# 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 os import numpy as np import tvm import topi import topi.testing from tvm.contrib.pickle_memoize import memoize from topi.util import get_const_tuple def verify_conv2d_hwcn(batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation=1): in_height = in_width = in_size A = tvm.placeholder((in_height, in_width, in_channel, batch), name='A') W = tvm.placeholder((kernel, kernel, in_channel, num_filter), name='W') B = tvm.placeholder((1, num_filter, 1), name='bias') a_shape = get_const_tuple(A.shape) w_shape = get_const_tuple(W.shape) b_shape = get_const_tuple(B.shape) dtype = A.dtype @memoize("topi.tests.test_topi_conv2d_hwcn.verify_hwcn") 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=b_shape).astype(dtype) dw_np = topi.testing.dilate_python(w_np, (dilation, dilation, 1, 1)) c1_np = topi.testing.conv2d_hwcn_python(a_np, dw_np, stride, padding) c2_np = c1_np + b_np c3_np = np.maximum(c2_np, 0) return a_np, w_np, b_np, c1_np, c2_np, c3_np a_np, w_np, b_np, c1_np, c2_np, c3_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): t_conv = topi.nn.conv2d(A, W, stride, padding, dilation, layout='HWCN') t_bias = topi.add(t_conv, B) t_relu = topi.nn.relu(t_bias) s1 = topi.generic.schedule_conv2d_hwcn([t_conv]) s2 = topi.generic.schedule_conv2d_hwcn([t_bias]) s3 = topi.generic.schedule_conv2d_hwcn([t_relu]) a = tvm.nd.array(a_np, ctx) w = tvm.nd.array(w_np, ctx) b = tvm.nd.array(b_np, ctx) conv_out = tvm.nd.array( np.zeros(get_const_tuple(t_conv.shape), dtype=t_conv.dtype), ctx) bias_out = tvm.nd.array( np.zeros(get_const_tuple(t_bias.shape), dtype=t_bias.dtype), ctx) relu_out = tvm.nd.array( np.zeros(get_const_tuple(t_relu.shape), dtype=t_relu.dtype), ctx) func1 = tvm.build(s1, [A, W, t_conv], device) func2 = tvm.build(s2, [A, W, B, t_bias], device) func3 = tvm.build(s3, [A, W, B, t_relu], device) func1(a, w, conv_out) func2(a, w, b, bias_out) func3(a, w, b, relu_out) tvm.testing.assert_allclose(conv_out.asnumpy(), c1_np, rtol=1e-5) tvm.testing.assert_allclose(bias_out.asnumpy(), c2_np, rtol=1e-5) tvm.testing.assert_allclose(relu_out.asnumpy(), c3_np, rtol=1e-5) for device in ['cuda', 'opencl', 'metal', 'rocm', 'vulkan', 'nvptx']: check_device(device) def test_conv2d_hwcn(): verify_conv2d_hwcn(1, 256, 32, 256, 3, 1, "SAME") verify_conv2d_hwcn(1, 256, 32, 256, 3, 1, "SAME") verify_conv2d_hwcn(4, 128, 16, 128, 5, 2, "SAME") verify_conv2d_hwcn(4, 128, 16, 256, 5, 2, "SAME") verify_conv2d_hwcn(1, 256, 32, 256, 3, 1, "VALID") verify_conv2d_hwcn(1, 256, 32, 256, 3, 1, "VALID") verify_conv2d_hwcn(4, 128, 16, 128, 5, 2, "VALID") verify_conv2d_hwcn(4, 128, 16, 256, 5, 2, "VALID") # dilation = 2 verify_conv2d_hwcn(1, 256, 32, 256, 3, 1, "SAME", dilation=2) if __name__ == "__main__": test_conv2d_hwcn()