1892 lines
129 KiB
Plaintext
1892 lines
129 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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"Detector de Paneles"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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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": "markdown",
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"metadata": {},
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"source": [
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"\n"
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]
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},
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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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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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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": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Using TensorFlow backend.\n"
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]
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},
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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{'panel': 1}\n",
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"\n",
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"\n",
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"Loading pretrained weights.\n",
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"\n",
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"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",
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"Instructions for updating:\n",
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"Colocations handled automatically by placer.\n",
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"WARNING:tensorflow:From /home/dl-desktop/Desktop/Rentadrone/model-definition/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",
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"Instructions for updating:\n",
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"Use tf.cast instead.\n",
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"WARNING:tensorflow:From /home/dl-desktop/Desktop/Rentadrone/model-definition/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",
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"Instructions for updating:\n",
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"Use tf.cast instead.\n",
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"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",
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"Instructions for updating:\n",
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"Deprecated in favor of operator or tf.math.divide.\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_panel.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",
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" divide_by_stddev=None)\n",
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"\n",
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" else :\n",
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" print('Wrong Backend')\n",
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"\n",
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"\n",
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"\n",
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" print('OK create model')\n",
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" #sgd = SGD(lr=config['train']['learning_rate'], momentum=0.9, decay=0.0, nesterov=False)\n",
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"\n",
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" # TODO: Set the path to the weights you want to load. only for ssd300 or ssd512\n",
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"\n",
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" weights_path = '../ssd_keras-master/VGG_ILSVRC_16_layers_fc_reduced.h5'\n",
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" print(\"\\nLoading pretrained weights VGG.\\n\")\n",
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" model.load_weights(weights_path, by_name=True)\n",
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"\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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" #adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)\n",
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" #sgd = SGD(lr=0.001, momentum=0.9, decay=0.0, nesterov=False)\n",
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" optimizer = Adam(lr=config['train']['learning_rate'], beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)\n",
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" ssd_loss = SSDLoss(neg_pos_ratio=3, alpha=1.0)\n",
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" model.compile(optimizer=optimizer, loss=ssd_loss.compute_loss)\n",
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"\n",
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" model.summary()\n"
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]
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},
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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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"Instanciar los generadores de datos y entrenamiento del modelo.\n",
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"\n",
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"*Cambio realizado para leer png y jpg. keras-ssd-master/data_generator/object_detection_2d_data_generator.py función parse_xml\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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"Processing image set 'train.txt': 100%|██████████| 1/1 [00:00<00:00, 3.02it/s]\n",
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"Processing image set 'test.txt': 100%|██████████| 1/1 [00:00<00:00, 2.48it/s]\n",
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"panel : 69\n",
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"cell : 423\n",
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"Number of images in the training dataset:\t 1\n",
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"Number of images in the validation dataset:\t 1\n",
