How to use metavar method in avocado

Best Python code snippet using avocado_python

image_param_list.py

Source:image_param_list.py

1import argparse2import random3import torch4epochs=605def params_np():6    7    parser_np = argparse.ArgumentParser(description='np')8    parser_np.add_argument('--no-cuda', action='store_true', default=False,9                           help='disables CUDA training')10    parser_np.add_argument('--seed', type=int, default=1, metavar='S',11                           help='random seed (default: 1)')12    parser_np.add_argument('--log-interval', type=int, default=1, metavar='N',13                           help='how many batches to wait before logging training status')14    parser_np.add_argument('--batch_size', type=int, default=8, metavar='N',15                           help='input batch size for training')16    parser_np.add_argument('--epochs', type=int, default=epochs, metavar='N',17                           help='number of epochs to train')18    19    parser_np.add_argument('--dim_x', type=int, default=2, metavar='N',20                           help='dimension of input')21    parser_np.add_argument('--dim_y', type=int, default=3, metavar='N',22                           help='dimension of output')23    24    parser_np.add_argument('--dim_h_lat', type=int, default=128, metavar='N',25                           help='dim of hidden units for encoders')26    parser_np.add_argument('--num_h_lat', type=int, default=3, metavar='N',27                           help='num of layers for encoders')28    parser_np.add_argument('--dim_lat', type=int, default=128, metavar='N',29                           help='dimension of z, the global latent variable') 30    31    parser_np.add_argument('--dim_h', type=int, default=128, metavar='N',32                           help='dim of hidden units for decoders')   33    parser_np.add_argument('--num_h', type=int, default=5, metavar='N') 34    parser_np.add_argument('--act_type', type=str, default='ReLU', metavar='N')   35    parser_np.add_argument('--amort_y', type=bool, default=False, metavar='N')36    37    args = parser_np.parse_args()38    args.cuda = not args.no_cuda and torch.cuda.is_available()39    torch.manual_seed(args.seed)40    random.seed(args.seed)41    device = torch.device("cuda" if args.cuda else "cpu")42    return args,random,device43def params_attn_np():44    45    parser_attn_np = argparse.ArgumentParser(description='attn_np')46    parser_attn_np.add_argument('--no-cuda', action='store_true', default=False,47                                help='disables CUDA training')48    parser_attn_np.add_argument('--seed', type=int, default=1, metavar='S',49                                help='random seed (default: 1)')50    parser_attn_np.add_argument('--log-interval', type=int, default=1, metavar='N',51                                help='how many batches to wait before logging training status')52    parser_attn_np.add_argument('--batch_size', type=int, default=8, metavar='N',53                                help='input batch size for training')54    parser_attn_np.add_argument('--epochs', type=int, default=epochs, metavar='N',55                                help='number of epochs to train')56    57    parser_attn_np.add_argument('--dim_x', type=int, default=2, metavar='N',58                                help='dimension of input')59    parser_attn_np.add_argument('--dim_y', type=int, default=3, metavar='N',60                                help='dimension of output')61    62    parser_attn_np.add_argument('--dim_h_lat', type=int, default=32, metavar='N',63                                help='dim of hidden units for encoders')64    parser_attn_np.add_argument('--num_h_lat', type=int, default=2, metavar='N',65                                help='num of layers for encoders')66    parser_attn_np.add_argument('--dim_lat', type=int, default=32, metavar='N',67                                help='dimension of z, the global latent variable')68    parser_attn_np.add_argument('--num_head', type=int, default=2, metavar='N',69                                help='num of heads for attention networks') 70    parser_attn_np.add_argument('--dim_emb_x', type=int, default=32, metavar='N',71                                help='num of heads for attention networks')    72    73    parser_attn_np.add_argument('--dim_h', type=int, default=128, metavar='N')   74    parser_attn_np.add_argument('--num_h', type=int, default=5, metavar='N') 75    parser_attn_np.add_argument('--act_type', type=str, default='ReLU', metavar='N')   76    parser_attn_np.add_argument('--amort_y', type=bool, default=False, metavar='N')77    78    args = parser_attn_np.parse_args()79    args.cuda = not args.no_cuda and torch.cuda.is_available()80    torch.manual_seed(args.seed)81    device = torch.device("cuda" if args.cuda else "cpu")82    return args,random,device83def params_moe_np():84    85    parser_moe_np = argparse.ArgumentParser(description='moe_nps')86    parser_moe_np.add_argument('--no-cuda', action='store_true', default=False,87                               help='disables CUDA training')88    parser_moe_np.add_argument('--seed', type=int, default=1, metavar='S',89                               help='random seed (default: 1)')90    parser_moe_np.add_argument('--log-interval', type=int, default=1, metavar='N',91                               help='how many batches to wait before logging training status')92    parser_moe_np.add_argument('--batch_size', type=int, default=32, metavar='N',93                               help='input batch size for training')94    parser_moe_np.add_argument('--epochs', type=int, default=epochs, metavar='N',95                               help='number of epochs to train') 96    parser_moe_np.add_argument('--dim_x', type=int, default=2, metavar='N',97                               