├── .DS_Store ├── .gitignore ├── .ipynb_checkpoints ├── model_control_tensorlayer-checkpoint.ipynb ├── model_keras-checkpoint.ipynb ├── model_tensorflow-checkpoint.ipynb └── nn_movidius-checkpoint.ipynb ├── .vscode └── settings.json ├── README.md ├── Step_1_Data_Collection ├── convert_to_tfrecords_once.py ├── copy_to_rpi │ ├── mount_disk │ └── rpi.py └── transmitter.py ├── Step_2&3a_Train&Run ├── .ipynb_checkpoints │ └── model_control_tensorlayer-checkpoint.ipynb ├── model_control_tensorlayer.ipynb └── rpi │ └── stream.sh ├── Step_3b_Run_with_NCS └── rpi_ncs.py ├── checkpoint ├── dataset_example ├── 000001.jpg ├── 000002.jpg ├── 000003.jpg ├── 000004.jpg ├── 000005.jpg ├── 000006.jpg ├── 000007.jpg ├── 000008.jpg ├── 000009.jpg ├── 000010.jpg ├── 000011.jpg ├── 000012.jpg ├── 000013.jpg ├── 000014.jpg ├── 000015.jpg ├── 000016.jpg ├── 000017.jpg ├── 000018.jpg ├── 000019.jpg ├── 000020.jpg ├── 000021.jpg ├── 000022.jpg ├── 000023.jpg ├── 000024.jpg ├── 000025.jpg ├── 000026.jpg ├── 000027.jpg ├── 000028.jpg ├── 000029.jpg ├── 000030.jpg ├── 000031.jpg ├── 000032.jpg ├── 000033.jpg ├── 000034.jpg ├── 000035.jpg ├── 000036.jpg ├── 000037.jpg ├── 000038.jpg ├── 000039.jpg ├── 000040.jpg ├── 000041.jpg ├── 000042.jpg ├── 000043.jpg ├── 000044.jpg ├── 000045.jpg ├── 000046.jpg ├── 000047.jpg ├── 000048.jpg ├── 000049.jpg ├── 000050.jpg ├── 000051.jpg ├── 000052.jpg ├── 000053.jpg ├── 000054.jpg ├── 000055.jpg ├── 000056.jpg ├── 000057.jpg ├── 000058.jpg ├── 000059.jpg ├── 000060.jpg ├── 000061.jpg ├── 000062.jpg ├── 000063.jpg ├── 000064.jpg ├── 000065.jpg ├── 000066.jpg ├── 000067.jpg ├── 000068.jpg ├── 000069.jpg ├── 000070.jpg ├── 000071.jpg ├── 000072.jpg ├── 000073.jpg ├── 000074.jpg ├── 000075.jpg ├── 000076.jpg ├── 000077.jpg ├── 000078.jpg ├── 000079.jpg ├── 000080.jpg ├── 000081.jpg ├── 000082.jpg ├── 000083.jpg ├── 000084.jpg ├── 000085.jpg ├── 000086.jpg ├── 000087.jpg ├── 000088.jpg ├── 000089.jpg ├── 000090.jpg ├── 000091.jpg ├── 000092.jpg ├── 000093.jpg ├── 000094.jpg ├── 000095.jpg ├── 000096.jpg ├── 000097.jpg ├── 000098.jpg ├── 000099.jpg ├── 000100.jpg ├── 000101.jpg ├── 000102.jpg ├── 000103.jpg ├── 000104.jpg ├── 000105.jpg ├── 000106.jpg ├── 000107.jpg ├── 000108.jpg ├── 000109.jpg ├── 000110.jpg ├── 000111.jpg ├── 000112.jpg ├── 000113.jpg ├── 000114.jpg ├── 000115.jpg ├── 000116.jpg ├── 000117.jpg ├── 000118.jpg ├── 000119.jpg ├── 000120.jpg ├── 000121.jpg ├── 000122.jpg ├── 000123.jpg ├── 000124.jpg ├── 000125.jpg ├── 000126.jpg ├── 000127.jpg ├── 000128.jpg ├── 000129.jpg ├── 000130.jpg ├── 000131.jpg ├── 000132.jpg ├── 000133.jpg ├── 000134.jpg ├── 000135.jpg ├── 000136.jpg ├── 000137.jpg ├── 000138.jpg ├── 000139.jpg ├── 000140.jpg ├── 000141.jpg ├── 000142.jpg ├── 000143.jpg ├── 000144.jpg ├── 000145.jpg ├── 000146.jpg ├── 000147.jpg ├── 000148.jpg ├── 000149.jpg ├── 000150.jpg ├── 000151.jpg ├── 000152.jpg ├── 000153.jpg ├── 000154.jpg ├── 000155.jpg ├── 000156.jpg ├── 000157.jpg ├── 000158.jpg ├── 000159.jpg ├── 000160.jpg ├── 000161.jpg ├── 000162.jpg ├── 000163.jpg ├── 000164.jpg ├── 000165.jpg ├── 000166.jpg ├── 000167.jpg ├── 000168.jpg ├── 000169.jpg ├── 000170.jpg ├── 000171.jpg ├── 000172.jpg ├── 000173.jpg ├── 000174.jpg ├── 000175.jpg ├── 000176.jpg ├── 000177.jpg ├── 000178.jpg ├── 000179.jpg ├── 000180.jpg ├── 000181.jpg ├── 000182.jpg ├── 000183.jpg ├── 000184.jpg ├── 000185.jpg ├── 000186.jpg ├── 000187.jpg ├── 000188.jpg ├── 000189.jpg ├── 000190.jpg ├── 000191.jpg ├── 000192.jpg ├── 000193.jpg ├── 000194.jpg ├── 000195.jpg ├── 000196.jpg ├── 000197.jpg ├── 000198.jpg ├── 000199.jpg ├── 000200.jpg ├── 000201.jpg ├── 000202.jpg ├── 000203.jpg ├── 000204.jpg ├── 000205.jpg ├── 000206.jpg ├── 000207.jpg ├── 000208.jpg ├── 000209.jpg ├── 000210.jpg ├── 000211.jpg ├── 000212.jpg ├── 000213.jpg ├── 000214.jpg ├── 000215.jpg ├── 000216.jpg ├── 000217.jpg ├── 000218.jpg ├── 000219.jpg ├── 000220.jpg ├── 000221.jpg ├── 000222.jpg ├── 000223.jpg ├── 000224.jpg ├── 000225.jpg ├── 000226.jpg ├── 000227.jpg ├── 000228.jpg ├── 000229.jpg ├── 000230.jpg ├── 000231.jpg ├── 000232.jpg ├── 000233.jpg ├── 000234.jpg ├── 000235.jpg ├── 000236.jpg ├── 000237.jpg ├── 000238.jpg ├── 000239.jpg ├── 000240.jpg ├── 000241.jpg ├── 000242.jpg ├── 000243.jpg ├── 000244.jpg ├── 000245.jpg ├── 000246.jpg ├── 000247.jpg ├── 000248.jpg ├── 000249.jpg ├── 000250.jpg ├── 000251.jpg ├── 000252.jpg ├── 000253.jpg ├── 000254.jpg ├── 000255.jpg ├── 000256.jpg ├── 000257.jpg ├── 000258.jpg ├── 000259.jpg ├── 000260.jpg ├── 000261.jpg ├── 000262.jpg ├── 000263.jpg ├── 000264.jpg ├── 000265.jpg ├── 000266.jpg ├── 000267.jpg ├── 000268.jpg ├── 000269.jpg ├── 000270.jpg ├── 000271.jpg ├── 000272.jpg ├── 000273.jpg ├── 000274.jpg ├── 000275.jpg ├── 000276.jpg ├── 000277.jpg ├── 000278.jpg ├── 000279.jpg ├── 000280.jpg ├── 000281.jpg ├── 000282.jpg ├── 000283.jpg ├── 000284.jpg ├── 000285.jpg ├── 000286.jpg ├── 000287.jpg ├── 000288.jpg ├── 000289.jpg ├── 000290.jpg ├── 000291.jpg ├── 000292.jpg ├── 000293.jpg ├── 000294.jpg ├── 000295.jpg ├── 000296.jpg ├── 000297.jpg ├── 000298.jpg ├── 000299.jpg ├── 000300.jpg ├── 000301.jpg ├── 000302.jpg ├── 000303.jpg ├── 000304.jpg ├── 000305.jpg ├── 000306.jpg ├── 000307.jpg ├── 000308.jpg ├── 000309.jpg ├── 000310.jpg ├── 000311.jpg ├── 000312.jpg ├── 000313.jpg ├── 000314.jpg ├── 000315.jpg ├── 000316.jpg ├── 000317.jpg ├── 000318.jpg ├── 000319.jpg ├── 000320.jpg ├── 000321.jpg ├── 000322.jpg ├── 000323.jpg ├── 000324.jpg ├── 000325.jpg ├── 000326.jpg ├── 000327.jpg ├── 000328.jpg ├── 000329.jpg ├── 000330.jpg ├── 000331.jpg ├── 000332.jpg ├── 000333.jpg ├── 000334.jpg ├── 000335.jpg ├── 000336.jpg ├── 000337.jpg ├── 000338.jpg ├── 000339.jpg ├── 000340.jpg ├── 000341.jpg └── steer_log.txt └── pics ├── TensorRider.jpg └── jupyter.jpg /.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/virtualwiz/TensorRider_Self_Driving_Car/3e725e10b2ea7f281e87d15675349a8093660062/.DS_Store -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | *.tfrecords 2 | *.npz 3 | */logs/* 4 | *.h5 5 | *.data-00000-of-00001 6 | *.meta 7 | *.index 8 | *.graph 9 | -------------------------------------------------------------------------------- /.ipynb_checkpoints/model_keras-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "name": "stderr", 10 | "output_type": "stream", 11 | "text": [ 12 | "/Users/matthew/anaconda3/envs/tensorflow/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: compiletime version 3.6 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.5\n", 13 | " return f(*args, **kwds)\n", 14 | "Using TensorFlow backend.\n" 15 | ] 16 | } 17 | ], 18 | "source": [ 19 | "import numpy as np\n", 20 | "import tensorflow as tf\n", 21 | "import keras\n", 22 | "from keras.datasets import mnist\n", 23 | "from keras.models import Model\n", 24 | "from keras.layers import Input, Dense, Dropout, Flatten, Activation\n", 25 | "from keras.layers import Conv2D, MaxPooling2D\n", 26 | "from keras import backend as K\n", 27 | "from matplotlib import pyplot as plt\n", 28 | "from keras.models import model_from_json\n", 29 | "from keras.utils import plot_model\n", 30 | "\n", 31 | "from IPython.display import SVG\n", 32 | "from keras.utils.vis_utils import model_to_dot" 33 | ] 34 | }, 35 | { 36 | "cell_type": "markdown", 37 | "metadata": {}, 38 | "source": [ 39 | "## Define the image preprocessing methods" 40 | ] 41 | }, 42 | { 43 | "cell_type": "code", 44 | "execution_count": null, 45 | "metadata": {}, 46 | "outputs": [], 47 | "source": [ 48 | "def preprocess(rgb):\n", 49 | "# r, g, b = rgb[:,:,0], rgb[:,:,1], rgb[:,:,2]\n", 50 | "# pd = 0.4 * r + 0.4 * g + 0.2 * b\n", 51 | "# pd = pd * (1. / 255) - 0.5\n", 52 | " pd=rgb * (1. / 255) #- 0.5\n", 53 | " return pd\n", 54 | "\n", 55 | "def prepareDataArrays(iterator):\n", 56 | " X = np.empty((0, 14400))\n", 57 | " y = np.empty((0,1))\n", 58 | " X_buffer = np.empty((0, 14400))\n", 59 | " y_buffer = np.empty((0,1))\n", 60 | " \n", 61 | " recordCounter = 0;\n", 62 | "\n", 63 | " for string_record in iterator:\n", 64 | " recordCounter += 1\n", 65 | "\n", 66 | " example = tf.train.Example()\n", 67 | " example.ParseFromString(string_record)\n", 68 | " imageString = (example.features.feature['image'].bytes_list.value[0])\n", 69 | " label = (example.features.feature['label'].int64_list.value[0])\n", 70 | " \n", 71 | " image = np.fromstring(imageString, dtype=np.uint8)\n", 72 | " image = image.reshape((60, 80, 3))\n", 73 | " image = preprocess(image)\n", 74 | " image = image.reshape((14400))\n", 75 | "\n", 76 | " X_buffer = np.append(X_buffer, [image], axis=0)\n", 77 | " y_buffer = np.append(y_buffer, label)\n", 78 | " \n", 79 | " if recordCounter % 100 == 0:\n", 80 | " print(recordCounter,end = '->')\n", 81 | " if recordCounter % 1000 == 0:\n", 82 | " print(\"Merging\")\n", 83 | " X = np.append(X, [X_buffer])\n", 84 | " y = np.append(y, [y_buffer])\n", 85 | " X_buffer = np.empty((0, 14400))\n", 86 | " y_buffer = np.empty((0,1))\n", 87 | " \n", 88 | " print(\"Done\") \n", 89 | " X = np.append(X, [X_buffer])\n", 90 | " y = np.append(y, y_buffer)\n", 91 | " \n", 92 | " X = X.reshape((recordCounter, 14400))\n", 93 | " y = y.reshape((recordCounter,))\n", 94 | " y = np.round(y / 6)\n", 95 | " y = y + 7\n", 96 | " return X, y" 97 | ] 98 | }, 99 | { 100 | "cell_type": "markdown", 101 | "metadata": {}, 102 | "source": [ 103 | "# Load the datasets for training " 104 | ] 105 | }, 106 | { 107 | "cell_type": "markdown", 108 | "metadata": {}, 109 | "source": [ 110 | "## (from tfrecord to RAM)" 111 | ] 112 | }, 113 | { 114 | "cell_type": "code", 115 | "execution_count": null, 116 | "metadata": {}, 117 | "outputs": [], 118 | "source": [ 119 | "trainIterator = tf.python_io.tf_record_iterator(path=\"train.tfrecords\")\n", 120 | "valIterator = tf.python_io.tf_record_iterator(path=\"val.tfrecords\")\n", 121 | "\n", 122 | "print(\"\\nTrain...