ResNet50RetinaNet - Toy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "from ipywidgets import IntProgress\n",
    "from keras_tqdm import TQDMNotebookCallback\n",
    "\n",
    "import keras\n",
    "import keras.preprocessing.image\n",
    "from keras.applications.imagenet_utils import get_file\n",
    "\n",
    "import keras_resnet.models\n",
    "import keras_retinanet.layers\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "import numpy as np\n",
    "import sys\n",
    "import math\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "def get_session():\n",
    "    config = tf.ConfigProto()\n",
    "    config.gpu_options.allow_growth = True\n",
    "    return tf.Session(config=config)\n",
    "\n",
    "keras.backend.tensorflow_backend.set_session(get_session())\n",
    "\n",
    "WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3.6/site-packages/ipykernel/__main__.py:45: UserWarning: Output \"classification_softmax\" missing from loss dictionary. We assume this was done on purpose, and we will not be expecting any data to be passed to \"classification_softmax\" during training.\n",
      "/usr/lib/python3.6/site-packages/ipykernel/__main__.py:45: UserWarning: Output \"boxes\" missing from loss dictionary. We assume this was done on purpose, and we will not be expecting any data to be passed to \"boxes\" during training.\n",
      "/usr/lib/python3.6/site-packages/ipykernel/__main__.py:45: UserWarning: Output \"focal_loss_5\" missing from loss dictionary. We assume this was done on purpose, and we will not be expecting any data to be passed to \"focal_loss_5\" during training.\n"
     ]
    }
   ],
   "source": [
    "def create_model(num_classes=2, num_anchors=9):\n",
    "    image = keras.layers.Input((512, 512, 3))\n",
    "    im_info = keras.layers.Input((3,))\n",
    "    gt_boxes = keras.layers.Input((None, 5))\n",
    "    \n",
    "    resnet = keras_resnet.models.ResNet50(image, include_top=False)\n",
    "    _, _, _, res5 = resnet.outputs\n",
    "\n",
    "    # predict anchor classes\n",
    "    classification = keras.layers.Conv2D(\n",
    "        filters=num_classes * num_anchors,\n",
    "        kernel_size=(3, 3),\n",
    "        strides=1,\n",
    "        padding='same',\n",
    "        name='pyramid_classification',\n",
    "        #kernel_initializer=keras.initializers.normal(mean=0.0, stddev=0.01, seed=None),\n",
    "        #bias_initializer=keras.initializers.zeros(),\n",
    "        bias_initializer=keras.initializers.constant(-math.log((1 - 0.01) / 0.01)),\n",
    "        kernel_initializer=keras.initializers.zeros(),\n",
    "    )(res5)\n",
    "    \n",
    "    # generate anchors and their labels\n",
    "    labels, _, anchors = keras_retinanet.layers.AnchorTarget(\n",
    "        features_shape=keras.backend.int_shape(classification)[1:3],\n",
    "        stride=32,\n",
    "        anchor_size=128,\n",
    "        name='boxes'\n",
    "    )([im_info, gt_boxes])\n",
    "    \n",
    "    # reshape classification to (-1, num_classes)\n",
    "    classification = keras.layers.Reshape((-1, num_classes), name='classification')(classification)\n",
    "    # compute classification losses\n",
    "    classification = keras.layers.Activation('softmax', name='classification_softmax')(classification)\n",
    "    cls_loss = keras_retinanet.layers.FocalLoss(num_classes=num_classes)([classification, labels])\n",
    "    \n",
    "    return keras.models.Model(inputs=[image, im_info, gt_boxes], outputs=[classification, labels, cls_loss, anchors])\n",
    "\n",
    "model = create_model()\n",
    "\n",
    "# load imagenet weights\n",
    "weights_path = get_file('resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5', WEIGHTS_PATH_NO_TOP, cache_subdir='models', md5_hash='a268eb855778b3df3c7506639542a6af')\n",
    "model.load_weights(weights_path, by_name=True)\n",
    "\n",
    "# compile model\n",
    "model.compile(loss=None, optimizer=keras.optimizers.adam(lr=0.01))\n",
    "#print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1c0a7ed588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 512, 512, 3) (1, 3) (1, 1, 5)\n"
     ]
    }
   ],
   "source": [
    "# generate really simple image with one object\n",
    "image = np.zeros((512, 512, 3), dtype=keras.backend.floatx())\n",
    "image[100:300, 100:300, :] = 1.0\n",
    "plt.imshow(image)\n",
    "plt.show()\n",
    "\n",
    "# create input batch blobs\n",
    "image_batch = np.expand_dims(image, axis=0)\n",
    "im_info_batch = np.array([[512, 512, 1.0]])\n",
    "gt_boxes_batch = np.array([[[100, 100, 300, 300, 1]]])\n",
    "\n",
