Module predict API can accept NDArray as input

python/mxnet/module/base_module.py

@@ -22,14 +22,15 @@
 import time
 import logging
 import warnings
+import numpy as np
 
 from .. import metric
 from .. import ndarray
 
 from ..context import cpu
 from ..model import BatchEndParam
 from ..initializer import Uniform
-from ..io import DataDesc
+from ..io import DataDesc, DataIter, DataBatch
 from ..base import _as_list
 
 
@@ -333,7 +334,7 @@ def predict(self, eval_data, num_batch=None, merge_batches=True, reset=True,
 
         Parameters
         ----------
-        eval_data : DataIter
+        eval_data : DataIter or NDArray or numpy array
             Evaluation data to run prediction on.
         num_batch : int
             Defaults to ``None``, indicates running all the batches in the data iterator.
@@ -363,6 +364,15 @@ def predict(self, eval_data, num_batch=None, merge_batches=True, reset=True,
         """
         assert self.binded and self.params_initialized
 
+        if isinstance(eval_data, (ndarray.NDArray, np.ndarray)):
+            if isinstance(eval_data, np.ndarray):
+                eval_data = ndarray.array(eval_data)
+            self.forward(DataBatch([eval_data]))
+            return self.get_outputs()[0]
+
+        if not isinstance(eval_data, DataIter):
+            raise ValueError('eval_data must be of type NDArray or DataIter')
+
         if reset:
             eval_data.reset()
 

tests/python/unittest/test_module.py

@@ -772,6 +772,8 @@ def test_forward_reshape():
              for_training=False, force_rebind=True)
     assert mod.predict(pred_dataiter).shape == tuple([10, num_class])
 
+@with_seed()
+def test_forward_types():
     #Test forward with other data batch API
     Batch = namedtuple('Batch', ['data'])
     data = mx.sym.Variable('data')
@@ -786,6 +788,18 @@ def test_forward_reshape():
     mod.forward(Batch(data2))
     assert mod.get_outputs()[0].shape == (3, 5)
 
+    #Test forward with other NDArray and np.ndarray inputs
+    data = mx.sym.Variable('data')
+    out = data * 2
+    mod = mx.mod.Module(symbol=out, label_names=None)
+    mod.bind(data_shapes=[('data', (1, 10))])
+    mod.init_params()
+    data1 = mx.nd.ones((1, 10))
+    assert mod.predict(data1).shape == (1, 10)
+    data2 = np.ones((1, 10))
+    assert mod.predict(data1).shape == (1, 10)
+
+
 
 if __name__ == '__main__':
     import nose