# ------------------------------------------------------------------------------ # Copyright (c) Microsoft # Licensed under the MIT License. # Written by Bin Xiao ([email protected]) # ------------------------------------------------------------------------------ from __future__ import absolute_import from __future__ import division from __future__ import print_function from yacs.config import CfgNode as CN # pose_resnet related params POSE_RESNET = CN() POSE_RESNET.NUM_LAYERS = 50 POSE_RESNET.DECONV_WITH_BIAS = False POSE_RESNET.NUM_DECONV_LAYERS = 3 POSE_RESNET.NUM_DECONV_FILTERS = [256, 256, 256] POSE_RESNET.NUM_DECONV_KERNELS = [4, 4, 4] POSE_RESNET.FINAL_CONV_KERNEL = 1 POSE_RESNET.PRETRAINED_LAYERS = ['*'] # pose_multi_resoluton_net related params POSE_HIGH_RESOLUTION_NET = CN() POSE_HIGH_RESOLUTION_NET.PRETRAINED_LAYERS = ['*'] POSE_HIGH_RESOLUTION_NET.STEM_INPLANES = 64 POSE_HIGH_RESOLUTION_NET.FINAL_CONV_KERNEL = 1 POSE_HIGH_RESOLUTION_NET.STAGE2 = CN() POSE_HIGH_RESOLUTION_NET.STAGE2.NUM_MODULES = 1
from yacs.config import CfgNode as CN
_C = CN()
_C.data = CN(
dict(
batch_size=8, # batch size
valid_size=16, # the first N examples are reserved for validation
sample_rate=22050, # Hz, sample rate
n_fft=2048, # fft frame size
win_length=1024, # window size
hop_length=256, # hop size between ajacent frame
# f_max=8000, # Hz, max frequency when converting to mel
n_mels=80, # mel bands
train_clip_seconds=0.5, # audio clip length(in seconds)
))
_C.model = CN(
dict(
upsample_factors=[16, 16],
n_stack=3,
n_loop=10,
filter_size=2,
residual_channels=128, # resiaudal channel in each flow
loss_type="mog",
output_dim=3, # single gaussian
log_scale_min=-9.0,
))
_C.training = CN(
dict(
lr=1e-3, # learning rates
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. # Modified by Pengfei Xian. All Rights Reserved. import os from yacs.config import CfgNode as CN _C = CN() _C.OUTPUT_DIR = "run/" _C.MULTI_RUN = False _C.TEST_ONLY = False # ---------------------------------------------------------------------------- # # DATALOADER # ---------------------------------------------------------------------------- # _C.DATALOADER = CN() _C.DATALOADER.NUM_CLASSES = 19 _C.DATALOADER.BASE_SIZE = 2048 _C.DATALOADER.CROP_SIZE = 513 _C.DATALOADER.BATCH_SIZE_TRAIN = 4 _C.DATALOADER.BATCH_SIZE_TEST = 4 _C.DATALOADER.BATCH_SIZE_VAL = 4 _C.DATALOADER.NUM_WORKERS = 4 _C.DATALOADER.PIN_MEMORY = True _C.DATALOADER.DATASET = "cityscapes" _C.DATALOADER.DATASET_PATH = "" _C.DATALOADER.IGNORE_INDEX = -1 # ---------------------------------------------------------------------------- # # Cudnn related params
# ----------------------------------------------------------------------------- # Convention about Training / Test specific parameters # ----------------------------------------------------------------------------- # Whenever an argument can be either used for training or for testing, the # corresponding name will be post-fixed by a _TRAIN for a training parameter, # or _TEST for a test-specific parameter. # For example, the number of images during training will be # IMAGES_PER_BATCH_TRAIN, while the number of images for testing will be # IMAGES_PER_BATCH_TEST # ----------------------------------------------------------------------------- # Config definition # ----------------------------------------------------------------------------- _C = CN() _C.MODEL = CN() _C.MODEL.RPN_ONLY = False _C.MODEL.MASK_ON = False _C.MODEL.DEVICE = "cuda" _C.MODEL.META_ARCHITECTURE = "GeneralizedRCNN" # If the WEIGHT starts with a catalog://, like :R-50, the code will look for # the path in paths_catalog. Else, it will use it as the specified absolute # path _C.MODEL.WEIGHT = "" # ----------------------------------------------------------------------------- # INPUT # -----------------------------------------------------------------------------
from yacs.config import CfgNode as CN _C = CN() _C.MODEL = CN() _C.MODEL.META_ARCHITECTURE = 'SSDDetector' _C.MODEL.DEVICE = "cuda" # match default boxes to any ground truth with jaccard overlap higher than a threshold (0.5) _C.MODEL.THRESHOLD = 0.5 _C.MODEL.NUM_CLASSES = 4 # Hard negative mining _C.MODEL.NEG_POS_RATIO = 3 _C.MODEL.CENTER_VARIANCE = 0.1 _C.MODEL.SIZE_VARIANCE = 0.2 # ---------------------------------------------------------------------------- # # Backbone # ---------------------------------------------------------------------------- # _C.MODEL.BACKBONE = CN() _C.MODEL.BACKBONE.NAME = 'light_vgg' #'vgg' _C.MODEL.BACKBONE.OUT_CHANNELS = (512, 1024, 512, 256, 256, 256) _C.MODEL.BACKBONE.PRETRAINED = True # ----------------------------------------------------------------------------- # PRIORS # ----------------------------------------------------------------------------- _C.MODEL.PRIORS = CN() _C.MODEL.PRIORS.FEATURE_MAPS = [38, 19, 10, 5, 3, 1] _C.MODEL.PRIORS.STRIDES = [8, 16, 32, 64, 100, 300] _C.MODEL.PRIORS.MIN_SIZES = [30, 60, 111, 162, 213, 264] _C.MODEL.PRIORS.MAX_SIZES = [60, 111, 162, 213, 264, 315]
def make_config():
cfg = CfgNode()
cfg.desc = "" # 对本次实验的简单描述，用于为tensorboard命名
cfg.stage = "train" # train or eval or test
cfg.device = "cpu" # cpu or cuda
cfg.device_ids = "" # if not set, use all gpus
cfg.output_dir = "/data/vehicle_reid/perspective_transform_feature/debug"
cfg.debug = False
cfg.train = CfgNode()
cfg.train.epochs = 120
cfg.data = CfgNode()
cfg.data.name = "VeRi776"
cfg.data.pkl_path = "../data_processing/veri776.pkl"
cfg.data.train_size = (256, 256)
cfg.data.valid_size = (256, 256)
cfg.data.pad = 10
cfg.data.re_prob = 0.5
cfg.data.with_mask = True
cfg.data.test_ext = ''
cfg.data.sampler = 'RandomIdentitySampler'
cfg.data.batch_size = 16
cfg.data.num_instances = 4
cfg.data.train_num_workers = 0
cfg.data.test_num_workers = 0
cfg.model = CfgNode()
cfg.model.name = "resnet50"
# If it is set to empty, we will download it from torchvision official website.
cfg.model.pretrain_path = ""
cfg.model.last_stride = 1
cfg.model.neck = 'bnneck'
cfg.model.neck_feat = 'after'
cfg.model.pretrain_choice = 'imagenet'
cfg.model.ckpt_period = 10
cfg.optim = CfgNode()
cfg.optim.name = 'Adam'
cfg.optim.base_lr = 3.5e-4
cfg.optim.bias_lr_factor = 1
cfg.optim.weight_decay = 0.0005
cfg.optim.momentum = 0.9
cfg.loss = CfgNode()
cfg.loss.losses = ["triplet", "id", "center", "local-triplet"]
cfg.loss.triplet_margin = 0.3
cfg.loss.normalize_feature = True
cfg.loss.id_epsilon = 0.1
cfg.loss.center_lr = 0.5
cfg.loss.center_weight = 0.0005
cfg.loss.tuplet_s = 64
cfg.loss.tuplet_beta = 0.1
cfg.scheduler = CfgNode()
cfg.scheduler.milestones = [40, 70]
cfg.scheduler.gamma = 0.1
cfg.scheduler.warmup_factor = 0.0
cfg.scheduler.warmup_iters = 10
cfg.scheduler.warmup_method = "linear"
cfg.test = CfgNode()
cfg.test.feat_norm = True
cfg.test.remove_junk = True
cfg.test.period = 10
cfg.test.device = "cuda"
cfg.test.model_path = "../outputs/veri776.pth"