help='dimension of input')98    parser_moe_np.add_argument('--dim_y', type=int, default=3, metavar='N',99                               help='dimension of output')100    101    parser_moe_np.add_argument('--dim_h_lat', type=int, default=128, metavar='N',102                               help='dim of hidden units for encoders')103    parser_moe_np.add_argument('--num_h_lat', type=int, default=3, metavar='N',104                               help='num of layers for encoders')105    parser_moe_np.add_argument('--dim_lat', type=int, default=128, metavar='N',106                               help='dimension of z, the global latent variable')107    108    parser_moe_np.add_argument('--num_lat', type=int, default=2, metavar='N',109                               help='num of latent variables')110    parser_moe_np.add_argument('--experts_in_gates', type=bool, default=False, metavar='N',111                               help='whether experts as input for encoders')112    parser_moe_np.add_argument('--dim_logit_h', type=int, default=32, metavar='N',113                               help='dim of hidden units for encoders') 114    parser_moe_np.add_argument('--num_logit_layers', type=int, default=2, metavar='N',115                               help='dim of hidden units for encoders')    116    parser_moe_np.add_argument('--temperature', type=int, default=0.1, metavar='N') 117    parser_moe_np.add_argument('--hard', type=bool, default=False, metavar='N')118    parser_moe_np.add_argument('--gumbel_max', type=bool, default=True, metavar='N')119    parser_moe_np.add_argument('--info_bottleneck', type=bool, default=False, metavar='N')    120    121    parser_moe_np.add_argument('--dim_h', type=int, default=128, metavar='N',122                               help='dim of hidden units for decoders')   123    parser_moe_np.add_argument('--num_h', type=int, default=5, metavar='N',124                               help='num of layers for decoders') 125    parser_moe_np.add_argument('--act_type', type=str, default='LeakyReLU', metavar='N',126                               help='type of activation units')   127    parser_moe_np.add_argument('--amort_y', type=bool, default=False, metavar='N')128    129    args = parser_moe_np.parse_args()130    args.cuda = not args.no_cuda and torch.cuda.is_available()131    torch.manual_seed(args.seed)132    random.seed(args.seed)133    device = torch.device("cuda" if args.cuda else "cpu")134    return args,random,device135def params_moe_condnp():136    137    parser_moe_condnp = argparse.ArgumentParser(description='moe_condnps')138    parser_moe_condnp.add_argument('--no-cuda', action='store_true', default=False,139                               help='disables CUDA training')140    parser_moe_condnp.add_argument('--seed', type=int, default=1, metavar='S',141                               help='random seed (default: 1)')142    parser_moe_condnp.add_argument('--log-interval', type=int, default=1, metavar='N',143                               help='how many batches to wait before logging training status')144    parser_moe_condnp.add_argument('--batch_size', type=int, default=8, metavar='N',145                               help='input batch size for training')146    parser_moe_condnp.add_argument('--epochs', type=int, default=epochs, metavar='N',147                               help='number of epochs to train')148    parser_moe_condnp.add_argument('--dim_x', type=int, default=2, metavar='N',149                               help='dimension of input')150    parser_moe_condnp.add_argument('--dim_y', type=int, default=3, metavar='N',151                               help='dimension of output')152    153    parser_moe_condnp.add_argument('--dim_h_lat', type=int, default=128, metavar='N',154                               help='dim of hidden units for encoders')155    parser_moe_condnp.add_argument('--num_h_lat', type=int, default=3, metavar='N',156                               help='num of layers for encoders')157    parser_moe_condnp.add_argument('--dim_lat', type=int, default=128, metavar='N',158                               help='dimension of z, the global latent variable')159    160    parser_moe_condnp.add_argument('--num_lat', type=int, default=2, metavar='N',161                               help='num of latent variables')162    parser_moe_condnp.add_argument('--experts_in_gates', type=bool, default=False, metavar='N',163                               help='whether experts as input for encoders')164    parser_moe_condnp.add_argument('--dim_logit_h', type=int, default=32, metavar='N',165                               help='dim of hidden units for encoders') 166    parser_moe_condnp.add_argument('--num_logit_layers', type=int, default=2, metavar='N',167                               help='dim of hidden units for encoders')    168    parser_moe_condnp.add_argument('--temperature', type=int, default=0.1, metavar='N') 169    parser_moe_condnp.add_argument('--hard', type=bool, default=False, metavar='N')170    parser_moe_condnp.add_argument('--gumbel_max', type=bool, default=True, metavar='N')171    parser_moe_condnp.add_argument('--info_bottleneck', type=bool, default=False, metavar='N')    172    173    parser_moe_condnp.add_argument('--dim_h', type=int, default=128, metavar='N')   174    parser_moe_condnp.add_argument('--num_h', type=int, default=5, metavar='N') 175    parser_moe_condnp.add_argument('--act_type', type=str, default='ReLU', metavar='N')   176    parser_moe_condnp.add_argument('--amort_y', type=bool, default=False, metavar='N')177    178    args = parser_moe_condnp.parse_args()179    args.cuda = not args.no_cuda and torch.cuda.is_available()180    torch.manual_seed(args.seed)181    random.seed(args.seed)182    device = torch.device("cuda" if args.cuda else "cpu")...

