test_local_topi_conv2d_nchw.py 4.13 KB
Newer Older
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
# 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.
17 18 19 20 21 22
"""Example code to do convolution.
Copied from topi/tests/python/test_topi_conv2d_nchw.py.
Should be removed once we fix OpenGL testing on Jenkins."""
import os
import numpy as np
import tvm
23
from tvm import te
24 25 26 27 28 29 30
import topi
from tvm.contrib.pickle_memoize import memoize
from topi.util import get_const_tuple

def verify_conv2d_nchw(batch, in_channel, in_size, num_filter, kernel, stride, padding):
    in_height = in_width = in_size

31 32
    A = te.placeholder((batch, in_channel, in_height, in_width), name='A')
    W = te.placeholder((num_filter, in_channel, kernel, kernel), name='W')
33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62
    B = topi.nn.conv2d_nchw(A, W, stride, padding)
    C = topi.nn.relu(B)

    a_shape = get_const_tuple(A.shape)
    w_shape = get_const_tuple(W.shape)
    dtype = A.dtype

    @memoize("topi.tests.test_topi_conv2d.verify_con2d_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_nchw_python(a_np, w_np, stride, 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):
            s1 = topi.generic.schedule_conv2d_nchw([B])
            s2 = topi.generic.schedule_conv2d_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)
63
        with tvm.target.build_config(auto_unroll_max_step=1400,
64 65 66 67 68
                              unroll_explicit=(device != "cuda")):
            func1 = tvm.build(s1, [A, W, B], device, name="conv2d_%d_%d_%d_%d_%d_%d_%d" % (batch, in_channel, in_size, num_filter, kernel, stride, padding))
            func2 = tvm.build(s2, [A, W, C], device, name="relu_%d_%d_%d_%d_%d_%d_%d" % (batch, in_channel, in_size, num_filter, kernel, stride, padding))
            func1(a, w, b)
            func2(a, w, c)
69 70
            tvm.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)
            tvm.testing.assert_allclose(c.asnumpy(), c_np, rtol=1e-5)
71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99

    for device in ['opengl']:
        check_device(device)


def test_conv2d_nchw():
    # ResNet18 worklaods
    verify_conv2d_nchw(1, 3, 224, 64, 7, 2, 3)
    verify_conv2d_nchw(1, 64, 56, 64, 3, 1, 1)
    verify_conv2d_nchw(1, 64, 56, 64, 1, 1, 0)
    verify_conv2d_nchw(1, 64, 56, 128, 3, 2, 1)
    verify_conv2d_nchw(1, 64, 56, 128, 1, 2, 0)
    verify_conv2d_nchw(1, 128, 28, 128, 3, 1, 1)
    verify_conv2d_nchw(1, 128, 28, 256, 3, 2, 1)
    verify_conv2d_nchw(1, 128, 28, 256, 1, 2, 0)
    verify_conv2d_nchw(1, 256, 14, 256, 3, 1, 1)
    verify_conv2d_nchw(1, 256, 14, 512, 3, 2, 1)
    verify_conv2d_nchw(1, 256, 14, 512, 1, 2, 0)
    verify_conv2d_nchw(1, 512, 7, 512, 3, 1, 1)
    # Vgg16 workloads
    verify_conv2d_nchw(1, 128, 122, 128, 3, 1, 1)
    # Super resolution workloads
    verify_conv2d_nchw(1, 1, 224, 64, 5, 1, 2)
    verify_conv2d_nchw(1, 64, 224, 64, 3, 1, 1)
    verify_conv2d_nchw(1, 64, 224, 32, 3, 1, 1)
    verify_conv2d_nchw(1, 32, 224, 9, 3, 1, 1)

if __name__ == "__main__":
    test_conv2d_nchw()