def __init__(self, input_type, input):
self.input_type = input_type
self.item_counter = 0
if input_type == 'json':
coco_annotation = COCO(input)
annotation_directory = os.path.join(os.getcwd(), os.path.dirname(input))
classes = ObjectDetectorJson.get_classes_from_coco_annotation(input)
self.json_data = ObjectDetectorJson.convert_coco_to_toolbox_format(coco_annotation, classes,
annotation_directory)
if input_type == "video":
self.cap = cv2.VideoCapture(input)
if input_type == "cam":
self.cap = cv2.VideoCapture(0)
batch_size = 32 # Number of images in the batch
class execution:
CUDA_VISIBLE_DEVICES = "0" # Environment variable to control cuda device used for training
per_process_gpu_memory_fraction = 0.5 # Fix extra memory allocation issue
allow_growth = True # Option which attempts to allocate only as much GPU memory based on runtime allocations
intra_op_parallelism_threads = 2
inter_op_parallelism_threads = 8
transformer_parallel_calls = 4 # Number of parallel threads in data transformer/augmentation
transformer_prefetch_size = 8 # Number of batches to prefetch
input_shape = (256, 256, 3
) # Input shape of the model (width, height, channels)
classes = ObjectDetectorJson.get_classes_from_coco_annotation(
os.path.join(current_dir, train.annotation_path))
model_dir = os.path.join(
os.path.dirname(os.path.abspath(__file__)), 'model'
) # Path to the folder where all training and evaluation artifacts will be located
if not os.path.exists(model_dir):
os.makedirs(model_dir)
def learning_rate_schedule(
): # Function which controls learning rate during training
import tensorflow as tf
step = tf.train.get_or_create_global_step()
lr = tf.case([(tf.less(step, 1000), lambda: tf.constant(0.0004)),
(tf.less(step, 10000), lambda: tf.constant(0.01)),
(tf.less(step, 40000), lambda: tf.constant(0.005)),
(tf.less(step, 55000), lambda: tf.constant(0.0005)),
transformer_prefetch_size = 1
class infer:
batch_size = 1
out_subdir = "predictions"
class execution:
CUDA_VISIBLE_DEVICES = ""
intra_op_parallelism_threads = 1
inter_op_parallelism_threads = 1
transformer_parallel_calls = 1
transformer_prefetch_size = 1
classes = ObjectDetectorJson.get_classes_from_coco_annotation(train.annotation_path)
input_shape = [128, 128, 3]
def optimizer(learning_rate):
import tensorflow as tf
optimizer = tf.train.MomentumOptimizer(learning_rate=learning_rate, momentum=0.95)
return optimizer
detector_params = {
"data_format": "NHWC",
"depth_multiplier": 0.35,
"initial_weights_path": path.join(root_dir, "data/test/model_ckpt/model.ckpt"),
"learning_rate": 0.001,
"mobilenet_version": "v2",
class infer:
batch_size = 1
out_subdir = "predictions"
class execution:
CUDA_VISIBLE_DEVICES = ""
intra_op_parallelism_threads = 1
inter_op_parallelism_threads = 1
transformer_parallel_calls = 1
transformer_prefetch_size = 1
classes = ObjectDetectorJson.get_classes_from_coco_annotation(
train.annotation_path)
input_shape = [128, 128, 3]
def optimizer(learning_rate):
import tensorflow as tf
optimizer = tf.train.MomentumOptimizer(learning_rate=learning_rate,
momentum=0.95)
return optimizer
detector_params = {
"data_format": "NHWC",
"depth_multiplier": 0.35,
"initial_weights_path": path.join(root_dir,
"data/test/model_ckpt/model.ckpt"),
out_subdir = "predictions" # Name of folder in model directory where output json files with detections will be saved
batch_size = 32 # Number of images in the batch
class execution:
CUDA_VISIBLE_DEVICES = "0" # Environment variable to control cuda device used for training
per_process_gpu_memory_fraction = 0.5 # Fix extra memory allocation issue
allow_growth = True # Option which attempts to allocate only as much GPU memory based on runtime allocations
intra_op_parallelism_threads = 2
inter_op_parallelism_threads = 8
transformer_parallel_calls = 4 # Number of parallel threads in data transformer/augmentation
transformer_prefetch_size = 8 # Number of batches to prefetch
input_shape = (256, 256, 3) # Input shape of the model (width, height, channels)
classes = ObjectDetectorJson.get_classes_from_coco_annotation(os.path.join(current_dir, train.annotation_path))
model_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'model') # Path to the folder where all training and evaluation artifacts will be located
if not os.path.exists(model_dir):
os.makedirs(model_dir)
def learning_rate_schedule(): # Function which controls learning rate during training
import tensorflow as tf
step = tf.train.get_or_create_global_step()
lr = tf.case([(tf.less(step, 1000), lambda: tf.constant(0.0004)),
(tf.less(step, 10000), lambda: tf.constant(0.01)),
(tf.less(step, 40000), lambda: tf.constant(0.005)),
(tf.less(step, 55000), lambda: tf.constant(0.0005)),
(tf.less(step, 65000), lambda: tf.constant(0.00005))])
return lr
batch_size = 32 # Number of images in the batch
class execution:
CUDA_VISIBLE_DEVICES = "0" # Environment variable to control cuda device used for training
per_process_gpu_memory_fraction = 0.5 # Fix extra memory allocation issue
allow_growth = True # Option which attempts to allocate only as much GPU memory based on runtime allocations
intra_op_parallelism_threads = 2
inter_op_parallelism_threads = 8
transformer_parallel_calls = 4 # Number of parallel threads in data transformer/augmentation
transformer_prefetch_size = 8 # Number of batches to prefetch
input_shape = (256, 256, 3
) # Input shape of the model (width, height, channels)
classes = ObjectDetectorJson.get_classes_from_coco_annotation(
os.path.join(CURRENT_DIR, train.annotation_path))
MODEL_DIR = os.path.join(
os.path.dirname(os.path.abspath(__file__)), 'model'
) # Path to the folder where all training and evaluation artifacts will be located
if not os.path.exists(MODEL_DIR):
os.makedirs(MODEL_DIR)
def learning_rate_schedule(
): # Function which controls learning rate during training
import tensorflow as tf
step = tf.train.get_or_create_global_step()
lr = tf.case([(tf.less(step, 1000), lambda: tf.constant(0.0004)),
(tf.less(step, 10000), lambda: tf.constant(0.01)),
(tf.less(step, 40000), lambda: tf.constant(0.005)),
(tf.less(step, 55000), lambda: tf.constant(0.0005)),
