def testInvalidRank(self):
with test_util.force_cpu():
sp_input = self._SparseTensor_2x5x6()
new_shape = np.array([3, 7], dtype=np.int64)
with self.assertRaises(ValueError):
sparse_ops.sparse_reset_shape(sp_input, new_shape)
def testInvalidRank(self):
with self.test_session(use_gpu=False):
sp_input = self._SparseTensor_2x5x6()
new_shape = np.array([3, 7], dtype=np.int64)
with self.assertRaises(ValueError):
sparse_ops.sparse_reset_shape(sp_input, new_shape)
def testInvalidDimensionSizeStatic(self):
sp_input = self._SparseTensor_2x5x6()
new_shape = np.array([3, 7, 5], dtype=np.int64)
with self.assertRaisesRegexp(ValueError,
"should have dimension sizes"):
sparse_ops.sparse_reset_shape(sp_input, new_shape)
def testInvalidRank(self):
with self.test_session(use_gpu=False):
sp_input = self._SparseTensor_2x5x6()
new_shape = np.array([3, 7], dtype=np.int64)
with self.assertRaises(ValueError):
sparse_ops.sparse_reset_shape(sp_input, new_shape)
def testInvalidRank(self):
with test_util.force_cpu():
sp_input = self._SparseTensor_2x5x6()
new_shape = np.array([3, 7], dtype=np.int64)
with self.assertRaises(ValueError):
sparse_ops.sparse_reset_shape(sp_input, new_shape)
def testInvalidDimensionSizeInputUnavailableInGraphConstruction(self):
sp_input = array_ops.sparse_placeholder(dtype=dtypes.int32)
with self.test_session(use_gpu=False) as sess:
new_shape = np.array([3, 7, 5], dtype=np.int64)
out = sparse_ops.sparse_reset_shape(sp_input, new_shape)
with self.assertRaisesOpError("x <= y did not hold element-wise"):
sess.run(out, feed_dict={sp_input: self._SparseTensorValue_2x5x6()})
def testInvalidDimensionSize(self):
with self.test_session(use_gpu=False) as sess:
sp_input = self._SparseTensor_2x5x6()
new_shape = np.array([3, 7, 5], dtype=np.int64)
out = sparse_ops.sparse_reset_shape(sp_input, new_shape)
with self.assertRaisesOpError("x <= y did not hold element-wise"):
sess.run(out)
def testInvalidRankNewShapeUnavailableInGraphConstruction(self):
with self.test_session(use_gpu=False) as sess:
new_shape = array_ops.placeholder(dtype=dtypes.int64)
sp_input = self._SparseTensor_2x5x6()
out = sparse_ops.sparse_reset_shape(sp_input, new_shape)
with self.assertRaisesOpError("x == y did not hold element-wise"):
sess.run(out, feed_dict={new_shape: np.array([3, 7], dtype=np.int64)})
def testInvalidDimensionSizeDynamic(self):
with self.session(use_gpu=False) as sess:
sp_input = self._SparseTensor_2x5x6()
new_shape = array_ops.placeholder(dtype=dtypes.int32)
out = sparse_ops.sparse_reset_shape(sp_input, new_shape)
with self.assertRaisesOpError("x <= y did not hold element-wise"):
sess.run(out, feed_dict={new_shape: [3, 7, 5]})
def testInvalidDimensionSizeDynamic(self):
with self.test_session(use_gpu=False) as sess:
sp_input = self._SparseTensor_2x5x6()
new_shape = array_ops.placeholder(dtype=dtypes.int32)
out = sparse_ops.sparse_reset_shape(sp_input, new_shape)
with self.assertRaisesOpError("x <= y did not hold element-wise"):
sess.run(out, feed_dict={new_shape: [3, 7, 5]})
