# Copyright 2015 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Tests for tensorflow.ops.tf.scatter.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.ops import state_ops from tensorflow.python.ops import variables from tensorflow.python.platform import test def _AsType(v, vtype): return v.astype(vtype) if isinstance(v, np.ndarray) else vtype(v) def _NumpyAdd(ref, indices, updates): # Since numpy advanced assignment does not support repeated indices, # we run a simple loop to perform scatter_add. for i, indx in np.ndenumerate(indices): ref[indx] += updates[i] def _NumpyAddScalar(ref, indices, update): for _, indx in np.ndenumerate(indices): ref[indx] += update def _NumpySub(ref, indices, updates): for i, indx in np.ndenumerate(indices): ref[indx] -= updates[i] def _NumpySubScalar(ref, indices, update): for _, indx in np.ndenumerate(indices): ref[indx] -= update def _NumpyMul(ref, indices, updates): for i, indx in np.ndenumerate(indices): ref[indx] *= updates[i] def _NumpyMulScalar(ref, indices, update): for _, indx in np.ndenumerate(indices): ref[indx] *= update def _NumpyDiv(ref, indices, updates): for i, indx in np.ndenumerate(indices): ref[indx] /= updates[i] def _NumpyDivScalar(ref, indices, update): for _, indx in np.ndenumerate(indices): ref[indx] /= update def _NumpyMin(ref, indices, updates): for i, indx in np.ndenumerate(indices): ref[indx] = np.minimum(ref[indx], updates[i]) def _NumpyMinScalar(ref, indices, update): for _, indx in np.ndenumerate(indices): ref[indx] = np.minimum(ref[indx], update) def _NumpyMax(ref, indices, updates): for i, indx in np.ndenumerate(indices): ref[indx] = np.maximum(ref[indx], updates[i]) def _NumpyMaxScalar(ref, indices, update): for _, indx in np.ndenumerate(indices): ref[indx] = np.maximum(ref[indx], update) def _NumpyUpdate(ref, indices, updates): for i, indx in np.ndenumerate(indices): ref[indx] = updates[i] def _NumpyUpdateScalar(ref, indices, update): for _, indx in np.ndenumerate(indices): ref[indx] = update _TF_OPS_TO_NUMPY = { state_ops.scatter_update: _NumpyUpdate, state_ops.scatter_add: _NumpyAdd, state_ops.scatter_sub: _NumpySub, state_ops.scatter_mul: _NumpyMul, state_ops.scatter_div: _NumpyDiv, state_ops.scatter_min: _NumpyMin, state_ops.scatter_max: _NumpyMax, } _TF_OPS_TO_NUMPY_SCALAR = { state_ops.scatter_update: _NumpyUpdateScalar, state_ops.scatter_add: _NumpyAddScalar, state_ops.scatter_sub: _NumpySubScalar, state_ops.scatter_mul: _NumpyMulScalar, state_ops.scatter_div: _NumpyDivScalar, state_ops.scatter_min: _NumpyMinScalar, state_ops.scatter_max: _NumpyMaxScalar, } class ScatterTest(test.TestCase): def _VariableRankTest(self, tf_scatter, vtype, itype, repeat_indices=False, updates_are_scalar=False): np.random.seed(8) with self.test_session(use_gpu=True): for indices_shape in (), (2,), (3, 7), (3, 4, 7): for extra_shape in (), (5,), (5, 9): # Generate random indices with no duplicates for easy numpy comparison size = np.prod(indices_shape, dtype=itype) first_dim = 3 * size indices = np.arange(first_dim) np.random.shuffle(indices) indices = indices[:size] if size > 1 and repeat_indices: # Add some random repeats. indices = indices[:size // 2] for _ in range(size - size // 2): # Randomly append some repeats. indices = np.append(indices, indices[np.random.randint(size // 2)]) np.random.shuffle(indices) indices = indices.reshape(indices_shape) if updates_are_scalar: updates = _AsType(np.random.randn(), vtype) else: updates = _AsType( np.random.randn(*(indices_shape + extra_shape)), vtype) # Clips small values to avoid division by zero. def clip_small_values(x): threshold = 1e-4 sign = np.sign(x) if isinstance(x, np.int32): threshold = 1 sign = np.random.choice([-1, 1]) return threshold * sign if np.abs(x) < threshold else x updates = np.vectorize(clip_small_values)(updates) old = _AsType(np.random.randn(*((first_dim,) + extra_shape)), vtype) # Scatter via numpy new = old.copy() if updates_are_scalar: np_scatter = _TF_OPS_TO_NUMPY_SCALAR[tf_scatter] else: np_scatter = _TF_OPS_TO_NUMPY[tf_scatter] np_scatter(new, indices, updates) # Scatter via tensorflow ref = variables.VariableV1(old) ref.initializer.run() tf_scatter(ref, indices, updates).eval() self.assertAllClose(ref.eval(), new) def _VariableRankTests(self, tf_scatter, repeat_indices=False, updates_are_scalar=False): vtypes = [np.float32, np.float64] if tf_scatter != state_ops.scatter_div: vtypes.append(np.int32) for vtype in vtypes: for