from argparse import ArgumentParser import argparse import os import json # def parse_and_load_from_model(parser): # args according to the loaded model # do not try to specify them from cmd line since they will be overwritten add_data_options(parser) add_model_options(parser) add_diffusion_options(parser) args = parser.parse_args() args_to_overwrite = [] for group_name in ['dataset', 'model', 'diffusion']: args_to_overwrite += get_args_per_group_name(parser, args, group_name) # load args from model model_path = get_model_path_from_args() print(f"model: {model_path}, dir_name: {os.path.dirname(model_path)}") args_path = os.path.join(os.path.dirname(model_path), 'args.json') assert os.path.exists(args_path), 'Arguments json file was not found!' with open(args_path, 'r') as fr: model_args = json.load(fr) for a in args_to_overwrite: if a in model_args.keys(): setattr(args, a, model_args[a]) elif 'cond_mode' in model_args: # backward compitability unconstrained = (model_args['cond_mode'] == 'no_cond') setattr(args, 'unconstrained', unconstrained) else: print('Warning: was not able to load [{}], using default value [{}] instead.'.format(a, args.__dict__[a])) if args.cond_mask_prob == 0: args.guidance_param = 1 return args def get_args_per_group_name(parser, args, group_name): for group in parser._action_groups: if group.title == group_name: group_dict = {a.dest: getattr(args, a.dest, None) for a in group._group_actions} return list(argparse.Namespace(**group_dict).__dict__.keys()) return ValueError('group_name was not found.') def get_model_path_from_args(): try: dummy_parser = ArgumentParser() dummy_parser.add_argument('model_path') dummy_args, _ = dummy_parser.parse_known_args() return dummy_args.model_path except: raise ValueError('model_path argument must be specified.') def add_base_options(parser): group = parser.add_argument_group('base') group.add_argument("--cuda", default=True, type=bool, help="Use cuda device, otherwise use CPU.") group.add_argument("--device", default=0, type=int, help="Device id to use.") group.add_argument("--seed", default=10, type=int, help="For fixing random seed.") group.add_argument("--batch_size", default=64, type=int, help="Batch size during training.") group.add_argument("--debug", action='store_true', help="If True, will run evaluation during training.") # rep_type group.add_argument("--rep_type", default="", type=str, help="If empty, will use defaults according to the specified dataset.") group.add_argument("--local_rank", default=0, type=int, help="Batch size during training.") ## for dist util ## group.add_argument("--nprocs", default=1, type=int, help="Batch size during training.") ## for dist util ## # denoising_stra ### 1) rep -> represetntions directly; 2) motion_to_rep group.add_argument("--denoising_stra", default="rep", type=str, help="Denoising strategy") # inter_optim group.add_argument("--inter_optim", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # diff_jts group.add_argument("--diff_jts", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # diff_basejtsrel group.add_argument("--diff_basejtsrel", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # diff_basejtse group.add_argument("--diff_basejtse", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # use_sep_models group.add_argument("--use_sep_models", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # use_vae group.add_argument("--use_vae", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # kl_weights group.add_argument("--kl_weights", default=0.0, type=float, help="Joint positions loss.") ### 1) rep -> represetntions directly; 2) motion_to_rep group.add_argument("--jts_sclae_stra", default="bbox", type=str, help="Denoising strategy") # use_sigmoid group.add_argument("--use_sigmoid", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # train_enc group.add_argument("--train_enc", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # train_diff ## group.add_argument("--train_diff", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") ## with_dec_pos_emb ---- whether to use pos emb ## group.add_argument("--without_dec_pos_emb", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # pred_diff_noise group.add_argument("--pred_diff_noise", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # pred_diff_noise group.add_argument("--deep_fuse_timeemb", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # use_ours_transformer_enc group.add_argument("--use_ours_transformer_enc", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") group.add_argument("--not_load_opt", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # resume_diff group.add_argument("--resume_diff", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # const_noise group.add_argument("--const_noise", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # set_attn_to_none group.add_argument("--set_attn_to_none", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # rnd_noise group.add_argument("--rnd_noise", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # jts_pred_loss_coeff # basejtsrel_pred_loss_coeff, # basejtse_along_normal_loss_coeff, basejtse_vt_normal_loss_coeff # jts_pred_loss_coeff