Udacity-AWS-MLE-ND-Project2-Build-a-ML-Workflow-For-Scones-Unlimited-On-Amazon-SageMaker.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deploy and monitor a machine learning workflow for Image Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting up this notebook\n",
    "\n",
    "Notes about the instance size and kernel setup: this notebook has been tested on\n",
    "\n",
    "1. The `Python 3 (Data Science)` kernel\n",
    "2. The `ml.t3.medium` Sagemaker notebook instance\n",
    "\n",
    "## Data Staging\n",
    "\n",
    "We'll use a sample dataset called CIFAR to simulate the challenges Scones Unlimited are facing in Image Classification. In order to start working with CIFAR we'll need to:\n",
    "\n",
    "1. Extract the data from a hosting service\n",
    "2. Transform it into a usable shape and format\n",
    "3. Load it into a production system\n",
    "\n",
    "In other words, we're going to do some simple ETL!\n",
    "\n",
    "### 1. Extract the data from the hosting service\n",
    "\n",
    "In the cell below, define a function `extract_cifar_data` that extracts python version of the CIFAR-100 dataset. The CIFAR dataaset is open source and generously hosted by the University of Toronto at: https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "\n",
    "def extract_cifar_data(url, filename=\"cifar.tar.gz\"):\n",
    "    \"\"\"A function for extracting the CIFAR-100 dataset and storing it as a gzipped file\n",
    "    \n",
    "    Arguments:\n",
    "    url      -- the URL where the dataset is hosted\n",
    "    filename -- the full path where the dataset will be written\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    # Todo: request the data from the data url\n",
    "    # Hint: use `requests.get` method\n",
    "    r = requests.get(url)\n",
    "    with open(filename, \"wb\") as file_context:\n",
    "        file_context.write(r.content)\n",
    "    return"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's test it out! Run the following cell and check whether a new file `cifar.tar.gz` is created in the file explorer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "extract_cifar_data(\"https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz\")     "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Transform the data into a usable shape and format\n",
    "\n",
    "Clearly, distributing the data as a gzipped archive makes sense for the hosting service! It saves on bandwidth, storage, and it's a widely-used archive format. In fact, it's so widely used that the Python community ships a utility for working with them, `tarfile`, as part of its Standard Library. Execute the following cell to decompress your extracted dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import tarfile\n",
    "\n",
    "with tarfile.open(\"cifar.tar.gz\", \"r:gz\") as tar:\n",
    "    tar.extractall()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A new folder `cifar-100-python` should be created, containing `meta`, `test`, and `train` files. These files are `pickles` and the [CIFAR homepage](https://www.cs.toronto.edu/~kriz/cifar.html) provides a simple script that can be used to load them. We've adapted the script below for you to run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "with open(\"./cifar-100-python/meta\", \"rb\") as f:\n",
    "    dataset_meta = pickle.load(f, encoding='bytes')\n",
    "\n",
    "with open(\"./cifar-100-python/test\", \"rb\") as f:\n",
    "    dataset_test = pickle.load(f, encoding='bytes')\n",
    "\n",
    "with open(\"./cifar-100-python/train\", \"rb\") as f:\n",
    "    dataset_train = pickle.load(f, encoding='bytes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys([b'filenames', b'batch_label', b'fine_labels', b'coarse_labels', b'data'])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Feel free to explore the datasets\n",
    "\n",
    "dataset_train.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As documented on the homepage, `b'data'` contains rows of 3073 unsigned integers, representing three channels (red, green, and blue) for one 32x32 pixel image per row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3072"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "32*32*3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a simple gut-check, let's transform one of our images. Each 1024 items in a row is a channel (red, green, then blue). Each 32 items in the channel are a row in the 32x32 image. Using python, we can stack these channels into a 32x32x3 array, and save it as a PNG file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Each 1024 in a row is a channel (red, green, then blue)\n",
    "row = dataset_train[b'data'][0]\n",
    "red, green, blue = row[0:1024], row[1024:2048], row[2048:]\n",
    "\n",
    "# Each 32 items in the channel are a row in the 32x32 image\n",
    "red = red.reshape(32,32)\n",
    "green = green.reshape(32,32)\n",
    "blue = blue.reshape(32,32)\n",
    "\n",
    "# Combine the channels into a 32x32x3 image!\n",
    "combined = np.dstack((red,green,blue))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a more concise version, consider the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# All in one:\n",
    "test_image = np.dstack((\n",
    "    row[0:1024].reshape(32,32),\n",
    "    row[1024:2048].reshape(32,32),\n",
    "    row[2048:].reshape(32,32)\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.imshow(test_image);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like a cow! Let's check the label. `dataset_meta` contains label names in order, and `dataset_train` has a list of labels for each row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_train[b'fine_labels'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our image has a label of `19`, so let's see what the 19th item is in the list of label names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'cattle'\n"
     ]
    }
   ],
   "source": [
    "print(dataset_meta[b'fine_label_names'][19])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok! 'cattle' sounds about right. By the way, using the previous two lines we can do:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'cattle'\n"
     ]
    }
   ],
   "source": [
    "n = 0\n",
    "print(dataset_meta[b'fine_label_names'][dataset_train[b'fine_labels'][n]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we know how to check labels, is there a way that we can also check file names? `dataset_train` also contains a `b'filenames'` key. Let's see what we have here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'bos_taurus_s_000507.png'\n"
     ]
    }
   ],
   "source": [
    "print(dataset_train[b'filenames'][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"Taurus\" is the name of a subspecies of cattle, so this looks like a pretty reasonable filename. To save an image we can also do:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "plt.imsave(\"file.png\", test_image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Your new PNG file should now appear in the file explorer -- go ahead and pop it open to see!\n",
    "\n",
    "Now that you know how to reshape the images, save them as files, and capture their filenames and labels, let's just capture all the bicycles and motorcycles and save them. Scones Unlimited can use a model that tells these apart to route delivery drivers automatically.\n",
    "\n",
    "In the following cell, identify the label numbers for Bicycles and Motorcycles:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'bicycle': 8\n",
      "b'motorcycle': 48\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Todo: Filter the dataset_train and dataset_meta objects to find the label numbers for Bicycle and Motorcycles\n",
    "\n",
    "for idx, x in enumerate(dataset_meta[b'fine_label_names']):\n",
    "    if x in [b'bicycle',b'motorcycle']:\n",
    "        print(f\"{x}: {idx}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Good job! We only need objects with label 8 and 48 -- this drastically simplifies our handling of the data! Below we construct a dataframe for you, and you can safely drop the rows that don't contain observations about bicycles and motorcycles. Fill in the missing lines below to drop all other rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train dataset shape : (1000, 3)\n",
      "Test dataset shape  : (200, 3)\n"
     ]
    }
   ],
   "source": [
    "# Construct the dataframe\n",
    "df_train = pd.DataFrame({\n",
    "    \"filenames\": dataset_train[b'filenames'],            # Filename\n",
    "    \"labels\": dataset_train[b'fine_labels'],             # Fine Label\n",
    "    \"row\": range(len(dataset_train[b'filenames']))       # Row Number\n",
    "})\n",
    "\n",
    "# Drop all rows from df_train where label is not 8 or 48\n",
    "df_train = df_train.loc[((df_train.labels==8) | (df_train.labels==48)),:]   #TODO: Fill in\n",
    "\n",
    "# Decode df_train.filenames so they are regular strings (remove b' i.e. byte format from string by decoding)\n",
    "df_train[\"filenames\"] = df_train[\"filenames\"].apply(lambda x: x.decode(\"utf-8\"))\n",
    "\n",
    "# Similar actions on test dataset\n",
    "df_test = pd.DataFrame({\n",
    "    \"filenames\": dataset_test[b'filenames'],           # Filename\n",
    "    \"labels\": dataset_test[b'fine_labels'],            # Fine Label (For evaluation)\n",
    "    \"row\": range(len(dataset_test[b'filenames']))      # Row Number\n",
    "})\n",
    "\n",
    "# Drop all rows from df_test where label is not 8 or 48\n",
    "df_test = df_test.loc[((df_test.labels==8) | (df_test.labels==48)),:]   #TODO: Fill in\n",
    "\n",
    "# Decode df_test.filenames so they are regular strings\n",
    "df_test[\"filenames\"] = df_test[\"filenames\"].apply(lambda x: x.decode(\"utf-8\")) # (remove b' i.e. byte format from string by decoding)\n",
    "\n",
    "print(\"Train dataset shape :\", df_train.shape)\n",
    "print(\"Test dataset shape  :\", df_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the data is filtered for just our classes, we can save all our images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!mkdir ./train\n",
    "!mkdir ./test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the previous sections we introduced you to several key snippets of code:\n",
    "\n",
    "1. Grabbing the image data:\n",
    "\n",
    "```python\n",
    "dataset_train[b'data'][0]\n",
    "```\n",
    "\n",
    "2. A simple idiom for stacking the image data into the right shape\n",
    "\n",
    "```python\n",
    "import numpy as np\n",
    "np.dstack((\n",
    "    row[0:1024].reshape(32,32),\n",
    "    row[1024:2048].reshape(32,32),\n",
    "    row[2048:].reshape(32,32)\n",
    "))\n",
    "```\n",
    "\n",
    "3. A simple `matplotlib` utility for saving images\n",
    "\n",
    "```python\n",
    "plt.imsave(path+row['filenames'], target)\n",
    "```\n",
    "\n",
    "Compose these together into a function that saves all the images into the `./test` and `./train` directories. Use the comments in the body of the `save_images` function below to guide your construction of the function:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filenames</th>\n",
       "      <th>labels</th>\n",
       "      <th>row</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>bike_s_000682.png</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>bike_s_000127.png</td>\n",
       "      <td>8</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>cycle_s_002598.png</td>\n",
       "      <td>8</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>minibike_s_000824.png</td>\n",
       "      <td>48</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>motorcycle_s_001856.png</td>\n",
       "      <td>48</td>\n",
       "      <td>195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>bicycle_s_000537.png</td>\n",
       "      <td>8</td>\n",
       "      <td>219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>moped_s_000169.png</td>\n",
       "      <td>48</td>\n",
       "      <td>251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>motorcycle_s_001453.png</td>\n",
       "      <td>48</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>safety_bike_s_000482.png</td>\n",
       "      <td>8</td>\n",
       "      <td>298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>dirt_bike_s_000124.png</td>\n",
       "      <td>48</td>\n",
       "      <td>370</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    filenames  labels  row\n",
       "16          bike_s_000682.png       8   16\n",
       "30          bike_s_000127.png       8   30\n",
       "130        cycle_s_002598.png       8  130\n",
       "152     minibike_s_000824.png      48  152\n",
       "195   motorcycle_s_001856.png      48  195\n",
       "219      bicycle_s_000537.png       8  219\n",
       "251        moped_s_000169.png      48  251\n",
       "252   motorcycle_s_001453.png      48  252\n",
       "298  safety_bike_s_000482.png       8  298\n",
       "370    dirt_bike_s_000124.png      48  370"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inspecting the train dataframe\n",
    "# Print first 10 training sample datapoints\n",
    "df_train.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filenames</th>\n",
       "      <th>labels</th>\n",
       "      <th>row</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>safety_bike_s_000390.png</td>\n",
       "      <td>8</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>bike_s_000658.png</td>\n",
       "      <td>8</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>velocipede_s_001744.png</td>\n",
       "      <td>8</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>bike_s_000643.png</td>\n",
       "      <td>8</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>ordinary_bicycle_s_000437.png</td>\n",
       "      <td>8</td>\n",
       "      <td>319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>motorcycle_s_000323.png</td>\n",
       "      <td>48</td>\n",
       "      <td>354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>safety_bike_s_001253.png</td>\n",
       "      <td>8</td>\n",
       "      <td>417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>cycle_s_001306.png</td>\n",
       "      <td>8</td>\n",
       "      <td>476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>532</th>\n",
       "      <td>velocipede_s_000863.png</td>\n",
       "      <td>8</td>\n",
       "      <td>532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>bicycle_s_000479.png</td>\n",
       "      <td>8</td>\n",
       "      <td>554</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         filenames  labels  row\n",
       "27        safety_bike_s_000390.png       8   27\n",
       "28               bike_s_000658.png       8   28\n",
       "116        velocipede_s_001744.png       8  116\n",
       "161              bike_s_000643.png       8  161\n",
       "319  ordinary_bicycle_s_000437.png       8  319\n",
       "354        motorcycle_s_000323.png      48  354\n",
       "417       safety_bike_s_001253.png       8  417\n",
       "476             cycle_s_001306.png       8  476\n",
       "532        velocipede_s_000863.png       8  532\n",
       "554           bicycle_s_000479.png       8  554"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Similarly checking the format of test dataframe\n",
    "df_test.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved: bike_s_000682.png to train/bike_s_000682.png\n",
      "Saved: bike_s_000127.png to train/bike_s_000127.png\n",
      "Saved: cycle_s_002598.png to train/cycle_s_002598.png\n",
      "Saved: minibike_s_000824.png to train/minibike_s_000824.png\n",
      "Saved: motorcycle_s_001856.png to train/motorcycle_s_001856.png\n",
      "Saved: bicycle_s_000537.png to train/bicycle_s_000537.png\n",
      "Saved: moped_s_000169.png to train/moped_s_000169.png\n",
      "Saved: motorcycle_s_001453.png to train/motorcycle_s_001453.png\n",
      "Saved: safety_bike_s_000482.png to train/safety_bike_s_000482.png\n",
      "Saved: dirt_bike_s_000124.png to train/dirt_bike_s_000124.png\n",
      "Saved: minibike_s_001863.png to train/minibike_s_001863.png\n",
      "Saved: minibike_s_001177.png to train/minibike_s_001177.png\n",
      "Saved: motorcycle_s_001792.png to train/motorcycle_s_001792.png\n",
      "Saved: minibike_s_001969.png to train/minibike_s_001969.png\n",
      "Saved: velocipede_s_001585.png to train/velocipede_s_001585.png\n",
      "Saved: moped_s_000035.png to train/moped_s_000035.png\n",
      "Saved: bike_s_000256.png to train/bike_s_000256.png\n",
      "Saved: minibike_s_000571.png to train/minibike_s_000571.png\n",
      "Saved: motorcycle_s_001119.png to train/motorcycle_s_001119.png\n",
      "Saved: moped_s_000009.png to train/moped_s_000009.png\n",
      "Saved: ordinary_bicycle_s_000023.png to train/ordinary_bicycle_s_000023.png\n",
      "Saved: motorcycle_s_001693.png to train/motorcycle_s_001693.png\n",
      "Saved: safety_bicycle_s_001570.png to train/safety_bicycle_s_001570.png\n",
      "Saved: safety_bike_s_000989.png to train/safety_bike_s_000989.png\n",
      "Saved: bicycle_s_001673.png to train/bicycle_s_001673.png\n",
      "Saved: ordinary_bicycle_s_000100.png to train/ordinary_bicycle_s_000100.png\n",
      "Saved: bike_s_000544.png to train/bike_s_000544.png\n",
      "Saved: motorcycle_s_001368.png to train/motorcycle_s_001368.png\n",
      "Saved: bicycle_s_000392.png to train/bicycle_s_000392.png\n",
      "Saved: motorbike_s_000221.png to train/motorbike_s_000221.png\n",
      "Saved: safety_bike_s_000311.png to train/safety_bike_s_000311.png\n",
      "Saved: cycle_s_001745.png to train/cycle_s_001745.png\n",
      "Saved: motorcycle_s_002030.png to train/motorcycle_s_002030.png\n",
      "Saved: motorcycle_s_000074.png to train/motorcycle_s_000074.png\n",
      "Saved: cycle_s_002978.png to train/cycle_s_002978.png\n",
      "Saved: bike_s_001679.png to train/bike_s_001679.png\n",
      "Saved: bicycle_s_000536.png to train/bicycle_s_000536.png\n",
      "Saved: bike_s_000926.png to train/bike_s_000926.png\n",
      "Saved: motorcycle_s_000070.png to train/motorcycle_s_000070.png\n",
      "Saved: safety_bicycle_s_000655.png to train/safety_bicycle_s_000655.png\n",
      "Saved: safety_bicycle_s_000427.png to train/safety_bicycle_s_000427.png\n",
      "Saved: minibike_s_000324.png to train/minibike_s_000324.png\n",
      "Saved: minibike_s_001631.png to train/minibike_s_001631.png\n",
      "Saved: motorcycle_s_000629.png to train/motorcycle_s_000629.png\n",
      "Saved: motorcycle_s_000605.png to train/motorcycle_s_000605.png\n",
      "Saved: motorcycle_s_001973.png to train/motorcycle_s_001973.png\n",
      "Saved: bike_s_000555.png to train/bike_s_000555.png\n",
      "Saved: bicycle_s_002762.png to train/bicycle_s_002762.png\n",
      "Saved: bicycle_s_000778.png to train/bicycle_s_000778.png\n",
      "Saved: bike_s_001613.png to train/bike_s_001613.png\n",
      "Saved: motorcycle_s_001180.png to train/motorcycle_s_001180.png\n",
      "Saved: minibike_s_001279.png to train/minibike_s_001279.png\n",
      "Saved: minibike_s_000451.png to train/minibike_s_000451.png\n",
      "Saved: motorbike_s_000346.png to train/motorbike_s_000346.png\n",
      "Saved: minibike_s_000117.png to train/minibike_s_000117.png\n",
      "Saved: bicycle_s_001448.png to train/bicycle_s_001448.png\n",
      "Saved: motorcycle_s_000790.png to train/motorcycle_s_000790.png\n",
      "Saved: bicycle_s_002373.png to train/bicycle_s_002373.png\n",
      "Saved: safety_bicycle_s_001202.png to train/safety_bicycle_s_001202.png\n",
      "Saved: motorcycle_s_000001.png to train/motorcycle_s_000001.png\n",
      "Saved: bike_s_001882.png to train/bike_s_001882.png\n",
      "Saved: bike_s_001113.png to train/bike_s_001113.png\n",
      "Saved: minibike_s_000362.png to train/minibike_s_000362.png\n",
      "Saved: cycle_s_000718.png to train/cycle_s_000718.png\n",
      "Saved: velocipede_s_001338.png to train/velocipede_s_001338.png\n",
      "Saved: minibike_s_001972.png to train/minibike_s_001972.png\n",
      "Saved: ordinary_bicycle_s_000107.png to train/ordinary_bicycle_s_000107.png\n",
      "Saved: motorcycle_s_000776.png to train/motorcycle_s_000776.png\n",
      "Saved: safety_bike_s_000934.png to train/safety_bike_s_000934.png\n",
      "Saved: bicycle_s_002218.png to train/bicycle_s_002218.png\n",
      "Saved: safety_bicycle_s_001240.png to train/safety_bicycle_s_001240.png\n",
      "Saved: ordinary_bicycle_s_000355.png to train/ordinary_bicycle_s_000355.png\n",
      "Saved: motorcycle_s_000454.png to train/motorcycle_s_000454.png\n",
      "Saved: motorcycle_s_000245.png to train/motorcycle_s_000245.png\n",
      "Saved: minibike_s_000743.png to train/minibike_s_000743.png\n",
      "Saved: safety_bike_s_000830.png to train/safety_bike_s_000830.png\n",
      "Saved: minibike_s_001573.png to train/minibike_s_001573.png\n",
      "Saved: motorbike_s_000141.png to train/motorbike_s_000141.png\n",
      "Saved: minibike_s_000652.png to train/minibike_s_000652.png\n",
      "Saved: motorbike_s_000058.png to train/motorbike_s_000058.png\n",
      "Saved: bicycle_s_000279.png to train/bicycle_s_000279.png\n",
      "Saved: safety_bike_s_001608.png to train/safety_bike_s_001608.png\n",
      "Saved: bicycle_s_000522.png to train/bicycle_s_000522.png\n",
      "Saved: bicycle_s_002569.png to train/bicycle_s_002569.png\n",
      "Saved: bicycle_s_000829.png to train/bicycle_s_000829.png\n",
      "Saved: velocipede_s_000586.png to train/velocipede_s_000586.png\n",
      "Saved: dirt_bike_s_000017.png to train/dirt_bike_s_000017.png\n",
      "Saved: cycle_s_001640.png to train/cycle_s_001640.png\n",
