diff options
Diffstat (limited to 'ethosu/vela/test/test_supported_operators.py')
| -rw-r--r-- | ethosu/vela/test/test_supported_operators.py | 38 |
1 files changed, 25 insertions, 13 deletions
diff --git a/ethosu/vela/test/test_supported_operators.py b/ethosu/vela/test/test_supported_operators.py index 1fb452cf..6e640b51 100644 --- a/ethosu/vela/test/test_supported_operators.py +++ b/ethosu/vela/test/test_supported_operators.py @@ -101,13 +101,16 @@ def test_constraint_tens_defined_shape(): assert not support.is_operator_supported(op) -def test_constraint_tens_shapeless(): - # Shapeless input is allowed if its of a certain type: - op = testutil.create_elemwise_op(Op.Mul, "scalar_mul", [1, 8, 8, 8], [], [1, 8, 8, 8]) - assert support.is_operator_supported(op) +def test_constraint_tens_output_shapeless(): # Shapeless output is not allowed at all: op = testutil.create_elemwise_op(Op.Mul, "scalar_mul", [1, 8, 8, 8], [1, 8, 8, 8], []) assert not support.is_operator_supported(op) + + +def test_constraint_tens_input_shapeless(): + # Shapeless input is allowed if its of a certain type: + op = testutil.create_elemwise_op(Op.Mul, "scalar_mul", [1, 8, 8, 8], [], [1, 8, 8, 8]) + assert support.is_operator_supported(op) # Invalid shapeless input due to op type: inp = Tensor([], DataType.uint8, "in") out = Tensor([1, 8, 8, 8], DataType.uint8, "out") @@ -124,11 +127,14 @@ def test_constraint_tens_shape_size(): def test_constraint_tens_dtype(): - # Tensors can only be of type uint8, int8, int16 (and int32) + # Tensors can only be of type uint8, int8, int16 and int32 inp = Tensor([1, 8, 8, 8], DataType.float32, "in") out = Tensor([1, 8, 8, 8], DataType.float32, "out") op = testutil.create_op(Op.Relu, [inp], out) assert not support.is_operator_supported(op) + + +def test_constraint_tens_int32_ops(): # For int32, only select op types are allowed: op = testutil.create_elemwise_op(Op.Mul, "scalar_mul", [1, 8, 8, 8], [], [1, 8, 8, 8], DataType.int32) assert support.is_operator_supported(op) @@ -150,6 +156,20 @@ def test_constraint_tens_dimension(): assert not support.is_operator_supported(op) +def test_constraint_tens_quant_none_check(): + # Tensors must have quantization parameters + op = testutil.create_elemwise_op(Op.Mul, "scalar_mul", [1, 8, 8, 8], [], [1, 8, 8, 8], ifm2_quant=None) + assert not support.is_operator_supported(op) + + +def test_constraint_tens_quant_scale(): + # Quantization scale cannot be infinit + qp = QuantizationParameters() + qp.scale_f32 = np.inf + op = testutil.create_elemwise_op(Op.Mul, "scalar_mul", [1, 8, 8, 8], [], [1, 8, 8, 8], ifm_quant=qp) + assert not support.is_operator_supported(op) + + def test_constraint_faf(): # Fused activation functions, if set, must be a valid op type inp = Tensor([1, 8, 8, 8], DataType.uint8, "in") @@ -157,11 +177,3 @@ def test_constraint_faf(): op = testutil.create_op(Op.Relu, [inp], out) op.activation = Op.Conv2D assert not support.is_operator_supported(op) - - -def test_constraint_tens_quant_scale(): - # Quantization scale cannot be infinit - op = testutil.create_elemwise_op(Op.Mul, "scalar_mul", [1, 8, 8, 8], [], [1, 8, 8, 8]) - op.inputs[0].quantization = QuantizationParameters() - op.inputs[0].quantization.scale_f32 = np.inf - assert not support.is_operator_supported(op) |