1. 将训练好的 GluonCV 网络导出为 JSON

如果您喜欢在 Python 中使用 GluonCV 进行训练和测试，那真是太棒了。在某些时候，您可能会问：“是否可以将现有模型部署到 Python 环境之外的地方？”

答案是“绝对可以！”，而且实际上超级简单。

本文将向您展示如何导出网络/模型以在 Python 之外的地方使用。

import gluoncv as gcv
from gluoncv.utils import export_block

首先，我们需要一个可以用来尝试的网络，一个预训练好的网络再好不过了

net = gcv.model_zoo.get_model('resnet18_v1', pretrained=True)
export_block('resnet18_v1', net, preprocess=True, layout='HWC')
print('Done.')

输出

Downloading /root/.mxnet/models/resnet18_v1-a0666292.zip from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/resnet18_v1-a0666292.zip...

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Done.

提示

使用 preprocess=True 将在网络上方添加一个默认的预处理层，该层将减去均值 [123.675, 116.28, 103.53]，除以标准差 [58.395, 57.12, 57.375]，并将原始图像 (B, H, W, C 且范围 [0, 255]) 转换为张量 (B, C, H, W) 作为网络输入。这是所有 GluonCV 预训练模型的默认预处理行为。有了这个预处理头，您可以在 C++ 中使用原始 RGB 图像，而无需显式应用这些操作。

上面的代码生成两个文件：xxxx.json 和 xxxx.params

import glob
print(glob.glob('*.json') + glob.glob('*.params'))

输出

['resnet18_v1-symbol.json', 'resnet18_v1-0000.params']

JSON 文件包含计算图，params 文件包含预训练权重。

可导出的网络不仅限于图像分类模型。我们还可以导出检测和分割网络

# YOLO
net = gcv.model_zoo.get_model('yolo3_darknet53_coco', pretrained=True)
export_block('yolo3_darknet53_coco', net)

# FCN
net = gcv.model_zoo.get_model('fcn_resnet50_ade', pretrained=True)
export_block('fcn_resnet50_ade', net)

# MaskRCNN
net = gcv.model_zoo.get_model('mask_rcnn_resnet50_v1b_coco', pretrained=True)
export_block('mask_rcnn_resnet50_v1b_coco', net)

输出

Downloading /root/.mxnet/models/yolo3_darknet53_coco-09767802.zip from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/yolo3_darknet53_coco-09767802.zip...

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Downloading /root/.mxnet/models/fcn_resnet50_ade-3479525a.zip from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/fcn_resnet50_ade-3479525a.zip...

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我们已经准备就绪。请查看其他教程，了解如何使用这些 JSON 和 params 文件。