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Add Mobilenetv1 on architectures #14

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Jun 29, 2024
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Add Mobilenetv1 on architectures #14

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Add Mobilenetv1 on architectures
HenryMantilla Jun 29, 2024
6c8dc77
Add Mobilenetv1 on architectures
HenryMantilla Jun 29, 2024
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1 change: 1 addition & 0 deletions automation/notebooks-table-data.csv
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Original file line number Diff line number Diff line change
Expand Up @@ -5,3 +5,4 @@ Network In Network, architectures/network-in-network.ipynb,,1312.4400
Xception, architectures/xception.ipynb,,1610.02357v3
Pixel Shuffle, modules/pixel-shuffle.ipynb,,1609.05158
U-Net, architectures/unet.ipynb,,1505.04597
Mobilenet V1, architectures/mobilenetv1.ipynb,,1704.04861
238 changes: 238 additions & 0 deletions notebooks/architectures/mobilenetv1.ipynb
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@@ -0,0 +1,238 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[![deep-learning-notes](https://github.com/semilleroCV/deep-learning-notes/raw/main/assets/banner-notebook.png)](https://github.com/semilleroCV/deep-learning-notes)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## MobileNet V1"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"%%capture\n",
"#@title **Install required packages**\n",
"\n",
"! pip install torchinfo"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"#@title **Importing libraries**\n",
"\n",
"import torch # 2.3.1+cu121\n",
"import torchinfo #1.8.0\n",
"\n",
"import torch.nn as nn\n",
"from torch import Tensor"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"torch version: 2.3.1+cu121\n",
"torchinfo version: 1.8.0\n"
]
}
],
"source": [
"# Note: Not all dependencies have the __version__ method.\n",
"\n",
"print(f\"torch version: {torch.__version__}\")\n",
"print(f\"torchinfo version: {torchinfo.__version__}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Mobilenet V1 architecture code"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"class ConvBlock(nn.Module):\n",
" def __init__(self, in_channels: int, out_channels: int, stride: int):\n",
" super(ConvBlock, self).__init__()\n",
"\n",
" self.conv_blk = nn.Sequential(\n",
" nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False),\n",
" nn.BatchNorm2d(out_channels),\n",
" nn.ReLU(),\n",
" )\n",
"\n",
" def forward(self, x):\n",
" return self.conv_blk(x)\n",
"\n",
"\n",
"class DepthwiseConvBlock(nn.Module):\n",
" def __init__(self, in_channels: int, out_channels: int, stride: int):\n",
" super(DepthwiseConvBlock, self).__init__()\n",
"\n",
" self.depthwise_conv_blk = nn.Sequential(\n",
" nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=stride, padding=1, groups=in_channels, bias=False),\n",
" nn.BatchNorm2d(in_channels),\n",
" nn.ReLU(inplace=True),\n",
" nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, bias=False),\n",
" nn.BatchNorm2d(out_channels),\n",
" nn.ReLU(inplace=True),\n",
" )\n",
"\n",
" def forward(self, x):\n",
" return self.depthwise_conv_blk(x)\n",
"\n",
"\n",
"class MobileNetV1(nn.Module):\n",
" def __init__(self, layer_config: list, depth_multiplier: int, num_classes: int = 1000):\n",
" super(MobileNetV1, self).__init__()\n",
"\n",
" \"\"\"depth multiplier is also called width_multiplier or alpha\"\"\"\n",
"\n",
" self.model = nn.Sequential()\n",
"\n",
" self.model.add_module('conv_blk_1', ConvBlock(3, 32, 2))\n",
"\n",
" for idx, params in enumerate(layer_config):\n",