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"Epoch 1/100\n",
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"\n",
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"Epoch 00001: LearningRateScheduler setting learning rate to 0.001.\n",
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"50/50 [==============================] - 200s 4s/step - loss: 13.2409 - val_loss: 9.9807\n",
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"\n",
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"Epoch 00001: val_loss improved from inf to 9.98075, saving model to experimento_ssd7_panel_cell.h5\n",
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"Epoch 2/100\n",
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"\n",
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"Epoch 00002: LearningRateScheduler setting learning rate to 0.001.\n",
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"50/50 [==============================] - 238s 5s/step - loss: 9.8864 - val_loss: 11.1452\n",
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"\n",
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"Epoch 00002: val_loss did not improve from 9.98075\n",
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"Epoch 3/100\n",
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"\n",
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"Epoch 00003: LearningRateScheduler setting learning rate to 0.001.\n",
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"50/50 [==============================] - 226s 5s/step - loss: 8.8060 - val_loss: 8.3006\n",
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"\n",
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"Epoch 00003: val_loss improved from 9.98075 to 8.30060, saving model to experimento_ssd7_panel_cell.h5\n",
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"Epoch 4/100\n",
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"\n",
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"Epoch 00004: LearningRateScheduler setting learning rate to 0.001.\n",
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"50/50 [==============================] - 199s 4s/step - loss: 7.4999 - val_loss: 8.9384\n",
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"\n",
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"Epoch 00004: val_loss did not improve from 8.30060\n",
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"Epoch 5/100\n",
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"\n",
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"Epoch 00005: LearningRateScheduler setting learning rate to 0.001.\n",
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"50/50 [==============================] - 187s 4s/step - loss: 7.4727 - val_loss: 7.9512\n",
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"\n",
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"Epoch 00005: val_loss improved from 8.30060 to 7.95121, saving model to experimento_ssd7_panel_cell.h5\n",
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"Epoch 6/100\n",
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"\n",
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"Epoch 00006: LearningRateScheduler setting learning rate to 0.001.\n",
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"50/50 [==============================] - 213s 4s/step - loss: 6.8813 - val_loss: 11.2544\n",
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"\n",
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"Epoch 00006: val_loss did not improve from 7.95121\n",
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"Epoch 7/100\n",
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"\n",
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"Epoch 00007: LearningRateScheduler setting learning rate to 0.001.\n",
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"50/50 [==============================] - 195s 4s/step - loss: 6.4775 - val_loss: 6.9093\n",
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"\n",
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"Epoch 00007: val_loss improved from 7.95121 to 6.90929, saving model to experimento_ssd7_panel_cell.h5\n",
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"Epoch 8/100\n",
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"\n",
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"Epoch 00008: LearningRateScheduler setting learning rate to 0.001.\n",
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"50/50 [==============================] - 212s 4s/step - loss: 6.9758 - val_loss: 8.6997\n",
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"\n",
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"Epoch 00008: val_loss did not improve from 6.90929\n",
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"Epoch 9/100\n",
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"\n",
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"Epoch 00009: LearningRateScheduler setting learning rate to 0.001.\n",
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"50/50 [==============================] - 199s 4s/step - loss: 6.1539 - val_loss: 10.9586\n",
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"\n",
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"Epoch 00009: val_loss did not improve from 6.90929\n",
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"Epoch 10/100\n",
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"\n",
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"Epoch 00010: LearningRateScheduler setting learning rate to 0.001.\n",
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"50/50 [==============================] - 206s 4s/step - loss: 5.9307 - val_loss: 8.4361\n",
|
|
"\n",
|
|
"Epoch 00010: val_loss did not improve from 6.90929\n",
|
|
"Epoch 11/100\n",
|
|
"\n",
|
|
"Epoch 00011: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 197s 4s/step - loss: 5.3895 - val_loss: 5.9796\n",
|
|
"\n",
|
|
"Epoch 00011: val_loss improved from 6.90929 to 5.97960, saving model to experimento_ssd7_panel_cell.h5\n",
|
|
"Epoch 12/100\n",
|
|
"\n",
|
|
"Epoch 00012: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 184s 4s/step - loss: 5.0889 - val_loss: 5.9283\n",
|
|
"\n",
|
|
"Epoch 00012: val_loss improved from 5.97960 to 5.92832, saving model to experimento_ssd7_panel_cell.h5\n",
