diff options
Diffstat (limited to 'ethosu/vela/test/test_supported_operators.py')
| -rw-r--r-- | ethosu/vela/test/test_supported_operators.py | 131 |
1 files changed, 8 insertions, 123 deletions
diff --git a/ethosu/vela/test/test_supported_operators.py b/ethosu/vela/test/test_supported_operators.py index cd331fd9..34ddb900 100644 --- a/ethosu/vela/test/test_supported_operators.py +++ b/ethosu/vela/test/test_supported_operators.py @@ -17,7 +17,6 @@ # Description: # Unit tests for support_operators import numpy as np -import pytest from ethosu.vela.data_type import DataType from ethosu.vela.operation import ActivationFunction @@ -529,14 +528,7 @@ def create_strided_slice_op(in_shape, out_shape, start_offsets, end_offsets): def create_pad_op( - in_shape, - out_shape, - padding, - in_dtype=DataType.int8, - out_dtype=DataType.int8, - pad_dtype=DataType.int32, - pad_setting=Padding.VALID, - kernel_size=3, + in_shape, out_shape, padding, in_dtype=DataType.int8, out_dtype=DataType.int8, pad_dtype=DataType.int32, ): qp = testutil.default_quant_params() in0 = Tensor(in_shape, in_dtype, "in") @@ -545,17 +537,6 @@ def create_pad_op( out = Tensor(out_shape, out_dtype, "out") out.quantization = qp.clone() op = testutil.create_op(Op.Pad, [in0, pad_tensor], out) - conv_out_tens = Tensor(in_shape, in_dtype, "output") - conv_out_tens.quantization = qp.clone() - weight_tens = Tensor([kernel_size, kernel_size, in_shape[-1], out_shape[-1]], in_dtype, "weights") - weight_tens.values = np.zeros(weight_tens.shape) - weight_tens.quant_values = np.zeros(weight_tens.shape, np.int8) - weight_tens.quantization = qp.clone() - bias_tens = Tensor(out_shape, pad_dtype, "biases") - attrs = {"padding": pad_setting, "stride_w": 2, "stride_h": 2, "dilation_w_factor": 1, "dilation_h_factor": 1} - attrs["strides"] = (1, attrs["stride_h"], attrs["stride_w"], 1) - conv2d_op = testutil.create_op(Op.Conv2DBias, [out, weight_tens, bias_tens], conv_out_tens, attrs) - conv2d_op.add_input_tensor(out) return op @@ -571,10 +552,16 @@ def test_constraint_padded_dimensions(): # Incorrect padding dimensions, can only pad width and height op = create_pad_op(in_shape=[1, 1, 1, 1], out_shape=[1, 3, 3, 1], padding=[[1, 1], [1, 1], [1, 1], [0, 0]],) assert not support.is_operator_supported(op) + op = create_pad_op(in_shape=[1, 1, 1, 1], out_shape=[1, 3, 3, 1], padding=[[1, 1], [1, 1], [0, 0]],) + assert support.is_operator_supported(op) + op = create_pad_op(in_shape=[1, 1, 1, 1], out_shape=[1, 3, 3, 1], padding=[[1, 1], [1, 1], [0, 1]],) + assert not support.is_operator_supported(op) def test_constraint_pad_shape(): - # PAD operator must be of shape (4,2) + # PAD operator must be of shape (3,2) or (4,2) + op = create_pad_op(in_shape=[1, 1, 1, 1], out_shape=[1, 3, 3, 1], padding=[[1, 1], [1, 1], [0, 0]]) + assert support.is_operator_supported(op) op = create_pad_op(in_shape=[1, 1, 1, 1], out_shape=[1, 3, 3, 1], padding=[[0, 0], [1, 1], [1, 1], [0, 0], [0, 0]],) assert not support.is_operator_supported(op) @@ -595,108 +582,6 @@ def test_constraint_pad_dtype(): assert not support.is_operator_supported(op) -def test_constraint_pad_consumer(): - # PAD operator must be followed by a valid consumer with Padding.VALID attribute - op = create_pad_op(in_shape=[1, 1, 1, 1], out_shape=[1, 3, 3, 1], padding=[[0, 0], [1, 1], [1, 1], [0, 0]],) - assert support.is_operator_supported(op) - op = create_pad_op( - in_shape=[1, 1, 1, 1], - out_shape=[1, 3, 3, 1], - padding=[[0, 0], [1, 1], [1, 1], [0, 