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Timothyxxx
2024-03-14 11:52:38 +08:00
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401
mm_agents/configs/seem_focall_unicl_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 (xueyan@cs.wisc.edu)
# --------------------------------------------------------
# Define Test/Trainer/Saving
PIPELINE: XDecoderPipeline
TRAINER: xdecoder
SAVE_DIR: '../../data/output/test'
base_path: "./"
# Resume Logistic
RESUME: false
WEIGHT: false
RESUME_FROM: ''
EVAL_AT_START: False
# Logging and Debug
WANDB: False
LOG_EVERY: 100
FIND_UNUSED_PARAMETERS: false
# Speed up training
FP16: false
PORT: '36873'
# misc
LOADER:
JOINT: False
KEY_DATASET: 'coco'
##################
# 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 # 77
WIDTH: 512
HEADS: 8
LAYERS: 12 # 6
AUTOGRESSIVE: True
BACKBONE:
NAME: focal
PRETRAINED: ''
LOAD_PRETRAINED: false
FOCAL:
PRETRAIN_IMG_SIZE: 224
PATCH_SIZE: 4
EMBED_DIM: 192
DEPTHS: [2, 2, 18, 2]
FOCAL_LEVELS: [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: 133
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: 5
TEXT_WEIGHT: 2.0
CLASS_WEIGHT: 0.5
RETRIEVAL:
ENABLED: False
LVIS:
ENABLED: True
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: True
INSTANCE_ON: True
PANOPTIC_ON: True
OVERLAP_THRESHOLD: 0.8
OBJECT_MASK_THRESHOLD: 0.8
SEM_SEG_POSTPROCESSING_BEFORE_INFERENCE: false
# 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: 20
IOU_ITER: 1
GROUNDING: False
# 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: ["coco_2017_train_panoptic_filtrefgumdval_with_sem_seg_caption_grounding_lvis",]
# TRAIN: ["coco_2017_train_panoptic_with_sem_seg_caption_grounding",]
TEST: ["coco_2017_val_panoptic_with_sem_seg", "pascalvoc_val_Point", "refcocog_val_umd"] # to evaluate instance and semantic performance as well
# TEST: ["pascalvoc_val_Point"] # [pascalvoc, openimage600, ade600, davis, cocomini], [Point, Scribble, Polygon, Circle, Box]
# TEST: ["cocomini_val_Point", "cocomini_val_Circle", "cocomini_val_Scribble", "cocomini_val_Polygon", "cocomini_val_Box"] # [pascalvoc, openimage600, ade600, davis, cocomini], [Point, Scribble, Polygon, Circle, Box]
# TEST: ["ade600_val_Point", "ade600_val_Circle", "ade600_val_Scribble", "ade600_val_Polygon", "ade600_val_Box"] # [pascalvoc, openimage600, ade600, davis, cocomini], [Point, Scribble, Polygon, Circle, Box]
# TEST: ["openimage600_val_Point", "openimage600_val_Circle", "openimage600_val_Scribble", "openimage600_val_Polygon", "openimage600_val_Box"] # [pascalvoc, openimage600, ade600, davis, cocomini], [Point, Scribble, Polygon, Circle, Box]
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: 8
MODEL_FILE: ''
AUG:
ENABLED: False
DATALOADER:
FILTER_EMPTY_ANNOTATIONS: False
NUM_WORKERS: 8
LOAD_PROPOSALS: False
SAMPLER_TRAIN: "TrainingSampler"
ASPECT_RATIO_GROUPING: True
COCO:
INPUT:
MIN_SIZE_TRAIN: 800
MAX_SIZE_TRAIN: 1333
MIN_SIZE_TRAIN_SAMPLING: 'choice'
MIN_SIZE_TEST: 800
MAX_SIZE_TEST: 1333
IMAGE_SIZE: 1024
MIN_SCALE: 0.1
MAX_SCALE: 2.0
DATASET_MAPPER_NAME: "coco_interactive"
IGNORE_VALUE: 255
COLOR_AUG_SSD: False
SIZE_DIVISIBILITY: 32
RANDOM_FLIP: "horizontal"
MASK_FORMAT: "polygon"
FORMAT: "RGB"
CROP:
ENABLED: True
DATASET:
DATASET: 'coco'
# Validation dataset
ADE20K:
INPUT:
MIN_SIZE_TRAIN: 640
MIN_SIZE_TRAIN_SAMPLING: "choice"
MIN_SIZE_TEST: 640
MAX_SIZE_TRAIN: 2560
MAX_SIZE_TEST: 2560
MASK_FORMAT: "polygon"
CROP:
ENABLED: True
TYPE: "absolute"
SIZE: (640, 640)
SINGLE_CATEGORY_MAX_AREA: 1.0
COLOR_AUG_SSD: True
SIZE_DIVISIBILITY: 640 # used in dataset mapper
DATASET_MAPPER_NAME: "mask_former_panoptic"
FORMAT: "RGB"
DATASET:
DATASET: 'ade'
SBD:
INPUT:
MIN_SIZE_TEST: 800
MAX_SIZE_TEST: 1333
DATALOADER:
FILTER_EMPTY_ANNOTATIONS: False
NUM_WORKERS: 0
LOAD_PROPOSALS: False
SAMPLER_TRAIN: "TrainingSampler"
ASPECT_RATIO_GROUPING: False
TEST:
BATCH_SIZE_TOTAL: 1
VOC:
INPUT:
MIN_SIZE_TEST: 800
MAX_SIZE_TEST: 1333
DATALOADER:
FILTER_EMPTY_ANNOTATIONS: False
NUM_WORKERS: 0
LOAD_PROPOSALS: False
SAMPLER_TRAIN: "TrainingSampler"
ASPECT_RATIO_GROUPING: False
TEST:
BATCH_SIZE_TOTAL: 8
DAVIS:
INPUT:
MIN_SIZE_TEST: 800
MAX_SIZE_TEST: 1333
DATALOADER:
FILTER_EMPTY_ANNOTATIONS: False
NUM_WORKERS: 0
LOAD_PROPOSALS: False
SAMPLER_TRAIN: "TrainingSampler"
ASPECT_RATIO_GROUPING: False
TEST:
BATCH_SIZE_TOTAL: 8
VOS:
INPUT:
MIN_SIZE_TEST: 800
MAX_SIZE_TEST: 1333
DATALOADER:
FILTER_EMPTY_ANNOTATIONS: False
NUM_WORKERS: 0
LOAD_PROPOSALS: False
SAMPLER_TRAIN: "TrainingSampler"
ASPECT_RATIO_GROUPING: False
TEST:
BATCH_SIZE_TOTAL: 1
REF:
INPUT:
PIXEL_MEAN: [123.675, 116.280, 103.530]
PIXEL_STD: [58.395, 57.120, 57.375]
MIN_SIZE_TEST: 512
MAX_SIZE_TEST: 1024
FORMAT: "RGB"
SPATIAL: False
DATALOADER:
FILTER_EMPTY_ANNOTATIONS: False
NUM_WORKERS: 4
LOAD_PROPOSALS: False
SAMPLER_TRAIN: "TrainingSampler"
ASPECT_RATIO_GROUPING: False
TEST:
BATCH_SIZE_TOTAL: 8
# 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

524
mm_agents/configs/semantic_sam_only_sa-1b_swinL.yaml
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# ------------------------------------------------------------------------
# Semantic SAM
# Copyright (c) MicroSoft, Inc. and its affiliates.
# Modified from OpenSeed https://github.com/IDEA-Research/OpenSeed by Feng Li.
# ------------------------------------------------------------------------
##################
# Task settings
##################
WEIGHT: ''
PORT: 53711
VERBOSE: true
OUTPUT_DIR: '../../data/output/test'
# misc
LOADER:
JOINT: True
KEY_DATASET: 'coco'
# model
MODEL:
NAME: interactive_mask_dino
HEAD: general_head
MASK_ON: false
KEYPOINT_ON: false
LOAD_PROPOSALS: false
DIM_PROJ: 512
BACKBONE_DIM: 768
BACKGROUND: False
WEIGHTS: ''
TEXT:
ARCH: noencoder # no language encoder for training only sa-1b data
NAME: transformer
TOKENIZER: clip
CONTEXT_LENGTH: 18 # 77
WIDTH: 512
HEADS: 8
LAYERS: 12 # 6
AUTOGRESSIVE: True
BACKBONE:
NAME: swin
PRETRAINED: 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth'
LOAD_PRETRAINED: true
SWIN:
PRETRAIN_IMG_SIZE: 384
PATCH_SIZE: 4
EMBED_DIM: 192
DEPTHS: [ 2, 2, 18, 2 ]
NUM_HEADS: [ 6, 12, 24, 48 ]
WINDOW_SIZE: 12
MLP_RATIO: 4.0
QKV_BIAS: true
QK_SCALE: ~
DROP_RATE: 0.0
ATTN_DROP_RATE: 0.0
DROP_PATH_RATE: 0.3
APE: false
PATCH_NORM: true
USE_CHECKPOINT: false
OUT_FEATURES: [ 'res2', 'res3', 'res4', 'res5' ]
ENCODER:
NAME: encoder_deform
IGNORE_VALUE: 255
NUM_CLASSES: 1
LOSS_WEIGHT: 1.0
CONVS_DIM: 256
MASK_DIM: 256
NORM: "GN"
IN_FEATURES: [ "res2", "res3", "res4", "res5" ]
DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES: [ "res3", "res4", "res5" ]
COMMON_STRIDE: 4
TRANSFORMER_ENC_LAYERS: 6
TOTAL_NUM_FEATURE_LEVELS: 4
NUM_FEATURE_LEVELS: 3
FEATURE_ORDER: "low2high"
DECODER:
NAME: interactive_mask_dino
TRANSFORMER_IN_FEATURE: "multi_scale_pixel_decoder"
MASK: True
BOX: True
PART: True
GROUNDING:
ENABLED: False
MAX_LEN: 5
TEXT_WEIGHT: 2.0
CLASS_WEIGHT: 0.5
CAPTION:
ENABLED: False
PHRASE_PROB: 0.0
SIM_THRES: 0.95
CAPTIONING:
ENABLED: False
STEP: 50
RETRIEVAL:
ENABLED: False
DIM_IMG: 768
ENSEMBLE: True
OPENIMAGE:
ENABLED: False
NEGATIVE_SAMPLES: 5
GROUNDING:
ENABLED: False
MAX_LEN: 5
DEEP_SUPERVISION: True
NO_OBJECT_WEIGHT: 0.1
CLASS_WEIGHT: 4.0
MASK_WEIGHT: 5.0
DICE_WEIGHT: 5.0
BOX_WEIGHT: 5.0
GIOU_WEIGHT: 2.0
IOU_WEIGHT: 1.0
COST_CLASS_WEIGHT: 4.0
COST_DICE_WEIGHT: 5.0
COST_MASK_WEIGHT: 5.0
COST_BOX_WEIGHT: 5.0
COST_GIOU_WEIGHT: 2.0
HIDDEN_DIM: 256
NUM_OBJECT_QUERIES: 0
NHEADS: 8
DROPOUT: 0.0
DIM_FEEDFORWARD: 2048
ENC_LAYERS: 0
PRE_NORM: False
ENFORCE_INPUT_PROJ: False
SIZE_DIVISIBILITY: 32
DEC_LAYERS: 9 # 9 decoder layers, add one for the loss on learnable query
TRAIN_NUM_POINTS: 12544
OVERSAMPLE_RATIO: 3.0
IMPORTANCE_SAMPLE_RATIO: 0.75
TWO_STAGE: False
INITIALIZE_BOX_TYPE: 'no'
DN: seg
DN_NOISE_SCALE: 0.4
DN_NUM: 100
INITIAL_PRED: False
LEARN_TGT: False
TOTAL_NUM_FEATURE_LEVELS: 4
SEMANTIC_CE_LOSS: False
PANO_BOX_LOSS: False
COCO: False
O365: False
SAM: True
PASCAL: False
RE_POINT: True
NUM_INTERACTIVE_TOKENS: 6
MAX_NUM_INSTANCE: 60
TEST:
SEMANTIC_ON: True
INSTANCE_ON: True
PANOPTIC_ON: True
BOX_INTERACTIVE: False
CLASSIFICATION_ON: False
OVERLAP_THRESHOLD: 0.8
OBJECT_MASK_THRESHOLD: 0.25
SEM_SEG_POSTPROCESSING_BEFORE_INFERENCE: false
TEST_FOUCUS_ON_BOX: False
PANO_TRANSFORM_EVAL: True
PANO_TEMPERATURE: 0.06
TEST:
EVAL_PERIOD: 500000
PRECISE_BN:
NUM_ITER: 1
ENABLED: False
AUG:
ENABLED: False
SAM:
INPUT:
MIN_SIZE_TEST: 800
MAX_SIZE_TEST: 1333
IMAGE_SIZE: 1024
MIN_SCALE: 0.99
MAX_SCALE: 1.01
DATASET_MAPPER_NAME: "sam"
IGNORE_VALUE: 255
COLOR_AUG_SSD: False
SIZE_DIVISIBILITY: 32
RANDOM_FLIP: "horizontal"
MASK_FORMAT: "polygon"
FORMAT: "RGB"
CROP:
ENABLED: True
DATASET:
DATASET: 'sam'
TEST:
DETECTIONS_PER_IMAGE: 100
NAME: coco_eval
IOU_TYPE: ['bbox', 'segm']
USE_MULTISCALE: false
BATCH_SIZE_TOTAL: 8
MODEL_FILE: ''
AUG:
ENABLED: False
TRAIN:
BATCH_SIZE_TOTAL: 1
BATCH_SIZE_PER_GPU: 1
SHUFFLE: true
DATALOADER:
FILTER_EMPTY_ANNOTATIONS: False
NUM_WORKERS: 4
LOAD_PROPOSALS: False
SAMPLER_TRAIN: "TrainingSampler"
ASPECT_RATIO_GROUPING: True
COCO:
INPUT:
MIN_SIZE_TEST: 800
MAX_SIZE_TEST: 1333
IMAGE_SIZE: 1024
MIN_SCALE: 0.1
MAX_SCALE: 2.0
DATASET_MAPPER_NAME: "coco_interactive_panoptic_lsj"
IGNORE_VALUE: 255
COLOR_AUG_SSD: False
SIZE_DIVISIBILITY: 32
RANDOM_FLIP: "horizontal"
MASK_FORMAT: "polygon"
FORMAT: "RGB"
CROP:
ENABLED: True
DATASET:
DATASET: 'coco'
TEST:
DETECTIONS_PER_IMAGE: 100
NAME: coco_eval
IOU_TYPE: ['bbox', 'segm']
USE_MULTISCALE: false
BATCH_SIZE_TOTAL: 1
MODEL_FILE: ''
AUG:
ENABLED: False
TRAIN:
BATCH_SIZE_TOTAL: 1
BATCH_SIZE_PER_GPU: 1
SHUFFLE: true
DATALOADER:
FILTER_EMPTY_ANNOTATIONS: False
NUM_WORKERS: 2
LOAD_PROPOSALS: False
SAMPLER_TRAIN: "TrainingSampler"
ASPECT_RATIO_GROUPING: True
VLP:
INPUT:
IMAGE_SIZE: 224
DATASET_MAPPER_NAME: "vlpretrain"
IGNORE_VALUE: 255
COLOR_AUG_SSD: False
SIZE_DIVISIBILITY: 32
MASK_FORMAT: "polygon"
FORMAT: "RGB"
CROP:
ENABLED: True
TRAIN:
BATCH_SIZE_TOTAL: 2
BATCH_SIZE_PER_GPU: 2
TEST:
BATCH_SIZE_TOTAL: 256
DATALOADER:
FILTER_EMPTY_ANNOTATIONS: False
NUM_WORKERS: 16
LOAD_PROPOSALS: False
SAMPLER_TRAIN: "TrainingSampler"
ASPECT_RATIO_GROUPING: True
INPUT:
PIXEL_MEAN: [123.675, 116.280, 103.530]
PIXEL_STD: [58.395, 57.120, 57.375]
DATASETS:
TRAIN: ["sam_train"]
# interactive segmentation evaluation.
TEST: ["coco_2017_val_panoptic_with_sem_seg_interactive_jointboxpoint"]
# TEST: ["sam_minival"]
CLASS_CONCAT: false
SIZE_DIVISIBILITY: 32
PROPOSAL_FILES_TRAIN: []
DATALOADER:
FILTER_EMPTY_ANNOTATIONS: False
NUM_WORKERS: 16
LOAD_PROPOSALS: False
SAMPLER_TRAIN: "TrainingSampler"
ASPECT_RATIO_GROUPING: True
# Detectron2 training config for optimizer and lr scheduler
SOLVER:
BASE_LR_END: 0.0
MOMENTUM: 0.9
NESTEROV: False
CHECKPOINT_PERIOD: 5000
IMS_PER_BATCH: 1
REFERENCE_WORLD_SIZE: 0
BIAS_LR_FACTOR: 1.0
WEIGHT_DECAY_BIAS: None
# original
BASE_LR: 0.0001
STEPS: [327778, 355092]
MAX_ITER: 368750
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
WEIGHT_DECAY_NORM: 0.0
WEIGHT_DECAY_EMBED: 0.0
CLIP_GRADIENTS:
ENABLED: True
CLIP_TYPE: "full_model"
CLIP_VALUE: 0.01
NORM_TYPE: 2.0
AMP:
ENABLED: True
# Evaluation Dataset
ADE20K:
INPUT:
MIN_SIZE_TRAIN: [320, 384, 448, 512, 576, 640, 704, 768, 832, 896, 960, 1024, 1088, 1152, 1216, 1280]
MIN_SIZE_TRAIN_SAMPLING: "choice"
MIN_SIZE_TEST: 640
MAX_SIZE_TRAIN: 2560
MAX_SIZE_TEST: 2560
MASK_FORMAT: "polygon"
CROP:
ENABLED: True
TYPE: "absolute"
SIZE: [640, 640]
SINGLE_CATEGORY_MAX_AREA: 1.0
IGNORE_VALUE: 255
COLOR_AUG_SSD: True
SIZE_DIVISIBILITY: 640 # used in dataset mapper
DATASET_MAPPER_NAME: "mask_former_panoptic"
FORMAT: "RGB"
DATASET:
DATASET: 'ade'
TRAIN:
ASPECT_RATIO_GROUPING: true
BATCH_SIZE_TOTAL: 16
BATCH_SIZE_PER_GPU: 2
SHUFFLE: true
TEST:
DETECTIONS_PER_IMAGE: 100
NAME: coco_eval
IOU_TYPE: ['bbox', 'segm']
USE_MULTISCALE: false
BATCH_SIZE_TOTAL: 8
MODEL_FILE: ''
AUG:
ENABLED: False
DATALOADER:
FILTER_EMPTY_ANNOTATIONS: False
NUM_WORKERS: 8
LOAD_PROPOSALS: False
SAMPLER_TRAIN: "TrainingSampler"
ASPECT_RATIO_GROUPING: True
#ADE20K:
# INPUT:
# MIN_SIZE_TRAIN: 640
# MIN_SIZE_TRAIN_SAMPLING: "choice"
# MIN_SIZE_TEST: 640
# MAX_SIZE_TRAIN: 2560
# MAX_SIZE_TEST: 2560
# MASK_FORMAT: "polygon"
# CROP:
# ENABLED: True
# TYPE: "absolute"
# SIZE: (640, 640)
# SINGLE_CATEGORY_MAX_AREA: 1.0
# COLOR_AUG_SSD: True
# SIZE_DIVISIBILITY: 640 # used in dataset mapper
# DATASET_MAPPER_NAME: "mask_former_panoptic"
# FORMAT: "RGB"
# DATASET:
# DATASET: 'ade'
# TEST:
# BATCH_SIZE_TOTAL: 8
REF:
INPUT:
PIXEL_MEAN: [123.675, 116.280, 103.530]
PIXEL_STD: [58.395, 57.120, 57.375]
MIN_SIZE_TEST: 512
MAX_SIZE_TEST: 1024
FORMAT: "RGB"
DATALOADER:
FILTER_EMPTY_ANNOTATIONS: False
NUM_WORKERS: 0
LOAD_PROPOSALS: False
SAMPLER_TRAIN: "TrainingSampler"
ASPECT_RATIO_GROUPING: False
TEST:
BATCH_SIZE_TOTAL: 8
SUN:
INPUT:
PIXEL_MEAN: [123.675, 116.280, 103.530]
PIXEL_STD: [58.395, 57.120, 57.375]
MIN_SIZE_TEST: 512
MAX_SIZE_TEST: 1024
DATALOADER:
FILTER_EMPTY_ANNOTATIONS: False
NUM_WORKERS: 0
LOAD_PROPOSALS: False
SAMPLER_TRAIN: "TrainingSampler"
ASPECT_RATIO_GROUPING: False
TEST:
BATCH_SIZE_TOTAL: 8
SCAN:
INPUT:
PIXEL_MEAN: [123.675, 116.280, 103.530]
PIXEL_STD: [58.395, 57.120, 57.375]
MIN_SIZE_TEST: 512
MAX_SIZE_TEST: 1024
DATALOADER:
FILTER_EMPTY_ANNOTATIONS: False
NUM_WORKERS: 0
LOAD_PROPOSALS: False
SAMPLER_TRAIN: "TrainingSampler"
ASPECT_RATIO_GROUPING: False
TEST:
BATCH_SIZE_TOTAL: 8
BDD:
INPUT:
PIXEL_MEAN: [123.675, 116.280, 103.530]
PIXEL_STD: [58.395, 57.120, 57.375]
MIN_SIZE_TEST: 800
MAX_SIZE_TEST: 1333
DATALOADER:
FILTER_EMPTY_ANNOTATIONS: False
NUM_WORKERS: 0
LOAD_PROPOSALS: False
SAMPLER_TRAIN: "TrainingSampler"
ASPECT_RATIO_GROUPING: False
TEST:
BATCH_SIZE_TOTAL: 8
CITY:
INPUT:
MIN_SIZE_TRAIN: [ 512, 614, 716, 819, 921, 1024, 1126, 1228, 1331, 1433, 1536, 1638, 1740, 1843, 1945, 2048 ]
MIN_SIZE_TRAIN_SAMPLING: "choice"
MIN_SIZE_TEST: 1024
MAX_SIZE_TRAIN: 4096
MAX_SIZE_TEST: 2048
CROP:
ENABLED: True
TYPE: "absolute"
SIZE: [ 512, 1024 ]
SINGLE_CATEGORY_MAX_AREA: 1.0
IGNORE_VALUE: 255
COLOR_AUG_SSD: True
SIZE_DIVISIBILITY: -1
FORMAT: "RGB"
DATASET_MAPPER_NAME: "mask_former_panoptic"
MASK_FORMAT: "polygon"
TEST:
EVAL_PERIOD: 5000
BATCH_SIZE_TOTAL: 1
AUG:
ENABLED: False
MIN_SIZES: [ 512, 768, 1024, 1280, 1536, 1792 ]
MAX_SIZE: 4096
FLIP: True
DATALOADER:
FILTER_EMPTY_ANNOTATIONS: True
NUM_WORKERS: 2
LOAD_PROPOSALS: False
SAMPLER_TRAIN: "TrainingSampler"
ASPECT_RATIO_GROUPING: True
TRAIN:
ASPECT_RATIO_GROUPING: true
BATCH_SIZE_TOTAL: 2
BATCH_SIZE_PER_GPU: 2
SHUFFLE: true
PSACAL_PART:
INPUT:
MIN_SIZE_TEST: 800
MAX_SIZE_TEST: 1333
IMAGE_SIZE: 1024
MIN_SCALE: 0.1
MAX_SCALE: 2.0
DATASET_MAPPER_NAME: "pascal_part_lsj"
IGNORE_VALUE: 255
COLOR_AUG_SSD: False
SIZE_DIVISIBILITY: 32
RANDOM_FLIP: "horizontal"
MASK_FORMAT: "polygon"
FORMAT: "RGB"
CROP:
ENABLED: True
MODEL:
MASK_ON: True
KEYPOINT_ON: False
LOAD_PROPOSALS: False
# DATASET:
# DATASET: 'coco'
TEST:
DETECTIONS_PER_IMAGE: 100
NAME: coco_eval
IOU_TYPE: ['bbox', 'segm']
USE_MULTISCALE: false
BATCH_SIZE_TOTAL: 8
MODEL_FILE: ''
AUG:
ENABLED: False
TRAIN:
BATCH_SIZE_TOTAL: 1
BATCH_SIZE_PER_GPU: 1
SHUFFLE: true
DATALOADER:
FILTER_EMPTY_ANNOTATIONS: False
NUM_WORKERS: 2
LOAD_PROPOSALS: False
SAMPLER_TRAIN: "TrainingSampler"
ASPECT_RATIO_GROUPING: True