|
|
"Epoch 13/100\n",
|
|
"\n",
|
|
"Epoch 00013: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 193s 4s/step - loss: 5.7916 - val_loss: 6.7706\n",
|
|
"\n",
|
|
"Epoch 00013: val_loss did not improve from 5.92832\n",
|
|
"Epoch 14/100\n",
|
|
"\n",
|
|
"Epoch 00014: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 222s 4s/step - loss: 5.3010 - val_loss: 7.8910\n",
|
|
"\n",
|
|
"Epoch 00014: val_loss did not improve from 5.92832\n",
|
|
"Epoch 15/100\n",
|
|
"\n",
|
|
"Epoch 00015: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 179s 4s/step - loss: 4.9873 - val_loss: 6.0389\n",
|
|
"\n",
|
|
"Epoch 00015: val_loss did not improve from 5.92832\n",
|
|
"Epoch 16/100\n",
|
|
"\n",
|
|
"Epoch 00016: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 182s 4s/step - loss: 5.4664 - val_loss: 6.4125\n",
|
|
"\n",
|
|
"Epoch 00016: val_loss did not improve from 5.92832\n",
|
|
"Epoch 17/100\n",
|
|
"\n",
|
|
"Epoch 00017: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 166s 3s/step - loss: 6.0094 - val_loss: 9.2918\n",
|
|
"\n",
|
|
"Epoch 00017: val_loss did not improve from 5.92832\n",
|
|
"Epoch 18/100\n",
|
|
"\n",
|
|
"Epoch 00018: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 181s 4s/step - loss: 5.1737 - val_loss: 7.6806\n",
|
|
"\n",
|
|
"Epoch 00018: val_loss did not improve from 5.92832\n",
|
|
"Epoch 19/100\n",
|
|
"\n",
|
|
"Epoch 00019: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 159s 3s/step - loss: 5.2708 - val_loss: 7.1096\n",
|
|
"\n",
|
|
"Epoch 00019: val_loss did not improve from 5.92832\n",
|
|
"Epoch 20/100\n",
|
|
"\n",
|
|
"Epoch 00020: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 173s 3s/step - loss: 5.4765 - val_loss: 5.4921\n",
|
|
"\n",
|
|
"Epoch 00020: val_loss improved from 5.92832 to 5.49211, saving model to experimento_ssd7_panel_cell.h5\n",
|
|
"Epoch 21/100\n",
|
|
"\n",
|
|
"Epoch 00021: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 170s 3s/step - loss: 4.6517 - val_loss: 6.6033\n",
|
|
"\n",
|
|
"Epoch 00021: val_loss did not improve from 5.49211\n",
|
|
"Epoch 22/100\n",
|
|
"\n",
|
|
"Epoch 00022: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 191s 4s/step - loss: 5.1432 - val_loss: 5.6549\n",
|
|
"\n",
|
|
"Epoch 00022: val_loss did not improve from 5.49211\n",
|
|
"Epoch 23/100\n",
|
|
"\n",
|
|
"Epoch 00023: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 159s 3s/step - loss: 5.4830 - val_loss: 5.8758\n",
|
|
"\n",
|
|
"Epoch 00023: val_loss did not improve from 5.49211\n",
|
|
"Epoch 24/100\n",
|
|
"\n",
|
|
"Epoch 00024: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 150s 3s/step - loss: 5.3366 - val_loss: 5.3871\n",
|
|
"\n",
|
|
"Epoch 00024: val_loss improved from 5.49211 to 5.38706, saving model to experimento_ssd7_panel_cell.h5\n",
|
|
"Epoch 25/100\n",
|
|
"\n",
|
|
"Epoch 00025: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 138s 3s/step - loss: 5.7189 - val_loss: 8.0760\n",
|
|
"\n",
|
|
"Epoch 00025: val_loss did not improve from 5.38706\n",
|
|
"Epoch 26/100\n",
|
|
"\n",
|
|
"Epoch 00026: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 144s 3s/step - loss: 6.0929 - val_loss: 12.6163\n",
|
|
"\n",
|
|
"Epoch 00026: val_loss did not improve from 5.38706\n",
|
|
"Epoch 27/100\n",
|
|
"\n",
|
|
"Epoch 00027: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 147s 3s/step - loss: 5.2239 - val_loss: 9.8536\n",
|
|
"\n",
|
|
"Epoch 00027: val_loss did not improve from 5.38706\n",
|
|
"Epoch 28/100\n",
|
|
"\n",
|
|
"Epoch 00028: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 158s 3s/step - loss: 5.4414 - val_loss: 6.4950\n",
|
|
"\n",
|
|
"Epoch 00028: val_loss did not improve from 5.38706\n",
|
|
"Epoch 29/100\n",
|
|
"\n",
|
|
"Epoch 00029: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 157s 3s/step - loss: 5.4436 - val_loss: 9.0002\n",
|
|
"\n",
|
|
"Epoch 00029: val_loss did not improve from 5.38706\n",
|
|
"Epoch 30/100\n",
|
|
"\n",
|
|
"Epoch 00030: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 162s 3s/step - loss: 4.9780 - val_loss: 4.9993\n",
|
|
"\n",
|
|
"Epoch 00030: val_loss improved from 5.38706 to 4.99925, saving model to experimento_ssd7_panel_cell.h5\n",
|
|
"Epoch 31/100\n",
|
|
"\n",
|
|
"Epoch 00031: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 140s 3s/step - loss: 4.9645 - val_loss: 5.6612\n",
|
|
"\n",
|
|
"Epoch 00031: val_loss did not improve from 4.99925\n",
|
|
"Epoch 32/100\n",
|
|
"\n",
|
|
"Epoch 00032: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 141s 3s/step - loss: 4.5982 - val_loss: 5.2083\n",
|
|
"\n",
|
|
"Epoch 00032: val_loss did not improve from 4.99925\n",
|
|
"Epoch 33/100\n",
|
|
"\n",
|
|
"Epoch 00033: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 143s 3s/step - loss: 4.3101 - val_loss: 6.4808\n",
|
|
"\n",
|
|
"Epoch 00033: val_loss did not improve from 4.99925\n",
|
|
"Epoch 34/100\n",
|
|
"\n",
|
|
"Epoch 00034: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 145s 3s/step - loss: 4.4252 - val_loss: 10.9472\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
"Epoch 00034: val_loss did not improve from 4.99925\n",
|
|
"Epoch 35/100\n",
|
|
"\n",
|
|
"Epoch 00035: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 153s 3s/step - loss: 4.4998 - val_loss: 7.1254\n",
|
|
"\n",
|
|
"Epoch 00035: val_loss did not improve from 4.99925\n",
|
|
"Epoch 36/100\n",
|
|
"\n",
|
|
"Epoch 00036: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 153s 3s/step - loss: 4.8952 - val_loss: 7.0446\n",
|
|
"\n",
|
|
"Epoch 00036: val_loss did not improve from 4.99925\n",
|
|
"Epoch 37/100\n",
|
|
"\n",
|
|
"Epoch 00037: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 154s 3s/step - loss: 4.9868 - val_loss: 9.3251\n",
|
|
"\n",
|
|
"Epoch 00037: val_loss did not improve from 4.99925\n",
|
|
"Epoch 38/100\n",
|
|
"\n",
|
|
"Epoch 00038: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 148s 3s/step - loss: 4.8918 - val_loss: 5.1689\n",
|
|
"\n",
|
|
"Epoch 00038: val_loss did not improve from 4.99925\n",
|
|
"Epoch 39/100\n",
|
|
"\n",
|
|
"Epoch 00039: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 143s 3s/step - loss: 4.5572 - val_loss: 4.9839\n",
|
|
"\n",
|
|
"Epoch 00039: val_loss improved from 4.99925 to 4.98394, saving model to experimento_ssd7_panel_cell.h5\n",
|
|
"Epoch 40/100\n",
|
|
"\n",
|
|
