def testFeedIndexedSlicesWithoutDenseShape(self):
with session.Session() as s:
values = np.array([1.0, 2.0]).astype(np.float32)
indices = np.array([[3, 2, 0], [4, 5, 1]]).astype(np.int64)
dense_shape = None
ind = ops.IndexedSlices(
array_ops.placeholder(dtype=np.float32, shape=(2, )),
array_ops.placeholder(dtype=np.int64, shape=(2, 3)), None)
ind_values = array_ops.identity(ind.values)
ind_indices = array_ops.identity(ind.indices)
ind2 = ops.IndexedSlices(ind_values, ind_indices)
# Feed with tuple
values_out, indices_out = s.run([ind_values, ind_indices],
{ind: (values, indices)})
self.assertAllEqual(values_out, values)
self.assertAllEqual(indices_out, indices)
# Feed with IndexedSlicesValue
values_out, indices_out = s.run(
[ind_values, ind_indices],
{ind: ops.IndexedSlicesValue(values, indices, dense_shape)})
self.assertAllEqual(values_out, values)
self.assertAllEqual(indices_out, indices)
# Feed with IndexedSlicesValue, fetch IndexedSlicesValue
ind2_out = s.run(
ind2,
{ind: ops.IndexedSlicesValue(values, indices, dense_shape)})
self.assertAllEqual(ind2_out.values, values)
self.assertAllEqual(ind2_out.indices, indices)
self.assertAllEqual(ind2_out.dense_shape, dense_shape)
def _to_numpy(a):
"""Converts tensors to numpy arrays.
Converts Tensors and EagerTensors to numpy arrays.
When eager execution is enabled, converts IndexedSlices
to IndexedSlicesValue with numpy indices/values
Args:
a: any value.
Returns:
If a is EagerTensor or Tensor, returns the evaluation of a by calling
numpy() or run().
If a is IndexedSlices and eager execution is enabled, calls numpy() on a's
fields. Otherwise returns a unchanged.
"""
if isinstance(a, ops.EagerTensor):
return a.numpy()
if isinstance(a, ops.Tensor):
sess = ops.get_default_session()
return sess.run(a)
if isinstance(a, ops.IndexedSlices) and context.executing_eagerly():
return ops.IndexedSlicesValue(indices=[x.numpy() for x in a.indices],
values=[x.numpy() for x in a.values],
dense_shape=a.dense_shape)
return a
def _eval_indexed_slices(a):
"""Converts IndexedSlices to IndexedSlicesValue with numpy indices/values.
When eager execution is enabled, converts IndexedSlices
to IndexedSlicesValue with numpy indices/values.
Args:
a: any value.
Returns:
If a is IndexedSlices and eager execution is enabled, calls numpy() on a's
fields. Otherwise returns a unchanged.
"""
if isinstance(a, ops.IndexedSlices) and context.executing_eagerly():
return ops.IndexedSlicesValue(indices=[x.numpy() for x in a.indices],
values=[x.numpy() for x in a.values],
dense_shape=a.dense_shape)
return a
def _eval_tensor(self, tensor):
if tensor is None:
return None
elif callable(tensor):
return self._eval_helper(tensor())
else:
try:
if sparse_tensor.is_sparse(tensor):
return sparse_tensor.SparseTensorValue(
tensor.indices.numpy(), tensor.values.numpy(),
tensor.dense_shape.numpy())
elif ragged_tensor.is_ragged(tensor):
return ragged_tensor_value.RaggedTensorValue(
self._eval_tensor(tensor.values),
self._eval_tensor(tensor.row_splits))
elif isinstance(tensor, ops.IndexedSlices):
return ops.IndexedSlicesValue(
values=tensor.values.numpy(),
indices=tensor.indices.numpy(),
dense_shape=tensor.dense_shape.numpy())
return tensor.numpy()
except AttributeError as e:
ValueError("Unsupported type %s." % type(tensor)), e
def _get_indexed_slices_value_from_fetches(fetched_vals):
return ops.IndexedSlicesValue(fetched_vals[0], fetched_vals[1],
fetched_vals[2]
if len(fetched_vals) == 3 else None)
