XShinnosuke : Deep Learning Framework

Descriptions

XShinnosuke(short as XS) is a high-level neural network framework which supports for both Dynamic Graph and Static Graph, and has almost the same API to Keras and Pytorch with slightly differences. It was written by Python only, and dedicated to realize experimentations quickly.

Here are some features of XS:

Based on Cupy(GPU version)/Numpy and native to Python.
Without any other 3rd-party deep learning library.
Keras and Pytorch style API, easy to start up.
Supports commonly used layers such as: Dense, Conv2D, MaxPooling2D, LSTM, SimpleRNN, etc, and commonly used function: conv2d, max_pool2d, relu, etc.
Sequential in Pytorch and Keras, Model in Keras and Module in Pytorch, all of them are supported by XS.
Training and inference supports for both dynamic graph and static graph.
Autograd is supported .

XS is compatible with: Python 3.x (3.7 is recommended) ==> C++ version

1. API docs 2. Notebook

Getting started

Compared with Pytorch and Keras

ResNet18(5 Epochs, 32 Batch_size)	XS_static_graph(cpu)	XS_dynamic_graph(cpu)	Pytorch(cpu)	Keras(cpu)
Speed(Ratio - seconds)	1x - 65.05	0.98x - 66.33	2.67x - 24.39	1.8x - 35.97
Memory(Ratio - GB)	1x - 0.47	0.47x- 0.22	0.55x - 0.26	0.96x - 0.45

ResNet18(5 Epochs, 32 Batch_size)	XS_static_graph(gpu)	XS_dynamic_graph(gpu)	Pytorch(gpu)	Keras(gpu)
Speed(Ratio - seconds)	1x - 9.64	1.02x - 9.45	3.47x - 2.78	1.07x - 9.04
Memory(Ratio - GB)	1x - 0.48	1.02x - 0.49	4.4x - 2.11	4.21x - 2.02

XS holds the best memory usage!

1. Static Graph

The core networks of XS is a model, which provide a way to combine layers. There are two model types: Sequential (a linear stack of layers) and Functional (build a graph for layers).

For Sequential model:

from xs.nn.models import Sequential

model = Sequential()

Using .add() to connect layers:

from xs.layers import Dense

model.add(Dense(out_features=500, activation='relu', input_shape=(784, )))  # must be specify input_shape if current layer is the first layer of model
model.add(Dense(out_features=10))

Once you have constructed your model, you should configure it with .compile() before training or inference:

model.compile(loss='cross_entropy', optimizer='sgd')

If your labels are one-hot encoded vectors/matrix, you shall specify loss as sparse_crossentropy, otherwise use crossentropy instead.

Use print(model) to see details of model:

***************************************************************************
Layer(type)               Output Shape         Param      Connected to   
###########################################################################
dense0 (Dense)            (None, 500)          392500     
              
---------------------------------------------------------------------------
dense1 (Dense)            (None, 10)           5010       dense0         
---------------------------------------------------------------------------
***************************************************************************
Total params: 397510
Trainable params: 397510
Non-trainable params: 0

Start training your network by fit():

# trainX and trainy are ndarrays
history = model.fit(trainX, trainy, batch_size=128, epochs=5)

Once completing training your model, you can save or load your model by save() / load(), respectively.

model.save(save_path)
model.load(model_path)

Evaluate your model performance by evaluate():

# testX and testy are Cupy/Numpy ndarray
accuracy, loss = model.evaluate(testX, testy, batch_size=128)

Inference through predict():

predict = model.predict(testX)

For Functional model:

from xs.nn.models import Model
from xs.layers import Input, Conv2D, MaxPooling2D, Flatten, Dense

X_input = Input(input_shape = (1, 28, 28))   # (channels, height, width)
X = Conv2D(8, (2, 2), activation='relu')(X_input)
X = MaxPooling2D((2, 2))(X)
X = Flatten()(X)
X = Dense(10)(X)
model = Model(inputs=X_input, outputs=X)  
model.compile(optimizer='sgd', loss='cross_entropy')
model.fit(trainX, trainy, batch_size=256, epochs=80)

Pass inputs and outputs layer to Model(), then compile and fit model as Sequentialmodel.