13
mm_agents/ops/functions/__init__.py
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@@ -1,13 +0,0 @@
# ------------------------------------------------------------------------------------------------
# Deformable DETR
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------------------------------
# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
# ------------------------------------------------------------------------------------------------
# Copyright (c) Facebook, Inc. and its affiliates.
# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
from .ms_deform_attn_func import MSDeformAttnFunction

72
mm_agents/ops/functions/ms_deform_attn_func.py
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# ------------------------------------------------------------------------------------------------
# Deformable DETR
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------------------------------
# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
# ------------------------------------------------------------------------------------------------
# Copyright (c) Facebook, Inc. and its affiliates.
# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import torch
import torch.nn.functional as F
from torch.autograd import Function
from torch.autograd.function import once_differentiable
try:
import MultiScaleDeformableAttention as MSDA
except ModuleNotFoundError as e:
info_string = (
"\n\nPlease compile MultiScaleDeformableAttention CUDA op with the following commands:\n"
"\t`cd mask2former/modeling/pixel_decoder/ops`\n"
"\t`sh make.sh`\n"
)
raise ModuleNotFoundError(info_string)
class MSDeformAttnFunction(Function):
@staticmethod
def forward(ctx, value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, im2col_step):
ctx.im2col_step = im2col_step
output = MSDA.ms_deform_attn_forward(
value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, ctx.im2col_step)
ctx.save_for_backward(value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights)
return output
@staticmethod
@once_differentiable
def backward(ctx, grad_output):
value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights = ctx.saved_tensors
grad_value, grad_sampling_loc, grad_attn_weight = \
MSDA.ms_deform_attn_backward(
value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, grad_output, ctx.im2col_step)
return grad_value, None, None, grad_sampling_loc, grad_attn_weight, None
def ms_deform_attn_core_pytorch(value, value_spatial_shapes, sampling_locations, attention_weights):
# for debug and test only,
# need to use cuda version instead
N_, S_, M_, D_ = value.shape
_, Lq_, M_, L_, P_, _ = sampling_locations.shape
value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes], dim=1)
sampling_grids = 2 * sampling_locations - 1
sampling_value_list = []
for lid_, (H_, W_) in enumerate(value_spatial_shapes):
# N_, H_*W_, M_, D_ -> N_, H_*W_, M_*D_ -> N_, M_*D_, H_*W_ -> N_*M_, D_, H_, W_
value_l_ = value_list[lid_].flatten(2).transpose(1, 2).reshape(N_*M_, D_, H_, W_)
# N_, Lq_, M_, P_, 2 -> N_, M_, Lq_, P_, 2 -> N_*M_, Lq_, P_, 2
sampling_grid_l_ = sampling_grids[:, :, :, lid_].transpose(1, 2).flatten(0, 1)
# N_*M_, D_, Lq_, P_
sampling_value_l_ = F.grid_sample(value_l_, sampling_grid_l_,
mode='bilinear', padding_mode='zeros', align_corners=False)
sampling_value_list.append(sampling_value_l_)
# (N_, Lq_, M_, L_, P_) -> (N_, M_, Lq_, L_, P_) -> (N_, M_, 1, Lq_, L_*P_)
attention_weights = attention_weights.transpose(1, 2).reshape(N_*M_, 1, Lq_, L_*P_)
output = (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights).sum(-1).view(N_, M_*D_, Lq_)
return output.transpose(1, 2).contiguous()

13
mm_agents/ops/make.sh
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@@ -1,13 +0,0 @@
#!/usr/bin/env bash
# ------------------------------------------------------------------------------------------------
# Deformable DETR
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------------------------------
# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
# ------------------------------------------------------------------------------------------------
# Copyright (c) Facebook, Inc. and its affiliates.
# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
python setup.py build install

12
mm_agents/ops/modules/__init__.py
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@@ -1,12 +0,0 @@
# ------------------------------------------------------------------------------------------------
# Deformable DETR
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------------------------------
# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
# ------------------------------------------------------------------------------------------------
# Copyright (c) Facebook, Inc. and its affiliates.
# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
from .ms_deform_attn import MSDeformAttn

125
mm_agents/ops/modules/ms_deform_attn.py
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@@ -1,125 +0,0 @@
# ------------------------------------------------------------------------------------------------
# Deformable DETR
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------------------------------
# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
# ------------------------------------------------------------------------------------------------
# Copyright (c) Facebook, Inc. and its affiliates.
# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import warnings
import math
import torch
from torch import nn
import torch.nn.functional as F
from torch.nn.init import xavier_uniform_, constant_
from ..functions import MSDeformAttnFunction
from ..functions.ms_deform_attn_func import ms_deform_attn_core_pytorch
def _is_power_of_2(n):
if (not isinstance(n, int)) or (n < 0):
raise ValueError("invalid input for _is_power_of_2: {} (type: {})".format(n, type(n)))
return (n & (n-1) == 0) and n != 0
class MSDeformAttn(nn.Module):
def __init__(self, d_model=256, n_levels=4, n_heads=8, n_points=4):
"""
Multi-Scale Deformable Attention Module
:param d_model hidden dimension
:param n_levels number of feature levels
:param n_heads number of attention heads
:param n_points number of sampling points per attention head per feature level
"""
super().__init__()
if d_model % n_heads != 0:
raise ValueError('d_model must be divisible by n_heads, but got {} and {}'.format(d_model, n_heads))
_d_per_head = d_model // n_heads
# you'd better set _d_per_head to a power of 2 which is more efficient in our CUDA implementation
if not _is_power_of_2(_d_per_head):
warnings.warn("You'd better set d_model in MSDeformAttn to make the dimension of each attention head a power of 2 "
"which is more efficient in our CUDA implementation.")
self.im2col_step = 128
self.d_model = d_model
self.n_levels = n_levels
self.n_heads = n_heads
self.n_points = n_points
self.sampling_offsets = nn.Linear(d_model, n_heads * n_levels * n_points * 2)
self.attention_weights = nn.Linear(d_model, n_heads * n_levels * n_points)
self.value_proj = nn.Linear(d_model, d_model)
self.output_proj = nn.Linear(d_model, d_model)
self._reset_parameters()
def _reset_parameters(self):
constant_(self.sampling_offsets.weight.data, 0.)
thetas = torch.arange(self.n_heads, dtype=torch.float32) * (2.0 * math.pi / self.n_heads)
grid_init = torch.stack([thetas.cos(), thetas.sin()], -1)
grid_init = (grid_init / grid_init.abs().max(-1, keepdim=True)[0]).view(self.n_heads, 1, 1, 2).repeat(1, self.n_levels, self.n_points, 1)
for i in range(self.n_points):
grid_init[:, :, i, :] *= i + 1
with torch.no_grad():
self.sampling_offsets.bias = nn.Parameter(grid_init.view(-1))
constant_(self.attention_weights.weight.data, 0.)
constant_(self.attention_weights.bias.data, 0.)
xavier_uniform_(self.value_proj.weight.data)
constant_(self.value_proj.bias.data, 0.)
xavier_uniform_(self.output_proj.weight.data)
constant_(self.output_proj.bias.data, 0.)
def forward(self, query, reference_points, input_flatten, input_spatial_shapes, input_level_start_index, input_padding_mask=None):
"""
:param query (N, Length_{query}, C)
:param reference_points (N, Length_{query}, n_levels, 2), range in [0, 1], top-left (0,0), bottom-right (1, 1), including padding area
or (N, Length_{query}, n_levels, 4), add additional (w, h) to form reference boxes
:param input_flatten (N, \sum_{l=0}^{L-1} H_l \cdot W_l, C)
:param input_spatial_shapes (n_levels, 2), [(H_0, W_0), (H_1, W_1), ..., (H_{L-1}, W_{L-1})]
:param input_level_start_index (n_levels, ), [0, H_0*W_0, H_0*W_0+H_1*W_1, H_0*W_0+H_1*W_1+H_2*W_2, ..., H_0*W_0+H_1*W_1+...+H_{L-1}*W_{L-1}]
:param input_padding_mask (N, \sum_{l=0}^{L-1} H_l \cdot W_l), True for padding elements, False for non-padding elements
:return output (N, Length_{query}, C)
"""
N, Len_q, _ = query.shape
N, Len_in, _ = input_flatten.shape
assert (input_spatial_shapes[:, 0] * input_spatial_shapes[:, 1]).sum() == Len_in
value = self.value_proj(input_flatten)
if input_padding_mask is not None:
value = value.masked_fill(input_padding_mask[..., None], float(0))
value = value.view(N, Len_in, self.n_heads, self.d_model // self.n_heads)
sampling_offsets = self.sampling_offsets(query).view(N, Len_q, self.n_heads, self.n_levels, self.n_points, 2)
attention_weights = self.attention_weights(query).view(N, Len_q, self.n_heads, self.n_levels * self.n_points)
attention_weights = F.softmax(attention_weights, -1).view(N, Len_q, self.n_heads, self.n_levels, self.n_points)
# N, Len_q, n_heads, n_levels, n_points, 2
if reference_points.shape[-1] == 2:
offset_normalizer = torch.stack([input_spatial_shapes[..., 1], input_spatial_shapes[..., 0]], -1)
sampling_locations = reference_points[:, :, None, :, None, :] \
+ sampling_offsets / offset_normalizer[None, None, None, :, None, :]
elif reference_points.shape[-1] == 4:
sampling_locations = reference_points[:, :, None, :, None, :2] \
+ sampling_offsets / self.n_points * reference_points[:, :, None, :, None, 2:] * 0.5
else:
raise ValueError(
'Last dim of reference_points must be 2 or 4, but get {} instead.'.format(reference_points.shape[-1]))
try:
output = MSDeformAttnFunction.apply(
value, input_spatial_shapes, input_level_start_index, sampling_locations, attention_weights, self.im2col_step)
except:
# CPU
output = ms_deform_attn_core_pytorch(value, input_spatial_shapes, sampling_locations, attention_weights)
# # For FLOPs calculation only
# output = ms_deform_attn_core_pytorch(value, input_spatial_shapes, sampling_locations, attention_weights)
output = self.output_proj(output)
return output

