add training and inference notebooks

notebooks/food-101-infer-gradio.ipynb

@@ -0,0 +1,287 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "87345732-d868-473b-b1a1-5c25839ce25b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from fastai.vision.all import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "79b9fbad-7b99-40fd-8768-b0a091bf85cb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/conda/envs/py310-cuda116/lib/python3.10/site-packages/paramiko/transport.py:236: CryptographyDeprecationWarning: Blowfish has been deprecated\n",
+      "  \"class\": algorithms.Blowfish,\n"
+     ]
+    }
+   ],
+   "source": [
+    "import gradio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "5409c6a7-5cae-42bb-8335-587a04471f22",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MODELS_PATH = Path(\"./models\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "4e836799-6858-438a-8d70-d95f98cf54f7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "EXAMPLES_PATH = Path('./examples')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9ed20c60-9f23-4795-bb4b-79b00af0f6d1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(#2) [Path('models/food-101-resnet34.pkl'),Path('models/food-101-resnet50.pkl')]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "MODELS_PATH.ls()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "0969ba8e-b0df-4550-a900-5d5a30fb0187",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(#9) [Path('examples/pad_thai.jpeg'),Path('examples/takoyaki.jpeg'),Path('examples/momo.jpeg'),Path('examples/falafel.jpeg'),Path('examples/paella.jpeg'),Path('examples/ravioli.jpeg'),Path('examples/huevos_rancheros.jpeg'),Path('examples/edamame.jpeg'),Path('examples/sushi.jpeg')]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "EXAMPLES_PATH.ls()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e9143742-c6bc-44f6-8ecd-3826502c84ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def label_func(filepath):\n",
+    "    return filepath.parent.name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c6ad64e8-f163-4472-b2f0-c0aa50ead4d8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn = load_learner(MODELS_PATH/'food-101-resnet50.pkl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d1370d20-fd51-4512-bd28-5f170d216c7b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['apple_pie', 'baby_back_ribs', 'baklava', 'beef_carpaccio', 'beef_tartare', 'beet_salad', 'beignets', 'bibimbap', 'bread_pudding', 'breakfast_burrito', 'bruschetta', 'caesar_salad', 'cannoli', 'caprese_salad', 'carrot_cake', 'ceviche', 'cheese_plate', 'cheesecake', 'chicken_curry', 'chicken_quesadilla', 'chicken_wings', 'chocolate_cake', 'chocolate_mousse', 'churros', 'clam_chowder', 'club_sandwich', 'crab_cakes', 'creme_brulee', 'croque_madame', 'cup_cakes', 'deviled_eggs', 'donuts', 'dumplings', 'edamame', 'eggs_benedict', 'escargots', 'falafel', 'filet_mignon', 'fish_and_chips', 'foie_gras', 'french_fries', 'french_onion_soup', 'french_toast', 'fried_calamari', 'fried_rice', 'frozen_yogurt', 'garlic_bread', 'gnocchi', 'greek_salad', 'grilled_cheese_sandwich', 'grilled_salmon', 'guacamole', 'gyoza', 'hamburger', 'hot_and_sour_soup', 'hot_dog', 'huevos_rancheros', 'hummus', 'ice_cream', 'lasagna', 'lobster_bisque', 'lobster_roll_sandwich', 'macaroni_and_cheese', 'macarons', 'miso_soup', 'mussels', 'nachos', 'omelette', 'onion_rings', 'oysters', 'pad_thai', 'paella', 'pancakes', 'panna_cotta', 'peking_duck', 'pho', 'pizza', 'pork_chop', 'poutine', 'prime_rib', 'pulled_pork_sandwich', 'ramen', 'ravioli', 'red_velvet_cake', 'risotto', 'samosa', 'sashimi', 'scallops', 'seaweed_salad', 'shrimp_and_grits', 'spaghetti_bolognese', 'spaghetti_carbonara', 'spring_rolls', 'steak', 'strawberry_shortcake', 'sushi', 'tacos', 'takoyaki', 'tiramisu', 'tuna_tartare', 'waffles']"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "labels = learn.dls.vocab\n",
+    "labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8f666b42-9fdd-45ca-81ca-7e98dd191369",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "a360dd6b-75a9-43e5-b91d-c6963ea462ea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def predict(img):\n",
+    "    img = PILImage.create(img)\n",
+    "    _pred, _pred_w_idx, probs = learn.predict(img)\n",
+    "    labels_probs = {labels[i]: float(probs[i]) for i, _ in enumerate(labels)}\n",
+    "    return labels_probs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "febc7266-8587-4530-811b-f2fa9117dcd5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('gradio_article.md') as f:\n",
+    "    article = f.read()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "8fd4ffb4-11ca-4b25-999c-cde2a4e236b4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/conda/envs/py310-cuda116/lib/python3.10/site-packages/gradio/interface.py:419: UserWarning: The `enable_queue` parameter in the `Interface`will be deprecated and may not work properly. Please use the `enable_queue` parameter in `launch()` instead\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on local URL:  http://localhost:9999/\n",
+      "\n",
+      "To create a public link, set `share=True` in `launch()`.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"900\"\n",
+       "            height=\"500\"\n",
+       "            src=\"http://localhost:9999/\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "            \n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.IFrame at 0x7f69f5315840>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(<fastapi.applications.FastAPI at 0x7f69f7595330>,\n",
+       " 'http://localhost:9999/',\n",
+       " None)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "interface_options = {\n",
+    "    \"title\": \"Food-101 Classifier\",\n",
+    "    \"description\": \"A food image classifier trained on the Food-101 (https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/) dataset with fastai with a ResNet50 CNN model.\",\n",
+    "    \"article\": article,\n",
+    "    \"examples\" : [f'{EXAMPLES_PATH}/{f.name}' for f in EXAMPLES_PATH.iterdir()],\n",
+    "    \"interpretation\": \"default\",\n",
+    "    \"layout\": \"horizontal\",\n",
+    "    \"allow_flagging\": \"never\",\n",
+    "    \"enable_queue\": True \n",
+    "}\n",
+    "\n",
+    "demo = gradio.Interface(fn=predict,\n",
+    "                      inputs=gradio.inputs.Image(shape=(512, 512)),\n",
+    "                      outputs=gradio.outputs.Label(num_top_classes=5),\n",
+    "                      **interface_options)\n",
+    "\n",
+    "demo_options = {\n",
+    "    \"inline\": True,\n",
+    "    \"inbrowser\": False,\n",
+    "    \"share\": False,\n",
+    "    \"show_error\": True,\n",
+    "    \"server_name\": \"0.0.0.0\",\n",
+    "    \"server_port\": 9999,\n",
+    "    \"enable_queue\": True,\n",
+    "}\n",
+    "\n",
+    "demo.launch(**demo_options)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "570f8a3c-367e-4a7f-808d-8fa2e925a444",
+   "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": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}