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train_mobilenet_ssd.py
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import sys
sys.path.append("/Path To /MobileNet-ssd-keras")
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint, LearningRateScheduler, EarlyStopping, ReduceLROnPlateau, TensorBoard
from keras import backend as K
from keras.models import load_model
from math import ceil
import numpy as np
from matplotlib import pyplot as plt
import tensorflow as tf
from models.ssd_mobilenet import ssd_300
from misc.keras_ssd_loss import SSDLoss, FocalLoss, weightedSSDLoss, weightedFocalLoss
from misc.keras_layer_AnchorBoxes import AnchorBoxes
from misc.keras_layer_L2Normalization import L2Normalization
from misc.ssd_box_encode_decode_utils import SSDBoxEncoder, decode_y, decode_y2
from misc.ssd_batch_generator import BatchGenerator
from keras.utils.training_utils import multi_gpu_model
import os
import keras
import argparse
os.environ['CUDA_VISIBLE_DEVICES'] = "1"
img_height = 300 # Height of the input images
img_width = 300 # Width of the input images
img_channels = 3 # Number of color channels of the input images
subtract_mean = [123, 117, 104] # The per-channel mean of the images in the dataset
swap_channels = True # The color channel order in the original SSD is BGR
n_classes = 20 # Number of positive classes, e.g. 20 for Pascal VOC, 80 for MS COCO
scales_voc = [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
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
scales = scales_voc
aspect_ratios = [[1.0, 2.0, 0.5],
[1.0, 2.0, 0.5, 3.0, 1.0 / 3.0],
[1.0, 2.0, 0.5, 3.0, 1.0 / 3.0],
[1.0, 2.0, 0.5, 3.0, 1.0 / 3.0],
[1.0, 2.0, 0.5],
[1.0, 2.0, 0.5]] # The anchor box aspect ratios used in the original SSD300; the order matters
two_boxes_for_ar1 = True
steps = [8, 16, 32, 64, 100, 300] # The space between two adjacent anchor box center points for each predictor layer.
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.
limit_boxes = False # Whether or not you want to limit the anchor boxes to lie entirely within the image boundaries
variances = [0.1, 0.1, 0.2,
0.2] # The variances by which the encoded target coordinates are scaled as in the original implementation
coords = 'centroids' # Whether the box coordinates to be used as targets for the model should be in the 'centroids', 'corners', or 'minmax' format, see documentation
normalize_coords = True
# 1: Build the Keras model
K.clear_session() # Clear previous models from memory.
def train(args):
model = ssd_300(mode = 'training',
image_size=(img_height, img_width, img_channels),
n_classes=n_classes,
l2_regularization=0.0005,
scales=scales,
aspect_ratios_per_layer=aspect_ratios,
two_boxes_for_ar1=two_boxes_for_ar1,
steps=steps,
offsets=offsets,
limit_boxes=limit_boxes,
variances=variances,
coords=coords,
normalize_coords=normalize_coords,
subtract_mean=subtract_mean,
divide_by_stddev=None,
swap_channels=swap_channels)
model.load_weights(args.weight_file, by_name=True,skip_mismatch=True)
predictor_sizes = [model.get_layer('conv11_mbox_conf').output_shape[1:3],
model.get_layer('conv13_mbox_conf').output_shape[1:3],
model.get_layer('conv14_2_mbox_conf').output_shape[1:3],
model.get_layer('conv15_2_mbox_conf').output_shape[1:3],
model.get_layer('conv16_2_mbox_conf').output_shape[1:3],
model.get_layer('conv17_2_mbox_conf').output_shape[1:3]]
adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=5e-04)
ssd_loss = SSDLoss(neg_pos_ratio=3, n_neg_min=0, alpha=1.0)
model.compile(optimizer=adam, loss=ssd_loss.compute_loss)
train_dataset = BatchGenerator(box_output_format=['class_id', 'xmin', 'ymin', 'xmax', 'ymax'])
val_dataset = BatchGenerator(box_output_format=['class_id', 'xmin', 'ymin', 'xmax', 'ymax'])
# 2: Parse the image and label lists for the training and validation datasets. This can take a while.
# TODO: Set the paths to the datasets here.
VOC_2007_images_dir = args.voc_dir_path + '/VOC2007/JPEGImages/'
VOC_2012_images_dir = args.voc_dir_path + '/VOC2012/JPEGImages/'
# The directories that contain the annotations.
VOC_2007_annotations_dir = args.voc_dir_path + '/VOC2007/Annotations/'
VOC_2012_annotations_dir = args.voc_dir_path + '/VOC2012/Annotations/'
