def e1001_3T_ucf101():
set_init_1()
project_variable.model_number = 20
project_variable.experiment_number = 1001
project_variable.sheet_number = 23
project_variable.device = 0
project_variable.end_epoch = 100
project_variable.batch_size = 30
project_variable.batch_size_val_test = 30
project_variable.load_model = True # exp, model, epoch, run
project_variable.load_from_fast = True
project_variable.use_dali = True
project_variable.dali_workers = 32
project_variable.dali_iterator_size = ['all', 'all', 0]
project_variable.nas = False
project_variable.stop_at_collapse = True
project_variable.early_stopping = True
project_variable.optimizer = 'adam'
project_variable.learning_rate = 0.00005
project_variable.use_adaptive_lr = True
main_file.run(project_variable)
def e4_conv3DTTN_jester():
set_init_1()
project_variable.model_number = 16
project_variable.experiment_number = 4
project_variable.sheet_number = 22
project_variable.device = 0
project_variable.end_epoch = 20
project_variable.repeat_experiments = 3
project_variable.batch_size = 2 * 27
# if you want all the data: train: 4200, val: 250, test: 250
# project_variable.data_points = [300 * 27, 50 * 27, 0 * 27]
project_variable.data_points = [30 * 27, 5 * 27, 0 * 27]
project_variable.stop_at_collapse = True
project_variable.early_stopping = True
project_variable.optimizer = 'adam'
project_variable.learning_rate = 0.0003
project_variable.use_adaptive_lr = True
project_variable.num_out_channels = [16, 32, 64, 128, 256]
project_variable.transformation_groups = project_variable.num_out_channels
project_variable.k0_groups = project_variable.num_out_channels
main_file.run(project_variable)
def e41_conv3T_jester():
set_init_1()
project_variable.model_number = 26 # GN 3T dynamic
project_variable.experiment_number = 41
project_variable.sheet_number = 22
project_variable.device = 2
project_variable.end_epoch = 100
project_variable.repeat_experiments = 1
project_variable.batch_size = 27
project_variable.batch_size_val_test = 27
project_variable.load_model = [30, 23, 28, 0]
project_variable.load_from_fast = True
project_variable.only_theta_final_layer = True
project_variable.use_dali = True
project_variable.dali_workers = 32
project_variable.dali_iterator_size = ['all', 'all', 0]
project_variable.nas = False
project_variable.stop_at_collapse = True
project_variable.early_stopping = True
project_variable.optimizer = 'adam'
project_variable.learning_rate = 0.005
project_variable.use_adaptive_lr = True
project_variable.num_out_channels = [0]
# wait_for_gpu(wait=True, device_num=project_variable.device)
main_file.run(project_variable)
def vis_conv3DTTN_jester():
set_init_1()
project_variable.model_number = 20
project_variable.experiment_number = 1011010010010
# TODO setup for visualization
project_variable.sheet_number = 22
project_variable.device = 0
project_variable.end_epoch = 1
project_variable.repeat_experiments = 1
project_variable.batch_size = 1 * 27
project_variable.data_points = [0 * 27, 1 * 27, 0 * 27]
project_variable.load_model = [10, 20, 2, 0] # exp, model, epoch, run
project_variable.inference_only_mode = True
project_variable.do_xai = True
project_variable.which_methods = ['gradient_method']
project_variable.which_layers = ['conv1']
project_variable.which_channels = [np.array([0, 1])]
project_variable.optimizer = 'adam'
project_variable.learning_rate = 0.00005
project_variable.use_adaptive_lr = True
project_variable.num_out_channels = [0]
main_file.run(project_variable)
def eRESNET_jester():
set_init_1()
project_variable.model_number = 20
project_variable.experiment_number = 8538953849588588
project_variable.sheet_number = 22
project_variable.device = 0
project_variable.end_epoch = 3
project_variable.repeat_experiments = 1
project_variable.batch_size = 1 * 27
project_variable.data_points = [1 * 27, 1 * 27, 0 * 27]
project_variable.load_model = True
