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biomed_seg_lang_v1.yaml
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# --------------------------------------------------------
# X-Decoder -- Generalized Decoding for Pixel, Image, and Language
# Copyright (c) 2022 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Xueyan Zou ([email protected])
# --------------------------------------------------------
# Define Test/Trainer/Saving
PIPELINE: XDecoderPipeline
TRAINER: xdecoder
SAVE_DIR: './output'
base_path: "./"
# Resume Logistic
RESUME: false
WEIGHT: false
RESUME_FROM: ''
EVAL_AT_START: false
SAVE_CHECKPOINT: True
# Logging and Debug
WANDB: False
LOG_EVERY: 100
FIND_UNUSED_PARAMETERS: false
# Speed up training
FP16: false
PORT: '36873'
# misc
LOADER:
JOINT: True
KEY_DATASET: ""
SAMPLE_PROB: "prop" # sampling probability proportional to data size. Use "equal" for each bach from all datasets
MIXING_LEVEL: 1 # num of different datasets for batch mixing on each GPU
RANDOM_SEED: 2024
STANDARD_TEXT_FOR_EVAL: False
##################
# Task settings
##################
VERBOSE: true
MODEL:
NAME: seem_model_v1
HEAD: xdecoder_head
MASK_ON: false
KEYPOINT_ON: false
LOAD_PROPOSALS: false
DIM_PROJ: 512
TEXT:
ARCH: vlpencoder
NAME: transformer
TOKENIZER: clip
CONTEXT_LENGTH: 77 #256 # 77
WIDTH: 512 # 768 # 512
HEADS: 8
LAYERS: 12 # 6
AUTOGRESSIVE: True
BACKBONE:
NAME: focal # focal_dw # focal
PRETRAINED: ''
LOAD_PRETRAINED: false
FOCAL:
PRETRAIN_IMG_SIZE: 224
PATCH_SIZE: 4
EMBED_DIM: 192 # 96 # 192
DEPTHS: [2, 2, 18, 2] # [2, 2, 6, 2] # [2, 2, 18, 2]
FOCAL_LEVELS: [4, 4, 4, 4] # [3, 3, 3, 3] # [4, 4, 4, 4]
FOCAL_WINDOWS: [3, 3, 3, 3]
DROP_PATH_RATE: 0.3
MLP_RATIO: 4.0
DROP_RATE: 0.0
PATCH_NORM: True
USE_CONV_EMBED: True
SCALING_MODULATOR: True
USE_CHECKPOINT: False
USE_POSTLN: true
USE_POSTLN_IN_MODULATION: false
USE_LAYERSCALE: True
OUT_FEATURES: ["res2", "res3", "res4", "res5"]
OUT_INDICES: [0, 1, 2, 3]
ENCODER:
NAME: transformer_encoder_fpn
IGNORE_VALUE: 255
NUM_CLASSES: 16
BINARY_CLASSES: False
LOSS_WEIGHT: 1.0
CONVS_DIM: 512
MASK_DIM: 512
NORM: "GN"
IN_FEATURES: ["res2", "res3", "res4", "res5"]
DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES: ["res3", "res4", "res5"]
COMMON_STRIDE: 4
TRANSFORMER_ENC_LAYERS: 6
DECODER:
NAME: seem_v1
TRANSFORMER_IN_FEATURE: "multi_scale_pixel_decoder"
MASK:
ENABLED: True
DETECTION: False
SPATIAL:
ENABLED: True
MAX_ITER: 1
GROUNDING:
ENABLED: True
MAX_LEN: 10
TEXT_WEIGHT: 2.0
CLASS_WEIGHT: 0.5
RETRIEVAL:
ENABLED: False
LVIS:
ENABLED: False
THRES: 0.7
OPENIMAGE:
ENABLED: False
NEGATIVE_SAMPLES: 5
GROUNDING:
ENABLED: False
MAX_LEN: 5
CAPTION:
ENABLED: False
PHRASE_PROB: 0.5
SIM_THRES: 0.95
DEEP_SUPERVISION: True
NO_OBJECT_WEIGHT: 0.1
GCLASS_WEIGHT: 0.4
GMASK_WEIGHT: 1.0
GDICE_WEIGHT: 1.0
SCLASS_WEIGHT: 0.4
SMASK_WEIGHT: 1.0
SDICE_WEIGHT: 1.0
OCLASS_WEIGHT: 0.4
OMASK_WEIGHT: 1.0
ODICE_WEIGHT: 1.0
CLASS_WEIGHT: 2.0
MASK_WEIGHT: 5.0
DICE_WEIGHT: 5.0
BBOX_WEIGHT: 5.0
GIOU_WEIGHT: 2.0
CAPTION_WEIGHT: 2.0
COST_SPATIAL:
CLASS_WEIGHT: 5.0
MASK_WEIGHT: 2.0
DICE_WEIGHT: 2.0
HIDDEN_DIM: 512
NUM_OBJECT_QUERIES: 101
NHEADS: 8
DROPOUT: 0.0
DIM_FEEDFORWARD: 2048
MAX_SPATIAL_LEN: [512, 512, 512, 512]
# ENC_LAYERS: 0
PRE_NORM: False
ENFORCE_INPUT_PROJ: False
SIZE_DIVISIBILITY: 32
TRAIN_NUM_POINTS: 12544
OVERSAMPLE_RATIO: 3.0
IMPORTANCE_SAMPLE_RATIO: 0.75
