otae chan classifier deployment.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.transforms import tfms_from_model\n",
    "from fastai.dataset import ImageClassifierData\n",
    "from fastai.conv_learner import ConvLearner\n",
    "import torchvision\n",
    "from fastai.dataset import open_image\n",
    "from torchvision.models import resnet34\n",
    "from fastai.transforms import transforms_side_on\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "PATH = \"data/\"\n",
    "sz=299\n",
    "arch=resnet34\n",
    "bs=28"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfms = tfms_from_model(arch, sz, aug_tfms=transforms_side_on, max_zoom=1.1)\n",
    "data = ImageClassifierData.from_paths(PATH, tfms=tfms, bs=bs, num_workers=4)\n",
    "learn = ConvLearner.pretrained(arch, data, precompute=True, ps=0.5)\n",
    "learn.unfreeze()\n",
    "learn.precompute = False\n",
    "learn.load('224_all_50_better')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not_otae  otae\r\n"
     ]
    }
   ],
   "source": [
    "!ls data/train/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'otae'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img = plt.imread(f'{PATH}train/otae/vlcsnap-2018-04-15-02h26m04s175.png')\n",
    "image = open_image(f'{PATH}train/otae/vlcsnap-2018-04-15-02h26m04s175.png')\n",
    "trn_tfms, val_tfms = tfms_from_model(arch, sz)\n",
    "im = val_tfms(image)\n",
    "preds = learn.predict_array(im[None])\n",
    "learn.data.classes[np.argmax(preds)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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