| # Copyright (C) 2020-2021 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_graph_optimiser |
| import numpy as np |
| import pytest |
| |
| from ethosu.vela.data_type import DataType |
| from ethosu.vela.graph_optimiser import optimise_graph |
| from ethosu.vela.nn_graph import NetworkType |
| from ethosu.vela.operation import Op |
| from ethosu.vela.operation import Padding |
| from ethosu.vela.rewrite_graph import verify_graph_health |
| from ethosu.vela.tensor import create_const_tensor |
| from ethosu.vela.tensor import Shape4D |
| from ethosu.vela.tensor import Tensor |
| from ethosu.vela.test import testutil |
| from ethosu.vela.tflite_graph_optimiser import calc_explicit_padding |
| from ethosu.vela.tflite_graph_optimiser import convert_batched_fc_shape |
| from ethosu.vela.tflite_graph_optimiser import replace_pad_by_hw_pad |
| from ethosu.vela.tflite_graph_optimiser import rewrite_fully_connected_input |
| |
| |
| def test_convert_batched_fc(): |
| """Tests shape conversion of batched fully connected""" |
| ifm_shape = [4, 8] |
| ifm = create_const_tensor("test_in", ifm_shape, np.uint8, np.zeros(ifm_shape)) |
| w_shape = [8, 4] |
| weights = create_const_tensor("weight_in", w_shape, np.uint8, np.zeros(w_shape)) |
| ofm = Tensor(ifm.shape, np.uint8, "test_out") |
| op = testutil.create_op(Op.FullyConnected, [ifm, weights], ofm) |
| |
| ifm.consumer_list.append(op) |
| |
| prev_op = op.clone() |
| prev_op.ifm_shapes = op.ifm_shapes.copy() |
| prev_op.ofm_shapes = op.ofm_shapes.copy() |
| |
| rewrite_fully_connected_input(op, None, None) |
| conv_op = convert_batched_fc_shape(op, None, None) |
| assert conv_op.ifm == prev_op.ifm |
| assert conv_op.ofm == prev_op.ofm |
| assert op.ifm_shapes[0] == Shape4D([1, 2, 2, 8]) |
| assert op.ofm_shapes[0] == Shape4D([1, 2, 2, 8]) |
| assert conv_op.type == Op.FullyConnected |
| assert len(conv_op.ifm.shape) == 2 |
| assert len(conv_op.ofm.shape) == 2 |
| assert conv_op.ifm.shape == conv_op.ofm.shape |
| |
| ifm.shape = [1, 8] |
| weights.shape = [8, 1] |
| ofm.shape = [1, 8] |
| op = testutil.create_op(Op.FullyConnected, [ifm, weights], ofm) |
| ifm.consumer_list.append(op) |
| |
| prev_op = op.clone() |
| prev_op.ifm_shapes = op.ifm_shapes.copy() |
| prev_op.ofm_shapes = op.ofm_shapes.copy() |
| |
| rewrite_fully_connected_input(op, None, None) |
| conv_op = convert_batched_fc_shape(op, None, None) |
| |
| assert conv_op.ifm == prev_op.ifm |
| assert conv_op.ofm == prev_op.ofm |
| assert op.ifm_shapes[0] == prev_op.ifm_shapes[0] |
| assert op.ofm_shapes[0] == prev_op.ofm_shapes[0] |
| assert conv_op.type == Op.FullyConnected |
| assert len(conv_op.ifm.shape) == 2 |
| assert len(conv_op.ofm.shape) == 2 |
| assert conv_op.ifm.shape == conv_op.ofm.shape |
| |
| |
| explicit_padding_test_data = [ |
| # Kernel size 2 |
| [(17, 1, 2, 1, 1), (1, 1)], |
| [(18, 1, 2, 0, 1), (0, 1)], |
| [(18, 1, 2, 1, 0), (1, 0)], |
| # Kernel size 3 |
| [(18, 2, 3, 1, 1), (1, 0)], |
| [(25, 2, 3, 1, 1), (1, 1)], |
| # Kernel size 4 |
| [(18, 1, 4, 1, 2), (1, 2)], |
| [(18, 1, 4, 2, 1), (2, 1)], |