cfg.test.max_rank = 50
cfg.test.rerank = False
cfg.test.lambda_ = 0.0
# split: When the CUDA memory is not sufficient,
# we can split the dataset into different parts
# for the computing of distance.
cfg.test.split = 0
cfg.logging = CfgNode()
cfg.logging.level = "info"
cfg.logging.period = 20
return cfg
from yacs.config import CfgNode as CN _C = CN() # Model _C.MODEL = CN() _C.MODEL.ARCH = "se_resnext50_32x4d" # check python train.py -h for available models _C.MODEL.IMG_SIZE = 224 # Train _C.TRAIN = CN() _C.TRAIN.OPT = "adam" # adam or sgd _C.TRAIN.WORKERS = 8 _C.TRAIN.LR = 0.001 _C.TRAIN.LR_DECAY_STEP = 20 _C.TRAIN.LR_DECAY_RATE = 0.2 _C.TRAIN.MOMENTUM = 0.9 _C.TRAIN.WEIGHT_DECAY = 0.0 _C.TRAIN.BATCH_SIZE = 64 _C.TRAIN.EPOCHS = 80 _C.TRAIN.AGE_STDDEV = 1.0 # Test _C.TEST = CN() _C.TEST.WORKERS = 8 _C.TEST.BATCH_SIZE = 32
# Copyright (c) SenseTime. All Rights Reserved. from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals from yacs.config import CfgNode as CN __C = CN() cfg = __C __C.META_ARC = "siamrpn_r50_l234_dwxcorr" __C.CUDA = True # ------------------------------------------------------------------------ # # Training options # ------------------------------------------------------------------------ # __C.TRAIN = CN() # Anchor Target # Positive anchor threshold __C.TRAIN.THR_HIGH = 0.6 # Negative anchor threshold __C.TRAIN.THR_LOW = 0.3 # Number of negative __C.TRAIN.NEG_NUM = 16
from yacs.config import CfgNode as CN
_C = CN()
_C.ENCODER_HIDDEN_LAYERS_MLP = [512, 256, 128, 64]
_C.DECODER_HIDDEN_LAYERS_MLP = [512, 256, 128, 64]
# _C.ENCODER_HIDDEN_LAYERS_MLP = [64, 32]
# _C.DECODER_HIDDEN_LAYERS_MLP = [64, 32]
def get_vae_defaults():
return _C.clone()
from yacs.config import CfgNode as CN
import os
_C = CN()
_C.name = "default"
_C.protocol = "ae"
# System settings -----------------------------------------------
_C.system = CN()
_C.system.num_gpu = 1
_C.system.num_workers = 4
_C.system.checkpoints_root = "training_checkpoints"
_C.system.stream = 'file'
_C.system.seed = 777
# Dataset opt. -----------------------------------------------
_C.dataset = CN()
_C.dataset.repeat_dataset = 1
_C.dataset.cache = 1
_C.dataset.img_size = 512
_C.dataset.augmentations = []
_C.dataset.transforms = [{"name": "ToTensor"}]
_C.dataset.root = CN(new_allowed=True)
_C.dataset.root.NIH = "/datasets/ilyas/ChestNets/images"
_C.dataset.root.JSRT = "/home/semyon/ds_project/data/pngversion"
data_annotation_root = '/home/semyon/ds_project/data'
# Train
_C.dataset.train = CN()
_C.dataset.train.csv_path = os.path.join(data_annotation_root, "train.csv")
_C.dataset.train.batch_size = 16
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from yacs.config import CfgNode as CN
# -----------------------------------------------------------------------------
# Convention about Training / Test specific parameters
# -----------------------------------------------------------------------------
# Whenever an argument can be either used for training or for testing, the
# corresponding name will be post-fixed by a _TRAIN for a training parameter,
# or _TEST for a test-specific parameter.
# For example, the number of images during training will be
# IMAGES_PER_BATCH_TRAIN, while the number of images for testing will be
# IMAGES_PER_BATCH_TEST
# _C.TASK = CN()
_C = CN()
# -----------------------------------------------------------------------------
# Config definition
# -----------------------------------------------------------------------------
_C.TASK = ""
_C.GPU = 0
_C.PRINT_FREQ = 10
_C.MODEL = CN()
_C.MODEL.ARCH = 'csnet'
_C.MODEL.BASIC_SPLIT = [
1,
]
_C.LOSS = CN()
from yacs.config import CfgNode _C = CfgNode() _C.GPUS = (0, ) _C.WORKERS = 4 # Cudnn related params _C.CUDNN = CfgNode() _C.CUDNN.BENCHMARK = True _C.CUDNN.DETERMINISTIC = False _C.CUDNN.ENABLED = True # common params for NETWORK _C.MODEL = CfgNode() _C.MODEL.NAME = 'pose_hrnet' _C.MODEL.NUM_JOINTS = 17 _C.MODEL.TAG_PER_JOINT = True _C.MODEL.IMAGE_SIZE = [256, 256] # width * height, ex: 192 * 256 _C.MODEL.EXTRA = CfgNode(new_allowed=True) _C.BOX_MODEL = CfgNode() _C.BOX_MODEL.THRESHOLD = 0.8 # testing _C.TEST = CfgNode() # size of images for each device # Test Model Epoch _C.TEST.POST_PROCESS = False _C.TEST.POSE_DRAW = False
