| # Copyright (C) 2021 Arm Limited or its affiliates. All rights reserved. |
| # |
| # SPDX-License-Identifier: Apache-2.0 |
| # |
| # Licensed under the Apache License, Version 2.0 (the License); you may |
| # not use this file except in compliance with the License. |
| # You may obtain a copy of the License at |
| # |
| # www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License is distributed on an AS IS BASIS, WITHOUT |
| # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| # See the License for the specific language governing permissions and |
| # limitations under the License. |
| # |
| # Description: |
| # Unit tests for tflite_model_semantic |
| import numpy as np |
| |
| from ethosu.vela.data_type import DataType |
| from ethosu.vela.operation import Op |
| from ethosu.vela.operation import Padding |
| from ethosu.vela.tensor import create_const_tensor |
| from ethosu.vela.tensor import QuantizationParameters |
| from ethosu.vela.tensor import Tensor |
| from ethosu.vela.test import testutil |
| from ethosu.vela.tflite_model_semantic import TFLiteSemantic |
| |
| semantic_checker = TFLiteSemantic() |
| |
| |
| def test_constraint_tens_no_dynamic(): |
| # Tensors cannot be dynamic (no shape, not a scalar) |
| op = testutil.create_op_with_quant_tensors(Op.Relu, [1, 8, 8, 8], []) |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| |
| |
| def test_constraint_tens_defined_shape(): |
| # Tensors cannot have None in them |
| op = testutil.create_op_with_quant_tensors(Op.Relu, [1, 8, None, 8], [1, 8, 8, 8]) |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| |
| |
| def test_constraint_tens_output_scalar(): |
| # Scalar output is not allowed at all: |
| op = testutil.create_elemwise_op(Op.Mul, "op", [1, 8, 8, 8], [1, 8, 8, 8], []) |
| op.ofm.values = 0.5 |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| |
| |
| def test_constraint_tens_input_scalar(): |
| # Shapeless input is allowed if its of a certain type: |
| op = testutil.create_elemwise_op(Op.Mul, "op", [1, 8, 8, 8], [], [1, 8, 8, 8]) |
| assert semantic_checker.is_operator_semantic_valid(op) |
| # Invalid shapeless input due to op type: |
| op = testutil.create_op_with_quant_tensors(Op.Relu, [], [1, 8, 8, 8]) |
| op.ifm.values = 0.5 |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| |
| |
| def test_constraint_tens_shape_size(): |
| # Tensors cannot be > 4D |
| op = testutil.create_op_with_quant_tensors(Op.Relu, [1, 1, 8, 8, 8], [1, 1, 8, 8, 8], set_ifm_ofm_shapes=False) |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| |
| |
| def test_constraint_tens_quant_none_check(): |
| # Tensors must have quantization parameters |
| op = testutil.create_elemwise_op(Op.Mul, "op", [1, 8, 8, 8], [], [1, 8, 8, 8], ifm2_quant=None) |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| |
| |
| def test_constraint_tens_quant_scale(): |
| # Quantization scale cannot be infinite |
| qp = QuantizationParameters() |
| qp.zero_point = 0 |
| qp.scale_f32 = np.inf |
| op = testutil.create_elemwise_op(Op.Mul, "op", [1, 8, 8, 8], [], [1, 8, 8, 8], ifm_quant=qp) |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| |
| |
| def test_constraint_fc_output_2d_not_supp(): |
| op = testutil.create_op_with_quant_tensors(Op.FullyConnected, [7, 4, 6], [3, 2, 2, 8], weights_shape=[1, 9, 1]) |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| op = testutil.create_op_with_quant_tensors(Op.FullyConnected, [12, 1, 6, 1], [3, 7, 4], weights_shape=[1, 1, 7, 1]) |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| op = testutil.create_op_with_quant_tensors(Op.FullyConnected, [4, 1, 4, 7], [1, 9], weights_shape=[12, 3]) |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| op = testutil.create_op_with_quant_tensors(Op.FullyConnected, [4], [9], weights_shape=[3, 2]) |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| |
| |
| def test_constraint_fc_output_2d_is_supp(): |