78
mm_agents/ops/setup.py
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@@ -1,78 +0,0 @@
# ------------------------------------------------------------------------------------------------
# Deformable DETR
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------------------------------
# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
# ------------------------------------------------------------------------------------------------
# Copyright (c) Facebook, Inc. and its affiliates.
# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
import os
import glob
import torch
from torch.utils.cpp_extension import CUDA_HOME
from torch.utils.cpp_extension import CppExtension
from torch.utils.cpp_extension import CUDAExtension
from setuptools import find_packages
from setuptools import setup
requirements = ["torch", "torchvision"]
def get_extensions():
this_dir = os.path.dirname(os.path.abspath(__file__))
extensions_dir = os.path.join(this_dir, "src")
main_file = glob.glob(os.path.join(extensions_dir, "*.cpp"))
source_cpu = glob.glob(os.path.join(extensions_dir, "cpu", "*.cpp"))
source_cuda = glob.glob(os.path.join(extensions_dir, "cuda", "*.cu"))
sources = main_file + source_cpu
extension = CppExtension
extra_compile_args = {"cxx": []}
define_macros = []
# Force cuda since torch ask for a device, not if cuda is in fact available.
if (os.environ.get('FORCE_CUDA') or torch.cuda.is_available()) and CUDA_HOME is not None:
extension = CUDAExtension
sources += source_cuda
define_macros += [("WITH_CUDA", None)]
extra_compile_args["nvcc"] = [
"-DCUDA_HAS_FP16=1",
"-D__CUDA_NO_HALF_OPERATORS__",
"-D__CUDA_NO_HALF_CONVERSIONS__",
"-D__CUDA_NO_HALF2_OPERATORS__",
]
else:
if CUDA_HOME is None:
raise NotImplementedError('CUDA_HOME is None. Please set environment variable CUDA_HOME.')
else:
raise NotImplementedError('No CUDA runtime is found. Please set FORCE_CUDA=1 or test it by running torch.cuda.is_available().')
sources = [os.path.join(extensions_dir, s) for s in sources]
include_dirs = [extensions_dir]
ext_modules = [
extension(
"MultiScaleDeformableAttention",
sources,
include_dirs=include_dirs,
define_macros=define_macros,
extra_compile_args=extra_compile_args,
)
]
return ext_modules
setup(
name="MultiScaleDeformableAttention",
version="1.0",
author="Weijie Su",
url="https://github.com/fundamentalvision/Deformable-DETR",
description="PyTorch Wrapper for CUDA Functions of Multi-Scale Deformable Attention",
packages=find_packages(exclude=("configs", "tests",)),
ext_modules=get_extensions(),
cmdclass={"build_ext": torch.utils.cpp_extension.BuildExtension},
)

46
mm_agents/ops/src/cpu/ms_deform_attn_cpu.cpp
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@@ -1,46 +0,0 @@
/*!
**************************************************************************************************
* Deformable DETR
* Copyright (c) 2020 SenseTime. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
**************************************************************************************************
* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
**************************************************************************************************
*/
/*!
* Copyright (c) Facebook, Inc. and its affiliates.
* Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
*/
#include <vector>
#include <ATen/ATen.h>
#include <ATen/cuda/CUDAContext.h>
at::Tensor
ms_deform_attn_cpu_forward(
const at::Tensor &value,
const at::Tensor &spatial_shapes,
const at::Tensor &level_start_index,
const at::Tensor &sampling_loc,
const at::Tensor &attn_weight,
const int im2col_step)
{
AT_ERROR("Not implement on cpu");
}
std::vector<at::Tensor>
ms_deform_attn_cpu_backward(
const at::Tensor &value,
const at::Tensor &spatial_shapes,
const at::Tensor &level_start_index,
const at::Tensor &sampling_loc,
const at::Tensor &attn_weight,
const at::Tensor &grad_output,
const int im2col_step)
{
AT_ERROR("Not implement on cpu");
}

38
mm_agents/ops/src/cpu/ms_deform_attn_cpu.h
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@@ -1,38 +0,0 @@
/*!
**************************************************************************************************
* Deformable DETR
* Copyright (c) 2020 SenseTime. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
**************************************************************************************************
* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
**************************************************************************************************
*/
/*!
* Copyright (c) Facebook, Inc. and its affiliates.
* Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
*/
#pragma once
#include <torch/extension.h>
at::Tensor
ms_deform_attn_cpu_forward(
const at::Tensor &value,
const at::Tensor &spatial_shapes,
const at::Tensor &level_start_index,
const at::Tensor &sampling_loc,
const at::Tensor &attn_weight,
const int im2col_step);
std::vector<at::Tensor>
ms_deform_attn_cpu_backward(
const at::Tensor &value,
const at::Tensor &spatial_shapes,
const at::Tensor &level_start_index,
const at::Tensor &sampling_loc,
const at::Tensor &attn_weight,
const at::Tensor &grad_output,
const int im2col_step);

158
mm_agents/ops/src/cuda/ms_deform_attn_cuda.cu
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@@ -1,158 +0,0 @@
/*!
**************************************************************************************************
* Deformable DETR
* Copyright (c) 2020 SenseTime. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
**************************************************************************************************
* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
**************************************************************************************************
*/
/*!
* Copyright (c) Facebook, Inc. and its affiliates.
* Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
*/
#include <vector>
#include "cuda/ms_deform_im2col_cuda.cuh"
#include <ATen/ATen.h>
#include <ATen/cuda/CUDAContext.h>
#include <cuda.h>
#include <cuda_runtime.h>
at::Tensor ms_deform_attn_cuda_forward(
const at::Tensor &value,
const at::Tensor &spatial_shapes,
const at::Tensor &level_start_index,
const at::Tensor &sampling_loc,
const at::Tensor &attn_weight,
const int im2col_step)
{
AT_ASSERTM(value.is_contiguous(), "value tensor has to be contiguous");
AT_ASSERTM(spatial_shapes.is_contiguous(), "spatial_shapes tensor has to be contiguous");
AT_ASSERTM(level_start_index.is_contiguous(), "level_start_index tensor has to be contiguous");
AT_ASSERTM(sampling_loc.is_contiguous(), "sampling_loc tensor has to be contiguous");
AT_ASSERTM(attn_weight.is_contiguous(), "attn_weight tensor has to be contiguous");
AT_ASSERTM(value.type().is_cuda(), "value must be a CUDA tensor");
AT_ASSERTM(spatial_shapes.type().is_cuda(), "spatial_shapes must be a CUDA tensor");
AT_ASSERTM(level_start_index.type().is_cuda(), "level_start_index must be a CUDA tensor");
AT_ASSERTM(sampling_loc.type().is_cuda(), "sampling_loc must be a CUDA tensor");
AT_ASSERTM(attn_weight.type().is_cuda(), "attn_weight must be a CUDA tensor");
const int batch = value.size(0);
const int spatial_size = value.size(1);
const int num_heads = value.size(2);
const int channels = value.size(3);
const int num_levels = spatial_shapes.size(0);
const int num_query = sampling_loc.size(1);
const int num_point = sampling_loc.size(4);
const int im2col_step_ = std::min(batch, im2col_step);
AT_ASSERTM(batch % im2col_step_ == 0, "batch(%d) must divide im2col_step(%d)", batch, im2col_step_);
auto output = at::zeros({batch, num_query, num_heads, channels}, value.options());
const int batch_n = im2col_step_;
auto output_n = output.view({batch/im2col_step_, batch_n, num_query, num_heads, channels});
auto per_value_size = spatial_size * num_heads * channels;
auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2;
auto per_attn_weight_size = num_query * num_heads * num_levels * num_point;
for (int n = 0; n < batch/im2col_step_; ++n)
{
auto columns = output_n.select(0, n);
AT_DISPATCH_FLOATING_TYPES(value.type(), "ms_deform_attn_forward_cuda", ([&] {
ms_deformable_im2col_cuda(at::cuda::getCurrentCUDAStream(),
value.data<scalar_t>() + n * im2col_step_ * per_value_size,
spatial_shapes.data<int64_t>(),
level_start_index.data<int64_t>(),
sampling_loc.data<scalar_t>() + n * im2col_step_ * per_sample_loc_size,
attn_weight.data<scalar_t>() + n * im2col_step_ * per_attn_weight_size,
batch_n, spatial_size, num_heads, channels, num_levels, num_query, num_point,
columns.data<scalar_t>());
}));
}
output = output.view({batch, num_query, num_heads*channels});
return output;
}
std::vector<at::Tensor> ms_deform_attn_cuda_backward(
const at::Tensor &value,
const at::Tensor &spatial_shapes,
const at::Tensor &level_start_index,
const at::Tensor &sampling_loc,
const at::Tensor &attn_weight,
const at::Tensor &grad_output,
const int im2col_step)
{
AT_ASSERTM(value.is_contiguous(), "value tensor has to be contiguous");
AT_ASSERTM(spatial_shapes.is_contiguous(), "spatial_shapes tensor has to be contiguous");
AT_ASSERTM(level_start_index.is_contiguous(), "level_start_index tensor has to be contiguous");
AT_ASSERTM(sampling_loc.is_contiguous(), "sampling_loc tensor has to be contiguous");
AT_ASSERTM(attn_weight.is_contiguous(), "attn_weight tensor has to be contiguous");
AT_ASSERTM(grad_output.is_contiguous(), "grad_output tensor has to be contiguous");
AT_ASSERTM(value.type().is_cuda(), "value must be a CUDA tensor");
AT_ASSERTM(spatial_shapes.type().is_cuda(), "spatial_shapes must be a CUDA tensor");
AT_ASSERTM(level_start_index.type().is_cuda(), "level_start_index must be a CUDA tensor");
AT_ASSERTM(sampling_loc.type().is_cuda(), "sampling_loc must be a CUDA tensor");
AT_ASSERTM(attn_weight.type().is_cuda(), "attn_weight must be a CUDA tensor");
AT_ASSERTM(grad_output.type().is_cuda(), "grad_output must be a CUDA tensor");
const int batch = value.size(0);
const int spatial_size = value.size(1);
const int num_heads = value.size(2);
const int channels = value.size(3);
const int num_levels = spatial_shapes.size(0);
const int num_query = sampling_loc.size(1);
const int num_point = sampling_loc.size(4);
const int im2col_step_ = std::min(batch, im2col_step);
AT_ASSERTM(batch % im2col_step_ == 0, "batch(%d) must divide im2col_step(%d)", batch, im2col_step_);
auto grad_value = at::zeros_like(value);
auto grad_sampling_loc = at::zeros_like(sampling_loc);
auto grad_attn_weight = at::zeros_like(attn_weight);
const int batch_n = im2col_step_;
auto per_value_size = spatial_size * num_heads * channels;
auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2;
auto per_attn_weight_size = num_query * num_heads * num_levels * num_point;
auto grad_output_n = grad_output.view({batch/im2col_step_, batch_n, num_query, num_heads, channels});
for (int n = 0; n < batch/im2col_step_; ++n)
{
auto grad_output_g = grad_output_n.select(0, n);
AT_DISPATCH_FLOATING_TYPES(value.type(), "ms_deform_attn_backward_cuda", ([&] {
ms_deformable_col2im_cuda(at::cuda::getCurrentCUDAStream(),
grad_output_g.data<scalar_t>(),
value.data<scalar_t>() + n * im2col_step_ * per_value_size,
spatial_shapes.data<int64_t>(),
level_start_index.data<int64_t>(),
sampling_loc.data<scalar_t>() + n * im2col_step_ * per_sample_loc_size,
attn_weight.data<scalar_t>() + n * im2col_step_ * per_attn_weight_size,
batch_n, spatial_size, num_heads, channels, num_levels, num_query, num_point,
grad_value.data<scalar_t>() + n * im2col_step_ * per_value_size,
grad_sampling_loc.data<scalar_t>() + n * im2col_step_ * per_sample_loc_size,
grad_attn_weight.data<scalar_t>() + n * im2col_step_ * per_attn_weight_size);
}));
}
return {
grad_value, grad_sampling_loc, grad_attn_weight
};
}

35
mm_agents/ops/src/cuda/ms_deform_attn_cuda.h
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/*!
**************************************************************************************************
* Deformable DETR
* Copyright (c) 2020 SenseTime. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
**************************************************************************************************
* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
**************************************************************************************************
*/
/*!
* Copyright (c) Facebook, Inc. and its affiliates.
* Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
*/
#pragma once
#include <torch/extension.h>
at::Tensor ms_deform_attn_cuda_forward(
const at::Tensor &value,
const at::Tensor &spatial_shapes,
const at::Tensor &level_start_index,
const at::Tensor &sampling_loc,
const at::Tensor &attn_weight,
const int im2col_step);
std::vector<at::Tensor> ms_deform_attn_cuda_backward(
const at::Tensor &value,
const at::Tensor &spatial_shapes,
const at::Tensor &level_start_index,
const at::Tensor &sampling_loc,
const at::Tensor &attn_weight,
const at::Tensor &grad_output,
const int im2col_step);

1332
mm_agents/ops/src/cuda/ms_deform_im2col_cuda.cuh
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67
mm_agents/ops/src/ms_deform_attn.h
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/*!
**************************************************************************************************
* Deformable DETR
* Copyright (c) 2020 SenseTime. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
**************************************************************************************************
* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
**************************************************************************************************
*/
/*!
* Copyright (c) Facebook, Inc. and its affiliates.
* Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
*/
#pragma once
#include "cpu/ms_deform_attn_cpu.h"
#ifdef WITH_CUDA
#include "cuda/ms_deform_attn_cuda.h"
#endif
at::Tensor
ms_deform_attn_forward(
const at::Tensor &value,
const at::Tensor &spatial_shapes,
const at::Tensor &level_start_index,
const at::Tensor &sampling_loc,
const at::Tensor &attn_weight,
const int im2col_step)
{
if (value.type().is_cuda())
{
#ifdef WITH_CUDA
return ms_deform_attn_cuda_forward(
value, spatial_shapes, level_start_index, sampling_loc, attn_weight, im2col_step);
#else
AT_ERROR("Not compiled with GPU support");
#endif
}
AT_ERROR("Not implemented on the CPU");
}
std::vector<at::Tensor>
ms_deform_attn_backward(
const at::Tensor &value,
const at::Tensor &spatial_shapes,
const at::Tensor &level_start_index,
const at::Tensor &sampling_loc,
const at::Tensor &attn_weight,
const at::Tensor &grad_output,
const int im2col_step)
{
if (value.type().is_cuda())
{
#ifdef WITH_CUDA
return ms_deform_attn_cuda_backward(
value, spatial_shapes, level_start_index, sampling_loc, attn_weight, grad_output, im2col_step);
#else
AT_ERROR("Not compiled with GPU support");
#endif
}
AT_ERROR("Not implemented on the CPU");
}

21
mm_agents/ops/src/vision.cpp
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/*!
**************************************************************************************************
* Deformable DETR
* Copyright (c) 2020 SenseTime. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
**************************************************************************************************
* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
**************************************************************************************************
*/
/*!
* Copyright (c) Facebook, Inc. and its affiliates.
* Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
*/
#include "ms_deform_attn.h"
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
m.def("ms_deform_attn_forward", &ms_deform_attn_forward, "ms_deform_attn_forward");
m.def("ms_deform_attn_backward", &ms_deform_attn_backward, "ms_deform_attn_backward");
}

92
mm_agents/ops/test.py
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# ------------------------------------------------------------------------------------------------
# Deformable DETR
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------------------------------
# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
# ------------------------------------------------------------------------------------------------
# Copyright (c) Facebook, Inc. and its affiliates.
# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import time
import torch
import torch.nn as nn
from torch.autograd import gradcheck
from functions.ms_deform_attn_func import MSDeformAttnFunction, ms_deform_attn_core_pytorch
N, M, D = 1, 2, 2
Lq, L, P = 2, 2, 2
shapes = torch.as_tensor([(6, 4), (3, 2)], dtype=torch.long).cuda()
level_start_index = torch.cat((shapes.new_zeros((1, )), shapes.prod(1).cumsum(0)[:-1]))
S = sum([(H*W).item() for H, W in shapes])
torch.manual_seed(3)
@torch.no_grad()
def check_forward_equal_with_pytorch_double():
value = torch.rand(N, S, M, D).cuda() * 0.01
sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda()
attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5
attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True)
im2col_step = 2
output_pytorch = ms_deform_attn_core_pytorch(value.double(), shapes, sampling_locations.double(), attention_weights.double()).detach().cpu()
output_cuda = MSDeformAttnFunction.apply(value.double(), shapes, level_start_index, sampling_locations.double(), attention_weights.double(), im2col_step).detach().cpu()
fwdok = torch.allclose(output_cuda, output_pytorch)
max_abs_err = (output_cuda - output_pytorch).abs().max()
max_rel_err = ((output_cuda - output_pytorch).abs() / output_pytorch.abs()).max()
print(f'* {fwdok} check_forward_equal_with_pytorch_double: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}')
@torch.no_grad()
def check_forward_equal_with_pytorch_float():
value = torch.rand(N, S, M, D).cuda() * 0.01
sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda()
attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5
attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True)
im2col_step = 2
output_pytorch = ms_deform_attn_core_pytorch(value, shapes, sampling_locations, attention_weights).detach().cpu()
output_cuda = MSDeformAttnFunction.apply(value, shapes, level_start_index, sampling_locations, attention_weights, im2col_step).detach().cpu()
fwdok = torch.allclose(output_cuda, output_pytorch, rtol=1e-2, atol=1e-3)
max_abs_err = (output_cuda - output_pytorch).abs().max()
max_rel_err = ((output_cuda - output_pytorch).abs() / output_pytorch.abs()).max()
print(f'* {fwdok} check_forward_equal_with_pytorch_float: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}')
def check_gradient_numerical(channels=4, grad_value=True, grad_sampling_loc=True, grad_attn_weight=True):
value = torch.rand(N, S, M, channels).cuda() * 0.01
sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda()
attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5
attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True)
im2col_step = 2
func = MSDeformAttnFunction.apply
value.requires_grad = grad_value
sampling_locations.requires_grad = grad_sampling_loc
attention_weights.requires_grad = grad_attn_weight
gradok = gradcheck(func, (value.double(), shapes, level_start_index, sampling_locations.double(), attention_weights.double(), im2col_step))
print(f'* {gradok} check_gradient_numerical(D={channels})')
if __name__ == '__main__':
check_forward_equal_with_pytorch_double()
check_forward_equal_with_pytorch_float()
for channels in [30, 32, 64, 71, 1025, 2048, 3096]:
check_gradient_numerical(channels, True, True, True)