"Epoch 00040: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 150s 3s/step - loss: 4.4722 - val_loss: 5.7133\n",
|
|
"\n",
|
|
"Epoch 00040: val_loss did not improve from 4.98394\n",
|
|
"Epoch 41/100\n",
|
|
"\n",
|
|
"Epoch 00041: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 152s 3s/step - loss: 4.9414 - val_loss: 5.5843\n",
|
|
"\n",
|
|
"Epoch 00041: val_loss did not improve from 4.98394\n",
|
|
"Epoch 42/100\n",
|
|
"\n",
|
|
"Epoch 00042: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 148s 3s/step - loss: 4.5857 - val_loss: 5.1884\n",
|
|
"\n",
|
|
"Epoch 00042: val_loss did not improve from 4.98394\n",
|
|
"Epoch 43/100\n",
|
|
"\n",
|
|
"Epoch 00043: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 149s 3s/step - loss: 4.7094 - val_loss: 6.7545\n",
|
|
"\n",
|
|
"Epoch 00043: val_loss did not improve from 4.98394\n",
|
|
"Epoch 44/100\n",
|
|
"\n",
|
|
"Epoch 00044: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 151s 3s/step - loss: 5.0428 - val_loss: 5.2691\n",
|
|
"\n",
|
|
"Epoch 00044: val_loss did not improve from 4.98394\n",
|
|
"Epoch 45/100\n",
|
|
"\n",
|
|
"Epoch 00045: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 146s 3s/step - loss: 4.9842 - val_loss: 6.5112\n",
|
|
"\n",
|
|
"Epoch 00045: val_loss did not improve from 4.98394\n",
|
|
"Epoch 46/100\n",
|
|
"\n",
|
|
"Epoch 00046: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 147s 3s/step - loss: 4.9108 - val_loss: 6.0670\n",
|
|
"\n",
|
|
"Epoch 00046: val_loss did not improve from 4.98394\n",
|
|
"Epoch 47/100\n",
|
|
"\n",
|
|
"Epoch 00047: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 155s 3s/step - loss: 4.6837 - val_loss: 5.8351\n",
|
|
"\n",
|
|
"Epoch 00047: val_loss did not improve from 4.98394\n",
|
|
"Epoch 48/100\n",
|
|
"\n",
|
|
"Epoch 00048: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 149s 3s/step - loss: 5.1042 - val_loss: 5.1778\n",
|
|
"\n",
|
|
"Epoch 00048: val_loss did not improve from 4.98394\n",
|
|
"Epoch 49/100\n",
|
|
"\n",
|
|
"Epoch 00049: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 144s 3s/step - loss: 4.1312 - val_loss: 5.9606\n",
|
|
"\n",
|
|
"Epoch 00049: val_loss did not improve from 4.98394\n",
|
|
"Epoch 50/100\n",
|
|
"\n",
|
|
"Epoch 00050: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 122s 2s/step - loss: 4.5373 - val_loss: 5.4351\n",
|
|
"\n",
|
|
"Epoch 00050: val_loss did not improve from 4.98394\n",
|
|
"Epoch 51/100\n",
|
|
"\n",
|
|
"Epoch 00051: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 135s 3s/step - loss: 4.8955 - val_loss: 6.0315\n",
|
|
"\n",
|
|
"Epoch 00051: val_loss did not improve from 4.98394\n",
|
|
"Epoch 52/100\n",
|
|
"\n",
|
|
"Epoch 00052: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 150s 3s/step - loss: 4.9445 - val_loss: 5.7199\n",
|
|
"\n",
|
|
"Epoch 00052: val_loss did not improve from 4.98394\n",
|
|
"Epoch 53/100\n",
|
|
"\n",
|
|
"Epoch 00053: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 139s 3s/step - loss: 3.9748 - val_loss: 5.5974\n",
|
|
"\n",
|
|
"Epoch 00053: val_loss did not improve from 4.98394\n",
|
|
"Epoch 54/100\n",
|
|
"\n",
|
|
"Epoch 00054: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 153s 3s/step - loss: 4.8783 - val_loss: 8.6056\n",
|
|
"\n",
|
|
"Epoch 00054: val_loss did not improve from 4.98394\n",
|
|
"Epoch 55/100\n",
|
|
"\n",
|
|
"Epoch 00055: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 141s 3s/step - loss: 4.1649 - val_loss: 6.0042\n",
|
|
"\n",
|
|
"Epoch 00055: val_loss did not improve from 4.98394\n",
|
|
"Epoch 56/100\n",
|
|
"\n",
|
|
"Epoch 00056: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 149s 3s/step - loss: 4.8997 - val_loss: 9.1298\n",
|
|
"\n",
|
|
"Epoch 00056: val_loss did not improve from 4.98394\n",
|
|
"Epoch 57/100\n",
|
|
"\n",
|
|
"Epoch 00057: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 151s 3s/step - loss: 4.4433 - val_loss: 7.1151\n",
|
|
"\n",
|
|
"Epoch 00057: val_loss did not improve from 4.98394\n",
|
|
"Epoch 58/100\n",
|
|
"\n",
|
|
"Epoch 00058: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 147s 3s/step - loss: 4.5827 - val_loss: 5.4356\n",
|
|
"\n",
|
|
"Epoch 00058: val_loss did not improve from 4.98394\n",
|
|
"Epoch 59/100\n",
|
|
"\n",
|
|
"Epoch 00059: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 137s 3s/step - loss: 3.9437 - val_loss: 4.7926\n",
|
|
"\n",
|
|
"Epoch 00059: val_loss improved from 4.98394 to 4.79262, saving model to experimento_ssd7_panel_cell.h5\n",
|
|
"Epoch 60/100\n",
|
|
"\n",
|
|
"Epoch 00060: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 125s 3s/step - loss: 4.0939 - val_loss: 5.7098\n",
|
|
"\n",
|
|
"Epoch 00060: val_loss did not improve from 4.79262\n",
|
|
"Epoch 61/100\n",
|
|
"\n",
|
|
"Epoch 00061: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 161s 3s/step - loss: 5.1152 - val_loss: 5.2079\n",
|
|
"\n",
|
|
"Epoch 00061: val_loss did not improve from 4.79262\n",
|
|
"Epoch 62/100\n",
|
|
"\n",
|
|
"Epoch 00062: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 144s 3s/step - loss: 4.2958 - val_loss: 4.9239\n",
|
|
"\n",
|
|
"Epoch 00062: val_loss did not improve from 4.79262\n",
|
|
"Epoch 63/100\n",
|
|
"\n",
|
|
"Epoch 00063: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 141s 3s/step - loss: 3.8241 - val_loss: 4.5443\n",
|
|
"\n",
|
|
"Epoch 00063: val_loss improved from 4.79262 to 4.54430, saving model to experimento_ssd7_panel_cell.h5\n",
|
|
"Epoch 64/100\n",
|
|
"\n",
|
|
"Epoch 00064: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 134s 3s/step - loss: 4.7252 - val_loss: 5.9445\n",
|
|
"\n",
|
|
"Epoch 00064: val_loss did not improve from 4.54430\n",
|
|
"Epoch 65/100\n",
|
|
"\n",
|
|
"Epoch 00065: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 154s 3s/step - loss: 4.4455 - val_loss: 4.8326\n",
|
|
"\n",
|
|
"Epoch 00065: val_loss did not improve from 4.54430\n",
|
|
"Epoch 66/100\n",
|
|
"\n",
|
|
"Epoch 00066: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 145s 3s/step - loss: 4.4054 - val_loss: 5.6441\n",
|
|
"\n",
|
|
"Epoch 00066: val_loss did not improve from 4.54430\n",
|
|
"Epoch 67/100\n",
|
|
"\n",
|
|
"Epoch 00067: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 124s 2s/step - loss: 4.4165 - val_loss: 6.8159\n",
|
|
"\n",
|
|
"Epoch 00067: val_loss did not improve from 4.54430\n",
|
|
"Epoch 68/100\n",
|
|
"\n",
