# 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. """Example code to do convolution.""" import numpy as np import tvm from tvm import autotvm 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_conv3d_ncdhw(batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation=1, add_bias=False, add_relu=False): print("Workload: (%d, %d, %d, %d, %d, %d, %d, %d)" % (batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation)) in_depth = in_height = in_width = in_size A = tvm.placeholder((batch, in_channel, in_depth, in_height, in_width), name='A') W = tvm.placeholder((num_filter, in_channel, kernel, kernel, kernel), name='W') bias = tvm.placeholder((num_filter, 1, 1, 1), name='bias') a_shape = get_const_tuple(A.shape) w_shape = get_const_tuple(W.shape) bias_shape = get_const_tuple(bias.shape) dtype = A.dtype @memoize("topi.tests.test_topi_conv3d_ncdhw.verify_conv3d_ncdhw") def get_ref_data(): a_np = np.random.uniform(size=a_shape).astype(dtype) w_np = np.random.uniform(size=w_shape).astype(dtype) b_np = np.random.uniform(size=bias_shape).astype(dtype) dw_np = topi.testing.dilate_python(w_np, (1, 1, dilation, dilation, dilation)) c_np = topi.testing.conv3d_ncdhw_python(a_np, dw_np, stride, padding) if add_bias: c_np += b_np if add_relu: c_np = np.maximum(c_np, 0) return a_np, w_np, b_np, c_np a_np, w_np, b_np, c_np = get_ref_data() def check_device(device): ctx = tvm.context(device, 0) if not ctx.exist: print("Skip because %s is not enabled" % device) return print("Running on target: %s" % device) with tvm.target.create(device): C = topi.nn.conv3d(A, W, (stride, stride, stride), (padding, padding, padding), (dilation, dilation, dilation), layout='NCDHW', out_dtype=dtype) if add_bias: C = topi.add(C, bias) if add_relu: C = topi.nn.relu(C) s = topi.generic.schedule_conv3d_ncdhw([C]) a = tvm.nd.array(a_np, ctx) w = tvm.nd.array(w_np, ctx) b = tvm.nd.array(b_np, ctx) c = tvm.nd.array(np.zeros(get_const_tuple(C.shape), dtype=C.dtype), ctx) if add_bias: func = tvm.build(s, [A, W, bias, C], device, name="relu_%d_%d_%d_%d_%d_%d_%d_%d" % (batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation)) func(a, w, b, c) else: func = tvm.build(s, [A, W, C], device, name="relu_%d_%d_%d_%d_%d_%d_%d_%d" % (batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation)) func(a, w, c) tvm.testing.assert_allclose(c.asnumpy(), c_np, rtol=1e-4) for device in get_all_backend(): with autotvm.tophub.context(device): # load tophub pre-tuned parameters check_device(device) def test_conv3d_ncdhw(): #3DCNN workloads verify_conv3d_ncdhw(1, 32, 32, 5, 1, 1, 0) verify_conv3d_ncdhw(1, 32, 32, 1, 1, 1, 0) verify_conv3d_ncdhw(1, 32, 32, 5, 1, 1, 1) verify_conv3d_ncdhw(1, 32, 32, 1, 1, 1, 1) # bias, relu verify_conv3d_ncdhw(1, 64, 56, 3, 1, 1, 1, add_relu=True) verify_conv3d_ncdhw(1, 64, 56, 3, 1, 1, 1, add_bias=True) verify_conv3d_ncdhw(1, 64, 56, 3, 1, 1, 1, add_bias=True, add_relu=True) # dilation = 2 verify_conv3d_ncdhw(1, 64, 56, 3, 3, 1, 1, dilation=2) # batch size verify_conv3d_ncdhw(4, 64, 56, 5, 3, 1, 1) # weird workloads verify_conv3d_ncdhw(2, 2, 2, 2, 2, 2, 2) verify_conv3d_ncdhw(3, 3, 3, 3, 3, 3, 3) if __name__ == "__main__": test_conv3d_ncdhw()