Compile ONNX Models

Author: Joshua Z. Zhang

This article is an introductory tutorial to deploy ONNX models with Relay.

For us to begin with, ONNX package must be installed.

A quick solution is to install protobuf compiler, and

pip install onnx --user

or please refer to offical site. https://github.com/onnx/onnx

import onnx
import numpy as np
import tvm
from tvm import te
import tvm.relay as relay
from tvm.contrib.download import download_testdata

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'])
model_path = download_testdata(model_url, 'super_resolution.onnx', module='onnx')
# now you have super_resolution.onnx on disk
onnx_model = onnx.load(model_path)

Out:

File /workspace/.tvm_test_data/onnx/super_resolution.onnx exists, skip.

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'
img_path = download_testdata(img_url, 'cat.png', module='data')
img = Image.open(img_path).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, :, :]

Out:

File /workspace/.tvm_test_data/data/cat.png exists, skip.

Compile the model with relay

target = 'llvm'

input_name = '1'
shape_dict = {input_name: x.shape}
mod, params = relay.frontend.from_onnx(onnx_model, shape_dict)

with tvm.transform.PassContext(opt_level=1):
    intrp = relay.build_module.create_executor('graph', mod, tvm.cpu(0), target)

Out:

/workspace/docs/../python/tvm/relay/frontend/onnx.py:2420: UserWarning: Mismatched attribute type in ' : kernel_shape'

==> Context: Bad node spec: input: "1" input: "2" output: "11" op_type: "Conv" attribute { name: "kernel_shape" ints: 5 ints: 5 } attribute { name: "strides" ints: 1 ints: 1 } attribute { name: "pads" ints: 2 ints: 2 ints: 2 ints: 2 } attribute { name: "dilations" ints: 1 ints: 1 } attribute { name: "group" i: 1 }
  warnings.warn(str(e))

Execute on TVM

dtype = 'float32'
tvm_output = intrp.evaluate()(tvm.nd.array(x.astype(dtype)), **params).asnumpy()

Out:

...47%, 0.01 MB, 654 KB/s, 0 seconds passed
...94%, 0.02 MB, 1246 KB/s, 0 seconds passed
...100%, 0.02 MB, 1828 KB/s, 0 seconds passed
Cannot find config for target=llvm -keys=cpu, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 32, 224, 224), 'float32'), ('TENSOR', (9, 32, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm -keys=cpu, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 224, 224), 'float32'), ('TENSOR', (32, 64, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm -keys=cpu, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 1, 224, 224), 'float32'), ('TENSOR', (64, 1, 5, 5), 'float32'), (1, 1), (2, 2, 2, 2), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.

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()
../../_images/sphx_glr_from_onnx_001.png

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