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ebc01fde48fa0f1ee9c1a1a0d17447c7c37fdf84
Photovoltaic_Fault_Detector/Panel_Detector_Fault_1.ipynb
dl-desktop ebc01fde48 Fault_Detector
2020-02-04 17:48:13 -03:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cargar el modelo ssd7 \n",
"(https://github.com/pierluigiferrari/ssd_keras#how-to-fine-tune-one-of-the-trained-models-on-your-own-dataset)\n",
"\n",
"Training del SSD7 (modelo reducido de SSD). Parámetros en config_7.json y descargar VGG_ILSVRC_16_layers_fc_reduced.h5\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Training on: \t{'1': 1}\n",
"\n",
"\n",
"Loading pretrained weights.\n",
"\n",
"WARNING:tensorflow:From /home/dl-desktop/anaconda3/envs/tf_gpu/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Colocations handled automatically by placer.\n",
"WARNING:tensorflow:From /home/dl-desktop/Desktop/Rentadrone/ssd_keras-master/keras_loss_function/keras_ssd_loss.py:133: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Use tf.cast instead.\n",
"WARNING:tensorflow:From /home/dl-desktop/Desktop/Rentadrone/ssd_keras-master/keras_loss_function/keras_ssd_loss.py:166: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Use tf.cast instead.\n",
"WARNING:tensorflow:From /home/dl-desktop/anaconda3/envs/tf_gpu/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:102: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Deprecated in favor of operator or tf.math.divide.\n"
]
}
],
"source": [
"from keras.optimizers import Adam, SGD\n",
"from keras.callbacks import ModelCheckpoint, LearningRateScheduler, TerminateOnNaN, CSVLogger\n",
"from keras import backend as K\n",
"from keras.models import load_model\n",
"from math import ceil\n",
"import numpy as np\n",
"from matplotlib import pyplot as plt\n",
"import os\n",
"import json\n",
"import xml.etree.cElementTree as ET\n",
"\n",
"import sys\n",
"sys.path += [os.path.abspath('../ssd_keras-master')]\n",
"\n",
"from keras_loss_function.keras_ssd_loss import SSDLoss\n",
"from keras_layers.keras_layer_AnchorBoxes import AnchorBoxes\n",
"from keras_layers.keras_layer_DecodeDetections import DecodeDetections\n",
"from keras_layers.keras_layer_DecodeDetectionsFast import DecodeDetectionsFast\n",
"from keras_layers.keras_layer_L2Normalization import L2Normalization\n",
"from ssd_encoder_decoder.ssd_input_encoder import SSDInputEncoder\n",
"from ssd_encoder_decoder.ssd_output_decoder import decode_detections, decode_detections_fast\n",
"from data_generator.object_detection_2d_data_generator import DataGenerator\n",
"from data_generator.object_detection_2d_geometric_ops import Resize\n",
"from data_generator.object_detection_2d_photometric_ops import ConvertTo3Channels\n",
"from data_generator.data_augmentation_chain_original_ssd import SSDDataAugmentation\n",
"from data_generator.object_detection_2d_misc_utils import apply_inverse_transforms\n",
"from eval_utils.average_precision_evaluator import Evaluator\n",
"from data_generator.data_augmentation_chain_variable_input_size import DataAugmentationVariableInputSize\n",
"from data_generator.data_augmentation_chain_constant_input_size import DataAugmentationConstantInputSize\n",
"\n",
"\n",
"def makedirs(path):\n",
" try:\n",
" os.makedirs(path)\n",
" except OSError:\n",
" if not os.path.isdir(path):\n",
" raise\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"K.tensorflow_backend._get_available_gpus()\n",
"\n",
"\n",
"def lr_schedule(epoch):\n",
" if epoch < 80:\n",
" return 0.001\n",
" elif epoch < 100:\n",
" return 0.0001\n",
" else:\n",
" return 0.00001\n",
"\n",
"config_path = 'config_300_fault_1.json'\n",
"\n",
"\n",
"with open(config_path) as config_buffer:\n",
" config = json.loads(config_buffer.read())\n",
"\n",
"###############################\n",
"# Parse the annotations\n",
"###############################\n",
"path_imgs_training = config['train']['train_image_folder']\n",
"path_anns_training = config['train']['train_annot_folder']\n",
"path_imgs_val = config['test']['test_image_folder']\n",
"path_anns_val = config['test']['test_annot_folder']\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",
"####################################\n",
"# Parameters\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",
