[GitHub] spidyDev closed pull request #10425: [MXNET-175] Raise user warning on mismatch between module data_names and data iter names

[GitBox <gi...@apache.org>]

spidyDev closed pull request #10425: [MXNET-175] Raise user warning on mismatch between module data_names and data iter names
URL: https://github.com/apache/incubator-mxnet/pull/10425
 
 
   

This is a PR merged from a forked repository.
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diff --git a/python/mxnet/io.py b/python/mxnet/io.py
index 884e9294741..3e25a2151ce 100644
--- a/python/mxnet/io.py
+++ b/python/mxnet/io.py
@@ -492,6 +492,7 @@ def getpad(self):
 def _init_data(data, allow_empty, default_name):
     """Convert data into canonical form."""
     assert (data is not None) or allow_empty
+    renamed_data = False
     if data is None:
         data = []
 
@@ -506,6 +507,9 @@ def _init_data(data, allow_empty, default_name):
         else:
             data = OrderedDict( # pylint: disable=redefined-variable-type
                 [('_%d_%s' % (i, default_name), d) for i, d in enumerate(data)])
+        # This is used to identify if the  data was originally a list, and was
+        # modified to a OrderedDict. Used when doing forward pass.
+        renamed_data = True
     if not isinstance(data, dict):
         raise TypeError("Input must be NDArray, numpy.ndarray, h5py.Dataset " + \
                 "a list of them or dict with them as values")
@@ -517,7 +521,7 @@ def _init_data(data, allow_empty, default_name):
                 raise TypeError(("Invalid type '%s' for %s, "  % (type(v), k)) + \
                                 "should be NDArray, numpy.ndarray or h5py.Dataset")
 
-    return list(sorted(data.items()))
+    return list(sorted(data.items())), renamed_data
 
 def _has_instance(data, dtype):
     """Return True if ``data`` has instance of ``dtype``.
@@ -645,8 +649,8 @@ def __init__(self, data, label=None, batch_size=1, shuffle=False,
                  label_name='softmax_label'):
         super(NDArrayIter, self).__init__(batch_size)
 
-        self.data = _init_data(data, allow_empty=False, default_name=data_name)
-        self.label = _init_data(label, allow_empty=True, default_name=label_name)
+        self.data, self.renamed_data = _init_data(data, allow_empty=False, default_name=data_name)
+        self.label, _ = _init_data(label, allow_empty=True, default_name=label_name)
 
         if ((_has_instance(self.data, CSRNDArray) or _has_instance(self.label, CSRNDArray)) and
                 (last_batch_handle != 'discard')):
diff --git a/python/mxnet/module/base_module.py b/python/mxnet/module/base_module.py
index c03f8e73cda..ed836d29c02 100644
--- a/python/mxnet/module/base_module.py
+++ b/python/mxnet/module/base_module.py
@@ -25,6 +25,7 @@
 
 from .. import metric
 from .. import ndarray
+from .. import io
 
 from ..context import cpu
 from ..model import BatchEndParam
@@ -78,6 +79,17 @@ def _parse_data_desc(data_names, label_names, data_shapes, label_shapes):
     return data_shapes, label_shapes
 
 
+def _check_data_names(eval_data, data_names):
+    """ Check if iterator data names match the data names provided in module"""
+    if isinstance(eval_data, io.NDArrayIter) and \
+            not eval_data.renamed_data and \
+            len(eval_data.data) and isinstance(eval_data.data[0], tuple) and \
+            dict(eval_data.data).keys() != data_names:
+        msg = "Data provided in data_names don't match names specified by iterator" \
+              " (%s vs. %s)"%(str(data_names), str(dict(eval_data.data).keys()))
+        warnings.warn(msg)
+
+
 class BaseModule(object):
     """The base class of a module.
 
@@ -244,6 +256,7 @@ def score(self, eval_data, eval_metric, num_batch=None, batch_end_callback=None,
         eval_metric.reset()
         actual_num_batch = 0
 
+        _check_data_names(eval_data, self.data_names)
         for nbatch, eval_batch in enumerate(eval_data):
             if num_batch is not None and nbatch == num_batch:
                 break
@@ -364,6 +377,7 @@ def predict(self, eval_data, num_batch=None, merge_batches=True, reset=True,
 
         output_list = []
 
+        _check_data_names(eval_data, self.data_names)
         for nbatch, eval_batch in enumerate(eval_data):
             if num_batch is not None and nbatch == num_batch:
                 break


 

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