Add data to .gitignore and untrack it
This commit is contained in:
parent
8fd720d9bd
commit
66ae608860
4
.gitignore
vendored
4
.gitignore
vendored
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@ -1,3 +1,7 @@
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# Data
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data
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img_align_celeba
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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@ -27,7 +27,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 1,
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"metadata": {
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"collapsed": false
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},
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@ -43,11 +43,11 @@
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"<torch._C.Generator at 0x7f04c8111fb0>"
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"<torch._C.Generator at 0x7f6ca81b2f10>"
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]
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},
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"metadata": {},
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"execution_count": 2
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"execution_count": 1
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}
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],
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"source": [
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@ -63,13 +63,48 @@
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 2,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# Root directory for dataset\ndataroot = \"data/celeba\"\n\n# Number of workers for dataloader\nworkers = 2\n\n# Batch size during training\nbatch_size = 128\n\n# Spatial size of training images. All images will be resized to this\n# size using a transformer.\nimage_size = 64\n\n# Number of channels in the training images. For color images this is 3\nnc = 3\n\n# Size of z latent vector (i.e. size of generator input)\nnz = 100\n\n# Size of feature maps in generator\nngf = 64\n\n# Size of feature maps in discriminator\nndf = 64\n\n# Number of training epochs\nnum_epochs = 5\n\n# Learning rate for optimizers\nlr = 0.0002\n\n# Beta1 hyperparam for Adam optimizers\nbeta1 = 0.5\n\n# Number of GPUs available. Use 0 for CPU mode.\nngpu = 1"
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"# Root directory for dataset\n",
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"dataroot = \"data/celeba\"\n",
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"\n",
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"# Number of workers for dataloader\n",
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"workers = 2\n",
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"\n",
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"# Batch size during training\n",
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"batch_size = 128\n",
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"\n",
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"# Spatial size of training images. All images will be resized to this\n",
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"# size using a transformer.\n",
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"image_size = 64\n",
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"\n",
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"# Number of channels in the training images. For color images this is 3\n",
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"nc = 3\n",
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"\n",
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"# Size of z latent vector (i.e. size of generator input)\n",
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"nz = 100\n",
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"\n",
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"# Size of feature maps in generator\n",
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"ngf = 64\n",
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"\n",
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"# Size of feature maps in discriminator\n",
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"ndf = 64\n",
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"\n",
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"# Number of training epochs\n",
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"num_epochs = 5\n",
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"\n",
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"# Learning rate for optimizers\n",
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"lr = 0.0002\n",
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"\n",
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"# Beta1 hyperparam for Adam optimizers\n",
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"beta1 = 0.5\n",
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"\n",
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"# Number of GPUs available. Use 0 for CPU mode.\n",
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"ngpu = 0"
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]
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},
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{
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@ -81,11 +116,27 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 3,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"outputs": [
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{
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"output_type": "error",
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"ename": "FileNotFoundError",
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"evalue": "[Errno 2] No such file or directory: 'data/celeba'",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m/tmp/ipykernel_2022/1968835037.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# We can use an image folder dataset the way we have it setup.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;31m# Create the dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m dataset = dset.ImageFolder(root=dataroot,\n\u001b[0m\u001b[1;32m 4\u001b[0m transform=transforms.Compose([\n\u001b[1;32m 5\u001b[0m \u001b[0mtransforms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mResize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage_size\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",
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"\u001b[0;32m~/git/PyTorch/.venv/lib/python3.9/site-packages/torchvision/datasets/folder.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, root, transform, target_transform, loader, is_valid_file)\u001b[0m\n\u001b[1;32m 308\u001b[0m \u001b[0mis_valid_file\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mCallable\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 309\u001b[0m ):\n\u001b[0;32m--> 310\u001b[0;31m super(ImageFolder, self).__init__(root, loader, IMG_EXTENSIONS if is_valid_file is None else None,\n\u001b[0m\u001b[1;32m 311\u001b[0m \u001b[0mtransform\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 312\u001b[0m \u001b[0mtarget_transform\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtarget_transform\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/git/PyTorch/.venv/lib/python3.9/site-packages/torchvision/datasets/folder.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, root, loader, extensions, transform, target_transform, is_valid_file)\u001b[0m\n\u001b[1;32m 143\u001b[0m super(DatasetFolder, self).__init__(root, transform=transform,\n\u001b[1;32m 144\u001b[0m target_transform=target_transform)\n\u001b[0;32m--> 145\u001b[0;31m \u001b[0mclasses\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclass_to_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind_classes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mroot\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 146\u001b[0m \u001b[0msamples\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mroot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclass_to_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextensions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_valid_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 147\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/git/PyTorch/.venv/lib/python3.9/site-packages/torchvision/datasets/folder.py\u001b[0m in \u001b[0;36mfind_classes\u001b[0;34m(self, directory)\u001b[0m\n\u001b[1;32m 219\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mTuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mList\u001b[0m \u001b[0mof\u001b[0m \u001b[0mall\u001b[0m \u001b[0mclasses\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mdictionary\u001b[0m \u001b[0mmapping\u001b[0m \u001b[0meach\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mto\u001b[0m \u001b[0man\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 220\u001b[0m \"\"\"\n\u001b[0;32m--> 221\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfind_classes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdirectory\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 222\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 223\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mAny\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAny\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",
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"\u001b[0;32m~/git/PyTorch/.venv/lib/python3.9/site-packages/torchvision/datasets/folder.py\u001b[0m in \u001b[0;36mfind_classes\u001b[0;34m(directory)\u001b[0m\n\u001b[1;32m 38\u001b[0m \u001b[0mSee\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;32mclass\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mDatasetFolder\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mdetails\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 39\u001b[0m \"\"\"\n\u001b[0;32m---> 40\u001b[0;31m \u001b[0mclasses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msorted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mentry\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mentry\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscandir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdirectory\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mentry\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_dir\u001b[0m\u001b[0;34m(\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[0m\u001b[1;32m 41\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mclasses\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mFileNotFoundError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Couldn't find any class folder in {directory}.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'data/celeba'"
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]
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}
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],
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"source": [
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"# We can use an image folder dataset the way we have it setup.\n# Create the dataset\ndataset = dset.ImageFolder(root=dataroot,\n transform=transforms.Compose([\n transforms.Resize(image_size),\n transforms.CenterCrop(image_size),\n transforms.ToTensor(),\n transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n ]))\n# Create the dataloader\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,\n shuffle=True, num_workers=workers)\n\n# Decide which device we want to run on\ndevice = torch.device(\"cuda:0\" if (torch.cuda.is_available() and ngpu > 0) else \"cpu\")\n\n# Plot some training images\nreal_batch = next(iter(dataloader))\nplt.figure(figsize=(8,8))\nplt.axis(\"off\")\nplt.title(\"Training Images\")\nplt.imshow(np.transpose(vutils.make_grid(real_batch[0].to(device)[:64], padding=2, normalize=True).cpu(),(1,2,0)))"
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]
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