tensorneko 0.2.3

Tensor Neural Engine Kompanion. An util library based on PyTorch and PyTorch Lightning.

TensorNeko

Tensor Neural Engine Kompanion. An util library based on PyTorch and PyTorch Lightning.

Install

pip install tensorneko

To use the library without PyTorch and PyTorch Lightning, you can install the util library (support Python 3.7 ~ 3.10 with limited features) with following command.

pip install tensorneko_util

Neko Layers, Modules and Architectures

Build an MLP with linear layers. The activation and normalization will be placed in the hidden layers.

784 -> 1024 -> 512 -> 10

import tensorneko as neko
import torch.nn

mlp = neko.module.MLP(
    neurons=[784, 1024, 512, 10],
    build_activation=torch.nn.ReLU,
    build_normalization=[
        lambda: torch.nn.BatchNorm1d(1024),
        lambda: torch.nn.BatchNorm1d(512)
    ],
    dropout_rate=0.5
)

Build a Conv2d with activation and normalization.

import tensorneko as neko
import torch.nn

conv2d = neko.layer.Conv2d(
    in_channels=256,
    out_channels=1024,
    kernel_size=(3, 3),
    padding=(1, 1),
    build_activation=torch.nn.ReLU,
    build_normalization=lambda: torch.nn.BatchNorm2d(256),
    normalization_after_activation=False
)

All architectures, modules and layers

Layers:

• Aggregation
• Concatenate
• Conv, Conv1d, Conv2d, Conv3d
• GaussianNoise
• ImageAttention, SeqAttention
• MaskedConv2d, MaskedConv2dA, MaskedConv2dB
• Linear
• Log
• PatchEmbedding2d
• PositionalEmbedding
• Reshape
• Stack
• VectorQuantizer

Modules:

• DenseBlock
• InceptionModule
• MLP
• ResidualBlock and ResidualModule
• AttentionModule, TransformerEncoderBlock and TransformerEncoder
• GatedConv

Architectures:

• AutoEncoder
• GAN
• WGAN
• VQVAE

Neko modules

All tensorneko.layer and tensorneko.module are NekoModule. They can be used in fn.py pipe operation.

from tensorneko.layer import Linear
from torch.nn import ReLU
import torch

linear0 = Linear(16, 128, build_activation=ReLU)
linear1 = Linear(128, 1)

f = linear0 >> linear1
print(f(torch.rand(16)).shape)
# torch.Size([1])

Neko IO

Easily load and save different modal data.

import tensorneko as neko
from tensorneko.io import json_data
from typing import List

# read video (Temporal, Channel, Height, Width)
video_tensor, audio_tensor, video_info = neko.io.read.video("path/to/video.mp4")
# write video
neko.io.write.video("path/to/video.mp4", 
    video_tensor, video_info.video_fps,
    audio_tensor, video_info.audio_fps
)

# read audio (Channel, Temporal)
audio_tensor, sample_rate = neko.io.read.audio("path/to/audio.wav")
# write audio
neko.io.write.audio("path/to/audio.wav", audio_tensor, sample_rate)

# read image (Channel, Height, Width) with float value in range [0, 1]
image_tensor = neko.io.read.image("path/to/image.png")
# write image
neko.io.write.image("path/to/image.png", image_tensor)
neko.io.write.image("path/to/image.jpg", image_tensor)

# read plain text
text_string = neko.io.read.text("path/to/text.txt")
# write plain text
neko.io.write.text("path/to/text.txt", text_string)

# read json as python dict or list
json_dict = neko.io.read.json("path/to/json.json")
# read json as an object
@json_data
class JsonData:
    x: int
    y: int

json_obj: List[JsonData] = neko.io.read.json("path/to/json.json", cls=List[JsonData])
# write json from python dict/list or json_data decorated object
neko.io.write.json("path/to/json.json", json_dict)
neko.io.write.json("path/to/json.json", json_obj)

Besides, the read/write for mat and pickle files is also supported.