"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",
"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",
"n_classes = len(labels) # Number of positive classes, e.g. 20 for Pascal VOC, 80 for MS COCO\n",
"scales_pascal = [0.01, 0.05, 0.1, 0.2, 0.37, 0.54, 0.71] # The anchor box scaling factors used in the original SSD300 for the Pascal VOC datasets\n",
"#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",
"scales = scales_pascal\n",
"aspect_ratios = [[1.0, 2.0, 0.5],\n",
" [1.0, 2.0, 0.5, 3.0, 1.0/3.0],\n",
" [1.0, 2.0, 0.5, 3.0, 1.0/3.0],\n",
" [1.0, 2.0, 0.5, 3.0, 1.0/3.0],\n",
" [1.0, 2.0, 0.5],\n",
" [1.0, 2.0, 0.5]] # The anchor box aspect ratios used in the original SSD300; the order matters\n",
"two_boxes_for_ar1 = True\n",
"steps = [8, 16, 32, 64, 100, 300] # The space between two adjacent anchor box center points for each predictor layer.\n",
"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",
"clip_boxes = False # Whether or not to clip the anchor boxes to lie entirely within the image boundaries\n",
"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",
"normalize_coords = True\n",
"\n",
"K.clear_session() # Clear previous models from memory.\n",
"\n",
"\n",
"model_path = config['train']['saved_weights_name']\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",
"if config['model']['backend'] == 'ssd7':\n",
" #weights_path = 'VGG_ILSVRC_16_layers_fc_reduced.h5'\n",
" 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",
" aspect_ratios = [0.5 ,1.0, 2.0] # The list of aspect ratios for the anchor boxes\n",
" two_boxes_for_ar1 = True # Whether or not you want to generate two anchor boxes for aspect ratio 1\n",
" steps = None # In case you'd like to set the step sizes for the anchor box grids manually; not recommended\n",
" offsets = None\n",
"\n",
"if os.path.exists(model_path):\n",
" print(\"\\nLoading pretrained weights.\\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",
" model = load_model(model_path, custom_objects={'AnchorBoxes': AnchorBoxes,\n",
" 'L2Normalization': L2Normalization,\n",
" 'compute_loss': ssd_loss.compute_loss})\n",
"\n",
"\n",
"else:\n",
" ####################################\n",
" # Build the Keras model.\n",
" ###################################\n",
"\n",
" if config['model']['backend'] == 'ssd300':\n",
" #weights_path = 'VGG_VOC0712Plus_SSD_300x300_ft_iter_160000.h5'\n",
" from models.keras_ssd300 import ssd_300 as ssd\n",
"\n",
" model = ssd(image_size=(img_height, img_width, img_channels),\n",
" n_classes=n_classes,\n",
" mode='training',\n",
" l2_regularization=0.0005,\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",
" normalize_coords=normalize_coords,\n",
" subtract_mean=mean_color,\n",
" swap_channels=swap_channels)\n",
"\n",
"\n",
" elif config['model']['backend'] == 'ssd7':\n",
" #weights_path = 'VGG_ILSVRC_16_layers_fc_reduced.h5'\n",
" from models.keras_ssd7 import build_model as ssd\n",
" 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",
" aspect_ratios = [0.5 ,1.0, 2.0] # The list of aspect ratios for the anchor boxes\n",
" two_boxes_for_ar1 = True # Whether or not you want to generate two anchor boxes for aspect ratio 1\n",
" steps = None # In case you'd like to set the step sizes for the anchor box grids manually; not recommended\n",
" offsets = None\n",
" model = ssd(image_size=(img_height, img_width, img_channels),\n",
" n_classes=n_classes,\n",
" mode='training',\n",
" l2_regularization=0.0005,\n",
" scales=scales,\n",
" aspect_ratios_global=aspect_ratios,\n",
" aspect_ratios_per_layer=None,\n",
" two_boxes_for_ar1=two_boxes_for_ar1,\n",
" steps=steps,\n",
" offsets=offsets,\n",
" clip_boxes=clip_boxes,\n",
" variances=variances,\n",
" normalize_coords=normalize_coords,\n",
" 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": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing image set 'train.txt': 100%|██████████| 783/783 [00:01<00:00, 511.30it/s]\n",
"Processing image set 'test.txt': 100%|██████████| 117/117 [00:00<00:00, 449.85it/s]\n",
"1 : 2246\n",
"Number of images in the training dataset:\t 783\n",
"Number of images in the validation dataset:\t 117\n",
"Epoch 1/100\n",
"\n",
"Epoch 00001: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 471s 942ms/step - loss: 3.8465 - val_loss: 3.9360\n",
"\n",