| [(19, 1, 4, 2, 2), (2, 2)], |
| # Kernel size 5 |
| [(19, 1, 5, 1, 2), (1, 2)], |
| [(19, 1, 5, 0, 2), (0, 2)], |
| [(19, 1, 5, 1, 0), (1, 0)], |
| # Kernel size 21 |
| [(41, 2, 21, 8, 10), (8, 10)], |
| [(41, 3, 21, 10, 10), (10, 9)], |
| [(42, 3, 21, 10, 10), (10, 8)], |
| [(42, 3, 21, 9, 10), (9, 9)], |
| [(41, 3, 21, 10, 6), (10, 6)], |
| ] |
| |
| |
| @pytest.mark.parametrize("test_input, expected_result", explicit_padding_test_data) |
| def test_calc_explicit_padding(test_input, expected_result): |
| input_size, stride, filter_size, explicit_pad_before, explicit_pad_after = test_input |
| before, after = calc_explicit_padding(input_size, stride, filter_size, explicit_pad_before, explicit_pad_after) |
| assert (before, after) == expected_result |
| |
| |
| def create_pad_and_conv2d( |
| in_shape, |
| out_shape, |
| padding, |
| in_dtype=DataType.int8, |
| out_dtype=DataType.int8, |
| pad_dtype=DataType.int32, |
| pad_setting=Padding.VALID, |
| kernel_size=3, |
| ): |
| """Creates Pad operator followed by a conv2d operator""" |
| 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) |
| op.run_on_npu = True |
| 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, in_dtype.as_numpy_type()) |
| 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) |
| conv2d_op.run_on_npu = True |
| return op, conv2d_op |
| |
| |
| def test_pad_followed_by_conv_is_removed(): |
| """ |
| Tests that the PAD operator is bypassed when followed by a convolution operator, |
| and that the padding of the convolution operation is correctly updated |
| """ |
| pad_op, conv2d_op = create_pad_and_conv2d( |
| in_shape=[1, 76, 75, 64], out_shape=[1, 76, 75, 64], padding=[[0, 0], [2, 1], [1, 1], [0, 0]], kernel_size=4 |
| ) |
| nng = testutil.create_graph([pad_op, conv2d_op]) |
| arch = testutil.create_arch() |
| |
| replace_pad_by_hw_pad(conv2d_op, nng, arch) |
| |
| op = nng.subgraphs[0].output_tensors[0].ops[0] |
| assert op.type == Op.Conv2DBias |
| assert op.attrs["padding"] == Padding.EXPLICIT |
| assert op.attrs["explicit_padding"] == (2, 1, 1, 1) |
| assert op.ifm.shape == [1, 76, 75, 64] |
| assert pad_op not in op.ifm.ops |
| |
| |
| 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, expect_pad_removed", leading_pad_test_data) |
| def test_leading_pad_size(top, left, kernel_size, expect_pad_removed): |
| # 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]] |
| pad_op, conv2d_op = create_pad_and_conv2d( |
| in_shape=[1, 11, 11, 1], out_shape=out_shape, padding=padding, kernel_size=kernel_size |
| ) |
| nng = testutil.create_graph([pad_op, conv2d_op]) |
| arch = testutil.create_arch() |
| replace_pad_by_hw_pad(conv2d_op, nng, arch) |
| op = nng.subgraphs[0].output_tensors[0].ops[0] |
| if expect_pad_removed: |
| assert op.attrs["padding"] == Padding.EXPLICIT |
| assert "explicit_padding" in op.attrs |
| assert op.ifm.shape == op.ofm.shape |
| assert pad_op not in op.ifm.ops |
| else: |
| assert pad_op in op.ifm.ops |
| assert op.attrs["padding"] == Padding.VALID |
| assert "explicit_padding" not in op.attrs |
| |