      "Saved: bicycle_s_000282.png to train/bicycle_s_000282.png\n",
      "Saved: motorcycle_s_000713.png to train/motorcycle_s_000713.png\n",
      "Saved: bike_s_000035.png to train/bike_s_000035.png\n",
      "Saved: bike_s_000299.png to train/bike_s_000299.png\n",
      "Saved: minibike_s_002198.png to train/minibike_s_002198.png\n",
      "Saved: safety_bicycle_s_001253.png to train/safety_bicycle_s_001253.png\n",
      "Saved: minibike_s_000110.png to train/minibike_s_000110.png\n",
      "Saved: motorcycle_s_001370.png to train/motorcycle_s_001370.png\n",
      "Saved: motorcycle_s_001363.png to train/motorcycle_s_001363.png\n",
      "Saved: ordinary_bicycle_s_000060.png to train/ordinary_bicycle_s_000060.png\n",
      "Saved: minibike_s_001653.png to train/minibike_s_001653.png\n",
      "Saved: bicycle_s_001338.png to train/bicycle_s_001338.png\n",
      "Saved: cycle_s_002053.png to train/cycle_s_002053.png\n",
      "Saved: velocipede_s_001056.png to train/velocipede_s_001056.png\n",
      "Saved: minibike_s_000402.png to train/minibike_s_000402.png\n",
      "Saved: motorbike_s_000124.png to train/motorbike_s_000124.png\n",
      "Saved: cycle_s_002638.png to train/cycle_s_002638.png\n",
      "Saved: velocipede_s_000949.png to train/velocipede_s_000949.png\n",
      "Saved: safety_bike_s_000921.png to train/safety_bike_s_000921.png\n",
      "Saved: ordinary_bicycle_s_000297.png to train/ordinary_bicycle_s_000297.png\n",
      "Saved: motorcycle_s_001580.png to train/motorcycle_s_001580.png\n",
      "Saved: velocipede_s_000139.png to train/velocipede_s_000139.png\n",
      "Saved: bicycle_s_000561.png to train/bicycle_s_000561.png\n",
      "Saved: ordinary_bicycle_s_000161.png to train/ordinary_bicycle_s_000161.png\n",
      "Saved: minibike_s_000518.png to train/minibike_s_000518.png\n",
      "Saved: motorcycle_s_000167.png to train/motorcycle_s_000167.png\n",
      "Saved: cycle_s_000318.png to train/cycle_s_000318.png\n",
      "Saved: velocipede_s_001958.png to train/velocipede_s_001958.png\n",
      "Saved: ordinary_bicycle_s_000201.png to train/ordinary_bicycle_s_000201.png\n",
      "Saved: bike_s_001462.png to train/bike_s_001462.png\n",
      "Saved: bicycle_s_000437.png to train/bicycle_s_000437.png\n",
      "Saved: velocipede_s_000825.png to train/velocipede_s_000825.png\n",
      "Saved: safety_bike_s_000100.png to train/safety_bike_s_000100.png\n",
      "Saved: minibike_s_000654.png to train/minibike_s_000654.png\n",
      "Saved: bicycle_s_000021.png to train/bicycle_s_000021.png\n",
      "Saved: moped_s_000030.png to train/moped_s_000030.png\n",
      "Saved: velocipede_s_001142.png to train/velocipede_s_001142.png\n",
      "Saved: motorbike_s_000117.png to train/motorbike_s_000117.png\n",
      "Saved: ordinary_bicycle_s_000036.png to train/ordinary_bicycle_s_000036.png\n",
      "Saved: motorcycle_s_000787.png to train/motorcycle_s_000787.png\n",
      "Saved: bike_s_000516.png to train/bike_s_000516.png\n",
      "Saved: motorcycle_s_000256.png to train/motorcycle_s_000256.png\n",
      "Saved: bicycle_s_002299.png to train/bicycle_s_002299.png\n",
      "Saved: velocipede_s_001883.png to train/velocipede_s_001883.png\n",
      "Saved: motorcycle_s_002143.png to train/motorcycle_s_002143.png\n",
      "Saved: cycle_s_000899.png to train/cycle_s_000899.png\n",
      "Saved: bicycle_s_002436.png to train/bicycle_s_002436.png\n",
      "Saved: safety_bicycle_s_000255.png to train/safety_bicycle_s_000255.png\n",
      "Saved: minibike_s_000565.png to train/minibike_s_000565.png\n",
      "Saved: bike_s_000034.png to train/bike_s_000034.png\n",
      "Saved: motorcycle_s_001064.png to train/motorcycle_s_001064.png\n",
      "Saved: minibike_s_001017.png to train/minibike_s_001017.png\n",
      "Saved: motorbike_s_000172.png to train/motorbike_s_000172.png\n",
      "Saved: motorcycle_s_000923.png to train/motorcycle_s_000923.png\n",
      "Saved: cycle_s_000583.png to train/cycle_s_000583.png\n",
      "Saved: motorcycle_s_000991.png to train/motorcycle_s_000991.png\n",
      "Saved: motorbike_s_000541.png to train/motorbike_s_000541.png\n",
      "Saved: motorcycle_s_001303.png to train/motorcycle_s_001303.png\n",
      "Saved: safety_bike_s_000567.png to train/safety_bike_s_000567.png\n",
      "Saved: minibike_s_000350.png to train/minibike_s_000350.png\n",
      "Saved: motorcycle_s_001905.png to train/motorcycle_s_001905.png\n",
      "Saved: bicycle_s_001247.png to train/bicycle_s_001247.png\n",
      "Saved: bicycle_s_001693.png to train/bicycle_s_001693.png\n",
      "Saved: bicycle_s_002258.png to train/bicycle_s_002258.png\n",
      "Saved: safety_bike_s_001552.png to train/safety_bike_s_001552.png\n",
      "Saved: cycle_s_002651.png to train/cycle_s_002651.png\n",
      "Saved: bicycle_s_000759.png to train/bicycle_s_000759.png\n",
      "Saved: motorcycle_s_001176.png to train/motorcycle_s_001176.png\n",
      "Saved: ordinary_bicycle_s_000432.png to train/ordinary_bicycle_s_000432.png\n",
      "Saved: minibike_s_000526.png to train/minibike_s_000526.png\n",
      "Saved: safety_bicycle_s_001049.png to train/safety_bicycle_s_001049.png\n",
      "Saved: minibike_s_001827.png to train/minibike_s_001827.png\n",
      "Saved: bike_s_001072.png to train/bike_s_001072.png\n",
      "Saved: bike_s_001876.png to train/bike_s_001876.png\n",
      "Saved: minibike_s_001511.png to train/minibike_s_001511.png\n",
      "Saved: bike_s_000237.png to train/bike_s_000237.png\n",
      "Saved: motorbike_s_000254.png to train/motorbike_s_000254.png\n",
      "Saved: bicycle_s_000180.png to train/bicycle_s_000180.png\n",
      "Saved: safety_bicycle_s_000359.png to train/safety_bicycle_s_000359.png\n",
      "Saved: motorcycle_s_000220.png to train/motorcycle_s_000220.png\n",
      "Saved: motorcycle_s_002225.png to train/motorcycle_s_002225.png\n",
      "Saved: minibike_s_001185.png to train/minibike_s_001185.png\n",
      "Saved: motorcycle_s_000025.png to train/motorcycle_s_000025.png\n",
      "Saved: safety_bike_s_000058.png to train/safety_bike_s_000058.png\n",
      "Saved: dirt_bike_s_000030.png to train/dirt_bike_s_000030.png\n",
      "Saved: minibike_s_002009.png to train/minibike_s_002009.png\n",
      "Saved: bicycle_s_000753.png to train/bicycle_s_000753.png\n",
      "Saved: safety_bicycle_s_001320.png to train/safety_bicycle_s_001320.png\n",
      "Saved: motorcycle_s_001287.png to train/motorcycle_s_001287.png\n",
      "Saved: ordinary_bicycle_s_000451.png to train/ordinary_bicycle_s_000451.png\n",
      "Saved: minibike_s_000885.png to train/minibike_s_000885.png\n",
      "Saved: motorcycle_s_000418.png to train/motorcycle_s_000418.png\n",
      "Saved: velocipede_s_000282.png to train/velocipede_s_000282.png\n",
      "Saved: cycle_s_001474.png to train/cycle_s_001474.png\n",
      "Saved: bicycle_s_000667.png to train/bicycle_s_000667.png\n",
      "Saved: minibike_s_001546.png to train/minibike_s_001546.png\n",
      "Saved: bicycle_s_001452.png to train/bicycle_s_001452.png\n",
      "Saved: motorcycle_s_000978.png to train/motorcycle_s_000978.png\n",
      "Saved: motorcycle_s_000447.png to train/motorcycle_s_000447.png\n",
      "Saved: minibike_s_000146.png to train/minibike_s_000146.png\n",
      "Saved: motorcycle_s_000300.png to train/motorcycle_s_000300.png\n",
      "Saved: safety_bicycle_s_001381.png to train/safety_bicycle_s_001381.png\n",
      "Saved: bike_s_001519.png to train/bike_s_001519.png\n",
      "Saved: minibike_s_001689.png to train/minibike_s_001689.png\n",
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      "Saved: bike_s_000722.png to train/bike_s_000722.png\n",
      "Saved: motorcycle_s_000508.png to train/motorcycle_s_000508.png\n",
      "Saved: motorcycle_s_002105.png to train/motorcycle_s_002105.png\n",
      "Saved: minibike_s_000165.png to train/minibike_s_000165.png\n",
      "Saved: cycle_s_002703.png to train/cycle_s_002703.png\n",
      "Saved: ordinary_bicycle_s_000216.png to train/ordinary_bicycle_s_000216.png\n",
      "Saved: motorcycle_s_001610.png to train/motorcycle_s_001610.png\n",
      "Saved: cycle_s_002779.png to train/cycle_s_002779.png\n",
      "Saved: bicycle_s_000668.png to train/bicycle_s_000668.png\n",
      "Saved: minibike_s_002186.png to train/minibike_s_002186.png\n",
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      "Saved: ordinary_bicycle_s_000016.png to train/ordinary_bicycle_s_000016.png\n",
      "Saved: motorcycle_s_001384.png to train/motorcycle_s_001384.png\n",
      "Saved: motorcycle_s_000431.png to train/motorcycle_s_000431.png\n",
      "Saved: bike_s_000302.png to train/bike_s_000302.png\n",
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      "Saved: motorcycle_s_001585.png to train/motorcycle_s_001585.png\n",
      "Saved: bike_s_001027.png to train/bike_s_001027.png\n",
      "Saved: bicycle_s_000396.png to train/bicycle_s_000396.png\n",
      "Saved: minibike_s_000149.png to train/minibike_s_000149.png\n",
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      "Saved: ordinary_bicycle_s_000155.png to train/ordinary_bicycle_s_000155.png\n",
      "Saved: cycle_s_003122.png to train/cycle_s_003122.png\n",
      "Saved: motorbike_s_000021.png to train/motorbike_s_000021.png\n",
      "Saved: safety_bicycle_s_001026.png to train/safety_bicycle_s_001026.png\n",
      "Saved: bike_s_001375.png to train/bike_s_001375.png\n",
      "Saved: safety_bicycle_s_000533.png to train/safety_bicycle_s_000533.png\n",
      "Saved: bike_s_000021.png to train/bike_s_000021.png\n",
      "Saved: minibike_s_000968.png to train/minibike_s_000968.png\n",
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      "Saved: bicycle_s_000463.png to train/bicycle_s_000463.png\n",
      "Saved: bike_s_001839.png to train/bike_s_001839.png\n",
      "Saved: minibike_s_000340.png to train/minibike_s_000340.png\n",
      "Saved: minibike_s_000831.png to train/minibike_s_000831.png\n",
      "Saved: motorcycle_s_001273.png to train/motorcycle_s_001273.png\n",
      "Saved: motorcycle_s_000807.png to train/motorcycle_s_000807.png\n",
      "Saved: bicycle_s_000156.png to train/bicycle_s_000156.png\n",
      "Saved: motorcycle_s_000732.png to train/motorcycle_s_000732.png\n",
      "Saved: dirt_bike_s_000119.png to train/dirt_bike_s_000119.png\n",
      "Saved: bicycle_s_000231.png to train/bicycle_s_000231.png\n",
      "Saved: ordinary_bicycle_s_000056.png to train/ordinary_bicycle_s_000056.png\n",
      "Saved: ordinary_bicycle_s_000022.png to train/ordinary_bicycle_s_000022.png\n",
      "Saved: safety_bicycle_s_001243.png to train/safety_bicycle_s_001243.png\n",
      "Saved: motorcycle_s_000894.png to train/motorcycle_s_000894.png\n",
      "Saved: motorcycle_s_001397.png to train/motorcycle_s_001397.png\n",
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      "Saved: motorcycle_s_000834.png to train/motorcycle_s_000834.png\n",
      "Saved: motorcycle_s_001238.png to train/motorcycle_s_001238.png\n",
      "Saved: minibike_s_000567.png to train/minibike_s_000567.png\n",
      "Saved: minibike_s_001512.png to train/minibike_s_001512.png\n",
      "Saved: bike_s_001945.png to train/bike_s_001945.png\n",
      "Saved: cycle_s_003148.png to train/cycle_s_003148.png\n",
      "Saved: safety_bicycle_s_000019.png to train/safety_bicycle_s_000019.png\n",
      "Saved: safety_bicycle_s_000167.png to train/safety_bicycle_s_000167.png\n",
      "Saved: motorcycle_s_001407.png to train/motorcycle_s_001407.png\n",
      "Saved: velocipede_s_001225.png to train/velocipede_s_001225.png\n",
      "Saved: moped_s_000168.png to train/moped_s_000168.png\n",
      "Saved: bicycle_s_000071.png to train/bicycle_s_000071.png\n",
      "Saved: minibike_s_002030.png to train/minibike_s_002030.png\n",
      "Saved: minibike_s_001294.png to train/minibike_s_001294.png\n",
      "Saved: minibike_s_001767.png to train/minibike_s_001767.png\n",
      "Saved: safety_bike_s_000984.png to train/safety_bike_s_000984.png\n",
      "Saved: bicycle_s_000782.png to train/bicycle_s_000782.png\n",
      "Saved: ordinary_bicycle_s_000095.png to train/ordinary_bicycle_s_000095.png\n",
      "Saved: cycle_s_001286.png to train/cycle_s_001286.png\n",
      "Saved: bike_s_001116.png to train/bike_s_001116.png\n",
      "Saved: motorcycle_s_002065.png to train/motorcycle_s_002065.png\n",
      "Saved: ordinary_bicycle_s_000433.png to train/ordinary_bicycle_s_000433.png\n",
      "Saved: motorcycle_s_002221.png to train/motorcycle_s_002221.png\n",
      "Saved: safety_bike_s_000054.png to train/safety_bike_s_000054.png\n",
      "Saved: bicycle_s_001174.png to train/bicycle_s_001174.png\n",
      "Saved: bicycle_s_002222.png to train/bicycle_s_002222.png\n",
      "Saved: velocipede_s_001361.png to train/velocipede_s_001361.png\n",
      "Saved: safety_bike_s_001012.png to train/safety_bike_s_001012.png\n",
      "Saved: minibike_s_000244.png to train/minibike_s_000244.png\n",
      "Saved: bicycle_s_000861.png to train/bicycle_s_000861.png\n",
      "Saved: minibike_s_000566.png to train/minibike_s_000566.png\n",
      "Saved: motorcycle_s_001244.png to train/motorcycle_s_001244.png\n",
      "Saved: bicycle_s_002715.png to train/bicycle_s_002715.png\n",
      "Saved: safety_bike_s_000963.png to train/safety_bike_s_000963.png\n",
      "Saved: motorcycle_s_001765.png to train/motorcycle_s_001765.png\n",
      "Saved: velocipede_s_000021.png to train/velocipede_s_000021.png\n",
      "Saved: motorcycle_s_000580.png to train/motorcycle_s_000580.png\n",
      "Saved: bike_s_001226.png to train/bike_s_001226.png\n",
      "Saved: motorcycle_s_000440.png to train/motorcycle_s_000440.png\n",
      "Saved: motorcycle_s_000144.png to train/motorcycle_s_000144.png\n",
      "Saved: cycle_s_002844.png to train/cycle_s_002844.png\n",
      "Saved: minibike_s_000485.png to train/minibike_s_000485.png\n",
      "Saved: ordinary_bicycle_s_000426.png to train/ordinary_bicycle_s_000426.png\n",
      "Saved: minibike_s_002196.png to train/minibike_s_002196.png\n",
      "Saved: bike_s_001897.png to train/bike_s_001897.png\n",
      "Saved: cycle_s_001374.png to train/cycle_s_001374.png\n",
      "Saved: motorcycle_s_000585.png to train/motorcycle_s_000585.png\n",
      "Saved: bike_s_000026.png to train/bike_s_000026.png\n",
      "Saved: bicycle_s_000235.png to train/bicycle_s_000235.png\n",
      "Saved: minibike_s_000108.png to train/minibike_s_000108.png\n",
      "Saved: motorcycle_s_000072.png to train/motorcycle_s_000072.png\n",
      "Saved: motorcycle_s_002234.png to train/motorcycle_s_002234.png\n",
      "Saved: bicycle_s_000785.png to train/bicycle_s_000785.png\n",
      "Saved: motorcycle_s_000669.png to train/motorcycle_s_000669.png\n",
      "Saved: bicycle_s_000051.png to train/bicycle_s_000051.png\n",
      "Saved: bike_s_001093.png to train/bike_s_001093.png\n",
      "Saved: velocipede_s_000049.png to train/velocipede_s_000049.png\n",
      "Saved: bike_s_000023.png to train/bike_s_000023.png\n",
      "Saved: motorcycle_s_001696.png to train/motorcycle_s_001696.png\n",
      "Saved: safety_bicycle_s_001113.png to train/safety_bicycle_s_001113.png\n",
      "Saved: motorcycle_s_000451.png to train/motorcycle_s_000451.png\n",
      "Saved: velocipede_s_000485.png to train/velocipede_s_000485.png\n",
      "Saved: motorcycle_s_001753.png to train/motorcycle_s_001753.png\n",
      "Saved: cycle_s_001439.png to train/cycle_s_001439.png\n",
      "Saved: minibike_s_002038.png to train/minibike_s_002038.png\n",
      "Saved: bicycle_s_000147.png to train/bicycle_s_000147.png\n",
      "Saved: cycle_s_002399.png to train/cycle_s_002399.png\n",
      "Saved: motorcycle_s_001565.png to train/motorcycle_s_001565.png\n",
      "Saved: bicycle_s_000159.png to train/bicycle_s_000159.png\n",
      "Saved: minibike_s_000035.png to train/minibike_s_000035.png\n",
      "Saved: safety_bicycle_s_000239.png to train/safety_bicycle_s_000239.png\n",
      "Saved: safety_bike_s_001640.png to train/safety_bike_s_001640.png\n",
      "Saved: minibike_s_000698.png to train/minibike_s_000698.png\n",
      "Saved: minibike_s_000967.png to train/minibike_s_000967.png\n",
      "Saved: safety_bike_s_000381.png to train/safety_bike_s_000381.png\n",
      "Saved: motorcycle_s_000216.png to train/motorcycle_s_000216.png\n",
      "Saved: bike_s_000855.png to train/bike_s_000855.png\n",
      "Saved: motorcycle_s_001715.png to train/motorcycle_s_001715.png\n",
      "Saved: motorcycle_s_002275.png to train/motorcycle_s_002275.png\n",
      "Saved: safety_bike_s_001338.png to train/safety_bike_s_001338.png\n",
      "Saved: safety_bicycle_s_001705.png to train/safety_bicycle_s_001705.png\n",
      "Saved: minibike_s_000855.png to train/minibike_s_000855.png\n",
      "Saved: bicycle_s_002132.png to train/bicycle_s_002132.png\n",
      "Saved: minibike_s_000218.png to train/minibike_s_000218.png\n",
      "Saved: safety_bike_s_000178.png to train/safety_bike_s_000178.png\n",
      "Saved: motorcycle_s_001584.png to train/motorcycle_s_001584.png\n",
      "Saved: safety_bicycle_s_000162.png to train/safety_bicycle_s_000162.png\n",
      "Saved: motorcycle_s_001392.png to train/motorcycle_s_001392.png\n",
      "Saved: bicycle_s_001814.png to train/bicycle_s_001814.png\n",
      "Saved: motorbike_s_000362.png to train/motorbike_s_000362.png\n",
      "Saved: minibike_s_000519.png to train/minibike_s_000519.png\n",
      "Saved: minibike_s_001417.png to train/minibike_s_001417.png\n",
      "Saved: safety_bike_s_000277.png to train/safety_bike_s_000277.png\n",
      "Saved: cycle_s_002090.png to train/cycle_s_002090.png\n",
      "Saved: bicycle_s_001168.png to train/bicycle_s_001168.png\n",
      "Saved: minibike_s_000498.png to train/minibike_s_000498.png\n",
      "Saved: motorcycle_s_001400.png to train/motorcycle_s_001400.png\n",
      "Saved: motorcycle_s_000320.png to train/motorcycle_s_000320.png\n",
      "Saved: minibike_s_001734.png to train/minibike_s_001734.png\n",
      "Saved: cycle_s_002659.png to train/cycle_s_002659.png\n",
      "Saved: bike_s_000990.png to train/bike_s_000990.png\n",
      "Saved: velocipede_s_000358.png to train/velocipede_s_000358.png\n",
      "Saved: safety_bicycle_s_000568.png to train/safety_bicycle_s_000568.png\n",
      "Saved: motorcycle_s_000312.png to train/motorcycle_s_000312.png\n",
      "Saved: motorcycle_s_001686.png to train/motorcycle_s_001686.png\n",
      "Saved: cycle_s_001472.png to train/cycle_s_001472.png\n",
      "Saved: minibike_s_000064.png to train/minibike_s_000064.png\n",
      "Saved: safety_bike_s_001784.png to train/safety_bike_s_001784.png\n",
      "Saved: motorcycle_s_002236.png to train/motorcycle_s_002236.png\n",
      "Saved: minibike_s_000020.png to train/minibike_s_000020.png\n",
      "Saved: motorcycle_s_001182.png to train/motorcycle_s_001182.png\n",
      "Saved: bicycle_s_000781.png to train/bicycle_s_000781.png\n",
      "Saved: ordinary_bicycle_s_000167.png to train/ordinary_bicycle_s_000167.png\n",
      "Saved: cycle_s_002666.png to train/cycle_s_002666.png\n",
      "Saved: dirt_bike_s_000134.png to train/dirt_bike_s_000134.png\n",
      "Saved: bike_s_000015.png to train/bike_s_000015.png\n",
      "Saved: bike_s_001767.png to train/bike_s_001767.png\n",
      "Saved: motorcycle_s_001790.png to train/motorcycle_s_001790.png\n",
      "Saved: bike_s_001915.png to train/bike_s_001915.png\n",
      "Saved: bike_s_001415.png to train/bike_s_001415.png\n",
      "Saved: motorbike_s_000578.png to train/motorbike_s_000578.png\n",
      "Saved: safety_bicycle_s_000532.png to train/safety_bicycle_s_000532.png\n",
      "Saved: ordinary_bicycle_s_000049.png to train/ordinary_bicycle_s_000049.png\n",
      "Saved: minibike_s_001639.png to train/minibike_s_001639.png\n",
      "Saved: bike_s_000129.png to train/bike_s_000129.png\n",
      "Saved: minibike_s_001565.png to train/minibike_s_001565.png\n",
      "Saved: bike_s_002203.png to train/bike_s_002203.png\n",
      "Saved: minibike_s_000401.png to train/minibike_s_000401.png\n",
      "Saved: safety_bicycle_s_001608.png to train/safety_bicycle_s_001608.png\n",
      "Saved: velocipede_s_000670.png to train/velocipede_s_000670.png\n",
      "Saved: safety_bicycle_s_000207.png to train/safety_bicycle_s_000207.png\n",
      "Saved: motorcycle_s_001853.png to train/motorcycle_s_001853.png\n",
      "Saved: bike_s_002222.png to train/bike_s_002222.png\n",
      "Saved: motorcycle_s_000004.png to train/motorcycle_s_000004.png\n",
      "Saved: motorcycle_s_000496.png to train/motorcycle_s_000496.png\n",
      "Saved: motorcycle_s_001492.png to train/motorcycle_s_001492.png\n",
      "Saved: bike_s_001761.png to train/bike_s_001761.png\n",
      "Saved: velocipede_s_001299.png to train/velocipede_s_001299.png\n",
      "Saved: bike_s_000040.png to train/bike_s_000040.png\n",
      "Saved: cycle_s_003162.png to train/cycle_s_003162.png\n",
      "Saved: velocipede_s_001880.png to train/velocipede_s_001880.png\n",
      "Saved: bike_s_002139.png to train/bike_s_002139.png\n",
      "Saved: bicycle_s_001583.png to train/bicycle_s_001583.png\n",
      "Saved: safety_bicycle_s_001706.png to train/safety_bicycle_s_001706.png\n",
      "Saved: bike_s_001200.png to train/bike_s_001200.png\n",
      "Saved: motorcycle_s_000699.png to train/motorcycle_s_000699.png\n",
      "Saved: cycle_s_000492.png to train/cycle_s_000492.png\n",
      "Saved: cycle_s_000222.png to train/cycle_s_000222.png\n",
      "Saved: motorcycle_s_002162.png to train/motorcycle_s_002162.png\n",
      "Saved: minibike_s_000409.png to train/minibike_s_000409.png\n",
      "Saved: cycle_s_001735.png to train/cycle_s_001735.png\n",
      "Saved: bike_s_000657.png to train/bike_s_000657.png\n",
      "Saved: safety_bike_s_000643.png to train/safety_bike_s_000643.png\n",
