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Jimexist/torchvision_GoogLeNet.ipynb

Created July 27, 2021 14:00
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Torch Vision GoogLeNet Model
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torchvision_GoogLeNet.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "d61a6c41",
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import torchvision"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "abe6863d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"('1.9.0', '0.10.0')"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"torch.__version__, torchvision.__version__"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "1b55aad8",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"GoogLeNet(\n",
" (conv1): BasicConv2d(\n",
" (conv): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
" (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (maxpool1): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
" (conv2): BasicConv2d(\n",
" (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (conv3): BasicConv2d(\n",
" (conv): Conv2d(64, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (maxpool2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
" (inception3a): Inception(\n",
" (branch1): BasicConv2d(\n",
" (conv): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (branch2): Sequential(\n",
" (0): BasicConv2d(\n",
" (conv): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (1): BasicConv2d(\n",
" (conv): Conv2d(96, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (branch3): Sequential(\n",
" (0): BasicConv2d(\n",
" (conv): Conv2d(192, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(16, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (1): BasicConv2d(\n",
" (conv): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (branch4): Sequential(\n",
" (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
" (1): BasicConv2d(\n",
" (conv): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" )\n",
" (inception3b): Inception(\n",
" (branch1): BasicConv2d(\n",
" (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (branch2): Sequential(\n",
" (0): BasicConv2d(\n",
" (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (1): BasicConv2d(\n",
" (conv): Conv2d(128, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (branch3): Sequential(\n",
" (0): BasicConv2d(\n",
" (conv): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (1): BasicConv2d(\n",
" (conv): Conv2d(32, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (branch4): Sequential(\n",
" (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
" (1): BasicConv2d(\n",
" (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" )\n",
" (maxpool3): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
" (inception4a): Inception(\n",
" (branch1): BasicConv2d(\n",
" (conv): Conv2d(480, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (branch2): Sequential(\n",
" (0): BasicConv2d(\n",
" (conv): Conv2d(480, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (1): BasicConv2d(\n",
" (conv): Conv2d(96, 208, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(208, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (branch3): Sequential(\n",
" (0): BasicConv2d(\n",
" (conv): Conv2d(480, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(16, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (1): BasicConv2d(\n",
" (conv): Conv2d(16, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (branch4): Sequential(\n",
" (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
" (1): BasicConv2d(\n",
" (conv): Conv2d(480, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" )\n",
" (inception4b): Inception(\n",
" (branch1): BasicConv2d(\n",
" (conv): Conv2d(512, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (branch2): Sequential(\n",
" (0): BasicConv2d(\n",
" (conv): Conv2d(512, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(112, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (1): BasicConv2d(\n",
" (conv): Conv2d(112, 224, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(224, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (branch3): Sequential(\n",
" (0): BasicConv2d(\n",
" (conv): Conv2d(512, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(24, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (1): BasicConv2d(\n",
" (conv): Conv2d(24, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (branch4): Sequential(\n",
" (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
" (1): BasicConv2d(\n",
" (conv): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" )\n",
" (inception4c): Inception(\n",
" (branch1): BasicConv2d(\n",
" (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (branch2): Sequential(\n",
" (0): BasicConv2d(\n",
" (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (1): BasicConv2d(\n",
" (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (branch3): Sequential(\n",
" (0): BasicConv2d(\n",
" (conv): Conv2d(512, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(24, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (1): BasicConv2d(\n",
" (conv): Conv2d(24, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (branch4): Sequential(\n",
" (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
" (1): BasicConv2d(\n",
" (conv): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" )\n",
" (inception4d): Inception(\n",
" (branch1): BasicConv2d(\n",
" (conv): Conv2d(512, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(112, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (branch2): Sequential(\n",
" (0): BasicConv2d(\n",
" (conv): Conv2d(512, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(144, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (1): BasicConv2d(\n",
" (conv): Conv2d(144, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(288, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (branch3): Sequential(\n",
" (0): BasicConv2d(\n",
" (conv): Conv2d(512, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (1): BasicConv2d(\n",
" (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (branch4): Sequential(\n",
" (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
" (1): BasicConv2d(\n",
" (conv): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" )\n",
" (inception4e): Inception(\n",
" (branch1): BasicConv2d(\n",
" (conv): Conv2d(528, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (branch2): Sequential(\n",
" (0): BasicConv2d(\n",
" (conv): Conv2d(528, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (1): BasicConv2d(\n",
" (conv): Conv2d(160, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (branch3): Sequential(\n",
" (0): BasicConv2d(\n",
" (conv): Conv2d(528, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (1): BasicConv2d(\n",
" (conv): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (branch4): Sequential(\n",
" (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
" (1): BasicConv2d(\n",
" (conv): Conv2d(528, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" )\n",
" (maxpool4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n",
" (inception5a): Inception(\n",
" (branch1): BasicConv2d(\n",
" (conv): Conv2d(832, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (branch2): Sequential(\n",
" (0): BasicConv2d(\n",
" (conv): Conv2d(832, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (1): BasicConv2d(\n",
" (conv): Conv2d(160, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (branch3): Sequential(\n",
" (0): BasicConv2d(\n",
" (conv): Conv2d(832, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (1): BasicConv2d(\n",
" (conv): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (branch4): Sequential(\n",
" (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
" (1): BasicConv2d(\n",
" (conv): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" )\n",
" (inception5b): Inception(\n",
" (branch1): BasicConv2d(\n",
" (conv): Conv2d(832, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (branch2): Sequential(\n",
" (0): BasicConv2d(\n",
" (conv): Conv2d(832, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (1): BasicConv2d(\n",
" (conv): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (branch3): Sequential(\n",
" (0): BasicConv2d(\n",
" (conv): Conv2d(832, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (1): BasicConv2d(\n",
" (conv): Conv2d(48, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (branch4): Sequential(\n",
" (0): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=True)\n",
" (1): BasicConv2d(\n",
" (conv): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" )\n",
" (aux1): InceptionAux(\n",
" (conv): BasicConv2d(\n",
" (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (fc1): Linear(in_features=2048, out_features=1024, bias=True)\n",
" (fc2): Linear(in_features=1024, out_features=1000, bias=True)\n",
" )\n",
" (aux2): InceptionAux(\n",
" (conv): BasicConv2d(\n",
" (conv): Conv2d(528, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (fc1): Linear(in_features=2048, out_features=1024, bias=True)\n",
" (fc2): Linear(in_features=1024, out_features=1000, bias=True)\n",
" )\n",
" (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
" (dropout): Dropout(p=0.2, inplace=False)\n",
" (fc): Linear(in_features=1024, out_features=1000, bias=True)\n",
")"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"torchvision.models.GoogLeNet(init_weights=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c7437d1d",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
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"language_info": {
"codemirror_mode": {
"name": "ipython",
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