Test_Xception.ipynb

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     "text": [
      "In C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The text.latex.unicode rcparam was deprecated in Matplotlib 3.0 and will be removed in 3.2.\n",
      "In C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The savefig.frameon rcparam was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
      "In C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The pgf.debug rcparam was deprecated in Matplotlib 3.0 and will be removed in 3.2.\n",
      "In C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The verbose.level rcparam was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
      "In C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The verbose.fileo rcparam was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pathlib\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "import glob\n",
    "import os\n",
    "from tensorflow.compat.v1 import ConfigProto\n",
    "from tensorflow.compat.v1 import InteractiveSession\n",
    "\n",
    "np.set_printoptions(threshold=np.inf)\n",
    "keras = tf.keras\n",
    "layers = tf.keras.layers\n",
    "config = ConfigProto()\n",
    "config.gpu_options.allow_growth = True\n",
    "session = InteractiveSession(config=config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_and_preprocess_image(path):\n",
    "    image = tf.io.read_file(path)\n",
    "    image = tf.image.decode_jpeg(image, channels=3)\n",
    "    #image = tf.image.resize(image, [224, 224])\n",
    "    image = tf.cast(image, tf.float32)\n",
    "    image = image/255.0  # normalize to [0,1] range\n",
    "    image = 2*image-1\n",
    "    return image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=tf.keras.models.load_model('F:/weightbodysize/addblxception.ckpt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = r'F:\\weightbodysize\\data\\test'\n",
    "data_root = pathlib.Path(data_dir)\n",
    "all_image_paths = list(data_root.glob('*/*'))\n",
    "all_image_paths = [str(path) for path in all_image_paths]\n",
    "all_image_labels = [pathlib.Path(path).parent.name for path in all_image_paths]\n",
    "image_count = len(all_image_paths)\n",
    "\n",
    "weight_labels = [[label.split('_')[1]] for label in all_image_labels]\n",
    "new_weight_labels=[]\n",
    "for n in weight_labels:\n",
    "    new_weight_labels.append(float(n[0]));  \n",
    "weight_labels=new_weight_labels\n",
    "\n",
    "bw_labels = [[label.split('_')[2]] for label in all_image_labels]\n",
    "new_bw_labels=[]\n",
    "for n in bw_labels:\n",
    "    new_bw_labels.append(float(n[0]));    \n",
    "bw_labels=new_bw_labels\n",
    "\n",
    "bh_labels = [[label.split('_')[3]] for label in all_image_labels]\n",
    "new_bh_labels=[]\n",
    "for n in bh_labels:\n",
    "    new_bh_labels.append(float(n[0]));    \n",
    "bh_labels=new_bh_labels\n",
    "\n",
    "hw_labels = [[label.split('_')[4]] for label in all_image_labels]\n",
    "new_hw_labels=[]\n",
    "for n in hw_labels:\n",
    "    new_hw_labels.append(float(n[0]));    \n",
    "hw_labels=new_hw_labels\n",
    "\n",
    "hh_labels = [[label.split('_')[5]] for label in all_image_labels]\n",
    "new_hh_labels=[]\n",
    "for n in hh_labels:\n",
    "    new_hh_labels.append(float(n[0]));    \n",
    "hh_labels=new_hh_labels\n",
    "\n",
    "bl_labels = [[label.split('_')[6]] for label in all_image_labels]\n",
    "new_bl_labels=[]\n",
    "for n in bl_labels:\n",
    "    new_bl_labels.append(float(n[0]));    \n",
    "bl_labels=new_bl_labels\n",
    "\n",
    "path_ds = tf.data.Dataset.from_tensor_slices(all_image_paths)\n",
    "AUTOTUNE = tf.data.experimental.AUTOTUNE\n",
    "image_ds = path_ds.map(load_and_preprocess_image, num_parallel_calls=AUTOTUNE)\n",
    "label_ds = tf.data.Dataset.from_tensor_slices((weight_labels, bw_labels, bh_labels, hw_labels, hh_labels, bl_labels))\n",
    "image_label_ds = tf.data.Dataset.zip((image_ds, label_ds))\n",
    "\n",
    "\n",
    "BATCH_SIZE =1\n",
    "val_data = image_label_ds.batch(BATCH_SIZE)\n",
    "\n",
    "for label in label_ds:\n",
    "    print(label)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#model.evaluate(val_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=model.predict(val_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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