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Thresholding based auto-labelling

Nov3
Joseph 2020-10-04 09:59:13 +05:30
parent 73d7a85f88
commit 2f553726bf
9 changed files with 95 additions and 7 deletions
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6
README.md
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@ -55,3 +55,9 @@ If you use Detectron2 in your research or wish to refer to the baseline results
year = {2019}
}
```
## Command
```python
python tools/train_net.py --num-gpus 4 --config-file ./configs/PascalVOC-Detection/faster_rcnn_R_50_C4.yamlSOLVER.IMS_PER_BATCH 4 SOLVER.BASE_LR 0.005
```

10
configs/PascalVOC-Detection/faster_rcnn_R_50_C4.yaml
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@ -5,14 +5,22 @@ MODEL:
RESNETS:
DEPTH: 50
ROI_HEADS:
NUM_CLASSES: 20
NUM_CLASSES: 21 # 0-19 Known class; 20 -> Unknown; 21 -> Background.
# NUM_CLASSES: 20
INPUT:
MIN_SIZE_TRAIN: (480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800)
MIN_SIZE_TEST: 800
DATASETS:
TRAIN: ('voc_2007_trainval', 'voc_2012_trainval')
TEST: ('voc_2007_test',)
SOLVER:
STEPS: (12000, 16000)
MAX_ITER: 18000 # 17.4 epochs
WARMUP_ITERS: 100
OUTPUT_DIR: "./output/al_threshold"
OWOD:
ENABLE_THRESHOLD_AUTOLABEL_UNK: True
NUM_UNK_PER_IMAGE: 1
ENABLE_UNCERTAINITY_AUTOLABEL_UNK: False
ENABLE_CLUSTERING: False

19
configs/PascalVOC-Detection/faster_rcnn_R_50_C4_eval.yaml 100644
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@ -0,0 +1,19 @@
_BASE_: "../Base-RCNN-C4.yaml"
MODEL:
WEIGHTS: "/home/joseph/workspace/OWOD/output/baseline_pascal/model_final.pth"
MASK_ON: False
RESNETS:
DEPTH: 50
ROI_HEADS:
NUM_CLASSES: 20
INPUT:
MIN_SIZE_TRAIN: (480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800)
MIN_SIZE_TEST: 800
DATASETS:
TRAIN: ('voc_2007_trainval', 'voc_2012_trainval')
TEST: ('voc_2007_test',)
SOLVER:
STEPS: (12000, 16000)
MAX_ITER: 18000 # 17.4 epochs
WARMUP_ITERS: 100
OUTPUT_DIR: "./output/baseline_pascal"

1
configs/PascalVOC-Detection/faster_rcnn_R_50_FPN.yaml
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@ -16,3 +16,4 @@ SOLVER:
STEPS: (12000, 16000)
MAX_ITER: 18000 # 17.4 epochs
WARMUP_ITERS: 100
OUTPUT_DIR: "./output/baseline_pascal_fpn"

9
detectron2/config/defaults.py
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@ -595,6 +595,15 @@ _C.TEST.AUG.FLIP = True
_C.TEST.PRECISE_BN = CN({"ENABLED": False})
_C.TEST.PRECISE_BN.NUM_ITER = 200
# ---------------------------------------------------------------------------- #
# OpenWorld Object Detection
# ---------------------------------------------------------------------------- #
_C.OWOD = CN()
_C.OWOD.ENABLE_THRESHOLD_AUTOLABEL_UNK = False
_C.OWOD.NUM_UNK_PER_IMAGE = 1
_C.OWOD.ENABLE_UNCERTAINITY_AUTOLABEL_UNK = False
_C.OWOD.ENABLE_CLUSTERING = False
# ---------------------------------------------------------------------------- #
# Misc options
# ---------------------------------------------------------------------------- #

3
detectron2/data/datasets/builtin.py
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@ -246,5 +246,6 @@ if __name__.endswith(".builtin"):
register_all_lvis(_root)
register_all_cityscapes(_root)
register_all_cityscapes_panoptic(_root)
register_all_pascal_voc(_root)
# register_all_pascal_voc(_root)
register_all_pascal_voc('/home/joseph/workspace/OWOD/datasets')
register_all_ade20k(_root)

2
detectron2/data/datasets/pascal_voc.py
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@ -17,7 +17,7 @@ __all__ = ["load_voc_instances", "register_pascal_voc"]
CLASS_NAMES = (
"aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat",
"chair", "cow", "diningtable", "dog", "horse", "motorbike", "person",
"pottedplant", "sheep", "sofa", "train", "tvmonitor"
"pottedplant", "sheep", "sofa", "train", "tvmonitor", "unknown"
)
# fmt: on

20
detectron2/evaluation/pascal_voc_evaluation.py
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@ -87,6 +87,9 @@ class PascalVOCDetectionEvaluator(DatasetEvaluator):
res_file_template = os.path.join(dirname, "{}.txt")