def testInvalidDimensionSize(self):
with self.test_session(use_gpu=False) as sess:
sp_input = self._SparseTensor_2x5x6()
new_shape = np.array([3, 7, 5], dtype=np.int64)
out = sparse_ops.sparse_reset_shape(sp_input, new_shape)
with self.assertRaisesOpError("x <= y did not hold element-wise"):
sess.run(out)
def testInvalidRankNewShapeUnavaibleInGraphConstruction(self):
with self.test_session(use_gpu=False) as sess:
new_shape = array_ops.placeholder(dtype=dtypes.int64)
sp_input = self._SparseTensor_2x5x6()
out = sparse_ops.sparse_reset_shape(sp_input, new_shape)
with self.assertRaisesOpError("x == y did not hold element-wise"):
sess.run(out, feed_dict={new_shape: np.array([3, 7], dtype=np.int64)})
def testInvalidDimensionSizeInputUnavailableInGraphConstruction(self):
sp_input = array_ops.sparse_placeholder(dtype=dtypes.int32)
with self.test_session(use_gpu=False) as sess:
new_shape = np.array([3, 7, 5], dtype=np.int64)
out = sparse_ops.sparse_reset_shape(sp_input, new_shape)
with self.assertRaisesOpError("x <= y did not hold element-wise"):
sess.run(out, feed_dict={sp_input: self._SparseTensorValue_2x5x6()})
def testTightBoundingBox(self):
with self.test_session(use_gpu=False) as sess:
sp_input = self._SparseTensor_2x5x6()
sp_output = sparse_ops.sparse_reset_shape(sp_input)
output = sess.run(sp_output)
self.assertAllEqual(output.indices, [[0, 0, 0], [0, 1, 0], [0, 1, 3], [1, 1, 4], [1, 3, 2], [1, 3, 3]])
self.assertAllEqual(output.values, [0, 10, 13, 14, 32, 33])
self.assertAllEqual(output.shape, [2, 4, 5])
def testTightBoundingBoxEmpty(self):
with self.test_session(use_gpu=False) as sess:
sp_input = self._SparseTensor_2x5x6_Empty()
sp_output = sparse_ops.sparse_reset_shape(sp_input)
output = sess.run(sp_output)
self.assertAllEqual(output.indices.shape, [0, 3])
self.assertAllEqual(output.values.shape, [0])
self.assertAllEqual(output.dense_shape, [0, 0, 0])
def testTightBoundingBoxEmpty(self):
with self.test_session(use_gpu=False) as sess:
sp_input = self._SparseTensor_2x5x6_Empty()
sp_output = sparse_ops.sparse_reset_shape(sp_input)
output = sess.run(sp_output)
self.assertAllEqual(output.indices.shape, [0, 3])
self.assertAllEqual(output.values.shape, [0])
self.assertAllEqual(output.dense_shape, [0, 0, 0])
def testTightBoundingBoxEmpty(self):
with test_util.force_cpu():
sp_input = self._SparseTensor_2x5x6_Empty()
sp_output = sparse_ops.sparse_reset_shape(sp_input)
output = self.evaluate(sp_output)
self.assertAllEqual(output.indices.shape, [0, 3])
self.assertAllEqual(output.values.shape, [0])
self.assertAllEqual(output.dense_shape, [0, 0, 0])
def testTightBoundingBoxEmpty(self):
with test_util.force_cpu():
sp_input = self._SparseTensor_2x5x6_Empty()
sp_output = sparse_ops.sparse_reset_shape(sp_input)
output = self.evaluate(sp_output)
self.assertAllEqual(output.indices.shape, [0, 3])
self.assertAllEqual(output.values.shape, [0])
self.assertAllEqual(output.dense_shape, [0, 0, 0])
def testTightBoundingBox(self):
with test_util.force_cpu():
sp_input = self._SparseTensor_2x5x6()
