1296 lines
77 KiB
Plaintext
1296 lines
77 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Cargar el modelo ssd7 \n",
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"(https://github.com/pierluigiferrari/ssd_keras#how-to-fine-tune-one-of-the-trained-models-on-your-own-dataset)\n",
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"\n",
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"Training del SSD7 (modelo reducido de SSD). Parámetros en config_7.json y descargar VGG_ILSVRC_16_layers_fc_reduced.h5\n",
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"\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"Training on: \t{'1': 1, '2': 2, '3': 3, '4': 4, '5': 5, '6': 6, '7': 7, '8': 8}\n",
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"\n",
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"OK create model\n",
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"\n",
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"Loading pretrained weights VGG.\n",
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"\n",
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"__________________________________________________________________________________________________\n",
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"Layer (type) Output Shape Param # Connected to \n",
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"==================================================================================================\n",
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"input_1 (InputLayer) (None, 400, 400, 3) 0 \n",
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"__________________________________________________________________________________________________\n",
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"identity_layer (Lambda) (None, 400, 400, 3) 0 input_1[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"conv1 (Conv2D) (None, 400, 400, 32) 2432 identity_layer[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"bn1 (BatchNormalization) (None, 400, 400, 32) 128 conv1[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"elu1 (ELU) (None, 400, 400, 32) 0 bn1[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"pool1 (MaxPooling2D) (None, 200, 200, 32) 0 elu1[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"conv2 (Conv2D) (None, 200, 200, 48) 13872 pool1[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"bn2 (BatchNormalization) (None, 200, 200, 48) 192 conv2[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"elu2 (ELU) (None, 200, 200, 48) 0 bn2[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"pool2 (MaxPooling2D) (None, 100, 100, 48) 0 elu2[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"conv3 (Conv2D) (None, 100, 100, 64) 27712 pool2[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"bn3 (BatchNormalization) (None, 100, 100, 64) 256 conv3[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"elu3 (ELU) (None, 100, 100, 64) 0 bn3[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"pool3 (MaxPooling2D) (None, 50, 50, 64) 0 elu3[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"conv4 (Conv2D) (None, 50, 50, 64) 36928 pool3[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"bn4 (BatchNormalization) (None, 50, 50, 64) 256 conv4[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"elu4 (ELU) (None, 50, 50, 64) 0 bn4[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"pool4 (MaxPooling2D) (None, 25, 25, 64) 0 elu4[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"conv5 (Conv2D) (None, 25, 25, 48) 27696 pool4[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"bn5 (BatchNormalization) (None, 25, 25, 48) 192 conv5[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"elu5 (ELU) (None, 25, 25, 48) 0 bn5[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"pool5 (MaxPooling2D) (None, 12, 12, 48) 0 elu5[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"conv6 (Conv2D) (None, 12, 12, 48) 20784 pool5[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"bn6 (BatchNormalization) (None, 12, 12, 48) 192 conv6[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"elu6 (ELU) (None, 12, 12, 48) 0 bn6[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"pool6 (MaxPooling2D) (None, 6, 6, 48) 0 elu6[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"conv7 (Conv2D) (None, 6, 6, 32) 13856 pool6[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"bn7 (BatchNormalization) (None, 6, 6, 32) 128 conv7[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"elu7 (ELU) (None, 6, 6, 32) 0 bn7[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"classes4 (Conv2D) (None, 50, 50, 36) 20772 elu4[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"classes5 (Conv2D) (None, 25, 25, 36) 15588 elu5[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"classes6 (Conv2D) (None, 12, 12, 36) 15588 elu6[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"classes7 (Conv2D) (None, 6, 6, 36) 10404 elu7[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"boxes4 (Conv2D) (None, 50, 50, 16) 9232 elu4[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"boxes5 (Conv2D) (None, 25, 25, 16) 6928 elu5[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"boxes6 (Conv2D) (None, 12, 12, 16) 6928 elu6[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"boxes7 (Conv2D) (None, 6, 6, 16) 4624 elu7[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"classes4_reshape (Reshape) (None, 10000, 9) 0 classes4[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"classes5_reshape (Reshape) (None, 2500, 9) 0 classes5[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"classes6_reshape (Reshape) (None, 576, 9) 0 classes6[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"classes7_reshape (Reshape) (None, 144, 9) 0 classes7[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"anchors4 (AnchorBoxes) (None, 50, 50, 4, 8) 0 boxes4[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"anchors5 (AnchorBoxes) (None, 25, 25, 4, 8) 0 boxes5[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"anchors6 (AnchorBoxes) (None, 12, 12, 4, 8) 0 boxes6[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"anchors7 (AnchorBoxes) (None, 6, 6, 4, 8) 0 boxes7[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"classes_concat (Concatenate) (None, 13220, 9) 0 classes4_reshape[0][0] \n",
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" classes5_reshape[0][0] \n",
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" classes6_reshape[0][0] \n",
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" classes7_reshape[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"boxes4_reshape (Reshape) (None, 10000, 4) 0 boxes4[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"boxes5_reshape (Reshape) (None, 2500, 4) 0 boxes5[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"boxes6_reshape (Reshape) (None, 576, 4) 0 boxes6[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"boxes7_reshape (Reshape) (None, 144, 4) 0 boxes7[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"anchors4_reshape (Reshape) (None, 10000, 8) 0 anchors4[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"anchors5_reshape (Reshape) (None, 2500, 8) 0 anchors5[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"anchors6_reshape (Reshape) (None, 576, 8) 0 anchors6[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"anchors7_reshape (Reshape) (None, 144, 8) 0 anchors7[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"classes_softmax (Activation) (None, 13220, 9) 0 classes_concat[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"boxes_concat (Concatenate) (None, 13220, 4) 0 boxes4_reshape[0][0] \n",
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" boxes5_reshape[0][0] \n",
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" boxes6_reshape[0][0] \n",
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" boxes7_reshape[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"anchors_concat (Concatenate) (None, 13220, 8) 0 anchors4_reshape[0][0] \n",
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" anchors5_reshape[0][0] \n",
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" anchors6_reshape[0][0] \n",
