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- .gitattributes +4 -4
- README.md +6 -5
- configs/.DS_Store +0 -0
- configs/ade20k/Base-ADE20K-UnifiedSegmentation.yaml +68 -0
- configs/ade20k/oneformer_R50_bs16_160k.yaml +58 -0
- configs/ade20k/oneformer_dinat_large_IN21k_384_bs16_160k.yaml +42 -0
- configs/ade20k/oneformer_swin_large_IN21k_384_bs16_160k.yaml +40 -0
- configs/cityscapes/.DS_Store +0 -0
- configs/cityscapes/Base-Cityscapes-UnifiedSegmentation.yaml +68 -0
- configs/cityscapes/oneformer_R50_bs16_90k.yaml +59 -0
- configs/cityscapes/oneformer_dinat_large_bs16_90k.yaml +22 -0
- configs/cityscapes/oneformer_swin_large_IN21k_384_bs16_90k.yaml +20 -0
- configs/coco/Base-COCO-UnifiedSegmentation.yaml +54 -0
- configs/coco/oneformer_R50_bs16_50ep.yaml +59 -0
- configs/coco/oneformer_dinat_large_bs16_100ep.yaml +22 -0
- configs/coco/oneformer_swin_large_IN21k_384_bs16_100ep.yaml +25 -0
- deform_setup.sh +21 -0
- demo/colormap.py +170 -0
- demo/defaults.py +77 -0
- demo/predictor.py +190 -0
- demo/visualizer.py +1350 -0
- examples/ade20k.jpeg +3 -0
- examples/cityscapes.png +3 -0
- examples/coco.jpeg +3 -0
- gradio_app.py +194 -0
- oneformer/.DS_Store +0 -0
- oneformer/__init__.py +9 -0
- oneformer/config.py +239 -0
- oneformer/data/__init__.py +2 -0
- oneformer/data/bpe_simple_vocab_16e6.txt +0 -0
- oneformer/data/bpe_simple_vocab_16e6.txt.gz +3 -0
- oneformer/data/build.py +117 -0
- oneformer/data/dataset_mappers/__init__.py +1 -0
- oneformer/data/dataset_mappers/coco_unified_new_baseline_dataset_mapper.py +341 -0
- oneformer/data/dataset_mappers/dataset_mapper.py +203 -0
- oneformer/data/dataset_mappers/oneformer_unified_dataset_mapper.py +375 -0
- oneformer/data/datasets/__init__.py +7 -0
- oneformer/data/datasets/register_ade20k_instance.py +56 -0
- oneformer/data/datasets/register_ade20k_panoptic.py +394 -0
- oneformer/data/datasets/register_cityscapes_panoptic.py +199 -0
- oneformer/data/datasets/register_coco_panoptic2instance.py +44 -0
- oneformer/data/datasets/register_coco_panoptic_annos_semseg.py +367 -0
- oneformer/data/tokenizer.py +193 -0
- oneformer/evaluation/__init__.py +3 -0
- oneformer/evaluation/cityscapes_evaluation.py +201 -0
- oneformer/evaluation/coco_evaluator.py +563 -0
- oneformer/evaluation/detection_coco_evaluator.py +723 -0
- oneformer/evaluation/evaluator.py +228 -0
- oneformer/evaluation/instance_evaluation.py +110 -0
- oneformer/modeling/.DS_Store +0 -0
.gitattributes
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README.md
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---
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title: OneFormer
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-
emoji:
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-
colorFrom:
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colorTo:
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sdk: gradio
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-
sdk_version: 3.
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app_file:
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pinned: false
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license: mit
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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---
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title: OneFormer
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+
emoji: 🎗️
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colorFrom: red
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colorTo: blue
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sdk: gradio
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sdk_version: 3.1.4
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app_file: gradio_app.py
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pinned: false
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license: mit
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+
python_version: 3.8.15
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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configs/.DS_Store
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Binary file (6.15 kB). View file
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configs/ade20k/Base-ADE20K-UnifiedSegmentation.yaml
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MODEL:
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BACKBONE:
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FREEZE_AT: 0
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NAME: "build_resnet_backbone"
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+
WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl"
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PIXEL_MEAN: [123.675, 116.280, 103.530]
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+
PIXEL_STD: [58.395, 57.120, 57.375]
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RESNETS:
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DEPTH: 50
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STEM_TYPE: "basic" # not used
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STEM_OUT_CHANNELS: 64
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STRIDE_IN_1X1: False
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OUT_FEATURES: ["res2", "res3", "res4", "res5"]
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# NORM: "SyncBN"
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RES5_MULTI_GRID: [1, 1, 1] # not used
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DATASETS:
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TRAIN: ("ade20k_panoptic_train",)
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TEST_PANOPTIC: ("ade20k_panoptic_val",)
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TEST_INSTANCE: ("ade20k_instance_val",)
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TEST_SEMANTIC: ("ade20k_sem_seg_val",)
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SOLVER:
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IMS_PER_BATCH: 16
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BASE_LR: 0.0001
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MAX_ITER: 160000
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WARMUP_FACTOR: 1.0
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WARMUP_ITERS: 0
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WEIGHT_DECAY: 0.05
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OPTIMIZER: "ADAMW"
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LR_SCHEDULER_NAME: "WarmupPolyLR"
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BACKBONE_MULTIPLIER: 0.1
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CLIP_GRADIENTS:
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ENABLED: True
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CLIP_TYPE: "full_model"
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CLIP_VALUE: 0.01
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NORM_TYPE: 2.0
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AMP:
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ENABLED: True
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INPUT:
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MIN_SIZE_TRAIN: !!python/object/apply:eval ["[int(x * 0.1 * 512) for x in range(5, 21)]"]
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MIN_SIZE_TRAIN_SAMPLING: "choice"
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MIN_SIZE_TEST: 512
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MAX_SIZE_TRAIN: 2048
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MAX_SIZE_TEST: 2048
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CROP:
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ENABLED: True
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TYPE: "absolute"
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SIZE: (512, 512)
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SINGLE_CATEGORY_MAX_AREA: 1.0
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COLOR_AUG_SSD: True
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SIZE_DIVISIBILITY: 512 # used in dataset mapper
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FORMAT: "RGB"
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DATASET_MAPPER_NAME: "oneformer_unified"
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MAX_SEQ_LEN: 77
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TASK_SEQ_LEN: 77
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TASK_PROB:
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SEMANTIC: 0.33
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INSTANCE: 0.66
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TEST:
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EVAL_PERIOD: 5000
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AUG:
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ENABLED: False
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MIN_SIZES: [256, 384, 512, 640, 768, 896]
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MAX_SIZE: 3584
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FLIP: True
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DATALOADER:
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FILTER_EMPTY_ANNOTATIONS: True
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NUM_WORKERS: 4
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VERSION: 2
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configs/ade20k/oneformer_R50_bs16_160k.yaml
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_BASE_: Base-ADE20K-UnifiedSegmentation.yaml
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MODEL:
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META_ARCHITECTURE: "OneFormer"
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SEM_SEG_HEAD:
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NAME: "OneFormerHead"
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IGNORE_VALUE: 255
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NUM_CLASSES: 150
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LOSS_WEIGHT: 1.0
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CONVS_DIM: 256
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MASK_DIM: 256
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NORM: "GN"
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# pixel decoder
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PIXEL_DECODER_NAME: "MSDeformAttnPixelDecoder"
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IN_FEATURES: ["res2", "res3", "res4", "res5"]
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DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES: ["res3", "res4", "res5"]
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COMMON_STRIDE: 4
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TRANSFORMER_ENC_LAYERS: 6
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ONE_FORMER:
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TRANSFORMER_DECODER_NAME: "ContrastiveMultiScaleMaskedTransformerDecoder"
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TRANSFORMER_IN_FEATURE: "multi_scale_pixel_decoder"
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DEEP_SUPERVISION: True
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NO_OBJECT_WEIGHT: 0.1
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CLASS_WEIGHT: 2.0
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MASK_WEIGHT: 5.0
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DICE_WEIGHT: 5.0
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CONTRASTIVE_WEIGHT: 0.5
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CONTRASTIVE_TEMPERATURE: 0.07
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HIDDEN_DIM: 256
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NUM_OBJECT_QUERIES: 150
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USE_TASK_NORM: True
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NHEADS: 8
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DROPOUT: 0.1
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DIM_FEEDFORWARD: 2048
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ENC_LAYERS: 0
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PRE_NORM: False
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ENFORCE_INPUT_PROJ: False
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SIZE_DIVISIBILITY: 32
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CLASS_DEC_LAYERS: 2
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DEC_LAYERS: 10 # 9 decoder layers, add one for the loss on learnable query
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TRAIN_NUM_POINTS: 12544
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OVERSAMPLE_RATIO: 3.0
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IMPORTANCE_SAMPLE_RATIO: 0.75
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TEXT_ENCODER:
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WIDTH: 256
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CONTEXT_LENGTH: 77
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NUM_LAYERS: 6
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VOCAB_SIZE: 49408
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PROJ_NUM_LAYERS: 2
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N_CTX: 16
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TEST:
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SEMANTIC_ON: True
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INSTANCE_ON: True
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PANOPTIC_ON: True
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OVERLAP_THRESHOLD: 0.8
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OBJECT_MASK_THRESHOLD: 0.8
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TASK: "panoptic"
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TEST:
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DETECTIONS_PER_IMAGE: 150
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configs/ade20k/oneformer_dinat_large_IN21k_384_bs16_160k.yaml
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_BASE_: oneformer_R50_bs16_160k.yaml
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MODEL:
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BACKBONE:
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NAME: "D2DiNAT"
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DiNAT:
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EMBED_DIM: 192
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MLP_RATIO: 2.0
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DEPTHS: [3, 4, 18, 5]
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NUM_HEADS: [6, 12, 24, 48]
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KERNEL_SIZE: 11
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DROP_PATH_RATE: 0.3
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DILATIONS: [[1, 20, 1], [1, 5, 1, 10], [1, 2, 1, 3, 1, 4, 1, 5, 1, 2, 1, 3, 1, 4, 1, 5, 1, 5], [1, 2, 1, 2, 1]]
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WEIGHTS: "dinat_large_in22k_in1k_384_11x11.pkl"
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+
PIXEL_MEAN: [123.675, 116.280, 103.530]
|
15 |
+
PIXEL_STD: [58.395, 57.120, 57.375]
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16 |
+
ONE_FORMER:
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17 |
+
NUM_OBJECT_QUERIES: 250
|
18 |
+
SOLVER:
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19 |
+
AMP:
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20 |
+
ENABLED: False
|
21 |
+
INPUT:
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22 |
+
MIN_SIZE_TRAIN: !!python/object/apply:eval ["[int(x * 0.1 * 640) for x in range(5, 21)]"]
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+
MIN_SIZE_TRAIN_SAMPLING: "choice"
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24 |
+
MIN_SIZE_TEST: 640
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25 |
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MAX_SIZE_TRAIN: 2560
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26 |
+
MAX_SIZE_TEST: 2560
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+
CROP:
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ENABLED: True
|
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+
TYPE: "absolute"
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+
SIZE: (640, 640)
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31 |
+
SINGLE_CATEGORY_MAX_AREA: 1.0
|
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+
COLOR_AUG_SSD: True
|
33 |
+
SIZE_DIVISIBILITY: 640 # used in dataset mapper
|
34 |
+
FORMAT: "RGB"
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35 |
+
TEST:
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36 |
+
DETECTIONS_PER_IMAGE: 250
|
37 |
+
EVAL_PERIOD: 5000
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38 |
+
AUG:
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39 |
+
ENABLED: False
|
40 |
+
MIN_SIZES: [320, 480, 640, 800, 960, 1120]
|
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+
MAX_SIZE: 4480
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+
FLIP: True
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configs/ade20k/oneformer_swin_large_IN21k_384_bs16_160k.yaml
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1 |
+
_BASE_: oneformer_R50_bs16_160k.yaml
|
2 |
+
MODEL:
|
3 |
+
BACKBONE:
|
4 |
+
NAME: "D2SwinTransformer"
|
5 |
+
SWIN:
|
6 |
+
EMBED_DIM: 192
|
7 |
+
DEPTHS: [2, 2, 18, 2]
|
8 |
+
NUM_HEADS: [6, 12, 24, 48]
|
9 |
+
WINDOW_SIZE: 12
|
10 |
+
APE: False
|
11 |
+
DROP_PATH_RATE: 0.3
|
12 |
+
PATCH_NORM: True
|
13 |
+
PRETRAIN_IMG_SIZE: 384
|
14 |
+
WEIGHTS: "swin_large_patch4_window12_384_22k.pkl"
|
15 |
+
PIXEL_MEAN: [123.675, 116.280, 103.530]
|
16 |
+
PIXEL_STD: [58.395, 57.120, 57.375]
|
17 |
+
ONE_FORMER:
|
18 |
+
NUM_OBJECT_QUERIES: 250
|
19 |
+
INPUT:
|
20 |
+
MIN_SIZE_TRAIN: !!python/object/apply:eval ["[int(x * 0.1 * 640) for x in range(5, 21)]"]
|
21 |
+
MIN_SIZE_TRAIN_SAMPLING: "choice"
|
22 |
+
MIN_SIZE_TEST: 640
|
23 |
+
MAX_SIZE_TRAIN: 2560
|
24 |
+
MAX_SIZE_TEST: 2560
|
25 |
+
CROP:
|
26 |
+
ENABLED: True
|
27 |
+
TYPE: "absolute"
|
28 |
+
SIZE: (640, 640)
|
29 |
+
SINGLE_CATEGORY_MAX_AREA: 1.0
|
30 |
+
COLOR_AUG_SSD: True
|
31 |
+
SIZE_DIVISIBILITY: 640 # used in dataset mapper
|
32 |
+
FORMAT: "RGB"
|
33 |
+
TEST:
|
34 |
+
DETECTIONS_PER_IMAGE: 250
|
35 |
+
EVAL_PERIOD: 5000
|
36 |
+
AUG:
|
37 |
+
ENABLED: False
|
38 |
+
MIN_SIZES: [320, 480, 640, 800, 960, 1120]
|
39 |
+
MAX_SIZE: 4480
|
40 |
+
FLIP: True
|
configs/cityscapes/.DS_Store
ADDED
Binary file (6.15 kB). View file
|
|
configs/cityscapes/Base-Cityscapes-UnifiedSegmentation.yaml
ADDED
@@ -0,0 +1,68 @@
|
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|
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|
|
|
|
|
|
|
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|
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|
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|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
MODEL:
|
2 |
+
BACKBONE:
|
3 |
+
FREEZE_AT: 0
|
4 |
+
NAME: "build_resnet_backbone"
|
5 |
+
WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl"
|
6 |
+
PIXEL_MEAN: [123.675, 116.280, 103.530]
|
7 |
+
PIXEL_STD: [58.395, 57.120, 57.375]
|
8 |
+
RESNETS:
|
9 |
+
DEPTH: 50
|
10 |
+
STEM_TYPE: "basic" # not used
|
11 |
+
STEM_OUT_CHANNELS: 64
|
12 |
+
STRIDE_IN_1X1: False
|
13 |
+
OUT_FEATURES: ["res2", "res3", "res4", "res5"]
|
14 |
+
NORM: "SyncBN" # use syncbn for cityscapes dataset
|
15 |
+
RES5_MULTI_GRID: [1, 1, 1] # not used
|
16 |
+
DATASETS:
|
17 |
+
TRAIN: ("cityscapes_fine_panoptic_train",)
|
18 |
+
TEST_PANOPTIC: ("cityscapes_fine_panoptic_val",)
|
19 |
+
TEST_INSTANCE: ("cityscapes_fine_instance_seg_val",)
|
20 |
+
TEST_SEMANTIC: ("cityscapes_fine_sem_seg_val",)
|
21 |
+
SOLVER:
|
22 |
+
IMS_PER_BATCH: 16
|
23 |
+
BASE_LR: 0.0001
|
24 |
+
MAX_ITER: 90000
|
25 |
+
WARMUP_FACTOR: 1.0
|
26 |
+
WARMUP_ITERS: 0
|
27 |
+
WEIGHT_DECAY: 0.05
|
28 |
+
OPTIMIZER: "ADAMW"
|
29 |
+
LR_SCHEDULER_NAME: "WarmupPolyLR"
|
30 |
+
BACKBONE_MULTIPLIER: 0.1
|
31 |
+
CLIP_GRADIENTS:
|
32 |
+
ENABLED: True
|
33 |
+
CLIP_TYPE: "full_model"
|
34 |
+
CLIP_VALUE: 0.01
|
35 |
+
NORM_TYPE: 2.0
|
36 |
+
AMP:
|
37 |
+
ENABLED: True
|
38 |
+
INPUT:
|
39 |
+
MIN_SIZE_TRAIN: !!python/object/apply:eval ["[int(x * 0.1 * 1024) for x in range(5, 21)]"]
|
40 |
+
MIN_SIZE_TRAIN_SAMPLING: "choice"
|
41 |
+
MIN_SIZE_TEST: 1024
|
42 |
+
MAX_SIZE_TRAIN: 4096
|
43 |
+
MAX_SIZE_TEST: 2048
|
44 |
+
CROP:
|
45 |
+
ENABLED: True
|
46 |
+
TYPE: "absolute"
|
47 |
+
SIZE: (512, 1024)
|
48 |
+
SINGLE_CATEGORY_MAX_AREA: 1.0
|
49 |
+
COLOR_AUG_SSD: True
|
50 |
+
SIZE_DIVISIBILITY: -1
|
51 |
+
FORMAT: "RGB"
|
52 |
+
DATASET_MAPPER_NAME: "oneformer_unified"
|
53 |
+
MAX_SEQ_LEN: 77
|
54 |
+
TASK_SEQ_LEN: 77
|
55 |
+
TASK_PROB:
|
56 |
+
SEMANTIC: 0.33
|
57 |
+
INSTANCE: 0.66
|
58 |
+
TEST:
|
59 |
+
EVAL_PERIOD: 5000
|
60 |
+
AUG:
|
61 |
+
ENABLED: False
|
62 |
+
MIN_SIZES: [512, 768, 1024, 1280, 1536, 1792]
|
63 |
+
MAX_SIZE: 4096
|
64 |
+
FLIP: True
|
65 |
+
DATALOADER:
|
66 |
+
FILTER_EMPTY_ANNOTATIONS: True
|
67 |
+
NUM_WORKERS: 4
|
68 |
+
VERSION: 2
|
configs/cityscapes/oneformer_R50_bs16_90k.yaml
ADDED
@@ -0,0 +1,59 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
_BASE_: Base-Cityscapes-UnifiedSegmentation.yaml
|
2 |
+
MODEL:
|
3 |
+
META_ARCHITECTURE: "OneFormer"
|
4 |
+
SEM_SEG_HEAD:
|
5 |
+
NAME: "OneFormerHead"
|
6 |
+
IGNORE_VALUE: 255
|
7 |
+
NUM_CLASSES: 19
|
8 |
+
LOSS_WEIGHT: 1.0
|
9 |
+
CONVS_DIM: 256
|
10 |
+
MASK_DIM: 256
|
11 |
+
NORM: "GN"
|
12 |
+
# pixel decoder
|
13 |
+
PIXEL_DECODER_NAME: "MSDeformAttnPixelDecoder"
|
14 |
+
IN_FEATURES: ["res2", "res3", "res4", "res5"]
|
15 |
+
DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES: ["res3", "res4", "res5"]
|
16 |
+
COMMON_STRIDE: 4
|
17 |
+
TRANSFORMER_ENC_LAYERS: 6
|
18 |
+
ONE_FORMER:
|
19 |
+
TRANSFORMER_DECODER_NAME: "ContrastiveMultiScaleMaskedTransformerDecoder"
|
20 |
+
TRANSFORMER_IN_FEATURE: "multi_scale_pixel_decoder"
|
21 |
+
DEEP_SUPERVISION: True
|
22 |
+
NO_OBJECT_WEIGHT: 0.1
|
23 |
+
CLASS_WEIGHT: 2.0
|
24 |
+
MASK_WEIGHT: 5.0
|
25 |
+
DICE_WEIGHT: 5.0
|
26 |
+
CONTRASTIVE_WEIGHT: 0.5
|
27 |
+
CONTRASTIVE_TEMPERATURE: 0.07
|
28 |
+
HIDDEN_DIM: 256
|
29 |
+
NUM_OBJECT_QUERIES: 150
|
30 |
+
USE_TASK_NORM: True
|
31 |
+
NHEADS: 8
|
32 |
+
DROPOUT: 0.1
|
33 |
+
DIM_FEEDFORWARD: 2048
|
34 |
+
ENC_LAYERS: 0
|
35 |
+
PRE_NORM: False
|
36 |
+
ENFORCE_INPUT_PROJ: False
|
37 |
+
SIZE_DIVISIBILITY: 32
|
38 |
+
ENC_LAYERS: 0
|
39 |
+
CLASS_DEC_LAYERS: 2
|
40 |
+
DEC_LAYERS: 10 # 9 decoder layers, add one for the loss on learnable query
|
41 |
+
TRAIN_NUM_POINTS: 12544
|
42 |
+
OVERSAMPLE_RATIO: 3.0
|
43 |
+
IMPORTANCE_SAMPLE_RATIO: 0.75
|
44 |
+
TEXT_ENCODER:
|
45 |
+
WIDTH: 256
|
46 |
+
CONTEXT_LENGTH: 77
|
47 |
+
NUM_LAYERS: 6
|
48 |
+
VOCAB_SIZE: 49408
|
49 |
+
PROJ_NUM_LAYERS: 2
|
50 |
+
N_CTX: 16
|
51 |
+
TEST:
|
52 |
+
SEMANTIC_ON: True
|
53 |
+
INSTANCE_ON: True
|
54 |
+
PANOPTIC_ON: True
|
55 |
+
OVERLAP_THRESHOLD: 0.8
|
56 |
+
OBJECT_MASK_THRESHOLD: 0.8
|
57 |
+
TASK: "panoptic"
|
58 |
+
TEST:
|
59 |
+
DETECTIONS_PER_IMAGE: 150
|
configs/cityscapes/oneformer_dinat_large_bs16_90k.yaml
ADDED
@@ -0,0 +1,22 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
_BASE_: oneformer_R50_bs16_90k.yaml
|
2 |
+
MODEL:
|
3 |
+
BACKBONE:
|
4 |
+
NAME: "D2DiNAT"
|
5 |
+
DiNAT:
|
6 |
+
EMBED_DIM: 192
|
7 |
+
MLP_RATIO: 2.0
|
8 |
+
DEPTHS: [3, 4, 18, 5]
|
9 |
+
NUM_HEADS: [6, 12, 24, 48]
|
10 |
+
KERNEL_SIZE: 7
|
11 |
+
DROP_PATH_RATE: 0.3
|
12 |
+
DILATIONS: [[1, 18, 1], [1, 5, 1, 9], [1, 2, 1, 3, 1, 4, 1, 2, 1, 3, 1, 4, 1, 2, 1, 3, 1, 4], [1, 2, 1, 2, 1]]
|
13 |
+
WEIGHTS: "dinat_large_in22k_224.pkl"
|
14 |
+
PIXEL_MEAN: [123.675, 116.280, 103.530]
|
15 |
+
PIXEL_STD: [58.395, 57.120, 57.375]
|
16 |
+
ONE_FORMER:
|
17 |
+
NUM_OBJECT_QUERIES: 250
|
18 |
+
SOLVER:
|
19 |
+
AMP:
|
20 |
+
ENABLED: False
|
21 |
+
TEST:
|
22 |
+
DETECTIONS_PER_IMAGE: 250
|
configs/cityscapes/oneformer_swin_large_IN21k_384_bs16_90k.yaml
ADDED
@@ -0,0 +1,20 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
_BASE_: oneformer_R50_bs16_90k.yaml
|
2 |
+
MODEL:
|
3 |
+
BACKBONE:
|
4 |
+
NAME: "D2SwinTransformer"
|
5 |
+
SWIN:
|
6 |
+
EMBED_DIM: 192
|
7 |
+
DEPTHS: [2, 2, 18, 2]
|
8 |
+
NUM_HEADS: [6, 12, 24, 48]
|
9 |
+
WINDOW_SIZE: 12
|
10 |
+
APE: False
|
11 |
+
DROP_PATH_RATE: 0.3
|
12 |
+
PATCH_NORM: True
|
13 |
+
PRETRAIN_IMG_SIZE: 384
|
14 |
+
WEIGHTS: "swin_large_patch4_window12_384_22k.pkl"
|
15 |
+
PIXEL_MEAN: [123.675, 116.280, 103.530]
|
16 |
+
PIXEL_STD: [58.395, 57.120, 57.375]
|
17 |
+
ONE_FORMER:
|
18 |
+
NUM_OBJECT_QUERIES: 250
|
19 |
+
TEST:
|
20 |
+
DETECTIONS_PER_IMAGE: 250
|
configs/coco/Base-COCO-UnifiedSegmentation.yaml
ADDED
@@ -0,0 +1,54 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
MODEL:
|
2 |
+
BACKBONE:
|
3 |
+
FREEZE_AT: 0
|
4 |
+
NAME: "build_resnet_backbone"
|
5 |
+
WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl"
|
6 |
+
PIXEL_MEAN: [123.675, 116.280, 103.530]
|
7 |
+
PIXEL_STD: [58.395, 57.120, 57.375]
|
8 |
+
RESNETS:
|
9 |
+
DEPTH: 50
|
10 |
+
STEM_TYPE: "basic" # not used
|
11 |
+
STEM_OUT_CHANNELS: 64
|
12 |
+
STRIDE_IN_1X1: False
|
13 |
+
OUT_FEATURES: ["res2", "res3", "res4", "res5"]
|
14 |
+
# NORM: "SyncBN"
|
15 |
+
RES5_MULTI_GRID: [1, 1, 1] # not used
|
16 |
+
DATASETS:
|
17 |
+
TRAIN: ("coco_2017_train_panoptic_with_sem_seg",)
|
18 |
+
TEST_PANOPTIC: ("coco_2017_val_panoptic_with_sem_seg",) # to evaluate instance and semantic performance as well
|
19 |
+
TEST_INSTANCE: ("coco_2017_val",)
|
20 |
+
TEST_SEMANTIC: ("coco_2017_val_panoptic_with_sem_seg",)
|
21 |
+
SOLVER:
|
22 |
+
IMS_PER_BATCH: 16
|
23 |
+
BASE_LR: 0.0001
|
24 |
+
STEPS: (327778, 355092)
|
25 |
+
MAX_ITER: 368750
|
26 |
+
WARMUP_FACTOR: 1.0
|
27 |
+
WARMUP_ITERS: 10
|
28 |
+
WEIGHT_DECAY: 0.05
|
29 |
+
OPTIMIZER: "ADAMW"
|
30 |
+
BACKBONE_MULTIPLIER: 0.1
|
31 |
+
CLIP_GRADIENTS:
|
32 |
+
ENABLED: True
|
33 |
+
CLIP_TYPE: "full_model"
|
34 |
+
CLIP_VALUE: 0.01
|
35 |
+
NORM_TYPE: 2.0
|
36 |
+
AMP:
|
37 |
+
ENABLED: True
|
38 |
+
INPUT:
|
39 |
+
IMAGE_SIZE: 1024
|
40 |
+
MIN_SCALE: 0.1
|
41 |
+
MAX_SCALE: 2.0
|
42 |
+
FORMAT: "RGB"
|
43 |
+
DATASET_MAPPER_NAME: "coco_unified_lsj"
|
44 |
+
MAX_SEQ_LEN: 77
|
45 |
+
TASK_SEQ_LEN: 77
|
46 |
+
TASK_PROB:
|
47 |
+
SEMANTIC: 0.33
|
48 |
+
INSTANCE: 0.66
|
49 |
+
TEST:
|
50 |
+
EVAL_PERIOD: 5000
|
51 |
+
DATALOADER:
|
52 |
+
FILTER_EMPTY_ANNOTATIONS: True
|
53 |
+
NUM_WORKERS: 4
|
54 |
+
VERSION: 2
|
configs/coco/oneformer_R50_bs16_50ep.yaml
ADDED
@@ -0,0 +1,59 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
_BASE_: Base-COCO-UnifiedSegmentation.yaml
|
2 |
+
MODEL:
|
3 |
+
META_ARCHITECTURE: "OneFormer"
|
4 |
+
SEM_SEG_HEAD:
|
5 |
+
NAME: "OneFormerHead"
|
6 |
+
IGNORE_VALUE: 255
|
7 |
+
NUM_CLASSES: 133
|
8 |
+
LOSS_WEIGHT: 1.0
|
9 |
+
CONVS_DIM: 256
|
10 |
+
MASK_DIM: 256
|
11 |
+
NORM: "GN"
|
12 |
+
# pixel decoder
|
13 |
+
PIXEL_DECODER_NAME: "MSDeformAttnPixelDecoder"
|
14 |
+
IN_FEATURES: ["res2", "res3", "res4", "res5"]
|
15 |
+
DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES: ["res3", "res4", "res5"]
|
16 |
+
COMMON_STRIDE: 4
|
17 |
+
TRANSFORMER_ENC_LAYERS: 6
|
18 |
+
ONE_FORMER:
|
19 |
+
TRANSFORMER_DECODER_NAME: "ContrastiveMultiScaleMaskedTransformerDecoder"
|
20 |
+
TRANSFORMER_IN_FEATURE: "multi_scale_pixel_decoder"
|
21 |
+
DEEP_SUPERVISION: True
|
22 |
+
NO_OBJECT_WEIGHT: 0.1
|
23 |
+
CLASS_WEIGHT: 2.0
|
24 |
+
MASK_WEIGHT: 5.0
|
25 |
+
DICE_WEIGHT: 5.0
|
26 |
+
CONTRASTIVE_WEIGHT: 0.5
|
27 |
+
CONTRASTIVE_TEMPERATURE: 0.07
|
28 |
+
HIDDEN_DIM: 256
|
29 |
+
NUM_OBJECT_QUERIES: 150
|
30 |
+
USE_TASK_NORM: True
|
31 |
+
NHEADS: 8
|
32 |
+
DROPOUT: 0.1
|
33 |
+
DIM_FEEDFORWARD: 2048
|
34 |
+
ENC_LAYERS: 0
|
35 |
+
PRE_NORM: False
|
36 |
+
ENFORCE_INPUT_PROJ: False
|
37 |
+
SIZE_DIVISIBILITY: 32
|
38 |
+
CLASS_DEC_LAYERS: 2
|
39 |
+
DEC_LAYERS: 10 # 9 decoder layers, add one for the loss on learnable query
|
40 |
+
TRAIN_NUM_POINTS: 12544
|
41 |
+
OVERSAMPLE_RATIO: 3.0
|
42 |
+
IMPORTANCE_SAMPLE_RATIO: 0.75
|
43 |
+
TEXT_ENCODER:
|
44 |
+
WIDTH: 256
|
45 |
+
CONTEXT_LENGTH: 77
|
46 |
+
NUM_LAYERS: 6
|
47 |
+
VOCAB_SIZE: 49408
|
48 |
+
PROJ_NUM_LAYERS: 2
|
49 |
+
N_CTX: 16
|
50 |
+
TEST:
|
51 |
+
SEMANTIC_ON: True
|
52 |
+
INSTANCE_ON: True
|
53 |
+
PANOPTIC_ON: True
|
54 |
+
DETECTION_ON: False
|
55 |
+
OVERLAP_THRESHOLD: 0.8
|
56 |
+
OBJECT_MASK_THRESHOLD: 0.8
|
57 |
+
TASK: "panoptic"
|
58 |
+
TEST:
|
59 |
+
DETECTIONS_PER_IMAGE: 150
|
configs/coco/oneformer_dinat_large_bs16_100ep.yaml
ADDED
@@ -0,0 +1,22 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
_BASE_: oneformer_R50_bs16_50ep.yaml
|
2 |
+
MODEL:
|
3 |
+
BACKBONE:
|
4 |
+
NAME: "D2DiNAT"
|
5 |
+
DiNAT:
|
6 |
+
EMBED_DIM: 192
|
7 |
+
MLP_RATIO: 2.0
|
8 |
+
DEPTHS: [3, 4, 18, 5]
|
9 |
+
NUM_HEADS: [6, 12, 24, 48]
|
10 |
+
KERNEL_SIZE: 11
|
11 |
+
DROP_PATH_RATE: 0.3
|
12 |
+
DILATIONS: [[1, 20, 1], [1, 5, 1, 10], [1, 2, 1, 3, 1, 4, 1, 5, 1, 2, 1, 3, 1, 4, 1, 5, 1, 5], [1, 2, 1, 2, 1]]
|
13 |
+
WEIGHTS: "dinat_large_in22k_in1k_384_11x11.pkl"
|
14 |
+
PIXEL_MEAN: [123.675, 116.280, 103.530]
|
15 |
+
PIXEL_STD: [58.395, 57.120, 57.375]
|
16 |
+
ONE_FORMER:
|
17 |
+
NUM_OBJECT_QUERIES: 150
|
18 |
+
SOLVER:
|
19 |
+
STEPS: (655556, 710184)
|
20 |
+
MAX_ITER: 737500
|
21 |
+
TEST:
|
22 |
+
DETECTIONS_PER_IMAGE: 150
|
configs/coco/oneformer_swin_large_IN21k_384_bs16_100ep.yaml
ADDED
@@ -0,0 +1,25 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
_BASE_: oneformer_R50_bs16_50ep.yaml
|
2 |
+
MODEL:
|
3 |
+
BACKBONE:
|
4 |
+
NAME: "D2SwinTransformer"
|
5 |
+
SWIN:
|
6 |
+
EMBED_DIM: 192
|
7 |
+
DEPTHS: [2, 2, 18, 2]
|
8 |
+
NUM_HEADS: [6, 12, 24, 48]
|
9 |
+
WINDOW_SIZE: 12
|
10 |
+
APE: False
|
11 |
+
DROP_PATH_RATE: 0.3
|
12 |
+
PATCH_NORM: True
|
13 |
+
PRETRAIN_IMG_SIZE: 384
|
14 |
+
WEIGHTS: "swin_large_patch4_window12_384_22k.pkl"
|
15 |
+
PIXEL_MEAN: [123.675, 116.280, 103.530]
|
16 |
+
PIXEL_STD: [58.395, 57.120, 57.375]
|
17 |
+
ONE_FORMER:
|
18 |
+
NUM_OBJECT_QUERIES: 150
|
19 |
+
SOLVER:
|
20 |
+
STEPS: (655556, 735184)
|
21 |
+
MAX_ITER: 737500
|
22 |
+
AMP:
|
23 |
+
ENABLED: False
|
24 |
+
TEST:
|
25 |
+
DETECTIONS_PER_IMAGE: 150
|
deform_setup.sh
ADDED
@@ -0,0 +1,21 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
#!/usr/bin/env bash
|
2 |
+
ln -s ./oneformer/modeling/pixel_decoder/ops/ ./
|
3 |
+
ls
|
4 |
+
cd ops/ && bash make.sh && cd ..
|
5 |
+
|
6 |
+
echo '----------------------------------------------------------------'
|
7 |
+
echo '----------------------------------------------------------------'
|
8 |
+
pip3 freeze | grep MultiScaleDeformableAttention
|
9 |
+
pip3 freeze | grep torch
|
10 |
+
pip3 freeze | grep detectron2
|
11 |
+
pip3 freeze | grep natten
|
12 |
+
echo '----------------------------------------------------------------'
|
13 |
+
echo '----------------------------------------------------------------'
|
14 |
+
|
15 |
+
echo '----------------------------------------------------------------'
|
16 |
+
echo '----------------------------------------------------------------'
|
17 |
+
cd /home/user/.pyenv/versions/3.8.15/lib/python3.8/site-packages
|
18 |
+
ls
|
19 |
+
ls | grep MultiScale
|
20 |
+
echo '----------------------------------------------------------------'
|
21 |
+
echo '----------------------------------------------------------------'
|
demo/colormap.py
ADDED
@@ -0,0 +1,170 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Copyright (c) Facebook, Inc. and its affiliates.
|
2 |
+
|
3 |
+
"""
|
4 |
+
An awesome colormap for really neat visualizations.
|
5 |
+
Copied from Detectron, and removed gray colors.
|
6 |
+
"""
|
7 |
+
|
8 |
+
import numpy as np
|
9 |
+
import random
|
10 |
+
random.seed(0)
|
11 |
+
|
12 |
+
__all__ = ["colormap", "random_color", "random_colors"]
|
13 |
+
|
14 |
+
# fmt: off
|
15 |
+
# RGB:
|
16 |
+
# _COLORS = np.array(
|
17 |
+
# [
|
18 |
+
# 0.000, 0.447, 0.741,
|
19 |
+
# 0.850, 0.325, 0.098,
|
20 |
+
# 0.929, 0.694, 0.125,
|
21 |
+
# 0.494, 0.184, 0.556,
|
22 |
+
# 0.466, 0.674, 0.188,
|
23 |
+
# 0.301, 0.745, 0.933,
|
24 |
+
# 0.635, 0.078, 0.184,
|
25 |
+
# 0.300, 0.300, 0.300,
|
26 |
+
# 0.600, 0.600, 0.600,
|
27 |
+
# 1.000, 0.000, 0.000,
|
28 |
+
# 1.000, 0.500, 0.000,
|
29 |
+
# 0.749, 0.749, 0.000,
|
30 |
+
# 0.000, 1.000, 0.000,
|
31 |
+
# 0.000, 0.000, 1.000,
|
32 |
+
# 0.667, 0.000, 1.000,
|
33 |
+
# 0.333, 0.333, 0.000,
|
34 |
+
# 0.333, 0.667, 0.000,
|
35 |
+
# 0.333, 1.000, 0.000,
|
36 |
+
# 0.667, 0.333, 0.000,
|
37 |
+
# 0.667, 0.667, 0.000,
|
38 |
+
# 0.667, 1.000, 0.000,
|
39 |
+
# 1.000, 0.333, 0.000,
|
40 |
+
# 1.000, 0.667, 0.000,
|
41 |
+
# 1.000, 1.000, 0.000,
|
42 |
+
# 0.000, 0.333, 0.500,
|
43 |
+
# 0.000, 0.667, 0.500,
|
44 |
+
# 0.000, 1.000, 0.500,
|
45 |
+
# 0.333, 0.000, 0.500,
|
46 |
+
# 0.333, 0.333, 0.500,
|
47 |
+
# 0.333, 0.667, 0.500,
|
48 |
+
# 0.333, 1.000, 0.500,
|
49 |
+
# 0.667, 0.000, 0.500,
|
50 |
+
# 0.667, 0.333, 0.500,
|
51 |
+
# 0.667, 0.667, 0.500,
|
52 |
+
# 0.667, 1.000, 0.500,
|
53 |
+
# 1.000, 0.000, 0.500,
|
54 |
+
# 1.000, 0.333, 0.500,
|
55 |
+
# 1.000, 0.667, 0.500,
|
56 |
+
# 1.000, 1.000, 0.500,
|
57 |
+
# 0.000, 0.333, 1.000,
|
58 |
+
# 0.000, 0.667, 1.000,
|
59 |
+
# 0.000, 1.000, 1.000,
|
60 |
+
# 0.333, 0.000, 1.000,
|
61 |
+
# 0.333, 0.333, 1.000,
|
62 |
+
# 0.333, 0.667, 1.000,
|
63 |
+
# 0.333, 1.000, 1.000,
|
64 |
+
# 0.667, 0.000, 1.000,
|
65 |
+
# 0.667, 0.333, 1.000,
|
66 |
+
# 0.667, 0.667, 1.000,
|
67 |
+
# 0.667, 1.000, 1.000,
|
68 |
+
# 1.000, 0.000, 1.000,
|
69 |
+
# 1.000, 0.333, 1.000,
|
70 |
+
# 1.000, 0.667, 1.000,
|
71 |
+
# 0.333, 0.000, 0.000,
|
72 |
+
# 0.500, 0.000, 0.000,
|
73 |
+
# 0.667, 0.000, 0.000,
|
74 |
+
# 0.833, 0.000, 0.000,
|
75 |
+
# 1.000, 0.000, 0.000,
|
76 |
+
# 0.000, 0.167, 0.000,
|
77 |
+
# 0.000, 0.333, 0.000,
|
78 |
+
# 0.000, 0.500, 0.000,
|
79 |
+
# 0.000, 0.667, 0.000,
|
80 |
+
# 0.000, 0.833, 0.000,
|
81 |
+
# 0.000, 1.000, 0.000,
|
82 |
+
# 0.000, 0.000, 0.167,
|
83 |
+
# 0.000, 0.000, 0.333,
|
84 |
+
# 0.000, 0.000, 0.500,
|
85 |
+
# 0.000, 0.000, 0.667,
|
86 |
+
# 0.000, 0.000, 0.833,
|
87 |
+
# 0.000, 0.000, 1.000,
|
88 |
+
# 0.000, 0.000, 0.000,
|
89 |
+
# 0.143, 0.143, 0.143,
|
90 |
+
# 0.857, 0.857, 0.857,
|
91 |
+
# 1.000, 1.000, 1.000
|
92 |
+
# ]
|
93 |
+
# ).astype(np.float32).reshape(-1, 3)
|
94 |
+
# fmt: on
|
95 |
+
|
96 |
+
_COLORS = []
|
97 |
+
|
98 |
+
|
99 |
+
def gen_color():
|
100 |
+
color = tuple(np.round(np.random.choice(range(256), size=3)/255, 3))
|
101 |
+
if color not in _COLORS and np.mean(color) != 0.0:
|
102 |
+
_COLORS.append(color)
|
103 |
+
else:
|
104 |
+
gen_color()
|
105 |
+
|
106 |
+
|
107 |
+
for _ in range(300):
|
108 |
+
gen_color()
|
109 |
+
|
110 |
+
|
111 |
+
def colormap(rgb=False, maximum=255):
|
112 |
+
"""
|
113 |
+
Args:
|
114 |
+
rgb (bool): whether to return RGB colors or BGR colors.
|
115 |
+
maximum (int): either 255 or 1
|
116 |
+
Returns:
|
117 |
+
ndarray: a float32 array of Nx3 colors, in range [0, 255] or [0, 1]
|
118 |
+
"""
|
119 |
+
assert maximum in [255, 1], maximum
|
120 |
+
c = _COLORS * maximum
|
121 |
+
if not rgb:
|
122 |
+
c = c[:, ::-1]
|
123 |
+
return c
|
124 |
+
|
125 |
+
|
126 |
+
def random_color(rgb=False, maximum=255):
|
127 |
+
"""
|
128 |
+
Args:
|
129 |
+
rgb (bool): whether to return RGB colors or BGR colors.
|
130 |
+
maximum (int): either 255 or 1
|
131 |
+
Returns:
|
132 |
+
ndarray: a vector of 3 numbers
|
133 |
+
"""
|
134 |
+
idx = np.random.randint(0, len(_COLORS))
|
135 |
+
ret = _COLORS[idx] * maximum
|
136 |
+
if not rgb:
|
137 |
+
ret = ret[::-1]
|
138 |
+
return ret
|
139 |
+
|
140 |
+
|
141 |
+
def random_colors(N, rgb=False, maximum=255):
|
142 |
+
"""
|
143 |
+
Args:
|
144 |
+
N (int): number of unique colors needed
|
145 |
+
rgb (bool): whether to return RGB colors or BGR colors.
|
146 |
+
maximum (int): either 255 or 1
|
147 |
+
Returns:
|
148 |
+
ndarray: a list of random_color
|
149 |
+
"""
|
150 |
+
indices = random.sample(range(len(_COLORS)), N)
|
151 |
+
ret = [_COLORS[i] * maximum for i in indices]
|
152 |
+
if not rgb:
|
153 |
+
ret = [x[::-1] for x in ret]
|
154 |
+
return ret
|
155 |
+
|
156 |
+
|
157 |
+
if __name__ == "__main__":
|
158 |
+
import cv2
|
159 |
+
|
160 |
+
size = 100
|
161 |
+
H, W = 10, 10
|
162 |
+
canvas = np.random.rand(H * size, W * size, 3).astype("float32")
|
163 |
+
for h in range(H):
|
164 |
+
for w in range(W):
|
165 |
+
idx = h * W + w
|
166 |
+
if idx >= len(_COLORS):
|
167 |
+
break
|
168 |
+
canvas[h * size : (h + 1) * size, w * size : (w + 1) * size] = _COLORS[idx]
|
169 |
+
cv2.imshow("a", canvas)
|
170 |
+
cv2.waitKey(0)
|
demo/defaults.py
ADDED
@@ -0,0 +1,77 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import torch
|
2 |
+
import detectron2.data.transforms as T
|
3 |
+
from detectron2.checkpoint import DetectionCheckpointer
|
4 |
+
from detectron2.data import (
|
5 |
+
MetadataCatalog,
|
6 |
+
)
|
7 |
+
from detectron2.modeling import build_model
|
8 |
+
|
9 |
+
|
10 |
+
__all__ = [
|
11 |
+
"DefaultPredictor",
|
12 |
+
]
|
13 |
+
|
14 |
+
|
15 |
+
class DefaultPredictor:
|
16 |
+
"""
|
17 |
+
Create a simple end-to-end predictor with the given config that runs on
|
18 |
+
single device for a single input image.
|
19 |
+
Compared to using the model directly, this class does the following additions:
|
20 |
+
1. Load checkpoint from `cfg.MODEL.WEIGHTS`.
|
21 |
+
2. Always take BGR image as the input and apply conversion defined by `cfg.INPUT.FORMAT`.
|
22 |
+
3. Apply resizing defined by `cfg.INPUT.{MIN,MAX}_SIZE_TEST`.
|
23 |
+
4. Take one input image and produce a single output, instead of a batch.
|
24 |
+
This is meant for simple demo purposes, so it does the above steps automatically.
|
25 |
+
This is not meant for benchmarks or running complicated inference logic.
|
26 |
+
If you'd like to do anything more complicated, please refer to its source code as
|
27 |
+
examples to build and use the model manually.
|
28 |
+
Attributes:
|
29 |
+
metadata (Metadata): the metadata of the underlying dataset, obtained from
|
30 |
+
cfg.DATASETS.TEST.
|
31 |
+
Examples:
|
32 |
+
::
|
33 |
+
pred = DefaultPredictor(cfg)
|
34 |
+
inputs = cv2.imread("input.jpg")
|
35 |
+
outputs = pred(inputs)
|
36 |
+
"""
|
37 |
+
|
38 |
+
def __init__(self, cfg):
|
39 |
+
self.cfg = cfg.clone() # cfg can be modified by model
|
40 |
+
self.model = build_model(self.cfg)
|
41 |
+
self.model.eval()
|
42 |
+
if len(cfg.DATASETS.TEST):
|
43 |
+
self.metadata = MetadataCatalog.get(cfg.DATASETS.TEST[0])
|
44 |
+
|
45 |
+
checkpointer = DetectionCheckpointer(self.model)
|
46 |
+
checkpointer.load(cfg.MODEL.WEIGHTS)
|
47 |
+
|
48 |
+
self.aug = T.ResizeShortestEdge(
|
49 |
+
[cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST], cfg.INPUT.MAX_SIZE_TEST
|
50 |
+
)
|
51 |
+
|
52 |
+
self.input_format = cfg.INPUT.FORMAT
|
53 |
+
assert self.input_format in ["RGB", "BGR"], self.input_format
|
54 |
+
|
55 |
+
def __call__(self, original_image, task):
|
56 |
+
"""
|
57 |
+
Args:
|
58 |
+
original_image (np.ndarray): an image of shape (H, W, C) (in BGR order).
|
59 |
+
Returns:
|
60 |
+
predictions (dict):
|
61 |
+
the output of the model for one image only.
|
62 |
+
See :doc:`/tutorials/models` for details about the format.
|
63 |
+
"""
|
64 |
+
with torch.no_grad(): # https://github.com/sphinx-doc/sphinx/issues/4258
|
65 |
+
# Apply pre-processing to image.
|
66 |
+
if self.input_format == "RGB":
|
67 |
+
# whether the model expects BGR inputs or RGB
|
68 |
+
original_image = original_image[:, :, ::-1]
|
69 |
+
height, width = original_image.shape[:2]
|
70 |
+
image = self.aug.get_transform(original_image).apply_image(original_image)
|
71 |
+
image = torch.as_tensor(image.astype("float32").transpose(2, 0, 1))
|
72 |
+
|
73 |
+
task = f"The task is {task}"
|
74 |
+
|
75 |
+
inputs = {"image": image, "height": height, "width": width, "task": task}
|
76 |
+
predictions = self.model([inputs])[0]
|
77 |
+
return predictions
|
demo/predictor.py
ADDED
@@ -0,0 +1,190 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Copyright (c) Facebook, Inc. and its affiliates.
|
2 |
+
# Copied from: https://github.com/facebookresearch/detectron2/blob/master/demo/predictor.py
|
3 |
+
import atexit
|
4 |
+
import bisect
|
5 |
+
import multiprocessing as mp
|
6 |
+
from collections import deque
|
7 |
+
|
8 |
+
import cv2
|
9 |
+
import torch
|
10 |
+
|
11 |
+
from detectron2.data import MetadataCatalog
|
12 |
+
from defaults import DefaultPredictor
|
13 |
+
from detectron2.utils.video_visualizer import VideoVisualizer
|
14 |
+
from visualizer import ColorMode, Visualizer
|
15 |
+
|
16 |
+
|
17 |
+
class VisualizationDemo(object):
|
18 |
+
def __init__(self, cfg, instance_mode=ColorMode.IMAGE, parallel=False):
|
19 |
+
"""
|
20 |
+
Args:
|
21 |
+
cfg (CfgNode):
|
22 |
+
instance_mode (ColorMode):
|
23 |
+
parallel (bool): whether to run the model in different processes from visualization.
