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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"id": "ClbDA89uqYc2",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 383
},
"outputId": "0dac9131-1d0c-416b-98fb-9dcc7e0dcf5b"
},
"outputs": [
{
"output_type": "error",
"ename": "ModuleNotFoundError",
"evalue": "No module named 'attention'",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-8-58da732233b1>\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtorch\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnn\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mfunctional\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mattention\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSelfAttention\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mVAE_AttentionBlock\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mModule\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'attention'",
"",
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
],
"errorDetails": {
"actions": [
{
"action": "open_url",
"actionText": "Open Examples",
"url": "/notebooks/snippets/importing_libraries.ipynb"
}
]
}
}
],
"source": [
"import torch\n",
"from torch import nn\n",
"from torch.nn import functional as F\n",
"from attention import SelfAttention\n",
"\n",
"class VAE_AttentionBlock(nn.Module):\n",
" def __init__(self, channels):\n",
" super.__init__()\n",
" self.groupnorm = nn.GroupNorm(32, channels)\n",
" self.attention = Attention(1, channels)\n",
" def forward(self, x):\n",
" residue=x\n",
" x=self.groupnorm(x)\n",
" n,c,h,w =x.shape\n",
" x=x.view(n,c,h*w)\n",
" x=x.transpose(-1,-2)\n",
" x=self.attention(x)\n",
" x=x.view((n,c,h,w))\n",
" x+=residue\n",
" return x\n",
"class VAE_ResidualBlock(nn.Module):\n",
" def __init__(self, in_channels, out_channels):\n",
" super.__init__()\n",
" self.groupnorm_1= nn.GroupNorm(32, in_channels)\n",
" self.conv_1=nn.Conv2d(in_channels, out_channels , kernel_size=3, padding=1)\n",
"\n",
" self.groupnorm_2= nn.GroupNorm(32, in_channels)\n",
" self.conv_2= nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1)\n",
"\n",
" if in_channels==out_channels:\n",
" self.residual_layer=nn.Identity()\n",
" else:\n",
" self.conv_2= nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=0)\n",
" def forward(self, x):\n",
" residue=x\n",
" x=self.groupnorm_1(x)\n",
" x=F.silu(x)\n",
" x=self.conv_2(x)\n",
" return x+self.residual_layer(residue)\n",
"class VAE_Decoder(nn.Sequential):\n",
" def __init__(self):\n",
" super.__init__(\n",
" nn.Conv2d(4,4, kernel_size=1, padding=0),\n",
" nn.Conv2d(4, 512, kernel_size=3, padding=1),\n",
" VAE_ResidualBlock(512, 512),\n",
" VAE_AttentionBlock(512),\n",
" VAE_ResidualBlock(512, 512),\n",
" VAE_ResidualBlock(512, 512),\n",
" VAE_ResidualBlock(512, 512),\n",
" VAE_ResidualBlock(512, 512),\n",
" VAE_ResidualBlock(512, 512),\n",
"\n",
" nn.Upsample(scale_factor=2),\n",
" nn.Conv2d(512, 512,kernel_size=3, padding=1),\n",
" VAE_ResidualBlock(512, 512),\n",
" VAE_ResidualBlock(512, 512),\n",
" VAE_ResidualBlock(512, 512),\n",
" nn.Upsample(scale_factor=2),\n",
" VAE_ResidualBlock(512, 256),\n",
" VAE_ResidualBlock(256, 256),\n",
" VAE_ResidualBlock(256, 256),\n",
" nn.Upsample(scale_factor=2),\n",
" nn.Conv2d(256, 256, kernel_size=3, padding=1),\n",
" VAE_ResidualBlock(256, 128),\n",
" VAE_ResidualBlock(128, 128),\n",
" VAE_ResidualBlock(128, 128),\n",
" nn.GroupNorm(32, 128),\n",
" nn.SiLU(),\n",
" nn.Conv2d(128, 3, kernel_size=3,padding=1)\n",
" )\n",
" def forward(self, x):\n",
" x/=0.18125\n",
" for module in self:\n",
" x=module(x)\n",
" return x\n",
"\n",
"\n"
]
}
]
} |