0]], - pad_setting=Padding.SAME, - ) - assert not support.is_operator_supported(op) - op_consumer = testutil.create_op_with_quant_tensors(Op.ConcatTFLite, [1, 1, 1, 4], [1, 1, 1, 8]) - op.ofm.consumer_list = [op_consumer] - assert not support.is_operator_supported(op) - op_consumer = testutil.create_elemwise_op(Op.Add, "op", [1, 3, 3, 1], [1, 3, 3, 1], [1, 3, 3, 1]) - op.ofm.consumer_list = [op_consumer] - assert not support.is_operator_supported(op) - - -pad_invalid_size_test_data = [ - (2, 1, 1, 1), - (1, 2, 1, 1), - (1, 1, 2, 1), - (1, 1, 1, 2), -] - - -@pytest.mark.parametrize("top, left, bottom, right", pad_invalid_size_test_data) -def test_constraint_pad_size(top, left, bottom, right): - # Tests PAD operator with a padding that is too high to be handled by the NPU - out_shape = [1, 11 + left + right, 11 + top + bottom, 1] - padding = [[0, 0], [top, bottom], [left, right], [0, 0]] - op = create_pad_op(in_shape=[1, 11, 11, 1], out_shape=out_shape, padding=padding,) - assert not support.is_operator_supported(op) - - -leading_pad_test_data = [ - (2, 2, 11, True), - (1, 2, 11, False), - (2, 1, 11, False), - (5, 2, 11, True), -] - - -@pytest.mark.parametrize("top, left, kernel_size, expected", leading_pad_test_data) -def test_constraint_leading_pad_size(top, left, kernel_size, expected): - # Tests PAD operator with big kernel size; top and left pad must be multiple of stride - out_shape = [1, 11 + left, 11 + top, 1] - padding = [[0, 0], [top, 0], [left, 0], [0, 0]] - op = create_pad_op(in_shape=[1, 11, 11, 1], out_shape=out_shape, padding=padding, kernel_size=kernel_size) - assert support.is_operator_supported(op) == expected - - -pad_avg_pool_test_data = [ - ((3, 3), (1, 1, 1, 1), True), - ((2, 4), (1, 2, 1, 2), True), - ((5, 3), (2, 1, 2, 1), True), - ((5, 3), (0, 1, 2, 1), True), - ((5, 3), (2, 0, 2, 1), True), - ((5, 3), (2, 1, 0, 1), True), - ((5, 3), (2, 1, 0, 1), True), - ((4, 4), (2, 2, 2, 2), True), - ((4, 4), (1, 2, 2, 2), False), - ((4, 4), (2, 1, 2, 2), False), - ((4, 4), (2, 2, 1, 2), False), - ((4, 4), (2, 2, 2, 1), False), -] - - -@pytest.mark.parametrize("k_size, padding, expected", pad_avg_pool_test_data) -def test_pad_followed_by_avg_pool(k_size, padding, expected): - # Tests PAD followed by AvgPool - k_w, k_h = k_size - top, left, bottom, right = padding - pad_values = [[0, 0], [top, bottom], [left, right], [0, 0]] - dtype = DataType.int8 - qp = testutil.default_quant_params() - in_shape = [1, 15, 17, 8] - out_shape = [1, in_shape[1] + top + bottom, in_shape[2] + left + right, in_shape[3]] - in0 = Tensor(in_shape, dtype, "in") - in0.quantization = qp - pad_tensor = create_const_tensor( - name="pad", shape=list(np.shape(pad_values)), values=pad_values, dtype=DataType.int32 - ) - out = Tensor(out_shape, dtype, "out") - out.quantization = qp.clone() - op = testutil.create_op(Op.Pad, [in0, pad_tensor], out) - pool_out_tens = Tensor(in_shape, dtype, "output") - pool_out_tens.quantization = qp.clone() - attrs = { - "padding": Padding.VALID, - "ksize": [1, k_w, k_h, 1], - "stride_w": 1, - "stride_h": 1, - "dilation_w_factor": 1, - "dilation_h_factor": 1, - } - pool_op = testutil.create_op(Op.AvgPool, [out], pool_out_tens, attrs) - pool_op.add_input_tensor(out) - assert support.is_operator_supported(op) == expected - - def create_strided_slice(): # Creates a valid strided slice operator with some valid inputs/outputs op = create_strided_slice_op([1, 10, 10, 10], [1, 5, 5, 10], [127, 2, 2, 0], [0, 7, -3, 0]) |