itype in (np.int32, np.int64): self._VariableRankTest(tf_scatter, vtype, itype, repeat_indices, updates_are_scalar) def testVariableRankUpdate(self): self._VariableRankTests(state_ops.scatter_update, False) def testVariableRankAdd(self): self._VariableRankTests(state_ops.scatter_add, False) def testVariableRankSub(self): self._VariableRankTests(state_ops.scatter_sub, False) def testVariableRankMul(self): self._VariableRankTests(state_ops.scatter_mul, False) def testVariableRankDiv(self): self._VariableRankTests(state_ops.scatter_div, False) def testVariableRankMin(self): self._VariableRankTests(state_ops.scatter_min, False) def testVariableRankMax(self): self._VariableRankTests(state_ops.scatter_max, False) def testRepeatIndicesAdd(self): self._VariableRankTests(state_ops.scatter_add, True) def testRepeatIndicesSub(self): self._VariableRankTests(state_ops.scatter_sub, True) def testRepeatIndicesMul(self): self._VariableRankTests(state_ops.scatter_mul, True) def testRepeatIndicesDiv(self): self._VariableRankTests(state_ops.scatter_div, True) def testRepeatIndicesMin(self): self._VariableRankTests(state_ops.scatter_min, True) def testRepeatIndicesMax(self): self._VariableRankTests(state_ops.scatter_max, True) def testVariableRankUpdateScalar(self): self._VariableRankTests(state_ops.scatter_update, False, True) def testVariableRankAddScalar(self): self._VariableRankTests(state_ops.scatter_add, False, True) def testVariableRankSubScalar(self): self._VariableRankTests(state_ops.scatter_sub, False, True) def testVariableRankMulScalar(self): self._VariableRankTests(state_ops.scatter_mul, False, True) def testVariableRankDivScalar(self): self._VariableRankTests(state_ops.scatter_div, False, True) def testVariableRankMinScalar(self): self._VariableRankTests(state_ops.scatter_min, False, True) def testVariableRankMaxScalar(self): self._VariableRankTests(state_ops.scatter_max, False, True) def testRepeatIndicesAddScalar(self): self._VariableRankTests(state_ops.scatter_add, True, True) def testRepeatIndicesSubScalar(self): self._VariableRankTests(state_ops.scatter_sub, True, True) def testRepeatIndicesMulScalar(self): self._VariableRankTests(state_ops.scatter_mul, True, True) def testRepeatIndicesDivScalar(self): self._VariableRankTests(state_ops.scatter_div, True, True) def testRepeatIndicesMinScalar(self): self._VariableRankTests(state_ops.scatter_min, True, True) def testRepeatIndicesMaxScalar(self): self._VariableRankTests(state_ops.scatter_max, True, True) def testBooleanScatterUpdate(self): if not test.is_gpu_available(): with self.test_session(use_gpu=False) as session: var = variables.Variable([True, False]) update0 = state_ops.scatter_update(var, 1, True) update1 = state_ops.scatter_update( var, constant_op.constant( 0, dtype=dtypes.int64), False) var.initializer.run() session.run([update0, update1]) self.assertAllEqual([False, True], var.eval()) def testScatterOutOfRangeCpu(self): for op, _ in _TF_OPS_TO_NUMPY.items(): params = np.array([1, 2, 3, 4, 5, 6]).astype(np.float32) updates = np.array([-3, -4, -5]).astype(np.float32) if not test.is_gpu_available(): with self.test_session(use_gpu=False): ref = variables.VariableV1(params) ref.initializer.run() # Indices all in range, no problem. indices = np.array([2, 0, 5]) op(ref, indices, updates).eval() # Test some out of range errors. indices = np.array([-1, 0, 5]) with self.assertRaisesOpError( r'indices\[0\] = -1 is not in \[0, 6\)'): op(ref, indices, updates).eval() indices = np.array([2, 0, 6]) with self.assertRaisesOpError(r'indices\[2\] = 6 is not in \[0, 6\)'): op(ref, indices, updates).eval() # TODO(fpmc): Re-enable this test when gpu_pip test actually runs on a GPU. def _disabledTestScatterOutOfRangeGpu(self): if test.is_gpu_available(): return for op, _ in _TF_OPS_TO_NUMPY.items(): params = np.array([1, 2, 3, 4, 5, 6]).astype(np.float32) updates = np.array([-3, -4, -5]).astype(np.float32) # With GPU, the code ignores indices that are out of range. # We don't test the implementation; just test there's no failures. with self.test_session(force_gpu=True): ref = variables.Variable(params) ref.initializer.run() # Indices all in range, no problem. indices = np.array([2, 0, 5]) op(ref, indices, updates).eval() # Indicies out of range should not fail. indices = np.array([-1, 0, 5]) op(ref, indices, updates).eval() indices = np.array([2, 0, 6]) op(ref, indices, updates).eval() if __name__ == '__main__': test.main()