def thumos(deno): print 'hey girl' model_name = 'graph_multi_video_with_L1_retF_tanh' criterion_str = 'MultiCrossEntropyMultiBranchWithL1_CASL' loss_weights = [1, 1, 1] plot_losses = True det_test = False lr = [0.001, 0.001, 0.001] multibranch = 1 branch_to_test = -2 print 'branch_to_test', branch_to_test attention = True k_vec = None gt_vec = False just_primary = False seed = 999 torch.backends.cudnn.deterministic = True random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) np.random.seed(seed) epoch_stuff = [250, 250] dataset = 'ucf' limit = None save_after = 50 test_mode = False save_outfs = False test_method = 'original' test_post_pend = '_' + test_method + '_class' model_nums = [249] retrain = False viz_mode = False viz_sim = False # post_pend = '_noBiasLastLayer' network_params = {} network_params['deno'] = deno network_params['in_out'] = [2048, 1024] network_params['feat_dim'] = [2048, 1024] network_params['feat_ret'] = True network_params['graph_size'] = 1 network_params['method'] = 'cos' network_params['sparsify'] = 'percent_0.5' network_params['graph_sum'] = attention network_params['non_lin'] = None network_params['aft_nonlin'] = 'RL_L2' network_params['sigmoid'] = True num_similar = 0 post_pend = 'changingSparsityAbs_' + str(num_similar) first_thresh = 0 class_weights = False test_after = 10 all_classes = False second_thresh = -0.9 det_class = -1 train_simple_mill_all_classes(model_name=model_name, lr=lr, dataset=dataset, network_params=network_params, limit=limit, epoch_stuff=epoch_stuff, batch_size=32, batch_size_val=32, save_after=save_after, test_mode=test_mode, class_weights=class_weights, test_after=test_after, all_classes=all_classes, just_primary=just_primary, model_nums=model_nums, retrain=retrain, viz_mode=viz_mode, second_thresh=second_thresh, first_thresh=first_thresh, det_class=det_class, post_pend=post_pend, viz_sim=viz_sim, gt_vec=gt_vec, loss_weights=loss_weights, multibranch=multibranch, branch_to_test=branch_to_test, k_vec=k_vec, attention=attention, test_pair=False, test_post_pend=test_post_pend, test_method=test_method, criterion_str=criterion_str, plot_losses=plot_losses, num_similar=num_similar, save_outfs=save_outfs, det_test=det_test)
def thumos_bce_actual(loss_weights, sparsify): print 'hey girl' model_name = 'graph_multi_video_with_L1_retF_tanh' criterion_str = 'BinaryCrossEntropyMultiBranchWithL1_CASL' # loss_weights = [1,1,0] plot_losses = True det_test = True # model_name = 'graph_multi_video_with_L1' # criterion_str = 'MultiCrossEntropyMultiBranchWithL1' # # criterion_str = 'BinaryCrossEntropyMultiBranchWithL1' # loss_weights = [1,1.] # plot_losses = False lr = [0.001, 0.001, 0.001] multibranch = 1 # loss_weights = [1,1] branch_to_test = -2 print 'branch_to_test', branch_to_test attention = True k_vec = None gt_vec = False just_primary = False seed = 999 torch.backends.cudnn.deterministic = True random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) np.random.seed(seed) epoch_stuff = [250, 250] dataset = 'ucf' limit = None save_after = 50 test_mode = False save_outfs = False # test_method = 'wtalc' # test_method = 'wtalc' # test_post_pend = '_'+test_method+'_tp_fp_conf' # test_method = 'best_worst_dot' # test_post_pend = '_'+test_method test_method = 'original' test_post_pend = '_' + test_method + '_diff_viz_multi_only' model_nums = [249] # ,349,399,449,499] retrain = False viz_mode = True viz_sim = False # post_pend = '_noBiasLastLayer' network_params = {} network_params['deno'] = 8 network_params['in_out'] = [2048, 1024] network_params['feat_dim'] = [2048, 