torchlit 0.1.4
torchlit - thin wrappers for Pytorch
License: MIT License
Requires Python: >3.5.2

# torchlit

`torchlit` is an in progress collection of Pytorch utilities and thin wrappers which can be used for various purposes.

With every project, I intend to add functionalities that are fairly genralized to be put as a boilerplate for different utilities.

It allows you to write less code while focusing on the model itself, rather than its verbosity and how the model is retrieved. Along with this, it consists of data utilities which can be used for purposes of loading data from dataframe, or from a folder for inference, etc.

### Sample usage

```
!pip install torchlit --q
```

```
    |████████████████████████████████| 911kB 5.4MB/s
    |████████████████████████████████| 102kB 7.3MB/s
    |████████████████████████████████| 81kB 6.7MB/s
    |████████████████████████████████| 7.6MB 9.3MB/s
    |████████████████████████████████| 81kB 7.4MB/s
    |████████████████████████████████| 102kB 9.5MB/s
```

```Python
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader, Dataset

import torchlit
```

```Python
class Net(torchlit.Model):
    def __init__(self):
        super(Net, self).__init__(F.cross_entropy, record=True, verbose=True)
        self.conv1 = nn.Conv2d(3, 6, 3)
        self.conv2 = nn.Conv2d(6, 12, 3)
        self.flatten = nn.Flatten()
        self.lin = nn.Linear(184512, 10)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.relu(self.conv2(x))
        x = self.flatten(x)
        return self.lin(x)

model = Net()
model
```

```
    Net(
      (conv1): Conv2d(3, 6, kernel_size=(3, 3), stride=(1, 1))
      (conv2): Conv2d(6, 12, kernel_size=(3, 3), stride=(1, 1))
      (flatten): Flatten(start_dim=1, end_dim=-1)
      (lin): Linear(in_features=184512, out_features=10, bias=True)
    )
```

```Python
train_ds = [(x, y) for x,y in zip(torch.randn((10, 3, 128, 128)), torch.randint(0, 10, (10,)))]
val_ds = [(x,y) for x,y in zip(torch.randn((3, 3, 128, 128)), torch.randint(0, 10, (3,)))]

train_dl = DataLoader(train_ds)
val_dl = DataLoader(val_ds)
```

```Python
EPOCHS = 10
model = Net()

for epoch in range(EPOCHS):
    for xb in train_dl:
        model.train_step(xb)

    for xb in val_dl:
        model.val_step(xb)

    model.epoch_end()
```

```

    Epoch [0]: train_loss: 2.3065271377563477, val_loss: 2.3060548305511475, val_acc: 0.0
    Epoch [1]: train_loss: 2.3065271377563477, val_loss: 2.3060548305511475, val_acc: 0.0
    Epoch [2]: train_loss: 2.3065271377563477, val_loss: 2.3060548305511475, val_acc: 0.0
    Epoch [3]: train_loss: 2.3065271377563477, val_loss: 2.3060548305511475, val_acc: 0.0
    Epoch [4]: train_loss: 2.3065271377563477, val_loss: 2.3060548305511475, val_acc: 0.0
    Epoch [5]: train_loss: 2.3065271377563477, val_loss: 2.3060548305511475, val_acc: 0.0
    Epoch [6]: train_loss: 2.3065271377563477, val_loss: 2.3060548305511475, val_acc: 0.0
    Epoch [7]: train_loss: 2.3065271377563477, val_loss: 2.3060548305511475, val_acc: 0.0
    Epoch [8]: train_loss: 2.3065271377563477, val_loss: 2.3060548305511475, val_acc: 0.0
    Epoch [9]: train_loss: 2.3065271377563477, val_loss: 2.3060548305511475, val_acc: 0.0
```

```Python
model.history
```

```
[{'epoch': 0,
  'train_loss': 2.3326268196105957,
  'val_acc': 0.0,
  'val_loss': 2.2232437133789062},
 {'epoch': 1,
  'train_loss': 2.3326268196105957,
  'val_acc': 0.0,
  'val_loss': 2.2232437133789062},
 {'epoch': 2,
  'train_loss': 2.3326268196105957,
  'val_acc': 0.0,
  'val_loss': 2.2232437133789062},
 {'epoch': 3,
  'train_loss': 2.3326268196105957,
  'val_acc': 0.0,
  'val_loss': 2.2232437133789062},
 {'epoch': 4,
  'train_loss': 2.3326268196105957,
  'val_acc': 0.0,
  'val_loss': 2.2232437133789062},
 {'epoch': 5,
  'train_loss': 2.3326268196105957,
  'val_acc': 0.0,
  'val_loss': 2.2232437133789062},
 {'epoch': 6,
  'train_loss': 2.3326268196105957,
  'val_acc': 0.0,
  'val_loss': 2.2232437133789062},
 {'epoch': 7,
  'train_loss': 2.3326268196105957,
  'val_acc': 0.0,
  'val_loss': 2.2232437133789062},
 {'epoch': 8,
  'train_loss': 2.3326268196105957,
  'val_acc': 0.0,
  'val_loss': 2.2232437133789062},
 {'epoch': 9,
  'train_loss': 2.3326268196105957,
  'val_acc': 0.0,
  'val_loss': 2.2232437133789062}]
```