| |
| def test_optimise_pad_followed_by_avg_pool(): |
| """ |
| Tests that the PAD operator is bypassed when followed by a average pool operator, |
| and that the average pool is converted to a depthwise |
| """ |
| # Create Pad operation followed by AvgPool |
| quant = testutil.default_quant_params() |
| in_tens = Tensor([1, 76, 75, 64], DataType.uint8, "input") |
| in_tens.quantization = quant |
| # Test with 3x2 input tensor |
| pad_input = create_const_tensor("pad_input", [3, 2], DataType.int32, [[2, 2], [1, 1], [0, 0]]) |
| temp_tens = Tensor([1, 79, 77, 64], DataType.uint8, "pad_out") |
| temp_tens.quantization = quant.clone() |
| out_tens = Tensor([1, 76, 75, 64], DataType.uint8, "output") |
| out_tens.quantization = quant.clone() |
| |
| pad_op = testutil.create_op(Op.Pad, [in_tens, pad_input], temp_tens) |
| attrs = { |
| "padding": Padding.VALID, |
| "ksize": [1, 5, 3, 1], |
| "stride_w": 2, |
| "stride_h": 2, |
| "dilation_w_factor": 1, |
| "dilation_h_factor": 1, |
| } |
| attrs["strides"] = (1, attrs["stride_h"], attrs["stride_w"], 1) |
| pad_op.run_on_npu = True |
| conv2d_op = testutil.create_op(Op.AvgPool, [temp_tens], out_tens, attrs) |
| conv2d_op.run_on_npu = True |
| nng = testutil.create_graph([pad_op, conv2d_op]) |
| arch = testutil.create_arch() |
| |
| replace_pad_by_hw_pad(conv2d_op, nng, arch) |
| |
| op = nng.subgraphs[0].output_tensors[0].ops[0] |
| assert op.type == Op.DepthwiseConv2DBias |
| assert op.attrs["padding"] == Padding.EXPLICIT |
| assert op.attrs["explicit_padding"] == (2, 1, 2, 1) |
| assert op.ifm.shape == [1, 76, 75, 64] |
| assert pad_op not in op.ifm.ops |
| # Check that bias and weight tensors have been added |
| assert op.bias.shape == [64] |
| assert op.weights.shape == [5, 3, 1, 64] |
| |
| |
| pad_avg_pool_test_data = [ |
| ((3, 3), (1, 1, 1, 1), True), |
| ((3, 3), (2, 1, 1, 1), False), |
| ((3, 3), (1, 2, 1, 1), False), |
| ((3, 3), (1, 1, 2, 1), False), |
| ((3, 3), (1, 1, 1, 2), False), |
| ((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, expect_pad_removed", pad_avg_pool_test_data) |
| def test_pad_followed_by_avg_pool(k_size, padding, expect_pad_removed): |
| # 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() |
| pad_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) |
| pad_op.run_on_npu = True |
| pool_op.run_on_npu = True |
| nng = testutil.create_graph([pad_op, pool_op]) |
| arch = testutil.create_arch() |
| nng = optimise_graph(nng, arch, NetworkType.TFLite) |
| sg = nng.subgraphs[0] |
| all_ops = sg.get_all_ops() |
| print("all_ops: ", all_ops) |
| # Pad should not be in the graph anymore, it should either have been removed or rewritten |
| assert not any(op.type == Op.Pad for op in all_ops) |
| op = nng.subgraphs[0].output_tensors[0].ops[0] |
| if expect_pad_removed: |
| # Expect rewrite to depthwise, PAD is removed |
| assert op.type == Op.DepthwiseConv2DBias |
| assert op.attrs["padding"] == Padding.EXPLICIT |
| assert any(pad > 0 for pad in op.attrs["explicit_padding"]) |
| assert op.ifm.shape == op.ofm.shape |