|
|
"Epoch 00068: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 162s 3s/step - loss: 5.0418 - val_loss: 4.8508\n",
|
|
"\n",
|
|
"Epoch 00068: val_loss did not improve from 4.54430\n",
|
|
"Epoch 69/100\n",
|
|
"\n",
|
|
"Epoch 00069: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 140s 3s/step - loss: 4.1512 - val_loss: 5.4053\n",
|
|
"\n",
|
|
"Epoch 00069: val_loss did not improve from 4.54430\n",
|
|
"Epoch 70/100\n",
|
|
"\n",
|
|
"Epoch 00070: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 148s 3s/step - loss: 4.6197 - val_loss: 5.2824\n",
|
|
"\n",
|
|
"Epoch 00070: val_loss did not improve from 4.54430\n",
|
|
"Epoch 71/100\n",
|
|
"\n",
|
|
"Epoch 00071: LearningRateScheduler setting learning rate to 0.001.\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"50/50 [==============================] - 152s 3s/step - loss: 4.2807 - val_loss: 5.5992\n",
|
|
"\n",
|
|
"Epoch 00071: val_loss did not improve from 4.54430\n",
|
|
"Epoch 72/100\n",
|
|
"\n",
|
|
"Epoch 00072: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 143s 3s/step - loss: 4.5368 - val_loss: 6.5207\n",
|
|
"\n",
|
|
"Epoch 00072: val_loss did not improve from 4.54430\n",
|
|
"Epoch 73/100\n",
|
|
"\n",
|
|
"Epoch 00073: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 141s 3s/step - loss: 4.0598 - val_loss: 5.2421\n",
|
|
"\n",
|
|
"Epoch 00073: val_loss did not improve from 4.54430\n",
|
|
"Epoch 74/100\n",
|
|
"\n",
|
|
"Epoch 00074: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 150s 3s/step - loss: 4.4861 - val_loss: 5.4182\n",
|
|
"\n",
|
|
"Epoch 00074: val_loss did not improve from 4.54430\n",
|
|
"Epoch 75/100\n",
|
|
"\n",
|
|
"Epoch 00075: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 144s 3s/step - loss: 4.5263 - val_loss: 4.3774\n",
|
|
"\n",
|
|
"Epoch 00075: val_loss improved from 4.54430 to 4.37742, saving model to experimento_ssd7_panel_cell.h5\n",
|
|
"Epoch 76/100\n",
|
|
"\n",
|
|
"Epoch 00076: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 148s 3s/step - loss: 3.8465 - val_loss: 4.5809\n",
|
|
"\n",
|
|
"Epoch 00076: val_loss did not improve from 4.37742\n",
|
|
"Epoch 77/100\n",
|
|
"\n",
|
|
"Epoch 00077: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 152s 3s/step - loss: 4.0495 - val_loss: 4.9745\n",
|
|
"\n",
|
|
"Epoch 00077: val_loss did not improve from 4.37742\n",
|
|
"Epoch 78/100\n",
|
|
"\n",
|
|
"Epoch 00078: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 152s 3s/step - loss: 4.6009 - val_loss: 13.4989\n",
|
|
"\n",
|
|
"Epoch 00078: val_loss did not improve from 4.37742\n",
|
|
"Epoch 79/100\n",
|
|
"\n",
|
|
"Epoch 00079: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 142s 3s/step - loss: 4.6687 - val_loss: 6.4490\n",
|
|
"\n",
|
|
"Epoch 00079: val_loss did not improve from 4.37742\n",
|
|
"Epoch 80/100\n",
|
|
"\n",
|
|
"Epoch 00080: LearningRateScheduler setting learning rate to 0.001.\n",
|
|
"50/50 [==============================] - 147s 3s/step - loss: 4.5297 - val_loss: 8.0478\n",
|
|
"\n",
|
|
"Epoch 00080: val_loss did not improve from 4.37742\n",
|
|
"Epoch 81/100\n",
|
|
"\n",
|
|
"Epoch 00081: LearningRateScheduler setting learning rate to 0.0001.\n",
|
|
"50/50 [==============================] - 141s 3s/step - loss: 4.2662 - val_loss: 5.7929\n",
|
|
"\n",
|
|
"Epoch 00081: val_loss did not improve from 4.37742\n",
|
|
"Epoch 82/100\n",
|
|
"\n",
|
|
"Epoch 00082: LearningRateScheduler setting learning rate to 0.0001.\n",
|
|
"50/50 [==============================] - 149s 3s/step - loss: 4.1048 - val_loss: 4.6117\n",
|
|
"\n",
|
|
"Epoch 00082: val_loss did not improve from 4.37742\n",
|
|
"Epoch 83/100\n",
|
|
"\n",
|
|
"Epoch 00083: LearningRateScheduler setting learning rate to 0.0001.\n",
|
|
"50/50 [==============================] - 156s 3s/step - loss: 3.9905 - val_loss: 4.5542\n",
|
|
"\n",
|
|
"Epoch 00083: val_loss did not improve from 4.37742\n",
|
|
"Epoch 84/100\n",
|
|
"\n",
|
|
"Epoch 00084: LearningRateScheduler setting learning rate to 0.0001.\n",
|
|
"50/50 [==============================] - 155s 3s/step - loss: 4.3129 - val_loss: 4.4676\n",
|
|
"\n",
|
|
"Epoch 00084: val_loss did not improve from 4.37742\n",
|
|
"Epoch 85/100\n",
|
|
"\n",
|
|
"Epoch 00085: LearningRateScheduler setting learning rate to 0.0001.\n",
|
|
"50/50 [==============================] - 156s 3s/step - loss: 3.7951 - val_loss: 4.4689\n",
|
|
"\n",
|
|
"Epoch 00085: val_loss did not improve from 4.37742\n",
|
|
"Epoch 86/100\n",
|
|
"\n",
|
|
"Epoch 00086: LearningRateScheduler setting learning rate to 0.0001.\n",
|
|
"50/50 [==============================] - 155s 3s/step - loss: 4.3618 - val_loss: 4.4048\n",
|
|
"\n",
|
|
"Epoch 00086: val_loss did not improve from 4.37742\n",
|
|
"Epoch 87/100\n",
|
|
"\n",
|
|
"Epoch 00087: LearningRateScheduler setting learning rate to 0.0001.\n",
|
|
"50/50 [==============================] - 156s 3s/step - loss: 4.3538 - val_loss: 4.6832\n",
|
|
"\n",
|
|
"Epoch 00087: val_loss did not improve from 4.37742\n",
|
|
"Epoch 88/100\n",
|
|
"\n",
|
|
"Epoch 00088: LearningRateScheduler setting learning rate to 0.0001.\n",
|
|
"50/50 [==============================] - 152s 3s/step - loss: 4.2076 - val_loss: 4.4796\n",
|
|
"\n",
|
|
"Epoch 00088: val_loss did not improve from 4.37742\n",
|
|
"Epoch 89/100\n",
|
|
"\n",
|
|
"Epoch 00089: LearningRateScheduler setting learning rate to 0.0001.\n",
|
|
"50/50 [==============================] - 146s 3s/step - loss: 4.1322 - val_loss: 4.5462\n",
|
|
"\n",
|
|
"Epoch 00089: val_loss did not improve from 4.37742\n",
|
|
"Epoch 90/100\n",
|
|
"\n",
|
|
"Epoch 00090: LearningRateScheduler setting learning rate to 0.0001.\n",
|
|
"50/50 [==============================] - 157s 3s/step - loss: 4.4995 - val_loss: 4.5660\n",
|
|
"\n",
|
|
"Epoch 00090: val_loss did not improve from 4.37742\n",
|
|
"Epoch 91/100\n",
|
|
"\n",
|
|
"Epoch 00091: LearningRateScheduler setting learning rate to 0.0001.\n",
|
|
"50/50 [==============================] - 158s 3s/step - loss: 4.2653 - val_loss: 4.5265\n",
|
|
"\n",
|
|
"Epoch 00091: val_loss did not improve from 4.37742\n",
|
|
"Epoch 92/100\n",
|
|
"\n",
|
|
"Epoch 00092: LearningRateScheduler setting learning rate to 0.0001.\n",
|
|
"50/50 [==============================] - 153s 3s/step - loss: 4.3702 - val_loss: 4.5276\n",
|
|
"\n",
|
|
