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How to use _c method in Molotov

Best Python code snippet using molotov_python

default.py

Source:default.py

1#!/usr/bin/env python32# Copyright (c) Facebook, Inc. and its affiliates.3# This source code is licensed under the MIT license found in the4# LICENSE file in the root directory of this source tree.5from typing import List, Optional, Union6import numpy as np7from habitat import get_config as get_task_config8from habitat.config import Config as CN9DEFAULT_CONFIG_DIR = "configs/"10CONFIG_FILE_SEPARATOR = ","11# -----------------------------------------------------------------------------12# EXPERIMENT CONFIG13# -----------------------------------------------------------------------------14_C = CN()15_C.BASE_TASK_CONFIG_PATH = "configs/tasks/pointnav.yaml"16_C.TASK_CONFIG = CN()  # task_config will be stored as a config node17_C.CMD_TRAILING_OPTS = []  # store command line options as list of strings18_C.TRAINER_NAME = "ppo"19_C.ENV_NAME = "NavRLEnv"20_C.SIMULATOR_GPU_ID = 021_C.TORCH_GPU_ID = 022_C.VIDEO_OPTION = ["disk", "tensorboard"]23_C.TENSORBOARD_DIR = "tb"24_C.VIDEO_DIR = "video_dir"25_C.TEST_EPISODE_COUNT = 226_C.EVAL_CKPT_PATH_DIR = "data/checkpoints"  # path to ckpt or path to ckpts dir27_C.NUM_PROCESSES = 1628_C.SENSORS = ["RGB_SENSOR", "DEPTH_SENSOR"]29_C.CHECKPOINT_FOLDER = "data/checkpoints"30_C.NUM_UPDATES = 1000031_C.LOG_INTERVAL = 1032_C.LOG_FILE = "train.log"33_C.CHECKPOINT_INTERVAL = 5034_C.VIS_INTERVAL = 20035# -----------------------------------------------------------------------------36# EVAL CONFIG37# -----------------------------------------------------------------------------38_C.EVAL = CN()39# The split to evaluate on40_C.EVAL.SPLIT = "val"41_C.EVAL.USE_CKPT_CONFIG = True42# -----------------------------------------------------------------------------43# REINFORCEMENT LEARNING (RL) ENVIRONMENT CONFIG44# -----------------------------------------------------------------------------45_C.RL = CN()46_C.RL.REWARD_MEASURE = "distance_to_goal"47_C.RL.SUCCESS_MEASURE = "spl"48_C.RL.SUCCESS_REWARD = 10.049_C.RL.SLACK_REWARD = -0.0150# -----------------------------------------------------------------------------51# PROXIMAL POLICY OPTIMIZATION (PPO)52# -----------------------------------------------------------------------------53_C.RL.PPO = CN()54_C.RL.PPO.clip_param = 0.255_C.RL.PPO.ppo_epoch = 456_C.RL.PPO.num_mini_batch = 1657_C.RL.PPO.value_loss_coef = 0.558_C.RL.PPO.entropy_coef = 0.0159_C.RL.PPO.lr = 7e-460_C.RL.PPO.eps = 1e-561_C.RL.PPO.max_grad_norm = 0.562_C.RL.PPO.num_steps = 563_C.RL.PPO.use_gae = True64_C.RL.PPO.use_linear_lr_decay = False65_C.RL.PPO.use_linear_clip_decay = False66_C.RL.PPO.gamma = 0.9967_C.RL.PPO.tau = 0.9568_C.RL.PPO.reward_window_size = 5069_C.RL.PPO.use_normalized_advantage = True70_C.RL.PPO.hidden_size = 51271# -----------------------------------------------------------------------------72# DECENTRALIZED DISTRIBUTED PROXIMAL POLICY OPTIMIZATION (DD-PPO)73# -----------------------------------------------------------------------------74_C.RL.DDPPO = CN()75_C.RL.DDPPO.sync_frac = 0.676_C.RL.DDPPO.distrib_backend = "GLOO"77_C.RL.DDPPO.rnn_type = "LSTM"78_C.RL.DDPPO.num_recurrent_layers = 279_C.RL.DDPPO.backbone = "resnet50"80_C.RL.DDPPO.pretrained_weights = "data/ddppo-models/gibson-2plus-resnet50.pth"81# Loads pretrained weights82_C.RL.DDPPO.pretrained = False83# Loads just the visual encoder backbone weights84_C.RL.DDPPO.pretrained_encoder = False85# Whether or not the visual encoder backbone will be trained86_C.RL.DDPPO.train_encoder = True87# Whether or not to reset the critic linear layer88_C.RL.DDPPO.reset_critic = True89# -----------------------------------------------------------------------------90# ORBSLAM2 BASELINE91# -----------------------------------------------------------------------------92_C.ORBSLAM2 = CN()93_C.ORBSLAM2.SLAM_VOCAB_PATH = "habitat_baselines/slambased/data/ORBvoc.txt"94_C.ORBSLAM2.SLAM_SETTINGS_PATH = (95    "habitat_baselines/slambased/data/mp3d3_small1k.yaml"96)97_C.ORBSLAM2.MAP_CELL_SIZE = 0.198_C.ORBSLAM2.MAP_SIZE = 4099_C.ORBSLAM2.CAMERA_HEIGHT = get_task_config().SIMULATOR.DEPTH_SENSOR.POSITION[100    1101]102_C.ORBSLAM2.BETA = 100103_C.ORBSLAM2.H_OBSTACLE_MIN = 0.3 * _C.ORBSLAM2.CAMERA_HEIGHT104_C.ORBSLAM2.H_OBSTACLE_MAX = 1.0 * _C.ORBSLAM2.CAMERA_HEIGHT105_C.ORBSLAM2.D_OBSTACLE_MIN = 0.1106_C.ORBSLAM2.D_OBSTACLE_MAX = 4.0107_C.ORBSLAM2.PREPROCESS_MAP = True108_C.ORBSLAM2.MIN_PTS_IN_OBSTACLE = (109    get_task_config().SIMULATOR.DEPTH_SENSOR.WIDTH / 2.0110)111_C.ORBSLAM2.ANGLE_TH = float(np.deg2rad(15))112_C.ORBSLAM2.DIST_REACHED_TH = 0.15113_C.ORBSLAM2.NEXT_WAYPOINT_TH = 0.5114_C.ORBSLAM2.NUM_ACTIONS = 3115_C.ORBSLAM2.DIST_TO_STOP = 0.05116_C.ORBSLAM2.PLANNER_MAX_STEPS = 500117_C.ORBSLAM2.DEPTH_DENORM = get_task_config().SIMULATOR.DEPTH_SENSOR.MAX_DEPTH118_C.attention = CN()119_C.attention.n_head = 4120_C.attention.d_model = 512 + 32 + 1121_C.attention.d_k = 512 + 32 + 1122_C.attention.d_v = 512 + 32 + 1123_C.attention.dropout = 0.1124_C.attention.lsh = CN()125_C.attention.lsh.bucket_size = 10126_C.attention.lsh.n_hashes = 4127_C.attention.lsh.add_local_attn_hash = False128_C.attention.lsh.causal = True129_C.attention.lsh.attn_chunks = 8130_C.attention.lsh.random_rotations_per_head = False131_C.attention.lsh.attend_across_buckets = True132_C.attention.lsh.allow_duplicate_attention = True133_C.attention.lsh.num_mem_kv = 0134_C.attention.lsh.one_value_head = False135_C.attention.lsh.full_attn_thres = 'none'136_C.attention.lsh.return_attn = False137_C.attention.lsh.post_attn_dropout = 0.1138_C.attention.lsh.dropout = 0.1139_C.attention.lsh.use_full_attn = False140_C.memory = CN()141_C.memory.embedding_size = 512 + 32142_C.memory.memory_size = 100143_C.memory.pose_dim = 5144def get_config(145    config_paths: Optional[Union[List[str], str]] = None,146    opts: Optional[list] = None,147) -> CN:148    r"""Create a unified config with default values overwritten by values from149    `config_paths` and overwritten by options from `opts`.150    Args:151        config_paths: List of config paths or string that contains comma152        separated list of config paths.153        opts: Config options (keys, values) in a list (e.g., passed from154        command line into the config. For example, `opts = ['FOO.BAR',155        0.5]`. Argument can be used for parameter sweeping or quick tests.156    """157    config = _C.clone()158    if config_paths:159        if isinstance(config_paths, str):160            if CONFIG_FILE_SEPARATOR in config_paths:161                config_paths = config_paths.split(CONFIG_FILE_SEPARATOR)162            else:163                config_paths = [config_paths]164        for config_path in config_paths:165            config.merge_from_file(config_path)166    config.TASK_CONFIG = get_task_config(config.BASE_TASK_CONFIG_PATH)167    if opts:168        config.CMD_TRAILING_OPTS = opts169        config.merge_from_list(opts)170    config.freeze()...