| # Check that bias and weight tensors have been added |
| assert len(op.bias.shape) > 0 |
| assert op.weights.shape is not None |
| else: |
| # Pad should have been rewritten to a number of average pool operations |
| assert all(op.type in (Op.AvgPool, Op.Const) for op in all_ops) |
| assert pool_op.type == Op.AvgPool |
| assert pool_op.attrs["padding"] == Padding.VALID |
| |
| |
| def test_remove_reshape(): |
| """ |
| Test that the expected reshape are removed in graph_optimisation |
| """ |
| |
| # Create tensors and operators Test1 |
| quant = testutil.default_quant_params() |
| |
| # create reshape1 op |
| ifm_shape = [64, 16] |
| reshape1_ofm_shape = [1, 4, 16, 16] |
| reshape1_ifm = create_const_tensor("reshape1_in", ifm_shape, DataType.uint8, np.zeros(ifm_shape)) |
| reshape1_ifm.quantization = quant |
| reshape1_ofm = create_const_tensor("reshape1_out", reshape1_ofm_shape, DataType.uint8, np.zeros(reshape1_ofm_shape)) |
| reshape1_ofm.quantization = quant |
| shape_tens = create_const_tensor("reshape1_shape", [1], DataType.int32, reshape1_ofm_shape) |
| reshape1_op = testutil.create_op(Op.Reshape, [reshape1_ifm, shape_tens], reshape1_ofm, set_ifm_ofm_shapes=False) |
| reshape1_op.attrs["new_shape"] = reshape1_ofm_shape |
| reshape1_op.run_on_npu = True |
| |
| # create conv op |
| conv_ofm = Tensor([1, 8, 8, 16], DataType.uint8, "output") |
| conv_ofm.quantization = quant.clone() |
| weight_tens = Tensor([1, 1, 16, 16], DataType.uint8, "weights") |
| weight_tens.values = np.zeros(weight_tens.shape, np.uint8) |
| weight_tens.quantization = quant.clone() |
| bias_tens = Tensor([16], DataType.int32, "biases") |
| |
| attrs = {"padding": Padding.SAME, "stride_w": 1, "stride_h": 1, "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, [reshape1_ofm, weight_tens, bias_tens], conv_ofm, attrs=attrs, set_ifm_ofm_shapes=False |
| ) |
| conv2d_op.run_on_npu = True |
| |
| # create reshape2 op |
| ofm_shape = [8, 8, 16] |
| reshape2_ofm = create_const_tensor("reshape2_out", ofm_shape, DataType.uint8, np.zeros(ofm_shape)) |
| reshape2_ofm.quantization = quant |
| shape_tens = create_const_tensor("reshape2_shape", [1], DataType.int32, ofm_shape) |
| reshape2_op = testutil.create_op(Op.Reshape, [conv_ofm, shape_tens], reshape2_ofm, set_ifm_ofm_shapes=False) |
| reshape2_op.attrs["new_shape"] = ofm_shape |
| reshape2_op.run_on_npu = True |
| |
| # Test1 no Reshape op is expected to remain in the NPU subgrapgh |
| # but first one will be put on CPU |
| # Network is Reshape-Conv-Reshape |
| # Result is Conv |
| nng = testutil.create_graph([reshape1_op, conv2d_op, reshape2_op]) |
| arch = testutil.create_arch() |
| assert verify_graph_health(nng) |
| nng = optimise_graph(nng, arch, NetworkType.TFLite, True) |
| assert verify_graph_health(nng) |
| |
| # Create tensors and operator Test2 |
| # create reshape op |
| reshape_ifm = create_const_tensor("reshape_in", ifm_shape, DataType.uint8, np.zeros(ifm_shape)) |
| reshape_ifm.quantization = quant |
| reshape_ofm = create_const_tensor("reshape1_out", reshape1_ofm_shape, DataType.uint8, np.zeros(reshape1_ofm_shape)) |
