Metadata-Version: 2.1
Name: torchlit
Version: 0.1.4
Summary: torchlit - thin wrappers for Pytorch
Home-page: https://github.com/himanshu-dutta/torchlit
Author: Himanshu Dutta
Author-email: meet.himanshu.dutta@gmail.com
License: MIT License
Description: # 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}]
        ```
        
Platform: linux
Platform: unix
Requires-Python: >3.5.2
Description-Content-Type: text/markdown