aps = defaultdict(list) # iou -> ap per class
# recs = defaultdict(list)
# precs = defaultdict(list)
for cls_id, cls_name in enumerate(self._class_names):
lines = predictions.get(cls_id, [""])
@ -103,10 +106,27 @@ class PascalVOCDetectionEvaluator(DatasetEvaluator):
use_07_metric=self._is_2007,
)
aps[thresh].append(ap * 100)
# recs[thresh].append(rec * 100)
# precs[thresh].append(prec * 100)
ret = OrderedDict()
mAP = {iou: np.mean(x) for iou, x in aps.items()}
ret["bbox"] = {"AP": np.mean(list(mAP.values())), "AP50": mAP[50], "AP75": mAP[75]}
# Extra logging of class-wise APs
self._logger.info(self._class_names)
self._logger.info("AP__: " + str(['%.1f' % x for x in list(np.mean([x for _, x in aps.items()], axis=0))]))
self._logger.info("AP50: " + str(['%.1f' % x for x in aps[50]]))
self._logger.info("AP75: " + str(['%.1f' % x for x in aps[75]]))
# self._logger.info("R__: " + str(['%.1f' % x for x in list(np.mean([x for _, x in recs.items()], axis=0))]))
# self._logger.info("R50: " + str(['%.1f' % x for x in recs[50]]))
# self._logger.info("R75: " + str(['%.1f' % x for x in recs[75]]))
#
# self._logger.info("P__: " + str(['%.1f' % x for x in list(np.mean([x for _, x in precs.items()], axis=0))]))
# self._logger.info("P50: " + str(['%.1f' % x for x in precs[50]]))
# self._logger.info("P75: " + str(['%.1f' % x for x in precs[75]]))
return ret

32
detectron2/modeling/roi_heads/roi_heads.py
View File

@ -2,6 +2,8 @@
import inspect
import logging
import numpy as np
import heapq
import operator
from typing import Dict, List, Optional, Tuple, Union
import torch
from torch import nn
@ -143,7 +145,9 @@ class ROIHeads(torch.nn.Module):
batch_size_per_image,
positive_fraction,
proposal_matcher,
proposal_append_gt=True
enable_thresold_autolabelling,
unk_k,
proposal_append_gt=True,
):
"""
NOTE: this interface is experimental.
@ -162,6 +166,8 @@ class ROIHeads(torch.nn.Module):
self.num_classes = num_classes
self.proposal_matcher = proposal_matcher
self.proposal_append_gt = proposal_append_gt
self.enable_thresold_autolabelling = enable_thresold_autolabelling
self.unk_k = unk_k
@classmethod
def from_config(cls, cfg):
@ -176,10 +182,13 @@ class ROIHeads(torch.nn.Module):
cfg.MODEL.ROI_HEADS.IOU_LABELS,
allow_low_quality_matches=False,
),
"enable_thresold_autolabelling": cfg.OWOD.ENABLE_THRESHOLD_AUTOLABEL_UNK,
"unk_k": cfg.OWOD.NUM_UNK_PER_IMAGE,
}
def _sample_proposals(
self, matched_idxs: torch.Tensor, matched_labels: torch.Tensor, gt_classes: torch.Tensor
self, matched_idxs: torch.Tensor, matched_labels: torch.Tensor, gt_classes: torch.Tensor,
objectness_logits: torch.Tensor = None,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Based on the matching between N proposals and M groundtruth,
@ -214,7 +223,22 @@ class ROIHeads(torch.nn.Module):
)
sampled_idxs = torch.cat([sampled_fg_idxs, sampled_bg_idxs], dim=0)
return sampled_idxs, gt_classes[sampled_idxs]
gt_classes_ss = gt_classes[sampled_idxs]
if self.enable_thresold_autolabelling:
matched_labels_ss = matched_labels[sampled_idxs]
pred_objectness_score_ss = objectness_logits[sampled_idxs]
# 1) Remove FG objectness score. 2) Sort and select top k. 3) Build and apply mask.
mask = torch.zeros((pred_objectness_score_ss.shape), dtype=torch.bool)
pred_objectness_score_ss[matched_labels_ss != 0] = -1
sorted_indices = list(zip(
*heapq.nlargest(self.unk_k, enumerate(pred_objectness_score_ss), key=operator.itemgetter(1))))[0]
for index in sorted_indices:
mask[index] = True
gt_classes_ss[mask] = self.num_classes - 1
return sampled_idxs, gt_classes_ss
@torch.no_grad()
def label_and_sample_proposals(
@ -269,7 +293,7 @@ class ROIHeads(torch.nn.Module):
)
matched_idxs, matched_labels = self.proposal_matcher(match_quality_matrix)
sampled_idxs, gt_classes = self._sample_proposals(
matched_idxs, matched_labels, targets_per_image.gt_classes
matched_idxs, matched_labels, targets_per_image.gt_classes, proposals_per_image.objectness_logits
)
# Set target attributes of the sampled proposals: