def __load_training_data(self):
train_dataset = BatchGenerator(
box_output_format=['class_id', 'xmin', 'xmax', 'ymin', 'ymax'])
train_classes = [
x[0] for x in join(self.path_to_train_directory,
walk(self.path_to_train_directory)) + '/'
]
train_labels = [x[0] for x in walk(self.path_to_train_directory)]
train_dataset.parse_csv(images_path=train_classes,
labels_path=self.cvsfile,
include_classes='all',
ret=False)
train_generator = train_dataset.generate(
batch_size=batch_size,
train=True,
ssd_box_encoder=ssd_box_encoder,
equalize=False,
translate=False,
scale=False,
full_crop_and_resize=(
self.img_train_height, self.img_train_width
), # This one is important because the Pascal VOC images vary in size
random_crop=False,
crop=False,
resize=False,
gray=True,
limit_boxes=
True, # While the anchor boxes are not being clipped, the ground truth boxes should be
include_thresh=0.4,
diagnostics=True)
n_train_samples = train_dataset.get_n_samples(
) # Get the number of samples in the training dataset to compute the epoch length below
return train_generator, n_train_samples
def __load_validation_data(self):
val_dataset = BatchGenerator(
box_output_format=['class_id', 'xmin', 'xmax', 'ymin', 'ymax'])
# 6: Create the validation set batch generator
validation_classes = [
x[0] for x in join(self.path_to_validation_directory,
walk(self.path_to_validation_directory)) + '/'
]
val_dataset.parse_csv(images_path=validation_classes,
labels_path=self.cvsfile_val,
include_classes='all',
ret=False)
val_generator = val_dataset.generate(
batch_size=self.batch_size,
train=True,
ssd_box_encoder=ssd_box_encoder,
equalize=False,
brightness=False,
flip=False,
translate=False,
scale=
False, # This one is important because the Pascal VOC images vary in size
full_crop_and_resize=(
self.img_train_height, self.img_train_width
), # This one is important because the Pascal VOC images vary in size
random_crop=False,
crop=False,
resize=False,
gray=True,
limit_boxes=True,
include_thresh=0.4,
diagnostics=False)
n_val_samples = val_dataset.get_n_samples()
return val_generator, n_val_samples
def train_hs512(lr=1e-4,
freeze_bn=False,
optim=None,
batch_size=8,
weights_path=None,
save_weights_only=True,
epochs=25):
if weights_path is None:
weights_path = 'VGG_VOC0712Plus_SSD_512x512_ft_iter_160000.h5'
img_height = 512 # 1088 // 2 # Height of the input images
img_width = 512 # 2048 // 2 # Width of the input images
img_channels = 3 # Number of color channels of the input images
# subtract_mean = [104, 117, 123] # The per-channel mean of the images in the dataset
subtract_mean = [138, 138,
138] # The per-channel mean of the images in the dataset
swap_channels = False
n_classes = 20 # The number of positive classes, e.g. 20 for Pascal VOC, 80 for MS COCO.
scales = [0.04, 0.1, 0.26, 0.42, 0.58, 0.74, 0.9, 1.06] # MS COCO scales
# scales = [0.07, 0.15, 0.3, 0.45, 0.6, 0.75, 0.9, 1.05]
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, 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
# The space between two adjacent anchor box center points for each predictor layer.
steps = [8, 16, 32, 64, 128, 256, 512]
offsets = [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
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]
coords = 'centroids'
normalize_coords = True
# 1: Build the Keras model
K.clear_session()
model, pred_sizes = ssd_512(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=None,
divide_by_stddev=None,
swap_channels=swap_channels,
return_predictor_sizes=True)
# 2: Load the trained VGG-16 weights into the model.
model.load_weights(weights_path, by_name=True)
# 3: Instantiate the optimizer and the SSD loss function and compile the model
if optim is None:
optim = SGD(lr=lr, momentum=0.9)
ssd_loss = SSDLoss(neg_pos_ratio=3, n_neg_min=0, alpha=1.0)
# 4: freeze the base model if needed
if freeze_bn:
for l in model.layers[:38]:
l.trainable = False
# 5: compile model
model.compile(optimizer=optim, loss=ssd_loss.compute_loss)
## Prepare data generation
# 1: Instantiate to `BatchGenerator` objects: One for training, one for validation.
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.
