""" Compile ONNX Models =================== **Author**: `Joshua Z. Zhang <https://zhreshold.github.io/>`_ This article is an introductory tutorial to deploy ONNX models with NNVM. For us to begin with, onnx module is required to be installed. A quick solution is to install protobuf compiler, and .. code-block:: bash pip install onnx --user or please refer to offical site. https://github.com/onnx/onnx """ import nnvm import tvm import onnx import numpy as np def download(url, path, overwrite=False): import os if os.path.isfile(path) and not overwrite: print('File {} existed, skip.'.format(path)) return print('Downloading from url {} to {}'.format(url, path)) try: import urllib.request urllib.request.urlretrieve(url, path) except: import urllib urllib.urlretrieve(url, path) ###################################################################### # Load pretrained ONNX model # --------------------------------------------- # The example super resolution model used here is exactly the same model in onnx tutorial # http://pytorch.org/tutorials/advanced/super_resolution_with_caffe2.html # we skip the pytorch model construction part, and download the saved onnx model model_url = ''.join(['https://gist.github.com/zhreshold/', 'bcda4716699ac97ea44f791c24310193/raw/', '93672b029103648953c4e5ad3ac3aadf346a4cdc/', 'super_resolution_0.2.onnx']) download(model_url, 'super_resolution.onnx', True) # now you have super_resolution.onnx on disk onnx_model = onnx.load_model('super_resolution.onnx') # we can load the graph as NNVM compatible model sym, params = nnvm.frontend.from_onnx(onnx_model) ###################################################################### # Load a test image # --------------------------------------------- # A single cat dominates the examples! from PIL import Image img_url = 'https://github.com/dmlc/mxnet.js/blob/master/data/cat.png?raw=true' download(img_url, 'cat.png') img = Image.open('cat.png').resize((224, 224)) img_ycbcr = img.convert("YCbCr") # convert to YCbCr img_y, img_cb, img_cr = img_ycbcr.split() x = np.array(img_y)[np.newaxis, np.newaxis, :, :] ###################################################################### # Compile the model on NNVM # --------------------------------------------- # We should be familiar with the process right now. import nnvm.compiler target = 'cuda' # assume first input name is data input_name = sym.list_input_names()[0] shape_dict = {input_name: x.shape} with nnvm.compiler.build_config(opt_level=3): graph, lib, params = nnvm.compiler.build(sym, target, shape_dict, params=params) ###################################################################### # Execute on TVM # --------------------------------------------- # The process is no different from other example from tvm.contrib import graph_runtime ctx = tvm.gpu(0) dtype = 'float32' m = graph_runtime.create(graph, lib, ctx) # set inputs m.set_input(input_name, tvm.nd.array(x.astype(dtype))) m.set_input(**params) # execute m.run() # get outputs output_shape = (1, 1, 672, 672) tvm_output = m.get_output(0, tvm.nd.empty(output_shape, dtype)).asnumpy() ###################################################################### # Display results # --------------------------------------------- # We put input and output image neck to neck from matplotlib import pyplot as plt out_y = Image.fromarray(np.uint8((tvm_output[0, 0]).clip(0, 255)), mode='L') out_cb = img_cb.resize(out_y.size, Image.BICUBIC) out_cr = img_cr.resize(out_y.size, Image.BICUBIC) result = Image.merge('YCbCr', [out_y, out_cb, out_cr]).convert('RGB') canvas = np.full((672, 672*2, 3), 255) canvas[0:224, 0:224, :] = np.asarray(img) canvas[:, 672:, :] = np.asarray(result) plt.imshow(canvas.astype(np.uint8)) plt.show()