"Epoch 00001: val_loss improved from inf to 3.93599, saving model to experimento_ssd300_fault_1.h5\n",
"Epoch 2/100\n",
"\n",
"Epoch 00002: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 465s 931ms/step - loss: 3.8139 - val_loss: 3.8815\n",
"\n",
"Epoch 00002: val_loss improved from 3.93599 to 3.88150, saving model to experimento_ssd300_fault_1.h5\n",
"Epoch 3/100\n",
"\n",
"Epoch 00003: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 460s 920ms/step - loss: 3.8367 - val_loss: 3.9229\n",
"\n",
"Epoch 00003: val_loss did not improve from 3.88150\n",
"Epoch 4/100\n",
"\n",
"Epoch 00004: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 458s 917ms/step - loss: 3.8556 - val_loss: 3.8905\n",
"\n",
"Epoch 00004: val_loss did not improve from 3.88150\n",
"Epoch 5/100\n",
"\n",
"Epoch 00005: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 453s 907ms/step - loss: 3.8205 - val_loss: 3.8369\n",
"\n",
"Epoch 00005: val_loss improved from 3.88150 to 3.83686, saving model to experimento_ssd300_fault_1.h5\n",
"Epoch 6/100\n",
"\n",
"Epoch 00006: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 450s 900ms/step - loss: 3.8164 - val_loss: 3.9733\n",
"\n",
"Epoch 00006: val_loss did not improve from 3.83686\n",
"Epoch 7/100\n",
"\n",
"Epoch 00007: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 453s 905ms/step - loss: 3.7874 - val_loss: 3.8792\n",
"\n",
"Epoch 00007: val_loss did not improve from 3.83686\n",
"Epoch 8/100\n",
"\n",
"Epoch 00008: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 451s 901ms/step - loss: 3.7895 - val_loss: 3.8497\n",
"\n",
"Epoch 00008: val_loss did not improve from 3.83686\n",
"Epoch 9/100\n",
"\n",
"Epoch 00009: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 454s 908ms/step - loss: 3.8056 - val_loss: 3.8965\n",
"\n",
"Epoch 00009: val_loss did not improve from 3.83686\n",
"Epoch 10/100\n",
"\n",
"Epoch 00010: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 457s 914ms/step - loss: 3.7874 - val_loss: 3.8854\n",
"\n",
"Epoch 00010: val_loss did not improve from 3.83686\n",
"Epoch 11/100\n",
"\n",
"Epoch 00011: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 458s 917ms/step - loss: 3.7937 - val_loss: 3.9264\n",
"\n",
"Epoch 00011: val_loss did not improve from 3.83686\n",
"Epoch 12/100\n",
"\n",
"Epoch 00012: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 456s 913ms/step - loss: 3.8105 - val_loss: 3.8769\n",
"\n",
"Epoch 00012: val_loss did not improve from 3.83686\n",
"Epoch 13/100\n",
"\n",
"Epoch 00013: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 460s 921ms/step - loss: 3.8102 - val_loss: 3.9104\n",
"\n",
"Epoch 00013: val_loss did not improve from 3.83686\n",
"Epoch 14/100\n",
"\n",
"Epoch 00014: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 456s 912ms/step - loss: 3.8034 - val_loss: 3.8571\n",
"\n",
"Epoch 00014: val_loss did not improve from 3.83686\n",
"Epoch 15/100\n",
"\n",
"Epoch 00015: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 458s 917ms/step - loss: 3.7412 - val_loss: 3.8471\n",
"\n",
"Epoch 00015: val_loss did not improve from 3.83686\n",
"Epoch 16/100\n",
"\n",
"Epoch 00016: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 450s 901ms/step - loss: 3.7816 - val_loss: 3.8868\n",
"\n",
"Epoch 00016: val_loss did not improve from 3.83686\n",
"Epoch 17/100\n",
"\n",
"Epoch 00017: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 451s 902ms/step - loss: 3.7849 - val_loss: 3.9379\n",
"\n",
"Epoch 00017: val_loss did not improve from 3.83686\n",
"Epoch 18/100\n",
"\n",
"Epoch 00018: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 450s 901ms/step - loss: 3.7739 - val_loss: 3.8811\n",
"\n",
"Epoch 00018: val_loss did not improve from 3.83686\n",
"Epoch 19/100\n",
"\n",
"Epoch 00019: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 451s 902ms/step - loss: 3.7704 - val_loss: 3.8714\n",
"\n",
"Epoch 00019: val_loss did not improve from 3.83686\n",
"Epoch 20/100\n",
"\n",
"Epoch 00020: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 450s 901ms/step - loss: 3.7367 - val_loss: 3.9438\n",
"\n",
"Epoch 00020: val_loss did not improve from 3.83686\n",
"Epoch 21/100\n",
"\n",
"Epoch 00021: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 450s 901ms/step - loss: 3.7554 - val_loss: 3.9248\n",
