Merge cfg

cfg.py

@@ -1,7 +1,1289 @@
 #!/usr/bin/env python3
 # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
 
-from .helpers.cfg import get_cfg
+"""Configs."""
+import math
+
+from fvcore.common.config import CfgNode
+
+# -----------------------------------------------------------------------------
+# Config definition
+# -----------------------------------------------------------------------------
+_C = CfgNode()
+
+# -----------------------------------------------------------------------------
+# Contrastive Model (for MoCo, SimCLR, SwAV, BYOL)
+# -----------------------------------------------------------------------------
+
+_C.CONTRASTIVE = CfgNode()
+
+# temperature used for contrastive losses
+_C.CONTRASTIVE.T = 0.07
+
+# output dimension for the loss
+_C.CONTRASTIVE.DIM = 128
+
+# number of training samples (for kNN bank)
+_C.CONTRASTIVE.LENGTH = 239975
+
+# the length of MoCo's and MemBanks' queues
+_C.CONTRASTIVE.QUEUE_LEN = 65536
+
+# momentum for momentum encoder updates
+_C.CONTRASTIVE.MOMENTUM = 0.5
+
+# wether to anneal momentum to value above with cosine schedule
+_C.CONTRASTIVE.MOMENTUM_ANNEALING = False
+
+# either memorybank, moco, simclr, byol, swav
+_C.CONTRASTIVE.TYPE = "mem"
+
+# wether to interpolate memorybank in time
+_C.CONTRASTIVE.INTERP_MEMORY = False
+
+# 1d or 2d (+temporal) memory
+_C.CONTRASTIVE.MEM_TYPE = "1d"
+
+# number of classes for online kNN evaluation
+_C.CONTRASTIVE.NUM_CLASSES_DOWNSTREAM = 400
+
+# use an MLP projection with these num layers
+_C.CONTRASTIVE.NUM_MLP_LAYERS = 1
+
+# dimension of projection and predictor MLPs
+_C.CONTRASTIVE.MLP_DIM = 2048
+
+# use BN in projection/prediction MLP
+_C.CONTRASTIVE.BN_MLP = False
+
+# use synchronized BN in projection/prediction MLP
+_C.CONTRASTIVE.BN_SYNC_MLP = False
+
+# shuffle BN only locally vs. across machines
+_C.CONTRASTIVE.LOCAL_SHUFFLE_BN = True
+
+# Wether to fill multiple clips (or just the first) into queue
+_C.CONTRASTIVE.MOCO_MULTI_VIEW_QUEUE = False
+
+# if sampling multiple clips per vid they need to be at least min frames apart
+_C.CONTRASTIVE.DELTA_CLIPS_MIN = -math.inf
+
+# if sampling multiple clips per vid they can be max frames apart
+_C.CONTRASTIVE.DELTA_CLIPS_MAX = math.inf
+
+# if non empty, use predictors with depth specified
+_C.CONTRASTIVE.PREDICTOR_DEPTHS = []
+
+# Wether to sequentially process multiple clips (=lower mem usage) or batch them
+_C.CONTRASTIVE.SEQUENTIAL = False
+
+# Wether to perform SimCLR loss across machines (or only locally)
+_C.CONTRASTIVE.SIMCLR_DIST_ON = True
+
+# Length of queue used in SwAV
+_C.CONTRASTIVE.SWAV_QEUE_LEN = 0
+
+# Wether to run online kNN evaluation during training
+_C.CONTRASTIVE.KNN_ON = True
+
+
+# ---------------------------------------------------------------------------- #
+# Batch norm options
+# ---------------------------------------------------------------------------- #
+_C.BN = CfgNode()
+
+# Precise BN stats.
+_C.BN.USE_PRECISE_STATS = False
+
+# Number of samples use to compute precise bn.
+_C.BN.NUM_BATCHES_PRECISE = 200
+
+# Weight decay value that applies on BN.
+_C.BN.WEIGHT_DECAY = 0.0
+
+# Norm type, options include `batchnorm`, `sub_batchnorm`, `sync_batchnorm`
+_C.BN.NORM_TYPE = "batchnorm"
+
+# Parameter for SubBatchNorm, where it splits the batch dimension into
+# NUM_SPLITS splits, and run BN on each of them separately independently.
+_C.BN.NUM_SPLITS = 1
+
+# Parameter for NaiveSyncBatchNorm, where the stats across `NUM_SYNC_DEVICES`
+# devices will be synchronized. `NUM_SYNC_DEVICES` cannot be larger than number of
+# devices per machine; if global sync is desired, set `GLOBAL_SYNC`.
+# By default ONLY applies to NaiveSyncBatchNorm3d; consider also setting
+# CONTRASTIVE.BN_SYNC_MLP if appropriate.
+_C.BN.NUM_SYNC_DEVICES = 1
+
+# Parameter for NaiveSyncBatchNorm. Setting `GLOBAL_SYNC` to True synchronizes
+# stats across all devices, across all machines; in this case, `NUM_SYNC_DEVICES`
+# must be set to None.
+# By default ONLY applies to NaiveSyncBatchNorm3d; consider also setting
+# CONTRASTIVE.BN_SYNC_MLP if appropriate.
+_C.BN.GLOBAL_SYNC = False
+
+# ---------------------------------------------------------------------------- #
+# Training options.
+# ---------------------------------------------------------------------------- #
+_C.TRAIN = CfgNode()
+
+# If True Train the model, else skip training.
+_C.TRAIN.ENABLE = True
+
+# Kill training if loss explodes over this ratio from the previous 5 measurements.
+# Only enforced if > 0.0
+_C.TRAIN.KILL_LOSS_EXPLOSION_FACTOR = 0.0
+
+# Dataset.
+_C.TRAIN.DATASET = "kinetics"
+
+# Total mini-batch size.
+_C.TRAIN.BATCH_SIZE = 64
+
+# Evaluate model on test data every eval period epochs.
+_C.TRAIN.EVAL_PERIOD = 10
+
+# Save model checkpoint every checkpoint period epochs.
+_C.TRAIN.CHECKPOINT_PERIOD = 10
+
+# Resume training from the latest checkpoint in the output directory.
+_C.TRAIN.AUTO_RESUME = True
+
+# Path to the checkpoint to load the initial weight.
+_C.TRAIN.CHECKPOINT_FILE_PATH = ""
+
+# Checkpoint types include `caffe2` or `pytorch`.
+_C.TRAIN.CHECKPOINT_TYPE = "pytorch"
+
+# If True, perform inflation when loading checkpoint.
+_C.TRAIN.CHECKPOINT_INFLATE = False
+
+# If True, reset epochs when loading checkpoint.
+_C.TRAIN.CHECKPOINT_EPOCH_RESET = False
+
+# If set, clear all layer names according to the pattern provided.
+_C.TRAIN.CHECKPOINT_CLEAR_NAME_PATTERN = ()  # ("backbone.",)
+
+# If True, use FP16 for activations
+_C.TRAIN.MIXED_PRECISION = False
+
+# if True, inflate some params from imagenet model.
+_C.TRAIN.CHECKPOINT_IN_INIT = False
+
+# ---------------------------------------------------------------------------- #
+# Augmentation options.
+# ---------------------------------------------------------------------------- #
+_C.AUG = CfgNode()
+
+# Whether to enable randaug.
+_C.AUG.ENABLE = False
+
+# Number of repeated augmentations to used during training.
+# If this is greater than 1, then the actual batch size is
+# TRAIN.BATCH_SIZE * AUG.NUM_SAMPLE.
+_C.AUG.NUM_SAMPLE = 1
+
+# Not used if using randaug.
+_C.AUG.COLOR_JITTER = 0.4
+
+# RandAug parameters.
+_C.AUG.AA_TYPE = "rand-m9-mstd0.5-inc1"
+
+# Interpolation method.
+_C.AUG.INTERPOLATION = "bicubic"
+
+# Probability of random erasing.
+_C.AUG.RE_PROB = 0.25
+
+# Random erasing mode.
+_C.AUG.RE_MODE = "pixel"
+
+# Random erase count.
+_C.AUG.RE_COUNT = 1
+
+# Do not random erase first (clean) augmentation split.
+_C.AUG.RE_SPLIT = False
+
+# Whether to generate input mask during image processing.
+_C.AUG.GEN_MASK_LOADER = False
+
+# If True, masking mode is "tube". Default is "cube".
+_C.AUG.MASK_TUBE = False
+
+# If True, masking mode is "frame". Default is "cube".
+_C.AUG.MASK_FRAMES = False
+
+# The size of generated masks.
+_C.AUG.MASK_WINDOW_SIZE = [8, 7, 7]
+
+# The ratio of masked tokens out of all tokens. Also applies to MViT supervised training
+_C.AUG.MASK_RATIO = 0.0
+
+# The maximum number of a masked block. None means no maximum limit. (Used only in image MaskFeat.)
+_C.AUG.MAX_MASK_PATCHES_PER_BLOCK = None
+
+# ---------------------------------------------------------------------------- #
+# Masked pretraining visualization options.
+# ---------------------------------------------------------------------------- #
+_C.VIS_MASK = CfgNode()
+
+# Whether to do visualization.
+_C.VIS_MASK.ENABLE = False
+
+# ---------------------------------------------------------------------------- #
+# MipUp options.
+# ---------------------------------------------------------------------------- #
+_C.MIXUP = CfgNode()
+
+# Whether to use mixup.
+_C.MIXUP.ENABLE = False
+
+# Mixup alpha.
+_C.MIXUP.ALPHA = 0.8
+
+# Cutmix alpha.
+_C.MIXUP.CUTMIX_ALPHA = 1.0
+
+# Probability of performing mixup or cutmix when either/both is enabled.
+_C.MIXUP.PROB = 1.0
+
+# Probability of switching to cutmix when both mixup and cutmix enabled.
+_C.MIXUP.SWITCH_PROB = 0.5
+
+# Label smoothing.
+_C.MIXUP.LABEL_SMOOTH_VALUE = 0.1
+
+# ---------------------------------------------------------------------------- #
+# Testing options
+# ---------------------------------------------------------------------------- #
+_C.TEST = CfgNode()
+
+# If True test the model, else skip the testing.
+_C.TEST.ENABLE = True
+
+# Dataset for testing.
+_C.TEST.DATASET = "kinetics"
+
+# Total mini-batch size
+_C.TEST.BATCH_SIZE = 8
+
+# Path to the checkpoint to load the initial weight.
+_C.TEST.CHECKPOINT_FILE_PATH = ""
+
+# Number of clips to sample from a video uniformly for aggregating the
+# prediction results.
+_C.TEST.NUM_ENSEMBLE_VIEWS = 10
+
+# Number of crops to sample from a frame spatially for aggregating the
+# prediction results.
+_C.TEST.NUM_SPATIAL_CROPS = 3
+
+# Checkpoint types include `caffe2` or `pytorch`.
+_C.TEST.CHECKPOINT_TYPE = "pytorch"
+# Path to saving prediction results file.
+_C.TEST.SAVE_RESULTS_PATH = ""
+
+_C.TEST.NUM_TEMPORAL_CLIPS = []
+# -----------------------------------------------------------------------------
+# ResNet options
+# -----------------------------------------------------------------------------
+_C.RESNET = CfgNode()
+
+# Transformation function.
+_C.RESNET.TRANS_FUNC = "bottleneck_transform"
+
+# Number of groups. 1 for ResNet, and larger than 1 for ResNeXt).
+_C.RESNET.NUM_GROUPS = 1
+
+# Width of each group (64 -> ResNet; 4 -> ResNeXt).
+_C.RESNET.WIDTH_PER_GROUP = 64
+
+# Apply relu in a inplace manner.
+_C.RESNET.INPLACE_RELU = True
+
+# Apply stride to 1x1 conv.
+_C.RESNET.STRIDE_1X1 = False
+
+#  If true, initialize the gamma of the final BN of each block to zero.
+_C.RESNET.ZERO_INIT_FINAL_BN = False
+
+#  If true, initialize the final conv layer of each block to zero.
+_C.RESNET.ZERO_INIT_FINAL_CONV = False
+
+# Number of weight layers.
+_C.RESNET.DEPTH = 50
+
+# If the current block has more than NUM_BLOCK_TEMP_KERNEL blocks, use temporal
+# kernel of 1 for the rest of the blocks.
+_C.RESNET.NUM_BLOCK_TEMP_KERNEL = [[3], [4], [6], [3]]
+
+# Size of stride on different res stages.
+_C.RESNET.SPATIAL_STRIDES = [[1], [2], [2], [2]]
+
+# Size of dilation on different res stages.
+_C.RESNET.SPATIAL_DILATIONS = [[1], [1], [1], [1]]
+
+# ---------------------------------------------------------------------------- #
+# X3D  options
+# See https://arxiv.org/abs/2004.04730 for details about X3D Networks.
+# ---------------------------------------------------------------------------- #
+_C.X3D = CfgNode()
+
+# Width expansion factor.
+_C.X3D.WIDTH_FACTOR = 1.0
+
+# Depth expansion factor.
+_C.X3D.DEPTH_FACTOR = 1.0
+
+# Bottleneck expansion factor for the 3x3x3 conv.