| op = testutil.create_op_with_quant_tensors(Op.FullyConnected, [4, 8, 8, 4], [32, 32], weights_shape=[4, 8, 8, 4]) |
| assert semantic_checker.is_operator_semantic_valid(op) |
| op = testutil.create_op_with_quant_tensors(Op.FullyConnected, [1, 1024], [16, 64], weights_shape=[1, 1024]) |
| assert semantic_checker.is_operator_semantic_valid(op) |
| op = testutil.create_op_with_quant_tensors(Op.FullyConnected, [12, 1], [3, 2, 1, 1], weights_shape=[12, 1, 1, 1]) |
| assert semantic_checker.is_operator_semantic_valid(op) |
| op = testutil.create_op_with_quant_tensors(Op.FullyConnected, [12, 1], [3, 2, 1], weights_shape=[12, 1, 1, 1]) |
| assert semantic_checker.is_operator_semantic_valid(op) |
| op = testutil.create_op_with_quant_tensors(Op.FullyConnected, [12, 1], [1, 1, 3, 2], weights_shape=[12, 1, 1, 1]) |
| assert semantic_checker.is_operator_semantic_valid(op) |
| op = testutil.create_op_with_quant_tensors(Op.FullyConnected, [12, 1, 1, 1], [1, 1, 1], weights_shape=[12, 1, 1, 1]) |
| assert semantic_checker.is_operator_semantic_valid(op) |
| op = testutil.create_op_with_quant_tensors( |
| Op.FullyConnected, [12, 1, 1, 1], [1, 1, 24], weights_shape=[12, 1, 1, 1] |
| ) |
| assert semantic_checker.is_operator_semantic_valid(op) |
| op = testutil.create_op_with_quant_tensors(Op.FullyConnected, [1, 1, 1, 1], [1, 3, 4], weights_shape=[1, 1, 1, 1]) |
| assert semantic_checker.is_operator_semantic_valid(op) |
| |
| |
| def test_constraint_conv_pass(): |
| # First test a simple conv passes |
| op = testutil.create_op_with_quant_tensors(Op.Conv2DBias, [1, 1, 1, 1], [1, 1, 1, 1], weights_shape=[1, 1, 1, 1]) |
| op.attrs = {"stride_w": 1, "stride_h": 1} |
| assert semantic_checker.is_operator_semantic_valid(op) |
| |
| |
| def test_constraint_stride_type(): |
| # Stride width and height must be integer types |
| op = testutil.create_op_with_quant_tensors(Op.Conv2DBias, [1, 8, 8, 8], [1, 8, 8, 8]) |
| op.attrs = {"stride_w": 1.5, "stride_h": "1"} |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| |
| |
| def test_constraint_dilation_type(): |
| # Dilation width and height must be integer types |
| op = testutil.create_op_with_quant_tensors(Op.Conv2DBias, [1, 8, 8, 8], [1, 8, 8, 8]) |
| op.attrs = {"stride_w": 1, "stride_h": 1, "dilation_w_factor": 1.5, "dilation_h_factor": "1"} |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| |
| |
| def test_constraint_quant_scale_inf(): |
| # Test handling IFM scale/OFM scale is infinite |
| op = testutil.create_op_with_quant_tensors(Op.Relu, [1, 8, 8, 8], [1, 8, 8, 8]) |
| op.ifm.quantization.scale_f32 = np.float32(1e9) |
| op.ofm.quantization.scale_f32 = np.float32(1e-35) |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| |
| |
| def test_constraint_ofm_scale_too_small(): |
| # Tests handling of OFM scale < 1e-38 |
| shp = [1, 10, 20, 16] |
| op = testutil.create_elemwise_op( |
| Op.Mul, |
| "mul", |
| shp, |
| shp, |
| shp, |
| ofm_quant=testutil.default_quant_params(), |
| ) |
| assert semantic_checker.is_operator_semantic_valid(op) |
| op.ofm.quantization.scale_f32 = 1e-43 |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| |
| |
| def test_constraint_matching_in_out_types(): |
| # Valid |
| op = testutil.create_op_with_quant_tensors(Op.AvgPool, [1, 8, 8, 8], [1, 8, 8, 8]) |
| op.attrs = {"stride_w": 2, "stride_h": 2, "filter_width": 2, "filter_height": 2, "padding": Padding.SAME} |
| assert semantic_checker.is_operator_semantic_valid(op) |
| # Invalid. datatypes for ifm and ofm must match (default uint8) |
| op.ifm.dtype = DataType.int8 |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| |
| |
| def test_constraint_filter_type(): |
| # Filter width/height must be integers |
| op = testutil.create_op_with_quant_tensors(Op.AvgPool, [1, 8, 8, 8], [1, 8, 8, 8]) |
| op.attrs = {"stride_w": 2, "stride_h": 2, "filter_width": 2.5, "filter_height": "2", "padding": Padding.SAME} |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| |