"Epoch 00092: val_loss did not improve from 4.37742\n",
|
|
"Epoch 93/100\n",
|
|
"\n",
|
|
"Epoch 00093: LearningRateScheduler setting learning rate to 0.0001.\n",
|
|
"50/50 [==============================] - 153s 3s/step - loss: 3.7340 - val_loss: 4.5439\n",
|
|
"\n",
|
|
"Epoch 00093: val_loss did not improve from 4.37742\n",
|
|
"Epoch 94/100\n",
|
|
"\n",
|
|
"Epoch 00094: LearningRateScheduler setting learning rate to 0.0001.\n",
|
|
"50/50 [==============================] - 151s 3s/step - loss: 4.0253 - val_loss: 4.3250\n",
|
|
"\n",
|
|
"Epoch 00094: val_loss improved from 4.37742 to 4.32498, saving model to experimento_ssd7_panel_cell.h5\n",
|
|
"Epoch 95/100\n",
|
|
"\n",
|
|
"Epoch 00095: LearningRateScheduler setting learning rate to 0.0001.\n",
|
|
"50/50 [==============================] - 143s 3s/step - loss: 4.0254 - val_loss: 4.6277\n",
|
|
"\n",
|
|
"Epoch 00095: val_loss did not improve from 4.32498\n",
|
|
"Epoch 96/100\n",
|
|
"\n",
|
|
"Epoch 00096: LearningRateScheduler setting learning rate to 0.0001.\n",
|
|
"50/50 [==============================] - 148s 3s/step - loss: 3.9857 - val_loss: 4.2953\n",
|
|
"\n",
|
|
"Epoch 00096: val_loss improved from 4.32498 to 4.29533, saving model to experimento_ssd7_panel_cell.h5\n",
|
|
"Epoch 97/100\n",
|
|
"\n",
|
|
"Epoch 00097: LearningRateScheduler setting learning rate to 0.0001.\n",
|
|
"50/50 [==============================] - 157s 3s/step - loss: 3.6750 - val_loss: 4.5637\n",
|
|
"\n",
|
|
"Epoch 00097: val_loss did not improve from 4.29533\n",
|
|
"Epoch 98/100\n",
|
|
"\n",
|
|
"Epoch 00098: LearningRateScheduler setting learning rate to 0.0001.\n",
|
|
"50/50 [==============================] - 154s 3s/step - loss: 3.7435 - val_loss: 4.3923\n",
|
|
"\n",
|
|
"Epoch 00098: val_loss did not improve from 4.29533\n",
|
|
"Epoch 99/100\n",
|
|
"\n",
|
|
"Epoch 00099: LearningRateScheduler setting learning rate to 0.0001.\n",
|
|
"50/50 [==============================] - 162s 3s/step - loss: 4.0930 - val_loss: 4.4010\n",
|
|
"\n",
|
|
"Epoch 00099: val_loss did not improve from 4.29533\n",
|
|
"Epoch 100/100\n",
|
|
"\n",
|
|
"Epoch 00100: LearningRateScheduler setting learning rate to 0.0001.\n",
|
|
"50/50 [==============================] - 134s 3s/step - loss: 3.8983 - val_loss: 4.4451\n",
|
|
"\n",
|
|
"Epoch 00100: val_loss did not improve from 4.29533\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 = ['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 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",
|
|
"\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": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 2,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"dict_keys(['val_loss', 'loss', 'lr'])\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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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_ssd7_panel.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.show()\n",
|
|
"\n",
|
|
"print(config['train']['saved_weights_name'])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Evaluación del Modelo"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Processing image set 'train.txt': 100%|██████████| 1/1 [00:00<00:00, 20.34it/s]\n",
|
|
"Processing image set 'test.txt': 100%|██████████| 1/1 [00:00<00:00, 20.49it/s]\n",
|
|
"Number of images in the evaluation dataset: 1\n",
|
|
"\n",
|
|
"Producing predictions batch-wise: 100%|██████████| 1/1 [00:00<00:00, 1.05it/s]\n",
|
|
"Matching predictions to ground truth, class 1/1.: 100%|██████████| 200/200 [00:00<00:00, 6558.57it/s]\n",
|
|
"Computing precisions and recalls, class 1/1\n",
|
|
"Computing average precision, class 1/1\n",
|
|
"200 instances of class panel with average precision: 0.8982\n",
|
|
"mAP using the weighted average of precisions among classes: 0.8982\n",
|
|
"mAP: 0.8982\n",
|
|
"panel AP 0.898\n",
|
|
"\n",
|
|
" mAP 0.898\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"\n",
|
|
"config_path = 'config_7_panel.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": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Cargar nuevamente el modelo desde los pesos.\n",
|
|
"Predicción"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
"Training on: \t{'panel': 1}\n",
|
|
"\n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"Layer (type) Output Shape Param # Connected to \n",
|
|
"==================================================================================================\n",
|
|
"input_1 (InputLayer) (None, 400, 400, 3) 0 \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"identity_layer (Lambda) (None, 400, 400, 3) 0 input_1[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"conv1 (Conv2D) (None, 400, 400, 32) 2432 identity_layer[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"bn1 (BatchNormalization) (None, 400, 400, 32) 128 conv1[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"elu1 (ELU) (None, 400, 400, 32) 0 bn1[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"pool1 (MaxPooling2D) (None, 200, 200, 32) 0 elu1[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"conv2 (Conv2D) (None, 200, 200, 48) 13872 pool1[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"bn2 (BatchNormalization) (None, 200, 200, 48) 192 conv2[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"elu2 (ELU) (None, 200, 200, 48) 0 bn2[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"pool2 (MaxPooling2D) (None, 100, 100, 48) 0 elu2[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"conv3 (Conv2D) (None, 100, 100, 64) 27712 pool2[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"bn3 (BatchNormalization) (None, 100, 100, 64) 256 conv3[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"elu3 (ELU) (None, 100, 100, 64) 0 bn3[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"pool3 (MaxPooling2D) (None, 50, 50, 64) 0 elu3[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"conv4 (Conv2D) (None, 50, 50, 64) 36928 pool3[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"bn4 (BatchNormalization) (None, 50, 50, 64) 256 conv4[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"elu4 (ELU) (None, 50, 50, 64) 0 bn4[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"pool4 (MaxPooling2D) (None, 25, 25, 64) 0 elu4[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"conv5 (Conv2D) (None, 25, 25, 48) 27696 