Neko preprocessing

import tensorneko as neko

# A video tensor with (120, 3, 720, 1280)
video = neko.io.read.video("example/video.mp4").video
# Get a resized tensor with (120, 3, 256, 256)
resized_video = neko.preprocess.resize_video(video, (256, 256))

All preprocessing utils

• resize_video
• resize_image
• padding_video
• padding_audio
• crop_with_padding
• frames2video

if ffmpeg is available, you can use below ffmpeg wrappers.

• video2frames
• merge_video_audio
• resample_video_fps
• mp32wav

Neko Visualization

Variable Web Watcher

Start a web server to watch the variable status when the program (e.g. training, inference, data preprocessing) is running.

import time
from tensorneko.visualization.watcher import *
data_list = ... # a list of data
def preprocessing(d): ...

# initialize the components
pb = ProgressBar("Processing", total=len(data_list))
logger = Logger("Log message")
var = Variable("Some Value", 0)
line_chart = LineChart("Line Chart", "x", "y")
view = View("Data preprocessing").add_all()

t0 = time.time()
# open server when the code block in running.
with Server(view, port=8000):
    for i, data in enumerate(data_list):
        preprocessing(data) # do some processing here
        
        x = time.time() - t0  # time since the start of the program
        y = i # processed number of data
        line_chart.add(x, y)  # add to the line chart
        logger.log("Some messages")  # log messages to the server
        var.value = ...  # keep tracking a variable
        pb.add(1)  # update the progress bar by add 1

When the script is running, go to 127.0.0.1:8000 to keep tracking the status.

Tensorboard Server

Simply run tensorboard server in Python script.

import tensorneko as neko

with neko.visualization.tensorboard.Server(port=6006):
    trainer.fit(model, dm)

Matplotlib wrappers

Display an image of (C, H, W) shape by plt.imshow wrapper.

import tensorneko as neko
import matplotlib.pyplot as plt

image_tensor = ...  # an image tensor with shape (C, H, W)
neko.visualization.matplotlib.imshow(image_tensor)
plt.show()

Predefined colors

Several aesthetic colors are predefined.

import tensorneko as neko
import matplotlib.pyplot as plt

# use with matplotlib
plt.plot(..., color=neko.visualization.Colors.RED)

# the palette for seaborn is also available
from tensorneko_util.visualization.seaborn import palette
import seaborn as sns
sns.set_palette(palette)

Neko Model

Build and train a simple model for classifying MNIST with MLP.

from typing import Optional, Union, Sequence, Dict, List

import torch.nn
from torch import Tensor
from torch.optim import Adam
from torchmetrics import Accuracy
from pytorch_lightning.callbacks import ModelCheckpoint

import tensorneko as neko
from tensorneko.util import get_activation, get_loss


class MnistClassifier(neko.NekoModel):

    def __init__(self, name: str, mlp_neurons: List[int], activation: str, dropout_rate: float, loss: str,
        learning_rate: float, weight_decay: float
    ):
        super().__init__(name)
        self.weight_decay = weight_decay
        self.learning_rate = learning_rate

        self.flatten = torch.nn.Flatten()
        self.mlp = neko.module.MLP(
            neurons=mlp_neurons,
            build_activation=get_activation(activation),
            dropout_rate=dropout_rate
        )
        self.loss_func = get_loss(loss)()
        self.acc_func = Accuracy()

    def forward(self, x):
        # (batch, 28, 28)
        x = self.flatten(x)
        # (batch, 768)
        x = self.mlp(x)
        # (batch, 10)
        return x

    def training_step(self, batch: Optional[Union[Tensor, Sequence[Tensor]]] = None, batch_idx: Optional[int] = None,
        optimizer_idx: Optional[int] = None, hiddens: Optional[Tensor] = None
    ) -> Dict[str, Tensor]:
        x, y = batch
        logit = self(x)
        prob = logit.sigmoid()
        loss = self.loss_func(logit, y)
        acc = self.acc_func(prob.max(dim=1)[1], y)
        return {"loss": loss, "acc": acc}