|
24 |
+
Useful since the visualization logic can be slow.
|
25 |
+
"""
|
26 |
+
self.metadata = MetadataCatalog.get(
|
27 |
+
cfg.DATASETS.TEST[0] if len(cfg.DATASETS.TEST) else "__unused"
|
28 |
+
)
|
29 |
+
if 'cityscapes_fine_sem_seg_val' in cfg.DATASETS.TEST[0]:
|
30 |
+
from cityscapesscripts.helpers.labels import labels
|
31 |
+
stuff_colors = [k.color for k in labels if k.trainId != 255]
|
32 |
+
self.metadata = self.metadata.set(stuff_colors=stuff_colors)
|
33 |
+
self.cpu_device = torch.device("cpu")
|
34 |
+
self.instance_mode = instance_mode
|
35 |
+
|
36 |
+
self.parallel = parallel
|
37 |
+
if parallel:
|
38 |
+
num_gpu = torch.cuda.device_count()
|
39 |
+
self.predictor = AsyncPredictor(cfg, num_gpus=num_gpu)
|
40 |
+
else:
|
41 |
+
self.predictor = DefaultPredictor(cfg)
|
42 |
+
|
43 |
+
def run_on_image(self, image, task, sem_gt, pan_gt, ins_gt, box_gt):
|
44 |
+
"""
|
45 |
+
Args:
|
46 |
+
image (np.ndarray): an image of shape (H, W, C) (in BGR order).
|
47 |
+
This is the format used by OpenCV.
|
48 |
+
Returns:
|
49 |
+
predictions (dict): the output of the model.
|
50 |
+
vis_output (VisImage): the visualized image output.
|
51 |
+
"""
|
52 |
+
vis_output = None
|
53 |
+
# Convert image from OpenCV BGR format to Matplotlib RGB format.
|
54 |
+
image = image[:, :, ::-1]
|
55 |
+
vis_output = {}
|
56 |
+
|
57 |
+
if task == 'panoptic':
|
58 |
+
visualizer = Visualizer(image, metadata=self.metadata, instance_mode=0)
|
59 |
+
predictions = self.predictor(image, "panoptic")
|
60 |
+
panoptic_seg, segments_info = predictions["panoptic_seg"]
|
61 |
+
vis_output['panoptic'] = visualizer.draw_panoptic_seg_predictions(
|
62 |
+
panoptic_seg.to(self.cpu_device), segments_info, alpha=1
|
63 |
+
)
|
64 |
+
|
65 |
+
# visualizer = Visualizer(image, metadata=self.metadata, instance_mode=0)
|
66 |
+
# vis_output['pan_gt'] = visualizer.draw_panoptic_seg(
|
67 |
+
# pan_gt[0].to(self.cpu_device), pan_gt[1], alpha=1
|
68 |
+
# )
|
69 |
+
|
70 |
+
if task == 'panoptic' or task == 'semantic':
|
71 |
+
visualizer = Visualizer(image, metadata=self.metadata, instance_mode=1)
|
72 |
+
predictions = self.predictor(image, "semantic")
|
73 |
+
vis_output['semantic'] = visualizer.draw_sem_seg(
|
74 |
+
predictions["sem_seg"].argmax(dim=0).to(self.cpu_device), alpha=1
|
75 |
+
)
|
76 |
+
|
77 |
+
# visualizer = Visualizer(image, metadata=self.metadata, instance_mode=1)
|
78 |
+
# vis_output['gt_sem'] = visualizer.draw_sem_seg(
|
79 |
+
# sem_gt.to(self.cpu_device), alpha=1
|
80 |
+
# )
|
81 |
+
|
82 |
+
if task == 'panoptic' or task == 'instance':
|
83 |
+
visualizer = Visualizer(image, metadata=self.metadata, instance_mode=2)
|
84 |
+
predictions = self.predictor(image, "instance")
|
85 |
+
instances = predictions["instances"].to(self.cpu_device)
|
86 |
+
vis_output['instance'] = visualizer.draw_instance_predictions(predictions=instances, alpha=1)
|
87 |
+
|
88 |
+
if 'boxes' in predictions:
|
89 |
+
boxes, labels, scores = predictions["boxes"]
|
90 |
+
visualizer = Visualizer(image, False, metadata=self.metadata, instance_mode=0)
|
91 |
+
vis_output['boxes'] = visualizer.draw_box_predictions(
|
92 |
+
boxes.to(self.cpu_device), labels.to(self.cpu_device), scores.to(self.cpu_device))
|
93 |
+
|
94 |
+
|
95 |
+
# visualizer = Visualizer(image, metadata=self.metadata, instance_mode=2)
|
96 |
+
# vis_output['ins_gt'] = visualizer.draw_instance_predictions(predictions=ins_gt.to(self.cpu_device), alpha=1)
|
97 |
+
# vis_output['input'] = visualizer.get_image(image)
|
98 |
+
|
99 |
+
return predictions, vis_output
|
100 |
+
|
101 |
+
|
102 |
+
class AsyncPredictor:
|
103 |
+
"""
|
104 |
+
A predictor that runs the model asynchronously, possibly on >1 GPUs.
|
105 |
+
Because rendering the visualization takes considerably amount of time,
|
106 |
+
this helps improve throughput a little bit when rendering videos.
|
107 |
+
"""
|
108 |
+
|
109 |
+
class _StopToken:
|
110 |
+
pass
|
111 |
+
|
112 |
+
class _PredictWorker(mp.Process):
|
113 |
+
def __init__(self, cfg, task_queue, result_queue):
|
114 |
+
self.cfg = cfg
|
115 |
+
self.task_queue = task_queue
|
116 |
+
self.result_queue = result_queue
|
117 |
+
super().__init__()
|
118 |
+
|
119 |
+
def run(self):
|
120 |
+
predictor = DefaultPredictor(self.cfg)
|
121 |
+
|
122 |
+
while True:
|
123 |
+
task = self.task_queue.get()
|
124 |
+
if isinstance(task, AsyncPredictor._StopToken):
|
125 |
+
break
|
126 |
+
idx, data = task
|
127 |
+
result = predictor(data)
|
128 |
+
self.result_queue.put((idx, result))
|
129 |
+
|
130 |
+
def __init__(self, cfg, num_gpus: int = 1):
|
131 |
+
"""
|
132 |
+
Args:
|
133 |
+
cfg (CfgNode):
|
134 |
+
num_gpus (int): if 0, will run on CPU
|
135 |
+
"""
|
136 |
+
num_workers = max(num_gpus, 1)
|
137 |
+
self.task_queue = mp.Queue(maxsize=num_workers * 3)
|
138 |
+
self.result_queue = mp.Queue(maxsize=num_workers * 3)
|
139 |
+
self.procs = []
|
140 |
+
for gpuid in range(max(num_gpus, 1)):
|
141 |
+
cfg = cfg.clone()
|
142 |
+
cfg.defrost()
|
143 |
+
cfg.MODEL.DEVICE = "cuda:{}".format(gpuid) if num_gpus > 0 else "cpu"
|
144 |
+
self.procs.append(
|
145 |
+
AsyncPredictor._PredictWorker(cfg, self.task_queue, self.result_queue)
|
146 |
+
)
|
147 |
+
|
148 |
+
self.put_idx = 0
|
149 |
+
self.get_idx = 0
|
150 |
+
self.result_rank = []
|
151 |
+
self.result_data = []
|
152 |
+
|
153 |
+
for p in self.procs:
|
154 |
+
p.start()
|
155 |
+
atexit.register(self.shutdown)
|
156 |
+
|
157 |
+
def put(self, image):
|
158 |
+
self.put_idx += 1
|
159 |
+
self.task_queue.put((self.put_idx, image))
|
160 |
+
|
161 |
+
def get(self):
|
162 |
+
self.get_idx += 1 # the index needed for this request
|
163 |
+
if len(self.result_rank) and self.result_rank[0] == self.get_idx:
|
164 |
+
res = self.result_data[0]
|
165 |
+
del self.result_data[0], self.result_rank[0]
|
166 |
+
return res
|
167 |
+
|
168 |
+
while True:
|
169 |
+
# make sure the results are returned in the correct order
|
170 |
+
idx, res = self.result_queue.get()
|
171 |
+
if idx == self.get_idx:
|
172 |
+
return res
|
173 |
+
insert = bisect.bisect(self.result_rank, idx)
|
174 |
+
self.result_rank.insert(insert, idx)
|
175 |
+
self.result_data.insert(insert, res)
|
176 |
+
|
177 |
+
def __len__(self):
|
178 |
+
return self.put_idx - self.get_idx
|
179 |
+
|
180 |
+
def __call__(self, image):
|
181 |
+
self.put(image)
|
182 |
+
return self.get()
|
183 |
+
|
184 |
+
def shutdown(self):
|
185 |
+
for _ in self.procs:
|
186 |
+
self.task_queue.put(AsyncPredictor._StopToken())
|
187 |
+
|
188 |
+
@property
|
189 |
+
def default_buffer_size(self):
|
190 |
+
return len(self.procs) * 5
|
demo/visualizer.py
ADDED
@@ -0,0 +1,1350 @@
|
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|
1 |
+
# Copyright (c) Facebook, Inc. and its affiliates.
|
2 |
+
import colorsys
|
3 |
+
import logging
|
4 |
+
import math
|
5 |
+
import numpy as np
|
6 |
+
from enum import Enum, unique
|
7 |
+
import cv2
|
8 |
+
import matplotlib as mpl
|
9 |
+
import matplotlib.colors as mplc
|
10 |
+
import matplotlib.figure as mplfigure
|
11 |
+
import pycocotools.mask as mask_util
|
12 |
+
import torch
|
13 |
+
from matplotlib.backends.backend_agg import FigureCanvasAgg
|
14 |
+
from PIL import Image
|
15 |
+
|
16 |
+
from detectron2.data import MetadataCatalog
|
17 |
+
from detectron2.structures import BitMasks, Boxes, BoxMode, Keypoints, PolygonMasks, RotatedBoxes
|
18 |
+
from detectron2.utils.file_io import PathManager
|
19 |
+
import random
|
20 |
+
random.seed(0)
|
21 |
+
from .colormap import random_color, _COLORS
|
22 |
+
logger = logging.getLogger(__name__)
|
23 |
+
|
24 |
+
__all__ = ["ColorMode", "VisImage", "Visualizer"]
|
25 |
+
|
26 |
+
|
27 |
+
_SMALL_OBJECT_AREA_THRESH = 1000
|
28 |
+
_LARGE_MASK_AREA_THRESH = 120000
|
29 |
+
_OFF_WHITE = (1.0, 1.0, 240.0 / 255)
|
30 |
+
_BLACK = (0, 0, 0)
|
31 |
+
_RED = (1.0, 0, 0)
|
32 |
+
|
33 |
+
_KEYPOINT_THRESHOLD = 0.05
|
34 |
+
|
35 |
+
|
36 |
+
def instance_color(rgb=False, idx=1, maximum=255):
|
37 |
+
"""
|
38 |
+
Args:
|
39 |
+
rgb (bool): whether to return RGB colors or BGR colors.
|
40 |
+
maximum (int): either 255 or 1
|
41 |
+
Returns:
|
42 |
+
ndarray: a vector of 3 numbers
|
43 |
+
"""
|
44 |
+
ret = _COLORS[idx] * maximum
|
45 |
+
if not rgb:
|
46 |
+
ret = ret[::-1]
|
47 |
+
return ret
|
48 |
+
|
49 |
+
@unique
|
50 |
+
class ColorMode(Enum):
|
51 |
+
"""
|
52 |
+
Enum of different color modes to use for instance visualizations.
|
53 |
+
"""
|
54 |
+
|
55 |
+
IMAGE = 0
|
56 |
+
"""
|
57 |
+
Picks a random color for every instance and overlay segmentations with low opacity.
|
58 |
+
"""
|
59 |
+
SEGMENTATION = 1
|
60 |
+
"""
|
61 |
+
Let instances of the same category have similar colors
|
62 |
+
(from metadata.thing_colors), and overlay them with
|
63 |
+
high opacity. This provides more attention on the quality of segmentation.
|
64 |
+
"""
|
65 |
+
IMAGE_BW = 2
|
66 |
+
"""
|
67 |
+
Same as IMAGE, but convert all areas without masks to gray-scale.
|
68 |
+
Only available for drawing per-instance mask predictions.
|
69 |
+
"""
|
70 |
+
|
71 |
+
|
72 |
+
class GenericMask:
|
73 |
+
"""
|
74 |
+
Attribute:
|
75 |
+
polygons (list[ndarray]): list[ndarray]: polygons for this mask.
|
76 |
+
Each ndarray has format [x, y, x, y, ...]
|
77 |
+
mask (ndarray): a binary mask
|
78 |
+
"""
|
79 |
+
|
80 |
+
def __init__(self, mask_or_polygons, height, width):
|
81 |
+
self._mask = self._polygons = self._has_holes = None
|
82 |
+
self.height = height
|
83 |
+
self.width = width
|
84 |
+
|
85 |
+
m = mask_or_polygons
|
86 |
+
if isinstance(m, dict):
|
87 |
+
# RLEs
|
88 |
+
assert "counts" in m and "size" in m
|
89 |
+
if isinstance(m["counts"], list): # uncompressed RLEs
|
90 |
+
h, w = m["size"]
|
91 |
+
assert h == height and w == width
|
92 |
+
m = mask_util.frPyObjects(m, h, w)
|
93 |
+
self._mask = mask_util.decode(m)[:, :]
|
94 |
+
return
|
95 |
+
|
96 |
+
if isinstance(m, list): # list[ndarray]
|
97 |
+
self._polygons = [np.asarray(x).reshape(-1) for x in m]
|
98 |
+
return
|
99 |
+
|
100 |
+
if isinstance(m, np.ndarray): # assumed to be a binary mask
|
101 |
+
assert m.shape[1] != 2, m.shape
|
102 |
+
assert m.shape == (
|
103 |
+
height,
|
104 |
+
width,
|
105 |
+
), f"mask shape: {m.shape}, target dims: {height}, {width}"
|
106 |
+
self._mask = m.astype("uint8")
|
107 |
+
return
|
108 |
+
|
109 |
+
raise ValueError("GenericMask cannot handle object {} of type '{}'".format(m, type(m)))
|
110 |
+
|
111 |
+
@property
|
112 |
+
def mask(self):
|
113 |
+
if self._mask is None:
|
114 |
+
self._mask = self.polygons_to_mask(self._polygons)
|
115 |
+
return self._mask
|
116 |
+
|
117 |
+
@property
|
118 |
+
def polygons(self):
|
119 |
+
if self._polygons is None:
|
120 |
+
self._polygons, self._has_holes = self.mask_to_polygons(self._mask)
|
121 |
+
return self._polygons
|
122 |
+
|
123 |
+
@property
|
124 |
+
def has_holes(self):
|
125 |
+
if self._has_holes is None:
|
126 |
+
if self._mask is not None:
|
127 |
+
self._polygons, self._has_holes = self.mask_to_polygons(self._mask)
|
128 |
+
else:
|
129 |
+
self._has_holes = False # if original format is polygon, does not have holes
|
130 |
+
return self._has_holes
|
131 |
+
|
132 |
+
def mask_to_polygons(self, mask):
|
133 |
+
# cv2.RETR_CCOMP flag retrieves all the contours and arranges them to a 2-level
|
134 |
+
# hierarchy. External contours (boundary) of the object are placed in hierarchy-1.
|
135 |
+
# Internal contours (holes) are placed in hierarchy-2.
|
136 |
+
# cv2.CHAIN_APPROX_NONE flag gets vertices of polygons from contours.
|
137 |
+
mask = np.ascontiguousarray(mask) # some versions of cv2 does not support incontiguous arr
|
138 |
+
res = cv2.findContours(mask.astype("uint8"), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)
|
139 |
+
hierarchy = res[-1]
|
140 |
+
if hierarchy is None: # empty mask
|
141 |
+
return [], False
|
142 |
+
has_holes = (hierarchy.reshape(-1, 4)[:, 3] >= 0).sum() > 0
|
143 |
+
res = res[-2]
|
144 |
+
res = [x.flatten() for x in res]
|
145 |
+
# These coordinates from OpenCV are integers in range [0, W-1 or H-1].
|
146 |
+
# We add 0.5 to turn them into real-value coordinate space. A better solution
|
147 |
+
# would be to first +0.5 and then dilate the returned polygon by 0.5.
|
148 |
+
res = [x + 0.5 for x in res if len(x) >= 6]
|
149 |
+
return res, has_holes
|
150 |
+
|
151 |
+
def polygons_to_mask(self, polygons):
|
152 |
+
rle = mask_util.frPyObjects(polygons, self.height, self.width)
|
153 |
+
rle = mask_util.merge(rle)
|
154 |
+
return mask_util.decode(rle)[:, :]
|
155 |
+
|
156 |
+
def area(self):
|
157 |
+
return self.mask.sum()
|
158 |
+
|
159 |
+
def bbox(self):
|
160 |
+
p = mask_util.frPyObjects(self.polygons, self.height, self.width)
|
161 |
+
p = mask_util.merge(p)
|
162 |
+
bbox = mask_util.toBbox(p)
|
163 |
+
bbox[2] += bbox[0]
|
164 |
+
bbox[3] += bbox[1]
|
165 |
+
return bbox
|
166 |
+
|
167 |
+
|
168 |
+
class _PanopticPrediction:
|
169 |
+
"""
|
170 |
+
Unify different panoptic annotation/prediction formats
|
171 |
+
"""
|
172 |
+
|
173 |
+
def __init__(self, panoptic_seg, segments_info, metadata=None):
|
174 |
+
if segments_info is None:
|
175 |
+
assert metadata is not None
|
176 |
+
# If "segments_info" is None, we assume "panoptic_img" is a
|
177 |
+
# H*W int32 image storing the panoptic_id in the format of
|
178 |
+
# category_id * label_divisor + instance_id. We reserve -1 for
|
179 |
+
# VOID label.
|
180 |
+
label_divisor = metadata.label_divisor
|
181 |
+
segments_info = []
|
182 |
+
for panoptic_label in np.unique(panoptic_seg.numpy()):
|
183 |
+
if panoptic_label == -1:
|
184 |
+
# VOID region.
|
185 |
+
continue
|
186 |
+
pred_class = panoptic_label // label_divisor
|
187 |
+
isthing = pred_class in metadata.thing_dataset_id_to_contiguous_id.values()
|
188 |
+
segments_info.append(
|
189 |
+
{
|
190 |
+
"id": int(panoptic_label),
|
191 |
+
"category_id": int(pred_class),
|
192 |
+
"isthing": bool(isthing),
|
193 |
+
}
|
194 |
+
)
|
195 |
+
del metadata
|
196 |
+
|
197 |
+
self._seg = panoptic_seg
|
198 |
+
|
199 |
+
self._sinfo = {s["id"]: s for s in segments_info} # seg id -> seg info
|
200 |
+
segment_ids, areas = torch.unique(panoptic_seg, sorted=True, return_counts=True)
|
201 |
+
areas = areas.numpy()
|
202 |
+
sorted_idxs = np.argsort(-areas)
|
203 |
+
self._seg_ids, self._seg_areas = segment_ids[sorted_idxs], areas[sorted_idxs]
|
204 |
+
self._seg_ids = self._seg_ids.tolist()
|
205 |
+
for sid, area in zip(self._seg_ids, self._seg_areas):
|
206 |
+
if sid in self._sinfo:
|
207 |
+
self._sinfo[sid]["area"] = float(area)
|
208 |
+
|
209 |
+
def non_empty_mask(self):
|
210 |
+
"""
|
211 |
+
Returns:
|
212 |
+
(H, W) array, a mask for all pixels that have a prediction
|
213 |
+
"""
|
214 |
+
empty_ids = []
|
215 |
+
for id in self._seg_ids:
|
216 |
+
if id not in self._sinfo:
|
217 |
+
empty_ids.append(id)
|
218 |
+
if len(empty_ids) == 0:
|
219 |
+
return np.zeros(self._seg.shape, dtype=np.uint8)
|
220 |
+
assert (
|
221 |
+
len(empty_ids) == 1
|
222 |
+
), ">1 ids corresponds to no labels. This is currently not supported"
|
223 |
+
return (self._seg != empty_ids[0]).numpy().astype(np.bool)
|
224 |
+
|
225 |
+
def semantic_masks(self):
|
226 |
+
for sid in self._seg_ids:
|
227 |
+
sinfo = self._sinfo.get(sid)
|
228 |
+
if sinfo is None or sinfo["isthing"]:
|
229 |
+
# Some pixels (e.g. id 0 in PanopticFPN) have no instance or semantic predictions.
|
230 |
+
continue
|
231 |
+
yield (self._seg == sid).numpy().astype(np.bool), sinfo
|
232 |
+
|
233 |
+
def instance_masks(self):
|
234 |
+
for sid in self._seg_ids:
|
235 |
+
sinfo = self._sinfo.get(sid)
|
236 |
+
if sinfo is None or not sinfo["isthing"]:
|
237 |
+
continue
|
238 |
+
mask = (self._seg == sid).numpy().astype(np.bool)
|
239 |
+
if mask.sum() > 0:
|
240 |
+
yield mask, sinfo
|
241 |
+
|
242 |
+
|
243 |
+
def _create_text_labels(classes, scores, class_names, is_crowd=None):
|
244 |
+
"""
|
245 |
+
Args:
|
246 |
+
classes (list[int] or None):
|
247 |
+
scores (list[float] or None):
|
248 |
+
class_names (list[str] or None):
|
249 |
+
is_crowd (list[bool] or None):
|
250 |
+
Returns:
|
251 |
+
list[str] or None
|
252 |
+
"""
|
253 |
+
labels = None
|
254 |
+
if classes is not None:
|
255 |
+
if class_names is not None and len(class_names) > 0:
|
256 |
+
labels = [class_names[i] for i in classes]
|
257 |
+
else:
|
258 |
+
labels = [str(i) for i in classes]
|
259 |
+
if scores is not None:
|
260 |
+
if labels is None:
|
261 |
+
labels = ["{:.0f}%".format(s * 100) for s in scores]
|
262 |
+
else:
|
263 |
+
labels = ["{} {:.0f}%".format(l, s * 100) for l, s in zip(labels, scores)]
|
264 |
+
if labels is not None and is_crowd is not None:
|
265 |
+
labels = [l + ("|crowd" if crowd else "") for l, crowd in zip(labels, is_crowd)]
|
266 |
+
return labels
|
267 |
+
|
268 |
+
|
269 |
+
class VisImage:
|
270 |
+
def __init__(self, img, scale=1.0):
|
271 |
+
"""
|
272 |
+
Args:
|
273 |
+
img (ndarray): an RGB image of shape (H, W, 3) in range [0, 255].
|
274 |
+
scale (float): scale the input image
|
275 |
+
"""
|
276 |
+
self.img = img
|
277 |
+
self.scale = scale
|
278 |
+
self.width, self.height = img.shape[1], img.shape[0]
|
279 |
+
self._setup_figure(img)
|
280 |
+
|
281 |
+
def _setup_figure(self, img):
|
282 |
+
"""
|
283 |
+
Args:
|
284 |
+
Same as in :meth:`__init__()`.
|
285 |
+
Returns:
|
286 |
+
fig (matplotlib.pyplot.figure): top level container for all the image plot elements.
|
287 |
+
ax (matplotlib.pyplot.Axes): contains figure elements and sets the coordinate system.
|
288 |
+
"""
|
289 |
+
fig = mplfigure.Figure(frameon=False)
|
290 |
+
self.dpi = fig.get_dpi()
|
291 |
+
# add a small 1e-2 to avoid precision lost due to matplotlib's truncation
|
292 |
+
# (https://github.com/matplotlib/matplotlib/issues/15363)
|
293 |
+
fig.set_size_inches(
|
294 |
+
(self.width * self.scale + 1e-2) / self.dpi,
|
295 |
+
(self.height * self.scale + 1e-2) / self.dpi,
|
296 |
+
)
|
297 |
+
self.canvas = FigureCanvasAgg(fig)
|
298 |
+
# self.canvas = mpl.backends.backend_cairo.FigureCanvasCairo(fig)
|
299 |
+
ax = fig.add_axes([0.0, 0.0, 1.0, 1.0])
|
300 |
+
ax.axis("off")
|
301 |
+
self.fig = fig
|
302 |
+
self.ax = ax
|
303 |
+
self.reset_image(img)
|
304 |
+
|
305 |
+
def reset_image(self, img):
|
306 |
+
"""
|
307 |
+
Args:
|
308 |
+
img: same as in __init__
|
309 |
+
"""
|
310 |
+
img = img.astype("uint8")
|
311 |
+
self.ax.imshow(img, extent=(0, self.width, self.height, 0), interpolation="nearest")
|
312 |
+
|
313 |
+
def save(self, filepath):
|
314 |
+
"""
|
315 |
+
Args:
|
316 |
+
filepath (str): a string that contains the absolute path, including the file name, where
|
317 |
+
the visualized image will be saved.
|
318 |
+
"""
|
319 |
+
self.fig.savefig(filepath)
|
320 |
+
|
321 |
+
def get_image(self):
|
322 |
+
"""
|
323 |
+
Returns:
|
324 |
+
ndarray:
|
325 |
+
the visualized image of shape (H, W, 3) (RGB) in uint8 type.
|
326 |
+
The shape is scaled w.r.t the input image using the given `scale` argument.
|
327 |
+
"""
|
328 |
+
canvas = self.canvas
|
329 |
+
s, (width, height) = canvas.print_to_buffer()
|
330 |
+
# buf = io.BytesIO() # works for cairo backend
|
331 |
+
# canvas.print_rgba(buf)
|
332 |
+
# width, height = self.width, self.height
|
333 |
+
# s = buf.getvalue()
|
334 |
+
|
335 |
+
buffer = np.frombuffer(s, dtype="uint8")
|
336 |
+
|
337 |
+
img_rgba = buffer.reshape(height, width, 4)
|
338 |
+
rgb, alpha = np.split(img_rgba, [3], axis=2)
|
339 |
+
return rgb.astype("uint8")
|
340 |
+
|
341 |
+
|
342 |
+
class Visualizer:
|
343 |
+
"""
|
344 |
+
Visualizer that draws data about detection/segmentation on images.
|
345 |
+
It contains methods like `draw_{text,box,circle,line,binary_mask,polygon}`
|
346 |
+
that draw primitive objects to images, as well as high-level wrappers like
|
347 |
+
`draw_{instance_predictions,sem_seg,panoptic_seg_predictions,dataset_dict}`
|
348 |
+
that draw composite data in some pre-defined style.
|
349 |
+
Note that the exact visualization style for the high-level wrappers are subject to change.
|
350 |
+
Style such as color, opacity, label contents, visibility of labels, or even the visibility
|
351 |
+
of objects themselves (e.g. when the object is too small) may change according
|
352 |
+
to different heuristics, as long as the results still look visually reasonable.
|
353 |
+
To obtain a consistent style, you can implement custom drawing functions with the
|
354 |
+
abovementioned primitive methods instead. If you need more customized visualization
|
355 |
+
styles, you can process the data yourself following their format documented in
|
356 |
+
tutorials (:doc:`/tutorials/models`, :doc:`/tutorials/datasets`). This class does not
|
357 |
+
intend to satisfy everyone's preference on drawing styles.
|
358 |
+
This visualizer focuses on high rendering quality rather than performance. It is not
|
359 |
+
designed to be used for real-time applications.
|
360 |
+
"""
|
361 |
+
|
362 |
+
# TODO implement a fast, rasterized version using OpenCV
|
363 |
+
|
364 |
+
def __init__(self, img_rgb, is_img=True, metadata=None, scale=1.0, instance_mode=ColorMode.IMAGE):
|
365 |
+
"""
|
366 |
+
Args:
|
367 |
+
img_rgb: a numpy array of shape (H, W, C), where H and W correspond to
|
368 |
+
the height and width of the image respectively. C is the number of
|
369 |
+
color channels. The image is required to be in RGB format since that
|
370 |
+
is a requirement of the Matplotlib library. The image is also expected
|
371 |
+
to be in the range [0, 255].
|
372 |
+
metadata (Metadata): dataset metadata (e.g. class names and colors)
|
373 |
+
instance_mode (ColorMode): defines one of the pre-defined style for drawing
|
374 |
+
instances on an image.
|
375 |
+
"""
|
376 |
+
if is_img:
|
377 |
+
self.img = np.asarray(img_rgb).clip(0, 255).astype(np.uint8)
|
378 |
+
else:
|
379 |
+
self.img = np.zeros_like(img_rgb).clip(0, 255).astype(np.uint8)
|
380 |
+
if metadata is None:
|
381 |
+
metadata = MetadataCatalog.get("__nonexist__")
|
382 |
+
self.metadata = metadata
|
383 |
+
self.output = VisImage(self.img, scale=scale)
|
384 |
+
self.cpu_device = torch.device("cpu")
|
385 |
+
|
386 |
+
# too small texts are useless, therefore clamp to 9
|
387 |
+
self._default_font_size = max(
|
388 |
+
np.sqrt(self.output.height * self.output.width) // 90, 10 // scale
|
389 |
+
)
|
390 |
+
self._instance_mode = instance_mode
|
391 |
+
self.keypoint_threshold = _KEYPOINT_THRESHOLD
|
392 |
+
|
393 |
+
def get_image(self, img):
|
394 |
+
img = np.asarray(img).clip(0, 255).astype(np.uint8)
|
395 |
+
return VisImage(img, scale=1.0)
|
396 |
+
|
397 |
+
def draw_box_predictions(
|
398 |
+
self,
|
399 |
+
boxes=None,
|
400 |
+
labels=None,
|
401 |
+
scores=None,
|
402 |
+
assigned_colors=None
|
403 |
+
):
|
404 |
+
"""
|
405 |
+
Args:
|
406 |
+
boxes (Boxes, RotatedBoxes or ndarray): either a :class:`Boxes`,
|
407 |
+
or an Nx4 numpy array of XYXY_ABS format for the N objects in a single image,
|
408 |
+
or a :class:`RotatedBoxes`,
|
409 |
+
or an Nx5 numpy array of (x_center, y_center, width, height, angle_degrees) format
|
410 |
+
for the N objects in a single image,
|
411 |
+
labels (list[str]): the text to be displayed for each instance.
|
412 |
+
assigned_colors (list[matplotlib.colors]): a list of colors, where each color
|
413 |
+
corresponds to each mask or box in the image. Refer to 'matplotlib.colors'
|
414 |
+
for full list of formats that the colors are accepted in.
|
415 |
+
Returns:
|
416 |
+
output (VisImage): image object with visualizations.
|
417 |
+
"""
|
418 |
+
num_instances = 0
|
419 |
+
boxes = self._convert_boxes(boxes)
|
420 |
+
classes = labels.tolist()
|
421 |
+
scores = scores.tolist()
|
422 |
+
labels = _create_text_labels(classes, scores, self.metadata.get("stuff_classes", None))
|
423 |
+
num_instances = len(boxes)
|
424 |
+
assert len(labels) == num_instances
|
425 |
+
if assigned_colors is None:
|
426 |
+
# assigned_colors = [random_color(rgb=True, maximum=1) for _ in range(num_instances)]
|
427 |
+
assigned_colors = [instance_color(rgb=True, idx=i, maximum=1) for i in range(num_instances)]
|
428 |
+
if num_instances == 0:
|
429 |
+
return self.output
|
430 |
+
|
431 |
+
# Display in largest to smallest order to reduce occlusion.
|
432 |
+
areas = None
|
433 |
+
areas = np.prod(boxes[:, 2:] - boxes[:, :2], axis=1)
|
434 |
+
|
435 |
+
if areas is not None:
|
436 |
+
sorted_idxs = np.argsort(-areas).tolist()
|
437 |
+
# Re-order overlapped instances in descending order.
|
438 |
+
boxes = boxes[sorted_idxs] if boxes is not None else None
|
439 |
+
labels = [labels[k] for k in sorted_idxs] if labels is not None else None
|
440 |
+
assigned_colors = [assigned_colors[idx] for idx in sorted_idxs]
|
441 |
+
|
442 |
+
for i in range(num_instances):
|
443 |
+
color = assigned_colors[i]
|
444 |
+
if boxes is not None:
|
445 |
+
self.draw_box(boxes[i], edge_color=color)
|
446 |
+
|
447 |
+
if labels is not None:
|
448 |
+
# first get a box
|
449 |
+
if boxes is not None:
|
450 |
+
x0, y0, x1, y1 = boxes[i]
|
451 |
+
text_pos = (x0, y0) # if drawing boxes, put text on the box corner.
|
452 |
+
horiz_align = "left"
|
453 |
+
else:
|
454 |
+
continue # drawing the box confidence for keypoints isn't very useful.
|
455 |
+
# for small objects, draw text at the side to avoid occlusion
|
456 |
+
instance_area = (y1 - y0) * (x1 - x0)
|
457 |
+
if (
|
458 |
+
instance_area < _SMALL_OBJECT_AREA_THRESH * self.output.scale
|
459 |
+
or y1 - y0 < 40 * self.output.scale
|
460 |
+
):
|
461 |
+
if y1 >= self.output.height - 5:
|
462 |
+
text_pos = (x1, y0)
|
463 |
+
else:
|
464 |
+
text_pos = (x0, y1)
|
465 |
+
|
466 |
+
height_ratio = (y1 - y0) / np.sqrt(self.output.height * self.output.width)
|
467 |
+
lighter_color = self._change_color_brightness(color, brightness_factor=0.7)
|
468 |
+
font_size = (
|
469 |
+
np.clip((height_ratio - 0.02) / 0.08 + 1, 1.2, 2)
|
470 |
+
* 0.5
|
471 |
+
* self._default_font_size
|
472 |
+
)
|
473 |
+
self.draw_text(
|
474 |
+
labels[i],
|
475 |
+
text_pos,
|
476 |
+
color=lighter_color,
|
477 |
+
horizontal_alignment=horiz_align,
|
478 |
+
font_size=font_size,
|
479 |
+
)
|
480 |
+
|
481 |
+
return self.output
|
482 |
+
|
483 |
+
|
484 |
+
def draw_instance_predictions(self, predictions, alpha=0.8, is_text=True):
|
485 |
+
"""
|
486 |
+
Draw instance-level prediction results on an image.
|
487 |
+
Args:
|
488 |
+
predictions (Instances): the output of an instance detection/segmentation
|
489 |
+
model. Following fields will be used to draw:
|
490 |
+
"pred_boxes", "pred_classes", "scores", "pred_masks" (or "pred_masks_rle").
|
491 |
+
Returns:
|
492 |
+
output (VisImage): image object with visualizations.
|
493 |
+
"""
|
494 |
+
boxes = predictions.pred_boxes if predictions.has("pred_boxes") else None
|
495 |
+
scores = predictions.scores if predictions.has("scores") else None
|
496 |
+
classes = predictions.pred_classes.tolist() if predictions.has("pred_classes") else None
|
497 |
+
labels = _create_text_labels(classes, scores, self.metadata.get("stuff_classes", None))
|
498 |
+
keypoints = predictions.pred_keypoints if predictions.has("pred_keypoints") else None
|
499 |
+
|
500 |
+
if predictions.has("pred_masks"):
|
501 |
+
masks = np.asarray(predictions.pred_masks)
|
502 |
+
masks = [GenericMask(x, self.output.height, self.output.width) for x in masks]
|
503 |
+
else:
|
504 |
+
masks = None
|
505 |
+
|
506 |
+
if self._instance_mode == ColorMode.SEGMENTATION and self.metadata.get("stuff_colors"):
|
507 |
+
# colors = [
|
508 |
+
# self._jitter([x / 255 for x in self.metadata.thing_colors[c]]) for c in classes
|
509 |
+
# ]
|
510 |
+
colors = [
|
511 |
+
instance_color(rgb=True, idx=c, maximum=1) for c in classes
|
512 |
+
]
|
513 |
+
else:
|
514 |
+
colors = None
|
515 |
+
|
516 |
+
if self._instance_mode == ColorMode.IMAGE_BW:
|
517 |
+
self.output.reset_image(
|
518 |
+
self._create_grayscale_image(
|
519 |
+
(predictions.pred_masks.any(dim=0) > 0).numpy()
|
520 |
+
if predictions.has("pred_masks")
|
521 |
+
else None
|
522 |
+
)
|
523 |
+
)
|
524 |
+
|
525 |
+
self.overlay_instances(
|
526 |
+
masks=masks,
|
527 |
+
boxes=boxes,
|
528 |
+
labels=labels,
|
529 |
+
keypoints=keypoints,
|
530 |
+
assigned_colors=colors,
|
531 |
+
alpha=alpha,
|
532 |
+
is_text=is_text,
|
533 |
+
)
|
534 |
+
return self.output
|
535 |
+
|
536 |
+
def draw_sem_seg(self, sem_seg, area_threshold=None, alpha=0.8, is_text=True):
|
537 |
+
"""
|
538 |
+
Draw semantic segmentation predictions/labels.
|
539 |
+
Args:
|
540 |
+
sem_seg (Tensor or ndarray): the segmentation of shape (H, W).
|
541 |
+
Each value is the integer label of the pixel.
|
542 |
+
area_threshold (int): segments with less than `area_threshold` are not drawn.
|
543 |
+
alpha (float): the larger it is, the more opaque the segmentations are.
|
544 |
+
Returns:
|
545 |
+
output (VisImage): image object with visualizations.
|
546 |
+
"""
|
547 |
+
if isinstance(sem_seg, torch.Tensor):
|
548 |
+
sem_seg = sem_seg.numpy()
|
549 |
+
labels, areas = np.unique(sem_seg, return_counts=True)
|
550 |
+
sorted_idxs = np.argsort(-areas).tolist()
|
551 |
+
labels = labels[sorted_idxs]
|
552 |
+
for label in filter(lambda l: l < len(self.metadata.stuff_classes), labels):
|
553 |
+
try:
|
554 |
+
mask_color = [x / 255 for x in self.metadata.stuff_colors[label]]
|
555 |
+
except (AttributeError, IndexError):
|
556 |
+
mask_color = None
|
557 |
+
|
558 |
+
binary_mask = (sem_seg == label).astype(np.uint8)
|
559 |
+
text = self.metadata.stuff_classes[label]
|
560 |
+
self.draw_binary_mask(
|
561 |
+
binary_mask,
|
562 |
+
color=mask_color,
|
563 |
+
edge_color=_OFF_WHITE,
|
564 |
+
text=text,
|
565 |
+
alpha=alpha,
|
566 |
+
area_threshold=area_threshold,
|
567 |
+
is_text=is_text,
|
568 |
+
)
|
569 |
+
return self.output
|
570 |
+
|
571 |
+
def draw_panoptic_seg(self, panoptic_seg, segments_info, area_threshold=None, alpha=0.7, is_text=True,):
|
572 |
+
"""
|
573 |
+
Draw panoptic prediction annotations or results.
|
574 |
+
Args:
|
575 |
+
panoptic_seg (Tensor): of shape (height, width) where the values are ids for each
|
576 |
+
segment.
|
577 |
+
segments_info (list[dict] or None): Describe each segment in `panoptic_seg`.
|
578 |
+
If it is a ``list[dict]``, each dict contains keys "id", "category_id".
|
579 |
+
If None, category id of each pixel is computed by
|
580 |
+
``pixel // metadata.label_divisor``.
|
581 |
+
area_threshold (int): stuff segments with less than `area_threshold` are not drawn.
|
582 |
+
Returns:
|
583 |
+
output (VisImage): image object with visualizations.
|
584 |
+
"""
|
585 |
+
pred = _PanopticPrediction(panoptic_seg, segments_info, self.metadata)
|
586 |
+
|
587 |
+
if self._instance_mode == ColorMode.IMAGE_BW:
|
588 |
+
self.output.reset_image(self._create_grayscale_image(pred.non_empty_mask()))
|
589 |
+
|
590 |
+
# draw mask for all semantic segments first i.e. "stuff"
|
591 |
+
for mask, sinfo in pred.semantic_masks():
|
592 |
+
category_idx = sinfo["category_id"]
|
593 |
+
try:
|
594 |
+
mask_color = [x / 255 for x in self.metadata.stuff_colors[category_idx]]
|
595 |
+
except AttributeError:
|
596 |
+
mask_color = None
|
597 |
+
|
598 |
+
text = self.metadata.stuff_classes[category_idx]
|
599 |
+
self.draw_binary_mask(
|
600 |
+
mask,
|
601 |
+
color=mask_color,
|
602 |
+
edge_color=_OFF_WHITE,
|
603 |
+
text=text,
|
604 |
+
alpha=alpha,
|
605 |
+
area_threshold=area_threshold,
|
606 |
+
is_text=is_text,
|
607 |
+
)
|
608 |
+
|
609 |
+
# draw mask for all instances second
|
610 |
+
all_instances = list(pred.instance_masks())
|
611 |
+
if len(all_instances) == 0:
|
612 |
+
return self.output
|
613 |
+
masks, sinfo = list(zip(*all_instances))
|
614 |
+
category_ids = [x["category_id"] for x in sinfo]
|
615 |
+
|
616 |
+
try:
|
617 |
+
scores = [x["score"] for x in sinfo]
|
618 |
+
except KeyError:
|
619 |
+
scores = None
|
620 |
+
labels = _create_text_labels(
|
621 |
+
category_ids, scores, self.metadata.stuff_classes, [x.get("iscrowd", 0) for x in sinfo]
|
622 |
+
)
|
623 |
+
|
624 |
+
try:
|
625 |
+
colors = [
|
626 |
+
self._jitter([x / 255 for x in self.metadata.stuff_colors[c]]) for c in category_ids
|
627 |
+
]
|
628 |
+
except AttributeError:
|
629 |
+
colors = None
|
630 |
+
self.overlay_instances(masks=masks, labels=labels, assigned_colors=colors, alpha=alpha, is_text=is_text)
|
631 |
+
|
632 |
+
return self.output
|
633 |
+
|
634 |
+
draw_panoptic_seg_predictions = draw_panoptic_seg # backward compatibility
|
635 |
+
|
636 |
+
def draw_dataset_dict(self, dic):
|
637 |
+
"""
|
638 |
+
Draw annotations/segmentaions in Detectron2 Dataset format.
|
639 |
+
Args:
|
640 |
+
dic (dict): annotation/segmentation data of one image, in Detectron2 Dataset format.
|
641 |
+
Returns:
|
642 |
+
output (VisImage): image object with visualizations.
|
643 |
+
"""
|
644 |
+
annos = dic.get("annotations", None)
|
645 |
+
if annos:
|
646 |
+
if "segmentation" in annos[0]:
|
647 |
+
masks = [x["segmentation"] for x in annos]
|
648 |
+
else:
|
649 |
+
masks = None
|
650 |
+
if "keypoints" in annos[0]:
|
651 |
+
keypts = [x["keypoints"] for x in annos]
|
652 |
+
keypts = np.array(keypts).reshape(len(annos), -1, 3)
|
653 |
+
else:
|
654 |
+
keypts = None
|
655 |
+
|
656 |
+
boxes = [
|
657 |
+
BoxMode.convert(x["bbox"], x["bbox_mode"], BoxMode.XYXY_ABS)
|
658 |
+
if len(x["bbox"]) == 4
|
659 |
+
else x["bbox"]
|
660 |
+
for x in annos
|
661 |
+
]
|
662 |
+
|
663 |
+
colors = None
|
664 |
+
category_ids = [x["category_id"] for x in annos]
|
665 |
+
if self._instance_mode == ColorMode.SEGMENTATION and self.metadata.get("stuff_colors"):
|
666 |
+
colors = [
|
667 |
+
self._jitter([x / 255 for x in self.metadata.stuff_colors[c]])
|
668 |
+
for c in category_ids
|
669 |
+
]
|
670 |
+
names = self.metadata.get("stuff_classes", None)
|
671 |
+
labels = _create_text_labels(
|
672 |
+
category_ids,
|
673 |
+
scores=None,
|
674 |
+
class_names=names,
|
675 |
+
is_crowd=[x.get("iscrowd", 0) for x in annos],
|
676 |
+
)
|
677 |
+
self.overlay_instances(
|
678 |
+
labels=labels, boxes=boxes, masks=masks, keypoints=keypts, assigned_colors=colors
|
679 |
+
)
|
680 |
+
|
681 |
+
sem_seg = dic.get("sem_seg", None)
|
682 |
+
if sem_seg is None and "sem_seg_file_name" in dic:
|
683 |
+
with PathManager.open(dic["sem_seg_file_name"], "rb") as f:
|
684 |
+
sem_seg = Image.open(f)
|
685 |
+
sem_seg = np.asarray(sem_seg, dtype="uint8")
|
686 |
+
if sem_seg is not None:
|
687 |
+
self.draw_sem_seg(sem_seg, area_threshold=0, alpha=0.5)
|
688 |
+
|
689 |
+
pan_seg = dic.get("pan_seg", None)
|
690 |
+
if pan_seg is None and "pan_seg_file_name" in dic:
|
691 |
+
with PathManager.open(dic["pan_seg_file_name"], "rb") as f:
|
692 |
+
pan_seg = Image.open(f)
|
693 |
+
pan_seg = np.asarray(pan_seg)
|
694 |
+
from panopticapi.utils import rgb2id
|
695 |
+
|
696 |
+
pan_seg = rgb2id(pan_seg)
|
697 |
+
if pan_seg is not None:
|
698 |
+
segments_info = dic["segments_info"]
|
699 |
+
pan_seg = torch.tensor(pan_seg)
|
700 |
+
self.draw_panoptic_seg(pan_seg, segments_info, area_threshold=0, alpha=0.5)
|
701 |
+
return self.output
|
702 |
+
|
703 |
+
def overlay_instances(
|
704 |
+
self,
|
705 |
+
*,
|
706 |
+
boxes=None,
|
707 |
+
labels=None,
|
708 |
+
masks=None,
|
709 |
+
keypoints=None,
|
710 |
+
assigned_colors=None,
|
711 |
+
alpha=0.5,
|
712 |
+
is_text=True,
|
713 |
+
):
|
714 |
+
"""
|
715 |
+
Args:
|
716 |
+
boxes (Boxes, RotatedBoxes or ndarray): either a :class:`Boxes`,
|
717 |
+
or an Nx4 numpy array of XYXY_ABS format for the N objects in a single image,
|
718 |
+
or a :class:`RotatedBoxes`,
|
719 |
+
or an Nx5 numpy array of (x_center, y_center, width, height, angle_degrees) format
|
720 |
+
for the N objects in a single image,
|
721 |
+
labels (list[str]): the text to be displayed for each instance.
|
722 |
+
masks (masks-like object): Supported types are:
|
723 |
+
* :class:`detectron2.structures.PolygonMasks`,
|
724 |
+
:class:`detectron2.structures.BitMasks`.
|
725 |
+
* list[list[ndarray]]: contains the segmentation masks for all objects in one image.
|
726 |
+
The first level of the list corresponds to individual instances. The second
|
727 |
+
level to all the polygon that compose the instance, and the third level
|
728 |
+
to the polygon coordinates. The third level should have the format of
|
729 |
+
[x0, y0, x1, y1, ..., xn, yn] (n >= 3).
|
730 |
+
* list[ndarray]: each ndarray is a binary mask of shape (H, W).
|
731 |
+
* list[dict]: each dict is a COCO-style RLE.
|
732 |
+
keypoints (Keypoint or array like): an array-like object of shape (N, K, 3),
|
733 |
+
where the N is the number of instances and K is the number of keypoints.
|
734 |
+
The last dimension corresponds to (x, y, visibility or score).
|
735 |
+
assigned_colors (list[matplotlib.colors]): a list of colors, where each color
|
736 |
+
corresponds to each mask or box in the image. Refer to 'matplotlib.colors'
|
737 |
+
for full list of formats that the colors are accepted in.
|
738 |
+
Returns:
|
739 |
+
output (VisImage): image object with visualizations.
