The previous model was compiled to work on the TVM runtime, but did not savez ( "imagenet_cat", data = img_data ) expand_dims ( norm_img_data, axis = 0 ) # Save to. shape ): norm_img_data = ( img_data / 255 - imagenet_mean ) / imagenet_stddev # Add batch dimension img_data = np. astype ( "float32" ) for i in range ( img_data. transpose ( img_data, ( 2, 0, 1 )) # Normalize according to ImageNet imagenet_mean = np. astype ( "float32" ) # ONNX expects NCHW input, so convert the array img_data = np. preprocess.py from import download_testdata from PIL import Image import numpy as np img_url = "" img_path = download_testdata ( img_url, "imagenet_cat.png", module = "data" ) # Resize it to 224x224 resized_image = Image. Quick Start Tutorial for Compiling Deep Learning Models.Optimizing Operators with Auto-scheduling.Optimizing Operators with Schedule Templates and AutoTVM. Working with Operators Using Tensor Expression.Compiling and Optimizing a Model with the Python Interface (AutoTVM).Compiling an Optimized Model with Tuning Data.Running the Model from The Compiled Module with TVMC.Compiling an ONNX Model to the TVM Runtime.
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