+_C.X3D.BOTTLENECK_FACTOR = 1.0  #
+
+# Dimensions of the last linear layer before classificaiton.
+_C.X3D.DIM_C5 = 2048
+
+# Dimensions of the first 3x3 conv layer.
+_C.X3D.DIM_C1 = 12
+
+# Whether to scale the width of Res2, default is false.
+_C.X3D.SCALE_RES2 = False
+
+# Whether to use a BatchNorm (BN) layer before the classifier, default is false.
+_C.X3D.BN_LIN5 = False
+
+# Whether to use channelwise (=depthwise) convolution in the center (3x3x3)
+# convolution operation of the residual blocks.
+_C.X3D.CHANNELWISE_3x3x3 = True
+
+# -----------------------------------------------------------------------------
+# Nonlocal options
+# -----------------------------------------------------------------------------
+_C.NONLOCAL = CfgNode()
+
+# Index of each stage and block to add nonlocal layers.
+_C.NONLOCAL.LOCATION = [[[]], [[]], [[]], [[]]]
+
+# Number of group for nonlocal for each stage.
+_C.NONLOCAL.GROUP = [[1], [1], [1], [1]]
+
+# Instatiation to use for non-local layer.
+_C.NONLOCAL.INSTANTIATION = "dot_product"
+
+
+# Size of pooling layers used in Non-Local.
+_C.NONLOCAL.POOL = [
+    # Res2
+    [[1, 2, 2], [1, 2, 2]],
+    # Res3
+    [[1, 2, 2], [1, 2, 2]],
+    # Res4
+    [[1, 2, 2], [1, 2, 2]],
+    # Res5
+    [[1, 2, 2], [1, 2, 2]],
+]
+
+# -----------------------------------------------------------------------------
+# Model options
+# -----------------------------------------------------------------------------
+_C.MODEL = CfgNode()
+
+# Model architecture.
+_C.MODEL.ARCH = "slowfast"
+
+# Model name
+_C.MODEL.MODEL_NAME = "SlowFast"
+
+# The number of classes to predict for the model.
+_C.MODEL.NUM_CLASSES = 400
+
+# Loss function.
+_C.MODEL.LOSS_FUNC = "cross_entropy"
+
+# Model architectures that has one single pathway.
+_C.MODEL.SINGLE_PATHWAY_ARCH = [
+    "2d",
+    "c2d",
+    "i3d",
+    "slow",
+    "x3d",
+    "mvit",
+    "maskmvit",
+]
+
+# Model architectures that has multiple pathways.
+_C.MODEL.MULTI_PATHWAY_ARCH = ["slowfast"]
+
+# Dropout rate before final projection in the backbone.
+_C.MODEL.DROPOUT_RATE = 0.5
+
+# Randomly drop rate for Res-blocks, linearly increase from res2 to res5
+_C.MODEL.DROPCONNECT_RATE = 0.0
+
+# The std to initialize the fc layer(s).
+_C.MODEL.FC_INIT_STD = 0.01
+
+# Activation layer for the output head.
+_C.MODEL.HEAD_ACT = "softmax"
+
+# Activation checkpointing enabled or not to save GPU memory.
+_C.MODEL.ACT_CHECKPOINT = False
+
+# If True, detach the final fc layer from the network, by doing so, only the
+# final fc layer will be trained.
+_C.MODEL.DETACH_FINAL_FC = False
+
+# If True, frozen batch norm stats during training.
+_C.MODEL.FROZEN_BN = False
+
+# If True, AllReduce gradients are compressed to fp16
+_C.MODEL.FP16_ALLREDUCE = False
+
+
+# -----------------------------------------------------------------------------
+# MViT options
+# -----------------------------------------------------------------------------
+_C.MVIT = CfgNode()
+
+# Options include `conv`, `max`.
+_C.MVIT.MODE = "conv"
+
+# If True, perform pool before projection in attention.
+_C.MVIT.POOL_FIRST = False
+
+# If True, use cls embed in the network, otherwise don't use cls_embed in transformer.
+_C.MVIT.CLS_EMBED_ON = True
+
+# Kernel size for patchtification.
+_C.MVIT.PATCH_KERNEL = [3, 7, 7]
+
+# Stride size for patchtification.
+_C.MVIT.PATCH_STRIDE = [2, 4, 4]
+
+# Padding size for patchtification.
+_C.MVIT.PATCH_PADDING = [2, 4, 4]
+
+# If True, use 2d patch, otherwise use 3d patch.
+_C.MVIT.PATCH_2D = False
+
+# Base embedding dimension for the transformer.
+_C.MVIT.EMBED_DIM = 96
+
+# Base num of heads for the transformer.
+_C.MVIT.NUM_HEADS = 1
+
+# Dimension reduction ratio for the MLP layers.
+_C.MVIT.MLP_RATIO = 4.0
+
+# If use, use bias term in attention fc layers.
+_C.MVIT.QKV_BIAS = True
+
+# Drop path rate for the tranfomer.
+_C.MVIT.DROPPATH_RATE = 0.1
+
+# The initial value of layer scale gamma. Set 0.0 to disable layer scale.
+_C.MVIT.LAYER_SCALE_INIT_VALUE = 0.0
+
+# Depth of the transformer.
+_C.MVIT.DEPTH = 16
+
+# Normalization layer for the transformer. Only layernorm is supported now.
+_C.MVIT.NORM = "layernorm"
+
+# Dimension multiplication at layer i. If 2.0 is used, then the next block will increase
+# the dimension by 2 times. Format: [depth_i: mul_dim_ratio]
+_C.MVIT.DIM_MUL = []
+
+# Head number multiplication at layer i. If 2.0 is used, then the next block will
+# increase the number of heads by 2 times. Format: [depth_i: head_mul_ratio]
+_C.MVIT.HEAD_MUL = []
+
+# Stride size for the Pool KV at layer i.
+# Format: [[i, stride_t_i, stride_h_i, stride_w_i], ...,]
+_C.MVIT.POOL_KV_STRIDE = []
+
+# Initial stride size for KV at layer 1. The stride size will be further reduced with
+# the raio of MVIT.DIM_MUL. If will overwrite MVIT.POOL_KV_STRIDE if not None.
+_C.MVIT.POOL_KV_STRIDE_ADAPTIVE = None
+
+# Stride size for the Pool Q at layer i.
+# Format: [[i, stride_t_i, stride_h_i, stride_w_i], ...,]
+_C.MVIT.POOL_Q_STRIDE = []
+
+# If not None, overwrite the KV_KERNEL and Q_KERNEL size with POOL_KVQ_CONV_SIZ.
+# Otherwise the kernel_size is [s + 1 if s > 1 else s for s in stride_size].
+_C.MVIT.POOL_KVQ_KERNEL = None
+
+# If True, perform no decay on positional embedding and cls embedding.
+_C.MVIT.ZERO_DECAY_POS_CLS = True
+
+# If True, use norm after stem.
+_C.MVIT.NORM_STEM = False
+
+# If True, perform separate positional embedding.
+_C.MVIT.SEP_POS_EMBED = False
+
+# Dropout rate for the MViT backbone.
+_C.MVIT.DROPOUT_RATE = 0.0
+
+# If True, use absolute positional embedding.
+_C.MVIT.USE_ABS_POS = True
+
+# If True, use relative positional embedding for spatial dimentions
+_C.MVIT.REL_POS_SPATIAL = False
+
+# If True, use relative positional embedding for temporal dimentions
+_C.MVIT.REL_POS_TEMPORAL = False
+
+# If True, init rel with zero
+_C.MVIT.REL_POS_ZERO_INIT = False
+
+# If True, using Residual Pooling connection
+_C.MVIT.RESIDUAL_POOLING = False
+
+# Dim mul in qkv linear layers of attention block instead of MLP
+_C.MVIT.DIM_MUL_IN_ATT = False
+
+# If True, using separate linear layers for Q, K, V in attention blocks.
+_C.MVIT.SEPARATE_QKV = False
+
+# The initialization scale factor for the head parameters.
+_C.MVIT.HEAD_INIT_SCALE = 1.0
+
+# Whether to use the mean pooling of all patch tokens as the output.
+_C.MVIT.USE_MEAN_POOLING = False
+
+# If True, use frozen sin cos positional embedding.
+_C.MVIT.USE_FIXED_SINCOS_POS = False
+
+# -----------------------------------------------------------------------------
+# Masked pretraining options
+# -----------------------------------------------------------------------------
+_C.MASK = CfgNode()
+
+# Whether to enable Masked style pretraining.
+_C.MASK.ENABLE = False
+
+# Whether to enable MAE (discard encoder tokens).
+_C.MASK.MAE_ON = False
+
+# Whether to enable random masking in mae
+_C.MASK.MAE_RND_MASK = False
+
+# Whether to do random masking per-frame in mae
+_C.MASK.PER_FRAME_MASKING = False
+
+# only predict loss on temporal strided patches, or predict full time extent
+_C.MASK.TIME_STRIDE_LOSS = True
+
+# Whether to normalize the pred pixel loss
+_C.MASK.NORM_PRED_PIXEL = True
+
+# Whether to fix initialization with inverse depth of layer for pretraining.
+_C.MASK.SCALE_INIT_BY_DEPTH = False
+
+# Base embedding dimension for the decoder transformer.
+_C.MASK.DECODER_EMBED_DIM = 512
+
+# Base embedding dimension for the decoder transformer.
+_C.MASK.DECODER_SEP_POS_EMBED = False
+
+# Use a KV kernel in decoder?
+_C.MASK.DEC_KV_KERNEL = []
+
+# Use a KV stride in decoder?
+_C.MASK.DEC_KV_STRIDE = []
+
+# The depths of features which are inputs of the prediction head.
+_C.MASK.PRETRAIN_DEPTH = [15]
+
+# The type of Masked pretraining prediction head.
+# Can be "separate", "separate_xformer".
+_C.MASK.HEAD_TYPE = "separate"
+
+# The depth of MAE's decoder
+_C.MASK.DECODER_DEPTH = 0
+
+# The weight of HOG target loss.
+_C.MASK.PRED_HOG = False
+# Reversible Configs
+_C.MVIT.REV = CfgNode()
+
+# Enable Reversible Model
+_C.MVIT.REV.ENABLE = False
+
+# Method to fuse the reversible paths
+# see :class: `TwoStreamFusion` for all the options
+_C.MVIT.REV.RESPATH_FUSE = "concat"
+
+# Layers to buffer activations at
+# (at least Q-pooling layers needed)
+_C.MVIT.REV.BUFFER_LAYERS = []
+
+# 'conv' or 'max' operator for the respath in Qpooling
+_C.MVIT.REV.RES_PATH = "conv"
+
+# Method to merge hidden states before Qpoolinglayers
+_C.MVIT.REV.PRE_Q_FUSION = "avg"
+
+# -----------------------------------------------------------------------------
+# SlowFast options
+# -----------------------------------------------------------------------------
+_C.SLOWFAST = CfgNode()
+
+# Corresponds to the inverse of the channel reduction ratio, $\beta$ between
+# the Slow and Fast pathways.
+_C.SLOWFAST.BETA_INV = 8
+
+# Corresponds to the frame rate reduction ratio, $\alpha$ between the Slow and
+# Fast pathways.
+_C.SLOWFAST.ALPHA = 8
+
+# Ratio of channel dimensions between the Slow and Fast pathways.
+_C.SLOWFAST.FUSION_CONV_CHANNEL_RATIO = 2
+
+# Kernel dimension used for fusing information from Fast pathway to Slow
+# pathway.
+_C.SLOWFAST.FUSION_KERNEL_SZ = 5
+
+
+# -----------------------------------------------------------------------------
+# Data options
+# -----------------------------------------------------------------------------
+_C.DATA = CfgNode()
+
+# The path to the data directory.
+_C.DATA.PATH_TO_DATA_DIR = ""
+
+# The separator used between path and label.
+_C.DATA.PATH_LABEL_SEPARATOR = " "
+
+# Video path prefix if any.
+_C.DATA.PATH_PREFIX = ""
+
+# The number of frames of the input clip.
+_C.DATA.NUM_FRAMES = 8
+
+# The video sampling rate of the input clip.
+_C.DATA.SAMPLING_RATE = 8
+
+# Eigenvalues for PCA jittering. Note PCA is RGB based.
+_C.DATA.TRAIN_PCA_EIGVAL = [0.225, 0.224, 0.229]
+
+# Eigenvectors for PCA jittering.
+_C.DATA.TRAIN_PCA_EIGVEC = [
+    [-0.5675, 0.7192, 0.4009],
+    [-0.5808, -0.0045, -0.8140],
+    [-0.5836, -0.6948, 0.4203],
+]
+
+# If a imdb have been dumpped to a local file with the following format:
+# `{"im_path": im_path, "class": cont_id}`
+# then we can skip the construction of imdb and load it from the local file.
+_C.DATA.PATH_TO_PRELOAD_IMDB = ""
+
+# The mean value of the video raw pixels across the R G B channels.
+_C.DATA.MEAN = [0.45, 0.45, 0.45]
+# List of input frame channel dimensions.
+
+_C.DATA.INPUT_CHANNEL_NUM = [3, 3]
+
+# The std value of the video raw pixels across the R G B channels.
