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test_topi_conv3d_ndhwc.py
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test_topi_conv3d_ndhwc.py
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# 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_conv3d_ndhwc(batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation=1):
if isinstance(in_size, tuple):
in_depth, in_height, in_width = in_size
else:
in_depth = in_height = in_width = in_size
if isinstance(kernel, tuple):
kernel_depth, kernel_height, kernel_width = kernel
else:
kernel_depth = kernel_height = kernel_width = kernel
A = tvm.placeholder((batch, in_depth, in_height, in_width, in_channel), name='A')
W = tvm.placeholder((kernel_depth, kernel_height, kernel_width, in_channel, num_filter), name='W')
B = topi.nn.conv3d_ndhwc(A, W, stride, padding, dilation)
a_shape = get_const_tuple(A.shape)
w_shape = get_const_tuple(W.shape)
dtype = A.dtype
@memoize("topi.tests.test_topi_conv3d_ndhwc.verify_ndhwc.v2")
def get_ref_data():
a_np = np.random.uniform(size=a_shape).astype(dtype)
w_np = np.random.uniform(size=w_shape).astype(dtype)
dw_np = topi.testing.dilate_python(w_np, (dilation, dilation, dilation, 1, 1))
b_np = topi.testing.conv3d_ndhwc_python(a_np, dw_np, stride, padding)
return a_np, w_np, b_np
a_np, w_np, b_np = get_ref_data()
def check_device(device):
if not tvm.module.enabled(device):
print("Skip because %s is not enabled" % device)
return
print("Running on target: %s" % device)
with tvm.target.create(device):
s = topi.generic.schedule_conv3d_ndhwc([B])
ctx = tvm.context(device, 0)
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)
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 ['llvm']:
check_device(device)
def test_conv3d_ndhwc():
verify_conv3d_ndhwc(1, 16, 32, 16, 3, 1, "SAME")
verify_conv3d_ndhwc(4, 32, 16, 32, 5, 2, "SAME")
verify_conv3d_ndhwc(4, 32, 16, 64, 5, 2, "SAME")
verify_conv3d_ndhwc(1, 64, 32, 64, 3, 1, "VALID")
verify_conv3d_ndhwc(1, 64, 32, 64, 3, 1, "VALID")
verify_conv3d_ndhwc(4, 32, 16, 32, 5, 2, "VALID")
verify_conv3d_ndhwc(4, 32, 16, 64, 5, 2, "VALID")
# dilation = 2
verify_conv3d_ndhwc(1, 64, 32, 64, 3, 1, "SAME", dilation=2)
verify_conv3d_ndhwc(1, 1, (20, 256, 256), 32, (1, 3, 3), (1, 2, 2), "SAME")
verify_conv3d_ndhwc(1, 1, (20, 256, 256), 32,
(1, 6, 6), (1, 2, 2), (0, 2, 2))
verify_conv3d_ndhwc(1, 4, (20, 256, 256), 8,
(1, 5, 5), (1, 2, 2), (0, 2, 2))
if __name__ == "__main__":
test_conv3d_ndhwc()