\")\n", 123 | "X_train, y_train = prepareDataArrays(trainIterator)\n", 124 | "print(\"\\nVal...\")\n", 125 | "X_val, y_val = prepareDataArrays(valIterator)" 126 | ] 127 | }, 128 | { 129 | "cell_type": "markdown", 130 | "metadata": {}, 131 | "source": [ 132 | "## (from RAM to NPZ file)" 133 | ] 134 | }, 135 | { 136 | "cell_type": "code", 137 | "execution_count": null, 138 | "metadata": {}, 139 | "outputs": [], 140 | "source": [ 141 | "np.savez(\"arrays_01color.npz\", xtr = X_train, ytr = y_train, xval = X_val, yval = y_val)" 142 | ] 143 | }, 144 | { 145 | "cell_type": "markdown", 146 | "metadata": {}, 147 | "source": [ 148 | "## (from NPZ file to RAM)" 149 | ] 150 | }, 151 | { 152 | "cell_type": "code", 153 | "execution_count": 2, 154 | "metadata": {}, 155 | "outputs": [], 156 | "source": [ 157 | "npRecall = np.load(\"arrays_01color.npz\")\n", 158 | "X_train = npRecall[\"xtr\"]\n", 159 | "y_train = npRecall[\"ytr\"]\n", 160 | "X_val = npRecall[\"xval\"]\n", 161 | "y_val = npRecall[\"yval\"]" 162 | ] 163 | }, 164 | { 165 | "cell_type": "markdown", 166 | "metadata": {}, 167 | "source": [ 168 | "## One-hot Encode" 169 | ] 170 | }, 171 | { 172 | "cell_type": "code", 173 | "execution_count": 3, 174 | "metadata": {}, 175 | "outputs": [], 176 | "source": [ 177 | "# (xt, yt), (xv, yv) = mnist.load_data()\n", 178 | "X_train = X_train.astype('float32')\n", 179 | "y_train = y_train.astype('uint8')\n", 180 | "X_val = X_val.astype('float32')\n", 181 | "y_val = y_val.astype('uint8')\n", 182 | "y_train = keras.utils.to_categorical(y_train, 15)\n", 183 | "y_val = keras.utils.to_categorical(y_val, 15)" 184 | ] 185 | }, 186 | { 187 | "cell_type": "markdown", 188 | "metadata": {}, 189 | "source": [ 190 | "## Define and compile the neural network" 191 | ] 192 | }, 193 | { 194 | "cell_type": "code", 195 | "execution_count": 5, 196 | "metadata": {}, 197 | "outputs": [], 198 | "source": [ 199 | "# model = Sequential()\n", 200 | "# model.add(Dense(2048, kernel_initializer=\"uniform\", activation=\"relu\", input_dim=14400))\n", 201 | "# model.add(Dense(1024, kernel_initializer=\"uniform\", activation=\"relu\"))\n", 202 | "# model.add(Dense(512, kernel_initializer=\"uniform\", activation=\"relu\"))\n", 203 | "# model.add(Dense(15))\n", 204 | "# model.add(Activation(\"softmax\"))\n", 205 | "\n", 206 | "inputs = Input(shape=(14400,))\n", 207 | "x = Dense(2048, activation='relu')(inputs)\n", 208 | "x = Dense(1024, activation='relu')(x)\n", 209 | "x = Dense(512, activation='relu')(x)\n", 210 | "predictions = Dense(15, activation='softmax')(x)" 211 | ] 212 | }, 213 | { 214 | "cell_type": "code", 215 | "execution_count": 6, 216 | "metadata": {}, 217 | "outputs": [], 218 | "source": [ 219 | "model = Model(inputs=inputs, outputs=predictions)\n", 220 | "model.compile(loss=keras.losses.categorical_crossentropy,\n", 221 | " optimizer=keras.optimizers.Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-08),\n", 222 | " metrics=['accuracy'])" 223 | ] 224 | }, 225 | { 226 | "cell_type": "code", 227 | "execution_count": 7, 228 | "metadata": { 229 | "scrolled": false 230 | }, 231 | "outputs": [ 232 | { 233 | "name": "stdout", 234 | "output_type": "stream", 235 | "text": [ 236 | "Train on 30000 samples, validate on 5000 samples\n", 237 | "Epoch 1/8\n", 238 | "30000/30000 [==============================] - 176s 6ms/step - loss: 1.3034 - acc: 0.5176 - val_loss: 1.1607 - val_acc: 0.5452\n", 239 | "Epoch 2/8\n", 240 | "30000/30000 [==============================] - 175s 6ms/step - loss: 1.1709 - acc: 0.5551 - val_loss: 1.1590 - val_acc: 0.5442\n", 241 | "Epoch 3/8\n", 242 | "30000/30000 [==============================] - 173s 6ms/step - loss: 1.1430 - acc: 0.5642 - val_loss: 1.1134 - val_acc: 0.5672\n", 243 | "Epoch 4/8\n", 244 | "30000/30000 [==============================] - 173s 6ms/step - loss: 1.1159 - acc: 0.5717 - val_loss: 1.1000 - val_acc: 0.5886\n", 245 | "Epoch 5/8\n", 246 | "30000/30000 [==============================] - 172s 6ms/step - loss: 1.0959 - acc: 0.5790 - val_loss: 1.0563 - val_acc: 0.5924\n", 247 | "Epoch 6/8\n", 248 | "30000/30000 [==============================] - 173s 6ms/step - loss: 1.0813 - acc: 0.5844 - val_loss: 1.0904 - val_acc: 0.5686\n", 249 | "Epoch 7/8\n", 250 | "30000/30000 [==============================] - 173s 6ms/step - loss: 1.0684 - acc: 0.5880 - val_loss: 1.1135 - val_acc: 0.5848\n", 251 | "Epoch 8/8\n", 252 | "30000/30000 [==============================] - 172s 6ms/step - loss: 1.0529 - acc: 0.5931 - val_loss: 1.1290 - val_acc: 0.5694\n" 253 | ] 254 | }, 255 | { 256 | "data": { 257 | "text/plain": [ 258 | "" 259 | ] 260 | }, 261 | "execution_count": 7, 262 | "metadata": {}, 263 | "output_type": "execute_result" 264 | } 265 | ], 266 | "source": [ 267 | "model.fit(X_train, y_train,\n", 268 | " batch_size=116,\n", 269 | " epochs=8,\n", 270 | " verbose=1,\n", 271 | " validation_data=(X_val, y_val))" 272 | ] 273 | }, 274 | { 275 | "cell_type": "code", 276 | "execution_count": 8, 277 | "metadata": {}, 278 | "outputs": [ 279 | { 280 | "data": { 281 | "text/plain": [ 282 | "'{\"config\": {\"name\": \"model_1\", \"input_layers\": [[\"input_2\", 0, 0]], \"layers\": [{\"config\": {\"name\": \"input_2\", \"batch_input_shape\": [null, 14400], \"sparse\": false, \"dtype\": \"float32\"}, \"name\": \"input_2\", \"class_name\": \"InputLayer\", \"inbound_nodes\": []}, {\"config\": {\"use_bias\": true, \"kernel_regularizer\": null, \"activation\": \"relu\", \"bias_regularizer\": null, \"name\": \"dense_2\", \"trainable\": true, \"activity_regularizer\": null, \"units\": 2048, \"kernel_constraint\": null, \"kernel_initializer\": {\"config\": {\"seed\": null, \"distribution\": \"uniform\", \"mode\": \"fan_avg\", \"scale\": 1.0}, \"class_name\": \"VarianceScaling\"}, \"bias_constraint\": null, \"bias_initializer\": {\"config\": {}, \"class_name\": \"Zeros\"}}, \"name\": \"dense_2\", \"class_name\": \"Dense\", \"inbound_nodes\": [[[\"input_2\", 0, 0, {}]]]}, {\"config\": {\"use_bias\": true, \"kernel_regularizer\": null, \"activation\": \"relu\", \"bias_regularizer\": null, \"name\": \"dense_3\", \"trainable\": true, \"activity_regularizer\": null, \"units\": 1024, \"kernel_constraint\": null, \"kernel_initializer\": {\"config\": {\"seed\": null, \"distribution\": \"uniform\", \"mode\": \"fan_avg\", \"scale\": 1.0}, \"class_name\": \"VarianceScaling\"}, \"bias_constraint\": null, \"bias_initializer\": {\"config\": {}, \"class_name\": \"Zeros\"}}, \"name\": \"dense_3\", \"class_name\": \"Dense\", \"inbound_nodes\": [[[\"dense_2\", 0, 0, {}]]]}, {\"config\": {\"use_bias\": true, \"kernel_regularizer\": null, \"activation\": \"relu\", \"bias_regularizer\": null, \"name\": \"dense_4\", \"trainable\": true, \"activity_regularizer\": null, \"units\": 512, \"kernel_constraint\": null, \"kernel_initializer\": {\"config\": {\"seed\": null, \"distribution\": \"uniform\", \"mode\": \"fan_avg\", \"scale\": 1.0}, \"class_name\": \"VarianceScaling\"}, \"bias_constraint\": null, \"bias_initializer\": {\"config\": {}, \"class_name\": \"Zeros\"}}, \"name\": \"dense_4\", \"class_name\": \"Dense\", \"inbound_nodes\": [[[\"dense_3\", 0, 0, {}]]]}, {\"config\": {\"use_bias\": true, \"kernel_regularizer\": null, \"activation\": \"softmax\", \"bias_regularizer\": null, \"name\": \"dense_5\", \"trainable\": true, \"activity_regularizer\": null, \"units\": 15, \"kernel_constraint\": null, \"kernel_initializer\": {\"config\": {\"seed\": null, \"distribution\": \"uniform\", \"mode\": \"fan_avg\", \"scale\": 1.0}, \"class_name\": \"VarianceScaling\"}, \"bias_constraint\": null, \"bias_initializer\": {\"config\": {}, \"class_name\": \"Zeros\"}}, \"name\": \"dense_5\", \"class_name\": \"Dense\", \"inbound_nodes\": [[[\"dense_4\", 0, 0, {}]]]}], \"output_layers\": [[\"dense_5\", 0, 0]]}, \"class_name\": \"Model\", \"backend\": \"tensorflow\", \"keras_version\": \"2.2.0\"}'" 283 | ] 284 | }, 285 | "execution_count": 8, 286 | "metadata": {}, 287 | "output_type": "execute_result" 288 | } 289 | ], 290 | "source": [ 291 | "model.save_weights('./weights.h5')\n", 292 | "model.to_json()" 293 | ] 294 | }, 295 | { 296 | "cell_type": "code", 297 | "execution_count": 2, 298 | "metadata": {}, 299 | "outputs": [ 300 | { 301 | "name": "stdout", 302 | "output_type": "stream", 303 | "text": [ 304 | "Loaded model from