"\n",
"Epoch 00021: val_loss did not improve from 3.83686\n",
"Epoch 22/100\n",
"\n",
"Epoch 00022: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 451s 902ms/step - loss: 3.7682 - val_loss: 3.9140\n",
"\n",
"Epoch 00022: val_loss did not improve from 3.83686\n",
"Epoch 23/100\n",
"\n",
"Epoch 00023: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 450s 901ms/step - loss: 3.7867 - val_loss: 3.8202\n",
"\n",
"Epoch 00023: val_loss improved from 3.83686 to 3.82025, saving model to experimento_ssd300_fault_1.h5\n",
"Epoch 24/100\n",
"\n",
"Epoch 00024: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 451s 902ms/step - loss: 3.7498 - val_loss: 3.8610\n",
"\n",
"Epoch 00024: val_loss did not improve from 3.82025\n",
"Epoch 25/100\n",
"\n",
"Epoch 00025: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 450s 901ms/step - loss: 3.7391 - val_loss: 3.8886\n",
"\n",
"Epoch 00025: val_loss did not improve from 3.82025\n",
"Epoch 26/100\n",
"\n",
"Epoch 00026: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 451s 902ms/step - loss: 3.7487 - val_loss: 3.8860\n",
"\n",
"Epoch 00026: val_loss did not improve from 3.82025\n",
"Epoch 27/100\n",
"\n",
"Epoch 00027: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 450s 901ms/step - loss: 3.6680 - val_loss: 3.3866\n",
"\n",
"Epoch 00027: val_loss improved from 3.82025 to 3.38664, saving model to experimento_ssd300_fault_1.h5\n",
"Epoch 28/100\n",
"\n",
"Epoch 00028: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 450s 901ms/step - loss: 3.6011 - val_loss: 3.3802\n",
"\n",
"Epoch 00028: val_loss improved from 3.38664 to 3.38020, saving model to experimento_ssd300_fault_1.h5\n",
"Epoch 29/100\n",
"\n",
"Epoch 00029: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 451s 902ms/step - loss: 3.5266 - val_loss: 3.3741\n",
"\n",
"Epoch 00029: val_loss improved from 3.38020 to 3.37413, saving model to experimento_ssd300_fault_1.h5\n",
"Epoch 30/100\n",
"\n",
"Epoch 00030: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 450s 901ms/step - loss: 3.5440 - val_loss: 3.3658\n",
"\n",
"Epoch 00030: val_loss improved from 3.37413 to 3.36583, saving model to experimento_ssd300_fault_1.h5\n",
"Epoch 31/100\n",
"\n",
"Epoch 00031: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 451s 902ms/step - loss: 3.5227 - val_loss: 3.2712\n",
"\n",
"Epoch 00031: val_loss improved from 3.36583 to 3.27118, saving model to experimento_ssd300_fault_1.h5\n",
"Epoch 32/100\n",
"\n",
"Epoch 00032: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 450s 901ms/step - loss: 3.5003 - val_loss: 3.2636\n",
"\n",
"Epoch 00032: val_loss improved from 3.27118 to 3.26357, saving model to experimento_ssd300_fault_1.h5\n",
"Epoch 33/100\n",
"\n",
"Epoch 00033: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 450s 901ms/step - loss: 3.5573 - val_loss: 3.2981\n",
"\n",
"Epoch 00033: val_loss did not improve from 3.26357\n",
"Epoch 34/100\n",
"\n",
"Epoch 00034: LearningRateScheduler setting learning rate to 0.001.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"500/500 [==============================] - 451s 902ms/step - loss: 3.5104 - val_loss: 3.3216\n",
"\n",
"Epoch 00034: val_loss did not improve from 3.26357\n",
"Epoch 35/100\n",
"\n",
"Epoch 00035: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 450s 901ms/step - loss: 3.4535 - val_loss: 3.2405\n",
"\n",
"Epoch 00035: val_loss improved from 3.26357 to 3.24054, saving model to experimento_ssd300_fault_1.h5\n",
"Epoch 36/100\n",
"\n",
"Epoch 00036: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 451s 902ms/step - loss: 3.4667 - val_loss: 3.2127\n",
"\n",
"Epoch 00036: val_loss improved from 3.24054 to 3.21267, saving model to experimento_ssd300_fault_1.h5\n",
"Epoch 37/100\n",
"\n",
"Epoch 00037: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 450s 901ms/step - loss: 3.4625 - val_loss: 3.2967\n",
"\n",
"Epoch 00037: val_loss did not improve from 3.21267\n",
"Epoch 38/100\n",
"\n",
"Epoch 00038: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 451s 902ms/step - loss: 3.4349 - val_loss: 3.2318\n",
"\n",
"Epoch 00038: val_loss did not improve from 3.21267\n",
"Epoch 39/100\n",
"\n",
"Epoch 00039: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 450s 901ms/step - loss: 3.4616 - val_loss: 3.2234\n",
