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Weibull distribution

pull/42/head
Joseph 2020-11-06 15:25:34 +05:30
parent 9321996ee8
commit 71480aa878
14 changed files with 200 additions and 18 deletions
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4
configs/Base-RCNN-C4-OWOD.yaml
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@ -24,10 +24,10 @@ SOLVER:
MAX_ITER: 90000
VERSION: 2
OWOD:
ENABLE_THRESHOLD_AUTOLABEL_UNK: False
ENABLE_THRESHOLD_AUTOLABEL_UNK: True
NUM_UNK_PER_IMAGE: 1
ENABLE_UNCERTAINITY_AUTOLABEL_UNK: False
ENABLE_CLUSTERING: False
ENABLE_CLUSTERING: True
CLUSTERING:
ITEMS_PER_CLASS: 20
START_ITER: 1000

4
configs/OWOD/t1.yaml
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@ -2,7 +2,7 @@ _BASE_: "../Base-RCNN-C4-OWOD.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
# WEIGHTS: "/home/fk1/workspace/OWOD/output/expr_training_with_unk_with_clustering_Z_DIMENSION_256/model_final.pth"
# WEIGHTS: "/home/fk1/workspace/OWOD/output/t1/model_final.pth"
# WEIGHTS: "/home/fk1/workspace/OWOD/output/t1_std_frcnn/model_final.pth"
DATASETS:
TRAIN: ('t1_voc_coco_2007_train', ) # t1_voc_coco_2007_train, t1_voc_coco_2007_ft
TEST: ('voc_coco_2007_test', ) # t1_voc_coco_2007_test, t1_voc_coco_2007_val
@ -10,7 +10,7 @@ SOLVER:
STEPS: (12000, 16000)
MAX_ITER: 18000
WARMUP_ITERS: 100
OUTPUT_DIR: "./output/t1_std_frcnn"
OUTPUT_DIR: "./output/t1_ENABLE_CLUSTERING_margin_5"
OWOD:
PREV_INTRODUCED_CLS: 0
CUR_INTRODUCED_CLS: 20

14
configs/OWOD/t1/t1_ft.yaml 100644
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@ -0,0 +1,14 @@
_BASE_: "../../Base-RCNN-C4-OWOD.yaml"
MODEL:
WEIGHTS: "/home/fk1/workspace/OWOD/output/t1_expr/model_final.pth"
DATASETS:
TRAIN: ('t1_voc_coco_2007_ft', ) # t1_voc_coco_2007_train, t1_voc_coco_2007_ft
TEST: (, ) # voc_coco_2007_test
SOLVER:
STEPS: (12000, 16000)
MAX_ITER: 18000
WARMUP_ITERS: 100
OUTPUT_DIR: "./output/t1_expr"
OWOD:
PREV_INTRODUCED_CLS: 0
CUR_INTRODUCED_CLS: 20

15
configs/OWOD/t1/t1_test.yaml 100644
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@ -0,0 +1,15 @@
_BASE_: "../../Base-RCNN-C4-OWOD.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
# WEIGHTS: "/home/fk1/workspace/OWOD/output/t1/model_final.pth"
DATASETS:
TRAIN: ('t1_voc_coco_2007_train', ) # t1_voc_coco_2007_train, t1_voc_coco_2007_ft
TEST: ('voc_coco_2007_test', ) # voc_coco_2007_test
SOLVER:
STEPS: (12000, 16000)
MAX_ITER: 18000
WARMUP_ITERS: 100
OUTPUT_DIR: "./output/t1_expr"
OWOD:
PREV_INTRODUCED_CLS: 0
CUR_INTRODUCED_CLS: 20

