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"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# !git clone https://github.com/facebookresearch/CutLER.git\n",
"\n",
"import os\n",
"from PIL import Image\n",
"from torch.utils.data import Dataset, DataLoader\n",
"import torchvision.transforms as transforms\n",
"\n",
"class CustomImageDataset(Dataset):\n",
" def __init__(self, image_dir, transform=None):\n",
" self.image_dir = image_dir\n",
" self.image_files = os.listdir(image_dir)\n",
" self.transform = transform\n",
"\n",
" def __len__(self):\n",
" return len(self.image_files)\n",
"\n",
" def __getitem__(self, idx):\n",
" img_path = os.path.join(self.image_dir, self.image_files[idx])\n",
" image = Image.open(img_path).convert(\"RGB\")\n",
" if self.transform:\n",
" image = self.transform(image)\n",
" return image\n",
"\n",
"transform = transforms.Compose([\n",
" transforms.Resize((480, 480)),\n",
" transforms.ToTensor(),\n",
" transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
"])\n",
"dataset = CustomImageDataset(\"img\", transform=transform)\n",
"dataloader = DataLoader(dataset, batch_size=16, shuffle=True)\n",
"\n",
"\n",
"#perform prediction\n",
"model.load_state_dict(torch.load(\"cutler_model_weights.pth\"))\n",
"model.eval() # Set the model to evaluation mode\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!git clone --recursive https://github.com/facebookresearch/CutLER"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "cutLer",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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