# project_variable.use_dali = True
# project_variable.dali_workers = 32
# project_variable.dali_iterator_size = ['all', 'all', 0]
# project_variable.nas = False
# project_variable.stop_at_collapse = True
# project_variable.early_stopping = True
project_variable.optimizer = 'adam'
project_variable.learning_rate = 0.0000005
project_variable.use_adaptive_lr = True
project_variable.num_out_channels = [0]
main_file.run(project_variable)
def e_test_3D_jester():
set_init_1()
project_variable.model_number = 14
project_variable.experiment_number = 1792792989823
project_variable.sheet_number = 22
project_variable.device = 0
project_variable.end_epoch = 30
project_variable.repeat_experiments = 1
project_variable.data_points = [30 * 27, 5 * 27, 0 * 27]
project_variable.optimizer = 'adam'
project_variable.learning_rate = 5e-4
project_variable.use_adaptive_lr = True
project_variable.num_out_channels = [32, 32, 64]
project_variable.transformation_groups = project_variable.num_out_channels
project_variable.k0_groups = project_variable.num_out_channels
project_variable.do_xai = False
project_variable.which_methods = ['gradient_method']
project_variable.which_layers = ['conv1', 'conv2', 'conv3']
project_variable.which_channels = [
np.arange(2), np.arange(2), np.arange(2)
]
main_file.run(project_variable)
def e22_conv3DTTN_jester():
set_init_1()
project_variable.model_number = 20
project_variable.experiment_number = 22
project_variable.sheet_number = 22
project_variable.device = 0
project_variable.end_epoch = 1
project_variable.repeat_experiments = 1
project_variable.batch_size = 1
project_variable.batch_size_val_test = 1
project_variable.xai_only_mode = True
project_variable.use_dali = True
project_variable.dali_workers = 32
project_variable.load_model = [13, 20, 14, 0] # exp, model, epoch, run
# project_variable.inference_only_mode = True
project_variable.do_xai = True
project_variable.which_methods = ['gradient_method']
project_variable.which_layers = ['conv1']
project_variable.which_channels = [np.array([0, 1])]
project_variable.optimizer = 'adam'
project_variable.learning_rate = 0.00005
project_variable.use_adaptive_lr = False
project_variable.num_out_channels = [0]
main_file.run(project_variable)
def e0_C3D_omgemo():
project_variable.model_number = 11
project_variable.end_epoch = 100
project_variable.dataset = 'omg_emotion'
project_variable.device = 2
project_variable.optimizer = 'adam'
project_variable.learning_rate = 5e-5
# project_variable.use_adaptive_lr = True
project_variable.num_out_channels = [12, 22]
project_variable.data_points = [50 * 7, 3 * 7, 5 * 7]
project_variable.label_size = 7
project_variable.batch_size = 14
project_variable.load_num_frames = 60 # 60
project_variable.label_type = 'categories'
project_variable.repeat_experiments = 1
project_variable.save_only_best_run = True
project_variable.same_training_data = True
project_variable.randomize_training_data = True
project_variable.balance_training_data = True
project_variable.experiment_state = 'new'
project_variable.sheet_number = 100
project_variable.eval_on = 'test'
project_variable.experiment_number = 1
main_file.run(project_variable)
def e28_conv3D_jester():
set_init_1()
project_variable.model_number = 25
project_variable.experiment_number = 28
project_variable.sheet_number = 22
project_variable.device = 0
project_variable.end_epoch = 100
project_variable.repeat_experiments = 1
project_variable.batch_size = 19
project_variable.batch_size_val_test = 19
project_variable.load_model = True
project_variable.load_from_fast = True
project_variable.use_dali = True
project_variable.dali_workers = 32
project_variable.dali_iterator_size = ['all', 'all', 0]
project_variable.nas = False
project_variable.stop_at_collapse = True
project_variable.early_stopping = True
project_variable.optimizer = 'adam'
project_variable.learning_rate = 0.000005
