weight_expr = relay.transpose(weight_expr, axes=[0, 1, 2, 4, 3])
new_attrs['tile_size'] = tile_size
+ new_attrs['channels'] = CO
new_data = data
new_kernel = te.placeholder((KH + tile_size - 1,
assert data_layout == "NCHW" and kernel_layout == "OIHW"
N, CI, H, W = get_const_tuple(data.shape)
CO, _, KH, KW = get_const_tuple(kernel.shape)
+ new_attrs['channels'] = CO
# pre-compute winograd_nnpack transform
# for winograd_nnpack_fp16, the the precompute prune pass must run on device,