optima2005 commented on a change in pull request #4639: [Relay/Topi][Op] Conv1D URL: https://github.com/apache/incubator-tvm/pull/4639#discussion_r365497175
########## File path: topi/tests/python/test_topi_conv1d.py ########## @@ -0,0 +1,103 @@ +# 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. +"""Test code for transposed convolution.""" +import numpy as np +import itertools +import tvm +import topi +import topi.testing +from tvm.contrib.pickle_memoize import memoize +from topi.util import get_const_tuple +from common import get_all_backend + + +def verify_conv1d(batch, + in_channels, + in_width, + filters, + kernel_size=3, + stride=1, + dilation=1, + padding='VALID', + layout='NCW'): + if layout == 'NCW': + in_shape = [batch, in_channels, in_width] + kernel_shape = [filters, in_channels, kernel_size] + else: + in_shape = [batch, in_width, in_channels] + kernel_shape = [kernel_size, in_channels, filters] + + dtype = 'float32' + A = tvm.placeholder(in_shape, name='A', dtype=dtype) + W = tvm.placeholder(kernel_shape, name='W', dtype=dtype) + + def get_ref_data(layout): + a_np = np.random.uniform(size=in_shape).astype(dtype) + w_np = np.random.uniform(size=kernel_shape).astype(dtype) + if layout == 'NWC': + np_in = np.transpose(a_np, [0, 2, 1]) + np_w = np.transpose(w_np, [2, 1, 0]) + else: + np_in = a_np + np_w = w_np + b_np = topi.testing.conv1d_ncw_python(np_in, np_w, stride, padding, dilation) + if layout == 'NWC': + b_np = np.transpose(b_np, [0, 2, 1]) + return a_np, w_np, b_np + + a_np, w_np, b_np = get_ref_data(layout) + + def check_device(device): + ctx = tvm.context(device, 0) + if not ctx.exist: + print("Skip because %s is not enabled" % device) + return + with tvm.target.create(device): + B = topi.nn.conv1d(A, W, stride, padding, dilation, layout, 'float32') + if layout == 'NCW': + s = topi.generic.schedule_conv1d_ncw([B]) + else: + s = topi.generic.schedule_conv1d_nwc([B]) + + a = tvm.nd.array(a_np, ctx) + w = tvm.nd.array(w_np, ctx) + b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=dtype), ctx) + + func = tvm.build(s, [A, W, B], device) + func(a, w, b) + tvm.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5) + + for device in get_all_backend(): + check_device(device) + + +def test_conv1d(): + for layout in ["NCW", "NWC"]: + # Most basic test case + verify_conv1d(1, 1, 8, 1, 3, 1, 1, 'VALID', layout) + # With padding + verify_conv1d(1, 1, 8, 1, 3, 1, 1, 'SAME', layout) + # Realistic dimensions + verify_conv1d(1, 16, 32, 16, 3, 1, 1, 'SAME', layout) + # With stride + verify_conv1d(1, 16, 32, 16, 3, 2, 1, 'SAME', layout) + # With dilation + verify_conv1d(1, 16, 32, 16, 3, 1, 2, 'SAME', layout) Review comment: Could you please add several more tests to cover multiple batch, and other kernel size, like 1, 2 ---------------------------------------------------------------- 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. For queries about this service, please contact Infrastructure at: [email protected] With regards, Apache Git Services