      "Saved: bike_s_000506.png to train/bike_s_000506.png\n",
      "Saved: minibike_s_001348.png to train/minibike_s_001348.png\n",
      "Saved: moped_s_000065.png to train/moped_s_000065.png\n",
      "Saved: bicycle_s_002729.png to train/bicycle_s_002729.png\n",
      "Saved: bicycle_s_001687.png to train/bicycle_s_001687.png\n",
      "Saved: bicycle_s_002338.png to train/bicycle_s_002338.png\n",
      "Saved: motorcycle_s_000459.png to train/motorcycle_s_000459.png\n",
      "Saved: bike_s_000593.png to train/bike_s_000593.png\n",
      "Saved: velocipede_s_001422.png to train/velocipede_s_001422.png\n",
      "Saved: ordinary_bicycle_s_000347.png to train/ordinary_bicycle_s_000347.png\n",
      "Saved: minibike_s_001870.png to train/minibike_s_001870.png\n",
      "Saved: bicycle_s_001757.png to train/bicycle_s_001757.png\n",
      "Saved: motorcycle_s_001016.png to train/motorcycle_s_001016.png\n",
      "Saved: motorbike_s_000041.png to train/motorbike_s_000041.png\n",
      "Saved: bike_s_000522.png to train/bike_s_000522.png\n",
      "Saved: ordinary_bicycle_s_000104.png to train/ordinary_bicycle_s_000104.png\n",
      "Saved: bike_s_000397.png to train/bike_s_000397.png\n",
      "Saved: minibike_s_000802.png to train/minibike_s_000802.png\n",
      "Saved: trail_bike_s_000016.png to train/trail_bike_s_000016.png\n",
      "Saved: motorcycle_s_000686.png to train/motorcycle_s_000686.png\n",
      "Saved: velocipede_s_001935.png to train/velocipede_s_001935.png\n",
      "Saved: safety_bike_s_000165.png to train/safety_bike_s_000165.png\n",
      "Saved: motorbike_s_000308.png to train/motorbike_s_000308.png\n",
      "Saved: bike_s_000018.png to train/bike_s_000018.png\n",
      "Saved: cycle_s_001976.png to train/cycle_s_001976.png\n",
      "Saved: minibike_s_000741.png to train/minibike_s_000741.png\n",
      "Saved: motorcycle_s_001236.png to train/motorcycle_s_001236.png\n",
      "Saved: moped_s_000071.png to train/moped_s_000071.png\n",
      "Saved: bike_s_000164.png to train/bike_s_000164.png\n",
      "Saved: bike_s_001739.png to train/bike_s_001739.png\n",
      "Saved: bicycle_s_001388.png to train/bicycle_s_001388.png\n",
      "Saved: bicycle_s_000149.png to train/bicycle_s_000149.png\n",
      "Saved: motorcycle_s_002197.png to train/motorcycle_s_002197.png\n",
      "Saved: motorcycle_s_000277.png to train/motorcycle_s_000277.png\n",
      "Saved: motorcycle_s_001690.png to train/motorcycle_s_001690.png\n",
      "Saved: minibike_s_002048.png to train/minibike_s_002048.png\n",
      "Saved: motorcycle_s_000253.png to train/motorcycle_s_000253.png\n",
      "Saved: safety_bike_s_001010.png to train/safety_bike_s_001010.png\n",
      "Saved: motorcycle_s_000785.png to train/motorcycle_s_000785.png\n",
      "Saved: ordinary_bicycle_s_000387.png to train/ordinary_bicycle_s_000387.png\n",
      "Saved: cycle_s_001309.png to train/cycle_s_001309.png\n",
      "Saved: motorcycle_s_002316.png to train/motorcycle_s_002316.png\n",
      "Saved: motorcycle_s_000340.png to train/motorcycle_s_000340.png\n",
      "Saved: moped_s_000124.png to train/moped_s_000124.png\n",
      "Saved: minibike_s_001917.png to train/minibike_s_001917.png\n",
      "Saved: bicycle_s_000951.png to train/bicycle_s_000951.png\n",
      "Saved: motorcycle_s_000002.png to train/motorcycle_s_000002.png\n",
      "Saved: motorcycle_s_001063.png to train/motorcycle_s_001063.png\n",
      "Saved: motorcycle_s_001641.png to train/motorcycle_s_001641.png\n",
      "Saved: bicycle_s_002129.png to train/bicycle_s_002129.png\n",
      "Saved: minibike_s_002182.png to train/minibike_s_002182.png\n",
      "Saved: minibike_s_000427.png to train/minibike_s_000427.png\n",
      "Saved: motorcycle_s_001438.png to train/motorcycle_s_001438.png\n",
      "Saved: motorcycle_s_000126.png to train/motorcycle_s_000126.png\n",
      "Saved: minibike_s_001865.png to train/minibike_s_001865.png\n",
      "Saved: motorcycle_s_000456.png to train/motorcycle_s_000456.png\n",
      "Saved: motorcycle_s_000252.png to train/motorcycle_s_000252.png\n",
      "Saved: bicycle_s_000043.png to train/bicycle_s_000043.png\n",
      "Saved: bicycle_s_001928.png to train/bicycle_s_001928.png\n",
      "Saved: safety_bike_s_000173.png to train/safety_bike_s_000173.png\n",
      "Saved: motorcycle_s_000696.png to train/motorcycle_s_000696.png\n",
      "Saved: motorcycle_s_001413.png to train/motorcycle_s_001413.png\n",
      "Saved: safety_bicycle_s_001303.png to train/safety_bicycle_s_001303.png\n",
      "Saved: motorcycle_s_001220.png to train/motorcycle_s_001220.png\n",
      "Saved: safety_bicycle_s_000079.png to train/safety_bicycle_s_000079.png\n",
      "Saved: bicycle_s_001681.png to train/bicycle_s_001681.png\n",
      "Saved: cycle_s_002015.png to train/cycle_s_002015.png\n",
      "Saved: cycle_s_002746.png to train/cycle_s_002746.png\n",
      "Saved: minibike_s_001250.png to train/minibike_s_001250.png\n",
      "Saved: safety_bicycle_s_000303.png to train/safety_bicycle_s_000303.png\n",
      "Saved: bike_s_000392.png to train/bike_s_000392.png\n",
      "Saved: minibike_s_000408.png to train/minibike_s_000408.png\n",
      "Saved: safety_bike_s_001355.png to train/safety_bike_s_001355.png\n",
      "Saved: motorcycle_s_000076.png to train/motorcycle_s_000076.png\n",
      "Saved: safety_bicycle_s_000322.png to train/safety_bicycle_s_000322.png\n",
      "Saved: bike_s_002090.png to train/bike_s_002090.png\n",
      "Saved: motorcycle_s_000796.png to train/motorcycle_s_000796.png\n",
      "Saved: motorbike_s_000225.png to train/motorbike_s_000225.png\n",
      "Saved: motorcycle_s_000050.png to train/motorcycle_s_000050.png\n",
      "Saved: bike_s_000474.png to train/bike_s_000474.png\n",
      "Saved: motorcycle_s_000127.png to train/motorcycle_s_000127.png\n",
      "Saved: motorcycle_s_002193.png to train/motorcycle_s_002193.png\n",
      "Saved: bicycle_s_002374.png to train/bicycle_s_002374.png\n",
      "Saved: minibike_s_001558.png to train/minibike_s_001558.png\n",
      "Saved: velocipede_s_001279.png to train/velocipede_s_001279.png\n",
      "Saved: minibike_s_001496.png to train/minibike_s_001496.png\n",
      "Saved: bicycle_s_001411.png to train/bicycle_s_001411.png\n",
      "Saved: minibike_s_001072.png to train/minibike_s_001072.png\n",
      "Saved: bicycle_s_001688.png to train/bicycle_s_001688.png\n",
      "Saved: safety_bike_s_000160.png to train/safety_bike_s_000160.png\n",
      "Saved: minibike_s_000820.png to train/minibike_s_000820.png\n",
      "Saved: ordinary_bicycle_s_000298.png to train/ordinary_bicycle_s_000298.png\n",
      "Saved: bicycle_s_000314.png to train/bicycle_s_000314.png\n",
      "Saved: motorcycle_s_001862.png to train/motorcycle_s_001862.png\n",
      "Saved: minibike_s_001575.png to train/minibike_s_001575.png\n",
      "Saved: minibike_s_000709.png to train/minibike_s_000709.png\n",
      "Saved: velocipede_s_000931.png to train/velocipede_s_000931.png\n",
      "Saved: safety_bike_s_000104.png to train/safety_bike_s_000104.png\n",
      "Saved: motorcycle_s_001197.png to train/motorcycle_s_001197.png\n",
      "Saved: motorbike_s_000115.png to train/motorbike_s_000115.png\n",
      "Saved: bicycle_s_002728.png to train/bicycle_s_002728.png\n",
      "Saved: safety_bike_s_001474.png to train/safety_bike_s_001474.png\n",
      "Saved: bike_s_002024.png to train/bike_s_002024.png\n",
      "Saved: cycle_s_000666.png to train/cycle_s_000666.png\n",
      "Saved: motorcycle_s_000617.png to train/motorcycle_s_000617.png\n",
      "Saved: safety_bike_s_000461.png to train/safety_bike_s_000461.png\n",
      "Saved: bike_s_001572.png to train/bike_s_001572.png\n",
      "Saved: motorcycle_s_000003.png to train/motorcycle_s_000003.png\n",
      "Saved: bicycle_s_002029.png to train/bicycle_s_002029.png\n",
      "Saved: minibike_s_000701.png to train/minibike_s_000701.png\n",
      "Saved: motorbike_s_000009.png to train/motorbike_s_000009.png\n",
      "Saved: minibike_s_001789.png to train/minibike_s_001789.png\n",
      "Saved: motorcycle_s_000417.png to train/motorcycle_s_000417.png\n",
      "Saved: motorcycle_s_000246.png to train/motorcycle_s_000246.png\n",
      "Saved: minibike_s_000886.png to train/minibike_s_000886.png\n",
      "Saved: motorcycle_s_000154.png to train/motorcycle_s_000154.png\n",
      "Saved: minibike_s_001169.png to train/minibike_s_001169.png\n",
      "Saved: cycle_s_002904.png to train/cycle_s_002904.png\n",
      "Saved: motorcycle_s_000772.png to train/motorcycle_s_000772.png\n",
      "Saved: velocipede_s_001158.png to train/velocipede_s_001158.png\n",
      "Saved: bicycle_s_002521.png to train/bicycle_s_002521.png\n",
      "Saved: minibike_s_001179.png to train/minibike_s_001179.png\n",
      "Saved: bicycle_s_001111.png to train/bicycle_s_001111.png\n",
      "Saved: bicycle_s_001205.png to train/bicycle_s_001205.png\n",
      "Saved: bicycle_s_000435.png to train/bicycle_s_000435.png\n",
      "Saved: minibike_s_001771.png to train/minibike_s_001771.png\n",
      "Saved: motorcycle_s_000545.png to train/motorcycle_s_000545.png\n",
      "Saved: moped_s_000004.png to train/moped_s_000004.png\n",
      "Saved: velocipede_s_001358.png to train/velocipede_s_001358.png\n",
      "Saved: motorcycle_s_001402.png to train/motorcycle_s_001402.png\n",
      "Saved: minibike_s_001836.png to train/minibike_s_001836.png\n",
      "Saved: motorcycle_s_002067.png to train/motorcycle_s_002067.png\n",
      "Saved: bicycle_s_002012.png to train/bicycle_s_002012.png\n",
      "Saved: motorcycle_s_000517.png to train/motorcycle_s_000517.png\n",
      "Saved: minibike_s_001345.png to train/minibike_s_001345.png\n",
      "Saved: minibike_s_000522.png to train/minibike_s_000522.png\n",
      "Saved: motorcycle_s_000700.png to train/motorcycle_s_000700.png\n",
      "Saved: motorcycle_s_001183.png to train/motorcycle_s_001183.png\n",
      "Saved: velocipede_s_001874.png to train/velocipede_s_001874.png\n",
      "Saved: cycle_s_003006.png to train/cycle_s_003006.png\n",
      "Saved: motorcycle_s_000170.png to train/motorcycle_s_000170.png\n",
      "Saved: ordinary_bicycle_s_000419.png to train/ordinary_bicycle_s_000419.png\n",
      "Saved: motorcycle_s_000284.png to train/motorcycle_s_000284.png\n",
      "Saved: motorbike_s_000035.png to train/motorbike_s_000035.png\n",
      "Saved: dirt_bike_s_000005.png to train/dirt_bike_s_000005.png\n",
      "Saved: motorcycle_s_001784.png to train/motorcycle_s_001784.png\n",
      "Saved: motorcycle_s_001848.png to train/motorcycle_s_001848.png\n",
      "Saved: minibike_s_002046.png to train/minibike_s_002046.png\n",
      "Saved: motorcycle_s_002317.png to train/motorcycle_s_002317.png\n",
      "Saved: bike_s_001980.png to train/bike_s_001980.png\n",
      "Saved: safety_bike_s_000950.png to train/safety_bike_s_000950.png\n",
      "Saved: bicycle_s_002237.png to train/bicycle_s_002237.png\n",
      "Saved: cycle_s_000042.png to train/cycle_s_000042.png\n",
      "Saved: bike_s_000628.png to train/bike_s_000628.png\n",
      "Saved: bike_s_000005.png to train/bike_s_000005.png\n",
      "Saved: ordinary_bicycle_s_000324.png to train/ordinary_bicycle_s_000324.png\n",
      "Saved: motorcycle_s_000346.png to train/motorcycle_s_000346.png\n",
      "Saved: safety_bicycle_s_000789.png to train/safety_bicycle_s_000789.png\n",
      "Saved: ordinary_bicycle_s_000011.png to train/ordinary_bicycle_s_000011.png\n",
      "Saved: minibike_s_001157.png to train/minibike_s_001157.png\n",
      "Saved: bike_s_000051.png to train/bike_s_000051.png\n",
      "Saved: motorcycle_s_000919.png to train/motorcycle_s_000919.png\n",
      "Saved: velocipede_s_001514.png to train/velocipede_s_001514.png\n",
      "Saved: bike_s_001592.png to train/bike_s_001592.png\n",
      "Saved: ordinary_bicycle_s_000066.png to train/ordinary_bicycle_s_000066.png\n",
      "Saved: bicycle_s_001470.png to train/bicycle_s_001470.png\n",
      "Saved: motorcycle_s_001695.png to train/motorcycle_s_001695.png\n",
      "Saved: motorcycle_s_001215.png to train/motorcycle_s_001215.png\n",
      "Saved: bike_s_002098.png to train/bike_s_002098.png\n",
      "Saved: bike_s_001928.png to train/bike_s_001928.png\n",
      "Saved: bicycle_s_001748.png to train/bicycle_s_001748.png\n",
      "Saved: motorcycle_s_001520.png to train/motorcycle_s_001520.png\n",
      "Saved: safety_bicycle_s_000728.png to train/safety_bicycle_s_000728.png\n",
      "Saved: motorcycle_s_000925.png to train/motorcycle_s_000925.png\n",
      "Saved: safety_bike_s_000914.png to train/safety_bike_s_000914.png\n",
      "Saved: safety_bike_s_001659.png to train/safety_bike_s_001659.png\n",
      "Saved: safety_bike_s_000155.png to train/safety_bike_s_000155.png\n",
      "Saved: bicycle_s_002153.png to train/bicycle_s_002153.png\n",
      "Saved: safety_bike_s_001715.png to train/safety_bike_s_001715.png\n",
      "Saved: bicycle_s_000035.png to train/bicycle_s_000035.png\n",
      "Saved: bicycle_s_002669.png to train/bicycle_s_002669.png\n",
      "Saved: bike_s_002277.png to train/bike_s_002277.png\n",
      "Saved: ordinary_bicycle_s_000265.png to train/ordinary_bicycle_s_000265.png\n",
      "Saved: bicycle_s_000369.png to train/bicycle_s_000369.png\n",
      "Saved: velocipede_s_001298.png to train/velocipede_s_001298.png\n",
      "Saved: minibike_s_001944.png to train/minibike_s_001944.png\n",
      "Saved: motorcycle_s_002215.png to train/motorcycle_s_002215.png\n",
      "Saved: ordinary_bicycle_s_000008.png to train/ordinary_bicycle_s_000008.png\n",
      "Saved: bicycle_s_000491.png to train/bicycle_s_000491.png\n",
      "Saved: motorcycle_s_000146.png to train/motorcycle_s_000146.png\n",
      "Saved: minibike_s_001261.png to train/minibike_s_001261.png\n",
      "Saved: safety_bicycle_s_000140.png to train/safety_bicycle_s_000140.png\n",
      "Saved: motorcycle_s_000407.png to train/motorcycle_s_000407.png\n",
      "Saved: minibike_s_000099.png to train/minibike_s_000099.png\n",
      "Saved: motorcycle_s_001767.png to train/motorcycle_s_001767.png\n",
      "Saved: safety_bike_s_000263.png to train/safety_bike_s_000263.png\n",
      "Saved: motorcycle_s_001205.png to train/motorcycle_s_001205.png\n",
      "Saved: motorbike_s_000134.png to train/motorbike_s_000134.png\n",
      "Saved: safety_bike_s_000245.png to train/safety_bike_s_000245.png\n",
      "Saved: motorcycle_s_000422.png to train/motorcycle_s_000422.png\n",
      "Saved: minibike_s_000800.png to train/minibike_s_000800.png\n",
      "Saved: minibike_s_001829.png to train/minibike_s_001829.png\n",
      "Saved: minibike_s_002124.png to train/minibike_s_002124.png\n",
      "Saved: bike_s_001414.png to train/bike_s_001414.png\n",
      "Saved: motorcycle_s_000423.png to train/motorcycle_s_000423.png\n",
      "Saved: motorcycle_s_001435.png to train/motorcycle_s_001435.png\n",
      "Saved: motorcycle_s_001237.png to train/motorcycle_s_001237.png\n",
      "Saved: motorcycle_s_001878.png to train/motorcycle_s_001878.png\n",
      "Saved: cycle_s_002093.png to train/cycle_s_002093.png\n",
      "Saved: velocipede_s_000265.png to train/velocipede_s_000265.png\n",
      "Saved: bike_s_001847.png to train/bike_s_001847.png\n",
      "Saved: cycle_s_000463.png to train/cycle_s_000463.png\n",
      "Saved: motorcycle_s_002140.png to train/motorcycle_s_002140.png\n",
      "Saved: motorcycle_s_001246.png to train/motorcycle_s_001246.png\n",
      "Saved: bike_s_001517.png to train/bike_s_001517.png\n",
      "Saved: motorcycle_s_000219.png to train/motorcycle_s_000219.png\n",
      "Saved: motorbike_s_000501.png to train/motorbike_s_000501.png\n",
      "Saved: velocipede_s_001872.png to train/velocipede_s_001872.png\n",
      "Saved: motorcycle_s_001337.png to train/motorcycle_s_001337.png\n",
      "Saved: motorcycle_s_000169.png to train/motorcycle_s_000169.png\n",
      "Saved: motorcycle_s_000347.png to train/motorcycle_s_000347.png\n",
      "Saved: ordinary_bicycle_s_000274.png to train/ordinary_bicycle_s_000274.png\n",
      "Saved: safety_bicycle_s_000196.png to train/safety_bicycle_s_000196.png\n",
      "Saved: bicycle_s_002049.png to train/bicycle_s_002049.png\n",
      "Saved: minibike_s_000010.png to train/minibike_s_000010.png\n",
      "Saved: motorcycle_s_002121.png to train/motorcycle_s_002121.png\n",
      "Saved: motorbike_s_000060.png to train/motorbike_s_000060.png\n",
      "Saved: motorbike_s_000534.png to train/motorbike_s_000534.png\n",
      "Saved: ordinary_bicycle_s_000286.png to train/ordinary_bicycle_s_000286.png\n",
      "Saved: bicycle_s_000723.png to train/bicycle_s_000723.png\n",
      "Saved: bicycle_s_000066.png to train/bicycle_s_000066.png\n",
      "Saved: motorcycle_s_000741.png to train/motorcycle_s_000741.png\n",
      "Saved: minibike_s_001168.png to train/minibike_s_001168.png\n",
      "Saved: bike_s_000001.png to train/bike_s_000001.png\n",
      "Saved: minibike_s_001491.png to train/minibike_s_001491.png\n",
      "Saved: motorcycle_s_000430.png to train/motorcycle_s_000430.png\n",
      "Saved: motorcycle_s_000026.png to train/motorcycle_s_000026.png\n",
      "Saved: bicycle_s_002448.png to train/bicycle_s_002448.png\n",
      "Saved: cycle_s_001412.png to train/cycle_s_001412.png\n",
      "Saved: motorcycle_s_000714.png to train/motorcycle_s_000714.png\n",
      "Saved: motorcycle_s_000319.png to train/motorcycle_s_000319.png\n",
      "Saved: safety_bicycle_s_001254.png to train/safety_bicycle_s_001254.png\n",
      "Saved: ordinary_bicycle_s_000205.png to train/ordinary_bicycle_s_000205.png\n",
      "Saved: bicycle_s_002400.png to train/bicycle_s_002400.png\n",
      "Saved: motorcycle_s_001126.png to train/motorcycle_s_001126.png\n",
      "Saved: bike_s_000364.png to train/bike_s_000364.png\n",
      "Saved: ordinary_bicycle_s_000096.png to train/ordinary_bicycle_s_000096.png\n",
      "Saved: bike_s_000204.png to train/bike_s_000204.png\n",
      "Saved: motorcycle_s_001181.png to train/motorcycle_s_001181.png\n",
      "Saved: minibike_s_001450.png to train/minibike_s_001450.png\n",
      "Saved: minibike_s_000130.png to train/minibike_s_000130.png\n",
      "Saved: motorcycle_s_002214.png to train/motorcycle_s_002214.png\n",
      "Saved: motorcycle_s_001263.png to train/motorcycle_s_001263.png\n",
      "Saved: minibike_s_000568.png to train/minibike_s_000568.png\n",
      "Saved: bicycle_s_000774.png to train/bicycle_s_000774.png\n",
      "Saved: ordinary_bicycle_s_000125.png to train/ordinary_bicycle_s_000125.png\n",
      "Saved: minibike_s_001731.png to train/minibike_s_001731.png\n",
      "Saved: bicycle_s_000243.png to train/bicycle_s_000243.png\n",
      "Saved: motorcycle_s_000029.png to train/motorcycle_s_000029.png\n",
      "Saved: velocipede_s_000023.png to train/velocipede_s_000023.png\n",
      "Saved: minibike_s_001701.png to train/minibike_s_001701.png\n",
      "Saved: ordinary_bicycle_s_000035.png to train/ordinary_bicycle_s_000035.png\n",
      "Saved: safety_bike_s_000244.png to train/safety_bike_s_000244.png\n",
      "Saved: bicycle_s_000137.png to train/bicycle_s_000137.png\n",
      "Saved: motorbike_s_000331.png to train/motorbike_s_000331.png\n",
      "Saved: motorcycle_s_000120.png to train/motorcycle_s_000120.png\n",
      "Saved: velocipede_s_000989.png to train/velocipede_s_000989.png\n",
      "Saved: moped_s_000135.png to train/moped_s_000135.png\n",
      "Saved: bicycle_s_000569.png to train/bicycle_s_000569.png\n",
      "Saved: safety_bicycle_s_001699.png to train/safety_bicycle_s_001699.png\n",
      "Saved: bicycle_s_000399.png to train/bicycle_s_000399.png\n",
      "Saved: bike_s_002033.png to train/bike_s_002033.png\n",
      "Saved: ordinary_bicycle_s_000280.png to train/ordinary_bicycle_s_000280.png\n",
      "Saved: bike_s_002283.png to train/bike_s_002283.png\n",
      "Saved: cycle_s_001413.png to train/cycle_s_001413.png\n",
      "Saved: dirt_bike_s_000059.png to train/dirt_bike_s_000059.png\n",
      "Saved: motorcycle_s_001699.png to train/motorcycle_s_001699.png\n",
      "Saved: motorcycle_s_000687.png to train/motorcycle_s_000687.png\n",
      "Saved: bike_s_000304.png to train/bike_s_000304.png\n",
      "Saved: motorcycle_s_002192.png to train/motorcycle_s_002192.png\n",
      "Saved: motorcycle_s_000887.png to train/motorcycle_s_000887.png\n",
      "Saved: bike_s_002042.png to train/bike_s_002042.png\n",
      "Saved: bicycle_s_000467.png to train/bicycle_s_000467.png\n",
      "Saved: motorcycle_s_001706.png to train/motorcycle_s_001706.png\n",
      "Saved: minibike_s_002176.png to train/minibike_s_002176.png\n",
      "Saved: safety_bicycle_s_000760.png to train/safety_bicycle_s_000760.png\n",
      "Saved: bicycle_s_000546.png to train/bicycle_s_000546.png\n",
      "Saved: safety_bike_s_000158.png to train/safety_bike_s_000158.png\n",
      "Saved: bicycle_s_001679.png to train/bicycle_s_001679.png\n",
      "Saved: motorcycle_s_000924.png to train/motorcycle_s_000924.png\n",
      "Saved: bicycle_s_000996.png to train/bicycle_s_000996.png\n",
      "Saved: motorcycle_s_000432.png to train/motorcycle_s_000432.png\n",
      "Saved: ordinary_bicycle_s_000277.png to train/ordinary_bicycle_s_000277.png\n",
      "Saved: bike_s_002164.png to train/bike_s_002164.png\n",
      "Saved: minibike_s_000036.png to train/minibike_s_000036.png\n",
      "Saved: motorbike_s_000062.png to train/motorbike_s_000062.png\n",
      "Saved: motorcycle_s_000133.png to train/motorcycle_s_000133.png\n",
      "Saved: safety_bike_s_000328.png to train/safety_bike_s_000328.png\n",
      "Saved: safety_bicycle_s_001028.png to train/safety_bicycle_s_001028.png\n",
      "Saved: minibike_s_001506.png to train/minibike_s_001506.png\n",
      "Saved: motorcycle_s_001549.png to train/motorcycle_s_001549.png\n",
