I change my code as follows:
```
import logging
import sys
import numpy as np
import tvm
from tvm import te
import topi
from topi.testing import conv2d_nchw_python
from tvm import autotvm
logging.getLogger('autotvm').setLevel(logging.DEBUG)
logging.getLogger('autotvm').addHandler(logging.StreamHandler(sys.stdout))
# the last layer in resnet
N, H, W, CO, CI, KH, KW, strides, padding = 1, 7, 7, 512, 512, 3, 3, (1, 1),
(1, 1)
data = te.placeholder((N, CI, H, W), name='data')
kernel = te.placeholder((CO, CI, KH, KW), name='kernel')
#conv = topi.nn.conv2d_nchw(data, kernel, strides, padding, dilation=1,
out_dtype='float32')
#cfg = autotvm.get_config()
task = autotvm.task.create("conv2d_nchw.cuda",
args=(data, kernel, strides, padding, 1, 'float32'),
target='cuda')
print(task.config_space)
```
Now it runs well.
I have another question: in the Tuning High Performance Convolution on NVIDIA
GPUs tutorial, the tuned operator is built by:
```
# apply history best from log file
with autotvm.apply_history_best('conv2d.log'):
with tvm.target.create("cuda"):
s, arg_bufs = conv2d_no_batching(N, H, W, CO, CI, KH, KW, strides,
padding)
func = tvm.build(s, arg_bufs)
```
How can I build the operator when I tune it with AutoTVM?
Thanks a lot.
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