Upload app_demo.ipynb

app_demo.ipynb

@@ -0,0 +1,243 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "097f69c4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import gradio\n",
+    "import torch\n",
+    "from torchvision import transforms\n",
+    "import torch.nn.functional as F\n",
+    "import timm\n",
+    "import torch.nn as nn\n",
+    "from PIL import Image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "065d0efd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "idx_to_class = {0: 'adidas', 1: 'converse', 2: 'new-balance', 3: 'nike', 4: 'reebok', 5: 'vans'}\n",
+    "num_classes = len(idx_to_class)\n",
+    "\n",
+    "mean = [0.485, 0.456, 0.406]\n",
+    "std =  [0.229, 0.224, 0.225]\n",
+    "test_transforms = transforms.Compose([transforms.Resize((224,224)),\n",
+    "                                      transforms.ToTensor(),\n",
+    "                                     transforms.Normalize(mean,std)\n",
+    "                                     ])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "6a5105de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def GetModel(model_name = 'efficientnet_b0',freeze = False):\n",
+    "    model = timm.create_model(model_name = model_name,pretrained=True)\n",
+    "    if freeze:\n",
+    "        for parameter in model.parameters():\n",
+    "            parameter.requires_grad = False\n",
+    "    \n",
+    "    in_features = model.classifier.in_features # 1792\n",
+    "    \n",
+    "    model.classifier = nn.Sequential(\n",
+    "                          nn.Linear(in_features, 100), \n",
+    "                          nn.BatchNorm1d(num_features=100),\n",
+    "                          nn.ReLU(),\n",
+    "                          nn.Dropout(),\n",
+    "                          nn.Linear(100, num_classes),\n",
+    "                                    )\n",
+    "    \n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "0b473e8c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def LoadModel(model, model_path):\n",
+    "    checkpoint = torch.load(model_path)\n",
+    "    model.load_state_dict(checkpoint['state_dict'])\n",
+    "    model.best_scores = checkpoint['best_stats']\n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "40b3fd21",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = LoadModel(GetModel(),\"snicker_model.pth\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "bc2bc9b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def GetClassProbs(img):\n",
+    "    with torch.no_grad():\n",
+    "        model.eval()\n",
+    "        #img = Image.open(img).convert(\"RGB\")\n",
+    "        img = test_transforms(img)\n",
+    "        img = img.unsqueeze(0)\n",
+    "        output = model(img)\n",
+    "        # remember softmax\n",
+    "        probs = F.softmax(output,dim=1)\n",
+    "        probs, indices = probs.topk(k=num_classes)\n",
+    "        probs = probs[0].tolist()\n",
+    "        indices = indices[0].tolist()\n",
+    "        classes = [idx_to_class[index] for index in indices]\n",
+    "        confidences = {classes[i]: round(probs[i],3) for i in range(num_classes)}  \n",
+    "\n",
+    "    return confidences"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "e070f2a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on local URL:  http://127.0.0.1:7862\n",
+      "Running on public URL: https://67885f1c-1326-46d9.gradio.live\n",
+      "\n",
+      "This share link expires in 72 hours. For free permanent hosting and GPU upgrades (NEW!), check out Spaces: https://huggingface.co/spaces\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><iframe src=\"https://67885f1c-1326-46d9.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import gradio as gr\n",
+    "examples = [\"samples/a.jpeg\",\"samples/c.jpeg\",\"samples/r.jpeg\"]\n",
+    "gr.Interface(fn=GetClassProbs, \n",
+    "             inputs=gr.Image(type=\"pil\"),\n",
+    "             outputs=gr.Label(num_top_classes=3),\n",
+    "             examples=examples).launch(share=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1f9eeb29",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5fab64d9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cf1b0fb5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f88d10d0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.13"
+  },
+  "latex_envs": {
+   "LaTeX_envs_menu_present": true,
+   "autoclose": false,
+   "autocomplete": false,
+   "bibliofile": "biblio.bib",
+   "cite_by": "apalike",
+   "current_citInitial": 1,
+   "eqLabelWithNumbers": true,
+   "eqNumInitial": 1,
+   "hotkeys": {
+    "equation": "Ctrl-E",
+    "itemize": "Ctrl-I"
+   },
+   "labels_anchors": false,
+   "latex_user_defs": false,
+   "report_style_numbering": false,
+   "user_envs_cfg": false
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}