      "Saved: bike_s_002292.png to train/bike_s_002292.png\n",
      "Saved: bike_s_000121.png to train/bike_s_000121.png\n",
      "Saved: motorcycle_s_001167.png to train/motorcycle_s_001167.png\n",
      "Saved: bicycle_s_000124.png to train/bicycle_s_000124.png\n",
      "Saved: motorcycle_s_002237.png to train/motorcycle_s_002237.png\n",
      "Saved: velocipede_s_001870.png to train/velocipede_s_001870.png\n",
      "Saved: moped_s_000119.png to train/moped_s_000119.png\n",
      "Saved: minibike_s_000121.png to train/minibike_s_000121.png\n",
      "Saved: motorcycle_s_000928.png to train/motorcycle_s_000928.png\n",
      "Saved: minibike_s_001881.png to train/minibike_s_001881.png\n",
      "Saved: bike_s_000679.png to train/bike_s_000679.png\n",
      "Saved: motorcycle_s_002183.png to train/motorcycle_s_002183.png\n",
      "Saved: bike_s_000111.png to train/bike_s_000111.png\n",
      "Saved: bike_s_001260.png to train/bike_s_001260.png\n",
      "Saved: safety_bicycle_s_000660.png to train/safety_bicycle_s_000660.png\n",
      "Saved: motorcycle_s_000695.png to train/motorcycle_s_000695.png\n",
      "Saved: safety_bicycle_s_000233.png to train/safety_bicycle_s_000233.png\n",
      "Saved: motorcycle_s_000792.png to train/motorcycle_s_000792.png\n",
      "Saved: motorcycle_s_000922.png to train/motorcycle_s_000922.png\n",
      "Saved: bicycle_s_002711.png to train/bicycle_s_002711.png\n",
      "Saved: bike_s_000390.png to train/bike_s_000390.png\n",
      "Saved: minibike_s_001094.png to train/minibike_s_001094.png\n",
      "Saved: minibike_s_000507.png to train/minibike_s_000507.png\n",
      "Saved: motorcycle_s_001213.png to train/motorcycle_s_001213.png\n",
      "Saved: bike_s_000154.png to train/bike_s_000154.png\n",
      "Saved: motorcycle_s_000223.png to train/motorcycle_s_000223.png\n",
      "Saved: bicycle_s_002661.png to train/bicycle_s_002661.png\n",
      "Saved: safety_bicycle_s_000232.png to train/safety_bicycle_s_000232.png\n",
      "Saved: bicycle_s_001663.png to train/bicycle_s_001663.png\n",
      "Saved: motorbike_s_000463.png to train/motorbike_s_000463.png\n",
      "Saved: minibike_s_002137.png to train/minibike_s_002137.png\n",
      "Saved: safety_bicycle_s_001651.png to train/safety_bicycle_s_001651.png\n",
      "Saved: motorcycle_s_000151.png to train/motorcycle_s_000151.png\n",
      "Saved: motorcycle_s_000217.png to train/motorcycle_s_000217.png\n",
      "Saved: bike_s_001877.png to train/bike_s_001877.png\n",
      "Saved: velocipede_s_001278.png to train/velocipede_s_001278.png\n",
      "Saved: minibike_s_001540.png to train/minibike_s_001540.png\n",
      "Saved: minibike_s_001885.png to train/minibike_s_001885.png\n",
      "Saved: velocipede_s_000012.png to train/velocipede_s_000012.png\n",
      "Saved: bicycle_s_001956.png to train/bicycle_s_001956.png\n",
      "Saved: ordinary_bicycle_s_000098.png to train/ordinary_bicycle_s_000098.png\n",
      "Saved: bicycle_s_002725.png to train/bicycle_s_002725.png\n",
      "Saved: velocipede_s_001210.png to train/velocipede_s_001210.png\n",
      "Saved: bicycle_s_000039.png to train/bicycle_s_000039.png\n",
      "Saved: motorcycle_s_000654.png to train/motorcycle_s_000654.png\n",
      "Saved: safety_bike_s_000079.png to train/safety_bike_s_000079.png\n",
      "Saved: motorcycle_s_002291.png to train/motorcycle_s_002291.png\n",
      "Saved: cycle_s_002168.png to train/cycle_s_002168.png\n",
      "Saved: bike_s_001078.png to train/bike_s_001078.png\n",
      "Saved: motorbike_s_000427.png to train/motorbike_s_000427.png\n",
      "Saved: minibike_s_001921.png to train/minibike_s_001921.png\n",
      "Saved: motorcycle_s_000917.png to train/motorcycle_s_000917.png\n",
      "Saved: velocipede_s_001920.png to train/velocipede_s_001920.png\n",
      "Saved: cycle_s_003026.png to train/cycle_s_003026.png\n",
      "Saved: motorcycle_s_000543.png to train/motorcycle_s_000543.png\n",
      "Saved: minibike_s_000491.png to train/minibike_s_000491.png\n",
      "Saved: motorcycle_s_001439.png to train/motorcycle_s_001439.png\n",
      "Saved: minibike_s_002084.png to train/minibike_s_002084.png\n",
      "Saved: bicycle_s_000775.png to train/bicycle_s_000775.png\n",
      "Saved: motorcycle_s_000262.png to train/motorcycle_s_000262.png\n",
      "Saved: moped_s_000237.png to train/moped_s_000237.png\n",
      "Saved: bicycle_s_000978.png to train/bicycle_s_000978.png\n",
      "Saved: bike_s_000162.png to train/bike_s_000162.png\n",
      "Saved: motorbike_s_000106.png to train/motorbike_s_000106.png\n",
      "Saved: motorcycle_s_000136.png to train/motorcycle_s_000136.png\n",
      "Saved: motorcycle_s_001319.png to train/motorcycle_s_001319.png\n",
      "Saved: safety_bicycle_s_000125.png to train/safety_bicycle_s_000125.png\n",
      "Saved: bicycle_s_001402.png to train/bicycle_s_001402.png\n",
      "Saved: minibike_s_000511.png to train/minibike_s_000511.png\n",
      "Saved: motorcycle_s_000332.png to train/motorcycle_s_000332.png\n",
      "Saved: motorcycle_s_001315.png to train/motorcycle_s_001315.png\n",
      "Saved: ordinary_bicycle_s_000031.png to train/ordinary_bicycle_s_000031.png\n",
      "Saved: motorcycle_s_000353.png to train/motorcycle_s_000353.png\n",
      "Saved: motorcycle_s_000521.png to train/motorcycle_s_000521.png\n",
      "Saved: safety_bicycle_s_000860.png to train/safety_bicycle_s_000860.png\n",
      "Saved: motorcycle_s_002153.png to train/motorcycle_s_002153.png\n",
      "Saved: safety_bicycle_s_000324.png to train/safety_bicycle_s_000324.png\n",
      "Saved: ordinary_bicycle_s_000112.png to train/ordinary_bicycle_s_000112.png\n",
      "Saved: motorbike_s_000068.png to train/motorbike_s_000068.png\n",
      "Saved: motorcycle_s_002254.png to train/motorcycle_s_002254.png\n",
      "Saved: motorcycle_s_000989.png to train/motorcycle_s_000989.png\n",
      "Saved: motorcycle_s_000622.png to train/motorcycle_s_000622.png\n",
      "Saved: bike_s_000545.png to train/bike_s_000545.png\n",
      "Saved: ordinary_bicycle_s_000099.png to train/ordinary_bicycle_s_000099.png\n",
      "Saved: safety_bike_s_001257.png to train/safety_bike_s_001257.png\n",
      "Saved: bicycle_s_001203.png to train/bicycle_s_001203.png\n",
      "Saved: safety_bicycle_s_001085.png to train/safety_bicycle_s_001085.png\n",
      "Saved: motorcycle_s_002222.png to train/motorcycle_s_002222.png\n",
      "Saved: safety_bicycle_s_001639.png to train/safety_bicycle_s_001639.png\n",
      "Saved: velocipede_s_001222.png to train/velocipede_s_001222.png\n",
      "Saved: minibike_s_000392.png to train/minibike_s_000392.png\n",
      "Saved: ordinary_bicycle_s_000308.png to train/ordinary_bicycle_s_000308.png\n",
      "Saved: motorcycle_s_001896.png to train/motorcycle_s_001896.png\n",
      "Saved: motorcycle_s_000325.png to train/motorcycle_s_000325.png\n",
      "Saved: bicycle_s_000986.png to train/bicycle_s_000986.png\n",
      "Saved: safety_bicycle_s_000373.png to train/safety_bicycle_s_000373.png\n",
      "Saved: safety_bicycle_s_000348.png to train/safety_bicycle_s_000348.png\n",
      "Saved: bicycle_s_002005.png to train/bicycle_s_002005.png\n",
      "Saved: minibike_s_002130.png to train/minibike_s_002130.png\n",
      "Saved: safety_bike_s_000867.png to train/safety_bike_s_000867.png\n",
      "Saved: bicycle_s_002475.png to train/bicycle_s_002475.png\n",
      "Saved: minibike_s_001193.png to train/minibike_s_001193.png\n",
      "Saved: minibike_s_001366.png to train/minibike_s_001366.png\n",
      "Saved: motorcycle_s_001711.png to train/motorcycle_s_001711.png\n",
      "Saved: minibike_s_001275.png to train/minibike_s_001275.png\n",
      "Saved: velocipede_s_001907.png to train/velocipede_s_001907.png\n",
      "Saved: motorcycle_s_000207.png to train/motorcycle_s_000207.png\n",
      "Saved: motorcycle_s_001409.png to train/motorcycle_s_001409.png\n",
      "Saved: safety_bicycle_s_000261.png to train/safety_bicycle_s_000261.png\n",
      "Saved: minibike_s_000738.png to train/minibike_s_000738.png\n",
      "Saved: motorcycle_s_001106.png to train/motorcycle_s_001106.png\n",
      "Saved: safety_bike_s_000198.png to train/safety_bike_s_000198.png\n",
      "Saved: minibike_s_001479.png to train/minibike_s_001479.png\n",
      "Saved: velocipede_s_000430.png to train/velocipede_s_000430.png\n",
      "Saved: bicycle_s_001409.png to train/bicycle_s_001409.png\n",
      "Saved: motorcycle_s_000938.png to train/motorcycle_s_000938.png\n",
      "Saved: motorcycle_s_000941.png to train/motorcycle_s_000941.png\n",
      "Saved: bicycle_s_000038.png to train/bicycle_s_000038.png\n",
      "Saved: minibike_s_001016.png to train/minibike_s_001016.png\n",
      "Saved: velocipede_s_001200.png to train/velocipede_s_001200.png\n",
      "Saved: bike_s_002109.png to train/bike_s_002109.png\n",
      "Saved: cycle_s_002092.png to train/cycle_s_002092.png\n",
      "Saved: motorcycle_s_002066.png to train/motorcycle_s_002066.png\n",
      "Saved: motorcycle_s_001920.png to train/motorcycle_s_001920.png\n",
      "Saved: minibike_s_000459.png to train/minibike_s_000459.png\n",
      "Saved: bicycle_s_001904.png to train/bicycle_s_001904.png\n",
      "Saved: minibike_s_001996.png to train/minibike_s_001996.png\n",
      "Saved: motorcycle_s_001611.png to train/motorcycle_s_001611.png\n",
      "Saved: velocipede_s_001141.png to train/velocipede_s_001141.png\n",
      "Saved: minibike_s_001747.png to train/minibike_s_001747.png\n",
      "Saved: safety_bicycle_s_001029.png to train/safety_bicycle_s_001029.png\n",
      "Saved: ordinary_bicycle_s_000029.png to train/ordinary_bicycle_s_000029.png\n",
      "Saved: minibike_s_002163.png to train/minibike_s_002163.png\n",
      "Saved: safety_bike_s_001148.png to train/safety_bike_s_001148.png\n",
      "Saved: cycle_s_003008.png to train/cycle_s_003008.png\n",
      "Saved: bicycle_s_000099.png to train/bicycle_s_000099.png\n",
      "Saved: minibike_s_000429.png to train/minibike_s_000429.png\n",
      "Saved: bike_s_000003.png to train/bike_s_000003.png\n",
      "Saved: cycle_s_002178.png to train/cycle_s_002178.png\n",
      "Saved: cycle_s_000639.png to train/cycle_s_000639.png\n",
      "Saved: motorcycle_s_000734.png to train/motorcycle_s_000734.png\n",
      "Saved: motorcycle_s_000309.png to train/motorcycle_s_000309.png\n",
      "Saved: bicycle_s_000371.png to train/bicycle_s_000371.png\n",
      "Saved: minibike_s_001270.png to train/minibike_s_001270.png\n",
      "Saved: minibike_s_000914.png to train/minibike_s_000914.png\n",
      "Saved: moped_s_000021.png to train/moped_s_000021.png\n",
      "Saved: motorcycle_s_000242.png to train/motorcycle_s_000242.png\n",
      "Saved: minibike_s_000314.png to train/minibike_s_000314.png\n",
      "Saved: motorcycle_s_000698.png to train/motorcycle_s_000698.png\n",
      "Saved: motorcycle_s_001360.png to train/motorcycle_s_001360.png\n",
      "Saved: ordinary_bicycle_s_000157.png to train/ordinary_bicycle_s_000157.png\n",
      "Saved: minibike_s_001838.png to train/minibike_s_001838.png\n",
      "Saved: bicycle_s_002247.png to train/bicycle_s_002247.png\n",
      "Saved: motorcycle_s_000027.png to train/motorcycle_s_000027.png\n",
      "Saved: minibike_s_000906.png to train/minibike_s_000906.png\n",
      "Saved: safety_bicycle_s_000500.png to train/safety_bicycle_s_000500.png\n",
      "Saved: minibike_s_002146.png to train/minibike_s_002146.png\n",
      "Saved: motorcycle_s_001242.png to train/motorcycle_s_001242.png\n",
      "Saved: motorcycle_s_001707.png to train/motorcycle_s_001707.png\n",
      "Saved: cycle_s_003043.png to train/cycle_s_003043.png\n",
      "Saved: safety_bicycle_s_001109.png to train/safety_bicycle_s_001109.png\n",
      "Saved: bike_s_001131.png to train/bike_s_001131.png\n",
      "Saved: motorcycle_s_001118.png to train/motorcycle_s_001118.png\n",
      "Saved: bike_s_001418.png to train/bike_s_001418.png\n",
      "Saved: bicycle_s_002550.png to train/bicycle_s_002550.png\n",
      "Saved: bicycle_s_002100.png to train/bicycle_s_002100.png\n",
      "Saved: motorcycle_s_000933.png to train/motorcycle_s_000933.png\n",
      "Saved: bicycle_s_002624.png to train/bicycle_s_002624.png\n",
      "Saved: safety_bicycle_s_001063.png to train/safety_bicycle_s_001063.png\n",
      "Saved: motorbike_s_000361.png to train/motorbike_s_000361.png\n",
      "Saved: safety_bicycle_s_000193.png to train/safety_bicycle_s_000193.png\n",
      "Saved: safety_bicycle_s_000092.png to train/safety_bicycle_s_000092.png\n",
      "Saved: motorcycle_s_000009.png to train/motorcycle_s_000009.png\n",
      "Saved: bicycle_s_001554.png to train/bicycle_s_001554.png\n",
      "Saved: motorcycle_s_001297.png to train/motorcycle_s_001297.png\n",
      "Saved: safety_bicycle_s_000296.png to train/safety_bicycle_s_000296.png\n",
      "Saved: safety_bike_s_001371.png to train/safety_bike_s_001371.png\n",
      "Saved: bicycle_s_001569.png to train/bicycle_s_001569.png\n",
      "Saved: motorcycle_s_001536.png to train/motorcycle_s_001536.png\n",
      "Saved: safety_bicycle_s_001659.png to train/safety_bicycle_s_001659.png\n",
      "Saved: ordinary_bicycle_s_000102.png to train/ordinary_bicycle_s_000102.png\n",
      "Saved: bike_s_002215.png to train/bike_s_002215.png\n",
      "Saved: bicycle_s_002410.png to train/bicycle_s_002410.png\n",
      "Saved: cycle_s_003147.png to train/cycle_s_003147.png\n",
      "Saved: bicycle_s_002759.png to train/bicycle_s_002759.png\n",
      "Saved: minibike_s_000335.png to train/minibike_s_000335.png\n",
      "Saved: safety_bike_s_000848.png to train/safety_bike_s_000848.png\n",
      "Saved: velocipede_s_000807.png to train/velocipede_s_000807.png\n",
      "Saved: bike_s_000024.png to train/bike_s_000024.png\n",
      "Saved: minibike_s_000435.png to train/minibike_s_000435.png\n",
      "Saved: minibike_s_001498.png to train/minibike_s_001498.png\n",
      "Saved: velocipede_s_001166.png to train/velocipede_s_001166.png\n",
      "Saved: motorcycle_s_001012.png to train/motorcycle_s_001012.png\n",
      "Saved: velocipede_s_000330.png to train/velocipede_s_000330.png\n",
      "Saved: motorcycle_s_000032.png to train/motorcycle_s_000032.png\n",
      "Saved: minibike_s_001597.png to train/minibike_s_001597.png\n",
      "Saved: motorcycle_s_002298.png to train/motorcycle_s_002298.png\n",
      "Saved: minibike_s_000102.png to train/minibike_s_000102.png\n",
      "Saved: motorcycle_s_001507.png to train/motorcycle_s_001507.png\n",
      "Saved: motorbike_s_000022.png to train/motorbike_s_000022.png\n",
      "Saved: motorcycle_s_000594.png to train/motorcycle_s_000594.png\n",
      "Saved: minibike_s_001539.png to train/minibike_s_001539.png\n",
      "Saved: bicycle_s_001228.png to train/bicycle_s_001228.png\n",
      "Saved: minibike_s_001344.png to train/minibike_s_001344.png\n",
      "Saved: motorcycle_s_002271.png to train/motorcycle_s_002271.png\n",
      "Saved: motorcycle_s_000791.png to train/motorcycle_s_000791.png\n",
      "Saved: ordinary_bicycle_s_000218.png to train/ordinary_bicycle_s_000218.png\n",
      "Saved: motorcycle_s_000221.png to train/motorcycle_s_000221.png\n",
      "Saved: minibike_s_001691.png to train/minibike_s_001691.png\n",
      "Saved: motorcycle_s_001209.png to train/motorcycle_s_001209.png\n",
      "Saved: ordinary_bicycle_s_000101.png to train/ordinary_bicycle_s_000101.png\n",
      "Saved: motorcycle_s_001326.png to train/motorcycle_s_001326.png\n",
      "Saved: minibike_s_001249.png to train/minibike_s_001249.png\n",
      "Saved: bicycle_s_000408.png to train/bicycle_s_000408.png\n",
      "Saved: minibike_s_000520.png to train/minibike_s_000520.png\n",
      "Saved: minibike_s_002218.png to train/minibike_s_002218.png\n",
      "Saved: minibike_s_001458.png to train/minibike_s_001458.png\n",
      "Saved: moped_s_000034.png to train/moped_s_000034.png\n",
      "Saved: motorbike_s_000119.png to train/motorbike_s_000119.png\n",
      "Saved: motorcycle_s_001027.png to train/motorcycle_s_001027.png\n",
      "Saved: cycle_s_001477.png to train/cycle_s_001477.png\n",
      "Saved: motorcycle_s_001710.png to train/motorcycle_s_001710.png\n",
      "Saved: minibike_s_000434.png to train/minibike_s_000434.png\n",
      "Saved: motorcycle_s_000979.png to train/motorcycle_s_000979.png\n",
      "Saved: safety_bike_s_001289.png to train/safety_bike_s_001289.png\n",
      "Saved: safety_bike_s_001560.png to train/safety_bike_s_001560.png\n",
      "Saved: minibike_s_000011.png to train/minibike_s_000011.png\n",
      "Saved: bicycle_s_001642.png to train/bicycle_s_001642.png\n",
      "Saved: minibike_s_000527.png to train/minibike_s_000527.png\n",
      "Saved: bicycle_s_001245.png to train/bicycle_s_001245.png\n",
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      "Saved: cycle_s_002503.png to train/cycle_s_002503.png\n",
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      "Saved: bike_s_000336.png to train/bike_s_000336.png\n",
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      "Saved: safety_bike_s_000390.png to test/safety_bike_s_000390.png\n",
      "Saved: bike_s_000658.png to test/bike_s_000658.png\n",
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      "Saved: motorcycle_s_000323.png to test/motorcycle_s_000323.png\n",
      "Saved: safety_bike_s_001253.png to test/safety_bike_s_001253.png\n",
      "Saved: cycle_s_001306.png to test/cycle_s_001306.png\n",
      "Saved: velocipede_s_000863.png to test/velocipede_s_000863.png\n",
      "Saved: bicycle_s_000479.png to test/bicycle_s_000479.png\n",
      "Saved: minibike_s_002173.png to test/minibike_s_002173.png\n",
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      "Saved: bike_s_000801.png to test/bike_s_000801.png\n",
      "Saved: motorbike_s_000433.png to test/motorbike_s_000433.png\n",
      "Saved: bike_s_001068.png to test/bike_s_001068.png\n",
      "Saved: motorcycle_s_000963.png to test/motorcycle_s_000963.png\n",
      "Saved: bike_s_001852.png to test/bike_s_001852.png\n",
      "Saved: motorcycle_s_000139.png to test/motorcycle_s_000139.png\n",
      "Saved: motorbike_s_000465.png to test/motorbike_s_000465.png\n",
      "Saved: bicycle_s_000513.png to test/bicycle_s_000513.png\n",
      "Saved: cycle_s_002305.png to test/cycle_s_002305.png\n",
      "Saved: motorcycle_s_000615.png to test/motorcycle_s_000615.png\n",
      "Saved: moped_s_000306.png to test/moped_s_000306.png\n",
      "Saved: minibike_s_000052.png to test/minibike_s_000052.png\n",
      "Saved: bike_s_001342.png to test/bike_s_001342.png\n",
      "Saved: cycle_s_001297.png to test/cycle_s_001297.png\n",
      "Saved: motorcycle_s_001955.png to test/motorcycle_s_001955.png\n",
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      "Saved: motorcycle_s_000494.png to test/motorcycle_s_000494.png\n",
      "Saved: motorcycle_s_001249.png to test/motorcycle_s_001249.png\n",
      "Saved: velocipede_s_000292.png to test/velocipede_s_000292.png\n",
      "Saved: minibike_s_001651.png to test/minibike_s_001651.png\n",
      "Saved: bicycle_s_001102.png to test/bicycle_s_001102.png\n",
      "Saved: safety_bike_s_001132.png to test/safety_bike_s_001132.png\n",
      "Saved: moped_s_000007.png to test/moped_s_000007.png\n",
      "Saved: motorcycle_s_000211.png to test/motorcycle_s_000211.png\n",
      "Saved: cycle_s_000970.png to test/cycle_s_000970.png\n",
      "Saved: motorcycle_s_002126.png to test/motorcycle_s_002126.png\n",
      "Saved: minibike_s_001732.png to test/minibike_s_001732.png\n",
      "Saved: minibike_s_001867.png to test/minibike_s_001867.png\n",
      "Saved: minibike_s_002227.png to test/minibike_s_002227.png\n",
      "Saved: cycle_s_000068.png to test/cycle_s_000068.png\n",
      "Saved: minibike_s_000880.png to test/minibike_s_000880.png\n",
      "Saved: moped_s_000033.png to test/moped_s_000033.png\n",
      "Saved: bicycle_s_001107.png to test/bicycle_s_001107.png\n",
      "Saved: bike_s_002116.png to test/bike_s_002116.png\n",
      "Saved: bike_s_001738.png to test/bike_s_001738.png\n",
      "Saved: bike_s_002009.png to test/bike_s_002009.png\n",
      "Saved: minibike_s_000947.png to test/minibike_s_000947.png\n",
      "Saved: bike_s_001159.png to test/bike_s_001159.png\n",
      "Saved: safety_bike_s_001065.png to test/safety_bike_s_001065.png\n",
      "Saved: motorbike_s_000005.png to test/motorbike_s_000005.png\n",
      "Saved: minibike_s_000075.png to test/minibike_s_000075.png\n",
      "Saved: motorcycle_s_001164.png to test/motorcycle_s_001164.png\n",
      "Saved: motorcycle_s_000679.png to test/motorcycle_s_000679.png\n",
      "Saved: motorcycle_s_001892.png to test/motorcycle_s_001892.png\n",
      "Saved: bike_s_000694.png to test/bike_s_000694.png\n",
      "Saved: velocipede_s_000106.png to test/velocipede_s_000106.png\n",
      "Saved: velocipede_s_000001.png to test/velocipede_s_000001.png\n",
      "Saved: motorcycle_s_002177.png to test/motorcycle_s_002177.png\n",
      "Saved: motorbike_s_000126.png to test/motorbike_s_000126.png\n",
      "Saved: safety_bicycle_s_001286.png to test/safety_bicycle_s_001286.png\n",
      "Saved: motorcycle_s_001936.png to test/motorcycle_s_001936.png\n",
      "Saved: safety_bike_s_001087.png to test/safety_bike_s_001087.png\n",
      "Saved: cycle_s_001648.png to test/cycle_s_001648.png\n",
      "Saved: minibike_s_000801.png to test/minibike_s_000801.png\n",
      "Saved: motorcycle_s_000427.png to test/motorcycle_s_000427.png\n",
      "Saved: minibike_s_001089.png to test/minibike_s_001089.png\n",
      "Saved: motorcycle_s_000530.png to test/motorcycle_s_000530.png\n",
      "Saved: velocipede_s_001232.png to test/velocipede_s_001232.png\n",
      "Saved: motorcycle_s_001261.png to test/motorcycle_s_001261.png\n",
      "Saved: velocipede_s_001466.png to test/velocipede_s_001466.png\n",