|
740 |
+
"""
|
741 |
+
num_instances = 0
|
742 |
+
if boxes is not None:
|
743 |
+
boxes = self._convert_boxes(boxes)
|
744 |
+
num_instances = len(boxes)
|
745 |
+
if masks is not None:
|
746 |
+
masks = self._convert_masks(masks)
|
747 |
+
if num_instances:
|
748 |
+
assert len(masks) == num_instances
|
749 |
+
else:
|
750 |
+
num_instances = len(masks)
|
751 |
+
if keypoints is not None:
|
752 |
+
if num_instances:
|
753 |
+
assert len(keypoints) == num_instances
|
754 |
+
else:
|
755 |
+
num_instances = len(keypoints)
|
756 |
+
keypoints = self._convert_keypoints(keypoints)
|
757 |
+
if labels is not None:
|
758 |
+
assert len(labels) == num_instances
|
759 |
+
if assigned_colors is None:
|
760 |
+
# assigned_colors = [random_color(rgb=True, maximum=1) for _ in range(num_instances)]
|
761 |
+
assigned_colors = [instance_color(rgb=True, idx=i, maximum=1) for i in range(num_instances)]
|
762 |
+
if num_instances == 0:
|
763 |
+
return self.output
|
764 |
+
if boxes is not None and boxes.shape[1] == 5:
|
765 |
+
return self.overlay_rotated_instances(
|
766 |
+
boxes=boxes, labels=labels, assigned_colors=assigned_colors
|
767 |
+
)
|
768 |
+
|
769 |
+
# Display in largest to smallest order to reduce occlusion.
|
770 |
+
areas = None
|
771 |
+
if boxes is not None:
|
772 |
+
areas = np.prod(boxes[:, 2:] - boxes[:, :2], axis=1)
|
773 |
+
elif masks is not None:
|
774 |
+
areas = np.asarray([x.area() for x in masks])
|
775 |
+
|
776 |
+
if areas is not None:
|
777 |
+
sorted_idxs = np.argsort(-areas).tolist()
|
778 |
+
# Re-order overlapped instances in descending order.
|
779 |
+
boxes = boxes[sorted_idxs] if boxes is not None else None
|
780 |
+
labels = [labels[k] for k in sorted_idxs] if labels is not None else None
|
781 |
+
masks = [masks[idx] for idx in sorted_idxs] if masks is not None else None
|
782 |
+
assigned_colors = [assigned_colors[idx] for idx in sorted_idxs]
|
783 |
+
keypoints = keypoints[sorted_idxs] if keypoints is not None else None
|
784 |
+
|
785 |
+
for i in range(num_instances):
|
786 |
+
color = assigned_colors[i]
|
787 |
+
if boxes is not None:
|
788 |
+
self.draw_box(boxes[i], edge_color=color)
|
789 |
+
|
790 |
+
if masks is not None:
|
791 |
+
for segment in masks[i].polygons:
|
792 |
+
self.draw_polygon(segment.reshape(-1, 2), color, alpha=alpha)
|
793 |
+
|
794 |
+
if labels is not None:
|
795 |
+
# first get a box
|
796 |
+
if boxes is not None:
|
797 |
+
x0, y0, x1, y1 = boxes[i]
|
798 |
+
text_pos = (x0, y0) # if drawing boxes, put text on the box corner.
|
799 |
+
horiz_align = "left"
|
800 |
+
elif masks is not None:
|
801 |
+
# skip small mask without polygon
|
802 |
+
if len(masks[i].polygons) == 0:
|
803 |
+
continue
|
804 |
+
|
805 |
+
x0, y0, x1, y1 = masks[i].bbox()
|
806 |
+
|
807 |
+
# draw text in the center (defined by median) when box is not drawn
|
808 |
+
# median is less sensitive to outliers.
|
809 |
+
text_pos = np.median(masks[i].mask.nonzero(), axis=1)[::-1]
|
810 |
+
horiz_align = "center"
|
811 |
+
else:
|
812 |
+
continue # drawing the box confidence for keypoints isn't very useful.
|
813 |
+
# for small objects, draw text at the side to avoid occlusion
|
814 |
+
instance_area = (y1 - y0) * (x1 - x0)
|
815 |
+
if (
|
816 |
+
instance_area < _SMALL_OBJECT_AREA_THRESH * self.output.scale
|
817 |
+
or y1 - y0 < 40 * self.output.scale
|
818 |
+
):
|
819 |
+
if y1 >= self.output.height - 5:
|
820 |
+
text_pos = (x1, y0)
|
821 |
+
else:
|
822 |
+
text_pos = (x0, y1)
|
823 |
+
|
824 |
+
height_ratio = (y1 - y0) / np.sqrt(self.output.height * self.output.width)
|
825 |
+
lighter_color = self._change_color_brightness(color, brightness_factor=0.7)
|
826 |
+
font_size = (
|
827 |
+
np.clip((height_ratio - 0.02) / 0.08 + 1, 1.2, 2)
|
828 |
+
* 0.5
|
829 |
+
* self._default_font_size
|
830 |
+
)
|
831 |
+
if is_text:
|
832 |
+
self.draw_text(
|
833 |
+
labels[i],
|
834 |
+
text_pos,
|
835 |
+
color=lighter_color,
|
836 |
+
horizontal_alignment=horiz_align,
|
837 |
+
font_size=font_size,
|
838 |
+
)
|
839 |
+
|
840 |
+
# draw keypoints
|
841 |
+
if keypoints is not None:
|
842 |
+
for keypoints_per_instance in keypoints:
|
843 |
+
self.draw_and_connect_keypoints(keypoints_per_instance)
|
844 |
+
|
845 |
+
return self.output
|
846 |
+
|
847 |
+
def overlay_rotated_instances(self, boxes=None, labels=None, assigned_colors=None):
|
848 |
+
"""
|
849 |
+
Args:
|
850 |
+
boxes (ndarray): an Nx5 numpy array of
|
851 |
+
(x_center, y_center, width, height, angle_degrees) format
|
852 |
+
for the N objects in a single image.
|
853 |
+
labels (list[str]): the text to be displayed for each instance.
|
854 |
+
assigned_colors (list[matplotlib.colors]): a list of colors, where each color
|
855 |
+
corresponds to each mask or box in the image. Refer to 'matplotlib.colors'
|
856 |
+
for full list of formats that the colors are accepted in.
|
857 |
+
Returns:
|
858 |
+
output (VisImage): image object with visualizations.
|
859 |
+
"""
|
860 |
+
num_instances = len(boxes)
|
861 |
+
|
862 |
+
if assigned_colors is None:
|
863 |
+
# assigned_colors = [random_color(rgb=True, maximum=1) for _ in range(num_instances)]
|
864 |
+
assigned_colors = [instance_color(rgb=True, idx=i, maximum=1) for i in range(num_instances)]
|
865 |
+
if num_instances == 0:
|
866 |
+
return self.output
|
867 |
+
|
868 |
+
# Display in largest to smallest order to reduce occlusion.
|
869 |
+
if boxes is not None:
|
870 |
+
areas = boxes[:, 2] * boxes[:, 3]
|
871 |
+
|
872 |
+
sorted_idxs = np.argsort(-areas).tolist()
|
873 |
+
# Re-order overlapped instances in descending order.
|
874 |
+
boxes = boxes[sorted_idxs]
|
875 |
+
labels = [labels[k] for k in sorted_idxs] if labels is not None else None
|
876 |
+
colors = [assigned_colors[idx] for idx in sorted_idxs]
|
877 |
+
|
878 |
+
for i in range(num_instances):
|
879 |
+
self.draw_rotated_box_with_label(
|
880 |
+
boxes[i], edge_color=colors[i], label=labels[i] if labels is not None else None
|
881 |
+
)
|
882 |
+
|
883 |
+
return self.output
|
884 |
+
|
885 |
+
def draw_and_connect_keypoints(self, keypoints):
|
886 |
+
"""
|
887 |
+
Draws keypoints of an instance and follows the rules for keypoint connections
|
888 |
+
to draw lines between appropriate keypoints. This follows color heuristics for
|
889 |
+
line color.
|
890 |
+
Args:
|
891 |
+
keypoints (Tensor): a tensor of shape (K, 3), where K is the number of keypoints
|
892 |
+
and the last dimension corresponds to (x, y, probability).
|
893 |
+
Returns:
|
894 |
+
output (VisImage): image object with visualizations.
|
895 |
+
"""
|
896 |
+
visible = {}
|
897 |
+
keypoint_names = self.metadata.get("keypoint_names")
|
898 |
+
for idx, keypoint in enumerate(keypoints):
|
899 |
+
|
900 |
+
# draw keypoint
|
901 |
+
x, y, prob = keypoint
|
902 |
+
if prob > self.keypoint_threshold:
|
903 |
+
self.draw_circle((x, y), color=_RED)
|
904 |
+
if keypoint_names:
|
905 |
+
keypoint_name = keypoint_names[idx]
|
906 |
+
visible[keypoint_name] = (x, y)
|
907 |
+
|
908 |
+
if self.metadata.get("keypoint_connection_rules"):
|
909 |
+
for kp0, kp1, color in self.metadata.keypoint_connection_rules:
|
910 |
+
if kp0 in visible and kp1 in visible:
|
911 |
+
x0, y0 = visible[kp0]
|
912 |
+
x1, y1 = visible[kp1]
|
913 |
+
color = tuple(x / 255.0 for x in color)
|
914 |
+
self.draw_line([x0, x1], [y0, y1], color=color)
|
915 |
+
|
916 |
+
# draw lines from nose to mid-shoulder and mid-shoulder to mid-hip
|
917 |
+
# Note that this strategy is specific to person keypoints.
|
918 |
+
# For other keypoints, it should just do nothing
|
919 |
+
try:
|
920 |
+
ls_x, ls_y = visible["left_shoulder"]
|
921 |
+
rs_x, rs_y = visible["right_shoulder"]
|
922 |
+
mid_shoulder_x, mid_shoulder_y = (ls_x + rs_x) / 2, (ls_y + rs_y) / 2
|
923 |
+
except KeyError:
|
924 |
+
pass
|
925 |
+
else:
|
926 |
+
# draw line from nose to mid-shoulder
|
927 |
+
nose_x, nose_y = visible.get("nose", (None, None))
|
928 |
+
if nose_x is not None:
|
929 |
+
self.draw_line([nose_x, mid_shoulder_x], [nose_y, mid_shoulder_y], color=_RED)
|
930 |
+
|
931 |
+
try:
|
932 |
+
# draw line from mid-shoulder to mid-hip
|
933 |
+
lh_x, lh_y = visible["left_hip"]
|
934 |
+
rh_x, rh_y = visible["right_hip"]
|
935 |
+
except KeyError:
|
936 |
+
pass
|
937 |
+
else:
|
938 |
+
mid_hip_x, mid_hip_y = (lh_x + rh_x) / 2, (lh_y + rh_y) / 2
|
939 |
+
self.draw_line([mid_hip_x, mid_shoulder_x], [mid_hip_y, mid_shoulder_y], color=_RED)
|
940 |
+
return self.output
|
941 |
+
|
942 |
+
"""
|
943 |
+
Primitive drawing functions:
|
944 |
+
"""
|
945 |
+
|
946 |
+
def draw_text(
|
947 |
+
self,
|
948 |
+
text,
|
949 |
+
position,
|
950 |
+
*,
|
951 |
+
font_size=None,
|
952 |
+
color="g",
|
953 |
+
horizontal_alignment="center",
|
954 |
+
rotation=0,
|
955 |
+
):
|
956 |
+
"""
|
957 |
+
Args:
|
958 |
+
text (str): class label
|
959 |
+
position (tuple): a tuple of the x and y coordinates to place text on image.
|
960 |
+
font_size (int, optional): font of the text. If not provided, a font size
|
961 |
+
proportional to the image width is calculated and used.
|
962 |
+
color: color of the text. Refer to `matplotlib.colors` for full list
|
963 |
+
of formats that are accepted.
|
964 |
+
horizontal_alignment (str): see `matplotlib.text.Text`
|
965 |
+
rotation: rotation angle in degrees CCW
|
966 |
+
Returns:
|
967 |
+
output (VisImage): image object with text drawn.
|
968 |
+
"""
|
969 |
+
if not font_size:
|
970 |
+
font_size = self._default_font_size
|
971 |
+
|
972 |
+
# since the text background is dark, we don't want the text to be dark
|
973 |
+
color = np.maximum(list(mplc.to_rgb(color)), 0.2)
|
974 |
+
color[np.argmax(color)] = max(0.8, np.max(color))
|
975 |
+
|
976 |
+
x, y = position
|
977 |
+
self.output.ax.text(
|
978 |
+
x,
|
979 |
+
y,
|
980 |
+
text,
|
981 |
+
size=font_size * self.output.scale,
|
982 |
+
family="sans-serif",
|
983 |
+
bbox={"facecolor": "black", "alpha": 0.8, "pad": 0.7, "edgecolor": "none"},
|
984 |
+
verticalalignment="top",
|
985 |
+
horizontalalignment=horizontal_alignment,
|
986 |
+
color=color,
|
987 |
+
zorder=10,
|
988 |
+
rotation=rotation,
|
989 |
+
)
|
990 |
+
return self.output
|
991 |
+
|
992 |
+
def draw_box(self, box_coord, alpha=1.0, edge_color="g", line_style="-"):
|
993 |
+
"""
|
994 |
+
Args:
|
995 |
+
box_coord (tuple): a tuple containing x0, y0, x1, y1 coordinates, where x0 and y0
|
996 |
+
are the coordinates of the image's top left corner. x1 and y1 are the
|
997 |
+
coordinates of the image's bottom right corner.
|
998 |
+
alpha (float): blending efficient. Smaller values lead to more transparent masks.
|
999 |
+
edge_color: color of the outline of the box. Refer to `matplotlib.colors`
|
1000 |
+
for full list of formats that are accepted.
|
1001 |
+
line_style (string): the string to use to create the outline of the boxes.
|
1002 |
+
Returns:
|
1003 |
+
output (VisImage): image object with box drawn.
|
1004 |
+
"""
|
1005 |
+
x0, y0, x1, y1 = box_coord
|
1006 |
+
width = x1 - x0
|
1007 |
+
height = y1 - y0
|
1008 |
+
|
1009 |
+
linewidth = 2
|
1010 |
+
|
1011 |
+
self.output.ax.add_patch(
|
1012 |
+
mpl.patches.Rectangle(
|
1013 |
+
(x0, y0),
|
1014 |
+
width,
|
1015 |
+
height,
|
1016 |
+
fill=False,
|
1017 |
+
edgecolor=edge_color,
|
1018 |
+
linewidth=linewidth * self.output.scale,
|
1019 |
+
alpha=alpha,
|
1020 |
+
linestyle=line_style,
|
1021 |
+
)
|
1022 |
+
)
|
1023 |
+
return self.output
|
1024 |
+
|
1025 |
+
def draw_rotated_box_with_label(
|
1026 |
+
self, rotated_box, alpha=0.5, edge_color="g", line_style="-", label=None
|
1027 |
+
):
|
1028 |
+
"""
|
1029 |
+
Draw a rotated box with label on its top-left corner.
|
1030 |
+
Args:
|
1031 |
+
rotated_box (tuple): a tuple containing (cnt_x, cnt_y, w, h, angle),
|
1032 |
+
where cnt_x and cnt_y are the center coordinates of the box.
|
1033 |
+
w and h are the width and height of the box. angle represents how
|
1034 |
+
many degrees the box is rotated CCW with regard to the 0-degree box.
|
1035 |
+
alpha (float): blending efficient. Smaller values lead to more transparent masks.
|
1036 |
+
edge_color: color of the outline of the box. Refer to `matplotlib.colors`
|
1037 |
+
for full list of formats that are accepted.
|
1038 |
+
line_style (string): the string to use to create the outline of the boxes.
|
1039 |
+
label (string): label for rotated box. It will not be rendered when set to None.
|
1040 |
+
Returns:
|
1041 |
+
output (VisImage): image object with box drawn.
|
1042 |
+
"""
|
1043 |
+
cnt_x, cnt_y, w, h, angle = rotated_box
|
1044 |
+
area = w * h
|
1045 |
+
# use thinner lines when the box is small
|
1046 |
+
linewidth = self._default_font_size / (
|
1047 |
+
6 if area < _SMALL_OBJECT_AREA_THRESH * self.output.scale else 3
|
1048 |
+
)
|
1049 |
+
|
1050 |
+
theta = angle * math.pi / 180.0
|
1051 |
+
c = math.cos(theta)
|
1052 |
+
s = math.sin(theta)
|
1053 |
+
rect = [(-w / 2, h / 2), (-w / 2, -h / 2), (w / 2, -h / 2), (w / 2, h / 2)]
|
1054 |
+
# x: left->right ; y: top->down
|
1055 |
+
rotated_rect = [(s * yy + c * xx + cnt_x, c * yy - s * xx + cnt_y) for (xx, yy) in rect]
|
1056 |
+
for k in range(4):
|
1057 |
+
j = (k + 1) % 4
|
1058 |
+
self.draw_line(
|
1059 |
+
[rotated_rect[k][0], rotated_rect[j][0]],
|
1060 |
+
[rotated_rect[k][1], rotated_rect[j][1]],
|
1061 |
+
color=edge_color,
|
1062 |
+
linestyle="--" if k == 1 else line_style,
|
1063 |
+
linewidth=linewidth,
|
1064 |
+
)
|
1065 |
+
|
1066 |
+
if label is not None:
|
1067 |
+
text_pos = rotated_rect[1] # topleft corner
|
1068 |
+
|
1069 |
+
height_ratio = h / np.sqrt(self.output.height * self.output.width)
|
1070 |
+
label_color = self._change_color_brightness(edge_color, brightness_factor=0.7)
|
1071 |
+
font_size = (
|
1072 |
+
np.clip((height_ratio - 0.02) / 0.08 + 1, 1.2, 2) * 0.5 * self._default_font_size
|
1073 |
+
)
|
1074 |
+
self.draw_text(label, text_pos, color=label_color, font_size=font_size, rotation=angle)
|
1075 |
+
|
1076 |
+
return self.output
|
1077 |
+
|
1078 |
+
def draw_circle(self, circle_coord, color, radius=3):
|
1079 |
+
"""
|
1080 |
+
Args:
|
1081 |
+
circle_coord (list(int) or tuple(int)): contains the x and y coordinates
|
1082 |
+
of the center of the circle.
|
1083 |
+
color: color of the polygon. Refer to `matplotlib.colors` for a full list of
|
1084 |
+
formats that are accepted.
|
1085 |
+
radius (int): radius of the circle.
|
1086 |
+
Returns:
|
1087 |
+
output (VisImage): image object with box drawn.
|
1088 |
+
"""
|
1089 |
+
x, y = circle_coord
|
1090 |
+
self.output.ax.add_patch(
|
1091 |
+
mpl.patches.Circle(circle_coord, radius=radius, fill=True, color=color)
|
1092 |
+
)
|
1093 |
+
return self.output
|
1094 |
+
|
1095 |
+
def draw_line(self, x_data, y_data, color, linestyle="-", linewidth=None):
|
1096 |
+
"""
|
1097 |
+
Args:
|
1098 |
+
x_data (list[int]): a list containing x values of all the points being drawn.
|
1099 |
+
Length of list should match the length of y_data.
|
1100 |
+
y_data (list[int]): a list containing y values of all the points being drawn.
|
1101 |
+
Length of list should match the length of x_data.
|
1102 |
+
color: color of the line. Refer to `matplotlib.colors` for a full list of
|
1103 |
+
formats that are accepted.
|
1104 |
+
linestyle: style of the line. Refer to `matplotlib.lines.Line2D`
|
1105 |
+
for a full list of formats that are accepted.
|
1106 |
+
linewidth (float or None): width of the line. When it's None,
|
1107 |
+
a default value will be computed and used.
|
1108 |
+
Returns:
|
1109 |
+
output (VisImage): image object with line drawn.
|
1110 |
+
"""
|
1111 |
+
if linewidth is None:
|
1112 |
+
linewidth = self._default_font_size / 3
|
1113 |
+
linewidth = max(linewidth, 1)
|
1114 |
+
self.output.ax.add_line(
|
1115 |
+
mpl.lines.Line2D(
|
1116 |
+
x_data,
|
1117 |
+
y_data,
|
1118 |
+
linewidth=linewidth * self.output.scale,
|
1119 |
+
color=color,
|
1120 |
+
linestyle=linestyle,
|
1121 |
+
)
|
1122 |
+
)
|
1123 |
+
return self.output
|
1124 |
+
|
1125 |
+
def draw_binary_mask(
|
1126 |
+
self, binary_mask, color=None, *, edge_color=None, text=None, alpha=0.5, area_threshold=10, is_text=True,
|
1127 |
+
):
|
1128 |
+
"""
|
1129 |
+
Args:
|
1130 |
+
binary_mask (ndarray): numpy array of shape (H, W), where H is the image height and
|
1131 |
+
W is the image width. Each value in the array is either a 0 or 1 value of uint8
|
1132 |
+
type.
|
1133 |
+
color: color of the mask. Refer to `matplotlib.colors` for a full list of
|
1134 |
+
formats that are accepted. If None, will pick a random color.
|
1135 |
+
edge_color: color of the polygon edges. Refer to `matplotlib.colors` for a
|
1136 |
+
full list of formats that are accepted.
|
1137 |
+
text (str): if None, will be drawn on the object
|
1138 |
+
alpha (float): blending efficient. Smaller values lead to more transparent masks.
|
1139 |
+
area_threshold (float): a connected component smaller than this area will not be shown.
|
1140 |
+
Returns:
|
1141 |
+
output (VisImage): image object with mask drawn.
|
1142 |
+
"""
|
1143 |
+
if color is None:
|
1144 |
+
color = random_color(rgb=True, maximum=1)
|
1145 |
+
color = mplc.to_rgb(color)
|
1146 |
+
|
1147 |
+
has_valid_segment = False
|
1148 |
+
binary_mask = binary_mask.astype("uint8") # opencv needs uint8
|
1149 |
+
mask = GenericMask(binary_mask, self.output.height, self.output.width)
|
1150 |
+
shape2d = (binary_mask.shape[0], binary_mask.shape[1])
|
1151 |
+
|
1152 |
+
if not mask.has_holes:
|
1153 |
+
# draw polygons for regular masks
|
1154 |
+
for segment in mask.polygons:
|
1155 |
+
area = mask_util.area(mask_util.frPyObjects([segment], shape2d[0], shape2d[1]))
|
1156 |
+
if area < (area_threshold or 0):
|
1157 |
+
continue
|
1158 |
+
has_valid_segment = True
|
1159 |
+
segment = segment.reshape(-1, 2)
|
1160 |
+
self.draw_polygon(segment, color=color, edge_color=edge_color, alpha=alpha)
|
1161 |
+
else:
|
1162 |
+
# TODO: Use Path/PathPatch to draw vector graphics:
|
1163 |
+
# https://stackoverflow.com/questions/8919719/how-to-plot-a-complex-polygon
|
1164 |
+
rgba = np.zeros(shape2d + (4,), dtype="float32")
|
1165 |
+
rgba[:, :, :3] = color
|
1166 |
+
rgba[:, :, 3] = (mask.mask == 1).astype("float32") * alpha
|
1167 |
+
has_valid_segment = True
|
1168 |
+
self.output.ax.imshow(rgba, extent=(0, self.output.width, self.output.height, 0))
|
1169 |
+
|
1170 |
+
if is_text:
|
1171 |
+
if text is not None and has_valid_segment:
|
1172 |
+
lighter_color = self._change_color_brightness(color, brightness_factor=0.7)
|
1173 |
+
self._draw_text_in_mask(binary_mask, text, lighter_color)
|
1174 |
+
return self.output
|
1175 |
+
|
1176 |
+
def draw_soft_mask(self, soft_mask, color=None, *, text=None, alpha=0.5):
|
1177 |
+
"""
|
1178 |
+
Args:
|
1179 |
+
soft_mask (ndarray): float array of shape (H, W), each value in [0, 1].
|
1180 |
+
color: color of the mask. Refer to `matplotlib.colors` for a full list of
|
1181 |
+
formats that are accepted. If None, will pick a random color.
|
1182 |
+
text (str): if None, will be drawn on the object
|
1183 |
+
alpha (float): blending efficient. Smaller values lead to more transparent masks.
|
1184 |
+
Returns:
|
1185 |
+
output (VisImage): image object with mask drawn.
|
1186 |
+
"""
|
1187 |
+
if color is None:
|
1188 |
+
color = random_color(rgb=True, maximum=1)
|
1189 |
+
color = mplc.to_rgb(color)
|
1190 |
+
|
1191 |
+
shape2d = (soft_mask.shape[0], soft_mask.shape[1])
|
1192 |
+
rgba = np.zeros(shape2d + (4,), dtype="float32")
|
1193 |
+
rgba[:, :, :3] = color
|
1194 |
+
rgba[:, :, 3] = soft_mask * alpha
|
1195 |
+
self.output.ax.imshow(rgba, extent=(0, self.output.width, self.output.height, 0))
|
1196 |
+
|
1197 |
+
if text is not None:
|
1198 |
+
lighter_color = self._change_color_brightness(color, brightness_factor=0.7)
|
1199 |
+
binary_mask = (soft_mask > 0.5).astype("uint8")
|
1200 |
+
# self._draw_text_in_mask(binary_mask, text, lighter_color)
|
1201 |
+
return self.output
|
1202 |
+
|
1203 |
+
def draw_polygon(self, segment, color, edge_color=None, alpha=0.5):
|
1204 |
+
"""
|
1205 |
+
Args:
|
1206 |
+
segment: numpy array of shape Nx2, containing all the points in the polygon.
|
1207 |
+
color: color of the polygon. Refer to `matplotlib.colors` for a full list of
|
1208 |
+
formats that are accepted.
|
1209 |
+
edge_color: color of the polygon edges. Refer to `matplotlib.colors` for a
|
1210 |
+
full list of formats that are accepted. If not provided, a darker shade
|
1211 |
+
of the polygon color will be used instead.
|
1212 |
+
alpha (float): blending efficient. Smaller values lead to more transparent masks.
|
1213 |
+
Returns:
|
1214 |
+
output (VisImage): image object with polygon drawn.
|
1215 |
+
"""
|
1216 |
+
if edge_color is None:
|
1217 |
+
# make edge color darker than the polygon color
|
1218 |
+
if alpha > 0.8:
|
1219 |
+
edge_color = self._change_color_brightness(color, brightness_factor=-0.7)
|
1220 |
+
else:
|
1221 |
+
edge_color = color
|
1222 |
+
edge_color = mplc.to_rgb(edge_color) + (1,)
|
1223 |
+
|
1224 |
+
polygon = mpl.patches.Polygon(
|
1225 |
+
segment,
|
1226 |
+
fill=True,
|
1227 |
+
facecolor=mplc.to_rgb(color) + (alpha,),
|
1228 |
+
edgecolor=edge_color,
|
1229 |
+
linewidth=max(self._default_font_size // 15 * self.output.scale, 1),
|
1230 |
+
)
|
1231 |
+
self.output.ax.add_patch(polygon)
|
1232 |
+
return self.output
|
1233 |
+
|
1234 |
+
"""
|
1235 |
+
Internal methods:
|
1236 |
+
"""
|
1237 |
+
|
1238 |
+
def _jitter(self, color):
|
1239 |
+
"""
|
1240 |
+
Randomly modifies given color to produce a slightly different color than the color given.
|
1241 |
+
Args:
|
1242 |
+
color (tuple[double]): a tuple of 3 elements, containing the RGB values of the color
|
1243 |
+
picked. The values in the list are in the [0.0, 1.0] range.
|
1244 |
+
Returns:
|
1245 |
+
jittered_color (tuple[double]): a tuple of 3 elements, containing the RGB values of the
|
1246 |
+
color after being jittered. The values in the list are in the [0.0, 1.0] range.
|
1247 |
+
"""
|
1248 |
+
color = mplc.to_rgb(color)
|
1249 |
+
vec = np.random.rand(3)
|
1250 |
+
# better to do it in another color space
|
1251 |
+
vec = vec / np.linalg.norm(vec) * 0.5
|
1252 |
+
res = np.clip(vec + color, 0, 1)
|
1253 |
+
return tuple(res)
|
1254 |
+
|
1255 |
+
def _create_grayscale_image(self, mask=None):
|
1256 |
+
"""
|
1257 |
+
Create a grayscale version of the original image.
|
1258 |
+
The colors in masked area, if given, will be kept.
|
1259 |
+
"""
|
1260 |
+
img_bw = self.img.astype("f4").mean(axis=2)
|
1261 |
+
img_bw = np.stack([img_bw] * 3, axis=2)
|
1262 |
+
if mask is not None:
|
1263 |
+
img_bw[mask] = self.img[mask]
|
1264 |
+
return img_bw
|
1265 |
+
|
1266 |
+
def _change_color_brightness(self, color, brightness_factor):
|
1267 |
+
"""
|
1268 |
+
Depending on the brightness_factor, gives a lighter or darker color i.e. a color with
|
1269 |
+
less or more saturation than the original color.
|
1270 |
+
Args:
|
1271 |
+
color: color of the polygon. Refer to `matplotlib.colors` for a full list of
|
1272 |
+
formats that are accepted.
|
1273 |
+
brightness_factor (float): a value in [-1.0, 1.0] range. A lightness factor of
|
1274 |
+
0 will correspond to no change, a factor in [-1.0, 0) range will result in
|
1275 |
+
a darker color and a factor in (0, 1.0] range will result in a lighter color.
|
1276 |
+
Returns:
|
1277 |
+
modified_color (tuple[double]): a tuple containing the RGB values of the
|
1278 |
+
modified color. Each value in the tuple is in the [0.0, 1.0] range.
|
1279 |
+
"""
|
1280 |
+
assert brightness_factor >= -1.0 and brightness_factor <= 1.0
|
1281 |
+
color = mplc.to_rgb(color)
|
1282 |
+
polygon_color = colorsys.rgb_to_hls(*mplc.to_rgb(color))
|
1283 |
+
modified_lightness = polygon_color[1] + (brightness_factor * polygon_color[1])
|
1284 |
+
modified_lightness = 0.0 if modified_lightness < 0.0 else modified_lightness
|
1285 |
+
modified_lightness = 1.0 if modified_lightness > 1.0 else modified_lightness
|
1286 |
+
modified_color = colorsys.hls_to_rgb(polygon_color[0], modified_lightness, polygon_color[2])
|
1287 |
+
return modified_color
|
1288 |
+
|
1289 |
+
def _convert_boxes(self, boxes):
|
1290 |
+
"""
|
1291 |
+
Convert different format of boxes to an NxB array, where B = 4 or 5 is the box dimension.
|
1292 |
+
"""
|
1293 |
+
if isinstance(boxes, Boxes) or isinstance(boxes, RotatedBoxes):
|
1294 |
+
return boxes.tensor.detach().numpy()
|
1295 |
+
else:
|
1296 |
+
return np.asarray(boxes)
|
1297 |
+
|
1298 |
+
def _convert_masks(self, masks_or_polygons):
|
1299 |
+
"""
|
1300 |
+
Convert different format of masks or polygons to a tuple of masks and polygons.
|
1301 |
+
Returns:
|
1302 |
+
list[GenericMask]:
|
1303 |
+
"""
|
1304 |
+
|
1305 |
+
m = masks_or_polygons
|
1306 |
+
if isinstance(m, PolygonMasks):
|
1307 |
+
m = m.polygons
|
1308 |
+
if isinstance(m, BitMasks):
|
1309 |
+
m = m.tensor.numpy()
|
1310 |
+
if isinstance(m, torch.Tensor):
|
1311 |
+
m = m.numpy()
|
1312 |
+
ret = []
|
1313 |
+
for x in m:
|
1314 |
+
if isinstance(x, GenericMask):
|
1315 |
+
ret.append(x)
|
1316 |
+
else:
|
1317 |
+
ret.append(GenericMask(x, self.output.height, self.output.width))
|
1318 |
+
return ret
|
1319 |
+
|
1320 |
+
def _draw_text_in_mask(self, binary_mask, text, color):
|
1321 |
+
"""
|
1322 |
+
Find proper places to draw text given a binary mask.
|
1323 |
+
"""
|
1324 |
+
# TODO sometimes drawn on wrong objects. the heuristics here can improve.
|
1325 |
+
_num_cc, cc_labels, stats, centroids = cv2.connectedComponentsWithStats(binary_mask, 8)
|
1326 |
+
if stats[1:, -1].size == 0:
|
1327 |
+
return
|
1328 |
+
largest_component_id = np.argmax(stats[1:, -1]) + 1
|
1329 |
+
|
1330 |
+
# draw text on the largest component, as well as other very large components.
|
1331 |
+
for cid in range(1, _num_cc):
|
1332 |
+
if cid == largest_component_id or stats[cid, -1] > _LARGE_MASK_AREA_THRESH:
|
1333 |
+
# median is more stable than centroid
|
1334 |
+
# center = centroids[largest_component_id]
|
1335 |
+
center = np.median((cc_labels == cid).nonzero(), axis=1)[::-1]
|
1336 |
+
self.draw_text(text, center, color=color)
|
1337 |
+
|
1338 |
+
def _convert_keypoints(self, keypoints):
|
1339 |
+
if isinstance(keypoints, Keypoints):
|
1340 |
+
keypoints = keypoints.tensor
|
1341 |
+
keypoints = np.asarray(keypoints)
|
1342 |
+
return keypoints
|
1343 |
+
|
1344 |
+
def get_output(self):
|
1345 |
+
"""
|
1346 |
+
Returns:
|
1347 |
+
output (VisImage): the image output containing the visualizations added
|
1348 |
+
to the image.
|
1349 |
+
"""
|
1350 |
+
return self.output
|
examples/ade20k.jpeg
ADDED
![]() |
Git LFS Details
|
examples/cityscapes.png
ADDED
![]() |
Git LFS Details
|
examples/coco.jpeg
ADDED
![]() |
Git LFS Details
|
gradio_app.py
ADDED
@@ -0,0 +1,194 @@
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|
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|
|
|
|
|
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|
|
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|
|
|
|
|
1 |
+
import subprocess
|
2 |
+
import torch
|
3 |
+
if torch.cuda.is_available():
|
4 |
+
subprocess.run('sh deform_setup.sh', shell=True)
|
5 |
+
|
6 |
+
print("Installed the dependencies!")
|
7 |
+
|
8 |
+
import numpy as np
|
9 |
+
from PIL import Image
|
10 |
+
import cv2
|
11 |
+
import imutils
|
12 |
+
|
13 |
+
from detectron2.config import get_cfg
|
14 |
+
from detectron2.projects.deeplab import add_deeplab_config
|
15 |
+
from detectron2.data import MetadataCatalog
|
16 |
+
|
17 |
+
from oneformer import (
|
18 |
+
add_oneformer_config,
|
19 |
+
add_common_config,
|
20 |
+
add_swin_config,
|
21 |
+
add_dinat_config,
|
22 |
+
)
|
23 |
+
|
24 |
+
from demo.defaults import DefaultPredictor
|
25 |
+
from demo.visualizer import Visualizer, ColorMode
|
26 |
+
|
27 |
+
import gradio as gr
|
28 |
+
|
29 |
+
KEY_DICT = {"Cityscapes (19 classes)": "cityscapes",
|
30 |
+
"COCO (133 classes)": "coco",
|
31 |
+
"ADE20K (150 classes)": "ade20k",}
|
32 |
+
|
33 |
+
SWIN_CFG_DICT = {"cityscapes": "configs/cityscapes/oneformer_swin_large_IN21k_384_bs16_90k.yaml",
|
34 |
+
"coco": "configs/coco/oneformer_swin_large_IN21k_384_bs16_100ep.yaml",
|
35 |
+
"ade20k": "configs/ade20k/oneformer_swin_large_IN21k_384_bs16_160k.yaml",}
|
36 |
+
|
37 |
+
SWIN_MODEL_DICT = {"cityscapes": "models/250_16_swin_l_oneformer_cityscapes_90k.pth",
|
38 |
+
"coco": "models/150_16_swin_l_oneformer_coco_100ep.pth",
|
39 |
+
"ade20k": "models/250_16_swin_l_oneformer_ade20k_160k.pth"}
|
40 |
+
|
41 |
+
DINAT_CFG_DICT = {"cityscapes": "configs/cityscapes/oneformer_dinat_large_bs16_90k.yaml",
|
42 |
+
"coco": "configs/coco/oneformer_dinat_large_bs16_100ep.yaml",
|
43 |
+
"ade20k": "configs/ade20k/oneformer_dinat_large_IN21k_384_bs16_160k.yaml",}
|
44 |
+
|
45 |
+
DINAT_MODEL_DICT = {"cityscapes": "models/250_16_dinat_l_oneformer_cityscapes_90k.pth",
|
46 |
+
"coco": "models/150_16_dinat_l_oneformer_coco_100ep.pth",
|
47 |
+
"ade20k": "models/250_16_dinat_l_oneformer_ade20k_160k.pth"}
|
48 |
+
|
49 |
+
MODEL_DICT = {"DiNAT-L": DINAT_MODEL_DICT,
|
50 |
+
"Swin-L": SWIN_MODEL_DICT }
|
51 |
+
|
52 |
+
CFG_DICT = {"DiNAT-L": DINAT_CFG_DICT,
|
53 |
+
"Swin-L": SWIN_CFG_DICT }
|
54 |
+
|
55 |
+
WIDTH_DICT = {"cityscapes": 512,
|
56 |
+
"coco": 512,
|
57 |
+
"ade20k": 640}
|
58 |
+
|
59 |
+
cpu_device = torch.device("cpu")
|
60 |
+
|
61 |
+
PREDICTORS = {
|
62 |
+
"DiNAT-L": {
|
63 |
+
"Cityscapes (19 classes)": None,
|
64 |
+
"COCO (133 classes)": None,
|
65 |
+
"ADE20K (150 classes)": None
|
66 |
+
},
|
67 |
+
"Swin-L": {
|
68 |
+
"Cityscapes (19 classes)": None,
|
69 |
+
"COCO (133 classes)": None,
|
70 |
+
"ADE20K (150 classes)": None
|
71 |
+
}
|
72 |
+
}
|
73 |
+
|
74 |
+
def setup_predictors():
|
75 |
+
for dataset in ["Cityscapes (19 classes)", "COCO (133 classes)", "ADE20K (150 classes)"]:
|
76 |
+
for backbone in ["DiNAT-L", "Swin-L"]:
|
77 |
+
cfg = setup_cfg(dataset, backbone)
|
78 |
+
PREDICTORS[backbone][dataset] = DefaultPredictor(cfg)
|
79 |
+
|
80 |
+
def setup_cfg(dataset, backbone):
|
81 |
+
# load config from file and command-line arguments
|
82 |
+
cfg = get_cfg()
|
83 |
+
add_deeplab_config(cfg)
|
84 |
+
add_common_config(cfg)
|
85 |
+
add_swin_config(cfg)
|
86 |
+
add_oneformer_config(cfg)
|
87 |
+
add_dinat_config(cfg)
|
88 |
+
dataset = KEY_DICT[dataset]
|
89 |
+
cfg_path = CFG_DICT[backbone][dataset]
|
90 |
+
cfg.merge_from_file(cfg_path)
|
91 |
+
if torch.cuda.is_available():
|
92 |
+
cfg.MODEL.DEVICE = 'cuda'
|
93 |
+
else:
|
94 |
+
cfg.MODEL.DEVICE = 'cpu'
|
95 |
+
cfg.MODEL.WEIGHTS = MODEL_DICT[backbone][dataset]
|
96 |
+
cfg.freeze()
|
97 |
+
return cfg
|
98 |
+
|
99 |
+
def setup_modules(dataset, backbone):
|
100 |
+
cfg = setup_cfg(dataset, backbone)
|
101 |
+
# predictor = DefaultPredictor(cfg)
|
102 |
+
predictor = PREDICTORS[backbone][dataset]
|
103 |
+
metadata = MetadataCatalog.get(
|
104 |
+
cfg.DATASETS.TEST_PANOPTIC[0] if len(cfg.DATASETS.TEST_PANOPTIC) else "__unused"
|
105 |
+
)
|
106 |
+
if 'cityscapes_fine_sem_seg_val' in cfg.DATASETS.TEST_PANOPTIC[0]:
|
107 |
+
from cityscapesscripts.helpers.labels import labels
|
108 |
+
stuff_colors = [k.color for k in labels if k.trainId != 255]
|
109 |
+
metadata = metadata.set(stuff_colors=stuff_colors)
|
110 |
+
|
111 |
+
return predictor, metadata
|
112 |
+
|
113 |
+
def panoptic_run(img, predictor, metadata):
|
114 |
+
visualizer = Visualizer(img[:, :, ::-1], metadata=metadata, instance_mode=ColorMode.IMAGE)
|
115 |
+
predictions = predictor(img, "panoptic")
|
116 |
+
panoptic_seg, segments_info = predictions["panoptic_seg"]
|
117 |
+
out = visualizer.draw_panoptic_seg_predictions(
|
118 |
+
panoptic_seg.to(cpu_device), segments_info, alpha=0.5
|
119 |
+
)
|
120 |
+
visualizer_map = Visualizer(img[:, :, ::-1], is_img=False, metadata=metadata, instance_mode=ColorMode.IMAGE)
|
121 |
+
out_map = visualizer_map.draw_panoptic_seg_predictions(
|
122 |
+
panoptic_seg.to(cpu_device), segments_info, alpha=1, is_text=False
|
123 |
+
)
|
124 |
+
return out, out_map
|
125 |
+
|
126 |
+
def instance_run(img, predictor, metadata):
|
127 |
+
visualizer = Visualizer(img[:, :, ::-1], metadata=metadata, instance_mode=ColorMode.IMAGE)
|
128 |
+
predictions = predictor(img, "instance")
|
129 |
+
instances = predictions["instances"].to(cpu_device)
|
130 |
+
out = visualizer.draw_instance_predictions(predictions=instances, alpha=0.5)
|
131 |
+
visualizer_map = Visualizer(img[:, :, ::-1], is_img=False, metadata=metadata, instance_mode=ColorMode.IMAGE)
|
132 |
+
out_map = visualizer_map.draw_instance_predictions(predictions=instances, alpha=1, is_text=False)
|
133 |
+
return out, out_map
|
134 |
+
|
135 |
+
def semantic_run(img, predictor, metadata):
|
136 |
+
visualizer = Visualizer(img[:, :, ::-1], metadata=metadata, instance_mode=ColorMode.IMAGE)
|
137 |
+
predictions = predictor(img, "semantic")
|
138 |
+
out = visualizer.draw_sem_seg(
|
139 |
+
predictions["sem_seg"].argmax(dim=0).to(cpu_device), alpha=0.5
|
140 |
+
)
|
141 |
+
visualizer_map = Visualizer(img[:, :, ::-1], is_img=False, metadata=metadata, instance_mode=ColorMode.IMAGE)
|
142 |
+
out_map = visualizer_map.draw_sem_seg(
|
143 |
+
predictions["sem_seg"].argmax(dim=0).to(cpu_device), alpha=1, is_text=False
|
144 |
+
)
|
145 |
+
return out, out_map
|
146 |
+
|
147 |
+
TASK_INFER = {"the task is panoptic": panoptic_run, "the task is instance": instance_run, "the task is semantic": semantic_run}
|
148 |
+
|
149 |
+
def segment(path, task, dataset, backbone):
|
150 |
+
predictor, metadata = setup_modules(dataset, backbone)
|
151 |
+
img = cv2.imread(path)
|
152 |
+
width = WIDTH_DICT[KEY_DICT[dataset]]
|
153 |
+
img = imutils.resize(img, width=width)
|
154 |
+
out, out_map = TASK_INFER[task](img, predictor, metadata)
|
155 |
+
out = Image.fromarray(out.get_image())
|
156 |
+
out_map = Image.fromarray(out_map.get_image())
|
157 |
+
return out, out_map
|
158 |
+
|
159 |
+
title = "OneFormer: One Transformer to Rule Universal Image Segmentation"
|
160 |
+
|
161 |
+
description = "<p style='color: #E0B941; font-size: 16px; font-weight: w600; text-align: center'> <a style='color: #E0B941;' href='https://praeclarumjj3.github.io/oneformer/' target='_blank'>Project Page</a> | <a style='color: #E0B941;' href='https://arxiv.org/abs/2211.06220' target='_blank'>OneFormer: One Transformer to Rule Universal Image Segmentation</a> | <a style='color: #E0B941;' href='https://github.com/SHI-Labs/OneFormer' target='_blank'>Github</a></p>" \
|
162 |
+
+ "<p style='color:royalblue; margin: 10px; font-size: 16px; font-weight: w400;'> \
|
163 |
+
[Note: Inference on CPU may take upto 2 minutes.] This is the official gradio demo for our paper <span style='color:#E0B941;'>OneFormer: One Transformer to Rule Universal Image Segmentation</span> To use OneFormer: <br> \
|
164 |
+
(1) <span style='color:#E0B941;'>Upload an Image</span> or <span style='color:#E0B941;'> select a sample image from the examples</span> <br> \
|
165 |
+
(2) Select the value of the <span style='color:#E0B941;'>Task Token Input</span> <br>\
|
166 |
+
(3) Select the <span style='color:#E0B941;'>Model</span> </p>"
|
167 |
+
|
168 |
+
# article =
|
169 |
+
|
170 |
+
# css = ".image-preview {height: 32rem; width: auto;} .output-image {height: 32rem; width: auto;} .panel-buttons { display: flex; flex-direction: row;}"
|
171 |
+
|
172 |
+
setup_predictors()
|
173 |
+
|
174 |
+
gradio_inputs = [gr.Image(source="upload", tool=None, label="Input Image",type="filepath"),
|
175 |
+
gr.inputs.Radio(choices=["the task is panoptic" ,"the task is instance", "the task is semantic"], type="value", default="the task is panoptic", label="Task Token Input"),
|
176 |
+
gr.inputs.Radio(choices=["COCO (133 classes)" ,"Cityscapes (19 classes)", "ADE20K (150 classes)"], type="value", default="Cityscapes (19 classes)", label="Model"),
|
177 |
+
gr.inputs.Radio(choices=["DiNAT-L" ,"Swin-L"], type="value", default="DiNAT-L", label="Backbone"),
|
178 |
+
]
|
179 |
+
gradio_outputs = [gr.Image(type="pil", label="Segmentation Overlay"), gr.Image(type="pil", label="Segmentation Map")]
|
180 |
+
|
181 |
+
|
182 |
+
examples = [["examples/coco.jpeg", "the task is panoptic", "COCO (133 classes)", "DiNAT-L"],
|
183 |
+
["examples/cityscapes.png", "the task is panoptic", "Cityscapes (19 classes)", "DiNAT-L"],
|
184 |
+
["examples/ade20k.jpeg", "the task is panoptic", "ADE20K (150 classes)", "DiNAT-L"]]
|
185 |
+
|
186 |
+
|
187 |
+
iface = gr.Interface(fn=segment, inputs=gradio_inputs,
|
188 |
+
outputs=gradio_outputs,
|
189 |
+
examples_per_page=5,
|
190 |
+
allow_flagging="never",
|
191 |
+
examples=examples, title=title,
|
192 |
+
description=description)
|
193 |
+
|
194 |
+
iface.launch(enable_queue=True)
|
oneformer/.DS_Store
ADDED
Binary file (6.15 kB). View file
|
|
oneformer/__init__.py
ADDED
@@ -0,0 +1,9 @@
|
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|
1 |
+
# Copyright (c) Facebook, Inc. and its affiliates.
|
2 |
+
from . import data # register all new datasets
|
3 |
+
from . import modeling
|
4 |
+
|
5 |
+
# config
|
6 |
+
from .config import *
|
7 |
+
|
8 |
+
# models
|
9 |
+
from .oneformer_model import OneFormer
|
oneformer/config.py
ADDED
@@ -0,0 +1,239 @@
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|
1 |
+
# -*- coding: utf-8 -*-
|
2 |
+
# Copyright (c) Facebook, Inc. and its affiliates.
|
3 |
+
from detectron2.config import CfgNode as CN
|
4 |
+
|
5 |
+
__all__ = ["add_common_config", "add_oneformer_config", "add_swin_config",
|
6 |
+
"add_dinat_config", "add_beit_adapter_config", "add_convnext_config"]
|
7 |
+
|
8 |
+
def add_common_config(cfg):
|
9 |
+
"""
|
10 |
+
Add config for common configuration
|
11 |
+
"""
|
12 |
+
# data config
|
13 |
+
# select the dataset mapper
|
14 |
+
cfg.INPUT.DATASET_MAPPER_NAME = "oneformer_unified"
|
15 |
+
# Color augmentation
|
16 |
+
cfg.INPUT.COLOR_AUG_SSD = False
|
17 |
+
# We retry random cropping until no single category in semantic segmentation GT occupies more
|
18 |
+
# than `SINGLE_CATEGORY_MAX_AREA` part of the crop.
|
19 |
+
cfg.INPUT.CROP.SINGLE_CATEGORY_MAX_AREA = 1.0
|
20 |
+
# Pad image and segmentation GT in dataset mapper.
|
21 |
+
cfg.INPUT.SIZE_DIVISIBILITY = -1
|
22 |
+
|
23 |
+
cfg.INPUT.TASK_SEQ_LEN = 77
|
24 |
+
cfg.INPUT.MAX_SEQ_LEN = 77
|
25 |
+
|
26 |
+
cfg.INPUT.TASK_PROB = CN()
|
27 |
+
cfg.INPUT.TASK_PROB.SEMANTIC = 0.33
|
28 |
+
cfg.INPUT.TASK_PROB.INSTANCE = 0.66
|
29 |
+
|
30 |
+
# test dataset
|
31 |
+
cfg.DATASETS.TEST_PANOPTIC = ("",)
|
32 |
+
cfg.DATASETS.TEST_INSTANCE = ("",)
|
33 |
+
cfg.DATASETS.TEST_SEMANTIC = ("",)
|
34 |
+
|
35 |
+
# solver config
|
36 |
+
# weight decay on embedding
|
37 |
+
cfg.SOLVER.WEIGHT_DECAY_EMBED = 0.0
|
38 |
+
# optimizer
|
39 |
+
cfg.SOLVER.OPTIMIZER = "ADAMW"
|
40 |
+
cfg.SOLVER.BACKBONE_MULTIPLIER = 0.1
|
41 |
+
|
42 |
+
# wandb
|
43 |
+
cfg.WANDB = CN()
|
44 |
+
cfg.WANDB.PROJECT = "unified_dense_recognition"
|
45 |
+
cfg.WANDB.NAME = None
|
46 |
+
|
47 |
+
cfg.MODEL.IS_TRAIN = False
|
48 |
+
cfg.MODEL.IS_DEMO = True
|
49 |
+
|
50 |
+
# text encoder config
|
51 |
+
cfg.MODEL.TEXT_ENCODER = CN()
|
52 |
+
|
53 |
+
cfg.MODEL.TEXT_ENCODER.WIDTH = 256
|
54 |
+
cfg.MODEL.TEXT_ENCODER.CONTEXT_LENGTH = 77
|
55 |
+
cfg.MODEL.TEXT_ENCODER.NUM_LAYERS = 12
|
56 |
+
cfg.MODEL.TEXT_ENCODER.VOCAB_SIZE = 49408
|
57 |
+
cfg.MODEL.TEXT_ENCODER.PROJ_NUM_LAYERS = 2
|
58 |
+
cfg.MODEL.TEXT_ENCODER.N_CTX = 16
|
59 |
+
|
60 |
+
# mask_former inference config
|
61 |
+
cfg.MODEL.TEST = CN()
|
62 |
+
cfg.MODEL.TEST.SEMANTIC_ON = True
|
63 |
+
cfg.MODEL.TEST.INSTANCE_ON = False
|
64 |
+
cfg.MODEL.TEST.PANOPTIC_ON = False
|
65 |
+
cfg.MODEL.TEST.DETECTION_ON = False
|
66 |
+
cfg.MODEL.TEST.OBJECT_MASK_THRESHOLD = 0.0
|
67 |
+
cfg.MODEL.TEST.OVERLAP_THRESHOLD = 0.0
|
68 |
+
cfg.MODEL.TEST.SEM_SEG_POSTPROCESSING_BEFORE_INFERENCE = False
|
69 |
+
cfg.MODEL.TEST.TASK = "panoptic"
|
70 |
+
|
71 |
+
# TEST AUG Slide
|
72 |
+
cfg.TEST.AUG.IS_SLIDE = False
|
73 |
+
cfg.TEST.AUG.CROP_SIZE = (640, 640)
|
74 |
+
cfg.TEST.AUG.STRIDE = (426, 426)
|
75 |
+
cfg.TEST.AUG.SCALE = (2048, 640)
|
76 |
+
cfg.TEST.AUG.SETR_MULTI_SCALE = True
|
77 |
+
cfg.TEST.AUG.KEEP_RATIO = True
|
78 |
+
cfg.TEST.AUG.SIZE_DIVISOR = 32
|
79 |
+
|
80 |
+
# pixel decoder config
|
81 |
+
cfg.MODEL.SEM_SEG_HEAD.MASK_DIM = 256
|
82 |
+
# adding transformer in pixel decoder
|
83 |
+
cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS = 0
|
84 |
+
# pixel decoder
|
85 |
+
cfg.MODEL.SEM_SEG_HEAD.PIXEL_DECODER_NAME = "BasePixelDecoder"
|
86 |
+
cfg.MODEL.SEM_SEG_HEAD.SEM_EMBED_DIM = 256
|
87 |
+
cfg.MODEL.SEM_SEG_HEAD.INST_EMBED_DIM = 256
|
88 |
+
|
89 |
+
# LSJ aug
|
90 |
+
cfg.INPUT.IMAGE_SIZE = 1024
|
91 |
+
cfg.INPUT.MIN_SCALE = 0.1
|
92 |
+
cfg.INPUT.MAX_SCALE = 2.0
|
93 |
+
|
94 |
+
# MSDeformAttn encoder configs
|
95 |
+
cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES = ["res3", "res4", "res5"]
|
96 |
+
cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_N_POINTS = 4
|
97 |
+
cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_N_HEADS = 8
|
98 |
+
|
99 |
+
def add_oneformer_config(cfg):
|
100 |
+
"""
|
101 |
+
Add config for ONE_FORMER.