15
configs/OWOD/t1/t1_train.yaml 100644
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@ -0,0 +1,15 @@
_BASE_: "../../Base-RCNN-C4-OWOD.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
# WEIGHTS: "/home/fk1/workspace/OWOD/output/t1/model_final.pth"
DATASETS:
TRAIN: ('t1_voc_coco_2007_train', ) # t1_voc_coco_2007_train, t1_voc_coco_2007_ft
TEST: (, ) # voc_coco_2007_test
SOLVER:
STEPS: (12000, 16000)
MAX_ITER: 18000
WARMUP_ITERS: 100
OUTPUT_DIR: "./output/t1_expr"
OWOD:
PREV_INTRODUCED_CLS: 0
CUR_INTRODUCED_CLS: 20

23
configs/OWOD/t1/t1_val.yaml 100644
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@ -0,0 +1,23 @@
_BASE_: "../../Base-RCNN-C4-OWOD.yaml"
MODEL:
# WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
# WEIGHTS: "/home/fk1/workspace/OWOD/output/t1_THRESHOLD_AUTOLABEL_UNK/model_final.pth"
WEIGHTS: "/home/fk1/workspace/OWOD/output/t1_ENABLE_CLUSTERING/model_final.pth"
# WEIGHTS: "/home/fk1/workspace/OWOD/output/t1_std_frcnn/model_final.pth"
DATASETS:
TRAIN: ('voc_coco_2007_val', ) # t1_voc_coco_2007_train, t1_voc_coco_2007_ft
TEST: ('voc_coco_2007_val', ) # voc_coco_2007_test
SOLVER:
STEPS: (12000, 16000)
MAX_ITER: 500
WARMUP_ITERS: 0
OUTPUT_DIR: "./output/t1_clustering_new_3"
OWOD:
PREV_INTRODUCED_CLS: 0
CUR_INTRODUCED_CLS: 20
COMPUTE_ENERGY: True
ENERGY_SAVE_PATH: 'energy'
SKIP_TRAINING_WHILE_EVAL: False
#OUTPUT_DIR: "./output/t1_std_frcnn_energy"

7
configs/OWOD/t2.yaml
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@ -1,9 +1,10 @@
_BASE_: "../Base-RCNN-C4-OWOD.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
# WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
WEIGHTS: "/home/fk1/workspace/OWOD/output/t1_ENABLE_CLUSTERING/model_final.pth"
DATASETS:
TRAIN: ('t2_train',)
TEST: ('voc_2007_test', 't2_test', 't3_test_unk', 't4_test_unk')
TRAIN: ('t2_voc_coco_2007_train', ) # t1_voc_coco_2007_train, t1_voc_coco_2007_ft
TEST: ('voc_coco_2007_test', )
SOLVER:
STEPS: (22000, 26000)
MAX_ITER: 28000

3
detectron2/config/defaults.py
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@ -613,6 +613,9 @@ _C.OWOD.CLUSTERING.Z_DIMENSION = 64
_C.OWOD.PREV_INTRODUCED_CLS = 0
_C.OWOD.CUR_INTRODUCED_CLS = 20
_C.OWOD.COMPUTE_ENERGY = False
_C.OWOD.ENERGY_SAVE_PATH = ''
_C.OWOD.SKIP_TRAINING_WHILE_EVAL = False
# ---------------------------------------------------------------------------- #
# Misc options

2
detectron2/data/build.py
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@ -263,7 +263,7 @@ def remove_prev_class_and_unk_instances(cfg, dataset_dicts):
# For training data.
prev_intro_cls = cfg.OWOD.PREV_INTRODUCED_CLS
curr_intro_cls = cfg.OWOD.CUR_INTRODUCED_CLS
valid_classes = range(prev_intro_cls, curr_intro_cls)
valid_classes = range(prev_intro_cls, prev_intro_cls + curr_intro_cls)
logger = logging.getLogger(__name__)
logger.info("Valid classes: " + str(valid_classes))

3
detectron2/engine/defaults.py
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@ -118,6 +118,9 @@ def default_setup(cfg, args):
if comm.is_main_process() and output_dir:
PathManager.mkdirs(output_dir)
if cfg.OWOD.COMPUTE_ENERGY:
PathManager.mkdirs(os.path.join(output_dir, cfg.OWOD.ENERGY_SAVE_PATH))
rank = comm.get_rank()
setup_logger(output_dir, distributed_rank=rank, name="fvcore")
logger = setup_logger(output_dir, distributed_rank=rank)

