def setUp(self):
self.model = C.ResNet50(
pretrained_model=None, initialW=chainer.initializers.Uniform(1), arch='he')
self.model.pool1 = lambda x: F.max_pooling_2d(
x, ksize=3, stride=2, cover_all=False)
self.x = np.zeros((1, 3, 224, 224), dtype=np.float32)
self.fn = 'ResNet50.onnx'
def export_onnx(input_image_path, output_path, gpu, only_output=True):
"""Export ResNet50 model to ONNX graph
'model.onnx' file will be exported under ``output_path``.
"""
model = C.ResNet50(pretrained_model='imagenet', arch='fb')
input_image = read_image(input_image_path)
input_image = scale(input_image, 256)
input_image = center_crop(input_image, (224, 224))
input_image -= model.mean
input_image = input_image[None, :]
if gpu >= 0:
model.to_gpu()
input_image = chainer.cuda.to_gpu(input_image)
if only_output:
os.makedirs(output_path, exist_ok=True)
name = os.path.join(output_path, 'model.onnx')
export(model, input_image, filename=name)
else:
# an input and output given by Chainer will be also emitted
# for using as test dataset
export_testcase(model, input_image, output_path)
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')
#!/usr/bin/env python # -*- coding: utf-8 -*- import sys import numpy as np import cupy import chainer import chainercv.links as C import onnx_chainer model = C.ResNet50(pretrained_model='imagenet', arch='he') #model = C.VGG16(pretrained_model='imagenet') model.to_gpu() # Pseudo input x = chainer.Variable(cupy.zeros((1, 3, 224, 224), dtype=np.float32)) onnx_chainer.export(model, x, filename='resnet50.onnx')
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import argparse
import chainer
import numpy as np
import chainercv.links as C
import onnx_chainer
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=int, default=-1)
parser.add_argument('--size', type=int, default=224)
args = parser.parse_args()
model = C.ResNet50()
if args.gpu >= 0:
chainer.cuda.get_device_from_id(args.gpu).use()
model.to_gpu()
# Pseudo input
x = model.xp.zeros((1, 3, args.size, args.size), dtype=np.float32)
onnx_chainer.export_testcase(model, x, 'resnet50_%d' % args.size)