"\n",
"Epoch 00039: val_loss did not improve from 3.21267\n",
"Epoch 40/100\n",
"\n",
"Epoch 00040: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 450s 901ms/step - loss: 3.4579 - val_loss: 3.2443\n",
"\n",
"Epoch 00040: val_loss did not improve from 3.21267\n",
"Epoch 41/100\n",
"\n",
"Epoch 00041: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 451s 902ms/step - loss: 3.4741 - val_loss: 3.1831\n",
"\n",
"Epoch 00041: val_loss improved from 3.21267 to 3.18308, saving model to experimento_ssd300_fault_1.h5\n",
"Epoch 42/100\n",
"\n",
"Epoch 00042: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 450s 901ms/step - loss: 3.4098 - val_loss: 3.1778\n",
"\n",
"Epoch 00042: val_loss improved from 3.18308 to 3.17781, saving model to experimento_ssd300_fault_1.h5\n",
"Epoch 43/100\n",
"\n",
"Epoch 00043: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 451s 902ms/step - loss: 3.4194 - val_loss: 3.3141\n",
"\n",
"Epoch 00043: val_loss did not improve from 3.17781\n",
"Epoch 44/100\n",
"\n",
"Epoch 00044: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 450s 901ms/step - loss: 3.4294 - val_loss: 3.1888\n",
"\n",
"Epoch 00044: val_loss did not improve from 3.17781\n",
"Epoch 45/100\n",
"\n",
"Epoch 00045: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 451s 902ms/step - loss: 3.4157 - val_loss: 3.2060\n",
"\n",
"Epoch 00045: val_loss did not improve from 3.17781\n",
"Epoch 46/100\n",
"\n",
"Epoch 00046: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 451s 901ms/step - loss: 3.4147 - val_loss: 3.1829\n",
"\n",
"Epoch 00046: val_loss did not improve from 3.17781\n",
"Epoch 47/100\n",
"\n",
"Epoch 00047: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 450s 901ms/step - loss: 3.4061 - val_loss: 3.2214\n",
"\n",
"Epoch 00047: val_loss did not improve from 3.17781\n",
"Epoch 48/100\n",
"\n",
"Epoch 00048: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 451s 902ms/step - loss: 3.4013 - val_loss: 3.1010\n",
"\n",
"Epoch 00048: val_loss improved from 3.17781 to 3.10097, saving model to experimento_ssd300_fault_1.h5\n",
"Epoch 49/100\n",
"\n",
"Epoch 00049: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 450s 901ms/step - loss: 3.4092 - val_loss: 3.1462\n",
"\n",
"Epoch 00049: val_loss did not improve from 3.10097\n",
"Epoch 50/100\n",
"\n",
"Epoch 00050: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 451s 902ms/step - loss: 3.3759 - val_loss: 3.1760\n",
"\n",
"Epoch 00050: val_loss did not improve from 3.10097\n",
"Epoch 51/100\n",
"\n",
"Epoch 00051: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 450s 901ms/step - loss: 3.4261 - val_loss: 3.1638\n",
"\n",
"Epoch 00051: val_loss did not improve from 3.10097\n",
"Epoch 52/100\n",
"\n",
"Epoch 00052: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 451s 902ms/step - loss: 3.3900 - val_loss: 3.2724\n",
"\n",
"Epoch 00052: val_loss did not improve from 3.10097\n",
"Epoch 53/100\n",
"\n",
"Epoch 00053: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 450s 901ms/step - loss: 3.3771 - val_loss: 3.1456\n",
"\n",
"Epoch 00053: val_loss did not improve from 3.10097\n",
"Epoch 54/100\n",
"\n",
"Epoch 00054: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 450s 901ms/step - loss: 3.3997 - val_loss: 3.2297\n",
"\n",
"Epoch 00054: val_loss did not improve from 3.10097\n",
"Epoch 55/100\n",
"\n",
"Epoch 00055: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 451s 902ms/step - loss: 3.3632 - val_loss: 3.1960\n",
"\n",
"Epoch 00055: val_loss did not improve from 3.10097\n",
"Epoch 56/100\n",
"\n",
"Epoch 00056: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 450s 901ms/step - loss: 3.3829 - val_loss: 3.1371\n",
"\n",
"Epoch 00056: val_loss did not improve from 3.10097\n",
"Epoch 57/100\n",
"\n",
"Epoch 00057: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 451s 902ms/step - loss: 3.3724 - val_loss: 3.1169\n",
"\n",
"Epoch 00057: val_loss did not improve from 3.10097\n",
"Epoch 58/100\n",
"\n",
"Epoch 00058: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 450s 901ms/step - loss: 3.3625 - val_loss: 3.2694\n",
"\n",
"Epoch 00058: val_loss did not improve from 3.10097\n",
"Epoch 59/100\n",
"\n",
"Epoch 00059: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 451s 902ms/step - loss: 3.3691 - val_loss: 3.1037\n",
