Delete clipseg/Quickstart.ipynb

clipseg/Quickstart.ipynb

@@ -1,107 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "import requests\n",
-    "\n",
-    "! wget https://owncloud.gwdg.de/index.php/s/ioHbRzFx6th32hn/download -O weights.zip\n",
-    "! unzip -d weights -j weights.zip\n",
-    "from models.clipseg import CLIPDensePredT\n",
-    "from PIL import Image\n",
-    "from torchvision import transforms\n",
-    "from matplotlib import pyplot as plt\n",
-    "\n",
-    "# load model\n",
-    "model = CLIPDensePredT(version='ViT-B/16', reduce_dim=64)\n",
-    "model.eval();\n",
-    "\n",
-    "# non-strict, because we only stored decoder weights (not CLIP weights)\n",
-    "model.load_state_dict(torch.load('weights/rd64-uni.pth', map_location=torch.device('cpu')), strict=False);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Load and normalize `example_image.jpg`. You can also load through an URL."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# load and normalize image\n",
-    "input_image = Image.open('example_image.jpg')\n",
-    "\n",
-    "# or load from URL...\n",
-    "# image_url = 'https://farm5.staticflickr.com/4141/4856248695_03475782dc_z.jpg'\n",
-    "# input_image = Image.open(requests.get(image_url, stream=True).raw)\n",
-    "\n",
-    "transform = transforms.Compose([\n",
-    "    transforms.ToTensor(),\n",
-    "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
-    "    transforms.Resize((352, 352)),\n",
-    "])\n",
-    "img = transform(input_image).unsqueeze(0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Predict and visualize (this might take a few seconds if running without GPU support)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "prompts = ['a glass', 'something to fill', 'wood', 'a jar']\n",
-    "\n",
-    "# predict\n",
-    "with torch.no_grad():\n",
-    "    preds = model(img.repeat(4,1,1,1), prompts)[0]\n",
-    "\n",
-    "# visualize prediction\n",
-    "_, ax = plt.subplots(1, 5, figsize=(15, 4))\n",
-    "[a.axis('off') for a in ax.flatten()]\n",
-    "ax[0].imshow(input_image)\n",
-    "[ax[i+1].imshow(torch.sigmoid(preds[i][0])) for i in range(4)];\n",
-    "[ax[i+1].text(0, -15, prompts[i]) for i in range(4)];"
-   ]
-  }
- ],
- "metadata": {
-  "interpreter": {
-   "hash": "800ed241f7db2bd3aa6942aa3be6809cdb30ee6b0a9e773dfecfa9fef1f4c586"
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.8.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}