# ------------------------------------------------------------------------------ # Copyright (c) Microsoft # Licensed under the MIT License. # Written by Bin Xiao ([email protected]) # ------------------------------------------------------------------------------ from __future__ import absolute_import from __future__ import division from __future__ import print_function from yacs.config import CfgNode as CN EFFICIENT_NET = CN() EFFICIENT_NET.NETTYPE = 'efficientnet-b0' EFFICIENT_NET.DECONV_WITH_BIAS = False EFFICIENT_NET.NUM_DECONV_LAYERS = 3 EFFICIENT_NET.NUM_DECONV_FILTERS = [256, 256, 256] EFFICIENT_NET.NUM_DECONV_KERNELS = [4, 4, 4] EFFICIENT_NET.FINAL_CONV_KERNEL = 1 EFFICIENT_NET.PRETRAINED_LAYERS = ['*'] # pose_resnet related params写在这儿是防止没有yaml吗 POSE_RESNET = CN() POSE_RESNET.NUM_LAYERS = 50 POSE_RESNET.DECONV_WITH_BIAS = False POSE_RESNET.NUM_DECONV_LAYERS = 3 POSE_RESNET.NUM_DECONV_FILTERS = [256, 256, 256] POSE_RESNET.NUM_DECONV_KERNELS = [4, 4, 4] POSE_RESNET.FINAL_CONV_KERNEL = 1 POSE_RESNET.PRETRAINED_LAYERS = ['*'] # pose_resnet related params
class Config():
"""
TODO Finish Documentation (Numpy Style)
"""
def __init__(self):
"""
Initialize all main class variables
"""
# initialize yacs config node (CfgNode)
self._CONFIG = CN()
# initialize main sections of settings with default values
self._initKittConfig()
self._initVisionConfig()
def create_argparser(self):
"""create command line parser using argparse
Arguments
---------
TODO Detail Arguments
"""
# initialize argparse parser
parser = argparse.ArgumentParser(description = "How to control Thor.")
# add arguments to parser
parser.add_argument('-c', '--config',
help="custom config file path",
required=False,
type=str,
default='source/Config/default.yaml')
parser.add_argument('opts',
help='modify default config using the command-line',
default=None,
nargs=argparse.REMAINDER)
# parse args
args = parser.parse_args()
# update config with argue yaml file
self._CONFIG.merge_from_file(args.config)
# update config with argued options
self._CONFIG.merge_from_list(args.opts)
# freeze the config to prevent corruption
self._CONFIG.freeze()
def _initKittConfig(self):
"""
TODO Finish Documentation (Numpy Style)
"""
# create Kitt section in config
self._CONFIG.KITT = CN()
# start Kitt module at runtime (True / False)
self._CONFIG.KITT.START = False
def _initVisionConfig(self):
"""Initialize configuration for Vision"""
# create Vision section in config
self._CONFIG.VISION = CN()
# start Vision module at runtime (True / False)
self._CONFIG.VISION.START = False
# webcam id to receive frames
self._CONFIG.VISION.CAM_NUMBER = 1 # usb cam on thor
# start detect package
self._CONFIG.VISION.DETECT = False
# frame rate to receive from video source
self._CONFIG.VISION.FPS = 30
# image size to receive from video source
self._CONFIG.VISION.IMAGE_SIZE = [640, 480]
# record camera footage
self._CONFIG.VISION.RECORD = False
# show the camera's view on screen
self._CONFIG.VISION.SHOW_VIEW = False
# image size to receive when using detect
self._CONFIG.VISION.TENSOR_IMAGE_SIZE = [800, 600]
# video path to receive frames
self._CONFIG.VISION.VIDEO_PATH = None
# ---------------
# getter methods
# ---------------
def getKittStart(self):
"""return boolean specifying whether to start Kitt at runtime"""
return self._CONFIG.KITT.START
def getVisionCamNum(self):
"""return integer value representing camera source"""
return self._CONFIG.VISION.CAM_NUMBER
def getVisionDetect(self):
"""return boolean attribute specifying whether or not to apply object detection"""
return self._CONFIG.VISION.DETECT
def getVisionFPS(self):
"""return integer value specifying frame rate to record"""
return self._CONFIG.VISION.FPS
def getVisionImageSize(self):
"""return list value specifying image size"""
return self._CONFIG.VISION.IMAGE_SIZE
def getVisionRecord(self):
"""return boolean value specifying whether to record"""
return self._CONFIG.VISION.RECORD
def getVisionShowView(self):
"""return boolean value specifying the initialization of visuals"""
return self._CONFIG.VISION.SHOW_VIEW
def getVisionStart(self):
"""return boolean specifying whether to start Vision at runtime"""
return self._CONFIG.VISION.START