1024] network_params['feat_ret'] = True network_params['graph_size'] = 1 network_params['method'] = 'cos' network_params['sparsify'] = sparsify # 'percent_0.5' # 'static_mid_minmin_maxmax' network_params['graph_sum'] = attention network_params['non_lin'] = None network_params['aft_nonlin'] = 'RL_L2' network_params['sigmoid'] = False # network_params['dropout'] = 0.8 num_similar = 0 post_pend = 'forplot_' + str(num_similar) first_thresh = -1 class_weights = False test_after = 10 all_classes = False second_thresh = scipy.special.logit(0.1) # -0.9 det_class = -1 train_simple_mill_all_classes(model_name=model_name, lr=lr, dataset=dataset, network_params=network_params, limit=limit, epoch_stuff=epoch_stuff, batch_size=32, batch_size_val=32, save_after=save_after, test_mode=test_mode, class_weights=class_weights, test_after=test_after, all_classes=all_classes, just_primary=just_primary, model_nums=model_nums, retrain=retrain, viz_mode=viz_mode, second_thresh=second_thresh, first_thresh=first_thresh, det_class=det_class, post_pend=post_pend, viz_sim=viz_sim, gt_vec=gt_vec, loss_weights=loss_weights, multibranch=multibranch, branch_to_test=branch_to_test, k_vec=k_vec, attention=attention, test_pair=False, test_post_pend=test_post_pend, test_method=test_method, criterion_str=criterion_str, plot_losses=plot_losses, num_similar=num_similar, save_outfs=save_outfs, det_test=True)
def charades_bce(): print 'hey girl NEW' model_name = 'graph_multi_video_with_L1_retF' # criterion_str = 'MultiCrossEntropyMultiBranchWithL1_CASL' criterion_str = 'BinaryCrossEntropyMultiBranchWithL1_CASL' loss_weights = [1,1,1] import torch.multiprocessing torch.multiprocessing.set_sharing_strategy('file_system') dataset = 'charades_i3d_charades_both' class_weights = False num_similar = 128 det_test = True plot_losses = True # model_file = ['../experiments/graph_multi_video_with_L1_retF/graph_multi_video_with_L1_retF_aft_nonlin_RL_L2_non_lin_None_sparsify_0.5_graph_size_2_sigmoid_False_graph_sum_True_deno_8_n_classes_157_in_out_2048_1024_feat_dim_2048_1024_feat_ret_True_method_cos_charades_i3d_charades_both/all_classes_False_just_primary_False_limit_None_cw_True_BinaryCrossEntropyMultiBranchWithL1_CASL_250_step_250_0.1_0.001_0.001_0.001_lw_1.00_1.00_1.00__cwOld_MulNumClasses_numSim_128/model_249.pt',250] # model_name = 'graph_multi_video_with_L1' # criterion_str = 'BinaryCrossEntropyMultiBranchWithL1' # loss_weights = [1,1] # plot_losses = False batch_size = 256 batch_size_val = 256 lr = [0.001,0.001, 0.001] # lr = [0.01,0.01, 0.01] multibranch = 1 # loss_weights = [1,1] branch_to_test = -2 print 'branch_to_test',branch_to_test attention = True k_vec = None gt_vec = False just_primary = False seed = 999 torch.backends.cudnn.deterministic = True random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) np.random.seed(seed) epoch_stuff = [250,250] limit = None save_after = 50 test_mode = False # test_method = 'wtalc' # test_method = 'wtalc' # test_post_pend = '_'+test_method+'_tp_fp_conf' # test_method = 'best_worst_dot' # test_post_pend = '_'+test_method test_method = 'original' test_post_pend = '_'+test_method+'_class' model_nums = [249] retrain = False viz_mode = False viz_sim = False # post_pend = '_noBiasLastLayer' network_params = {} network_params['deno'] = 8 network_params['in_out'] = [2048,1024] network_params['feat_dim'] = [2048,1024] network_params['feat_ret']=True network_params['graph_size'] = 2 network_params['method'] = 'cos' network_params['sparsify'] = 'percent_0.5' network_params['graph_sum'] = attention network_params['non_lin'] = None