+_C.DATA.STD = [0.225, 0.225, 0.225]
+
+# The spatial augmentation jitter scales for training.
+_C.DATA.TRAIN_JITTER_SCALES = [256, 320]
+
+# The relative scale range of Inception-style area based random resizing augmentation.
+# If this is provided, DATA.TRAIN_JITTER_SCALES above is ignored.
+_C.DATA.TRAIN_JITTER_SCALES_RELATIVE = []
+
+# The relative aspect ratio range of Inception-style area based random resizing
+# augmentation.
+_C.DATA.TRAIN_JITTER_ASPECT_RELATIVE = []
+
+# If True, perform stride length uniform temporal sampling.
+_C.DATA.USE_OFFSET_SAMPLING = False
+
+# Whether to apply motion shift for augmentation.
+_C.DATA.TRAIN_JITTER_MOTION_SHIFT = False
+
+# The spatial crop size for training.
+_C.DATA.TRAIN_CROP_SIZE = 224
+
+# The spatial crop size for testing.
+_C.DATA.TEST_CROP_SIZE = 256
+
+# Input videos may has different fps, convert it to the target video fps before
+# frame sampling.
+_C.DATA.TARGET_FPS = 30
+
+# JITTER TARGET_FPS by +- this number randomly
+_C.DATA.TRAIN_JITTER_FPS = 0.0
+
+# Decoding backend, options include `pyav` or `torchvision`
+_C.DATA.DECODING_BACKEND = "torchvision"
+
+# Decoding resize to short size (set to native size for best speed)
+_C.DATA.DECODING_SHORT_SIZE = 256
+
+# if True, sample uniformly in [1 / max_scale, 1 / min_scale] and take a
+# reciprocal to get the scale. If False, take a uniform sample from
+# [min_scale, max_scale].
+_C.DATA.INV_UNIFORM_SAMPLE = False
+
+# If True, perform random horizontal flip on the video frames during training.
+_C.DATA.RANDOM_FLIP = True
+
+# If True, calculdate the map as metric.
+_C.DATA.MULTI_LABEL = False
+
+# Method to perform the ensemble, options include "sum" and "max".
+_C.DATA.ENSEMBLE_METHOD = "sum"
+
+# If True, revert the default input channel (RBG <-> BGR).
+_C.DATA.REVERSE_INPUT_CHANNEL = False
+
+# how many samples (=clips) to decode from a single video
+_C.DATA.TRAIN_CROP_NUM_TEMPORAL = 1
+
+# how many spatial samples to crop from a single clip
+_C.DATA.TRAIN_CROP_NUM_SPATIAL = 1
+
+# color random percentage for grayscale conversion
+_C.DATA.COLOR_RND_GRAYSCALE = 0.0
+
+# loader can read .csv file in chunks of this chunk size
+_C.DATA.LOADER_CHUNK_SIZE = 0
+
+# if LOADER_CHUNK_SIZE > 0, define overall length of .csv file
+_C.DATA.LOADER_CHUNK_OVERALL_SIZE = 0
+
+# for chunked reading, dataloader can skip rows in (large)
+# training csv file
+_C.DATA.SKIP_ROWS = 0
+
+# The separator used between path and label.
+_C.DATA.PATH_LABEL_SEPARATOR = " "
+
+# augmentation probability to convert raw decoded video to
+# grayscale temporal difference
+_C.DATA.TIME_DIFF_PROB = 0.0
+
+# Apply SSL-based SimCLR / MoCo v1/v2 color augmentations,
+#  with params below
+_C.DATA.SSL_COLOR_JITTER = False
+
+# color jitter percentage for brightness, contrast, saturation
+_C.DATA.SSL_COLOR_BRI_CON_SAT = [0.4, 0.4, 0.4]
+
+# color jitter percentage for hue
+_C.DATA.SSL_COLOR_HUE = 0.1
+
+# SimCLR / MoCo v2 augmentations on/off
+_C.DATA.SSL_MOCOV2_AUG = False
+
+# SimCLR / MoCo v2 blur augmentation minimum gaussian sigma
+_C.DATA.SSL_BLUR_SIGMA_MIN = [0.0, 0.1]
+
+# SimCLR / MoCo v2 blur augmentation maximum gaussian sigma
+_C.DATA.SSL_BLUR_SIGMA_MAX = [0.0, 2.0]
+
+
+# If combine train/val split as training for in21k
+_C.DATA.IN22K_TRAINVAL = False
+
+# If not None, use IN1k as val split when training in21k
+_C.DATA.IN22k_VAL_IN1K = ""
+
+# Large resolution models may use different crop ratios
+_C.DATA.IN_VAL_CROP_RATIO = 0.875  # 224/256 = 0.875
+
+# don't use real video for kinetics.py
+_C.DATA.DUMMY_LOAD = False
+
+# ---------------------------------------------------------------------------- #
+# Optimizer options
+# ---------------------------------------------------------------------------- #
+_C.SOLVER = CfgNode()
+
+# Base learning rate.
+_C.SOLVER.BASE_LR = 0.1
+
+# Learning rate policy (see utils/lr_policy.py for options and examples).
+_C.SOLVER.LR_POLICY = "cosine"
+
+# Final learning rates for 'cosine' policy.
+_C.SOLVER.COSINE_END_LR = 0.0
+
+# Exponential decay factor.
+_C.SOLVER.GAMMA = 0.1
+
+# Step size for 'exp' and 'cos' policies (in epochs).
+_C.SOLVER.STEP_SIZE = 1
+
+# Steps for 'steps_' policies (in epochs).
+_C.SOLVER.STEPS = []
+
+# Learning rates for 'steps_' policies.
+_C.SOLVER.LRS = []
+
+# Maximal number of epochs.
+_C.SOLVER.MAX_EPOCH = 300
+
+# Momentum.
+_C.SOLVER.MOMENTUM = 0.9
+
+# Momentum dampening.
+_C.SOLVER.DAMPENING = 0.0
+
+# Nesterov momentum.
+_C.SOLVER.NESTEROV = True
+
+# L2 regularization.
+_C.SOLVER.WEIGHT_DECAY = 1e-4
+
+# Start the warm up from SOLVER.BASE_LR * SOLVER.WARMUP_FACTOR.
+_C.SOLVER.WARMUP_FACTOR = 0.1
+
+# Gradually warm up the SOLVER.BASE_LR over this number of epochs.
+_C.SOLVER.WARMUP_EPOCHS = 0.0
+
+# The start learning rate of the warm up.
+_C.SOLVER.WARMUP_START_LR = 0.01
+
+# Optimization method.
+_C.SOLVER.OPTIMIZING_METHOD = "sgd"
+
+# Base learning rate is linearly scaled with NUM_SHARDS.
+_C.SOLVER.BASE_LR_SCALE_NUM_SHARDS = False
+
+# If True, start from the peak cosine learning rate after warm up.
+_C.SOLVER.COSINE_AFTER_WARMUP = False
+
+# If True, perform no weight decay on parameter with one dimension (bias term, etc).
+_C.SOLVER.ZERO_WD_1D_PARAM = False
+
+# Clip gradient at this value before optimizer update
+_C.SOLVER.CLIP_GRAD_VAL = None
+
+# Clip gradient at this norm before optimizer update
+_C.SOLVER.CLIP_GRAD_L2NORM = None
+
+# LARS optimizer
+_C.SOLVER.LARS_ON = False
+
+# The layer-wise decay of learning rate. Set to 1. to disable.
+_C.SOLVER.LAYER_DECAY = 1.0
+
+# Adam's beta
+_C.SOLVER.BETAS = (0.9, 0.999)
+# ---------------------------------------------------------------------------- #
+# Misc options
+# ---------------------------------------------------------------------------- #
+
+# The name of the current task; e.g. "ssl"/"sl" for (self)supervised learning
+_C.TASK = ""
+
+# Number of GPUs to use (applies to both training and testing).
+_C.NUM_GPUS = 1
+
+# Number of machine to use for the job.
+_C.NUM_SHARDS = 1
+
+# The index of the current machine.
+_C.SHARD_ID = 0
+
+# Output basedir.
+_C.OUTPUT_DIR = "."
+
+# Note that non-determinism may still be present due to non-deterministic
+# operator implementations in GPU operator libraries.
+_C.RNG_SEED = 1
+
+# Log period in iters.
+_C.LOG_PERIOD = 10
+
+# If True, log the model info.
+_C.LOG_MODEL_INFO = True
+
+# Distributed backend.
+_C.DIST_BACKEND = "nccl"
+
+# ---------------------------------------------------------------------------- #
+# Benchmark options
+# ---------------------------------------------------------------------------- #
+_C.BENCHMARK = CfgNode()
+
+# Number of epochs for data loading benchmark.
+_C.BENCHMARK.NUM_EPOCHS = 5
+
+# Log period in iters for data loading benchmark.
+_C.BENCHMARK.LOG_PERIOD = 100
+
+# If True, shuffle dataloader for epoch during benchmark.
+_C.BENCHMARK.SHUFFLE = True
+
+
+# ---------------------------------------------------------------------------- #
+# Common train/test data loader options
+# ---------------------------------------------------------------------------- #
+_C.DATA_LOADER = CfgNode()
+
+# Number of data loader workers per training process.
+_C.DATA_LOADER.NUM_WORKERS = 8
+
+# Load data to pinned host memory.
+_C.DATA_LOADER.PIN_MEMORY = True
+
+# Enable multi thread decoding.
+_C.DATA_LOADER.ENABLE_MULTI_THREAD_DECODE = False
+
+
+# ---------------------------------------------------------------------------- #
+# Detection options.
+# ---------------------------------------------------------------------------- #
+_C.DETECTION = CfgNode()
+
+# Whether enable video detection.
+_C.DETECTION.ENABLE = False
+
+# Aligned version of RoI. More details can be found at slowfast/models/head_helper.py
+_C.DETECTION.ALIGNED = True
+
+# Spatial scale factor.
+_C.DETECTION.SPATIAL_SCALE_FACTOR = 16
+
+# RoI tranformation resolution.
+_C.DETECTION.ROI_XFORM_RESOLUTION = 7
+
+
+# -----------------------------------------------------------------------------
+# AVA Dataset options
+# -----------------------------------------------------------------------------
+_C.AVA = CfgNode()
+
+# Directory path of frames.
+_C.AVA.FRAME_DIR = "/mnt/fair-flash3-east/ava_trainval_frames.img/"
+
+# Directory path for files of frame lists.
+_C.AVA.FRAME_LIST_DIR = (
+    "/mnt/vol/gfsai-flash3-east/ai-group/users/haoqifan/ava/frame_list/"
+)
+
+# Directory path for annotation files.
+_C.AVA.ANNOTATION_DIR = (
+    "/mnt/vol/gfsai-flash3-east/ai-group/users/haoqifan/ava/frame_list/"
+)
+
+# Filenames of training samples list files.
+_C.AVA.TRAIN_LISTS = ["train.csv"]
+
+# Filenames of test samples list files.
+_C.AVA.TEST_LISTS = ["val.csv"]
+
+# Filenames of box list files for training. Note that we assume files which
+# contains predicted boxes will have a suffix "predicted_boxes" in the
+# filename.
+_C.AVA.TRAIN_GT_BOX_LISTS = ["ava_train_v2.2.csv"]
+_C.AVA.TRAIN_PREDICT_BOX_LISTS = []
+
+# Filenames of box list files for test.
+_C.AVA.TEST_PREDICT_BOX_LISTS = ["ava_val_predicted_boxes.csv"]
+
+# This option controls the score threshold for the predicted boxes to use.
+_C.AVA.DETECTION_SCORE_THRESH = 0.9
+
+# If use BGR as the format of input frames.
+_C.AVA.BGR = False
+
+# Training augmentation parameters
+# Whether to use color augmentation method.
+_C.AVA.TRAIN_USE_COLOR_AUGMENTATION = False
+
+# Whether to only use PCA jitter augmentation when using color augmentation
+# method (otherwise combine with color jitter method).
+_C.AVA.TRAIN_PCA_JITTER_ONLY = True
+
+# Whether to do horizontal flipping during test.
+_C.AVA.TEST_FORCE_FLIP = False
+
+# Whether to use full test set for validation split.
+_C.AVA.FULL_TEST_ON_VAL = False
+
+# The name of the file to the ava label map.
+_C.AVA.LABEL_MAP_FILE = "ava_action_list_v2.2_for_activitynet_2019.pbtxt"
+
+# The name of the file to the ava exclusion.
+_C.AVA.EXCLUSION_FILE = "ava_val_excluded_timestamps_v2.2.csv"
+
+# The name of the file to the ava groundtruth.
+_C.AVA.GROUNDTRUTH_FILE = "ava_val_v2.2.csv"
+
+# Backend to process image, includes `pytorch` and `cv2`.
+_C.AVA.IMG_PROC_BACKEND = "cv2"
+
+# ---------------------------------------------------------------------------- #
+# Multigrid training options
+# See https://arxiv.org/abs/1912.00998 for details about multigrid training.
+# ---------------------------------------------------------------------------- #
+_C.MULTIGRID = CfgNode()
+
+# Multigrid training allows us to train for more epochs with fewer iterations.
+# This hyperparameter specifies how many times more epochs to train.
+# The default setting in paper trains for 1.5x more epochs than baseline.