project_variable.use_adaptive_lr = True
project_variable.num_out_channels = [0]
main_file.run(project_variable)
def e_test_3DTTN_dhg():
set_init_3()
project_variable.end_epoch = 1
project_variable.repeat_experiments = 1
project_variable.experiment_number = 777656767
project_variable.sheet_number = 21
project_variable.device = 2
project_variable.model_number = 11
project_variable.data_points = [0 * 14, 1 * 14, 0 * 14]
project_variable.batch_size = 1 * 14
project_variable.inference_only_mode = True
project_variable.load_model = [68, 11, 199, 0]
project_variable.optimizer = 'adam'
project_variable.learning_rate = 1e-4
project_variable.use_adaptive_lr = True
project_variable.num_out_channels = [6, 16]
project_variable.do_xai = True
# project_variable.return_ind = True
project_variable.which_methods = ['gradient_method']
# if 'zeiler2014' in project_variable.which_methods:
# project_variable.return_ind = True
# project_variable.which_layers = ['conv1']
# project_variable.which_channels = [np.arange(2)]
project_variable.which_layers = ['conv1', 'conv2']
project_variable.which_channels = [np.arange(6), np.arange(16)]
main_file.run(project_variable)
def e71_3D_dhg():
set_init_3()
project_variable.end_epoch = 1
project_variable.repeat_experiments = 1
project_variable.experiment_number = 71
project_variable.sheet_number = 21
project_variable.device = 2
project_variable.model_number = 12
project_variable.data_points = [0 * 14, 2 * 14, 0 * 14]
project_variable.batch_size = 2 * 14
project_variable.inference_only_mode = True
project_variable.load_model = [69, 12, 99, 3]
project_variable.optimizer = 'adam'
project_variable.learning_rate = 0.001
project_variable.use_adaptive_lr = False
project_variable.num_out_channels = [6, 16]
project_variable.do_xai = True
# project_variable.which_methods = ['gradient_method']
project_variable.which_methods = [
'erhan2009', 'zeiler2014', 'gradient_method'
]
project_variable.return_ind = True
project_variable.which_layers = ['conv1', 'conv2']
project_variable.which_channels = [np.arange(6), np.arange(16)]
main_file.run(project_variable)
def e67_test_3D_dhg():
set_init_3()
project_variable.end_epoch = 100
project_variable.repeat_experiments = 1
project_variable.experiment_number = 67
project_variable.sheet_number = 21
project_variable.device = 2
project_variable.model_number = 11
project_variable.data_points = [140 * 14, 20 * 14, 1 * 14]
project_variable.batch_size = 2 * 14
project_variable.optimizer = 'adam'
project_variable.learning_rate = 1e-4
project_variable.use_adaptive_lr = True
project_variable.num_out_channels = [6, 16]
project_variable.do_xai = True
project_variable.which_methods = ['erhan2009', 'zeiler2014']
if 'zeiler2014' in project_variable.which_methods:
project_variable.return_ind = True
project_variable.which_layers = ['conv1', 'conv2']
project_variable.which_channels = [np.arange(6), np.arange(16)]
main_file.run(project_variable)
def e36_conv3DTTN_jester():
set_init_1()
project_variable.model_number = 20 # RN18 3T
project_variable.experiment_number = 36
project_variable.sheet_number = 22
project_variable.device = 2
project_variable.end_epoch = 100
project_variable.repeat_experiments = 1
project_variable.batch_size = 32
project_variable.batch_size_val_test = 32
project_variable.load_model = [32, 20, 13, 0]
project_variable.load_from_fast = True
project_variable.use_dali = True
project_variable.dali_workers = 32
project_variable.dali_iterator_size = ['all', 'all', 0]
project_variable.nas = False
project_variable.stop_at_collapse = True
project_variable.early_stopping = True
project_variable.optimizer = 'adam'
project_variable.learning_rate = 0.00005
project_variable.use_adaptive_lr = True
project_variable.num_out_channels = [0]
wait_for_gpu(wait=True, device_num=project_variable.device)
main_file.run(project_variable)
def e001_3T_kinetics():
set_init_1()