0
mm_agents/task_adapter/sam/__init__.py
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mm_agents/task_adapter/sam/tasks/__Init__.py
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from .inference_sam_m2m_auto import *
from .inference_sam_m2m_interactive import *

103
mm_agents/task_adapter/sam/tasks/inference_sam_m2m_auto.py
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# --------------------------------------------------------
# Semantic-SAM: Segment and Recognize Anything at Any Granularity
# Copyright (c) 2023 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Hao Zhang (hzhangcx@connect.ust.hk)
# --------------------------------------------------------
import torch
import numpy as np
from torchvision import transforms
from task_adapter.utils.visualizer import Visualizer
from typing import Tuple
from PIL import Image
from detectron2.data import MetadataCatalog
import matplotlib.pyplot as plt
import cv2
import io
from segment_anything import SamAutomaticMaskGenerator
metadata = MetadataCatalog.get('coco_2017_train_panoptic')
def inference_sam_m2m_auto(model, image, text_size, label_mode='1', alpha=0.1, anno_mode=['Mask']):
t = []
t.append(transforms.Resize(int(text_size), interpolation=Image.BICUBIC))
transform1 = transforms.Compose(t)
image_ori = transform1(image)
image_ori = np.asarray(image_ori)
mask_generator = SamAutomaticMaskGenerator(model)
outputs = mask_generator.generate(image_ori)
from task_adapter.utils.visualizer import Visualizer
visual = Visualizer(image_ori, metadata=metadata)
sorted_anns = sorted(outputs, key=(lambda x: x['area']), reverse=True)
label = 1
# for ann in sorted_anns:
# mask = ann['segmentation']
# color_mask = np.random.random((1, 3)).tolist()[0]
# # color_mask = [int(c*255) for c in color_mask]
# demo = visual.draw_binary_mask_with_number(mask, text=str(label), label_mode=label_mode, alpha=alpha, anno_mode=anno_mode)
# label += 1
# im = demo.get_image()
mask_map = np.zeros(image_ori.shape, dtype=np.uint8)
for i, ann in enumerate(sorted_anns):
mask = ann['segmentation']
color_mask = np.random.random((1, 3)).tolist()[0]
# color_mask = [int(c*255) for c in color_mask]
demo = visual.draw_binary_mask_with_number(mask, text=str(label), label_mode=label_mode, alpha=alpha, anno_mode=anno_mode)
# assign the mask to the mask_map
mask_map[mask == 1] = label
label += 1
im = demo.get_image()
# fig=plt.figure(figsize=(10, 10))
# plt.imshow(image_ori)
# show_anns(outputs)
# fig.canvas.draw()
# im=Image.frombytes('RGB', fig.canvas.get_width_height(), fig.canvas.tostring_rgb())
return im, sorted_anns
def remove_small_regions(
mask: np.ndarray, area_thresh: float, mode: str
) -> Tuple[np.ndarray, bool]:
"""
Removes small disconnected regions and holes in a mask. Returns the
mask and an indicator of if the mask has been modified.
"""
import cv2 # type: ignore
assert mode in ["holes", "islands"]
correct_holes = mode == "holes"
working_mask = (correct_holes ^ mask).astype(np.uint8)
n_labels, regions, stats, _ = cv2.connectedComponentsWithStats(working_mask, 8)
sizes = stats[:, -1][1:] # Row 0 is background label
small_regions = [i + 1 for i, s in enumerate(sizes) if s < area_thresh]
if len(small_regions) == 0:
return mask, False
fill_labels = [0] + small_regions
if not correct_holes:
fill_labels = [i for i in range(n_labels) if i not in fill_labels]
# If every region is below threshold, keep largest
if len(fill_labels) == 0:
fill_labels = [int(np.argmax(sizes)) + 1]
mask = np.isin(regions, fill_labels)
return mask, True
def show_anns(anns):
if len(anns) == 0:
return
sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True)
ax = plt.gca()
ax.set_autoscale_on(False)
polygons = []
color = []
for ann in sorted_anns:
m = ann['segmentation']
img = np.ones((m.shape[0], m.shape[1], 3))
color_mask = np.random.random((1, 3)).tolist()[0]
for i in range(3):
img[:,:,i] = color_mask[i]
ax.imshow(np.dstack((img, m*0.35)))

221
mm_agents/task_adapter/sam/tasks/inference_sam_m2m_interactive.py
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@@ -1,221 +0,0 @@
# --------------------------------------------------------
# Semantic-SAM: Segment and Recognize Anything at Any Granularity
# Copyright (c) 2023 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Hao Zhang (hzhangcx@connect.ust.hk)
# --------------------------------------------------------
import torch
import torch.nn.functional as F
import numpy as np
from torchvision import transforms
from task_adapter.utils.visualizer import Visualizer
from typing import Tuple
from PIL import Image
from detectron2.data import MetadataCatalog
from kornia.contrib import distance_transform
import matplotlib.pyplot as plt
import cv2
import io
metadata = MetadataCatalog.get('coco_2017_train_panoptic')
from segment_anything import SamAutomaticMaskGenerator
from segment_anything.utils.amg import (
MaskData,
area_from_rle,
batch_iterator,
batched_mask_to_box,
box_xyxy_to_xywh,
build_all_layer_point_grids,
calculate_stability_score,
coco_encode_rle,
generate_crop_boxes,
is_box_near_crop_edge,
mask_to_rle_pytorch,
remove_small_regions,
rle_to_mask,
uncrop_boxes_xyxy,
uncrop_masks,
uncrop_points,
)
def sam_interactive_mask(mask_generator, points, in_points, in_labels, mask_input):
masks, iou_preds, _ = mask_generator.predictor.predict_torch(
in_points,
in_labels,
mask_input=mask_input,
multimask_output=True,
return_logits=True,
)
nm,_,h,w = masks.shape
# Serialize predictions and store in MaskData
data = MaskData(
masks=masks.flatten(0, 1),
iou_preds=iou_preds.flatten(0, 1),
points=torch.as_tensor(points.repeat(masks.shape[1], axis=0)),
)
del masks
# Calculate stability score
data["stability_score"] = calculate_stability_score(
data["masks"], mask_generator.predictor.model.mask_threshold, mask_generator.stability_score_offset
)
masks = data["masks"].reshape(nm, -1, h, w)
scores = (data['iou_preds'] + data['stability_score']).reshape(nm, -1)
index = torch.stack([torch.arange(nm).cuda(), scores.argmax(dim=1)]).tolist()
return masks[index]
def inference_sam_m2m_interactive(model, image, spatial_masks, text_size, label_mode='1', alpha=0.1, anno_mode=['Mask']):
t = []
t.append(transforms.Resize(int(text_size), interpolation=Image.BICUBIC))
transform1 = transforms.Compose(t)
image_ori = transform1(image)
image_ori = np.asarray(image_ori)
images = torch.from_numpy(image_ori.copy()).permute(2,0,1).cuda()
orig_size = images.shape[-2:]
orig_h, orig_w = orig_size
crop_box = [0,0,orig_w,orig_h]
spatial_masks = spatial_masks[:, None].float().cuda()
spatial_masks = F.interpolate(spatial_masks, size=(orig_h, orig_w), mode='bicubic', align_corners=False) > 0
# generate single center point
# n,_,h,w = spatial_masks.shape
# mask_dt = (distance_transform((~F.pad(spatial_masks, pad=(1, 1, 1, 1), mode='constant', value=0)).float())[:,:,1:-1,1:-1]).reshape(n,-1)
# max_xy_idx = torch.stack([torch.arange(n), mask_dt.max(dim=-1)[1].cpu()]).tolist()
# next_mask = torch.zeros(spatial_masks.shape, device=torch.cuda.current_device()).bool()
# next_mask = next_mask.view(n,-1)
# next_mask[max_xy_idx] = True
# next_mask = next_mask.reshape((n,1,h,w))
# points = next_mask.nonzero()[:,2:].flip(dims=[1]).cpu().numpy()
# stack sampled points
acc_points = []
for i in range(len(spatial_masks)):
points = spatial_masks[i:i+1].nonzero()[:,2:].flip(dims=[1]).cpu().numpy()
rand_ids = np.random.choice(points.shape[0], size=40, replace=True)
points = points[rand_ids]
acc_points.append(points)
_np = len(acc_points)
points = np.concatenate(acc_points)
mask_generator = SamAutomaticMaskGenerator(model)
mask_generator.predictor.set_image(image_ori)
im_size = image_ori.shape[:-1]
transformed_points = mask_generator.predictor.transform.apply_coords(points, im_size)
in_points = torch.as_tensor(transformed_points, device=mask_generator.predictor.device).reshape(_np,-1,2).transpose(0,1)
in_labels = torch.ones((in_points.shape[0], _np), dtype=torch.int, device=mask_generator.predictor.device)
masks = sam_interactive_mask(mask_generator, points, in_points.transpose(0,1), in_labels.transpose(0,1), None)
masks = masks > 0.0
iou_preds = torch.ones(masks.shape[0], dtype=torch.float32)
points = torch.zeros((masks.shape[0], 2), dtype=torch.float32)
mask_data = MaskData(
masks=masks,
iou_preds=iou_preds,
points=points,
)
mask_data["stability_score"] = torch.ones(masks.shape[0], dtype=torch.float32)
del masks
mask_data["boxes"] = batched_mask_to_box(mask_data["masks"])
mask_data["crop_boxes"] = torch.tensor([crop_box for _ in range(len(mask_data["boxes"]))])
# Compress to RLE
mask_data["masks"] = uncrop_masks(mask_data["masks"], crop_box, orig_h, orig_w)
mask_data["rles"] = mask_to_rle_pytorch(mask_data["masks"])
del mask_data["masks"]
mask_data["segmentations"] = [rle_to_mask(rle) for rle in mask_data["rles"]]
# Write mask records
outputs = []
for idx in range(len(mask_data["segmentations"])):
ann = {
"segmentation": mask_data["segmentations"][idx],
"area": area_from_rle(mask_data["rles"][idx]),
"bbox": box_xyxy_to_xywh(mask_data["boxes"][idx]).tolist(),
"predicted_iou": mask_data["iou_preds"][idx].item(),
"point_coords": [mask_data["points"][idx].tolist()],
"stability_score": mask_data["stability_score"][idx].item(),
"crop_box": box_xyxy_to_xywh(mask_data["crop_boxes"][idx]).tolist(),
}
outputs.append(ann)
from task_adapter.utils.visualizer import Visualizer
visual = Visualizer(image_ori, metadata=metadata)
sorted_anns = sorted(outputs, key=(lambda x: x['area']), reverse=True)
label = 1
# for ann in sorted_anns:
# mask = ann['segmentation']
# demo = visual.draw_binary_mask_with_number(mask, text=str(label), label_mode=label_mode, alpha=alpha, anno_mode=anno_mode)
# label += 1
# im = demo.get_image()
mask_map = np.zeros(image_ori.shape, dtype=np.uint8)
for i, ann in enumerate(sorted_anns):
mask = ann['segmentation']
color_mask = np.random.random((1, 3)).tolist()[0]
# color_mask = [int(c*255) for c in color_mask]
demo = visual.draw_binary_mask_with_number(mask, text=str(label), label_mode=label_mode, alpha=alpha, anno_mode=anno_mode)
# assign the mask to the mask_map
mask_map[mask == 1] = label
label += 1
im = demo.get_image()
# fig=plt.figure(figsize=(10, 10))
# plt.imshow(image_ori)
# show_anns(outputs)
# fig.canvas.draw()
# im=Image.frombytes('RGB', fig.canvas.get_width_height(), fig.canvas.tostring_rgb())
return im, sorted_anns
def remove_small_regions(
mask: np.ndarray, area_thresh: float, mode: str
) -> Tuple[np.ndarray, bool]:
"""
Removes small disconnected regions and holes in a mask. Returns the
mask and an indicator of if the mask has been modified.
"""
import cv2 # type: ignore
assert mode in ["holes", "islands"]
correct_holes = mode == "holes"
working_mask = (correct_holes ^ mask).astype(np.uint8)
n_labels, regions, stats, _ = cv2.connectedComponentsWithStats(working_mask, 8)
sizes = stats[:, -1][1:] # Row 0 is background label
small_regions = [i + 1 for i, s in enumerate(sizes) if s < area_thresh]
if len(small_regions) == 0:
return mask, False
fill_labels = [0] + small_regions
if not correct_holes:
fill_labels = [i for i in range(n_labels) if i not in fill_labels]
# If every region is below threshold, keep largest
if len(fill_labels) == 0:
fill_labels = [int(np.argmax(sizes)) + 1]
mask = np.isin(regions, fill_labels)
return mask, True
def show_anns(anns):
if len(anns) == 0:
return
sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True)
ax = plt.gca()
ax.set_autoscale_on(False)
polygons = []
color = []
for ann in sorted_anns:
m = ann['segmentation']
img = np.ones((m.shape[0], m.shape[1], 3))
color_mask = np.random.random((1, 3)).tolist()[0]
for i in range(3):
img[:,:,i] = color_mask[i]
ax.imshow(np.dstack((img, m*0.35)))

0
mm_agents/task_adapter/seem/__init__.py
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3
mm_agents/task_adapter/seem/tasks/__init__.py
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@@ -1,3 +0,0 @@
from .interactive_seem_m2m_auto import *
from .inference_seem_pano import *
from .inference_seem_interactive import *

