[GitHub] [incubator-mxnet] dai-ichiro commented on issue #20985: Is there onnx import support in 1.9.0

[GitBox <gi...@apache.org>]

dai-ichiro commented on issue #20985:
URL: https://github.com/apache/incubator-mxnet/issues/20985#issuecomment-1087073867

   https://github.com/apache/incubator-mxnet/issues/20769
   
   ```
   from PIL import Image
   import numpy as np
   import mxnet as mx
   from mxnet.gluon.utils import download
   
   import onnx
   from mxnet.contrib.onnx.onnx2mx.import_onnx import GraphProto
   
   img_url = 'https://s3.amazonaws.com/onnx-mxnet/examples/super_res_input.jpg'
   fname = download(img_url)
   
   model_url = 'https://s3.amazonaws.com/onnx-mxnet/examples/super_resolution.onnx'
   onnx_model_file = download(model_url)
   
   graph = GraphProto()
   model_proto = onnx.load_model(onnx_model_file)
   
   sym, arg, aux = graph.from_onnx(model_proto.graph, opset_version=10)
   # 10 is dummy number.
   
   img = Image.open(fname).resize((224, 224))
   img_ycbcr = img.convert("YCbCr")
   img_y, img_cb, img_cr = img_ycbcr.split()
   test_image = np.array(img_y)[np.newaxis, np.newaxis, :, :]
   
   data_names = [graph_input for graph_input in sym.list_inputs()
                         if graph_input not in arg and graph_input not in aux]
   
   mod = mx.mod.Module(symbol=sym, data_names=data_names, context=mx.cpu(), label_names=None)
   mod.bind(for_training=False, data_shapes=[(data_names[0],test_image.shape)], label_shapes=None)
   mod.set_params(arg_params=arg, aux_params=aux, allow_missing=True, allow_extra=True)
   
   from collections import namedtuple
   Batch = namedtuple('Batch', ['data'])
   
   mod.forward(Batch([mx.nd.array(test_image)]))
   
   output = mod.get_outputs()[0][0][0]
   img_out_y = Image.fromarray(np.uint8((output.asnumpy().clip(0, 255))))
   
   result_img = Image.merge(
   "YCbCr", [
                   img_out_y,
                   img_cb.resize(img_out_y.size, Image.BICUBIC),
                   img_cr.resize(img_out_y.size, Image.BICUBIC)
   ]).convert("RGB")
   result_img.save("super_res_output.jpg")
   ```


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