network_params['aft_nonlin']='RL_L2' network_params['sigmoid'] = False # network_params['dropout'] = 0.8 # post_pend = '' post_pend = '_marginloss_numSim_'+str(num_similar) # +'_sumnomean_noExclusiveCASL_NEW_noMax' first_thresh=-1 test_after = 50 all_classes = False second_thresh = scipy.special.logit(0.05) det_class = -1 print 'this is it! are you ready????' raw_input() train_simple_mill_all_classes (model_name = model_name, lr = lr, dataset = dataset, network_params = network_params, limit = limit, epoch_stuff= epoch_stuff, batch_size = batch_size, batch_size_val = batch_size_val, save_after = save_after, test_mode = test_mode, class_weights = class_weights, test_after = test_after, all_classes = all_classes, just_primary = just_primary, model_nums = model_nums, retrain = retrain, viz_mode = viz_mode, second_thresh = second_thresh, first_thresh = first_thresh, det_class = det_class, post_pend = post_pend, viz_sim = viz_sim, gt_vec = gt_vec, loss_weights = loss_weights, multibranch = multibranch, branch_to_test = branch_to_test, k_vec = k_vec, attention = attention, test_pair = False, test_post_pend = test_post_pend, test_method = test_method, criterion_str = criterion_str, plot_losses = plot_losses, det_test = det_test, num_similar = num_similar)
def charades(deno, gs=1): print 'hey girl NEW' model_name = 'graph_multi_video_with_L1_retF_tanh' criterion_str = 'MultiCrossEntropyMultiBranchWithL1_CASL' # criterion_str = 'BinaryCrossEntropyMultiBranchWithL1_CASL' loss_weights = [1, 1, 1] dataset = 'charades_i3d_charades_both' class_weights = False num_similar = 128 det_test = False plot_losses = True # model_name = 'graph_multi_video_with_L1' # criterion_str = 'BinaryCrossEntropyMultiBranchWithL1' # loss_weights = [1,1] # plot_losses = False batch_size = 256 batch_size_val = 256 lr = [0.001, 0.001, 0.001] # lr = [0.01,0.01, 0.01] multibranch = 1 # loss_weights = [1,1] branch_to_test = -2 print 'branch_to_test', branch_to_test attention = True k_vec = None gt_vec = False just_primary = False seed = 999 torch.backends.cudnn.deterministic = True random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) np.random.seed(seed) epoch_stuff = [250, 250] limit = None save_after = 50 test_mode = True save_outfs = True # test_method = 'wtalc' # test_method = 'wtalc' # test_post_pend = '_'+test_method+'_tp_fp_conf' # test_method = 'best_worst_dot' # test_post_pend = '_'+test_method test_method = 'original' test_post_pend = '_' + test_method + '_class_merged' model_nums = [249] retrain = False viz_mode = False viz_sim = False # post_pend = '_noBiasLastLayer' network_params = {} network_params['deno'] = deno network_params['in_out'] = [2048, 1024] network_params['feat_dim'] = [2048, 1024] network_params['feat_ret'] = True network_params['graph_size'] = gs network_params['method'] = 'cos' network_params['sparsify'] = 'percent_0.5' network_params['graph_sum'] = attention network_params['non_lin'] = None network_params['aft_nonlin'] = 'RL_L2' network_params['sigmoid'] = True # network_params['dropout'] = 0.8 # post_pend = '' post_pend = 'changingSparsityAbs_numSim_' + str(num_similar) # '_cwNo_justPos_MulNumClasses_numSim_'+str(num_similar) # +'_sumnomean_noExclusiveCASL_NEW_noMax' first_thresh = 0 # scipy.special.logit(0.1) # test_after = 50 all_classes = False second_thresh = -0.9 # scipy.special.logit(0.1) det_class = -1 # print 'this is it! are you