| reshape_ofm.quantization = quant |
| shape_tens = create_const_tensor("reshape1_shape", [1], DataType.int32, reshape1_ofm_shape) |
| reshape_op = testutil.create_op(Op.Reshape, [reshape_ifm, shape_tens], reshape_ofm, set_ifm_ofm_shapes=False) |
| reshape_op.attrs["new_shape"] = reshape1_ofm_shape |
| reshape_op.run_on_npu = True |
| |
| # Test2 Reshape ifm/ofm is sg input/output. |
| # Reshape op is expected to be replaced by a AvgPool 'NOP'. |
| # |
| # Network is Reshape |
| # expected is AvgPool |
| nng = testutil.create_graph([reshape_op]) |
| assert verify_graph_health(nng) |
| nng = optimise_graph(nng, arch, NetworkType.TFLite, True) |
| assert verify_graph_health(nng) |
| |
| |
| def test_remove_squeeze(): |
| """ |
| Tests that the expected squeeze are removed in graph_optimisation |
| """ |
| |
| # Create tensors and operators Test1 |
| quant = testutil.default_quant_params() |
| |
| # create conv op |
| ifm_shape = [1, 1, 1, 1024] |
| conv_ifm = create_const_tensor("conv_in", ifm_shape, DataType.uint8, np.zeros(ifm_shape)) |
| conv_ifm.quantization = quant |
| conv_ofm = Tensor([1, 1, 1, 1001], DataType.uint8, "output") |
| conv_ofm.quantization = quant.clone() |
| weight_tens = Tensor([1, 1, 1024, 1001], DataType.uint8, "weights") |
| weight_tens.values = np.zeros(weight_tens.shape, np.uint8) |
| weight_tens.quantization = quant.clone() |
| bias_tens = Tensor([1001], DataType.int32, "biases") |
| |
| attrs = {"padding": Padding.SAME, "stride_w": 1, "stride_h": 1, "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, [conv_ifm, weight_tens, bias_tens], conv_ofm, attrs=attrs, set_ifm_ofm_shapes=False |
| ) |
| conv2d_op.run_on_npu = True |
| |
| # create squeeze op |
| ofm_shape = [1, 1001] |
| squeeze_ofm = create_const_tensor("squeeze_out", ofm_shape, DataType.uint8, np.zeros(ofm_shape)) |
| squeeze_ofm.quantization = quant.clone() |
| squeeze_op = testutil.create_op(Op.Squeeze, [conv_ofm], squeeze_ofm, set_ifm_ofm_shapes=False) |
| squeeze_op.attrs["squeeze_dims"] = [1, 2] |
| squeeze_op.run_on_npu = True |
| |
| # Test1 no Squeeze op is expected to remain in the NPU subgrapgh |
| # |
| # Network is Conv-Squeeze |
| # Result is Conv |
| nng = testutil.create_graph([conv2d_op, squeeze_op]) |
| arch = testutil.create_arch() |
| assert verify_graph_health(nng) |
| nng = optimise_graph(nng, arch, NetworkType.TFLite, True) |
| assert verify_graph_health(nng) |
| |
| # Create tensors and operator Test2 |
| # create squeeze op |
| ifm_shape = [1, 1, 1, 1001] |
| squeeze_ifm = create_const_tensor("squeeze_in", ifm_shape, DataType.uint8, np.zeros(ifm_shape)) |
| squeeze_ifm.quantization = quant |
| squeeze_ofm = create_const_tensor("squeeze_out", ofm_shape, DataType.uint8, np.zeros(ofm_shape)) |
| squeeze_ofm.quantization = quant.clone() |
| squeeze_op = testutil.create_op(Op.Squeeze, [squeeze_ifm], squeeze_ofm, set_ifm_ofm_shapes=False) |
| squeeze_op.attrs["squeeze_dims"] = [1, 2] |
| squeeze_op.run_on_npu = True |
| |
| # Test2 Squeeze ifm/ofm is sg input/output. |
