def __init__(self,
name,
batch_size = 128,
epochs = 200,
learning_rate = 0.01,
lr_decay_scheme = 1,
weight_decay = 0.0005,
data_key = 'ucf',
dropout = 0.5,
load_epoch_pt = -1,
name_finetuning = None,
name_experiment = None,
reset_fc7 = True,
reset_fc6 = True,
freeze_layer = 'input',
rgb = 0.3,
num_test = 25,
split = 1
):
super(Finetuning_AR_RGB, self).__init__(name=name, batch_size=batch_size, epochs=epochs,
learning_rate=learning_rate, lr_decay_scheme=lr_decay_scheme, weight_decay=weight_decay,
data_key=data_key, dropout=dropout, name_finetuning=name_finetuning,
name_experiment=name_experiment, reset_fc7=reset_fc7, load_epoch_pt=load_epoch_pt,
freeze_layer=freeze_layer, split=split, reset_fc6=reset_fc6)
self.rgb = rgb
self.num_test = num_test
self.list_infos += [('rgb', rgb), ('num_test', num_test)]
self.dataset_type = Dataset_RGB
self.tracker = Tracker_classification()
self.optimizer = optim.SGD(filter(lambda p: p.requires_grad, self.net.parameters()),
lr=self.learning_rate, momentum=0.9, weight_decay=self.weight_decay)
self.criterion = nn.CrossEntropyLoss()
def __init__(self,
name,
batch_size=30,
epochs=200,
learning_rate=0.01,
lr_decay_scheme=1,
weight_decay=0.0005,
arch='caffe_bn',
data_key='ucf',
modalities=['rgb', 'of'],
num_frames_flow=10,
num_frames_cod=4,
split_channels=True,
dropout=0.5,
high_motion=1,
time_flip=True):
super(Pretraining_Concat,
self).__init__(name=name,
batch_size=batch_size,
epochs=epochs,
learning_rate=learning_rate,
lr_decay_scheme=lr_decay_scheme,
weight_decay=weight_decay,
arch=arch,
data_key=data_key,
split_channels=split_channels,
dropout=dropout)
self.num_frames_flow = num_frames_flow
self.num_frames_cod = num_frames_cod
self.high_motion = high_motion
self.time_flip = time_flip
self.modalities = modalities
self.list_infos += [('num_frames_flow', num_frames_flow),
('num_frames_cod', num_frames_cod),
('high_motion', high_motion),
('time_flip', time_flip),
('modalities', modalities)]
self.net = Concat(arch=self.arch,
num_frames=self.num_frames_flow,
dropout=self.dropout,
modalities=self.modalities)
self.tracker = Tracker_classification()
self.optimizer = optim.SGD(self.net.parameters(),
lr=self.learning_rate,
momentum=0.9,
weight_decay=self.weight_decay)
self.dataset_type = Dataset_Two_Stream
self.criterion = nn.CrossEntropyLoss()
def __init__(self,
name,
batch_size = 128,
epochs = 200,
learning_rate = 0.01,
lr_decay_scheme = 1,
weight_decay = 0.0005,
data_key = 'ucf',
dropout = 0.5,
load_epoch_pt = -1,
name_finetuning = None,
reset_fc7 = True,
reset_fc6 = True,
freeze_layer = 'input',
num_test = 25,
nodiff = False,
time_flip = False,
split = 1
):
super(Finetuning_AR_COD, self).__init__(name=name, batch_size=batch_size, epochs=epochs,
learning_rate=learning_rate, lr_decay_scheme=lr_decay_scheme, weight_decay=weight_decay,
data_key=data_key, dropout=dropout, name_finetuning=name_finetuning,
reset_fc7=reset_fc7, load_epoch_pt=load_epoch_pt, freeze_layer=freeze_layer, split=split,
reset_fc6=reset_fc6)
self.num_test = num_test
self.num_frames = int(self.net.input_dim / 3)
self.nodiff = nodiff
self.time_flip = time_flip
self.list_infos += [('num_test', num_test), ('num_frames', self.num_frames), ('nodiff', nodiff),
('time_flip', time_flip)]
self.dataset_type = Dataset_COD
self.tracker = Tracker_classification()
self.optimizer = optim.SGD(filter(lambda p: p.requires_grad, self.net.parameters()),
lr=self.learning_rate, momentum=0.9, weight_decay=self.weight_decay)
self.criterion = nn.CrossEntropyLoss()
def _reconfigure_tracker_test(self): self.tracker = Tracker_classification(mode='multi_frame')
def _reconfigure_tracker_train(self): self.tracker = Tracker_classification()
def _reconfigure_tracker_test(self):
self.tracker = Tracker_classification(with_nonzeros=True)