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# Template for the dataset config
data:
# The code still looks too complicated. I should refactor this again (do I have time?...)
train:
_target_: data.dynamic_dataloader.DynamicTorchDataset
num_workers: 8
max_img_per_gpu: 48
# Shuffling in PyTorch DataLoader can sometimes copy large dicts and exceed CPU memory
# (see: https://github.com/pytorch/pytorch/issues/13246).
# To avoid this, set shuffle=False and enable common_config.inside_random=True instead.
shuffle: True
pin_memory: False
common_config: # common config for evaluation
fix_img_num: -1 # -1 means do not fix the number of images
fix_aspect_ratio: 1.0
load_track: False
track_num: 1024
training: True
inside_random: True
img_size: 224
patch_size: 14
rescale: True
rescale_aug: True
landscape_check: False
debug: False
get_nearby: True
load_depth: True
img_nums: [2, 24]
max_img_per_gpu: 48
allow_duplicate_img: True
repeat_batch: False
augs:
cojitter: True
cojitter_ratio: 0.3
scales: [0.8, 1.2]
aspects: [0.33, 1.0]
color_jitter:
brightness: 0.5
contrast: 0.5
saturation: 0.5
hue: 0.1
p: 0.9
gray_scale: True
gau_blur: False
val:
_target_: data.dynamic_dataloader.DynamicTorchDataset
num_workers: 8
max_img_per_gpu: 48
# Shuffling in PyTorch DataLoader can sometimes copy large dicts and exceed CPU memory
# (see: https://github.com/pytorch/pytorch/issues/13246).
# To avoid this, set shuffle=False and enable common_config.inside_random=True instead.
shuffle: True
pin_memory: False
common_config: # common config for evaluation
fix_img_num: -1 # -1 means do not fix the number of images
fix_aspect_ratio: 1.0
load_track: False
track_num: 1024
training: False
inside_random: True
img_size: 224
patch_size: 14
rescale: True
rescale_aug: False
landscape_check: False
debug: False
get_nearby: True
load_depth: True
img_nums: [2, 12]
allow_duplicate_img: True
augs:
cojitter: False
cojitter_ratio: 0.5
scales: null
aspects: [1.0, 1.0]
color_jitter: null
gray_scale: False
gau_blur: False |