def e1002_3D_ucf101(): set_init_1() project_variable.model_number = 25 project_variable.experiment_number = 1002 project_variable.sheet_number = 23 project_variable.device = 1 project_variable.end_epoch = 100 project_variable.batch_size = 20 project_variable.batch_size_val_test = 20 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 = 0.000005 project_variable.use_adaptive_lr = True main_file.run(project_variable)
def e002_dots(): set_init_1_frames() project_variable.dataset = 'dots_frames' # project_variable.dataset = 'ucf101' # project_variable.model_number = 56 # lenet5 3t project_variable.model_number = 55 # lenet5 2D project_variable.experiment_number = 2 project_variable.device = 0 project_variable.end_epoch = 10 project_variable.batch_size = 32 project_variable.batch_size_val_test = 32 # project_variable.inference_only_mode = True project_variable.inference_only_mode = False project_variable.load_model = False # loading model from scratch # project_variable.load_from_fast = True # UNUSED? 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.stop_at_collapse = False # project_variable.early_stopping = True project_variable.early_stopping = False project_variable.optimizer = 'adam' # project_variable.learning_rate = 0.05 project_variable.learning_rate = 0.0005 project_variable.use_adaptive_lr = True main_file.run(project_variable)
def e001_3T_kinetics(): set_init_1() project_variable.model_number = 23 # googlenet project_variable.experiment_number = 1 project_variable.device = 2 project_variable.end_epoch = 200 project_variable.batch_size = 18 # 5 about 3000 project_variable.batch_size_val_test = 30 # 30 about 3000 # project_variable.inference_only_mode = True project_variable.inference_only_mode = False # project_variable.save_model_every_x_epochs = 1 project_variable.load_model = True # loading pre-trained on ImageNet 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.stop_at_collapse = False 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 e1011_3T_ucf101(): set_init_1() project_variable.nin = True project_variable.train_nin_mode = 'joint' project_variable.model_number = 52 # TACoNet project_variable.experiment_number = 1011 project_variable.sheet_number = 22 project_variable.device = 1 project_variable.end_epoch = 1 project_variable.repeat_experiments = 1 project_variable.batch_size = 20 project_variable.batch_size_val_test = 20 project_variable.load_model = None 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] main_file.run(project_variable)
def e1014_3T_ucf101(): set_init_1() project_variable.model_number = 23 project_variable.experiment_number = 1014 project_variable.sheet_number = 23 project_variable.device = 1 project_variable.end_epoch = 100 project_variable.batch_size = 15 project_variable.batch_size_val_test = 15 project_variable.load_model = [1, 23, 4, 0] # 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 utils.wait_for_gpu(wait=True, device_num=project_variable.device, threshold=9700) main_file.run(project_variable)
def e101_conv3D_jester(): set_init_1() project_variable.model_number = 21 # 3Dresnet18 from scratch project_variable.experiment_number = 101 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 = 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 = 0.000005 project_variable.use_adaptive_lr = True project_variable.num_out_channels = [0] main_file.run(project_variable)
def e1010_3T_ucf101(): set_init_1() project_variable.nin = False project_variable.model_number = 51 # convnet3T project_variable.experiment_number = 38 project_variable.sheet_number = 23 project_variable.device = 1 project_variable.end_epoch = 1 project_variable.repeat_experiments = 1 project_variable.batch_size = 20 project_variable.batch_size_val_test = 20 project_variable.load_model = None 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] # utils.wait_for_gpu(wait=True, device_num=project_variable.device) main_file.run(project_variable)
def e45_conv3T_jester(): set_init_1() project_variable.dataset = 'tiny_jester' project_variable.nin = True project_variable.train_nin_mode = 'joint' project_variable.model_number = 54 # alexnet-taco project_variable.experiment_number = 45 project_variable.sheet_number = 22 project_variable.device = 0 project_variable.end_epoch = 100 project_variable.repeat_experiments = 3 project_variable.batch_size = 20 project_variable.batch_size_val_test = 20 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 = 0.00001 project_variable.use_adaptive_lr = True project_variable.num_out_channels = [0] # utils.wait_for_gpu(wait=True, device_num=project_variable.device) main_file.run(project_variable)
def e40_conv3T_jester(): set_init_1() project_variable.dataset = 'tiny_jester' project_variable.nin = False # project_variable.train_nin_mode = 'nin_only' project_variable.model_number = 51 # convnet3T project_variable.experiment_number = 40 project_variable.sheet_number = 22 project_variable.device = 2 project_variable.end_epoch = 100 project_variable.repeat_experiments = 10 project_variable.batch_size = 20 project_variable.batch_size_val_test = 20 project_variable.load_model = None 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, threshold=2000) main_file.run(project_variable)