sp_output = sparse_ops.sparse_reset_shape(sp_input)
output = self.evaluate(sp_output)
self.assertAllEqual(output.indices, [[0, 0, 0], [0, 1, 0], [0, 1, 3],
[1, 1, 4], [1, 3, 2], [1, 3, 3]])
self.assertAllEqual(output.values, [0, 10, 13, 14, 32, 33])
self.assertAllEqual(output.dense_shape, [2, 4, 5])
def testTightBoundingBox(self):
with test_util.force_cpu():
sp_input = self._SparseTensor_2x5x6()
sp_output = sparse_ops.sparse_reset_shape(sp_input)
output = self.evaluate(sp_output)
self.assertAllEqual(output.indices, [[0, 0, 0], [0, 1, 0], [0, 1, 3],
[1, 1, 4], [1, 3, 2], [1, 3, 3]])
self.assertAllEqual(output.values, [0, 10, 13, 14, 32, 33])
self.assertAllEqual(output.dense_shape, [2, 4, 5])
def testInputUnavaibleInGraphConstructionOk(self):
with self.test_session(use_gpu=False) as sess:
sp_input = array_ops.sparse_placeholder(dtype=dtypes.int32)
new_shape = np.array([3, 6, 7], dtype=np.int64)
sp_output = sparse_ops.sparse_reset_shape(sp_input, new_shape)
output = sess.run(sp_output, feed_dict={sp_input: self._SparseTensorValue_2x5x6()})
self.assertAllEqual(output.indices, [[0, 0, 0], [0, 1, 0], [0, 1, 3], [1, 1, 4], [1, 3, 2], [1, 3, 3]])
self.assertAllEqual(output.values, [0, 10, 13, 14, 32, 33])
self.assertAllEqual(output.shape, [3, 6, 7])
def testTightBoundingBox(self):
with self.test_session(use_gpu=False) as sess:
sp_input = self._SparseTensor_2x5x6()
sp_output = sparse_ops.sparse_reset_shape(sp_input)
output = sess.run(sp_output)
self.assertAllEqual(output.indices, [[0, 0, 0], [0, 1, 0], [0, 1, 3],
[1, 1, 4], [1, 3, 2], [1, 3, 3]])
self.assertAllEqual(output.values, [0, 10, 13, 14, 32, 33])
self.assertAllEqual(output.dense_shape, [2, 4, 5])
def testBasic(self):
with self.test_session(use_gpu=False) as sess:
sp_input = self._SparseTensor_2x5x6()
new_shape = np.array([3, 6, 7], dtype=np.int64)
sp_output = sparse_ops.sparse_reset_shape(sp_input, new_shape)
output = sess.run(sp_output)
self.assertAllEqual(output.indices, [[0, 0, 0], [0, 1, 0], [0, 1, 3], [1, 1, 4], [1, 3, 2], [1, 3, 3]])
self.assertAllEqual(output.values, [0, 10, 13, 14, 32, 33])
self.assertAllEqual(output.shape, [3, 6, 7])
def testInputUnavailableInGraphConstructionOk(self):
with test_util.force_cpu():
sp_input = self._SparseTensorValue_2x5x6()
new_shape = np.array([3, 6, 7], dtype=np.int64)
sp_output = sparse_ops.sparse_reset_shape(sp_input, new_shape)
output = self.evaluate(sp_output)
self.assertAllEqual(output.indices, [[0, 0, 0], [0, 1, 0], [0, 1, 3],
[1, 1, 4], [1, 3, 2], [1, 3, 3]])
self.assertAllEqual(output.values, [0, 10, 13, 14, 32, 33])
self.assertAllEqual(output.dense_shape, [3, 6, 7])
def testInputUnavailableInGraphConstructionOk(self):
with test_util.force_cpu():
sp_input = self._SparseTensorValue_2x5x6()
new_shape = np.array([3, 6, 7], dtype=np.int64)
sp_output = sparse_ops.sparse_reset_shape(sp_input, new_shape)
output = self.evaluate(sp_output)
self.assertAllEqual(output.indices, [[0, 0, 0], [0, 1, 0], [0, 1, 3],
[1, 1, 4], [1, 3, 2], [1, 3, 3]])
self.assertAllEqual(output.values, [0, 10, 13, 14, 32, 33])