# The paths to the image sets.
VOC_2007_train_image_set_filename = args.voc_dir_path + '/VOC2007/ImageSets/Main/trainval.txt'
VOC_2012_train_image_set_filename = args.voc_dir_path + '/VOC2012/ImageSets/Main/trainval.txt'
VOC_2007_val_image_set_filename = args.voc_dir_path + '/VOC2007/ImageSets/Main/test.txt'
# The XML parser needs to now what object class names to look for and in which order to map them to integers.
classes = ['background',
'aeroplane', 'bicycle', 'bird', 'boat',
'bottle', 'bus', 'car', 'cat',
'chair', 'cow', 'diningtable', 'dog',
'horse', 'motorbike', 'person', 'pottedplant',
'sheep', 'sofa', 'train', 'tvmonitor']
train_dataset.parse_xml(images_dirs=[VOC_2007_images_dir,
VOC_2012_images_dir],
image_set_filenames=[VOC_2007_train_image_set_filename,
VOC_2012_train_image_set_filename],
annotations_dirs=[VOC_2007_annotations_dir,
VOC_2012_annotations_dir],
classes=classes,
include_classes='all',
exclude_truncated=False,
exclude_difficult=False,
ret=False)
val_dataset.parse_xml(images_dirs=[VOC_2007_images_dir],
image_set_filenames=[VOC_2007_val_image_set_filename],
annotations_dirs=[VOC_2007_annotations_dir],
classes=classes,
include_classes='all',
exclude_truncated=False,
exclude_difficult=False,
ret=False
)
# 3: Instantiate an encoder that can encode ground truth labels into the format needed by the SSD loss function.
ssd_box_encoder = SSDBoxEncoder(img_height=img_height,
img_width=img_width,
n_classes=n_classes,
predictor_sizes=predictor_sizes,
min_scale=None,
max_scale=None,
scales=scales,
aspect_ratios_global=None,
aspect_ratios_per_layer=aspect_ratios,
two_boxes_for_ar1=two_boxes_for_ar1,
steps=steps,
offsets=offsets,
limit_boxes=limit_boxes,
variances=variances,
pos_iou_threshold=0.5,
neg_iou_threshold=0.2,
coords=coords,
normalize_coords=normalize_coords)
batch_size = args.batch_size
train_generator = train_dataset.generate(batch_size=batch_size,
shuffle=True,
train=True,
ssd_box_encoder=ssd_box_encoder,
convert_to_3_channels=True,
equalize=False,
brightness=(0.5, 2, 0.5),
flip=0.5,
translate=False,
scale=False,
max_crop_and_resize=(img_height, img_width, 1, 3),
# This one is important because the Pascal VOC images vary in size
random_pad_and_resize=(img_height, img_width, 1, 3, 0.5),
# This one is important because the Pascal VOC images vary in size
random_crop=False,
crop=False,
resize=False,
gray=False,
limit_boxes=True,
# While the anchor boxes are not being clipped, the ground truth boxes should be
include_thresh=0.4)
val_generator = val_dataset.generate(batch_size=batch_size,
shuffle=True,
train=True,
ssd_box_encoder=ssd_box_encoder,
convert_to_3_channels=True,
equalize=False,
brightness=(0.5, 2, 0.5),
flip=0.5,
translate=False,
scale=False,
max_crop_and_resize=(img_height, img_width, 1, 3),
# This one is important because the Pascal VOC images vary in size
random_pad_and_resize=(img_height, img_width, 1, 3, 0.5),
# This one is important because the Pascal VOC images vary in size
random_crop=False,
crop=False,
resize=False,
gray=False,
limit_boxes=True,
# While the anchor boxes are not being clipped, the ground truth boxes should be
include_thresh=0.4)
# Get the number of samples in the training and validations datasets to compute the epoch lengths below.
n_train_samples = train_dataset.get_n_samples()
n_val_samples = val_dataset.get_n_samples()
def lr_schedule(epoch):
if epoch <= 300:
return 0.001
else:
return 0.0001
learning_rate_scheduler = LearningRateScheduler(schedule=lr_schedule)
checkpoint_path = args.checkpoint_path + "/ssd300_epoch-{epoch:02d}.h5"
checkpoint = ModelCheckpoint(checkpoint_path)
log_path = args.checkpoint_path + "/logs"
tensorborad = TensorBoard(log_dir=log_path,
histogram_freq=0, write_graph=True, write_images=False)
callbacks = [checkpoint,tensorborad,learning_rate_scheduler]
# TODO: Set the number of epochs to train for.
epochs = args.epochs
intial_epoch = args.intial_epoch
history = model.fit_generator(generator=train_generator,
steps_per_epoch=ceil(n_train_samples)/batch_size,
verbose=1,
initial_epoch=intial_epoch,
epochs=epochs,
validation_data=val_generator,
validation_steps=ceil(n_val_samples)/batch_size,
callbacks=callbacks
)
if __name__== "__main__":
parser = argparse.ArgumentParser(description='Evaluation script')
parser.add_argument('--voc_dir_path', type=str,
help='VOCdevkit directory path')
parser.add_argument('--weight_file',type=str,
help='weight file path')
parser.add_argument('--epochs',type=int,
help='Number of epochs', default = 500)
parser.add_argument('--intial_epoch',type=int,
help='intial_epoch', default=0)
parser.add_argument('--checkpoint_path',type=str,
help='Path to save checkpoint')
parser.add_argument('--batch_size',type=int,
help='batch_size', default=32)
args = parser.parse_args()
train(args)