group.add_argument("--jts_pred_loss_coeff", default=20.0, type=float, help="Joint positions loss.") # basejtsrel_pred_loss_coeff group.add_argument("--basejtsrel_pred_loss_coeff", default=20.0, type=float, help="Joint positions loss.") # basejtse_along_normal_loss_coeff group.add_argument("--basejtse_along_normal_loss_coeff", default=20.0, type=float, help="Joint positions loss.") # basejtse_vt_normal_loss_coeff group.add_argument("--basejtse_vt_normal_loss_coeff", default=20.0, type=float, help="Joint positions loss.") # wo_e_normalization group.add_argument("--wo_e_normalization", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # wo_rel_normalization group.add_argument("--wo_rel_normalization", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # use_dec_rel_v2 group.add_argument("--use_dec_rel_v2", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # pred_basejtsrel_avgjts ### pred_basejtsrel_avgjts ### group.add_argument("--pred_basejtsrel_avgjts", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") #only_first_clip group.add_argument("--only_first_clip", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # single_frame_noise group.add_argument("--single_frame_noise", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # use_t group.add_argument("--use_t", default=1, type=int, help="Joint positions loss.") # not_add_noise group.add_argument("--not_add_noise", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # not_cond_base group.add_argument("--not_cond_base", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # not_pred_avg_jts group.add_argument("--not_pred_avg_jts", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # diff_spatial group.add_argument("--diff_spatial", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # pred_joints_offset group.add_argument("--pred_joints_offset", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # not_diff_avgjts group.add_argument("--not_diff_avgjts", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") ## joint_std_v2 group.add_argument("--joint_std_v2", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # joint_std_v3 group.add_argument("--joint_std_v3", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # diff_latents group.add_argument("--diff_latents", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # use_canon_joints group.add_argument("--use_canon_joints", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # use_var_sched group.add_argument("--use_var_sched", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # e_normalization_stra group.add_argument("--e_normalization_stra", default="cent", type=str, help="If empty, will use defaults according to the specified dataset.") # diff_realbasejtsrel group.add_argument("--diff_realbasejtsrel", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # real_basejtsrel_norm_stra group.add_argument("--real_basejtsrel_norm_stra", default="none", type=str, help="If empty, will use defaults according to the specified dataset.") # diff_realbasejtsrel_to_joints ## basejtsrel_to_joints group.add_argument("--diff_realbasejtsrel_to_joints", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # use_abs_jts_pos group.add_argument("--use_abs_jts_pos", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # use_abs_jts_for_encoding group.add_argument("--use_abs_jts_for_encoding", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # use_abs_jts_for_encoding_obj_base group.add_argument("--use_abs_jts_for_encoding_obj_base", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # use_objbase_v2 group.add_argument("--use_objbase_v2", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # use_objbase_v3 group.add_argument("--use_objbase_v3", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # use_jts_pert_realbasejtsrel group.add_argument("--use_jts_pert_realbasejtsrel", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # use_objbase_out_v3 group.add_argument("--use_objbase_out_v3", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # nn_base_pts group.add_argument("--nn_base_pts", default=700, type=int, help="Joint positions loss.") # use_objbase_v4, use_objbase_out_v4 group.add_argument("--use_objbase_v4", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") group.add_argument("--use_objbase_out_v4", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # ### objbase_v5, use_objbase_out_v5 ### group.add_argument("--use_objbase_v5", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") group.add_argument("--use_objbase_out_v5", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # out_objbase_v5_bundle_out group.add_argument("--out_objbase_v5_bundle_out", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # add_noise_onjts group.add_argument("--add_noise_onjts", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # add_noise_onjts_single group.add_argument("--add_noise_onjts_single", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # v5_out_not_cond_base group.add_argument("--v5_out_not_cond_base", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # v5_out_not_cond_base group.add_argument("--use_objbase_v6", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # use_objbase_v7 