+_C.MULTIGRID.EPOCH_FACTOR = 1.5
+
+# Enable short cycles.
+_C.MULTIGRID.SHORT_CYCLE = False
+# Short cycle additional spatial dimensions relative to the default crop size.
+_C.MULTIGRID.SHORT_CYCLE_FACTORS = [0.5, 0.5**0.5]
+
+_C.MULTIGRID.LONG_CYCLE = False
+# (Temporal, Spatial) dimensions relative to the default shape.
+_C.MULTIGRID.LONG_CYCLE_FACTORS = [
+    (0.25, 0.5**0.5),
+    (0.5, 0.5**0.5),
+    (0.5, 1),
+    (1, 1),
+]
+
+# While a standard BN computes stats across all examples in a GPU,
+# for multigrid training we fix the number of clips to compute BN stats on.
+# See https://arxiv.org/abs/1912.00998 for details.
+_C.MULTIGRID.BN_BASE_SIZE = 8
+
+# Multigrid training epochs are not proportional to actual training time or
+# computations, so _C.TRAIN.EVAL_PERIOD leads to too frequent or rare
+# evaluation. We use a multigrid-specific rule to determine when to evaluate:
+# This hyperparameter defines how many times to evaluate a model per long
+# cycle shape.
+_C.MULTIGRID.EVAL_FREQ = 3
+
+# No need to specify; Set automatically and used as global variables.
+_C.MULTIGRID.LONG_CYCLE_SAMPLING_RATE = 0
+_C.MULTIGRID.DEFAULT_B = 0
+_C.MULTIGRID.DEFAULT_T = 0
+_C.MULTIGRID.DEFAULT_S = 0
+
+# -----------------------------------------------------------------------------
+# Tensorboard Visualization Options
+# -----------------------------------------------------------------------------
+_C.TENSORBOARD = CfgNode()
+
+# Log to summary writer, this will automatically.
+# log loss, lr and metrics during train/eval.
+_C.TENSORBOARD.ENABLE = False
+# Provide path to prediction results for visualization.
+# This is a pickle file of [prediction_tensor, label_tensor]
+_C.TENSORBOARD.PREDICTIONS_PATH = ""
+# Path to directory for tensorboard logs.
+# Default to to cfg.OUTPUT_DIR/runs-{cfg.TRAIN.DATASET}.
+_C.TENSORBOARD.LOG_DIR = ""
+# Path to a json file providing class_name - id mapping
+# in the format {"class_name1": id1, "class_name2": id2, ...}.
+# This file must be provided to enable plotting confusion matrix
+# by a subset or parent categories.
+_C.TENSORBOARD.CLASS_NAMES_PATH = ""
+
+# Path to a json file for categories -> classes mapping
+# in the format {"parent_class": ["child_class1", "child_class2",...], ...}.
+_C.TENSORBOARD.CATEGORIES_PATH = ""
+
+# Config for confusion matrices visualization.
+_C.TENSORBOARD.CONFUSION_MATRIX = CfgNode()
+# Visualize confusion matrix.
+_C.TENSORBOARD.CONFUSION_MATRIX.ENABLE = False
+# Figure size of the confusion matrices plotted.
+_C.TENSORBOARD.CONFUSION_MATRIX.FIGSIZE = [8, 8]
+# Path to a subset of categories to visualize.
+# File contains class names separated by newline characters.
+_C.TENSORBOARD.CONFUSION_MATRIX.SUBSET_PATH = ""
+
+# Config for histogram visualization.
+_C.TENSORBOARD.HISTOGRAM = CfgNode()
+# Visualize histograms.
+_C.TENSORBOARD.HISTOGRAM.ENABLE = False
+# Path to a subset of classes to plot histograms.
+# Class names must be separated by newline characters.
+_C.TENSORBOARD.HISTOGRAM.SUBSET_PATH = ""
+# Visualize top-k most predicted classes on histograms for each
+# chosen true label.
+_C.TENSORBOARD.HISTOGRAM.TOPK = 10
+# Figure size of the histograms plotted.
+_C.TENSORBOARD.HISTOGRAM.FIGSIZE = [8, 8]
+
+# Config for layers' weights and activations visualization.
+# _C.TENSORBOARD.ENABLE must be True.
+_C.TENSORBOARD.MODEL_VIS = CfgNode()
+
+# If False, skip model visualization.
+_C.TENSORBOARD.MODEL_VIS.ENABLE = False
+
+# If False, skip visualizing model weights.
+_C.TENSORBOARD.MODEL_VIS.MODEL_WEIGHTS = False
+
+# If False, skip visualizing model activations.
+_C.TENSORBOARD.MODEL_VIS.ACTIVATIONS = False
+
+# If False, skip visualizing input videos.
+_C.TENSORBOARD.MODEL_VIS.INPUT_VIDEO = False
+
+
+# List of strings containing data about layer names and their indexing to
+# visualize weights and activations for. The indexing is meant for
+# choosing a subset of activations outputed by a layer for visualization.
+# If indexing is not specified, visualize all activations outputed by the layer.
+# For each string, layer name and indexing is separated by whitespaces.
+# e.g.: [layer1 1,2;1,2, layer2, layer3 150,151;3,4]; this means for each array `arr`
+# along the batch dimension in `layer1`, we take arr[[1, 2], [1, 2]]
+_C.TENSORBOARD.MODEL_VIS.LAYER_LIST = []
+# Top-k predictions to plot on videos
+_C.TENSORBOARD.MODEL_VIS.TOPK_PREDS = 1
+# Colormap to for text boxes and bounding boxes colors
+_C.TENSORBOARD.MODEL_VIS.COLORMAP = "Pastel2"
+# Config for visualization video inputs with Grad-CAM.
+# _C.TENSORBOARD.ENABLE must be True.
+_C.TENSORBOARD.MODEL_VIS.GRAD_CAM = CfgNode()
+# Whether to run visualization using Grad-CAM technique.
+_C.TENSORBOARD.MODEL_VIS.GRAD_CAM.ENABLE = True
+# CNN layers to use for Grad-CAM. The number of layers must be equal to
+# number of pathway(s).
+_C.TENSORBOARD.MODEL_VIS.GRAD_CAM.LAYER_LIST = []
+# If True, visualize Grad-CAM using true labels for each instances.
+# If False, use the highest predicted class.
+_C.TENSORBOARD.MODEL_VIS.GRAD_CAM.USE_TRUE_LABEL = False
+# Colormap to for text boxes and bounding boxes colors
+_C.TENSORBOARD.MODEL_VIS.GRAD_CAM.COLORMAP = "viridis"
+
+# Config for visualization for wrong prediction visualization.
+# _C.TENSORBOARD.ENABLE must be True.
+_C.TENSORBOARD.WRONG_PRED_VIS = CfgNode()
+_C.TENSORBOARD.WRONG_PRED_VIS.ENABLE = False
+# Folder tag to origanize model eval videos under.
+_C.TENSORBOARD.WRONG_PRED_VIS.TAG = "Incorrectly classified videos."
+# Subset of labels to visualize. Only wrong predictions with true labels
+# within this subset is visualized.
+_C.TENSORBOARD.WRONG_PRED_VIS.SUBSET_PATH = ""
+
+
+# ---------------------------------------------------------------------------- #
+# Demo options
+# ---------------------------------------------------------------------------- #
+_C.DEMO = CfgNode()
+
+# Run model in DEMO mode.
+_C.DEMO.ENABLE = False
+
+# Path to a json file providing class_name - id mapping
+# in the format {"class_name1": id1, "class_name2": id2, ...}.
+_C.DEMO.LABEL_FILE_PATH = ""
+
+# Specify a camera device as input. This will be prioritized
+# over input video if set.
+# If -1, use input video instead.
+_C.DEMO.WEBCAM = -1
+
+# Path to input video for demo.
+_C.DEMO.INPUT_VIDEO = ""
+# Custom width for reading input video data.
+_C.DEMO.DISPLAY_WIDTH = 0
+# Custom height for reading input video data.
+_C.DEMO.DISPLAY_HEIGHT = 0
+# Path to Detectron2 object detection model configuration,
+# only used for detection tasks.
+_C.DEMO.DETECTRON2_CFG = "COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml"
+# Path to Detectron2 object detection model pre-trained weights.
+_C.DEMO.DETECTRON2_WEIGHTS = "detectron2://COCO-Detection/faster_rcnn_R_50_FPN_3x/137849458/model_final_280758.pkl"
+# Threshold for choosing predicted bounding boxes by Detectron2.
+_C.DEMO.DETECTRON2_THRESH = 0.9
+# Number of overlapping frames between 2 consecutive clips.
+# Increase this number for more frequent action predictions.
+# The number of overlapping frames cannot be larger than
+# half of the sequence length `cfg.DATA.NUM_FRAMES * cfg.DATA.SAMPLING_RATE`
+_C.DEMO.BUFFER_SIZE = 0
+# If specified, the visualized outputs will be written this a video file of
+# this path. Otherwise, the visualized outputs will be displayed in a window.
+_C.DEMO.OUTPUT_FILE = ""
+# Frames per second rate for writing to output video file.
+# If not set (-1), use fps rate from input file.
+_C.DEMO.OUTPUT_FPS = -1
+# Input format from demo video reader ("RGB" or "BGR").
+_C.DEMO.INPUT_FORMAT = "BGR"
+# Draw visualization frames in [keyframe_idx - CLIP_VIS_SIZE, keyframe_idx + CLIP_VIS_SIZE] inclusively.
+_C.DEMO.CLIP_VIS_SIZE = 10
+# Number of processes to run video visualizer.
+_C.DEMO.NUM_VIS_INSTANCES = 2
+
+# Path to pre-computed predicted boxes
+_C.DEMO.PREDS_BOXES = ""
+# Whether to run in with multi-threaded video reader.
+_C.DEMO.THREAD_ENABLE = False
+# Take one clip for every `DEMO.NUM_CLIPS_SKIP` + 1 for prediction and visualization.
+# This is used for fast demo speed by reducing the prediction/visualiztion frequency.
+# If -1, take the most recent read clip for visualization. This mode is only supported
+# if `DEMO.THREAD_ENABLE` is set to True.
+_C.DEMO.NUM_CLIPS_SKIP = 0
+# Path to ground-truth boxes and labels (optional)
+_C.DEMO.GT_BOXES = ""
+# The starting second of the video w.r.t bounding boxes file.
+_C.DEMO.STARTING_SECOND = 900
+# Frames per second of the input video/folder of images.
+_C.DEMO.FPS = 30
+# Visualize with top-k predictions or predictions above certain threshold(s).
+# Option: {"thres", "top-k"}
+_C.DEMO.VIS_MODE = "thres"
+# Threshold for common class names.
+_C.DEMO.COMMON_CLASS_THRES = 0.7
+# Theshold for uncommon class names. This will not be
+# used if `_C.DEMO.COMMON_CLASS_NAMES` is empty.
+_C.DEMO.UNCOMMON_CLASS_THRES = 0.3
+# This is chosen based on distribution of examples in
+# each classes in AVA dataset.
+_C.DEMO.COMMON_CLASS_NAMES = [
+    "watch (a person)",
+    "talk to (e.g., self, a person, a group)",
+    "listen to (a person)",
+    "touch (an object)",
+    "carry/hold (an object)",
+    "walk",
+    "sit",
+    "lie/sleep",
+    "bend/bow (at the waist)",
+]
+# Slow-motion rate for the visualization. The visualized portions of the
+# video will be played `_C.DEMO.SLOWMO` times slower than usual speed.
+_C.DEMO.SLOWMO = 1
+
+
+def assert_and_infer_cfg(cfg):
+    # BN assertions.
+    if cfg.BN.USE_PRECISE_STATS:
+        assert cfg.BN.NUM_BATCHES_PRECISE >= 0
+    # TRAIN assertions.
+    assert cfg.TRAIN.CHECKPOINT_TYPE in ["pytorch", "caffe2"]
+    assert cfg.NUM_GPUS == 0 or cfg.TRAIN.BATCH_SIZE % cfg.NUM_GPUS == 0
+
+    # TEST assertions.
+    assert cfg.TEST.CHECKPOINT_TYPE in ["pytorch", "caffe2"]
+    assert cfg.NUM_GPUS == 0 or cfg.TEST.BATCH_SIZE % cfg.NUM_GPUS == 0
+
+    # RESNET assertions.
+    assert cfg.RESNET.NUM_GROUPS > 0
+    assert cfg.RESNET.WIDTH_PER_GROUP > 0
+    assert cfg.RESNET.WIDTH_PER_GROUP % cfg.RESNET.NUM_GROUPS == 0
+
+    # Execute LR scaling by num_shards.
+    if cfg.SOLVER.BASE_LR_SCALE_NUM_SHARDS:
+        cfg.SOLVER.BASE_LR *= cfg.NUM_SHARDS
+        cfg.SOLVER.WARMUP_START_LR *= cfg.NUM_SHARDS
+        cfg.SOLVER.COSINE_END_LR *= cfg.NUM_SHARDS
+
+    # General assertions.