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" anchors7_reshape[0][0] \n",
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"__________________________________________________________________________________________________\n",
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"predictions (Concatenate) (None, 13220, 21) 0 classes_softmax[0][0] \n",
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" boxes_concat[0][0] \n",
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" anchors_concat[0][0] \n",
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"==================================================================================================\n",
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"Total params: 234,688\n",
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"Trainable params: 234,016\n",
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"Non-trainable params: 672\n",
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"__________________________________________________________________________________________________\n"
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]
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}
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],
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"source": [
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"from keras.optimizers import Adam, SGD\n",
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"from keras.callbacks import ModelCheckpoint, LearningRateScheduler, TerminateOnNaN, CSVLogger\n",
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"from keras import backend as K\n",
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"from keras.models import load_model\n",
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"from math import ceil\n",
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"import numpy as np\n",
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"from matplotlib import pyplot as plt\n",
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"import os\n",
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"import json\n",
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"import xml.etree.cElementTree as ET\n",
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"\n",
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"import sys\n",
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"sys.path += [os.path.abspath('../ssd_keras-master')]\n",
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"\n",
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"from keras_loss_function.keras_ssd_loss import SSDLoss\n",
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"from keras_layers.keras_layer_AnchorBoxes import AnchorBoxes\n",
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"from keras_layers.keras_layer_DecodeDetections import DecodeDetections\n",
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"from keras_layers.keras_layer_DecodeDetectionsFast import DecodeDetectionsFast\n",
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"from keras_layers.keras_layer_L2Normalization import L2Normalization\n",
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"from ssd_encoder_decoder.ssd_input_encoder import SSDInputEncoder\n",
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"from ssd_encoder_decoder.ssd_output_decoder import decode_detections, decode_detections_fast\n",
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"from data_generator.object_detection_2d_data_generator import DataGenerator\n",
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"from data_generator.object_detection_2d_geometric_ops import Resize\n",
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"from data_generator.object_detection_2d_photometric_ops import ConvertTo3Channels\n",
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"from data_generator.data_augmentation_chain_original_ssd import SSDDataAugmentation\n",
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"from data_generator.object_detection_2d_misc_utils import apply_inverse_transforms\n",
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"from eval_utils.average_precision_evaluator import Evaluator\n",
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"from data_generator.data_augmentation_chain_variable_input_size import DataAugmentationVariableInputSize\n",
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"from data_generator.data_augmentation_chain_constant_input_size import DataAugmentationConstantInputSize\n",
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"\n",
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"\n",
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"def makedirs(path):\n",
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" try:\n",
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" os.makedirs(path)\n",
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" except OSError:\n",
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" if not os.path.isdir(path):\n",
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" raise\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"K.tensorflow_backend._get_available_gpus()\n",
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"\n",
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"\n",
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"def lr_schedule(epoch):\n",
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" if epoch < 80:\n",
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" return 0.001\n",
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" elif epoch < 100:\n",
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" return 0.0001\n",
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" else:\n",
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" return 0.00001\n",
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"\n",
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"config_path = 'config_7_fault.json'\n",
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"\n",
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"\n",
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"with open(config_path) as config_buffer:\n",
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" config = json.loads(config_buffer.read())\n",
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"\n",
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"###############################\n",
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"# Parse the annotations\n",
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"###############################\n",
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"path_imgs_training = config['train']['train_image_folder']\n",
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"path_anns_training = config['train']['train_annot_folder']\n",
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"path_imgs_val = config['test']['test_image_folder']\n",
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"path_anns_val = config['test']['test_annot_folder']\n",
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"labels = config['model']['labels']\n",
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"categories = {}\n",
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"#categories = {\"Razor\": 1, \"Gun\": 2, \"Knife\": 3, \"Shuriken\": 4} #la categoría 0 es la background\n",
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"for i in range(len(labels)): categories[labels[i]] = i+1\n",
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"print('\\nTraining on: \\t' + str(categories) + '\\n')\n",
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"\n",
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"####################################\n",
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"# Parameters\n",
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"###################################\n",
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" #%%\n",
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"img_height = config['model']['input'] # Height of the model input images\n",
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"img_width = config['model']['input'] # Width of the model input images\n",