DEC_LAYERS: 10 # 9 decoder layers, add one for the loss on learnable query
TOP_GROUNDING_LAYERS: 10
TOP_CAPTION_LAYERS: 10
TOP_SPATIAL_LAYERS: 10
TOP_OPENIMAGE_LAYERS: 10
TEST:
SEMANTIC_ON: False
INSTANCE_ON: False
PANOPTIC_ON: False
OVERLAP_THRESHOLD: 0.8
OBJECT_MASK_THRESHOLD: 0.8
SEM_SEG_POSTPROCESSING_BEFORE_INFERENCE: true
# Spatial sampler
STROKE_SAMPLER:
MAX_CANDIDATE: 1
CANDIDATE_PROBS: [0.25, 0.25, 0.25, 0.25] # for training only
CANDIDATE_NAMES: ["Point", "Polygon", "Scribble", "Circle"]
DILATION: 3
CIRCLE:
NUM_STROKES: 5
STROKE_PRESET: ['object_like', 'object_like_middle', 'object_like_small']
STROKE_PROB: [0.33, 0.33, 0.33]
SCRIBBLE:
NUM_STROKES: 5
STROKE_PRESET: ['rand_curve', 'rand_curve_small']
STROKE_PROB: [0.5, 0.5]
POINT:
NUM_POINTS: 20
POLYGON:
MAX_POINTS: 9
EVAL:
MODE: 'best' # best/random/best_random
NEGATIVE: False
MAX_ITER: 1
IOU_ITER: 1
GROUNDING: True
# Multi-modal Architecture, order matters
ATTENTION_ARCH:
VARIABLE:
queries: ['object', 'grounding', 'spatial']
tokens: ['grounding', 'spatial']
memories: ['spatial']
SELF_ATTENTION:
queries:
object: ['queries_object']
grounding: ['queries_grounding', 'tokens_grounding']
spatial: ['queries_spatial', 'tokens_spatial', 'memories_spatial']
tokens:
grounding: ['queries_grounding', 'tokens_grounding']
spatial: ['tokens_spatial']
memories:
spatial: ['memories_spatial']
CROSS_ATTENTION:
queries:
object: True
grounding: True
spatial: True
memories:
spatial: True
tokens:
grounding: False
spatial: False
MASKING: ['tokens_spatial', 'tokens_grounding']
DUPLICATION:
queries:
grounding: 'queries_object'
spatial: 'queries_object'
SPATIAL_MEMORIES: 32
QUERY_NUMBER: 3
DATASETS:
TRAIN: [
'biomed_BiomedParseData-Demo_demo' # Add your registered training datasets here
]
TEST: [
'biomed_BiomedParseData-Demo_demo' # Add your registered test datasets here
]
CLASS_CONCAT: false
SIZE_DIVISIBILITY: 32
PROPOSAL_FILES_TRAIN: []
INPUT:
PIXEL_MEAN: [123.675, 116.280, 103.530]
PIXEL_STD: [58.395, 57.120, 57.375]
TRAIN:
ASPECT_RATIO_GROUPING: true
BATCH_SIZE_TOTAL: 4
BATCH_SIZE_PER_GPU: 4
SHUFFLE: true
TEST:
DETECTIONS_PER_IMAGE: 100
NAME: coco_eval
IOU_TYPE: ['bbox', 'segm']
USE_MULTISCALE: false
BATCH_SIZE_TOTAL: 4
MODEL_FILE: ''
AUG:
ENABLED: False
DATALOADER:
FILTER_EMPTY_ANNOTATIONS: False
NUM_WORKERS: 8
LOAD_PROPOSALS: False
SAMPLER_TRAIN: "TrainingSampler"
ASPECT_RATIO_GROUPING: True
BioMed:
INPUT:
PIXEL_MEAN: [64.284, 59.293, 59.962]
PIXEL_STD: [62.484, 60.865, 59.835]
DATASET_MAPPER_NAME: "biomed_interactive"
MIN_SIZE_TRAIN: 900
MAX_SIZE_TRAIN: 1100
MIN_SIZE_TRAIN_SAMPLING: 'choice'
MIN_SIZE_TEST: 900
MAX_SIZE_TEST: 1100
IMAGE_SIZE: 1024
MIN_SCALE: 0.9
MAX_SCALE: 1.1
IGNORE_VALUE: 255
COLOR_AUG_SSD: False
SIZE_DIVISIBILITY: 32
RANDOM_FLIP: "none"
RANDOM_ROTATE: False
MASK_FORMAT: "polygon"
MIN_AREA: 30
FORMAT: "RGB"
SPATIAL: True
CROP:
ENABLED: True
DATASET:
DATASET: "biomed"
# Detectron2 training config for optimizer and lr scheduler
SOLVER:
BASE_LR: 0.0001
STEPS: [0.88889, 0.96296]
MAX_ITER: 1
GAMMA: 0.1
WARMUP_FACTOR: 1.0
WARMUP_ITERS: 10
WARMUP_METHOD: "linear"
WEIGHT_DECAY: 0.05
OPTIMIZER: "ADAMW"
LR_SCHEDULER_NAME: "WarmupMultiStepLR"
LR_MULTIPLIER:
backbone: 0.1
lang_encoder: 0.1
FIX_PARAM:
backbone: True
lang_encoder: True
pixel_decoder: True
WEIGHT_DECAY_NORM: 0.0
WEIGHT_DECAY_EMBED: 0.0
CLIP_GRADIENTS:
ENABLED: True
CLIP_TYPE: "full_model"
CLIP_VALUE: 5.0 # 0.01
NORM_TYPE: 2.0
MAX_NUM_EPOCHS: 50