      "Saved: motorcycle_s_001269.png to test/motorcycle_s_001269.png\n",
      "Saved: bicycle_s_000776.png to test/bicycle_s_000776.png\n",
      "Saved: motorcycle_s_001348.png to test/motorcycle_s_001348.png\n",
      "Saved: motorcycle_s_002026.png to test/motorcycle_s_002026.png\n",
      "Saved: safety_bike_s_000540.png to test/safety_bike_s_000540.png\n",
      "Saved: safety_bicycle_s_000280.png to test/safety_bicycle_s_000280.png\n",
      "Saved: bike_s_000131.png to test/bike_s_000131.png\n",
      "Saved: cycle_s_000010.png to test/cycle_s_000010.png\n",
      "Saved: minibike_s_000828.png to test/minibike_s_000828.png\n",
      "Saved: minibike_s_000288.png to test/minibike_s_000288.png\n",
      "Saved: velocipede_s_001355.png to test/velocipede_s_001355.png\n",
      "Saved: safety_bicycle_s_001575.png to test/safety_bicycle_s_001575.png\n",
      "Saved: safety_bike_s_001690.png to test/safety_bike_s_001690.png\n",
      "Saved: cycle_s_001044.png to test/cycle_s_001044.png\n",
      "Saved: motorcycle_s_000063.png to test/motorcycle_s_000063.png\n",
      "Saved: velocipede_s_001379.png to test/velocipede_s_001379.png\n",
      "Saved: minibike_s_001441.png to test/minibike_s_001441.png\n",
      "Saved: motorcycle_s_000797.png to test/motorcycle_s_000797.png\n",
      "Saved: velocipede_s_001790.png to test/velocipede_s_001790.png\n",
      "Saved: motorbike_s_000135.png to test/motorbike_s_000135.png\n",
      "Saved: cycle_s_002613.png to test/cycle_s_002613.png\n",
      "Saved: motorcycle_s_001960.png to test/motorcycle_s_001960.png\n",
      "Saved: minibike_s_000913.png to test/minibike_s_000913.png\n",
      "Saved: motorcycle_s_000685.png to test/motorcycle_s_000685.png\n",
      "Saved: ordinary_bicycle_s_000431.png to test/ordinary_bicycle_s_000431.png\n",
      "Saved: minibike_s_002051.png to test/minibike_s_002051.png\n",
      "Saved: motorcycle_s_000446.png to test/motorcycle_s_000446.png\n",
      "Saved: velocipede_s_001699.png to test/velocipede_s_001699.png\n",
      "Saved: minibike_s_000792.png to test/minibike_s_000792.png\n",
      "Saved: bicycle_s_002458.png to test/bicycle_s_002458.png\n",
      "Saved: minibike_s_000505.png to test/minibike_s_000505.png\n",
      "Saved: minibike_s_000573.png to test/minibike_s_000573.png\n",
      "Saved: velocipede_s_000369.png to test/velocipede_s_000369.png\n",
      "Saved: moped_s_000064.png to test/moped_s_000064.png\n",
      "Saved: minibike_s_001893.png to test/minibike_s_001893.png\n",
      "Saved: motorcycle_s_000878.png to test/motorcycle_s_000878.png\n",
      "Saved: motorcycle_s_000042.png to test/motorcycle_s_000042.png\n",
      "Saved: motorcycle_s_000606.png to test/motorcycle_s_000606.png\n",
      "Saved: minibike_s_002230.png to test/minibike_s_002230.png\n",
      "Saved: motorcycle_s_001519.png to test/motorcycle_s_001519.png\n",
      "Saved: motorcycle_s_000512.png to test/motorcycle_s_000512.png\n",
      "Saved: cycle_s_001915.png to test/cycle_s_001915.png\n",
      "Saved: safety_bike_s_000541.png to test/safety_bike_s_000541.png\n",
      "Saved: safety_bike_s_000192.png to test/safety_bike_s_000192.png\n",
      "Saved: bicycle_s_000030.png to test/bicycle_s_000030.png\n",
      "Saved: motorcycle_s_001385.png to test/motorcycle_s_001385.png\n",
      "Saved: minibike_s_000960.png to test/minibike_s_000960.png\n",
      "Saved: safety_bike_s_000791.png to test/safety_bike_s_000791.png\n",
      "Saved: bike_s_000071.png to test/bike_s_000071.png\n",
      "Saved: motorcycle_s_000876.png to test/motorcycle_s_000876.png\n",
      "Saved: ordinary_bicycle_s_000350.png to test/ordinary_bicycle_s_000350.png\n",
      "Saved: motorcycle_s_000602.png to test/motorcycle_s_000602.png\n",
      "Saved: motorcycle_s_000222.png to test/motorcycle_s_000222.png\n",
      "Saved: minibike_s_000309.png to test/minibike_s_000309.png\n",
      "Saved: motorcycle_s_001679.png to test/motorcycle_s_001679.png\n",
      "Saved: minibike_s_001473.png to test/minibike_s_001473.png\n",
      "Saved: velocipede_s_001633.png to test/velocipede_s_001633.png\n",
      "Saved: safety_bike_s_000438.png to test/safety_bike_s_000438.png\n",
      "Saved: safety_bicycle_s_000315.png to test/safety_bicycle_s_000315.png\n",
      "Saved: motorcycle_s_002112.png to test/motorcycle_s_002112.png\n",
      "Saved: bike_s_000163.png to test/bike_s_000163.png\n",
      "Saved: bicycle_s_000059.png to test/bicycle_s_000059.png\n",
      "Saved: bicycle_s_001789.png to test/bicycle_s_001789.png\n",
      "Saved: safety_bike_s_000699.png to test/safety_bike_s_000699.png\n",
      "Saved: motorbike_s_000324.png to test/motorbike_s_000324.png\n",
      "Saved: motorcycle_s_000485.png to test/motorcycle_s_000485.png\n",
      "Saved: motorcycle_s_001687.png to test/motorcycle_s_001687.png\n",
      "Saved: minibike_s_000497.png to test/minibike_s_000497.png\n",
      "Saved: minibike_s_001547.png to test/minibike_s_001547.png\n",
      "Saved: minibike_s_000055.png to test/minibike_s_000055.png\n",
      "Saved: ordinary_bicycle_s_000169.png to test/ordinary_bicycle_s_000169.png\n",
      "Saved: minibike_s_000290.png to test/minibike_s_000290.png\n",
      "Saved: velocipede_s_001335.png to test/velocipede_s_001335.png\n",
      "Saved: bicycle_s_001804.png to test/bicycle_s_001804.png\n",
      "Saved: safety_bike_s_000488.png to test/safety_bike_s_000488.png\n",
      "Saved: bike_s_001216.png to test/bike_s_001216.png\n",
      "Saved: motorbike_s_000121.png to test/motorbike_s_000121.png\n",
      "Saved: bike_s_001073.png to test/bike_s_001073.png\n",
      "Saved: safety_bicycle_s_001153.png to test/safety_bicycle_s_001153.png\n",
      "Saved: motorcycle_s_000171.png to test/motorcycle_s_000171.png\n",
      "Saved: velocipede_s_000041.png to test/velocipede_s_000041.png\n",
      "Saved: motorcycle_s_001782.png to test/motorcycle_s_001782.png\n",
      "Saved: motorbike_s_000629.png to test/motorbike_s_000629.png\n",
      "Saved: bicycle_s_000977.png to test/bicycle_s_000977.png\n",
      "Saved: motorcycle_s_001906.png to test/motorcycle_s_001906.png\n",
      "Saved: bike_s_002208.png to test/bike_s_002208.png\n",
      "Saved: minibike_s_000254.png to test/minibike_s_000254.png\n",
      "Saved: safety_bicycle_s_001322.png to test/safety_bicycle_s_001322.png\n",
      "Saved: bike_s_000041.png to test/bike_s_000041.png\n",
      "Saved: bicycle_s_000031.png to test/bicycle_s_000031.png\n",
      "Saved: bicycle_s_000779.png to test/bicycle_s_000779.png\n",
      "Saved: velocipede_s_001277.png to test/velocipede_s_001277.png\n",
      "Saved: safety_bike_s_001088.png to test/safety_bike_s_001088.png\n",
      "Saved: motorcycle_s_001971.png to test/motorcycle_s_001971.png\n",
      "Saved: bike_s_001784.png to test/bike_s_001784.png\n",
      "Saved: motorcycle_s_000825.png to test/motorcycle_s_000825.png\n",
      "Saved: dirt_bike_s_000001.png to test/dirt_bike_s_000001.png\n",
      "Saved: cycle_s_001214.png to test/cycle_s_001214.png\n",
      "Saved: cycle_s_002964.png to test/cycle_s_002964.png\n",
      "Saved: ordinary_bicycle_s_000284.png to test/ordinary_bicycle_s_000284.png\n",
      "Saved: bicycle_s_002735.png to test/bicycle_s_002735.png\n",
      "Saved: motorcycle_s_000450.png to test/motorcycle_s_000450.png\n",
      "Saved: motorcycle_s_000049.png to test/motorcycle_s_000049.png\n",
      "Saved: motorcycle_s_000866.png to test/motorcycle_s_000866.png\n",
      "Saved: velocipede_s_001201.png to test/velocipede_s_001201.png\n",
      "Saved: bike_s_000457.png to test/bike_s_000457.png\n",
      "Saved: bike_s_000330.png to test/bike_s_000330.png\n",
      "Saved: minibike_s_001605.png to test/minibike_s_001605.png\n",
      "Saved: minibike_s_000398.png to test/minibike_s_000398.png\n",
      "Saved: motorcycle_s_000007.png to test/motorcycle_s_000007.png\n",
      "Saved: motorcycle_s_000040.png to test/motorcycle_s_000040.png\n",
      "Saved: motorcycle_s_000739.png to test/motorcycle_s_000739.png\n",
      "Saved: motorbike_s_000333.png to test/motorbike_s_000333.png\n",
      "Saved: safety_bike_s_001481.png to test/safety_bike_s_001481.png\n",
      "Saved: ordinary_bicycle_s_000158.png to test/ordinary_bicycle_s_000158.png\n",
      "Saved: motorcycle_s_001508.png to test/motorcycle_s_001508.png\n",
      "Saved: motorcycle_s_000060.png to test/motorcycle_s_000060.png\n",
      "Saved: cycle_s_002661.png to test/cycle_s_002661.png\n",
      "Saved: bicycle_s_001047.png to test/bicycle_s_001047.png\n",
      "Saved: bicycle_s_000777.png to test/bicycle_s_000777.png\n",
      "Saved: safety_bicycle_s_000235.png to test/safety_bicycle_s_000235.png\n",
      "Saved: bike_s_000487.png to test/bike_s_000487.png\n",
      "Saved: ordinary_bicycle_s_000113.png to test/ordinary_bicycle_s_000113.png\n",
      "Saved: trail_bike_s_000073.png to test/trail_bike_s_000073.png\n",
      "Saved: bike_s_000941.png to test/bike_s_000941.png\n",
      "Saved: motorcycle_s_000141.png to test/motorcycle_s_000141.png\n",
      "Saved: ordinary_bicycle_s_000105.png to test/ordinary_bicycle_s_000105.png\n",
      "Saved: bicycle_s_001218.png to test/bicycle_s_001218.png\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "def save_images(imgs_details_df, imgs_dataset, target_folderpath):\n",
    "    '''\n",
    "    This function takes the following inputs and saves all images to the 'target_folder' in the local directory\n",
    "    inputs:\n",
    "    imgs_details_df: DataFrame containing details of images like filenames, finelabels (specific image labels) \n",
    "                     and row number details \n",
    "    imgs_dataset   : Original CIFAR-100 images dataset containing image data in the row major form that further \n",
    "                     needs to be transformed into 3 channeled image array\n",
    "    target_folder  : Output folder where the transformed images are saved\n",
    "    '''\n",
    "    \n",
    "    # Saving dataset 'row' numbers of corresponding images from 'imgs_details_df' dataframe\n",
    "    row_number_imgs_data = imgs_details_df['row'] \n",
    "    \n",
    "    # Corresponding image filenames in the dataset\n",
    "    imgs_file_names = imgs_details_df['filenames']\n",
    "    \n",
    "    for row, img_name in zip(row_number_imgs_data, imgs_file_names):\n",
    "        \n",
    "        #Grab the image data in row-major form\n",
    "        img = imgs_dataset[b'data'][row]\n",
    "\n",
    "        # Consolidated stacking/reshaping from earlier (For all 3-R,G,B channels)\n",
    "        target = np.dstack((\n",
    "                        img[0:1024].reshape(32,32,1),\n",
    "                        img[1024:2048].reshape(32,32,1),\n",
    "                        img[2048:].reshape(32,32,1)\n",
    "                         ))\n",
    "    \n",
    "        # Save the image to target folder (local directory)\n",
    "        try:\n",
    "            save_img_filepath = os.path.join(target_folderpath, img_name)\n",
    "            plt.imsave(save_img_filepath, target)\n",
    "            \n",
    "            # For printing the saved image filepath in correct format\n",
    "            raw_save_img_filepath = r\"{}\".format(save_img_filepath)\n",
    "            nraw_save_img_filepath = os.path.normpath(raw_save_img_filepath)\n",
    "            print(f\"Saved: {img_name} to {nraw_save_img_filepath}\")\n",
    "            \n",
    "        # Return any signal data you want for debugging\n",
    "        except RuntimeError as e:\n",
    "            return f\"Error: {e} \\nAn error encountered while saving {img_name} file to the target folder located at {target_folderpath}\"\n",
    "\n",
    "## TODO: save ALL images using the save_images function\n",
    "\n",
    "# Save all transformed train dataset images in \"./train\"\n",
    "save_images(imgs_details_df= df_train,\n",
    "            imgs_dataset= dataset_train, \n",
    "            target_folderpath= \"./train\")\n",
    "\n",
    "# Save all transformed test dataset images in \"./test\"\n",
    "save_images(imgs_details_df= df_test,\n",
    "            imgs_dataset= dataset_test, \n",
    "            target_folderpath= \"./test\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Load the data\n",
    "\n",
    "Now we can load the data into S3.\n",
    "\n",
    "Using the sagemaker SDK grab the current region, execution role, and bucket."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Default Bucket : sagemaker-us-east-1-820297565403\n",
      "AWS Region     : us-east-1\n",
      "RoleArn        : arn:aws:iam::820297565403:role/service-role/AmazonSageMaker-ExecutionRole-20231010T025997\n"
     ]
    }
   ],
   "source": [
    "import sagemaker\n",
    "\n",
    "sess = sagemaker.Session()\n",
    "\n",
    "bucket= sess.default_bucket() ## TODO: fill in\n",
    "print(\"Default Bucket : {}\".format(bucket))\n",
    "\n",
    "region = sess.boto_region_name ## TODO: fill in\n",
    "print(\"AWS Region     : {}\".format(region))\n",
    "\n",
    "role = sagemaker.get_execution_role() ## TODO: fill in\n",
    "print(\"RoleArn        : {}\".format(role))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this data we can easily sync your data up into S3!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "upload: train/bicycle_s_000017.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000017.png\n",
      "upload: train/bicycle_s_000066.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000066.png\n",
      "upload: train/bicycle_s_000021.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000021.png\n",
      "upload: train/bicycle_s_000035.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000035.png\n",
      "upload: train/bicycle_s_000071.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000071.png\n",
      "upload: train/bicycle_s_000038.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000038.png\n",
      "upload: train/bicycle_s_000180.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000180.png\n",
      "upload: train/bicycle_s_000039.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000039.png\n",
      "upload: train/bicycle_s_000099.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000099.png\n",
      "upload: train/bicycle_s_000137.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000137.png\n",
      "upload: train/bicycle_s_000156.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000156.png\n",
      "upload: train/bicycle_s_000159.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000159.png\n",
      "upload: train/bicycle_s_000149.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000149.png\n",
      "upload: train/bicycle_s_000392.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000392.png\n",
      "upload: train/bicycle_s_000043.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000043.png\n",
      "upload: train/bicycle_s_000051.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000051.png\n",
      "upload: train/bicycle_s_000231.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000231.png\n",
      "upload: train/bicycle_s_000243.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000243.png\n",
      "upload: train/bicycle_s_000314.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000314.png\n",
      "upload: train/bicycle_s_000124.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000124.png\n",
      "upload: train/bicycle_s_000147.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000147.png\n",
      "upload: train/bicycle_s_000282.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000282.png\n",
      "upload: train/bicycle_s_000399.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000399.png\n",
      "upload: train/bicycle_s_000435.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000435.png\n",
      "upload: train/bicycle_s_000408.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000408.png\n",
      "upload: train/bicycle_s_000437.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000437.png\n",
      "upload: train/bicycle_s_000463.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000463.png\n",
      "upload: train/bicycle_s_000235.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000235.png\n",
      "upload: train/bicycle_s_000396.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000396.png\n",
      "upload: train/bicycle_s_000369.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000369.png\n",
      "upload: train/bicycle_s_000371.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000371.png\n",
      "upload: train/bicycle_s_000279.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000279.png\n",
      "upload: train/bicycle_s_000537.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000537.png\n",
      "upload: train/bicycle_s_000467.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000467.png\n",
      "upload: train/bicycle_s_000569.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000569.png\n",
      "upload: train/bicycle_s_000522.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000522.png\n",
      "upload: train/bicycle_s_000546.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000546.png\n",
      "upload: train/bicycle_s_000536.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000536.png\n",
      "upload: train/bicycle_s_000491.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000491.png\n",
      "upload: train/bicycle_s_000667.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000667.png\n",
      "upload: train/bicycle_s_000561.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000561.png\n",
      "upload: train/bicycle_s_000753.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000753.png\n",
      "upload: train/bicycle_s_000774.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000774.png\n",
      "upload: train/bicycle_s_000723.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000723.png\n",
      "upload: train/bicycle_s_000759.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000759.png\n",
      "upload: train/bicycle_s_000782.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000782.png\n",
      "upload: train/bicycle_s_000781.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000781.png\n",
      "upload: train/bicycle_s_000775.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000775.png\n",
      "upload: train/bicycle_s_000778.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000778.png\n",
      "upload: train/bicycle_s_000668.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000668.png\n",
      "upload: train/bicycle_s_000822.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000822.png\n",
      "upload: train/bicycle_s_000785.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000785.png\n",
      "upload: train/bicycle_s_000829.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000829.png\n",
      "upload: train/bicycle_s_000861.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000861.png\n",
      "upload: train/bicycle_s_000978.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000978.png\n",
      "upload: train/bicycle_s_000996.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000996.png\n",
      "upload: train/bicycle_s_000986.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000986.png\n",
      "upload: train/bicycle_s_000951.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_000951.png\n",
      "upload: train/bicycle_s_001168.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_001168.png\n",
      "upload: train/bicycle_s_001111.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_001111.png\n",
      "upload: train/bicycle_s_001002.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_001002.png\n",
      "upload: train/bicycle_s_001174.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_001174.png\n",
      "upload: train/bicycle_s_001228.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_001228.png\n",
      "upload: train/bicycle_s_001245.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_001245.png\n",
      "upload: train/bicycle_s_001338.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_001338.png\n",
      "upload: train/bicycle_s_001388.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_001388.png\n",
      "upload: train/bicycle_s_001348.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_001348.png\n",
      "upload: train/bicycle_s_001402.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_001402.png\n",
      "upload: train/bicycle_s_001247.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_001247.png\n",
      "upload: train/bicycle_s_001205.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_001205.png\n",
      "upload: train/bicycle_s_001203.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_001203.png\n",
      "upload: train/bicycle_s_001411.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_001411.png\n",
      "upload: train/bicycle_s_001470.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_001470.png\n",
      "upload: train/bicycle_s_001448.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_001448.png\n",
      "upload: train/bicycle_s_001569.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_001569.png\n",
      "upload: train/bicycle_s_001642.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_001642.png\n",
      "upload: train/bicycle_s_001554.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_001554.png\n",
      "upload: train/bicycle_s_001663.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_001663.png\n",
      "upload: train/bicycle_s_001452.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_001452.png\n",
      "upload: train/bicycle_s_001583.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_001583.png\n",
