230204_main_weapon_inference_playground.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# License: BSD\n",
    "# Author: Sasank Chilamkurthy\n",
    "\n",
    "from __future__ import print_function, division\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.optim import lr_scheduler\n",
    "import numpy as np\n",
    "import torchvision\n",
    "from torchvision import datasets, models, transforms\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import os\n",
    "import copy\n",
    "from PIL import Image\n",
    "import torch.nn.functional as F\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "plt.ion()   # interactive mode\n",
    "\n",
    "\n",
    "class ImageTransform():\n",
    "    def __init__(self, mean, std):\n",
    "        self.data_transform = transforms.Compose([\n",
    "            transforms.ToTensor(),\n",
    "            transforms.Normalize(mean, std)\n",
    "        ])\n",
    "\n",
    "    def __call__(self, img):\n",
    "        return self.data_transform(img)\n",
    "    \n",
    "mean = (0.5,)\n",
    "std = (0.5,)\n",
    "\n",
    "transform = ImageTransform(mean, std)\n",
    "\n",
    "f = open(\"main_weapon_list.txt\",\"r\")\n",
    "class_names= []\n",
    "\n",
    "for x in f:\n",
    "    class_names.append(x.rstrip(\"\\n\"))\n",
    "    #以下のようにしてしまうと、改行コードがlistに入ってしまうため注意\n",
    "    #list_row.append(x)\n",
    "f.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = Image.open(\"52-Gal.jpg\")\n",
    "\n",
    "inputs = transform(img)\n",
    "inputs = inputs.unsqueeze(0).to(device)\n",
    "\n",
    "model = torch.load('main_weapons_classification_weight.pth')\n",
    "model.eval()  ## torch.nn.Module.eval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top-1 52-Gal 100.00%\n",
      "Top-2 96-Gal 0.00%\n",
      "Top-3 Bloblobber 0.00%\n"
     ]
    }
   ],
   "source": [
    "with torch.no_grad():\n",
    "    outputs = model(inputs)\n",
    "    batch_probs = F.softmax(outputs, dim=1)\n",
    "    batch_probs, batch_indices = batch_probs.sort(dim=1, descending=True)\n",
    "\n",
    "    for probs, indices in zip(batch_probs, batch_indices):\n",
    "        for k in range(3):\n",
    "            print(f\"Top-{k + 1} {class_names[indices[k]]} {probs[k]:.2%}\")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "バッチ処理できるようになりたい"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'dataloaders' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [7], line 7\u001b[0m\n\u001b[1;32m      4\u001b[0m images_so_far \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n\u001b[1;32m      6\u001b[0m \u001b[39mwith\u001b[39;00m torch\u001b[39m.\u001b[39mno_grad():\n\u001b[0;32m----> 7\u001b[0m     \u001b[39mfor\u001b[39;00m i, (inputs, labels) \u001b[39min\u001b[39;00m \u001b[39menumerate\u001b[39m(dataloaders[\u001b[39m'\u001b[39m\u001b[39mval\u001b[39m\u001b[39m'\u001b[39m]):\n\u001b[1;32m      8\u001b[0m         inputs \u001b[39m=\u001b[39m inputs\u001b[39m.\u001b[39mto(device)\n\u001b[1;32m      9\u001b[0m         labels \u001b[39m=\u001b[39m labels\u001b[39m.\u001b[39mto(device)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'dataloaders' is not defined"
     ]
    }
   ],
   "source": [
    "num_images=6\n",
    "was_training = model.training\n",
    "model.eval()\n",
    "images_so_far = 0\n",
    "\n",
    "with torch.no_grad():\n",
    "    for i, (inputs, labels) in enumerate(dataloaders['val']):\n",
    "        inputs = inputs.to(device)\n",
    "        labels = labels.to(device)\n",
    "\n",
    "        outputs = model(inputs)\n",
    "        _, preds = torch.max(outputs, 1)\n",
    "\n",
    "        for j in range(inputs.size()[0]):\n",
    "            print('predicted: {}'.format(class_names[preds[j]]))\n",