def getVisionTensorImageSize(self):
"""return boolean specifying output of object detection"""
return self._CONFIG.VISION.TENSOR_IMAGE_SIZE
def getVisionVideoPath(self):
"""return path to prerecorded video(s)"""
return self._CONFIG.VISION.VIDEO_PATH
from os.path import join from yacs.config import CfgNode from config.const import PROJECT_ROOT _C = CfgNode() _C.SEED = 42 # train _C.TRAIN = CfgNode() _C.TRAIN.BATCH_SIZE = 256 _C.TRAIN.DISTRIBUTED_BACKEND = 'ddp' _C.TRAIN.GPUS = 2 _C.TRAIN.LR = 0.01 _C.TRAIN.MAX_EPOCHS = 1 _C.TRAIN.MODEL_TYPE = 'simple_cnn' _C.TRAIN.MOMENTUM = 0.9 _C.TRAIN.OPTIMIZER_TYPE = 'sgd' _C.TRAIN.SCHEDULER_TYPE = 'step_lr' _C.TRAIN.STEP_SIZE = 5 _C.MLFLOW = CfgNode() _C.MLFLOW.EXPERIMENT_NAME = 'Default' _C.OPTUNA = CfgNode() _C.OPTUNA.N_TRIALS = 2 _C.OPTUNA.TIMEOUT = 60 * 60 _C.DATA = CfgNode() _C.DATA.CACHE_DIR = join(PROJECT_ROOT, '.data')
""" ------------------------------------------------- File Name: config.py Author: Zhonghao Huang Date: 2019/10/22 Description: Global Configure. ------------------------------------------------- """ from yacs.config import CfgNode as CN cfg = CN() cfg.output_dir = '' cfg.device = 'cuda' cfg.device_id = '3' cfg.structure = 'fixed' cfg.loss = "logistic" cfg.drift = 0.001 cfg.d_repeats = 1 cfg.use_ema = True cfg.ema_decay = 0.999 cfg.num_works = 4 cfg.num_samples = 36 cfg.feedback_factor = 10 cfg.checkpoint_factor = 10 # ---------------------------------------------------------------------------- # # Options for scheduler
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. import os from yacs.config import CfgNode as CN # ----------------------------------------------------------------------------- # Config definition # ----------------------------------------------------------------------------- _C = CN() _C.MODEL = CN() # ----------------------------------------------------------------------------- # INPUT # ----------------------------------------------------------------------------- _C.INPUT = CN() # Size of the smallest side of the image during training _C.INPUT.HEIGHT = 600 _C.INPUT.WIDTH = 400 # Values to be used for image normalization _C.INPUT.PIXEL_MEAN = [0.485, 0.456, 0.406] # Values to be used for image normalization _C.INPUT.PIXEL_STD = [0.229, 0.224, 0.225] # ----------------------------------------------------------------------------- # DataLoader # ----------------------------------------------------------------------------- _C.DATALOADER = CN() # Number of data loading threads _C.DATALOADER.NUM_WORKERS = 4 _C.DATALOADER.DEBUG = False
# ------------------------------------------------------------------------------ # Copyright (c) Microsoft # Licensed under the MIT License. # Written by Bin Xiao ([email protected]) # ------------------------------------------------------------------------------ from __future__ import absolute_import from __future__ import division from __future__ import print_function from yacs.config import CfgNode as CN # pose_multi_resoluton_net related params POSENET = CN() POSENET.PRETRAINED_LAYERS = ['*'] POSENET.STEM_INPLANES = 64 POSENET.FINAL_CONV_KERNEL = 1 POSENET.STAGE2 = CN() POSENET.STAGE2.NUM_MODULES = 4 POSENET.STAGE2.NUM_BRANCHES = 4 POSENET.STAGE2.NUM_BLOCKS = [4, 4, 4, 4] POSENET.STAGE2.NUM_CHANNELS = [32, 64, 128, 256] POSENET.STAGE2.BLOCK = 'BASIC' POSENET.STAGE2.FUSE_METHOD = 'SUM' MODEL_EXTRAS = { 'posenet': POSENET, }
# -*- coding: utf-8 -*- # @Author : LG from yacs.config import CfgNode as CN import os ### 参数请结合自身项目设定，才能跑出较好的效果。 project_root = os.getcwd() _C = CN() _C.FILE = CN() _C.FILE.PRETRAIN_WEIGHT_ROOT = project_root+'/Weights/pretrained' # 会使用到的预训练模型 _C.FILE.MODEL_SAVE_ROOT = project_root+'/Weights/trained' # 训练模型的保存 _C.FILE.VGG16_WEIGHT = 'vgg16_reducedfc.pth' # vgg预训练模型 _C.DEVICE = CN() _C.DEVICE.MAINDEVICE = 'cuda:0' # 主gpu _C.DEVICE.TRAIN_DEVICES = [0,1] # 训练gpu _C.DEVICE.TEST_DEVICES = [0,1] # 检测gpu _C.MODEL = CN() _C.MODEL.INPUT = CN() _C.MODEL.INPUT.IMAGE_SIZE = 300 # 模型输入尺寸 _C.MODEL.INPUT.PIXEL_MEAN = [0, 0, 0] # 数据集均值 _C.MODEL.INPUT.PIXEL_STD = [1, 1, 1] # 数据集方差
# Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. """Configuration file (powered by YACS).""" import argparse import os import sys from pycls.core.io import cache_url from yacs.config import CfgNode as CfgNode # Global config object _C = CfgNode() # Example usage: # from core.config import cfg cfg = _C # ------------------------------------------------------------------------------------ # # Model options # ------------------------------------------------------------------------------------ # _C.MODEL = CfgNode() # Model type _C.MODEL.TYPE = "" # Number of weight layers _C.MODEL.DEPTH = 0