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"img_channels = 3 # Number of color channels of the model input images\n",
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"mean_color = [123, 117, 104] # The per-channel mean of the images in the dataset. Do not change this value if you're using any of the pre-trained weights.\n",
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"swap_channels = [2, 1, 0] # The color channel order in the original SSD is BGR, so we'll have the model reverse the color channel order of the input images.\n",
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"n_classes = len(labels) # Number of positive classes, e.g. 20 for Pascal VOC, 80 for MS COCO\n",
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"scales_pascal = [0.1, 0.2, 0.37, 0.54, 0.71, 0.88, 1.05] # The anchor box scaling factors used in the original SSD300 for the Pascal VOC datasets\n",
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"#scales_coco = [0.07, 0.15, 0.33, 0.51, 0.69, 0.87, 1.05] # The anchor box scaling factors used in the original SSD300 for the MS COCO datasets\n",
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"scales = scales_pascal\n",
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"aspect_ratios = [[1.0, 2.0, 0.5],\n",
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" [1.0, 2.0, 0.5, 3.0, 1.0/3.0],\n",
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" [1.0, 2.0, 0.5, 3.0, 1.0/3.0],\n",
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" [1.0, 2.0, 0.5, 3.0, 1.0/3.0],\n",
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" [1.0, 2.0, 0.5],\n",
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" [1.0, 2.0, 0.5]] # The anchor box aspect ratios used in the original SSD300; the order matters\n",
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"two_boxes_for_ar1 = True\n",
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"steps = [8, 16, 32, 64, 100, 300] # The space between two adjacent anchor box center points for each predictor layer.\n",
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"offsets = [0.5, 0.5, 0.5, 0.5, 0.5, 0.5] # The offsets of the first anchor box center points from the top and left borders of the image as a fraction of the step size for each predictor layer.\n",
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"clip_boxes = False # Whether or not to clip the anchor boxes to lie entirely within the image boundaries\n",
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"variances = [0.1, 0.1, 0.2, 0.2] # The variances by which the encoded target coordinates are divided as in the original implementation\n",
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"normalize_coords = True\n",
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"\n",
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"K.clear_session() # Clear previous models from memory.\n",
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"\n",
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"\n",
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"model_path = config['train']['saved_weights_name']\n",
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"# 3: Instantiate an optimizer and the SSD loss function and compile the model.\n",
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"# If you want to follow the original Caffe implementation, use the preset SGD\n",
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"# optimizer, otherwise I'd recommend the commented-out Adam optimizer.\n",
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"\n",
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"\n",
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"if config['model']['backend'] == 'ssd7':\n",
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" #weights_path = 'VGG_ILSVRC_16_layers_fc_reduced.h5'\n",
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" scales = [0.08, 0.16, 0.32, 0.64, 0.96] # An explicit list of anchor box scaling factors. If this is passed, it will override `min_scale` and `max_scale`.\n",
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" aspect_ratios = [0.5 ,1.0, 2.0] # The list of aspect ratios for the anchor boxes\n",
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" two_boxes_for_ar1 = True # Whether or not you want to generate two anchor boxes for aspect ratio 1\n",
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" steps = None # In case you'd like to set the step sizes for the anchor box grids manually; not recommended\n",
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" offsets = None\n",
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"\n",
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"if os.path.exists(model_path):\n",
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" print(\"\\nLoading pretrained weights.\\n\")\n",
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" # We need to create an SSDLoss object in order to pass that to the model loader.\n",
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" ssd_loss = SSDLoss(neg_pos_ratio=3, alpha=1.0)\n",
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"\n",
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" K.clear_session() # Clear previous models from memory.\n",
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" model = load_model(model_path, custom_objects={'AnchorBoxes': AnchorBoxes,\n",
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" 'L2Normalization': L2Normalization,\n",
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" 'compute_loss': ssd_loss.compute_loss})\n",
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"\n",
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"\n",
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"else:\n",
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" ####################################\n",
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" # Build the Keras model.\n",
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" ###################################\n",
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"\n",
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" if config['model']['backend'] == 'ssd300':\n",
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" #weights_path = 'VGG_VOC0712Plus_SSD_300x300_ft_iter_160000.h5'\n",
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" from models.keras_ssd300 import ssd_300 as ssd\n",
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"\n",
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" model = ssd_300(image_size=(img_height, img_width, img_channels),\n",
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" n_classes=n_classes,\n",
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" mode='training',\n",
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" l2_regularization=0.0005,\n",
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" scales=scales,\n",
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" aspect_ratios_per_layer=aspect_ratios,\n",
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" two_boxes_for_ar1=two_boxes_for_ar1,\n",
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" steps=steps,\n",
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" offsets=offsets,\n",
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" clip_boxes=clip_boxes,\n",
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" variances=variances,\n",
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" normalize_coords=normalize_coords,\n",
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" subtract_mean=mean_color,\n",
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" swap_channels=swap_channels)\n",
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"\n",
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"\n",
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" elif config['model']['backend'] == 'ssd7':\n",
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" #weights_path = 'VGG_ILSVRC_16_layers_fc_reduced.h5'\n",
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" from models.keras_ssd7 import build_model as ssd\n",