      "upload: train/bicycle_s_001409.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_001409.png\n",
      "upload: train/bicycle_s_001687.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_001687.png\n",
      "upload: train/bicycle_s_001679.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_001679.png\n",
      "upload: train/bicycle_s_001673.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_001673.png\n",
      "upload: train/bicycle_s_001688.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_001688.png\n",
      "upload: train/bicycle_s_001757.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_001757.png\n",
      "upload: train/bicycle_s_001681.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_001681.png\n",
      "upload: train/bicycle_s_001693.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_001693.png\n",
      "upload: train/bicycle_s_001814.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_001814.png\n",
      "upload: train/bicycle_s_001748.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_001748.png\n",
      "upload: train/bicycle_s_001904.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_001904.png\n",
      "upload: train/bicycle_s_001928.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_001928.png\n",
      "upload: train/bicycle_s_002012.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_002012.png\n",
      "upload: train/bicycle_s_001956.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_001956.png\n",
      "upload: train/bicycle_s_002029.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_002029.png\n",
      "upload: train/bicycle_s_002005.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_002005.png\n",
      "upload: train/bicycle_s_002129.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_002129.png\n",
      "upload: train/bicycle_s_002132.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_002132.png\n",
      "upload: train/bicycle_s_002222.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_002222.png\n",
      "upload: train/bicycle_s_002153.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_002153.png\n",
      "upload: train/bicycle_s_002100.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_002100.png\n",
      "upload: train/bicycle_s_002237.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_002237.png\n",
      "upload: train/bicycle_s_002049.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_002049.png\n",
      "upload: train/bicycle_s_002338.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_002338.png\n",
      "upload: train/bicycle_s_002374.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_002374.png\n",
      "upload: train/bicycle_s_002400.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_002400.png\n",
      "upload: train/bicycle_s_002436.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_002436.png\n",
      "upload: train/bicycle_s_002448.png to s3://sagemaker-us-east-1-820297565403/train/bicycle_s_002448.png\n",
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      "upload: train/motorcycle_s_000319.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_000319.png\n",
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      "upload: train/motorcycle_s_000320.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_000320.png\n",
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      "upload: train/motorcycle_s_000346.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_000346.png\n",
      "upload: train/motorcycle_s_000407.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_000407.png\n",
      "upload: train/motorcycle_s_000340.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_000340.png\n",
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      "upload: train/motorcycle_s_000430.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_000430.png\n",
      "upload: train/motorcycle_s_000422.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_000422.png\n",
      "upload: train/motorcycle_s_000417.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_000417.png\n",
      "upload: train/motorcycle_s_000418.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_000418.png\n",
      "upload: train/motorcycle_s_000423.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_000423.png\n",
      "upload: train/motorcycle_s_000431.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_000431.png\n",
      "upload: train/motorcycle_s_000432.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_000432.png\n",
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      "upload: train/motorcycle_s_000459.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_000459.png\n",
      "upload: train/motorcycle_s_000440.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_000440.png\n",
      "upload: train/motorcycle_s_000508.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_000508.png\n",
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      "upload: train/motorcycle_s_000496.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_000496.png\n",
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      "upload: train/motorcycle_s_000451.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_000451.png\n",
      "upload: train/motorcycle_s_000517.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_000517.png\n",
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      "upload: train/motorcycle_s_000542.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_000542.png\n",
      "upload: train/motorcycle_s_000580.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_000580.png\n",
      "upload: train/motorcycle_s_000585.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_000585.png\n",
      "upload: train/motorcycle_s_000617.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_000617.png\n",
      "upload: train/motorcycle_s_000622.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_000622.png\n",
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      "upload: train/motorcycle_s_000734.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_000734.png\n",
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      "upload: train/motorcycle_s_000919.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_000919.png\n",
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      "upload: train/motorcycle_s_001027.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_001027.png\n",
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      "upload: train/motorcycle_s_001409.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_001409.png\n",
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      "upload: train/motorcycle_s_001407.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_001407.png\n",
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      "upload: train/motorcycle_s_001400.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_001400.png\n",
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      "upload: train/motorcycle_s_001439.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_001439.png\n",
      "upload: train/motorcycle_s_001507.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_001507.png\n",
      "upload: train/motorcycle_s_001520.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_001520.png\n",
      "upload: train/motorcycle_s_001536.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_001536.png\n",
      "upload: train/motorcycle_s_001549.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_001549.png\n",
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      "upload: train/motorcycle_s_001584.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_001584.png\n",
      "upload: train/motorcycle_s_001565.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_001565.png\n",
      "upload: train/motorcycle_s_001580.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_001580.png\n",
      "upload: train/motorcycle_s_001587.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_001587.png\n",
      "upload: train/motorcycle_s_001563.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_001563.png\n",
      "upload: train/motorcycle_s_001611.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_001611.png\n",
      "upload: train/motorcycle_s_001610.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_001610.png\n",
      "upload: train/motorcycle_s_001623.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_001623.png\n",
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      "upload: train/motorcycle_s_001686.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_001686.png\n",
      "upload: train/motorcycle_s_001585.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_001585.png\n",
      "upload: train/motorcycle_s_001622.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_001622.png\n",
      "upload: train/motorcycle_s_001696.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_001696.png\n",
      "upload: train/motorcycle_s_001707.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_001707.png\n",
      "upload: train/motorcycle_s_001695.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_001695.png\n",
      "upload: train/motorcycle_s_001711.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_001711.png\n",
      "upload: train/motorcycle_s_001693.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_001693.png\n",
      "upload: train/motorcycle_s_001715.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_001715.png\n",
      "upload: train/motorcycle_s_001690.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_001690.png\n",
      "upload: train/motorcycle_s_001706.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_001706.png\n",
      "upload: train/motorcycle_s_001699.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_001699.png\n",
      "upload: train/motorcycle_s_001714.png to s3://sagemaker-us-east-1-820297565403/train/motorcycle_s_001714.png\n",
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      "upload: test/safety_bike_s_001087.png to s3://sagemaker-us-east-1-820297565403/test/safety_bike_s_001087.png\n",
      "upload: test/safety_bike_s_001132.png to s3://sagemaker-us-east-1-820297565403/test/safety_bike_s_001132.png\n",
      "upload: test/safety_bike_s_000438.png to s3://sagemaker-us-east-1-820297565403/test/safety_bike_s_000438.png\n",
      "upload: test/safety_bike_s_000192.png to s3://sagemaker-us-east-1-820297565403/test/safety_bike_s_000192.png\n",
      "upload: test/safety_bike_s_001088.png to s3://sagemaker-us-east-1-820297565403/test/safety_bike_s_001088.png\n",
      "upload: test/safety_bike_s_001253.png to s3://sagemaker-us-east-1-820297565403/test/safety_bike_s_001253.png\n",
      "upload: test/safety_bike_s_001690.png to s3://sagemaker-us-east-1-820297565403/test/safety_bike_s_001690.png\n",
      "upload: test/velocipede_s_000369.png to s3://sagemaker-us-east-1-820297565403/test/velocipede_s_000369.png\n",
      "upload: test/safety_bike_s_001481.png to s3://sagemaker-us-east-1-820297565403/test/safety_bike_s_001481.png\n",
      "upload: test/velocipede_s_000041.png to s3://sagemaker-us-east-1-820297565403/test/velocipede_s_000041.png\n",
      "upload: test/trail_bike_s_000073.png to s3://sagemaker-us-east-1-820297565403/test/trail_bike_s_000073.png\n",
      "upload: test/velocipede_s_000863.png to s3://sagemaker-us-east-1-820297565403/test/velocipede_s_000863.png\n",
      "upload: test/velocipede_s_000106.png to s3://sagemaker-us-east-1-820297565403/test/velocipede_s_000106.png\n",
      "upload: test/velocipede_s_000001.png to s3://sagemaker-us-east-1-820297565403/test/velocipede_s_000001.png\n",
      "upload: test/velocipede_s_000292.png to s3://sagemaker-us-east-1-820297565403/test/velocipede_s_000292.png\n",
      "upload: test/velocipede_s_001277.png to s3://sagemaker-us-east-1-820297565403/test/velocipede_s_001277.png\n",
      "upload: test/velocipede_s_001232.png to s3://sagemaker-us-east-1-820297565403/test/velocipede_s_001232.png\n",
      "upload: test/velocipede_s_001466.png to s3://sagemaker-us-east-1-820297565403/test/velocipede_s_001466.png\n",
      "upload: test/velocipede_s_001335.png to s3://sagemaker-us-east-1-820297565403/test/velocipede_s_001335.png\n",
      "upload: test/velocipede_s_001699.png to s3://sagemaker-us-east-1-820297565403/test/velocipede_s_001699.png\n",
      "upload: test/velocipede_s_001355.png to s3://sagemaker-us-east-1-820297565403/test/velocipede_s_001355.png\n",
      "upload: test/velocipede_s_001633.png to s3://sagemaker-us-east-1-820297565403/test/velocipede_s_001633.png\n",
      "upload: test/velocipede_s_001744.png to s3://sagemaker-us-east-1-820297565403/test/velocipede_s_001744.png\n",
      "upload: test/velocipede_s_001201.png to s3://sagemaker-us-east-1-820297565403/test/velocipede_s_001201.png\n",
      "upload: test/velocipede_s_001379.png to s3://sagemaker-us-east-1-820297565403/test/velocipede_s_001379.png\n",
      "upload: test/velocipede_s_001790.png to s3://sagemaker-us-east-1-820297565403/test/velocipede_s_001790.png\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "os.environ[\"DEFAULT_S3_BUCKET\"] = bucket\n",
    "!aws s3 sync ./train s3://${DEFAULT_S3_BUCKET}/train/\n",
    "!aws s3 sync ./test s3://${DEFAULT_S3_BUCKET}/test/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And that's it! You can check the bucket and verify that the items were uploaded.\n",
    "\n",
    "## Model Training\n",
    "\n",
    "For Image Classification, Sagemaker [also expects metadata](https://docs.aws.amazon.com/sagemaker/latest/dg/image-classification.html) e.g. in the form of TSV files with labels and filepaths. We can generate these using our Pandas DataFrames from earlier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def to_metadata_file(df, prefix):\n",
    "    df[\"s3_path\"] = df[\"filenames\"]\n",
    "    df[\"labels\"] = df[\"labels\"].apply(lambda x: 0 if x==8 else 1)\n",
    "    return df[[\"row\", \"labels\", \"s3_path\"]].to_csv(\n",
    "        f\"{prefix}.lst\", sep=\"\\t\", index=False, header=False\n",
    "    )\n",
    "    \n",
    "to_metadata_file(df_train.copy(), \"train\")\n",
    "to_metadata_file(df_test.copy(), \"test\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also upload our manifest files:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import boto3\n",
    "\n",
    "# Upload files\n",
    "boto3.Session().resource('s3').Bucket(\n",
    "    bucket).Object('train.lst').upload_file('./train.lst')\n",
    "boto3.Session().resource('s3').Bucket(\n",
    "    bucket).Object('test.lst').upload_file('./test.lst')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `bucket` and `region` info we can get the latest prebuilt container to run our training job, and define an output location on our s3 bucket for the model. Use the `image_uris` function from the SageMaker SDK to retrieve the latest `image-classification` image below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:sagemaker.image_uris:Same images used for training and inference. Defaulting to image scope: inference.\n",
      "WARNING:sagemaker.image_uris:Defaulting to the only supported framework/algorithm version: 1. Ignoring framework/algorithm version: 1.\n",
      "INFO:sagemaker.image_uris:Ignoring unnecessary instance type: None.\n"
     ]
    }
   ],
   "source": [
    "# Use the image_uris function to retrieve the latest 'image-classification' image \n",
    "from sagemaker import get_execution_role\n",
    "from sagemaker.model import Model\n",
    "from sagemaker import image_uris\n",
    "\n",
    "\n",
    "algo_image = image_uris.retrieve(\n",
    "    framework='image-classification',\n",
    "    region = region,\n",
    "    version = 1\n",
    ")\n",
    "s3_output_location = f\"s3://{bucket}/models/image_model\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're ready to create an estimator! Create an estimator `img_classifier_model` that uses one instance of `ml.p3.2xlarge`. Ensure that y ou use the output location we defined above - we'll be referring to that later!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "img_classifier_model_default=sagemaker.estimator.Estimator(\n",
    "    image_uri=algo_image,\n",
    "    role = role,\n",
    "    instance_count = 1,\n",
    "    instance_type='ml.p3.2xlarge',\n",
    "    output_location = s3_output_location,\n",
    "    sagemaker_session = sess\n",
    "    ## TODO: define your estimator options    \n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also set a few key hyperparameters and define the inputs for our model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "img_classifier_model_default.set_hyperparameters(\n",
    "    image_shape='3,32,32',\n",
    "    num_classes=2,\n",
    "    num_training_samples=len(df_train)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `image-classification` image uses four input channels with very specific input parameters. For convenience, we've provided them below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sagemaker.debugger import Rule, rule_configs\n",
    "from sagemaker.session import TrainingInput\n",
    "model_inputs = {\n",
    "        \"train\": sagemaker.inputs.TrainingInput(\n",
    "            s3_data=f\"s3://{bucket}/train/\",\n",
    "            content_type=\"application/x-image\"\n",
    "        ),\n",
    "        \"validation\": sagemaker.inputs.TrainingInput(\n",
    "            s3_data=f\"s3://{bucket}/test/\",\n",
    "            content_type=\"application/x-image\"\n",
    "        ),\n",
    "        \"train_lst\": sagemaker.inputs.TrainingInput(\n",
    "            s3_data=f\"s3://{bucket}/train.lst\",\n",
    "            content_type=\"application/x-image\"\n",
    "        ),\n",
    "        \"validation_lst\": sagemaker.inputs.TrainingInput(\n",
    "            s3_data=f\"s3://{bucket}/test.lst\",\n",
    "            content_type=\"application/x-image\"\n",
    "        )\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great, now we can train the model using the model_inputs. In the cell below, call the `fit` method on our model,:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:sagemaker:Creating training-job with name: image-classification-2023-10-10-06-19-26-694\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-10-10 06:19:26 Starting - Starting the training job...\n",
      "2023-10-10 06:19:52 Starting - Preparing the instances for training.........\n",
      "2023-10-10 06:21:12 Downloading - Downloading input data......\n",
      "2023-10-10 06:22:02 Training - Downloading the training image............\n",
      "2023-10-10 06:24:08 Training - Training image download completed. Training in progress...\u001b[34mDocker entrypoint called with argument(s): train\u001b[0m\n",
      "\u001b[34mRunning default environment configuration script\u001b[0m\n",
      "\u001b[34mNvidia gpu devices, drivers and cuda toolkit versions (only available on hosts with GPU):\u001b[0m\n",
      "\u001b[34mTue Oct 10 06:24:32 2023       \u001b[0m\n",
      "\u001b[34m+---------------------------------------------------------------------------------------+\u001b[0m\n",
      "\u001b[34m| NVIDIA-SMI 535.104.12             Driver Version: 535.104.12   CUDA Version: 12.2     |\u001b[0m\n",
      "\u001b[34m|-----------------------------------------+----------------------+----------------------+\u001b[0m\n",
      "\u001b[34m| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\u001b[0m\n",
      "\u001b[34m| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\u001b[0m\n",
      "\u001b[34m|                                         |                      |               MIG M. |\u001b[0m\n",
      "\u001b[34m|=========================================+======================+======================|\u001b[0m\n",
      "\u001b[34m|   0  Tesla V100-SXM2-16GB           On  | 00000000:00:1E.0 Off |                    0 |\u001b[0m\n",
      "\u001b[34m| N/A   36C    P0              25W / 300W |      0MiB / 16384MiB |      0%      Default |\u001b[0m\n",
      "\u001b[34m|                                         |                      |                  N/A |\u001b[0m\n",
      "\u001b[34m+-----------------------------------------+----------------------+----------------------+\n",
      "                                                                                         \u001b[0m\n",
      "\u001b[34m+---------------------------------------------------------------------------------------+\u001b[0m\n",
      "\u001b[34m| Processes:                                                                            |\u001b[0m\n",
      "\u001b[34m|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\u001b[0m\n",
      "\u001b[34m|        ID   ID                                                             Usage      |\u001b[0m\n",
      "\u001b[34m|=======================================================================================|\u001b[0m\n",
      "\u001b[34m|  No running processes found                                                           |\u001b[0m\n",
      "\u001b[34m+---------------------------------------------------------------------------------------+\u001b[0m\n",
      "\u001b[34mChecking for nvidia driver and cuda compatibility.\u001b[0m\n",
      "\u001b[34mCUDA Compatibility driver provided.\u001b[0m\n",
      "\u001b[34mProceeding with compatibility check between driver, cuda-toolkit and cuda-compat.\u001b[0m\n",
      "\u001b[34mDetected cuda-toolkit version: 11.1.\u001b[0m\n",
      "\u001b[34mDetected cuda-compat version: 455.32.00.\u001b[0m\n",
      "\u001b[34mDetected Nvidia driver version: 535.104.12.\u001b[0m\n",