from yacs.config import CfgNode as CN # ----------------------------------------------------------------------------- # Config definition # ----------------------------------------------------------------------------- _C = CN() _C.DIR = "ckpt/ade20k-resnet50dilated-ppm_deepsup" # ----------------------------------------------------------------------------- # Dataset # ----------------------------------------------------------------------------- _C.DATASET = CN() _C.DATASET.root_dataset = "./data/" _C.DATASET.list_train = "./data/training.odgt" _C.DATASET.list_val = "./data/validation.odgt" _C.DATASET.color_mat = "./data/color150.mat" _C.DATASET.object_info = "./data/object150_info.csv" _C.DATASET.num_class = 150 # multiscale train/test, size of short edge (int or tuple) _C.DATASET.imgSizes = (300, 375, 450, 525, 600) # maximum input image size of long edge _C.DATASET.imgMaxSize = 1000 # maxmimum downsampling rate of the network _C.DATASET.padding_constant = 8 # downsampling rate of the segmentation label _C.DATASET.segm_downsampling_rate = 8 # randomly horizontally flip images when train/test _C.DATASET.random_flip = True # -----------------------------------------------------------------------------
# ----------------------------------------------------------------------------- # Convention about Training / Test specific parameters # ----------------------------------------------------------------------------- # Whenever an argument can be either used for training or for testing, the # corresponding name will be post-fixed by a _TRAIN for a training parameter, # or _TEST for a test-specific parameter. # For example, the maximum image side during training will be # INPUT.MAX_SIZE_TRAIN, while for testing it will be # INPUT.MAX_SIZE_TEST # ----------------------------------------------------------------------------- # Config definition # ----------------------------------------------------------------------------- _C = CN() _C.MODEL = CN() _C.MODEL.RPN_ONLY = False _C.MODEL.MASK_ON = False _C.MODEL.RETINANET_ON = False _C.MODEL.KEYPOINT_ON = False _C.MODEL.DEVICE = "cuda" _C.MODEL.META_ARCHITECTURE = "GeneralizedRCNN" _C.MODEL.CLS_AGNOSTIC_BBOX_REG = False _C.MODEL.RPN_VERSION = 0 _C.MODEL.ROI_VERSION = 0 # If the WEIGHT starts with a catalog://, like :R-50, the code will look for # the path in paths_catalog. Else, it will use it as the specified absolute
# Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. """Configuration file (powered by YACS).""" import argparse import os import sys from pycls.core.io import cache_url from yacs.config import CfgNode as CN # Global config object _C = CN() # Example usage: # from core.config import cfg cfg = _C # ------------------------------------------------------------------------------------ # # Model options # ------------------------------------------------------------------------------------ # _C.MODEL = CN() # Model type _C.MODEL.TYPE = "" # Number of weight layers _C.MODEL.DEPTH = 0
# ------------------------------------------------------------------------------ # Copyright (c) Microsoft # Licensed under the MIT License. # Written by Bin Xiao ([email protected]) # ------------------------------------------------------------------------------ from __future__ import absolute_import from __future__ import division from __future__ import print_function import os from yacs.config import CfgNode as CN _C = CN() _C.OUTPUT_DIR = '' _C.LOG_DIR = '' _C.DATA_DIR = '' _C.GPUS = (0, 1) _C.WORKERS = 4 _C.PRINT_FREQ = 20 _C.AUTO_RESUME = False _C.PIN_MEMORY = True _C.RANK = 0 # Cudnn related params _C.CUDNN = CN() _C.CUDNN.BENCHMARK = True _C.CUDNN.DETERMINISTIC = False _C.CUDNN.ENABLED = True
import os
from yacs.config import CfgNode as CN
r"""Learning algorithm settings