"\n",
"Epoch 00059: val_loss did not improve from 3.10097\n",
"Epoch 60/100\n",
"\n",
"Epoch 00060: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 450s 901ms/step - loss: 3.3976 - val_loss: 3.1110\n",
"\n",
"Epoch 00060: val_loss did not improve from 3.10097\n",
"Epoch 61/100\n",
"\n",
"Epoch 00061: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 451s 901ms/step - loss: 3.3747 - val_loss: 3.1192\n",
"\n",
"Epoch 00061: val_loss did not improve from 3.10097\n",
"Epoch 62/100\n",
"\n",
"Epoch 00062: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 452s 903ms/step - loss: 3.3526 - val_loss: 3.1612\n",
"\n",
"Epoch 00062: val_loss did not improve from 3.10097\n",
"Epoch 63/100\n",
"\n",
"Epoch 00063: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 452s 903ms/step - loss: 3.4435 - val_loss: 3.1396\n",
"\n",
"Epoch 00063: val_loss did not improve from 3.10097\n",
"Epoch 64/100\n",
"\n",
"Epoch 00064: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 452s 903ms/step - loss: 3.3811 - val_loss: 3.1575\n",
"\n",
"Epoch 00064: val_loss did not improve from 3.10097\n",
"Epoch 65/100\n",
"\n",
"Epoch 00065: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 451s 902ms/step - loss: 3.3493 - val_loss: 3.2337\n",
"\n",
"Epoch 00065: val_loss did not improve from 3.10097\n",
"Epoch 66/100\n",
"\n",
"Epoch 00066: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 451s 903ms/step - loss: 3.3510 - val_loss: 3.1230\n",
"\n",
"Epoch 00066: val_loss did not improve from 3.10097\n",
"Epoch 67/100\n",
"\n",
"Epoch 00067: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 451s 902ms/step - loss: 3.3569 - val_loss: 3.1419\n",
"\n",
"Epoch 00067: val_loss did not improve from 3.10097\n",
"Epoch 68/100\n",
"\n",
"Epoch 00068: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 451s 901ms/step - loss: 3.3372 - val_loss: 3.0772\n",
"\n",
"Epoch 00068: val_loss improved from 3.10097 to 3.07717, saving model to experimento_ssd300_fault_1.h5\n",
"Epoch 69/100\n",
"\n",
"Epoch 00069: LearningRateScheduler setting learning rate to 0.001.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"500/500 [==============================] - 449s 898ms/step - loss: 3.3732 - val_loss: 3.1180\n",
"\n",
"Epoch 00069: val_loss did not improve from 3.07717\n",
"Epoch 70/100\n",
"\n",
"Epoch 00070: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 451s 901ms/step - loss: 3.3438 - val_loss: 3.1318\n",
"\n",
"Epoch 00070: val_loss did not improve from 3.07717\n",
"Epoch 71/100\n",
"\n",
"Epoch 00071: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 451s 902ms/step - loss: 3.3451 - val_loss: 3.1492\n",
"\n",
"Epoch 00071: val_loss did not improve from 3.07717\n",
"Epoch 72/100\n",
"\n",
"Epoch 00072: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 451s 901ms/step - loss: 3.3055 - val_loss: 3.1092\n",
"\n",
"Epoch 00072: val_loss did not improve from 3.07717\n",
"Epoch 73/100\n",
"\n",
"Epoch 00073: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 450s 901ms/step - loss: 3.3299 - val_loss: 3.2583\n",
"\n",
"Epoch 00073: val_loss did not improve from 3.07717\n",
"Epoch 74/100\n",
"\n",
"Epoch 00074: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 451s 902ms/step - loss: 3.3542 - val_loss: 3.1427\n",
"\n",
"Epoch 00074: val_loss did not improve from 3.07717\n",
"Epoch 75/100\n",
"\n",
"Epoch 00075: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 450s 901ms/step - loss: 3.3353 - val_loss: 3.1750\n",
"\n",
"Epoch 00075: val_loss did not improve from 3.07717\n",
"Epoch 76/100\n",
"\n",
"Epoch 00076: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 451s 902ms/step - loss: 3.3239 - val_loss: 3.1659\n",
"\n",
"Epoch 00076: val_loss did not improve from 3.07717\n",
"Epoch 77/100\n",
"\n",
"Epoch 00077: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 450s 901ms/step - loss: 3.3305 - val_loss: 3.0835\n",
"\n",
"Epoch 00077: val_loss did not improve from 3.07717\n",
"Epoch 78/100\n",
"\n",
"Epoch 00078: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 451s 902ms/step - loss: 3.3211 - val_loss: 3.1030\n",
"\n",
"Epoch 00078: val_loss did not improve from 3.07717\n",
"Epoch 79/100\n",
"\n",
"Epoch 00079: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 450s 901ms/step - loss: 3.3223 - val_loss: 3.1195\n",