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" scales = [0.08, 0.16, 0.32, 0.64, 0.96] # An explicit list of anchor box scaling factors. If this is passed, it will override `min_scale` and `max_scale`.\n",
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" aspect_ratios = [0.5 ,1.0, 2.0] # The list of aspect ratios for the anchor boxes\n",
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" two_boxes_for_ar1 = True # Whether or not you want to generate two anchor boxes for aspect ratio 1\n",
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" steps = None # In case you'd like to set the step sizes for the anchor box grids manually; not recommended\n",
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" offsets = None\n",
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" model = ssd(image_size=(img_height, img_width, img_channels),\n",
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" n_classes=n_classes,\n",
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" mode='training',\n",
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" l2_regularization=0.0005,\n",
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" scales=scales,\n",
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" aspect_ratios_global=aspect_ratios,\n",
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" aspect_ratios_per_layer=None,\n",
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" two_boxes_for_ar1=two_boxes_for_ar1,\n",
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" steps=steps,\n",
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" offsets=offsets,\n",
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" clip_boxes=clip_boxes,\n",
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" variances=variances,\n",
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" normalize_coords=normalize_coords,\n",
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" subtract_mean=None,\n",
|
|
" divide_by_stddev=None)\n",
|
|
"\n",
|
|
" else :\n",
|
|
" print('Wrong Backend')\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
" print('OK create model')\n",
|
|
" #sgd = SGD(lr=config['train']['learning_rate'], momentum=0.9, decay=0.0, nesterov=False)\n",
|
|
"\n",
|
|
" # TODO: Set the path to the weights you want to load. only for ssd300 or ssd512\n",
|
|
"\n",
|
|
" weights_path = '../ssd_keras-master/VGG_ILSVRC_16_layers_fc_reduced.h5'\n",
|
|
" print(\"\\nLoading pretrained weights VGG.\\n\")\n",
|
|
" model.load_weights(weights_path, by_name=True)\n",
|
|
"\n",
|
|
" # 3: Instantiate an optimizer and the SSD loss function and compile the model.\n",
|
|
" # If you want to follow the original Caffe implementation, use the preset SGD\n",
|
|
" # optimizer, otherwise I'd recommend the commented-out Adam optimizer.\n",
|
|
"\n",
|
|
"\n",
|
|
" #adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)\n",
|
|
" #sgd = SGD(lr=0.001, momentum=0.9, decay=0.0, nesterov=False)\n",
|
|
" optimizer = Adam(lr=config['train']['learning_rate'], beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)\n",
|
|
" ssd_loss = SSDLoss(neg_pos_ratio=3, alpha=1.0)\n",
|
|
" model.compile(optimizer=optimizer, loss=ssd_loss.compute_loss)\n",
|
|
"\n",
|
|
" model.summary()\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Instanciar los generadores de datos y entrenamiento del modelo.\n",
|
|
"\n",
|
|
"*Cambio realizado para leer png y jpg. keras-ssd-master/data_generator/object_detection_2d_data_generator.py función parse_xml\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Processing image set 'train.txt': 100%|██████████| 603/603 [00:00<00:00, 718.60it/s]\n",
|
|
"Processing image set 'test.txt': 100%|██████████| 67/67 [00:00<00:00, 469.34it/s]\n",
|
|
"1 : 595\n",
|
|
"2 : 39\n",
|
|
"3 : 20\n",
|
|
"4 : 191\n",
|
|
"5 : 6\n",
|
|
"7 : 17\n",
|
|
"Number of images in the training dataset:\t 603\n",
|
|
"Number of images in the validation dataset:\t 67\n",
|
|
"Epoch 1/100\n",
|
|
"\n",
|
|
"Epoch 00001: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 216s 4s/step - loss: 15.2071 - val_loss: 12.0999\n",
|
|
"\n",
|
|
"Epoch 00001: val_loss improved from inf to 12.09990, saving model to experimento_ssd7_fault.h5\n",
|
|
"Epoch 2/100\n",
|
|
"\n",
|
|
"Epoch 00002: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 200s 4s/step - loss: 12.5243 - val_loss: 9.2978\n",
|
|
"\n",
|
|
"Epoch 00002: val_loss improved from 12.09990 to 9.29781, saving model to experimento_ssd7_fault.h5\n",
|
|
"Epoch 3/100\n",
|
|
"\n",
|
|
"Epoch 00003: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 202s 4s/step - loss: 11.0985 - val_loss: 8.5945\n",
|
|
"\n",
|
|
"Epoch 00003: val_loss improved from 9.29781 to 8.59453, saving model to experimento_ssd7_fault.h5\n",
|
|
"Epoch 4/100\n",
|
|
"\n",
|
|
"Epoch 00004: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 200s 4s/step - loss: 9.9140 - val_loss: 7.7708\n",
|
|
"\n",
|
|
"Epoch 00004: val_loss improved from 8.59453 to 7.77083, saving model to experimento_ssd7_fault.h5\n",
|
|
"Epoch 5/100\n",
|
|
"\n",
|
|
"Epoch 00005: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 204s 4s/step - loss: 8.8886 - val_loss: 7.3329\n",
|
|
"\n",
|
|
"Epoch 00005: val_loss improved from 7.77083 to 7.33291, saving model to experimento_ssd7_fault.h5\n",
|
|
"Epoch 6/100\n",
|
|
"\n",
|
|
"Epoch 00006: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 201s 4s/step - loss: 8.2391 - val_loss: 7.4923\n",
|
|
"\n",
|
|
"Epoch 00006: val_loss did not improve from 7.33291\n",
|
|
"Epoch 7/100\n",
|
|
"\n",
|
|
"Epoch 00007: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 200s 4s/step - loss: 7.8558 - val_loss: 6.3696\n",
|
|
"\n",
|
|
"Epoch 00007: val_loss improved from 7.33291 to 6.36964, saving model to experimento_ssd7_fault.h5\n",
|
|
"Epoch 8/100\n",
|
|
"\n",
|
|
"Epoch 00008: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 24506s 490s/step - loss: 7.2554 - val_loss: 6.6485\n",
|
|
"\n",
|
|
"Epoch 00008: val_loss did not improve from 6.36964\n",
|
|
"Epoch 9/100\n",
|
|
"\n",
|
|
"Epoch 00009: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 221s 4s/step - loss: 7.4554 - val_loss: 5.6804\n",
|
|
"\n",
|
|
"Epoch 00009: val_loss improved from 6.36964 to 5.68039, saving model to experimento_ssd7_fault.h5\n",
|
|
"Epoch 10/100\n",
|
|
"\n",
|
|
"Epoch 00010: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 220s 4s/step - loss: 6.7250 - val_loss: 6.7484\n",
|
|
"\n",
|
|
"Epoch 00010: val_loss did not improve from 5.68039\n",
|
|
"Epoch 11/100\n",
|
|
"\n",
|
|
"Epoch 00011: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 222s 4s/step - loss: 6.9921 - val_loss: 5.0112\n",
|
|
"\n",
|
|
"Epoch 00011: val_loss improved from 5.68039 to 5.01120, saving model to experimento_ssd7_fault.h5\n",
|
|
"Epoch 12/100\n",
|
|
"\n",
|
|
"Epoch 00012: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 299s 6s/step - loss: 6.9904 - val_loss: 10.5203\n",
|
|
"\n",
|
|
"Epoch 00012: val_loss did not improve from 5.01120\n",
|
|
"Epoch 13/100\n",
|
|
"\n",
|
|
"Epoch 00013: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 216s 4s/step - loss: 6.5806 - val_loss: 13.1147\n",
|
|
"\n",
|
|
"Epoch 00013: val_loss did not improve from 5.01120\n",
|
|
"Epoch 14/100\n",
|
|
"\n",
|
|
"Epoch 00014: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 208s 4s/step - loss: 6.4404 - val_loss: 7.3660\n",
|
|
"\n",
|
|
"Epoch 00014: val_loss did not improve from 5.01120\n",
|
|
"Epoch 15/100\n",
|
|
"\n",
|
|
"Epoch 00015: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 210s 4s/step - loss: 6.7263 - val_loss: 6.3213\n",
|
|
"\n",
|
|
"Epoch 00015: val_loss did not improve from 5.01120\n",
|
|
"Epoch 16/100\n",
|
|
"\n",
|
|
"Epoch 00016: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 207s 4s/step - loss: 6.5043 - val_loss: 12.7072\n",
|
|
"\n",
|
|
"Epoch 00016: val_loss did not improve from 5.01120\n",
|
|
"Epoch 17/100\n",
|
|
"\n",
|
|
"Epoch 00017: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 208s 4s/step - loss: 6.3426 - val_loss: 10.2291\n",
|
|
"\n",
|
|
"Epoch 00017: val_loss did not improve from 5.01120\n",
|
|
"Epoch 18/100\n",
|
|
"\n",
|
|
"Epoch 00018: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 209s 4s/step - loss: 6.7835 - val_loss: 12.4165\n",