disk\n" 305 | ] 306 | } 307 | ], 308 | "source": [ 309 | "json_file = open('model.json', 'r')\n", 310 | "loaded_model_json = json_file.read()\n", 311 | "json_file.close()\n", 312 | "model = model_from_json(loaded_model_json)\n", 313 | "model.load_weights(\"weights.h5\")\n", 314 | "print(\"Loaded model from disk\")" 315 | ] 316 | }, 317 | { 318 | "cell_type": "code", 319 | "execution_count": 9, 320 | "metadata": {}, 321 | "outputs": [], 322 | "source": [ 323 | "saver = tf.train.Saver()\n", 324 | "with K.get_session() as sess:\n", 325 | " K.set_learning_phase(0)\n", 326 | " saver.save(sess, './tfmodel/model')" 327 | ] 328 | }, 329 | { 330 | "cell_type": "code", 331 | "execution_count": 10, 332 | "metadata": {}, 333 | "outputs": [ 334 | { 335 | "data": { 336 | "image/svg+xml": [ 337 | "\n", 338 | "\n", 339 | "G\n", 340 | "\n", 341 | "\n", 342 | "\n", 343 | "4799897272\n", 344 | "\n", 345 | "input_2: InputLayer\n", 346 | "\n", 347 | "\n", 348 | "\n", 349 | "4799897328\n", 350 | "\n", 351 | "dense_2: Dense\n", 352 | "\n", 353 | "\n", 354 | "\n", 355 | "4799897272->4799897328\n", 356 | "\n", 357 | "\n", 358 | "\n", 359 | "\n", 360 | "\n", 361 | "4799897552\n", 362 | "\n", 363 | "dense_3: Dense\n", 364 | "\n", 365 | "\n", 366 | "\n", 367 | "4799897328->4799897552\n", 368 | "\n", 369 | "\n", 370 | "\n", 371 | "\n", 372 | "\n", 373 | "8256397552\n", 374 | "\n", 375 | "dense_4: Dense\n", 376 | "\n", 377 | "\n", 378 | "\n", 379 | "4799897552->8256397552\n", 380 | "\n", 381 | "\n", 382 | "\n", 383 | "\n", 384 | "\n", 385 | "8256400912\n", 386 | "\n", 387 | "dense_5: Dense\n", 388 | "\n", 389 | "\n", 390 | "\n", 391 | "8256397552->8256400912\n", 392 | "\n", 393 | "\n", 394 | "\n", 395 | "\n", 396 | "" 397 | ], 398 | "text/plain": [ 399 | "" 400 | ] 401 | }, 402 | "execution_count": 10, 403 | "metadata": {}, 404 | "output_type": "execute_result" 405 | } 406 | ], 407 | "source": [ 408 | "SVG(model_to_dot(model).create(prog='dot', format='svg'))" 409 | ] 410 | }, 411 | { 412 | "cell_type": "code", 413 | "execution_count": null, 414 | "metadata": {}, 415 | "outputs": [], 416 | "source": [] 417 | } 418 | ], 419 | "metadata": { 420 | "kernelspec": { 421 | "display_name": "Python 3", 422 | "language": "python", 423 | "name": "python3" 424 | }, 425 | "language_info": { 426 | "codemirror_mode": { 427 | "name": "ipython", 428 | "version": 3 429 | }, 430 | "file_extension": ".py", 431 | "mimetype": "text/x-python", 432 | "name": "python", 433 | "nbconvert_exporter": "python", 434 | "pygments_lexer": "ipython3", 435 | "version": "3.5.5" 436 | } 437 | }, 438 | "nbformat": 4, 439 | "nbformat_minor": 2 440 | } 441 | -------------------------------------------------------------------------------- /.ipynb_checkpoints/model_tensorflow-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 2, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "name": "stderr", 10 | "output_type": "stream", 11 | "text": [ 12 | "/Users/matthew/anaconda3/envs/tensorflow/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: compiletime version 3.6 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.5\n", 13 | " return f(*args, **kwds)\n", 14 | "Using TensorFlow backend.\n" 15 | ] 16 | } 17 | ], 18 | "source": [ 19 | "import numpy as np\n", 20 | "import tensorflow as tf\n", 21 | "import keras\n", 22 | "import argparse\n", 23 | "import sys\n", 24 | "import tempfile\n", 25 | "\n", 26 | "# import keras\n", 27 | "# from keras.datasets import mnist\n", 28 | "# from keras.models import Model\n", 29 | "# from keras.layers import Input, Dense, Dropout, Flatten, Activation\n", 30 | "# from keras.layers import Conv2D, MaxPooling2D\n", 31 | "# from keras import backend as K\n", 32 | "# from keras.models import model_from_json\n", 33 | "# from keras.utils import plot_model\n", 34 | "\n", 35 | "# from IPython.display import SVG\n", 36 | "# from keras.utils.vis_utils import model_to_dot" 37 | ] 38 | }, 39 | { 40 | "cell_type": "markdown", 41 | "metadata": {}, 42 | "source": [ 43 | "## Define the image preprocessing methods" 44 | ] 45 | }, 46 | { 47 | "cell_type": "code", 48 | "execution_count": null, 49 | "metadata": {}, 50 | "outputs": [], 51 | "source": [ 52 | "def preprocess(rgb):\n", 53 | "# r, g, b = rgb[:,:,0], rgb[:,:,1], rgb[:,:,2]\n", 54 | "# pd = 0.4 * r + 0.4 * g + 0.2 * b\n", 55 | "# pd = pd * (1. / 255) - 0.5\n", 56 | " pd=rgb * (1. / 255) #- 0.5\n", 57 | " return pd\n", 58 | "\n", 59 | "def prepareDataArrays(iterator):\n", 60 | " X = np.empty((0, 14400))\n", 61 | " y = np.empty((0,1))\n", 62 | " X_buffer = np.empty((0, 14400))\n", 63 | " y_buffer = np.empty((0,1))\n", 64 | " \n", 65 | " recordCounter = 0;\n", 66 | "\n", 67 | " for string_record in iterator:\n", 68 | " recordCounter += 1\n", 69 | "\n", 70 | " example = tf.train.Example()\n", 71 | " example.ParseFromString(string_record)\n", 72 | " imageString = (example.features.feature['image'].bytes_list.value[0])\n", 73 | " label = (example.features.feature['label'].int64_list.value[0])\n", 74 | " \n", 75 | " image = np.fromstring(imageString, dtype=np.uint8)\n", 76 | " image = image.reshape((60, 80, 3))\n", 77 | " image = preprocess(image)\n", 78 | " image = image.reshape((14400))\n", 79 | "\n", 80 | " X_buffer = np.append(X_buffer, [image], axis=0)\n", 81 | " y_buffer = np.append(y_buffer, label)\n", 82 | " \n", 83 | " if recordCounter % 100 == 0:\n", 84 | " print(recordCounter,end = '->')\n", 85 | " if recordCounter % 1000 == 0:\n", 86 | " print(\"Merging\")\n", 87 | " X = np.append(X, [X_buffer])\n", 88 | " y = np.append(y, [y_buffer])\n", 89 | " X_buffer = np.empty((0, 14400))\n", 90 | " y_buffer = np.empty((0,1))\n", 91 | " \n", 92 | " print(\"Done\") \n", 93 | " X = np.append(X, [X_buffer])\n", 94 | " y = np.append(y, y_buffer)\n", 95 | " \n", 96 | " X = X.reshape((recordCounter, 14400))\n", 97 | " y = y.reshape((recordCounter,))\n", 98 | " y = np.round(y / 6)\n", 99 | " y = y + 7\n", 100 | " return X, y" 101 | ] 102 | }, 103 | { 104 | "cell_type": "markdown", 105 | "metadata": {}, 106 | "source": [ 107 | "# Load the datasets for training " 108 | ] 109 | }, 110 | { 111 | "cell_type": "markdown", 112 | "metadata": {}, 113 | "source": [ 114 | "## (from tfrecord to RAM)" 115 | ] 116 | }, 117 | { 118 | "cell_type": "code", 119 | "execution_count": null, 120 | "metadata": {}, 121 | "outputs": [], 122 | "source": [ 123 | "trainIterator = tf.python_io.tf_record_iterator(path=\"train.tfrecords\")\n", 124 | "valIterator = tf.python_io.tf_record_iterator(path=\"val.tfrecords\")\n", 125 | "\n", 126 | "print(\"\\nTrain...\")\n", 127 | "X_train, y_train = prepareDataArrays(trainIterator)\n", 128 | "print(\"\\nVal...\")\n", 129 | "X_val, y_val = prepareDataArrays(valIterator)" 130 | ] 131 | }, 132 | { 133 | "cell_type": "markdown", 134 | "metadata": {}, 135 | "source": [ 136 | "## (from RAM to NPZ file)" 137 | ] 138 | }, 139 | { 140 | "cell_type": "code", 141 | "execution_count": null, 142 | "metadata": {}, 143 | "outputs": [], 144 | "source": [ 145 | "np.savez(\"arrays_01color.npz\", xtr = X_train, ytr = y_train, xval = X_val, yval = y_val)" 146 | ] 147 | }, 148 | { 149 | "cell_type": "markdown", 150 | "metadata": {}, 151 | "source": [ 152 | "## (from NPZ file to RAM)" 153 | ] 154 | }, 155 | { 156 | "cell_type": "code", 157 | "execution_count": 4, 158 | "metadata": {}, 159 | "outputs": [], 160 | "source": [ 161 | "npRecall = np.load(\"arrays_01color.npz\")\n", 162 | "X_train = npRecall[\"xtr\"]\n", 163 | "y_train = npRecall[\"ytr\"]\n", 164 | "X_val = npRecall[\"xval\"]\n", 165 | "y_val = npRecall[\"yval\"]" 166 | ] 167 | }, 168 | { 169 | "cell_type": "markdown", 170 | "metadata": {}, 171 | "source": [ 172 | "## One-hot Encode" 173 | ] 174 | }, 175 | { 176 | "cell_type": "code", 177 | "execution_count": 3, 178 | "metadata": {}, 179 | "outputs": [], 180 | "source": [ 181 | "# (xt, yt), (xv, yv) = mnist.load_data()\n", 182 | "X_train = X_train.astype('float32')\n", 183 | "y_train = y_train.astype('uint8')\n", 184 | "X_val = X_val.astype('float32')\n", 185 | "y_val = y_val.astype('uint8')\n", 186 | "y_train = keras.utils.to_categorical(y_train, 15)\n", 187 | "y_val = keras.utils.to_categorical(y_val, 15)" 188 | ] 189 | }, 190 | { 191 | "cell_type": "markdown", 192 | "metadata": {}, 193 | "source": [ 194 | "## Define and compile the neural network" 195 | ] 196 | }, 197 | { 198 | "cell_type": "code", 199 | "execution_count": 5, 200 | "metadata": {}, 201 | "outputs": [], 202 | "source": [ 203 | "def deepnn(x):\n", 204 | " \n", 205 | " with tf.name_scope('reshape'):\n", 206 | " x_image = tf.reshape(x, [-1, 14400])\n", 207 | "\n", 208 | " with tf.name_scope('fc1'):\n", 209 | " W_fc1 = weight_variable([14400, 2048])\n", 210 | " b_fc1 = bias_variable([2048])\n", 211 | " h_fc1 = tf.nn.relu(tf.matmul(x_image, W_fc1) + b_fc1)\n", 212 | " \n", 213 | " with tf.name_scope('fc2'):\n", 214 | " W_fc2 = weight_variable([2048, 1024])\n", 215 | " b_fc2 = bias_variable([1024])\n", 216 | " h_fc2 = tf.nn.relu(tf.matmul(h_fc1, W_fc2) + b_fc2)\n", 217 | " \n", 218 | " with tf.name_scope('fc3'):\n", 219 | " W_fc3 = weight_variable([1024, 512])\n", 220 | " b_fc3 = bias_variable([512])\n", 221 | " h_fc3 = tf.nn.relu(tf.matmul(h_fc2, W_fc3) + b_fc3)\n", 222 | "\n", 223 | " with tf.name_scope('fc4'):\n", 224 | " W_fc4 = weight_variable([512, 15])\n", 225 | " b_fc4 = bias_variable([15])\n", 226 | " y_conv = tf.matmul(h_fc3, W_fc4) + b_fc4\n", 227 | " \n", 228 | " return y_conv\n", 229 | "\n", 230 | "def weight_variable(shape):\n", 231 | " \"\"\"weight_variable generates a weight variable of a given shape.