test_deep_icy_model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from config import *\n",
    "import torch \n",
    "from unet import UNet\n",
    "from torchvision.transforms import ToTensor\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load model architecture and weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = UNet(3,3)\n",
    "model.load_state_dict(torch.load('weights'))\n",
    "model.cuda()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test the model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "image = plt.imread(os.path.join('test_images','image_test.jpg'))/255\n",
    "tensor = ToTensor()(image)[None].cuda().to(torch.float32)\n",
    "\n",
    "### Post processing: apply exponential\n",
    "segmentation = torch.exp(model(tensor))[0].cpu().detach().numpy()\n",
    "segmented_image = np.transpose(segmentation , (1,2,0))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create figure\n",
    "fig = plt.figure(figsize=(10, 7))\n",
    "\n",
    "# setting values to rows and column variables\n",
    "rows = 1\n",
    "columns = 2\n",
    "\n",
    "# Adds a subplot at the 1st position\n",
    "fig.add_subplot(rows, columns, 1)\n",
    "\n",
    "# showing image\n",
    "plt.imshow(image)\n",
    "plt.axis(\"off\")\n",
    "plt.title(\"image\")\n",
    "\n",
    "# Adds a subplot at the 2nd position\n",
    "fig.add_subplot(rows, columns, 2)\n",
    "\n",
    "# showing image\n",
    "plt.imshow(segmented_image)\n",
    "plt.axis(\"off\")\n",
    "plt.title(\"Segmentation result\")"
   ]
  }
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