"""
_C = CN()
# --------------------------------------------------------------------- #
# computation device & media options #
# --------------------------------------------------------------------- #
_C.GPU = 0 # Only gpu mode is supported now
_C.DISTRIBUTED = True
_C.MULTIPROC_DIST = True
_C.DIST_URL = 'tcp://10.198.87.144:8008'
# _C.DIST_URL = 'tcp://224.66.41.62:23456'
_C.DIST_BACKEND = 'nccl'
_C.WORLD_SIZE = 1
_C.RANK = 0
# --------------------------------------------------------------------- #
# miscellaneous options #
# --------------------------------------------------------------------- #
_C.OUTPUT_DIR = "../models/"
_C.CHECKPOINT = ""
_C.PRETRAINED = "../models/s" # Pretrained unsupervised model for downstream tasks
# _C.SEED = None
_C.LOGGER = ('MetricLogger', 'TensorboardLogger')
# --------------------------------------------------------------------- #
# model options #
# ----------------------------------------------------------------------------- # Convention about Training / Test specific parameters # ----------------------------------------------------------------------------- # Whenever an argument can be either used for training or for testing, the # corresponding name will be post-fixed by a _TRAIN for a training parameter, # or _TEST for a test-specific parameter. # For example, the number of images during training will be # IMAGES_PER_BATCH_TRAIN, while the number of images for testing will be # IMAGES_PER_BATCH_TEST # ----------------------------------------------------------------------------- # Config definition # ----------------------------------------------------------------------------- _C = CN() _C.MODEL = CN() _C.MODEL.DEVICE = "cuda:0" _C.MODEL.NAME = 'seresnet50' # 'resnet50' 'seresnet50' 'densenet196' _C.MODEL.LAST_STRIDE = 1 _C.MODEL.PRETRAIN_PATH = '' _C.MODEL.PRETRAIN_PATH_SE = '' _C.MODEL.PRETRAIN_PATH_DENSE = '' # ----------------------------------------------------------------------------- # APNet # ----------------------------------------------------------------------------- _C.APNET = CN() _C.APNET.LEVEL = 0 # -----------------------------------------------------------------------------
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. from yacs.config import CfgNode # ----------------------------------------------------------------------------- # Config definition # ----------------------------------------------------------------------------- _C = CfgNode() _C.DATA_ROOT = './data/coco' _C.DATASET_ABS = 'Phys' _C.BASE = '' # ----------------------------------------------------------------------------- # INPUT # ----------------------------------------------------------------------------- _C.INPUT = CfgNode() _C.INPUT.PRELOAD_TO_MEMORY = False _C.INPUT.IMAGE_MEAN = [0, 0, 0] _C.INPUT.IMAGE_STD = [1.0, 1.0, 1.0] _C.INPUT.PHYRE_USE_EMBEDDING = False _C.INPUT.IMAGE_CHANNEL = 3 # ---------------------------------------------------------------------------- # # Solver # ---------------------------------------------------------------------------- # _C.SOLVER = CfgNode() _C.SOLVER.BASE_LR = 0.001 _C.SOLVER.BIAS_LR_FACTOR = 2 _C.SOLVER.LR_GAMMA = 0.1 _C.SOLVER.VAL_INTERVAL = 16000 _C.SOLVER.WEIGHT_DECAY = 0.0005
from yacs.config import CfgNode as CN _C = CN() # numpy random seed _C.NUMPY_RANDOM_SEED = 1234 #----------------------------------------------------------------------------- # Bullet configuration. #----------------------------------------------------------------------------- _C.BULLET = CN() # If "GUI", run real-time pybullet built-in visualizer; if "DIRECT", not. _C.BULLET.MODE = "GUI" # timestep in physics simulation. _C.BULLET.TIMESTEP = 0.0002 # maximum duration of each simulation (-1 if indefinite) _C.BULLET.MAX_TIME = -1. # numSolverIteration parameter of bullet _C.BULLET.NUM_SOLVER_ITERATIONS = 20 # gravity constant _C.BULLET.GRAVITY = -9.81 #----------------------------------------------------------------------------- # Scene set configuration #----------------------------------------------------------------------------- # Number of scenes _C.NUM_SCENES = 10 #----------------------------------------------------------------------------- # Object pose initialization configuration.