| |
| def test_constraint_matching_shapes(): |
| # Softmax requires the ifm and ofm shapes to match |
| op = testutil.create_op_with_quant_tensors(Op.Softmax, [1, 1, 1, 8], [1, 2, 2, 4]) |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| op = testutil.create_op_with_quant_tensors(Op.Softmax, [1, 1, 1, 8], [1, 1, 1, 8]) |
| assert semantic_checker.is_operator_semantic_valid(op) |
| |
| |
| def test_constraint_beta_value_range(): |
| # beta must be positive |
| op = testutil.create_op_with_quant_tensors(Op.Softmax, [1, 1, 1, 8], [1, 1, 1, 8]) |
| op.attrs["beta"] = -1.0 |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| op.attrs["beta"] = 0.0 |
| assert semantic_checker.is_operator_semantic_valid(op) |
| |
| |
| def test_constraint_splitv_inferred(): |
| # SplitV requires a maximum of one inferred shape (-1) |
| qp = testutil.default_quant_params() |
| op = testutil.create_op_with_quant_tensors(Op.SplitV, [1, 1, 1, 8], [1, 1, 1, 8]) |
| sizes = create_const_tensor("sizes", [1, 1, 1, 4], DataType.int16, [[[[0, -1, 2, -1]]]], np.int16, quantization=qp) |
| op.add_input_tensor(sizes) |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| op = testutil.create_op_with_quant_tensors(Op.SplitV, [1, 1, 1, 8], [1, 1, 1, 8]) |
| sizes = create_const_tensor("sizes", [1, 1, 1, 4], DataType.int16, [[[[0, 1, 2, -1]]]], np.int16, quantization=qp) |
| op.add_input_tensor(sizes) |
| assert semantic_checker.is_operator_semantic_valid(op) |
| |
| |
| def test_constraint_concat_pass(): |
| # A working concat |
| op = testutil.create_op_with_quant_tensors(Op.ConcatTFLite, [1, 1, 1, 4], [1, 1, 1, 8]) |
| ifm2 = Tensor([1, 1, 1, 4], DataType.uint8, "in2") |
| ifm2.quantization = testutil.default_quant_params() |
| op.add_input_tensor(ifm2) |
| op.attrs["axis"] = 3 |
| assert semantic_checker.is_operator_semantic_valid(op) |
| |
| |
| def test_constraint_axis_exists(): |
| # Missing axis attribute |
| op = testutil.create_op_with_quant_tensors(Op.ConcatTFLite, [1, 1, 1, 4], [1, 1, 1, 8]) |
| ifm2 = Tensor([1, 1, 1, 4], DataType.uint8, "in2") |
| ifm2.quantization = testutil.default_quant_params() |
| op.add_input_tensor(ifm2) |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| |
| |
| def test_constraint_axis_valid(): |
| # Invalid axis attribute |
| op = testutil.create_op_with_quant_tensors(Op.ConcatTFLite, [1, 1, 1, 4], [1, 1, 1, 8]) |
| ifm2 = Tensor([1, 1, 1, 4], DataType.uint8, "in2") |
| ifm2.quantization = testutil.default_quant_params() |
| op.add_input_tensor(ifm2) |
| op.attrs["axis"] = 7 |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| |
| |
| def test_constraint_matching_dimensionality(): |
| # Mismatching dimensionality: 4D+2D=4D |
| op = testutil.create_op_with_quant_tensors(Op.ConcatTFLite, [1, 1, 1, 4], [1, 1, 1, 8]) |
| ifm2 = Tensor([1, 4], DataType.uint8, "in2") |
| ifm2.quantization = testutil.default_quant_params() |
| op.add_input_tensor(ifm2) |
| op.attrs["axis"] = 3 |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| |
| |
| def test_constraint_valid_dimensions(): |
| # Mismatching dimension value: |
| # ifm2 has w and h as 2, which is not the axis to concat and doesnt match ifm1 or ofm |
| op = testutil.create_op_with_quant_tensors(Op.ConcatTFLite, [1, 1, 1, 4], [1, 1, 1, 8]) |
| ifm2 = Tensor([1, 2, 2, 4], DataType.uint8, "in2") |
| ifm2.quantization = testutil.default_quant_params() |
| op.add_input_tensor(ifm2) |
| op.attrs["axis"] = 3 |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| |
| |
| def create_strided_slice_op(in_shape, out_shape, start_offsets, end_offsets): |
| qp = testutil.default_quant_params() |
| in0 = Tensor(in_shape, DataType.uint8, "in") |
| in0.quantization = qp |
| in1 = create_const_tensor("begin", [len(start_offsets)], DataType.uint8, start_offsets, quantization=qp) |
| in2 = create_const_tensor("end", [len(end_offsets)], DataType.uint8, end_offsets, quantization=qp) |
