341 lines
11 KiB
Python
341 lines
11 KiB
Python
# 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.framework import test_util
|
|
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.cached_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.Variable(old)
|
|
self.evaluate(ref.initializer)
|
|
self.evaluate(tf_scatter(ref, indices, updates))
|
|
self.assertAllClose(self.evaluate(ref), 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.session(use_gpu=False):
|
|
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)
|
|
self.evaluate(var.initializer)
|
|
|
|
self.evaluate([update0, update1])
|
|
|
|
self.assertAllEqual([False, True], self.evaluate(var))
|
|
|
|
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.session(use_gpu=False):
|
|
ref = variables.Variable(params)
|
|
self.evaluate(ref.initializer)
|
|
|
|
# Indices all in range, no problem.
|
|
indices = np.array([2, 0, 5])
|
|
self.evaluate(op(ref, indices, updates))
|
|
|
|
# Test some out of range errors.
|
|
indices = np.array([-1, 0, 5])
|
|
with self.assertRaisesOpError(
|
|
r'indices\[0\] = -1 is not in \[0, 6\)'):
|
|
self.evaluate(op(ref, indices, updates))
|
|
|
|
indices = np.array([2, 0, 6])
|
|
with self.assertRaisesOpError(r'indices\[2\] = 6 is not in \[0, 6\)'):
|
|
self.evaluate(op(ref, indices, updates))
|
|
|
|
# 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 test_util.force_gpu():
|
|
ref = variables.Variable(params)
|
|
self.evaluate(ref.initializer)
|
|
|
|
# Indices all in range, no problem.
|
|
indices = np.array([2, 0, 5])
|
|
self.evaluate(op(ref, indices, updates))
|
|
|
|
# Indices out of range should not fail.
|
|
indices = np.array([-1, 0, 5])
|
|
self.evaluate(op(ref, indices, updates))
|
|
indices = np.array([2, 0, 6])
|
|
self.evaluate(op(ref, indices, updates))
|
|
|
|
|
|
if __name__ == '__main__':
|
|
test.main()
|