|
|
"\n",
|
|
"Epoch 00018: val_loss did not improve from 5.01120\n",
|
|
"Epoch 19/100\n",
|
|
"\n",
|
|
"Epoch 00019: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 208s 4s/step - loss: 6.2405 - val_loss: 11.0467\n",
|
|
"\n",
|
|
"Epoch 00019: val_loss did not improve from 5.01120\n",
|
|
"Epoch 20/100\n",
|
|
"\n",
|
|
"Epoch 00020: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 208s 4s/step - loss: 6.4472 - val_loss: 8.6781\n",
|
|
"\n",
|
|
"Epoch 00020: val_loss did not improve from 5.01120\n",
|
|
"Epoch 21/100\n",
|
|
"\n",
|
|
"Epoch 00021: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 14740s 295s/step - loss: 6.4947 - val_loss: 5.3276\n",
|
|
"\n",
|
|
"Epoch 00021: val_loss did not improve from 5.01120\n",
|
|
"Epoch 22/100\n",
|
|
"\n",
|
|
"Epoch 00022: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 206s 4s/step - loss: 6.6102 - val_loss: 6.9214\n",
|
|
"\n",
|
|
"Epoch 00022: val_loss did not improve from 5.01120\n",
|
|
"Epoch 23/100\n",
|
|
"\n",
|
|
"Epoch 00023: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 207s 4s/step - loss: 6.0585 - val_loss: 4.6572\n",
|
|
"\n",
|
|
"Epoch 00023: val_loss improved from 5.01120 to 4.65718, saving model to experimento_ssd7_fault.h5\n",
|
|
"Epoch 24/100\n",
|
|
"\n",
|
|
"Epoch 00024: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 209s 4s/step - loss: 5.9899 - val_loss: 9.6784\n",
|
|
"\n",
|
|
"Epoch 00024: val_loss did not improve from 4.65718\n",
|
|
"Epoch 25/100\n",
|
|
"\n",
|
|
"Epoch 00025: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 206s 4s/step - loss: 6.4963 - val_loss: 7.5584\n",
|
|
"\n",
|
|
"Epoch 00025: val_loss did not improve from 4.65718\n",
|
|
"Epoch 26/100\n",
|
|
"\n",
|
|
"Epoch 00026: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 206s 4s/step - loss: 6.2881 - val_loss: 4.7997\n",
|
|
"\n",
|
|
"Epoch 00026: val_loss did not improve from 4.65718\n",
|
|
"Epoch 27/100\n",
|
|
"\n",
|
|
"Epoch 00027: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
" 3/50 [>.............................] - ETA: 3:14 - loss: 5.8079"
|
|
]
|
|
},
|
|
{
|
|
"ename": "KeyboardInterrupt",
|
|
"evalue": "",
|
|
"output_type": "error",
|
|
"traceback": [
|
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
|
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
|
|
"\u001b[0;32m<ipython-input-22-bec118ff3f60>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 201\u001b[0m \u001b[0mvalidation_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mceil\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval_dataset_size\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mbatch_size\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\u001b[1;32m 202\u001b[0m \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 203\u001b[0;31m verbose = 1 if config['train']['debug'] else 2)\n\u001b[0m\u001b[1;32m 204\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 205\u001b[0m \u001b[0mhistory_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'train'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'saved_weights_name'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'_history'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
"\u001b[0;32m~/anaconda3/envs/model/lib/python3.6/site-packages/keras/legacy/interfaces.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 89\u001b[0m warnings.warn('Update your `' + object_name + '` call to the ' +\n\u001b[1;32m 90\u001b[0m 'Keras 2 API: ' + signature, stacklevel=2)\n\u001b[0;32m---> 91\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\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[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 92\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 93\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
"\u001b[0;32m~/anaconda3/envs/model/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m 1416\u001b[0m \u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1417\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1418\u001b[0;31m initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m 1419\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1420\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0minterfaces\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegacy_generator_methods_support\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
"\u001b[0;32m~/anaconda3/envs/model/lib/python3.6/site-packages/keras/engine/training_generator.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m 215\u001b[0m outs = model.train_on_batch(x, y,\n\u001b[1;32m 216\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 217\u001b[0;31m class_weight=class_weight)\n\u001b[0m\u001b[1;32m 218\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 219\u001b[0m \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
"\u001b[0;32m~/anaconda3/envs/model/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[0;34m(self, x, y, sample_weight, class_weight)\u001b[0m\n\u001b[1;32m 1215\u001b[0m \u001b[0mins\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0msample_weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1216\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_train_function\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\u001b[0;32m-> 1217\u001b[0;31m \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1218\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0munpack_singleton\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1219\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
"\u001b[0;32m~/anaconda3/envs/model/lib/python3.6/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 2713\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2714\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2715\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[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2716\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2717\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 \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minputs\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",
|
|
"\u001b[0;32m~/anaconda3/envs/model/lib/python3.6/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 2673\u001b[0m \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_metadata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2674\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2675\u001b[0;31m \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2676\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mfetched\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\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\u001b[1;32m 2677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
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"\u001b[0;32m~/anaconda3/envs/model/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1380\u001b[0m ret = tf_session.TF_SessionRunCallable(\n\u001b[1;32m 1381\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1382\u001b[0;31m run_metadata_ptr)\n\u001b[0m\u001b[1;32m 1383\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1384\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[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
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]
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}
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],
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"source": [
|
|
"#ENTRENAMIENTO DE MODELO\n",
|
|
"#####################################################################\n",
|
|
"# Instantiate two `DataGenerator` objects: One for training, one for validation.\n",
|
|
"######################################################################\n",
|
|
"# Optional: If you have enough memory, consider loading the images into memory for the reasons explained above.\n",
|
|
"\n",
|
|
"train_dataset = DataGenerator(load_images_into_memory=False, hdf5_dataset_path=None)\n",
|
|
"val_dataset = DataGenerator(load_images_into_memory=False, hdf5_dataset_path=None)\n",
|
|
"\n",
|
|
"# 2: Parse the image and label lists for the training and validation datasets. This can take a while.\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"# The XML parser needs to now what object class names to look for and in which order to map them to integers.\n",