" \"\"\"layer_params: List -> (in_channels, out_channels, stride)\"\"\"\n",
" self.model.add_module(f\"dw_blk_{idx}\",DepthwiseConvBlock(int(params[0]*depth_multiplier),\n",
" (params[1]*depth_multiplier), params[2]))\n",
" \n",
" self.model.add_module('pool', nn.AdaptiveAvgPool2d(1))\n",
" self.model.add_module('flatten', nn.Flatten())\n",
" self.model.add_module('fc',nn.Linear(1024, num_classes))\n",
"\n",
" def forward(self, x):\n",
" return self.model(x)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"==========================================================================================\n",
"Layer (type:depth-idx) Output Shape Param #\n",
"==========================================================================================\n",
"MobileNetV1 [1, 1000] --\n",
"├─Sequential: 1-1 [1, 1000] --\n",
"│ └─ConvBlock: 2-1 [1, 32, 112, 112] --\n",
"│ │ └─Sequential: 3-1 [1, 32, 112, 112] 928\n",
"│ └─DepthwiseConvBlock: 2-2 [1, 64, 112, 112] --\n",
"│ │ └─Sequential: 3-2 [1, 64, 112, 112] 2,528\n",
"│ └─DepthwiseConvBlock: 2-3 [1, 128, 56, 56] --\n",
"│ │ └─Sequential: 3-3 [1, 128, 56, 56] 9,152\n",
"│ └─DepthwiseConvBlock: 2-4 [1, 128, 56, 56] --\n",
"│ │ └─Sequential: 3-4 [1, 128, 56, 56] 18,048\n",
"│ └─DepthwiseConvBlock: 2-5 [1, 256, 28, 28] --\n",
"│ │ └─Sequential: 3-5 [1, 256, 28, 28] 34,688\n",
"│ └─DepthwiseConvBlock: 2-6 [1, 256, 28, 28] --\n",
"│ │ └─Sequential: 3-6 [1, 256, 28, 28] 68,864\n",
"│ └─DepthwiseConvBlock: 2-7 [1, 512, 14, 14] --\n",
"│ │ └─Sequential: 3-7 [1, 512, 14, 14] 134,912\n",
"│ └─DepthwiseConvBlock: 2-8 [1, 512, 14, 14] --\n",
"│ │ └─Sequential: 3-8 [1, 512, 14, 14] 268,800\n",
"│ └─DepthwiseConvBlock: 2-9 [1, 512, 14, 14] --\n",
"│ │ └─Sequential: 3-9 [1, 512, 14, 14] 268,800\n",
"│ └─DepthwiseConvBlock: 2-10 [1, 512, 14, 14] --\n",
"│ │ └─Sequential: 3-10 [1, 512, 14, 14] 268,800\n",
"│ └─DepthwiseConvBlock: 2-11 [1, 512, 14, 14] --\n",
"│ │ └─Sequential: 3-11 [1, 512, 14, 14] 268,800\n",
"│ └─DepthwiseConvBlock: 2-12 [1, 512, 14, 14] --\n",
"│ │ └─Sequential: 3-12 [1, 512, 14, 14] 268,800\n",
"│ └─DepthwiseConvBlock: 2-13 [1, 1024, 7, 7] --\n",
"│ │ └─Sequential: 3-13 [1, 1024, 7, 7] 531,968\n",
"│ └─DepthwiseConvBlock: 2-14 [1, 1024, 7, 7] --\n",
"│ │ └─Sequential: 3-14 [1, 1024, 7, 7] 1,061,888\n",
"│ └─AdaptiveAvgPool2d: 2-15 [1, 1024, 1, 1] --\n",
"│ └─Flatten: 2-16 [1, 1024] --\n",
"│ └─Linear: 2-17 [1, 1000] 1,025,000\n",
"==========================================================================================\n",
"Total params: 4,231,976\n",
"Trainable params: 4,231,976\n",
"Non-trainable params: 0\n",
"Total mult-adds (M): 568.76\n",
"==========================================================================================\n",
"Input size (MB): 0.60\n",
"Forward/backward pass size (MB): 80.69\n",
"Params size (MB): 16.93\n",
"Estimated Total Size (MB): 98.22\n",
"=========================================================================================="
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Declare parameters for depth-wise separable convolution layers -> (in_channels, out_channels, stride)\n",
"dw_params = [\n",
" (32, 64, 1),\n",
" (64, 128, 2),\n",
" (128, 128, 1),\n",
" (128, 256, 2),\n",
" (256, 256, 1),\n",
" (256, 512, 2),\n",
" (512, 512, 1),\n",
" (512, 512, 1),\n",
" (512, 512, 1),\n",
" (512, 512, 1),\n",
" (512, 512, 1),\n",
" (512, 1024, 2),\n",
" (1024, 1024, 1),\n",
"]\n",
"\n",
"model = MobileNetV1(layer_config=dw_params, depth_multiplier=1, num_classes=1000)\n",
"torchinfo.summary(model, (3, 224, 224), batch_dim = 0)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}