    def validation_step(self, batch: Optional[Union[Tensor, Sequence[Tensor]]] = None, batch_idx: Optional[int] = None,
        dataloader_idx: Optional[int] = None
    ) -> Dict[str, Tensor]:
        x, y = batch
        logit = self(x)
        prob = logit.sigmoid()
        loss = self.loss_func(logit, y)
        acc = self.acc_func(prob.max(dim=1)[1], y)
        return {"loss": loss, "acc": acc}

    def configure_optimizers(self):
        optimizer = Adam(self.parameters(), lr=self.learning_rate, betas=(0.5, 0.9), weight_decay=self.weight_decay)
        return {
            "optimizer": optimizer
        }


model = MnistClassifier("mnist_mlp_classifier", [784, 1024, 512, 10], "ReLU", 0.5, "CrossEntropyLoss", 1e-4, 1e-4)

dm = ...  # The MNIST datamodule from PyTorch Lightning

trainer = neko.NekoTrainer(log_every_n_steps=100, gpus=1, logger=model.name, precision=32,
    callbacks=[ModelCheckpoint(dirpath="./ckpt",
        save_last=True, filename=model.name + "-{epoch}-{val_acc:.3f}", monitor="val_acc", mode="max"
    )])

trainer.fit(model, dm)

Neko Callbacks

Some simple but useful pytorch-lightning callbacks are provided.

• DisplayMetricsCallback
• EarlyStoppingLR: Early stop training when learning rate reaches threshold.

Neko Notebook Helpers

Here are some helper functions to better interact with Jupyter Notebook.

import tensorneko as neko
# display a video
neko.notebook.display.video("path/to/video.mp4")
# display an audio
neko.notebook.display.audio("path/to/audio.wav")
# display a code file
neko.notebook.display.code("path/to/code.java")

Neko Debug Tools

Get the default values from ArgumentParser args. It's convenient to use this in the notebook.

from argparse import ArgumentParser
from tensorneko.debug import get_parser_default_args

parser = ArgumentParser()
parser.add_argument("integers", type=int, nargs="+", default=[1, 2, 3])
parser.add_argument("--sum", dest="accumulate", action="store_const", const=sum, default=max)
args = get_parser_default_args(parser)

print(args.integers)  # [1, 2, 3]
print(args.accumulate)  # <function sum at ...>

Neko Evaluation

Some metrics function for evaluation are provided.

• iou_1d
• iou_2d
• psnr_video
• psnr_image
• ssim_video
• ssim_image

Neko Utilities

Functional

__: The arguments to pipe operator. (Inspired from fn.py)

from tensorneko.util import __, _
result = __(20) >> (_ + 1) >> (_ * 2) >> __.get
print(result)
# 42

Seq and Stream: A collection wrapper for method chaining with concurrent supporting.

from tensorneko.util import Seq, Stream, _
from tensorneko_util.backend.parallel import ParallelType
# using method chaining
seq = Seq.of(1, 2, 3).map(_ + 1).filter(_ % 2 == 0).map(_ * 2).take(2).to_list()
# return [4, 8]

# using bit shift operator to chain the sequence
seq = Seq.of(1, 2, 3) << Seq.of(2, 3, 4) << [3, 4, 5]
# return Seq(1, 2, 3, 2, 3, 4, 3, 4, 5)

# run concurrent with `for_each` for Stream
if __name__ == '__main__':
    Stream.of(1, 2, 3, 4).for_each(print, progress_bar=True, parallel_type=ParallelType.PROCESS)

Option: A monad for dealing with data.

from tensorneko.util import return_option

@return_option
def get_data():
    if some_condition:
        return 1
    else:
        return None

def process_data(n: int):
    if condition(n):
        return n
    else:
        return None
    

data = get_data()
data = data.map(process_data).get_or_else(-1)  # if the response is None, return -1

Eval: A monad for lazy evaluation.

from tensorneko.util import Eval

@Eval.always
def call_by_name_var():
    return 42

@Eval.later
def call_by_need_var():
    return 43

@Eval.now
def call_by_value_var():
    return 44


print(call_by_name_var.value)  # 42

Reactive

This library provides event bus based reactive tools. The API integrates the Python type annotation syntax.