"""Default packnet_sfm configuration parameters (overridable in configs/*.yaml) """ import os from yacs.config import CfgNode as CN ######################################################################################################################## cfg = CN() cfg.name = '' # Run name cfg.debug = False # Debugging flag ######################################################################################################################## ### ARCH ######################################################################################################################## cfg.arch = CN() cfg.arch.seed = 42 # Random seed for Pytorch/Numpy initialization cfg.arch.min_epochs = 1 # Minimum number of epochs cfg.arch.max_epochs = 50 # Maximum number of epochs ######################################################################################################################## ### CHECKPOINT ######################################################################################################################## cfg.checkpoint = CN() cfg.checkpoint.filepath = '' # Checkpoint filepath to save data cfg.checkpoint.save_top_k = 5 # Number of best models to save cfg.checkpoint.monitor = 'loss' # Metric to monitor for logging cfg.checkpoint.monitor_index = 0 # Dataset index for the metric to monitor cfg.checkpoint.mode = 'auto' # Automatically determine direction of improvement (increase or decrease) cfg.checkpoint.s3_path = '' # s3 path for AWS model syncing cfg.checkpoint.s3_frequency = 1 # How often to s3 sync ######################################################################################################################## ### SAVE ########################################################################################################################
# ----------------------------------------------------------------------------- # Convention about Training / Test specific parameters # ----------------------------------------------------------------------------- # Whenever an argument can be either used for training or for testing, the # corresponding name will be post-fixed by a _TRAIN for a training parameter, # or _TEST for a test-specific parameter. # For example, the number of images during training will be # IMAGES_PER_BATCH_TRAIN, while the number of images for testing will be # IMAGES_PER_BATCH_TEST # ----------------------------------------------------------------------------- # Config definition # ----------------------------------------------------------------------------- _C = CN() _C.MODEL = CN() _C.MODEL.RPN_ONLY = False _C.MODEL.MASK_ON = False _C.MODEL.DEVICE = "cuda" _C.MODEL.META_ARCHITECTURE = "GeneralizedRCNN" # If the WEIGHT starts with a catalog://, like :R-50, the code will look for # the path in paths_catalog. Else, it will use it as the specified absolute # path _C.MODEL.WEIGHT = "" # ----------------------------------------------------------------------------- # INPUT
def test_merge_cfg_from_cfg(self):
# Test: merge from clone
cfg = get_cfg()
s = "dummy0"
cfg2 = cfg.clone()
cfg2.MODEL.TYPE = s
cfg.merge_from_other_cfg(cfg2)
assert cfg.MODEL.TYPE == s
# Test: merge from yaml
s = "dummy1"
cfg2 = CN.load_cfg(cfg.dump())
cfg2.MODEL.TYPE = s
cfg.merge_from_other_cfg(cfg2)
assert cfg.MODEL.TYPE == s
# Test: merge with a valid key
s = "dummy2"
cfg2 = CN()
cfg2.MODEL = CN()
cfg2.MODEL.TYPE = s
cfg.merge_from_other_cfg(cfg2)
assert cfg.MODEL.TYPE == s
# Test: merge with an invalid key
s = "dummy3"
cfg2 = CN()
cfg2.FOO = CN()
cfg2.FOO.BAR = s
with self.assertRaises(KeyError):
cfg.merge_from_other_cfg(cfg2)
# Test: merge with converted type
cfg2 = CN()
cfg2.TRAIN = CN()
cfg2.TRAIN.SCALES = [1]
cfg.merge_from_other_cfg(cfg2)
assert type(cfg.TRAIN.SCALES) is tuple
assert cfg.TRAIN.SCALES[0] == 1
# Test str (bytes) <-> unicode conversion for py2
if PY2:
cfg.A_UNICODE_KEY = u"foo"
cfg2 = CN()
cfg2.A_UNICODE_KEY = b"bar"
cfg.merge_from_other_cfg(cfg2)
assert type(cfg.A_UNICODE_KEY) == unicode # noqa: F821
assert cfg.A_UNICODE_KEY == u"bar"
# Test: merge with invalid type
cfg2 = CN()
cfg2.TRAIN = CN()
cfg2.TRAIN.SCALES = 1
with self.assertRaises(ValueError):
cfg.merge_from_other_cfg(cfg2)