I am trying to run a ResNet-18 model on a Jetson TX2 with an input data shape of (1, 3, 228, 304) instead of (1, 3, 224, 224). I am cross-compiling and using target="cuda" and target_host="llvm -target=aarch64-linux-gnu". Both the host machine on which I’m compiling and the TX2 are running cuda-8.0 and llvm-4.0.
When I try to compile the ResNet-18 model with the input data shape of (1, 3, 228, 304). I get the following error:
tvm._ffi.base.TVMError: [16:00:46] /home/dwofk/tvm/src/schedule/message_passing.cc:36: Check failed: match iter_var(threadIdx.x, Range(min=0, extent=8), threadIdx.x) domain already inferred, cannot prove their extents are the same 5 vs 8
Error during compile graph
--------------------------
Graph(%input0,
%input1,
%input2,
%input3,
%input4) {
%input0, shape=[1,128,29,38]
%input1, shape=[128,128,3,3]
%input2, shape=[128,1,1]
%input3, shape=[128,1,1]
%input4, shape=[1,128,29,38]
%2 = conv2d(%input0, %input1, channels='128', use_bias='False', strides='(1, 1)', kernel_size='(3, 3)', dilation='(1, 1)', groups='1', padding='(1, 1)'), shape=[1,128,29,38]
%4 = broadcast_mul(%2, %input2), shape=[1,128,29,38]
%6 = broadcast_add(%4, %input3), shape=[1,128,29,38]
%8 = elemwise_add(%6, %input4), shape=[1,128,29,38]
%9 = relu(%8), shape=[1,128,29,38]
ret %9
}
graph_attr_keys = [shape, shape_num_unknown_nodes, dtype, dtype_num_unknown_nodes]
I noticed that this issue is similar to the one in https://github.com/dmlc/nnvm/issues/239. That issue was resolved by adding an extra elif condition in the conv2d_56_64_128 schedule. I attempted to resolve my own issue by adding a similar extra elif condition right below:
if mark % 8 == 0 and mark % 7 == 0:
num_thread_x = 8
vthread_x = 7
elif mark % 4 == 0 and mark % 7 == 0:
num_thread_x = 4
vthread_x = 7
elif mark % 2 == 0 and mark % 19 == 0:
num_thread_x = 2
vthread_x = 19
This seems to resolve my issue. I am wondering if this was the correct approach. Or is there a more optimal solution?