Add PyTorch snippets

Author: futureprogrammer360

README.md

@@ -14,21 +14,24 @@ The easiest way to install Python Data Science Snippets is through [Package Cont
 * [Matplotlib](#matplotlib)
 * [Scikit-learn](#scikit-learn)
 * [Keras](#keras)
+* [PyTorch](#pytorch)
 
 ### Imports
 
 Import snippets start with `i` followed by the package's import alias.
 
-| Trigger    | Description                       |
-|------------|-----------------------------------|
-| `ikeras`   | `from tensorflow import keras`    |
-| `inp`      | `import numpy as np`              |
-| `ipd`      | `import pandas as pd`             |
-| `iplt`     | `import matplotlib.pyplot as plt` |
-| `isklearn` | `from sklearn.$1 import $2`       |
-| `isns`     | `import seaborn as sns`           |
-| `itf`      | `import tensorflow as tf`         |
-| `itorch`   | `import torch`                    |
+| Trigger    | Description                               |
+|------------|-------------------------------------------|
+| `ikeras`   | `from tensorflow import keras`            |
+| `inp`      | `import numpy as np`                      |
+| `ipd`      | `import pandas as pd`                     |
+| `iplt`     | `import matplotlib.pyplot as plt`         |
+| `isklearn` | `from sklearn.$1 import $2`               |
+| `isns`     | `import seaborn as sns`                   |
+| `itf`      | `import tensorflow as tf`                 |
+| `itorch`   | `import torch`                            |
+| `inn`      | `from torch import nn`                    |
+| `idl`      | `from torch.utils.data import DataLoader` |
 
 ### NumPy
 
@@ -89,4 +92,12 @@ Import snippets start with `i` followed by the package's import alias.
 | `save`       | `model.save`              |
 | `sequential` | `keras.Sequential`        |
 
+### PyTorch
+
+| Trigger      | Description                   |
+|--------------|-------------------------------|
+| `dataloader` | `torch.utils.data.DataLoader` |
+| `device`     | `torch.device (cuda/cpu)`     |
+| `module`     | `torch.nn.Module`             |
+
 The snippet files are in the `snippets` folder of [this GitHub repository](https://github.com/futureprogrammer360/Python-Data-Science-Snippets).

snippets/imports/dl.sublime-snippet

@@ -0,0 +1,8 @@
+<snippet>
+    <content><![CDATA[
+from torch.utils.data import DataLoader
+]]></content>
+    <tabTrigger>idl</tabTrigger>
+    <scope>source.python</scope>
+    <description>from torch.utils.data import DataLoader</description>
+</snippet>

snippets/imports/nn.sublime-snippet

@@ -0,0 +1,8 @@
+<snippet>
+    <content><![CDATA[
+from torch import nn
+]]></content>
+    <tabTrigger>inn</tabTrigger>
+    <scope>source.python</scope>
+    <description>from torch import nn</description>
+</snippet>

snippets/torch/dataloader.sublime-snippet

@@ -0,0 +1,8 @@
+<snippet>
+    <content><![CDATA[
+${1:dataloader} = ${2:torch.utils.data.}DataLoader(${3:dataset}, batch_size=${4:1})
+]]></content>
+    <tabTrigger>dataloader</tabTrigger>
+    <scope>source.python</scope>
+    <description>torch.utils.data.DataLoader</description>
+</snippet>

snippets/torch/device.sublime-snippet

@@ -0,0 +1,8 @@
+<snippet>
+    <content><![CDATA[
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+]]></content>
+    <tabTrigger>device</tabTrigger>
+    <scope>source.python</scope>
+    <description>torch device (cuda/cpu)</description>
+</snippet>

snippets/torch/module.sublime-snippet

@@ -0,0 +1,15 @@
+<snippet>
+    <content><![CDATA[
+class ${1:Model}(${2:nn.}Module):
+    def __init__(self$3):
+        super(${1:Model}, self).__init__()
+        self.${4:layer} = ${5:${2:nn.}Linear}($0)
+
+    def forward(self, x):
+        x = self.${4:layer}(x)
+        return x
+]]></content>
+    <tabTrigger>module</tabTrigger>
+    <scope>source.python</scope>
+    <description>torch.nn.Module</description>
+</snippet>