| # Squeeze op is expected to be replaced by a AvgPool 'NOP'. |
| # |
| # Network is Squeeze |
| # expected is AvgPool |
| nng = testutil.create_graph([squeeze_op]) |
| assert verify_graph_health(nng) |
| nng = optimise_graph(nng, arch, NetworkType.TFLite, True) |
| assert verify_graph_health(nng) |
| |
| |
| def test_remove_expand_dims(): |
| """ |
| Tests that the expected ExpandDims are removed in graph_optimisation |
| """ |
| |
| # Create tensors and operators Test1 |
| quant = testutil.default_quant_params() |
| |
| # create ExpandDims op |
| ifm_shape = [4, 16, 16] |
| ofm_shape = [1, 4, 16, 16] |
| expand_dims_ifm = create_const_tensor("expand_dims_in", ifm_shape, DataType.uint8, np.zeros(ifm_shape)) |
| expand_dims_ifm.quantization = quant |
| expand_dims_ofm = create_const_tensor("expand_dims_out", ofm_shape, DataType.uint8, np.zeros(ofm_shape)) |
| expand_dims_ofm.quantization = quant.clone() |
| dim_tens = create_const_tensor("dim_tens", [], DataType.uint8, 1) |
| expand_dims_op = testutil.create_op( |
| Op.ExpandDims, [expand_dims_ifm, dim_tens], expand_dims_ofm, set_ifm_ofm_shapes=False |
| ) |
| expand_dims_op.run_on_npu = True |
| |
| # create conv op |
| conv_ofm = Tensor([1, 8, 8, 16], DataType.uint8, "output") |
| conv_ofm.quantization = quant.clone() |
| weight_tens = Tensor([1, 1, 16, 16], DataType.uint8, "weights") |
| weight_tens.values = np.zeros(weight_tens.shape, np.uint8) |
| weight_tens.quantization = quant.clone() |
| bias_tens = Tensor([16], DataType.int32, "biases") |
| |
| attrs = {"padding": Padding.SAME, "stride_w": 1, "stride_h": 1, "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, [expand_dims_ofm, weight_tens, bias_tens], conv_ofm, attrs=attrs, set_ifm_ofm_shapes=False |
| ) |
| conv2d_op.run_on_npu = True |
| |
| # Test1 no ExpandDims op is expected to remain in the NPU subgrapgh |
| # |
| # Network is ExpandDims-Conv |
| # Result is Conv |
| nng = testutil.create_graph([expand_dims_op, conv2d_op]) |
| arch = testutil.create_arch() |
| assert verify_graph_health(nng) |
| nng = optimise_graph(nng, arch, NetworkType.TFLite, True) |
| assert verify_graph_health(nng) |
| |
| # create ExpandDims op |
| expand_dims_ifm = create_const_tensor("expand_dims_in", ifm_shape, DataType.uint8, np.zeros(ifm_shape)) |
| expand_dims_ifm.quantization = quant |
| expand_dims_ofm = create_const_tensor("expand_dims_out", ofm_shape, DataType.uint8, np.zeros(ofm_shape)) |
| expand_dims_ofm.quantization = quant.clone() |
| dim_tens = create_const_tensor("dim_tens", [], DataType.uint8, 1) |
| expand_dims_op = testutil.create_op( |
| Op.ExpandDims, [expand_dims_ifm, dim_tens], expand_dims_ofm, set_ifm_ofm_shapes=False |
| ) |
| expand_dims_op.run_on_npu = True |
| |
| # Test2 ExpandDims ifm/ofm is sg input/output. |
| # ExpandDims op is expected to be replaced by a AvgPool 'NOP'. |
| # |
| # Network is ExpandDims |
| # expected is AvgPool |
| nng = testutil.create_graph([expand_dims_op]) |
| assert verify_graph_health(nng) |
| nng = optimise_graph(nng, arch, NetworkType.TFLite, True) |
| assert verify_graph_health(nng) |