382
mm_agents/task_adapter/seem/tasks/automatic_mask_generator.py
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@@ -1,382 +0,0 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
import torch.nn as nn
from torchvision.ops.boxes import batched_nms, box_area # type: ignore
from typing import Any, Dict, List, Optional, Tuple
from segment_anything.modeling import Sam
from segment_anything.utils.amg import (
MaskData,
area_from_rle,
batch_iterator,
batched_mask_to_box,
box_xyxy_to_xywh,
build_all_layer_point_grids,
calculate_stability_score,
coco_encode_rle,
generate_crop_boxes,
is_box_near_crop_edge,
mask_to_rle_pytorch,
remove_small_regions,
rle_to_mask,
uncrop_boxes_xyxy,
uncrop_masks,
uncrop_points,
)
class SeemAutomaticMaskGenerator:
def __init__(
self,
model: Sam,
points_per_side: Optional[int] = 32,
points_per_batch: int = 64,
pred_iou_thresh: float = 0.9,
stability_score_thresh: float = 0.5,
stability_score_offset: float = 1.0,
box_nms_thresh: float = 0.7,
crop_n_layers: int = 0,
crop_nms_thresh: float = 0.7,
crop_overlap_ratio: float = 512 / 1500,
crop_n_points_downscale_factor: int = 1,
point_grids: Optional[List[np.ndarray]] = None,
min_mask_region_area: int = 0,
output_mode: str = "binary_mask",
) -> None:
"""
Using a SAM model, generates masks for the entire image.
Generates a grid of point prompts over the image, then filters
low quality and duplicate masks. The default settings are chosen
for SAM with a ViT-H backbone.
Arguments:
model (Sam): The SAM model to use for mask prediction.
points_per_side (int or None): The number of points to be sampled
along one side of the image. The total number of points is
points_per_side**2. If None, 'point_grids' must provide explicit
point sampling.
points_per_batch (int): Sets the number of points run simultaneously
by the model. Higher numbers may be faster but use more GPU memory.
pred_iou_thresh (float): A filtering threshold in [0,1], using the
model's predicted mask quality.
stability_score_thresh (float): A filtering threshold in [0,1], using
the stability of the mask under changes to the cutoff used to binarize
the model's mask predictions.
stability_score_offset (float): The amount to shift the cutoff when
calculated the stability score.
box_nms_thresh (float): The box IoU cutoff used by non-maximal
suppression to filter duplicate masks.
crop_n_layers (int): If >0, mask prediction will be run again on
crops of the image. Sets the number of layers to run, where each
layer has 2**i_layer number of image crops.
crop_nms_thresh (float): The box IoU cutoff used by non-maximal
suppression to filter duplicate masks between different crops.
crop_overlap_ratio (float): Sets the degree to which crops overlap.
In the first crop layer, crops will overlap by this fraction of
the image length. Later layers with more crops scale down this overlap.
crop_n_points_downscale_factor (int): The number of points-per-side
sampled in layer n is scaled down by crop_n_points_downscale_factor**n.
point_grids (list(np.ndarray) or None): A list over explicit grids
of points used for sampling, normalized to [0,1]. The nth grid in the
list is used in the nth crop layer. Exclusive with points_per_side.
min_mask_region_area (int): If >0, postprocessing will be applied
to remove disconnected regions and holes in masks with area smaller
than min_mask_region_area. Requires opencv.
output_mode (str): The form masks are returned in. Can be 'binary_mask',
'uncompressed_rle', or 'coco_rle'. 'coco_rle' requires pycocotools.
For large resolutions, 'binary_mask' may consume large amounts of
memory.
"""
assert (points_per_side is None) != (
point_grids is None
), "Exactly one of points_per_side or point_grid must be provided."
if points_per_side is not None:
self.point_grids = build_all_layer_point_grids(
points_per_side,
crop_n_layers,
crop_n_points_downscale_factor,
)
elif point_grids is not None:
self.point_grids = point_grids
else:
raise ValueError("Can't have both points_per_side and point_grid be None.")
assert output_mode in [
"binary_mask",
"uncompressed_rle",
"coco_rle",
], f"Unknown output_mode {output_mode}."
if output_mode == "coco_rle":
from pycocotools import mask as mask_utils # type: ignore # noqa: F401
if min_mask_region_area > 0:
import cv2 # type: ignore # noqa: F401
self.predictor = model
self.points_per_batch = points_per_batch
self.pred_iou_thresh = pred_iou_thresh
self.stability_score_thresh = stability_score_thresh
self.stability_score_offset = stability_score_offset
self.box_nms_thresh = box_nms_thresh
self.crop_n_layers = crop_n_layers
self.crop_nms_thresh = crop_nms_thresh
self.crop_overlap_ratio = crop_overlap_ratio
self.crop_n_points_downscale_factor = crop_n_points_downscale_factor
self.min_mask_region_area = min_mask_region_area
self.output_mode = output_mode
# dilate conv
self.dilation = nn.Conv2d(in_channels=1, out_channels=1, kernel_size=7, stride=1, padding=3, bias=False)
self.dilation.weight.data.fill_(1.0)
self.dilation.cuda()
@torch.no_grad()
def generate(self, image: np.ndarray) -> List[Dict[str, Any]]:
"""
Generates masks for the given image.
Arguments:
image (np.ndarray): The image to generate masks for, in HWC uint8 format.
Returns:
list(dict(str, any)): A list over records for masks. Each record is
a dict containing the following keys:
segmentation (dict(str, any) or np.ndarray): The mask. If
output_mode='binary_mask', is an array of shape HW. Otherwise,
is a dictionary containing the RLE.
bbox (list(float)): The box around the mask, in XYWH format.
area (int): The area in pixels of the mask.
predicted_iou (float): The model's own prediction of the mask's
quality. This is filtered by the pred_iou_thresh parameter.
point_coords (list(list(float))): The point coordinates input
to the model to generate this mask.
stability_score (float): A measure of the mask's quality. This
is filtered on using the stability_score_thresh parameter.
crop_box (list(float)): The crop of the image used to generate
the mask, given in XYWH format.
"""
# Generate masks
mask_data = self._generate_masks(image)
# Filter small disconnected regions and holes in masks
if self.min_mask_region_area > 0:
mask_data = self.postprocess_small_regions(
mask_data,
self.min_mask_region_area,
max(self.box_nms_thresh, self.crop_nms_thresh),
)
# Encode masks
if self.output_mode == "coco_rle":
mask_data["segmentations"] = [coco_encode_rle(rle) for rle in mask_data["rles"]]
elif self.output_mode == "binary_mask":
mask_data["segmentations"] = [rle_to_mask(rle) for rle in mask_data["rles"]]
else:
mask_data["segmentations"] = mask_data["rles"]
# Write mask records
curr_anns = []
for idx in range(len(mask_data["segmentations"])):
ann = {
"segmentation": mask_data["segmentations"][idx],
"area": area_from_rle(mask_data["rles"][idx]),
"bbox": box_xyxy_to_xywh(mask_data["boxes"][idx]).tolist(),
"predicted_iou": mask_data["iou_preds"][idx].item(),
"point_coords": [mask_data["points"][idx].tolist()],
"stability_score": mask_data["stability_score"][idx].item(),
"crop_box": box_xyxy_to_xywh(mask_data["crop_boxes"][idx]).tolist(),
}
curr_anns.append(ann)
return curr_anns
def _generate_masks(self, image: np.ndarray) -> MaskData:
orig_size = image.shape[-2:]
crop_boxes, layer_idxs = generate_crop_boxes(
orig_size, self.crop_n_layers, self.crop_overlap_ratio
)
# Iterate over image crops
data = MaskData()
for crop_box, layer_idx in zip(crop_boxes, layer_idxs):
crop_data = self._process_crop(image, crop_box, layer_idx, orig_size)
data.cat(crop_data)
# Remove duplicate masks between crops
if len(crop_boxes) > 1:
# Prefer masks from smaller crops
scores = 1 / box_area(data["crop_boxes"])
scores = scores.to(data["boxes"].device)
keep_by_nms = batched_nms(
data["boxes"].float(),
scores,
torch.zeros_like(data["boxes"][:, 0]), # categories
iou_threshold=self.crop_nms_thresh,
)
data.filter(keep_by_nms)
data.to_numpy()
return data
def _process_crop(
self,
image: np.ndarray,
crop_box: List[int],
crop_layer_idx: int,
orig_size: Tuple[int, ...],
) -> MaskData:
# Crop the image and calculate embeddings
x0, y0, x1, y1 = crop_box
cropped_im = image#[y0:y1, x0:x1, :]
cropped_im_size = cropped_im.shape[-2:]
# self.predictor.set_image(cropped_im)
# Get points for this crop
points_scale = np.array(cropped_im_size)[None, ::-1]
points_for_image = self.point_grids[crop_layer_idx] #* points_scale
# Generate masks for this crop in batches
data = MaskData()
self.enc_features=None
for (points,) in batch_iterator(self.points_per_batch, points_for_image):
batch_data = self._process_batch(cropped_im, points, cropped_im_size, crop_box, orig_size)
data.cat(batch_data)
del batch_data
# Remove duplicates within this crop.
keep_by_nms = batched_nms(
data["boxes"].float(),
data["iou_preds"],
torch.zeros(len(data["boxes"])), # categories
iou_threshold=self.box_nms_thresh,
)
data.filter(keep_by_nms)
# Return to the original image frame
data["boxes"] = uncrop_boxes_xyxy(data["boxes"], crop_box)
data["crop_boxes"] = torch.tensor([crop_box for _ in range(len(data["rles"]))])
return data
def _process_batch(
self,
images,
points: np.ndarray,
im_size: Tuple[int, ...],
crop_box: List[int],
orig_size: Tuple[int, ...],
) -> MaskData:
orig_h, orig_w = orig_size
data = {"image": images, "height": orig_h, "width": orig_w}
points = torch.tensor(points,dtype=torch.float).to(images.device)
# prepare interactive mask for seem
abs_points = (points * torch.tensor(orig_size)[None,:].to(points.device)).long()
abs_masks = torch.zeros((len(points), orig_h, orig_w), dtype=torch.bool).to(device=points.device)
abs_masks[torch.arange(0, abs_points.size(0))[:,None], abs_points[:,0:1], abs_points[:,1:2]] = True
abs_masks = self.dilation(abs_masks[:,None].float())[:,0] > 0
data['spatial_query'] = {'rand_shape': abs_masks[:,None]}
batch_inputs = [data]
if self.enc_features is None:
masks, iou_preds, mask_features, transformer_encoder_features, multi_scale_features = self.predictor.model.evaluate_demo(batch_inputs, None, None, return_features=True)
self.enc_features = (mask_features, transformer_encoder_features, multi_scale_features)
else:
masks, iou_preds = self.predictor.model.evaluate_demo(batch_inputs, self.enc_features[0], self.enc_features[1], self.enc_features[2])
data = MaskData(
masks=masks,
iou_preds=iou_preds,
points=points,
)
del masks
# Filter by predicted IoU
if self.pred_iou_thresh > 0.0:
keep_mask = data["iou_preds"] > self.pred_iou_thresh
data.filter(keep_mask)
# Calculate stability score
data["stability_score"] = calculate_stability_score(
data["masks"], 0.0, self.stability_score_offset
)
if self.stability_score_thresh > 0.0:
keep_mask = data["stability_score"] >= self.stability_score_thresh
data.filter(keep_mask)
# Threshold masks and calculate boxes
data["masks"] = data["masks"] > 0.0
data["boxes"] = batched_mask_to_box(data["masks"])
# Filter boxes that touch crop boundaries
keep_mask = ~is_box_near_crop_edge(data["boxes"], crop_box, [0, 0, orig_w, orig_h])
if not torch.all(keep_mask):
data.filter(keep_mask)
# Compress to RLE
data["masks"] = uncrop_masks(data["masks"], crop_box, orig_h, orig_w)
data["rles"] = mask_to_rle_pytorch(data["masks"])
del data["masks"]
return data
@staticmethod
def postprocess_small_regions(
mask_data: MaskData, min_area: int, nms_thresh: float
) -> MaskData:
"""
Removes small disconnected regions and holes in masks, then reruns
box NMS to remove any new duplicates.
Edits mask_data in place.
Requires open-cv as a dependency.
"""
if len(mask_data["rles"]) == 0:
return mask_data
# Filter small disconnected regions and holes
new_masks = []
scores = []
for rle in mask_data["rles"]:
mask = rle_to_mask(rle)
mask, changed = remove_small_regions(mask, min_area, mode="holes")
unchanged = not changed
mask, changed = remove_small_regions(mask, min_area, mode="islands")
unchanged = unchanged and not changed
new_masks.append(torch.as_tensor(mask).unsqueeze(0))
# Give score=0 to changed masks and score=1 to unchanged masks
# so NMS will prefer ones that didn't need postprocessing
scores.append(float(unchanged))
# Recalculate boxes and remove any new duplicates
masks = torch.cat(new_masks, dim=0)
boxes = batched_mask_to_box(masks)
keep_by_nms = batched_nms(
boxes.float(),
torch.as_tensor(scores),
torch.zeros_like(boxes[:, 0]), # categories
iou_threshold=nms_thresh,
)
# Only recalculate RLEs for masks that have changed
for i_mask in keep_by_nms:
if scores[i_mask] == 0.0:
mask_torch = masks[i_mask].unsqueeze(0)
mask_data["rles"][i_mask] = mask_to_rle_pytorch(mask_torch)[0]
mask_data["boxes"][i_mask] = boxes[i_mask] # update res directly
mask_data.filter(keep_by_nms)
return mask_data

169
mm_agents/task_adapter/seem/tasks/inference_seem_interactive.py
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@@ -1,169 +0,0 @@
# --------------------------------------------------------
# Semantic-SAM: Segment and Recognize Anything at Any Granularity
# Copyright (c) 2023 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Hao Zhang (hzhangcx@connect.ust.hk)
# --------------------------------------------------------
import torch
import torch.nn.functional as F
import numpy as np
from torchvision import transforms
from task_adapter.utils.visualizer import Visualizer
from typing import Tuple
from PIL import Image
from detectron2.data import MetadataCatalog
import matplotlib.pyplot as plt
import cv2
import io
from .automatic_mask_generator import SeemAutomaticMaskGenerator
metadata = MetadataCatalog.get('coco_2017_train_panoptic')
from segment_anything.utils.amg import (
MaskData,
area_from_rle,
batch_iterator,
batched_mask_to_box,
box_xyxy_to_xywh,
build_all_layer_point_grids,
calculate_stability_score,
coco_encode_rle,
generate_crop_boxes,
is_box_near_crop_edge,
mask_to_rle_pytorch,
remove_small_regions,
rle_to_mask,
uncrop_boxes_xyxy,
uncrop_masks,
uncrop_points,
)
def inference_seem_interactive(model, image, spatial_masks, text_size, label_mode='1', alpha=0.1, anno_mode=['Mask']):
t = []
t.append(transforms.Resize(int(text_size), interpolation=Image.BICUBIC))
transform1 = transforms.Compose(t)
image_ori = transform1(image)
image_ori = np.asarray(image_ori)
images = torch.from_numpy(image_ori.copy()).permute(2,0,1).cuda()
orig_size = images.shape[-2:]
orig_h, orig_w = orig_size
crop_box = [0,0,orig_w,orig_h]
data = {"image": images, "height": orig_h, "width": orig_w}
spatial_masks = spatial_masks[:, None].float().cuda()
spatial_masks = F.interpolate(spatial_masks, size=(orig_h, orig_w), mode='bicubic', align_corners=False) > 0
data['spatial_query'] = {'rand_shape': spatial_masks}
model.model.metadata = metadata
masks, _ = model.model.evaluate_demo([data])
masks = masks > 0.0
iou_preds = torch.ones(masks.shape[0], dtype=torch.float32)
points = torch.zeros((masks.shape[0], 2), dtype=torch.float32)
mask_data = MaskData(
masks=masks,
iou_preds=iou_preds,
points=points,
)
mask_data["stability_score"] = torch.ones(masks.shape[0], dtype=torch.float32)
del masks
mask_data["boxes"] = batched_mask_to_box(mask_data["masks"])
mask_data["crop_boxes"] = torch.tensor([crop_box for _ in range(len(mask_data["boxes"]))])
# Compress to RLE
mask_data["masks"] = uncrop_masks(mask_data["masks"], crop_box, orig_h, orig_w)
mask_data["rles"] = mask_to_rle_pytorch(mask_data["masks"])
del mask_data["masks"]
mask_data["segmentations"] = [rle_to_mask(rle) for rle in mask_data["rles"]]
# Write mask records
outputs = []
for idx in range(len(mask_data["segmentations"])):
ann = {
"segmentation": mask_data["segmentations"][idx],
"area": area_from_rle(mask_data["rles"][idx]),
"bbox": box_xyxy_to_xywh(mask_data["boxes"][idx]).tolist(),
"predicted_iou": mask_data["iou_preds"][idx].item(),
"point_coords": [mask_data["points"][idx].tolist()],
"stability_score": mask_data["stability_score"][idx].item(),
"crop_box": box_xyxy_to_xywh(mask_data["crop_boxes"][idx]).tolist(),
}
outputs.append(ann)
from task_adapter.utils.visualizer import Visualizer
visual = Visualizer(image_ori, metadata=metadata)
sorted_anns = sorted(outputs, key=(lambda x: x['area']), reverse=True)
label = 1
# for ann in sorted_anns:
# mask = ann['segmentation']
# color_mask = np.random.random((1, 3)).tolist()[0]
# # color_mask = [int(c*255) for c in color_mask]
# demo = visual.draw_binary_mask_with_number(mask, text=str(label), label_mode=label_mode, alpha=alpha, anno_mode=anno_mode)
# label += 1
# im = demo.get_image()
mask_map = np.zeros(image_ori.shape, dtype=np.uint8)
for i, ann in enumerate(sorted_anns):
mask = ann['segmentation']
color_mask = np.random.random((1, 3)).tolist()[0]
# color_mask = [int(c*255) for c in color_mask]
demo = visual.draw_binary_mask_with_number(mask, text=str(label), label_mode=label_mode, alpha=alpha, anno_mode=anno_mode)
# assign the mask to the mask_map
mask_map[mask == 1] = label
label += 1
im = demo.get_image()
# fig=plt.figure(figsize=(10, 10))
# plt.imshow(image_ori)
# show_anns(outputs)
# fig.canvas.draw()
# im=Image.frombytes('RGB', fig.canvas.get_width_height(), fig.canvas.tostring_rgb())
return im, sorted_anns
def remove_small_regions(
mask: np.ndarray, area_thresh: float, mode: str
) -> Tuple[np.ndarray, bool]:
"""
Removes small disconnected regions and holes in a mask. Returns the
mask and an indicator of if the mask has been modified.
"""
import cv2 # type: ignore
assert mode in ["holes", "islands"]
correct_holes = mode == "holes"
working_mask = (correct_holes ^ mask).astype(np.uint8)
n_labels, regions, stats, _ = cv2.connectedComponentsWithStats(working_mask, 8)
sizes = stats[:, -1][1:] # Row 0 is background label
small_regions = [i + 1 for i, s in enumerate(sizes) if s < area_thresh]
if len(small_regions) == 0:
return mask, False
fill_labels = [0] + small_regions
if not correct_holes:
fill_labels = [i for i in range(n_labels) if i not in fill_labels]
# If every region is below threshold, keep largest
if len(fill_labels) == 0:
fill_labels = [int(np.argmax(sizes)) + 1]
mask = np.isin(regions, fill_labels)
return mask, True
def show_anns(anns):
if len(anns) == 0:
return
sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True)
ax = plt.gca()
ax.set_autoscale_on(False)
polygons = []
color = []
for ann in sorted_anns:
m = ann['segmentation']
img = np.ones((m.shape[0], m.shape[1], 3))
color_mask = np.random.random((1, 3)).tolist()[0]
for i in range(3):
img[:,:,i] = color_mask[i]
ax.imshow(np.dstack((img, m*0.35)))

