def setup_method(self):
"""
Set up parameters for test methods
"""
Logger.init()
Logger.remove_file_logger()
self.params = DmdsParams()
# get one entry from the database
Config.add_config('./config.ini')
collection_details = ("local_mongodb", "depth", "driving_stereo")
scenes = [
"2018-10-26-15-24-18",
"2018-10-19-09-30-39",
]
self.train_data = []
self.val_data = []
self.collection_details = []
# get ids
for scene_token in scenes:
td, vd = load_ids(collection_details,
data_split=(80, 20),
limit=100,
shuffle_data=False,
mongodb_filter={"scene_token": scene_token},
sort_by={"timestamp": 1})
self.train_data.append(td)
self.val_data.append(vd)
self.collection_details.append(collection_details)
def setup_method(self):
Logger.init()
Logger.remove_file_logger()
self.params = CenternetParams(len(OD_CLASS_MAPPING))
self.params.REGRESSION_FIELDS["l_shape"].active = True
self.params.REGRESSION_FIELDS["3d_info"].active = True
# get some entries from the database
Config.add_config('./config.ini')
self.collection_details = ("local_mongodb", "labels", "nuscenes_train")
# Create Data Generators
self.train_data, self.val_data = load_ids(self.collection_details,
data_split=(70, 30),
limit=250)
def setup_method(self):
"""
Set up parameters for test methods
"""
Logger.init()
Logger.remove_file_logger()
self.params = SemsegParams()
# get one entry from the database
Config.add_config('./config.ini')
self.collection_details = ("local_mongodb", "labels", "comma10k")
# Create Data Generators
self.train_data, self.val_data = load_ids(self.collection_details,
data_split=(70, 30),
limit=30)
def setup_method(self):
Logger.init()
Logger.remove_file_logger()
self.params = MultitaskParams(len(OD_CLASS_MAPPING.items()))
# get one entry from the database
Config.add_config('./config.ini')
self.collection_details = ("local_mongodb", "labels", "nuscenes_train")
# Create Data Generators
self.td, self.vd = load_ids(
self.collection_details,
data_split=(70, 30),
shuffle_data=True,
limit=30
)
def setup_method(self):
"""
Set up parameters for test methods
"""
Logger.init()
Logger.remove_file_logger()
self.params = CentertrackerParams(len(OD_CLASS_MAPPING))
# get some entries from the database
Config.add_config('./config.ini')
self.collection_details = ("local_mongodb", "labels", "kitti")
# Create Data Generators
self.train_data, self.val_data = load_ids(
self.collection_details,
data_split=(70, 30),
limit=100
)
def setup_method(self):
"""
Set up parameters for test methods
"""
Logger.init()
Logger.remove_file_logger()
self.params = Params()
# get one entry from the database
Config.add_config('./config.ini')
collection_details = ("local_mongodb", "labels", "nuscenes_train")
# get ids
td, vd = load_ids(
collection_details,
data_split=(70, 30),
limit=100,
shuffle_data=True,
)
self.train_data = [td]
self.val_data = [vd]
self.collection_details = [collection_details]
show_pygame.show(x, y_true, y_pred)
return result
return call_custom_callbacks
if __name__ == "__main__":
Logger.init()
Logger.remove_file_logger()
params = MultitaskParams(len(OD_CLASS_MAPPING.items()))
Config.add_config('./config.ini')
con = ("local_mongodb", "labels", "nuscenes_train")
td, vd = load_ids(
con,
data_split=(90, 10),
shuffle_data=True
)
train_data = [td]
val_data = [vd]
collection_details = [con]
train_gen = MongoDBGenerator(
collection_details,
train_data,
batch_size=params.BATCH_SIZE,
processors=[ProcessImages(params, [0, 0])],
shuffle_data=True
)
val_gen = MongoDBGenerator(
# "2018-10-11-17-08-31",
# "2018-08-13-17-45-03",
# "2018-08-13-15-32-19",
# "2018-07-31-11-22-31",
# "2018-07-31-11-07-48",
]
train_data = []
val_data = []
collection_details = []
# get ids
for scene_token in scenes:
td, vd = load_ids(
con,
data_split=(95, 5),
shuffle_data=False,
mongodb_filter={"scene_token": scene_token},
sort_by={"timestamp": 1}
)
train_data.append(td)
val_data.append(vd)
collection_details.append(con)
processors = [ProcessImages(params)]
train_gen = MongoDBGenerator(
collection_details,
train_data,
batch_size=params.BATCH_SIZE,
processors=processors,
data_group_size=2,
continues_data_selection=True,
from models.depth import create_model, Params, ProcessImages
from models.depth.loss import DepthLoss
from common.utils import set_weights
if __name__ == "__main__":
Logger.init()
Logger.remove_file_logger()
params = Params()
# get one entry from the database
Config.add_config('./config.ini')
con = ("local_mongodb", "labels", "driving_stereo")
# con = ("local_mongodb", "labels", "nuscenes_train")
td, vd = load_ids(con, data_split=(87, 13), shuffle_data=True)
train_data = [td]
val_data = [vd]
collection_details = [con]
train_gen = MongoDBGenerator(collection_details,
train_data,
batch_size=params.BATCH_SIZE,
processors=[ProcessImages(params, [0, 0])],
shuffle_data=True)
val_gen = MongoDBGenerator(collection_details,
val_data,
batch_size=params.BATCH_SIZE,
processors=[ProcessImages(params, False)],
shuffle_data=True)
[tf.config.experimental.VirtualDeviceConfiguration(memory_limit=4864)])
if __name__ == "__main__":
Logger.init()
Logger.remove_file_logger()
params = CentertrackerParams(len(OD_CLASS_MAPPING))
params.REGRESSION_FIELDS["l_shape"].active = False
params.REGRESSION_FIELDS["3d_info"].active = False
Config.add_config('./config.ini')
collection_details = ("local_mongodb", "labels", "kitti")
# Create Data Generators
train_data, val_data = load_ids(collection_details,
data_split=(82, 18),
shuffle_data=True)
processors = [CenterTrackerProcess(params)]
train_gen = MongoDBGenerator(collection_details,
train_data,
batch_size=params.BATCH_SIZE,
processors=processors)
val_gen = MongoDBGenerator(collection_details,
val_data,
batch_size=params.BATCH_SIZE,
processors=processors)
loss = CenterTrackerProcess(params)
# metrics = [loss.class_focal_loss, loss.r_offset_loss, loss.fullbox_loss]
metrics = []
