diff --git a/python/paddle/fluid/data_feeder.py b/python/paddle/fluid/data_feeder.py index c280ff21eec8d1a90b8be9102d7eae119f38f2b1..7b70d19de5ca309441bdc1404e6e601af3c5b892 100644 --- a/python/paddle/fluid/data_feeder.py +++ b/python/paddle/fluid/data_feeder.py @@ -71,10 +71,25 @@ class DataToLoDTensorConverter(object): for each_data in data: self._feed_impl_(each_data, lod[1:], lod_level - 1) + def _check_shape(self, shape): + for s1, s2 in zip(self.shape, shape): + if s1 != s2 and s1 >= 0 and s2 >= 0: + raise ValueError( + "Shape not match. What is defined in data layer is {}, but receive {}". + format(self.shape, shape)) + def done(self): arr = numpy.array(self.data, dtype=self.dtype) - if self.shape and len(arr.shape) != len(self.shape): - arr = arr.reshape(self.shape) + if self.shape: + if len(arr.shape) != len(self.shape): + try: + arr = arr.reshape(self.shape) + except ValueError: + raise ValueError( + "Reshape error. What is defined in data layer is {}, but receive {}" + .format(self.shape, arr.shape)) + else: + self._check_shape(arr.shape) t = core.LoDTensor() t.set(arr, self.place) if self.lod_level > 0: @@ -152,17 +167,8 @@ class DataFeeder(object): raise TypeError("Feed list should contain a list of variable") self.feed_dtypes.append(each_var.dtype) self.feed_names.append(each_var.name) - shape = each_var.shape - batch_size_dim = -1 - for i, s in enumerate(shape): - if s < 0: - batch_size_dim = i - break - if batch_size_dim == -1: - raise ValueError("Variable {0} must has a batch size dimension", - each_var.name) self.feed_lod_level.append(each_var.lod_level) - self.feed_shapes.append(shape) + self.feed_shapes.append(each_var.shape) self.place = place diff --git a/python/paddle/fluid/tests/test_data_feeder.py b/python/paddle/fluid/tests/test_data_feeder.py index 01de564aa438e5f14a5c578f7bbbfb475155ca55..16a33fd3ab3c794494687ba39278e327560686ec 100644 --- a/python/paddle/fluid/tests/test_data_feeder.py +++ b/python/paddle/fluid/tests/test_data_feeder.py @@ -30,6 +30,12 @@ class TestDataFeeder(unittest.TestCase): self.assertEqual(result['image'].recursive_sequence_lengths(), []) self.assertEqual(result['label'].recursive_sequence_lengths(), []) + try: + result = feeder.feed([([0] * 783, [9]), ([1] * 783, [1])]) + self.assertTrue(False) + except ValueError: + self.assertTrue(True) + def test_lod_level_1_converter(self): # lod_level = 1 # each sentence has a different number of words