76
detectron2/engine/train_loop.py
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@ -7,6 +7,9 @@ import numpy as np
import time
import weakref
import torch
import os
from matplotlib import pyplot
from reliability.Fitters import Fit_Weibull_3P
import detectron2.utils.comm as comm
from detectron2.utils.events import EventStorage
@ -138,6 +141,8 @@ class TrainerBase:
try:
self.before_train()
for self.iter in range(start_iter, max_iter):
if self.cfg.OWOD.SKIP_TRAINING_WHILE_EVAL:
continue
self.before_step()
self.run_step()
self.after_step()
@ -152,13 +157,80 @@ class TrainerBase:
self.after_train()
def before_train(self):
if self.cfg.OWOD.SKIP_TRAINING_WHILE_EVAL:
logger.info('Skipping training as cfg.OWOD.SKIP_TRAINING_WHILE_EVAL flag is set.')
for h in self._hooks:
h.before_train()
def after_train(self):
self.storage.iter = self.iter
for h in self._hooks:
h.after_train()
if self.cfg.OWOD.COMPUTE_ENERGY:
logger = logging.getLogger(__name__)
logger.info("Going to analyse the energy files...")
self.analyse_energy()
for h in self._hooks:
if 'EvalHook' not in str(type(h)):
h.after_train()
else:
for h in self._hooks:
h.after_train()
def analyse_energy(self):
files = os.listdir(os.path.join(self.cfg.OUTPUT_DIR, self.cfg.OWOD.ENERGY_SAVE_PATH))
unk = []
known = []
logger = logging.getLogger(__name__)
for id, file in enumerate(files):
path = os.path.join(self.cfg.OUTPUT_DIR, self.cfg.OWOD.ENERGY_SAVE_PATH, file)
try:
logits, classes = torch.load(path)
except:
logger.info('Not able to load ' + path + ". Continuing...")
continue
num_seen_classes = self.cfg.OWOD.PREV_INTRODUCED_CLS + self.cfg.OWOD.CUR_INTRODUCED_CLS
lse = torch.logsumexp(logits[:, :num_seen_classes], dim=1)
# lse = torch.logsumexp(logits[:, :-2], dim=1)
for i, cls in enumerate(classes):
if cls == self.cfg.MODEL.ROI_HEADS.NUM_CLASSES:
continue
if cls == self.cfg.MODEL.ROI_HEADS.NUM_CLASSES-1:
unk.append(lse[i].detach().cpu().tolist())
else:
known.append(lse[i].detach().cpu().tolist())
if id % 100 == 0:
logger.info("Analysing " + str(id) + " / " + str(len(files)))
if id == 10:
break
logger.info('len(unk): ' + str(len(unk)))
logger.info('len(known): '+ str(len(known)))
logger.info('Fitting Weibull distribution...')
wb_dist_param = []
wb_unk = Fit_Weibull_3P(failures=unk, show_probability_plot=False, print_results=False)
wb_dist_param.append({"scale_unk": wb_unk.alpha, "shape_unk": wb_unk.beta, "shift_unk": wb_unk.gamma})
wb_known = Fit_Weibull_3P(failures=known, show_probability_plot=False, print_results=False)
wb_dist_param.append(
{"scale_known": wb_known.alpha, "shape_known": wb_known.beta, "shift_known": wb_known.gamma})
param_save_location = os.path.join(self.cfg.OUTPUT_DIR,
'energy_dist_' + str(self.cfg.OWOD.PREV_INTRODUCED_CLS
+ self.cfg.OWOD.CUR_INTRODUCED_CLS) + '.pkl')
logger.info('Pickling the parameters to ' + param_save_location)
torch.save(wb_dist_param, param_save_location)
logger.info('Plotting the computed energy values...')
bins = np.linspace(2, 15, 500)
pyplot.hist(known, bins, alpha=0.5, label='known')
pyplot.hist(unk, bins, alpha=0.5, label='unk')
pyplot.legend(loc='upper right')
pyplot.savefig(os.path.join(self.cfg.OUTPUT_DIR, 'energy.png'))
def before_step(self):
# Maintain the invariant that storage.iter == trainer.iter

