def main(save_id, gen_p, train_p, eval_p, backbone_id, return_eval=False, use_bottleneck=True, file_id=""): print("save_id: {0}, train_p : {1}, eval_p: {2}, backbone_id: {3}, ".format(save_id, train_p, eval_p, backbone_id)) from model_def import define_model model_dict = define_model(backbone_id) num_segments = model_dict["num_segments"] bottleneck_size = model_dict["bottleneck_size"] dense_sample = model_dict["dense_sample"] dense_rate = model_dict["dense_rate"] dir_name = os.path.join("saved_models", save_id) # lfd_params if not os.path.exists(dir_name): os.makedirs(dir_name) filename = os.path.join(dir_name, "../model") lfd_params = default_model_args(save_id=save_id, log_dir=dir_name, num_segments=num_segments, bottleneck_size=bottleneck_size, dense_sample=dense_sample, dense_rate=dense_rate) # parse_model_args() if gen_p: print("Generating ITR Files") model = Classifier(lfd_params, filename, backbone_id, use_feature_extractor=True, use_spatial_lstm=False, spatial_train=False, use_bottleneck=use_bottleneck) generate_iad_files(lfd_params, model, "train", backbone=backbone_id) generate_iad_files(lfd_params, model, "evaluation", backbone=backbone_id) if train_p: model = Classifier(lfd_params, filename, backbone_id, use_feature_extractor=False, use_spatial_lstm=True, spatial_train=True, use_bottleneck=use_bottleneck) model = train(lfd_params, model, verbose=True, input_dtype="iad") model.save_model() if eval_p: model = Classifier(lfd_params, filename, backbone_id, use_feature_extractor=False, use_spatial_lstm=True, spatial_train=False, use_bottleneck=use_bottleneck) train_df = evaluate(lfd_params, model, mode="train", input_dtype="iad") train_df["mode"] = ["train"]*len(train_df) eval_df = evaluate(lfd_params, model, mode="evaluation", verbose=True, input_dtype="iad") eval_df["mode"] = ["evaluation"] * len(eval_df) df = pd.concat([train_df, eval_df]) if return_eval: return df df["repeat"] = ["1"]*len(df) out_filename = os.path.join(lfd_params.args.output_dir, "output_" + save_id + file_id+".csv") df.to_csv(out_filename) print("Output placed in: " + out_filename)
def exec_classifier_backbone(args): # Train if args.eval_only: model = Classifier(lfd_params, filename, backbone_id, use_feature_extractor=False, use_spatial=True, spatial_train=True, use_bottleneck=use_bottleneck) model = train(lfd_params, model, verbose=True) model.save_model() # Evaluate model = Classifier(lfd_params, filename, backbone_id, use_feature_extractor=False, use_spatial=True, spatial_train=False, use_bottleneck=use_bottleneck) train_df = evaluate(lfd_params, model, mode="train") train_df["mode"] = ["train"] * len(train_df) eval_df = evaluate(lfd_params, model, mode="evaluation", verbose=True) eval_df["mode"] = ["evaluation"] * len(eval_df) df = pd.concat([train_df, eval_df]) df["repeat"] = ["1"] * len(df) out_filename = os.path.join(lfd_params.args.output_dir, "output_" + save_id + "_spatial.csv") df.to_csv(out_filename) print("Output placed in: " + out_filename) return 0
def main(save_id, gen_p, train_p, eval_p, backbone_id, use_bottleneck=True): from model_def import define_model model_dict = define_model(backbone_id) num_segments = model_dict["num_segments"] bottleneck_size = model_dict["bottleneck_size"] dense_sample = model_dict["dense_sample"] dense_rate = model_dict["dense_rate"] dir_name = os.path.join("saved_models", save_id) # lfd_params if not os.path.exists(dir_name): os.makedirs(dir_name) filename = os.path.join(dir_name, "../model") lfd_params = default_model_args(save_id=save_id, log_dir=dir_name, num_segments=num_segments, bottleneck_size=bottleneck_size, dense_sample=dense_sample, dense_rate=dense_rate) if gen_p: # Generate IADs print("Generating ITR Files") model = Classifier(lfd_params, filename, backbone_id, use_feature_extractor=True, use_spatial_lstm=False, spatial_train=False, use_bottleneck=use_bottleneck) generate_iad_files(lfd_params, model, "train", backbone=backbone_id) generate_iad_files(lfd_params, model, "evaluation", backbone=backbone_id) if train_p: print("Training Policy") model = PolicyLearner(lfd_params, filename, backbone_id, use_feature_extractor=False, use_spatial_lstm=True, spatial_train=True, policy_train=True, use_bottleneck=use_bottleneck) # Train policy learner model = train(lfd_params, model, verbose=False, input_dtype="iad", ablation=False) model.save_model() if eval_p: model = PolicyLearner(lfd_params, filename, backbone_id, use_feature_extractor=False, use_spatial_lstm=True, policy_train=False, use_bottleneck=use_bottleneck) df = evaluate_action_trace(lfd_params, model, verbose=True, input_dtype="iad") out_filename = os.path.join(lfd_params.args.output_dir, "output_" + save_id + "_action_trace.csv") df.to_csv(out_filename) print("Output placed in: " + out_filename) df = evaluate_action_trace(lfd_params, model, input_dtype="iad", ablation=True, verbose=True, mode="train") out_filename = os.path.join( lfd_params.args.output_dir, "output_" + save_id + "_action_trace_ablation_train.csv") df.to_csv(out_filename) print("Output placed in: " + out_filename) df = evaluate_action_trace(lfd_params, model, input_dtype="iad", ablation=True, verbose=True, mode="evaluation") out_filename = os.path.join( lfd_params.args.output_dir, "output_" + save_id + "_action_trace_ablation_eval.csv") df.to_csv(out_filename) print("Output placed in: " + out_filename)