|
|
"classes = ['background' ] + labels\n",
|
|
"\n",
|
|
"train_dataset.parse_xml(images_dirs= [config['train']['train_image_folder']],\n",
|
|
" image_set_filenames=[config['train']['train_image_set_filename']],\n",
|
|
" annotations_dirs=[config['train']['train_annot_folder']],\n",
|
|
" classes=classes,\n",
|
|
" include_classes='all',\n",
|
|
" #classes = ['background', 'panel', 'cell'], \n",
|
|
" #include_classes=classes,\n",
|
|
" exclude_truncated=False,\n",
|
|
" exclude_difficult=False,\n",
|
|
" ret=False)\n",
|
|
"\n",
|
|
"val_dataset.parse_xml(images_dirs= [config['test']['test_image_folder']],\n",
|
|
" image_set_filenames=[config['test']['test_image_set_filename']],\n",
|
|
" annotations_dirs=[config['test']['test_annot_folder']],\n",
|
|
" classes=classes,\n",
|
|
" include_classes='all',\n",
|
|
" #classes = ['background', 'panel', 'cell'], \n",
|
|
" #include_classes=classes,\n",
|
|
" exclude_truncated=False,\n",
|
|
" exclude_difficult=False,\n",
|
|
" ret=False)\n",
|
|
"\n",
|
|
"#########################\n",
|
|
"# 3: Set the batch size.\n",
|
|
"#########################\n",
|
|
"batch_size = config['train']['batch_size'] # Change the batch size if you like, or if you run into GPU memory issues.\n",
|
|
"\n",
|
|
"##########################\n",
|
|
"# 4: Set the image transformations for pre-processing and data augmentation options.\n",
|
|
"##########################\n",
|
|
"# For the training generator:\n",
|
|
"\n",
|
|
"\n",
|
|
"# For the validation generator:\n",
|
|
"convert_to_3_channels = ConvertTo3Channels()\n",
|
|
"resize = Resize(height=img_height, width=img_width)\n",
|
|
"\n",
|
|
"######################################3\n",
|
|
"# 5: Instantiate an encoder that can encode ground truth labels into the format needed by the SSD loss function.\n",
|
|
"#########################################\n",
|
|
"# The encoder constructor needs the spatial dimensions of the model's predictor layers to create the anchor boxes.\n",
|
|
"if config['model']['backend'] == 'ssd300':\n",
|
|
" predictor_sizes = [model.get_layer('conv4_3_norm_mbox_conf').output_shape[1:3],\n",
|
|
" model.get_layer('fc7_mbox_conf').output_shape[1:3],\n",
|
|
" model.get_layer('conv6_2_mbox_conf').output_shape[1:3],\n",
|
|
" model.get_layer('conv7_2_mbox_conf').output_shape[1:3],\n",
|
|
" model.get_layer('conv8_2_mbox_conf').output_shape[1:3],\n",
|
|
" model.get_layer('conv9_2_mbox_conf').output_shape[1:3]]\n",
|
|
" ssd_input_encoder = SSDInputEncoder(img_height=img_height,\n",
|
|
" img_width=img_width,\n",
|
|
" n_classes=n_classes,\n",
|
|
" predictor_sizes=predictor_sizes,\n",
|
|
" scales=scales,\n",
|
|
" aspect_ratios_per_layer=aspect_ratios,\n",
|
|
" two_boxes_for_ar1=two_boxes_for_ar1,\n",
|
|
" steps=steps,\n",
|
|
" offsets=offsets,\n",
|
|
" clip_boxes=clip_boxes,\n",
|
|
" variances=variances,\n",
|
|
" matching_type='multi',\n",
|
|
" pos_iou_threshold=0.5,\n",
|
|
" neg_iou_limit=0.5,\n",
|
|
" normalize_coords=normalize_coords)\n",
|
|
"\n",
|
|
"elif config['model']['backend'] == 'ssd7':\n",
|
|
" predictor_sizes = [model.get_layer('classes4').output_shape[1:3],\n",
|
|
" model.get_layer('classes5').output_shape[1:3],\n",
|
|
" model.get_layer('classes6').output_shape[1:3],\n",
|
|
" model.get_layer('classes7').output_shape[1:3]]\n",
|
|
" ssd_input_encoder = SSDInputEncoder(img_height=img_height,\n",
|
|
" img_width=img_width,\n",
|
|
" n_classes=n_classes,\n",
|
|
" predictor_sizes=predictor_sizes,\n",
|
|
" scales=scales,\n",
|
|
" aspect_ratios_global=aspect_ratios,\n",
|
|
" two_boxes_for_ar1=two_boxes_for_ar1,\n",
|
|
" steps=steps,\n",
|
|
" offsets=offsets,\n",
|
|
" clip_boxes=clip_boxes,\n",
|
|
" variances=variances,\n",
|
|
" matching_type='multi',\n",
|
|
" pos_iou_threshold=0.5,\n",
|
|
" neg_iou_limit=0.3,\n",
|
|
" normalize_coords=normalize_coords)\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
" \n",
|
|
"data_augmentation_chain = DataAugmentationVariableInputSize(resize_height = img_height,\n",
|
|
" resize_width = img_width,\n",
|
|
" random_brightness=(-48, 48, 0.5),\n",
|
|
" random_contrast=(0.5, 1.8, 0.5),\n",
|
|
" random_saturation=(0.5, 1.8, 0.5),\n",
|
|
" random_hue=(18, 0.5),\n",
|
|
" random_flip=0.5,\n",
|
|
" n_trials_max=3,\n",
|
|
" clip_boxes=True,\n",
|
|
" overlap_criterion='area',\n",
|
|
" bounds_box_filter=(0.3, 1.0),\n",
|
|
" bounds_validator=(0.5, 1.0),\n",
|
|
" n_boxes_min=1,\n",
|
|
" background=(0,0,0))\n",
|
|
"#######################\n",
|
|
"# 6: Create the generator handles that will be passed to Keras' `fit_generator()` function.\n",
|
|
"#######################\n",
|
|
"\n",
|
|
"train_generator = train_dataset.generate(batch_size=batch_size,\n",
|
|
" shuffle=True,\n",
|
|
" transformations= [data_augmentation_chain],\n",
|
|
" label_encoder=ssd_input_encoder,\n",
|
|
" returns={'processed_images',\n",
|
|
" 'encoded_labels'},\n",
|
|
" keep_images_without_gt=False)\n",
|
|
"\n",
|
|
"val_generator = val_dataset.generate(batch_size=batch_size,\n",
|
|
" shuffle=False,\n",
|
|
" transformations=[convert_to_3_channels,\n",
|
|
" resize],\n",
|
|
" label_encoder=ssd_input_encoder,\n",
|
|
" returns={'processed_images',\n",
|
|
" 'encoded_labels'},\n",
|
|
" keep_images_without_gt=False)\n",
|
|
"\n",
|
|
"# Summary instance training\n",
|
|
"category_train_list = []\n",
|
|
"for image_label in train_dataset.labels:\n",
|
|
" category_train_list += [i[0] for i in image_label]\n",
|
|
"summary_category_training = {train_dataset.classes[i]: category_train_list.count(i) for i in list(set(category_train_list))}\n",
|
|
"for i in summary_category_training.keys():\n",
|
|
" print(i, ': {:.0f}'.format(summary_category_training[i]))\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"# Get the number of samples in the training and validations datasets.\n",
|
|
"train_dataset_size = train_dataset.get_dataset_size()\n",
|
|
"val_dataset_size = val_dataset.get_dataset_size()\n",
|
|
"\n",
|
|
"print(\"Number of images in the training dataset:\\t{:>6}\".format(train_dataset_size))\n",
|
|
"print(\"Number of images in the validation dataset:\\t{:>6}\".format(val_dataset_size))\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"##########################\n",
|
|
"# Define model callbacks.\n",
|
|
"#########################\n",
|
|
"\n",
|
|
"# TODO: Set the filepath under which you want to save the model.\n",
|
|
"model_checkpoint = ModelCheckpoint(filepath= config['train']['saved_weights_name'],\n",
|
|
" monitor='val_loss',\n",
|
|
" verbose=1,\n",
|
|
" save_best_only=True,\n",
|
|
" save_weights_only=False,\n",
|
|
" mode='auto',\n",
|
|
" period=1)\n",
|
|
"#model_checkpoint.best =\n",
|
|
"\n",
|
|
"csv_logger = CSVLogger(filename='log.csv',\n",
|
|
" separator=',',\n",
|
|
" append=True)\n",
|
|
"\n",
|
|
"learning_rate_scheduler = LearningRateScheduler(schedule=lr_schedule,\n",
|
|
" verbose=1)\n",
|
|
"\n",
|
|
"terminate_on_nan = TerminateOnNaN()\n",
|
|
"\n",
|
|
"callbacks = [model_checkpoint,\n",
|
|
" csv_logger,\n",
|
|
" learning_rate_scheduler,\n",
|
|
" terminate_on_nan]\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"batch_images, batch_labels = next(train_generator)\n",
|
|
"\n",
|
|
"\n",
|
|
"initial_epoch = 0\n",
|
|
"final_epoch = 100 #config['train']['nb_epochs']\n",
|
|
"steps_per_epoch = 50\n",
|
|
"\n",
|
|
"history = model.fit_generator(generator=train_generator,\n",
|
|
" steps_per_epoch=steps_per_epoch,\n",
|
|
" epochs=final_epoch,\n",
|
|
" callbacks=callbacks,\n",
|
|
" validation_data=val_generator,\n",
|
|
" validation_steps=ceil(val_dataset_size/batch_size),\n",
|
|
" initial_epoch=initial_epoch,\n",
|
|
" verbose = 1 if config['train']['debug'] else 2)\n",
|
|
"\n",
|
|
"history_path = config['train']['saved_weights_name'].split('.')[0] + '_history'\n",
|
|
"\n",
|
|
"np.save(history_path, history.history)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 20,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 23,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"ename": "FileNotFoundError",
|
|
"evalue": "[Errno 2] No such file or directory: 'experimento_ssd7_fault_history.npy'",
