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##################
# Trainer settings
##################
TASK: UniCLTask
NAME: 'Example Eval Configuration'
SAVE_TIMER_LOG: true
# TUTORIAL STEP 1: CHOOSE SAVE DIR
SAVE_DIR: ''
LOG_EVERY: 10
LOGLEVEL_OVERRIDE: INFO
LOG_GPU_MEM: true
RESUME: False
RESET_DATA_LOADER: false
FP16: true
ZERO_STAGE: 0
DEEPSPEED: false
# ZERO_STAGE: 1
AMP: PYTORCH
# USE_APEX_DDP: false
# USE_APEX_AMP: false
# USE_HIT: false
FIND_UNUSED_PARAMETERS: false
SAVE_PER_OPTIM_STEPS: 500
EVAL_PER_OPTIM_STEPS: 250
EVAL_AT_START: False
# SAVE_PER_UPDATE_NUM: -1
# EVAL_PER_UPDATE_NUM: 0 # 0: do evaluation when saving checkpoint, -1: don't do evaluation
NO_AUTO_LR_SCALING: true
GRAD_CLIPPING: 1.0 #0.07
SET_SAMPLER_EPOCH: true
DONT_LOAD_MODEL: true
user_dir: "./MainzVision" # lower case due to it is used in mainz as such
##################
# Task settings
##################
VERBOSE: true
WORKERS: 6
PIN_MEMORY: true
IMAGE_ENCODER:
NAME: davit_v1
NUM_CLASSES: 0
#IMAGE_SIZE: [384, 384]
IMAGE_SIZE: [480, 480]
LOAD_PRETRAINED: true
PRETRAINED: ''
PRETRAINED_LAYERS: '*'
IMAGE_MEAN: [0.485, 0.456, 0.406]
IMAGE_STD: [0.229, 0.224, 0.225]
SPEC:
DROP_RATE: 0.1
DROP_PATH_RATE: 0.2
PATCH_SIZE: [7, 3, 3, 3]
PATCH_STRIDE: [4, 2, 2, 2]
PATCH_PADDING: [3, 1, 1, 1]
PATCH_PRENORM: [false, true, true, true]
DIM_EMBED: [256, 512, 1024, 2048]
NUM_HEADS: [8, 16, 32, 64]
NUM_GROUPS: [8, 16, 32, 64]
DEPTHS: [1, 1, 9, 1]
WINDOW_SIZE: 12
ENABLE_CHECKPOINT: true
LANG_ENCODER:
NAME: transformer
LOAD_PRETRAINED: false
PRETRAINED: ''
PRETRAINED_LAYERS: '*'
TOKENIZER: clip
CONTEXT_LENGTH: 77
WIDTH: 1024
HEADS: 16
LAYERS: 16
AUTOGRESSIVE: false
UNICL_MODEL:
DIM_PROJECTION: 1024
GATHER_TENSORS: true
LOAD_PRETRAINED: true
# TUTORIAL STEP 2: CHOOSE MODEL PATH
PRETRAINED: ''
PRETRAINED_LAYERS: '*'
AUG:
MIXUP_PROB: 0.0
MIXUP: 0.8
MIXCUT: 1.0
MIXCUT_MINMAX: []
MIXUP_SWITCH_PROB: 0.5
MIXUP_MODE: 'batch'
SCALE: [0.8, 1.0]
RATIO: [0.75, 1.3333333]
INTERPOLATION: 'bicubic'
TORCHVISION_AUG:
AUTO_AUGMENT: ta_wide
RE_PROB: 0.25
HFLIP: 0.0
VFLIP: 0.0
LOSS:
LOSS: UniCL
DATASET:
DATASET: 'image_text_pairs_v2'
TEXT_FORMAT: 'json'
ROOT: ''
TRAIN_SET: 'mimic_cxr_v2-chestxray14-chexpertv4-irma2009_v2-rsnaboneage-mura-bingmedicalfewshot'
DATA_FORMAT: 'tsv'
SAMPLER: 'default'
LOADER: 'default'
TOKEN_FILE: ''
#PROMPT_ENGINEERING: False
#SAMPLER: 'chunk'
#LOADER: 'azcopy'
#TOKEN_FILE: 'cliptrainingpairs.txt'
#TEST_SET: 'MarsAtrain'
# TUTORIAL STEP 3: CHOOSE ALL BELOW EVAL PATHS (THESE ARE ALL OPTIONAL EXTRA EVALS)
# Note how one eval is ZIP format and the other is TSV format.
EVALDATASET_LTCXR_S100_N100_TEXT_CLASSIFIER:
TEXT_FORMAT: json
FORMAT: 'zip'
SPLIT: 'NIH-CXR-LT'
ZIP_FILE: ''
ZIP_MAP_FILE: ''
LABEL_FILE: ''
IMAGE_TSV: ''
TEXT_TSV: ''
CWEIGHT_FILE: ''
ZS_MODE: 2
ZS_WEIGHT: 1.0
KNN: 100
# CLASSIFICATION_SETS: ['NIH-CXR-LT']
# NUM_CLASSES: [20]
# TUTORIAL STEP 4: SET THE DEFAULT ZEROSHOT EVAL (THIS IS THE MANDATORY EVAL)
ZEROSHOT_EVAL_DATASET:
FORMAT: 'zip'
SPLIT: 'NIH-CXR-LT'
ZIP_FILE: ''
ZIP_MAP_FILE: ''
LABEL_FILE: ''
EVALUATION_SPLITS: ['cls-zeroshot-eval']
TEST:
BATCH_SIZE_PER_GPU: 8
MODEL_FILE: ''
CENTER_CROP: false
TRAIN:
BATCH_SIZE_TOTAL: 1024
BATCH_SIZE_PER_GPU: 16
SHUFFLE: true
WEIGHT_SMOOTHING:
decay: 0.999
use_cpu: False
eval_smoothed_weight: True
START_LEARNING_RATE: 0.00001
# MAX_NUM_EPOCHS: 2
MAX_NUM_EPOCHS: 100
OPTIMIZER: AdamW # adam
OPTIMIZER_PARAMS:
weight_decay: 0.2 #0.1
CUSTOMIZED_PARAMS_CONF:
NO_WEIGHT_DECAY_MODULES: ['dw', 'norm']
WEIGHT_DECAY_PATTERNS:
"\\.bias$": 0.0
"logit_scale": 0.0
"positional_embedding": 0.0
"token_embedding": 0.0
LR_SCHEDULER: TimmScheduler
LR_SCHEDULER_PARAMS:
sched: cosine
warmup_steps: 5
warmup_lr: 0.000000001
min_lr: 0.000000001
# GRADIENT_ACCUMULATE_STEP will be updated by:
# BATCH_SIZE_TOTAL // (BATCH_SIZE_PER_GPU * world_size)
GRADIENT_ACCUMULATE_STEP: -1
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