\"\"\"\n", 232 | " initial = tf.truncated_normal(shape, stddev=0.1)\n", 233 | " return tf.Variable(initial)\n", 234 | "\n", 235 | "def bias_variable(shape):\n", 236 | " \"\"\"bias_variable generates a bias variable of a given shape.\"\"\"\n", 237 | " initial = tf.constant(0.1, shape=shape)\n", 238 | " return tf.Variable(initial)\n" 239 | ] 240 | }, 241 | { 242 | "cell_type": "code", 243 | "execution_count": 6, 244 | "metadata": {}, 245 | "outputs": [], 246 | "source": [ 247 | "train_dataset = tf.data.TFRecordDataset('./train.tfrecords')\n", 248 | "val_dataset = tf.data.TFRecordDataset('./val.tfrecords')\n", 249 | "\n", 250 | "def _parse_function(example_proto):\n", 251 | " keys_to_features = {'X':tf.FixedLenFeature((), tf.float32),\n", 252 | " 'y': tf.FixedLenFeature((), tf.int64, default_value=0)}\n", 253 | " parsed_features = tf.parse_single_example(example_proto, keys_to_features)\n", 254 | " return parsed_features['X'], parsed_features['y']\n", 255 | "\n", 256 | "train_dataset = train_dataset.map(_parse_function)\n", 257 | "iterator = train_dataset.make_one_shot_iterator()\n", 258 | "X, y = iterator.get_next()" 259 | ] 260 | }, 261 | { 262 | "cell_type": "code", 263 | "execution_count": 9, 264 | "metadata": {}, 265 | "outputs": [ 266 | { 267 | "ename": "InvalidArgumentError", 268 | "evalue": "Feature: X (data type: float) is required but could not be found.\n\t [[Node: ParseSingleExample/ParseSingleExample = ParseSingleExample[Tdense=[DT_FLOAT, DT_INT64], dense_keys=[\"X\", \"y\"], dense_shapes=[[], []], num_sparse=0, sparse_keys=[], sparse_types=[]](arg0, ParseSingleExample/Const, ParseSingleExample/Reshape)]]\n\t [[Node: IteratorGetNext = IteratorGetNext[output_shapes=[[], []], output_types=[DT_FLOAT, DT_INT64], _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"](OneShotIterator)]]", 269 | "output_type": "error", 270 | "traceback": [ 271 | "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", 272 | "\u001b[0;31mInvalidArgumentError\u001b[0m Traceback (most recent call last)", 273 | "\u001b[0;32m~/anaconda3/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m 1321\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1322\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1323\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", 274 | "\u001b[0;32m~/anaconda3/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m 1306\u001b[0m return self._call_tf_sessionrun(\n\u001b[0;32m-> 1307\u001b[0;31m options, feed_dict, fetch_list, target_list, run_metadata)\n\u001b[0m\u001b[1;32m 1308\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", 275 | "\u001b[0;32m~/anaconda3/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_call_tf_sessionrun\u001b[0;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\u001b[0m\n\u001b[1;32m 1408\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1409\u001b[0;31m run_metadata)\n\u001b[0m\u001b[1;32m 1410\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", 276 | "\u001b[0;31mInvalidArgumentError\u001b[0m: Feature: X (data type: float) is required but could not be found.\n\t [[Node: ParseSingleExample/ParseSingleExample = ParseSingleExample[Tdense=[DT_FLOAT, DT_INT64], dense_keys=[\"X\", \"y\"], dense_shapes=[[], []], num_sparse=0, sparse_keys=[], sparse_types=[]](arg0, ParseSingleExample/Const, ParseSingleExample/Reshape)]]\n\t [[Node: IteratorGetNext = IteratorGetNext[output_shapes=[[], []], output_types=[DT_FLOAT, DT_INT64], _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"](OneShotIterator)]]", 277 | "\nDuring handling of the above exception, another exception occurred:\n", 278 | "\u001b[0;31mInvalidArgumentError\u001b[0m Traceback (most recent call last)", 279 | "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0msess\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mInteractiveSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0msess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", 280 | "\u001b[0;32m~/anaconda3/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m 898\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 899\u001b[0m result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 900\u001b[0;31m run_metadata_ptr)\n\u001b[0m\u001b[1;32m 901\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 902\u001b[0m \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", 281 | "\u001b[0;32m~/anaconda3/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m 1133\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1134\u001b[0m results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1135\u001b[0;31m feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m 1136\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1137\u001b[0m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", 282 | "\u001b[0;32m~/anaconda3/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m 1314\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1315\u001b[0m return self._do_call(_run_fn, feeds, fetches, targets, options,\n\u001b[0;32m-> 1316\u001b[0;31m run_metadata)\n\u001b[0m\u001b[1;32m 1317\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1318\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", 283 | "\u001b[0;32m~/anaconda3/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m 1333\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1334\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1335\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode_def\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1336\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1337\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_extend_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", 284 | "\u001b[0;31mInvalidArgumentError\u001b[0m: Feature: X (data type: float) is required but could not be found.\n\t [[Node: ParseSingleExample/ParseSingleExample = ParseSingleExample[Tdense=[DT_FLOAT, DT_INT64], dense_keys=[\"X\", \"y\"], dense_shapes=[[], []], num_sparse=0, sparse_keys=[], sparse_types=[]](arg0, ParseSingleExample/Const, ParseSingleExample/Reshape)]]\n\t [[Node: IteratorGetNext = IteratorGetNext[output_shapes=[[], []], output_types=[DT_FLOAT, DT_INT64], _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"](OneShotIterator)]]" 285 | ] 286 | } 287 | ], 288 | "source": [ 289 | "sess=tf.InteractiveSession()\n", 290 | "sess.run(X)" 291 | ] 292 | }, 293 | { 294 | "cell_type": "code", 295 | "execution_count": 8, 296 | "metadata": { 297 | "scrolled": false 298 | }, 299 | "outputs": [ 300 | { 301 | "name": "stdout", 302 | "output_type": "stream", 303 | "text": [ 304 | "Saving graph to: ./tfgraph/\n" 305 | ] 306 | }, 307 | { 308 | "ename": "NameError", 309 | "evalue": "name 'tensors' is not defined", 310 | "output_type": "error", 311 | "traceback": [ 312 | "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", 313 | "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", 314 | "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 44\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 45\u001b[0m batch = tf.train.batch(\n\u001b[0;32m---> 46\u001b[0;31m \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 47\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[0mnum_threads\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", 315 | "\u001b[0;31mNameError\u001b[0m: name 'tensors' is not defined" 316 | ] 317 | } 318 | ], 319 | "source": [ 320 | "train_dataset = tf.data.TFRecordDataset('./train.tfrecords')\n", 321 | "val_dataset = tf.data.TFRecordDataset('./val.tfrecords')\n", 322 | "\n", 323 | "\n", 324 | "# Create the model\n", 325 | "x = tf.placeholder(tf.float32, [None, 14400])\n", 326 | "\n", 327 | "# Define loss and optimizer\n", 328 | "y_ = tf.placeholder(tf.int64, [None])\n", 329 | "\n", 330 | "# Build the graph for the deep net\n", 331 | "y_conv = deepnn(x)\n", 332 | "\n", 333 | "with tf.name_scope('loss'):\n", 334 | " cross_entropy = tf.losses.sparse_softmax_cross_entropy(\n", 335 | " labels=y_, logits=y_conv)\n", 336 | " cross_entropy = tf.reduce_mean(cross_entropy)\n", 337 | "\n", 338 | "with tf.name_scope('adam_optimizer'):\n", 339 | " train_step = tf.train.AdamOptimizer(0.0001).minimize(cross_entropy)\n", 340 | "\n", 341 | "with tf.name_scope('accuracy'):\n", 342 | " correct_prediction = tf.equal(tf.argmax(y_conv, 1), y_)\n", 343 | " correct_prediction = tf.cast(correct_prediction, tf.float32)\n", 344 | " accuracy = tf.reduce_mean(correct_prediction)\n", 345 | "\n", 346 | "graph_location = './tfgraph/'\n", 347 | "print('Saving graph to: %s' % graph_location)\n", 348 | "train_writer = tf.summary.FileWriter(graph_location)\n", 349 | "train_writer.add_graph(tf.get_default_graph())\n", 350 | "\n", 351 | "with tf.Session() as sess:\n", 352 | " sess.run(tf.global_variables_initializer())\n", 353 | " for i in range(2000):\n", 354 | " batch = tf.train.batch(\n", 355 | " tensors,\n", 356 | " batch_size,\n", 357 | " num_threads=1,\n", 358 | " capacity=32,\n", 359 | " enqueue_many=True,\n", 360 | " shapes=None,\n", 361 | " dynamic_pad=False,\n", 362 | " allow_smaller_final_batch=False,\n", 363 | " shared_name=None,\n", 364 | " name=None\n", 365 | " )\n", 366 | " if i % 100 == 0:\n", 367 | " train_accuracy = accuracy.eval(feed_dict={\n", 368 | " x: batch[0], y_: batch[1]})\n", 369 | " print('step %d, training accuracy %g' % (i, train_accuracy))\n", 370 | " train_step.run(feed_dict={x: batch[0], y_: batch[1]})" 371 | ] 372 | }, 373 | { 374 | "cell_type": "code", 375 | "execution_count": 10, 376 | "metadata": {}, 377 | "outputs": [], 378 | "source": [ 379 | "saver = tf.train.Saver()\n", 380 | "with K.get_session() as sess:\n", 381 | " K.set_learning_phase(0)\n", 382 | " saver.save(sess, './tfmodel/model')" 383 | ] 384 | }, 385 | { 386 | "cell_type": "code", 387 | "execution_count": 11, 388 | "metadata": {}, 389 | "outputs": [ 390 | { 391 | "data": { 392 | "image/svg+xml": [ 393 | "\n", 394 | "\n", 395 | "G\n", 396 | "\n", 397 | "\n", 398 | "\n", 399 | "4836983696\n", 400 | "\n", 401 | "pixels: InputLayer\n", 402 | "\n", 403 | "\n", 404 | "\n", 405 | "4836983640\n", 406 | "\n", 407 | "relu1: Dense\n", 408 | "\n", 409 | "\n", 410 | "\n", 411 | "4836983696->4836983640\n", 412 | "\n", 413 | "\n", 414 | "\n", 415 | "\n", 416 | "\n", 417 | "4836984088\n", 418 | "\n", 419 | "relu2: Dense\n", 420 | "\n", 421 | "\n", 422 | "\n", 423 | "4836983640->4836984088\n", 424 | "\n", 425 | "\n", 426 | "\n", 427 | "\n", 428 | "\n", 429 | "4836984536\n", 430 | "\n", 431 | "relu3: Dense\n", 432 | "\n", 433 | "\n", 434 | "\n", 435 | "4836984088->4836984536\n", 436 | "\n", 437 | "\n", 438 | "\n", 439 | "\n", 440 | "\n", 441 | "4836986776\n", 442 | "\n", 443 | "onehot: Dense\n", 444 | "\n", 445 | "\n", 446 | "\n", 447 | "4836984536->4836986776\n", 448 | "\n", 449 | "\n", 450 | "\n", 451 | "\n", 452 | "" 453 | ], 454 | "text/plain": [ 455 | "" 456 | ] 457 | }, 458 | "execution_count": 11, 459 | "metadata": {}, 460 | "output_type": "execute_result" 461 | } 462 | ], 463 | "source": [ 464 | "SVG(model_to_dot(model).create(prog='dot', format='svg'))" 465 | ] 466 | }, 467 | { 468 | "cell_type": "code", 469 | "execution_count": null, 470 | "metadata": {}, 471 | "outputs": [], 472 | "source": [] 473 | } 474 | ], 475 | "metadata": { 476 | "kernelspec": { 477 | "display_name": "Python 3", 478 | "language": "python", 479 | "name": "python3" 480 | }, 481 | "language_info": { 482 | "codemirror_mode": { 483 | "name": "ipython", 484 | "version": 3 485 | }, 486 | "file_extension": ".py", 487 | "mimetype": "text/x-python", 488 | "name": "python", 489 | "nbconvert_exporter": "python", 490 | "pygments_lexer": "ipython3", 491 | "version": "3.5.5" 492 | } 493 | }, 494 | "nbformat": 4, 495 | "nbformat_minor": 2 496 | } 497 | -------------------------------------------------------------------------------- /.ipynb_checkpoints/nn_movidius-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "name": "stderr", 10 | "output_type": "stream", 11 | "text": [ 12 | "/Users/matthew/anaconda3/envs/tensorflow/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: compiletime version 3.6 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.5\n", 13 | " return f(*args, **kwds)\n", 14 | "Using TensorFlow backend.\n" 15 | ] 16 | } 17 | ], 18 | "source": [ 19 | "import numpy as np\n", 20 | "import tensorflow as tf\n", 21 | "import keras\n", 22 | "from keras.datasets import mnist\n", 23 | "from keras.models import Sequential\n", 24 | "from keras.layers import Dense, Dropout, Flatten, Activation\n", 25 | "from keras.layers import Conv2D, MaxPooling2D\n", 26 | "from keras import backend as K\n", 27 | "from matplotlib import pyplot as plt" 28 | ] 29 | }, 30 | { 31 | "cell_type": "markdown", 32 | "metadata": {}, 33 | "source": [ 34 | "## Define the image preprocessing methods" 35 | ] 36 | }, 37 | { 38 | "cell_type": "code", 39 | "execution_count": null, 40 | "metadata": {}, 41 | "outputs": [], 42 | "source": [ 43 | "def preprocess(rgb):\n", 44 | "# r, g, b = rgb[:,:,0], rgb[:,:,1], rgb[:,:,2]\n", 45 | "# pd = 0.4 * r + 0.4 * g + 0.2 * b\n", 46 | "# pd = pd * (1. / 255) - 0.5\n", 47 | " pd=rgb * (1. / 255) #- 0.5\n", 48 | " return pd\n", 49 | "\n", 50 | "def prepareDataArrays(iterator):\n", 51 | " X = np.empty((0, 14400))\n", 52 | " y = np.empty((0,1))\n", 53 | " X_buffer = np.empty((0, 14400))\n", 54 | " y_buffer = np.empty((0,1))\n", 55 | " \n", 56 | " recordCounter = 0;\n", 57 | "\n", 58 | " for string_record in iterator:\n", 59 | " recordCounter += 1\n", 60 | "\n", 61 | " example = tf.train.Example()\n", 62 | " example.ParseFromString(string_record)\n", 63 | " imageString = (example.features.feature['image'].bytes_list.value[0])\n", 64 | " label = (example.features.feature['label'].int64_list.value[0])\n", 65 | " \n", 66 | " image = np.fromstring(imageString, dtype=np.uint8)\n", 67 | " image = image.reshape((60, 80, 3))\n", 68 | " image = preprocess(image)\n", 69 | " image = image.reshape((14400))\n", 70 | "\n", 71 | " X_buffer = np.append(X_buffer, [image], axis=0)\n", 72 | " y_buffer = np.append(y_buffer, label)\n", 73 | " \n", 74 | " if recordCounter % 100 == 0:\n", 75 | " print(recordCounter,end = '->')\n", 76 | " if recordCounter % 1000 == 0:\n", 77 | " print(\"Merging\")\n", 78 | " X = np.append(X, [X_buffer])\n", 79 | " y = np.append(y, [y_buffer])\n", 80 | " X_buffer = np.empty((0, 14400))\n", 81 | " y_buffer = np.empty((0,1))\n", 82 | " \n", 83 | " print(\"Done\") \n", 84 | " X = np.append(X, [X_buffer])\n", 85 | " y = np.append(y, y_buffer)\n", 86 | " \n", 87 | " X = X.reshape((recordCounter, 14400))\n", 88 | " y = y.reshape((recordCounter,))\n", 89 | " y = np.round(y / 6)\n", 90 | " y = y + 7\n", 91 | " return X, y" 92 | ] 93 | }, 94 | { 95 | "cell_type": "markdown", 96 | "metadata": {}, 97 | "source": [ 98 | "# Load the datasets for training " 99 | ] 100 | }, 101 | { 102 | "cell_type": "markdown", 103 | "metadata": {}, 104 | "source": [ 105 | "## (from tfrecord to RAM)" 106 | ] 107 | }, 108 | { 109 | "cell_type": "code", 110 | "execution_count": null, 111 | "metadata": {}, 112 | "outputs": [], 113 | "source": [ 114 | "trainIterator = tf.python_io.tf_record_iterator(path=\"train.tfrecords\")\n", 115 | "valIterator = tf.python_io.tf_record_iterator(path=\"val.tfrecords\")\n", 116 | "\n", 117 | "print(\"\\nTrain...\")\n", 118 | "X_train, y_train = prepareDataArrays(trainIterator)\n", 119 | "print(\"\\nVal...\")\n", 120 | "X_val, y_val = prepareDataArrays(valIterator)" 121 | ] 122 | }, 123 | { 124 | "cell_type": "markdown", 125 | "metadata": {}, 126 | "source": [ 127 | "## (from RAM to NPZ file)" 128 | ] 129 | }, 130 | { 131 | "cell_type": "code", 132 | "execution_count": null, 133 | "metadata": {}, 134 | "outputs": [], 135 | "source": [ 136 | "np.savez(\"arrays_01color.npz\", xtr = X_train, ytr = y_train, xval = X_val, yval = y_val)" 137 | ] 138 | }, 139 | { 140 | "cell_type": "markdown", 141 | "metadata": {}, 142 | "source": [ 143 | "## (from NPZ file to RAM)" 144 | ] 145 | }, 146 | { 147 | "cell_type": "code", 148 | "execution_count": 2, 149 | "metadata": {}, 150 | "outputs": [], 151 | "source": [ 152 | "npRecall = np.load(\"arrays_01color.npz\")\n", 153 | "X_train = npRecall[\"xtr\"]\n", 154 | "y_train = npRecall[\"ytr\"]\n", 155 | "X_val = npRecall[\"xval\"]\n", 156 | "y_val = npRecall[\"yval\"]" 157 | ] 158 | }, 159 | { 160 | "cell_type": "markdown", 161 | "metadata": {}, 162 | "source": [ 163 | "## One-hot Encode" 164 | ] 165 | }, 166 | { 167 | "cell_type": "code", 168 | "execution_count": 3, 169 | "metadata": {}, 170 | "outputs": [], 171 | "source": [ 172 | "# (xt, yt), (xv, yv) = mnist.load_data()\n", 173 | "X_train = X_train.astype('float32')\n", 174 | "y_train = y_train.astype('uint8')\n", 175 | "X_val = X_val.astype('float32')\n", 176 | "y_val = y_val.astype('uint8')\n", 177 | "y_train = keras.utils.to_categorical(y_train, 15)\n", 178 | "y_val = keras.utils.to_categorical(y_val, 15)" 179 | ] 180 | }, 181 | { 182 | "cell_type": "code", 183 | "execution_count": 4, 184 | "metadata": {}, 185 | "outputs": [ 186 | { 187 | "data": { 188 | "text/plain": [ 189 | "" 190 | ] 191 | }, 192 | "execution_count": 4, 193 | "metadata": {}, 194 | "output_type": "execute_result" 195 | }, 196 | { 197 | "data": { 198 | "image/png": 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\n", 199 | "text/plain": [ 200 | "