|
|
"output_type": "error",
|
|
"traceback": [
|
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
|
"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
|
|
"\u001b[0;32m<ipython-input-23-7b6943048be5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mhistory_path\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'train'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'saved_weights_name'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'_history'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mhist_load\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhistory_path\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'.npy'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mallow_pickle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\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\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhist_load\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\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",
|
|
"\u001b[0;32m~/anaconda3/envs/model/lib/python3.6/site-packages/numpy/lib/npyio.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(file, mmap_mode, allow_pickle, fix_imports, encoding)\u001b[0m\n\u001b[1;32m 382\u001b[0m \u001b[0mown_fid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 383\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbasestring\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\u001b[0;32m--> 384\u001b[0;31m \u001b[0mfid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 385\u001b[0m \u001b[0mown_fid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 386\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mis_pathlib_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\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",
|
|
"\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'experimento_ssd7_fault_history.npy'"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"#Graficar aprendizaje\n",
|
|
"\n",
|
|
"history_path =config['train']['saved_weights_name'].split('.')[0] + '_history'\n",
|
|
"\n",
|
|
"hist_load = np.load(history_path + '.npy',allow_pickle=True).item()\n",
|
|
"\n",
|
|
"print(hist_load.keys())\n",
|
|
"\n",
|
|
"# summarize history for loss\n",
|
|
"plt.plot(hist_load['loss'])\n",
|
|
"plt.plot(hist_load['val_loss'])\n",
|
|
"plt.title('model loss')\n",
|
|
"plt.ylabel('loss')\n",
|
|
"plt.xlabel('epoch')\n",
|
|
"plt.legend(['train', 'test'], loc='upper left')\n",
|
|
"plt.show()\n",
|
|
"\n",
|
|
"print(config['train']['saved_weights_name'])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Evaluación del Modelo"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
"\n",
|
|
"Processing image set 'train.txt': 0%| | 0/603 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
|
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"\n",
|
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"Processing image set 'train.txt': 8%|▊ | 51/603 [00:00<00:01, 508.16it/s]\u001b[A\u001b[A\n",
|
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"\n",
|
|
"Processing image set 'train.txt': 22%|██▏ | 130/603 [00:00<00:00, 568.11it/s]\u001b[A\u001b[A\n",
|
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"\n",
|
|
"Processing image set 'train.txt': 35%|███▍ | 211/603 [00:00<00:00, 623.50it/s]\u001b[A\u001b[A\n",
|
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"\n",
|
|
"Processing image set 'train.txt': 47%|████▋ | 282/603 [00:00<00:00, 646.28it/s]\u001b[A\u001b[A\n",
|
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"\n",
|
|
"Processing image set 'train.txt': 61%|██████▏ | 370/603 [00:00<00:00, 699.94it/s]\u001b[A\u001b[A\n",
|
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"\n",
|
|
"Processing image set 'train.txt': 72%|███████▏ | 435/603 [00:00<00:00, 669.90it/s]\u001b[A\u001b[A\n",
|
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"\n",
|
|
"Processing image set 'train.txt': 86%|████████▌ | 517/603 [00:00<00:00, 708.21it/s]\u001b[A\u001b[A\n",
|
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"\n",
|
|
"Processing image set 'train.txt': 100%|██████████| 603/603 [00:00<00:00, 747.93it/s]\u001b[A\u001b[A\n",
|
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"\n",
|
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"Processing image set 'test.txt': 0%| | 0/67 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
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"\n",
|
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"Processing image set 'test.txt': 85%|████████▌ | 57/67 [00:00<00:00, 569.15it/s]\u001b[A\u001b[A\n",
|
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"\n",
|
|
"Processing image set 'test.txt': 100%|██████████| 67/67 [00:00<00:00, 532.24it/s]\u001b[A\u001b[ANumber of images in the evaluation dataset: 67\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
" 0%| | 0/17 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
|
|
"\n",
|
|
"Producing predictions batch-wise: 0%| | 0/17 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
|
|
"\n",
|
|
"Producing predictions batch-wise: 6%|▌ | 1/17 [00:18<04:54, 18.43s/it]\u001b[A\u001b[A\n",
|
|
"\n",
|
|
"Producing predictions batch-wise: 12%|█▏ | 2/17 [00:39<04:48, 19.23s/it]\u001b[A\u001b[A\n",
|
|
"\n",
|
|
"Producing predictions batch-wise: 18%|█▊ | 3/17 [00:59<04:34, 19.60s/it]\u001b[A\u001b[A\n",
|
|
"\n",
|
|
"Producing predictions batch-wise: 24%|██▎ | 4/17 [01:18<04:08, 19.13s/it]\u001b[A\u001b[A"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"\n",
|
|
"config_path = 'config_7_fault.json'\n",
|
|
"\n",
|
|
"with open(config_path) as config_buffer:\n",
|
|
" config = json.loads(config_buffer.read())\n",
|
|
"\n",
|
|
" \n",
|
|
"model_mode = 'training'\n",
|
|
"# TODO: Set the path to the `.h5` file of the model to be loaded.\n",
|
|
"model_path = config['train']['saved_weights_name']\n",
|
|
"\n",
|
|
"# We need to create an SSDLoss object in order to pass that to the model loader.\n",
|
|
"ssd_loss = SSDLoss(neg_pos_ratio=3, alpha=1.0)\n",
|
|
"\n",
|
|
"K.clear_session() # Clear previous models from memory.\n",
|
|
"\n",
|
|
"model = load_model(model_path, custom_objects={'AnchorBoxes': AnchorBoxes,\n",
|
|
" 'L2Normalization': L2Normalization,\n",
|
|
" 'DecodeDetections': DecodeDetections,\n",
|
|
" 'compute_loss': ssd_loss.compute_loss})\n",
|
|
"\n",
|
|
"\n",
|
|
" \n",
|
|
"train_dataset = DataGenerator(load_images_into_memory=False, hdf5_dataset_path=None)\n",
|
|
"val_dataset = DataGenerator(load_images_into_memory=False, hdf5_dataset_path=None)\n",
|
|
"\n",
|
|
"# 2: Parse the image and label lists for the training and validation datasets. This can take a while.\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"# The XML parser needs to now what object class names to look for and in which order to map them to integers.\n",
|
|
"classes = ['background' ] + labels\n",
|
|
"\n",
|
|
"train_dataset.parse_xml(images_dirs= [config['train']['train_image_folder']],\n",
|
|
" image_set_filenames=[config['train']['train_image_set_filename']],\n",
|
|
" annotations_dirs=[config['train']['train_annot_folder']],\n",
|
|
" classes=classes,\n",
|
|
" include_classes='all',\n",
|
|
" #classes = ['background', 'panel', 'cell'], \n",
|
|
" #include_classes=classes,\n",
|
|
" exclude_truncated=False,\n",
|
|
" exclude_difficult=False,\n",
|
|
" ret=False)\n",
|
|
"\n",
|
|
"val_dataset.parse_xml(images_dirs= [config['test']['test_image_folder']],\n",
|
|
" image_set_filenames=[config['test']['test_image_set_filename']],\n",
|
|
" annotations_dirs=[config['test']['test_annot_folder']],\n",
|
|
" classes=classes,\n",
|
|
" include_classes='all',\n",
|
|
" #classes = ['background', 'panel', 'cell'], \n",
|
|
" #include_classes=classes,\n",
|
|
" exclude_truncated=False,\n",
|
|
" exclude_difficult=False,\n",
|
|
" ret=False)\n",
|
|
"\n",
|
|
"#########################\n",
|
|
"# 3: Set the batch size.\n",
|
|
"#########################\n",
|
|
"batch_size = config['train']['batch_size'] # Change the batch size if you like, or if you run into GPU memory issues.\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"evaluator = Evaluator(model=model,\n",
|
|