| in3 = create_const_tensor("strides", [len(end_offsets)], DataType.uint8, len(end_offsets) * [1], quantization=qp) |
| out = Tensor(out_shape, DataType.uint8, "out") |
| out.quantization = qp |
| attrs = {"ellipsis_mask": 0, "new_axis_mask": 0, "shrink_axis_mask": 0, "begin_mask": 0, "end_mask": 0} |
| return testutil.create_op(Op.StridedSlice, [in0, in1, in2, in3], out, attrs=attrs) |
| |
| |
| def create_pad_op( |
| 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") |
| in0.quantization = qp |
| pad_tensor = create_const_tensor(name="pad", shape=list(np.shape(padding)), 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) |
| return op |
| |
| |
| def test_constraint_pad_input_count(): |
| # Incorrect number of input tensors (2) |
| 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 semantic_checker.is_operator_semantic_valid(op) |
| op.add_input_tensor(op.inputs[0].clone()) |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| |
| |
| 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]) |
| op.attrs["begin_mask"] = 1 |
| op.attrs["end_mask"] = 9 |
| assert semantic_checker.is_operator_semantic_valid(op) |
| return op |
| |
| |
| def test_constraint_stridedslice_input_count(): |
| # Wrong number of input tensors |
| op = create_strided_slice() |
| op.add_input_tensor(op.inputs[0].clone()) |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| |
| |
| def test_constraint_stridedslice_inputs_const(): |
| # begin, end, stride values must not be None |
| op = create_strided_slice() |
| op.inputs[1].values = None |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| op = create_strided_slice() |
| op.inputs[2].values = None |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| op = create_strided_slice() |
| op.inputs[3].values = None |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| |
| |
| def test_constraint_ellipsis_mask(): |
| # Unsemantic_checkered ellipsis mask |
| op = create_strided_slice() |
| op.attrs["ellipsis_mask"] = 1 |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| |
| |
| def test_constraint_axis_masks(): |
| op = create_strided_slice() |
| # Setting one of new_axis_mask/shrink_axis_mask to non-zero is ok |
| op.attrs["new_axis_mask"] = 2 |
| assert semantic_checker.is_operator_semantic_valid(op) |
| op = create_strided_slice() |
| op.attrs["shrink_axis_mask"] = 3 |
| assert semantic_checker.is_operator_semantic_valid(op) |
| # But setting both to non-zero is not semantic_checkered |
| op.attrs["new_axis_mask"] = 2 |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| |
| |
| def test_constraint_slice_ranges(): |
| # Examples where end offset <= begin offset |
| op = create_strided_slice() |
| op.inputs[1].values = [0, 7, 2, 0] |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| op = create_strided_slice() |
| op.inputs[2].values = [0, 7, 2, 0] |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| op = create_strided_slice() |
| op.attrs["begin_mask"] = 0 |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| op = create_strided_slice() |
| op.attrs["end_mask"] = 0 |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| |
| |
| def test_constraint_matching_inputs_types(): |
| # input data types must match (default is uint8) |
| op = testutil.create_elemwise_op(Op.Mul, "op", [1, 8, 8, 8], [1, 8, 8, 8], [1, 8, 8, 8]) |
| op.ifm2.dtype = DataType.int8 |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| |
| |
| def test_constraint_matching_signed(): |
| # signed inputs require output to also be signed |
| op = testutil.create_elemwise_op(Op.Mul, "op", [1, 8, 8, 8], [1, 8, 8, 8], [1, 8, 8, 8], datatype=DataType.int8) |
| op.ofm.dtype = DataType.uint8 |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| |
| |
| def test_constraint_unsigned_valid(): |
| # unsigned inputs require output to be either: |
| op = testutil.create_elemwise_op(Op.Mul, "op", [1, 8, 8, 8], [1, 8, 8, 8], [1, 8, 8, 8]) |
| # the same (default uint8) |
