diff options
| -rw-r--r-- | ethosu/vela/graph_optimiser.py | 4 | ||||
| -rw-r--r-- | ethosu/vela/supported_operators.py | 8 | ||||
| -rw-r--r-- | ethosu/vela/test/test_supported_operators.py | 23 |
3 files changed, 26 insertions, 9 deletions
diff --git a/ethosu/vela/graph_optimiser.py b/ethosu/vela/graph_optimiser.py index 642f1349..7c60368d 100644 --- a/ethosu/vela/graph_optimiser.py +++ b/ethosu/vela/graph_optimiser.py @@ -1472,8 +1472,8 @@ def convert_mean_to_depthwise_conv_or_avgpool(op, arch, nng): dims = len(shape) # Height and width axes have different index depending on dimensions - if axis.shape == []: # single axis - axis = int(axis.values) + if len(axis.shape) <= 1: # single axis + axis = int(axis.values) if len(axis.shape) == 0 else axis.values[0] if dims in (2, 3): if axis == 0: h, w = shape[axis], 1 diff --git a/ethosu/vela/supported_operators.py b/ethosu/vela/supported_operators.py index 5bf2c459..dfa27199 100644 --- a/ethosu/vela/supported_operators.py +++ b/ethosu/vela/supported_operators.py @@ -1040,11 +1040,11 @@ class SupportedOperators: def constraint_mean_axis(op): "Axis indices must correspond to height and width axes" dims = len(op.inputs[0].shape) - axis = op.inputs[1].values if op.inputs[1].shape == [] else list(op.inputs[1].values) + axis = int(op.inputs[1].values) if op.inputs[1].shape == [] else list(op.inputs[1].values) if dims == 2 or dims == 3: - valid = axis in (0, 1, [0, 1], [1, 0]) + valid = axis in (0, 1, [0], [1], [0, 1], [1, 0]) elif dims == 4: - valid = axis in (1, 2, [1, 2], [2, 1]) + valid = axis in (1, 2, [1], [2], [1, 2], [2, 1]) return valid, f"Axis is {axis}" @classmethod @@ -1082,7 +1082,7 @@ class SupportedOperators: keep_dims is set to True and IFM datatype is int8""" shape = op.ifm.shape - axis = op.inputs[1].values if op.inputs[1].shape == [] else list(op.inputs[1].values) + axis = int(op.inputs[1].values) if op.inputs[1].shape == [] else list(op.inputs[1].values) # doesn't apply, size is checked by constraint_mean_height_width_product_avgpool # and constraint_mean_height_width_product if ( diff --git a/ethosu/vela/test/test_supported_operators.py b/ethosu/vela/test/test_supported_operators.py index 355b472c..666a5ecc 100644 --- a/ethosu/vela/test/test_supported_operators.py +++ b/ethosu/vela/test/test_supported_operators.py @@ -840,12 +840,15 @@ def test_constraint_keep_dims_ifm_ofm(): assert support.is_operator_supported(op) -def create_mean(input_shape, output_shape, indices, datatype, attrs): +def create_mean(input_shape, output_shape, axis, datatype, attrs): ifm = Tensor(input_shape, datatype, "in") ifm.quantization = testutil.default_quant_params() - indices = create_const_tensor("indices", [len(indices)], DataType.int32, indices, np.uint8) ofm = Tensor(output_shape, datatype, "out") ofm.quantization = testutil.default_quant_params() + if type(axis) is list: + indices = create_const_tensor("indices", [len(axis)], DataType.int32, axis, np.uint8) + elif type(axis) is int: + indices = create_const_tensor("indices", [], DataType.int32, axis, np.uint8) op = testutil.create_op(Op.Mean, [ifm, indices], ofm, attrs) return op @@ -859,8 +862,22 @@ def test_mean_dtype(): def test_mean_axis(): - op = create_mean([1, 6, 6, 16], [1, 1, 1, 16], [1], DataType.int8, {"keep_dims": True}) + op = create_mean([1, 6, 6, 16], [1, 1, 1, 16], 0, DataType.int8, {"keep_dims": True}) + assert not support.is_operator_supported(op) + op = create_mean([1, 6, 6, 16], [1, 1, 1, 16], [3], DataType.int8, {"keep_dims": True}) + assert not support.is_operator_supported(op) + op = create_mean([1, 6, 6, 16], [1, 1, 1, 16], [1, 3], DataType.int8, {"keep_dims": True}) assert not support.is_operator_supported(op) + op = create_mean([1, 6, 6, 16], [1, 1, 1, 16], [0, 1], DataType.int8, {"keep_dims": True}) + assert not support.is_operator_supported(op) + op = create_mean([1, 6, 6, 16], [1, 1, 1, 16], [1, 2], DataType.int8, {"keep_dims": True}) + assert support.is_operator_supported(op) + op = create_mean([1, 6, 6, 16], [1, 1, 1, 16], [1], DataType.int8, {"keep_dims": True}) + assert support.is_operator_supported(op) + op = create_mean([1, 6, 6, 16], [1, 1, 1, 16], 2, DataType.int8, {"keep_dims": True}) + assert support.is_operator_supported(op) + op = create_mean([1, 6, 6, 16], [1, 1, 1, 16], [2, 1], DataType.int8, {"keep_dims": True}) + assert support.is_operator_supported(op) def test_mean_hw_product(): |