diff options
Diffstat (limited to 'ethosu/vela/test/test_supported_operators.py')
| -rw-r--r-- | ethosu/vela/test/test_supported_operators.py | 29 |
1 files changed, 19 insertions, 10 deletions
diff --git a/ethosu/vela/test/test_supported_operators.py b/ethosu/vela/test/test_supported_operators.py index d8fbb98f..36213b73 100644 --- a/ethosu/vela/test/test_supported_operators.py +++ b/ethosu/vela/test/test_supported_operators.py @@ -518,7 +518,13 @@ 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 + in_shape, + out_shape, + padding, + in_dtype=DataType.int8, + out_dtype=DataType.int8, + pad_dtype=DataType.int32, + pad_setting=Padding.VALID, ): qp = testutil.default_quant_params() in0 = Tensor(in_shape, in_dtype, "in") @@ -527,19 +533,17 @@ 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(in_shape, 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([in_shape[-1]], pad_dtype, "biases") - attrs = {"padding": Padding.VALID, "stride_w": 2, "stride_h": 2, "dilation_w_factor": 1, "dilation_h_factor": 1} + 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.Conv2D, [out, weight_tens, bias_tens], conv_out_tens, attrs) + conv2d_op = testutil.create_op(Op.Conv2DBias, [out, weight_tens, bias_tens], conv_out_tens, attrs) conv2d_op.add_input_tensor(out) - conv2d_op.set_ifm_ofm_shapes() return op @@ -581,11 +585,16 @@ def test_constraint_pad_dtype(): 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], [0, 0]],) - conv_op = op.ofm.consumers()[0] - conv_op.attrs["Padding"] = Padding.SAME + 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.Concat, [1, 1, 1, 4], [1, 1, 1, 8]) + 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_op_with_quant_tensors(Op.AvgPool, [1, 8, 8, 8], [1, 8, 8, 8]) |