|
102 |
+
"""
|
103 |
+
|
104 |
+
# mask_former model config
|
105 |
+
cfg.MODEL.ONE_FORMER = CN()
|
106 |
+
|
107 |
+
# loss
|
108 |
+
cfg.MODEL.ONE_FORMER.DEEP_SUPERVISION = True
|
109 |
+
cfg.MODEL.ONE_FORMER.NO_OBJECT_WEIGHT = 0.1
|
110 |
+
cfg.MODEL.ONE_FORMER.CLASS_WEIGHT = 1.0
|
111 |
+
cfg.MODEL.ONE_FORMER.DICE_WEIGHT = 1.0
|
112 |
+
cfg.MODEL.ONE_FORMER.MASK_WEIGHT = 20.0
|
113 |
+
cfg.MODEL.ONE_FORMER.CONTRASTIVE_WEIGHT = 0.5
|
114 |
+
cfg.MODEL.ONE_FORMER.CONTRASTIVE_TEMPERATURE = 0.07
|
115 |
+
|
116 |
+
# transformer config
|
117 |
+
cfg.MODEL.ONE_FORMER.NHEADS = 8
|
118 |
+
cfg.MODEL.ONE_FORMER.DROPOUT = 0.1
|
119 |
+
cfg.MODEL.ONE_FORMER.DIM_FEEDFORWARD = 2048
|
120 |
+
cfg.MODEL.ONE_FORMER.ENC_LAYERS = 0
|
121 |
+
cfg.MODEL.ONE_FORMER.CLASS_DEC_LAYERS = 2
|
122 |
+
cfg.MODEL.ONE_FORMER.DEC_LAYERS = 6
|
123 |
+
cfg.MODEL.ONE_FORMER.PRE_NORM = False
|
124 |
+
|
125 |
+
cfg.MODEL.ONE_FORMER.HIDDEN_DIM = 256
|
126 |
+
cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES = 120
|
127 |
+
cfg.MODEL.ONE_FORMER.NUM_OBJECT_CTX = 16
|
128 |
+
cfg.MODEL.ONE_FORMER.USE_TASK_NORM = True
|
129 |
+
|
130 |
+
cfg.MODEL.ONE_FORMER.TRANSFORMER_IN_FEATURE = "res5"
|
131 |
+
cfg.MODEL.ONE_FORMER.ENFORCE_INPUT_PROJ = False
|
132 |
+
|
133 |
+
# Sometimes `backbone.size_divisibility` is set to 0 for some backbone (e.g. ResNet)
|
134 |
+
# you can use this config to override
|
135 |
+
cfg.MODEL.ONE_FORMER.SIZE_DIVISIBILITY = 32
|
136 |
+
|
137 |
+
# transformer module
|
138 |
+
cfg.MODEL.ONE_FORMER.TRANSFORMER_DECODER_NAME = "ContrastiveMultiScaleMaskedTransformerDecoder"
|
139 |
+
|
140 |
+
# point loss configs
|
141 |
+
# Number of points sampled during training for a mask point head.
|
142 |
+
cfg.MODEL.ONE_FORMER.TRAIN_NUM_POINTS = 112 * 112
|
143 |
+
# Oversampling parameter for PointRend point sampling during training. Parameter `k` in the
|
144 |
+
# original paper.
|
145 |
+
cfg.MODEL.ONE_FORMER.OVERSAMPLE_RATIO = 3.0
|
146 |
+
# Importance sampling parameter for PointRend point sampling during training. Parametr `beta` in
|
147 |
+
# the original paper.
|
148 |
+
cfg.MODEL.ONE_FORMER.IMPORTANCE_SAMPLE_RATIO = 0.75
|
149 |
+
|
150 |
+
def add_swin_config(cfg):
|
151 |
+
"""
|
152 |
+
Add config forSWIN Backbone.
|
153 |
+
"""
|
154 |
+
|
155 |
+
# swin transformer backbone
|
156 |
+
cfg.MODEL.SWIN = CN()
|
157 |
+
cfg.MODEL.SWIN.PRETRAIN_IMG_SIZE = 224
|
158 |
+
cfg.MODEL.SWIN.PATCH_SIZE = 4
|
159 |
+
cfg.MODEL.SWIN.EMBED_DIM = 96
|
160 |
+
cfg.MODEL.SWIN.DEPTHS = [2, 2, 6, 2]
|
161 |
+
cfg.MODEL.SWIN.NUM_HEADS = [3, 6, 12, 24]
|
162 |
+
cfg.MODEL.SWIN.WINDOW_SIZE = 7
|
163 |
+
cfg.MODEL.SWIN.MLP_RATIO = 4.0
|
164 |
+
cfg.MODEL.SWIN.QKV_BIAS = True
|
165 |
+
cfg.MODEL.SWIN.QK_SCALE = None
|
166 |
+
cfg.MODEL.SWIN.DROP_RATE = 0.0
|
167 |
+
cfg.MODEL.SWIN.ATTN_DROP_RATE = 0.0
|
168 |
+
cfg.MODEL.SWIN.DROP_PATH_RATE = 0.3
|
169 |
+
cfg.MODEL.SWIN.APE = False
|
170 |
+
cfg.MODEL.SWIN.PATCH_NORM = True
|
171 |
+
cfg.MODEL.SWIN.OUT_FEATURES = ["res2", "res3", "res4", "res5"]
|
172 |
+
cfg.MODEL.SWIN.USE_CHECKPOINT = False
|
173 |
+
## Semask additions
|
174 |
+
cfg.MODEL.SWIN.SEM_WINDOW_SIZE = 7
|
175 |
+
cfg.MODEL.SWIN.NUM_SEM_BLOCKS = 1
|
176 |
+
|
177 |
+
def add_dinat_config(cfg):
|
178 |
+
"""
|
179 |
+
Add config for NAT Backbone.
|
180 |
+
"""
|
181 |
+
|
182 |
+
# DINAT transformer backbone
|
183 |
+
cfg.MODEL.DiNAT = CN()
|
184 |
+
cfg.MODEL.DiNAT.DEPTHS = [3, 4, 18, 5]
|
185 |
+
cfg.MODEL.DiNAT.OUT_FEATURES = ["res2", "res3", "res4", "res5"]
|
186 |
+
cfg.MODEL.DiNAT.EMBED_DIM = 64
|
187 |
+
cfg.MODEL.DiNAT.MLP_RATIO = 3.0
|
188 |
+
cfg.MODEL.DiNAT.NUM_HEADS = [2, 4, 8, 16]
|
189 |
+
cfg.MODEL.DiNAT.DROP_PATH_RATE = 0.2
|
190 |
+
cfg.MODEL.DiNAT.KERNEL_SIZE = 7
|
191 |
+
cfg.MODEL.DiNAT.DILATIONS = [[1, 16, 1], [1, 4, 1, 8], [1, 2, 1, 3, 1, 4], [1, 2, 1, 2, 1]]
|
192 |
+
cfg.MODEL.DiNAT.OUT_INDICES = (0, 1, 2, 3)
|
193 |
+
cfg.MODEL.DiNAT.QKV_BIAS = True
|
194 |
+
cfg.MODEL.DiNAT.QK_SCALE = None
|
195 |
+
cfg.MODEL.DiNAT.DROP_RATE = 0
|
196 |
+
cfg.MODEL.DiNAT.ATTN_DROP_RATE = 0.
|
197 |
+
cfg.MODEL.DiNAT.IN_PATCH_SIZE = 4
|
198 |
+
|
199 |
+
def add_convnext_config(cfg):
|
200 |
+
"""
|
201 |
+
Add config for ConvNeXt Backbone.
|
202 |
+
"""
|
203 |
+
|
204 |
+
# swin transformer backbone
|
205 |
+
cfg.MODEL.CONVNEXT = CN()
|
206 |
+
cfg.MODEL.CONVNEXT.IN_CHANNELS = 3
|
207 |
+
cfg.MODEL.CONVNEXT.DEPTHS = [3, 3, 27, 3]
|
208 |
+
cfg.MODEL.CONVNEXT.DIMS = [192, 384, 768, 1536]
|
209 |
+
cfg.MODEL.CONVNEXT.DROP_PATH_RATE = 0.4
|
210 |
+
cfg.MODEL.CONVNEXT.LSIT = 1.0
|
211 |
+
cfg.MODEL.CONVNEXT.OUT_INDICES = [0, 1, 2, 3]
|
212 |
+
cfg.MODEL.CONVNEXT.OUT_FEATURES = ["res2", "res3", "res4", "res5"]
|
213 |
+
|
214 |
+
def add_beit_adapter_config(cfg):
|
215 |
+
"""
|
216 |
+
Add config for BEiT Adapter Backbone.
|
217 |
+
"""
|
218 |
+
|
219 |
+
# beit adapter backbone
|
220 |
+
cfg.MODEL.BEiTAdapter = CN()
|
221 |
+
cfg.MODEL.BEiTAdapter.IMG_SIZE = 640
|
222 |
+
cfg.MODEL.BEiTAdapter.PATCH_SIZE = 16
|
223 |
+
cfg.MODEL.BEiTAdapter.EMBED_DIM = 1024
|
224 |
+
cfg.MODEL.BEiTAdapter.DEPTH = 24
|
225 |
+
cfg.MODEL.BEiTAdapter.NUM_HEADS = 16
|
226 |
+
cfg.MODEL.BEiTAdapter.MLP_RATIO = 4
|
227 |
+
cfg.MODEL.BEiTAdapter.QKV_BIAS = True
|
228 |
+
cfg.MODEL.BEiTAdapter.USE_ABS_POS_EMB = False
|
229 |
+
cfg.MODEL.BEiTAdapter.USE_REL_POS_BIAS = True
|
230 |
+
cfg.MODEL.BEiTAdapter.INIT_VALUES = 1e-6
|
231 |
+
cfg.MODEL.BEiTAdapter.DROP_PATH_RATE = 0.3
|
232 |
+
cfg.MODEL.BEiTAdapter.CONV_INPLANE = 64
|
233 |
+
cfg.MODEL.BEiTAdapter.N_POINTS = 4
|
234 |
+
cfg.MODEL.BEiTAdapter.DEFORM_NUM_HEADS = 16
|
235 |
+
cfg.MODEL.BEiTAdapter.CFFN_RATIO = 0.25
|
236 |
+
cfg.MODEL.BEiTAdapter.DEFORM_RATIO = 0.5
|
237 |
+
cfg.MODEL.BEiTAdapter.WITH_CP = True
|
238 |
+
cfg.MODEL.BEiTAdapter.INTERACTION_INDEXES=[[0, 5], [6, 11], [12, 17], [18, 23]]
|
239 |
+
cfg.MODEL.BEiTAdapter.OUT_FEATURES = ["res2", "res3", "res4", "res5"]
|
oneformer/data/__init__.py
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
# Copyright (c) Facebook, Inc. and its affiliates.
|
2 |
+
from . import datasets
|
oneformer/data/bpe_simple_vocab_16e6.txt
ADDED
The diff for this file is too large to render.
See raw diff
|
|
oneformer/data/bpe_simple_vocab_16e6.txt.gz
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:924691ac288e54409236115652ad4aa250f48203de50a9e4722a6ecd48d6804a
|
3 |
+
size 1356917
|
oneformer/data/build.py
ADDED
@@ -0,0 +1,117 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
|
|
|
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|
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|
|
|
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|
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|
|
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|
|
|
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|
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|
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|
|
|
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|
|
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|
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|
|
|
|
|
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|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Copyright (c) Facebook, Inc. and its affiliates.
|
2 |
+
from typing import Any, Callable, Dict, List, Optional, Union
|
3 |
+
import torch.utils.data as torchdata
|
4 |
+
|
5 |
+
from detectron2.config import configurable
|
6 |
+
|
7 |
+
|
8 |
+
from detectron2.data.common import DatasetFromList, MapDataset
|
9 |
+
from detectron2.data.dataset_mapper import DatasetMapper
|
10 |
+
from detectron2.data.samplers import (
|
11 |
+
InferenceSampler,
|
12 |
+
)
|
13 |
+
from detectron2.data.build import (
|
14 |
+
get_detection_dataset_dicts,
|
15 |
+
trivial_batch_collator
|
16 |
+
)
|
17 |
+
"""
|
18 |
+
This file contains the default logic to build a dataloader for training or testing.
|
19 |
+
"""
|
20 |
+
|
21 |
+
__all__ = [
|
22 |
+
"build_detection_test_loader",
|
23 |
+
]
|
24 |
+
|
25 |
+
|
26 |
+
def _test_loader_from_config(cfg, dataset_name, mapper=None):
|
27 |
+
"""
|
28 |
+
Uses the given `dataset_name` argument (instead of the names in cfg), because the
|
29 |
+
standard practice is to evaluate each test set individually (not combining them).
|
30 |
+
"""
|
31 |
+
if isinstance(dataset_name, str):
|
32 |
+
dataset_name = [dataset_name]
|
33 |
+
|
34 |
+
dataset = get_detection_dataset_dicts(
|
35 |
+
dataset_name,
|
36 |
+
filter_empty=False,
|
37 |
+
proposal_files=[
|
38 |
+
cfg.DATASETS.PROPOSAL_FILES_TEST[list(cfg.DATASETS.TEST).index(x)] for x in dataset_name
|
39 |
+
]
|
40 |
+
if cfg.MODEL.LOAD_PROPOSALS
|
41 |
+
else None,
|
42 |
+
)
|
43 |
+
if mapper is None:
|
44 |
+
mapper = DatasetMapper(cfg, False)
|
45 |
+
return {
|
46 |
+
"dataset": dataset,
|
47 |
+
"mapper": mapper,
|
48 |
+
"num_workers": cfg.DATALOADER.NUM_WORKERS,
|
49 |
+
"sampler": InferenceSampler(len(dataset))
|
50 |
+
if not isinstance(dataset, torchdata.IterableDataset)
|
51 |
+
else None,
|
52 |
+
}
|
53 |
+
|
54 |
+
|
55 |
+
@configurable(from_config=_test_loader_from_config)
|
56 |
+
def build_detection_test_loader(
|
57 |
+
dataset: Union[List[Any], torchdata.Dataset],
|
58 |
+
*,
|
59 |
+
mapper: Callable[[Dict[str, Any]], Any],
|
60 |
+
sampler: Optional[torchdata.Sampler] = None,
|
61 |
+
batch_size: int = 1,
|
62 |
+
num_workers: int = 0,
|
63 |
+
collate_fn: Optional[Callable[[List[Any]], Any]] = None,
|
64 |
+
) -> torchdata.DataLoader:
|
65 |
+
"""
|
66 |
+
Similar to `build_detection_train_loader`, with default batch size = 1,
|
67 |
+
and sampler = :class:`InferenceSampler`. This sampler coordinates all workers
|
68 |
+
to produce the exact set of all samples.
|
69 |
+
|
70 |
+
Args:
|
71 |
+
dataset: a list of dataset dicts,
|
72 |
+
or a pytorch dataset (either map-style or iterable). They can be obtained
|
73 |
+
by using :func:`DatasetCatalog.get` or :func:`get_detection_dataset_dicts`.
|
74 |
+
mapper: a callable which takes a sample (dict) from dataset
|
75 |
+
and returns the format to be consumed by the model.
|
76 |
+
When using cfg, the default choice is ``DatasetMapper(cfg, is_train=False)``.
|
77 |
+
sampler: a sampler that produces
|
78 |
+
indices to be applied on ``dataset``. Default to :class:`InferenceSampler`,
|
79 |
+
which splits the dataset across all workers. Sampler must be None
|
80 |
+
if `dataset` is iterable.
|
81 |
+
batch_size: the batch size of the data loader to be created.
|
82 |
+
Default to 1 image per worker since this is the standard when reporting
|
83 |
+
inference time in papers.
|
84 |
+
num_workers: number of parallel data loading workers
|
85 |
+
collate_fn: same as the argument of `torch.utils.data.DataLoader`.
|
86 |
+
Defaults to do no collation and return a list of data.
|
87 |
+
|
88 |
+
Returns:
|
89 |
+
DataLoader: a torch DataLoader, that loads the given detection
|
90 |
+
dataset, with test-time transformation and batching.
|
91 |
+
|
92 |
+
Examples:
|
93 |
+
::
|
94 |
+
data_loader = build_detection_test_loader(
|
95 |
+
DatasetRegistry.get("my_test"),
|
96 |
+
mapper=DatasetMapper(...))
|
97 |
+
|
98 |
+
# or, instantiate with a CfgNode:
|
99 |
+
data_loader = build_detection_test_loader(cfg, "my_test")
|
100 |
+
"""
|
101 |
+
if isinstance(dataset, list):
|
102 |
+
dataset = DatasetFromList(dataset, copy=False)
|
103 |
+
if mapper is not None:
|
104 |
+
dataset = MapDataset(dataset, mapper)
|
105 |
+
if isinstance(dataset, torchdata.IterableDataset):
|
106 |
+
assert sampler is None, "sampler must be None if dataset is IterableDataset"
|
107 |
+
else:
|
108 |
+
if sampler is None:
|
109 |
+
sampler = InferenceSampler(len(dataset))
|
110 |
+
return torchdata.DataLoader(
|
111 |
+
dataset,
|
112 |
+
batch_size=batch_size,
|
113 |
+
sampler=sampler,
|
114 |
+
drop_last=False,
|
115 |
+
num_workers=num_workers,
|
116 |
+
collate_fn=trivial_batch_collator if collate_fn is None else collate_fn,
|
117 |
+
)
|
oneformer/data/dataset_mappers/__init__.py
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
|
oneformer/data/dataset_mappers/coco_unified_new_baseline_dataset_mapper.py
ADDED
@@ -0,0 +1,341 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# ------------------------------------------------------------------------------
|
2 |
+
# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/data/dataset_mappers/coco_panoptic_new_baseline_dataset_mapper.py
|
3 |
+
# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
|
4 |
+
# ------------------------------------------------------------------------------
|
5 |
+
|
6 |
+
import copy
|
7 |
+
import logging
|
8 |
+
|
9 |
+
import numpy as np
|
10 |
+
import torch
|
11 |
+
|
12 |
+
from detectron2.data import MetadataCatalog
|
13 |
+
from detectron2.config import configurable
|
14 |
+
from detectron2.data import detection_utils as utils
|
15 |
+
from detectron2.data import transforms as T
|
16 |
+
from detectron2.structures import BitMasks, Instances
|
17 |
+
from oneformer.utils.box_ops import masks_to_boxes
|
18 |
+
from oneformer.data.tokenizer import SimpleTokenizer, Tokenize
|
19 |
+
|
20 |
+
__all__ = ["COCOUnifiedNewBaselineDatasetMapper"]
|
21 |
+
|
22 |
+
|
23 |
+
def build_transform_gen(cfg, is_train):
|
24 |
+
"""
|
25 |
+
Create a list of default :class:`Augmentation` from config.
|
26 |
+
Now it includes resizing and flipping.
|
27 |
+
Returns:
|
28 |
+
list[Augmentation]
|
29 |
+
"""
|
30 |
+
assert is_train, "Only support training augmentation"
|
31 |
+
image_size = cfg.INPUT.IMAGE_SIZE
|
32 |
+
min_scale = cfg.INPUT.MIN_SCALE
|
33 |
+
max_scale = cfg.INPUT.MAX_SCALE
|
34 |
+
|
35 |
+
augmentation = []
|
36 |
+
|
37 |
+
if cfg.INPUT.RANDOM_FLIP != "none":
|
38 |
+
augmentation.append(
|
39 |
+
T.RandomFlip(
|
40 |
+
horizontal=cfg.INPUT.RANDOM_FLIP == "horizontal",
|
41 |
+
vertical=cfg.INPUT.RANDOM_FLIP == "vertical",
|
42 |
+
)
|
43 |
+
)
|
44 |
+
|
45 |
+
augmentation.extend([
|
46 |
+
T.ResizeScale(
|
47 |
+
min_scale=min_scale, max_scale=max_scale, target_height=image_size, target_width=image_size
|
48 |
+
),
|
49 |
+
T.FixedSizeCrop(crop_size=(image_size, image_size)),
|
50 |
+
])
|
51 |
+
|
52 |
+
return augmentation
|
53 |
+
|
54 |
+
|
55 |
+
# This is specifically designed for the COCO dataset.
|
56 |
+
class COCOUnifiedNewBaselineDatasetMapper:
|
57 |
+
"""
|
58 |
+
A callable which takes a dataset dict in Detectron2 Dataset format,
|
59 |
+
and map it into a format used by OneFormer.
|
60 |
+
|
61 |
+
This dataset mapper applies the same transformation as DETR for COCO panoptic segmentation.
|
62 |
+
|
63 |
+
The callable currently does the following:
|
64 |
+
|
65 |
+
1. Read the image from "file_name"
|
66 |
+
2. Applies geometric transforms to the image and annotation
|
67 |
+
3. Find and applies suitable cropping to the image and annotation
|
68 |
+
4. Prepare image and annotation to Tensors
|
69 |
+
"""
|
70 |
+
|
71 |
+
@configurable
|
72 |
+
def __init__(
|
73 |
+
self,
|
74 |
+
is_train=True,
|
75 |
+
*,
|
76 |
+
num_queries,
|
77 |
+
tfm_gens,
|
78 |
+
meta,
|
79 |
+
image_format,
|
80 |
+
max_seq_len,
|
81 |
+
task_seq_len,
|
82 |
+
semantic_prob,
|
83 |
+
instance_prob,
|
84 |
+
):
|
85 |
+
"""
|
86 |
+
NOTE: this interface is experimental.
|
87 |
+
Args:
|
88 |
+
is_train: for training or inference
|
89 |
+
augmentations: a list of augmentations or deterministic transforms to apply
|
90 |
+
crop_gen: crop augmentation
|
91 |
+
tfm_gens: data augmentation
|
92 |
+
image_format: an image format supported by :func:`detection_utils.read_image`.
|
93 |
+
"""
|
94 |
+
self.tfm_gens = tfm_gens
|
95 |
+
logging.getLogger(__name__).info(
|
96 |
+
"[COCOUnifiedNewBaselineDatasetMapper] Full TransformGens used in training: {}".format(
|
97 |
+
str(self.tfm_gens)
|
98 |
+
)
|
99 |
+
)
|
100 |
+
|
101 |
+
self.img_format = image_format
|
102 |
+
self.is_train = is_train
|
103 |
+
self.meta = meta
|
104 |
+
self.ignore_label = self.meta.ignore_label
|
105 |
+
self.num_queries = num_queries
|
106 |
+
|
107 |
+
self.things = []
|
108 |
+
for k,v in self.meta.thing_dataset_id_to_contiguous_id.items():
|
109 |
+
self.things.append(v)
|
110 |
+
self.class_names = self.meta.stuff_classes
|
111 |
+
self.text_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=max_seq_len)
|
112 |
+
self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len)
|
113 |
+
self.semantic_prob = semantic_prob
|
114 |
+
self.instance_prob = instance_prob
|
115 |
+
|
116 |
+
@classmethod
|
117 |
+
def from_config(cls, cfg, is_train=True):
|
118 |
+
# Build augmentation
|
119 |
+
tfm_gens = build_transform_gen(cfg, is_train)
|
120 |
+
dataset_names = cfg.DATASETS.TRAIN
|
121 |
+
meta = MetadataCatalog.get(dataset_names[0])
|
122 |
+
|
123 |
+
ret = {
|
124 |
+
"is_train": is_train,
|
125 |
+
"meta": meta,
|
126 |
+
"tfm_gens": tfm_gens,
|
127 |
+
"image_format": cfg.INPUT.FORMAT,
|
128 |
+
"num_queries": cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES - cfg.MODEL.TEXT_ENCODER.N_CTX,
|
129 |
+
"task_seq_len": cfg.INPUT.TASK_SEQ_LEN,
|
130 |
+
"max_seq_len": cfg.INPUT.MAX_SEQ_LEN,
|
131 |
+
"semantic_prob": cfg.INPUT.TASK_PROB.SEMANTIC,
|
132 |
+
"instance_prob": cfg.INPUT.TASK_PROB.INSTANCE,
|
133 |
+
}
|
134 |
+
return ret
|
135 |
+
|
136 |
+
def _get_semantic_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj):
|
137 |
+
instances = Instances(image_shape)
|
138 |
+
|
139 |
+
classes = []
|
140 |
+
texts = ["a semantic photo"] * self.num_queries
|
141 |
+
masks = []
|
142 |
+
label = np.ones_like(pan_seg_gt) * self.ignore_label
|
143 |
+
|
144 |
+
for segment_info in segments_info:
|
145 |
+
class_id = segment_info["category_id"]
|
146 |
+
if not segment_info["iscrowd"]:
|
147 |
+
mask = pan_seg_gt == segment_info["id"]
|
148 |
+
if not np.all(mask == False):
|
149 |
+
if class_id not in classes:
|
150 |
+
cls_name = self.class_names[class_id]
|
151 |
+
classes.append(class_id)
|
152 |
+
masks.append(mask)
|
153 |
+
num_class_obj[cls_name] += 1
|
154 |
+
else:
|
155 |
+
idx = classes.index(class_id)
|
156 |
+
masks[idx] += mask
|
157 |
+
masks[idx] = np.clip(masks[idx], 0, 1).astype(np.bool)
|
158 |
+
label[mask] = class_id
|
159 |
+
|
160 |
+
num = 0
|
161 |
+
for i, cls_name in enumerate(self.class_names):
|
162 |
+
if num_class_obj[cls_name] > 0:
|
163 |
+
for _ in range(num_class_obj[cls_name]):
|
164 |
+
if num >= len(texts):
|
165 |
+
break
|
166 |
+
texts[num] = f"a photo with a {cls_name}"
|
167 |
+
num += 1
|
168 |
+
|
169 |
+
classes = np.array(classes)
|
170 |
+
instances.gt_classes = torch.tensor(classes, dtype=torch.int64)
|
171 |
+
if len(masks) == 0:
|
172 |
+
# Some image does not have annotation (all ignored)
|
173 |
+
instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))
|
174 |
+
instances.gt_bboxes = torch.zeros((0, 4))
|
175 |
+
else:
|
176 |
+
masks = BitMasks(
|
177 |
+
torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])
|
178 |
+
)
|
179 |
+
instances.gt_masks = masks.tensor
|
180 |
+
# Placeholder bounding boxes for stuff regions. Note that these are not used during training.
|
181 |
+
instances.gt_bboxes = torch.stack([torch.tensor([0., 0., 1., 1.])] * instances.gt_masks.shape[0])
|
182 |
+
return instances, texts, label
|
183 |
+
|
184 |
+
def _get_instance_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj):
|
185 |
+
instances = Instances(image_shape)
|
186 |
+
|
187 |
+
classes = []
|
188 |
+
texts = ["an instance photo"] * self.num_queries
|
189 |
+
masks = []
|
190 |
+
label = np.ones_like(pan_seg_gt) * self.ignore_label
|
191 |
+
|
192 |
+
for segment_info in segments_info:
|
193 |
+
class_id = segment_info["category_id"]
|
194 |
+
if class_id in self.things:
|
195 |
+
if not segment_info["iscrowd"]:
|
196 |
+
mask = pan_seg_gt == segment_info["id"]
|
197 |
+
if not np.all(mask == False):
|
198 |
+
cls_name = self.class_names[class_id]
|
199 |
+
classes.append(class_id)
|
200 |
+
masks.append(mask)
|
201 |
+
num_class_obj[cls_name] += 1
|
202 |
+
label[mask] = class_id
|
203 |
+
|
204 |
+
num = 0
|
205 |
+
for i, cls_name in enumerate(self.class_names):
|
206 |
+
if num_class_obj[cls_name] > 0:
|
207 |
+
for _ in range(num_class_obj[cls_name]):
|
208 |
+
if num >= len(texts):
|
209 |
+
break
|
210 |
+
texts[num] = f"a photo with a {cls_name}"
|
211 |
+
num += 1
|
212 |
+
|
213 |
+
classes = np.array(classes)
|
214 |
+
instances.gt_classes = torch.tensor(classes, dtype=torch.int64)
|
215 |
+
if len(masks) == 0:
|
216 |
+
# Some image does not have annotation (all ignored)
|
217 |
+
instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))
|
218 |
+
instances.gt_bboxes = torch.zeros((0, 4))
|
219 |
+
else:
|
220 |
+
masks = BitMasks(
|
221 |
+
torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])
|
222 |
+
)
|
223 |
+
instances.gt_masks = masks.tensor
|
224 |
+
instances.gt_bboxes = masks_to_boxes(instances.gt_masks)
|
225 |
+
return instances, texts, label
|
226 |
+
|
227 |
+
def _get_panoptic_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj):
|
228 |
+
instances = Instances(image_shape)
|
229 |
+
|
230 |
+
classes = []
|
231 |
+
texts = ["a panoptic photo"] * self.num_queries
|
232 |
+
masks = []
|
233 |
+
label = np.ones_like(pan_seg_gt) * self.ignore_label
|
234 |
+
|
235 |
+
for segment_info in segments_info:
|
236 |
+
class_id = segment_info["category_id"]
|
237 |
+
if not segment_info["iscrowd"]:
|
238 |
+
mask = pan_seg_gt == segment_info["id"]
|
239 |
+
if not np.all(mask == False):
|
240 |
+
cls_name = self.class_names[class_id]
|
241 |
+
classes.append(class_id)
|
242 |
+
masks.append(mask)
|
243 |
+
num_class_obj[cls_name] += 1
|
244 |
+
label[mask] = class_id
|
245 |
+
|
246 |
+
num = 0
|
247 |
+
for i, cls_name in enumerate(self.class_names):
|
248 |
+
if num_class_obj[cls_name] > 0:
|
249 |
+
for _ in range(num_class_obj[cls_name]):
|
250 |
+
if num >= len(texts):
|
251 |
+
break
|
252 |
+
texts[num] = f"a photo with a {cls_name}"
|
253 |
+
num += 1
|
254 |
+
|
255 |
+
classes = np.array(classes)
|
256 |
+
instances.gt_classes = torch.tensor(classes, dtype=torch.int64)
|
257 |
+
if len(masks) == 0:
|
258 |
+
# Some image does not have annotation (all ignored)
|
259 |
+
instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))
|
260 |
+
instances.gt_bboxes = torch.zeros((0, 4))
|
261 |
+
else:
|
262 |
+
masks = BitMasks(
|
263 |
+
torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])
|
264 |
+
)
|
265 |
+
instances.gt_masks = masks.tensor
|
266 |
+
instances.gt_bboxes = masks_to_boxes(instances.gt_masks)
|
267 |
+
for i in range(instances.gt_classes.shape[0]):
|
268 |
+
# Placeholder bounding boxes for stuff regions. Note that these are not used during training.
|
269 |
+
if instances.gt_classes[i].item() not in self.things:
|
270 |
+
instances.gt_bboxes[i] = torch.tensor([0., 0., 1., 1.])
|
271 |
+
return instances, texts, label
|
272 |
+
|
273 |
+
def __call__(self, dataset_dict):
|
274 |
+
"""
|
275 |
+
Args:
|
276 |
+
dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.
|
277 |
+
|
278 |
+
Returns:
|
279 |
+
dict: a format that builtin models in detectron2 accept
|
280 |
+
"""
|
281 |
+
dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below
|
282 |
+
image = utils.read_image(dataset_dict["file_name"], format=self.img_format)
|
283 |
+
utils.check_image_size(dataset_dict, image)
|
284 |
+
|
285 |
+
image, transforms = T.apply_transform_gens(self.tfm_gens, image)
|
286 |
+
image_shape = image.shape[:2] # h, w
|
287 |
+
|
288 |
+
# Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,
|
289 |
+
# but not efficient on large generic data structures due to the use of pickle & mp.Queue.
|
290 |
+
# Therefore it's important to use torch.Tensor.
|
291 |
+
dataset_dict["image"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))
|
292 |
+
|
293 |
+
if not self.is_train:
|
294 |
+
# USER: Modify this if you want to keep them for some reason.
|
295 |
+
dataset_dict.pop("annotations", None)
|
296 |
+
return dataset_dict
|
297 |
+
|
298 |
+
# semantic segmentation
|
299 |
+
if "sem_seg_file_name" in dataset_dict:
|
300 |
+
# PyTorch transformation not implemented for uint16, so converting it to double first
|
301 |
+
sem_seg_gt = utils.read_image(dataset_dict.pop("sem_seg_file_name")).astype("double")
|
302 |
+
sem_seg_gt = transforms.apply_segmentation(sem_seg_gt)
|
303 |
+
else:
|
304 |
+
sem_seg_gt = None
|
305 |
+
|
306 |
+
if "pan_seg_file_name" in dataset_dict:
|
307 |
+
pan_seg_gt = utils.read_image(dataset_dict.pop("pan_seg_file_name"), "RGB")
|
308 |
+
segments_info = dataset_dict["segments_info"]
|
309 |
+
|
310 |
+
# apply the same transformation to panoptic segmentation
|
311 |
+
pan_seg_gt = transforms.apply_segmentation(pan_seg_gt)
|
312 |
+
|
313 |
+
from panopticapi.utils import rgb2id
|
314 |
+
pan_seg_gt = rgb2id(pan_seg_gt)
|
315 |
+
|
316 |
+
prob_task = np.random.uniform(0,1.)
|
317 |
+
|
318 |
+
num_class_obj = {}
|
319 |
+
|
320 |
+
for name in self.class_names:
|
321 |
+
num_class_obj[name] = 0
|
322 |
+
|
323 |
+
if prob_task < self.semantic_prob:
|
324 |
+
task = "The task is semantic"
|
325 |
+
instances, text, sem_seg = self._get_semantic_dict(pan_seg_gt, image_shape, segments_info, num_class_obj)
|
326 |
+
elif prob_task < self.instance_prob:
|
327 |
+
task = "The task is instance"
|
328 |
+
instances, text, sem_seg = self._get_instance_dict(pan_seg_gt, image_shape, segments_info, num_class_obj)
|
329 |
+
else:
|
330 |
+
task = "The task is panoptic"
|
331 |
+
instances, text, sem_seg = self._get_panoptic_dict(pan_seg_gt, image_shape, segments_info, num_class_obj)
|
332 |
+
|
333 |
+
|
334 |
+
dataset_dict["sem_seg"] = torch.from_numpy(sem_seg).long()
|
335 |
+
dataset_dict["instances"] = instances
|
336 |
+
dataset_dict["orig_shape"] = image_shape
|
337 |
+
dataset_dict["task"] = task
|
338 |
+
dataset_dict["text"] = text
|
339 |
+
dataset_dict["thing_ids"] = self.things
|
340 |
+
|
341 |
+
return dataset_dict
|
oneformer/data/dataset_mappers/dataset_mapper.py
ADDED
@@ -0,0 +1,203 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# ------------------------------------------------------------------------------
|
2 |
+
# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/data/dataset_mapper.py
|
3 |
+
# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
|
4 |
+
# ------------------------------------------------------------------------------
|
5 |
+
|
6 |
+
import copy
|
7 |
+
import logging
|
8 |
+
import numpy as np
|
9 |
+
from typing import List, Optional, Union
|
10 |
+
import torch
|
11 |
+
|
12 |
+
from detectron2.config import configurable
|
13 |
+
|
14 |
+
from detectron2.data import detection_utils as utils
|
15 |
+
from detectron2.data import transforms as T
|
16 |
+
from oneformer.data.tokenizer import SimpleTokenizer, Tokenize
|
17 |
+
|
18 |
+
__all__ = ["DatasetMapper"]
|
19 |
+
|
20 |
+
|
21 |
+
class DatasetMapper:
|
22 |
+
"""
|
23 |
+
A callable which takes a dataset dict in Detectron2 Dataset format,
|
24 |
+
and map it into a format used by the model.
|
25 |
+
|
26 |
+
This is the default callable to be used to map your dataset dict into training data.
|
27 |
+
You may need to follow it to implement your own one for customized logic,
|
28 |
+
such as a different way to read or transform images.
|
29 |
+
See :doc:`/tutorials/data_loading` for details.
|
30 |
+
|
31 |
+
The callable currently does the following:
|
32 |
+
|
33 |
+
1. Read the image from "file_name"
|
34 |
+
2. Applies cropping/geometric transforms to the image and annotations
|
35 |
+
3. Prepare data and annotations to Tensor and :class:`Instances`
|
36 |
+
"""
|
37 |
+
|
38 |
+
@configurable
|
39 |
+
def __init__(
|
40 |
+
self,
|
41 |
+
is_train: bool,
|
42 |
+
*,
|
43 |
+
augmentations: List[Union[T.Augmentation, T.Transform]],
|
44 |
+
image_format: str,
|
45 |
+
task_seq_len: int,
|
46 |
+
task: str = "panoptic",
|
47 |
+
use_instance_mask: bool = False,
|
48 |
+
use_keypoint: bool = False,
|
49 |
+
instance_mask_format: str = "polygon",
|
50 |
+
keypoint_hflip_indices: Optional[np.ndarray] = None,
|
51 |
+
precomputed_proposal_topk: Optional[int] = None,
|
52 |
+
recompute_boxes: bool = False,
|
53 |
+
):
|
54 |
+
"""
|
55 |
+
NOTE: this interface is experimental.
|
56 |
+
|
57 |
+
Args:
|
58 |
+
is_train: whether it's used in training or inference
|
59 |
+
augmentations: a list of augmentations or deterministic transforms to apply
|
60 |
+
image_format: an image format supported by :func:`detection_utils.read_image`.
|
61 |
+
use_instance_mask: whether to process instance segmentation annotations, if available
|
62 |
+
use_keypoint: whether to process keypoint annotations if available
|
63 |
+
instance_mask_format: one of "polygon" or "bitmask". Process instance segmentation
|
64 |
+
masks into this format.
|
65 |
+
keypoint_hflip_indices: see :func:`detection_utils.create_keypoint_hflip_indices`
|
66 |
+
precomputed_proposal_topk: if given, will load pre-computed
|
67 |
+
proposals from dataset_dict and keep the top k proposals for each image.
|
68 |
+
recompute_boxes: whether to overwrite bounding box annotations
|
69 |
+
by computing tight bounding boxes from instance mask annotations.
|
70 |
+
"""
|
71 |
+
if recompute_boxes:
|
72 |
+
assert use_instance_mask, "recompute_boxes requires instance masks"
|
73 |
+
# fmt: off
|
74 |
+
self.is_train = is_train
|
75 |
+
self.augmentations = T.AugmentationList(augmentations)
|
76 |
+
self.image_format = image_format
|
77 |
+
self.use_instance_mask = use_instance_mask
|
78 |
+
self.instance_mask_format = instance_mask_format
|
79 |
+
self.use_keypoint = use_keypoint
|
80 |
+
self.keypoint_hflip_indices = keypoint_hflip_indices
|
81 |
+
self.proposal_topk = precomputed_proposal_topk
|
82 |
+
self.recompute_boxes = recompute_boxes
|
83 |
+
self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len)
|
84 |
+
self.task = task
|
85 |
+
assert self.task in ["panoptic", "semantic", "instance"]
|
86 |
+
|
87 |
+
# fmt: on
|
88 |
+
logger = logging.getLogger(__name__)
|
89 |
+
mode = "training" if is_train else "inference"
|
90 |
+
logger.info(f"[DatasetMapper] Augmentations used in {mode}: {augmentations}")
|
91 |
+
|
92 |
+
@classmethod
|
93 |
+
def from_config(cls, cfg, is_train: bool = True):
|
94 |
+
augs = utils.build_augmentation(cfg, is_train)
|
95 |
+
if cfg.INPUT.CROP.ENABLED and is_train:
|
96 |
+
augs.insert(0, T.RandomCrop(cfg.INPUT.CROP.TYPE, cfg.INPUT.CROP.SIZE))
|
97 |
+
recompute_boxes = cfg.MODEL.MASK_ON
|
98 |
+
else:
|
99 |
+
recompute_boxes = False
|
100 |
+
|
101 |
+
ret = {
|
102 |
+
"is_train": is_train,
|
103 |
+
"augmentations": augs,
|
104 |
+
"image_format": cfg.INPUT.FORMAT,
|
105 |
+
"use_instance_mask": cfg.MODEL.MASK_ON,
|
106 |
+
"instance_mask_format": cfg.INPUT.MASK_FORMAT,
|
107 |
+
"use_keypoint": cfg.MODEL.KEYPOINT_ON,
|
108 |
+
"task_seq_len": cfg.INPUT.TASK_SEQ_LEN,
|
109 |
+
"recompute_boxes": recompute_boxes,
|
110 |
+
"task": cfg.MODEL.TEST.TASK,
|
111 |
+
}
|
112 |
+
|
113 |
+
if cfg.MODEL.KEYPOINT_ON:
|
114 |
+
ret["keypoint_hflip_indices"] = utils.create_keypoint_hflip_indices(cfg.DATASETS.TRAIN)
|
115 |
+
|
116 |
+
if cfg.MODEL.LOAD_PROPOSALS:
|
117 |
+
ret["precomputed_proposal_topk"] = (
|
118 |
+
cfg.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TRAIN
|
119 |
+
if is_train
|
120 |
+
else cfg.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TEST
|
121 |
+
)
|
122 |
+
return ret
|
123 |
+
|
124 |
+
def _transform_annotations(self, dataset_dict, transforms, image_shape):
|
125 |
+
# USER: Modify this if you want to keep them for some reason.
|
126 |
+
for anno in dataset_dict["annotations"]:
|
127 |
+
if not self.use_instance_mask:
|
128 |
+
anno.pop("segmentation", None)
|
129 |
+
if not self.use_keypoint:
|
130 |
+
anno.pop("keypoints", None)
|
131 |
+
|
132 |
+
# USER: Implement additional transformations if you have other types of data
|
133 |
+
annos = [
|
134 |
+
utils.transform_instance_annotations(
|
135 |
+
obj, transforms, image_shape, keypoint_hflip_indices=self.keypoint_hflip_indices
|
136 |
+
)
|
137 |
+
for obj in dataset_dict.pop("annotations")
|
138 |
+
if obj.get("iscrowd", 0) == 0
|
139 |
+
]
|
140 |
+
instances = utils.annotations_to_instances(
|
141 |
+
annos, image_shape, mask_format=self.instance_mask_format
|
142 |
+
)
|
143 |
+
|
144 |
+
# After transforms such as cropping are applied, the bounding box may no longer
|
145 |
+
# tightly bound the object. As an example, imagine a triangle object
|
146 |
+
# [(0,0), (2,0), (0,2)] cropped by a box [(1,0),(2,2)] (XYXY format). The tight
|
147 |
+
# bounding box of the cropped triangle should be [(1,0),(2,1)], which is not equal to
|
148 |
+
# the intersection of original bounding box and the cropping box.
|
149 |
+
if self.recompute_boxes:
|
150 |
+
instances.gt_boxes = instances.gt_masks.get_bounding_boxes()
|
151 |
+
dataset_dict["instances"] = utils.filter_empty_instances(instances)
|
152 |
+
|
153 |
+
def __call__(self, dataset_dict):
|
154 |
+
"""
|
155 |
+
Args:
|
156 |
+
dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.
