fastorch 0.0.2

Using pytorch easier and faster

Fastorch

Pytorch is convenient and easy to use, while Keras is designed to experiment quickly. Do you want to possess both Pytorch's convenience and Keras' fast experimentation? The answer is fastorch.

Traditional classification task training flow in pytorch

################################# import dependencies ###############################
import torch.nn as nn
import torch
from torch.utils.data import Dataset, DataLoader

################################# define your network ###############################
class network(nn.Module):
    def __init__(self):
        super(network, self).__init__()
        self.fc = nn.Sequential(
            nn.Linear(1000, 500),
            nn.ReLU(True),
            nn.Linear(500, 500),
            nn.ReLU(True),
            nn.Linear(500, 500),
            nn.ReLU(True),
            nn.Linear(500, 500),
            nn.ReLU(True),
            nn.Linear(500, 100),
            nn.ReLU(True),
            nn.Linear(100, 100),
            nn.ReLU(True),
            nn.Linear(100, 10),
        )

    def forward(self, x):
        return self.fc(x)

################################## prepare dataset ##################################
class mydataset(Dataset):
    def __init__(self, x, y):
        self.x = x
        self.y = y

    def __getitem__(self, item):
        return self.x[item], self.y[item]

    def __len__(self):
        return self.x.size(0)
# for simplicity, randomly generate datas, 
X = torch.randn(5000, 1000)
Y = torch.randint(0, 10, size=(5000,))
BATCH_SIZE = 100
trainset = mydataset(X, Y)
train_loader = DataLoader(trainset, batch_size=BATCH_SIZE, shuffle=True)

########################### assign optimizer and criterion ##########################
optimizer = torch.optim.SGD(net.parameters(), lr=0.01)
criterion = nn.CrossEntropyLoss()

############################## define and run training ##############################
device = "cuda" if torch.cuda.is_available() else "cpu"
EPOCH = 10
net = network().to(device)
for epoch in range(EPOCH):
    for x, y in train_loader:
        x, y = x.to(device), y.to(device)
        optimizer.zero_grad()
        out = net(x)
        loss = criterion(out, y)
        loss.backward()
        optimizer.step()
        print('Epoch %d -> loss: %.4f' % (epoch, loss.item()))

It's simple and straightforward, but is that enough? Below gives fastorch resolution.

Training with fastorch

################################# import dependencies ###############################
import torch
import fastorch

################################# define your network ###############################
'''
inherit from fastorch in your own network
'''
class network(fastorch.models.Model):
    def __init__(self):
        super(network, self).__init__()
        self.fc = nn.Sequential(
            nn.Linear(1000, 500),
            nn.ReLU(True),
            nn.Linear(500, 500),
            nn.ReLU(True),
            nn.Linear(500, 500),
            nn.ReLU(True),
            nn.Linear(500, 500),
            nn.ReLU(True),
            nn.Linear(500, 100),
            nn.ReLU(True),
            nn.Linear(100, 100),
            nn.ReLU(True),
            nn.Linear(100, 10),
        )

    def forward(self, x):
        return self.fc(x)

################################## prepare dataset ##################################
# for simplicity, randomly generate datas, 
X = torch.randn(5000, 1000)
Y = torch.randint(0, 10, size=(5000,))

#################################### run training ###################################
net = network()
net.fit(x=X, y=Y, optimizer='sgd', criterion='cross_entropy', batch_size=BATCH_SIZE, epochs=1, verbose=1)
'''
Epoch[1/1]
 2100/5000 [========>***********] - 1s - 68ms/batch -batch_loss: 2.2993 -batch_acc: 0.1200
'''

Installation

Before installing Fastorch, please install the following dependencies:

• python >= 3.0 (3.6 is recommend)
• pytorch (1.0+ is recommend)

Then you can install Fastorchby using pip:

$ pip install fastorch

Supports

When using fastorch.models.Model, make sure your network is inherited from fastorch.models.Model.

- fastorch.models.Model

fit(train_dataset=None, x=None, y=None, optimizer=None, criterion=None, transform=None, batch_size=None, epochs=1, verbose=1, print_acc=True, callbacks=None, validation_dataset=None, validation_split=0.0, validation_data=None, validation_transform=None, shuffle=True, initial_epoch=0, steps_per_epoch=None, device=None, **kwargs)

• train_dataset: training dataset, which inherited from torch.utils.data.Dataset. Make sure x and y are not None when train_dataset is None.
• x: training data, it can be Tensor銆乣ndarrayorList`. You need pass x and y when train_dataset is None.
• y: training target, it can be Tensor銆乣ndarrayorList`. You need pass x and y when train_dataset is None.
• optimizer: model optimizer, it can be str or an instance of pytorch optimizer, when passing str('sgd', 'adam'), fastorch will automatically declare the corresponding pytorch optimizer.
• criterion: loss function, it can be str or an instance of pytorch objective function , when passing str('mse', 'cross_entropy', etc.), fastorch will automatically declare the corresponding pytorch objective function .
• transform: applying torchvision.transforms on x and y or train_dataset.
• batch_size: training batch_size, default is 32 if not specified.
• epochs: training stop epochs, default is 1 if not specified.
• verbose: log display mode, 0 = silent, 1 = progress bar, 2 = print each line.
• print_acc: True or False, passing True if you are doing classification task.
• callbacks: waiting for implemented.
• validation_dataset: validation dataset, which inherited from torch.utils.data.Dataset.
• validation_split: split validation data from training data's ratio, make sure x and y are not None when using this argument.
• validation_data: tuple of validation x and validation y.
• validation_transform: applying torchvision.transforms on validation_data or data split by validation_split.
• shuffle: whether shuffle the training data.
• initial_epoch: start training epoch.
• steps_per_epoch: int or None, if specified, batch_size will be set as total_sample_nums / steps_per_epoch.
• device: specify device, otherwise fastorch will set device according to torch.cuda.is_available().
• **kwargs: arguments used for torch.utils.data.Dataloader, etc.