defaults.py

Source:defaults.py

1from yacs.config import CfgNode as CN2_C = CN()3_C.MODEL = CN()4_C.MODEL.META_ARCHITECTURE = 'SSDDetector'5_C.MODEL.DEVICE = "cuda"6# match default boxes to any ground truth with jaccard overlap higher than a threshold (0.5)7_C.MODEL.THRESHOLD = 0.58_C.MODEL.NUM_CLASSES = 219# Hard negative mining10_C.MODEL.NEG_POS_RATIO = 3 # negative : positive = 3:111_C.MODEL.CENTER_VARIANCE = 0.112_C.MODEL.SIZE_VARIANCE = 0.213# ---------------------------------------------------------------------------- #14# Backbone15# ---------------------------------------------------------------------------- #16_C.MODEL.BACKBONE = CN()17_C.MODEL.BACKBONE.NAME = 'vgg'18_C.MODEL.BACKBONE.OUT_CHANNELS = (512, 1024, 512, 256, 256, 256)19_C.MODEL.BACKBONE.PRETRAINED = True20_C.MODEL.BACKBONE.ISFREEZE = False21_C.MODEL.BACKBONE.RFBTYPE = 'NONE' # NONE or Advanced or Basic or Original22# for hrnet23_C.MODEL.BACKBONE.C = 3224_C.MODEL.BACKBONE.BN_MOMENTUM = 0.125# ---------------------------------------------------------------------------- #26# Neck27# ---------------------------------------------------------------------------- #28_C.MODEL.NECK = CN()29_C.MODEL.NECK.NAME = 'NONE'30# -----------------------------------------------------------------------------31# PRIORS32# -----------------------------------------------------------------------------33_C.MODEL.PRIORS = CN()34_C.MODEL.PRIORS.FEATURE_MAPS = [38, 19, 10, 5, 3, 1] # éè¦ä¾æ®ç½ç»çç¹å¾å¾å¤§å°æ¥è¿è¡è°è35_C.MODEL.PRIORS.STRIDES = [8, 16, 32, 64, 100, 300]36# MIN_SIZES å MAX_SIZES æ¯ç± ratio çåæ°è®¡ç®åºæ¥ç, ä½æ¯ MIN_SIZES å MAX æ¯ä¾æ®æ°æ®éçå°ºåº¦åå¸æ¥è®¡ç®ç, å³å®ä¸åç¹å¾å¾ä¸prior boxçè¾¹é¿37_C.MODEL.PRIORS.MIN_SIZES = [30, 60, 111, 162, 213, 264] # these parameters are for voc0712 dataset 38_C.MODEL.PRIORS.MAX_SIZES = [60, 111, 162, 213, 264, 315]39_C.MODEL.PRIORS.ASPECT_RATIOS = [[2], [2, 3], [2, 3], [2, 3], [2], [2]]40# When has 1 aspect ratio, every location has 4 boxes, 2 ratio 6 boxes.41# #boxes = 2 + #ratio * 242_C.MODEL.PRIORS.BOXES_PER_LOCATION = [4, 6, 6, 6, 4, 4]  # number of boxes per feature map location43_C.MODEL.PRIORS.CLIP = True44# -----------------------------------------------------------------------------45# Box Head46# -----------------------------------------------------------------------------47_C.MODEL.BOX_HEAD = CN()48_C.MODEL.BOX_HEAD.NAME = 'SSDBoxHead'49_C.MODEL.BOX_HEAD.PREDICTOR = 'SSDBoxPredictor'50# -----------------------------------------------------------------------------51# INPUT52# -----------------------------------------------------------------------------53_C.INPUT = CN()54# Image size55_C.INPUT.IMAGE_SIZE = 30056# Values to be used for image normalization, RGB layout57_C.INPUT.PIXEL_MEAN = [123, 117, 104]58# -----------------------------------------------------------------------------59# Dataset60# -----------------------------------------------------------------------------61_C.DATASETS = CN()62# List of the dataset names for training, as present in paths_catalog.py63_C.DATASETS.TRAIN = ()64# List of the dataset names for testing, as present in paths_catalog.py65_C.DATASETS.TEST = ()66# -----------------------------------------------------------------------------67# DataLoader68# -----------------------------------------------------------------------------69_C.DATA_LOADER = CN()70# Number of data loading threads71_C.DATA_LOADER.NUM_WORKERS = 872_C.DATA_LOADER.PIN_MEMORY = True73# ---------------------------------------------------------------------------- #74# Solver75# ---------------------------------------------------------------------------- #76_C.SOLVER = CN()77# train configs78_C.SOLVER.MAX_ITER = 12000079_C.SOLVER.LR_STEPS = [80000, 100000]80_C.SOLVER.GAMMA = 0.181_C.SOLVER.BATCH_SIZE = 3282_C.SOLVER.LR = 1e-3 # original 1e-383_C.SOLVER.MOMENTUM = 0.984_C.SOLVER.WEIGHT_DECAY = 5e-485_C.SOLVER.WARMUP_FACTOR = 1.0 / 3 # original 1.0 / 386_C.SOLVER.WARMUP_ITERS = 500 # original 50087# ---------------------------------------------------------------------------- #88# Specific test options89# ---------------------------------------------------------------------------- #90_C.TEST = CN()91_C.TEST.NMS_THRESHOLD = 0.4592_C.TEST.CONFIDENCE_THRESHOLD = 0.0193_C.TEST.MAX_PER_CLASS = -194_C.TEST.MAX_PER_IMAGE = 10095_C.TEST.BATCH_SIZE = 10...

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