|
157 |
+
|
158 |
+
Returns:
|
159 |
+
dict: a format that builtin models in detectron2 accept
|
160 |
+
"""
|
161 |
+
dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below
|
162 |
+
# USER: Write your own image loading if it's not from a file
|
163 |
+
image = utils.read_image(dataset_dict["file_name"], format=self.image_format)
|
164 |
+
utils.check_image_size(dataset_dict, image)
|
165 |
+
|
166 |
+
task = f"The task is {self.task}"
|
167 |
+
dataset_dict["task"] = task
|
168 |
+
|
169 |
+
# USER: Remove if you don't do semantic/panoptic segmentation.
|
170 |
+
if "sem_seg_file_name" in dataset_dict:
|
171 |
+
sem_seg_gt = utils.read_image(dataset_dict.pop("sem_seg_file_name"), "L").squeeze(2)
|
172 |
+
else:
|
173 |
+
sem_seg_gt = None
|
174 |
+
|
175 |
+
aug_input = T.AugInput(image, sem_seg=sem_seg_gt)
|
176 |
+
transforms = self.augmentations(aug_input)
|
177 |
+
image, sem_seg_gt = aug_input.image, aug_input.sem_seg
|
178 |
+
|
179 |
+
image_shape = image.shape[:2] # h, w
|
180 |
+
# Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,
|
181 |
+
# but not efficient on large generic data structures due to the use of pickle & mp.Queue.
|
182 |
+
# Therefore it's important to use torch.Tensor.
|
183 |
+
dataset_dict["image"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))
|
184 |
+
if sem_seg_gt is not None:
|
185 |
+
dataset_dict["sem_seg"] = torch.as_tensor(sem_seg_gt.astype("long"))
|
186 |
+
|
187 |
+
# USER: Remove if you don't use pre-computed proposals.
|
188 |
+
# Most users would not need this feature.
|
189 |
+
if self.proposal_topk is not None:
|
190 |
+
utils.transform_proposals(
|
191 |
+
dataset_dict, image_shape, transforms, proposal_topk=self.proposal_topk
|
192 |
+
)
|
193 |
+
|
194 |
+
if not self.is_train:
|
195 |
+
# USER: Modify this if you want to keep them for some reason.
|
196 |
+
dataset_dict.pop("annotations", None)
|
197 |
+
dataset_dict.pop("sem_seg_file_name", None)
|
198 |
+
return dataset_dict
|
199 |
+
|
200 |
+
if "annotations" in dataset_dict:
|
201 |
+
self._transform_annotations(dataset_dict, transforms, image_shape)
|
202 |
+
|
203 |
+
return dataset_dict
|
oneformer/data/dataset_mappers/oneformer_unified_dataset_mapper.py
ADDED
@@ -0,0 +1,375 @@
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|
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|
|
|
|
|
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|
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|
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|
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|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# ------------------------------------------------------------------------------
|
2 |
+
# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/data/dataset_mappers/mask_former_panoptic_dataset_mapper.py
|
3 |
+
# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
|
4 |
+
# ------------------------------------------------------------------------------
|
5 |
+
|
6 |
+
import copy
|
7 |
+
import logging
|
8 |
+
import os
|
9 |
+
|
10 |
+
import numpy as np
|
11 |
+
import torch
|
12 |
+
from torch.nn import functional as F
|
13 |
+
|
14 |
+
from detectron2.config import configurable
|
15 |
+
from detectron2.data import detection_utils as utils
|
16 |
+
from detectron2.data import transforms as T
|
17 |
+
from detectron2.structures import BitMasks, Instances
|
18 |
+
from detectron2.data import MetadataCatalog
|
19 |
+
from detectron2.projects.point_rend import ColorAugSSDTransform
|
20 |
+
from oneformer.utils.box_ops import masks_to_boxes
|
21 |
+
from oneformer.data.tokenizer import SimpleTokenizer, Tokenize
|
22 |
+
|
23 |
+
__all__ = ["OneFormerUnifiedDatasetMapper"]
|
24 |
+
|
25 |
+
|
26 |
+
class OneFormerUnifiedDatasetMapper:
|
27 |
+
"""
|
28 |
+
A callable which takes a dataset dict in Detectron2 Dataset format,
|
29 |
+
and map it into a format used by OneFormer for universal segmentation.
|
30 |
+
|
31 |
+
The callable currently does the following:
|
32 |
+
|
33 |
+
1. Read the image from "file_name"
|
34 |
+
2. Applies geometric transforms to the image and annotation
|
35 |
+
3. Find and applies suitable cropping to the image and annotation
|
36 |
+
4. Prepare image and annotation to Tensors
|
37 |
+
"""
|
38 |
+
|
39 |
+
@configurable
|
40 |
+
def __init__(
|
41 |
+
self,
|
42 |
+
is_train=True,
|
43 |
+
*,
|
44 |
+
name,
|
45 |
+
num_queries,
|
46 |
+
meta,
|
47 |
+
augmentations,
|
48 |
+
image_format,
|
49 |
+
ignore_label,
|
50 |
+
size_divisibility,
|
51 |
+
task_seq_len,
|
52 |
+
max_seq_len,
|
53 |
+
semantic_prob,
|
54 |
+
instance_prob,
|
55 |
+
):
|
56 |
+
"""
|
57 |
+
NOTE: this interface is experimental.
|
58 |
+
Args:
|
59 |
+
is_train: for training or inference
|
60 |
+
augmentations: a list of augmentations or deterministic transforms to apply
|
61 |
+
image_format: an image format supported by :func:`detection_utils.read_image`.
|
62 |
+
ignore_label: the label that is ignored to evaluation
|
63 |
+
size_divisibility: pad image size to be divisible by this value
|
64 |
+
"""
|
65 |
+
self.is_train = is_train
|
66 |
+
self.meta = meta
|
67 |
+
self.name = name
|
68 |
+
self.tfm_gens = augmentations
|
69 |
+
self.img_format = image_format
|
70 |
+
self.ignore_label = ignore_label
|
71 |
+
self.size_divisibility = size_divisibility
|
72 |
+
self.num_queries = num_queries
|
73 |
+
|
74 |
+
logger = logging.getLogger(__name__)
|
75 |
+
mode = "training" if is_train else "inference"
|
76 |
+
logger.info(f"[{self.__class__.__name__}] Augmentations used in {mode}: {augmentations}")
|
77 |
+
|
78 |
+
self.things = []
|
79 |
+
for k,v in self.meta.thing_dataset_id_to_contiguous_id.items():
|
80 |
+
self.things.append(v)
|
81 |
+
self.class_names = self.meta.stuff_classes
|
82 |
+
self.text_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=max_seq_len)
|
83 |
+
self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len)
|
84 |
+
self.semantic_prob = semantic_prob
|
85 |
+
self.instance_prob = instance_prob
|
86 |
+
|
87 |
+
@classmethod
|
88 |
+
def from_config(cls, cfg, is_train=True):
|
89 |
+
# Build augmentation
|
90 |
+
augs = [
|
91 |
+
T.ResizeShortestEdge(
|
92 |
+
cfg.INPUT.MIN_SIZE_TRAIN,
|
93 |
+
cfg.INPUT.MAX_SIZE_TRAIN,
|
94 |
+
cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING,
|
95 |
+
)
|
96 |
+
]
|
97 |
+
if cfg.INPUT.CROP.ENABLED:
|
98 |
+
augs.append(
|
99 |
+
T.RandomCrop_CategoryAreaConstraint(
|
100 |
+
cfg.INPUT.CROP.TYPE,
|
101 |
+
cfg.INPUT.CROP.SIZE,
|
102 |
+
cfg.INPUT.CROP.SINGLE_CATEGORY_MAX_AREA,
|
103 |
+
cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE,
|
104 |
+
)
|
105 |
+
)
|
106 |
+
if cfg.INPUT.COLOR_AUG_SSD:
|
107 |
+
augs.append(ColorAugSSDTransform(img_format=cfg.INPUT.FORMAT))
|
108 |
+
augs.append(T.RandomFlip())
|
109 |
+
|
110 |
+
# Assume always applies to the training set.
|
111 |
+
dataset_names = cfg.DATASETS.TRAIN
|
112 |
+
meta = MetadataCatalog.get(dataset_names[0])
|
113 |
+
ignore_label = meta.ignore_label
|
114 |
+
|
115 |
+
ret = {
|
116 |
+
"is_train": is_train,
|
117 |
+
"meta": meta,
|
118 |
+
"name": dataset_names[0],
|
119 |
+
"num_queries": cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES - cfg.MODEL.TEXT_ENCODER.N_CTX,
|
120 |
+
"task_seq_len": cfg.INPUT.TASK_SEQ_LEN,
|
121 |
+
"max_seq_len": cfg.INPUT.MAX_SEQ_LEN,
|
122 |
+
"augmentations": augs,
|
123 |
+
"image_format": cfg.INPUT.FORMAT,
|
124 |
+
"ignore_label": ignore_label,
|
125 |
+
"size_divisibility": cfg.INPUT.SIZE_DIVISIBILITY,
|
126 |
+
"semantic_prob": cfg.INPUT.TASK_PROB.SEMANTIC,
|
127 |
+
"instance_prob": cfg.INPUT.TASK_PROB.INSTANCE,
|
128 |
+
}
|
129 |
+
return ret
|
130 |
+
|
131 |
+
def _get_semantic_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj):
|
132 |
+
pan_seg_gt = pan_seg_gt.numpy()
|
133 |
+
instances = Instances(image_shape)
|
134 |
+
|
135 |
+
classes = []
|
136 |
+
texts = ["a semantic photo"] * self.num_queries
|
137 |
+
masks = []
|
138 |
+
label = np.ones_like(pan_seg_gt) * self.ignore_label
|
139 |
+
|
140 |
+
for segment_info in segments_info:
|
141 |
+
class_id = segment_info["category_id"]
|
142 |
+
if not segment_info["iscrowd"]:
|
143 |
+
mask = pan_seg_gt == segment_info["id"]
|
144 |
+
if not np.all(mask == False):
|
145 |
+
if class_id not in classes:
|
146 |
+
cls_name = self.class_names[class_id]
|
147 |
+
classes.append(class_id)
|
148 |
+
masks.append(mask)
|
149 |
+
num_class_obj[cls_name] += 1
|
150 |
+
else:
|
151 |
+
idx = classes.index(class_id)
|
152 |
+
masks[idx] += mask
|
153 |
+
masks[idx] = np.clip(masks[idx], 0, 1).astype(np.bool)
|
154 |
+
label[mask] = class_id
|
155 |
+
|
156 |
+
num = 0
|
157 |
+
for i, cls_name in enumerate(self.class_names):
|
158 |
+
if num_class_obj[cls_name] > 0:
|
159 |
+
for _ in range(num_class_obj[cls_name]):
|
160 |
+
if num >= len(texts):
|
161 |
+
break
|
162 |
+
texts[num] = f"a photo with a {cls_name}"
|
163 |
+
num += 1
|
164 |
+
|
165 |
+
classes = np.array(classes)
|
166 |
+
instances.gt_classes = torch.tensor(classes, dtype=torch.int64)
|
167 |
+
if len(masks) == 0:
|
168 |
+
# Some image does not have annotation (all ignored)
|
169 |
+
instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))
|
170 |
+
instances.gt_bboxes = torch.zeros((0, 4))
|
171 |
+
else:
|
172 |
+
masks = BitMasks(
|
173 |
+
torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])
|
174 |
+
)
|
175 |
+
instances.gt_masks = masks.tensor
|
176 |
+
# Placeholder bounding boxes for stuff regions. Note that these are not used during training.
|
177 |
+
instances.gt_bboxes = torch.stack([torch.tensor([0., 0., 1., 1.])] * instances.gt_masks.shape[0])
|
178 |
+
return instances, texts, label
|
179 |
+
|
180 |
+
def _get_instance_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj):
|
181 |
+
pan_seg_gt = pan_seg_gt.numpy()
|
182 |
+
instances = Instances(image_shape)
|
183 |
+
|
184 |
+
classes = []
|
185 |
+
texts = ["an instance photo"] * self.num_queries
|
186 |
+
masks = []
|
187 |
+
label = np.ones_like(pan_seg_gt) * self.ignore_label
|
188 |
+
|
189 |
+
for segment_info in segments_info:
|
190 |
+
class_id = segment_info["category_id"]
|
191 |
+
if class_id in self.things:
|
192 |
+
if not segment_info["iscrowd"]:
|
193 |
+
mask = pan_seg_gt == segment_info["id"]
|
194 |
+
if not np.all(mask == False):
|
195 |
+
cls_name = self.class_names[class_id]
|
196 |
+
classes.append(class_id)
|
197 |
+
masks.append(mask)
|
198 |
+
num_class_obj[cls_name] += 1
|
199 |
+
label[mask] = class_id
|
200 |
+
|
201 |
+
num = 0
|
202 |
+
for i, cls_name in enumerate(self.class_names):
|
203 |
+
if num_class_obj[cls_name] > 0:
|
204 |
+
for _ in range(num_class_obj[cls_name]):
|
205 |
+
if num >= len(texts):
|
206 |
+
break
|
207 |
+
texts[num] = f"a photo with a {cls_name}"
|
208 |
+
num += 1
|
209 |
+
|
210 |
+
classes = np.array(classes)
|
211 |
+
instances.gt_classes = torch.tensor(classes, dtype=torch.int64)
|
212 |
+
if len(masks) == 0:
|
213 |
+
# Some image does not have annotation (all ignored)
|
214 |
+
instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))
|
215 |
+
instances.gt_bboxes = torch.zeros((0, 4))
|
216 |
+
else:
|
217 |
+
masks = BitMasks(
|
218 |
+
torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])
|
219 |
+
)
|
220 |
+
instances.gt_masks = masks.tensor
|
221 |
+
instances.gt_bboxes = masks_to_boxes(instances.gt_masks)
|
222 |
+
return instances, texts, label
|
223 |
+
|
224 |
+
def _get_panoptic_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj):
|
225 |
+
pan_seg_gt = pan_seg_gt.numpy()
|
226 |
+
instances = Instances(image_shape)
|
227 |
+
|
228 |
+
classes = []
|
229 |
+
texts = ["a panoptic photo"] * self.num_queries
|
230 |
+
masks = []
|
231 |
+
label = np.ones_like(pan_seg_gt) * self.ignore_label
|
232 |
+
|
233 |
+
for segment_info in segments_info:
|
234 |
+
class_id = segment_info["category_id"]
|
235 |
+
if not segment_info["iscrowd"]:
|
236 |
+
mask = pan_seg_gt == segment_info["id"]
|
237 |
+
if not np.all(mask == False):
|
238 |
+
cls_name = self.class_names[class_id]
|
239 |
+
classes.append(class_id)
|
240 |
+
masks.append(mask)
|
241 |
+
num_class_obj[cls_name] += 1
|
242 |
+
label[mask] = class_id
|
243 |
+
|
244 |
+
num = 0
|
245 |
+
for i, cls_name in enumerate(self.class_names):
|
246 |
+
if num_class_obj[cls_name] > 0:
|
247 |
+
for _ in range(num_class_obj[cls_name]):
|
248 |
+
if num >= len(texts):
|
249 |
+
break
|
250 |
+
texts[num] = f"a photo with a {cls_name}"
|
251 |
+
num += 1
|
252 |
+
|
253 |
+
classes = np.array(classes)
|
254 |
+
instances.gt_classes = torch.tensor(classes, dtype=torch.int64)
|
255 |
+
if len(masks) == 0:
|
256 |
+
# Some image does not have annotation (all ignored)
|
257 |
+
instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))
|
258 |
+
instances.gt_bboxes = torch.zeros((0, 4))
|
259 |
+
else:
|
260 |
+
masks = BitMasks(
|
261 |
+
torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])
|
262 |
+
)
|
263 |
+
instances.gt_masks = masks.tensor
|
264 |
+
instances.gt_bboxes = masks_to_boxes(instances.gt_masks)
|
265 |
+
for i in range(instances.gt_classes.shape[0]):
|
266 |
+
# Placeholder bounding boxes for stuff regions. Note that these are not used during training.
|
267 |
+
if instances.gt_classes[i].item() not in self.things:
|
268 |
+
instances.gt_bboxes[i] = torch.tensor([0., 0., 1., 1.])
|
269 |
+
return instances, texts, label
|
270 |
+
|
271 |
+
def __call__(self, dataset_dict):
|
272 |
+
"""
|
273 |
+
Args:
|
274 |
+
dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.
|
275 |
+
|
276 |
+
Returns:
|
277 |
+
dict: a format that builtin models in detectron2 accept
|
278 |
+
"""
|
279 |
+
assert self.is_train, "OneFormerUnifiedDatasetMapper should only be used for training!"
|
280 |
+
|
281 |
+
dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below
|
282 |
+
image = utils.read_image(dataset_dict["file_name"], format=self.img_format)
|
283 |
+
utils.check_image_size(dataset_dict, image)
|
284 |
+
|
285 |
+
# semantic segmentation
|
286 |
+
if "sem_seg_file_name" in dataset_dict:
|
287 |
+
# PyTorch transformation not implemented for uint16, so converting it to double first
|
288 |
+
sem_seg_gt = utils.read_image(dataset_dict.pop("sem_seg_file_name")).astype("double")
|
289 |
+
else:
|
290 |
+
sem_seg_gt = None
|
291 |
+
|
292 |
+
# panoptic segmentation
|
293 |
+
if "pan_seg_file_name" in dataset_dict:
|
294 |
+
pan_seg_gt = utils.read_image(dataset_dict.pop("pan_seg_file_name"), "RGB")
|
295 |
+
segments_info = dataset_dict["segments_info"]
|
296 |
+
else:
|
297 |
+
pan_seg_gt = None
|
298 |
+
segments_info = None
|
299 |
+
|
300 |
+
if pan_seg_gt is None:
|
301 |
+
raise ValueError(
|
302 |
+
"Cannot find 'pan_seg_file_name' for panoptic segmentation dataset {}.".format(
|
303 |
+
dataset_dict["file_name"]
|
304 |
+
)
|
305 |
+
)
|
306 |
+
|
307 |
+
aug_input = T.AugInput(image, sem_seg=sem_seg_gt)
|
308 |
+
aug_input, transforms = T.apply_transform_gens(self.tfm_gens, aug_input)
|
309 |
+
image = aug_input.image
|
310 |
+
if sem_seg_gt is not None:
|
311 |
+
sem_seg_gt = aug_input.sem_seg
|
312 |
+
|
313 |
+
# apply the same transformation to panoptic segmentation
|
314 |
+
pan_seg_gt = transforms.apply_segmentation(pan_seg_gt)
|
315 |
+
|
316 |
+
from panopticapi.utils import rgb2id
|
317 |
+
|
318 |
+
pan_seg_gt = rgb2id(pan_seg_gt)
|
319 |
+
|
320 |
+
# Pad image and segmentation label here!
|
321 |
+
image = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))
|
322 |
+
if sem_seg_gt is not None:
|
323 |
+
sem_seg_gt = torch.as_tensor(sem_seg_gt.astype("long"))
|
324 |
+
pan_seg_gt = torch.as_tensor(pan_seg_gt.astype("long"))
|
325 |
+
|
326 |
+
if self.size_divisibility > 0:
|
327 |
+
image_size = (image.shape[-2], image.shape[-1])
|
328 |
+
padding_size = [
|
329 |
+
0,
|
330 |
+
self.size_divisibility - image_size[1],
|
331 |
+
0,
|
332 |
+
self.size_divisibility - image_size[0],
|
333 |
+
]
|
334 |
+
image = F.pad(image, padding_size, value=128).contiguous()
|
335 |
+
if sem_seg_gt is not None:
|
336 |
+
sem_seg_gt = F.pad(sem_seg_gt, padding_size, value=self.ignore_label).contiguous()
|
337 |
+
pan_seg_gt = F.pad(
|
338 |
+
pan_seg_gt, padding_size, value=0
|
339 |
+
).contiguous() # 0 is the VOID panoptic label
|
340 |
+
|
341 |
+
image_shape = (image.shape[-2], image.shape[-1]) # h, w
|
342 |
+
|
343 |
+
# Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,
|
344 |
+
# but not efficient on large generic data structures due to the use of pickle & mp.Queue.
|
345 |
+
# Therefore it's important to use torch.Tensor.
|
346 |
+
dataset_dict["image"] = image
|
347 |
+
|
348 |
+
if "annotations" in dataset_dict:
|
349 |
+
raise ValueError("Pemantic segmentation dataset should not have 'annotations'.")
|
350 |
+
|
351 |
+
prob_task = np.random.uniform(0,1.)
|
352 |
+
|
353 |
+
num_class_obj = {}
|
354 |
+
|
355 |
+
for name in self.class_names:
|
356 |
+
num_class_obj[name] = 0
|
357 |
+
|
358 |
+
if prob_task < self.semantic_prob:
|
359 |
+
task = "The task is semantic"
|
360 |
+
instances, text, sem_seg = self._get_semantic_dict(pan_seg_gt, image_shape, segments_info, num_class_obj)
|
361 |
+
elif prob_task < self.instance_prob:
|
362 |
+
task = "The task is instance"
|
363 |
+
instances, text, sem_seg = self._get_instance_dict(pan_seg_gt, image_shape, segments_info, num_class_obj)
|
364 |
+
else:
|
365 |
+
task = "The task is panoptic"
|
366 |
+
instances, text, sem_seg = self._get_panoptic_dict(pan_seg_gt, image_shape, segments_info, num_class_obj)
|
367 |
+
|
368 |
+
dataset_dict["sem_seg"] = torch.from_numpy(sem_seg).long()
|
369 |
+
dataset_dict["instances"] = instances
|
370 |
+
dataset_dict["orig_shape"] = image_shape
|
371 |
+
dataset_dict["task"] = task
|
372 |
+
dataset_dict["text"] = text
|
373 |
+
dataset_dict["thing_ids"] = self.things
|
374 |
+
|
375 |
+
return dataset_dict
|
oneformer/data/datasets/__init__.py
ADDED
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from . import (
|
2 |
+
register_ade20k_panoptic,
|
3 |
+
register_cityscapes_panoptic,
|
4 |
+
register_coco_panoptic_annos_semseg,
|
5 |
+
register_ade20k_instance,
|
6 |
+
register_coco_panoptic2instance,
|
7 |
+
)
|
oneformer/data/datasets/register_ade20k_instance.py
ADDED
@@ -0,0 +1,56 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# ------------------------------------------------------------------------------
|
2 |
+
# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/data/datasets/register_ade20k_instance.py
|
3 |
+
# ------------------------------------------------------------------------------
|
4 |
+
|
5 |
+
import json
|
6 |
+
import logging
|
7 |
+
import numpy as np
|
8 |
+
import os
|
9 |
+
from PIL import Image
|
10 |
+
|
11 |
+
from detectron2.data import DatasetCatalog, MetadataCatalog
|
12 |
+
from detectron2.data.datasets.coco import load_coco_json, register_coco_instances
|
13 |
+
from detectron2.utils.file_io import PathManager
|
14 |
+
|
15 |
+
ADE_CATEGORIES = [{'id': 7, 'name': 'bed'}, {'id': 8, 'name': 'windowpane'}, {'id': 10, 'name': 'cabinet'}, {'id': 12, 'name': 'person'}, {'id': 14, 'name': 'door'}, {'id': 15, 'name': 'table'}, {'id': 18, 'name': 'curtain'}, {'id': 19, 'name': 'chair'}, {'id': 20, 'name': 'car'}, {'id': 22, 'name': 'painting'}, {'id': 23, 'name': 'sofa'}, {'id': 24, 'name': 'shelf'}, {'id': 27, 'name': 'mirror'}, {'id': 30, 'name': 'armchair'}, {'id': 31, 'name': 'seat'}, {'id': 32, 'name': 'fence'}, {'id': 33, 'name': 'desk'}, {'id': 35, 'name': 'wardrobe'}, {'id': 36, 'name': 'lamp'}, {'id': 37, 'name': 'bathtub'}, {'id': 38, 'name': 'railing'}, {'id': 39, 'name': 'cushion'}, {'id': 41, 'name': 'box'}, {'id': 42, 'name': 'column'}, {'id': 43, 'name': 'signboard'}, {'id': 44, 'name': 'chest of drawers'}, {'id': 45, 'name': 'counter'}, {'id': 47, 'name': 'sink'}, {'id': 49, 'name': 'fireplace'}, {'id': 50, 'name': 'refrigerator'}, {'id': 53, 'name': 'stairs'}, {'id': 55, 'name': 'case'}, {'id': 56, 'name': 'pool table'}, {'id': 57, 'name': 'pillow'}, {'id': 58, 'name': 'screen door'}, {'id': 62, 'name': 'bookcase'}, {'id': 64, 'name': 'coffee table'}, {'id': 65, 'name': 'toilet'}, {'id': 66, 'name': 'flower'}, {'id': 67, 'name': 'book'}, {'id': 69, 'name': 'bench'}, {'id': 70, 'name': 'countertop'}, {'id': 71, 'name': 'stove'}, {'id': 72, 'name': 'palm'}, {'id': 73, 'name': 'kitchen island'}, {'id': 74, 'name': 'computer'}, {'id': 75, 'name': 'swivel chair'}, {'id': 76, 'name': 'boat'}, {'id': 78, 'name': 'arcade machine'}, {'id': 80, 'name': 'bus'}, {'id': 81, 'name': 'towel'}, {'id': 82, 'name': 'light'}, {'id': 83, 'name': 'truck'}, {'id': 85, 'name': 'chandelier'}, {'id': 86, 'name': 'awning'}, {'id': 87, 'name': 'streetlight'}, {'id': 88, 'name': 'booth'}, {'id': 89, 'name': 'television receiver'}, {'id': 90, 'name': 'airplane'}, {'id': 92, 'name': 'apparel'}, {'id': 93, 'name': 'pole'}, {'id': 95, 'name': 'bannister'}, {'id': 97, 'name': 'ottoman'}, {'id': 98, 'name': 'bottle'}, {'id': 102, 'name': 'van'}, {'id': 103, 'name': 'ship'}, {'id': 104, 'name': 'fountain'}, {'id': 107, 'name': 'washer'}, {'id': 108, 'name': 'plaything'}, {'id': 110, 'name': 'stool'}, {'id': 111, 'name': 'barrel'}, {'id': 112, 'name': 'basket'}, {'id': 115, 'name': 'bag'}, {'id': 116, 'name': 'minibike'}, {'id': 118, 'name': 'oven'}, {'id': 119, 'name': 'ball'}, {'id': 120, 'name': 'food'}, {'id': 121, 'name': 'step'}, {'id': 123, 'name': 'trade name'}, {'id': 124, 'name': 'microwave'}, {'id': 125, 'name': 'pot'}, {'id': 126, 'name': 'animal'}, {'id': 127, 'name': 'bicycle'}, {'id': 129, 'name': 'dishwasher'}, {'id': 130, 'name': 'screen'}, {'id': 132, 'name': 'sculpture'}, {'id': 133, 'name': 'hood'}, {'id': 134, 'name': 'sconce'}, {'id': 135, 'name': 'vase'}, {'id': 136, 'name': 'traffic light'}, {'id': 137, 'name': 'tray'}, {'id': 138, 'name': 'ashcan'}, {'id': 139, 'name': 'fan'}, {'id': 142, 'name': 'plate'}, {'id': 143, 'name': 'monitor'}, {'id': 144, 'name': 'bulletin board'}, {'id': 146, 'name': 'radiator'}, {'id': 147, 'name': 'glass'}, {'id': 148, 'name': 'clock'}, {'id': 149, 'name': 'flag'}]
|
16 |
+
|
17 |
+
|
18 |
+
_PREDEFINED_SPLITS = {
|
19 |
+
# point annotations without masks
|
20 |
+
"ade20k_instance_train": (
|
21 |
+
"ADEChallengeData2016/images/training",
|
22 |
+
"ADEChallengeData2016/ade20k_instance_train.json",
|
23 |
+
),
|
24 |
+
"ade20k_instance_val": (
|
25 |
+
"ADEChallengeData2016/images/validation",
|
26 |
+
"ADEChallengeData2016/ade20k_instance_val.json",
|
27 |
+
),
|
28 |
+
}
|
29 |
+
|
30 |
+
|
31 |
+
def _get_ade_instances_meta():
|
32 |
+
thing_ids = [k["id"] for k in ADE_CATEGORIES]
|
33 |
+
assert len(thing_ids) == 100, len(thing_ids)
|
34 |
+
# Mapping from the incontiguous ADE category id to an id in [0, 99]
|
35 |
+
thing_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(thing_ids)}
|
36 |
+
thing_classes = [k["name"] for k in ADE_CATEGORIES]
|
37 |
+
ret = {
|
38 |
+
"thing_dataset_id_to_contiguous_id": thing_dataset_id_to_contiguous_id,
|
39 |
+
"thing_classes": thing_classes,
|
40 |
+
}
|
41 |
+
return ret
|
42 |
+
|
43 |
+
|
44 |
+
def register_all_ade20k_instance(root):
|
45 |
+
for key, (image_root, json_file) in _PREDEFINED_SPLITS.items():
|
46 |
+
# Assume pre-defined datasets live in `./datasets`.
|
47 |
+
register_coco_instances(
|
48 |
+
key,
|
49 |
+
_get_ade_instances_meta(),
|
50 |
+
os.path.join(root, json_file) if "://" not in json_file else json_file,
|
51 |
+
os.path.join(root, image_root),
|
52 |
+
)
|
53 |
+
|
54 |
+
|
55 |
+
_root = os.getenv("DETECTRON2_DATASETS", "datasets")
|
56 |
+
register_all_ade20k_instance(_root)
|
oneformer/data/datasets/register_ade20k_panoptic.py
ADDED
@@ -0,0 +1,394 @@
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|
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|
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|
|
|
|
|
|
|
|
|
1 |
+
# ------------------------------------------------------------------------------
|
2 |
+
# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/data/datasets/register_ade20k_panoptic.py
|
3 |
+
# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
|
4 |
+
# ------------------------------------------------------------------------------
|
5 |
+
|
6 |
+
import json
|
7 |
+
import os
|
8 |
+
|
9 |
+
from detectron2.data import DatasetCatalog, MetadataCatalog
|
10 |
+
from detectron2.utils.file_io import PathManager
|
11 |
+
|
12 |
+
ADE20K_150_CATEGORIES = [
|
13 |
+
{"color": [120, 120, 120], "id": 0, "isthing": 0, "name": "wall"},
|
14 |
+
{"color": [180, 120, 120], "id": 1, "isthing": 0, "name": "building"},
|
15 |
+
{"color": [6, 230, 230], "id": 2, "isthing": 0, "name": "sky"},
|
16 |
+
{"color": [80, 50, 50], "id": 3, "isthing": 0, "name": "floor"},
|
17 |
+
{"color": [4, 200, 3], "id": 4, "isthing": 0, "name": "tree"},
|
18 |
+
{"color": [120, 120, 80], "id": 5, "isthing": 0, "name": "ceiling"},
|
19 |
+
{"color": [140, 140, 140], "id": 6, "isthing": 0, "name": "road, route"},
|
20 |
+
{"color": [204, 5, 255], "id": 7, "isthing": 1, "name": "bed"},
|
21 |
+
{"color": [230, 230, 230], "id": 8, "isthing": 1, "name": "window "},
|
22 |
+
{"color": [4, 250, 7], "id": 9, "isthing": 0, "name": "grass"},
|
23 |
+
{"color": [224, 5, 255], "id": 10, "isthing": 1, "name": "cabinet"},
|
24 |
+
{"color": [235, 255, 7], "id": 11, "isthing": 0, "name": "sidewalk, pavement"},
|
25 |
+
{"color": [150, 5, 61], "id": 12, "isthing": 1, "name": "person"},
|
26 |
+
{"color": [120, 120, 70], "id": 13, "isthing": 0, "name": "earth, ground"},
|
27 |
+
{"color": [8, 255, 51], "id": 14, "isthing": 1, "name": "door"},
|
28 |
+
{"color": [255, 6, 82], "id": 15, "isthing": 1, "name": "table"},
|
29 |
+
{"color": [143, 255, 140], "id": 16, "isthing": 0, "name": "mountain, mount"},
|
30 |
+
{"color": [204, 255, 4], "id": 17, "isthing": 0, "name": "plant"},
|
31 |
+
{"color": [255, 51, 7], "id": 18, "isthing": 1, "name": "curtain"},
|
32 |
+
{"color": [204, 70, 3], "id": 19, "isthing": 1, "name": "chair"},
|
33 |
+
{"color": [0, 102, 200], "id": 20, "isthing": 1, "name": "car"},
|
34 |
+
{"color": [61, 230, 250], "id": 21, "isthing": 0, "name": "water"},
|
35 |
+
{"color": [255, 6, 51], "id": 22, "isthing": 1, "name": "painting, picture"},
|
36 |
+
{"color": [11, 102, 255], "id": 23, "isthing": 1, "name": "sofa"},
|
37 |
+
{"color": [255, 7, 71], "id": 24, "isthing": 1, "name": "shelf"},
|
38 |
+
{"color": [255, 9, 224], "id": 25, "isthing": 0, "name": "house"},
|
39 |
+
{"color": [9, 7, 230], "id": 26, "isthing": 0, "name": "sea"},
|
40 |
+
{"color": [220, 220, 220], "id": 27, "isthing": 1, "name": "mirror"},
|
41 |
+
{"color": [255, 9, 92], "id": 28, "isthing": 0, "name": "rug"},
|
42 |
+
{"color": [112, 9, 255], "id": 29, "isthing": 0, "name": "field"},
|
43 |
+
{"color": [8, 255, 214], "id": 30, "isthing": 1, "name": "armchair"},
|
44 |
+
{"color": [7, 255, 224], "id": 31, "isthing": 1, "name": "seat"},
|
45 |
+
{"color": [255, 184, 6], "id": 32, "isthing": 1, "name": "fence"},
|
46 |
+
{"color": [10, 255, 71], "id": 33, "isthing": 1, "name": "desk"},
|
47 |
+
{"color": [255, 41, 10], "id": 34, "isthing": 0, "name": "rock, stone"},
|
48 |
+
{"color": [7, 255, 255], "id": 35, "isthing": 1, "name": "wardrobe, closet, press"},
|
49 |
+
{"color": [224, 255, 8], "id": 36, "isthing": 1, "name": "lamp"},
|
50 |
+
{"color": [102, 8, 255], "id": 37, "isthing": 1, "name": "tub"},
|
51 |
+
{"color": [255, 61, 6], "id": 38, "isthing": 1, "name": "rail"},
|
52 |
+
{"color": [255, 194, 7], "id": 39, "isthing": 1, "name": "cushion"},
|
53 |
+
{"color": [255, 122, 8], "id": 40, "isthing": 0, "name": "base, pedestal, stand"},
|
54 |
+
{"color": [0, 255, 20], "id": 41, "isthing": 1, "name": "box"},
|
55 |
+
{"color": [255, 8, 41], "id": 42, "isthing": 1, "name": "column, pillar"},
|
56 |
+
{"color": [255, 5, 153], "id": 43, "isthing": 1, "name": "signboard, sign"},
|
57 |
+
{
|
58 |
+
"color": [6, 51, 255],
|
59 |
+
"id": 44,
|
60 |
+
"isthing": 1,
|
61 |
+
"name": "chest of drawers, chest, bureau, dresser",
|
62 |
+
},
|
63 |
+
{"color": [235, 12, 255], "id": 45, "isthing": 1, "name": "counter"},
|
64 |
+
{"color": [160, 150, 20], "id": 46, "isthing": 0, "name": "sand"},
|
65 |
+
{"color": [0, 163, 255], "id": 47, "isthing": 1, "name": "sink"},
|
66 |
+
{"color": [140, 140, 140], "id": 48, "isthing": 0, "name": "skyscraper"},
|
67 |
+
{"color": [250, 10, 15], "id": 49, "isthing": 1, "name": "fireplace"},
|
68 |
+
{"color": [20, 255, 0], "id": 50, "isthing": 1, "name": "refrigerator, icebox"},
|
69 |
+
{"color": [31, 255, 0], "id": 51, "isthing": 0, "name": "grandstand, covered stand"},
|
70 |
+
{"color": [255, 31, 0], "id": 52, "isthing": 0, "name": "path"},
|
71 |
+
{"color": [255, 224, 0], "id": 53, "isthing": 1, "name": "stairs"},
|
72 |
+
{"color": [153, 255, 0], "id": 54, "isthing": 0, "name": "runway"},
|
73 |
+
{"color": [0, 0, 255], "id": 55, "isthing": 1, "name": "case, display case, showcase, vitrine"},
|
74 |
+
{
|
75 |
+
"color": [255, 71, 0],
|
76 |
+
"id": 56,
|
77 |
+
"isthing": 1,
|
78 |
+
"name": "pool table, billiard table, snooker table",
|
79 |
+
},
|
80 |
+
{"color": [0, 235, 255], "id": 57, "isthing": 1, "name": "pillow"},
|
81 |
+
{"color": [0, 173, 255], "id": 58, "isthing": 1, "name": "screen door, screen"},
|
82 |
+
{"color": [31, 0, 255], "id": 59, "isthing": 0, "name": "stairway, staircase"},
|
83 |
+
{"color": [11, 200, 200], "id": 60, "isthing": 0, "name": "river"},
|
84 |
+
{"color": [255, 82, 0], "id": 61, "isthing": 0, "name": "bridge, span"},
|
85 |
+
{"color": [0, 255, 245], "id": 62, "isthing": 1, "name": "bookcase"},
|
86 |
+
{"color": [0, 61, 255], "id": 63, "isthing": 0, "name": "blind, screen"},
|
87 |
+
{"color": [0, 255, 112], "id": 64, "isthing": 1, "name": "coffee table"},
|
88 |
+
{
|
89 |
+
"color": [0, 255, 133],
|
90 |
+
"id": 65,
|
91 |
+
"isthing": 1,
|
92 |
+
"name": "toilet, can, commode, crapper, pot, potty, stool, throne",
|
93 |
+
},
|
94 |
+
{"color": [255, 0, 0], "id": 66, "isthing": 1, "name": "flower"},
|
95 |
+
{"color": [255, 163, 0], "id": 67, "isthing": 1, "name": "book"},
|
96 |
+
{"color": [255, 102, 0], "id": 68, "isthing": 0, "name": "hill"},
|
97 |
+
{"color": [194, 255, 0], "id": 69, "isthing": 1, "name": "bench"},
|
98 |
+
{"color": [0, 143, 255], "id": 70, "isthing": 1, "name": "countertop"},
|
99 |
+
{"color": [51, 255, 0], "id": 71, "isthing": 1, "name": "stove"},
|
100 |
+
{"color": [0, 82, 255], "id": 72, "isthing": 1, "name": "palm, palm tree"},
|
101 |
+
{"color": [0, 255, 41], "id": 73, "isthing": 1, "name": "kitchen island"},
|
102 |
+
{"color": [0, 255, 173], "id": 74, "isthing": 1, "name": "computer"},
|
103 |
+
{"color": [10, 0, 255], "id": 75, "isthing": 1, "name": "swivel chair"},
|
104 |
+
{"color": [173, 255, 0], "id": 76, "isthing": 1, "name": "boat"},
|
105 |
+
{"color": [0, 255, 153], "id": 77, "isthing": 0, "name": "bar"},
|
106 |
+
{"color": [255, 92, 0], "id": 78, "isthing": 1, "name": "arcade machine"},
|
107 |
+
{"color": [255, 0, 255], "id": 79, "isthing": 0, "name": "hovel, hut, hutch, shack, shanty"},
|
108 |
+
{"color": [255, 0, 245], "id": 80, "isthing": 1, "name": "bus"},
|
109 |
+
{"color": [255, 0, 102], "id": 81, "isthing": 1, "name": "towel"},
|
110 |
+
{"color": [255, 173, 0], "id": 82, "isthing": 1, "name": "light"},
|
111 |
+
{"color": [255, 0, 20], "id": 83, "isthing": 1, "name": "truck"},
|
112 |
+
{"color": [255, 184, 184], "id": 84, "isthing": 0, "name": "tower"},
|
113 |
+
{"color": [0, 31, 255], "id": 85, "isthing": 1, "name": "chandelier"},
|
114 |
+
{"color": [0, 255, 61], "id": 86, "isthing": 1, "name": "awning, sunshade, sunblind"},
|
115 |
+
{"color": [0, 71, 255], "id": 87, "isthing": 1, "name": "street lamp"},
|
116 |
+
{"color": [255, 0, 204], "id": 88, "isthing": 1, "name": "booth"},
|
117 |
+
{"color": [0, 255, 194], "id": 89, "isthing": 1, "name": "tv"},
|
118 |
+
{"color": [0, 255, 82], "id": 90, "isthing": 1, "name": "plane"},
|
119 |
+
{"color": [0, 10, 255], "id": 91, "isthing": 0, "name": "dirt track"},
|
120 |
+
{"color": [0, 112, 255], "id": 92, "isthing": 1, "name": "clothes"},
|
121 |
+
{"color": [51, 0, 255], "id": 93, "isthing": 1, "name": "pole"},
|
122 |
+
{"color": [0, 194, 255], "id": 94, "isthing": 0, "name": "land, ground, soil"},
|
123 |
+
{
|
124 |
+
"color": [0, 122, 255],
|
125 |
+
"id": 95,
|
126 |
+
"isthing": 1,
|
127 |
+
"name": "bannister, banister, balustrade, balusters, handrail",
|
128 |
+
},
|
129 |
+
{
|
130 |
+
"color": [0, 255, 163],
|
131 |
+
"id": 96,
|
132 |
+
"isthing": 0,
|
133 |
+
"name": "escalator, moving staircase, moving stairway",
|
134 |
+
},
|
135 |
+
{
|
136 |
+
"color": [255, 153, 0],
|
137 |
+
"id": 97,
|
138 |
+
"isthing": 1,
|
139 |
+
"name": "ottoman, pouf, pouffe, puff, hassock",
|
140 |
+
},
|
141 |
+
{"color": [0, 255, 10], "id": 98, "isthing": 1, "name": "bottle"},
|
142 |
+
{"color": [255, 112, 0], "id": 99, "isthing": 0, "name": "buffet, counter, sideboard"},
|
143 |
+
{
|
144 |
+
"color": [143, 255, 0],
|
145 |
+
"id": 100,
|
146 |
+
"isthing": 0,
|
147 |
+
"name": "poster, posting, placard, notice, bill, card",
|
148 |
+
},
|
149 |
+
{"color": [82, 0, 255], "id": 101, "isthing": 0, "name": "stage"},
|
150 |
+
{"color": [163, 255, 0], "id": 102, "isthing": 1, "name": "van"},
|
151 |
+
{"color": [255, 235, 0], "id": 103, "isthing": 1, "name": "ship"},
|
152 |
+
{"color": [8, 184, 170], "id": 104, "isthing": 1, "name": "fountain"},
|
153 |
+
{
|
154 |
+
"color": [133, 0, 255],
|
155 |
+
"id": 105,
|
156 |
+
"isthing": 0,
|
157 |
+
"name": "conveyer belt, conveyor belt, conveyer, conveyor, transporter",
|
158 |
+
},
|
159 |
+
{"color": [0, 255, 92], "id": 106, "isthing": 0, "name": "canopy"},
|
160 |
+
{
|
161 |
+
"color": [184, 0, 255],
|
162 |
+
"id": 107,
|
163 |
+
"isthing": 1,
|
164 |
+
"name": "washer, automatic washer, washing machine",
|
165 |
+
},
|
166 |
+
{"color": [255, 0, 31], "id": 108, "isthing": 1, "name": "plaything, toy"},
|
167 |
+
{"color": [0, 184, 255], "id": 109, "isthing": 0, "name": "pool"},
|
168 |
+
{"color": [0, 214, 255], "id": 110, "isthing": 1, "name": "stool"},
|
169 |
+
{"color": [255, 0, 112], "id": 111, "isthing": 1, "name": "barrel, cask"},
|
170 |
+
{"color": [92, 255, 0], "id": 112, "isthing": 1, "name": "basket, handbasket"},
|
171 |
+
{"color": [0, 224, 255], "id": 113, "isthing": 0, "name": "falls"},
|
172 |
+
{"color": [112, 224, 255], "id": 114, "isthing": 0, "name": "tent"},
|
173 |
+
{"color": [70, 184, 160], "id": 115, "isthing": 1, "name": "bag"},
|
174 |
+
{"color": [163, 0, 255], "id": 116, "isthing": 1, "name": "minibike, motorbike"},
|
175 |
+
{"color": [153, 0, 255], "id": 117, "isthing": 0, "name": "cradle"},
|
176 |
+
{"color": [71, 255, 0], "id": 118, "isthing": 1, "name": "oven"},
|
177 |
+
{"color": [255, 0, 163], "id": 119, "isthing": 1, "name": "ball"},
|
178 |
+
{"color": [255, 204, 0], "id": 120, "isthing": 1, "name": "food, solid food"},
|
179 |
+
{"color": [255, 0, 143], "id": 121, "isthing": 1, "name": "step, stair"},
|
180 |
+
{"color": [0, 255, 235], "id": 122, "isthing": 0, "name": "tank, storage tank"},
|
181 |
+
{"color": [133, 255, 0], "id": 123, "isthing": 1, "name": "trade name"},
|
182 |
+
{"color": [255, 0, 235], "id": 124, "isthing": 1, "name": "microwave"},
|
183 |
+
{"color": [245, 0, 255], "id": 125, "isthing": 1, "name": "pot"},
|
184 |
+
{"color": [255, 0, 122], "id": 126, "isthing": 1, "name": "animal"},
|
185 |
+
{"color": [255, 245, 0], "id": 127, "isthing": 1, "name": "bicycle"},
|
186 |
+
{"color": [10, 190, 212], "id": 128, "isthing": 0, "name": "lake"},
|
187 |
+
{"color": [214, 255, 0], "id": 129, "isthing": 1, "name": "dishwasher"},
|
188 |
+
{"color": [0, 204, 255], "id": 130, "isthing": 1, "name": "screen"},
|
189 |
+
{"color": [20, 0, 255], "id": 131, "isthing": 0, "name": "blanket, cover"},
|
190 |
+
{"color": [255, 255, 0], "id": 132, "isthing": 1, "name": "sculpture"},
|
191 |
+
{"color": [0, 153, 255], "id": 133, "isthing": 1, "name": "hood, exhaust hood"},
|
192 |
+
{"color": [0, 41, 255], "id": 134, "isthing": 1, "name": "sconce"},
|
193 |
+
{"color": [0, 255, 204], "id": 135, "isthing": 1, "name": "vase"},
|
194 |
+
{"color": [41, 0, 255], "id": 136, "isthing": 1, "name": "traffic light"},
|
195 |
+
{"color": [41, 255, 0], "id": 137, "isthing": 1, "name": "tray"},
|
196 |
+
{"color": [173, 0, 255], "id": 138, "isthing": 1, "name": "trash can"},
|
197 |
+
{"color": [0, 245, 255], "id": 139, "isthing": 1, "name": "fan"},
|
198 |
+
{"color": [71, 0, 255], "id": 140, "isthing": 0, "name": "pier"},
|
199 |
+
{"color": [122, 0, 255], "id": 141, "isthing": 0, "name": "crt screen"},
|
200 |
+
{"color": [0, 255, 184], "id": 142, "isthing": 1, "name": "plate"},
|
201 |
+
{"color": [0, 92, 255], "id": 143, "isthing": 1, "name": "monitor"},
|
202 |
+
{"color": [184, 255, 0], "id": 144, "isthing": 1, "name": "bulletin board"},
|
203 |
+
{"color": [0, 133, 255], "id": 145, "isthing": 0, "name": "shower"},
|
204 |
+
{"color": [255, 214, 0], "id": 146, "isthing": 1, "name": "radiator"},
|
205 |
+
{"color": [25, 194, 194], "id": 147, "isthing": 1, "name": "glass, drinking glass"},
|
206 |
+
{"color": [102, 255, 0], "id": 148, "isthing": 1, "name": "clock"},
|
207 |
+
{"color": [92, 0, 255], "id": 149, "isthing": 1, "name": "flag"},
|
208 |
+
]
|
209 |
+
|
210 |
+
ADE20k_COLORS = [k["color"] for k in ADE20K_150_CATEGORIES]
|
211 |
+
|
212 |
+
MetadataCatalog.get("ade20k_sem_seg_train").set(
|
213 |
+
stuff_colors=ADE20k_COLORS[:],
|
214 |
+
)
|
215 |
+
|
216 |
+
MetadataCatalog.get("ade20k_sem_seg_val").set(
|
217 |
+
stuff_colors=ADE20k_COLORS[:],
|
218 |
+
)
|
219 |
+
|
220 |
+
|
221 |
+
def load_ade20k_panoptic_json(json_file, image_dir, gt_dir, semseg_dir, meta):
|
222 |
+
"""
|
223 |
+
Args:
|
224 |
+
image_dir (str): path to the raw dataset. e.g., "~/coco/train2017".
|
225 |
+
gt_dir (str): path to the raw annotations. e.g., "~/coco/panoptic_train2017".
|
226 |
+
json_file (str): path to the json file. e.g., "~/coco/annotations/panoptic_train2017.json".
|
227 |
+
Returns:
|
228 |
+
list[dict]: a list of dicts in Detectron2 standard format. (See
|
229 |
+
`Using Custom Datasets </tutorials/datasets.html>`_ )
|
230 |
+
"""
|
231 |
+
|
232 |
+
def _convert_category_id(segment_info, meta):
|
233 |
+
if segment_info["category_id"] in meta["thing_dataset_id_to_contiguous_id"]:
|
234 |
+
segment_info["category_id"] = meta["thing_dataset_id_to_contiguous_id"][
|
235 |
+
segment_info["category_id"]
|
236 |
+
]
|
237 |
+
segment_info["isthing"] = True
|
238 |
+
else:
|
239 |
+
segment_info["category_id"] = meta["stuff_dataset_id_to_contiguous_id"][
|
240 |
+
segment_info["category_id"]
|
241 |
+
]
|
242 |
+
segment_info["isthing"] = False
|
243 |
+
return segment_info
|
244 |
+
|
245 |
+
with PathManager.open(json_file) as f:
|
246 |
+
json_info = json.load(f)
|
247 |
+
|
248 |
+
ret = []
|
249 |
+
for ann in json_info["annotations"]:
|
250 |
+
image_id = ann["image_id"]
|
251 |
+
# TODO: currently we assume image and label has the same filename but
|
252 |
+
# different extension, and images have extension ".jpg" for COCO. Need
|
253 |
+
# to make image extension a user-provided argument if we extend this
|
254 |
+
# function to support other COCO-like datasets.
|
255 |
+
image_file = os.path.join(image_dir, os.path.splitext(ann["file_name"])[0] + ".jpg")
|
256 |
+
label_file = os.path.join(gt_dir, ann["file_name"])
|
257 |
+
sem_label_file = os.path.join(semseg_dir, ann["file_name"])
|
258 |
+
segments_info = [_convert_category_id(x, meta) for x in ann["segments_info"]]
|
259 |
+
ret.append(
|
260 |
+
{
|
261 |
+
"file_name": image_file,
|
262 |
+
"image_id": image_id,
|
263 |
+
"pan_seg_file_name": label_file,
|
264 |
+
"sem_seg_file_name": sem_label_file,
|
265 |
+
"segments_info": segments_info,
|
266 |
+
}
|
267 |
+
)
|
268 |
+
assert len(ret), f"No images found in {image_dir}!"
