v0.2.0

[leonwanghui]

Released 2021-06-07.

Major Features and Improvements

• Add text module to provide the basic dataset loading and preprocessing in NLP scenarios. #53 #73
• Upgrade the version of mindspore module dependencies from v1.1.1 to v1.2.0. #81 #84
• Refactor the Client and Server communication interface in serving module. #76
• Added server_path, start FlaskServer and add host and port parameters. #77
• Implement TinyMS hub module to enable loading lots of pre-trained models, incluidng lenet5_v1, resnet50_v1, alexnet_v1, vgg16_v1, mobilenet_v2 and ssd300_v1. #86 #93
• Publish the TinyMS Hub contributing guidelines in public to welcome pre-trained model from the comunity. #91
• Refactor the model network entrypoint method to provide the unified interface. #85

Model Park

• Add 5 models support: AlexNet, DenseNet100, VGG16, SentimentNet, Bert. #59 #89 #63 #67

API Change

• Refactor the serving entrypoint function with Client and Server class interface.

v0.1.0	v0.2.0
from tinyms.serving import start_server, server_started, list_servables, predict, shutdown start_server() if server_started(): list_servables() predict('example.jpg', 'servable_name', dataset_name='mnist') shutdown()	from tinyms.serving import Client, Server server = Server() server.start_server() client = Client() client.list_servables() client.predict('example.jpg', 'servable_name', dataset_name='mnist') server.shutdown()

• Add a new interface load in model module to support load MindIR graph directly to perform model inference operation.

v0.2.0

>>> import tinyms as ts >>> import tinyms.layers as layers >>> from tinyms.model import Model, load >>> >>> net = layers.Conv2d(1, 1, kernel_size=3) >>> model = Model(net) >>> input = ts.ones([1, 1, 3, 3]) >>> model.export(input, "net", file_format="MINDIR") ... >>> net = load("net.mindir") >>> print(net(input)) [[[[ 0.02548009 0.04010789 0.03120251] [ 0.00268656 0.02353744 0.03807815] [-0.00896441 -0.00303641 0.01502199]]]]

• Add hub.load method to easily load pretrained model and apply model evaluation and inference operation.

v0.2.0

from PIL import Image from tinyms import hub from tinyms.vision import mnist_transform from tinyms.model import Model img = Image.open(img_path) img = mnist_transform(img) # load LeNet5 pretrained model net= hub.load('tinyms/0.2/lenet5_v1_mnist', class_num=10) model = Model(net) res = model.predict(ts.expand_dims(ts.array(img), 0)).asnumpy() print("The label is:", mnist_transform.postprocess(res))

For the detailed API changes, please find TinyMS Python API in API Documentation.

Backwards Incompatible Change

None

Bug fixes

• Fix some bugs when serving in Windows operating system. #74
• Set batch_norm as True by default in VGG16 to fix the converge problem of accuracy. #90

Contributors

Great thanks go to these wonderful people:

@zjuter0126, @Mickls, @leonwanghui, @hannibalhuang, @hellowaywewe, @huxiaoman7

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This discussion was created from the release v0.2.0.