pool4[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"bn5 (BatchNormalization) (None, 25, 25, 48) 192 conv5[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"elu5 (ELU) (None, 25, 25, 48) 0 bn5[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"pool5 (MaxPooling2D) (None, 12, 12, 48) 0 elu5[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"conv6 (Conv2D) (None, 12, 12, 48) 20784 pool5[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"bn6 (BatchNormalization) (None, 12, 12, 48) 192 conv6[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"elu6 (ELU) (None, 12, 12, 48) 0 bn6[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"pool6 (MaxPooling2D) (None, 6, 6, 48) 0 elu6[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"conv7 (Conv2D) (None, 6, 6, 32) 13856 pool6[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"bn7 (BatchNormalization) (None, 6, 6, 32) 128 conv7[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"elu7 (ELU) (None, 6, 6, 32) 0 bn7[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"classes4 (Conv2D) (None, 50, 50, 8) 4616 elu4[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"classes5 (Conv2D) (None, 25, 25, 8) 3464 elu5[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"classes6 (Conv2D) (None, 12, 12, 8) 3464 elu6[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"classes7 (Conv2D) (None, 6, 6, 8) 2312 elu7[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"boxes4 (Conv2D) (None, 50, 50, 16) 9232 elu4[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"boxes5 (Conv2D) (None, 25, 25, 16) 6928 elu5[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"boxes6 (Conv2D) (None, 12, 12, 16) 6928 elu6[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"boxes7 (Conv2D) (None, 6, 6, 16) 4624 elu7[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"classes4_reshape (Reshape) (None, 10000, 2) 0 classes4[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"classes5_reshape (Reshape) (None, 2500, 2) 0 classes5[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"classes6_reshape (Reshape) (None, 576, 2) 0 classes6[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"classes7_reshape (Reshape) (None, 144, 2) 0 classes7[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"anchors4 (AnchorBoxes) (None, 50, 50, 4, 8) 0 boxes4[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"anchors5 (AnchorBoxes) (None, 25, 25, 4, 8) 0 boxes5[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"anchors6 (AnchorBoxes) (None, 12, 12, 4, 8) 0 boxes6[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"anchors7 (AnchorBoxes) (None, 6, 6, 4, 8) 0 boxes7[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"classes_concat (Concatenate) (None, 13220, 2) 0 classes4_reshape[0][0] \n",
|
|
" classes5_reshape[0][0] \n",
|
|
" classes6_reshape[0][0] \n",
|
|
" classes7_reshape[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"boxes4_reshape (Reshape) (None, 10000, 4) 0 boxes4[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"boxes5_reshape (Reshape) (None, 2500, 4) 0 boxes5[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"boxes6_reshape (Reshape) (None, 576, 4) 0 boxes6[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"boxes7_reshape (Reshape) (None, 144, 4) 0 boxes7[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"anchors4_reshape (Reshape) (None, 10000, 8) 0 anchors4[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"anchors5_reshape (Reshape) (None, 2500, 8) 0 anchors5[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"anchors6_reshape (Reshape) (None, 576, 8) 0 anchors6[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"anchors7_reshape (Reshape) (None, 144, 8) 0 anchors7[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"classes_softmax (Activation) (None, 13220, 2) 0 classes_concat[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"boxes_concat (Concatenate) (None, 13220, 4) 0 boxes4_reshape[0][0] \n",
|
|
" boxes5_reshape[0][0] \n",
|
|
" boxes6_reshape[0][0] \n",
|
|
" boxes7_reshape[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"anchors_concat (Concatenate) (None, 13220, 8) 0 anchors4_reshape[0][0] \n",
|
|
" anchors5_reshape[0][0] \n",
|
|
" anchors6_reshape[0][0] \n",
|
|
" anchors7_reshape[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"predictions (Concatenate) (None, 13220, 14) 0 classes_softmax[0][0] \n",
|
|
" boxes_concat[0][0] \n",
|
|
" anchors_concat[0][0] \n",
|
|
"==================================================================================================\n",
|
|
"Total params: 186,192\n",
|
|
"Trainable params: 185,520\n",
|
|
"Non-trainable params: 672\n",
|
|
"__________________________________________________________________________________________________\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"#############################\n",
|
|
"####Prediction\n",
|
|
"#############################\n",
|
|
"\n",
|
|
"from imageio import imread\n",
|
|
"from keras.preprocessing import image\n",
|
|
"import time\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",
|
|
"config_path = 'config_7_panel.json'\n",
|
|
"input_path = ['panel_jpg/Mision_1/', 'panel_jpg/Mision_2/']\n",
|
|
"output_path = 'result_ssd7_panel/'\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 = 0.5\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",
|
|
"model.summary()\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Tiempo Total: 1.040\n",
|
|
"Tiempo promedio por imagen: 0.104\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": 28,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"__________________________________________________________________________________________________\n",
|
|
"Layer (type) Output Shape Param # Connected to \n",
|
|
"==================================================================================================\n",
|
|