project_variable.model_number = 23 # googlenet
project_variable.experiment_number = 2000
project_variable.sheet_number = 23
project_variable.device = 1
project_variable.end_epoch = 200
project_variable.batch_size = 1
project_variable.batch_size_val_test = 1
project_variable.inference_only_mode = True
project_variable.load_model = False
project_variable.load_from_fast = True
project_variable.use_dali = True
project_variable.dali_workers = 32
project_variable.dali_iterator_size = ['all', 'all', 0]
project_variable.nas = False
project_variable.stop_at_collapse = True
project_variable.early_stopping = True
project_variable.optimizer = 'adam'
project_variable.learning_rate = 0.00005
project_variable.use_adaptive_lr = True
main_file.run(project_variable)
def e2():
project_variable.device = 1
project_variable.model_number = 0
project_variable.experiment_number = 2
project_variable.load_model = [2, 0, 35]
if not project_variable.debug_mode:
project_variable.start_epoch = 35
main_file.run(project_variable)
def pilot_2():
project_variable.device = 0
project_variable.model_number = 0
project_variable.experiment_number = 1
project_variable.end_epoch = 1
project_variable.load_model = [0, 0, 99]
project_variable.save_data = False
main_file.run(project_variable)
def e3_3D_omgemo():
set_init_1()
project_variable.experiment_number = 3
project_variable.sheet_number = 20
project_variable.device = 0
project_variable.learning_rate = 1e-3
project_variable.use_adaptive_lr = True
project_variable.num_out_channels = [6, 16]
main_file.run(project_variable)
def e6_3D_omgemo():
set_init_1()
project_variable.experiment_number = 6
project_variable.sheet_number = 20
project_variable.device = 0
project_variable.learning_rate = 1e-4
project_variable.use_adaptive_lr = False
project_variable.num_out_channels = [12, 22]
main_file.run(project_variable)
def pilot():
project_variable.device = 0
project_variable.model_number = 0
project_variable.experiment_number = 0
project_variable.batch_size = 20
# project_variable.save_model = False
# project_variable.save_data = False
main_file.run(project_variable)
def e1_3D_omgemo():
set_init_1()
project_variable.experiment_state = 'crashed'
project_variable.experiment_number = 1
project_variable.sheet_number = 20
project_variable.device = 2
project_variable.learning_rate = 1e-3
project_variable.use_adaptive_lr = False
project_variable.num_out_channels = [6, 16]
main_file.run(project_variable)
def e39_3D_dhg():
set_init_1()
project_variable.experiment_number = 39
project_variable.sheet_number = 21
project_variable.device = 0
project_variable.optimizer = 'adam'
project_variable.learning_rate = 1e-3
project_variable.use_adaptive_lr = True
project_variable.num_out_channels = [8, 18]
main_file.run(project_variable)
def e33_3D_dhg():
set_init_2()
project_variable.experiment_number = 33
project_variable.sheet_number = 21
project_variable.device = 1
project_variable.optimizer = 'sgd'
project_variable.learning_rate = 5e-7
project_variable.use_adaptive_lr = True
project_variable.num_out_channels = [6, 16]
main_file.run(project_variable)
def e10_3D_dhg():
set_init_1()
project_variable.experiment_number = 10
project_variable.sheet_number = 21
project_variable.device = 2
project_variable.optimizer = 'sgd'
project_variable.learning_rate = 1e-8
project_variable.use_adaptive_lr = False
project_variable.num_out_channels = [6, 16]
main_file.run(project_variable)
def e44_3D_dhg():
set_init_2()
project_variable.experiment_number = 44
project_variable.sheet_number = 21
project_variable.device = 2
project_variable.optimizer = 'adam'
project_variable.learning_rate = 1e-4
project_variable.use_adaptive_lr = True
project_variable.num_out_channels = [12, 22]
main_file.run(project_variable)
def e17_3D_dhg():
set_init_1()
project_variable.experiment_number = 17
project_variable.sheet_number = 21
project_variable.device = 2
