testing.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing Generative Adversarial Network (GAN)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "\n",
    "from training.generator import Generator\n",
    "from training import utils\n",
    "from training.settings import *\n",
    "\n",
    "utils.reset_rand()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check GPU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "utils.check_gpu()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "MODEL_PATH = 'models/faces_256.pt'\n",
    "\n",
    "model = Generator().to(DEVICE)\n",
    "model.load_state_dict(torch.load(MODEL_PATH, map_location = DEVICE))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mean W image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "image = model.generate_one(0.0)\n",
    "\n",
    "plt.imshow(image)\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "SAVE_PATH = None\t# Path to save the generated image (None to not save)\n",
    "SHAPE = (8, 6)\t\t# Shape of the grid of images\n",
    "PSI = 0.7\t\t\t# Psi value to use for the generation"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "images = model.generate_grid(SHAPE, PSI)\n",
    "\n",
    "if SAVE_PATH is not None:\n",
    "\tImage.fromarray(images).save(SAVE_PATH)\n",
    "\n",
    "plt.figure(figsize = (20, 20))\n",
    "plt.imshow(images)\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "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": "3.10.11"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "7dc26219aa9f3ff1475f89dfde9802a67188480d81af861fc512482f0a9e031a"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}