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pytorch-stuff/Pruning_VGG16.ipynb

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import torchvision\n",
"from torchvision import models"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"vgg16 = models.vgg16(pretrained=True)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"VGG(\n",
" (features): Sequential(\n",
" (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (1): ReLU(inplace=True)\n",
" (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (3): ReLU(inplace=True)\n",
" (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
" (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (6): ReLU(inplace=True)\n",
" (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (8): ReLU(inplace=True)\n",
" (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
" (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (11): ReLU(inplace=True)\n",
" (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (13): ReLU(inplace=True)\n",
" (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (15): ReLU(inplace=True)\n",
" (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
" (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (18): ReLU(inplace=True)\n",
" (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (20): ReLU(inplace=True)\n",
" (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (22): ReLU(inplace=True)\n",
" (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
" (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (25): ReLU(inplace=True)\n",
" (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (27): ReLU(inplace=True)\n",
" (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (29): ReLU(inplace=True)\n",
" (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
" )\n",
" (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))\n",
" (classifier): Sequential(\n",
" (0): Linear(in_features=25088, out_features=4096, bias=True)\n",
" (1): ReLU(inplace=True)\n",
" (2): Dropout(p=0.5, inplace=False)\n",
" (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
" (4): ReLU(inplace=True)\n",
" (5): Dropout(p=0.5, inplace=False)\n",
" (6): Linear(in_features=4096, out_features=1000, bias=True)\n",
" )\n",
")\n"
]
}
],
"source": [
"print(vgg16)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"odict_items([('0', Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))), ('1', ReLU(inplace=True)), ('2', Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))), ('3', ReLU(inplace=True)), ('4', MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)), ('5', Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))), ('6', ReLU(inplace=True)), ('7', Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))), ('8', ReLU(inplace=True)), ('9', MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)), ('10', Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))), ('11', ReLU(inplace=True)), ('12', Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))), ('13', ReLU(inplace=True)), ('14', Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))), ('15', ReLU(inplace=True)), ('16', MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)), ('17', Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))), ('18', ReLU(inplace=True)), ('19', Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))), ('20', ReLU(inplace=True)), ('21', Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))), ('22', ReLU(inplace=True)), ('23', MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)), ('24', Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))), ('25', ReLU(inplace=True)), ('26', Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))), ('27', ReLU(inplace=True)), ('28', Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))), ('29', ReLU(inplace=True)), ('30', MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False))])\n"
]
}
],
"source": [
"print(vgg16.features._modules.items())"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Layer: 0\n",
"Name: 0\n",
"Module: Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
"Layer: 2\n",
"Name: 2\n",
"Module: Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
"Layer: 5\n",
"Name: 5\n",
"Module: Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
"Layer: 7\n",
"Name: 7\n",
"Module: Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
"Layer: 10\n",
"Name: 10\n",
"Module: Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
"Layer: 12\n",
"Name: 12\n",
"Module: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
"Layer: 14\n",
"Name: 14\n",
"Module: Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
"Layer: 17\n",
"Name: 17\n",
"Module: Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
"Layer: 19\n",
"Name: 19\n",
"Module: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
"Layer: 21\n",
"Name: 21\n",
"Module: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
"Layer: 24\n",
"Name: 24\n",
"Module: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
"Layer: 26\n",
"Name: 26\n",
"Module: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
"Layer: 28\n",
"Name: 28\n",
"Module: Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"
]
}
],
"source": [
"for layer, (name, module) in enumerate(vgg16.features._modules.items()):\n",
" if isinstance(module, torch.nn.modules.conv.Conv2d):\n",
" print(\"Layer: {}\".format(layer))\n",
" print(\"Name: {}\".format(name))\n",
" print(\"Module: {}\".format(module))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<generator object Module.parameters at 0x7f00a2681d50>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vgg16.features.parameters()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<bound method VGG.forward of VGG(\n",
" (features): Sequential(\n",
" (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (1): ReLU(inplace=True)\n",
" (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (3): ReLU(inplace=True)\n",
" (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
" (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (6): ReLU(inplace=True)\n",
" (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (8): ReLU(inplace=True)\n",
" (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
" (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (11): ReLU(inplace=True)\n",
" (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (13): ReLU(inplace=True)\n",
" (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (15): ReLU(inplace=True)\n",
" (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
" (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (18): ReLU(inplace=True)\n",
" (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (20): ReLU(inplace=True)\n",
" (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (22): ReLU(inplace=True)\n",
" (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
" (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (25): ReLU(inplace=True)\n",
" (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (27): ReLU(inplace=True)\n",
" (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (29): ReLU(inplace=True)\n",
" (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
" )\n",
" (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))\n",
" (classifier): Sequential(\n",
" (0): Linear(in_features=25088, out_features=4096, bias=True)\n",
" (1): ReLU(inplace=True)\n",
" (2): Dropout(p=0.5, inplace=False)\n",
" (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
" (4): ReLU(inplace=True)\n",
" (5): Dropout(p=0.5, inplace=False)\n",
" (6): Linear(in_features=4096, out_features=1000, bias=True)\n",
" )\n",
")>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vgg16.forward"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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