Python VGG16 Examples

Example #1

def main():
    model = C.VGG16(pretrained_model='imagenet')
    save_as_onnx_then_import_from_nnvm(model, 'vgg16.onnx')

    model = C.ResNet50(pretrained_model='imagenet', arch='he')
    # Change cover_all option to False to match the default behavior of MXNet's pooling
    model.pool1 = lambda x: F.max_pooling_2d(
        x, ksize=3, stride=2, cover_all=False)
    save_as_onnx_then_import_from_nnvm(model, 'resnet50.onnx')

Example #2

def convert_model_to_onnx(input_shape, onnx_file_path):
    # Export Chainer model to ONNX
    model = L.VGG16(pretrained_model='imagenet')

    # Pseudo input
    x = np.zeros(input_shape, dtype=np.float32)

    # Don't forget to set train flag off!
    chainer.config.train = False

    onnx_chainer.export(model, x, filename=onnx_file_path)

Example #3

File: test_rt.py Project: harusametime/onnx-chainer

 def setUp(self):
     self.model = C.VGG16(pretrained_model=None,
                          initialW=chainer.initializers.Uniform(1))
     self.x = np.zeros((1, 3, 224, 224), dtype=np.float32)
     self.fn = 'VGG16.onnx'

Example #4

import numpy as np

import chainer
import chainercv.links as C
import onnx_chainer

model = C.VGG16(pretrained_model='imagenet')

# Pseudo input
x = np.zeros((1, 3, 224, 224), dtype=np.float32)

onnx_chainer.export(model, x, filename='vgg16.onnx')