# 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 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_conv2d_transpose_nchw(batch, in_channel, in_size, num_filter, kernel, stride, padding, output_padding): in_height = in_width = in_size A = tvm.placeholder((batch, in_channel, in_height, in_width), name='A') W = tvm.placeholder((in_channel, num_filter, kernel, kernel), name='W') a_shape = get_const_tuple(A.shape) w_shape = get_const_tuple(W.shape) dtype = A.dtype @memoize("topi.tests.test_topi_conv2d_transpose.verify_conv2d_transpose_nchw") 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 = topi.testing.conv2d_transpose_nchw_python(a_np, w_np, stride, padding, output_padding) c_np = np.maximum(b_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): B = topi.nn.conv2d_transpose_nchw(A, W, [stride, stride], [padding, padding], A.dtype, output_padding) C = topi.nn.relu(B) s1 = topi.generic.schedule_conv2d_transpose_nchw([B]) s2 = topi.generic.schedule_conv2d_transpose_nchw([C]) 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=B.dtype), ctx) c = tvm.nd.array(np.zeros(get_const_tuple(C.shape), dtype=C.dtype), ctx) func1 = tvm.build(s1, [A, W, B], device) func2 = tvm.build(s2, [A, W, C], device) func1(a, w, b) func2(a, w, c) tvm.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5) tvm.testing.assert_allclose(c.asnumpy(), c_np, rtol=1e-5) for device in get_all_backend(): check_device(device) def test_conv2d_transpose_nchw(): verify_conv2d_transpose_nchw(1, 3, 224, 32, 3, 1, 0, (0, 0)) verify_conv2d_transpose_nchw(1, 3, 224, 32, 3, 2, 1, (0, 0)) verify_conv2d_transpose_nchw(1, 3, 224, 32, 3, 2, 1, (1, 0)) verify_conv2d_transpose_nchw(1, 3, 224, 32, 2, 2, 0, (0, 0)) verify_conv2d_transpose_nchw(1, 3, 224, 32, 2, 2, 0, (1, 1)) verify_conv2d_transpose_nchw(1, 32, 32, 128, 5, 1, 0, (0, 0)) verify_conv2d_transpose_nchw(1, 32, 32, 128, 5, 2, 1, (0, 0)) if __name__ == "__main__": test_conv2d_transpose_nchw()