mehrdadh commented on code in PR #11250: URL: https://github.com/apache/tvm/pull/11250#discussion_r874187865
########## tests/python/relay/strategy/arm_cpu/test_conv2d_nhwc.py: ########## @@ -0,0 +1,163 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you 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 +# +# http://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. +import sys +import numpy as np +import pytest +import tvm +import tvm.testing +from tvm import relay +from tvm.micro.testing.aot_test_utils import ( + AOTTestModel, + AOT_CORSTONE300_RUNNER, + generate_ref_data, + compile_and_run, +) + + +class BasicConv2dTests: + @tvm.testing.requires_corstone300 + def test_conv2d( + self, + data_shape, + kernel_size, + kernel_layout, + num_filter, + strides, + padding, + dilation, + dtype, + schedule_name, + ): + """Test a subgraph with a single conv2d operator.""" + ishape = data_shape + wshape = (*kernel_size, data_shape[-1], num_filter) + + weight_data = np.random.randint(low=-10, high=10, size=wshape, dtype=dtype) + + input0 = relay.var("input", relay.TensorType(ishape, dtype)) + weight0 = relay.const(weight_data) + out0 = relay.op.nn.conv2d( + input0, + weight0, + kernel_size=kernel_size, + strides=strides, + padding=padding, + dilation=(dilation, dilation), + data_layout="NHWC", + kernel_layout="HWIO", + out_dtype="int32", + out_layout="NHWC", + ) + ref_mod = tvm.IRModule.from_expr(relay.Function([input0], out0)) + + input1 = relay.var("input", relay.TensorType(ishape, dtype)) + + if kernel_layout == "HWOI": + weight1 = relay.const(np.moveaxis(weight_data, 2, -1)) + elif kernel_layout == "HWIO": + weight1 = relay.const(weight_data) + + out1 = relay.op.nn.conv2d( + input1, + weight1, + kernel_size=kernel_size, + strides=strides, + padding=padding, + dilation=(dilation, dilation), + data_layout="NHWC", + kernel_layout=kernel_layout, + out_dtype="int32", + out_layout="NHWC", + ) + mod = tvm.IRModule.from_expr(relay.Function([input1], out1)) + + inputs = {"input": np.random.randint(low=-128, high=127, size=ishape, dtype=dtype)} + output_list = generate_ref_data(ref_mod, inputs) + + compile_and_run( + AOTTestModel(module=mod, inputs=inputs, outputs=output_list), + runner=AOT_CORSTONE300_RUNNER, + interface_api="c", + use_unpacked_api=True, + target_opts={ + "-keys": "arm_cpu", + "-mcpu": "cortex-m7", + }, + schedule_name=schedule_name, + ) + + +class TestConv2d_DSP_HWOI(BasicConv2dTests): + """This test is for conv2d_nhwc_dsp.arm_cpu schedule.""" + + data_shape, kernel_size, num_filter, strides, padding, dilation = tvm.testing.parameters( + # TODO(mehrdadh): Fails due to https://github.com/apache/tvm/issues/11216 + # ((1, 32, 32, 1), (3, 3), 12, 1, 0, 1), + # ((1, 32, 10, 3), (3, 3), 16, 1, 0, 1), + # ((1, 49, 10, 1), (10, 4), 64, (2, 1), (4, 1, 5, 1), 1), + ((1, 32, 32, 16), (3, 3), 16, 1, (0, 2, 2, 0), 1), + ((1, 32, 32, 16), (3, 3), 16, 1, 0, 1), + ((1, 32, 32, 16), (3, 3), 16, 1, 0, 1), + ((1, 32, 32, 16), (3, 3), 16, 1, (0, 2, 2, 0), 2), + ((1, 32, 32, 16), (3, 3), 16, 1, (1, 1, 2, 2), 2), + # from Keyword Spotting model from MLPerfTiny models + # TODO(mehrdad): Fails due to https://github.com/apache/tvm/issues/11216 + # ((1, 49, 10, 1), (10, 4), 64, (2, 2), (4, 1, 5, 1), 1), + # from Visual Wake Word model from MLPerfTiny models + # TODO(mehrdadh): fails due to https://github.com/apache/tvm/issues/11216 + # ((1, 96, 96, 3), (3, 3), 8, (2, 2), (0, 0, 1, 1), 1), + # from Image Classification model from MLPerfTiny models + ((1, 16, 16, 32), (1, 1), 64, (2, 2), 0, 1), + ((4, 16, 16, 8), (5, 5), 8, 2, (0, 4, 4, 0), 1), + ((4, 16, 16, 8), (5, 5), 16, 2, (0, 4, 4, 0), 1), + ((4, 16, 16, 8), (5, 5), 8, 2, 0, 1), + ((4, 16, 16, 8), (5, 5), 16, 2, 0, 1), + ((1, 16, 16, 8), (3, 3), 16, 2, (0, 0, 1, 1), 1), + ((1, 16, 16, 8), (3, 3), 16, 2, (1, 1, 2, 2), 1), + ((1, 16, 16, 8), (5, 5), 16, 2, (3, 3, 2, 2), 1), + ((1, 16, 16, 8), (3, 3), 16, 2, (0, 1, 2, 3), 1), + ) + dtype = tvm.testing.parameter("int8", "int16") + kernel_layout = tvm.testing.parameter("HWOI") + schedule_name = tvm.testing.parameter("conv2d_nhwc_dsp.arm_cpu") + + +class TestConv2d_HWIO(BasicConv2dTests): + """This test is for conv2d_nhwc_spatial_pack.arm_cpu schedule.""" + + data_shape, kernel_size, num_filter, strides, padding, dilation = tvm.testing.parameters( + ((1, 32, 32, 1), (3, 3), 12, 1, 0, 1), + ((1, 32, 10, 3), (3, 3), 16, 1, 0, 1), + ((1, 49, 10, 1), (10, 4), 64, (2, 1), (4, 1, 5, 1), 1), + ((1, 32, 32, 16), (3, 3), 16, 1, (0, 2, 2, 0), 1), + ((1, 32, 32, 16), (3, 3), 16, 1, 0, 1), + ((1, 32, 32, 16), (3, 3), 16, 1, 0, 1), + ((1, 32, 32, 16), (3, 3), 16, 1, (0, 2, 2, 0), 2), + ((1, 32, 32, 16), (3, 3), 16, 1, (1, 1, 2, 2), 2), + ) + dtype = tvm.testing.parameter("int8", "int16") + kernel_layout = tvm.testing.parameter("HWIO") + schedule_name = tvm.testing.parameter("conv2d_nhwc_spatial_pack.arm_cpu") + + +# TODO(mehrdadh): Add test for conv2d_NHWC_quantized_native.arm_cpu Review Comment: removed them. -- This is an automated message from the Apache Git Service. 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