key == 'loss3/classifier_weight':
+        if key == 'loss3/classifier_weight' or key == 'loss3/classifier/weight':
             tmp = tensor.to_numpy(val)
             tmp = tmp.reshape(tmp.shape[::-1])
             val.copy_from_numpy(np.transpose(tmp))

http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/c5b454d1/python/singa/net.py
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diff --git a/python/singa/net.py b/python/singa/net.py
index 8d6bc85..12b0ead 100644
--- a/python/singa/net.py
+++ b/python/singa/net.py
@@ -239,15 +239,21 @@ class FeedForwardNet(object):
         output layers.
 
         Currently only support nets with a single output layer
+        TODO(yujian) to handle multiple outputs from the network
 
         Args:
             x: input data, a single input Tensor or a dict: layer name -> Tensor
 
         Returns:
             a single output tensor as the prediction result.
+
         '''
+
         xx = self.forward(kEval, x)
-        return tensor.softmax(xx)
+        if type(xx) is dict:
+            return tensor.softmax(list(xx.values())[0])
+        else:
+            return tensor.softmax(xx)
 
     def topo_sort(self, layers, src_of_layer):
         '''Topology sort of layers.