+    assert cfg.SHARD_ID < cfg.NUM_SHARDS
+    return cfg
+
+
+def get_cfg():
+    return _C.clone()
 
 def load_config(path_to_config=None):
     # Setup cfg.

helpers/cfg.py

@@ -1,1286 +0,0 @@
-#!/usr/bin/env python3
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-
-"""Configs."""
-import math
-
-from fvcore.common.config import CfgNode
-
-# -----------------------------------------------------------------------------
-# Config definition
-# -----------------------------------------------------------------------------
-_C = CfgNode()
-
-# -----------------------------------------------------------------------------
-# Contrastive Model (for MoCo, SimCLR, SwAV, BYOL)
-# -----------------------------------------------------------------------------
-
-_C.CONTRASTIVE = CfgNode()
-
-# temperature used for contrastive losses
-_C.CONTRASTIVE.T = 0.07
-
-# output dimension for the loss
-_C.CONTRASTIVE.DIM = 128
-
-# number of training samples (for kNN bank)
-_C.CONTRASTIVE.LENGTH = 239975
-
-# the length of MoCo's and MemBanks' queues
-_C.CONTRASTIVE.QUEUE_LEN = 65536
-
-# momentum for momentum encoder updates
-_C.CONTRASTIVE.MOMENTUM = 0.5
-
-# wether to anneal momentum to value above with cosine schedule
-_C.CONTRASTIVE.MOMENTUM_ANNEALING = False
-
-# either memorybank, moco, simclr, byol, swav
-_C.CONTRASTIVE.TYPE = "mem"
-
-# wether to interpolate memorybank in time
-_C.CONTRASTIVE.INTERP_MEMORY = False
-
-# 1d or 2d (+temporal) memory
-_C.CONTRASTIVE.MEM_TYPE = "1d"
-
-# number of classes for online kNN evaluation
-_C.CONTRASTIVE.NUM_CLASSES_DOWNSTREAM = 400
-
-# use an MLP projection with these num layers
-_C.CONTRASTIVE.NUM_MLP_LAYERS = 1
-
-# dimension of projection and predictor MLPs
-_C.CONTRASTIVE.MLP_DIM = 2048
-
-# use BN in projection/prediction MLP
-_C.CONTRASTIVE.BN_MLP = False
-
-# use synchronized BN in projection/prediction MLP
-_C.CONTRASTIVE.BN_SYNC_MLP = False
-
-# shuffle BN only locally vs. across machines
-_C.CONTRASTIVE.LOCAL_SHUFFLE_BN = True
-
-# Wether to fill multiple clips (or just the first) into queue
-_C.CONTRASTIVE.MOCO_MULTI_VIEW_QUEUE = False
-
-# if sampling multiple clips per vid they need to be at least min frames apart
-_C.CONTRASTIVE.DELTA_CLIPS_MIN = -math.inf
-
-# if sampling multiple clips per vid they can be max frames apart
-_C.CONTRASTIVE.DELTA_CLIPS_MAX = math.inf
-
-# if non empty, use predictors with depth specified
-_C.CONTRASTIVE.PREDICTOR_DEPTHS = []
-
-# Wether to sequentially process multiple clips (=lower mem usage) or batch them
-_C.CONTRASTIVE.SEQUENTIAL = False
-
-# Wether to perform SimCLR loss across machines (or only locally)
-_C.CONTRASTIVE.SIMCLR_DIST_ON = True
-
-# Length of queue used in SwAV
-_C.CONTRASTIVE.SWAV_QEUE_LEN = 0
-
-# Wether to run online kNN evaluation during training
-_C.CONTRASTIVE.KNN_ON = True
-
-
-# ---------------------------------------------------------------------------- #
-# Batch norm options
-# ---------------------------------------------------------------------------- #
-_C.BN = CfgNode()
-
-# Precise BN stats.
-_C.BN.USE_PRECISE_STATS = False
-
-# Number of samples use to compute precise bn.
-_C.BN.NUM_BATCHES_PRECISE = 200
-
-# Weight decay value that applies on BN.
-_C.BN.WEIGHT_DECAY = 0.0
-
-# Norm type, options include `batchnorm`, `sub_batchnorm`, `sync_batchnorm`
-_C.BN.NORM_TYPE = "batchnorm"
-
-# Parameter for SubBatchNorm, where it splits the batch dimension into
-# NUM_SPLITS splits, and run BN on each of them separately independently.
-_C.BN.NUM_SPLITS = 1
-
-# Parameter for NaiveSyncBatchNorm, where the stats across `NUM_SYNC_DEVICES`
-# devices will be synchronized. `NUM_SYNC_DEVICES` cannot be larger than number of
-# devices per machine; if global sync is desired, set `GLOBAL_SYNC`.
-# By default ONLY applies to NaiveSyncBatchNorm3d; consider also setting
-# CONTRASTIVE.BN_SYNC_MLP if appropriate.
-_C.BN.NUM_SYNC_DEVICES = 1
-
-# Parameter for NaiveSyncBatchNorm. Setting `GLOBAL_SYNC` to True synchronizes
-# stats across all devices, across all machines; in this case, `NUM_SYNC_DEVICES`
-# must be set to None.
-# By default ONLY applies to NaiveSyncBatchNorm3d; consider also setting
-# CONTRASTIVE.BN_SYNC_MLP if appropriate.
-_C.BN.GLOBAL_SYNC = False
-
-# ---------------------------------------------------------------------------- #
-# Training options.
-# ---------------------------------------------------------------------------- #
-_C.TRAIN = CfgNode()
-
-# If True Train the model, else skip training.
-_C.TRAIN.ENABLE = True
-
-# Kill training if loss explodes over this ratio from the previous 5 measurements.
-# Only enforced if > 0.0
-_C.TRAIN.KILL_LOSS_EXPLOSION_FACTOR = 0.0
-
-# Dataset.
-_C.TRAIN.DATASET = "kinetics"
-
-# Total mini-batch size.
-_C.TRAIN.BATCH_SIZE = 64
-
-# Evaluate model on test data every eval period epochs.
-_C.TRAIN.EVAL_PERIOD = 10
-
-# Save model checkpoint every checkpoint period epochs.
-_C.TRAIN.CHECKPOINT_PERIOD = 10
-
-# Resume training from the latest checkpoint in the output directory.
-_C.TRAIN.AUTO_RESUME = True
-
-# Path to the checkpoint to load the initial weight.
-_C.TRAIN.CHECKPOINT_FILE_PATH = ""
-
-# Checkpoint types include `caffe2` or `pytorch`.
-_C.TRAIN.CHECKPOINT_TYPE = "pytorch"
-
-# If True, perform inflation when loading checkpoint.
-_C.TRAIN.CHECKPOINT_INFLATE = False
-
-# If True, reset epochs when loading checkpoint.
-_C.TRAIN.CHECKPOINT_EPOCH_RESET = False
-
-# If set, clear all layer names according to the pattern provided.
-_C.TRAIN.CHECKPOINT_CLEAR_NAME_PATTERN = ()  # ("backbone.",)
-
-# If True, use FP16 for activations
-_C.TRAIN.MIXED_PRECISION = False
-
-# if True, inflate some params from imagenet model.
-_C.TRAIN.CHECKPOINT_IN_INIT = False
-
-# ---------------------------------------------------------------------------- #
-# Augmentation options.
-# ---------------------------------------------------------------------------- #
-_C.AUG = CfgNode()
-
-# Whether to enable randaug.
-_C.AUG.ENABLE = False
-
-# Number of repeated augmentations to used during training.
-# If this is greater than 1, then the actual batch size is
-# TRAIN.BATCH_SIZE * AUG.NUM_SAMPLE.
-_C.AUG.NUM_SAMPLE = 1
-
-# Not used if using randaug.
-_C.AUG.COLOR_JITTER = 0.4
-
-# RandAug parameters.
-_C.AUG.AA_TYPE = "rand-m9-mstd0.5-inc1"
-
-# Interpolation method.
-_C.AUG.INTERPOLATION = "bicubic"
-
-# Probability of random erasing.
-_C.AUG.RE_PROB = 0.25
-
-# Random erasing mode.
-_C.AUG.RE_MODE = "pixel"
-
-# Random erase count.
-_C.AUG.RE_COUNT = 1
-
-# Do not random erase first (clean) augmentation split.
-_C.AUG.RE_SPLIT = False
-
-# Whether to generate input mask during image processing.
-_C.AUG.GEN_MASK_LOADER = False
-
-# If True, masking mode is "tube". Default is "cube".
-_C.AUG.MASK_TUBE = False
-
-# If True, masking mode is "frame". Default is "cube".
-_C.AUG.MASK_FRAMES = False
-
-# The size of generated masks.
-_C.AUG.MASK_WINDOW_SIZE = [8, 7, 7]
-
-# The ratio of masked tokens out of all tokens. Also applies to MViT supervised training
-_C.AUG.MASK_RATIO = 0.0
-
-# The maximum number of a masked block. None means no maximum limit. (Used only in image MaskFeat.)
-_C.AUG.MAX_MASK_PATCHES_PER_BLOCK = None
-
-# ---------------------------------------------------------------------------- #
-# Masked pretraining visualization options.
-# ---------------------------------------------------------------------------- #
-_C.VIS_MASK = CfgNode()
-
-# Whether to do visualization.
-_C.VIS_MASK.ENABLE = False
-
-# ---------------------------------------------------------------------------- #
-# MipUp options.
-# ---------------------------------------------------------------------------- #
-_C.MIXUP = CfgNode()
-
-# Whether to use mixup.
-_C.MIXUP.ENABLE = False
-
-# Mixup alpha.
-_C.MIXUP.ALPHA = 0.8
-
-# Cutmix alpha.
-_C.MIXUP.CUTMIX_ALPHA = 1.0
-
-# Probability of performing mixup or cutmix when either/both is enabled.
-_C.MIXUP.PROB = 1.0
-
-# Probability of switching to cutmix when both mixup and cutmix enabled.
-_C.MIXUP.SWITCH_PROB = 0.5
-
-# Label smoothing.
-_C.MIXUP.LABEL_SMOOTH_VALUE = 0.1
-
-# ---------------------------------------------------------------------------- #
-# Testing options
-# ---------------------------------------------------------------------------- #
-_C.TEST = CfgNode()
-
-# If True test the model, else skip the testing.
-_C.TEST.ENABLE = True
-
-# Dataset for testing.
-_C.TEST.DATASET = "kinetics"
-
-# Total mini-batch size
-_C.TEST.BATCH_SIZE = 8
-
-# Path to the checkpoint to load the initial weight.
-_C.TEST.CHECKPOINT_FILE_PATH = ""
-
-# Number of clips to sample from a video uniformly for aggregating the
-# prediction results.
-_C.TEST.NUM_ENSEMBLE_VIEWS = 10
-
-# Number of crops to sample from a frame spatially for aggregating the
-# prediction results.
-_C.TEST.NUM_SPATIAL_CROPS = 3
-
-# Checkpoint types include `caffe2` or `pytorch`.
-_C.TEST.CHECKPOINT_TYPE = "pytorch"
-# Path to saving prediction results file.
-_C.TEST.SAVE_RESULTS_PATH = ""
-
-_C.TEST.NUM_TEMPORAL_CLIPS = []
-# -----------------------------------------------------------------------------
-# ResNet options
-# -----------------------------------------------------------------------------
-_C.RESNET = CfgNode()
-
-# Transformation function.
-_C.RESNET.TRANS_FUNC = "bottleneck_transform"
-
-# Number of groups. 1 for ResNet, and larger than 1 for ResNeXt).
-_C.RESNET.NUM_GROUPS = 1
-
-# Width of each group (64 -> ResNet; 4 -> ResNeXt).
-_C.RESNET.WIDTH_PER_GROUP = 64
-
-# Apply relu in a inplace manner.
-_C.RESNET.INPLACE_RELU = True
-
-# Apply stride to 1x1 conv.
-_C.RESNET.STRIDE_1X1 = False
-
-#  If true, initialize the gamma of the final BN of each block to zero.
-_C.RESNET.ZERO_INIT_FINAL_BN = False
-
-#  If true, initialize the final conv layer of each block to zero.
-_C.RESNET.ZERO_INIT_FINAL_CONV = False
-
-# Number of weight layers.
-_C.RESNET.DEPTH = 50
-
-# If the current block has more than NUM_BLOCK_TEMP_KERNEL blocks, use temporal
-# kernel of 1 for the rest of the blocks.
-_C.RESNET.NUM_BLOCK_TEMP_KERNEL = [[3], [4], [6], [3]]
-
-# Size of stride on different res stages.
-_C.RESNET.SPATIAL_STRIDES = [[1], [2], [2], [2]]
-
-# Size of dilation on different res stages.
-_C.RESNET.SPATIAL_DILATIONS = [[1], [1], [1], [1]]
-
-# ---------------------------------------------------------------------------- #
-# X3D  options
-# See https://arxiv.org/abs/2004.04730 for details about X3D Networks.
-# ---------------------------------------------------------------------------- #
-_C.X3D = CfgNode()
-
-# Width expansion factor.
-_C.X3D.WIDTH_FACTOR = 1.0
-
-# Depth expansion factor.
-_C.X3D.DEPTH_FACTOR = 1.0
-
-# Bottleneck expansion factor for the 3x3x3 conv.
