tajer

Taller [taˈʎer] is the spanish word for workshop, a good place to store tools, such as useful (PyTorch) functions.

You can easily install it with

pip3 install git+https://github.com/joh-fischer/tajer.git#egg=tajer

Utils

See tajer.utils.py for more information...

Neural network layers

from tajer.nn import ResidualBlock

# depthwise separable convolution (https://arxiv.org/abs/1704.04861)
from tajer.nn import DepthwiseSeparableConv2D

# attention layers (https://arxiv.org/abs/1706.03762)
from tajer.nn import MultiHeadAttention, ConvAttention

# linear attention (https://arxiv.org/abs/1812.01243)
from tajer.nn import LinearConvAttention

# Convolutional block attention module (https://arxiv.org/abs/1807.06521)
from tajer.nn import CBAM

# 1D sinusoidal time embedding (https://arxiv.org/abs/1706.03762)
from tajer.nn import TimeEmbedding

Distributed PyTorch

In tajer/distributed/min_DDP.py you can find a minimum working example of single-node, multi-gpu training with PyTorch, as well as a README.md that shows you how to use it. All communication between processes, as well as the multiprocess spawn is handled by the functions defined in distributed_pytorch.py.

Logging

Command line and txt logger

This function returns a logger that prints to the command line and writes all outputs also to a text log file.

from tajer.log import get_logger

logger = get_logger('log_dir', dist_rank=0)

logger.info("...")
logger.warning("...")

Logger class

Here is a small example of how it works.

import torch
from tajer.log import Logger

logger = Logger('./logs',
                # create log-folder: './logs/model1/22-07-07_121028'
                'experiment_name', timestamp=True,
                # include tensorboard SummaryWriter
                tensorboard=True)

logger.log_hparams({'lr': 1e-4,
                    'optimizer': 'Adam'})

for epoch in range(2):
    logger.init_epoch(epoch)  # initialize epoch to aggregate values

    # training
    for step in range(4):
        logger.log_metrics({'loss': torch.rand(1), 'acc': torch.rand(1)},
                           phase='train', aggregate=True)

    # write to tensorboard
    logger.tensorboard.add_scalar('train/loss', logger.epoch['loss'].avg)

    # validation simulation
    for step in range(2):
        logger.log_metrics({'val_loss': torch.rand(1)},
                           phase='val', aggregate=True)

        print('Running average:', logger.epoch['val_loss'].avg)
        print('Running sum:', logger.epoch['val_loss'].sum)

logger.save()