Improve the x86 auto-tune tutorial (apache#3609)

tutorials/autotvm/tune_relay_x86.py

@@ -64,7 +64,7 @@ def get_network(name, batch_size):
         # an example for mxnet model
         from mxnet.gluon.model_zoo.vision import get_model
         block = get_model('resnet18_v1', pretrained=True)
-        mod, params = relay.frontend.from_mxnet(block, shape={'data': input_shape}, dtype=dtype)
+        mod, params = relay.frontend.from_mxnet(block, shape={input_name: input_shape}, dtype=dtype)
         net = mod["main"]
         net = relay.Function(net.params, relay.nn.softmax(net.body), None, net.type_params, net.attrs)
         mod = relay.Module.from_expr(net)
@@ -86,6 +86,10 @@ def get_network(name, batch_size):
 log_file = "%s.log" % model_name
 graph_opt_sch_file = "%s_graph_opt.log" % model_name
 
+# Set the input name of the graph
+# For ONNX models, it is typically "0".
+input_name = "data"
+
 # Set number of threads used for tuning based on the number of
 # physical CPU cores on your machine.
 num_threads = 1
@@ -166,7 +170,7 @@ def tune_kernels(tasks,
 def tune_graph(graph, dshape, records, opt_sch_file, use_DP=True):
     target_op = [relay.nn.conv2d]
     Tuner = DPTuner if use_DP else PBQPTuner
-    executor = Tuner(graph, {"data": dshape}, records, target_op, target)
+    executor = Tuner(graph, {input_name: dshape}, records, target_op, target)
     executor.benchmark_layout_transform(min_exec_num=2000)
     executor.run()
     executor.write_opt_sch2record_file(opt_sch_file)
@@ -198,7 +202,7 @@ def tune_and_evaluate(tuning_opt):
         ctx = tvm.cpu()
         data_tvm = tvm.nd.array((np.random.uniform(size=data_shape)).astype(dtype))
         module = runtime.create(graph, lib, ctx)
-        module.set_input('data', data_tvm)
+        module.set_input(input_name, data_tvm)
         module.set_input(**params)
 
         # evaluate