    "            imshow(inputs.cpu().data[j])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "pic should be PIL Image or ndarray. Got <class 'list'>",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[7], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m imgs \u001b[39m=\u001b[39m [img, img, img, img]\n\u001b[0;32m----> 2\u001b[0m inputs \u001b[39m=\u001b[39m transform(imgs)\n\u001b[1;32m      3\u001b[0m inputs \u001b[39m=\u001b[39m inputs\u001b[39m.\u001b[39munsqueeze(\u001b[39m0\u001b[39m)\u001b[39m.\u001b[39mto(device)\n",
      "Cell \u001b[0;32mIn[2], line 9\u001b[0m, in \u001b[0;36mImageTransform.__call__\u001b[0;34m(self, img)\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__call__\u001b[39m(\u001b[39mself\u001b[39m, img):\n\u001b[0;32m----> 9\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdata_transform(img)\n",
      "File \u001b[0;32m~/.python/current/lib/python3.10/site-packages/torchvision/transforms/transforms.py:94\u001b[0m, in \u001b[0;36mCompose.__call__\u001b[0;34m(self, img)\u001b[0m\n\u001b[1;32m     92\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__call__\u001b[39m(\u001b[39mself\u001b[39m, img):\n\u001b[1;32m     93\u001b[0m     \u001b[39mfor\u001b[39;00m t \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtransforms:\n\u001b[0;32m---> 94\u001b[0m         img \u001b[39m=\u001b[39m t(img)\n\u001b[1;32m     95\u001b[0m     \u001b[39mreturn\u001b[39;00m img\n",
      "File \u001b[0;32m~/.python/current/lib/python3.10/site-packages/torchvision/transforms/transforms.py:134\u001b[0m, in \u001b[0;36mToTensor.__call__\u001b[0;34m(self, pic)\u001b[0m\n\u001b[1;32m    126\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__call__\u001b[39m(\u001b[39mself\u001b[39m, pic):\n\u001b[1;32m    127\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    128\u001b[0m \u001b[39m    Args:\u001b[39;00m\n\u001b[1;32m    129\u001b[0m \u001b[39m        pic (PIL Image or numpy.ndarray): Image to be converted to tensor.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    132\u001b[0m \u001b[39m        Tensor: Converted image.\u001b[39;00m\n\u001b[1;32m    133\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 134\u001b[0m     \u001b[39mreturn\u001b[39;00m F\u001b[39m.\u001b[39;49mto_tensor(pic)\n",
      "File \u001b[0;32m~/.python/current/lib/python3.10/site-packages/torchvision/transforms/functional.py:138\u001b[0m, in \u001b[0;36mto_tensor\u001b[0;34m(pic)\u001b[0m\n\u001b[1;32m    136\u001b[0m     _log_api_usage_once(to_tensor)\n\u001b[1;32m    137\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (F_pil\u001b[39m.\u001b[39m_is_pil_image(pic) \u001b[39mor\u001b[39;00m _is_numpy(pic)):\n\u001b[0;32m--> 138\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mTypeError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mpic should be PIL Image or ndarray. Got \u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mtype\u001b[39m(pic)\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n\u001b[1;32m    140\u001b[0m \u001b[39mif\u001b[39;00m _is_numpy(pic) \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m _is_numpy_image(pic):\n\u001b[1;32m    141\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mpic should be 2/3 dimensional. Got \u001b[39m\u001b[39m{\u001b[39;00mpic\u001b[39m.\u001b[39mndim\u001b[39m}\u001b[39;00m\u001b[39m dimensions.\u001b[39m\u001b[39m\"\u001b[39m)\n",
      "\u001b[0;31mTypeError\u001b[0m: pic should be PIL Image or ndarray. Got <class 'list'>"
     ]
    }
   ],
   "source": [
    "imgs = [img, img, img, img]\n",
    "inputs = transform(imgs)\n",
    "inputs = inputs.unsqueeze(0).to(device)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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