import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.applications.vgg16 import VGG16, preprocess_input
import helpers
import rpn
import faster_rcnn
args = helpers.handle_args()
if args.handle_gpu:
helpers.handle_gpu_compatibility()
batch_size = 8
epochs = 50
load_weights = False
hyper_params = helpers.get_hyper_params()
VOC_train_data, VOC_info = helpers.get_dataset("voc/2007", "train+validation")
VOC_val_data, _ = helpers.get_dataset("voc/2007", "test")
VOC_train_total_items = helpers.get_total_item_size(VOC_info, "train+validation")
VOC_val_total_items = helpers.get_total_item_size(VOC_info, "test")
step_size_train = helpers.get_step_size(VOC_train_total_items, batch_size)
step_size_val = helpers.get_step_size(VOC_val_total_items, batch_size)
labels = helpers.get_labels(VOC_info)
# We add 1 class for background
hyper_params["total_labels"] = len(labels) + 1
# If you want to use different dataset and don't know max height and width values
# You can use calculate_max_height_width method in helpers
max_height, max_width = helpers.VOC["max_height"], helpers.VOC["max_width"]
VOC_train_data = VOC_train_data.map(lambda x : helpers.preprocessing(x, max_height, max_width))
VOC_val_data = VOC_val_data.map(lambda x : helpers.preprocessing(x, max_height, max_width))
import tensorflow as tf
from tensorflow.keras.applications.vgg16 import VGG16, preprocess_input
from tensorflow.keras.models import load_model, Sequential
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
import helpers
import rpn
import faster_rcnn
args = helpers.handle_args()
if args.handle_gpu:
helpers.handle_gpu_compatibility()
batch_size = 1
# If you have trained faster rcnn model you can load weights from faster rcnn model
load_weights_from_frcnn = False
hyper_params = helpers.get_hyper_params(nms_topn=10)
VOC_test_data, VOC_info = helpers.get_dataset("voc/2007", "test")
labels = helpers.get_labels(VOC_info)
# We add 1 class for background
hyper_params["total_labels"] = len(labels) + 1
# If you want to use different dataset and don't know max height and width values
# You can use calculate_max_height_width method in helpers
max_height, max_width = helpers.VOC["max_height"], helpers.VOC["max_width"]
VOC_test_data = VOC_test_data.map(lambda x : helpers.preprocessing(x, max_height, max_width))
padded_shapes, padding_values = helpers.get_padded_batch_params()
VOC_test_data = VOC_test_data.padded_batch(batch_size, padded_shapes=padded_shapes, padding_values=padding_values)
base_model = VGG16(include_top=False)
if hyper_params["stride"] == 16:
import tensorflow as tf
from tensorflow.keras.applications.vgg16 import VGG16, preprocess_input
from tensorflow.keras.models import load_model, Sequential
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
import helpers
import rpn
import faster_rcnn
args = helpers.handle_args()
if args.handle_gpu:
helpers.handle_gpu_compatibility()
batch_size = 1
# If you have trained faster rcnn model you can load weights from faster rcnn model
load_weights_from_frcnn = False
hyper_params = helpers.get_hyper_params(post_nms_topn=10)
VOC_test_data, VOC_info = helpers.get_dataset("voc/2007", "test")
labels = helpers.get_labels(VOC_info)
# We add 1 class for background
hyper_params["total_labels"] = len(labels) + 1
# If you want to use different dataset and don't know max height and width values
# You can use calculate_max_height_width method in helpers
max_height, max_width = helpers.VOC["max_height"], helpers.VOC["max_width"]
VOC_test_data = VOC_test_data.map(
lambda x: helpers.preprocessing(x, max_height, max_width))
padded_shapes, padding_values = helpers.get_padded_batch_params()
VOC_test_data = VOC_test_data.padded_batch(batch_size,
padded_shapes=padded_shapes,
padding_values=padding_values)