test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path = r'F:\\studies\\datascience\\python\\pytorch\\finger_count\\data\\train\\4\\IMG_20200609_200550.jpg'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "image = cv2.imread(file_path, cv2.IMREAD_GRAYSCALE)\n",
    "image = cv2.resize(image, (50, 50)).astype(np.float32)/255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2cb0d7589e8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = torch.Tensor(image).view(-1, 1, 50, 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.conv1 = nn.Conv2d(1, 4, 3)\n",
    "        self.maxpool1 = nn.MaxPool2d(3, 3)\n",
    "        self.conv2 = nn.Conv2d(4, 8, 3)\n",
    "        self.maxpool2 = nn.MaxPool2d(3, 3)\n",
    "        self.flatten_shape = None\n",
    "        x = torch.randn([50,50]).view(-1,1,50,50)\n",
    "        x = self.conv(x)\n",
    "        self.fc1 = nn.Linear(self.flatten_shape, 128)\n",
    "        self.fc2 = nn.Linear(128, 5)\n",
    "    \n",
    "    def conv(self, x):\n",
    "        x = F.relu(self.conv1(x))\n",
    "        x = self.maxpool1(x)\n",
    "        x = F.relu(self.conv2(x))\n",
    "        x = self.maxpool2(x)\n",
    "        if self.flatten_shape is None:\n",
    "            self.flatten_shape = x.shape[1] * x.shape[2] * x.shape[3]\n",
    "        return x\n",
    "    \n",
    "    def forward(self, x):\n",
    "        x = self.conv(x)\n",
    "        x = x.view(-1, self.flatten_shape)\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.softmax(self.fc2(x), dim=1)\n",
    "#         x = self.fc2(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of fingers: 4\n"
     ]
    }
   ],
   "source": [
    "model = Net()\n",
    "model.load_state_dict(torch.load(os.path.join(os.getcwd(), 'model.pth')))\n",
    "model.eval()\n",
    "y_hat = model.forward(x)\n",
    "print('number of fingers: ' + str(torch.argmax(y_hat, dim=1).item() + 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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