|
269 |
+
assert PathManager.isfile(ret[0]["file_name"]), ret[0]["file_name"]
|
270 |
+
assert PathManager.isfile(ret[0]["pan_seg_file_name"]), ret[0]["pan_seg_file_name"]
|
271 |
+
assert PathManager.isfile(ret[0]["sem_seg_file_name"]), ret[0]["sem_seg_file_name"]
|
272 |
+
return ret
|
273 |
+
|
274 |
+
|
275 |
+
def register_ade20k_panoptic(
|
276 |
+
name, metadata, image_root, panoptic_root, semantic_root, panoptic_json, instances_json=None,
|
277 |
+
):
|
278 |
+
"""
|
279 |
+
Register a "standard" version of ADE20k panoptic segmentation dataset named `name`.
|
280 |
+
The dictionaries in this registered dataset follows detectron2's standard format.
|
281 |
+
Hence it's called "standard".
|
282 |
+
Args:
|
283 |
+
name (str): the name that identifies a dataset,
|
284 |
+
e.g. "ade20k_panoptic_train"
|
285 |
+
metadata (dict): extra metadata associated with this dataset.
|
286 |
+
image_root (str): directory which contains all the images
|
287 |
+
panoptic_root (str): directory which contains panoptic annotation images in COCO format
|
288 |
+
panoptic_json (str): path to the json panoptic annotation file in COCO format
|
289 |
+
sem_seg_root (none): not used, to be consistent with
|
290 |
+
`register_coco_panoptic_separated`.
|
291 |
+
instances_json (str): path to the json instance annotation file
|
292 |
+
"""
|
293 |
+
panoptic_name = name
|
294 |
+
DatasetCatalog.register(
|
295 |
+
panoptic_name,
|
296 |
+
lambda: load_ade20k_panoptic_json(
|
297 |
+
panoptic_json, image_root, panoptic_root, semantic_root, metadata
|
298 |
+
),
|
299 |
+
)
|
300 |
+
MetadataCatalog.get(panoptic_name).set(
|
301 |
+
panoptic_root=panoptic_root,
|
302 |
+
image_root=image_root,
|
303 |
+
panoptic_json=panoptic_json,
|
304 |
+
json_file=instances_json,
|
305 |
+
evaluator_type="ade20k_panoptic_seg",
|
306 |
+
ignore_label=255,
|
307 |
+
label_divisor=1000,
|
308 |
+
**metadata,
|
309 |
+
)
|
310 |
+
|
311 |
+
|
312 |
+
_PREDEFINED_SPLITS_ADE20K_PANOPTIC = {
|
313 |
+
"ade20k_panoptic_train": (
|
314 |
+
"ADEChallengeData2016/images/training",
|
315 |
+
"ADEChallengeData2016/ade20k_panoptic_train",
|
316 |
+
"ADEChallengeData2016/ade20k_panoptic_train.json",
|
317 |
+
"ADEChallengeData2016/annotations_detectron2/training",
|
318 |
+
"ADEChallengeData2016/ade20k_instance_train.json",
|
319 |
+
),
|
320 |
+
"ade20k_panoptic_val": (
|
321 |
+
"ADEChallengeData2016/images/validation",
|
322 |
+
"ADEChallengeData2016/ade20k_panoptic_val",
|
323 |
+
"ADEChallengeData2016/ade20k_panoptic_val.json",
|
324 |
+
"ADEChallengeData2016/annotations_detectron2/validation",
|
325 |
+
"ADEChallengeData2016/ade20k_instance_val.json",
|
326 |
+
),
|
327 |
+
}
|
328 |
+
|
329 |
+
|
330 |
+
def get_metadata():
|
331 |
+
meta = {}
|
332 |
+
# The following metadata maps contiguous id from [0, #thing categories +
|
333 |
+
# #stuff categories) to their names and colors. We have to replica of the
|
334 |
+
# same name and color under "thing_*" and "stuff_*" because the current
|
335 |
+
# visualization function in D2 handles thing and class classes differently
|
336 |
+
# due to some heuristic used in Panoptic FPN. We keep the same naming to
|
337 |
+
# enable reusing existing visualization functions.
|
338 |
+
thing_classes = [k["name"] for k in ADE20K_150_CATEGORIES if k["isthing"] == 1]
|
339 |
+
thing_colors = [k["color"] for k in ADE20K_150_CATEGORIES if k["isthing"] == 1]
|
340 |
+
stuff_classes = [k["name"] for k in ADE20K_150_CATEGORIES]
|
341 |
+
stuff_colors = [k["color"] for k in ADE20K_150_CATEGORIES]
|
342 |
+
|
343 |
+
meta["thing_classes"] = thing_classes
|
344 |
+
meta["thing_colors"] = thing_colors
|
345 |
+
meta["stuff_classes"] = stuff_classes
|
346 |
+
meta["stuff_colors"] = stuff_colors
|
347 |
+
|
348 |
+
# Convert category id for training:
|
349 |
+
# category id: like semantic segmentation, it is the class id for each
|
350 |
+
# pixel. Since there are some classes not used in evaluation, the category
|
351 |
+
# id is not always contiguous and thus we have two set of category ids:
|
352 |
+
# - original category id: category id in the original dataset, mainly
|
353 |
+
# used for evaluation.
|
354 |
+
# - contiguous category id: [0, #classes), in order to train the linear
|
355 |
+
# softmax classifier.
|
356 |
+
thing_dataset_id_to_contiguous_id = {}
|
357 |
+
stuff_dataset_id_to_contiguous_id = {}
|
358 |
+
|
359 |
+
for i, cat in enumerate(ADE20K_150_CATEGORIES):
|
360 |
+
if cat["isthing"]:
|
361 |
+
thing_dataset_id_to_contiguous_id[cat["id"]] = i
|
362 |
+
# else:
|
363 |
+
# stuff_dataset_id_to_contiguous_id[cat["id"]] = i
|
364 |
+
|
365 |
+
# in order to use sem_seg evaluator
|
366 |
+
stuff_dataset_id_to_contiguous_id[cat["id"]] = i
|
367 |
+
|
368 |
+
meta["thing_dataset_id_to_contiguous_id"] = thing_dataset_id_to_contiguous_id
|
369 |
+
meta["stuff_dataset_id_to_contiguous_id"] = stuff_dataset_id_to_contiguous_id
|
370 |
+
|
371 |
+
return meta
|
372 |
+
|
373 |
+
|
374 |
+
def register_all_ade20k_panoptic(root):
|
375 |
+
metadata = get_metadata()
|
376 |
+
for (
|
377 |
+
prefix,
|
378 |
+
(image_root, panoptic_root, panoptic_json, semantic_root, instance_json),
|
379 |
+
) in _PREDEFINED_SPLITS_ADE20K_PANOPTIC.items():
|
380 |
+
# The "standard" version of COCO panoptic segmentation dataset,
|
381 |
+
# e.g. used by Panoptic-DeepLab
|
382 |
+
register_ade20k_panoptic(
|
383 |
+
prefix,
|
384 |
+
metadata,
|
385 |
+
os.path.join(root, image_root),
|
386 |
+
os.path.join(root, panoptic_root),
|
387 |
+
os.path.join(root, semantic_root),
|
388 |
+
os.path.join(root, panoptic_json),
|
389 |
+
os.path.join(root, instance_json),
|
390 |
+
)
|
391 |
+
|
392 |
+
|
393 |
+
_root = os.getenv("DETECTRON2_DATASETS", "datasets")
|
394 |
+
register_all_ade20k_panoptic(_root)
|
oneformer/data/datasets/register_cityscapes_panoptic.py
ADDED
@@ -0,0 +1,199 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# ------------------------------------------------------------------------------
|
2 |
+
# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/data/datasets/cityscapes_panoptic.py
|
3 |
+
# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
|
4 |
+
# ------------------------------------------------------------------------------
|
5 |
+
|
6 |
+
import json
|
7 |
+
import logging
|
8 |
+
import os
|
9 |
+
|
10 |
+
from detectron2.data import DatasetCatalog, MetadataCatalog
|
11 |
+
from detectron2.data.datasets.builtin_meta import CITYSCAPES_CATEGORIES
|
12 |
+
from detectron2.utils.file_io import PathManager
|
13 |
+
|
14 |
+
"""
|
15 |
+
This file contains functions to register the Cityscapes panoptic dataset to the DatasetCatalog.
|
16 |
+
"""
|
17 |
+
|
18 |
+
|
19 |
+
logger = logging.getLogger(__name__)
|
20 |
+
|
21 |
+
|
22 |
+
def get_cityscapes_panoptic_files(image_dir, gt_dir, json_info):
|
23 |
+
files = []
|
24 |
+
# scan through the directory
|
25 |
+
cities = PathManager.ls(image_dir)
|
26 |
+
logger.info(f"{len(cities)} cities found in '{image_dir}'.")
|
27 |
+
image_dict = {}
|
28 |
+
for city in cities:
|
29 |
+
city_img_dir = os.path.join(image_dir, city)
|
30 |
+
for basename in PathManager.ls(city_img_dir):
|
31 |
+
image_file = os.path.join(city_img_dir, basename)
|
32 |
+
|
33 |
+
suffix = "_leftImg8bit.png"
|
34 |
+
assert basename.endswith(suffix), basename
|
35 |
+
basename = os.path.basename(basename)[: -len(suffix)]
|
36 |
+
|
37 |
+
image_dict[basename] = image_file
|
38 |
+
|
39 |
+
for ann in json_info["annotations"]:
|
40 |
+
image_file = image_dict.get(ann["image_id"], None)
|
41 |
+
assert image_file is not None, "No image {} found for annotation {}".format(
|
42 |
+
ann["image_id"], ann["file_name"]
|
43 |
+
)
|
44 |
+
label_file = os.path.join(gt_dir, ann["file_name"])
|
45 |
+
segments_info = ann["segments_info"]
|
46 |
+
files.append((image_file, label_file, segments_info))
|
47 |
+
|
48 |
+
assert len(files), "No images found in {}".format(image_dir)
|
49 |
+
assert PathManager.isfile(files[0][0]), files[0][0]
|
50 |
+
assert PathManager.isfile(files[0][1]), files[0][1]
|
51 |
+
return files
|
52 |
+
|
53 |
+
|
54 |
+
def load_cityscapes_panoptic(image_dir, gt_dir, gt_json, meta):
|
55 |
+
"""
|
56 |
+
Args:
|
57 |
+
image_dir (str): path to the raw dataset. e.g., "~/cityscapes/leftImg8bit/train".
|
58 |
+
gt_dir (str): path to the raw annotations. e.g.,
|
59 |
+
"~/cityscapes/gtFine/cityscapes_panoptic_train".
|
60 |
+
gt_json (str): path to the json file. e.g.,
|
61 |
+
"~/cityscapes/gtFine/cityscapes_panoptic_train.json".
|
62 |
+
meta (dict): dictionary containing "thing_dataset_id_to_contiguous_id"
|
63 |
+
and "stuff_dataset_id_to_contiguous_id" to map category ids to
|
64 |
+
contiguous ids for training.
|
65 |
+
|
66 |
+
Returns:
|
67 |
+
list[dict]: a list of dicts in Detectron2 standard format. (See
|
68 |
+
`Using Custom Datasets </tutorials/datasets.html>`_ )
|
69 |
+
"""
|
70 |
+
|
71 |
+
def _convert_category_id(segment_info, meta):
|
72 |
+
if segment_info["category_id"] in meta["thing_dataset_id_to_contiguous_id"]:
|
73 |
+
segment_info["category_id"] = meta["thing_dataset_id_to_contiguous_id"][
|
74 |
+
segment_info["category_id"]
|
75 |
+
]
|
76 |
+
else:
|
77 |
+
segment_info["category_id"] = meta["stuff_dataset_id_to_contiguous_id"][
|
78 |
+
segment_info["category_id"]
|
79 |
+
]
|
80 |
+
return segment_info
|
81 |
+
|
82 |
+
assert os.path.exists(
|
83 |
+
gt_json
|
84 |
+
), "Please run `python cityscapesscripts/preparation/createPanopticImgs.py` to generate label files." # noqa
|
85 |
+
|
86 |
+
|
87 |
+
with open(gt_json) as f:
|
88 |
+
json_info = json.load(f)
|
89 |
+
|
90 |
+
files = get_cityscapes_panoptic_files(image_dir, gt_dir, json_info)
|
91 |
+
ret = []
|
92 |
+
for image_file, label_file, segments_info in files:
|
93 |
+
sem_label_file = (
|
94 |
+
image_file.replace("leftImg8bit", "gtFine").split(".")[0] + "_labelTrainIds.png"
|
95 |
+
)
|
96 |
+
segments_info = [_convert_category_id(x, meta) for x in segments_info]
|
97 |
+
ret.append(
|
98 |
+
{
|
99 |
+
"file_name": image_file,
|
100 |
+
"image_id": "_".join(
|
101 |
+
os.path.splitext(os.path.basename(image_file))[0].split("_")[:3]
|
102 |
+
),
|
103 |
+
"sem_seg_file_name": sem_label_file,
|
104 |
+
"pan_seg_file_name": label_file,
|
105 |
+
"segments_info": segments_info,
|
106 |
+
}
|
107 |
+
)
|
108 |
+
assert len(ret), f"No images found in {image_dir}!"
|
109 |
+
assert PathManager.isfile(
|
110 |
+
ret[0]["sem_seg_file_name"]
|
111 |
+
), "Please generate labelTrainIds.png with cityscapesscripts/preparation/createTrainIdLabelImgs.py" # noqa
|
112 |
+
assert PathManager.isfile(
|
113 |
+
ret[0]["pan_seg_file_name"]
|
114 |
+
), "Please generate panoptic annotation with python cityscapesscripts/preparation/createPanopticImgs.py" # noqa
|
115 |
+
return ret
|
116 |
+
|
117 |
+
|
118 |
+
_RAW_CITYSCAPES_PANOPTIC_SPLITS = {
|
119 |
+
"cityscapes_fine_panoptic_train": (
|
120 |
+
"cityscapes/leftImg8bit/train",
|
121 |
+
"cityscapes/gtFine/cityscapes_panoptic_train",
|
122 |
+
"cityscapes/gtFine/cityscapes_panoptic_train.json",
|
123 |
+
),
|
124 |
+
"cityscapes_fine_panoptic_val": (
|
125 |
+
"cityscapes/leftImg8bit/val",
|
126 |
+
"cityscapes/gtFine/cityscapes_panoptic_val",
|
127 |
+
"cityscapes/gtFine/cityscapes_panoptic_val.json",
|
128 |
+
),
|
129 |
+
# "cityscapes_fine_panoptic_test": not supported yet
|
130 |
+
}
|
131 |
+
|
132 |
+
|
133 |
+
def register_all_cityscapes_panoptic(root):
|
134 |
+
meta = {}
|
135 |
+
# The following metadata maps contiguous id from [0, #thing categories +
|
136 |
+
# #stuff categories) to their names and colors. We have to replica of the
|
137 |
+
# same name and color under "thing_*" and "stuff_*" because the current
|
138 |
+
# visualization function in D2 handles thing and class classes differently
|
139 |
+
# due to some heuristic used in Panoptic FPN. We keep the same naming to
|
140 |
+
# enable reusing existing visualization functions.
|
141 |
+
thing_classes = [k["name"] for k in CITYSCAPES_CATEGORIES]
|
142 |
+
thing_colors = [k["color"] for k in CITYSCAPES_CATEGORIES]
|
143 |
+
stuff_classes = [k["name"] for k in CITYSCAPES_CATEGORIES]
|
144 |
+
stuff_colors = [k["color"] for k in CITYSCAPES_CATEGORIES]
|
145 |
+
|
146 |
+
meta["thing_classes"] = thing_classes
|
147 |
+
meta["thing_colors"] = thing_colors
|
148 |
+
meta["stuff_classes"] = stuff_classes
|
149 |
+
meta["stuff_colors"] = stuff_colors
|
150 |
+
|
151 |
+
# There are three types of ids in cityscapes panoptic segmentation:
|
152 |
+
# (1) category id: like semantic segmentation, it is the class id for each
|
153 |
+
# pixel. Since there are some classes not used in evaluation, the category
|
154 |
+
# id is not always contiguous and thus we have two set of category ids:
|
155 |
+
# - original category id: category id in the original dataset, mainly
|
156 |
+
# used for evaluation.
|
157 |
+
# - contiguous category id: [0, #classes), in order to train the classifier
|
158 |
+
# (2) instance id: this id is used to differentiate different instances from
|
159 |
+
# the same category. For "stuff" classes, the instance id is always 0; for
|
160 |
+
# "thing" classes, the instance id starts from 1 and 0 is reserved for
|
161 |
+
# ignored instances (e.g. crowd annotation).
|
162 |
+
# (3) panoptic id: this is the compact id that encode both category and
|
163 |
+
# instance id by: category_id * 1000 + instance_id.
|
164 |
+
thing_dataset_id_to_contiguous_id = {}
|
165 |
+
stuff_dataset_id_to_contiguous_id = {}
|
166 |
+
|
167 |
+
for k in CITYSCAPES_CATEGORIES:
|
168 |
+
if k["isthing"] == 1:
|
169 |
+
thing_dataset_id_to_contiguous_id[k["id"]] = k["trainId"]
|
170 |
+
else:
|
171 |
+
stuff_dataset_id_to_contiguous_id[k["id"]] = k["trainId"]
|
172 |
+
|
173 |
+
meta["thing_dataset_id_to_contiguous_id"] = thing_dataset_id_to_contiguous_id
|
174 |
+
meta["stuff_dataset_id_to_contiguous_id"] = stuff_dataset_id_to_contiguous_id
|
175 |
+
|
176 |
+
for key, (image_dir, gt_dir, gt_json) in _RAW_CITYSCAPES_PANOPTIC_SPLITS.items():
|
177 |
+
image_dir = os.path.join(root, image_dir)
|
178 |
+
gt_dir = os.path.join(root, gt_dir)
|
179 |
+
gt_json = os.path.join(root, gt_json)
|
180 |
+
|
181 |
+
if key in DatasetCatalog.list():
|
182 |
+
DatasetCatalog.remove(key)
|
183 |
+
|
184 |
+
DatasetCatalog.register(
|
185 |
+
key, lambda x=image_dir, y=gt_dir, z=gt_json: load_cityscapes_panoptic(x, y, z, meta)
|
186 |
+
)
|
187 |
+
MetadataCatalog.get(key).set(
|
188 |
+
panoptic_root=gt_dir,
|
189 |
+
image_root=image_dir,
|
190 |
+
panoptic_json=gt_json,
|
191 |
+
gt_dir=gt_dir.replace("cityscapes_panoptic_", ""),
|
192 |
+
evaluator_type="cityscapes_panoptic_seg",
|
193 |
+
ignore_label=255,
|
194 |
+
label_divisor=1000,
|
195 |
+
**meta,
|
196 |
+
)
|
197 |
+
|
198 |
+
_root = os.getenv("DETECTRON2_DATASETS", "datasets")
|
199 |
+
register_all_cityscapes_panoptic(_root)
|
oneformer/data/datasets/register_coco_panoptic2instance.py
ADDED
@@ -0,0 +1,44 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# ------------------------------------------------------------------------------
|
2 |
+
# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/data/datasets/builtin.py
|
3 |
+
# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
|
4 |
+
# ------------------------------------------------------------------------------
|
5 |
+
|
6 |
+
|
7 |
+
"""
|
8 |
+
This file registers pre-defined datasets at hard-coded paths, and their metadata.
|
9 |
+
|
10 |
+
We hard-code metadata for common datasets. This will enable:
|
11 |
+
1. Consistency check when loading the datasets
|
12 |
+
2. Use models on these standard datasets directly and run demos,
|
13 |
+
without having to download the dataset annotations
|
14 |
+
|
15 |
+
We hard-code some paths to the dataset that's assumed to
|
16 |
+
exist in "./datasets/".
|
17 |
+
|
18 |
+
Users SHOULD NOT use this file to create new dataset / metadata for new dataset.
|
19 |
+
To add new dataset, refer to the tutorial "docs/DATASETS.md".
|
20 |
+
"""
|
21 |
+
|
22 |
+
import os
|
23 |
+
from detectron2.data.datasets.builtin_meta import _get_builtin_metadata
|
24 |
+
from detectron2.data.datasets.coco import register_coco_instances
|
25 |
+
|
26 |
+
|
27 |
+
_PREDEFINED_SPLITS_COCO = {
|
28 |
+
"coco_2017_val_panoptic2instance": ("coco/val2017", "coco/annotations/panoptic2instances_val2017.json"),
|
29 |
+
}
|
30 |
+
|
31 |
+
|
32 |
+
def register_panoptic2instances_coco(root):
|
33 |
+
for key, (image_root, json_file) in _PREDEFINED_SPLITS_COCO.items():
|
34 |
+
# Assume pre-defined datasets live in `./datasets`.
|
35 |
+
register_coco_instances(
|
36 |
+
key,
|
37 |
+
_get_builtin_metadata("coco"),
|
38 |
+
os.path.join(root, json_file) if "://" not in json_file else json_file,
|
39 |
+
os.path.join(root, image_root),
|
40 |
+
)
|
41 |
+
|
42 |
+
|
43 |
+
_root = os.path.expanduser(os.getenv("DETECTRON2_DATASETS", "datasets"))
|
44 |
+
register_panoptic2instances_coco(_root)
|
oneformer/data/datasets/register_coco_panoptic_annos_semseg.py
ADDED
@@ -0,0 +1,367 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
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|
|
|
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|
|
|
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|
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|
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|
|
|
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|
|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
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|
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|
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|
|
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|
|
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|
|
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|
|
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|
|
|
|
|
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|
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|
|
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|
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|
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|
|
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|
|
|
|
|
|
|
|
|
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|
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|
|
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|
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|
|
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|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# ------------------------------------------------------------------------------
|
2 |
+
# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/data/datasets/register_coco_panoptic_annos_semseg.py
|
3 |
+
# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
|
4 |
+
# ------------------------------------------------------------------------------
|
5 |
+
|
6 |
+
import json
|
7 |
+
import os
|
8 |
+
|
9 |
+
from detectron2.data import DatasetCatalog, MetadataCatalog
|
10 |
+
from detectron2.data.datasets import load_sem_seg
|
11 |
+
from detectron2.data.datasets.builtin_meta import COCO_CATEGORIES
|
12 |
+
from detectron2.utils.file_io import PathManager
|
13 |
+
import contextlib
|
14 |
+
import logging
|
15 |
+
import io
|
16 |
+
from fvcore.common.timer import Timer
|
17 |
+
import pycocotools.mask as mask_util
|
18 |
+
from detectron2.structures import BoxMode
|
19 |
+
|
20 |
+
|
21 |
+
logger = logging.getLogger(__name__)
|
22 |
+
|
23 |
+
|
24 |
+
_PREDEFINED_SPLITS_COCO_PANOPTIC = {
|
25 |
+
"coco_2017_train_panoptic": (
|
26 |
+
# This is the original panoptic annotation directory
|
27 |
+
"coco/panoptic_train2017",
|
28 |
+
"coco/annotations/panoptic_train2017.json",
|
29 |
+
# This directory contains semantic annotations that are
|
30 |
+
# converted from panoptic annotations.
|
31 |
+
# It is used by PanopticFPN.
|
32 |
+
# You can use the script at detectron2/datasets/prepare_panoptic_fpn.py
|
33 |
+
# to create these directories.
|
34 |
+
"coco/panoptic_semseg_train2017",
|
35 |
+
),
|
36 |
+
"coco_2017_val_panoptic": (
|
37 |
+
"coco/panoptic_val2017",
|
38 |
+
"coco/annotations/panoptic_val2017.json",
|
39 |
+
"coco/panoptic_semseg_val2017",
|
40 |
+
),
|
41 |
+
}
|
42 |
+
|
43 |
+
def load_coco_instance_json(json_file, image_root, dataset_name=None):
|
44 |
+
from pycocotools.coco import COCO
|
45 |
+
|
46 |
+
timer = Timer()
|
47 |
+
json_file = PathManager.get_local_path(json_file)
|
48 |
+
with contextlib.redirect_stdout(io.StringIO()):
|
49 |
+
coco_api = COCO(json_file)
|
50 |
+
if timer.seconds() > 1:
|
51 |
+
logger.info("Loading {} takes {:.2f} seconds.".format(json_file, timer.seconds()))
|
52 |
+
|
53 |
+
id_map = None
|
54 |
+
if dataset_name is not None:
|
55 |
+
meta = MetadataCatalog.get(dataset_name)
|
56 |
+
cat_ids = sorted(coco_api.getCatIds())
|
57 |
+
cats = coco_api.loadCats(cat_ids)
|
58 |
+
# The categories in a custom json file may not be sorted.
|
59 |
+
thing_classes = [c["name"] for c in sorted(cats, key=lambda x: x["id"])]
|
60 |
+
meta.thing_classes = thing_classes
|
61 |
+
|
62 |
+
# In COCO, certain category ids are artificially removed,
|
63 |
+
# and by convention they are always ignored.
|
64 |
+
# We deal with COCO's id issue and translate
|
65 |
+
# the category ids to contiguous ids in [0, 80).
|
66 |
+
|
67 |
+
# It works by looking at the "categories" field in the json, therefore
|
68 |
+
# if users' own json also have incontiguous ids, we'll
|
69 |
+
# apply this mapping as well but print a warning.
|
70 |
+
if not (min(cat_ids) == 1 and max(cat_ids) == len(cat_ids)):
|
71 |
+
if "coco" not in dataset_name:
|
72 |
+
logger.warning(
|
73 |
+
"""
|
74 |
+
Category ids in annotations are not in [1, #categories]! We'll apply a mapping for you.
|
75 |
+
"""
|
76 |
+
)
|
77 |
+
id_map = {v: i for i, v in enumerate(cat_ids)}
|
78 |
+
meta.thing_dataset_id_to_contiguous_id = id_map
|
79 |
+
|
80 |
+
# sort indices for reproducible results
|
81 |
+
img_ids = sorted(coco_api.imgs.keys())
|
82 |
+
# imgs is a list of dicts, each looks something like:
|
83 |
+
# {'license': 4,
|
84 |
+
# 'url': 'http://farm6.staticflickr.com/5454/9413846304_881d5e5c3b_z.jpg',
|
85 |
+
# 'file_name': 'COCO_val2014_000000001268.jpg',
|
86 |
+
# 'height': 427,
|
87 |
+
# 'width': 640,
|
88 |
+
# 'date_captured': '2013-11-17 05:57:24',
|
89 |
+
# 'id': 1268}
|
90 |
+
imgs = coco_api.loadImgs(img_ids)
|
91 |
+
# anns is a list[list[dict]], where each dict is an annotation
|
92 |
+
# record for an object. The inner list enumerates the objects in an image
|
93 |
+
# and the outer list enumerates over images. Example of anns[0]:
|
94 |
+
# [{'segmentation': [[192.81,
|
95 |
+
# 247.09,
|
96 |
+
# ...
|
97 |
+
# 219.03,
|
98 |
+
# 249.06]],
|
99 |
+
# 'area': 1035.749,
|
100 |
+
# 'iscrowd': 0,
|
101 |
+
# 'image_id': 1268,
|
102 |
+
# 'bbox': [192.81, 224.8, 74.73, 33.43],
|
103 |
+
# 'category_id': 16,
|
104 |
+
# 'id': 42986},
|
105 |
+
# ...]
|
106 |
+
anns = [coco_api.imgToAnns[img_id] for img_id in img_ids]
|
107 |
+
total_num_valid_anns = sum([len(x) for x in anns])
|
108 |
+
total_num_anns = len(coco_api.anns)
|
109 |
+
if total_num_valid_anns < total_num_anns:
|
110 |
+
logger.warning(
|
111 |
+
f"{json_file} contains {total_num_anns} annotations, but only "
|
112 |
+
f"{total_num_valid_anns} of them match to images in the file."
|
113 |
+
)
|
114 |
+
|
115 |
+
if "minival" not in json_file:
|
116 |
+
# The popular valminusminival & minival annotations for COCO2014 contain this bug.
|
117 |
+
# However the ratio of buggy annotations there is tiny and does not affect accuracy.
|
118 |
+
# Therefore we explicitly white-list them.
|
119 |
+
ann_ids = [ann["id"] for anns_per_image in anns for ann in anns_per_image]
|
120 |
+
assert len(set(ann_ids)) == len(ann_ids), "Annotation ids in '{}' are not unique!".format(
|
121 |
+
json_file
|
122 |
+
)
|
123 |
+
|
124 |
+
imgs_anns = list(zip(imgs, anns))
|
125 |
+
logger.info("Loaded {} images in COCO format from {}".format(len(imgs_anns), json_file))
|
126 |
+
|
127 |
+
dataset_dicts = {}
|
128 |
+
|
129 |
+
ann_keys = ["iscrowd", "bbox", "keypoints", "category_id"]
|
130 |
+
|
131 |
+
num_instances_without_valid_segmentation = 0
|
132 |
+
|
133 |
+
for (img_dict, anno_dict_list) in imgs_anns:
|
134 |
+
record = {}
|
135 |
+
record["file_name"] = os.path.join(image_root, img_dict["file_name"])
|
136 |
+
record["height"] = img_dict["height"]
|
137 |
+
record["width"] = img_dict["width"]
|
138 |
+
image_id = record["image_id"] = img_dict["id"]
|
139 |
+
|
140 |
+
objs = []
|
141 |
+
for anno in anno_dict_list:
|
142 |
+
# Check that the image_id in this annotation is the same as
|
143 |
+
# the image_id we're looking at.
|
144 |
+
# This fails only when the data parsing logic or the annotation file is buggy.
|
145 |
+
|
146 |
+
# The original COCO valminusminival2014 & minival2014 annotation files
|
147 |
+
# actually contains bugs that, together with certain ways of using COCO API,
|
148 |
+
# can trigger this assertion.
|
149 |
+
assert anno["image_id"] == image_id
|
150 |
+
|
151 |
+
assert anno.get("ignore", 0) == 0, '"ignore" in COCO json file is not supported.'
|
152 |
+
|
153 |
+
obj = {key: anno[key] for key in ann_keys if key in anno}
|
154 |
+
if "bbox" in obj and len(obj["bbox"]) == 0:
|
155 |
+
raise ValueError(
|
156 |
+
f"One annotation of image {image_id} contains empty 'bbox' value! "
|
157 |
+
"This json does not have valid COCO format."
|
158 |
+
)
|
159 |
+
|
160 |
+
segm = anno.get("segmentation", None)
|
161 |
+
if segm: # either list[list[float]] or dict(RLE)
|
162 |
+
if isinstance(segm, dict):
|
163 |
+
if isinstance(segm["counts"], list):
|
164 |
+
# convert to compressed RLE
|
165 |
+
segm = mask_util.frPyObjects(segm, *segm["size"])
|
166 |
+
else:
|
167 |
+
# filter out invalid polygons (< 3 points)
|
168 |
+
segm = [poly for poly in segm if len(poly) % 2 == 0 and len(poly) >= 6]
|
169 |
+
if len(segm) == 0:
|
170 |
+
num_instances_without_valid_segmentation += 1
|
171 |
+
continue # ignore this instance
|
172 |
+
obj["segmentation"] = segm
|
173 |
+
|
174 |
+
keypts = anno.get("keypoints", None)
|
175 |
+
if keypts: # list[int]
|
176 |
+
for idx, v in enumerate(keypts):
|
177 |
+
if idx % 3 != 2:
|
178 |
+
# COCO's segmentation coordinates are floating points in [0, H or W],
|
179 |
+
# but keypoint coordinates are integers in [0, H-1 or W-1]
|
180 |
+
# Therefore we assume the coordinates are "pixel indices" and
|
181 |
+
# add 0.5 to convert to floating point coordinates.
|
182 |
+
keypts[idx] = v + 0.5
|
183 |
+
obj["keypoints"] = keypts
|
184 |
+
|
185 |
+
obj["bbox_mode"] = BoxMode.XYWH_ABS
|
186 |
+
if id_map:
|
187 |
+
annotation_category_id = obj["category_id"]
|
188 |
+
try:
|
189 |
+
obj["category_id"] = id_map[annotation_category_id]
|
190 |
+
except KeyError as e:
|
191 |
+
raise KeyError(
|
192 |
+
f"Encountered category_id={annotation_category_id} "
|
193 |
+
"but this id does not exist in 'categories' of the json file."
|
194 |
+
) from e
|
195 |
+
objs.append(obj)
|
196 |
+
record["annotations"] = objs
|
197 |
+
dataset_dicts[image_id] = record
|
198 |
+
|
199 |
+
if num_instances_without_valid_segmentation > 0:
|
200 |
+
logger.warning(
|
201 |
+
"Filtered out {} instances without valid segmentation. ".format(
|
202 |
+
num_instances_without_valid_segmentation
|
203 |
+
)
|
204 |
+
+ "There might be issues in your dataset generation process. Please "
|
205 |
+
"check https://detectron2.readthedocs.io/en/latest/tutorials/datasets.html carefully"
|
206 |
+
)
|
207 |
+
return dataset_dicts
|
208 |
+
|
209 |
+
def get_metadata():
|
210 |
+
meta = {}
|
211 |
+
# The following metadata maps contiguous id from [0, #thing categories +
|
212 |
+
# #stuff categories) to their names and colors. We have to replica of the
|
213 |
+
# same name and color under "thing_*" and "stuff_*" because the current
|
214 |
+
# visualization function in D2 handles thing and class classes differently
|
215 |
+
# due to some heuristic used in Panoptic FPN. We keep the same naming to
|
216 |
+
# enable reusing existing visualization functions.
|
217 |
+
thing_classes = [k["name"] for k in COCO_CATEGORIES if k["isthing"] == 1]
|
218 |
+
thing_colors = [k["color"] for k in COCO_CATEGORIES if k["isthing"] == 1]
|
219 |
+
stuff_classes = [k["name"] for k in COCO_CATEGORIES]
|
220 |
+
stuff_colors = [k["color"] for k in COCO_CATEGORIES]
|
221 |
+
|
222 |
+
meta["thing_classes"] = thing_classes
|
223 |
+
meta["thing_colors"] = thing_colors
|
224 |
+
meta["stuff_classes"] = stuff_classes
|
225 |
+
meta["stuff_colors"] = stuff_colors
|
226 |
+
|
227 |
+
# Convert category id for training:
|
228 |
+
# category id: like semantic segmentation, it is the class id for each
|
229 |
+
# pixel. Since there are some classes not used in evaluation, the category
|
230 |
+
# id is not always contiguous and thus we have two set of category ids:
|
231 |
+
# - original category id: category id in the original dataset, mainly
|
232 |
+
# used for evaluation.
|
233 |
+
# - contiguous category id: [0, #classes), in order to train the linear
|
234 |
+
# softmax classifier.
|
235 |
+
thing_dataset_id_to_contiguous_id = {}
|
236 |
+
stuff_dataset_id_to_contiguous_id = {}
|
237 |
+
|
238 |
+
for i, cat in enumerate(COCO_CATEGORIES):
|
239 |
+
if cat["isthing"]:
|
240 |
+
thing_dataset_id_to_contiguous_id[cat["id"]] = i
|
241 |
+
# else:
|
242 |
+
# stuff_dataset_id_to_contiguous_id[cat["id"]] = i
|
243 |
+
|
244 |
+
# in order to use sem_seg evaluator
|
245 |
+
stuff_dataset_id_to_contiguous_id[cat["id"]] = i
|
246 |
+
|
247 |
+
meta["thing_dataset_id_to_contiguous_id"] = thing_dataset_id_to_contiguous_id
|
248 |
+
meta["stuff_dataset_id_to_contiguous_id"] = stuff_dataset_id_to_contiguous_id
|
249 |
+
|
250 |
+
return meta
|
251 |
+
|
252 |
+
|
253 |
+
def load_coco_panoptic_json(json_file, instances_json, instances_name, image_dir, gt_dir, semseg_dir, meta):
|
254 |
+
"""
|
255 |
+
Args:
|
256 |
+
image_dir (str): path to the raw dataset. e.g., "~/coco/train2017".
|
257 |
+
gt_dir (str): path to the raw annotations. e.g., "~/coco/panoptic_train2017".
|
258 |
+
json_file (str): path to the json file. e.g., "~/coco/annotations/panoptic_train2017.json".
|
259 |
+
Returns:
|
260 |
+
list[dict]: a list of dicts in Detectron2 standard format. (See
|
261 |
+
`Using Custom Datasets </tutorials/datasets.html>`_ )
|
262 |
+
"""
|
263 |
+
|
264 |
+
def _convert_category_id(segment_info, meta):
|
265 |
+
if segment_info["category_id"] in meta["thing_dataset_id_to_contiguous_id"]:
|
266 |
+
segment_info["category_id"] = meta["thing_dataset_id_to_contiguous_id"][
|
267 |
+
segment_info["category_id"]
|
268 |
+
]
|
269 |
+
segment_info["isthing"] = True
|
270 |
+
else:
|
271 |
+
segment_info["category_id"] = meta["stuff_dataset_id_to_contiguous_id"][
|
272 |
+
segment_info["category_id"]
|
273 |
+
]
|
274 |
+
segment_info["isthing"] = False
|
275 |
+
return segment_info
|
276 |
+
|
277 |
+
with PathManager.open(json_file) as f:
|
278 |
+
json_info = json.load(f)
|
279 |
+
|
280 |
+
instance_data_dicts = load_coco_instance_json(instances_json, image_dir.replace("panoptic_", ""), instances_name)
|
281 |
+
|
282 |
+
ret = []
|
283 |
+
for ann in json_info["annotations"]:
|
284 |
+
image_id = int(ann["image_id"])
|
285 |
+
# TODO: currently we assume image and label has the same filename but
|
286 |
+
# different extension, and images have extension ".jpg" for COCO. Need
|
287 |
+
# to make image extension a user-provided argument if we extend this
|
288 |
+
# function to support other COCO-like datasets.
|
289 |
+
image_file = os.path.join(image_dir, os.path.splitext(ann["file_name"])[0] + ".jpg")
|
290 |
+
label_file = os.path.join(gt_dir, ann["file_name"])
|
291 |
+
sem_label_file = os.path.join(semseg_dir, ann["file_name"])
|
292 |
+
segments_info = [_convert_category_id(x, meta) for x in ann["segments_info"]]
|
293 |
+
ret.append(
|
294 |
+
{
|
295 |
+
"file_name": image_file,
|
296 |
+
"image_id": image_id,
|
297 |
+
"pan_seg_file_name": label_file,
|
298 |
+
"sem_seg_file_name": sem_label_file,
|
299 |
+
"segments_info": segments_info,
|
300 |
+
"annotations": instance_data_dicts[image_id]["annotations"],
|
301 |
+
}
|
302 |
+
)
|
303 |
+
assert len(ret), f"No images found in {image_dir}!"
|
304 |
+
assert PathManager.isfile(ret[0]["file_name"]), ret[0]["file_name"]
|
305 |
+
assert PathManager.isfile(ret[0]["pan_seg_file_name"]), ret[0]["pan_seg_file_name"]
|
306 |
+
assert PathManager.isfile(ret[0]["sem_seg_file_name"]), ret[0]["sem_seg_file_name"]
|
307 |
+
return ret
|
308 |
+
|
309 |
+
|
310 |
+
def register_coco_panoptic_annos_sem_seg(
|
311 |
+
name, metadata, image_root, panoptic_root, panoptic_json, sem_seg_root, instances_json, instances_name,
|
312 |
+
):
|
313 |
+
panoptic_name = name
|
314 |
+
delattr(MetadataCatalog.get(panoptic_name), "thing_classes")
|
315 |
+
delattr(MetadataCatalog.get(panoptic_name), "thing_colors")
|
316 |
+
MetadataCatalog.get(panoptic_name).set(
|
317 |
+
thing_classes=metadata["thing_classes"],
|
318 |
+
thing_colors=metadata["thing_colors"],
|
319 |
+
# thing_dataset_id_to_contiguous_id=metadata["thing_dataset_id_to_contiguous_id"],
|
320 |
+
)
|
321 |
+
|
322 |
+
# the name is "coco_2017_train_panoptic_with_sem_seg" and "coco_2017_val_panoptic_with_sem_seg"
|
323 |
+
semantic_name = name + "_with_sem_seg"
|
324 |
+
DatasetCatalog.register(
|
325 |
+
semantic_name,
|
326 |
+
lambda: load_coco_panoptic_json(panoptic_json, instances_json, instances_name, image_root, panoptic_root, sem_seg_root, metadata),
|
327 |
+
)
|
328 |
+
MetadataCatalog.get(semantic_name).set(
|
329 |
+
sem_seg_root=sem_seg_root,
|
330 |
+
panoptic_root=panoptic_root,
|
331 |
+
image_root=image_root,
|
332 |
+
panoptic_json=panoptic_json,
|
333 |
+
json_file=instances_json,
|
334 |
+
evaluator_type="coco_panoptic_seg",
|
335 |
+
ignore_label=255,
|
336 |
+
label_divisor=1000,
|
337 |
+
**metadata,
|
338 |
+
)
|
339 |
+
|
340 |
+
|
341 |
+
def register_all_coco_panoptic_annos_sem_seg(root):
|
342 |
+
for (
|
343 |
+
prefix,
|
344 |
+
(panoptic_root, panoptic_json, semantic_root),
|
345 |
+
) in _PREDEFINED_SPLITS_COCO_PANOPTIC.items():
|
346 |
+
|
347 |
+
prefix_instances = prefix[: -len("_panoptic")]
|
348 |
+
instances_meta = MetadataCatalog.get(prefix_instances)
|
349 |
+
image_root, instances_json = instances_meta.image_root, instances_meta.json_file
|
350 |
+
|
351 |
+
if 'val' in instances_json:
|
352 |
+
instances_json = instances_json.replace('instances_', 'panoptic2instances_')
|
353 |
+
|
354 |
+
register_coco_panoptic_annos_sem_seg(
|
355 |
+
prefix,
|
356 |
+
get_metadata(),
|
357 |
+
image_root,
|
358 |
+
os.path.join(root, panoptic_root),
|
359 |
+
os.path.join(root, panoptic_json),
|
360 |
+
os.path.join(root, semantic_root),
|
361 |
+
instances_json,
|
362 |
+
prefix_instances,
|
363 |
+
)
|
364 |
+
|
365 |
+
|
366 |
+
_root = os.getenv("DETECTRON2_DATASETS", "datasets")
|
367 |
+
register_all_coco_panoptic_annos_sem_seg(_root)
|
oneformer/data/tokenizer.py
ADDED
@@ -0,0 +1,193 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
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|
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|
|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
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# -------------------------------------------------------------------------
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# MIT License
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#
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# Copyright (c) 2021 OpenAI
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#
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# Permission is hereby granted, free of charge, to any person obtaining a copy
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# of this software and associated documentation files (the "Software"), to deal
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+
# in the Software without restriction, including without limitation the rights
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# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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# copies of the Software, and to permit persons to whom the Software is
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# furnished to do so, subject to the following conditions:
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#
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# The above copyright notice and this permission notice shall be included in all
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# copies or substantial portions of the Software.
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#
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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# SOFTWARE.
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#
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# Modified by Jiarui Xu
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# -------------------------------------------------------------------------
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import gzip
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import html
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+
import os
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from functools import lru_cache
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+
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import ftfy
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import regex as re
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import torch
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@lru_cache()
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+
def default_bpe():
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return os.path.join(os.path.dirname(os.path.abspath(__file__)), 'bpe_simple_vocab_16e6.txt.gz')
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+
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+
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@lru_cache()
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def bytes_to_unicode():
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"""Returns list of utf-8 byte and a corresponding list of unicode strings.
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+
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The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab
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if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for decent
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coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup tables
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between utf-8 bytes and unicode strings. And avoids mapping to whitespace/control characters the bpe code barfs on.
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"""
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bs = list(range(ord('!'), ord('~') + 1)) + list(range(ord('¡'), ord('¬') + 1)) + list(range(ord('®'), ord('ÿ') + 1))
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+
cs = bs[:]
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+
n = 0
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+
for b in range(2**8):
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+
if b not in bs:
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bs.append(b)
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cs.append(2**8 + n)
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n += 1
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cs = [chr(n) for n in cs]
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return dict(zip(bs, cs))
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+
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+
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def get_pairs(word):
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"""Return set of symbol pairs in a word.
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+
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Word is represented as tuple of symbols (symbols being variable-length strings).
