test.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LSTM UNET TEST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from model import *\n",
    "from data import *\n",
    "import os\n",
    "import sys\n",
    "\n",
    "ROOTPATH = os.path.abspath(\"./\")\n",
    "sys.path.append(ROOTPATH)\n",
    "train_dir = \"./TRAIN/\"\n",
    "val_dir = \"./VAL/\" \n",
    "\n",
    "test_dir = \"./test/\"\n",
    "\n",
    "all_list = os.listdir(train_dir)\n",
    "train_dataset = [train_dir+s_dir+'/' for s_dir in all_list]\n",
    "\n",
    "all_list = os.listdir(val_dir)\n",
    "val_dataset = [val_dir+s_dir+'/' for s_dir in all_list]\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create data generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_generator = data_generator(train_dataset,batch_size=1)\n",
    "val_data_generator = data_generator(val_dataset,batch_size=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tensorboard records (optional) \n",
    "need install tensorboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.callbacks import TensorBoard\n",
    "class TB(TensorBoard):\n",
    "    def __init__(self, log_every=1, **kwargs):\n",
    "        super().__init__(**kwargs)\n",
    "        self.log_every = log_every\n",
    "        self.counter = 0\n",
    "    \n",
    "    def on_batch_end(self, batch, logs=None):\n",
    "        self.counter+=1\n",
    "        if self.counter%self.log_every==0:\n",
    "            for name, value in logs.items():\n",
    "                if name in ['batch', 'size']:\n",
    "                    continue\n",
    "                summary = tf.Summary()\n",
    "                summary_value = summary.value.add()\n",
    "                summary_value.simple_value = value.item()\n",
    "                summary_value.tag = name\n",
    "                self.writer.add_summary(summary, self.counter)\n",
    "            self.writer.flush()\n",
    "        \n",
    "        super().on_batch_end(batch, logs)\n",
    "        \n",
    "tensorboard_log = TB(log_dir='./Graph', histogram_freq=0, write_graph=True, write_images=True)\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compile model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LSTMUnet()\n",
    "model_checkpoint = ModelCheckpoint('lstm_unet.hdf5', monitor='loss',verbose=1, save_best_only=True)\n",
    "print(model.summary())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.fit_generator(\n",
    "    train_data_generator,\n",
    "    validation_data=val_data_generator,\n",
    "    validation_steps =400,\n",
    "    steps_per_epoch=2325,\n",
    "    epochs=15,\n",
    "    callbacks=[model_checkpoint, tensorboard_log])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_val = LSTMUnet()\n",
    "model_val.load_weights('lstm_3.hdf5') # load your pretrained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_dir = \"/cole_driver/dff/PLS3Bx10/\"\n",
    "test_dir2 = \"/cole_driver/dff/PLS3Sx50/\" #change to your test folder\n",
    "\n",
    "input_ = data_feed(test_dir)\n",
    "input_s = np.zeros((1,)+input_.shape,dtype=np.uint8)\n",
    "input_s[0] = input_\n",
    "\n",
    "out_d = model_val.predict(input_s)\n",
    "imd = plt.imshow(out_d[0,::,::,0])\n",
    "plt.colorbar()"
   ]
  }
 ],
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