"\n",
"Epoch 00079: val_loss did not improve from 3.07717\n",
"Epoch 80/100\n",
"\n",
"Epoch 00080: LearningRateScheduler setting learning rate to 0.001.\n",
"500/500 [==============================] - 450s 901ms/step - loss: 3.3430 - val_loss: 3.2754\n",
"\n",
"Epoch 00080: val_loss did not improve from 3.07717\n",
"Epoch 81/100\n",
"\n",
"Epoch 00081: LearningRateScheduler setting learning rate to 0.0001.\n",
"500/500 [==============================] - 451s 902ms/step - loss: 3.1907 - val_loss: 2.9731\n",
"\n",
"Epoch 00081: val_loss improved from 3.07717 to 2.97306, saving model to experimento_ssd300_fault_1.h5\n",
"Epoch 82/100\n",
"\n",
"Epoch 00082: LearningRateScheduler setting learning rate to 0.0001.\n",
"500/500 [==============================] - 451s 901ms/step - loss: 3.1456 - val_loss: 2.9711\n",
"\n",
"Epoch 00082: val_loss improved from 2.97306 to 2.97114, saving model to experimento_ssd300_fault_1.h5\n",
"Epoch 83/100\n",
"\n",
"Epoch 00083: LearningRateScheduler setting learning rate to 0.0001.\n",
"500/500 [==============================] - 451s 902ms/step - loss: 3.1248 - val_loss: 2.9670\n",
"\n",
"Epoch 00083: val_loss improved from 2.97114 to 2.96699, saving model to experimento_ssd300_fault_1.h5\n",
"Epoch 84/100\n",
"\n",
"Epoch 00084: LearningRateScheduler setting learning rate to 0.0001.\n",
"500/500 [==============================] - 450s 901ms/step - loss: 3.1167 - val_loss: 2.9557\n",
"\n",
"Epoch 00084: val_loss improved from 2.96699 to 2.95567, saving model to experimento_ssd300_fault_1.h5\n",
"Epoch 85/100\n",
"\n",
"Epoch 00085: LearningRateScheduler setting learning rate to 0.0001.\n",
"500/500 [==============================] - 451s 902ms/step - loss: 3.1287 - val_loss: 2.9472\n",
"\n",
"Epoch 00085: val_loss improved from 2.95567 to 2.94721, saving model to experimento_ssd300_fault_1.h5\n",
"Epoch 86/100\n",
"\n",
"Epoch 00086: LearningRateScheduler setting learning rate to 0.0001.\n",
"500/500 [==============================] - 451s 901ms/step - loss: 3.1195 - val_loss: 2.9572\n",
"\n",
"Epoch 00086: val_loss did not improve from 2.94721\n",
"Epoch 87/100\n",
"\n",
"Epoch 00087: LearningRateScheduler setting learning rate to 0.0001.\n",
"500/500 [==============================] - 451s 901ms/step - loss: 3.0942 - val_loss: 2.9739\n",
"\n",
"Epoch 00087: val_loss did not improve from 2.94721\n",
"Epoch 88/100\n",
"\n",
"Epoch 00088: LearningRateScheduler setting learning rate to 0.0001.\n",
"500/500 [==============================] - 451s 902ms/step - loss: 3.0693 - val_loss: 2.9428\n",
"\n",
"Epoch 00088: val_loss improved from 2.94721 to 2.94277, saving model to experimento_ssd300_fault_1.h5\n",
"Epoch 89/100\n",
"\n",
"Epoch 00089: LearningRateScheduler setting learning rate to 0.0001.\n",
"500/500 [==============================] - 450s 901ms/step - loss: 3.0901 - val_loss: 2.9392\n",
"\n",
"Epoch 00089: val_loss improved from 2.94277 to 2.93917, saving model to experimento_ssd300_fault_1.h5\n",
"Epoch 90/100\n",
"\n",
"Epoch 00090: LearningRateScheduler setting learning rate to 0.0001.\n",
"500/500 [==============================] - 451s 902ms/step - loss: 3.0916 - val_loss: 2.9386\n",
"\n",
"Epoch 00090: val_loss improved from 2.93917 to 2.93864, saving model to experimento_ssd300_fault_1.h5\n",
"Epoch 91/100\n",
"\n",
"Epoch 00091: LearningRateScheduler setting learning rate to 0.0001.\n",
"500/500 [==============================] - 450s 901ms/step - loss: 3.0853 - val_loss: 2.9484\n",
"\n",
"Epoch 00091: val_loss did not improve from 2.93864\n",
"Epoch 92/100\n",
"\n",
"Epoch 00092: LearningRateScheduler setting learning rate to 0.0001.\n",
"500/500 [==============================] - 451s 902ms/step - loss: 3.0696 - val_loss: 2.9277\n",
"\n",
"Epoch 00092: val_loss improved from 2.93864 to 2.92770, saving model to experimento_ssd300_fault_1.h5\n",
"Epoch 93/100\n",
"\n",
"Epoch 00093: LearningRateScheduler setting learning rate to 0.0001.\n",
"500/500 [==============================] - 451s 901ms/step - loss: 3.0827 - val_loss: 2.9312\n",
"\n",
"Epoch 00093: val_loss did not improve from 2.92770\n",
"Epoch 94/100\n",
"\n",
"Epoch 00094: LearningRateScheduler setting learning rate to 0.0001.\n",