images_path_root = './Datasets/'
train_combined_labels = './Datasets/train_combined_ssd_512.txt'
val_labels = './Datasets/val_ssd_512.txt'
train_dataset.parse_csv(images_dir=images_path_root,
labels_filename=train_combined_labels,
input_format=[
'image_name', 'class_id', 'xmin', 'xmax',
'ymin', 'ymax'
])
val_dataset.parse_csv(images_dir=images_path_root,
labels_filename=val_labels,
input_format=[
'image_name', 'class_id', 'xmin', 'xmax', 'ymin',
'ymax'
])
# 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=pred_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)
# 4: Set the image processing / data augmentation options and create generator handles.
train_generator = train_dataset.generate(
batch_size=batch_size,
shuffle=True,
train=True,
ssd_box_encoder=ssd_box_encoder,
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=True,
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,
equalize=False,
brightness=False,
flip=False,
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=True,
limit_boxes=True,
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()
# ## 4. Run the training
fingerprint = 'ssd{:%Y-%m-%d_%H-%M-%S}'.format(datetime.datetime.now())
tbCallBack = TensorBoard(log_dir='./Graph/{}'.format(fingerprint),
histogram_freq=0,
batch_size=batch_size,
write_graph=True)
checkpointer = ModelCheckpoint(
'./saved/{{val_loss:.2f}}__{}_best_weights.h5'.format(fingerprint),
monitor='val_loss',
verbose=1,
save_best_only=save_weights_only,
save_weights_only=True,
mode='auto',
period=1)
learning_rate_reduction = ReduceLROnPlateau(monitor='val_loss',
patience=5,
verbose=1,
factor=0.5,
min_lr=1e-6)
stopper = EarlyStopping(monitor='val_loss', min_delta=0.001, patience=10)
epochs = 30
history = model.fit_generator(
generator=train_generator,
steps_per_epoch=ceil(n_train_samples / batch_size),
epochs=epochs,
callbacks=[checkpointer, learning_rate_reduction, stopper, tbCallBack],
validation_data=val_generator,
validation_steps=ceil(n_val_samples / batch_size))
coords=coords,
normalize_coords=normalize_coords)
#--------------------------------------------------------------------------------------------------------------
# 5: Create the training set batch generator
classes = ['background', 'nation']
train_dataset = BatchGenerator(
images_path=data_path,
include_classes='all',
box_output_format=['class_id', 'xmin', 'xmax', 'ymin', 'ymax']
) # This is the format in which the generator is supposed to output the labels. At the moment it **must** be the format set here.
train_dataset.parse_csv(
labels_path=data_path + '/train_labels.csv',
input_format=['image_name', 'xmin', 'xmax', 'ymin', 'ymax', 'class_id']
) # This is the order of the first six columns in the CSV file that contains the labels for your dataset. If your labels are in XML format, maybe the XML parser will be helpful, check the documentation.
# Change the online data augmentation settings as you like
train_generator = train_dataset.generate(
batch_size=batch_size,
train=True,
ssd_box_encoder=ssd_box_encoder,
equalize=False,
brightness=(0.5, 2, 0.5),
flip=0.5,
translate=((0, 30), (0, 30), 0.5),
scale=(0.75, 1.2, 0.5),
random_crop=(
300, 300, 1,
3), # This one is important because the Pascal VOC images vary in size
val_dataset = BatchGenerator(
box_output_format=['class_id', 'xmin', 'xmax', 'ymin', 'ymax'])
# train_images_path = './sample/data/udacity_driving_datasets/'
# train_labels_path = './sample/data/udacity_driving_datasets/train_labels.csv'
# val_images_path = './sample/data/udacity_driving_datasets/'
# val_labels_path = './sample/data/udacity_driving_datasets/val_labels.csv'
train_images_path = './data/train'
train_labels_path = './train_labels.csv'
val_images_path = './data/train'
val_labels_path = './val_labels.csv'
train_dataset.parse_csv(
images_dir=train_images_path,
labels_filename=train_labels_path,
input_format=[
'image_name', 'xmin', 'xmax', 'ymin', 'ymax', 'class_id'
], # This is the order of the first six columns in the CSV file that contains the labels for your dataset. If your labels are in XML format, maybe the XML parser will be helpful, check the documentation.
include_classes='all')
val_dataset.parse_csv(
images_dir=val_images_path,
labels_filename=val_labels_path,
input_format=['image_name', 'xmin', 'xmax', 'ymin', 'ymax', 'class_id'],
include_classes='all')
predictor_sizes = [
model.get_layer('classes4').output_shape[1:3],
model.get_layer('classes5').output_shape[1:3],
model.get_layer('classes6').output_shape[1:3],
model.get_layer('classes7').output_shape[1:3]
'compute_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'])
train_images_dir = '/home/udacity_driving_datasets'
train_labels_filename = '/home/udacity_driving_datasets/labels_train.csv'
val_images_dir = '/home/udacity_driving_datasets'
val_labels_filename = '/home/udacity_driving_datasets/labels_val.csv'
train_dataset.parse_csv(
images_dir=train_images_dir,
labels_filename=train_labels_filename,
input_format=['image_name', 'xmin', 'xmax', 'ymin', 'ymax', 'class_id'],
include_classes='all')
val_dataset.parse_csv(
images_dir=val_images_dir,
labels_filename=val_labels_filename,
input_format=['image_name', 'xmin', 'xmax', 'ymin', 'ymax', 'class_id'],
include_classes='all')
predictor_sizes = [
model.get_layer('classes4').output_shape[1:3],
model.get_layer('classes5').output_shape[1:3],
model.get_layer('classes6').output_shape[1:3],
model.get_layer('classes7').output_shape[1:3]
]