24
detectron2/evaluation/pascal_voc_evaluation.py
View File

@ -46,6 +46,7 @@ class PascalVOCDetectionEvaluator(DatasetEvaluator):
self.prev_intro_cls = cfg.OWOD.PREV_INTRODUCED_CLS
self.curr_intro_cls = cfg.OWOD.CUR_INTRODUCED_CLS
self.total_num_class = cfg.MODEL.ROI_HEADS.NUM_CLASSES
self.known_classes = self._class_names[:self.prev_intro_cls + self.curr_intro_cls]
def reset(self):
self._predictions = defaultdict(list) # class name -> list of prediction strings
@ -57,6 +58,7 @@ class PascalVOCDetectionEvaluator(DatasetEvaluator):
boxes = instances.pred_boxes.tensor.numpy()
scores = instances.scores.tolist()
classes = instances.pred_classes.tolist()
logits = instances.logits
for box, score, cls in zip(boxes, scores, classes):
xmin, ymin, xmax, ymax = box
# The inverse of data loading logic in `datasets/pascal_voc.py`
@ -108,6 +110,7 @@ class PascalVOCDetectionEvaluator(DatasetEvaluator):
cls_name,
ovthresh=thresh / 100.0,
use_07_metric=self._is_2007,
known_classes=self.known_classes
)
aps[thresh].append(ap * 100)
# recs[thresh].append(rec * 100)
@ -166,14 +169,27 @@ class PascalVOCDetectionEvaluator(DatasetEvaluator):
@lru_cache(maxsize=None)
def parse_rec(filename):
def parse_rec(filename, known_classes):
"""Parse a PASCAL VOC xml file."""
VOC_CLASS_NAMES_COCOFIED = [
"airplane", "dining table", "motorcycle",
"potted plant", "couch", "tv"
]
BASE_VOC_CLASS_NAMES = [
"aeroplane", "diningtable", "motorbike",
"pottedplant", "sofa", "tvmonitor"
]
with PathManager.open(filename) as f:
tree = ET.parse(f)
objects = []
for obj in tree.findall("object"):
obj_struct = {}
obj_struct["name"] = obj.find("name").text
cls_name = obj.find("name").text
if cls_name in VOC_CLASS_NAMES_COCOFIED:
cls_name = BASE_VOC_CLASS_NAMES[VOC_CLASS_NAMES_COCOFIED.index(cls_name)]
if cls_name not in known_classes:
cls_name = 'unknown'
obj_struct["name"] = cls_name
# obj_struct["pose"] = obj.find("pose").text
# obj_struct["truncated"] = int(obj.find("truncated").text)
obj_struct["difficult"] = int(obj.find("difficult").text)
@ -221,7 +237,7 @@ def voc_ap(rec, prec, use_07_metric=False):
return ap
def voc_eval(detpath, annopath, imagesetfile, classname, ovthresh=0.5, use_07_metric=False):
def voc_eval(detpath, annopath, imagesetfile, classname, ovthresh=0.5, use_07_metric=False, known_classes=None):
"""rec, prec, ap = voc_eval(detpath,
annopath,
imagesetfile,
@ -254,7 +270,7 @@ def voc_eval(detpath, annopath, imagesetfile, classname, ovthresh=0.5, use_07_me
# load annots
recs = {}
for imagename in imagenames:
recs[imagename] = parse_rec(annopath.format(imagename))
recs[imagename] = parse_rec(annopath.format(imagename), tuple(known_classes))
# extract gt objects for this class
class_recs = {}

