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default.yaml
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default.yaml
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# @package _group_
# The top few go into the dataset object to load as per these
num_frames: 16
frame_rate: null # Null => Defaults or natural frame rate of the video
# Allow for an option to clip the long original clip into subclips. This is
# useful I want features for multiple past clips, so I just read and process a
# really long one and then crop it up. By default crop into 1 subclip -- same
# as input
subclips:
# TODO Need to use relative interpolation here
num_frames: ${..num_frames}
stride: ${..num_frames}
# Load segmentation labels only if a classifier on the past is being applied
load_seg_labels: ${model.classifier_on_past}
# Get rid of the next 2 params.. not sure what they are for
train_bs_multiplier: 5
val_clips_per_video: 1
workers: 10
# Scale image to this size before cropping
# scale_w can be -1, in which case it will scale the shorter size to
# scale_h.
scale_h: 128
scale_w: 174
# Ht and wd of the crop from the above resized video. Set to null for no
# cropping.
crop_size: 112
# Mean/std for centering the image
mean: [0.43216, 0.394666, 0.37645]
std: [0.22803, 0.22145, 0.216989]
# Augmentations. Note, set these all to default, or "0", such that they are
# not applied. Change it in the txt file to add it at training time, since
# this ConfigGroup object will be copied for both train and test time.
flip_p: 0.5 # Left-right flip 50% at train time. Not used during eval.
scale_pix_val: 1.0 # Scale the pixel values by this number. Useful to scale from 0-1 values to 0-255.
reverse_channels: false # Reverse channels, i.e. convert from RGB->BGR
color_jitter_brightness: 0.0
color_jitter_contrast: 0.0
color_jitter_saturation: 0.0
color_jitter_hue: 0.0
# Use distributed sampler or not. For certain data loader settings, such
# as when using the dense_sampler for feature extraction, that automatically
# samples different clips for different workers, so set this to false when
# using it
use_dist_sampler: true
# Test time augmentations. Only used in the eval code
eval_num_crops: 1
eval_flip_crops: False