add caffe_predict to test

image_classification/caffe2paddle.py

@@ -205,10 +205,35 @@ class ModelConverter(object):
             self.params[file_name] = (param_conf, data.flatten())
         return name
 
+    def caffe_predict(self,
+                      img,
+                      mean_file='./caffe/imagenet/ilsvrc_2012_mean.npy'):
+        net = self.net
+
+        mu = np.load(mean_file)
+        mu = mu.mean(1).mean(1)
+
+        transformer = caffe.io.Transformer({
+            'data': net.blobs['data'].data.shape
+        })
+        transformer.set_transpose('data', (2, 0, 1))
+        transformer.set_mean('data', mu)
+        transformer.set_raw_scale('data', 255)
+        transformer.set_channel_swap('data', (2, 1, 0))
+        im = caffe.io.load_image(img)
+
+        net.blobs['data'].data[...] = transformer.preprocess('data', im)
+        out = net.forward()
+
+        output_prob = net.blobs['prob'].data[0].flatten()
+        print np.sort(output_prob)[::-1]
+        print np.argsort(output_prob)[::-1]
+        print 'predicted class is:', output_prob.argmax()
+
 
 if __name__ == "__main__":
     converter = ModelConverter("./VGG_ILSVRC_16_layers_deploy.prototxt",
                                "./VGG_ILSVRC_16_layers.caffemodel",
-                               "/Users/baidu/caffe/caffe/python/paddle_model",
                                "test_vgg16.tar.gz")
     converter.convert()
+    converter.caffe_predict(img='./caffe/examples/images/cat.jpg')