" 201 | ] 202 | }, 203 | "metadata": {}, 204 | "output_type": "display_data" 205 | } 206 | ], 207 | "source": [ 208 | "Xt=X_train[1,:].reshape(60,80,3)\n", 209 | "plt.imshow(Xt)" 210 | ] 211 | }, 212 | { 213 | "cell_type": "markdown", 214 | "metadata": {}, 215 | "source": [ 216 | "## Define and compile the neural network" 217 | ] 218 | }, 219 | { 220 | "cell_type": "code", 221 | "execution_count": 5, 222 | "metadata": {}, 223 | "outputs": [], 224 | "source": [ 225 | "model = Sequential()\n", 226 | "model.add(Dense(2048, kernel_initializer=\"uniform\", activation=\"relu\", input_dim=14400))\n", 227 | "model.add(Dense(1024, kernel_initializer=\"uniform\", activation=\"relu\"))\n", 228 | "model.add(Dense(512, kernel_initializer=\"uniform\", activation=\"relu\"))\n", 229 | "model.add(Dense(15))\n", 230 | "model.add(Activation(\"softmax\"))" 231 | ] 232 | }, 233 | { 234 | "cell_type": "code", 235 | "execution_count": 6, 236 | "metadata": {}, 237 | "outputs": [], 238 | "source": [ 239 | "model.compile(loss=keras.losses.categorical_crossentropy,\n", 240 | " optimizer=keras.optimizers.Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-08),\n", 241 | " metrics=['accuracy'])" 242 | ] 243 | }, 244 | { 245 | "cell_type": "code", 246 | "execution_count": 7, 247 | "metadata": { 248 | "scrolled": false 249 | }, 250 | "outputs": [ 251 | { 252 | "name": "stdout", 253 | "output_type": "stream", 254 | "text": [ 255 | "Train on 30000 samples, validate on 5000 samples\n", 256 | "Epoch 1/20\n", 257 | "30000/30000 [==============================] - 179s 6ms/step - loss: 1.2759 - acc: 0.5325 - val_loss: 1.2184 - val_acc: 0.5296\n", 258 | "Epoch 2/20\n", 259 | "30000/30000 [==============================] - 173s 6ms/step - loss: 1.1639 - acc: 0.5562 - val_loss: 1.1667 - val_acc: 0.5396\n", 260 | "Epoch 3/20\n", 261 | "30000/30000 [==============================] - 172s 6ms/step - loss: 1.1221 - acc: 0.5690 - val_loss: 1.1080 - val_acc: 0.5840\n", 262 | "Epoch 4/20\n", 263 | "30000/30000 [==============================] - 172s 6ms/step - loss: 1.1120 - acc: 0.5750 - val_loss: 1.0783 - val_acc: 0.5726\n", 264 | "Epoch 5/20\n", 265 | "30000/30000 [==============================] - 172s 6ms/step - loss: 1.0796 - acc: 0.5858 - val_loss: 1.0489 - val_acc: 0.5948\n", 266 | "Epoch 6/20\n", 267 | "30000/30000 [==============================] - 172s 6ms/step - loss: 1.0656 - acc: 0.5900 - val_loss: 1.1104 - val_acc: 0.5634\n", 268 | "Epoch 7/20\n", 269 | "30000/30000 [==============================] - 172s 6ms/step - loss: 1.0495 - acc: 0.5929 - val_loss: 1.0429 - val_acc: 0.5860\n", 270 | "Epoch 8/20\n", 271 | "30000/30000 [==============================] - 172s 6ms/step - loss: 1.0293 - acc: 0.5970 - val_loss: 1.0199 - val_acc: 0.5982\n", 272 | "Epoch 9/20\n", 273 | "30000/30000 [==============================] - 172s 6ms/step - loss: 1.0157 - acc: 0.6025 - val_loss: 1.0185 - val_acc: 0.5910\n", 274 | "Epoch 10/20\n", 275 | "30000/30000 [==============================] - 174s 6ms/step - loss: 0.9911 - acc: 0.6066 - val_loss: 1.1694 - val_acc: 0.5236\n", 276 | "Epoch 11/20\n", 277 | "30000/30000 [==============================] - 174s 6ms/step - loss: 0.9690 - acc: 0.6169 - val_loss: 1.0029 - val_acc: 0.5918\n", 278 | "Epoch 12/20\n", 279 | "30000/30000 [==============================] - 174s 6ms/step - loss: 0.9544 - acc: 0.6191 - val_loss: 1.2426 - val_acc: 0.5152\n", 280 | "Epoch 13/20\n", 281 | " 1044/30000 [>.............................] - ETA: 2:43 - loss: 1.0498 - acc: 0.5987" 282 | ] 283 | }, 284 | { 285 | "ename": "KeyboardInterrupt", 286 | "evalue": "", 287 | "output_type": "error", 288 | "traceback": [ 289 | "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", 290 | "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", 291 | "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m 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\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1042\u001b[0;31m validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m 1043\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1044\u001b[0m def evaluate(self, x=None, y=None,\n", 293 | "\u001b[0;32m~/anaconda3/envs/tensorflow/lib/python3.5/site-packages/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mfit_loop\u001b[0;34m(model, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[1;32m 197\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m 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"\u001b[0;32m~/anaconda3/envs/tensorflow/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m 2659\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_legacy_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2660\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2661\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2662\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2663\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mpy_any\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mis_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m 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334 | } 335 | }, 336 | "nbformat": 4, 337 | "nbformat_minor": 2 338 | } 339 | -------------------------------------------------------------------------------- /.vscode/settings.json: -------------------------------------------------------------------------------- 1 | { 2 | "python.pythonPath": "D:\\Anaconda3\\python.exe" 3 | } -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # TensorRider 自动驾驶车 2 | 3 | ![](pics/TensorRider.jpg) 4 | 5 | TensorRider是一种基于BP神经网络，对驾驶场景具有学习能力的的简易自动驾驶车模型。目前，TensorRider仅能实现基本的车道保持功能，即在学习了操作者的遥控驾驶行为后，根据经验对类似的场景做出自动驾驶行为。 6 | 7 | TensorRider使用Google TensorFlow作为算法框架，可以在CPU或支持CUDA的NVIDIA GPU上进行训练。 8 | 9 | 完成训练后，可以使用一台计算机（Linux，macOS或Windows）作为TensorRider的计算服务器，令实验小车在线运行，也可以通过在TensorRider的Raspberry Pi上连接Intel Movidius Neural Compute Stick(NCS)，实现离线运行。 10 | 11 | # 硬件需求 12 | 13 | * TensorRider自动驾驶车模型，包括Raspberry Pi，摄像头，电机驱动电路等 14 | * 运行Linux，Apple macOS 10.12+或Microsoft Windows 7+的计算机 15 | * 带有模拟摇杆的游戏控制器（手柄） 16 | * （非必需）兼容CUDA的NVIDIA GPU，Intel Movidius NCS 17 | * 连接质量良好的无线局域网 18 | 19 | # 使用方法 20 | 21 | TensorRider实现基于学习的自动驾驶分为3个阶段：收集数据，建立模型和自动运行。 22 | 23 | ## 1.收集数据 24 | 25 | 在这一步骤中，车辆模型在人工指引下，边行驶边记录摄像头拍摄的图像，同时记录下拍摄图像时车辆模型的转向角度。当收集了足够多的图像-转向角度数据后，即可以这些数据为依据，训练神经网络。 26 | 27 | 我们假设你已经在计算机上安装好了```Python3```和```Jupyter Notebook```。首先在计算机端安装依赖的python软件包。 28 | 29 | ``` 30 | pip3 install pygame 31 | ``` 32 | 33 | *注：我们假设你已掌握使用pip安装软件包的方法，以及在虚拟环境中安装软件包的方法。例如根据你使用的python环境的不同，你可能需要使用conda install或pip install。* 34 | 35 | 目录```Step_1_Data_Collection```内包含进行数据采集所需的程序。先使用SFTP或NFS等方式，将```copy_to_rpi```目录中的文件传输至实验小车端的Raspberry Pi上。在Raspberry Pi上连接一个使用FAT32文件系统的USB闪存盘，并启动Raspberry Pi。 36 | 37 | 使用SSH登录Raspberry Pi，建立USB闪存盘的挂载点 38 | 39 | ``` 40 | sudo mkdir /mnt/pdisk 41 | ``` 42 | 43 | 然后运行 44 | 45 | ``` 46 | sudo mount -o uid=pi,gid=pi /dev/sda1 /mnt/pdisk/ 47 | ``` 48 | 49 | 挂载USB闪存盘。 50 | 51 | *你也可以选择将数据存储在Raspberry Pi的SD卡，甚至内存文件系统中。但是比起使用网络传输采集的大量文件，使用USB闪存盘拷贝是更加快速的方式。如果你希望变更文件的存储位置，可在```rpi.py```的第161--163行中更改。* 52 | 53 | 挂载完成后运行```rpi.py```。如果实验小车上安装了状态指示灯，在接收到控制数据之前，红色指示灯每3秒闪烁一次。接着，在计算机上，在```transmitter.py```的第9行写入小车的局域网IP地址，然后运行```transmitter.py```。你将看到终端持续打印出读取到的手柄数据。如果与实验小车的连接正常，小车上的指示灯将转为绿色闪烁。 54 | 55 | 向前轻推控制器的左侧手柄，实验小车开始加速行驶，向后拉左侧手柄可使小车减速。向左、右方向推动右侧手柄，可使实验小车转向。若需要急停，可同时按下```L2```+```R2```键，实验小车立即停车。 56 | 57 | 熟悉实验小车的操控方法后，可在适当的时机按下```START```键，实验小车开始收集行驶数据。再按一次```START```停止收集数据。 58 | 59 | *数据收集建议至少持续30min。在收集数据时，需要使用较慢的恒定速度行驶，因为小车的转弯半径与速度相关。可以同时按下```L1```+```R1```键将小车设置为推荐的训练速度。* 60 | 61 | 数据收集完成后，卸载USB闪存盘或安全地关闭Raspberry Pi，将闪存盘中记录的图像移至计算机，作为一个数据集。然后清空闪存盘，重复上述步骤，再收集一些规模较小的数据集，用作测试数据集。 62 | 63 | Google公司的TensorFlow项目介绍了一种适用于表示数据集的二进制文件格式```TFRecords```，我们可将收集到的jpg图像及转向角度，转换为这种格式，便于稍后使用。 64 | 65 | 将需要转换的数据集目录（内有jpg图片及转向角度txt文件）放在```Step_1_Data_Collection```目录中，并重命名为```dataset```，然后运行 66 | 67 | ``` 68 | python3 convert_to_tfrecords_once.py 69 | ``` 70 | 71 | 程序会要求输出需要转换的图片数量，可以输入总的图片个数，或只转换一部分图像。转换完成后，将生成的```record.tfrecords```根据需要重命名。下一步中需要两个数据集：1个规模较大（约10000张图像）的训练集，名为```train.tfrecords```，和1个规模较小（2000-3000张图像左右）的测试集，名为```val.tfrecords```。 72 | 73 | ## 2.建立控制模型 74 | 75 | 这一步骤将加载数据集，建立BP神经网络，并使用上一步获得的数据集进行训练。 76 | 如果需要，先启动安装有TensorFlow的虚拟环境，使用Jupyter Notebook打开```Step_2&3a_Train&Run```目录中的```model_control_tensorlayer.ipynb```，按照Jupyter Notebook中的说明执行程序即可。 77 | 78 | ![](pics/jupyter.jpg) 79 | 80 | *PS 我使用的是一台游戏型笔记本电脑，装有CUDA并使用NVIDIA Geforce GTX 1050显卡进行神经网络训练。在我的机器上加载40000张图片作为训练集，大约训练20分钟后达到收敛，之后出现了较明显的过拟合现象。希望可以给读者一个大致印象。* 81 | 82 | 当运行完```保存模型```步骤，你的模型已经生成完成，可以用来控制自动驾驶车了。你可根据需要选择模型保存的格式，在我们的程序中，在线运行和离线运行需要不同的模型描述文件格式。具体可参考Notebook中的说明。 83 | 84 | ## 3a.在线运行 85 | 86 | 在线运行是指，实验小车将拍摄到的图像发至计算机，由计算机执行神经网络计算。完成后将计算结果以遥控指令的形式重新发回实验小车。此过程循环运行，即可实现自动驾驶，驾驶过程中小车与计算机一直保持网络连接。 87 | 88 | 移至Notebook的```开始在线运行```部分，安装和导入所需的程序库。 89 | 90 | 我们需要在Raspberry Pi上安装```mjpg-streamer```来发送视频流。*软件```mjpg-streamer```是Tom Stoeveken的作品，感谢作者的无私奉献。* 91 | 92 | 使用SSH等方式登录Raspberry Pi终端，运行 93 | 94 | ``` 95 | sudo apt-get install git cmake libjpeg8-dev 96 | git clone https://github.com/virtualwiz/mjpg-streamer.git 97 | cd mjpg-streamer-experimental 98 | make 99 | sudo make install 100 | ``` 101 | 102 | 将编译和安装```mjpg-streamer```。 103 | 104 | 首先在后台启动```rpi.py```并持续运行。（小车接受遥控指令，指示灯闪烁） 105 | 106 | ``` 107 | python3 rpi.py& 108 | ``` 109 | 110 | 然后运行以下命令，启动mjpg服务器 111 | 112 | ``` 113 | ./usr/local/bin/mjpg_streamer -o "output_http.so -w ./www" -i "input_raspicam.so -x 80 -y 60 -fps 30" 114 | ``` 115 | 116 | *你可以将此命令写为shell脚本，保存在Raspberry Pi上，以方便使用。