ready????' # raw_input() train_simple_mill_all_classes(model_name=model_name, lr=lr, dataset=dataset, network_params=network_params, limit=limit, epoch_stuff=epoch_stuff, batch_size=batch_size, batch_size_val=batch_size_val, save_after=save_after, test_mode=test_mode, class_weights=class_weights, test_after=test_after, all_classes=all_classes, just_primary=just_primary, model_nums=model_nums, retrain=retrain, viz_mode=viz_mode, second_thresh=second_thresh, first_thresh=first_thresh, det_class=det_class, post_pend=post_pend, viz_sim=viz_sim, gt_vec=gt_vec, loss_weights=loss_weights, multibranch=multibranch, branch_to_test=branch_to_test, k_vec=k_vec, attention=attention, test_pair=False, test_post_pend=test_post_pend, test_method=test_method, criterion_str=criterion_str, plot_losses=plot_losses, det_test=det_test, num_similar=num_similar, save_outfs=save_outfs)
def activitynet_bce(): print 'hey girl' model_name = 'graph_multi_video_with_L1_retF_tanh' # criterion_str = 'MultiCrossEntropyMultiBranchWithSigmoidWithL1_CASL' criterion_str = 'MultiCrossEntropyMultiBranchWithL1_CASL' # criterion_str = 'BinaryCrossEntropyMultiBranchWithL1_CASL' loss_weights = [1,1,1] plot_losses = True # import torch.multiprocessing # torch.multiprocessing.set_sharing_strategy('file_system') # model_name = 'graph_multi_video_with_L1' # # criterion_str = 'MultiCrossEntropyMultiBranchWithL1' # criterion_str = 'BinaryCrossEntropyMultiBranchWithL1' # loss_weights = [1,1.] # plot_losses = False lr = [0.001,0.001, 0.001] multibranch = 1 num_similar = 128 # det_test = False batch_size = 256 batch_size_val = 256 # loss_weights = [1,1] branch_to_test = -2 print 'branch_to_test',branch_to_test attention = True k_vec = None gt_vec = False just_primary = False seed = 999 torch.backends.cudnn.deterministic = True random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) np.random.seed(seed) epoch_stuff = [250,250] dataset = 'activitynet' limit = None save_after = 50 test_mode = False save_outfs = False # test_method = 'wtalc' # test_method = 'wtalc' # test_post_pend = '_'+test_method+'_tp_fp_conf' # test_method = 'best_worst_dot' # test_post_pend = '_'+test_method test_method = 'original' test_post_pend = '_'+test_method+'_class' model_nums = [249] retrain = False viz_mode = False viz_sim = False # post_pend = '_noBiasLastLayer' network_params = {} network_params['deno'] = 1 network_params['in_out'] = [2048,1024] network_params['feat_dim'] = [2048,1024] network_params['feat_ret']=True network_params['graph_size'] = 1 network_params['method'] = 'cos' network_params['sparsify'] = 'percent_0.5' network_params['graph_sum'] = attention network_params['non_lin'] = None network_params['aft_nonlin']='RL_L2' network_params['sigmoid'] = True # post_pend = '0to4_weighting5_changingSparsityAbs_'+str(num_similar) # class_weights = False post_pend = 'gs1_changingSparsityAbs_'+str(num_similar) class_weights = False first_thresh=0 # scipy.special.logit(0.1) test_after = 10 all_classes = False second_thresh = -0.9 # scipy.special.logit(0.1) # 'max_-4' # scipy.special.logit(0.1) # -5.144 # 0.1 # 'otsu_per_class_pmfthresh_justpos_0' # 'min_max_per_class_pmfthresh_0' # 'otsu_per_class_gt' # -10.970 # -9.514 # 0.5 # -11.699 # -5.144 # 0.5 det_class = -1 train_simple_mill_all_classes (model_name = model_name, lr = lr, dataset = dataset, network_params = network_params, limit = limit, epoch_stuff= epoch_stuff, batch_size = batch_size, batch_size_val = batch_size_val, save_after = save_after, test_mode = test_mode, class_weights = class_weights, test_after = test_after, all_classes = all_classes, just_primary = just_primary, model_nums = model_nums, retrain = retrain, viz_mode = viz_mode, second_thresh = second_thresh, first_thresh = first_thresh, det_class = det_class, post_pend = post_pend, viz_sim = viz_sim, gt_vec = gt_vec, loss_weights = loss_weights, multibranch = multibranch, branch_to_test = branch_to_test, k_vec = k_vec, attention = attention, test_pair = False, test_post_pend = test_post_pend, test_method = test_method, criterion_str = criterion_str, plot_losses = plot_losses, num_similar = num_similar, save_outfs = save_outfs, det_test = True)