arguments.py

Source:arguments.py

1# Copyright (c) 2018-present, Facebook, Inc.2# All rights reserved.3#4# This source code is licensed under the license found in the5# LICENSE file in the root directory of this source tree.6#7import argparse8def parse_args():9    parser = argparse.ArgumentParser(description='Training script')10    # General arguments11    parser.add_argument('-d', '--dataset', default='h36m', type=str, metavar='NAME', help='target dataset') # h36m or humaneva12    parser.add_argument('-k', '--keypoints', default='cpn_ft_h36m_dbb', type=str, metavar='NAME', help='2D detections to use')13    parser.add_argument('-str', '--subjects-train', default='S1,S5,S6,S7,S8', type=str, metavar='LIST',14                        help='training subjects separated by comma')15    parser.add_argument('-ste', '--subjects-test', default='S9,S11', type=str, metavar='LIST', help='test subjects separated by comma')16    parser.add_argument('-sun', '--subjects-unlabeled', default='', type=str, metavar='LIST',17                        help='unlabeled subjects separated by comma for self-supervision')18    parser.add_argument('-a', '--actions', default='*', type=str, metavar='LIST',19                        help='actions to train/test on, separated by comma, or * for all')20    parser.add_argument('-c', '--checkpoint', default='checkpoint', type=str, metavar='PATH',21                        help='checkpoint directory')22    parser.add_argument('--checkpoint-frequency', default=10, type=int, metavar='N',23                        help='create a checkpoint every N epochs')24    parser.add_argument('-r', '--resume', default='', type=str, metavar='FILENAME',25                        help='checkpoint to resume (file name)')26    parser.add_argument('--evaluate', default='', type=str, metavar='FILENAME', help='checkpoint to evaluate (file name)')27    parser.add_argument('--render', action='store_true', help='visualize a particular video')28    parser.add_argument('--by-subject', action='store_true', help='break down error by subject (on evaluation)')29    parser.add_argument('--export-training-curves', action='store_true', help='save training curves as .png images')30    # Model arguments31    parser.add_argument('-s', '--stride', default=1, type=int, metavar='N', help='chunk size to use during training')32    parser.add_argument('-e', '--epochs', default=60, type=int, metavar='N', help='number of training epochs')33    parser.add_argument('-b', '--batch-size', default=1024, type=int, metavar='N', help='batch size in terms of predicted frames')34    parser.add_argument('-drop', '--dropout', default=0.25, type=float, metavar='P', help='dropout probability')35    parser.add_argument('-lr', '--learning-rate', default=0.001, type=float, metavar='LR', help='initial learning rate')36    parser.add_argument('-lrd', '--lr-decay', default=0.95, type=float, metavar='LR', help='learning rate decay per epoch')37    parser.add_argument('-no-da', '--no-data-augmentation', dest='data_augmentation', action='store_false',38                        help='disable train-time flipping')39    parser.add_argument('-no-tta', '--no-test-time-augmentation', dest='test_time_augmentation', action='store_false',40                        help='disable test-time flipping')41    parser.add_argument('-arc', '--architecture', default='3,3,3', type=str, metavar='LAYERS', help='filter