      "\u001b[34mNvidia driver compatible with cuda-toolkit. Disabling cuda-compat.\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:24:36 INFO 139811818182464] Reading default configuration from /opt/amazon/lib/python3.7/site-packages/image_classification/default-input.json: {'use_pretrained_model': 0, 'num_layers': 152, 'epochs': 30, 'learning_rate': 0.1, 'lr_scheduler_factor': 0.1, 'optimizer': 'sgd', 'momentum': 0, 'weight_decay': 0.0001, 'beta_1': 0.9, 'beta_2': 0.999, 'eps': 1e-08, 'gamma': 0.9, 'mini_batch_size': 32, 'image_shape': '3,224,224', 'precision_dtype': 'float32'}\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:24:36 INFO 139811818182464] Merging with provided configuration from /opt/ml/input/config/hyperparameters.json: {'image_shape': '3,32,32', 'num_classes': '2', 'num_training_samples': '1000'}\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:24:36 INFO 139811818182464] Final configuration: {'use_pretrained_model': 0, 'num_layers': 152, 'epochs': 30, 'learning_rate': 0.1, 'lr_scheduler_factor': 0.1, 'optimizer': 'sgd', 'momentum': 0, 'weight_decay': 0.0001, 'beta_1': 0.9, 'beta_2': 0.999, 'eps': 1e-08, 'gamma': 0.9, 'mini_batch_size': 32, 'image_shape': '3,32,32', 'precision_dtype': 'float32', 'num_classes': '2', 'num_training_samples': '1000'}\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:24:36 INFO 139811818182464] Searching for .lst files in /opt/ml/input/data/train_lst.\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:24:36 INFO 139811818182464] Creating record files for train.lst\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:24:36 INFO 139811818182464] Done creating record files...\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:24:36 INFO 139811818182464] Searching for .lst files in /opt/ml/input/data/validation_lst.\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:24:36 INFO 139811818182464] Creating record files for test.lst\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:24:36 INFO 139811818182464] Done creating record files...\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:24:36 INFO 139811818182464] use_pretrained_model: 0\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:24:36 INFO 139811818182464] multi_label: 0\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:24:36 INFO 139811818182464] Performing random weight initialization\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:24:36 INFO 139811818182464] ---- Parameters ----\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:24:36 INFO 139811818182464] num_layers: 152\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:24:36 INFO 139811818182464] data type: <class 'numpy.float32'>\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:24:36 INFO 139811818182464] epochs: 30\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:24:36 INFO 139811818182464] optimizer: sgd\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:24:36 INFO 139811818182464] momentum: 0.9\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:24:36 INFO 139811818182464] weight_decay: 0.0001\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:24:36 INFO 139811818182464] learning_rate: 0.1\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:24:36 INFO 139811818182464] num_training_samples: 1000\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:24:36 INFO 139811818182464] mini_batch_size: 32\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:24:36 INFO 139811818182464] image_shape: 3,32,32\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:24:36 INFO 139811818182464] num_classes: 2\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:24:36 INFO 139811818182464] augmentation_type: None\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:24:36 INFO 139811818182464] kv_store: device\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:24:36 INFO 139811818182464] checkpoint_frequency not set, will store the best model\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:24:36 INFO 139811818182464] --------------------\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:24:36 INFO 139811818182464] Setting number of threads: 7\u001b[0m\n",
      "\u001b[34m[06:24:40] /opt/brazil-pkg-cache/packages/AIAlgorithmsMXNet/AIAlgorithmsMXNet-1.4.x_ecl_Cuda_11.1.x.231.0/AL2_x86_64/generic-flavor/src/src/operator/nn/./cudnn/./cudnn_algoreg-inl.h:97: Running performance tests to find the best convolution algorithm, this can take a while... (setting env variable MXNET_CUDNN_AUTOTUNE_DEFAULT to 0 to disable)\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:24:45 INFO 139811818182464] Epoch[0] Batch [20]#011Speed: 121.899 samples/sec#011accuracy=0.595238\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:24:47 INFO 139811818182464] Epoch[0] Train-accuracy=0.635081\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:24:47 INFO 139811818182464] Epoch[0] Time cost=6.895\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:24:47 INFO 139811818182464] Epoch[0] Validation-accuracy=0.505208\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:24:48 INFO 139811818182464] Storing the best model with validation accuracy: 0.505208\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:24:48 INFO 139811818182464] Saved checkpoint to \"/opt/ml/model/image-classification-0001.params\"\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:24:51 INFO 139811818182464] Epoch[1] Batch [20]#011Speed: 190.871 samples/sec#011accuracy=0.732143\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:24:53 INFO 139811818182464] Epoch[1] Train-accuracy=0.690524\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:24:53 INFO 139811818182464] Epoch[1] Time cost=5.091\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:24:53 INFO 139811818182464] Epoch[1] Validation-accuracy=0.593750\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:24:54 INFO 139811818182464] Storing the best model with validation accuracy: 0.593750\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:24:54 INFO 139811818182464] Saved checkpoint to \"/opt/ml/model/image-classification-0002.params\"\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:24:57 INFO 139811818182464] Epoch[2] Batch [20]#011Speed: 191.921 samples/sec#011accuracy=0.733631\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:24:59 INFO 139811818182464] Epoch[2] Train-accuracy=0.735887\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:24:59 INFO 139811818182464] Epoch[2] Time cost=4.969\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:25:00 INFO 139811818182464] Epoch[2] Validation-accuracy=0.786458\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:25:00 INFO 139811818182464] Storing the best model with validation accuracy: 0.786458\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:25:00 INFO 139811818182464] Saved checkpoint to \"/opt/ml/model/image-classification-0003.params\"\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:25:04 INFO 139811818182464] Epoch[3] Batch [20]#011Speed: 184.894 samples/sec#011accuracy=0.744048\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:25:05 INFO 139811818182464] Epoch[3] Train-accuracy=0.761089\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:25:05 INFO 139811818182464] Epoch[3] Time cost=5.107\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:25:06 INFO 139811818182464] Epoch[3] Validation-accuracy=0.808036\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:25:06 INFO 139811818182464] Storing the best model with validation accuracy: 0.808036\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:25:07 INFO 139811818182464] Saved checkpoint to \"/opt/ml/model/image-classification-0004.params\"\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:25:10 INFO 139811818182464] Epoch[4] Batch [20]#011Speed: 188.535 samples/sec#011accuracy=0.790179\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:25:12 INFO 139811818182464] Epoch[4] Train-accuracy=0.781250\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:25:12 INFO 139811818182464] Epoch[4] Time cost=5.018\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:25:12 INFO 139811818182464] Epoch[4] Validation-accuracy=0.828125\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:25:12 INFO 139811818182464] Storing the best model with validation accuracy: 0.828125\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:25:13 INFO 139811818182464] Saved checkpoint to \"/opt/ml/model/image-classification-0005.params\"\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:25:16 INFO 139811818182464] Epoch[5] Batch [20]#011Speed: 191.516 samples/sec#011accuracy=0.809524\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:25:18 INFO 139811818182464] Epoch[5] Train-accuracy=0.797379\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:25:18 INFO 139811818182464] Epoch[5] Time cost=4.972\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:25:18 INFO 139811818182464] Epoch[5] Validation-accuracy=0.817708\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:25:22 INFO 139811818182464] Epoch[6] Batch [20]#011Speed: 192.204 samples/sec#011accuracy=0.845238\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:25:24 INFO 139811818182464] Epoch[6] Train-accuracy=0.838710\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:25:24 INFO 139811818182464] Epoch[6] Time cost=4.974\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:25:24 INFO 139811818182464] Epoch[6] Validation-accuracy=0.770833\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:25:28 INFO 139811818182464] Epoch[7] Batch [20]#011Speed: 192.680 samples/sec#011accuracy=0.839286\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:25:30 INFO 139811818182464] Epoch[7] Train-accuracy=0.834677\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:25:30 INFO 139811818182464] Epoch[7] Time cost=4.950\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:25:30 INFO 139811818182464] Epoch[7] Validation-accuracy=0.714286\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:25:34 INFO 139811818182464] Epoch[8] Batch [20]#011Speed: 189.306 samples/sec#011accuracy=0.827381\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:25:36 INFO 139811818182464] Epoch[8] Train-accuracy=0.819556\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:25:36 INFO 139811818182464] Epoch[8] Time cost=5.033\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:25:36 INFO 139811818182464] Epoch[8] Validation-accuracy=0.796875\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:25:40 INFO 139811818182464] Epoch[9] Batch [20]#011Speed: 192.843 samples/sec#011accuracy=0.860119\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:25:42 INFO 139811818182464] Epoch[9] Train-accuracy=0.853831\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:25:42 INFO 139811818182464] Epoch[9] Time cost=4.945\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:25:42 INFO 139811818182464] Epoch[9] Validation-accuracy=0.812500\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:25:46 INFO 139811818182464] Epoch[10] Batch [20]#011Speed: 190.737 samples/sec#011accuracy=0.885417\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:25:48 INFO 139811818182464] Epoch[10] Train-accuracy=0.873992\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:25:48 INFO 139811818182464] Epoch[10] Time cost=5.013\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:25:48 INFO 139811818182464] Epoch[10] Validation-accuracy=0.796875\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:25:52 INFO 139811818182464] Epoch[11] Batch [20]#011Speed: 191.332 samples/sec#011accuracy=0.901786\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:25:54 INFO 139811818182464] Epoch[11] Train-accuracy=0.890121\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:25:54 INFO 139811818182464] Epoch[11] Time cost=4.968\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:25:54 INFO 139811818182464] Epoch[11] Validation-accuracy=0.799107\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:25:58 INFO 139811818182464] Epoch[12] Batch [20]#011Speed: 191.722 samples/sec#011accuracy=0.904762\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:26:00 INFO 139811818182464] Epoch[12] Train-accuracy=0.900202\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:26:00 INFO 139811818182464] Epoch[12] Time cost=4.976\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:26:00 INFO 139811818182464] Epoch[12] Validation-accuracy=0.807292\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:26:04 INFO 139811818182464] Epoch[13] Batch [20]#011Speed: 185.066 samples/sec#011accuracy=0.903274\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:26:06 INFO 139811818182464] Epoch[13] Train-accuracy=0.909274\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:26:06 INFO 139811818182464] Epoch[13] Time cost=5.104\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:26:06 INFO 139811818182464] Epoch[13] Validation-accuracy=0.838542\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:26:06 INFO 139811818182464] Storing the best model with validation accuracy: 0.838542\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:26:07 INFO 139811818182464] Saved checkpoint to \"/opt/ml/model/image-classification-0014.params\"\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:26:10 INFO 139811818182464] Epoch[14] Batch [20]#011Speed: 191.373 samples/sec#011accuracy=0.933036\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:26:12 INFO 139811818182464] Epoch[14] Train-accuracy=0.914315\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:26:12 INFO 139811818182464] Epoch[14] Time cost=4.976\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:26:12 INFO 139811818182464] Epoch[14] Validation-accuracy=0.822917\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:26:16 INFO 139811818182464] Epoch[15] Batch [20]#011Speed: 192.191 samples/sec#011accuracy=0.922619\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:26:18 INFO 139811818182464] Epoch[15] Train-accuracy=0.919355\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:26:18 INFO 139811818182464] Epoch[15] Time cost=4.975\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:26:18 INFO 139811818182464] Epoch[15] Validation-accuracy=0.839286\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:26:19 INFO 139811818182464] Storing the best model with validation accuracy: 0.839286\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:26:19 INFO 139811818182464] Saved checkpoint to \"/opt/ml/model/image-classification-0016.params\"\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:26:22 INFO 139811818182464] Epoch[16] Batch [20]#011Speed: 191.650 samples/sec#011accuracy=0.947917\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:26:24 INFO 139811818182464] Epoch[16] Train-accuracy=0.944556\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:26:24 INFO 139811818182464] Epoch[16] Time cost=4.993\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:26:24 INFO 139811818182464] Epoch[16] Validation-accuracy=0.796875\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:26:28 INFO 139811818182464] Epoch[17] Batch [20]#011Speed: 190.981 samples/sec#011accuracy=0.962798\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:26:30 INFO 139811818182464] Epoch[17] Train-accuracy=0.961694\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:26:30 INFO 139811818182464] Epoch[17] Time cost=4.992\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:26:30 INFO 139811818182464] Epoch[17] Validation-accuracy=0.786458\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:26:34 INFO 139811818182464] Epoch[18] Batch [20]#011Speed: 191.361 samples/sec#011accuracy=0.891369\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:26:36 INFO 139811818182464] Epoch[18] Train-accuracy=0.904234\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:26:36 INFO 139811818182464] Epoch[18] Time cost=4.974\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:26:36 INFO 139811818182464] Epoch[18] Validation-accuracy=0.802083\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:26:40 INFO 139811818182464] Epoch[19] Batch [20]#011Speed: 191.612 samples/sec#011accuracy=0.970238\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:26:42 INFO 139811818182464] Epoch[19] Train-accuracy=0.962702\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:26:42 INFO 139811818182464] Epoch[19] Time cost=4.965\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:26:42 INFO 139811818182464] Epoch[19] Validation-accuracy=0.758929\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:26:46 INFO 139811818182464] Epoch[20] Batch [20]#011Speed: 191.600 samples/sec#011accuracy=0.976190\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:26:48 INFO 139811818182464] Epoch[20] Train-accuracy=0.975806\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:26:48 INFO 139811818182464] Epoch[20] Time cost=4.976\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:26:48 INFO 139811818182464] Epoch[20] Validation-accuracy=0.838542\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:26:52 INFO 139811818182464] Epoch[21] Batch [20]#011Speed: 191.765 samples/sec#011accuracy=0.967262\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:26:54 INFO 139811818182464] Epoch[21] Train-accuracy=0.973790\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:26:54 INFO 139811818182464] Epoch[21] Time cost=4.978\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:26:54 INFO 139811818182464] Epoch[21] Validation-accuracy=0.812500\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:26:58 INFO 139811818182464] Epoch[22] Batch [20]#011Speed: 191.661 samples/sec#011accuracy=0.986607\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:27:00 INFO 139811818182464] Epoch[22] Train-accuracy=0.976815\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:27:00 INFO 139811818182464] Epoch[22] Time cost=4.973\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:27:00 INFO 139811818182464] Epoch[22] Validation-accuracy=0.796875\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:27:04 INFO 139811818182464] Epoch[23] Batch [20]#011Speed: 187.198 samples/sec#011accuracy=0.970238\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:27:06 INFO 139811818182464] Epoch[23] Train-accuracy=0.970766\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:27:06 INFO 139811818182464] Epoch[23] Time cost=5.072\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:27:06 INFO 139811818182464] Epoch[23] Validation-accuracy=0.794643\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:27:10 INFO 139811818182464] Epoch[24] Batch [20]#011Speed: 191.444 samples/sec#011accuracy=0.977679\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:27:12 INFO 139811818182464] Epoch[24] Train-accuracy=0.971774\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:27:12 INFO 139811818182464] Epoch[24] Time cost=4.967\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:27:12 INFO 139811818182464] Epoch[24] Validation-accuracy=0.854167\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:27:13 INFO 139811818182464] Storing the best model with validation accuracy: 0.854167\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:27:13 INFO 139811818182464] Saved checkpoint to \"/opt/ml/model/image-classification-0025.params\"\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:27:16 INFO 139811818182464] Epoch[25] Batch [20]#011Speed: 191.829 samples/sec#011accuracy=0.953869\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:27:18 INFO 139811818182464] Epoch[25] Train-accuracy=0.958669\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:27:18 INFO 139811818182464] Epoch[25] Time cost=4.972\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:27:18 INFO 139811818182464] Epoch[25] Validation-accuracy=0.817708\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:27:22 INFO 139811818182464] Epoch[26] Batch [20]#011Speed: 190.859 samples/sec#011accuracy=0.964286\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:27:24 INFO 139811818182464] Epoch[26] Train-accuracy=0.968750\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:27:24 INFO 139811818182464] Epoch[26] Time cost=4.989\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:27:24 INFO 139811818182464] Epoch[26] Validation-accuracy=0.828125\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:27:28 INFO 139811818182464] Epoch[27] Batch [20]#011Speed: 193.562 samples/sec#011accuracy=0.977679\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:27:30 INFO 139811818182464] Epoch[27] Train-accuracy=0.980847\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:27:30 INFO 139811818182464] Epoch[27] Time cost=4.942\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:27:30 INFO 139811818182464] Epoch[27] Validation-accuracy=0.816964\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:27:34 INFO 139811818182464] Epoch[28] Batch [20]#011Speed: 190.203 samples/sec#011accuracy=0.998512\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:27:36 INFO 139811818182464] Epoch[28] Train-accuracy=0.997984\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:27:36 INFO 139811818182464] Epoch[28] Time cost=4.987\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:27:36 INFO 139811818182464] Epoch[28] Validation-accuracy=0.822917\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:27:40 INFO 139811818182464] Epoch[29] Batch [20]#011Speed: 191.319 samples/sec#011accuracy=0.998512\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:27:42 INFO 139811818182464] Epoch[29] Train-accuracy=0.998992\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:27:42 INFO 139811818182464] Epoch[29] Time cost=4.994\u001b[0m\n",
      "\u001b[34m[10/10/2023 06:27:42 INFO 139811818182464] Epoch[29] Validation-accuracy=0.828125\u001b[0m\n",
      "\n",
      "2023-10-10 06:27:49 Uploading - Uploading generated training model\n",
      "2023-10-10 06:28:15 Completed - Training job completed\n",
      "Training seconds: 423\n",
      "Billable seconds: 423\n"
     ]
    }
   ],
   "source": [
    "## TODO: train your model\n",