def fcasl(feat_dim): print 'hey girl' model_name = 'fcasl_multi_video_with_L1_retF' criterion_str = 'MultiCrossEntropyMultiBranchFakeL1_CASL' loss_weights = [1, 1, 1] plot_losses = True det_test = True # model_name = 'graph_multi_video_with_L1' # criterion_str = 'MultiCrossEntropyMultiBranchWithL1' # # criterion_str = 'BinaryCrossEntropyMultiBranchWithL1' # loss_weights = [1,1.] # plot_losses = False lr = [0.001, 0.001] multibranch = 1 # loss_weights = [1,1] branch_to_test = -2 print 'branch_to_test', branch_to_test attention = True k_vec = None gt_vec = False just_primary = False seed = 999 torch.backends.cudnn.deterministic = True random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) np.random.seed(seed) epoch_stuff = [250, 250] dataset = 'ucf' limit = None save_after = 50 test_mode = False save_outfs = False # test_method = 'wtalc' # test_method = 'wtalc' # test_post_pend = '_'+test_method+'_tp_fp_conf' # test_method = 'best_worst_dot' # test_post_pend = '_'+test_method test_method = 'original' test_post_pend = '_' + test_method model_nums = [249] # ,349,399,449,499] retrain = False viz_mode = False viz_sim = False # post_pend = '_noBiasLastLayer' network_params = {} network_params['deno'] = 8 network_params['feat_dim'] = [2048, feat_dim] network_params['feat_ret'] = True network_params['sigmoid'] = True num_similar = 0 post_pend = 'comparison_' + str(num_similar) first_thresh = 0 class_weights = False test_after = 10 all_classes = False second_thresh = -0.9 det_class = -1 train_simple_mill_all_classes(model_name=model_name, lr=lr, dataset=dataset, network_params=network_params, limit=limit, epoch_stuff=epoch_stuff, batch_size=32, batch_size_val=32, save_after=save_after, test_mode=test_mode, class_weights=class_weights, test_after=test_after, all_classes=all_classes, just_primary=just_primary, model_nums=model_nums, retrain=retrain, viz_mode=viz_mode, second_thresh=second_thresh, first_thresh=first_thresh, det_class=det_class, post_pend=post_pend, viz_sim=viz_sim, gt_vec=gt_vec, loss_weights=loss_weights, multibranch=multibranch, branch_to_test=branch_to_test, k_vec=k_vec, attention=attention, test_pair=False, test_post_pend=test_post_pend, test_method=test_method, criterion_str=criterion_str, plot_losses=plot_losses, num_similar=num_similar, save_outfs=save_outfs, det_test=det_test)
def graph_norm_game(): print 'hey girl' # raw_input() model_name = 'graph_multi_video_norm_game' criterion_str = 'MultiCrossEntropyMultiBranchWithL1' loss_weights = [1,1] plot_losses = False # model_name = 'graph_multi_video_with_L1' # criterion_str = 'MultiCrossEntropyMultiBranchWithL1' # loss_weights = [1,1] # plot_losses = False lr = [0.001,0.001, 0.001] multibranch = 1 branch_to_test = -2 print 'branch_to_test',branch_to_test attention = True k_vec = None gt_vec = False just_primary = False seed = 999 torch.backends.cudnn.deterministic = True random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) np.random.seed(seed) epoch_stuff = [250,250] dataset = 'ucf' limit = None save_after = 50 test_mode = False test_method = 'original' test_post_pend = '_'+test_method+'_class' model_nums = [249] retrain = False viz_mode = False viz_sim = False network_params = {} network_params['deno'] = 8 network_params['in_out'] = [2048,1024] network_params['feat_dim'] = [2048,1024] network_params['feat_ret']=False network_params['graph_size'] = 1 network_params['method'] = 'cos' network_params['sparsify'] = None network_params['graph_sum'] = attention network_params['non_lin'] = None network_params['aft_nonlin']='RL_L2' network_params['sigmoid'] = False # network_params['nosum'] = 5 # post_pend = '_rnsmaxways' network_params['nosum'] = 4 post_pend = '_rnbothways' first_thresh=0 class_weights = True test_after = 10 all_classes = False second_thresh = 0.5 det_class = -1 train_simple_mill_all_classes (model_name = model_name, lr = lr, dataset = dataset, network_params = network_params, limit = limit, epoch_stuff= epoch_stuff, batch_size = 32, batch_size_val = 32, save_after = save_after, test_mode = test_mode, class_weights = class_weights, test_after = test_after, all_classes = all_classes, just_primary = just_primary, model_nums = model_nums, retrain = retrain, viz_mode = viz_mode, second_thresh = second_thresh, first_thresh = first_thresh, det_class = det_class, post_pend = post_pend, viz_sim = viz_sim, gt_vec = gt_vec, loss_weights = loss_weights, multibranch = multibranch, branch_to_test = branch_to_test, k_vec = k_vec, attention = attention, test_pair = False, test_post_pend = test_post_pend, test_method = test_method, criterion_str = criterion_str, plot_losses = plot_losses)
def graph_charades_everything_sim(): print 'hey girl charades' # raw_input() model_name = 'graph_multi_video_with_L1_retF' criterion_str = 'MultiCrossEntropyMultiBranchWithL1_CASL' loss_weights = [1,1,1] # loss_weights = [1,1,0] # dataset = 'charades_vgg_16_rgb_npy' dataset = 'charades_i3d_both' class_weights = True num_similar = 128 det_test = False batch_size = 256 batch_size_val = 256 plot_losses = True # model_name = 'graph_multi_video_with_L1' # criterion_str = 'MultiCrossEntropyMultiBranchWithL1' # loss_weights = [1,1] # plot_losses = False lr = [0.001,0.001, 0.001] multibranch = 1 # loss_weights = [1,1] branch_to_test = -2 print 'branch_to_test',branch_to_test attention = True k_vec = None gt_vec = False just_primary = False seed = 999 torch.backends.cudnn.deterministic = True random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) np.random.seed(seed) epoch_stuff = [250,250] # dataset = 'multithumos' limit = None save_after = 50 test_mode = True # test_method = 'wtalc' # test_method = 'wtalc' # test_post_pend = '_'+test_method+'_tp_fp_conf' # test_method = 'best_worst_dot' # test_post_pend = '_'+test_method test_method = 'original' test_post_pend = '_'+test_method+'_class' model_nums = None retrain = False viz_mode = False viz_sim = False # post_pend = '_noBiasLastLayer' network_params = {} network_params['deno'] = 8 network_params['in_out'] = [2048,1024] network_params['feat_dim'] = [2048,1024] network_params['feat_ret']=True network_params['graph_size'] = 2 network_params['method'] = 'cos' network_params['sparsify'] = 0.5 network_params['graph_sum'] = attention network_params['non_lin'] = None network_params['aft_nonlin']='RL_L2' network_params['sigmoid'] = False # network_params['dropout'] = 0.8 post_pend = '_noLimit' post_pend = '_numSim_'+str(num_similar)+'_sumnomean_noExclusiveCASL_NEW_noMax' # post_pend = '_numSim_'+str(num_similar)+'_' first_thresh=0 test_after = 50 all_classes = False second_thresh = 0.5 det_class = -1 # print 'this is it! are you ready????' # raw_input() train_simple_mill_all_classes (model_name = model_name, lr = lr, dataset = dataset, network_params = network_params, limit = limit, epoch_stuff= epoch_stuff, batch_size = batch_size, batch_size_val = batch_size_val, save_after = save_after, test_mode = test_mode, class_weights = class_weights, test_after = test_after, all_classes = all_classes, just_primary = just_primary, model_nums = model_nums, retrain = retrain, viz_mode = viz_mode, second_thresh = second_thresh, first_thresh = first_thresh, det_class = det_class, post_pend = post_pend, viz_sim = viz_sim, gt_vec = gt_vec, loss_weights = loss_weights, multibranch = multibranch, branch_to_test = branch_to_test, k_vec = k_vec, attention = attention, test_pair = False, test_post_pend = test_post_pend, test_method = test_method, criterion_str = criterion_str, plot_losses = plot_losses, det_test = det_test, num_similar = num_similar, save_outfs = True)
def thumos(deno): print 'hey girl' model_name = 'graph_multi_video_with_L1_retF_tanh' criterion_str = 'MultiCrossEntropyMultiBranchWithL1_CASL' loss_weights = [1, 1, 1] plot_losses = True # learning rate for [\phi, graph layer, final linear layer] lr = [0.001, 0.001, 0.001] seed = 999 torch.backends.cudnn.deterministic = True random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) np.random.seed(seed) # string to switch between datasets dataset = 'ucf' #num segments to include per video during training. in case of memory problems limit = None # epoch_stuff = [num epochs after which to reduce lr, total num epochs] epoch_stuff = [250, 250] save_after = 50 # string to append to experiment folder post_pend = 'denoExp' test_after = 10 # set det_test to False if localization results on val not needed during training det_test = True # model numbers to test. model_nums = [249] # set test mode to true to test a trained model test_mode = False save_outfs = False test_method = 'original' test_post_pend = '_' + test_method + '_class' # number of similar class videos to contain in each training batch. imp when num classes>batchsize num_similar = 0 retrain = False viz_mode = False viz_sim = False network_params = {} network_params['deno'] = deno network_params['in_out'] = [2048, 1024] network_params['feat_dim'] = [2048, 1024] network_params['feat_ret'] = True # graph_size controls number of videos per graph during training (see supp Fig 1) network_params['graph_size'] = 1 network_params['method'] = 'cos' network_params['sparsify'] = 'percent_0.5' attention = True network_params['graph_sum'] = attention network_params['non_lin'] = None network_params['aft_nonlin'] = 'RL_L2' network_params['sigmoid'] = True # default params. no need to change. first_thresh = 0 class_weights = False all_classes = False second_thresh = -0.9 det_class = -1 multibranch = 1 branch_to_test = -2 k_vec = None gt_vec = False just_primary = False train_simple_mill_all_classes(model_name=model_name, lr=lr, dataset=dataset, network_params=network_params, limit=limit, epoch_stuff=epoch_stuff, batch_size=32, batch_size_val=32, save_after=save_after, test_mode=test_mode, class_weights=class_weights, test_after=test_after, all_classes=all_classes, just_primary=just_primary, model_nums=model_nums, retrain=retrain, viz_mode=viz_mode, second_thresh=second_thresh, first_thresh=first_thresh, det_class=det_class, post_pend=post_pend, viz_sim=viz_sim, gt_vec=gt_vec, loss_weights=loss_weights, multibranch=multibranch, branch_to_test=branch_to_test, k_vec=k_vec, attention=attention, test_pair=False, test_post_pend=test_post_pend, test_method=test_method, criterion_str=criterion_str, plot_losses=plot_losses, num_similar=num_similar, save_outfs=save_outfs, det_test=det_test)