evaluate(dataset=None, dataloader=None, x=None, y=None, transform=None, batch_size=None, verbose=1, criterion=None, print_acc=True, steps=None, device=None, **kwargs)

• dataset: evaluate dataset, which inherited from torch.utils.data.Dataset. Make sure dataloader or x and y are not None when datasetis None.

• dataloader: evaluate dataloader, which inherited from torch.utils.data.Dataloader. Make sure datasetor x and y are not None when dataloaderis None.

• x: test data, it can be Tensor銆乣ndarrayorList`. You need pass x and y when dataset or dataloader is None.

• y: test target, it can be Tensor銆乣ndarrayorList`. You need pass x and y when dataset or dataloader is None.

• transform: applying torchvision.transforms on x and y or dataset.

• batch_size: evaluate batch_size, default is 32 if not specified.

• verbose: log display mode, 0 = silent, 1 = progress bar, 2 = print each line.

• criterion: loss function, it can be str or an instance of pytorch objective function , when passing str('mse', 'cross_entropy', etc.), fastorch will automatically declare the corresponding pytorch objective function .

• print_acc: True or False, passing True if you are doing classification task.

• steps: int or None, if specified, batch_size will be set as total_sample_nums / steps.

• device: specify device, otherwise fastorch will set device according to torch.cuda.is_available().

• **kwargs: arguments used for torch.utils.data.Dataloader, etc.

- fastorch.functional

fit(model=None, train_dataset=None, x=None, y=None, optimizer=None, criterion=None, transform=None, batch_size=None, epochs=1, verbose=1, print_acc=True, callbacks=None, validation_dataset=None, validation_split=0.0, validation_data=None, validation_transform=None, shuffle=True, initial_epoch=0, steps_per_epoch=None, device=None, **kwargs)

• model: your network, must be specify.

• train_dataset: training dataset, which inherited from torch.utils.data.Dataset. Make sure x and y are not None when train_dataset is None.

• x: training data, it can be Tensor銆乣ndarrayorList`. You need pass x and y when train_dataset is None.

• y: training target, it can be Tensor銆乣ndarrayorList`. You need pass x and y when train_dataset is None.

• optimizer: model optimizer, it can be str or an instance of pytorch optimizer, when passing str('sgd', 'adam'), fastorch will automatically declare the corresponding pytorch optimizer.

• criterion: loss function, it can be str or an instance of pytorch objective function , when passing str('mse', 'cross_entropy', etc.), fastorch will automatically declare the corresponding pytorch objective function .

• transform: applying torchvision.transforms on x and y or train_dataset.

• batch_size: training batch_size, default is 32 if not specified.

• epochs: training stop epochs, default is 1 if not specified.

• verbose: log display mode, 0 = silent, 1 = progress bar, 2 = print each line.

• print_acc: True or False, passing True if you are doing classification task.

• callbacks: waiting for implemented.

• validation_dataset: validation dataset, which inherited from torch.utils.data.Dataset.

• validation_split: split validation data from training data's ratio, make sure x and y are not None when using this argument.

• validation_data: tuple of validation x and validation y.

• validation_transform: applying torchvision.transforms on validation_data or data split by validation_split.

• shuffle: whether shuffle the training data.

• initial_epoch: start training epoch.

• steps_per_epoch: int or None, if specified, batch_size will be set as total_sample_nums / steps_per_epoch.

• device: specify device, otherwise fastorch will set device according to torch.cuda.is_available().

• **kwargs: arguments used for torch.utils.data.Dataloader, etc.

evaluate((model=None, dataset=None, dataloader=None, x=None, y=None, transform=None, batch_size=None, verbose=1, criterion=None, print_acc=True, steps=None, device=None, **kwargs)

• model: your network, must be specify.

• dataset: evaluate dataset, which inherited from torch.utils.data.Dataset. Make sure dataloader or x and y are not None when datasetis None.

• dataloader: evaluate dataloader, which inherited from torch.utils.data.Dataloader. Make sure datasetor x and y are not None when dataloaderis None.

• x: test data, it can be Tensor銆乣ndarrayorList`. You need pass x and y when dataset or dataloader is None.

• y: test target, it can be Tensor銆乣ndarrayorList`. You need pass x and y when dataset or dataloader is None.

• transform: applying torchvision.transforms on x and y or dataset.

• batch_size: evaluate batch_size, default is 32 if not specified.

• verbose: log display mode, 0 = silent, 1 = progress bar, 2 = print each line.

• criterion: loss function, it can be str or an instance of pytorch objective function , when passing str('mse', 'cross_entropy', etc.), fastorch will automatically declare the corresponding pytorch objective function .

• print_acc: True or False, passing True if you are doing classification task.

• steps: int or None, if specified, batch_size will be set as total_sample_nums / steps.

• device: specify device, otherwise fastorch will set device according to torch.cuda.is_available().

• **kwargs: arguments used for torch.utils.data.Dataloader, etc.