group.add_argument("--use_objbase_v7", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # predicted_info_fn group.add_argument("--predicted_info_fn", default="", type=str, help="If empty, will use defaults according to the specified dataset.") # only_cmb_finger group.add_argument("--only_cmb_finger", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # use_vox_data group.add_argument("--use_vox_data", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # v5_in_not_base_pos group.add_argument("--v5_in_not_base_pos", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # v5_in_not_base group.add_argument("--v5_in_not_base", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # v5_in_without_glb group.add_argument("--v5_in_without_glb", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # finetune_with_cond group.add_argument("--finetune_with_cond", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # in_eval group.add_argument("--in_eval", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # finetune_with_cond_rel; finetune_with_cond_jtsobj group.add_argument("--finetune_with_cond_rel", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # finetune_with_cond_jtsobj group.add_argument("--finetune_with_cond_jtsobj", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # sel_basepts_idx group.add_argument("--sel_basepts_idx", default=0, type=int, help="Joint positions loss.") # test_tag group.add_argument("--test_tag", default="", type=str, help="If empty, will use defaults according to the specified dataset.") # finetune_cond_obj_feats_dim group.add_argument("--finetune_cond_obj_feats_dim", default=3, type=int, help="Joint positions loss.") # cad_model_fn group.add_argument("--cad_model_fn", default="", type=str, help="If empty, will use defaults according to the specified dataset.") # cad_model_fn # group.add_argument("--cad_model_fn", default="", type=str, # help="If empty, will use defaults according to the specified dataset.") # diff_joint_quants group.add_argument("--diff_joint_quants", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # joint_quants_nn group.add_argument("--joint_quants_nn", default=2, type=int, help="Joint positions loss.") # use_same_noise_for_rep ### whether to use the same noise for representations denoising ## # group.add_argument("--use_same_noise_for_rep", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # use_temporal_rep_v2 group.add_argument("--use_temporal_rep_v2", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # use_arti_obj group.add_argument("--use_arti_obj", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # pert_type group.add_argument("--pert_type", default="gaussian", type=str, help="If empty, will use defaults according to the specified dataset.") # use_anchors # group.add_argument("--use_anchors", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # with_glb_info group.add_argument("--with_glb_info", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # phy_guided_sampling group.add_argument("--phy_guided_sampling", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # diff_hand_params group.add_argument("--diff_hand_params", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") group.add_argument("--corr_fn", default="", type=str, help="If empty, will use defaults according to the specified dataset.") # group.add_argument("--augment", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # train_all_clips group.add_argument("--train_all_clips", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # use_predicted_infos group.add_argument("--use_predicted_infos", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # start_idx ### start_idx here for the starting idxes 3 group.add_argument("--start_idx", default=50, type=int, help="Joint positions loss.") # theta_dim group.add_argument("--theta_dim", default=24, type=int, help="Joint positions loss.") # use_interpolated_infos group.add_argument("--use_interpolated_infos", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # use_reverse group.add_argument("--use_reverse", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # predicted_info_fn_jts_only group.add_argument("--predicted_info_fn_jts_only", default="", type=str, help="If empty, will use defaults according to the specified dataset.") # select_part_idx group.add_argument("--select_part_idx", default=-1, type=int, help="Joint positions loss.") # not_canon_rep group.add_argument("--not_canon_rep", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # scale_obj group.add_argument("--scale_obj", default=1, type=int, help="Joint positions loss.") group.add_argument("--resplit", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # use_arctic group.add_argument("--use_arctic", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") group.add_argument("--use_left", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") # use_pose_pred group.add_argument("--use_pose_pred", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") group.add_argument("--use_hho", default=False, action='store_true', help="Use cuda device, otherwise use CPU.") def add_diffusion_options(parser): group = parser.add_argument_group('diffusion') group.add_argument("--noise_schedule", default='cosine', choices=['linear', 