164
mm_agents/task_adapter/seem/tasks/inference_seem_pano.py
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@@ -1,164 +0,0 @@
# --------------------------------------------------------
# Semantic-SAM: Segment and Recognize Anything at Any Granularity
# Copyright (c) 2023 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Hao Zhang (hzhangcx@connect.ust.hk)
# --------------------------------------------------------
import torch
import numpy as np
from torchvision import transforms
from task_adapter.utils.visualizer import Visualizer
from typing import Tuple
from PIL import Image
from detectron2.data import MetadataCatalog
import matplotlib.pyplot as plt
import cv2
import io
from .automatic_mask_generator import SeemAutomaticMaskGenerator
metadata = MetadataCatalog.get('coco_2017_train_panoptic')
from segment_anything.utils.amg import (
MaskData,
area_from_rle,
batch_iterator,
batched_mask_to_box,
box_xyxy_to_xywh,
build_all_layer_point_grids,
calculate_stability_score,
coco_encode_rle,
generate_crop_boxes,
is_box_near_crop_edge,
mask_to_rle_pytorch,
remove_small_regions,
rle_to_mask,
uncrop_boxes_xyxy,
uncrop_masks,
uncrop_points,
)
def inference_seem_pano(model, image, text_size, label_mode='1', alpha=0.1, anno_mode=['Mask']):
t = []
t.append(transforms.Resize(int(text_size), interpolation=Image.BICUBIC))
transform1 = transforms.Compose(t)
image_ori = transform1(image)
image_ori = np.asarray(image_ori)
images = torch.from_numpy(image_ori.copy()).permute(2,0,1).cuda()
orig_size = images.shape[-2:]
orig_h, orig_w = orig_size
crop_box = [0,0,orig_w,orig_h]
data = {"image": images, "height": orig_h, "width": orig_w}
batch_inputs = [data]
model.model.metadata = metadata
outputs = model.model.evaluate(batch_inputs)
pano_mask = outputs[0]['panoptic_seg'][0]
pano_info = outputs[0]['panoptic_seg'][1]
masks = []
for seg_info in pano_info:
masks += [pano_mask == seg_info['id']]
masks = torch.stack(masks, dim=0)
iou_preds = torch.ones(masks.shape[0], dtype=torch.float32)
points = torch.zeros((masks.shape[0], 2), dtype=torch.float32)
mask_data = MaskData(
masks=masks,
iou_preds=iou_preds,
points=points,
)
mask_data["stability_score"] = torch.ones(masks.shape[0], dtype=torch.float32)
del masks
mask_data["boxes"] = batched_mask_to_box(mask_data["masks"])
mask_data["crop_boxes"] = torch.tensor([crop_box for _ in range(len(mask_data["boxes"]))])
# Compress to RLE
mask_data["masks"] = uncrop_masks(mask_data["masks"], crop_box, orig_h, orig_w)
mask_data["rles"] = mask_to_rle_pytorch(mask_data["masks"])
del mask_data["masks"]
mask_data["segmentations"] = [rle_to_mask(rle) for rle in mask_data["rles"]]
# Write mask records
outputs = []
for idx in range(len(mask_data["segmentations"])):
ann = {
"segmentation": mask_data["segmentations"][idx],
"area": area_from_rle(mask_data["rles"][idx]),
"bbox": box_xyxy_to_xywh(mask_data["boxes"][idx]).tolist(),
"predicted_iou": mask_data["iou_preds"][idx].item(),
"point_coords": [mask_data["points"][idx].tolist()],
"stability_score": mask_data["stability_score"][idx].item(),
"crop_box": box_xyxy_to_xywh(mask_data["crop_boxes"][idx]).tolist(),
}
outputs.append(ann)
from task_adapter.utils.visualizer import Visualizer
visual = Visualizer(image_ori, metadata=metadata)
# create a full zero image as the image_orig
sorted_anns = sorted(outputs, key=(lambda x: x['area']), reverse=True)
label = 1
mask_map = np.zeros(image_ori.shape, dtype=np.uint8)
for i, ann in enumerate(sorted_anns):
mask = ann['segmentation']
color_mask = np.random.random((1, 3)).tolist()[0]
# color_mask = [int(c*255) for c in color_mask]
demo = visual.draw_binary_mask_with_number(mask, text=str(label), label_mode=label_mode, alpha=alpha, anno_mode=anno_mode)
# assign the mask to the mask_map
mask_map[mask == 1] = label
label += 1
im = demo.get_image()
# fig=plt.figure(figsize=(10, 10))
# plt.imshow(image_ori)
# show_anns(outputs)
# fig.canvas.draw()
# im=Image.frombytes('RGB', fig.canvas.get_width_height(), fig.canvas.tostring_rgb())
return im, sorted_anns
def remove_small_regions(
mask: np.ndarray, area_thresh: float, mode: str
) -> Tuple[np.ndarray, bool]:
"""
Removes small disconnected regions and holes in a mask. Returns the
mask and an indicator of if the mask has been modified.
"""
import cv2 # type: ignore
assert mode in ["holes", "islands"]
correct_holes = mode == "holes"
working_mask = (correct_holes ^ mask).astype(np.uint8)
n_labels, regions, stats, _ = cv2.connectedComponentsWithStats(working_mask, 8)
sizes = stats[:, -1][1:] # Row 0 is background label
small_regions = [i + 1 for i, s in enumerate(sizes) if s < area_thresh]
if len(small_regions) == 0:
return mask, False
fill_labels = [0] + small_regions
if not correct_holes:
fill_labels = [i for i in range(n_labels) if i not in fill_labels]
# If every region is below threshold, keep largest
if len(fill_labels) == 0:
fill_labels = [int(np.argmax(sizes)) + 1]
mask = np.isin(regions, fill_labels)
return mask, True
def show_anns(anns):
if len(anns) == 0:
return
sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True)
ax = plt.gca()
ax.set_autoscale_on(False)
polygons = []
color = []
for ann in sorted_anns:
m = ann['segmentation']
img = np.ones((m.shape[0], m.shape[1], 3))
color_mask = np.random.random((1, 3)).tolist()[0]
for i in range(3):
img[:,:,i] = color_mask[i]
ax.imshow(np.dstack((img, m*0.35)))

93
mm_agents/task_adapter/seem/tasks/interactive_seem_m2m_auto.py
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@@ -1,93 +0,0 @@
# --------------------------------------------------------
# Semantic-SAM: Segment and Recognize Anything at Any Granularity
# Copyright (c) 2023 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Hao Zhang (hzhangcx@connect.ust.hk)
# --------------------------------------------------------
import torch
import numpy as np
from torchvision import transforms
from task_adapter.utils.visualizer import Visualizer
from typing import Tuple
from PIL import Image
from detectron2.data import MetadataCatalog
import matplotlib.pyplot as plt
import cv2
import io
from .automatic_mask_generator import SeemAutomaticMaskGenerator
metadata = MetadataCatalog.get('coco_2017_train_panoptic')
def interactive_seem_m2m_auto(model, image, text_size, label_mode='1', alpha=0.1, anno_mode=['Mask']):
t = []
t.append(transforms.Resize(int(text_size), interpolation=Image.BICUBIC))
transform1 = transforms.Compose(t)
image_ori = transform1(image)
image_ori = np.asarray(image_ori)
images = torch.from_numpy(image_ori.copy()).permute(2,0,1).cuda()
mask_generator = SeemAutomaticMaskGenerator(model)
outputs = mask_generator.generate(images)
from task_adapter.utils.visualizer import Visualizer
visual = Visualizer(image_ori, metadata=metadata)
sorted_anns = sorted(outputs, key=(lambda x: x['area']), reverse=True)
label = 1
for ann in sorted_anns:
mask = ann['segmentation']
color_mask = np.random.random((1, 3)).tolist()[0]
# color_mask = [int(c*255) for c in color_mask]
demo = visual.draw_binary_mask_with_number(mask, text=str(label), label_mode=label_mode, alpha=alpha, anno_mode=anno_mode)
label += 1
im = demo.get_image()
# fig=plt.figure(figsize=(10, 10))
# plt.imshow(image_ori)
# show_anns(outputs)
# fig.canvas.draw()
# im=Image.frombytes('RGB', fig.canvas.get_width_height(), fig.canvas.tostring_rgb())
return im
def remove_small_regions(
mask: np.ndarray, area_thresh: float, mode: str
) -> Tuple[np.ndarray, bool]:
"""
Removes small disconnected regions and holes in a mask. Returns the
mask and an indicator of if the mask has been modified.
"""
import cv2 # type: ignore
assert mode in ["holes", "islands"]
correct_holes = mode == "holes"
working_mask = (correct_holes ^ mask).astype(np.uint8)
n_labels, regions, stats, _ = cv2.connectedComponentsWithStats(working_mask, 8)
sizes = stats[:, -1][1:] # Row 0 is background label
small_regions = [i + 1 for i, s in enumerate(sizes) if s < area_thresh]
if len(small_regions) == 0:
return mask, False
fill_labels = [0] + small_regions
if not correct_holes:
fill_labels = [i for i in range(n_labels) if i not in fill_labels]
# If every region is below threshold, keep largest
if len(fill_labels) == 0:
fill_labels = [int(np.argmax(sizes)) + 1]
mask = np.isin(regions, fill_labels)
return mask, True
def show_anns(anns):
if len(anns) == 0:
return
sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True)
ax = plt.gca()
ax.set_autoscale_on(False)
polygons = []
color = []
for ann in sorted_anns:
m = ann['segmentation']
img = np.ones((m.shape[0], m.shape[1], 3))
color_mask = np.random.random((1, 3)).tolist()[0]
for i in range(3):
img[:,:,i] = color_mask[i]
ax.imshow(np.dstack((img, m*0.35)))

6
mm_agents/task_adapter/semantic_sam/tasks/__init__.py
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@@ -1,6 +0,0 @@
from .interactive_idino_m2m import interactive_infer_image as interactive_infer_image_idino_m2m
from .interactive_idino_m2m import interactive_infer_image_semantic, interactive_infer_image_3l
from .inference_semsam_m2m_auto import inference_semsam_m2m_auto
from .interactive_idino_1o1_box import interactive_infer_image_box as interactive_infer_image_idino_m2m_box
from .automatic_mask_generator import prompt_switch
from .interactive_predictor import SemanticSAMPredictor

393
mm_agents/task_adapter/semantic_sam/tasks/automatic_mask_generator.py
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@@ -1,393 +0,0 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
from torchvision.ops.boxes import batched_nms, box_area # type: ignore
from typing import Any, Dict, List, Optional, Tuple
# from
# from .modeling import Sam
# from .predictor import SamPredictor
from semantic_sam.utils.sam_utils.amg import (
MaskData,
area_from_rle,
batch_iterator,
batched_mask_to_box,
box_xyxy_to_xywh,
build_all_layer_point_grids,
calculate_stability_score,
coco_encode_rle,
generate_crop_boxes,
is_box_near_crop_edge,
mask_to_rle_pytorch,
remove_small_regions,
rle_to_mask,
uncrop_boxes_xyxy,
uncrop_masks,
uncrop_points,
)
def prompt_switch(p):
p = int(p)
if p == 1:
return 3
if p == 2:
return 2
if p == 3:
return 0
if p == 4:
return 4
if p == 5:
return 1
if p == 6:
return 5
else:
raise NotImplementedError
class SemanticSamAutomaticMaskGenerator:
def __init__(
self,
model,
points_per_side: Optional[int] = 32,
points_per_batch: int = 200,
pred_iou_thresh: float = 0.88,
stability_score_thresh: float = 0.92,
stability_score_offset: float = 1.0,
box_nms_thresh: float = 0.7,
crop_n_layers: int = 0,
crop_nms_thresh: float = 0.7,
crop_overlap_ratio: float = 512 / 1500,
crop_n_points_downscale_factor: int = 1,
point_grids: Optional[List[np.ndarray]] = None,
min_mask_region_area: int = 10,
output_mode: str = "binary_mask",
level: list = [1, 2, 3, 4, 5, 6],
) -> None:
"""
Using a SAM model, generates masks for the entire image.
Generates a grid of point prompts over the image, then filters
low quality and duplicate masks. The default settings are chosen
for SAM with a ViT-H backbone.
Arguments:
model (Sam): The SAM model to use for mask prediction.
points_per_side (int or None): The number of points to be sampled
along one side of the image. The total number of points is
points_per_side**2. If None, 'point_grids' must provide explicit
point sampling.
points_per_batch (int): Sets the number of points run simultaneously
by the model. Higher numbers may be faster but use more GPU memory.
pred_iou_thresh (float): A filtering threshold in [0,1], using the
model's predicted mask quality.
stability_score_thresh (float): A filtering threshold in [0,1], using
the stability of the mask under changes to the cutoff used to binarize
the model's mask predictions.
stability_score_offset (float): The amount to shift the cutoff when
calculated the stability score.
box_nms_thresh (float): The box IoU cutoff used by non-maximal
suppression to filter duplicate masks.
crops_n_layers (int): If >0, mask prediction will be run again on
crops of the image. Sets the number of layers to run, where each
layer has 2**i_layer number of image crops.
crops_nms_thresh (float): The box IoU cutoff used by non-maximal
suppression to filter duplicate masks between different crops.
crop_overlap_ratio (float): Sets the degree to which crops overlap.
In the first crop layer, crops will overlap by this fraction of
the image length. Later layers with more crops scale down this overlap.
crop_n_points_downscale_factor (int): The number of points-per-side
sampled in layer n is scaled down by crop_n_points_downscale_factor**n.
point_grids (list(np.ndarray) or None): A list over explicit grids
of points used for sampling, normalized to [0,1]. The nth grid in the
list is used in the nth crop layer. Exclusive with points_per_side.
min_mask_region_area (int): If >0, postprocessing will be applied
to remove disconnected regions and holes in masks with area smaller
than min_mask_region_area. Requires opencv.
output_mode (str): The form masks are returned in. Can be 'binary_mask',
'uncompressed_rle', or 'coco_rle'. 'coco_rle' requires pycocotools.
For large resolutions, 'binary_mask' may consume large amounts of
memory.
"""
self.level = [prompt_switch(l) for l in level]
assert (points_per_side is None) != (
point_grids is None
), "Exactly one of points_per_side or point_grid must be provided."
if points_per_side is not None:
self.point_grids = build_all_layer_point_grids(
points_per_side,
crop_n_layers,
crop_n_points_downscale_factor,
)
elif point_grids is not None:
self.point_grids = point_grids
else:
raise ValueError("Can't have both points_per_side and point_grid be None.")
assert output_mode in [
"binary_mask",
"uncompressed_rle",
"coco_rle",
], f"Unknown output_mode {output_mode}."
if output_mode == "coco_rle":
from pycocotools import mask as mask_utils # type: ignore # noqa: F401
if min_mask_region_area > 0:
import cv2 # type: ignore # noqa: F401
self.predictor = model
self.points_per_batch = points_per_batch
self.pred_iou_thresh = pred_iou_thresh
self.stability_score_thresh = stability_score_thresh
self.stability_score_offset = stability_score_offset
self.box_nms_thresh = box_nms_thresh
self.crop_n_layers = crop_n_layers
self.crop_nms_thresh = crop_nms_thresh
self.crop_overlap_ratio = crop_overlap_ratio
self.crop_n_points_downscale_factor = crop_n_points_downscale_factor
self.min_mask_region_area = min_mask_region_area
self.output_mode = output_mode
@torch.no_grad()
def generate(self, image: np.ndarray) -> List[Dict[str, Any]]:
"""
Generates masks for the given image.
Arguments:
image (np.ndarray): The image to generate masks for, in HWC uint8 format.
Returns:
list(dict(str, any)): A list over records for masks. Each record is
a dict containing the following keys:
segmentation (dict(str, any) or np.ndarray): The mask. If
output_mode='binary_mask', is an array of shape HW. Otherwise,
is a dictionary containing the RLE.
bbox (list(float)): The box around the mask, in XYWH format.
area (int): The area in pixels of the mask.
predicted_iou (float): The model's own prediction of the mask's
quality. This is filtered by the pred_iou_thresh parameter.
point_coords (list(list(float))): The point coordinates input
to the model to generate this mask.
stability_score (float): A measure of the mask's quality. This
is filtered on using the stability_score_thresh parameter.
crop_box (list(float)): The crop of the image used to generate
the mask, given in XYWH format.
"""
# Generate masks
mask_data = self._generate_masks(image)
# Filter small disconnected regions and holes in masks
if self.min_mask_region_area > 0:
mask_data = self.postprocess_small_regions(
mask_data,
self.min_mask_region_area,
max(self.box_nms_thresh, self.crop_nms_thresh),
)
# Encode masks
if self.output_mode == "coco_rle":
mask_data["segmentations"] = [coco_encode_rle(rle) for rle in mask_data["rles"]]
elif self.output_mode == "binary_mask":
mask_data["segmentations"] = [rle_to_mask(rle) for rle in mask_data["rles"]]
else:
mask_data["segmentations"] = mask_data["rles"]
# Write mask records
curr_anns = []
for idx in range(len(mask_data["segmentations"])):
ann = {
"segmentation": mask_data["segmentations"][idx],
"area": area_from_rle(mask_data["rles"][idx]),
"bbox": box_xyxy_to_xywh(mask_data["boxes"][idx]).tolist(),
"predicted_iou": mask_data["iou_preds"][idx].item(),
"point_coords": [mask_data["points"][idx].tolist()],
"stability_score": mask_data["stability_score"][idx].item(),
"crop_box": box_xyxy_to_xywh(mask_data["crop_boxes"][idx]).tolist(),
}
curr_anns.append(ann)
return curr_anns
def _generate_masks(self, image: np.ndarray) -> MaskData:
orig_size = image.shape[-2:]
crop_boxes, layer_idxs = generate_crop_boxes(
orig_size, self.crop_n_layers, self.crop_overlap_ratio
)
# Iterate over image crops
assert len(crop_boxes)==1
data = MaskData()
# import ipdb; ipdb.set_trace()
for crop_box, layer_idx in zip(crop_boxes, layer_idxs):
crop_data = self._process_crop(image, crop_box, layer_idx, orig_size)
data.cat(crop_data)
# import ipdb; ipdb.set_trace()
# Remove duplicate masks between crops
if len(crop_boxes) > 1:
# Prefer masks from smaller crops
scores = 1 / box_area(data["crop_boxes"])
scores = scores.to(data["boxes"].device)
keep_by_nms = batched_nms(
data["boxes"].float(),
scores,
torch.zeros(len(data["boxes"])), # categories
iou_threshold=self.crop_nms_thresh,
)
data.filter(keep_by_nms)
data.to_numpy()
return data
def _process_crop(
self,
image: np.ndarray,
crop_box: List[int],
crop_layer_idx: int,
orig_size: Tuple[int, ...],
) -> MaskData:
# Crop the image and calculate embeddings
x0, y0, x1, y1 = crop_box
cropped_im = image#[y0:y1, x0:x1, :]
cropped_im_size = cropped_im.shape[-2:]
# self.predictor.set_image(cropped_im)
# Get points for this crop
points_scale = np.array(cropped_im_size)[None, ::-1]
points_for_image = self.point_grids[crop_layer_idx] #* points_scale
# Generate masks for this crop in batches
data = MaskData()
self.enc_features=None
# import ipdb; ipdb.set_trace()
for (points,) in batch_iterator(self.points_per_batch, points_for_image):
batch_data = self._process_batch(cropped_im,points, cropped_im_size, crop_box, orig_size)
data.cat(batch_data)
del batch_data
keep_by_nms = batched_nms(
data["boxes"].float(),
data["iou_preds"],
torch.zeros(len(data["boxes"])), # categories
iou_threshold=self.box_nms_thresh,
)
# import ipdb; ipdb.set_trace()
data.filter(keep_by_nms)
# import ipdb; ipdb.set_trace()
# Return to the original image frame
data["boxes"] = uncrop_boxes_xyxy(data["boxes"], crop_box)
data["crop_boxes"] = torch.tensor([crop_box for _ in range(len(data["rles"]))])
return data
def _process_batch(
self,
images,
points: np.ndarray,
im_size: Tuple[int, ...],
crop_box: List[int],
orig_size: Tuple[int, ...],
) -> MaskData:
orig_h, orig_w = orig_size
data = {"image": images, "height": orig_h, "width": orig_w}
points=torch.tensor(points,dtype=torch.float).to(images.device)
points = torch.cat([points, points.new_tensor([[0.005, 0.005]]).repeat(len(points), 1)], dim=-1)
data['targets'] = [dict()]
data['targets'][0]['points']=points
data['targets'][0]['pb']=points.new_tensor([0.]*len(points))
batch_inputs = [data]
if self.enc_features is None:
masks, iou_preds,mask_features,multi_scale_features= self.predictor.model.evaluate_demo(batch_inputs,None,None,return_features=True, level=self.level)
self.enc_features=(mask_features,multi_scale_features)
else:
masks, iou_preds= self.predictor.model.evaluate_demo(batch_inputs,None,None,self.enc_features[0],self.enc_features[1], level=self.level)
data = MaskData(
masks=masks,
iou_preds=iou_preds.flatten(),
points=torch.as_tensor(points[:,None].repeat(1,len(self.level), 1).view(-1,4)),
)
del masks
# Filter by predicted IoU
keep_mask = data["iou_preds"] > self.pred_iou_thresh
data.filter(keep_mask)
# Calculate stability score
data["stability_score"] = calculate_stability_score(
data["masks"], 0.0, self.stability_score_offset
)
# if self.stability_score_thresh > 0.0:
keep_mask = data["stability_score"] >= self.stability_score_thresh
data.filter(keep_mask)
# Threshold masks and calculate boxes
data["masks"] = data["masks"] > 0.0
data["boxes"] = batched_mask_to_box(data["masks"])
# Filter boxes that touch crop boundaries
keep_mask = ~is_box_near_crop_edge(data["boxes"], crop_box, [0, 0, orig_w, orig_h])
if not torch.all(keep_mask):
data.filter(keep_mask)
# Compress to RLE
data["masks"] = uncrop_masks(data["masks"], crop_box, orig_h, orig_w)
data["rles"] = mask_to_rle_pytorch(data["masks"])
del data["masks"]
return data
@staticmethod
def postprocess_small_regions(
mask_data: MaskData, min_area: int, nms_thresh: float
) -> MaskData:
"""
Removes small disconnected regions and holes in masks, then reruns
box NMS to remove any new duplicates.
Edits mask_data in place.
Requires open-cv as a dependency.
"""
if len(mask_data["rles"]) == 0:
return mask_data
# Filter small disconnected regions and holes
new_masks = []
scores = []
for rle in mask_data["rles"]:
mask = rle_to_mask(rle)
mask, changed = remove_small_regions(mask, min_area, mode="holes")
unchanged = not changed
mask, changed = remove_small_regions(mask, min_area, mode="islands")
unchanged = unchanged and not changed
new_masks.append(torch.as_tensor(mask).unsqueeze(0))
# Give score=0 to changed masks and score=1 to unchanged masks
# so NMS will prefer ones that didn't need postprocessing
scores.append(float(unchanged))
# Recalculate boxes and remove any new duplicates
masks = torch.cat(new_masks, dim=0)
boxes = batched_mask_to_box(masks)
keep_by_nms = batched_nms(
boxes.float(),
torch.as_tensor(scores),
torch.zeros(len(boxes)), # categories
iou_threshold=nms_thresh,
)
# Only recalculate RLEs for masks that have changed
for i_mask in keep_by_nms:
if scores[i_mask] == 0.0:
mask_torch = masks[i_mask].unsqueeze(0)
mask_data["rles"][i_mask] = mask_to_rle_pytorch(mask_torch)[0]
mask_data["boxes"][i_mask] = boxes[i_mask] # update res directly
mask_data.filter(keep_by_nms)
return mask_data