widths separated by comma')42    parser.add_argument('--causal', action='store_true', help='use causal convolutions for real-time processing')43    parser.add_argument('-ch', '--channels', default=1024, type=int, metavar='N', help='number of channels in convolution layers')44    # Experimental45    parser.add_argument('--subset', default=1, type=float, metavar='FRACTION', help='reduce dataset size by fraction')46    parser.add_argument('--downsample', default=1, type=int, metavar='FACTOR', help='downsample frame rate by factor (semi-supervised)')47    parser.add_argument('--warmup', default=1, type=int, metavar='N', help='warm-up epochs for semi-supervision')48    parser.add_argument('--no-eval', action='store_true', help='disable epoch evaluation while training (small speed-up)')49    parser.add_argument('--dense', action='store_true', help='use dense convolutions instead of dilated convolutions')50    parser.add_argument('--disable-optimizations', action='store_true', help='disable optimized model for single-frame predictions')51    parser.add_argument('--linear-projection', action='store_true', help='use only linear coefficients for semi-supervised projection')52    parser.add_argument('--no-bone-length', action='store_false', dest='bone_length_term',53                        help='disable bone length term in semi-supervised settings')54    parser.add_argument('--no-proj', action='store_true', help='disable projection for semi-supervised setting')55    56    # Visualization57    parser.add_argument('--viz-subject', type=str, metavar='STR', help='subject to render')58    parser.add_argument('--viz-action', type=str, metavar='STR', help='action to render')59    parser.add_argument('--viz-camera', type=int, default=0, metavar='N', help='camera to render')60    parser.add_argument('--viz-video', type=str, metavar='PATH', help='path to input video')61    parser.add_argument('--viz-skip', type=int, default=0, metavar='N', help='skip first N frames of input video')62    parser.add_argument('--viz-output', type=str, metavar='PATH', help='output file name (.gif or .mp4)')63    parser.add_argument('--viz-export', type=str, metavar='PATH', help='output file name for coordinates')64    parser.add_argument('--viz-bitrate', type=int, default=3000, metavar='N', help='bitrate for mp4 videos')65    parser.add_argument('--viz-no-ground-truth', action='store_true', help='do not show ground-truth poses')66    parser.add_argument('--viz-limit', type=int, default=-1, metavar='N', help='only render first N frames')67    parser.add_argument('--viz-downsample', type=int, default=1, metavar='N', help='downsample FPS by a factor N')68    parser.add_argument('--viz-size', type=int, default=5, metavar='N', help='image size')69    70    parser.set_defaults(bone_length_term=True)71    parser.set_defaults(data_augmentation=True)72    parser.set_defaults(test_time_augmentation=True)73    74    args = parser.parse_args()75    # Check invalid configuration76    if args.resume and args.evaluate:77        print('Invalid flags: --resume and --evaluate cannot be set at the same time')78        exit()79        80    if args.export_training_curves and args.no_eval:81        print('Invalid flags: --export-training-curves and --no-eval cannot be set at the same time')82        exit()...