'cosine'], type=str, help="Noise schedule type") group.add_argument("--diffusion_steps", default=1000, type=int, help="Number of diffusion steps (denoted T in the paper)") group.add_argument("--sigma_small", default=True, type=bool, help="Use smaller sigma values.") def add_model_options(parser): group = parser.add_argument_group('model') group.add_argument("--arch", default='trans_enc', choices=['trans_enc', 'trans_dec', 'gru'], type=str, help="Architecture types as reported in the paper.") group.add_argument("--emb_trans_dec", default=False, type=bool, help="For trans_dec architecture only, if true, will inject condition as a class token" " (in addition to cross-attention).") group.add_argument("--layers", default=8, type=int, help="Number of layers.") group.add_argument("--latent_dim", default=512, type=int, help="Transformer/GRU width.") group.add_argument("--cond_mask_prob", default=.1, type=float, help="The probability of masking the condition during training." " For classifier-free guidance learning.") group.add_argument("--lambda_rcxyz", default=0.0, type=float, help="Joint positions loss.") group.add_argument("--lambda_vel", default=0.0, type=float, help="Joint velocity loss.") group.add_argument("--lambda_fc", default=0.0, type=float, help="Foot contact loss.") group.add_argument("--unconstrained", action='store_true', help="Model is trained unconditionally. That is, it is constrained by neither text nor action. " "Currently tested on HumanAct12 only.") def add_data_options(parser): group = parser.add_argument_group('dataset') group.add_argument("--dataset", default='humanml', choices=['humanml', 'kit', 'humanact12', 'uestc', 'motion_ours'], type=str, help="Dataset name (choose from list).") group.add_argument("--data_dir", default="", type=str, help="If empty, will use defaults according to the specified dataset.") group.add_argument("--single_seq_path", default="", type=str, help="If empty, will use defaults according to the specified dataset.") # window_size group.add_argument("--window_size", default=30, type=int, help="Number of learning rate anneal steps.") # inst_normalization; inst_normalization group.add_argument("--inst_normalization", action='store_true', default=False, help="If True, will enable to use an already existing save_dir.") def add_training_options(parser): group = parser.add_argument_group('training') group.add_argument("--model_path", default="", type=str, ## model_path --> model_path help="Path to model####.pt file to be sampled.") group.add_argument("--input_text", default='', type=str, help="Path to a text file lists text prompts to be synthesized. If empty, will take text prompts from dataset.") group.add_argument("--save_dir", required=True, type=str, help="Path to save checkpoints and results.") group.add_argument("--overwrite", action='store_true', help="If True, will enable to use an already existing save_dir.") group.add_argument("--train_platform_type", default='NoPlatform', choices=['NoPlatform', 'ClearmlPlatform', 'TensorboardPlatform'], type=str, help="Choose platform to log results. NoPlatform means no logging.") group.add_argument("--lr", default=1e-4, type=float, help="Learning rate.") group.add_argument("--weight_decay", default=0.0, type=float, help="Optimizer weight decay.") group.add_argument("--lr_anneal_steps", default=0, type=int, help="Number of learning rate anneal steps.") group.add_argument("--eval_batch_size", default=32, type=int, help="Batch size during evaluation loop. Do not change this unless you know what you are doing. " "T2m precision calculation is based on fixed batch size 32.") group.add_argument("--eval_split", default='test', choices=['val', 'test'], type=str, help="Which split to evaluate on during training.") group.add_argument("--eval_during_training", action='store_true', help="If True, will run evaluation during training.") group.add_argument("--eval_rep_times", default=3, type=int, help="Number of repetitions for evaluation loop during training.") group.add_argument("--eval_num_samples", default=1_000, type=int, help="If -1, will use all samples in the specified split.") group.add_argument("--log_interval", default=1_000, type=int, help="Log losses each N steps") group.add_argument("--save_interval", default=50_000, type=int, help="Save checkpoints and run evaluation each N steps") group.add_argument("--num_steps", default=600_000000, type=int, help="Training will stop after the specified number of steps.") group.add_argument("--num_frames", default=60, type=int, help="Limit for the maximal number of frames. In HumanML3D and KIT this field is ignored.") group.add_argument("--resume_checkpoint", default="", type=str, help="If not empty, will start from the specified checkpoint (path to model###.pt file).") # group.add_argument("--debug", action='store_true', # help="If True, will run evaluation during training.") ## with_dec_pos_emb def add_sampling_options(parser): group = parser.add_argument_group('sampling') group.add_argument("--model_path", required=True, type=str, help="Path to model####.pt file to be sampled.") group.add_argument("--output_dir", default='', type=str, help="Path to results dir (auto created