def testMatrixAndVectorBatchShapesSameRankButPermuted(self):
batch_shape = [6, 3, 2]
for static_batch_shape in [
tf.TensorShape(batch_shape), tf.TensorShape(None)]:
with self.test_session():
mat = self._rng.rand(2, 3, 4, 6)
vec = operator_pd.flip_matrix_to_vector(
mat, batch_shape, static_batch_shape)
vec_v = vec.eval()
self.assertAllEqual((6, 3, 2, 4), vec_v.shape)
def testVectorBatchShapeLongerThanMatrixBatchShape(self):
batch_shape = [2, 3, 2, 3]
for static_batch_shape in [
tf.TensorShape(batch_shape), tf.TensorShape(None)]:
with self.test_session():
mat = self._rng.rand(2, 3, 4, 6)
vec = operator_pd.flip_matrix_to_vector(
mat, batch_shape, static_batch_shape)
vec_v = vec.eval()
self.assertAllEqual((2, 3, 2, 3, 4), vec_v.shape)
def testVectorBatchShapeLongerThanMatrixBatchShape(self):
batch_shape = [2, 3, 2, 3]
for static_batch_shape in [
tf.TensorShape(batch_shape), tf.TensorShape(None)]:
with self.test_session():
mat = self._rng.rand(2, 3, 4, 6)
vec = operator_pd.flip_matrix_to_vector(
mat, batch_shape, static_batch_shape)
vec_v = vec.eval()
self.assertAllEqual((2, 3, 2, 3, 4), vec_v.shape)
def testMatrixAndVectorBatchShapesSameRankButPermuted(self):
batch_shape = [6, 3, 2]
for static_batch_shape in [
tf.TensorShape(batch_shape), tf.TensorShape(None)]:
with self.test_session():
mat = self._rng.rand(2, 3, 4, 6)
vec = operator_pd.flip_matrix_to_vector(
mat, batch_shape, static_batch_shape)
vec_v = vec.eval()
self.assertAllEqual((6, 3, 2, 4), vec_v.shape)
def testMatrixBatchShapeHasASingletonThatVecBatchShapeDoesnt(self):
batch_shape = [6, 3]
for static_batch_shape in [
tf.TensorShape(batch_shape), tf.TensorShape(None)]:
with self.test_session():
mat = self._rng.rand(1, 3, 4, 6)
vec = operator_pd.flip_matrix_to_vector(
mat, batch_shape, static_batch_shape)
vec_v = vec.eval()
self.assertAllEqual((6, 3, 4), vec_v.shape)
self.assertAllEqual(mat[0, 2, 3, 4], vec_v[4, 2, 3])
def test_matrix_and_vector_batch_shapes_the_same(self):
batch_shape = [6, 2, 3]
for static_batch_shape in [
tf.TensorShape(batch_shape), tf.TensorShape(None)]:
with self.test_session():
mat = self._rng.rand(2, 3, 4, 6)
vec = operator_pd.flip_matrix_to_vector(
mat, batch_shape, static_batch_shape)
vec_v = vec.eval()
self.assertAllEqual((6, 2, 3, 4), vec_v.shape)
self.assertAllEqual(mat[1, 2, 3, 4], vec_v[4, 1, 2, 3])
def testMatrixBatchShapeHasASingletonThatVecBatchShapeDoesnt(self):
batch_shape = [6, 3]
for static_batch_shape in [
tf.TensorShape(batch_shape), tf.TensorShape(None)]:
with self.test_session():
mat = self._rng.rand(1, 3, 4, 6)
vec = operator_pd.flip_matrix_to_vector(
mat, batch_shape, static_batch_shape)
vec_v = vec.eval()
self.assertAllEqual((6, 3, 4), vec_v.shape)
self.assertAllEqual(mat[0, 2, 3, 4], vec_v[4, 2, 3])
def test_matrix_and_vector_batch_shapes_the_same(self):
batch_shape = [6, 2, 3]
for static_batch_shape in [
tf.TensorShape(batch_shape), tf.TensorShape(None)]:
with self.test_session():
mat = self._rng.rand(2, 3, 4, 6)
vec = operator_pd.flip_matrix_to_vector(
mat, batch_shape, static_batch_shape)
vec_v = vec.eval()
self.assertAllEqual((6, 2, 3, 4), vec_v.shape)
self.assertAllEqual(mat[1, 2, 3, 4], vec_v[4, 1, 2, 3])