"input_1 (InputLayer) (None, 400, 400, 3) 0 \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"identity_layer (Lambda) (None, 400, 400, 3) 0 input_1[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"conv1 (Conv2D) (None, 400, 400, 32) 2432 identity_layer[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"bn1 (BatchNormalization) (None, 400, 400, 32) 128 conv1[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"elu1 (ELU) (None, 400, 400, 32) 0 bn1[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"pool1 (MaxPooling2D) (None, 200, 200, 32) 0 elu1[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"conv2 (Conv2D) (None, 200, 200, 48) 13872 pool1[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"bn2 (BatchNormalization) (None, 200, 200, 48) 192 conv2[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"elu2 (ELU) (None, 200, 200, 48) 0 bn2[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"pool2 (MaxPooling2D) (None, 100, 100, 48) 0 elu2[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"conv3 (Conv2D) (None, 100, 100, 64) 27712 pool2[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"bn3 (BatchNormalization) (None, 100, 100, 64) 256 conv3[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"elu3 (ELU) (None, 100, 100, 64) 0 bn3[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"pool3 (MaxPooling2D) (None, 50, 50, 64) 0 elu3[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"conv4 (Conv2D) (None, 50, 50, 64) 36928 pool3[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"bn4 (BatchNormalization) (None, 50, 50, 64) 256 conv4[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"elu4 (ELU) (None, 50, 50, 64) 0 bn4[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"pool4 (MaxPooling2D) (None, 25, 25, 64) 0 elu4[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"conv5 (Conv2D) (None, 25, 25, 48) 27696 pool4[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"bn5 (BatchNormalization) (None, 25, 25, 48) 192 conv5[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"elu5 (ELU) (None, 25, 25, 48) 0 bn5[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"pool5 (MaxPooling2D) (None, 12, 12, 48) 0 elu5[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"conv6 (Conv2D) (None, 12, 12, 48) 20784 pool5[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"bn6 (BatchNormalization) (None, 12, 12, 48) 192 conv6[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"elu6 (ELU) (None, 12, 12, 48) 0 bn6[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"pool6 (MaxPooling2D) (None, 6, 6, 48) 0 elu6[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"conv7 (Conv2D) (None, 6, 6, 32) 13856 pool6[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"bn7 (BatchNormalization) (None, 6, 6, 32) 128 conv7[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"elu7 (ELU) (None, 6, 6, 32) 0 bn7[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"classes4 (Conv2D) (None, 50, 50, 12) 6924 elu4[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"classes5 (Conv2D) (None, 25, 25, 12) 5196 elu5[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"classes6 (Conv2D) (None, 12, 12, 12) 5196 elu6[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"classes7 (Conv2D) (None, 6, 6, 12) 3468 elu7[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"boxes4 (Conv2D) (None, 50, 50, 16) 9232 elu4[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"boxes5 (Conv2D) (None, 25, 25, 16) 6928 elu5[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"boxes6 (Conv2D) (None, 12, 12, 16) 6928 elu6[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"boxes7 (Conv2D) (None, 6, 6, 16) 4624 elu7[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"classes4_reshape (Reshape) (None, 10000, 3) 0 classes4[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"classes5_reshape (Reshape) (None, 2500, 3) 0 classes5[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"classes6_reshape (Reshape) (None, 576, 3) 0 classes6[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"classes7_reshape (Reshape) (None, 144, 3) 0 classes7[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"anchors4 (AnchorBoxes) (None, 50, 50, 4, 8) 0 boxes4[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"anchors5 (AnchorBoxes) (None, 25, 25, 4, 8) 0 boxes5[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"anchors6 (AnchorBoxes) (None, 12, 12, 4, 8) 0 boxes6[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"anchors7 (AnchorBoxes) (None, 6, 6, 4, 8) 0 boxes7[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"classes_concat (Concatenate) (None, 13220, 3) 0 classes4_reshape[0][0] \n",
|
|
" classes5_reshape[0][0] \n",
|
|
" classes6_reshape[0][0] \n",
|
|
" classes7_reshape[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"boxes4_reshape (Reshape) (None, 10000, 4) 0 boxes4[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"boxes5_reshape (Reshape) (None, 2500, 4) 0 boxes5[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"boxes6_reshape (Reshape) (None, 576, 4) 0 boxes6[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"boxes7_reshape (Reshape) (None, 144, 4) 0 boxes7[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"anchors4_reshape (Reshape) (None, 10000, 8) 0 anchors4[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"anchors5_reshape (Reshape) (None, 2500, 8) 0 anchors5[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"anchors6_reshape (Reshape) (None, 576, 8) 0 anchors6[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"anchors7_reshape (Reshape) (None, 144, 8) 0 anchors7[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"classes_softmax (Activation) (None, 13220, 3) 0 classes_concat[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"boxes_concat (Concatenate) (None, 13220, 4) 0 boxes4_reshape[0][0] \n",
|
|
" boxes5_reshape[0][0] \n",
|
|
" boxes6_reshape[0][0] \n",
|
|
" boxes7_reshape[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"anchors_concat (Concatenate) (None, 13220, 8) 0 anchors4_reshape[0][0] \n",
|
|
" anchors5_reshape[0][0] \n",
|
|
" anchors6_reshape[0][0] \n",
|
|
" anchors7_reshape[0][0] \n",
|
|
"__________________________________________________________________________________________________\n",
|
|
"predictions (Concatenate) (None, 13220, 15) 0 classes_softmax[0][0] \n",
|
|
" boxes_concat[0][0] \n",
|
|
" anchors_concat[0][0] \n",
|
|
"==================================================================================================\n",
|
|
"Total params: 193,120\n",
|
|
"Trainable params: 192,448\n",
|
|
"Non-trainable params: 672\n",
|
|
"__________________________________________________________________________________________________\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"model.summary()"
|
|
]
|
|
},
|
|
{
|
|
"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
|
|
}
|