by the script). " "If empty, will create dir in parallel to checkpoint.") group.add_argument("--num_samples", default=10, type=int, help="Maximal number of prompts to sample, " "if loading dataset from file, this field will be ignored.") group.add_argument("--num_repetitions", default=3, type=int, help="Number of repetitions, per sample (text prompt/action)") group.add_argument("--guidance_param", default=2.5, type=float, help="For classifier-free sampling - specifies the s parameter, as defined in the paper.") def add_generate_options(parser): group = parser.add_argument_group('generate') group.add_argument("--motion_length", default=6.0, type=float, help="The length of the sampled motion [in seconds]. " "Maximum is 9.8 for HumanML3D (text-to-motion), and 2.0 for HumanAct12 (action-to-motion)") group.add_argument("--input_text", default='', type=str, help="Path to a text file lists text prompts to be synthesized. If empty, will take text prompts from dataset.") group.add_argument("--action_file", default='', type=str, help="Path to a text file that lists names of actions to be synthesized. Names must be a subset of dataset/uestc/info/action_classes.txt if sampling from uestc, " "or a subset of [warm_up,walk,run,jump,drink,lift_dumbbell,sit,eat,turn steering wheel,phone,boxing,throw] if sampling from humanact12. " "If no file is specified, will take action names from dataset.") group.add_argument("--text_prompt", default='', type=str, help="A text prompt to be generated. If empty, will take text prompts from dataset.") group.add_argument("--action_name", default='', type=str, help="An action name to be generated. If empty, will take text prompts from dataset.") # group.add_argument("--debug", action='store_true', # help="If True, will run evaluation during training.") def add_edit_options(parser): group = parser.add_argument_group('edit') group.add_argument("--edit_mode", default='in_between', choices=['in_between', 'upper_body'], type=str, help="Defines which parts of the input motion will be edited.\n" "(1) in_between - suffix and prefix motion taken from input motion, " "middle motion is generated.\n" "(2) upper_body - lower body joints taken from input motion, " "upper body is generated.") group.add_argument("--text_condition", default='', type=str, help="Editing will be conditioned on this text prompt. " "If empty, will perform unconditioned editing.") group.add_argument("--prefix_end", default=0.25, type=float, help="For in_between editing - Defines the end of input prefix (ratio from all frames).") group.add_argument("--suffix_start", default=0.75, type=float, help="For in_between editing - Defines the start of input suffix (ratio from all frames).") def add_evaluation_options(parser): group = parser.add_argument_group('eval') group.add_argument("--model_path", required=True, type=str, help="Path to model####.pt file to be sampled.") group.add_argument("--eval_mode", default='wo_mm', choices=['wo_mm', 'mm_short', 'debug', 'full'], type=str, help="wo_mm (t2m only) - 20 repetitions without multi-modality metric; " "mm_short (t2m only) - 5 repetitions with multi-modality metric; " "debug - short run, less accurate results." "full (a2m only) - 20 repetitions.") group.add_argument("--guidance_param", default=2.5, type=float, help="For classifier-free sampling - specifies the s parameter, as defined in the paper.") def get_cond_mode(args): # unconstrained, constrained, text conditional # if args.unconstrained: cond_mode = 'no_cond' # elif args.dataset in ['kit', 'humanml', 'motion_ours']: # cond_mode = 'text' elif args.dataset in ['kit', 'humanml', 'motion_ours']: cond_mode = 'text' else: cond_mode = 'action' print(f"dataset: {args.dataset}, cond_mode: {cond_mode}") return cond_mode def train_args(): #### === useufl arguments for training === #### parser = ArgumentParser() add_base_options(parser) add_data_options(parser) add_model_options(parser) add_diffusion_options(parser) add_training_options(parser) return parser.parse_args() def generate_args(): parser = ArgumentParser() # args specified by the user: (all other will be loaded from the model) add_base_options(parser) add_sampling_options(parser) add_generate_options(parser) args = parse_and_load_from_model(parser) cond_mode = get_cond_mode(args) print(f"cond_mode: {cond_mode}") if (args.input_text or args.text_prompt) and cond_mode != 'text': raise Exception('Arguments input_text and text_prompt should not be used for an action condition. Please use action_file or action_name.') elif args.action_file or args.action_name and cond_mode != 'action': raise Exception('Arguments action_file and action_name should not be used for a text condition. Please use input_text or text_prompt.') return args def edit_args(): parser = ArgumentParser() # args specified by the user: (all other will be loaded from the model) add_base_options(parser) add_sampling_options(parser) add_edit_options(parser) return parse_and_load_from_model(parser) def evaluation_parser(): parser = ArgumentParser() # args specified by the user: (all other will be loaded from the model) add_base_options(parser) add_evaluation_options(parser) return parse_and_load_from_model(parser)