diff options
Diffstat (limited to 'ethosu/vela/test/test_tflite_supported_operators.py')
| -rw-r--r-- | ethosu/vela/test/test_tflite_supported_operators.py | 33 |
1 files changed, 17 insertions, 16 deletions
diff --git a/ethosu/vela/test/test_tflite_supported_operators.py b/ethosu/vela/test/test_tflite_supported_operators.py index d091531d..6a0b58e3 100644 --- a/ethosu/vela/test/test_tflite_supported_operators.py +++ b/ethosu/vela/test/test_tflite_supported_operators.py @@ -1,4 +1,4 @@ -# SPDX-FileCopyrightText: Copyright 2020-2022 Arm Limited and/or its affiliates <open-source-office@arm.com> +# SPDX-FileCopyrightText: Copyright 2020-2023 Arm Limited and/or its affiliates <open-source-office@arm.com> # # SPDX-License-Identifier: Apache-2.0 # @@ -303,55 +303,55 @@ def test_constraint_resize(): for resize_op in Op.op_set(Op.is_resize_op): # IFM W and H == 1 op = testutil.create_op_with_quant_tensors(resize_op, [1, 1, 1, 8], [1, 8, 8, 8]) - op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [8, 8], np.int32)) + op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [8, 8])) assert support.is_operator_supported(op) # IFM == OFM op = testutil.create_op_with_quant_tensors(resize_op, [1, 8, 8, 8], [1, 8, 8, 8]) - op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [8, 8], np.int32)) + op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [8, 8])) assert support.is_operator_supported(op) # IFM x2 == OFM ; align_corners = False op = testutil.create_op_with_quant_tensors(resize_op, [1, 4, 4, 8], [1, 8, 8, 8]) - op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [8, 8], np.int32)) + op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [8, 8])) assert support.is_operator_supported(op) # IFM x4 == OFM ; align_corners = False op = testutil.create_op_with_quant_tensors(resize_op, [1, 4, 4, 8], [1, 16, 16, 8]) - op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [16, 16], np.int32)) + op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [16, 16])) assert support.is_operator_supported(op) # IFM x8 == OFM ; align_corners = False op = testutil.create_op_with_quant_tensors(resize_op, [1, 4, 4, 8], [1, 32, 32, 8]) - op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [32, 32], np.int32)) + op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [32, 32])) assert support.is_operator_supported(op) # IFM -1 x2 == OFM -1 ; align_corners = True op = testutil.create_op_with_quant_tensors(resize_op, [1, 4, 4, 8], [1, 7, 7, 8]) - op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [7, 7], np.int32)) + op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [7, 7])) op.attrs["align_corners"] = True assert support.is_operator_supported(op) # IFM -1 x4 == OFM -1 ; align_corners = True op = testutil.create_op_with_quant_tensors(resize_op, [1, 4, 4, 8], [1, 13, 13, 8]) - op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [13, 13], np.int32)) + op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [13, 13])) op.attrs["align_corners"] = True assert support.is_operator_supported(op) # IFM -1 x8 == OFM -1 ; align_corners = True op = testutil.create_op_with_quant_tensors(resize_op, [1, 4, 4, 8], [1, 25, 25, 8]) - op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [25, 25], np.int32)) + op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [25, 25])) op.attrs["align_corners"] = True assert support.is_operator_supported(op) # Invalid case - upscale size op = testutil.create_op_with_quant_tensors(resize_op, [1, 4, 4, 8], [1, 17, 17, 8]) - op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [17, 17], np.int32)) + op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [17, 17])) assert not support.is_operator_supported(op) # Invalid case - upscale size with align corners op = testutil.create_op_with_quant_tensors(resize_op, [1, 4, 4, 8], [1, 15, 15, 8]) - op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [15, 15], np.int32)) + op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [15, 15])) op.attrs["align_corners"] = True assert not support.is_operator_supported(op) @@ -360,7 +360,7 @@ def test_constraint_resize_size(): for resize_op in Op.op_set(Op.is_resize_op): # Invalid case - size != ofm size op = testutil.create_op_with_quant_tensors(resize_op, [1, 4, 4, 8], [1, 8, 8, 8]) - op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [7, 7], np.int32)) + op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [7, 7])) assert not support.is_operator_supported(op) @@ -368,7 +368,7 @@ def test_constraint_resize_attrs(): for resize_op in Op.op_set(Op.is_resize_op): # Invalid case - both align corners and half-pixel centers op = testutil.create_op_with_quant_tensors(resize_op, [1, 4, 4, 8], [1, 8, 8, 8]) - op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [8, 8], np.int32)) + op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [8, 8])) op.attrs["align_corners"] = True op.attrs["half_pixel_centers"] = True assert not support.is_operator_supported(op) @@ -395,7 +395,8 @@ def create_pad_op( qp = testutil.default_quant_params() in0 = Tensor(in_shape, in_dtype, "in") in0.quantization = qp - pad_tensor = create_const_tensor(name="pad", shape=list(np.shape(padding)), values=padding, dtype=pad_dtype) + shape = [] if padding == [] else list(np.shape(padding)) + pad_tensor = create_const_tensor(name="pad", shape=shape, values=padding, dtype=pad_dtype) out = Tensor(out_shape, out_dtype, "out") out.quantization = qp.clone() op = testutil.create_op(Op.Pad, [in0, pad_tensor], out) @@ -587,9 +588,9 @@ def create_mean(input_shape, output_shape, axis, datatype, attrs): 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) + indices = create_const_tensor("indices", [len(axis)], DataType.int32, axis) elif type(axis) is int: - indices = create_const_tensor("indices", [], DataType.int32, axis, np.uint8) + indices = create_const_tensor("indices", [], DataType.int32, axis) op = testutil.create_op(Op.Mean, [ifm, indices], ofm, attrs) return op |