2. Dynamic Graph

First design your own network, make sure your network is inherited from Module and override the __init__() and forward() function:

from xs.nn.models import Module
from xs.layers import Conv2D, ReLU, Flatten, Dense
import xs.nn.functional as F

class MyNet(Module):
    def __init__(self):
        super().__init__()
        self.conv1 = Conv2D(out_channels=8, kernel_size=3)  # don't need to specify in_channels, which is simple than Pytorch
        self.relu = ReLU(inplace=True)
        self.flat = Flatten()
        self.fc = Dense(10)

    def forward(self, x, *args):
        x = self.conv1(x)
        x = self.relu(x)
        x = F.max_pool2d(x, kernel_size=2)
        x = self.flat(x)
        x = self.fc(x)
        return x

Then manually set the training/ testing flow:

from xs.nn.optimizers import SGD
from xs.utils.data import DataSet, DataLoader
import xs.nn as nn
import numpy as np

# random generate data
X = np.random.randn(100, 3, 12, 12)
Y = np.random.randint(0, 10, (100, ))
# generate training dataloader
train_dataset = DataSet(X, Y)
train_loader = DataLoader(dataset=train_dataset, batch_size=10, shuffle=True)
# initialize net
net = MyNet()
# specify optimizer and critetion
optimizer = SGD(net.parameters(), lr=0.1)
critetion = nn.CrossEntropyLoss()
# start training
EPOCH = 5
for epoch in range(EPOCH):
    for x, y in train_loader:
        optimizer.zero_grad()
        out = net(x)
        loss = critetion(out, y)
        loss.backward()
        optimizer.step()
        train_acc = critetion.calc_acc(out, y)
        print(f'epoch -> {epoch}, train_acc: {train_acc}, train_loss: {loss.item()}')

Building an image classification model, a question answering system or any other model is just as convenient and fast~

Autograd

XS autograd supports for basic operators such as: +, -, *, \, **, etc and some common functions: matmul(), mean(), sum(), log(), view(), etc .

from xs.nn import Tensor

a = Tensor(5, requires_grad=True)
b = Tensor(10, requires_grad=True)
c = Tensor(3, requires_grad=True)
x = (a + b) * c
y = x ** 2
print('x: ', x)  # x:  Variable(45.0, requires_grad=True, grad_fn=<MultiplyBackward>)
print('y: ', y)  # y:  Variable(2025.0, requires_grad=True, grad_fn=<PowBackward>)
x.retain_grad()
y.backward()
print('x grad:', x.grad)  # x grad: 90.0
print('c grad:', c.grad)  # c grad: 1350.0
print('b grad:', b.grad)  # b grad: 270.0
print('a grad:', a.grad)  # a grad: 270.0

Here are examples of autograd.

Installation

Before installing XS, please install the following dependencies:

Numpy

Cupy (Optional)

Then you can install XS by using pip:

$ pip install xshinnosuke

Supports

functional

admm
mm
relu
flatten
conv2d
max_pool2d
avg_pool2d
reshape
sigmoid
tanh
softmax
dropout2d
batch_norm
groupnorm2d
layernorm
pad_2d
embedding

Two basic class:

- Layer:

Dense
Flatten
Conv2D
MaxPooling2D
AvgPooling2D
ChannelMaxPooling
ChannelAvgPooling
Activation
Input
Dropout
BatchNormalization
LayerNormalization
GroupNormalization
TimeDistributed
SimpleRNN
LSTM
Embedding
ZeroPadding2D
Add
Multiply
Matmul
Log
Negative
Exp
Sum
Abs
Mean
Pow

- Tenosr:

Parameter

Optimizers

SGD
Momentum
RMSprop
AdaGrad
AdaDelta
Adam

Waiting for implemented more

Objectives

MSELoss
MAELoss
BCELoss
SparseCrossEntropy
CrossEntropyLoss

Activations

ReLU
Sigmoid
Tanh
Softmax

Initializations

Zeros
Ones
Uniform
LecunUniform
GlorotUniform
HeUniform
Normal
LecunNormal
GlorotNormal
HeNormal
Orthogonal

Regularizes

waiting for implement.

Preprocess

to_categorical (convert inputs to one-hot vector/matrix)
pad_sequences (pad sequences to the same length)

Contact

Email: eleven_1111@outlook.com

E1eveNn/xshinnosuke