"500/500 [==============================] - 450s 901ms/step - loss: 3.0772 - val_loss: 2.9390\n",
"\n",
"Epoch 00094: val_loss did not improve from 2.92770\n",
"Epoch 95/100\n",
"\n",
"Epoch 00095: LearningRateScheduler setting learning rate to 0.0001.\n",
"500/500 [==============================] - 451s 902ms/step - loss: 3.0609 - val_loss: 2.9373\n",
"\n",
"Epoch 00095: val_loss did not improve from 2.92770\n",
"Epoch 96/100\n",
"\n",
"Epoch 00096: LearningRateScheduler setting learning rate to 0.0001.\n",
"500/500 [==============================] - 450s 901ms/step - loss: 3.0587 - val_loss: 2.9377\n",
"\n",
"Epoch 00096: val_loss did not improve from 2.92770\n",
"Epoch 97/100\n",
"\n",
"Epoch 00097: LearningRateScheduler setting learning rate to 0.0001.\n",
"500/500 [==============================] - 451s 902ms/step - loss: 3.0529 - val_loss: 2.9221\n",
"\n",
"Epoch 00097: val_loss improved from 2.92770 to 2.92209, saving model to experimento_ssd300_fault_1.h5\n",
"Epoch 98/100\n",
"\n",
"Epoch 00098: LearningRateScheduler setting learning rate to 0.0001.\n",
"500/500 [==============================] - 450s 901ms/step - loss: 3.0698 - val_loss: 2.9095\n",
"\n",
"Epoch 00098: val_loss improved from 2.92209 to 2.90946, saving model to experimento_ssd300_fault_1.h5\n",
"Epoch 99/100\n",
"\n",
"Epoch 00099: LearningRateScheduler setting learning rate to 0.0001.\n",
"500/500 [==============================] - 450s 901ms/step - loss: 3.0672 - val_loss: 2.9138\n",
"\n",
"Epoch 00099: val_loss did not improve from 2.90946\n",
"Epoch 100/100\n",
"\n",
"Epoch 00100: LearningRateScheduler setting learning rate to 0.0001.\n",
"500/500 [==============================] - 451s 902ms/step - loss: 3.0530 - val_loss: 2.9209\n",
"\n",
"Epoch 00100: val_loss did not improve from 2.90946\n"
]
}
],
"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 = classes, \n",
" include_classes= [1],\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 = classes, \n",
" include_classes=[1],\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 = 500\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*10),\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": null,
"metadata": {},
"outputs": [],
"source": [
"classes"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"dict_keys(['val_loss', 'loss', 'lr'])\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"experimento_ssd300_fault_1.h5\n"
]
}
],
"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.ylim((0, 10)) \n",
"plt.show()\n",
"\n",
"print(config['train']['saved_weights_name'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Evaluación del Modelo"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'json' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-1-85ffd401afdf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig_path\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mconfig_buffer\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mconfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig_buffer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\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[0m\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'json' is not defined"
]
}
],
"source": [
"\n",
"config_path = 'config_300_fault_1.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": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"24"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ceil(val_dataset_size/batch_size)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cargar nuevamente el modelo desde los pesos.\n",
"Predicción"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Training on: \t{'1': 1}\n",
"\n"
]
}
],
"source": [
"from imageio import imread\n",
"from keras.preprocessing import image\n",
"import time\n",
"\n",
"config_path = 'config_300_fault_1.json'\n",
"input_path = ['fault_jpg_1/']\n",
"output_path = 'result_ssd300_fault_1/'\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 = 0.25\n",
"score_threshold_iou = 0.5\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": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tiempo Total: 27.768\n",
"Tiempo promedio por imagen: 1.111\n",
"OK\n"
]
}
],
"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
}
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