TestingTheModel.ipynb

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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
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   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "import PIL.Image as Image\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "classes =[ 'Mantled Howler', 'Patas Monkey', 'Bald Uakari', 'Japanese Macaque', 'Pygmy Marmoset', 'White Headed Capuchin', 'Silvery Marmoset', 'Common Squirrel Monkey', 'Black Headed Night Monkey', 'Nilgiri Langur']"
   ]
  },
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     "text": [
      "C:\\Users\\asus\\AppData\\Local\\Temp\\ipykernel_12832\\2808078871.py:1: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  model = torch.load('best_model.pth')\n"
     ]
    }
   ],
   "source": [
    "model = torch.load('best_model.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean = [0.4363, 0.4328, 0.3291]\n",
    "std = [0.2129, 0.2075, 0.2038]\n",
    "\n",
    "image_transforms = transforms.Compose([\n",
    "    transforms.Resize((224, 224)), \n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(torch.Tensor(mean), torch.Tensor(std))\n",
    "])"
   ]
  },
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   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def classify(model, image_transforms, image_path, classes):\n",
    "    model.eval()\n",
    "    image = Image.open(image_path)\n",
    "    image = image_transforms(image).float()\n",
    "    image = image.unsqueeze(0)\n",
    "    \n",
    "    output = model(image)\n",
    "    _ , pred = torch.max(output.data, 1)\n",
    "    \n",
    "    print(classes[pred.item()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "checking_img = os.listdir(path='./Model_check_imgs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image name: indian mac.jpg\n",
      "Japanese Macaque\n",
      "\n",
      "\n",
      "Image name: japanese_macaque.jpg\n",
      "Japanese Macaque\n",
      "\n",
      "\n",
      "Image name: Mantled Howler.jpg\n",
      "Mantled Howler\n",
      "\n",
      "\n",
      "Image name: Nilgiri Langur.jpg\n",
      "Nilgiri Langur\n",
      "\n",
      "\n",
      "Image name: Picsart_24-04-30_21-32-50-403.jpg\n",
      "Japanese Macaque\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for img in checking_img:\n",
    "    print(f\"Image name: {img}\")\n",
    "    classify(model, image_transforms, f\"./Model_check_imgs/{img}\", classes)\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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