DATASET: 'k400'

# Dataset Folder
FRAME_REPO: "/home/hzhang57/"
TRN_LIST_TXT: "/home/hzhang57/k400/trainValTest/train.txt"
VAL_LIST_TXT: "/home/hzhang57/k400/trainValTest/val.txt"
TEST_LIST_TXT: "/home/hzhang57/k400/trainValTest/val.txt"
FRAME_FORMAT: "img_{:05d}.jpg"
USE_SUBSET: False

# TRAINING-CFG
LABEL_SMOOTH: 0.01

PIXEL_MEAN: [127.5, 127.5, 127.5]
PIXEL_STD: [0.5, 0.5, 0.5]

TRN_BATCH: 1 # Batch Per GPU, This setting is on 8x3090Ti
VAL_BATCH: 1 # Batch Per GPU, This setting is on 8x3090Ti

LR_TYPE: 'sgd'
LR_SCHEME: 'step'
LR: 0.01     # This is 8 Clips settings, use linear-scale rule to adjust this value
LR_STEPS: [10, 15, 18]
EPOCH: 18
MOMENTUM: 0.9
WEIGHT_DECAY: 0.0 # 5e-4
GRADIENT_ACCUMULATION_STEPS: 10 # 8
CLIP_GD: 1.0

# Data Proces 
# 1. Input Clip
T_SIZE: 8
T_STEP: 32
#DENSE_OR_UNIFORM: "uniform"
UNIFORM_TWICE: True

DENSE_OR_UNIFORM: "dense"
VAL_SAMPLE_TIMES: 1 ## Set to 10 when evaluating, Set to 1 when training for time-saving
VAL_FLIP: False
# Multiple cropped patchs in Test, 
# "3": Left ,Center, Right
## Not Implemented: "5": Top-Left, Top-Right, Center, Bot-Left, Bot-Right
VAL_TEST_AUG: 3 # Multiple cropped patchs in Test, 

# 2. Resize and Crop
TRN_PATCH_SIZE: 384
TRN_SHORT_SIDE_RANGE: [384, 512]

VAL_PATCH_SIZE: 384
VAL_SHORT_SIDE: 384

# Backbone
NET: 'TokShift'
VER: 3
NUM_CLASS: 400
BASE_NET: 'ViT-B_16'
CONSENSUS_TYPE: 'avg'
DROP_OUT: 0.0
PARTIAL_BN: False # True Froze all BN
PRINT_SPEC: True
PRETRAIN:  'imagenet'
IS_SHIFT: True # Select shift
SHIFT_DIV: 4
USE_PRECISE_BN: 0
DROP_BLOCK: 0.0
#USE_PRECISE_BN: 0
