Released v1.0.11! Provide pre-trained models, added new functions, and better compatibility with ONNX.

Last week, we released nnabla packages v1.0.11! The main features updated from the previous version are described below:

• Spotlight:

Subpackage nnabla.models:

This adds nnabla.models API which allows users to use state-of-the-art pretrained models for both inference and training, without having to manually train the model from scratch, as shown below:

from nnabla.models.imagenet import ResNet
model = ResNet(18)
batch_size = 1
x = nn.Variable((batch_size,) + model.input_shape)
y = model(x, training=False)

Following models pretrained on ImageNet are available;

•ResNet-18,34,50,101,152

•MobileNetV2

•SqueezeNet

•SENet-154

• Functions:

Add functions: IsInf, IsNaN, ResetNaN, ResetInf, and Where

Add clear_buffer flag to forward_all

Add 3D support for pooling functions.

[Experimental] PyTorch-like Functions

Added AMSGRAD

Serialization of SolverState

Add CuDNN max and average pooling for 3D case.

Use dedicated function to determine workspace size for alogorithm.

• Utilities:

Improve onnx exporter

improve onnx import

[Experimental] Trainer API

On memory api

Support multiple dataset in .proto

Added learing rate scheduler

Add graph converters for inference

• Examples:

DeepLabv3

Self-attention GAN

Efficient Neural Architecture Search (ENAS)