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"""
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pairs = set()
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prev_char = word[0]
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for char in word[1:]:
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pairs.add((prev_char, char))
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prev_char = char
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+
return pairs
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+
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+
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+
def basic_clean(text):
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text = ftfy.fix_text(text)
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text = html.unescape(html.unescape(text))
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return text.strip()
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+
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+
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def whitespace_clean(text):
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text = re.sub(r'\s+', ' ', text)
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text = text.strip()
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return text
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+
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class Tokenize:
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def __init__(self, tokenizer, max_seq_len=77, truncate=True):
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self.tokenizer = tokenizer
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self.max_seq_len = max_seq_len
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self.truncate = truncate
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+
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+
def __call__(self, texts):
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expanded_dim = False
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if isinstance(texts, str):
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texts = [texts]
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expanded_dim = True
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+
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sot_token = self.tokenizer.encoder['<|startoftext|>']
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eot_token = self.tokenizer.encoder['<|endoftext|>']
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+
all_tokens = [[sot_token] + self.tokenizer.encode(text) + [eot_token] for text in texts]
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result = torch.zeros(len(all_tokens), self.max_seq_len, dtype=torch.long)
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+
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for i, tokens in enumerate(all_tokens):
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+
if len(tokens) > self.max_seq_len:
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+
if self.truncate:
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+
tokens = tokens[:self.max_seq_len]
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tokens[-1] = eot_token
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+
else:
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raise RuntimeError(f'Input {texts[i]} is too long for context length {self.max_seq_len}')
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result[i, :len(tokens)] = torch.tensor(tokens)
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+
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if expanded_dim:
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return result[0]
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+
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return result
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+
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+
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class SimpleTokenizer(object):
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+
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def __init__(self, bpe_path: str = default_bpe()):
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self.byte_encoder = bytes_to_unicode()
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self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
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merges = gzip.open(bpe_path).read().decode('utf-8').split('\n')
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merges = merges[1:49152 - 256 - 2 + 1]
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merges = [tuple(merge.split()) for merge in merges]
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vocab = list(bytes_to_unicode().values())
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vocab = vocab + [v + '</w>' for v in vocab]
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for merge in merges:
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vocab.append(''.join(merge))
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vocab.extend(['<|startoftext|>', '<|endoftext|>'])
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self.encoder = dict(zip(vocab, range(len(vocab))))
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self.decoder = {v: k for k, v in self.encoder.items()}
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self.bpe_ranks = dict(zip(merges, range(len(merges))))
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self.cache = {'<|startoftext|>': '<|startoftext|>', '<|endoftext|>': '<|endoftext|>'}
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self.pat = re.compile(
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r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""",
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re.IGNORECASE)
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+
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def bpe(self, token):
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if token in self.cache:
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return self.cache[token]
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word = tuple(token[:-1]) + (token[-1] + '</w>', )
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pairs = get_pairs(word)
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+
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if not pairs:
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return token + '</w>'
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+
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while True:
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bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float('inf')))
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if bigram not in self.bpe_ranks:
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break
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first, second = bigram
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new_word = []
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i = 0
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while i < len(word):
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+
try:
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j = word.index(first, i)
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new_word.extend(word[i:j])
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i = j
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except: # noqa: E722
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new_word.extend(word[i:])
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break
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+
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if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
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new_word.append(first + second)
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+
i += 2
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+
else:
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new_word.append(word[i])
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i += 1
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new_word = tuple(new_word)
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word = new_word
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if len(word) == 1:
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break
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else:
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pairs = get_pairs(word)
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word = ' '.join(word)
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self.cache[token] = word
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return word
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+
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def encode(self, text):
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bpe_tokens = []
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text = whitespace_clean(basic_clean(text)).lower()
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+
for token in re.findall(self.pat, text):
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+
token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))
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+
bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' '))
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return bpe_tokens
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+
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+
def decode(self, tokens):
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text = ''.join([self.decoder[token] for token in tokens])
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text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors='replace').replace('</w>', ' ')
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return text
|
oneformer/evaluation/__init__.py
ADDED
@@ -0,0 +1,3 @@
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from .detection_coco_evaluator import *
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+
from .coco_evaluator import *
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+
from .cityscapes_evaluation import CityscapesInstanceEvaluator
|
oneformer/evaluation/cityscapes_evaluation.py
ADDED
@@ -0,0 +1,201 @@
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|
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+
# ------------------------------------------------------------------------------
|
2 |
+
# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/evaluation/cityscapes_evaluation.py
|
3 |
+
# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
|
4 |
+
# ------------------------------------------------------------------------------
|
5 |
+
|
6 |
+
import glob
|
7 |
+
import logging
|
8 |
+
import numpy as np
|
9 |
+
import os
|
10 |
+
import tempfile
|
11 |
+
from collections import OrderedDict
|
12 |
+
import torch
|
13 |
+
from PIL import Image
|
14 |
+
|
15 |
+
from detectron2.data import MetadataCatalog
|
16 |
+
from detectron2.utils import comm
|
17 |
+
from detectron2.utils.file_io import PathManager
|
18 |
+
|
19 |
+
from .evaluator import DatasetEvaluator
|
20 |
+
|
21 |
+
|
22 |
+
class CityscapesEvaluator(DatasetEvaluator):
|
23 |
+
"""
|
24 |
+
Base class for evaluation using cityscapes API.
|
25 |
+
"""
|
26 |
+
|
27 |
+
def __init__(self, dataset_name):
|
28 |
+
"""
|
29 |
+
Args:
|
30 |
+
dataset_name (str): the name of the dataset.
|
31 |
+
It must have the following metadata associated with it:
|
32 |
+
"thing_classes", "gt_dir".
|
33 |
+
"""
|
34 |
+
self._metadata = MetadataCatalog.get(dataset_name)
|
35 |
+
self._cpu_device = torch.device("cpu")
|
36 |
+
self._logger = logging.getLogger(__name__)
|
37 |
+
|
38 |
+
def reset(self):
|
39 |
+
self._working_dir = tempfile.TemporaryDirectory(prefix="cityscapes_eval_")
|
40 |
+
self._temp_dir = self._working_dir.name
|
41 |
+
# All workers will write to the same results directory
|
42 |
+
# TODO this does not work in distributed training
|
43 |
+
assert (
|
44 |
+
comm.get_local_size() == comm.get_world_size()
|
45 |
+
), "CityscapesEvaluator currently do not work with multiple machines."
|
46 |
+
self._temp_dir = comm.all_gather(self._temp_dir)[0]
|
47 |
+
if self._temp_dir != self._working_dir.name:
|
48 |
+
self._working_dir.cleanup()
|
49 |
+
self._logger.info(
|
50 |
+
"Writing cityscapes results to temporary directory {} ...".format(self._temp_dir)
|
51 |
+
)
|
52 |
+
|
53 |
+
|
54 |
+
class CityscapesInstanceEvaluator(CityscapesEvaluator):
|
55 |
+
"""
|
56 |
+
Evaluate instance segmentation results on cityscapes dataset using cityscapes API.
|
57 |
+
|
58 |
+
Note:
|
59 |
+
* It does not work in multi-machine distributed training.
|
60 |
+
* It contains a synchronization, therefore has to be used on all ranks.
|
61 |
+
* Only the main process runs evaluation.
|
62 |
+
"""
|
63 |
+
|
64 |
+
def process(self, inputs, outputs):
|
65 |
+
from cityscapesscripts.helpers.labels import name2label
|
66 |
+
|
67 |
+
for input, output in zip(inputs, outputs):
|
68 |
+
file_name = input["file_name"]
|
69 |
+
basename = os.path.splitext(os.path.basename(file_name))[0]
|
70 |
+
pred_txt = os.path.join(self._temp_dir, basename + "_pred.txt")
|
71 |
+
|
72 |
+
if "instances" in output:
|
73 |
+
output = output["instances"].to(self._cpu_device)
|
74 |
+
num_instances = len(output)
|
75 |
+
with open(pred_txt, "w") as fout:
|
76 |
+
for i in range(num_instances):
|
77 |
+
pred_class = output.pred_classes[i]
|
78 |
+
classes = self._metadata.stuff_classes[pred_class]
|
79 |
+
class_id = name2label[classes].id
|
80 |
+
score = output.scores[i]
|
81 |
+
mask = output.pred_masks[i].numpy().astype("uint8")
|
82 |
+
png_filename = os.path.join(
|
83 |
+
self._temp_dir, basename + "_{}_{}.png".format(i, classes)
|
84 |
+
)
|
85 |
+
|
86 |
+
Image.fromarray(mask * 255).save(png_filename)
|
87 |
+
fout.write(
|
88 |
+
"{} {} {}\n".format(os.path.basename(png_filename), class_id, score)
|
89 |
+
)
|
90 |
+
else:
|
91 |
+
# Cityscapes requires a prediction file for every ground truth image.
|
92 |
+
with open(pred_txt, "w") as fout:
|
93 |
+
pass
|
94 |
+
|
95 |
+
def evaluate(self):
|
96 |
+
"""
|
97 |
+
Returns:
|
98 |
+
dict: has a key "segm", whose value is a dict of "AP" and "AP50".
|
99 |
+
"""
|
100 |
+
comm.synchronize()
|
101 |
+
if comm.get_rank() > 0:
|
102 |
+
return
|
103 |
+
import cityscapesscripts.evaluation.evalInstanceLevelSemanticLabeling as cityscapes_eval
|
104 |
+
|
105 |
+
self._logger.info("Evaluating results under {} ...".format(self._temp_dir))
|
106 |
+
|
107 |
+
# set some global states in cityscapes evaluation API, before evaluating
|
108 |
+
cityscapes_eval.args.predictionPath = os.path.abspath(self._temp_dir)
|
109 |
+
cityscapes_eval.args.predictionWalk = None
|
110 |
+
cityscapes_eval.args.JSONOutput = False
|
111 |
+
cityscapes_eval.args.colorized = False
|
112 |
+
cityscapes_eval.args.gtInstancesFile = os.path.join(self._temp_dir, "gtInstances.json")
|
113 |
+
|
114 |
+
# These lines are adopted from
|
115 |
+
# https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalInstanceLevelSemanticLabeling.py # noqa
|
116 |
+
gt_dir = PathManager.get_local_path(self._metadata.gt_dir)
|
117 |
+
groundTruthImgList = glob.glob(os.path.join(gt_dir, "*", "*_gtFine_instanceIds.png"))
|
118 |
+
assert len(
|
119 |
+
groundTruthImgList
|
120 |
+
), "Cannot find any ground truth images to use for evaluation. Searched for: {}".format(
|
121 |
+
cityscapes_eval.args.groundTruthSearch
|
122 |
+
)
|
123 |
+
predictionImgList = []
|
124 |
+
for gt in groundTruthImgList:
|
125 |
+
predictionImgList.append(cityscapes_eval.getPrediction(gt, cityscapes_eval.args))
|
126 |
+
results = cityscapes_eval.evaluateImgLists(
|
127 |
+
predictionImgList, groundTruthImgList, cityscapes_eval.args
|
128 |
+
)["averages"]
|
129 |
+
|
130 |
+
ret = OrderedDict()
|
131 |
+
ret["segm"] = {"AP": results["allAp"] * 100, "AP50": results["allAp50%"] * 100}
|
132 |
+
self._working_dir.cleanup()
|
133 |
+
return ret
|
134 |
+
|
135 |
+
|
136 |
+
class CityscapesSemSegEvaluator(CityscapesEvaluator):
|
137 |
+
"""
|
138 |
+
Evaluate semantic segmentation results on cityscapes dataset using cityscapes API.
|
139 |
+
|
140 |
+
Note:
|
141 |
+
* It does not work in multi-machine distributed training.
|
142 |
+
* It contains a synchronization, therefore has to be used on all ranks.
|
143 |
+
* Only the main process runs evaluation.
|
144 |
+
"""
|
145 |
+
|
146 |
+
def process(self, inputs, outputs):
|
147 |
+
from cityscapesscripts.helpers.labels import trainId2label
|
148 |
+
|
149 |
+
for input, output in zip(inputs, outputs):
|
150 |
+
file_name = input["file_name"]
|
151 |
+
basename = os.path.splitext(os.path.basename(file_name))[0]
|
152 |
+
pred_filename = os.path.join(self._temp_dir, basename + "_pred.png")
|
153 |
+
|
154 |
+
output = output["sem_seg"].argmax(dim=0).to(self._cpu_device).numpy()
|
155 |
+
pred = 255 * np.ones(output.shape, dtype=np.uint8)
|
156 |
+
for train_id, label in trainId2label.items():
|
157 |
+
if label.ignoreInEval:
|
158 |
+
continue
|
159 |
+
pred[output == train_id] = label.id
|
160 |
+
Image.fromarray(pred).save(pred_filename)
|
161 |
+
|
162 |
+
def evaluate(self):
|
163 |
+
comm.synchronize()
|
164 |
+
if comm.get_rank() > 0:
|
165 |
+
return
|
166 |
+
# Load the Cityscapes eval script *after* setting the required env var,
|
167 |
+
# since the script reads CITYSCAPES_DATASET into global variables at load time.
|
168 |
+
import cityscapesscripts.evaluation.evalPixelLevelSemanticLabeling as cityscapes_eval
|
169 |
+
|
170 |
+
self._logger.info("Evaluating results under {} ...".format(self._temp_dir))
|
171 |
+
|
172 |
+
# set some global states in cityscapes evaluation API, before evaluating
|
173 |
+
cityscapes_eval.args.predictionPath = os.path.abspath(self._temp_dir)
|
174 |
+
cityscapes_eval.args.predictionWalk = None
|
175 |
+
cityscapes_eval.args.JSONOutput = False
|
176 |
+
cityscapes_eval.args.colorized = False
|
177 |
+
|
178 |
+
# These lines are adopted from
|
179 |
+
# https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalPixelLevelSemanticLabeling.py # noqa
|
180 |
+
gt_dir = PathManager.get_local_path(self._metadata.gt_dir)
|
181 |
+
groundTruthImgList = glob.glob(os.path.join(gt_dir, "*", "*_gtFine_labelIds.png"))
|
182 |
+
assert len(
|
183 |
+
groundTruthImgList
|
184 |
+
), "Cannot find any ground truth images to use for evaluation. Searched for: {}".format(
|
185 |
+
cityscapes_eval.args.groundTruthSearch
|
186 |
+
)
|
187 |
+
predictionImgList = []
|
188 |
+
for gt in groundTruthImgList:
|
189 |
+
predictionImgList.append(cityscapes_eval.getPrediction(cityscapes_eval.args, gt))
|
190 |
+
results = cityscapes_eval.evaluateImgLists(
|
191 |
+
predictionImgList, groundTruthImgList, cityscapes_eval.args
|
192 |
+
)
|
193 |
+
ret = OrderedDict()
|
194 |
+
ret["sem_seg"] = {
|
195 |
+
"IoU": 100.0 * results["averageScoreClasses"],
|
196 |
+
"iIoU": 100.0 * results["averageScoreInstClasses"],
|
197 |
+
"IoU_sup": 100.0 * results["averageScoreCategories"],
|
198 |
+
"iIoU_sup": 100.0 * results["averageScoreInstCategories"],
|
199 |
+
}
|
200 |
+
self._working_dir.cleanup()
|
201 |
+
return ret
|
oneformer/evaluation/coco_evaluator.py
ADDED
@@ -0,0 +1,563 @@
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|
|
|
|
|
|
|
|
1 |
+
# ------------------------------------------------------------------------------
|
2 |
+
# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/evaluation/coco_evaluation.py
|
3 |
+
# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
|
4 |
+
# ------------------------------------------------------------------------------
|
5 |
+
|
6 |
+
import contextlib
|
7 |
+
import copy
|
8 |
+
import io
|
9 |
+
import itertools
|
10 |
+
import json
|
11 |
+
import logging
|
12 |
+
import numpy as np
|
13 |
+
import os
|
14 |
+
import pickle
|
15 |
+
from collections import OrderedDict
|
16 |
+
import pycocotools.mask as mask_util
|
17 |
+
import torch
|
18 |
+
from pycocotools.coco import COCO
|
19 |
+
from pycocotools.cocoeval import COCOeval
|
20 |
+
from tabulate import tabulate
|
21 |
+
|
22 |
+
import detectron2.utils.comm as comm
|
23 |
+
from detectron2.config import CfgNode
|
24 |
+
from detectron2.data import MetadataCatalog
|
25 |
+
from detectron2.data.datasets.coco import convert_to_coco_json
|
26 |
+
from detectron2.structures import Boxes, BoxMode, pairwise_iou
|
27 |
+
from detectron2.utils.file_io import PathManager
|
28 |
+
from detectron2.utils.logger import create_small_table
|
29 |
+
|
30 |
+
from .evaluator import DatasetEvaluator
|
31 |
+
|
32 |
+
try:
|
33 |
+
from detectron2.evaluation.fast_eval_api import COCOeval_opt
|
34 |
+
except ImportError:
|
35 |
+
COCOeval_opt = COCOeval
|
36 |
+
|
37 |
+
|
38 |
+
class COCOEvaluator(DatasetEvaluator):
|
39 |
+
"""
|
40 |
+
Evaluate AP for instance detection/segmentation, AP
|
41 |
+
for keypoint detection outputs using COCO's metrics.
|
42 |
+
See http://cocodataset.org/#detection-eval and
|
43 |
+
http://cocodataset.org/#keypoints-eval to understand its metrics.
|
44 |
+
The metrics range from 0 to 100 (instead of 0 to 1), where a -1 or NaN means
|
45 |
+
the metric cannot be computed (e.g. due to no predictions made).
|
46 |
+
|
47 |
+
In addition to COCO, this evaluator is able to support any bounding box detection,
|
48 |
+
instance segmentation, or keypoint detection dataset.
|
49 |
+
"""
|
50 |
+
|
51 |
+
def __init__(
|
52 |
+
self,
|
53 |
+
dataset_name,
|
54 |
+
tasks=None,
|
55 |
+
distributed=True,
|
56 |
+
output_dir=None,
|
57 |
+
*,
|
58 |
+
max_dets_per_image=None,
|
59 |
+
use_fast_impl=True,
|
60 |
+
kpt_oks_sigmas=(),
|
61 |
+
allow_cached_coco=True,
|
62 |
+
):
|
63 |
+
"""
|
64 |
+
Args:
|
65 |
+
dataset_name (str): name of the dataset to be evaluated.
|
66 |
+
It must have either the following corresponding metadata:
|
67 |
+
|
68 |
+
"json_file": the path to the COCO format annotation
|
69 |
+
|
70 |
+
Or it must be in detectron2's standard dataset format
|
71 |
+
so it can be converted to COCO format automatically.
|
72 |
+
tasks (tuple[str]): tasks that can be evaluated under the given
|
73 |
+
configuration. A task is one of "bbox", "segm", "keypoints".
|
74 |
+
By default, will infer this automatically from predictions.
|
75 |
+
distributed (True): if True, will collect results from all ranks and run evaluation
|
76 |
+
in the main process.
|
77 |
+
Otherwise, will only evaluate the results in the current process.
|
78 |
+
output_dir (str): optional, an output directory to dump all
|
79 |
+
results predicted on the dataset. The dump contains two files:
|
80 |
+
|
81 |
+
1. "instances_predictions.pth" a file that can be loaded with `torch.load` and
|
82 |
+
contains all the results in the format they are produced by the model.
|
83 |
+
2. "coco_instances_results.json" a json file in COCO's result format.
|
84 |
+
max_dets_per_image (int): limit on the maximum number of detections per image.
|
85 |
+
By default in COCO, this limit is to 100, but this can be customized
|
86 |
+
to be greater, as is needed in evaluation metrics AP fixed and AP pool
|
87 |
+
(see https://arxiv.org/pdf/2102.01066.pdf)
|
88 |
+
This doesn't affect keypoint evaluation.
|
89 |
+
use_fast_impl (bool): use a fast but **unofficial** implementation to compute AP.
|
90 |
+
Although the results should be very close to the official implementation in COCO
|
91 |
+
API, it is still recommended to compute results with the official API for use in
|
92 |
+
papers. The faster implementation also uses more RAM.
|
93 |
+
kpt_oks_sigmas (list[float]): The sigmas used to calculate keypoint OKS.
|
94 |
+
See http://cocodataset.org/#keypoints-eval
|
95 |
+
When empty, it will use the defaults in COCO.
|
96 |
+
Otherwise it should be the same length as ROI_KEYPOINT_HEAD.NUM_KEYPOINTS.
|
97 |
+
allow_cached_coco (bool): Whether to use cached coco json from previous validation
|
98 |
+
runs. You should set this to False if you need to use different validation data.
|
99 |
+
Defaults to True.
|
100 |
+
"""
|
101 |
+
self._logger = logging.getLogger(__name__)
|
102 |
+
self._distributed = distributed
|
103 |
+
self._output_dir = output_dir
|
104 |
+
|
105 |
+
if use_fast_impl and (COCOeval_opt is COCOeval):
|
106 |
+
self._logger.info("Fast COCO eval is not built. Falling back to official COCO eval.")
|
107 |
+
use_fast_impl = False
|
108 |
+
self._use_fast_impl = use_fast_impl
|
109 |
+
|
110 |
+
# COCOeval requires the limit on the number of detections per image (maxDets) to be a list
|
111 |
+
# with at least 3 elements. The default maxDets in COCOeval is [1, 10, 100], in which the
|
112 |
+
# 3rd element (100) is used as the limit on the number of detections per image when
|
113 |
+
# evaluating AP. COCOEvaluator expects an integer for max_dets_per_image, so for COCOeval,
|
114 |
+
# we reformat max_dets_per_image into [1, 10, max_dets_per_image], based on the defaults.
|
115 |
+
if max_dets_per_image is None:
|
116 |
+
max_dets_per_image = [1, 10, 100]
|
117 |
+
else:
|
118 |
+
max_dets_per_image = [1, 10, max_dets_per_image]
|
119 |
+
self._max_dets_per_image = max_dets_per_image
|
120 |
+
|
121 |
+
if tasks is not None and isinstance(tasks, CfgNode):
|
122 |
+
kpt_oks_sigmas = (
|
123 |
+
tasks.TEST.KEYPOINT_OKS_SIGMAS if not kpt_oks_sigmas else kpt_oks_sigmas
|
124 |
+
)
|
125 |
+
self._logger.warn(
|
126 |
+
"COCO Evaluator instantiated using config, this is deprecated behavior."
|
127 |
+
" Please pass in explicit arguments instead."
|
128 |
+
)
|
129 |
+
self._tasks = None # Infering it from predictions should be better
|
130 |
+
else:
|
131 |
+
self._tasks = tasks
|
132 |
+
|
133 |
+
self._cpu_device = torch.device("cpu")
|
134 |
+
|
135 |
+
self._metadata = MetadataCatalog.get(dataset_name)
|
136 |
+
if not hasattr(self._metadata, "json_file"):
|
137 |
+
if output_dir is None:
|
138 |
+
raise ValueError(
|
139 |
+
"output_dir must be provided to COCOEvaluator "
|
140 |
+
"for datasets not in COCO format."
|
141 |
+
)
|
142 |
+
self._logger.info(f"Trying to convert '{dataset_name}' to COCO format ...")
|
143 |
+
|
144 |
+
cache_path = os.path.join(output_dir, f"{dataset_name}_coco_format.json")
|
145 |
+
self._metadata.json_file = cache_path
|
146 |
+
convert_to_coco_json(dataset_name, cache_path, allow_cached=allow_cached_coco)
|
147 |
+
|
148 |
+
json_file = PathManager.get_local_path(self._metadata.json_file)
|
149 |
+
with contextlib.redirect_stdout(io.StringIO()):
|
150 |
+
self._coco_api = COCO(json_file)
|
151 |
+
|
152 |
+
# Test set json files do not contain annotations (evaluation must be
|
153 |
+
# performed using the COCO evaluation server).
|
154 |
+
self._do_evaluation = "annotations" in self._coco_api.dataset
|
155 |
+
if self._do_evaluation:
|
156 |
+
self._kpt_oks_sigmas = kpt_oks_sigmas
|
157 |
+
|
158 |
+
def reset(self):
|
159 |
+
self._predictions = []
|
160 |
+
|
161 |
+
def process(self, inputs, outputs):
|
162 |
+
"""
|
163 |
+
Args:
|
164 |
+
inputs: the inputs to a COCO model (e.g., GeneralizedRCNN).
|
165 |
+
It is a list of dict. Each dict corresponds to an image and
|
166 |
+
contains keys like "height", "width", "file_name", "image_id".
|
167 |
+
outputs: the outputs of a COCO model. It is a list of dicts with key
|
168 |
+
"instances" that contains :class:`Instances`.
|
169 |
+
"""
|
170 |
+
for input, output in zip(inputs, outputs):
|
171 |
+
prediction = {"image_id": input["image_id"]}
|
172 |
+
|
173 |
+
if "instances" in output:
|
174 |
+
instances = output["instances"].to(self._cpu_device)
|
175 |
+
prediction["instances"] = instances_to_coco_json(instances, input["image_id"])
|
176 |
+
if len(prediction) > 1:
|
177 |
+
self._predictions.append(prediction)
|
178 |
+
|
179 |
+
def evaluate(self, img_ids=None):
|
180 |
+
"""
|
181 |
+
Args:
|
182 |
+
img_ids: a list of image IDs to evaluate on. Default to None for the whole dataset
|
183 |
+
"""
|
184 |
+
if self._distributed:
|
185 |
+
comm.synchronize()
|
186 |
+
predictions = comm.gather(self._predictions, dst=0)
|
187 |
+
predictions = list(itertools.chain(*predictions))
|
188 |
+
|
189 |
+
if not comm.is_main_process():
|
190 |
+
return {}
|
191 |
+
else:
|
192 |
+
predictions = self._predictions
|
193 |
+
|
194 |
+
if len(predictions) == 0:
|
195 |
+
self._logger.warning("[COCOEvaluator] Did not receive valid predictions.")
|
196 |
+
return {}
|
197 |
+
|
198 |
+
if self._output_dir:
|
199 |
+
PathManager.mkdirs(self._output_dir)
|
200 |
+
file_path = os.path.join(self._output_dir, "instances_predictions.pth")
|
201 |
+
with PathManager.open(file_path, "wb") as f:
|
202 |
+
torch.save(predictions, f)
|
203 |
+
|
204 |
+
self._results = OrderedDict()
|
205 |
+
if "instances" in predictions[0]:
|
206 |
+
self._eval_predictions(predictions, img_ids=img_ids)
|
207 |
+
# Copy so the caller can do whatever with results
|
208 |
+
return copy.deepcopy(self._results)
|
209 |
+
|
210 |
+
def _tasks_from_predictions(self, predictions):
|
211 |
+
"""
|
212 |
+
Get COCO API "tasks" (i.e. iou_type) from COCO-format predictions.
|
213 |
+
"""
|
214 |
+
for pred in predictions:
|
215 |
+
if "segmentation" in pred:
|
216 |
+
tasks = {"segm"}
|
217 |
+
if "keypoints" in pred:
|
218 |
+
tasks.add("keypoints")
|
219 |
+
return sorted(tasks)
|
220 |
+
|
221 |
+
def _eval_predictions(self, predictions, img_ids=None):
|
222 |
+
"""
|
223 |
+
Evaluate predictions. Fill self._results with the metrics of the tasks.
|
224 |
+
"""
|
225 |
+
self._logger.info("Preparing results for COCO format ...")
|
226 |
+
coco_results = list(itertools.chain(*[x["instances"] for x in predictions]))
|
227 |
+
tasks = self._tasks or self._tasks_from_predictions(coco_results)
|
228 |
+
|
229 |
+
# unmap the category ids for COCO
|
230 |
+
if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"):
|
231 |
+
dataset_id_to_contiguous_id = self._metadata.thing_dataset_id_to_contiguous_id
|
232 |
+
all_contiguous_ids = list(dataset_id_to_contiguous_id.values())
|
233 |
+
num_classes = len(all_contiguous_ids)
|
234 |
+
assert min(all_contiguous_ids) == 0 and max(all_contiguous_ids) == num_classes - 1
|
235 |
+
|
236 |
+
reverse_id_mapping = {v: k for k, v in dataset_id_to_contiguous_id.items()}
|
237 |
+
for result in coco_results:
|
238 |
+
category_id = result["category_id"]
|
239 |
+
assert category_id < num_classes, (
|
240 |
+
f"A prediction has class={category_id}, "
|
241 |
+
f"but the dataset only has {num_classes} classes and "
|
242 |
+
f"predicted class id should be in [0, {num_classes - 1}]."
|
243 |
+
)
|
244 |
+
result["category_id"] = reverse_id_mapping[category_id]
|
245 |
+
|
246 |
+
if self._output_dir:
|
247 |
+
file_path = os.path.join(self._output_dir, "coco_instances_results.json")
|
248 |
+
self._logger.info("Saving results to {}".format(file_path))
|
249 |
+
with PathManager.open(file_path, "w") as f:
|
250 |
+
f.write(json.dumps(coco_results))
|
251 |
+
f.flush()
|
252 |
+
|
253 |
+
if not self._do_evaluation:
|
254 |
+
self._logger.info("Annotations are not available for evaluation.")
|
255 |
+
return
|
256 |
+
|
257 |
+
self._logger.info(
|
258 |
+
"Evaluating predictions with {} COCO API...".format(
|
259 |
+
"unofficial" if self._use_fast_impl else "official"
|
260 |
+
)
|
261 |
+
)
|
262 |
+
for task in sorted(tasks):
|
263 |
+
assert task in {"segm", "keypoints"}, f"Got unknown task: {task}!"
|
264 |
+
coco_eval = (
|
265 |
+
_evaluate_predictions_on_coco(
|
266 |
+
self._coco_api,
|
267 |
+
coco_results,
|
268 |
+
task,
|
269 |
+
kpt_oks_sigmas=self._kpt_oks_sigmas,
|
270 |
+
use_fast_impl=self._use_fast_impl,
|
271 |
+
img_ids=img_ids,
|
272 |
+
max_dets_per_image=self._max_dets_per_image,
|
273 |
+
)
|
274 |
+
if len(coco_results) > 0
|
275 |
+
else None # cocoapi does not handle empty results very well
|
276 |
+
)
|
277 |
+
|
278 |
+
res = self._derive_coco_results(
|
279 |
+
coco_eval, task, class_names=self._metadata.get("thing_classes")
|
280 |
+
)
|
281 |
+
self._results[task] = res
|
282 |
+
|
283 |
+
def _derive_coco_results(self, coco_eval, iou_type, class_names=None):
|
284 |
+
"""
|
285 |
+
Derive the desired score numbers from summarized COCOeval.
|
286 |
+
|
287 |
+
Args:
|
288 |
+
coco_eval (None or COCOEval): None represents no predictions from model.
|
289 |
+
iou_type (str):
|
290 |
+
class_names (None or list[str]): if provided, will use it to predict
|
291 |
+
per-category AP.
|
292 |
+
|
293 |
+
Returns:
|
294 |
+
a dict of {metric name: score}
|
295 |
+
"""
|
296 |
+
|
297 |
+
metrics = {
|
298 |
+
"segm": ["AP", "AP50", "AP75", "APs", "APm", "APl"],
|
299 |
+
"keypoints": ["AP", "AP50", "AP75", "APm", "APl"],
|
300 |
+
}[iou_type]
|
301 |
+
|
302 |
+
if coco_eval is None:
|
303 |
+
self._logger.warn("No predictions from the model!")
|
304 |
+
return {metric: float("nan") for metric in metrics}
|
305 |
+
|
306 |
+
# the standard metrics
|
307 |
+
results = {
|
308 |
+
metric: float(coco_eval.stats[idx] * 100 if coco_eval.stats[idx] >= 0 else "nan")
|
309 |
+
for idx, metric in enumerate(metrics)
|
310 |
+
}
|
311 |
+
self._logger.info(
|
312 |
+
"Evaluation results for {}: \n".format(iou_type) + create_small_table(results)
|
313 |
+
)
|
314 |
+
if not np.isfinite(sum(results.values())):
|
315 |
+
self._logger.info("Some metrics cannot be computed and is shown as NaN.")
|
316 |
+
|
317 |
+
if class_names is None or len(class_names) <= 1:
|
318 |
+
return results
|
319 |
+
# Compute per-category AP
|
320 |
+
# from https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L222-L252 # noqa
|
321 |
+
precisions = coco_eval.eval["precision"]
|
322 |
+
# precision has dims (iou, recall, cls, area range, max dets)
|
323 |
+
assert len(class_names) == precisions.shape[2]
|
324 |
+
|
325 |
+
results_per_category = []
|
326 |
+
for idx, name in enumerate(class_names):
|
327 |
+
# area range index 0: all area ranges
|
328 |
+
# max dets index -1: typically 100 per image
|
329 |
+
precision = precisions[:, :, idx, 0, -1]
|
330 |
+
precision = precision[precision > -1]
|
331 |
+
ap = np.mean(precision) if precision.size else float("nan")
|
332 |
+
results_per_category.append(("{}".format(name), float(ap * 100)))
|
333 |
+
|
334 |
+
# tabulate it
|
335 |
+
N_COLS = min(6, len(results_per_category) * 2)
|
336 |
+
results_flatten = list(itertools.chain(*results_per_category))
|
337 |
+
results_2d = itertools.zip_longest(*[results_flatten[i::N_COLS] for i in range(N_COLS)])
|
338 |
+
table = tabulate(
|
339 |
+
results_2d,
|
340 |
+
tablefmt="pipe",
|
341 |
+
floatfmt=".3f",
|
342 |
+
headers=["category", "AP"] * (N_COLS // 2),
|
343 |
+
numalign="left",
|
344 |
+
)
|
345 |
+
self._logger.info("Per-category {} AP: \n".format(iou_type) + table)
|
346 |
+
|
347 |
+
results.update({"AP-" + name: ap for name, ap in results_per_category})
|
348 |
+
return results
|
349 |
+
|
350 |
+
|
351 |
+
def instances_to_coco_json(instances, img_id):
|
352 |
+
"""
|
353 |
+
Dump an "Instances" object to a COCO-format json that's used for evaluation.
|
354 |
+
|
355 |
+
Args:
|
356 |
+
instances (Instances):
|
357 |
+
img_id (int): the image id
|
358 |
+
|
359 |
+
Returns:
|
360 |
+
list[dict]: list of json annotations in COCO format.
|
361 |
+
"""
|
362 |
+
num_instance = len(instances)
|
363 |
+
if num_instance == 0:
|
364 |
+
return []
|
365 |
+
|
366 |
+
scores = instances.scores.tolist()
|
367 |
+
classes = instances.pred_classes.tolist()
|
368 |
+
|
369 |
+
has_mask = instances.has("pred_masks")
|
370 |
+
if has_mask:
|
371 |
+
# use RLE to encode the masks, because they are too large and takes memory
|
372 |
+
# since this evaluator stores outputs of the entire dataset
|
373 |
+
rles = [
|
374 |
+
mask_util.encode(np.array(mask[:, :, None], order="F", dtype="uint8"))[0]
|
375 |
+
for mask in instances.pred_masks
|
376 |
+
]
|
377 |
+
for rle in rles:
|
378 |
+
# "counts" is an array encoded by mask_util as a byte-stream. Python3's
|
379 |
+
# json writer which always produces strings cannot serialize a bytestream
|
380 |
+
# unless you decode it. Thankfully, utf-8 works out (which is also what
|
381 |
+
# the pycocotools/_mask.pyx does).
|
382 |
+
rle["counts"] = rle["counts"].decode("utf-8")
|
383 |
+
|
384 |
+
has_keypoints = instances.has("pred_keypoints")
|
385 |
+
if has_keypoints:
|
386 |
+
keypoints = instances.pred_keypoints
|
387 |
+
|
388 |
+
results = []
|
389 |
+
for k in range(num_instance):
|
390 |
+
result = {
|
391 |
+
"image_id": img_id,
|
392 |
+
"category_id": classes[k],
|
393 |
+
"score": scores[k],
|
394 |
+
}
|
395 |
+
if has_mask:
|
396 |
+
result["segmentation"] = rles[k]
|
397 |
+
if has_keypoints:
|
398 |
+
# In COCO annotations,
|
399 |
+
# keypoints coordinates are pixel indices.
|
400 |
+
# However our predictions are floating point coordinates.
|
401 |
+
# Therefore we subtract 0.5 to be consistent with the annotation format.
|
402 |
+
# This is the inverse of data loading logic in `datasets/coco.py`.
|
403 |
+
keypoints[k][:, :2] -= 0.5
|
404 |
+
result["keypoints"] = keypoints[k].flatten().tolist()
|
405 |
+
results.append(result)
|
406 |
+
return results
|
407 |
+
|
408 |
+
def _evaluate_predictions_on_coco(
|
409 |
+
coco_gt,
|
410 |
+
coco_results,
|
411 |
+
iou_type,
|
412 |
+
kpt_oks_sigmas=None,
|
413 |
+
use_fast_impl=True,
|
414 |
+
img_ids=None,
|
415 |
+
max_dets_per_image=None,
|
416 |
+
):
|
417 |
+
"""
|
418 |
+
Evaluate the coco results using COCOEval API.
|
419 |
+
"""
|
420 |
+
assert len(coco_results) > 0
|
421 |
+
|
422 |
+
if iou_type == "segm":
|
423 |
+
coco_results = copy.deepcopy(coco_results)
|
424 |
+
# When evaluating mask AP, if the results contain bbox, cocoapi will
|
425 |
+
# use the box area as the area of the instance, instead of the mask area.
|
426 |
+
# This leads to a different definition of small/medium/large.
|
427 |
+
# We remove the bbox field to let mask AP use mask area.
|
428 |
+
for c in coco_results:
|
429 |
+
c.pop("bbox", None)
|
430 |
+
|
431 |
+
coco_dt = coco_gt.loadRes(coco_results)
|
432 |
+
coco_eval = (COCOeval_opt if use_fast_impl else COCOeval)(coco_gt, coco_dt, iou_type)
|
433 |
+
# For COCO, the default max_dets_per_image is [1, 10, 100].
|
434 |
+
if max_dets_per_image is None:
|
435 |
+
max_dets_per_image = [1, 10, 100] # Default from COCOEval
|
436 |
+
else:
|
437 |
+
assert (
|
438 |
+
len(max_dets_per_image) >= 3
|
439 |
+
), "COCOeval requires maxDets (and max_dets_per_image) to have length at least 3"
|
440 |
+
# In the case that user supplies a custom input for max_dets_per_image,
|
441 |
+
# apply COCOevalMaxDets to evaluate AP with the custom input.
|
442 |
+
if max_dets_per_image[2] != 100:
|
443 |
+
coco_eval = COCOevalMaxDets(coco_gt, coco_dt, iou_type)
|
444 |
+
if iou_type != "keypoints":
|
445 |
+
coco_eval.params.maxDets = max_dets_per_image
|
446 |
+
|
447 |
+
if img_ids is not None:
|
448 |
+
coco_eval.params.imgIds = img_ids
|
449 |
+
|
450 |
+
if iou_type == "keypoints":
|
451 |
+
# Use the COCO default keypoint OKS sigmas unless overrides are specified
|
452 |
+
if kpt_oks_sigmas:
|
453 |
+
assert hasattr(coco_eval.params, "kpt_oks_sigmas"), "pycocotools is too old!"
|
454 |
+
coco_eval.params.kpt_oks_sigmas = np.array(kpt_oks_sigmas)
|
455 |
+
# COCOAPI requires every detection and every gt to have keypoints, so
|
456 |
+
# we just take the first entry from both
|
457 |
+
num_keypoints_dt = len(coco_results[0]["keypoints"]) // 3
|
458 |
+
num_keypoints_gt = len(next(iter(coco_gt.anns.values()))["keypoints"]) // 3
|
459 |
+
num_keypoints_oks = len(coco_eval.params.kpt_oks_sigmas)
|
460 |
+
assert num_keypoints_oks == num_keypoints_dt == num_keypoints_gt, (
|
461 |
+
f"[COCOEvaluator] Prediction contain {num_keypoints_dt} keypoints. "
|
462 |
+
f"Ground truth contains {num_keypoints_gt} keypoints. "
|
463 |
+
f"The length of cfg.TEST.KEYPOINT_OKS_SIGMAS is {num_keypoints_oks}. "
|
464 |
+
"They have to agree with each other. For meaning of OKS, please refer to "
|
465 |
+
"http://cocodataset.org/#keypoints-eval."
|
466 |
+
)
|
467 |
+
|
468 |
+
coco_eval.evaluate()
|
469 |
+
coco_eval.accumulate()
|
470 |
+
coco_eval.summarize()
|
471 |
+
|
472 |
+
return coco_eval
|
473 |
+
|
474 |
+
|
475 |
+
class COCOevalMaxDets(COCOeval):
|
476 |
+
"""
|
477 |
+
Modified version of COCOeval for evaluating AP with a custom
|
478 |
+
maxDets (by default for COCO, maxDets is 100)
|
479 |
+
"""
|
480 |
+
|
481 |
+
def summarize(self):
|
482 |
+
"""
|
483 |
+
Compute and display summary metrics for evaluation results given
|
484 |
+
a custom value for max_dets_per_image
|
485 |
+
"""
|
486 |
+
|
487 |
+
def _summarize(ap=1, iouThr=None, areaRng="all", maxDets=100):
|
488 |
+
p = self.params
|
489 |
+
iStr = " {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} ] = {:0.3f}"
|
490 |
+
titleStr = "Average Precision" if ap == 1 else "Average Recall"
|
491 |
+
typeStr = "(AP)" if ap == 1 else "(AR)"
|
492 |
+
iouStr = (
|
493 |
+
"{:0.2f}:{:0.2f}".format(p.iouThrs[0], p.iouThrs[-1])
|
494 |
+
if iouThr is None
|
495 |
+
else "{:0.2f}".format(iouThr)
|
496 |
+
)
|
497 |
+
|
498 |
+
aind = [i for i, aRng in enumerate(p.areaRngLbl) if aRng == areaRng]
|
499 |
+
mind = [i for i, mDet in enumerate(p.maxDets) if mDet == maxDets]
|
500 |
+
if ap == 1:
|
501 |
+
# dimension of precision: [TxRxKxAxM]
|
502 |
+
s = self.eval["precision"]
|
503 |
+
# IoU
|
504 |
+
if iouThr is not None:
|
505 |
+
t = np.where(iouThr == p.iouThrs)[0]
|
506 |
+
s = s[t]
|
507 |
+
s = s[:, :, :, aind, mind]
|
508 |
+
else:
|
509 |
+
# dimension of recall: [TxKxAxM]
|
510 |
+
s = self.eval["recall"]
|
511 |
+
if iouThr is not None:
|
512 |
+
t = np.where(iouThr == p.iouThrs)[0]
|
513 |
+
s = s[t]
|
514 |
+
s = s[:, :, aind, mind]
|
515 |
+
if len(s[s > -1]) == 0:
|
516 |
+
mean_s = -1
|
517 |
+
else:
|
518 |
+
mean_s = np.mean(s[s > -1])
|
519 |
+
print(iStr.format(titleStr, typeStr, iouStr, areaRng, maxDets, mean_s))
|
520 |
+
return mean_s
|
521 |
+
|
522 |
+
def _summarizeDets():
|
523 |
+
stats = np.zeros((12,))
|
524 |
+
# Evaluate AP using the custom limit on maximum detections per image
|
525 |
+
stats[0] = _summarize(1, maxDets=self.params.maxDets[2])
|
526 |
+
stats[1] = _summarize(1, iouThr=0.5, maxDets=self.params.maxDets[2])
|
527 |
+
stats[2] = _summarize(1, iouThr=0.75, maxDets=self.params.maxDets[2])
|
528 |
+
stats[3] = _summarize(1, areaRng="small", maxDets=self.params.maxDets[2])
|
529 |
+
stats[4] = _summarize(1, areaRng="medium", maxDets=self.params.maxDets[2])
|
530 |
+
stats[5] = _summarize(1, areaRng="large", maxDets=self.params.maxDets[2])
|
531 |
+
stats[6] = _summarize(0, maxDets=self.params.maxDets[0])
|
532 |
+
stats[7] = _summarize(0, maxDets=self.params.maxDets[1])
|
533 |
+
stats[8] = _summarize(0, maxDets=self.params.maxDets[2])
|
534 |
+
stats[9] = _summarize(0, areaRng="small", maxDets=self.params.maxDets[2])
|
535 |
+
stats[10] = _summarize(0, areaRng="medium", maxDets=self.params.maxDets[2])
|
536 |
+
stats[11] = _summarize(0, areaRng="large", maxDets=self.params.maxDets[2])
|
537 |
+
return stats
|
538 |
+
|
539 |
+
def _summarizeKps():
|
540 |
+
stats = np.zeros((10,))
|
541 |
+
stats[0] = _summarize(1, maxDets=20)
|
542 |
+
stats[1] = _summarize(1, maxDets=20, iouThr=0.5)
|
543 |
+
stats[2] = _summarize(1, maxDets=20, iouThr=0.75)
|
544 |
+
stats[3] = _summarize(1, maxDets=20, areaRng="medium")
|
545 |
+
stats[4] = _summarize(1, maxDets=20, areaRng="large")
|
546 |
+
stats[5] = _summarize(0, maxDets=20)
|
547 |
+
stats[6] = _summarize(0, maxDets=20, iouThr=0.5)
|
548 |
+
stats[7] = _summarize(0, maxDets=20, iouThr=0.75)
|
549 |
+
stats[8] = _summarize(0, maxDets=20, areaRng="medium")
|
550 |
+
stats[9] = _summarize(0, maxDets=20, areaRng="large")
|
551 |
+
return stats
|
552 |
+
|
553 |
+
if not self.eval:
|
554 |
+
raise Exception("Please run accumulate() first")
|
555 |
+
iouType = self.params.iouType
|
556 |
+
if iouType == "segm":
|
557 |
+
summarize = _summarizeDets
|
558 |
+
elif iouType == "keypoints":
|
559 |
+
summarize = _summarizeKps
|
560 |
+
self.stats = summarize()
|
561 |
+
|
562 |
+
def __str__(self):
|
563 |
+
self.summarize()
|
oneformer/evaluation/detection_coco_evaluator.py
ADDED
@@ -0,0 +1,723 @@
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|
|
|
|
|
|
|
|
1 |
+
# ------------------------------------------------------------------------------
|
2 |
+
# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/evaluation/coco_evaluation.py
|
3 |
+
# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
|
4 |
+
# ------------------------------------------------------------------------------
|
5 |
+
|
6 |
+
import contextlib
|
7 |
+
import copy
|
8 |
+
import io
|
9 |
+
import itertools
|
10 |
+
import json
|
11 |
+
import logging
|
12 |
+
import numpy as np
|
13 |
+
import os
|
14 |
+
import pickle
|
15 |
+
from collections import OrderedDict
|
16 |
+
import pycocotools.mask as mask_util
|
17 |
+
import torch
|
18 |
+
from pycocotools.coco import COCO
|
19 |
+
from pycocotools.cocoeval import COCOeval
|
20 |
+
from tabulate import tabulate
|
21 |
+
|
22 |
+
import detectron2.utils.comm as comm
|
23 |
+
from detectron2.config import CfgNode
|
24 |
+
from detectron2.data import MetadataCatalog
|
25 |
+
from detectron2.data.datasets.coco import convert_to_coco_json
|
26 |
+
from detectron2.structures import Boxes, BoxMode, pairwise_iou
|
27 |
+
from detectron2.utils.file_io import PathManager
|
28 |
+
from detectron2.utils.logger import create_small_table
|
29 |
+
|
30 |
+
from .evaluator import DatasetEvaluator
|
31 |
+
|
32 |
+
try:
|
33 |
+
from detectron2.evaluation.fast_eval_api import COCOeval_opt
|
34 |
+
except ImportError:
|
35 |
+
COCOeval_opt = COCOeval
|
36 |
+
|
37 |
+
|
38 |
+
class DetectionCOCOEvaluator(DatasetEvaluator):
|
39 |
+
"""
|
40 |
+
Evaluate AR for object proposals, AP for instance detection/segmentation, AP
|
41 |
+
for keypoint detection outputs using COCO's metrics.
|
42 |
+
See http://cocodataset.org/#detection-eval and
|
43 |
+
http://cocodataset.org/#keypoints-eval to understand its metrics.
|
44 |
+
The metrics range from 0 to 100 (instead of 0 to 1), where a -1 or NaN means
|
45 |
+
the metric cannot be computed (e.g. due to no predictions made).
|
46 |
+
|
47 |
+
In addition to COCO, this evaluator is able to support any bounding box detection,
|
48 |
+
instance segmentation, or keypoint detection dataset.
|
49 |
+
"""
|
50 |
+
|
51 |
+
def __init__(
|
52 |
+
self,
|
53 |
+
dataset_name,
|
54 |
+
tasks=None,
|
55 |
+
distributed=True,
|
56 |
+
output_dir=None,
|
57 |
+
*,
|
58 |
+
max_dets_per_image=None,
|
59 |
+
use_fast_impl=True,
|
60 |
+
kpt_oks_sigmas=(),
|
61 |
+
allow_cached_coco=True,
|
62 |
+
):
|
63 |
+
"""
|
64 |
+
Args:
|
65 |
+
dataset_name (str): name of the dataset to be evaluated.
|
66 |
+
It must have either the following corresponding metadata:
|
67 |
+
|
68 |
+
"json_file": the path to the COCO format annotation
|
69 |
+
|
70 |
+
Or it must be in detectron2's standard dataset format
|
71 |
+
so it can be converted to COCO format automatically.
|
72 |
+
tasks (tuple[str]): tasks that can be evaluated under the given
|
73 |
+
configuration. A task is one of "bbox", "segm", "keypoints".
|
74 |
+
By default, will infer this automatically from predictions.
|
75 |
+
distributed (True): if True, will collect results from all ranks and run evaluation
|
76 |
+
in the main process.
|
77 |
+
Otherwise, will only evaluate the results in the current process.
|
78 |
+
output_dir (str): optional, an output directory to dump all
|
79 |
+
results predicted on the dataset. The dump contains two files:
|
80 |
+
|
81 |
+
1. "instances_predictions.pth" a file that can be loaded with `torch.load` and
|
82 |
+
contains all the results in the format they are produced by the model.
|
83 |
+
2. "coco_instances_results.json" a json file in COCO's result format.
|
84 |
+
max_dets_per_image (int): limit on the maximum number of detections per image.
|
85 |
+
By default in COCO, this limit is to 100, but this can be customized
|
86 |
+
to be greater, as is needed in evaluation metrics AP fixed and AP pool
|
87 |
+
(see https://arxiv.org/pdf/2102.01066.pdf)
|
88 |
+
This doesn't affect keypoint evaluation.
|
89 |
+
use_fast_impl (bool): use a fast but **unofficial** implementation to compute AP.
|
90 |
+
Although the results should be very close to the official implementation in COCO
|
91 |
+
API, it is still recommended to compute results with the official API for use in
|
92 |
+
papers. The faster implementation also uses more RAM.
|
93 |
+
kpt_oks_sigmas (list[float]): The sigmas used to calculate keypoint OKS.
|
94 |
+
See http://cocodataset.org/#keypoints-eval
|
95 |
+
When empty, it will use the defaults in COCO.
|
96 |
+
Otherwise it should be the same length as ROI_KEYPOINT_HEAD.NUM_KEYPOINTS.
|
97 |
+
allow_cached_coco (bool): Whether to use cached coco json from previous validation
|
98 |
+
runs. You should set this to False if you need to use different validation data.
|
99 |
+
Defaults to True.
|
100 |
+
"""
|
101 |
+
self._logger = logging.getLogger(__name__)
|
102 |
+
self._distributed = distributed
|
103 |
+
self._output_dir = output_dir
|
104 |
+
|
105 |
+
if use_fast_impl and (COCOeval_opt is COCOeval):
|
106 |
+
self._logger.info("Fast COCO eval is not built. Falling back to official COCO eval.")
|
107 |
+
use_fast_impl = False
|
108 |
+
self._use_fast_impl = use_fast_impl
|
109 |
+
|
110 |
+
# COCOeval requires the limit on the number of detections per image (maxDets) to be a list
|
111 |
+
# with at least 3 elements. The default maxDets in COCOeval is [1, 10, 100], in which the
|
112 |
+
# 3rd element (100) is used as the limit on the number of detections per image when
|
113 |
+
# evaluating AP. COCOEvaluator expects an integer for max_dets_per_image, so for COCOeval,
|
114 |
+
# we reformat max_dets_per_image into [1, 10, max_dets_per_image], based on the defaults.
|
115 |
+
if max_dets_per_image is None:
|
116 |
+
max_dets_per_image = [1, 10, 100]
|
117 |
+
else:
|
118 |
+
max_dets_per_image = [1, 10, max_dets_per_image]
|
119 |
+
self._max_dets_per_image = max_dets_per_image
|
120 |
+
|
121 |
+
if tasks is not None and isinstance(tasks, CfgNode):
|
122 |
+
kpt_oks_sigmas = (
|
123 |
+
tasks.TEST.KEYPOINT_OKS_SIGMAS if not kpt_oks_sigmas else kpt_oks_sigmas
|
124 |
+
)
|
125 |
+
self._logger.warn(
|
126 |
+
"COCO Evaluator instantiated using config, this is deprecated behavior."
|
127 |
+
" Please pass in explicit arguments instead."
|
128 |
+
)
|
129 |
+
self._tasks = None # Infering it from predictions should be better
|
130 |
+
else:
|
131 |
+
self._tasks = tasks
|
132 |
+
|
133 |
+
self._cpu_device = torch.device("cpu")
|
134 |
+
|
135 |
+
self._metadata = MetadataCatalog.get(dataset_name)
|
136 |
+
if not hasattr(self._metadata, "json_file"):
|
137 |
+
if output_dir is None:
|
138 |
+
raise ValueError(
|
139 |
+
"output_dir must be provided to COCOEvaluator "
|
140 |
+
"for datasets not in COCO format."
