whenxuan: update the readme

Author: wwhenxuan

README.md

@@ -26,9 +26,24 @@ We only develop and test with PyTorch. Please make sure to install it from [PyTo
 
 ## Usage <a id="Usage"></a>
 
+**The core of the channel attention mechanism lies in its invariance between input and output.** Therefore, we can easily embed this module into a certain location in a neural network to further improve the model's performance. 
 
+~~~python
+import torch
+from channel_attention import SEAttention
 
+# 1D Time Series Data with (batch_size, channels, seq_len)
+inputs = torch.rand(8, 16, 128)
+attn = SEAttention(n_dims=1, n_channels=16, reduction=4)
+print(attn(inputs).shape)
 
+# 2D Image Data with (batch_size, channels, height, width)
+inputs_2d = torch.rand(8, 16, 64, 64)
+attn_2d = SEAttention(n_dims=2, n_channels=16, reduction=4)
+print(attn_2d(inputs_2d).shape)
+~~~
+
+When the number of input channels is small, the channel attention mechanism is very lightweight and does not significantly increase computational complexity.
 
 ## Modules <a id="Modules"></a>
 
@@ -50,5 +65,12 @@ We only develop and test with PyTorch. Please make sure to install it from [PyTo
     <img width="80%" src="images/SpatialAttention.png">
 </div>
 
+#### 4. [`ConvBlockAttention`](https://github.com/wwhenxuan/Channel-Attention/blob/main/channel_attention/spatial_attention.py): [[paper]]() The Convolutional Block Attention Module (CBAM) combining Channel Attention and Spatial Attention.
+
+<div align="center">
+    <img width="80%" src="images/ConvBlockAttention.png">
+</div>
+
+
 
 ## Experiments <a id="Experiments"></a>

images/ConvBlockAttention.png

@@ -433,7 +433,20 @@
    "id": "88291fd7",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "import torch\n",
+    "from channel_attention import SEAttention\n",
+    "\n",
+    "# 1D Time Series Data with (batch_size, channels, seq_len)\n",
+    "inputs = torch.rand(8, 16, 128)\n",
+    "attn = SEAttention(n_dims=1, n_channels=16, reduction=4)\n",
+    "print(attn(inputs).shape)\n",
+    "\n",
+    "# 2D Image Data with (batch_size, channels, height, width)\n",
+    "inputs_2d = torch.rand(8, 16, 64, 64)\n",
+    "attn_2d = SEAttention(n_dims=2, n_channels=16, reduction=4)\n",
+    "print(attn_2d(inputs_2d).shape)"
+   ]
   }
  ],
  "metadata": {