    "inputs = [image_batch, im_info_batch, gt_boxes_batch]\n",
    "\n",
    "print(image_batch.shape, im_info_batch.shape, gt_boxes_batch.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "999 3.118030463156174e-066\r"
     ]
    }
   ],
   "source": [
    "# train for some iterations\n",
    "for i in range(1000):\n",
    "    print('{} {}'.format(i, model.train_on_batch(inputs, None)), end='\\r')\n",
    "    sys.stdout.flush()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predict the same image again\n",
    "classification, labels, cls_loss, anchors = model.predict_on_batch(inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 2304) (1, 2304, 2) (1, 2304, 4)\n",
      "1.0 [ 0.01348912  0.98651087]\n",
      "1.0 [ 0.00627385  0.99372619]\n",
      "1.0 [ 0.00209964  0.99790037]\n",
      "1.0 [ 0.00452386  0.99547619]\n",
      "1.0 [ 0.0193282   0.98067182]\n",
      "1.0 [ 0.00812897  0.991871  ]\n",
      "1.0 [ 0.00952255  0.99047744]\n",
      "1.0 [  1.06148713e-04   9.99893785e-01]\n",
      "1.0 [ 0.05200059  0.94799942]\n",
      "1.0 [ 0.00240846  0.99759161]\n",
      "1.0 [ 0.02541689  0.97458309]\n",
      "1.0 [ 0.00573512  0.9942649 ]\n",
      "1.0 [  1.14641634e-05   9.99988556e-01]\n",
      "1.0 [  2.84824437e-05   9.99971509e-01]\n",
      "1.0 [  8.69676078e-05   9.99912977e-01]\n",
      "1.0 [ 0.03039886  0.96960115]\n",
      "1.0 [  2.02974588e-05   9.99979734e-01]\n",
      "1.0 [  4.90425955e-05   9.99951005e-01]\n",
      "1.0 [  2.19084352e-04   9.99780953e-01]\n",
      "1.0 [  4.56347625e-04   9.99543607e-01]\n",
      "1.0 [ 0.00981079  0.99018925]\n",
      "1.0 [  7.80278788e-05   9.99921918e-01]\n",
      "1.0 [  4.56221518e-04   9.99543726e-01]\n",
      "1.0 [ 0.00191325  0.99808681]\n",
      "1.0 [ 0.07593685  0.92406309]\n",
      "1.0 [ 0.00128312  0.99871695]\n",
      "1.0 [ 0.00962798  0.99037194]\n",
      "1.0 [  8.06932861e-04   9.99193013e-01]\n",
      "1.0 [ 0.00115529  0.99884474]\n",
      "1.0 [ 0.00167788  0.99832207]\n",
      "1.0 [ 0.00101345  0.99898654]\n",
      "1.0 [ 0.00865112  0.99134892]\n",
      "1.0 [ 0.0377437   0.96225631]\n",
      "1.0 [ 0.02015107  0.97984892]\n",
      "1.0 [ 0.00976999  0.99023002]\n",
      "1.0 [ 0.00167574  0.99832422]\n"
     ]
    },
    {
     "data": {
      "image/png": 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nHOpY2XbT24XT1neWtruuA2bAyBIAQENYAgBoCEsAAI3LecwSz7TlWXuXdE22udSxNK4N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\noCEsAQA0hCUAgMb/B6PPLpmm/IWJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1bf4318ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the positive anchors\n",
    "indices = np.where(labels[0, :] > 0)[0]\n",
    "im = image_batch[0, ...].copy()\n",
    "print(labels.shape, classification.shape, anchors.shape)\n",
    "for idx in indices:\n",
    "    # print the label and the classification scores\n",
    "    print(labels[0, idx], classification[0, idx, :])\n",
    "    b = anchors[0, idx, :].astype(int)\n",
    "    cv2.rectangle(im, (b[0], b[1]), (b[2], b[3]), (0, 1, 0), 3)\n",
    "    \n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.imshow(im)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate anchors for a certain image size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras_retinanet.layers\n",
    "\n",
    "im_info = keras.layers.Input((3,))\n",
    "gt_boxes = keras.layers.Input((None, 5))\n",
    "\n",
    "shapes = [(64, 64), (32, 32), (16, 16), (8, 8), (4, 4)]\n",
    "strides = [8, 16, 32, 64, 128]\n",
    "sizes = [32, 64, 128, 256, 512]\n",
    "idx = 2\n",
    "\n",
    "labels, bbox_reg_targets, anchors = keras_retinanet.layers.AnchorTarget(shapes[idx], strides[idx], sizes[idx])([im_info, gt_boxes])\n",
    "anchor_model = keras.models.Model(inputs=[im_info, gt_boxes], outputs=[labels, anchors])\n",
    "\n",
    "l, a = anchor_model.predict([im_info_batch, gt_boxes_batch])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nHOpY2XbT24XT1neWtruuA2bAyBIAQENYAgBoCEsAAI3LecwSz7TlWXuXdE22udSxNK4N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\noCEsAQA0hCUAgMb/B6PPLpmm/IWJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1bf3943748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show labeled anchors\n",
    "indices = np.where(l[0, :] > 0)[0]\n",
    "im = image_batch[0, ...].copy()\n",
    "for idx in indices:\n",
    "    b = a[0, idx, :].astype(int)\n",
    "    cv2.rectangle(im, (b[0], b[1]), (b[2], b[3]), (0, 1, 0), 3)\n",
    "    \n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.imshow(im)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}