guberti commented on code in PR #13051: URL: https://github.com/apache/tvm/pull/13051#discussion_r999605483
########## tests/python/relay/strategy/arm_cpu/test_generalized_conv2d.py: ########## @@ -0,0 +1,122 @@ +# 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. +"""Helper class for testing variations of 2D convolution. Should be used by subclassing +`GeneralizedConv2dTests`, and then setting the arguments using tvm.testing.parameter(s).""" + +from numpy.random import randint +import tvm +import tvm.testing +from tvm import relay +from tvm.testing.aot import AOTTestModel, compile_and_run, generate_ref_data +from tvm.micro.testing.aot_test_utils import AOT_CORSTONE300_RUNNER +from tvm.topi.utils import change_ndarray_layout + + +class GeneralizedConv2dTests: + """Superclass which can be used to test regular, depthwise, or grouped conv2D. Cannot be used + for 5D data formats (NCHWc and such) as written, but could be extended. Might also be worth + abstracting some of this logic into an even more general class that could be used for other + operators. + + Note that data_shape should always be a tuple of length four indicating the data shape in NHWC + format (it will later be reshaped according to the given data_layout), and kernel_size should be + a length two tuple giving the height and width of the kernel.""" + + @tvm.testing.requires_corstone300 + def test_conv2d( + self, + data_shape, + kernel_size, + num_filter, + in_dtype, + strides, + padding, + groups, + dilation, + data_layout, + kernel_layout, + out_layout, + schedule_name, + ): + """Test a subgraph with a single conv2d operator.""" + + ref_input_data = randint(low=-128, high=127, size=data_shape, dtype=in_dtype) + ref_input_var = relay.var("input", relay.TensorType(data_shape, in_dtype)) # NHWC layout + kernel_shape = (*kernel_size, data_shape[-1] // groups, num_filter) # HWIO layout + ref_kernel_data = randint(low=-10, high=10, size=kernel_shape, dtype=in_dtype) + + # Our x86 depthwise implementation only supports HWOI with NHWC, so we need to change our + # kernel layout to work around this. We can't just change the whole thing to HWIO or + # something else, as then group conv2d would not work. Eventually, we should switch to using + # TensorFlow to create the reference output so we can ensure our implementation is right. Review Comment: Done, see #13137. I also link to it from the comment. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: commits-unsubscr...@tvm.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org