self.assertAllEqual(output.dense_shape, [3, 6, 7])
def testBasic(self):
with self.test_session(use_gpu=False) as sess:
sp_input = self._SparseTensor_2x5x6()
new_shape = np.array([3, 6, 7], dtype=np.int64)
sp_output = sparse_ops.sparse_reset_shape(sp_input, new_shape)
output = sess.run(sp_output)
self.assertAllEqual(output.indices, [[0, 0, 0], [0, 1, 0], [0, 1, 3],
[1, 1, 4], [1, 3, 2], [1, 3, 3]])
self.assertAllEqual(output.values, [0, 10, 13, 14, 32, 33])
self.assertAllEqual(output.dense_shape, [3, 6, 7])
def testFeedInputUnavailableInGraphConstructionOk(self):
with self.session(use_gpu=False) as sess:
sp_input = array_ops.sparse_placeholder(dtype=dtypes.int32)
new_shape = np.array([3, 6, 7], dtype=np.int64)
sp_output = sparse_ops.sparse_reset_shape(sp_input, new_shape)
output = sess.run(sp_output,
feed_dict={sp_input: self._SparseTensorValue_2x5x6()})
self.assertAllEqual(output.indices, [[0, 0, 0], [0, 1, 0], [0, 1, 3],
[1, 1, 4], [1, 3, 2], [1, 3, 3]])
self.assertAllEqual(output.values, [0, 10, 13, 14, 32, 33])
self.assertAllEqual(output.dense_shape, [3, 6, 7])
def pad_fn(k=k,
t=t,
ndims=ndims,
num_frames=num_frames,
new_shape=new_shape):
"""Pads the tensor."""
if isinstance(t, sparse_tensor.SparseTensor):
return sparse_ops.sparse_reset_shape(t, new_shape)
else:
# Padding is n * 2 tensor where n is the ndims or rank of the padded
# tensor.
paddings = array_ops.stack([[0, 0], [0, list_size - num_frames]] +
[[0, 0]] * (ndims - 2))
return array_ops.pad(
t,
paddings,
constant_values=array_ops.squeeze(
example_feature_spec[k].default_value[0]))
def pad_fn(k=k,
t=t,
ndims=ndims,
num_frames=num_frames,
new_shape=new_shape):
"""Pads the tensor."""
if isinstance(t, sparse_tensor.SparseTensor):
return sparse_ops.sparse_reset_shape(t, new_shape)
else:
# Padding is n * 2 tensor where n is the ndims or rank of the padded
# tensor.
if ndims == 2:
paddings = array_ops.stack([[0, list_size - num_frames, ],[0, 0]])
else:
paddings = array_ops.stack([[0, list_size - num_frames,] ])
default_val = tf.cast(0, tf.int64)
if name_to_sequence_features[k].dtype == tf.string:
default_val = ""
return array_ops.pad(
t,
paddings,
constant_values=array_ops.squeeze(default_val)
)
def testStaticShapeInfoPreservedWhenNewShapeIsProvidedAndStatic(self):
sp_input = self._SparseTensor_2x5x6()
new_shape = np.array([3, 6, 7], dtype=np.int64)
sp_output = sparse_ops.sparse_reset_shape(sp_input, new_shape)
self.assertAllEqual([3, 6, 7], sp_output.get_shape())
def testStaticShapeInfoPreservedWhenNewShapeIsProvidedAndStatic(self): sp_input = self._SparseTensor_2x5x6() new_shape = np.array([3, 6, 7], dtype=np.int64) sp_output = sparse_ops.sparse_reset_shape(sp_input, new_shape) self.assertAllEqual([3, 6, 7], sp_output.get_shape())
def testInvalidDimensionSizeStatic(self):
sp_input = self._SparseTensor_2x5x6()
new_shape = np.array([3, 7, 5], dtype=np.int64)
with self.assertRaisesRegexp(ValueError, "should have dimension sizes"):
sparse_ops.sparse_reset_shape(sp_input, new_shape)