# useful decorators for default event bus
from tensorneko.util import (
    subscribe, # run in the main thread
    subscribe_thread, # run in a new thread
    subscribe_async, # run async
    subscribe_process # run in a new process
)
# Event base type
from tensorneko.util import Event

class LogEvent(Event):
    def __init__(self, message: str):
        self.message = message

# the event argument should be annotated correctly
@subscribe
def log_information(event: LogEvent):
    print(event.message)

@subscribe_thread
def log_information_thread(event: LogEvent):
    print(event.message, "in another thread")

if __name__ == '__main__':
    # emit an event, and then the event handler will be invoked
    # The sequential order is not guaranteed
    LogEvent("Hello world!")
    # one possible output:
    # Hello world! in another thread
    # Hello world!

Multiple Dispatch

dispatch: Multi-dispatch implementation for Python.

To my knowledge, 3 popular multi-dispatch libraries still have critical limitations. plum doesn't support static methods, mutipledispatch doesn't support Python type annotation syntax and multimethod doesn't support default argument. TensorNeko can do it all.

from tensorneko.util import dispatch

class DispatchExample:

    @staticmethod
    @dispatch
    def go() -> None:
        print("Go0")

    @staticmethod
    @dispatch
    def go(x: int) -> None:
        print("Go1")

    @staticmethod
    @dispatch
    def go(x: float, y: float = 1.0) -> None:
        print("Go2")

@dispatch
def come(x: int) -> str:
    return "Come1"

@dispatch.of(str)
def come(x) -> str:
    return "Come2"

Miscellaneous

StringGetter: Get PyTorch class from string.

import tensorneko as neko
activation = neko.util.get_activation("leakyRelu")()

Seed: The universal seed for numpy, torch and Python random.

from tensorneko.util import Seed
from torch.utils.data import DataLoader

# set seed to 42 for all numpy, torch and python random
Seed.set(42)

# Apply seed to parallel workers of DataLoader
DataLoader(
    train_dataset,
    batch_size=batch_size,
    num_workers=num_workers,
    worker_init_fn=Seed.get_loader_worker_init(),
    generator=Seed.get_torch_generator()
)

Timer: A timer for measuring the time.

from tensorneko.util import Timer
import time

# use as a context manager with single time
with Timer():
    time.sleep(1)

# use as a context manager with multiple segments
with Timer() as t:
    time.sleep(1)
    t.time("sleep A")
    time.sleep(1)
    t.time("sleep B")
    time.sleep(1)

# use as a decorator
@Timer()
def f():
    time.sleep(1)
    print("f")

Singleton: A decorator to make a class as a singleton. Inspired from Scala/Kotlin.

from tensorneko.util import Singleton

@Singleton
class MyObject:
    def __init__(self):
        self.value = 0

    def add(self, value):
        self.value += value
        return self.value


print(MyObject.value)  # 0
MyObject.add(1)
print(MyObject.value)  # 1

Besides, many miscellaneous functions are also provided.

Functions list (in tensorneko_util):

• generate_inf_seq
• compose
• listdir
• with_printed
• ifelse
• dict_add
• as_list
• identity
• list_to_dict
• get_tensorneko_util_path

Functions list (in tensorneko):

• reduce_dict_by
• summarize_dict_by
• with_printed_shape
• is_bad_num
• count_parameters