-_C.X3D.BOTTLENECK_FACTOR = 1.0  #
-
-# Dimensions of the last linear layer before classificaiton.
-_C.X3D.DIM_C5 = 2048
-
-# Dimensions of the first 3x3 conv layer.
-_C.X3D.DIM_C1 = 12
-
-# Whether to scale the width of Res2, default is false.
-_C.X3D.SCALE_RES2 = False
-
-# Whether to use a BatchNorm (BN) layer before the classifier, default is false.
-_C.X3D.BN_LIN5 = False
-
-# Whether to use channelwise (=depthwise) convolution in the center (3x3x3)
-# convolution operation of the residual blocks.
-_C.X3D.CHANNELWISE_3x3x3 = True
-
-# -----------------------------------------------------------------------------
-# Nonlocal options
-# -----------------------------------------------------------------------------
-_C.NONLOCAL = CfgNode()
-
-# Index of each stage and block to add nonlocal layers.
-_C.NONLOCAL.LOCATION = [[[]], [[]], [[]], [[]]]
-
-# Number of group for nonlocal for each stage.
-_C.NONLOCAL.GROUP = [[1], [1], [1], [1]]
-
-# Instatiation to use for non-local layer.
-_C.NONLOCAL.INSTANTIATION = "dot_product"
-
-
-# Size of pooling layers used in Non-Local.
-_C.NONLOCAL.POOL = [
-    # Res2
-    [[1, 2, 2], [1, 2, 2]],
-    # Res3
-    [[1, 2, 2], [1, 2, 2]],
-    # Res4
-    [[1, 2, 2], [1, 2, 2]],
-    # Res5
-    [[1, 2, 2], [1, 2, 2]],
-]
-
-# -----------------------------------------------------------------------------
-# Model options
-# -----------------------------------------------------------------------------
-_C.MODEL = CfgNode()
-
-# Model architecture.
-_C.MODEL.ARCH = "slowfast"
-
-# Model name
-_C.MODEL.MODEL_NAME = "SlowFast"
-
-# The number of classes to predict for the model.
-_C.MODEL.NUM_CLASSES = 400
-
-# Loss function.
-_C.MODEL.LOSS_FUNC = "cross_entropy"
-
-# Model architectures that has one single pathway.
-_C.MODEL.SINGLE_PATHWAY_ARCH = [
-    "2d",
-    "c2d",
-    "i3d",
-    "slow",
-    "x3d",
-    "mvit",
-    "maskmvit",
-]
-
-# Model architectures that has multiple pathways.
-_C.MODEL.MULTI_PATHWAY_ARCH = ["slowfast"]
-
-# Dropout rate before final projection in the backbone.
-_C.MODEL.DROPOUT_RATE = 0.5
-
-# Randomly drop rate for Res-blocks, linearly increase from res2 to res5
-_C.MODEL.DROPCONNECT_RATE = 0.0
-
-# The std to initialize the fc layer(s).
-_C.MODEL.FC_INIT_STD = 0.01
-
-# Activation layer for the output head.
-_C.MODEL.HEAD_ACT = "softmax"
-
-# Activation checkpointing enabled or not to save GPU memory.
-_C.MODEL.ACT_CHECKPOINT = False
-
-# If True, detach the final fc layer from the network, by doing so, only the
-# final fc layer will be trained.
-_C.MODEL.DETACH_FINAL_FC = False
-
-# If True, frozen batch norm stats during training.
-_C.MODEL.FROZEN_BN = False
-
-# If True, AllReduce gradients are compressed to fp16
-_C.MODEL.FP16_ALLREDUCE = False
-
-
-# -----------------------------------------------------------------------------
-# MViT options
-# -----------------------------------------------------------------------------
-_C.MVIT = CfgNode()
-
-# Options include `conv`, `max`.
-_C.MVIT.MODE = "conv"
-
-# If True, perform pool before projection in attention.
-_C.MVIT.POOL_FIRST = False
-
-# If True, use cls embed in the network, otherwise don't use cls_embed in transformer.
-_C.MVIT.CLS_EMBED_ON = True
-
-# Kernel size for patchtification.
-_C.MVIT.PATCH_KERNEL = [3, 7, 7]
-
-# Stride size for patchtification.
-_C.MVIT.PATCH_STRIDE = [2, 4, 4]
-
-# Padding size for patchtification.
-_C.MVIT.PATCH_PADDING = [2, 4, 4]
-
-# If True, use 2d patch, otherwise use 3d patch.
-_C.MVIT.PATCH_2D = False
-
-# Base embedding dimension for the transformer.
-_C.MVIT.EMBED_DIM = 96
-
-# Base num of heads for the transformer.
-_C.MVIT.NUM_HEADS = 1
-
-# Dimension reduction ratio for the MLP layers.
-_C.MVIT.MLP_RATIO = 4.0
-
-# If use, use bias term in attention fc layers.
-_C.MVIT.QKV_BIAS = True
-
-# Drop path rate for the tranfomer.
-_C.MVIT.DROPPATH_RATE = 0.1
-
-# The initial value of layer scale gamma. Set 0.0 to disable layer scale.
-_C.MVIT.LAYER_SCALE_INIT_VALUE = 0.0
-
-# Depth of the transformer.
-_C.MVIT.DEPTH = 16
-
-# Normalization layer for the transformer. Only layernorm is supported now.
-_C.MVIT.NORM = "layernorm"
-
-# Dimension multiplication at layer i. If 2.0 is used, then the next block will increase
-# the dimension by 2 times. Format: [depth_i: mul_dim_ratio]
-_C.MVIT.DIM_MUL = []
-
-# Head number multiplication at layer i. If 2.0 is used, then the next block will
-# increase the number of heads by 2 times. Format: [depth_i: head_mul_ratio]
-_C.MVIT.HEAD_MUL = []
-
-# Stride size for the Pool KV at layer i.
-# Format: [[i, stride_t_i, stride_h_i, stride_w_i], ...,]
-_C.MVIT.POOL_KV_STRIDE = []
-
-# Initial stride size for KV at layer 1. The stride size will be further reduced with
-# the raio of MVIT.DIM_MUL. If will overwrite MVIT.POOL_KV_STRIDE if not None.
-_C.MVIT.POOL_KV_STRIDE_ADAPTIVE = None
-
-# Stride size for the Pool Q at layer i.
-# Format: [[i, stride_t_i, stride_h_i, stride_w_i], ...,]
-_C.MVIT.POOL_Q_STRIDE = []
-
-# If not None, overwrite the KV_KERNEL and Q_KERNEL size with POOL_KVQ_CONV_SIZ.
-# Otherwise the kernel_size is [s + 1 if s > 1 else s for s in stride_size].
-_C.MVIT.POOL_KVQ_KERNEL = None
-
-# If True, perform no decay on positional embedding and cls embedding.
-_C.MVIT.ZERO_DECAY_POS_CLS = True
-
-# If True, use norm after stem.
-_C.MVIT.NORM_STEM = False
-
-# If True, perform separate positional embedding.
-_C.MVIT.SEP_POS_EMBED = False
-
-# Dropout rate for the MViT backbone.
-_C.MVIT.DROPOUT_RATE = 0.0
-
-# If True, use absolute positional embedding.
-_C.MVIT.USE_ABS_POS = True
-
-# If True, use relative positional embedding for spatial dimentions
-_C.MVIT.REL_POS_SPATIAL = False
-
-# If True, use relative positional embedding for temporal dimentions
-_C.MVIT.REL_POS_TEMPORAL = False
-
-# If True, init rel with zero
-_C.MVIT.REL_POS_ZERO_INIT = False
-
-# If True, using Residual Pooling connection
-_C.MVIT.RESIDUAL_POOLING = False
-
-# Dim mul in qkv linear layers of attention block instead of MLP
-_C.MVIT.DIM_MUL_IN_ATT = False
-
-# If True, using separate linear layers for Q, K, V in attention blocks.
-_C.MVIT.SEPARATE_QKV = False
-
-# The initialization scale factor for the head parameters.
-_C.MVIT.HEAD_INIT_SCALE = 1.0
-
-# Whether to use the mean pooling of all patch tokens as the output.
-_C.MVIT.USE_MEAN_POOLING = False
-
-# If True, use frozen sin cos positional embedding.
-_C.MVIT.USE_FIXED_SINCOS_POS = False
-
-# -----------------------------------------------------------------------------
-# Masked pretraining options
-# -----------------------------------------------------------------------------
-_C.MASK = CfgNode()
-
-# Whether to enable Masked style pretraining.
-_C.MASK.ENABLE = False
-
-# Whether to enable MAE (discard encoder tokens).
-_C.MASK.MAE_ON = False
-
-# Whether to enable random masking in mae
-_C.MASK.MAE_RND_MASK = False
-
-# Whether to do random masking per-frame in mae
-_C.MASK.PER_FRAME_MASKING = False
-
-# only predict loss on temporal strided patches, or predict full time extent
-_C.MASK.TIME_STRIDE_LOSS = True
-
-# Whether to normalize the pred pixel loss
-_C.MASK.NORM_PRED_PIXEL = True
-
-# Whether to fix initialization with inverse depth of layer for pretraining.
-_C.MASK.SCALE_INIT_BY_DEPTH = False
-
-# Base embedding dimension for the decoder transformer.
-_C.MASK.DECODER_EMBED_DIM = 512
-
-# Base embedding dimension for the decoder transformer.
-_C.MASK.DECODER_SEP_POS_EMBED = False
-
-# Use a KV kernel in decoder?
-_C.MASK.DEC_KV_KERNEL = []
-
-# Use a KV stride in decoder?
-_C.MASK.DEC_KV_STRIDE = []
-
-# The depths of features which are inputs of the prediction head.
-_C.MASK.PRETRAIN_DEPTH = [15]
-
-# The type of Masked pretraining prediction head.
-# Can be "separate", "separate_xformer".
-_C.MASK.HEAD_TYPE = "separate"
-
-# The depth of MAE's decoder
-_C.MASK.DECODER_DEPTH = 0
-
-# The weight of HOG target loss.
-_C.MASK.PRED_HOG = False
-# Reversible Configs
-_C.MVIT.REV = CfgNode()
-
-# Enable Reversible Model
-_C.MVIT.REV.ENABLE = False
-
-# Method to fuse the reversible paths
-# see :class: `TwoStreamFusion` for all the options
-_C.MVIT.REV.RESPATH_FUSE = "concat"
-
-# Layers to buffer activations at
-# (at least Q-pooling layers needed)
-_C.MVIT.REV.BUFFER_LAYERS = []
-
-# 'conv' or 'max' operator for the respath in Qpooling
-_C.MVIT.REV.RES_PATH = "conv"
-
-# Method to merge hidden states before Qpoolinglayers
-_C.MVIT.REV.PRE_Q_FUSION = "avg"
-
-# -----------------------------------------------------------------------------
-# SlowFast options
-# -----------------------------------------------------------------------------
-_C.SLOWFAST = CfgNode()
-
-# Corresponds to the inverse of the channel reduction ratio, $\beta$ between
-# the Slow and Fast pathways.
-_C.SLOWFAST.BETA_INV = 8
-
-# Corresponds to the frame rate reduction ratio, $\alpha$ between the Slow and
-# Fast pathways.
-_C.SLOWFAST.ALPHA = 8
-
-# Ratio of channel dimensions between the Slow and Fast pathways.
-_C.SLOWFAST.FUSION_CONV_CHANNEL_RATIO = 2
-
-# Kernel dimension used for fusing information from Fast pathway to Slow
-# pathway.
-_C.SLOWFAST.FUSION_KERNEL_SZ = 5
-
-
-# -----------------------------------------------------------------------------
-# Data options
-# -----------------------------------------------------------------------------
-_C.DATA = CfgNode()
-
-# The path to the data directory.
-_C.DATA.PATH_TO_DATA_DIR = ""
-
-# The separator used between path and label.
-_C.DATA.PATH_LABEL_SEPARATOR = " "
-
-# Video path prefix if any.
-_C.DATA.PATH_PREFIX = ""
-
-# The number of frames of the input clip.
-_C.DATA.NUM_FRAMES = 8
-
-# The video sampling rate of the input clip.
-_C.DATA.SAMPLING_RATE = 8
-
-# Eigenvalues for PCA jittering. Note PCA is RGB based.
-_C.DATA.TRAIN_PCA_EIGVAL = [0.225, 0.224, 0.229]
-
-# Eigenvectors for PCA jittering.
-_C.DATA.TRAIN_PCA_EIGVEC = [
-    [-0.5675, 0.7192, 0.4009],
-    [-0.5808, -0.0045, -0.8140],
-    [-0.5836, -0.6948, 0.4203],
-]
-
-# If a imdb have been dumpped to a local file with the following format:
-# `{"im_path": im_path, "class": cont_id}`
-# then we can skip the construction of imdb and load it from the local file.
-_C.DATA.PATH_TO_PRELOAD_IMDB = ""
-
-# The mean value of the video raw pixels across the R G B channels.
-_C.DATA.MEAN = [0.45, 0.45, 0.45]
-# List of input frame channel dimensions.
-
-_C.DATA.INPUT_CHANNEL_NUM = [3, 3]
-
-# The std value of the video raw pixels across the R G B channels.