generate_parser.py

Source:generate_parser.py

1import argparse2def get_args():3    parser = argparse.ArgumentParser(description='PyTorch Soft Actor-Critic Args')4    parser.add_argument('--env_name', default="HalfCheetah-v2",5                        help='Mujoco Gym environment (default: HalfCheetah-v2)')6    parser.add_argument('--policy', default="Gaussian",7                        help='Policy Type: Gaussian | Deterministic (default: Gaussian)')8    parser.add_argument('--eval', type=bool, default=True,9                        help='Evaluates a policy a policy every 10 episode (default: True)')10    parser.add_argument('--gamma', type=float, default=0.99, metavar='G',11                        help='discount factor for reward (default: 0.99)')12    parser.add_argument('--tau', type=float, default=0.005, metavar='G',13                        help='target smoothing coefficient(Ï) (default: 0.005)')14    parser.add_argument('--lr', type=float, default=0.0003, metavar='G',15                        help='learning rate (default: 0.0003)')16    parser.add_argument('--alpha', type=float, default=0.2, metavar='G',17                        help='Temperature parameter Î± determines the relative importance of the entropy\18                                term against the reward (default: 0.2)')19    parser.add_argument('--automatic_entropy_tuning', type=int, default=0, metavar='G',20                        help='Automaically adjust Î± (default: False)')21    parser.add_argument('--seed', type=int, default=123456, metavar='N',22                        help='random seed (default: 123456)')23    parser.add_argument('--batch_size', type=int, default=256, metavar='N',24                        help='batch size (default: 256)')25    parser.add_argument('--num_steps', type=int, default=1000001, metavar='N',26                        help='maximum number of steps (default: 1000000)')27    parser.add_argument('--hidden_size', type=int, default=256, metavar='N',28                        help='hidden size (default: 256)')29    parser.add_argument('--updates_per_step', type=int, default=1, metavar='N',30                        help='model updates per simulator step (default: 1)')31    parser.add_argument('--start_steps', type=int, default=10000, metavar='N',32                        help='Steps sampling random actions (default: 10000)')33    parser.add_argument('--target_update_interval', type=int, default=1, metavar='N',34                        help='Value target update per no. of updates per step (default: 1)')35    parser.add_argument('--replay_size', type=int, default=100000, metavar='N', help='size of replay buffer (default: 10000000)')36    parser.add_argument('--cuda', type=int, default=1, metavar='N', help='run on CUDA (default: False)')37    #38    parser.add_argument('--replay_filter_coeff', type=float, default=0.5, metavar='N', help='0. to 1.')39    parser.add_argument('--iter_filter_coeff', type=float, default=0.5, metavar='N', help='0. to inf')40    parser.add_argument('--policy_sampler_type', type=str, default='uniform', metavar='N', help='stratified,uniform,filtered')41    parser.add_argument('--algo', type=str, default='BC', metavar='N', help='stratified,uniform,filtered')42    parser.add_argument('--num_particles', type=int, default=256, metavar='N', help='stratified,uniform,filtered')43    parser.add_argument('--num_clusters', type=int, default=256, metavar='N', help='stratified,uniform,filtered')44    # optimizers stuff45    parser.add_argument('--expert_params_path', type=str, default='models/', metavar='N')46    parser.add_argument('--policy_optim', type=str, default='Adam', metavar='N')47    parser.add_argument('--critic_optim', type=str, default='Adam', metavar='N')48    # sls args49    parser.add_argument('--sls_beta_b', type=float, default=0.9, metavar='N')50    parser.add_argument('--sls_c', type=float, default=0.5, metavar='N')51    parser.add_argument('--sls_gamma', type=float, default=2.0, metavar='N')52    parser.add_argument('--sls_beta_f', type=float, default=2.0, metavar='N')53    parser.add_argument('--log_init_step_size', type=float, default=4., metavar='N')54    parser.add_argument('--sls_eta_max', type=float, default=100., metavar='N')55    # project56    parser.add_argument('--project', type=str, default='optimizers-in-bc', metavar='N')57    parser.add_argument('--group', type=str, default='static-args', metavar='N')58    parser.add_argument('--data_file_location', type=str, default='./data/examples/', metavar='N')59    # BC stuff60    parser.add_argument('--behavior_type', type=str, default='static-args', metavar='N')61    parser.add_argument('--expert_mode', type=int, default=0, metavar='N')62    parser.add_argument('--model_type', type=str, default='nn', metavar='N')63    parser.add_argument('--bandwidth', type=float, default=0., metavar='N')64    # better grid-search65    parser.add_argument('--log_lr', type=float, default=-3., metavar='N')66    parser.add_argument('--log_eps', type=float, default=-5., metavar='N')67    parser.add_argument('--use_log_lr', type=int, default=1, metavar='N')68    parser.add_argument('--transform_dist', type=int, default=1, metavar='N')69    parser.add_argument('--nonlin', type=str, default='relu', metavar='N')70    parser.add_argument('--clamp', type=int, default=0, metavar='N')71    # sps stuff72    parser.add_argument('--use_torch_dataloader', type=int, default=1, metavar='N')73    parser.add_argument('--sps_eps', type=int, default=0, metavar='N')74    parser.add_argument('--batch_in_step', type=int, default=0, metavar='N')75    # world models stuff76    parser.add_argument('--train_world_model', type=int, default=0, metavar='N')77    parser.add_argument('--mpc_algo', type=str, default='WM', metavar='N')78    parser.add_argument('--dyna_optim', type=str, default='Adam', metavar='N')79    parser.add_argument('--log_gan_scale', type=float, default=0., metavar='N')80    parser.add_argument('--marginal_steps', type=int, default=15, metavar='N')81    parser.add_argument('--policy_updates', type=int, default=1, metavar='N')82    parser.add_argument('--dyna_model_type', type=str, default='nn', metavar='N')83    parser.add_argument('--dyna_bandwidth', type=float, default=0., metavar='N')84    # parse85    args, _ = parser.parse_known_args()...

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