project_variable.randomize_training_data = False
project_variable.optimizer = 'adam'
project_variable.learning_rate = 1e-3
project_variable.use_adaptive_lr = False
project_variable.num_out_channels = [6, 16]
main_file.run(project_variable)
def e48_3D_dhg():
set_init_2()
project_variable.experiment_number = 48
project_variable.sheet_number = 21
project_variable.device = 0
project_variable.optimizer = 'adam'
project_variable.learning_rate = 1e-4
project_variable.use_adaptive_lr = True
project_variable.num_out_channels = [12, 22]
project_variable.transformation_groups = project_variable.num_out_channels
project_variable.k0_groups = project_variable.num_out_channels
main_file.run(project_variable)
def e1_3D_jester():
set_init_1()
project_variable.model_number = 12
project_variable.experiment_number = 1
project_variable.sheet_number = 22
project_variable.device = 0
project_variable.data_points = [50 * 27, 5 * 27, 0 * 27]
project_variable.optimizer = 'adam'
project_variable.learning_rate = 1e-3
project_variable.use_adaptive_lr = False
project_variable.num_out_channels = [6, 16]
main_file.run(project_variable)
def e63_3D_dhg():
set_init_3()
project_variable.experiment_number = 63
project_variable.sheet_number = 21
project_variable.device = 0
project_variable.model_number = 11
project_variable.data_points = [10 * 14, 20 * 14, 40 * 14]
project_variable.batch_size = 2 * 14
project_variable.optimizer = 'adam'
project_variable.learning_rate = 1e-4
project_variable.use_adaptive_lr = True
project_variable.num_out_channels = [6, 16]
main_file.run(project_variable)
def e10_conv3DTTN_jester():
set_init_1()
project_variable.model_number = 20
project_variable.experiment_number = 10
project_variable.sheet_number = 22
project_variable.device = 0
project_variable.end_epoch = 3
project_variable.repeat_experiments = 1
project_variable.batch_size = 1 * 27
project_variable.data_points = [1 * 27, 1 * 27, 0 * 27]
project_variable.load_model = True
project_variable.optimizer = 'adam'
project_variable.learning_rate = 0.00005
project_variable.use_adaptive_lr = True
project_variable.num_out_channels = [0]
main_file.run(project_variable)
def run_single_experiment(project_variable, lr, epochs, out_channels, device,
model_number):
project_variable.nas = True
project_variable.device = device
project_variable.end_epoch = epochs
project_variable.learning_rate = lr
project_variable.num_out_channels = out_channels
project_variable.model_number = model_number
project_variable.save_data = False
project_variable.save_model = False
project_variable.save_graphs = False
project_variable.dataset = 'jester'
# if you want all the data: train: 150, val: 10, test: 10
# total_dp = {'train': 118562, 'val': 7393, 'test': 7394}
project_variable.num_in_channels = 3
# project_variable.data_points = [2 * 27, 1 * 27, 0 * 27]
project_variable.batch_size = 2 * 27
project_variable.use_dali = True
project_variable.dali_workers = 8
# for now, use 'all' for val, since idk how to reset the iterator
project_variable.dali_iterator_size = [5 * 27, 'all', 0]
project_variable.label_size = 27
project_variable.load_num_frames = 30
project_variable.label_type = 'categories'
project_variable.use_adaptive_lr = True
project_variable.repeat_experiments = 1
project_variable.save_only_best_run = True
project_variable.same_training_data = True
project_variable.randomize_training_data = True
project_variable.balance_training_data = True
project_variable.theta_init = None
project_variable.srxy_init = 'eye'
project_variable.weight_transform = 'seq'
project_variable.experiment_state = 'new'
project_variable.eval_on = 'val'
project_variable.experiment_number = 1792792989823
project_variable.sheet_number = 22
project_variable.repeat_experiments = 1
project_variable.data_points = [30 * 27, 5 * 27, 0 * 27]
project_variable.optimizer = 'adam'
return main_file.run(project_variable)