-_C.DATA.STD = [0.225, 0.225, 0.225]
-
-# The spatial augmentation jitter scales for training.
-_C.DATA.TRAIN_JITTER_SCALES = [256, 320]
-
-# The relative scale range of Inception-style area based random resizing augmentation.
-# If this is provided, DATA.TRAIN_JITTER_SCALES above is ignored.
-_C.DATA.TRAIN_JITTER_SCALES_RELATIVE = []
-
-# The relative aspect ratio range of Inception-style area based random resizing
-# augmentation.
-_C.DATA.TRAIN_JITTER_ASPECT_RELATIVE = []
-
-# If True, perform stride length uniform temporal sampling.
-_C.DATA.USE_OFFSET_SAMPLING = False
-
-# Whether to apply motion shift for augmentation.
-_C.DATA.TRAIN_JITTER_MOTION_SHIFT = False
-
-# The spatial crop size for training.
-_C.DATA.TRAIN_CROP_SIZE = 224
-
-# The spatial crop size for testing.
-_C.DATA.TEST_CROP_SIZE = 256
-
-# Input videos may has different fps, convert it to the target video fps before
-# frame sampling.
-_C.DATA.TARGET_FPS = 30
-
-# JITTER TARGET_FPS by +- this number randomly
-_C.DATA.TRAIN_JITTER_FPS = 0.0
-
-# Decoding backend, options include `pyav` or `torchvision`
-_C.DATA.DECODING_BACKEND = "torchvision"
-
-# Decoding resize to short size (set to native size for best speed)
-_C.DATA.DECODING_SHORT_SIZE = 256
-
-# if True, sample uniformly in [1 / max_scale, 1 / min_scale] and take a
-# reciprocal to get the scale. If False, take a uniform sample from
-# [min_scale, max_scale].
-_C.DATA.INV_UNIFORM_SAMPLE = False
-
-# If True, perform random horizontal flip on the video frames during training.
-_C.DATA.RANDOM_FLIP = True
-
-# If True, calculdate the map as metric.
-_C.DATA.MULTI_LABEL = False
-
-# Method to perform the ensemble, options include "sum" and "max".
-_C.DATA.ENSEMBLE_METHOD = "sum"
-
-# If True, revert the default input channel (RBG <-> BGR).
-_C.DATA.REVERSE_INPUT_CHANNEL = False
-
-# how many samples (=clips) to decode from a single video
-_C.DATA.TRAIN_CROP_NUM_TEMPORAL = 1
-
-# how many spatial samples to crop from a single clip
-_C.DATA.TRAIN_CROP_NUM_SPATIAL = 1
-
-# color random percentage for grayscale conversion
-_C.DATA.COLOR_RND_GRAYSCALE = 0.0
-
-# loader can read .csv file in chunks of this chunk size
-_C.DATA.LOADER_CHUNK_SIZE = 0
-
-# if LOADER_CHUNK_SIZE > 0, define overall length of .csv file
-_C.DATA.LOADER_CHUNK_OVERALL_SIZE = 0
-
-# for chunked reading, dataloader can skip rows in (large)
-# training csv file
-_C.DATA.SKIP_ROWS = 0
-
-# The separator used between path and label.
-_C.DATA.PATH_LABEL_SEPARATOR = " "
-
-# augmentation probability to convert raw decoded video to
-# grayscale temporal difference
-_C.DATA.TIME_DIFF_PROB = 0.0
-
-# Apply SSL-based SimCLR / MoCo v1/v2 color augmentations,
-#  with params below
-_C.DATA.SSL_COLOR_JITTER = False
-
-# color jitter percentage for brightness, contrast, saturation
-_C.DATA.SSL_COLOR_BRI_CON_SAT = [0.4, 0.4, 0.4]
-
-# color jitter percentage for hue
-_C.DATA.SSL_COLOR_HUE = 0.1
-
-# SimCLR / MoCo v2 augmentations on/off
-_C.DATA.SSL_MOCOV2_AUG = False
-
-# SimCLR / MoCo v2 blur augmentation minimum gaussian sigma
-_C.DATA.SSL_BLUR_SIGMA_MIN = [0.0, 0.1]
-
-# SimCLR / MoCo v2 blur augmentation maximum gaussian sigma
-_C.DATA.SSL_BLUR_SIGMA_MAX = [0.0, 2.0]
-
-
-# If combine train/val split as training for in21k
-_C.DATA.IN22K_TRAINVAL = False
-
-# If not None, use IN1k as val split when training in21k
-_C.DATA.IN22k_VAL_IN1K = ""
-
-# Large resolution models may use different crop ratios
-_C.DATA.IN_VAL_CROP_RATIO = 0.875  # 224/256 = 0.875
-
-# don't use real video for kinetics.py
-_C.DATA.DUMMY_LOAD = False
-
-# ---------------------------------------------------------------------------- #
-# Optimizer options
-# ---------------------------------------------------------------------------- #
-_C.SOLVER = CfgNode()
-
-# Base learning rate.
-_C.SOLVER.BASE_LR = 0.1
-
-# Learning rate policy (see utils/lr_policy.py for options and examples).
-_C.SOLVER.LR_POLICY = "cosine"
-
-# Final learning rates for 'cosine' policy.
-_C.SOLVER.COSINE_END_LR = 0.0
-
-# Exponential decay factor.
-_C.SOLVER.GAMMA = 0.1
-
-# Step size for 'exp' and 'cos' policies (in epochs).
-_C.SOLVER.STEP_SIZE = 1
-
-# Steps for 'steps_' policies (in epochs).
-_C.SOLVER.STEPS = []
-
-# Learning rates for 'steps_' policies.
-_C.SOLVER.LRS = []
-
-# Maximal number of epochs.
-_C.SOLVER.MAX_EPOCH = 300
-
-# Momentum.
-_C.SOLVER.MOMENTUM = 0.9
-
-# Momentum dampening.
-_C.SOLVER.DAMPENING = 0.0
-
-# Nesterov momentum.
-_C.SOLVER.NESTEROV = True
-
-# L2 regularization.
-_C.SOLVER.WEIGHT_DECAY = 1e-4
-
-# Start the warm up from SOLVER.BASE_LR * SOLVER.WARMUP_FACTOR.
-_C.SOLVER.WARMUP_FACTOR = 0.1
-
-# Gradually warm up the SOLVER.BASE_LR over this number of epochs.
-_C.SOLVER.WARMUP_EPOCHS = 0.0
-
-# The start learning rate of the warm up.
-_C.SOLVER.WARMUP_START_LR = 0.01
-
-# Optimization method.
-_C.SOLVER.OPTIMIZING_METHOD = "sgd"
-
-# Base learning rate is linearly scaled with NUM_SHARDS.
-_C.SOLVER.BASE_LR_SCALE_NUM_SHARDS = False
-
-# If True, start from the peak cosine learning rate after warm up.
-_C.SOLVER.COSINE_AFTER_WARMUP = False
-
-# If True, perform no weight decay on parameter with one dimension (bias term, etc).
-_C.SOLVER.ZERO_WD_1D_PARAM = False
-
-# Clip gradient at this value before optimizer update
-_C.SOLVER.CLIP_GRAD_VAL = None
-
-# Clip gradient at this norm before optimizer update
-_C.SOLVER.CLIP_GRAD_L2NORM = None
-
-# LARS optimizer
-_C.SOLVER.LARS_ON = False
-
-# The layer-wise decay of learning rate. Set to 1. to disable.
-_C.SOLVER.LAYER_DECAY = 1.0
-
-# Adam's beta
-_C.SOLVER.BETAS = (0.9, 0.999)
-# ---------------------------------------------------------------------------- #
-# Misc options
-# ---------------------------------------------------------------------------- #
-
-# The name of the current task; e.g. "ssl"/"sl" for (self)supervised learning
-_C.TASK = ""
-
-# Number of GPUs to use (applies to both training and testing).
-_C.NUM_GPUS = 1
-
-# Number of machine to use for the job.
-_C.NUM_SHARDS = 1
-
-# The index of the current machine.
-_C.SHARD_ID = 0
-
-# Output basedir.
-_C.OUTPUT_DIR = "."
-
-# Note that non-determinism may still be present due to non-deterministic
-# operator implementations in GPU operator libraries.
-_C.RNG_SEED = 1
-
-# Log period in iters.
-_C.LOG_PERIOD = 10
-
-# If True, log the model info.
-_C.LOG_MODEL_INFO = True
-
-# Distributed backend.
-_C.DIST_BACKEND = "nccl"
-
-# ---------------------------------------------------------------------------- #
-# Benchmark options
-# ---------------------------------------------------------------------------- #
-_C.BENCHMARK = CfgNode()
-
-# Number of epochs for data loading benchmark.
-_C.BENCHMARK.NUM_EPOCHS = 5
-
-# Log period in iters for data loading benchmark.
-_C.BENCHMARK.LOG_PERIOD = 100
-
-# If True, shuffle dataloader for epoch during benchmark.
-_C.BENCHMARK.SHUFFLE = True
-
-
-# ---------------------------------------------------------------------------- #
-# Common train/test data loader options
-# ---------------------------------------------------------------------------- #
-_C.DATA_LOADER = CfgNode()
-
-# Number of data loader workers per training process.
-_C.DATA_LOADER.NUM_WORKERS = 8
-
-# Load data to pinned host memory.
-_C.DATA_LOADER.PIN_MEMORY = True
-
-# Enable multi thread decoding.
-_C.DATA_LOADER.ENABLE_MULTI_THREAD_DECODE = False
-
-
-# ---------------------------------------------------------------------------- #
-# Detection options.
-# ---------------------------------------------------------------------------- #
-_C.DETECTION = CfgNode()
-
-# Whether enable video detection.
-_C.DETECTION.ENABLE = False
-
-# Aligned version of RoI. More details can be found at slowfast/models/head_helper.py
-_C.DETECTION.ALIGNED = True
-
-# Spatial scale factor.
-_C.DETECTION.SPATIAL_SCALE_FACTOR = 16
-
-# RoI tranformation resolution.
-_C.DETECTION.ROI_XFORM_RESOLUTION = 7
-
-
-# -----------------------------------------------------------------------------
-# AVA Dataset options
-# -----------------------------------------------------------------------------
-_C.AVA = CfgNode()
-
-# Directory path of frames.
-_C.AVA.FRAME_DIR = "/mnt/fair-flash3-east/ava_trainval_frames.img/"
-
-# Directory path for files of frame lists.
-_C.AVA.FRAME_LIST_DIR = (
-    "/mnt/vol/gfsai-flash3-east/ai-group/users/haoqifan/ava/frame_list/"
-)
-
-# Directory path for annotation files.
-_C.AVA.ANNOTATION_DIR = (
-    "/mnt/vol/gfsai-flash3-east/ai-group/users/haoqifan/ava/frame_list/"
-)
-
-# Filenames of training samples list files.
-_C.AVA.TRAIN_LISTS = ["train.csv"]
-
-# Filenames of test samples list files.
-_C.AVA.TEST_LISTS = ["val.csv"]
-
-# Filenames of box list files for training. Note that we assume files which
-# contains predicted boxes will have a suffix "predicted_boxes" in the
-# filename.
-_C.AVA.TRAIN_GT_BOX_LISTS = ["ava_train_v2.2.csv"]
-_C.AVA.TRAIN_PREDICT_BOX_LISTS = []
-
-# Filenames of box list files for test.
-_C.AVA.TEST_PREDICT_BOX_LISTS = ["ava_val_predicted_boxes.csv"]
-
-# This option controls the score threshold for the predicted boxes to use.
-_C.AVA.DETECTION_SCORE_THRESH = 0.9
-
-# If use BGR as the format of input frames.
-_C.AVA.BGR = False
-
-# Training augmentation parameters
-# Whether to use color augmentation method.
-_C.AVA.TRAIN_USE_COLOR_AUGMENTATION = False
-
-# Whether to only use PCA jitter augmentation when using color augmentation
-# method (otherwise combine with color jitter method).
-_C.AVA.TRAIN_PCA_JITTER_ONLY = True
-
-# Whether to do horizontal flipping during test.
-_C.AVA.TEST_FORCE_FLIP = False
-
-# Whether to use full test set for validation split.
-_C.AVA.FULL_TEST_ON_VAL = False
-
-# The name of the file to the ava label map.
-_C.AVA.LABEL_MAP_FILE = "ava_action_list_v2.2_for_activitynet_2019.pbtxt"
-
-# The name of the file to the ava exclusion.
-_C.AVA.EXCLUSION_FILE = "ava_val_excluded_timestamps_v2.2.csv"
-
-# The name of the file to the ava groundtruth.
-_C.AVA.GROUNDTRUTH_FILE = "ava_val_v2.2.csv"
-
-# Backend to process image, includes `pytorch` and `cv2`.
-_C.AVA.IMG_PROC_BACKEND = "cv2"
-
-# ---------------------------------------------------------------------------- #
-# Multigrid training options
-# See https://arxiv.org/abs/1912.00998 for details about multigrid training.
-# ---------------------------------------------------------------------------- #
-_C.MULTIGRID = CfgNode()
-
-# Multigrid training allows us to train for more epochs with fewer iterations.
-# This hyperparameter specifies how many times more epochs to train.
-# The default setting in paper trains for 1.5x more epochs than baseline.