108
mm_agents/task_adapter/semantic_sam/tasks/inference_semsam_m2m_auto.py
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@@ -1,108 +0,0 @@
# --------------------------------------------------------
# Semantic-SAM: Segment and Recognize Anything at Any Granularity
# Copyright (c) 2023 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Hao Zhang (hzhangcx@connect.ust.hk)
# --------------------------------------------------------
import torch
import numpy as np
from torchvision import transforms
from task_adapter.utils.visualizer import Visualizer
from typing import Tuple
from PIL import Image
from detectron2.data import MetadataCatalog
import matplotlib.pyplot as plt
import cv2
import io
from .automatic_mask_generator import SemanticSamAutomaticMaskGenerator
metadata = MetadataCatalog.get('coco_2017_train_panoptic')
def inference_semsam_m2m_auto(model, image, level, all_classes, all_parts, thresh, text_size, hole_scale, island_scale, semantic, refimg=None, reftxt=None, audio_pth=None, video_pth=None, label_mode='1', alpha=0.1, anno_mode=['Mask']):
t = []
t.append(transforms.Resize(int(text_size), interpolation=Image.BICUBIC))
transform1 = transforms.Compose(t)
image_ori = transform1(image)
image_ori = np.asarray(image_ori)
images = torch.from_numpy(image_ori.copy()).permute(2,0,1).cuda()
mask_generator = SemanticSamAutomaticMaskGenerator(model,points_per_side=32,
pred_iou_thresh=0.88,
stability_score_thresh=0.92,
min_mask_region_area=10,
level=level,
)
outputs = mask_generator.generate(images)
from task_adapter.utils.visualizer import Visualizer
visual = Visualizer(image_ori, metadata=metadata)
sorted_anns = sorted(outputs, key=(lambda x: x['area']), reverse=True)
label = 1
# for ann in sorted_anns:
# mask = ann['segmentation']
# color_mask = np.random.random((1, 3)).tolist()[0]
# # color_mask = [int(c*255) for c in color_mask]
# demo = visual.draw_binary_mask_with_number(mask, text=str(label), label_mode=label_mode, alpha=alpha, anno_mode=anno_mode)
# label += 1
# im = demo.get_image()
mask_map = np.zeros(image_ori.shape, dtype=np.uint8)
for i, ann in enumerate(sorted_anns):
mask = ann['segmentation']
color_mask = np.random.random((1, 3)).tolist()[0]
# color_mask = [int(c*255) for c in color_mask]
demo = visual.draw_binary_mask_with_number(mask, text=str(label), label_mode=label_mode, alpha=alpha, anno_mode=anno_mode)
# assign the mask to the mask_map
mask_map[mask == 1] = label
label += 1
im = demo.get_image()
# fig=plt.figure(figsize=(10, 10))
# plt.imshow(image_ori)
# show_anns(outputs)
# fig.canvas.draw()
# im=Image.frombytes('RGB', fig.canvas.get_width_height(), fig.canvas.tostring_rgb())
return im, sorted_anns
def remove_small_regions(
mask: np.ndarray, area_thresh: float, mode: str
) -> Tuple[np.ndarray, bool]:
"""
Removes small disconnected regions and holes in a mask. Returns the
mask and an indicator of if the mask has been modified.
"""
import cv2 # type: ignore
assert mode in ["holes", "islands"]
correct_holes = mode == "holes"
working_mask = (correct_holes ^ mask).astype(np.uint8)
n_labels, regions, stats, _ = cv2.connectedComponentsWithStats(working_mask, 8)
sizes = stats[:, -1][1:] # Row 0 is background label
small_regions = [i + 1 for i, s in enumerate(sizes) if s < area_thresh]
if len(small_regions) == 0:
return mask, False
fill_labels = [0] + small_regions
if not correct_holes:
fill_labels = [i for i in range(n_labels) if i not in fill_labels]
# If every region is below threshold, keep largest
if len(fill_labels) == 0:
fill_labels = [int(np.argmax(sizes)) + 1]
mask = np.isin(regions, fill_labels)
return mask, True
def show_anns(anns):
if len(anns) == 0:
return
sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True)
ax = plt.gca()
ax.set_autoscale_on(False)
polygons = []
color = []
for ann in sorted_anns:
m = ann['segmentation']
img = np.ones((m.shape[0], m.shape[1], 3))
color_mask = np.random.random((1, 3)).tolist()[0]
for i in range(3):
img[:,:,i] = color_mask[i]
ax.imshow(np.dstack((img, m*0.35)))

144
mm_agents/task_adapter/semantic_sam/tasks/interactive_idino_1o1_box.py
View File

@@ -1,144 +0,0 @@
# --------------------------------------------------------
# Semantic-SAM: Segment and Recognize Anything at Any Granularity
# Copyright (c) 2023 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Hao Zhang (hzhangcx@connect.ust.hk)
# --------------------------------------------------------
import torch
import numpy as np
from torchvision import transforms
from task_adapter.utils.visualizer import Visualizer
from typing import Tuple
from PIL import Image
from detectron2.data import MetadataCatalog
from detectron2.structures import BitMasks
from semantic_sam.utils import box_ops
metadata = MetadataCatalog.get('coco_2017_train_panoptic')
def interactive_infer_image_box(model, image,all_classes,all_parts, thresh,text_size,hole_scale,island_scale,semantic, refimg=None, reftxt=None, audio_pth=None, video_pth=None):
t = []
t.append(transforms.Resize(int(text_size), interpolation=Image.BICUBIC))
transform1 = transforms.Compose(t)
image_ori = transform1(image['image'])
mask_ori = transform1(image['mask'])
width = image_ori.size[0]
height = image_ori.size[1]
image_ori = np.asarray(image_ori)
images = torch.from_numpy(image_ori.copy()).permute(2,0,1).cuda()
all_classes, all_parts=all_classes.strip().strip("\"[]").split(':'),all_parts.strip().strip("\"[]").split(':')
data = {"image": images, "height": height, "width": width}
mask_ori = np.asarray(mask_ori)[:,:,0:1].copy()
mask_ori = torch.from_numpy(mask_ori).permute(2,0,1)[0]
flaten_mask = mask_ori.unsqueeze(0)
# import ipdb; ipdb.set_trace()
points=mask_ori.nonzero().float().to(images.device)
if len(points)==0:
point_=point=points.new_tensor([[0.5,0.5,0.5,0.5]])
else:
mean_point=points.mean(0)[None]
box_xyxy = BitMasks(flaten_mask > 0).get_bounding_boxes().tensor
h = mask_ori.shape[0]
w = mask_ori.shape[1]
box_xywh = (box_ops.box_xyxy_to_cxcywh(box_xyxy) / torch.as_tensor([w, h, w, h])).cuda()
# point_=points.mean(0)[None]
# point=point_.clone()
# point[0, 0] = point_[0, 0] / mask_ori.shape[0]
# point[0, 1] = point_[0, 1] / mask_ori.shape[1]
# point = point[:, [1, 0]]
point=box_xywh
data['targets'] = [dict()]
data['targets'][0]['points']=point
data['targets'][0]['pb']=point.new_tensor([1.])
batch_inputs = [data]
masks,ious = model.model.evaluate_demo(batch_inputs,all_classes,all_parts, task='demo_box')
pred_masks_poses = masks
reses=[]
ious=ious[0,0]
ids=torch.argsort(ious,descending=True)
text_res=''
try:
thresh=float(thresh)
except Exception:
thresh=0.0
mask_ls=[]
ious_res=[]
areas=[]
for i,(pred_masks_pos,iou) in enumerate(zip(pred_masks_poses[ids],ious[ids])):
iou=round(float(iou),2)
texts=f'{iou}'
mask=(pred_masks_pos>0.0).cpu().numpy()
area=mask.sum()
conti=False
if iou<thresh:
conti=True
for m in mask_ls:
if np.logical_and(mask,m).sum()/np.logical_or(mask,m).sum()>0.95:
conti=True
break
if i == len(pred_masks_poses[ids])-1 and mask_ls==[]:
conti=False
if conti:
continue
ious_res.append(iou)
mask_ls.append(mask)
areas.append(area)
mask,_=remove_small_regions(mask,int(hole_scale),mode="holes")
mask,_=remove_small_regions(mask,int(island_scale),mode="islands")
mask=(mask).astype(np.float)
out_txt = texts
visual = Visualizer(image_ori, metadata=metadata)
color=[0.,0.,1.0]
demo = visual.draw_binary_mask(mask, color=color, text=texts)
demo = visual.draw_box(box_xyxy[0])
res = demo.get_image()
# point_x0=max(0,int(point_[0, 1])-3)
# point_x1=min(mask_ori.shape[1],int(point_[0, 1])+3)
# point_y0 = max(0, int(point_[0, 0]) - 3)
# point_y1 = min(mask_ori.shape[0], int(point_[0, 0]) + 3)
# res[point_y0:point_y1,point_x0:point_x1,0]=255
# res[point_y0:point_y1,point_x0:point_x1,1]=0
# res[point_y0:point_y1,point_x0:point_x1,2]=0
reses.append(Image.fromarray(res))
text_res=text_res+';'+out_txt
ids=list(torch.argsort(torch.tensor(areas),descending=False))
ids = [int(i) for i in ids]
torch.cuda.empty_cache()
return reses,[reses[i] for i in ids]
def remove_small_regions(
mask: np.ndarray, area_thresh: float, mode: str
) -> Tuple[np.ndarray, bool]:
"""
Removes small disconnected regions and holes in a mask. Returns the
mask and an indicator of if the mask has been modified.
"""
import cv2 # type: ignore
assert mode in ["holes", "islands"]
correct_holes = mode == "holes"
working_mask = (correct_holes ^ mask).astype(np.uint8)
n_labels, regions, stats, _ = cv2.connectedComponentsWithStats(working_mask, 8)
sizes = stats[:, -1][1:] # Row 0 is background label
small_regions = [i + 1 for i, s in enumerate(sizes) if s < area_thresh]
if len(small_regions) == 0:
return mask, False
fill_labels = [0] + small_regions
if not correct_holes:
fill_labels = [i for i in range(n_labels) if i not in fill_labels]
# If every region is below threshold, keep largest
if len(fill_labels) == 0:
fill_labels = [int(np.argmax(sizes)) + 1]
mask = np.isin(regions, fill_labels)
return mask, True