    "img_classifier_model_default.fit(model_inputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If all goes well, you'll end up with a model topping out above `.8` validation accuracy. With only 1000 training samples in the CIFAR dataset, that's pretty good. We could definitely pursue data augmentation & gathering more samples to help us improve further, but for now let's proceed to deploy our model.\n",
    "\n",
    "### Getting ready to deploy\n",
    "\n",
    "To begin with, let's configure Model Monitor to track our deployment. We'll define a `DataCaptureConfig` below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sagemaker.model_monitor import DataCaptureConfig\n",
    "\n",
    "data_capture_config = DataCaptureConfig(\n",
    "    ## TODO: Set config options\n",
    "    destination_s3_uri=f\"s3://{bucket}/data_capture\",\n",
    "    sampling_percentage=100,\n",
    "    enable_capture=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note the `destination_s3_uri` parameter: At the end of the project, we can explore the `data_capture` directory in S3 to find crucial data about the inputs and outputs Model Monitor has observed on our model endpoint over time.\n",
    "\n",
    "With that done, deploy your model on a single `ml.m5.xlarge` instance with the data capture config attached:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:sagemaker:Creating model with name: image-classification-2023-10-10-06-28-45-621\n",
      "INFO:sagemaker:Creating endpoint-config with name image-classification-2023-10-10-06-28-45-621\n",
      "INFO:sagemaker:Creating endpoint with name image-classification-2023-10-10-06-28-45-621\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------!image-classification-2023-10-10-06-28-45-621\n"
     ]
    }
   ],
   "source": [
    "deployment = img_classifier_model_default.deploy(\n",
    "    ## TODO: fill in deployment options\n",
    "    data_capture_config=data_capture_config,\n",
    "    initial_instance_count = 1,\n",
    "    instance_type = 'ml.m5.xlarge'\n",
    "    )\n",
    "\n",
    "endpoint = deployment.endpoint_name\n",
    "print(endpoint)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note the endpoint name for later as well.\n",
    "\n",
    "Next, instantiate a Predictor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sagemaker.predictor import Predictor\n",
    "\n",
    "predictor = Predictor(endpoint_name = endpoint)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the code snippet below we are going to prepare one of your saved images for prediction. Use the predictor to process the `payload`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sagemaker.serializers import IdentitySerializer\n",
    "import base64\n",
    "\n",
    "predictor.serializer = IdentitySerializer(\"image/png\")\n",
    "with open(\"./test/bicycle_s_001789.png\", \"rb\") as f:\n",
    "    payload = f.read()\n",
    "\n",
    "    \n",
    "inference = predictor.predict(payload)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Your `inference` object is an array of two values, the predicted probability value for each of your classes (bicycle and motorcycle respectively.) So, for example, a value of `b'[0.91, 0.09]'` indicates the probability of being a bike is 91% and being a motorcycle is 9%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'[0.9997171759605408, 0.0002827658609021455]'\n"
     ]
    }
   ],
   "source": [
    "print(inference)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Draft Lambdas and Step Function Workflow\n",
    "\n",
    "Your operations team uses Step Functions to orchestrate serverless workflows. One of the nice things about Step Functions is that [workflows can call other workflows](https://docs.aws.amazon.com/step-functions/latest/dg/connect-stepfunctions.html), so the team can easily plug your workflow into the broader production architecture for Scones Unlimited.\n",
    "\n",
    "In this next stage you're going to write and deploy three Lambda functions, and then use the Step Functions visual editor to chain them together! Our functions are going to work with a simple data object:\n",
    "\n",
    "```python\n",
    "{\n",
    "    \"inferences\": [], # Output of predictor.predict\n",
    "    \"s3_key\": \"\", # Source data S3 key\n",
    "    \"s3_bucket\": \"\", # Source data S3 bucket\n",
    "    \"image_data\": \"\"  # base64 encoded string containing the image data\n",
    "}\n",
    "```\n",
    "\n",
    "A good test object that you can use for Lambda tests and Step Function executions, throughout the next section, might look like this:\n",
    "\n",
    "```python\n",
    "{\n",
    "  \"image_data\": \"\",\n",
    "  \"s3_bucket\": MY_BUCKET_NAME, # Fill in with your bucket\n",
    "  \"s3_key\": \"test/bicycle_s_000513.png\"\n",
    "}\n",
    "```\n",
    "\n",
    "Using these fields, your functions can read and write the necessary data to execute your workflow. Let's start with the first function. Your first Lambda function will copy an object from S3, base64 encode it, and then return it to the step function as `image_data` in an event.\n",
    "\n",
    "Go to the Lambda dashboard and create a new Lambda function with a descriptive name like \"serializeImageData\" and select thr 'Python 3.8' runtime. Add the same permissions as the SageMaker role you created earlier. (Reminder: you do this in the Configuration tab under \"Permissions\"). Once you're ready, use the starter code below to craft your Lambda handler:\n",
    "\n",
    "```python\n",
    "import json\n",
    "import boto3\n",
    "import base64\n",
    "\n",
    "s3 = boto3.client('s3')\n",
    "\n",
    "def lambda_handler(event, context):\n",
    "    \"\"\"A function to serialize target data from S3\"\"\"\n",
    "    \n",
    "    # Get the s3 address from the Step Function event input\n",
    "    key = ## TODO: fill in\n",
    "    bucket = ## TODO: fill in\n",
    "    \n",
    "    # Download the data from s3 to /tmp/image.png\n",
    "    ## TODO: fill in\n",
    "    \n",
    "    # We read the data from a file\n",
    "    with open(\"/tmp/image.png\", \"rb\") as f:\n",
    "        image_data = base64.b64encode(f.read())\n",
    "\n",
    "    # Pass the data back to the Step Function\n",
    "    print(\"Event:\", event.keys())\n",
    "    return {\n",
    "        'statusCode': 200,\n",
    "        'body': {\n",
    "            \"image_data\": image_data,\n",
    "            \"s3_bucket\": bucket,\n",
    "            \"s3_key\": key,\n",
    "            \"inferences\": []\n",
    "        }\n",
    "    }\n",
    "```\n",
    "\n",
    "The next function is responsible for the classification part - we're going to take the image output from the previous function, decode it, and then pass inferences back to the the Step Function.\n",
    "\n",
    "Because this Lambda will have runtime dependencies (i.e. the SageMaker SDK) you'll need to package them in your function. *Key reading:* https://docs.aws.amazon.com/lambda/latest/dg/python-package-create.html#python-package-create-with-dependency\n",
    "\n",
    "Create a new Lambda function with the same rights and a descriptive name, then fill in the starter code below for your classifier Lambda.\n",
    "\n",
    "```python\n",
    "import json\n",
    "import sagemaker\n",
    "import base64\n",
    "from sagemaker.serializers import IdentitySerializer\n",
    "\n",
    "# Fill this in with the name of your deployed model\n",
    "ENDPOINT = ## TODO: fill in\n",
    "\n",
    "def lambda_handler(event, context):\n",
    "\n",
    "    # Decode the image data\n",
    "    image = base64.b64decode(## TODO: fill in)\n",
    "\n",
    "    # Instantiate a Predictor\n",
    "    predictor = ## TODO: fill in\n",
    "\n",
    "    # For this model the IdentitySerializer needs to be \"image/png\"\n",
    "    predictor.serializer = IdentitySerializer(\"image/png\")\n",
    "    \n",
    "    # Make a prediction:\n",
    "    inferences = ## TODO: fill in\n",
    "    \n",
    "    # We return the data back to the Step Function    \n",
    "    event[\"inferences\"] = inferences.decode('utf-8')\n",
    "    return {\n",
    "        'statusCode': 200,\n",
    "        'body': json.dumps(event)\n",
    "    }\n",
    "```\n",
    "\n",
    "Finally, we need to filter low-confidence inferences. Define a threshold between 1.00 and 0.000 for your model: what is reasonble for you? If the model predicts at `.70` for it's highest confidence label, do we want to pass that inference along to downstream systems? Make one last Lambda function and tee up the same permissions:\n",
    "\n",
    "```python\n",
    "import json\n",
    "\n",
    "\n",
    "THRESHOLD = .93\n",
    "\n",
    "\n",
    "def lambda_handler(event, context):\n",
    "    \n",
    "    # Grab the inferences from the event\n",
    "    inferences = ## TODO: fill in\n",
    "    \n",
    "    # Check if any values in our inferences are above THRESHOLD\n",
    "    meets_threshold = ## TODO: fill in\n",
    "    \n",
    "    # If our threshold is met, pass our data back out of the\n",
    "    # Step Function, else, end the Step Function with an error\n",
    "    if meets_threshold:\n",
    "        pass\n",
    "    else:\n",
    "        raise(\"THRESHOLD_CONFIDENCE_NOT_MET\")\n",
    "\n",
    "    return {\n",
    "        'statusCode': 200,\n",
    "        'body': json.dumps(event)\n",
    "    }\n",
    "```\n",
    "Once you have tested the lambda functions, save the code for each lambda function in a python script called 'lambda.py'.\n",
    "\n",
    "With your lambdas in place, you can use the Step Functions visual editor to construct a workflow that chains them together. In the Step Functions console you'll have the option to author a Standard step function *Visually*.\n",
    "\n",
    "When the visual editor opens, you'll have many options to add transitions in your workflow. We're going to keep it simple and have just one: to invoke Lambda functions. Add three of them chained together. For each one, you'll be able to select the Lambda functions you just created in the proper order, filter inputs and outputs, and give them descriptive names.\n",
    "\n",
    "Make sure that you:\n",
    "\n",
    "1. Are properly filtering the inputs and outputs of your invokations (e.g. `$.body`)\n",
    "2. Take care to remove the error handling from the last function - it's supposed to \"fail loudly\" for your operations colleagues!\n",
    "\n",
    "Take a screenshot of your working step function in action and export the step function as JSON for your submission package."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Great! Now you can use the files in `./test` as test files for our workflow. Depending on our threshold, our workflow should reliably pass predictions about images from `./test` on to downstream systems, while erroring out for inferences below our confidence threshold!\n",
    "\n",
    "### Testing and Evaluation\n",
    "\n",
    "Do several step function invokations using data from the `./test` folder. This process should give you confidence that the workflow both *succeeds* AND *fails* as expected. In addition, SageMaker Model Monitor will generate recordings of your data and inferences which we can visualize.\n",
    "\n",
    "Here's a function that can help you generate test inputs for your invokations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\"image_data\": \"\", \"s3_bucket\": \"sagemaker-us-east-1-820297565403\", \"s3_key\": \"test/velocipede_s_001201.png\"}'"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import random\n",
    "import boto3\n",
    "import json\n",
    "\n",
    "\n",
    "def generate_test_case():\n",
    "    # Setup s3 in boto3\n",
    "    s3 = boto3.resource('s3')\n",
    "    \n",
    "    # Randomly pick from sfn or test folders in our bucket\n",
    "    objects = s3.Bucket(bucket).objects.filter(Prefix=\"test\")\n",
    "    \n",
    "    # Grab any random object key from that folder!\n",
    "    obj = random.choice([x.key for x in objects])\n",
    "    \n",
    "    return json.dumps({\n",
    "        \"image_data\": \"\",\n",
    "        \"s3_bucket\": bucket,\n",
    "        \"s3_key\": obj\n",
    "    })\n",
    "generate_test_case()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the Step Function dashboard for your new function, you can create new executions and copy in the generated test cases. Do several executions so that you can generate data you can evaluate and visualize.\n",
    "\n",
    "Once you've done several executions, let's visualize the record of our inferences. Pull in the JSONLines data from your inferences like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['captured_data/02-47-417-24de15bf-2723-4395-87ee-5457aabdf252.jsonl',\n",
       " 'captured_data/06-01-333-85460d96-6f83-497d-ad38-b280cb797a58.jsonl',\n",
       " 'captured_data/07-18-094-53753431-09ef-4b37-a2ca-e4d89c9320fe.jsonl',\n",
       " 'captured_data/08-40-188-2e22ffd3-69ab-4a10-b07d-ace0044d8022.jsonl',\n",
       " 'captured_data/09-49-468-d5b30e66-3e3d-443f-882e-7db793688a29.jsonl']"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sagemaker.s3 import S3Downloader\n",
    "\n",
    "# In S3 your data will be saved to a datetime-aware path\n",
    "# Find a path related to a datetime you're interested in\n",
    "data_path = f\"s3://{bucket}/data_capture/image-classification-2023-10-10-06-28-45-621/AllTraffic/2023/10/10/09/\"\n",
    "\n",
    "S3Downloader.download(data_path, \"captured_data\")\n",
    "\n",
    "# Feel free to repeat this multiple times and pull in more data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data are in JSONLines format, where multiple valid JSON objects are stacked on top of eachother in a single `jsonl` file. We'll import an open-source library, `jsonlines` that was purpose built for parsing this format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: jsonlines in /opt/conda/lib/python3.7/site-packages (3.1.0)\n",
      "Requirement already satisfied: attrs>=19.2.0 in /opt/conda/lib/python3.7/site-packages (from jsonlines) (23.1.0)\n",
      "Requirement already satisfied: typing-extensions in /opt/conda/lib/python3.7/site-packages (from jsonlines) (4.7.1)\n",
      "Requirement already satisfied: importlib-metadata in /opt/conda/lib/python3.7/site-packages (from attrs>=19.2.0->jsonlines) (6.7.0)\n",
      "Requirement already satisfied: zipp>=0.5 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata->attrs>=19.2.0->jsonlines) (2.2.0)\n",
      "\u001b[33mDEPRECATION: pyodbc 4.0.0-unsupported has a non-standard version number. pip 23.3 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pyodbc or contact the author to suggest that they release a version with a conforming version number. Discussion can be found at https://github.com/pypa/pip/issues/12063\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install jsonlines\n",
    "import jsonlines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can extract the data from each of the source files:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "# List the file names we downloaded\n",
    "file_handles = os.listdir(\"./captured_data\")\n",
    "\n",
    "# Dump all the data into an array\n",
    "json_data = []\n",
    "for jsonl in file_handles:\n",
    "    with jsonlines.open(f\"./captured_data/{jsonl}\") as f:\n",
    "        json_data.append(f.read())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data should now be a list of dictionaries, with significant nesting. We'll give you an example of some code that grabs data out of the objects and visualizes it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0.0006853179074823856, 0.9993146657943726], '2023-10-10T09:08:40Z')"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define how we'll get our data\n",
    "def simple_getter(obj):\n",
    "    inferences = obj[\"captureData\"][\"endpointOutput\"][\"data\"]\n",
    "    timestamp = obj[\"eventMetadata\"][\"inferenceTime\"]\n",
    "    return json.loads(inferences), timestamp\n",
    "\n",
    "simple_getter(json_data[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, here's an example of a visualization you can build with this data. In this last part, you will take some time and build your own - the captured data has the input images, the resulting inferences, and the timestamps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
      "INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Populate the data for the x and y axis\n",
    "%matplotlib inline\n",
    "THRESHOLD = 0.70\n",
    "x = []\n",
    "y = []\n",
    "for obj in json_data:\n",
    "    inference, timestamp = simple_getter(obj)\n",
    "    \n",
    "    y.append(max(inference))\n",
    "    x.append(timestamp)\n",
    "\n",
    "# Todo: here is an visualization example, take some time to build another visual that helps monitor the result\n",
    "# Plot the data\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.scatter(x, y, c=['r' if k<THRESHOLD else 'b' for k in y ])\n",
    "plt.axhline(y=THRESHOLD, color='g', linestyle='--')\n",
    "plt.ylim(bottom=0,top=1.02)\n",
    "\n",
    "# Add labels\n",
    "plt.ylabel(\"Confidence\")\n",
    "plt.xlabel(\"Timestamps\")\n",
    "plt.suptitle(\"Observed Recent Inferences\", size=14, y=0.97, fontweight='bold')\n",
    "plt.title(\"Pictured with confidence threshold for production use\", size=12)\n",
    "\n",
    "# Give it some pizzaz!\n",
    "plt.style.use(\"Solarize_Light2\")\n",
    "plt.gcf().autofmt_xdate()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Todo: build your own visualization\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0.0006853179074823856, 0.9993146657943726],\n",
       " '2023-10-10T09:08:40Z',\n",
       " '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')"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# My Simple Getter:\n",
    "def my_simple_getter(obj):\n",
    "    inferences = obj[\"captureData\"][\"endpointOutput\"][\"data\"]\n",
    "    timestamp = obj[\"eventMetadata\"][\"inferenceTime\"]\n",
    "    images = obj[\"captureData\"][\"endpointInput\"][\"data\"]\n",
    "    return json.loads(inferences), timestamp, images\n",
    "\n",
    "# Example: first object retrieved\n",
    "my_simple_getter(json_data[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
      "INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
      "INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
      "INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
      "INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
      "INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing images and their inferences in comparison to the assumed threshold\n",
    "%matplotlib inline\n",
    "import io\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "from PIL import Image\n",
    "\n",
    "x = []\n",
    "y = []\n",
    "THRESHOLD = 0.93\n",
    "for idx, obj in enumerate(json_data):\n",
    "    inference, timestamp, image = my_simple_getter(obj)\n",
    "    img = base64.b64decode(image)     # decode string using base64\n",
    "    img = io.BytesIO(img) \n",
    "    img = Image.open(img)             # Open image\n",
    "    \n",
    "    # Plotting subplots to display Images along with their performances\n",
    "    f = plt.figure(figsize=(12, 6))\n",
    "    # f.suptitle(\"Observed Recent Inferences\", size=16)\n",
    "    f.suptitle(\"Observed Recent Inference\", size=15, y=1.04, fontweight='bold')\n",
    "    \n",
    "    \n",
    "    # Plot Test Image\n",
    "    ax1 = f.add_subplot(121)\n",
    "    ax1.set_title(f\"Test Image {idx+1}\", size=13, y=1.11)\n",
    "    ax1.imshow(img, interpolation='nearest')\n",
    "    \n",
    "    \n",
    "    # Plot Inference and Time Data\n",
    "    ax2 = f.add_subplot(122)\n",
    "    ax2.scatter(timestamp, max(inference), c=['r' if max(inference)<THRESHOLD else 'b'])\n",
    "    ax2.axhline(y=THRESHOLD, color='g', linestyle='--')\n",
    "    ax2.set_ylim(bottom=0,top=1.02)\n",
    "    # Add labels\n",
    "    ax2.set_ylabel(\"Confidence\")\n",
    "    ax2.set_title(\"Pictured with confidence threshold\\n for production use\", size=13, y=1.02)\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Congratulations!\n",
    "\n",
    "You've reached the end of the project. In this project you created an event-drivent ML workflow that can be incorporated into the Scones Unlimited production architecture. You used the SageMaker Estimator API to deploy your SageMaker Model and Endpoint, and you used AWS Lambda and Step Functions to orchestrate your ML workflow. Using SageMaker Model Monitor, you instrumented and observed your Endpoint, and at the end of the project you built a visualization to help stakeholders understand the performance of the Endpoint over time. If you're up for it, you can even go further with these stretch goals:\n",
    "\n",
    "* Extend your workflow to incorporate more classes: the CIFAR dataset includes other vehicles that Scones Unlimited can identify with this model.\n",
    "* Modify your event driven workflow: can you rewrite your Lambda functions so that the workflow can process multiple image inputs in parallel? Can the Step Function \"fan out\" to accomodate this new workflow?\n",
    "* Consider the test data generator we provided for you. Can we use it to create a \"dummy data\" generator, to simulate a continuous stream of input data? Or a big paralell load of data?\n",
    "* What if we want to get notified every time our step function errors out? Can we use the Step Functions visual editor in conjunction with a service like SNS to accomplish this? Try it out!\n",
    "\n",
    "\n"
   ]
  }
 ],
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