[Tutorial][Executor] Fix the usage of executors in tutorials (apache#…

…8586) * fix: executor usage for keras tutorial * fix: executor usage for onnx tutorial * [Tutorial][Executor] Fix executors in tutorials
AndrewZhaoLuo · Sep 1, 2021 · a5cb1a9 · a5cb1a9
1 parent 3d43446
commit a5cb1a9
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Showing 3 changed files with 8 additions and 5 deletions.
diff --git a/tutorials/dev/bring_your_own_datatypes.py b/tutorials/dev/bring_your_own_datatypes.py
@@ -257,8 +257,9 @@ def get_cat_image():
 ######################################################################
 # It's easy to execute MobileNet with native TVM:
 
+ex = tvm.relay.create_executor("graph", mod=module, params=params)
 input = get_cat_image()
-result = tvm.relay.create_executor("graph", mod=module).evaluate()(input, **params).numpy()
+result = ex.evaluate()(input).numpy()
 # print first 10 elements
 print(result.flatten()[:10])
 

diff --git a/tutorials/frontend/from_keras.py b/tutorials/frontend/from_keras.py
@@ -103,14 +103,14 @@
 # due to a latent bug. Note that the pass context only has an effect within
 # evaluate() and is not captured by create_executor().
 with tvm.transform.PassContext(opt_level=0):
-    model = relay.build_module.create_executor("graph", mod, dev, target).evaluate()
+    model = relay.build_module.create_executor("graph", mod, dev, target, params).evaluate()
 
 
 ######################################################################
 # Execute on TVM
 # ---------------
 dtype = "float32"
-tvm_out = model(tvm.nd.array(data.astype(dtype)), **params)
+tvm_out = model(tvm.nd.array(data.astype(dtype)))
 top1_tvm = np.argmax(tvm_out.numpy()[0])
 
 #####################################################################

diff --git a/tutorials/frontend/from_onnx.py b/tutorials/frontend/from_onnx.py
@@ -92,13 +92,15 @@
 mod, params = relay.frontend.from_onnx(onnx_model, shape_dict)
 
 with tvm.transform.PassContext(opt_level=1):
-    compiled = relay.build_module.create_executor("graph", mod, tvm.cpu(0), target).evaluate()
+    executor = relay.build_module.create_executor(
+        "graph", mod, tvm.cpu(0), target, params
+    ).evaluate()
 
 ######################################################################
 # Execute on TVM
 # ---------------------------------------------
 dtype = "float32"
-tvm_output = compiled(tvm.nd.array(x.astype(dtype)), **params).numpy()
+tvm_output = executor(tvm.nd.array(x.astype(dtype))).numpy()
 
 ######################################################################
 # Display results