* 117 | 118 | 此时，实验小车已建立一个视频流，并等待接收控制命令。返回Jupyter Notebook中，执行```预览图像```Cell，查看小车传回的实时图像。图像传输应当没有明显可感知的延迟。 119 | 120 | 一切就绪后，在Jupyter Notebook中启动图像刷新和控制线程（见Notebook内说明），闭合小车电机驱动开关，即可观察自动驾驶控制效果。 121 | 122 | ## 3b.离线运行 123 | 124 | 离线运行是指，在实验小车上安装Intel Movidius神经网络计算棒，实验小车行驶时无需连接计算机作为服务器的运行方式，这也是更加接近现实的运行方式。 125 | 126 | 根据Intel的说明，要实现离线运行，需要先在Raspberry Pi上安装Intel提供的Movidius SDK 1(legacy)，这需要Raspberry Pi使用16GB以上的microSD卡。 127 | 128 | 可参考 https://movidius.github.io/ncsdk/install.html 中介绍的Raspberry Pi Installation部分，安装该SDK。这里将Intel提供的说明简述如下： 129 | 130 | 1. 运行```sudo nano /etc/dphys-swapfile```，编辑配置文件，将```CONF_SWAPSIZE```参数的值设置为1024以上。这将扩大swapfile，以保证顺利编译。 131 | 132 | 2. 运行```sudo /etc/init.d/dphys-swapfile restart```，重新启动swapfile服务。 133 | 134 | 3. 运行以下命令来下载、编译并安装NCSDK 1。 135 | 136 | ``` 137 | wget https://ncs-forum-uploads.s3.amazonaws.com/ncsdk/ncsdk-01_12_00_01-full/ncsdk-1.12.00.01.tar.gz 138 | tar xvf ncsdk-1.12.00.01.tar.gz 139 | cd ncsdk-1.12.00.01 140 | make install 141 | make examples 142 | ``` 143 | 144 | *PS 我在进行到这一步骤时，等待了很长时间，并遇到了多种奇怪的错误提示。因为Raspberry Pi的性能有限，再加上中国较为复杂的网络环境，一次编译安装NCSDK会耗费数小时的时间，且过程中出现了一些警告和错误。经过数次尝试才成功安装。* 145 | 146 | 安装完成后，即可开始使用Movidius编译器，将模型编译为计算棒可以使用的文件。将Jupyter保存的三个文件： 147 | 148 | * *.index 149 | * *.data-00000-of-00001 150 | * *.mnist_model.meta 151 | 152 | 使用SFTP等方式发送到Raspberry Pi。进入放置这些文件的目录，运行 153 | 154 | ``` 155 | mvNCCompile filename.meta -s 12 -in x -on bias_add -o ncs.graph 156 | ``` 157 | 158 | 编译完成后，即可得到```ncs.graph```计算图文件。 159 | 160 | 要开始离线运行，将```Step_3b_Run_with_NCS```目录中的```rpi_ncs.py```发至Raspberry Pi，放置在与```ncs.graph```同一级目录下，执行 161 | 162 | ``` 163 | python3 rpi_ncs.py 164 | ``` 165 | 166 | 即可开始离线运行。 167 | 168 | # 后记 169 | 170 | 此项目是作者大学本科期间完成的作业，时间非常仓促，很多代码写得不够仔细，也有很多地方没有实现性能的最优化，甚至可以说性能很差。如今Google TensorFlow等程序更新迅速，作者难以保证这份程序可以长期与最新的计算机软件保持兼容，但希望能给和我一样喜欢玩电子的同学一个参考，恳请读者多提意见。 171 | -------------------------------------------------------------------------------- /Step_1_Data_Collection/convert_to_tfrecords_once.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from skimage import io 3 | from matplotlib import pyplot as plt 4 | import linecache 5 | 6 | import tensorflow as tf 7 | 8 | NUM_OF_IMAGES = input("Number of images?\r\n") 9 | 10 | def _bytes_feature(value): 11 | return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value])) 12 | 13 | def _int64_feature(value): 14 | return tf.train.Feature(int64_list=tf.train.Int64List(value=[value])) 15 | 16 | def _float32_feature(value): 17 | return tf.train.Feature(float_list=tf.train.FloatList(value=value)) 18 | 19 | tfrecords_filename = 'record.tfrecords' 20 | tfWriter = tf.python_io.TFRecordWriter(tfrecords_filename) 21 | 22 | for fileIndex in range(1, int(NUM_OF_IMAGES)+1): 23 | image = io.imread("./dataset/" + "%0*d" %(6, fileIndex) + ".jpg") 24 | 25 | # height, width, depth = image.shape 26 | imageString = image.tostring() 27 | 28 | direction = int(linecache.getline(r"./dataset/steer_log.txt", fileIndex)) 29 | 30 | example = tf.train.Example(features=tf.train.Features(feature={ 31 | 'image': _bytes_feature(imageString), 32 | 'label': _int64_feature(direction) 33 | })) 34 | 35 | print("Writing record " + "%0*d" %(6, fileIndex) + ".jpg " + "with label " + str(direction)) 36 | tfWriter.write(example.SerializeToString()) 37 | 38 | tfWriter.close() 39 | -------------------------------------------------------------------------------- /Step_1_Data_Collection/copy_to_rpi/mount_disk: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | sudo mount -o uid=pi,gid=pi /dev/sda1 /mnt/pdisk/ 3 | -------------------------------------------------------------------------------- /Step_1_Data_Collection/copy_to_rpi/rpi.py: -------------------------------------------------------------------------------- 1 | import RPi.GPIO as io 2 | import socket 3 | import threading 4 | from picamera import PiCamera 5 | from time import ctime,sleep 6 | import os 7 | 8 | addr=('',51423) 9 | s=socket.socket(socket.AF_INET,socket.SOCK_DGRAM) 10 | s.bind(addr) 11 | 12 | MOTOR_IO_LEFT = 13 13 | MOTOR_IO_RIGHT = 15 14 | MOTOR_IO_LEFT_REV = 19 15 | MOTOR_IO_RIGHT_REV = 21 16 | 17 | MOTOR_PWM_FREQUENCY = 120 18 | 19 | BCN_IO_RED = 12 20 | BCN_IO_GREEN = 11 21 | BCN_LIGHT_PERIOD = 0.05 22 | BCN_LIGHT_WAIT = 0.3 23 | BCN_INTERVAL = 3 24 | 25 | FRAME_WIDTH = 80 26 | FRAME_HEIGHT = 60 27 | FRAME_FPS = 30 28 | 29 | bcnStatus = 0 30 | loggerStatus = False 31 | 32 | camera = PiCamera() 33 | camera.resolution = (FRAME_WIDTH, FRAME_HEIGHT) 34 | camera.framerate = FRAME_FPS 35 | 36 | io.setmode(io.BOARD) 37 | 38 | io.setup(MOTOR_IO_LEFT, io.OUT) 39 | io.setup(MOTOR_IO_RIGHT, io.OUT) 40 | io.setup(MOTOR_IO_LEFT_REV, io.OUT) 41 | io.setup(MOTOR_IO_RIGHT_REV, io.OUT) 42 | 43 | io.setup(BCN_IO_RED, io.OUT) 44 | io.setup(BCN_IO_GREEN, io.OUT) 45 | 46 | MotorPwmL = io.PWM(MOTOR_IO_LEFT, MOTOR_PWM_FREQUENCY) 47 | MotorPwmR = io.PWM(MOTOR_IO_RIGHT, MOTOR_PWM_FREQUENCY) 48 | MotorPwmL.start(0) 49 | MotorPwmR.start(0) 50 | 51 | MotorPwmRevL = io.PWM(MOTOR_IO_LEFT_REV, MOTOR_PWM_FREQUENCY) 52 | MotorPwmRevR = io.PWM(MOTOR_IO_RIGHT_REV, MOTOR_PWM_FREQUENCY) 53 | MotorPwmRevL.start(0) 54 | MotorPwmRevR.start(0) 55 | 56 | def flashBeacon(): 57 | if bcnStatus == 0: 58 | io.output(BCN_IO_RED,True) 59 | sleep(BCN_LIGHT_WAIT) 60 | io.output(BCN_IO_RED,False) 61 | elif bcnStatus == 1: 62 | io.output(BCN_IO_GREEN,True) 63 | sleep(BCN_LIGHT_PERIOD) 64 | io.output(BCN_IO_GREEN,False) 65 | elif bcnStatus == 2: 66 | io.output(BCN_IO_RED,True) 67 | io.output(BCN_IO_GREEN,True) 68 | sleep(BCN_LIGHT_PERIOD) 69 | io.output(BCN_IO_RED,False) 70 | io.output(BCN_IO_GREEN,False) 71 | sleep(BCN_LIGHT_WAIT) 72 | io.output(BCN_IO_RED,True) 73 | sleep(BCN_LIGHT_PERIOD) 74 | io.output(BCN_IO_RED,False) 75 | 76 | def setBeaconStatus(status): 77 | global bcnStatus 78 | bcnStatus = status 79 | 80 | 81 | def Range_Limiter(val,range_min,range_max): 82 | if val > range_max: 83 | val = range_max 84 | elif val < range_min: 85 | val = range_min 86 | return val 87 | 88 | class Car: 89 | def __init__(self): 90 | self.speed = 0 91 | self.direction = 0 92 | self.pwmDutyLeft = 0 93 | self.pwmDutyRight = 0 94 | 95 | def writeMotor(self): 96 | # self.pwmDutyLeft = self.speed + (self.direction * (self.speed / 100)) 97 | # self.pwmDutyRight = self.speed - (self.direction * (self.speed / 100)) 98 | # self.pwmDutyLeft = Range_Limiter(self.pwmDutyLeft, 0, 100) 99 | # self.pwmDutyRight = Range_Limiter(self.pwmDutyRight, 0, 100) 100 | self.pwmDutyLeft = self.speed + self.direction 101 | self.pwmDutyRight = self.speed - self.direction 102 | self.pwmDutyLeft = Range_Limiter(self.pwmDutyLeft, -100, 100) 103 | self.pwmDutyRight = Range_Limiter(self.pwmDutyRight, -100, 100) 104 | # print(self.pwmDutyLeft, self.pwmDutyRight) 105 | 106 | if self.pwmDutyLeft >= 0: 107 | MotorPwmRevL.ChangeDutyCycle(0) 108 | MotorPwmL.ChangeDutyCycle(self.pwmDutyLeft) 109 | elif self.pwmDutyLeft < 0: 110 | MotorPwmL.ChangeDutyCycle(0) 111 | MotorPwmRevL.ChangeDutyCycle(-(self.pwmDutyLeft)) 112 | 113 | if self.pwmDutyRight >= 0: 114 | MotorPwmRevR.ChangeDutyCycle(0) 115 | MotorPwmR.ChangeDutyCycle(self.pwmDutyRight) 116 | elif self.pwmDutyRight < 0: 117 | MotorPwmR.ChangeDutyCycle(0) 118 | MotorPwmRevR.ChangeDutyCycle(-(self.pwmDutyRight)) 119 | 120 | 121 | rider = Car() 122 | logText = "N/A" 123 | 124 | def Controller_ReceiveAndWrite(): 125 | global loggerStatus 126 | global logText 127 | 128 | while True: 129 | data,addr = s.recvfrom(2048) 130 | data = str(data) 131 | # print(data) 132 | if rider.speed > 0 and loggerStatus == False: 133 | setBeaconStatus(2) 134 | elif rider.speed == 0 and loggerStatus == False: 135 | setBeaconStatus(1) 136 | 137 | if data == "R": 138 | if loggerStatus == False: 139 | loggerStatus = True 140 | else: 141 | loggerStatus = False 142 | 143 | else: 144 | rider.speed = int(float(data[0:data.find(r',')])) 145 | rider.direction = int(float(data[data.find(r',') + 1:-1] + data[-1])) 146 | logText = str(rider.direction) 147 | rider.writeMotor() 148 | sleep(0.1) 149 | 150 | def Beacon_Write(): 151 | while True: 152 | flashBeacon() 153 | sleep(BCN_INTERVAL) 154 | 155 | def Logger(): 156 | global loggerStatus 157 | global logText 158 | while True: 159 | if loggerStatus == True: 160 | setBeaconStatus(0) 161 | logFile = open('/mnt/pdisk/steer_log.txt', 'w') 162 | # for filename in camera.capture_continuous('/home/pi/img/{counter:06d}.jpg', use_video_port=True): 163 | for filename in camera.capture_continuous('/mnt/pdisk/{counter:06d}.jpg', use_video_port=True): 164 | # print('Captured %s' % filename) 165 | logFile.write(logText) 166 | logFile.write("\r\n") 167 | sleep(0.05) 168 | if loggerStatus == False: 169 | logFile.close( ) 170 | break 171 | sleep(0.1) 172 | 173 | ControlThread = threading.Thread(target = Controller_ReceiveAndWrite) 174 | ControlThread.start() 175 | BeaconThread = threading.Thread(target = Beacon_Write) 176 | BeaconThread.start() 177 | LoggerThread = threading.Thread(target = Logger) 178 | LoggerThread.start() -------------------------------------------------------------------------------- /Step_1_Data_Collection/transmitter.py: -------------------------------------------------------------------------------- 1 | import socket 2 | from sys import exit 3 | import pygame 4 | import threading 5 | from time import ctime,sleep 6 | import string 7 | 8 | # CONFIG 9 | addr=('192.168.31.204',51423) 10 | deadzone = 0.1 11 | # END OF CONFIG 12 | 13 | s=socket.socket(socket.AF_INET,socket.SOCK_DGRAM) 14 | 15 | class Car: 16 | def __init__(self): 17 | self.speed = 0 18 | self.acceleration = -2 19 | self.direction = 0 20 | self.differential = 40 21 | 22 | def Controller_DeadZoneCancellation(rawData): 23 | if(abs(rawData) <= deadzone): 24 | rawData = 0 25 | return rawData 26 | 27 | def Range_Limiter(val,range_min,range_max): 28 | if val > range_max: 29 | val = range_max 30 | elif val < range_min: 31 | val = range_min 32 | return val 33 | 34 | def Controller_ReadAndSend(): 35 | while True: 36 | pygame.event.pump() 37 | stickVal_Acc = Controller_DeadZoneCancellation(controller.get_axis(1)) 38 | stickVal_Dir = Controller_DeadZoneCancellation(controller.get_axis(2)) 39 | rider_local.speed += rider_local.acceleration * stickVal_Acc 40 | rider_local.speed = Range_Limiter(rider_local.speed, -20, 100) 41 | rider_local.direction = stickVal_Dir * rider_local.differential 42 | 43 | # if controller.get_button(9) == 1: 44 | # print("Recording Triggered!") 