Description

Fully Convolutional Network (FCN) was proposed in a paper [1]. FCN's architecture is shown in the figure below.

This repository offers a supervised-training code of FCN on all the subset datasets of the UCR archive. The datasets are automatically downloaded when relevant codes are executed. They, also, can be manually downloaded here. The trained FCN will be used to compute the FID (Fréchet Inception Distance) score for evaluation of generated time series.

pip installation

pip install supervised-fcn

Load a Pretrained FCN

from supervised_FCN.example_pretrained_model_loading import load_pretrained_FCN

subset_dataset_name = ...  # 'Adiac'
fcn = load_pretrained_FCN(subset_dataset_name)
fcn.eval()

You can do the forward propagation as follows:

x = torch.rand((1, 1, 176))  # (batch_size, in_channels, length)
out = fcn(x)  # (batch_size, n_classes); an output right before the softmax layer.

You can obtain the representation (feature) vector (i.e., a vector right after the last pooling layer) as follows:

x = torch.rand((1, 1, 176))  # (batch_size, in_channels, length)
z = fcn(x, return_feature_vector=True)  # (batch_size, feature_dim)

Compute FID and IS

FID

from supervised_FCN.example_compute_FID import calculate_fid

x_real = torch.rand((1, 1, 176))  # (batch_size, in_channels, length)
x_fake = torch.rand((1, 1, 176))  # (batch_size, in_channels, length)

z_real = fcn(x_real, return_feature_vector=True)  # (batch_size, feature_dim)
z_fake = fcn(x_fake, return_feature_vector=True)  # (batch_size, feature_dim)

fid_score = calculate_fid(z_real.cpu().detach().numpy(), z_fake.cpu().detach().numpy())

IS

from supervised_FCN.example_compute_IS import calculate_inception_score

x = torch.rand((1, 1, 176))  # (batch_size, in_channels, length)
out = fcn(x)  # (batch_size, n_classes); an output right before the softmax layer.
p_yx = torch.nn.functional.softmax(out, dim=-1)  # p(y|x); (batch_size, n_classes)

IS_mean, IS_std = calculate_inception_score(p_yx.cpu().detach().numpy())

Training

Prerequisite for Training

You need to download the UCR archive dataset and put it in supervised_FCN/datasets/. You can download it from here. Then, your supervised_FCN/datasets directory should have supervised_FCN/datasets/UCRArchive_2018.

Training

supervised_FCN/train_fcn.py: runs training of FCN on a subset dataset from the UCR archive.

supervised_FCN/configs/config.yaml: is where you can set parameters and hyper-parameters for dataset loading and training.

Training History

The training histories of FCN on all the UCR subset datasets are available here.

A training and test dataset split is the same as provided in the UCR archive, and a test dataset is used as a validation set during training for better training progress tracking.

Optimizer

• initial learning rate: 1e-3
• learning rate scheduler: cosine learning rate scheduler
• optimizer: AdamW
• weight decay: 1e-5
• max epochs: 1000
• batch size: 256

Example Templates

supervised_FCN/example_data_loading.py: a template for loading a subset dataset from the UCR archive.

supervised_FCN/example_pretrained_model_loading.py: a template for loading a pretrained FCN.

supervised_FCN/example_compute_FID.py: an example of computing the FID score.

supervised_FCN/example_compute_IS.py: an example of computing the IS (Inception Score).

Reference

[1] Wang, Zhiguang, Weizhong Yan, and Tim Oates. "Time series classification from scratch with deep neural networks: A strong baseline." 2017 International joint conference on neural networks (IJCNN). IEEE, 2017.