diff --git a/evaluation/UMAP_ResNet50.ipynb b/evaluation/UMAP_ResNet50_LHQ.ipynb similarity index 99% rename from evaluation/UMAP_ResNet50.ipynb rename to evaluation/UMAP_ResNet50_LHQ.ipynb index 10f956a25b876a38f71a9e1dea7fc85cbf5c6229..082221d5d2e1b388ac0558dcd1c8786298dc41f5 100644 --- a/evaluation/UMAP_ResNet50.ipynb +++ b/evaluation/UMAP_ResNet50_LHQ.ipynb @@ -32,7 +32,11 @@ "import numpy as np\n", "import seaborn as sns\n", "import math\n", - "import random" + "import random\n", + "from torchvision.models import resnet50\n", + "from PIL import Image\n", + "from torch.utils.data import DataLoader\n", + "import torchvision.transforms as transforms" ] }, { @@ -124,7 +128,7 @@ "metadata": {}, "outputs": [], "source": [ - "def filter_df(df, x=(0,0), y=(0,0), sample=10, flag='both'):\n", + "def filter_df(df,path_to_training_data,path_to_samples, x=(0,0), y=(0,0), sample=10, flag='both'):\n", " \n", " filtered_df = df[(df['umap_x'].between(*x)) & (df['umap_y'].between(*y))]\n", "\n", @@ -244,11 +248,6 @@ "metadata": {}, "outputs": [], "source": [ - "from torchvision.models import resnet50\n", - "from PIL import Image\n", - "from torch.utils.data import DataLoader\n", - "import torchvision.transforms as transforms\n", - "\n", "def extract_resnet_features(path, sample='all'):\n", " if sample == 'all':\n", " sample_size = -1\n",