" n_classes=n_classes,\n",
|
|
" data_generator=val_dataset,\n",
|
|
" model_mode='training')\n",
|
|
"\n",
|
|
"results = evaluator(img_height=img_height,\n",
|
|
" img_width=img_width,\n",
|
|
" batch_size=4,\n",
|
|
" data_generator_mode='resize',\n",
|
|
" round_confidences=False,\n",
|
|
" matching_iou_threshold=0.5,\n",
|
|
" border_pixels='include',\n",
|
|
" sorting_algorithm='quicksort',\n",
|
|
" average_precision_mode='sample',\n",
|
|
" num_recall_points=11,\n",
|
|
" ignore_neutral_boxes=True,\n",
|
|
" return_precisions=True,\n",
|
|
" return_recalls=True,\n",
|
|
" return_average_precisions=True,\n",
|
|
" verbose=True)\n",
|
|
"\n",
|
|
"mean_average_precision, average_precisions, precisions, recalls = results\n",
|
|
"total_instances = []\n",
|
|
"precisions = []\n",
|
|
"\n",
|
|
"for i in range(1, len(average_precisions)):\n",
|
|
" \n",
|
|
" print('{:.0f} instances of class'.format(len(recalls[i])),\n",
|
|
" classes[i], 'with average precision: {:.4f}'.format(average_precisions[i]))\n",
|
|
" total_instances.append(len(recalls[i]))\n",
|
|
" precisions.append(average_precisions[i])\n",
|
|
"\n",
|
|
"if sum(total_instances) == 0:\n",
|
|
" \n",
|
|
" print('No test instances found.')\n",
|
|
"\n",
|
|
"else:\n",
|
|
"\n",
|
|
" print('mAP using the weighted average of precisions among classes: {:.4f}'.format(sum([a * b for a, b in zip(total_instances, precisions)]) / sum(total_instances)))\n",
|
|
" print('mAP: {:.4f}'.format(sum(precisions) / sum(x > 0 for x in total_instances)))\n",
|
|
"\n",
|
|
" for i in range(1, len(average_precisions)):\n",
|
|
" print(\"{:<14}{:<6}{}\".format(classes[i], 'AP', round(average_precisions[i], 3)))\n",
|
|
" print()\n",
|
|
" print(\"{:<14}{:<6}{}\".format('','mAP', round(mean_average_precision, 3)))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 27,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"8"
|
|
]
|
|
},
|
|
"execution_count": 27,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"n_classes"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Cargar nuevamente el modelo desde los pesos.\n",
|
|
"Predicción"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
"Training on: \t{'panel': 1}\n",
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"from imageio import imread\n",
|
|
"from keras.preprocessing import image\n",
|
|
"import time\n",
|
|
"\n",
|
|
"config_path = 'config_7_fault.json'\n",
|
|
"input_path = ['panel_jpg/Mision_1/', 'panel_jpg/Mision_2/']\n",
|
|
"output_path = 'result_ssd7_fault/'\n",
|
|
"\n",
|
|
"with open(config_path) as config_buffer:\n",
|
|
" config = json.loads(config_buffer.read())\n",
|
|
"\n",
|
|
"makedirs(output_path)\n",
|
|
"###############################\n",
|
|
"# Parse the annotations\n",
|
|
"###############################\n",
|
|
"score_threshold = 0.8\n",
|
|
"score_threshold_iou = 0.3\n",
|
|
"labels = config['model']['labels']\n",
|
|
"categories = {}\n",
|
|
"#categories = {\"Razor\": 1, \"Gun\": 2, \"Knife\": 3, \"Shuriken\": 4} #la categoría 0 es la background\n",
|
|
"for i in range(len(labels)): categories[labels[i]] = i+1\n",
|
|
"print('\\nTraining on: \\t' + str(categories) + '\\n')\n",
|
|
"\n",
|
|
"img_height = config['model']['input'] # Height of the model input images\n",
|
|
"img_width = config['model']['input'] # Width of the model input images\n",
|
|
"img_channels = 3 # Number of color channels of the model input images\n",
|
|
"n_classes = len(labels) # Number of positive classes, e.g. 20 for Pascal VOC, 80 for MS COCO\n",
|
|
"classes = ['background'] + labels\n",
|
|
"\n",
|
|
"model_mode = 'training'\n",
|
|
"# TODO: Set the path to the `.h5` file of the model to be loaded.\n",
|
|
"model_path = config['train']['saved_weights_name']\n",
|
|
"\n",
|
|
"# We need to create an SSDLoss object in order to pass that to the model loader.\n",
|
|
"ssd_loss = SSDLoss(neg_pos_ratio=3, alpha=1.0)\n",
|
|
"\n",
|
|
"K.clear_session() # Clear previous models from memory.\n",
|
|
"\n",
|
|
"model = load_model(model_path, custom_objects={'AnchorBoxes': AnchorBoxes,\n",
|
|
" 'L2Normalization': L2Normalization,\n",
|
|
" 'DecodeDetections': DecodeDetections,\n",
|
|
" 'compute_loss': ssd_loss.compute_loss})\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"image_paths = []\n",
|
|
"for inp in input_path:\n",
|
|
" if os.path.isdir(inp):\n",
|
|
" for inp_file in os.listdir(inp):\n",
|
|
" image_paths += [inp + inp_file]\n",
|
|
" else:\n",
|
|
" image_paths += [inp]\n",
|
|
"\n",
|
|
"image_paths = [inp_file for inp_file in image_paths if (inp_file[-4:] in ['.jpg', '.png', 'JPEG'])]\n",
|
|
"times = []\n",
|
|
"\n",
|
|
"\n",
|
|
"for img_path in image_paths:\n",
|
|
" orig_images = [] # Store the images here.\n",
|
|
" input_images = [] # Store resized versions of the images here.\n",
|
|
" #print(img_path)\n",
|
|
"\n",
|
|
" # preprocess image for network\n",
|
|
" orig_images.append(imread(img_path))\n",
|
|
" img = image.load_img(img_path, target_size=(img_height, img_width))\n",
|
|
" img = image.img_to_array(img)\n",
|
|
" input_images.append(img)\n",
|
|
" input_images = np.array(input_images)\n",
|
|
" # process image\n",
|
|
" start = time.time()\n",
|
|
" y_pred = model.predict(input_images)\n",
|
|
" y_pred_decoded = decode_detections(y_pred,\n",
|
|
" confidence_thresh=score_threshold,\n",
|
|
" iou_threshold=score_threshold_iou,\n",
|
|
" top_k=200,\n",
|
|
" normalize_coords=True,\n",
|
|
" img_height=img_height,\n",
|
|
" img_width=img_width)\n",
|
|
"\n",
|
|
"\n",
|
|
" #print(\"processing time: \", time.time() - start)\n",
|
|
" times.append(time.time() - start)\n",
|
|
" # correct for image scale\n",
|
|
"\n",
|
|
" # visualize detections\n",
|
|
" # Set the colors for the bounding boxes\n",
|
|
" colors = plt.cm.brg(np.linspace(0, 1, 21)).tolist()\n",
|
|
"\n",
|
|
" plt.figure(figsize=(20,12))\n",
|
|
" plt.imshow(orig_images[0],cmap = 'gray')\n",
|
|
"\n",
|
|
" current_axis = plt.gca()\n",
|
|
" #print(y_pred)\n",
|
|
" for box in y_pred_decoded[0]:\n",
|
|
" # Transform the predicted bounding boxes for the 300x300 image to the original image dimensions.\n",
|
|
"\n",
|
|
" xmin = box[2] * orig_images[0].shape[1] / img_width\n",
|
|
" ymin = box[3] * orig_images[0].shape[0] / img_height\n",
|
|
" xmax = box[4] * orig_images[0].shape[1] / img_width\n",
|
|
" ymax = box[5] * orig_images[0].shape[0] / img_height\n",
|
|
"\n",
|
|
" color = colors[int(box[0])]\n",
|
|
" label = '{}: {:.2f}'.format(classes[int(box[0])], box[1])\n",
|
|
" current_axis.add_patch(plt.Rectangle((xmin, ymin), xmax-xmin, ymax-ymin, color=color, fill=False, linewidth=2))\n",
|
|
" current_axis.text(xmin, ymin, label, size='x-large', color='white', bbox={'facecolor':color, 'alpha':1.0})\n",
|
|
"\n",
|
|
" #plt.figure(figsize=(15, 15))\n",
|
|
" #plt.axis('off')\n",
|
|
" save_path = output_path + img_path.split('/')[-1]\n",
|
|
" plt.savefig(save_path)\n",
|
|
" plt.close()\n",
|
|
" \n",
|
|
"file = open(output_path + 'time.txt','w')\n",
|
|
"\n",
|
|
"file.write('Tiempo promedio:' + str(np.mean(times)))\n",
|
|
"\n",
|
|
"file.close()\n",
|
|
"print('Tiempo Total: {:.3f}'.format(np.sum(times)))\n",
|
|
"print('Tiempo promedio por imagen: {:.3f}'.format(np.mean(times)))\n",
|
|
"print('OK')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"panel : 69\n",
|
|
"cell : 423\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"\n",
|
|
"# Summary instance training\n",
|
|
"category_train_list = []\n",
|
|
"for image_label in train_dataset.labels:\n",
|
|
" category_train_list += [i[0] for i in train_dataset.labels[0]]\n",
|
|
"summary_category_training = {train_dataset.classes[i]: category_train_list.count(i) for i in list(set(category_train_list))}\n",
|
|
"for i in summary_category_training.keys():\n",
|
|
" print(i, ': {:.0f}'.format(summary_category_training[i]))\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 4,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"1 : 6030\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"for i in summary_category_training.keys():\n",
|
|
" print(i, ': {:.0f}'.format(summary_category_training[i]))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.6.8"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|