-_C.MULTIGRID.EPOCH_FACTOR = 1.5
-
-# Enable short cycles.
-_C.MULTIGRID.SHORT_CYCLE = False
-# Short cycle additional spatial dimensions relative to the default crop size.
-_C.MULTIGRID.SHORT_CYCLE_FACTORS = [0.5, 0.5**0.5]
-
-_C.MULTIGRID.LONG_CYCLE = False
-# (Temporal, Spatial) dimensions relative to the default shape.
-_C.MULTIGRID.LONG_CYCLE_FACTORS = [
-    (0.25, 0.5**0.5),
-    (0.5, 0.5**0.5),
-    (0.5, 1),
-    (1, 1),
-]
-
-# While a standard BN computes stats across all examples in a GPU,
-# for multigrid training we fix the number of clips to compute BN stats on.
-# See https://arxiv.org/abs/1912.00998 for details.
-_C.MULTIGRID.BN_BASE_SIZE = 8
-
-# Multigrid training epochs are not proportional to actual training time or
-# computations, so _C.TRAIN.EVAL_PERIOD leads to too frequent or rare
-# evaluation. We use a multigrid-specific rule to determine when to evaluate:
-# This hyperparameter defines how many times to evaluate a model per long
-# cycle shape.
-_C.MULTIGRID.EVAL_FREQ = 3
-
-# No need to specify; Set automatically and used as global variables.
-_C.MULTIGRID.LONG_CYCLE_SAMPLING_RATE = 0
-_C.MULTIGRID.DEFAULT_B = 0
-_C.MULTIGRID.DEFAULT_T = 0
-_C.MULTIGRID.DEFAULT_S = 0
-
-# -----------------------------------------------------------------------------
-# Tensorboard Visualization Options
-# -----------------------------------------------------------------------------
-_C.TENSORBOARD = CfgNode()
-
-# Log to summary writer, this will automatically.
-# log loss, lr and metrics during train/eval.
-_C.TENSORBOARD.ENABLE = False
-# Provide path to prediction results for visualization.
-# This is a pickle file of [prediction_tensor, label_tensor]
-_C.TENSORBOARD.PREDICTIONS_PATH = ""
-# Path to directory for tensorboard logs.
-# Default to to cfg.OUTPUT_DIR/runs-{cfg.TRAIN.DATASET}.
-_C.TENSORBOARD.LOG_DIR = ""
-# Path to a json file providing class_name - id mapping
-# in the format {"class_name1": id1, "class_name2": id2, ...}.
-# This file must be provided to enable plotting confusion matrix
-# by a subset or parent categories.
-_C.TENSORBOARD.CLASS_NAMES_PATH = ""
-
-# Path to a json file for categories -> classes mapping
-# in the format {"parent_class": ["child_class1", "child_class2",...], ...}.
-_C.TENSORBOARD.CATEGORIES_PATH = ""
-
-# Config for confusion matrices visualization.
-_C.TENSORBOARD.CONFUSION_MATRIX = CfgNode()
-# Visualize confusion matrix.
-_C.TENSORBOARD.CONFUSION_MATRIX.ENABLE = False
-# Figure size of the confusion matrices plotted.
-_C.TENSORBOARD.CONFUSION_MATRIX.FIGSIZE = [8, 8]
-# Path to a subset of categories to visualize.
-# File contains class names separated by newline characters.
-_C.TENSORBOARD.CONFUSION_MATRIX.SUBSET_PATH = ""
-
-# Config for histogram visualization.
-_C.TENSORBOARD.HISTOGRAM = CfgNode()
-# Visualize histograms.
-_C.TENSORBOARD.HISTOGRAM.ENABLE = False
-# Path to a subset of classes to plot histograms.
-# Class names must be separated by newline characters.
-_C.TENSORBOARD.HISTOGRAM.SUBSET_PATH = ""
-# Visualize top-k most predicted classes on histograms for each
-# chosen true label.
-_C.TENSORBOARD.HISTOGRAM.TOPK = 10
-# Figure size of the histograms plotted.
-_C.TENSORBOARD.HISTOGRAM.FIGSIZE = [8, 8]
-
-# Config for layers' weights and activations visualization.
-# _C.TENSORBOARD.ENABLE must be True.
-_C.TENSORBOARD.MODEL_VIS = CfgNode()
-
-# If False, skip model visualization.
-_C.TENSORBOARD.MODEL_VIS.ENABLE = False
-
-# If False, skip visualizing model weights.
-_C.TENSORBOARD.MODEL_VIS.MODEL_WEIGHTS = False
-
-# If False, skip visualizing model activations.
-_C.TENSORBOARD.MODEL_VIS.ACTIVATIONS = False
-
-# If False, skip visualizing input videos.
-_C.TENSORBOARD.MODEL_VIS.INPUT_VIDEO = False
-
-
-# List of strings containing data about layer names and their indexing to
-# visualize weights and activations for. The indexing is meant for
-# choosing a subset of activations outputed by a layer for visualization.
-# If indexing is not specified, visualize all activations outputed by the layer.
-# For each string, layer name and indexing is separated by whitespaces.
-# e.g.: [layer1 1,2;1,2, layer2, layer3 150,151;3,4]; this means for each array `arr`
-# along the batch dimension in `layer1`, we take arr[[1, 2], [1, 2]]
-_C.TENSORBOARD.MODEL_VIS.LAYER_LIST = []
-# Top-k predictions to plot on videos
-_C.TENSORBOARD.MODEL_VIS.TOPK_PREDS = 1
-# Colormap to for text boxes and bounding boxes colors
-_C.TENSORBOARD.MODEL_VIS.COLORMAP = "Pastel2"
-# Config for visualization video inputs with Grad-CAM.
-# _C.TENSORBOARD.ENABLE must be True.
-_C.TENSORBOARD.MODEL_VIS.GRAD_CAM = CfgNode()
-# Whether to run visualization using Grad-CAM technique.
-_C.TENSORBOARD.MODEL_VIS.GRAD_CAM.ENABLE = True
-# CNN layers to use for Grad-CAM. The number of layers must be equal to
-# number of pathway(s).
-_C.TENSORBOARD.MODEL_VIS.GRAD_CAM.LAYER_LIST = []
-# If True, visualize Grad-CAM using true labels for each instances.
-# If False, use the highest predicted class.
-_C.TENSORBOARD.MODEL_VIS.GRAD_CAM.USE_TRUE_LABEL = False
-# Colormap to for text boxes and bounding boxes colors
-_C.TENSORBOARD.MODEL_VIS.GRAD_CAM.COLORMAP = "viridis"
-
-# Config for visualization for wrong prediction visualization.
-# _C.TENSORBOARD.ENABLE must be True.
-_C.TENSORBOARD.WRONG_PRED_VIS = CfgNode()
-_C.TENSORBOARD.WRONG_PRED_VIS.ENABLE = False
-# Folder tag to origanize model eval videos under.
-_C.TENSORBOARD.WRONG_PRED_VIS.TAG = "Incorrectly classified videos."
-# Subset of labels to visualize. Only wrong predictions with true labels
-# within this subset is visualized.
-_C.TENSORBOARD.WRONG_PRED_VIS.SUBSET_PATH = ""
-
-
-# ---------------------------------------------------------------------------- #
-# Demo options
-# ---------------------------------------------------------------------------- #
-_C.DEMO = CfgNode()
-
-# Run model in DEMO mode.
-_C.DEMO.ENABLE = False
-
-# Path to a json file providing class_name - id mapping
-# in the format {"class_name1": id1, "class_name2": id2, ...}.
-_C.DEMO.LABEL_FILE_PATH = ""
-
-# Specify a camera device as input. This will be prioritized
-# over input video if set.
-# If -1, use input video instead.
-_C.DEMO.WEBCAM = -1
-
-# Path to input video for demo.
-_C.DEMO.INPUT_VIDEO = ""
-# Custom width for reading input video data.
-_C.DEMO.DISPLAY_WIDTH = 0
-# Custom height for reading input video data.
-_C.DEMO.DISPLAY_HEIGHT = 0
-# Path to Detectron2 object detection model configuration,
-# only used for detection tasks.
-_C.DEMO.DETECTRON2_CFG = "COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml"
-# Path to Detectron2 object detection model pre-trained weights.
-_C.DEMO.DETECTRON2_WEIGHTS = "detectron2://COCO-Detection/faster_rcnn_R_50_FPN_3x/137849458/model_final_280758.pkl"
-# Threshold for choosing predicted bounding boxes by Detectron2.
-_C.DEMO.DETECTRON2_THRESH = 0.9
-# Number of overlapping frames between 2 consecutive clips.
-# Increase this number for more frequent action predictions.
-# The number of overlapping frames cannot be larger than
-# half of the sequence length `cfg.DATA.NUM_FRAMES * cfg.DATA.SAMPLING_RATE`
-_C.DEMO.BUFFER_SIZE = 0
-# If specified, the visualized outputs will be written this a video file of
-# this path. Otherwise, the visualized outputs will be displayed in a window.
-_C.DEMO.OUTPUT_FILE = ""
-# Frames per second rate for writing to output video file.
-# If not set (-1), use fps rate from input file.
-_C.DEMO.OUTPUT_FPS = -1
-# Input format from demo video reader ("RGB" or "BGR").
-_C.DEMO.INPUT_FORMAT = "BGR"
-# Draw visualization frames in [keyframe_idx - CLIP_VIS_SIZE, keyframe_idx + CLIP_VIS_SIZE] inclusively.
-_C.DEMO.CLIP_VIS_SIZE = 10
-# Number of processes to run video visualizer.
-_C.DEMO.NUM_VIS_INSTANCES = 2
-
-# Path to pre-computed predicted boxes
-_C.DEMO.PREDS_BOXES = ""
-# Whether to run in with multi-threaded video reader.
-_C.DEMO.THREAD_ENABLE = False
-# Take one clip for every `DEMO.NUM_CLIPS_SKIP` + 1 for prediction and visualization.
-# This is used for fast demo speed by reducing the prediction/visualiztion frequency.
-# If -1, take the most recent read clip for visualization. This mode is only supported
-# if `DEMO.THREAD_ENABLE` is set to True.
-_C.DEMO.NUM_CLIPS_SKIP = 0
-# Path to ground-truth boxes and labels (optional)
-_C.DEMO.GT_BOXES = ""
-# The starting second of the video w.r.t bounding boxes file.
-_C.DEMO.STARTING_SECOND = 900
-# Frames per second of the input video/folder of images.
-_C.DEMO.FPS = 30
-# Visualize with top-k predictions or predictions above certain threshold(s).
-# Option: {"thres", "top-k"}
-_C.DEMO.VIS_MODE = "thres"
-# Threshold for common class names.
-_C.DEMO.COMMON_CLASS_THRES = 0.7
-# Theshold for uncommon class names. This will not be
-# used if `_C.DEMO.COMMON_CLASS_NAMES` is empty.
-_C.DEMO.UNCOMMON_CLASS_THRES = 0.3
-# This is chosen based on distribution of examples in
-# each classes in AVA dataset.
-_C.DEMO.COMMON_CLASS_NAMES = [
-    "watch (a person)",
-    "talk to (e.g., self, a person, a group)",
-    "listen to (a person)",
-    "touch (an object)",
-    "carry/hold (an object)",
-    "walk",
-    "sit",
-    "lie/sleep",
-    "bend/bow (at the waist)",
-]
-# Slow-motion rate for the visualization. The visualized portions of the
-# video will be played `_C.DEMO.SLOWMO` times slower than usual speed.
-_C.DEMO.SLOWMO = 1
-
-
-def assert_and_infer_cfg(cfg):
-    # BN assertions.
-    if cfg.BN.USE_PRECISE_STATS:
-        assert cfg.BN.NUM_BATCHES_PRECISE >= 0
-    # TRAIN assertions.
-    assert cfg.TRAIN.CHECKPOINT_TYPE in ["pytorch", "caffe2"]
-    assert cfg.NUM_GPUS == 0 or cfg.TRAIN.BATCH_SIZE % cfg.NUM_GPUS == 0
-
-    # TEST assertions.
-    assert cfg.TEST.CHECKPOINT_TYPE in ["pytorch", "caffe2"]
-    assert cfg.NUM_GPUS == 0 or cfg.TEST.BATCH_SIZE % cfg.NUM_GPUS == 0
-
-    # RESNET assertions.
-    assert cfg.RESNET.NUM_GROUPS > 0
-    assert cfg.RESNET.WIDTH_PER_GROUP > 0
-    assert cfg.RESNET.WIDTH_PER_GROUP % cfg.RESNET.NUM_GROUPS == 0
-
-    # Execute LR scaling by num_shards.
-    if cfg.SOLVER.BASE_LR_SCALE_NUM_SHARDS:
-        cfg.SOLVER.BASE_LR *= cfg.NUM_SHARDS
-        cfg.SOLVER.WARMUP_START_LR *= cfg.NUM_SHARDS
-        cfg.SOLVER.COSINE_END_LR *= cfg.NUM_SHARDS
-
-    # General assertions.
-    assert cfg.SHARD_ID < cfg.NUM_SHARDS
-    return cfg
-
-
-def get_cfg():
-    return _C.clone()