| assert semantic_checker.is_operator_semantic_valid(op) |
| op.ofm.dtype = DataType.int8 |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| op.ofm.dtype = DataType.int16 |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| # or int32 |
| op.ofm.dtype = DataType.int32 |
| assert semantic_checker.is_operator_semantic_valid(op) |
| |
| |
| def test_constraint_matching_either_shapes(): |
| # BINARY CASE |
| # At least one ifm shape must match ofm's shape |
| op = testutil.create_elemwise_op(Op.Add, "op", [1, 4], [4, 4], [4, 4]) |
| assert semantic_checker.is_operator_semantic_valid(op) |
| op = testutil.create_elemwise_op(Op.Add, "op", [4, 4], [1, 4], [4, 4]) |
| assert semantic_checker.is_operator_semantic_valid(op) |
| op = testutil.create_elemwise_op(Op.Add, "op", [4, 4], [4, 4], [2, 2]) |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| op = testutil.create_elemwise_op(Op.Add, "op", [1, 4, 1, 16], [1, 1, 4, 1], [1, 4, 4, 16]) |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| op = testutil.create_elemwise_op(Op.Add, "op", [1, 1, 4, 1], [1, 4, 1, 16], [1, 4, 4, 16]) |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| |
| # UNARY CASE |
| # No second input so this is treated the same as requiring ifm shape to match ofm shape |
| op = testutil.create_elemwise_op(Op.CLZ, "op", [2, 2], None, [2, 2], datatype=DataType.int32) |
| assert semantic_checker.is_operator_semantic_valid(op) |
| op = testutil.create_elemwise_op(Op.CLZ, "op", [4, 4], None, [2, 2], datatype=DataType.int32) |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| |
| |
| def test_constraint_alpha_valid(): |
| # Alpha can only be negative for int8 and uint8 |
| op = testutil.create_elemwise_op(Op.LeakyRelu, "op", [2, 2], None, [2, 2], DataType.int16) |
| op.attrs["alpha"] = 0 |
| assert semantic_checker.is_operator_semantic_valid(op) |
| op.attrs["alpha"] = -1 |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| op = testutil.create_elemwise_op(Op.LeakyRelu, "op", [2, 2], None, [2, 2], DataType.int8) |
| op.attrs["alpha"] = 0 |
| assert semantic_checker.is_operator_semantic_valid(op) |
| op.attrs["alpha"] = -1 |
| assert semantic_checker.is_operator_semantic_valid(op) |
| |
| |
| def test_constraint_hardswish_dtype(): |
| # HardSwish operator dtype should be int8 or uint8, and input dtype must match output |
| # UINT8 |
| op = testutil.create_op_with_quant_tensors(Op.HardSwish, [1, 8, 8, 8], [1, 8, 8, 8]) |
| assert semantic_checker.is_operator_semantic_valid(op) |
| # INT8 |
| op = testutil.create_op_with_quant_tensors(Op.HardSwish, [1, 8, 8, 8], [1, 8, 8, 8], datatype=DataType.int8) |
| assert semantic_checker.is_operator_semantic_valid(op) |
| |
| # Invalid |
| op = testutil.create_op_with_quant_tensors(Op.HardSwish, [1, 8, 8, 8], [1, 8, 8, 8], datatype=DataType.int16) |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| op = testutil.create_op_with_quant_tensors(Op.HardSwish, [1, 8, 8, 8], [1, 8, 8, 8], datatype=DataType.uint16) |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| op = testutil.create_op_with_quant_tensors(Op.HardSwish, [1, 8, 8, 8], [1, 8, 8, 8], datatype=DataType.int32) |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| |
| in_tens = Tensor([1, 8, 8, 8], DataType.int8, "in") |
| out_tens = Tensor([1, 8, 8, 8], DataType.uint8, "out") |
| op = testutil.create_op(Op.HardSwish, [in_tens], out_tens) |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| |
| |
| def test_constraint_keep_dims_ifm_ofm(): |
| op = testutil.create_op_with_quant_tensors(Op.FullyConnected, [4, 8, 8, 4], [32, 32], weights_shape=[4, 8, 8, 4]) |
| op.attrs["keep_num_dims"] = True |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| op.attrs["keep_num_dims"] = False |
| assert semantic_checker.is_operator_semantic_valid(op) |
| |
| |
| def create_mean(input_shape, output_shape, axis, datatype, attrs): |
| ifm = Tensor(input_shape, datatype, "in") |
