A post-processing script will be used here ： #!python.
TVMC DOWNLOAD PROBLEM DOWNLOAD
In fact, each model has its own output tensor Format, We can download one here resnet50 Output lookup table, Extract information from, And print out.
TVMC DOWNLOAD PROBLEM HOW TO
In this case, The machine running the compiled model is the same platform, But you can also use RPC Platform operation test provided in, adopt python -m run -help see RPC How to use it. Stay tar In the package, There is a compiled model runtime library ,tvmc Encapsulates the tvm Of runtime Interface, After operation ,tvmc Will give a prediction. With the compiled model and the converted picture, We can test the effect of the model ： python -m run \ savez ( "imagenet_cat", data =img_data ) Run the compiled model
expand_dims (norm_img_data, axis = 0 ) # Save to. Norm_img_data = (img_data / 255 - imagenet_mean ) / imagenet_stddev # Add batch dimension astype ( "float32" ) for i in range (img_data. transpose (img_data, ( 2, 0, 1 ) ) # Normalize according to ImageNet astype ( "float32" ) # ONNX expects NCHW input, so convert the array Img_path = download_testdata (img_url, "imagenet_cat.png", module = "data" ) # Resize it to 224x224 # if you have problem of download,just use images above
TVMC DOWNLOAD PROBLEM INSTALL
When doing pre-processing and post-processing, Need to use pillow modular, If not, Can be installed like this pip3 install pillow. Ībout resnet50, Images need to use ImageNet The format of, Put one below pre-processing and post-processing Example. I like cats very much, Here it is tvm It's the same in the tutorial, Just use this picture of the cat. When using, We need to prepare ：Įach model will have the desired input size, data type, Data format and so on, So for a picture, Alignment is usually required for preprocessing or post-processing. Test use tvm Of runtime api, Of course tvmc It's integrated inside. We have compiled the model module, Now we need to test the effect. These things can be loaded directly by your application, The model can go through TVM Of runtime API call. mod.json Express relay Text file of calculation diagram.mod.params Contains the pre training data of the model.mod.so This is actually a model, It's just compiled into c++ Shared library ,TVM Of runtime Will load and call it.You'll see model There are three files in it ： Just to recap, The compiled model will exist after being compressed tar Inside the compressed package.
If you click this article directly, Maybe you don't know how the compiled model came from, stamp here. Test use TVM Compile the resnet50 stay CPU Effect on
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