322
mm_agents/task_adapter/semantic_sam/tasks/interactive_idino_m2m.py
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@@ -1,322 +0,0 @@
# --------------------------------------------------------
# Semantic-SAM: Segment and Recognize Anything at Any Granularity
# Copyright (c) 2023 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Hao Zhang (hzhangcx@connect.ust.hk)
# --------------------------------------------------------
import torch
import numpy as np
from torchvision import transforms
from task_adapter.utils.visualizer import Visualizer
from typing import Tuple
from PIL import Image
from detectron2.data import MetadataCatalog
metadata = MetadataCatalog.get('coco_2017_train_panoptic')
def interactive_infer_image(model, image,all_classes,all_parts, thresh,text_size,hole_scale,island_scale,semantic, refimg=None, reftxt=None, audio_pth=None, video_pth=None, label_mode='1', alpha=0.1, anno_mode=['Mask']):
t = []
t.append(transforms.Resize(int(text_size), interpolation=Image.BICUBIC))
transform1 = transforms.Compose(t)
image_ori = transform1(image['image'])
mask_ori = transform1(image['mask'])
width = image_ori.size[0]
height = image_ori.size[1]
image_ori = np.asarray(image_ori)
images = torch.from_numpy(image_ori.copy()).permute(2,0,1).cuda()
all_classes, all_parts=all_classes.strip().strip("\"[]").split(':'),all_parts.strip().strip("\"[]").split(':')
data = {"image": images, "height": height, "width": width}
mask_ori = np.asarray(mask_ori)[:,:,0:1].copy()
mask_ori = torch.from_numpy(mask_ori).permute(2,0,1)[0]
points=mask_ori.nonzero().float().to(images.device)
if len(points)==0:
point_=point=points.new_tensor([[0.5,0.5,0.006,0.006]])
else:
point_=points.mean(0)[None]
point=point_.clone()
point[0, 0] = point_[0, 0] / mask_ori.shape[0]
point[0, 1] = point_[0, 1] / mask_ori.shape[1]
point = point[:, [1, 0]]
point=torch.cat([point,points.new_tensor([[0.005,0.005]])],dim=-1)
data['targets'] = [dict()]
data['targets'][0]['points']=point
data['targets'][0]['pb']=point.new_tensor([0.])
batch_inputs = [data]
masks,ious = model.model.evaluate_demo(batch_inputs,all_classes,all_parts)
pred_masks_poses = masks
reses=[]
ious=ious[0,0]
ids=torch.argsort(ious,descending=True)
text_res=''
try:
thresh=float(thresh)
except Exception:
thresh=0.0
mask_ls=[]
ious_res=[]
areas=[]
for i,(pred_masks_pos,iou) in enumerate(zip(pred_masks_poses[ids],ious[ids])):
iou=round(float(iou),2)
texts=f'{iou}'
mask=(pred_masks_pos>0.0).cpu().numpy()
area=mask.sum()
conti=False
if iou<thresh:
conti=True
for m in mask_ls:
if np.logical_and(mask,m).sum()/np.logical_or(mask,m).sum()>0.95:
conti=True
break
if i == len(pred_masks_poses[ids])-1 and mask_ls==[]:
conti=False
if conti:
continue
ious_res.append(iou)
mask_ls.append(mask)
areas.append(area)
mask,_=remove_small_regions(mask,int(hole_scale),mode="holes")
mask,_=remove_small_regions(mask,int(island_scale),mode="islands")
mask=(mask).astype(np.float)
out_txt = texts
visual = Visualizer(image_ori, metadata=metadata)
color=[0.,0.,1.0]
# demo = visual.draw_binary_mask(mask, color=color, text=texts)
demo = visual.draw_binary_mask_with_number(mask, text=str(label), label_mode=label_mode, alpha=alpha, anno_mode=anno_mode)
res = demo.get_image()
point_x0=max(0,int(point_[0, 1])-3)
point_x1=min(mask_ori.shape[1],int(point_[0, 1])+3)
point_y0 = max(0, int(point_[0, 0]) - 3)
point_y1 = min(mask_ori.shape[0], int(point_[0, 0]) + 3)
# res[point_y0:point_y1,point_x0:point_x1,0]=255
# res[point_y0:point_y1,point_x0:point_x1,1]=0
# res[point_y0:point_y1,point_x0:point_x1,2]=0
reses.append(Image.fromarray(res))
text_res=text_res+';'+out_txt
ids=list(torch.argsort(torch.tensor(areas),descending=False))
ids = [int(i) for i in ids]
torch.cuda.empty_cache()
return reses,[reses[i] for i in ids]
def interactive_infer_image_3l(model, image,all_classes,all_parts, thresh,text_size,hole_scale,island_scale,semantic, refimg=None, reftxt=None, audio_pth=None, video_pth=None):
t = []
t.append(transforms.Resize(int(text_size), interpolation=Image.BICUBIC))
transform1 = transforms.Compose(t)
image_ori = transform1(image['image'])
mask_ori = transform1(image['mask'])
width = image_ori.size[0]
height = image_ori.size[1]
image_ori = np.asarray(image_ori)
images = torch.from_numpy(image_ori.copy()).permute(2,0,1).cuda()
all_classes, all_parts=all_classes.strip().strip("\"[]").split(':'),all_parts.strip().strip("\"[]").split(':')
data = {"image": images, "height": height, "width": width}
mask_ori = np.asarray(mask_ori)[:,:,0:1].copy()
mask_ori = torch.from_numpy(mask_ori).permute(2,0,1)[0]
points=mask_ori.nonzero().float().to(images.device)
if len(points)==0:
point_=point=points.new_tensor([[0.5,0.5,0.006,0.006]])
else:
point_=points.mean(0)[None]
point=point_.clone()
point[0, 0] = point_[0, 0] / mask_ori.shape[0]
point[0, 1] = point_[0, 1] / mask_ori.shape[1]
point = point[:, [1, 0]]
point=torch.cat([point,points.new_tensor([[0.005,0.005]])],dim=-1)
data['targets'] = [dict()]
data['targets'][0]['points']=point
data['targets'][0]['pb']=point.new_tensor([0.])
batch_inputs = [data]
masks, ious, pred_class, pred_class_score = model.model.evaluate_demo(batch_inputs,all_classes,all_parts, level=[0,1,2])
pred_masks_poses = masks
reses=[]
ious=ious[0,0]
ids=torch.argsort(ious,descending=True)
text_res=''
try:
thresh=float(thresh)
except Exception:
thresh=0.0
mask_ls=[]
ious_res=[]
areas=[]
new_pred_class = []
new_pred_class_score = []
for i in ids:
new_pred_class_score.append(pred_class_score[i])
new_pred_class.append(pred_class[i])
# import ipdb; ipdb.set_trace()
for i,(pred_masks_pos,iou, cls_name, cls_score) in enumerate(zip(pred_masks_poses[ids],ious[ids], new_pred_class, new_pred_class_score)):
iou=round(float(iou),2)
texts=f'{iou}_{cls_name}_{cls_score}'
mask=(pred_masks_pos>0.0).cpu().numpy()
area=mask.sum()
conti=False
if iou<thresh:
conti=True
for m in mask_ls:
if np.logical_and(mask,m).sum()/np.logical_or(mask,m).sum()>0.95:
conti=True
break
if i == len(pred_masks_poses[ids])-1 and mask_ls==[]:
conti=False
if conti:
continue
ious_res.append(iou)
mask_ls.append(mask)
areas.append(area)
mask,_=remove_small_regions(mask,int(hole_scale),mode="holes")
mask,_=remove_small_regions(mask,int(island_scale),mode="islands")
mask=(mask).astype(np.float)
out_txt = texts
visual = Visualizer(image_ori, metadata=metadata)
color=[0.,0.,1.0]
demo = visual.draw_binary_mask(mask, color=color, text=texts)
res = demo.get_image()
point_x0=max(0,int(point_[0, 1])-3)
point_x1=min(mask_ori.shape[1],int(point_[0, 1])+3)
point_y0 = max(0, int(point_[0, 0]) - 3)
point_y1 = min(mask_ori.shape[0], int(point_[0, 0]) + 3)
res[point_y0:point_y1,point_x0:point_x1,0]=255
res[point_y0:point_y1,point_x0:point_x1,1]=0
res[point_y0:point_y1,point_x0:point_x1,2]=0
reses.append(Image.fromarray(res))
text_res=text_res+';'+out_txt
ids=list(torch.argsort(torch.tensor(areas),descending=False))
ids = [int(i) for i in ids]
torch.cuda.empty_cache()
return reses,[reses[i] for i in ids]
def interactive_infer_image_semantic(model, image,all_classes,all_parts, thresh,text_size,hole_scale,island_scale,semantic, refimg=None, reftxt=None, audio_pth=None, video_pth=None):
t = []
t.append(transforms.Resize(int(text_size), interpolation=Image.BICUBIC))
transform1 = transforms.Compose(t)
image_ori = transform1(image['image'])
mask_ori = transform1(image['mask'])
width = image_ori.size[0]
height = image_ori.size[1]
image_ori = np.asarray(image_ori)
images = torch.from_numpy(image_ori.copy()).permute(2,0,1).cuda()
all_classes, all_parts=all_classes.strip().strip("\"[]").split(':'),all_parts.strip().strip("\"[]").split(':')
data = {"image": images, "height": height, "width": width}
mask_ori = np.asarray(mask_ori)[:,:,0:1].copy()
mask_ori = torch.from_numpy(mask_ori).permute(2,0,1)[0]
points=mask_ori.nonzero().float().to(images.device)
if len(points)==0:
point_=point=points.new_tensor([[0.5,0.5,0.006,0.006]])
else:
point_=points.mean(0)[None]
point=point_.clone()
point[0, 0] = point_[0, 0] / mask_ori.shape[0]
point[0, 1] = point_[0, 1] / mask_ori.shape[1]
point = point[:, [1, 0]]
point=torch.cat([point,points.new_tensor([[0.005,0.005]])],dim=-1)
data['targets'] = [dict()]
data['targets'][0]['points']=point
data['targets'][0]['pb']=point.new_tensor([0.])
data['targets'][0]['pb']=point.new_tensor([1.])
batch_inputs = [data]
masks,ious = model.model.evaluate_demo(batch_inputs,all_classes,all_parts)
pred_masks_poses = masks
reses=[]
ious=ious[0,0]
ids=torch.argsort(ious,descending=True)
text_res=''
try:
thresh=float(thresh)
except Exception:
thresh=0.0
mask_ls=[]
ious_res=[]
areas=[]
for i,(pred_masks_pos,iou) in enumerate(zip(pred_masks_poses[ids],ious[ids])):
iou=round(float(iou),2)
texts=f'{iou}'
mask=(pred_masks_pos>0.0).cpu().numpy()
area=mask.sum()
conti=False
if iou<thresh:
conti=True
for m in mask_ls:
if np.logical_and(mask,m).sum()/np.logical_or(mask,m).sum()>0.95:
conti=True
break
if i == len(pred_masks_poses[ids])-1 and mask_ls==[]:
conti=False
if conti:
continue
ious_res.append(iou)
mask_ls.append(mask)
areas.append(area)
mask,_=remove_small_regions(mask,int(hole_scale),mode="holes")
mask,_=remove_small_regions(mask,int(island_scale),mode="islands")
mask=(mask).astype(np.float)
out_txt = texts
visual = Visualizer(image_ori, metadata=metadata)
color=[0.,0.,1.0]
demo = visual.draw_binary_mask(mask, color=color, text=texts)
res = demo.get_image()
point_x0=max(0,int(point_[0, 1])-3)
point_x1=min(mask_ori.shape[1],int(point_[0, 1])+3)
point_y0 = max(0, int(point_[0, 0]) - 3)
point_y1 = min(mask_ori.shape[0], int(point_[0, 0]) + 3)
res[point_y0:point_y1,point_x0:point_x1,0]=255
res[point_y0:point_y1,point_x0:point_x1,1]=0
res[point_y0:point_y1,point_x0:point_x1,2]=0
reses.append(Image.fromarray(res))
text_res=text_res+';'+out_txt
ids=list(torch.argsort(torch.tensor(areas),descending=False))
ids = [int(i) for i in ids]
torch.cuda.empty_cache()
return reses,[reses[i] for i in ids]
def remove_small_regions(
mask: np.ndarray, area_thresh: float, mode: str
) -> Tuple[np.ndarray, bool]:
"""
Removes small disconnected regions and holes in a mask. Returns the
mask and an indicator of if the mask has been modified.
"""
import cv2 # type: ignore
assert mode in ["holes", "islands"]
correct_holes = mode == "holes"
working_mask = (correct_holes ^ mask).astype(np.uint8)
n_labels, regions, stats, _ = cv2.connectedComponentsWithStats(working_mask, 8)
sizes = stats[:, -1][1:] # Row 0 is background label
small_regions = [i + 1 for i, s in enumerate(sizes) if s < area_thresh]
if len(small_regions) == 0:
return mask, False
fill_labels = [0] + small_regions
if not correct_holes:
fill_labels = [i for i in range(n_labels) if i not in fill_labels]
# If every region is below threshold, keep largest
if len(fill_labels) == 0:
fill_labels = [int(np.argmax(sizes)) + 1]
mask = np.isin(regions, fill_labels)
return mask, True

139
mm_agents/task_adapter/semantic_sam/tasks/interactive_predictor.py
View File

@@ -1,139 +0,0 @@
import torch
import numpy as np
from torchvision import transforms
from task_adapter.utils.visualizer import Visualizer
from typing import Tuple
from PIL import Image
from detectron2.data import MetadataCatalog
metadata = MetadataCatalog.get('coco_2017_train_panoptic')
class SemanticSAMPredictor:
def __init__(self, model, thresh=0.5, text_size=640, hole_scale=100, island_scale=100):
"""
thresh: iou thresh to filter low confidence objects
text_size: resize the input image short edge for the model to process
hole_scale: fill in small holes as in SAM
island_scale: remove small regions as in SAM
"""
self.model = model
self.thresh = thresh
self.text_size = hole_scale
self.hole_scale = hole_scale
self.island_scale = island_scale
self.point = None
def predict(self, image_ori, image, point=None):
"""
produce up to 6 prediction results for each click
"""
width = image_ori.shape[0]
height = image_ori.shape[1]
data = {"image": image, "height": height, "width": width}
# import ipdb; ipdb.set_trace()
if point is None:
point = torch.tensor([[0.5, 0.5, 0.006, 0.006]]).cuda()
else:
point = torch.tensor(point).cuda()
point_ = point
point = point_.clone()
point[0, 0] = point_[0, 0]
point[0, 1] = point_[0, 1]
# point = point[:, [1, 0]]
point = torch.cat([point, point.new_tensor([[0.005, 0.005]])], dim=-1)
self.point = point[:, :2].clone()*(torch.tensor([width, height]).to(point))
data['targets'] = [dict()]
data['targets'][0]['points'] = point
data['targets'][0]['pb'] = point.new_tensor([0.])
batch_inputs = [data]
masks, ious = self.model.model.evaluate_demo(batch_inputs)
return masks, ious
def process_multi_mask(self, masks, ious, image_ori):
pred_masks_poses = masks
reses = []
ious = ious[0, 0]
ids = torch.argsort(ious, descending=True)
text_res = ''
mask_ls = []
ious_res = []
areas = []
for i, (pred_masks_pos, iou) in enumerate(zip(pred_masks_poses[ids], ious[ids])):
iou = round(float(iou), 2)
texts = f'{iou}'
mask = (pred_masks_pos > 0.0).cpu().numpy()
area = mask.sum()
conti = False
if iou < self.thresh:
conti = True
for m in mask_ls:
if np.logical_and(mask, m).sum() / np.logical_or(mask, m).sum() > 0.95:
conti = True
break
if i == len(pred_masks_poses[ids]) - 1 and mask_ls == []:
conti = False
if conti:
continue
ious_res.append(iou)
mask_ls.append(mask)
areas.append(area)
mask, _ = self.remove_small_regions(mask, int(self.hole_scale), mode="holes")
mask, _ = self.remove_small_regions(mask, int(self.island_scale), mode="islands")
mask = (mask).astype(np.float)
out_txt = texts
visual = Visualizer(image_ori, metadata=metadata)
color = [0., 0., 1.0]
demo = visual.draw_binary_mask(mask, color=color, text=texts)
res = demo.get_image()
point_x0 = max(0, int(self.point[0, 0]) - 3)
point_x1 = min(image_ori.shape[1], int(self.point[0, 0]) + 3)
point_y0 = max(0, int(self.point[0, 1]) - 3)
point_y1 = min(image_ori.shape[0], int(self.point[0, 1]) + 3)
res[point_y0:point_y1, point_x0:point_x1, 0] = 255
res[point_y0:point_y1, point_x0:point_x1, 1] = 0
res[point_y0:point_y1, point_x0:point_x1, 2] = 0
reses.append(Image.fromarray(res))
text_res = text_res + ';' + out_txt
ids = list(torch.argsort(torch.tensor(areas), descending=False))
ids = [int(i) for i in ids]
torch.cuda.empty_cache()
return reses, [reses[i] for i in ids]
def predict_masks(self, image_ori, image, point=None):
masks, ious = self.predict(image_ori, image, point)
return self.process_multi_mask(masks, ious, image_ori)
@staticmethod
def remove_small_regions(
mask: np.ndarray, area_thresh: float, mode: str
) -> Tuple[np.ndarray, bool]:
"""
Removes small disconnected regions and holes in a mask. Returns the
mask and an indicator of if the mask has been modified.
"""
import cv2 # type: ignore
assert mode in ["holes", "islands"]
correct_holes = mode == "holes"
working_mask = (correct_holes ^ mask).astype(np.uint8)
n_labels, regions, stats, _ = cv2.connectedComponentsWithStats(working_mask, 8)
sizes = stats[:, -1][1:] # Row 0 is background label
small_regions = [i + 1 for i, s in enumerate(sizes) if s < area_thresh]
if len(small_regions) == 0:
return mask, False
fill_labels = [0] + small_regions
if not correct_holes:
fill_labels = [i for i in range(n_labels) if i not in fill_labels]
# If every region is below threshold, keep largest
if len(fill_labels) == 0:
fill_labels = [int(np.argmax(sizes)) + 1]
mask = np.isin(regions, fill_labels)
return mask, True

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mm_agents/task_adapter/utils/visualizer.py
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