def test_raises_if_rank_is_not_scalar_static(self): with self.test_session(): tensor = constant_op.constant((42, 43), name="my_tensor") desired_ranks = (np.array(1, dtype=np.int32), np.array((2, 1), dtype=np.int32)) with self.assertRaisesRegexp(ValueError, "Rank must be a scalar"): check_ops.assert_rank_in(tensor, desired_ranks)
def test_raises_if_rank_is_not_scalar_static(self): tensor = constant_op.constant((42, 43), name="my_tensor") desired_ranks = ( np.array(1, dtype=np.int32), np.array((2, 1), dtype=np.int32)) with self.assertRaisesRegexp(ValueError, "Rank must be a scalar"): check_ops.assert_rank_in(tensor, desired_ranks)
def test_raises_if_rank_is_not_integer_static(self): with self.test_session(): tensor = constant_op.constant((42, 43), name="my_tensor") with self.assertRaisesRegexp(TypeError, "must be of type <dtype: 'int32'>"): check_ops.assert_rank_in(tensor, ( 1, .5, ))
def test_rank_one_tensor_raises_if_rank_mismatches_static_rank(self): with self.test_session(): tensor_rank1 = constant_op.constant((42, 43), name="my_tensor") with self.assertRaisesRegexp(ValueError, "my_tensor.*rank"): with ops.control_dependencies([ check_ops.assert_rank_in(tensor_rank1, (0, 2))]): array_ops.identity(tensor_rank1).eval()
def test_rank_one_tensor_doesnt_raise_if_rank_matches_static_rank(self): with self.test_session(): tensor_rank1 = constant_op.constant([42, 43], name="my_tensor") for desired_ranks in ((0, 1, 2), (1, 0, 2), (1, 2, 0)): with ops.control_dependencies([ check_ops.assert_rank_in(tensor_rank1, desired_ranks)]): array_ops.identity(tensor_rank1).eval()
def test_rank_zero_tensor_doesnt_raise_if_rank_matches_dynamic_rank(self): with self.test_session(): tensor_rank0 = array_ops.placeholder(dtypes.float32, name="my_tensor") for desired_ranks in ((0, 1, 2), (1, 0, 2), (1, 2, 0)): with ops.control_dependencies([ check_ops.assert_rank_in(tensor_rank0, desired_ranks)]): array_ops.identity(tensor_rank0).eval(feed_dict={tensor_rank0: 42.0})
def test_rank_zero_tensor_raises_if_rank_mismatch_dynamic_rank(self): with self.test_session(): tensor_rank0 = array_ops.placeholder(dtypes.float32, name="my_tensor") with ops.control_dependencies([ check_ops.assert_rank_in(tensor_rank0, (1, 2), message="fail")]): with self.assertRaisesOpError("fail.*my_tensor.*rank"): array_ops.identity(tensor_rank0).eval(feed_dict={tensor_rank0: 42.0})
def test_rank_zero_tensor_raises_if_rank_mismatch_static_rank(self): tensor_rank0 = constant_op.constant(42, name="my_tensor") with self.assertRaisesRegexp( ValueError, "fail.*must have rank.*in.*1.*2"): with ops.control_dependencies([ check_ops.assert_rank_in(tensor_rank0, (1, 2), message="fail")]): self.evaluate(array_ops.identity(tensor_rank0))
def test_rank_zero_tensor_raises_if_rank_mismatch_static_rank(self): with self.test_session(): tensor_rank0 = constant_op.constant(42, name="my_tensor") with self.assertRaisesRegexp( ValueError, "fail.*my_tensor.*must have rank.*in.*1.*2"): with ops.control_dependencies([ check_ops.assert_rank_in(tensor_rank0, (1, 2), message="fail")]): array_ops.identity(tensor_rank0).eval()
def test_rank_one_tensor_raises_if_rank_mismatches_dynamic_rank(self): with self.test_session(): tensor_rank1 = array_ops.placeholder(dtypes.float32, name="my_tensor") with ops.control_dependencies([ check_ops.assert_rank_in(tensor_rank1, (0, 2))]): with self.assertRaisesOpError("my_tensor.*rank"): array_ops.identity(tensor_rank1).eval(feed_dict={ tensor_rank1: (42.0, 43.0) })
def test_raises_if_rank_is_not_integer_dynamic(self): with self.test_session(): tensor = constant_op.constant( (42, 43), dtype=dtypes.float32, name="my_tensor") rank_tensor = array_ops.placeholder(dtypes.float32, name="rank_tensor") with self.assertRaisesRegexp(TypeError, "must be of type <dtype: 'int32'>"): with ops.control_dependencies( [check_ops.assert_rank_in(tensor, (1, rank_tensor))]): array_ops.identity(tensor).eval(feed_dict={rank_tensor: .5})
def test_raises_if_rank_is_not_scalar_dynamic(self): with self.test_session(): tensor = constant_op.constant( (42, 43), dtype=dtypes.float32, name="my_tensor") desired_ranks = ( array_ops.placeholder(dtypes.int32, name="rank0_tensor"), array_ops.placeholder(dtypes.int32, name="rank1_tensor")) with self.assertRaisesOpError("Rank must be a scalar"): with ops.control_dependencies( (check_ops.assert_rank_in(tensor, desired_ranks),)): array_ops.identity(tensor).eval(feed_dict={ desired_ranks[0]: 1, desired_ranks[1]: [2, 1], })
def test_raises_if_rank_is_not_integer_static(self): with self.test_session(): tensor = constant_op.constant((42, 43), name="my_tensor") with self.assertRaisesRegexp(TypeError, "must be of type <dtype: 'int32'>"): check_ops.assert_rank_in(tensor, (1, .5,))
def test_rank_zero_tensor_doesnt_raise_if_rank_matches_static_rank(self): tensor_rank0 = constant_op.constant(42, name="my_tensor") for desired_ranks in ((0, 1, 2), (1, 0, 2), (1, 2, 0)): with ops.control_dependencies([ check_ops.assert_rank_in(tensor_rank0, desired_ranks)]): self.evaluate(array_ops.identity(tensor_rank0))