| ifm.quantization = testutil.default_quant_params() |
| 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 |
| |
| |
| def test_mean_dtype(): |
| op = create_mean([1, 6, 6, 16], [1, 1, 1, 16], [1, 2], DataType.int8, {"keep_dims": True}) |
| assert semantic_checker.is_operator_semantic_valid(op) |
| op.ifm.dtype = DataType.int16 |
| op.ofm.dtype = DataType.int16 |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| |
| |
| def test_mean_axis(): |
| op = create_mean([1, 6, 6, 16], [1, 1, 1, 16], 0, DataType.int8, {"keep_dims": True}) |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| op = create_mean([1, 6, 6, 16], [1, 1, 1, 16], [3], DataType.int8, {"keep_dims": True}) |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| op = create_mean([1, 6, 6, 16], [1, 1, 1, 16], [1, 3], DataType.int8, {"keep_dims": True}) |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| op = create_mean([1, 6, 6, 16], [1, 1, 1, 16], [0, 1], DataType.int8, {"keep_dims": True}) |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| op = create_mean([1, 6, 6, 16], [1, 1, 1, 16], [1, 2], DataType.int8, {"keep_dims": True}) |
| assert semantic_checker.is_operator_semantic_valid(op) |
| op = create_mean([1, 6, 6, 16], [1, 1, 1, 16], [1], DataType.int8, {"keep_dims": True}) |
| assert semantic_checker.is_operator_semantic_valid(op) |
| op = create_mean([1, 6, 6, 16], [1, 1, 1, 16], 2, DataType.int8, {"keep_dims": True}) |
| assert semantic_checker.is_operator_semantic_valid(op) |
| op = create_mean([1, 6, 6, 16], [1, 1, 1, 16], [2, 1], DataType.int8, {"keep_dims": True}) |
| assert semantic_checker.is_operator_semantic_valid(op) |
| |
| |
| def test_matching_in_out_quant(): |
| # quantisation parameters of ifm and ofm should match. |
| quant = testutil.default_quant_params() |
| # create reshape op |
| ifm_shape = [64, 16] |
| ofm_shape = [1, 4, 16, 16] |
| ifm = create_const_tensor("reshape_in", ifm_shape, DataType.uint8, np.zeros(ifm_shape)) |
| ifm.quantization = quant |
| ofm = create_const_tensor("reshape_out", ofm_shape, DataType.uint8, np.zeros(ofm_shape)) |
| ofm.quantization = quant.clone() |
| shape_tens = create_const_tensor("shape", [1], DataType.int32, ofm_shape) |
| op = testutil.create_op(Op.Reshape, [ifm, shape_tens], ofm, set_ifm_ofm_shapes=False) |
| op.attrs["new_shape"] = ofm_shape |
| |
| # Matching quantisation parameters |
| assert semantic_checker.is_operator_semantic_valid(op) |
| |
| # Different zp |
| ofm.quantization.zero_point = 32 |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| |
| # Different scale |
| ofm.quantization.zero_point = 0 |
| ofm.quantization.scale_f32 = 0.9 |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| |
| # Squeeze op diff quant |
| # create squeeze op |
| ifm_shape = [1, 1, 1, 1001] |
| ofm_shape = [1, 1001] |
| ifm = create_const_tensor("squeeze_in", ifm_shape, DataType.uint8, np.zeros(ifm_shape)) |
| ifm.quantization = quant |
| ofm = create_const_tensor("squeeze_out", ofm_shape, DataType.uint8, np.zeros(ofm_shape)) |
| ofm.quantization = quant.clone() |
| ofm.quantization.zero_point = 32 |
| op = testutil.create_op(Op.Squeeze, [ifm], ofm, set_ifm_ofm_shapes=False) |
| op.attrs["squeeze_dims"] = [1, 2] |
| assert not semantic_checker.is_operator_semantic_valid(op) |
| |
| # ExpandDims diff quant |
| quant = testutil.default_quant_params() |
| # create expand_dims op |
| ifm_shape = [4, 16, 16] |
| ofm_shape = [1, 4, 16, 16] |
| ifm = create_const_tensor("expand_dims_in", ifm_shape, DataType.uint8, np.zeros(ifm_shape)) |
| ifm.quantization = quant |
| ofm = create_const_tensor("expand_dims_out", ofm_shape, DataType.uint8, np.zeros(ofm_shape)) |
| ofm.quantization = quant.clone() |
| ofm.quantization.zero_point = 32 |
| dim = create_const_tensor("expand_dims_dim", [], DataType.uint8, 0) |
| op = testutil.create_op(Op.ExpandDims, [ifm, dim], ofm, set_ifm_ofm_shapes=False) |
| assert not semantic_checker.is_operator_semantic_valid(op) |