def e37_conv3T_jester(): set_init_1() project_variable.nin = True project_variable.train_nin_mode = 'nin_only' project_variable.model_number = 50 # RN18 3T project_variable.experiment_number = 37 project_variable.sheet_number = 22 project_variable.device = 1 project_variable.end_epoch = 5 project_variable.repeat_experiments = 1 project_variable.batch_size = 1 project_variable.batch_size_val_test = 1 project_variable.load_model = [31, 20, 8, 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 e009_3T_kinetics(): set_init_1() project_variable.dataset = 'kinetics400' project_variable.label_size = 400 project_variable.model_number = 23 # googlenet project_variable.experiment_number = 9 project_variable.device = 1 project_variable.end_epoch = 200 project_variable.batch_size = 17 # 5 about 3000 # project_variable.batch_size = 10 # 5 about 3000 project_variable.batch_size_val_test = 29 # 30 about 3000 # project_variable.inference_only_mode = True project_variable.inference_only_mode = False # project_variable.eval_on = 'test' project_variable.eval_on = 'val' # project_variable.load_model = True # loading pre-trained on ImageNet project_variable.load_model = [6, 23, 7, 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 = 0.00007 project_variable.use_adaptive_lr = True utils.wait_for_gpu(wait=True, device_num=project_variable.device, threshold=9700) main_file.run(project_variable)
def e002_3T_kinetics(): set_init_1() project_variable.dataset = 'kinetics400_metaclass' project_variable.label_size = 39 project_variable.model_number = 23 # googlenet project_variable.experiment_number = 2 project_variable.device = 1 project_variable.end_epoch = 200 project_variable.batch_size = 17 # 5 about 3000 project_variable.batch_size_val_test = 29 # 30 about 3000 # project_variable.inference_only_mode = True project_variable.inference_only_mode = False # project_variable.eval_on = 'test' project_variable.eval_on = 'val' # TODO: standardize the test data # project_variable.save_model_every_x_epochs = 1 project_variable.load_model = True # loading pre-trained on ImageNet # project_variable.load_model = [2, 23, 10, 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 = False main_file.run(project_variable)
def e010_dots(): set_init_1_frames() project_variable.dataset = 'dots_avi' # project_variable.dataset = 'ucf101' project_variable.model_number = 57 # simplenet3t, 1 conv layer project_variable.experiment_number = 10 project_variable.load_num_frames = 15 project_variable.device = 0 project_variable.end_epoch = 20 project_variable.batch_size = 32 project_variable.batch_size_val_test = 32 # project_variable.inference_only_mode = True project_variable.inference_only_mode = False project_variable.load_model = False # loading model from scratch # project_variable.load_from_fast = True # UNUSED? project_variable.use_dali = True project_variable.dali_workers = 32 project_variable.dali_iterator_size = ['all', 'all', 0] project_variable.nas = False project_variable.save_all_models = True # project_variable.stop_at_collapse = True project_variable.stop_at_collapse = False # project_variable.early_stopping = True project_variable.early_stopping = False project_variable.optimizer = 'sgd' # project_variable.learning_rate = 0.03 # project_variable.learning_rate = 0.05 project_variable.learning_rate = 0.08 project_variable.use_adaptive_lr = True main_file.run(project_variable)
def e29_conv3T_jester(): set_init_1() project_variable.model_number = 20 project_variable.experiment_number = 29 project_variable.sheet_number = 22 project_variable.device = 1 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 = 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 = 0.00005 project_variable.use_adaptive_lr = True project_variable.num_out_channels = [0] go = False while not go: gpu_available = get_gpu_memory_map() if gpu_available[project_variable.device] < 100: go = True else: print('waiting for gpu...') time.sleep(10) main_file.run(project_variable)
def generation_loop_with_cnn(device_num=0): ''' rad_dot_m_t, num_dots_low_t, num_dots_high_t = parameters 2, 10, 20 rad_dot_m_r, num_dots_low_r, num_dots_high_r = parameters 2, 10, 20 num_dots_low_s, num_dots_high_s, radius_dot_min_s_pos_dir, radius_dot_max_s_pos_dir, radius_dot_min_s_neg_dir, radius_dot_max_s_neg_dir = parameters 10, 11, 3, 4, 3, 4 ''' # parameters = [2, 10, 20, 2, 10, 20, 10, 11, 3, 4, 3, 4] random_acc = 1 / 3 val_acc = 1 e = 0.05 it = 1 while val_acc > random_acc + e: opt_remove_dataset() parameters = sample_params() make_dataset('train', 500, 30, 33, parameters) make_dataset('val', 200, 30, 33, parameters) pv = config_pv(device_num) val_acc = main_file.run(pv) if val_acc > random_acc + e: print('%d: val_acc: %f. parameters tried: %s' % (it, val_acc, str(parameters))) save_results(val_acc, parameters) else: print('OPTIMAL PARAMETERS FOUND!') print('%d: val_acc: %f. parameters: %s' % (it, val_acc, str(parameters))) save_results(val_acc, parameters) it = it + 1