45 | # temp = rider_local.speed 46 | # rider_local.speed = -1 # As a signal to start data recording 47 | # msgCtrl_Udp = str(rider_local.speed) + "," + str(rider_local.direction) 48 | # s.sendto(msgCtrl_Udp.encode('utf-8'), addr) 49 | # rider_local.speed = temp 50 | 51 | if controller.get_button(9) == 1: 52 | s.sendto("R".encode('utf-8'), addr) 53 | print("Recording Triggered!") 54 | sleep(0.5) #To prevent data congestion 55 | 56 | 57 | if controller.get_button(6) + controller.get_button(7) == 2: 58 | print("Brake!") 59 | rider_local.speed = 0 #Emergency Stop 60 | 61 | if controller.get_button(4) + controller.get_button(5) == 2: 62 | print("Train!") 63 | rider_local.speed = 25 #Training Speed 64 | 65 | if controller.get_button(8) == 1: 66 | s.close() 67 | exit() 68 | 69 | msgCtrl_Udp = str(rider_local.speed) + "," + str(rider_local.direction) 70 | s.sendto(msgCtrl_Udp.encode('utf-8'), addr) 71 | print("speed:",round(rider_local.speed,3),"direction",round(rider_local.direction,3)) 72 | sleep(0.15) 73 | 74 | pygame.joystick.init() 75 | pygame.display.init() 76 | 77 | if pygame.joystick.get_count() == 1: 78 | controller = pygame.joystick.Joystick(0) 79 | print("Using controller:",controller.get_name()) 80 | elif pygame.joystick.get_count() == 0: 81 | print("Controller Not Found.") 82 | exit() 83 | else: 84 | print("Multiple controllers found.") 85 | exit() 86 | 87 | controller.init() 88 | print("Controller initialized.") 89 | 90 | rider_local = Car() 91 | SendThread = threading.Thread(target = Controller_ReadAndSend) 92 | SendThread.start() 93 | -------------------------------------------------------------------------------- /Step_2&3a_Train&Run/rpi/stream.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | ./mjpg_streamer -o "output_http.so -w ./www" -i "input_raspicam.so -x 80 -y 60 -fps 30" 3 | 4 | -------------------------------------------------------------------------------- /Step_3b_Run_with_NCS/rpi_ncs.py: -------------------------------------------------------------------------------- 1 | from mvnc import mvncapi as mvnc 2 | import numpy as np 3 | import cv2 4 | import os 5 | import sys 6 | from typing import List 7 | from time import ctime,sleep 8 | from picamera import PiCamera 9 | import io 10 | import RPi.GPIO as gpio 11 | import threading 12 | 13 | MOTOR_IO_LEFT = 13 14 | MOTOR_IO_RIGHT = 15 15 | MOTOR_IO_LEFT_REV = 19 16 | MOTOR_IO_RIGHT_REV = 21 17 | 18 | MOTOR_PWM_FREQUENCY = 120 19 | 20 | BCN_IO_RED = 12 21 | BCN_IO_GREEN = 11 22 | BCN_LIGHT_PERIOD = 0.05 23 | BCN_LIGHT_WAIT = 0.3 24 | BCN_INTERVAL = 10 25 | 26 | FRAME_WIDTH = 80 27 | FRAME_HEIGHT = 60 28 | FRAME_FPS = 30 29 | 30 | bcnStatus = 0 31 | 32 | def flashBeacon(): 33 | if bcnStatus == 0: 34 | gpio.output(BCN_IO_RED,True) 35 | gpio.output(BCN_IO_GREEN,True) 36 | sleep(BCN_LIGHT_PERIOD) 37 | gpio.output(BCN_IO_GREEN,False) 38 | sleep(BCN_LIGHT_WAIT) 39 | gpio.output(BCN_IO_RED,False) 40 | elif bcnStatus == 1: 41 | gpio.output(BCN_IO_GREEN,True) 42 | sleep(BCN_LIGHT_PERIOD) 43 | gpio.output(BCN_IO_GREEN,False) 44 | elif bcnStatus == 2: 45 | gpio.output(BCN_IO_RED,True) 46 | gpio.output(BCN_IO_GREEN,True) 47 | sleep(BCN_LIGHT_PERIOD) 48 | gpio.output(BCN_IO_RED,False) 49 | gpio.output(BCN_IO_GREEN,False) 50 | sleep(BCN_LIGHT_WAIT) 51 | gpio.output(BCN_IO_RED,True) 52 | sleep(BCN_LIGHT_PERIOD) 53 | gpio.output(BCN_IO_RED,False) 54 | 55 | def setBeaconStatus(status): 56 | global bcnStatus 57 | bcnStatus = status 58 | 59 | 60 | def Range_Limiter(val,range_min,range_max): 61 | if val > range_max: 62 | val = range_max 63 | elif val < range_min: 64 | val = range_min 65 | return val 66 | 67 | class Car: 68 | def __init__(self): 69 | self.speed = 0 70 | self.direction = 0 71 | self.pwmDutyLeft = 0 72 | self.pwmDutyRight = 0 73 | 74 | def writeMotor(self): 75 | # self.pwmDutyLeft = self.speed + (self.direction * (self.speed / 100)) 76 | # self.pwmDutyRight = self.speed - (self.direction * (self.speed / 100)) 77 | # self.pwmDutyLeft = Range_Limiter(self.pwmDutyLeft, 0, 100) 78 | # self.pwmDutyRight = Range_Limiter(self.pwmDutyRight, 0, 100) 79 | self.pwmDutyLeft = self.speed + self.direction 80 | self.pwmDutyRight = self.speed - self.direction 81 | self.pwmDutyLeft = Range_Limiter(self.pwmDutyLeft, -100, 100) 82 | self.pwmDutyRight = Range_Limiter(self.pwmDutyRight, -100, 100) 83 | # print(self.pwmDutyLeft, self.pwmDutyRight) 84 | 85 | if self.pwmDutyLeft >= 0: 86 | MotorPwmRevL.ChangeDutyCycle(0) 87 | MotorPwmL.ChangeDutyCycle(self.pwmDutyLeft) 88 | elif self.pwmDutyLeft < 0: 89 | MotorPwmL.ChangeDutyCycle(0) 90 | MotorPwmRevL.ChangeDutyCycle(-(self.pwmDutyLeft)) 91 | 92 | if self.pwmDutyRight >= 0: 93 | MotorPwmRevR.ChangeDutyCycle(0) 94 | MotorPwmR.ChangeDutyCycle(self.pwmDutyRight) 95 | elif self.pwmDutyRight < 0: 96 | MotorPwmR.ChangeDutyCycle(0) 97 | MotorPwmRevR.ChangeDutyCycle(-(self.pwmDutyRight)) 98 | 99 | 100 | rider = Car() 101 | 102 | print("Initializing hardware") 103 | camera = PiCamera() 104 | camera.resolution = (FRAME_WIDTH, FRAME_HEIGHT) 105 | camera.framerate = FRAME_FPS 106 | 107 | gpio.setmode(gpio.BOARD) 108 | 109 | gpio.setup(MOTOR_IO_LEFT, gpio.OUT) 110 | gpio.setup(MOTOR_IO_RIGHT, gpio.OUT) 111 | gpio.setup(MOTOR_IO_LEFT_REV, gpio.OUT) 112 | gpio.setup(MOTOR_IO_RIGHT_REV, gpio.OUT) 113 | 114 | gpio.setup(BCN_IO_RED, gpio.OUT) 115 | gpio.setup(BCN_IO_GREEN, gpio.OUT) 116 | 117 | MotorPwmL = gpio.PWM(MOTOR_IO_LEFT, MOTOR_PWM_FREQUENCY) 118 | MotorPwmR = gpio.PWM(MOTOR_IO_RIGHT, MOTOR_PWM_FREQUENCY) 119 | MotorPwmL.start(0) 120 | MotorPwmR.start(0) 121 | 122 | MotorPwmRevL = gpio.PWM(MOTOR_IO_LEFT_REV, MOTOR_PWM_FREQUENCY) 123 | MotorPwmRevR = gpio.PWM(MOTOR_IO_RIGHT_REV, MOTOR_PWM_FREQUENCY) 124 | MotorPwmRevL.start(0) 125 | MotorPwmRevR.start(0) 126 | 127 | 128 | print("Starting NCS") 129 | devices = mvnc.EnumerateDevices() 130 | if len(devices) == 0: 131 | print('Error - No devices found') 132 | 133 | device = mvnc.Device(devices[0]) 134 | device.OpenDevice() 135 | 136 | 137 | print("Reading graph file") 138 | graph_filename = './ncs.graph' 139 | try : 140 | with open(graph_filename, mode='rb') as f: 141 | in_memory_graph = f.read() 142 | except : 143 | print ("Error reading graph file: " + graph_filename) 144 | 145 | print("Downloading graph to NCS") 146 | graph = device.AllocateGraph(in_memory_graph) 147 | 148 | 149 | def Controller_ReceiveAndWrite(): 150 | while True: 151 | stream = io.BytesIO() 152 | camera.capture(stream, format='jpeg', use_video_port=True) 153 | data = np.fromstring(stream.getvalue(), dtype=np.uint8) 154 | image = cv2.imdecode(data, 1) 155 | image = image[:, :, ::-1] 156 | image = image.reshape((14400,)).astype(np.float16) 157 | image = image / 255 158 | # image = np.zeros((14400,), dtype=np.float16) //for NCS benchmark only 159 | if (graph.LoadTensor(image.astype(np.float16), 'user object')): 160 | output, userobj = graph.GetResult() 161 | decision = (np.argmax(output) - 7) * 6 162 | print(decision) 163 | 164 | rider.speed = 5 165 | rider.direction = decision 166 | rider.writeMotor() 167 | 168 | def Beacon_Write(): 169 | while True: 170 | flashBeacon() 171 | sleep(BCN_INTERVAL) 172 | 173 | ControlThread = threading.Thread(target = Controller_ReceiveAndWrite) 174 | ControlThread.start() 175 | BeaconThread = threading.Thread(target = Beacon_Write) 176 | BeaconThread.start() -------------------------------------------------------------------------------- /checkpoint: -------------------------------------------------------------------------------- 1 | model_checkpoint_path: "tl_model" 2 | all_model_checkpoint_paths: "tl_model" 3 | -------------------------------------------------------------------------------- /dataset_example/000001.jpg: 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