|
141 |
+
)
|
142 |
+
self._logger.info(f"Trying to convert '{dataset_name}' to COCO format ...")
|
143 |
+
|
144 |
+
cache_path = os.path.join(output_dir, f"{dataset_name}_coco_format.json")
|
145 |
+
self._metadata.json_file = cache_path
|
146 |
+
convert_to_coco_json(dataset_name, cache_path, allow_cached=allow_cached_coco)
|
147 |
+
|
148 |
+
json_file = PathManager.get_local_path(self._metadata.json_file)
|
149 |
+
with contextlib.redirect_stdout(io.StringIO()):
|
150 |
+
self._coco_api = COCO(json_file)
|
151 |
+
|
152 |
+
# Test set json files do not contain annotations (evaluation must be
|
153 |
+
# performed using the COCO evaluation server).
|
154 |
+
self._do_evaluation = "annotations" in self._coco_api.dataset
|
155 |
+
if self._do_evaluation:
|
156 |
+
self._kpt_oks_sigmas = kpt_oks_sigmas
|
157 |
+
|
158 |
+
def reset(self):
|
159 |
+
self._predictions = []
|
160 |
+
|
161 |
+
def process(self, inputs, outputs):
|
162 |
+
"""
|
163 |
+
Args:
|
164 |
+
inputs: the inputs to a COCO model (e.g., GeneralizedRCNN).
|
165 |
+
It is a list of dict. Each dict corresponds to an image and
|
166 |
+
contains keys like "height", "width", "file_name", "image_id".
|
167 |
+
outputs: the outputs of a COCO model. It is a list of dicts with key
|
168 |
+
"box_instances" that contains :class:`Instances`.
|
169 |
+
"""
|
170 |
+
for input, output in zip(inputs, outputs):
|
171 |
+
prediction = {"image_id": input["image_id"]}
|
172 |
+
|
173 |
+
if "box_instances" in output:
|
174 |
+
instances = output["box_instances"].to(self._cpu_device)
|
175 |
+
prediction["box_instances"] = instances_to_coco_json(instances, input["image_id"])
|
176 |
+
if "proposals" in output:
|
177 |
+
prediction["proposals"] = output["proposals"].to(self._cpu_device)
|
178 |
+
if len(prediction) > 1:
|
179 |
+
self._predictions.append(prediction)
|
180 |
+
|
181 |
+
def evaluate(self, img_ids=None):
|
182 |
+
"""
|
183 |
+
Args:
|
184 |
+
img_ids: a list of image IDs to evaluate on. Default to None for the whole dataset
|
185 |
+
"""
|
186 |
+
if self._distributed:
|
187 |
+
comm.synchronize()
|
188 |
+
predictions = comm.gather(self._predictions, dst=0)
|
189 |
+
predictions = list(itertools.chain(*predictions))
|
190 |
+
|
191 |
+
if not comm.is_main_process():
|
192 |
+
return {}
|
193 |
+
else:
|
194 |
+
predictions = self._predictions
|
195 |
+
|
196 |
+
if len(predictions) == 0:
|
197 |
+
self._logger.warning("[COCOEvaluator] Did not receive valid predictions.")
|
198 |
+
return {}
|
199 |
+
|
200 |
+
if self._output_dir:
|
201 |
+
PathManager.mkdirs(self._output_dir)
|
202 |
+
file_path = os.path.join(self._output_dir, "instances_predictions.pth")
|
203 |
+
with PathManager.open(file_path, "wb") as f:
|
204 |
+
torch.save(predictions, f)
|
205 |
+
|
206 |
+
self._results = OrderedDict()
|
207 |
+
if "proposals" in predictions[0]:
|
208 |
+
self._eval_box_proposals(predictions)
|
209 |
+
if "box_instances" in predictions[0]:
|
210 |
+
self._eval_predictions(predictions, img_ids=img_ids)
|
211 |
+
# Copy so the caller can do whatever with results
|
212 |
+
return copy.deepcopy(self._results)
|
213 |
+
|
214 |
+
def _tasks_from_predictions(self, predictions):
|
215 |
+
"""
|
216 |
+
Get COCO API "tasks" (i.e. iou_type) from COCO-format predictions.
|
217 |
+
"""
|
218 |
+
tasks = {"bbox"}
|
219 |
+
for pred in predictions:
|
220 |
+
if "keypoints" in pred:
|
221 |
+
tasks.add("keypoints")
|
222 |
+
return sorted(tasks)
|
223 |
+
|
224 |
+
def _eval_predictions(self, predictions, img_ids=None):
|
225 |
+
"""
|
226 |
+
Evaluate predictions. Fill self._results with the metrics of the tasks.
|
227 |
+
"""
|
228 |
+
self._logger.info("Preparing results for COCO format ...")
|
229 |
+
coco_results = list(itertools.chain(*[x["box_instances"] for x in predictions]))
|
230 |
+
tasks = self._tasks or self._tasks_from_predictions(coco_results)
|
231 |
+
|
232 |
+
# unmap the category ids for COCO
|
233 |
+
if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"):
|
234 |
+
dataset_id_to_contiguous_id = self._metadata.thing_dataset_id_to_contiguous_id
|
235 |
+
all_contiguous_ids = list(dataset_id_to_contiguous_id.values())
|
236 |
+
num_classes = len(all_contiguous_ids)
|
237 |
+
assert min(all_contiguous_ids) == 0 and max(all_contiguous_ids) == num_classes - 1
|
238 |
+
|
239 |
+
reverse_id_mapping = {v: k for k, v in dataset_id_to_contiguous_id.items()}
|
240 |
+
for result in coco_results:
|
241 |
+
category_id = result["category_id"]
|
242 |
+
assert category_id < num_classes, (
|
243 |
+
f"A prediction has class={category_id}, "
|
244 |
+
f"but the dataset only has {num_classes} classes and "
|
245 |
+
f"predicted class id should be in [0, {num_classes - 1}]."
|
246 |
+
)
|
247 |
+
result["category_id"] = reverse_id_mapping[category_id]
|
248 |
+
|
249 |
+
if self._output_dir:
|
250 |
+
file_path = os.path.join(self._output_dir, "coco_instances_results.json")
|
251 |
+
self._logger.info("Saving results to {}".format(file_path))
|
252 |
+
with PathManager.open(file_path, "w") as f:
|
253 |
+
f.write(json.dumps(coco_results))
|
254 |
+
f.flush()
|
255 |
+
|
256 |
+
if not self._do_evaluation:
|
257 |
+
self._logger.info("Annotations are not available for evaluation.")
|
258 |
+
return
|
259 |
+
|
260 |
+
self._logger.info(
|
261 |
+
"Evaluating predictions with {} COCO API...".format(
|
262 |
+
"unofficial" if self._use_fast_impl else "official"
|
263 |
+
)
|
264 |
+
)
|
265 |
+
for task in sorted(tasks):
|
266 |
+
assert task in {"bbox", "keypoints"}, f"Got unknown task: {task}!"
|
267 |
+
coco_eval = (
|
268 |
+
_evaluate_predictions_on_coco(
|
269 |
+
self._coco_api,
|
270 |
+
coco_results,
|
271 |
+
task,
|
272 |
+
kpt_oks_sigmas=self._kpt_oks_sigmas,
|
273 |
+
use_fast_impl=self._use_fast_impl,
|
274 |
+
img_ids=img_ids,
|
275 |
+
max_dets_per_image=self._max_dets_per_image,
|
276 |
+
)
|
277 |
+
if len(coco_results) > 0
|
278 |
+
else None # cocoapi does not handle empty results very well
|
279 |
+
)
|
280 |
+
|
281 |
+
res = self._derive_coco_results(
|
282 |
+
coco_eval, task, class_names=self._metadata.get("thing_classes")
|
283 |
+
)
|
284 |
+
self._results[task] = res
|
285 |
+
|
286 |
+
def _eval_box_proposals(self, predictions):
|
287 |
+
"""
|
288 |
+
Evaluate the box proposals in predictions.
|
289 |
+
Fill self._results with the metrics for "box_proposals" task.
|
290 |
+
"""
|
291 |
+
if self._output_dir:
|
292 |
+
# Saving generated box proposals to file.
|
293 |
+
# Predicted box_proposals are in XYXY_ABS mode.
|
294 |
+
bbox_mode = BoxMode.XYXY_ABS.value
|
295 |
+
ids, boxes, objectness_logits = [], [], []
|
296 |
+
for prediction in predictions:
|
297 |
+
ids.append(prediction["image_id"])
|
298 |
+
boxes.append(prediction["proposals"].proposal_boxes.tensor.numpy())
|
299 |
+
objectness_logits.append(prediction["proposals"].objectness_logits.numpy())
|
300 |
+
|
301 |
+
proposal_data = {
|
302 |
+
"boxes": boxes,
|
303 |
+
"objectness_logits": objectness_logits,
|
304 |
+
"ids": ids,
|
305 |
+
"bbox_mode": bbox_mode,
|
306 |
+
}
|
307 |
+
with PathManager.open(os.path.join(self._output_dir, "box_proposals.pkl"), "wb") as f:
|
308 |
+
pickle.dump(proposal_data, f)
|
309 |
+
|
310 |
+
if not self._do_evaluation:
|
311 |
+
self._logger.info("Annotations are not available for evaluation.")
|
312 |
+
return
|
313 |
+
|
314 |
+
self._logger.info("Evaluating bbox proposals ...")
|
315 |
+
res = {}
|
316 |
+
areas = {"all": "", "small": "s", "medium": "m", "large": "l"}
|
317 |
+
for limit in [100, 1000]:
|
318 |
+
for area, suffix in areas.items():
|
319 |
+
stats = _evaluate_box_proposals(predictions, self._coco_api, area=area, limit=limit)
|
320 |
+
key = "AR{}@{:d}".format(suffix, limit)
|
321 |
+
res[key] = float(stats["ar"].item() * 100)
|
322 |
+
self._logger.info("Proposal metrics: \n" + create_small_table(res))
|
323 |
+
self._results["box_proposals"] = res
|
324 |
+
|
325 |
+
def _derive_coco_results(self, coco_eval, iou_type, class_names=None):
|
326 |
+
"""
|
327 |
+
Derive the desired score numbers from summarized COCOeval.
|
328 |
+
|
329 |
+
Args:
|
330 |
+
coco_eval (None or COCOEval): None represents no predictions from model.
|
331 |
+
iou_type (str):
|
332 |
+
class_names (None or list[str]): if provided, will use it to predict
|
333 |
+
per-category AP.
|
334 |
+
|
335 |
+
Returns:
|
336 |
+
a dict of {metric name: score}
|
337 |
+
"""
|
338 |
+
|
339 |
+
metrics = {
|
340 |
+
"bbox": ["AP", "AP50", "AP75", "APs", "APm", "APl"],
|
341 |
+
"keypoints": ["AP", "AP50", "AP75", "APm", "APl"],
|
342 |
+
}[iou_type]
|
343 |
+
|
344 |
+
if coco_eval is None:
|
345 |
+
self._logger.warn("No predictions from the model!")
|
346 |
+
return {metric: float("nan") for metric in metrics}
|
347 |
+
|
348 |
+
# the standard metrics
|
349 |
+
results = {
|
350 |
+
metric: float(coco_eval.stats[idx] * 100 if coco_eval.stats[idx] >= 0 else "nan")
|
351 |
+
for idx, metric in enumerate(metrics)
|
352 |
+
}
|
353 |
+
self._logger.info(
|
354 |
+
"Evaluation results for {}: \n".format(iou_type) + create_small_table(results)
|
355 |
+
)
|
356 |
+
if not np.isfinite(sum(results.values())):
|
357 |
+
self._logger.info("Some metrics cannot be computed and is shown as NaN.")
|
358 |
+
|
359 |
+
if class_names is None or len(class_names) <= 1:
|
360 |
+
return results
|
361 |
+
# Compute per-category AP
|
362 |
+
# from https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L222-L252 # noqa
|
363 |
+
precisions = coco_eval.eval["precision"]
|
364 |
+
# precision has dims (iou, recall, cls, area range, max dets)
|
365 |
+
assert len(class_names) == precisions.shape[2]
|
366 |
+
|
367 |
+
results_per_category = []
|
368 |
+
for idx, name in enumerate(class_names):
|
369 |
+
# area range index 0: all area ranges
|
370 |
+
# max dets index -1: typically 100 per image
|
371 |
+
precision = precisions[:, :, idx, 0, -1]
|
372 |
+
precision = precision[precision > -1]
|
373 |
+
ap = np.mean(precision) if precision.size else float("nan")
|
374 |
+
results_per_category.append(("{}".format(name), float(ap * 100)))
|
375 |
+
|
376 |
+
# tabulate it
|
377 |
+
N_COLS = min(6, len(results_per_category) * 2)
|
378 |
+
results_flatten = list(itertools.chain(*results_per_category))
|
379 |
+
results_2d = itertools.zip_longest(*[results_flatten[i::N_COLS] for i in range(N_COLS)])
|
380 |
+
table = tabulate(
|
381 |
+
results_2d,
|
382 |
+
tablefmt="pipe",
|
383 |
+
floatfmt=".3f",
|
384 |
+
headers=["category", "AP"] * (N_COLS // 2),
|
385 |
+
numalign="left",
|
386 |
+
)
|
387 |
+
self._logger.info("Per-category {} AP: \n".format(iou_type) + table)
|
388 |
+
|
389 |
+
results.update({"AP-" + name: ap for name, ap in results_per_category})
|
390 |
+
return results
|
391 |
+
|
392 |
+
|
393 |
+
def instances_to_coco_json(instances, img_id):
|
394 |
+
"""
|
395 |
+
Dump an "Instances" object to a COCO-format json that's used for evaluation.
|
396 |
+
|
397 |
+
Args:
|
398 |
+
instances (Instances):
|
399 |
+
img_id (int): the image id
|
400 |
+
|
401 |
+
Returns:
|
402 |
+
list[dict]: list of json annotations in COCO format.
|
403 |
+
"""
|
404 |
+
num_instance = len(instances)
|
405 |
+
if num_instance == 0:
|
406 |
+
return []
|
407 |
+
|
408 |
+
boxes = instances.pred_boxes.tensor.numpy()
|
409 |
+
boxes = BoxMode.convert(boxes, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS)
|
410 |
+
boxes = boxes.tolist()
|
411 |
+
scores = instances.scores.tolist()
|
412 |
+
classes = instances.pred_classes.tolist()
|
413 |
+
|
414 |
+
has_mask = instances.has("pred_masks")
|
415 |
+
if has_mask:
|
416 |
+
# use RLE to encode the masks, because they are too large and takes memory
|
417 |
+
# since this evaluator stores outputs of the entire dataset
|
418 |
+
rles = [
|
419 |
+
mask_util.encode(np.array(mask[:, :, None], order="F", dtype="uint8"))[0]
|
420 |
+
for mask in instances.pred_masks
|
421 |
+
]
|
422 |
+
for rle in rles:
|
423 |
+
# "counts" is an array encoded by mask_util as a byte-stream. Python3's
|
424 |
+
# json writer which always produces strings cannot serialize a bytestream
|
425 |
+
# unless you decode it. Thankfully, utf-8 works out (which is also what
|
426 |
+
# the pycocotools/_mask.pyx does).
|
427 |
+
rle["counts"] = rle["counts"].decode("utf-8")
|
428 |
+
|
429 |
+
has_keypoints = instances.has("pred_keypoints")
|
430 |
+
if has_keypoints:
|
431 |
+
keypoints = instances.pred_keypoints
|
432 |
+
|
433 |
+
results = []
|
434 |
+
for k in range(num_instance):
|
435 |
+
result = {
|
436 |
+
"image_id": img_id,
|
437 |
+
"category_id": classes[k],
|
438 |
+
"bbox": boxes[k],
|
439 |
+
"score": scores[k],
|
440 |
+
}
|
441 |
+
if has_mask:
|
442 |
+
result["segmentation"] = rles[k]
|
443 |
+
if has_keypoints:
|
444 |
+
# In COCO annotations,
|
445 |
+
# keypoints coordinates are pixel indices.
|
446 |
+
# However our predictions are floating point coordinates.
|
447 |
+
# Therefore we subtract 0.5 to be consistent with the annotation format.
|
448 |
+
# This is the inverse of data loading logic in `datasets/coco.py`.
|
449 |
+
keypoints[k][:, :2] -= 0.5
|
450 |
+
result["keypoints"] = keypoints[k].flatten().tolist()
|
451 |
+
results.append(result)
|
452 |
+
return results
|
453 |
+
|
454 |
+
|
455 |
+
# inspired from Detectron:
|
456 |
+
# https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L255 # noqa
|
457 |
+
def _evaluate_box_proposals(dataset_predictions, coco_api, thresholds=None, area="all", limit=None):
|
458 |
+
"""
|
459 |
+
Evaluate detection proposal recall metrics. This function is a much
|
460 |
+
faster alternative to the official COCO API recall evaluation code. However,
|
461 |
+
it produces slightly different results.
|
462 |
+
"""
|
463 |
+
# Record max overlap value for each gt box
|
464 |
+
# Return vector of overlap values
|
465 |
+
areas = {
|
466 |
+
"all": 0,
|
467 |
+
"small": 1,
|
468 |
+
"medium": 2,
|
469 |
+
"large": 3,
|
470 |
+
"96-128": 4,
|
471 |
+
"128-256": 5,
|
472 |
+
"256-512": 6,
|
473 |
+
"512-inf": 7,
|
474 |
+
}
|
475 |
+
area_ranges = [
|
476 |
+
[0**2, 1e5**2], # all
|
477 |
+
[0**2, 32**2], # small
|
478 |
+
[32**2, 96**2], # medium
|
479 |
+
[96**2, 1e5**2], # large
|
480 |
+
[96**2, 128**2], # 96-128
|
481 |
+
[128**2, 256**2], # 128-256
|
482 |
+
[256**2, 512**2], # 256-512
|
483 |
+
[512**2, 1e5**2],
|
484 |
+
] # 512-inf
|
485 |
+
assert area in areas, "Unknown area range: {}".format(area)
|
486 |
+
area_range = area_ranges[areas[area]]
|
487 |
+
gt_overlaps = []
|
488 |
+
num_pos = 0
|
489 |
+
|
490 |
+
for prediction_dict in dataset_predictions:
|
491 |
+
predictions = prediction_dict["proposals"]
|
492 |
+
|
493 |
+
# sort predictions in descending order
|
494 |
+
# TODO maybe remove this and make it explicit in the documentation
|
495 |
+
inds = predictions.objectness_logits.sort(descending=True)[1]
|
496 |
+
predictions = predictions[inds]
|
497 |
+
|
498 |
+
ann_ids = coco_api.getAnnIds(imgIds=prediction_dict["image_id"])
|
499 |
+
anno = coco_api.loadAnns(ann_ids)
|
500 |
+
gt_boxes = [
|
501 |
+
BoxMode.convert(obj["bbox"], BoxMode.XYWH_ABS, BoxMode.XYXY_ABS)
|
502 |
+
for obj in anno
|
503 |
+
if obj["iscrowd"] == 0
|
504 |
+
]
|
505 |
+
gt_boxes = torch.as_tensor(gt_boxes).reshape(-1, 4) # guard against no boxes
|
506 |
+
gt_boxes = Boxes(gt_boxes)
|
507 |
+
gt_areas = torch.as_tensor([obj["area"] for obj in anno if obj["iscrowd"] == 0])
|
508 |
+
|
509 |
+
if len(gt_boxes) == 0 or len(predictions) == 0:
|
510 |
+
continue
|
511 |
+
|
512 |
+
valid_gt_inds = (gt_areas >= area_range[0]) & (gt_areas <= area_range[1])
|
513 |
+
gt_boxes = gt_boxes[valid_gt_inds]
|
514 |
+
|
515 |
+
num_pos += len(gt_boxes)
|
516 |
+
|
517 |
+
if len(gt_boxes) == 0:
|
518 |
+
continue
|
519 |
+
|
520 |
+
if limit is not None and len(predictions) > limit:
|
521 |
+
predictions = predictions[:limit]
|
522 |
+
|
523 |
+
overlaps = pairwise_iou(predictions.proposal_boxes, gt_boxes)
|
524 |
+
|
525 |
+
_gt_overlaps = torch.zeros(len(gt_boxes))
|
526 |
+
for j in range(min(len(predictions), len(gt_boxes))):
|
527 |
+
# find which proposal box maximally covers each gt box
|
528 |
+
# and get the iou amount of coverage for each gt box
|
529 |
+
max_overlaps, argmax_overlaps = overlaps.max(dim=0)
|
530 |
+
|
531 |
+
# find which gt box is 'best' covered (i.e. 'best' = most iou)
|
532 |
+
gt_ovr, gt_ind = max_overlaps.max(dim=0)
|
533 |
+
assert gt_ovr >= 0
|
534 |
+
# find the proposal box that covers the best covered gt box
|
535 |
+
box_ind = argmax_overlaps[gt_ind]
|
536 |
+
# record the iou coverage of this gt box
|
537 |
+
_gt_overlaps[j] = overlaps[box_ind, gt_ind]
|
538 |
+
assert _gt_overlaps[j] == gt_ovr
|
539 |
+
# mark the proposal box and the gt box as used
|
540 |
+
overlaps[box_ind, :] = -1
|
541 |
+
overlaps[:, gt_ind] = -1
|
542 |
+
|
543 |
+
# append recorded iou coverage level
|
544 |
+
gt_overlaps.append(_gt_overlaps)
|
545 |
+
gt_overlaps = (
|
546 |
+
torch.cat(gt_overlaps, dim=0) if len(gt_overlaps) else torch.zeros(0, dtype=torch.float32)
|
547 |
+
)
|
548 |
+
gt_overlaps, _ = torch.sort(gt_overlaps)
|
549 |
+
|
550 |
+
if thresholds is None:
|
551 |
+
step = 0.05
|
552 |
+
thresholds = torch.arange(0.5, 0.95 + 1e-5, step, dtype=torch.float32)
|
553 |
+
recalls = torch.zeros_like(thresholds)
|
554 |
+
# compute recall for each iou threshold
|
555 |
+
for i, t in enumerate(thresholds):
|
556 |
+
recalls[i] = (gt_overlaps >= t).float().sum() / float(num_pos)
|
557 |
+
# ar = 2 * np.trapz(recalls, thresholds)
|
558 |
+
ar = recalls.mean()
|
559 |
+
return {
|
560 |
+
"ar": ar,
|
561 |
+
"recalls": recalls,
|
562 |
+
"thresholds": thresholds,
|
563 |
+
"gt_overlaps": gt_overlaps,
|
564 |
+
"num_pos": num_pos,
|
565 |
+
}
|
566 |
+
|
567 |
+
|
568 |
+
def _evaluate_predictions_on_coco(
|
569 |
+
coco_gt,
|
570 |
+
coco_results,
|
571 |
+
iou_type,
|
572 |
+
kpt_oks_sigmas=None,
|
573 |
+
use_fast_impl=True,
|
574 |
+
img_ids=None,
|
575 |
+
max_dets_per_image=None,
|
576 |
+
):
|
577 |
+
"""
|
578 |
+
Evaluate the coco results using COCOEval API.
|
579 |
+
"""
|
580 |
+
assert len(coco_results) > 0
|
581 |
+
|
582 |
+
if iou_type == "segm":
|
583 |
+
coco_results = copy.deepcopy(coco_results)
|
584 |
+
# When evaluating mask AP, if the results contain bbox, cocoapi will
|
585 |
+
# use the box area as the area of the instance, instead of the mask area.
|
586 |
+
# This leads to a different definition of small/medium/large.
|
587 |
+
# We remove the bbox field to let mask AP use mask area.
|
588 |
+
for c in coco_results:
|
589 |
+
c.pop("bbox", None)
|
590 |
+
|
591 |
+
coco_dt = coco_gt.loadRes(coco_results)
|
592 |
+
coco_eval = (COCOeval_opt if use_fast_impl else COCOeval)(coco_gt, coco_dt, iou_type)
|
593 |
+
# For COCO, the default max_dets_per_image is [1, 10, 100].
|
594 |
+
if max_dets_per_image is None:
|
595 |
+
max_dets_per_image = [1, 10, 100] # Default from COCOEval
|
596 |
+
else:
|
597 |
+
assert (
|
598 |
+
len(max_dets_per_image) >= 3
|
599 |
+
), "COCOeval requires maxDets (and max_dets_per_image) to have length at least 3"
|
600 |
+
# In the case that user supplies a custom input for max_dets_per_image,
|
601 |
+
# apply COCOevalMaxDets to evaluate AP with the custom input.
|
602 |
+
if max_dets_per_image[2] != 100:
|
603 |
+
coco_eval = COCOevalMaxDets(coco_gt, coco_dt, iou_type)
|
604 |
+
if iou_type != "keypoints":
|
605 |
+
coco_eval.params.maxDets = max_dets_per_image
|
606 |
+
|
607 |
+
if img_ids is not None:
|
608 |
+
coco_eval.params.imgIds = img_ids
|
609 |
+
|
610 |
+
if iou_type == "keypoints":
|
611 |
+
# Use the COCO default keypoint OKS sigmas unless overrides are specified
|
612 |
+
if kpt_oks_sigmas:
|
613 |
+
assert hasattr(coco_eval.params, "kpt_oks_sigmas"), "pycocotools is too old!"
|
614 |
+
coco_eval.params.kpt_oks_sigmas = np.array(kpt_oks_sigmas)
|
615 |
+
# COCOAPI requires every detection and every gt to have keypoints, so
|
616 |
+
# we just take the first entry from both
|
617 |
+
num_keypoints_dt = len(coco_results[0]["keypoints"]) // 3
|
618 |
+
num_keypoints_gt = len(next(iter(coco_gt.anns.values()))["keypoints"]) // 3
|
619 |
+
num_keypoints_oks = len(coco_eval.params.kpt_oks_sigmas)
|
620 |
+
assert num_keypoints_oks == num_keypoints_dt == num_keypoints_gt, (
|
621 |
+
f"[COCOEvaluator] Prediction contain {num_keypoints_dt} keypoints. "
|
622 |
+
f"Ground truth contains {num_keypoints_gt} keypoints. "
|
623 |
+
f"The length of cfg.TEST.KEYPOINT_OKS_SIGMAS is {num_keypoints_oks}. "
|
624 |
+
"They have to agree with each other. For meaning of OKS, please refer to "
|
625 |
+
"http://cocodataset.org/#keypoints-eval."
|
626 |
+
)
|
627 |
+
|
628 |
+
coco_eval.evaluate()
|
629 |
+
coco_eval.accumulate()
|
630 |
+
coco_eval.summarize()
|
631 |
+
|
632 |
+
return coco_eval
|
633 |
+
|
634 |
+
|
635 |
+
class COCOevalMaxDets(COCOeval):
|
636 |
+
"""
|
637 |
+
Modified version of COCOeval for evaluating AP with a custom
|
638 |
+
maxDets (by default for COCO, maxDets is 100)
|
639 |
+
"""
|
640 |
+
|
641 |
+
def summarize(self):
|
642 |
+
"""
|
643 |
+
Compute and display summary metrics for evaluation results given
|
644 |
+
a custom value for max_dets_per_image
|
645 |
+
"""
|
646 |
+
|
647 |
+
def _summarize(ap=1, iouThr=None, areaRng="all", maxDets=100):
|
648 |
+
p = self.params
|
649 |
+
iStr = " {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} ] = {:0.3f}"
|
650 |
+
titleStr = "Average Precision" if ap == 1 else "Average Recall"
|
651 |
+
typeStr = "(AP)" if ap == 1 else "(AR)"
|
652 |
+
iouStr = (
|
653 |
+
"{:0.2f}:{:0.2f}".format(p.iouThrs[0], p.iouThrs[-1])
|
654 |
+
if iouThr is None
|
655 |
+
else "{:0.2f}".format(iouThr)
|
656 |
+
)
|
657 |
+
|
658 |
+
aind = [i for i, aRng in enumerate(p.areaRngLbl) if aRng == areaRng]
|
659 |
+
mind = [i for i, mDet in enumerate(p.maxDets) if mDet == maxDets]
|
660 |
+
if ap == 1:
|
661 |
+
# dimension of precision: [TxRxKxAxM]
|
662 |
+
s = self.eval["precision"]
|
663 |
+
# IoU
|
664 |
+
if iouThr is not None:
|
665 |
+
t = np.where(iouThr == p.iouThrs)[0]
|
666 |
+
s = s[t]
|
667 |
+
s = s[:, :, :, aind, mind]
|
668 |
+
else:
|
669 |
+
# dimension of recall: [TxKxAxM]
|
670 |
+
s = self.eval["recall"]
|
671 |
+
if iouThr is not None:
|
672 |
+
t = np.where(iouThr == p.iouThrs)[0]
|
673 |
+
s = s[t]
|
674 |
+
s = s[:, :, aind, mind]
|
675 |
+
if len(s[s > -1]) == 0:
|
676 |
+
mean_s = -1
|
677 |
+
else:
|
678 |
+
mean_s = np.mean(s[s > -1])
|
679 |
+
print(iStr.format(titleStr, typeStr, iouStr, areaRng, maxDets, mean_s))
|
680 |
+
return mean_s
|
681 |
+
|
682 |
+
def _summarizeDets():
|
683 |
+
stats = np.zeros((12,))
|
684 |
+
# Evaluate AP using the custom limit on maximum detections per image
|
685 |
+
stats[0] = _summarize(1, maxDets=self.params.maxDets[2])
|
686 |
+
stats[1] = _summarize(1, iouThr=0.5, maxDets=self.params.maxDets[2])
|
687 |
+
stats[2] = _summarize(1, iouThr=0.75, maxDets=self.params.maxDets[2])
|
688 |
+
stats[3] = _summarize(1, areaRng="small", maxDets=self.params.maxDets[2])
|
689 |
+
stats[4] = _summarize(1, areaRng="medium", maxDets=self.params.maxDets[2])
|
690 |
+
stats[5] = _summarize(1, areaRng="large", maxDets=self.params.maxDets[2])
|
691 |
+
stats[6] = _summarize(0, maxDets=self.params.maxDets[0])
|
692 |
+
stats[7] = _summarize(0, maxDets=self.params.maxDets[1])
|
693 |
+
stats[8] = _summarize(0, maxDets=self.params.maxDets[2])
|
694 |
+
stats[9] = _summarize(0, areaRng="small", maxDets=self.params.maxDets[2])
|
695 |
+
stats[10] = _summarize(0, areaRng="medium", maxDets=self.params.maxDets[2])
|
696 |
+
stats[11] = _summarize(0, areaRng="large", maxDets=self.params.maxDets[2])
|
697 |
+
return stats
|
698 |
+
|
699 |
+
def _summarizeKps():
|
700 |
+
stats = np.zeros((10,))
|
701 |
+
stats[0] = _summarize(1, maxDets=20)
|
702 |
+
stats[1] = _summarize(1, maxDets=20, iouThr=0.5)
|
703 |
+
stats[2] = _summarize(1, maxDets=20, iouThr=0.75)
|
704 |
+
stats[3] = _summarize(1, maxDets=20, areaRng="medium")
|
705 |
+
stats[4] = _summarize(1, maxDets=20, areaRng="large")
|
706 |
+
stats[5] = _summarize(0, maxDets=20)
|
707 |
+
stats[6] = _summarize(0, maxDets=20, iouThr=0.5)
|
708 |
+
stats[7] = _summarize(0, maxDets=20, iouThr=0.75)
|
709 |
+
stats[8] = _summarize(0, maxDets=20, areaRng="medium")
|
710 |
+
stats[9] = _summarize(0, maxDets=20, areaRng="large")
|
711 |
+
return stats
|
712 |
+
|
713 |
+
if not self.eval:
|
714 |
+
raise Exception("Please run accumulate() first")
|
715 |
+
iouType = self.params.iouType
|
716 |
+
if iouType == "segm" or iouType == "bbox":
|
717 |
+
summarize = _summarizeDets
|
718 |
+
elif iouType == "keypoints":
|
719 |
+
summarize = _summarizeKps
|
720 |
+
self.stats = summarize()
|
721 |
+
|
722 |
+
def __str__(self):
|
723 |
+
self.summarize()
|
oneformer/evaluation/evaluator.py
ADDED
@@ -0,0 +1,228 @@
|
|
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|
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|
|
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|
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|
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|
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|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# ------------------------------------------------------------------------------
|
2 |
+
# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/evaluation/evaluator.py
|
3 |
+
# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
|
4 |
+
# ------------------------------------------------------------------------------
|
5 |
+
|
6 |
+
import datetime
|
7 |
+
import logging
|
8 |
+
import time
|
9 |
+
from collections import OrderedDict, abc
|
10 |
+
from contextlib import ExitStack, contextmanager
|
11 |
+
from typing import List, Union
|
12 |
+
import torch
|
13 |
+
from torch import nn
|
14 |
+
|
15 |
+
from detectron2.utils.comm import get_world_size, is_main_process
|
16 |
+
from detectron2.utils.logger import log_every_n_seconds
|
17 |
+
|
18 |
+
|
19 |
+
class DatasetEvaluator:
|
20 |
+
"""
|
21 |
+
Base class for a dataset evaluator.
|
22 |
+
|
23 |
+
The function :func:`inference_on_dataset` runs the model over
|
24 |
+
all samples in the dataset, and have a DatasetEvaluator to process the inputs/outputs.
|
25 |
+
|
26 |
+
This class will accumulate information of the inputs/outputs (by :meth:`process`),
|
27 |
+
and produce evaluation results in the end (by :meth:`evaluate`).
|
28 |
+
"""
|
29 |
+
|
30 |
+
def reset(self):
|
31 |
+
"""
|
32 |
+
Preparation for a new round of evaluation.
|
33 |
+
Should be called before starting a round of evaluation.
|
34 |
+
"""
|
35 |
+
pass
|
36 |
+
|
37 |
+
def process(self, inputs, outputs):
|
38 |
+
"""
|
39 |
+
Process the pair of inputs and outputs.
|
40 |
+
If they contain batches, the pairs can be consumed one-by-one using `zip`:
|
41 |
+
|
42 |
+
.. code-block:: python
|
43 |
+
|
44 |
+
for input_, output in zip(inputs, outputs):
|
45 |
+
# do evaluation on single input/output pair
|
46 |
+
...
|
47 |
+
|
48 |
+
Args:
|
49 |
+
inputs (list): the inputs that's used to call the model.
|
50 |
+
outputs (list): the return value of `model(inputs)`
|
51 |
+
"""
|
52 |
+
pass
|
53 |
+
|
54 |
+
def evaluate(self):
|
55 |
+
"""
|
56 |
+
Evaluate/summarize the performance, after processing all input/output pairs.
|
57 |
+
|
58 |
+
Returns:
|
59 |
+
dict:
|
60 |
+
A new evaluator class can return a dict of arbitrary format
|
61 |
+
as long as the user can process the results.
|
62 |
+
In our train_net.py, we expect the following format:
|
63 |
+
|
64 |
+
* key: the name of the task (e.g., bbox)
|
65 |
+
* value: a dict of {metric name: score}, e.g.: {"AP50": 80}
|
66 |
+
"""
|
67 |
+
pass
|
68 |
+
|
69 |
+
|
70 |
+
class DatasetEvaluators(DatasetEvaluator):
|
71 |
+
"""
|
72 |
+
Wrapper class to combine multiple :class:`DatasetEvaluator` instances.
|
73 |
+
|
74 |
+
This class dispatches every evaluation call to
|
75 |
+
all of its :class:`DatasetEvaluator`.
|
76 |
+
"""
|
77 |
+
|
78 |
+
def __init__(self, evaluators):
|
79 |
+
"""
|
80 |
+
Args:
|
81 |
+
evaluators (list): the evaluators to combine.
|
82 |
+
"""
|
83 |
+
super().__init__()
|
84 |
+
self._evaluators = evaluators
|
85 |
+
|
86 |
+
def reset(self):
|
87 |
+
for evaluator in self._evaluators:
|
88 |
+
evaluator.reset()
|
89 |
+
|
90 |
+
def process(self, inputs, outputs):
|
91 |
+
for evaluator in self._evaluators:
|
92 |
+
evaluator.process(inputs, outputs)
|
93 |
+
|
94 |
+
def evaluate(self):
|
95 |
+
results = OrderedDict()
|
96 |
+
for evaluator in self._evaluators:
|
97 |
+
result = evaluator.evaluate()
|
98 |
+
if is_main_process() and result is not None:
|
99 |
+
for k, v in result.items():
|
100 |
+
assert (
|
101 |
+
k not in results
|
102 |
+
), "Different evaluators produce results with the same key {}".format(k)
|
103 |
+
results[k] = v
|
104 |
+
return results
|
105 |
+
|
106 |
+
|
107 |
+
def inference_on_dataset(
|
108 |
+
model, data_loader, evaluator: Union[DatasetEvaluator, List[DatasetEvaluator], None]
|
109 |
+
):
|
110 |
+
"""
|
111 |
+
Run model on the data_loader and evaluate the metrics with evaluator.
|
112 |
+
Also benchmark the inference speed of `model.__call__` accurately.
|
113 |
+
The model will be used in eval mode.
|
114 |
+
|
115 |
+
Args:
|
116 |
+
model (callable): a callable which takes an object from
|
117 |
+
`data_loader` and returns some outputs.
|
118 |
+
|
119 |
+
If it's an nn.Module, it will be temporarily set to `eval` mode.
|
120 |
+
If you wish to evaluate a model in `training` mode instead, you can
|
121 |
+
wrap the given model and override its behavior of `.eval()` and `.train()`.
|
122 |
+
data_loader: an iterable object with a length.
|
123 |
+
The elements it generates will be the inputs to the model.
|
124 |
+
evaluator: the evaluator(s) to run. Use `None` if you only want to benchmark,
|
125 |
+
but don't want to do any evaluation.
|
126 |
+
|
127 |
+
Returns:
|
128 |
+
The return value of `evaluator.evaluate()`
|
129 |
+
"""
|
130 |
+
num_devices = get_world_size()
|
131 |
+
logger = logging.getLogger(__name__)
|
132 |
+
logger.info("Start inference on {} batches".format(len(data_loader)))
|
133 |
+
|
134 |
+
total = len(data_loader) # inference data loader must have a fixed length
|
135 |
+
if evaluator is None:
|
136 |
+
# create a no-op evaluator
|
137 |
+
evaluator = DatasetEvaluators([])
|
138 |
+
if isinstance(evaluator, abc.MutableSequence):
|
139 |
+
evaluator = DatasetEvaluators(evaluator)
|
140 |
+
evaluator.reset()
|
141 |
+
|
142 |
+
num_warmup = min(5, total - 1)
|
143 |
+
start_time = time.perf_counter()
|
144 |
+
total_data_time = 0
|
145 |
+
total_compute_time = 0
|
146 |
+
total_eval_time = 0
|
147 |
+
with ExitStack() as stack:
|
148 |
+
if isinstance(model, nn.Module):
|
149 |
+
stack.enter_context(inference_context(model))
|
150 |
+
stack.enter_context(torch.no_grad())
|
151 |
+
|
152 |
+
start_data_time = time.perf_counter()
|
153 |
+
for idx, inputs in enumerate(data_loader):
|
154 |
+
total_data_time += time.perf_counter() - start_data_time
|
155 |
+
if idx == num_warmup:
|
156 |
+
start_time = time.perf_counter()
|
157 |
+
total_data_time = 0
|
158 |
+
total_compute_time = 0
|
159 |
+
total_eval_time = 0
|
160 |
+
|
161 |
+
start_compute_time = time.perf_counter()
|
162 |
+
outputs = model(inputs)
|
163 |
+
if torch.cuda.is_available():
|
164 |
+
torch.cuda.synchronize()
|
165 |
+
total_compute_time += time.perf_counter() - start_compute_time
|
166 |
+
|
167 |
+
start_eval_time = time.perf_counter()
|
168 |
+
evaluator.process(inputs, outputs)
|
169 |
+
total_eval_time += time.perf_counter() - start_eval_time
|
170 |
+
|
171 |
+
iters_after_start = idx + 1 - num_warmup * int(idx >= num_warmup)
|
172 |
+
data_seconds_per_iter = total_data_time / iters_after_start
|
173 |
+
compute_seconds_per_iter = total_compute_time / iters_after_start
|
174 |
+
eval_seconds_per_iter = total_eval_time / iters_after_start
|
175 |
+
total_seconds_per_iter = (time.perf_counter() - start_time) / iters_after_start
|
176 |
+
if idx >= num_warmup * 2 or compute_seconds_per_iter > 5:
|
177 |
+
eta = datetime.timedelta(seconds=int(total_seconds_per_iter * (total - idx - 1)))
|
178 |
+
log_every_n_seconds(
|
179 |
+
logging.INFO,
|
180 |
+
(
|
181 |
+
f"Inference done {idx + 1}/{total}. "
|
182 |
+
f"Dataloading: {data_seconds_per_iter:.4f} s/iter. "
|
183 |
+
f"Inference: {compute_seconds_per_iter:.4f} s/iter. "
|
184 |
+
f"Eval: {eval_seconds_per_iter:.4f} s/iter. "
|
185 |
+
f"Total: {total_seconds_per_iter:.4f} s/iter. "
|
186 |
+
f"ETA={eta}"
|
187 |
+
),
|
188 |
+
n=5,
|
189 |
+
)
|
190 |
+
start_data_time = time.perf_counter()
|
191 |
+
|
192 |
+
# Measure the time only for this worker (before the synchronization barrier)
|
193 |
+
total_time = time.perf_counter() - start_time
|
194 |
+
total_time_str = str(datetime.timedelta(seconds=total_time))
|
195 |
+
# NOTE this format is parsed by grep
|
196 |
+
logger.info(
|
197 |
+
"Total inference time: {} ({:.6f} s / iter per device, on {} devices)".format(
|
198 |
+
total_time_str, total_time / (total - num_warmup), num_devices
|
199 |
+
)
|
200 |
+
)
|
201 |
+
total_compute_time_str = str(datetime.timedelta(seconds=int(total_compute_time)))
|
202 |
+
logger.info(
|
203 |
+
"Total inference pure compute time: {} ({:.6f} s / iter per device, on {} devices)".format(
|
204 |
+
total_compute_time_str, total_compute_time / (total - num_warmup), num_devices
|
205 |
+
)
|
206 |
+
)
|
207 |
+
|
208 |
+
results = evaluator.evaluate()
|
209 |
+
# An evaluator may return None when not in main process.
|
210 |
+
# Replace it by an empty dict instead to make it easier for downstream code to handle
|
211 |
+
if results is None:
|
212 |
+
results = {}
|
213 |
+
return results
|
214 |
+
|
215 |
+
|
216 |
+
@contextmanager
|
217 |
+
def inference_context(model):
|
218 |
+
"""
|
219 |
+
A context where the model is temporarily changed to eval mode,
|
220 |
+
and restored to previous mode afterwards.
|
221 |
+
|
222 |
+
Args:
|
223 |
+
model: a torch Module
|
224 |
+
"""
|
225 |
+
training_mode = model.training
|
226 |
+
model.eval()
|
227 |
+
yield
|
228 |
+
model.train(training_mode)
|
oneformer/evaluation/instance_evaluation.py
ADDED
@@ -0,0 +1,110 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# ------------------------------------------------------------------------------
|
2 |
+
# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/evaluation/instance_evaluation.py
|
3 |
+
# ------------------------------------------------------------------------------
|
4 |
+
|
5 |
+
import contextlib
|
6 |
+
import copy
|
7 |
+
import io
|
8 |
+
import itertools
|
9 |
+
import json
|
10 |
+
import logging
|
11 |
+
import numpy as np
|
12 |
+
import os
|
13 |
+
import pickle
|
14 |
+
from collections import OrderedDict
|
15 |
+
import pycocotools.mask as mask_util
|
16 |
+
import torch
|
17 |
+
from pycocotools.coco import COCO
|
18 |
+
from pycocotools.cocoeval import COCOeval
|
19 |
+
from tabulate import tabulate
|
20 |
+
|
21 |
+
import detectron2.utils.comm as comm
|
22 |
+
from detectron2.config import CfgNode
|
23 |
+
from detectron2.data import MetadataCatalog
|
24 |
+
from detectron2.data.datasets.coco import convert_to_coco_json
|
25 |
+
from detectron2.evaluation.coco_evaluation import COCOEvaluator, _evaluate_predictions_on_coco
|
26 |
+
from detectron2.evaluation.fast_eval_api import COCOeval_opt
|
27 |
+
from detectron2.structures import Boxes, BoxMode, pairwise_iou
|
28 |
+
from detectron2.utils.file_io import PathManager
|
29 |
+
from detectron2.utils.logger import create_small_table
|
30 |
+
|
31 |
+
|
32 |
+
# modified from COCOEvaluator for instance segmetnat
|
33 |
+
class InstanceSegEvaluator(COCOEvaluator):
|
34 |
+
"""
|
35 |
+
Evaluate AR for object proposals, AP for instance detection/segmentation, AP
|
36 |
+
for keypoint detection outputs using COCO's metrics.
|
37 |
+
See http://cocodataset.org/#detection-eval and
|
38 |
+
http://cocodataset.org/#keypoints-eval to understand its metrics.
|
39 |
+
The metrics range from 0 to 100 (instead of 0 to 1), where a -1 or NaN means
|
40 |
+
the metric cannot be computed (e.g. due to no predictions made).
|
41 |
+
|
42 |
+
In addition to COCO, this evaluator is able to support any bounding box detection,
|
43 |
+
instance segmentation, or keypoint detection dataset.
|
44 |
+
"""
|
45 |
+
|
46 |
+
def _eval_predictions(self, predictions, img_ids=None):
|
47 |
+
"""
|
48 |
+
Evaluate predictions. Fill self._results with the metrics of the tasks.
|
49 |
+
"""
|
50 |
+
self._logger.info("Preparing results for COCO format ...")
|
51 |
+
coco_results = list(itertools.chain(*[x["instances"] for x in predictions]))
|
52 |
+
tasks = self._tasks or self._tasks_from_predictions(coco_results)
|
53 |
+
|
54 |
+
# unmap the category ids for COCO
|
55 |
+
if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"):
|
56 |
+
dataset_id_to_contiguous_id = self._metadata.thing_dataset_id_to_contiguous_id
|
57 |
+
# all_contiguous_ids = list(dataset_id_to_contiguous_id.values())
|
58 |
+
# num_classes = len(all_contiguous_ids)
|
59 |
+
# assert min(all_contiguous_ids) == 0 and max(all_contiguous_ids) == num_classes - 1
|
60 |
+
|
61 |
+
reverse_id_mapping = {v: k for k, v in dataset_id_to_contiguous_id.items()}
|
62 |
+
for result in coco_results:
|
63 |
+
category_id = result["category_id"]
|
64 |
+
# assert category_id < num_classes, (
|
65 |
+
# f"A prediction has class={category_id}, "
|
66 |
+
# f"but the dataset only has {num_classes} classes and "
|
67 |
+
# f"predicted class id should be in [0, {num_classes - 1}]."
|
68 |
+
# )
|
69 |
+
assert category_id in reverse_id_mapping, (
|
70 |
+
f"A prediction has class={category_id}, "
|
71 |
+
f"but the dataset only has class ids in {dataset_id_to_contiguous_id}."
|
72 |
+
)
|
73 |
+
result["category_id"] = reverse_id_mapping[category_id]
|
74 |
+
|
75 |
+
if self._output_dir:
|
76 |
+
file_path = os.path.join(self._output_dir, "coco_instances_results.json")
|
77 |
+
self._logger.info("Saving results to {}".format(file_path))
|
78 |
+
with PathManager.open(file_path, "w") as f:
|
79 |
+
f.write(json.dumps(coco_results))
|
80 |
+
f.flush()
|
81 |
+
|
82 |
+
if not self._do_evaluation:
|
83 |
+
self._logger.info("Annotations are not available for evaluation.")
|
84 |
+
return
|
85 |
+
|
86 |
+
self._logger.info(
|
87 |
+
"Evaluating predictions with {} COCO API...".format(
|
88 |
+
"unofficial" if self._use_fast_impl else "official"
|
89 |
+
)
|
90 |
+
)
|
91 |
+
for task in sorted(tasks):
|
92 |
+
assert task in {"bbox", "segm", "keypoints"}, f"Got unknown task: {task}!"
|
93 |
+
coco_eval = (
|
94 |
+
_evaluate_predictions_on_coco(
|
95 |
+
self._coco_api,
|
96 |
+
coco_results,
|
97 |
+
task,
|
98 |
+
kpt_oks_sigmas=self._kpt_oks_sigmas,
|
99 |
+
use_fast_impl=self._use_fast_impl,
|
100 |
+
img_ids=img_ids,
|
101 |
+
max_dets_per_image=self._max_dets_per_image,
|
102 |
+
)
|
103 |
+
if len(coco_results) > 0
|
104 |
+
else None # cocoapi does not handle empty results very well
|
105 |
+
)
|
106 |
+
|
107 |
+
res = self._derive_coco_results(
|
108 |
+
coco_eval, task, class_names=self._metadata.get("thing_classes")
|
109 |
+
)
|
110 |
+
self._results[task] = res
|
oneformer/modeling/.DS_Store
ADDED
Binary file (6.15 kB). View file
|
|