15
detectron2/modeling/roi_heads/fast_rcnn.py
View File

@ -47,7 +47,7 @@ Naming convention:
"""
def fast_rcnn_inference(boxes, scores, image_shapes, score_thresh, nms_thresh, topk_per_image):
def fast_rcnn_inference(boxes, scores, image_shapes, predictions, score_thresh, nms_thresh, topk_per_image):
"""
Call `fast_rcnn_inference_single_image` for all images.
@ -75,15 +75,15 @@ def fast_rcnn_inference(boxes, scores, image_shapes, score_thresh, nms_thresh, t
"""
result_per_image = [
fast_rcnn_inference_single_image(
boxes_per_image, scores_per_image, image_shape, score_thresh, nms_thresh, topk_per_image
boxes_per_image, scores_per_image, image_shape, score_thresh, nms_thresh, topk_per_image, prediction
)
for scores_per_image, boxes_per_image, image_shape in zip(scores, boxes, image_shapes)
for scores_per_image, boxes_per_image, image_shape, prediction in zip(scores, boxes, image_shapes, predictions)
]
return [x[0] for x in result_per_image], [x[1] for x in result_per_image]
def fast_rcnn_inference_single_image(
boxes, scores, image_shape, score_thresh, nms_thresh, topk_per_image
boxes, scores, image_shape, score_thresh, nms_thresh, topk_per_image, prediction
):
"""
Single-image inference. Return bounding-box detection results by thresholding
@ -96,12 +96,15 @@ def fast_rcnn_inference_single_image(
Returns:
Same as `fast_rcnn_inference`, but for only one image.
"""
logits = prediction
valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
if not valid_mask.all():
boxes = boxes[valid_mask]
scores = scores[valid_mask]
logits = logits[valid_mask]
scores = scores[:, :-1]
logits = logits[:, :-1]
num_bbox_reg_classes = boxes.shape[1] // 4
# Convert to Boxes to use the `clip` function ...
boxes = Boxes(boxes.reshape(-1, 4))
@ -119,17 +122,20 @@ def fast_rcnn_inference_single_image(
else:
boxes = boxes[filter_mask]
scores = scores[filter_mask]
logits = logits[filter_inds[:,0]]
# 2. Apply NMS for each class independently.
keep = batched_nms(boxes, scores, filter_inds[:, 1], nms_thresh)
if topk_per_image >= 0:
keep = keep[:topk_per_image]
boxes, scores, filter_inds = boxes[keep], scores[keep], filter_inds[keep]
logits = logits[keep]
result = Instances(image_shape)
result.pred_boxes = Boxes(boxes)
result.scores = scores
result.pred_classes = filter_inds[:, 1]
result.logits = logits
return result, filter_inds[:, 0]
@ -734,6 +740,7 @@ class FastRCNNOutputLayers(nn.Module):
boxes,
scores,
image_shapes,
predictions,
self.test_score_thresh,
self.test_nms_thresh,
self.test_topk_per_image,

13
detectron2/modeling/roi_heads/roi_heads.py
View File

@ -3,6 +3,8 @@ import inspect
import logging
import numpy as np
import heapq
import os
import shortuuid
import operator
import shortuuid
from typing import Dict, List, Optional, Tuple, Union
@ -385,6 +387,8 @@ class Res5ROIHeads(ROIHeads):
sampling_ratio = cfg.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO
self.mask_on = cfg.MODEL.MASK_ON
self.enable_clustering = cfg.OWOD.ENABLE_CLUSTERING
self.compute_energy_flag = cfg.OWOD.COMPUTE_ENERGY
self.energy_save_path = os.path.join(cfg.OUTPUT_DIR, cfg.OWOD.ENERGY_SAVE_PATH)
# fmt: on
assert not cfg.MODEL.KEYPOINT_ON
assert len(self.in_features) == 1
@ -443,6 +447,13 @@ class Res5ROIHeads(ROIHeads):
location = '/home/fk1/workspace/OWOD/output/features/' + shortuuid.uuid() + '.pkl'
torch.save(data, location)
def compute_energy(self, predictions, proposals):
gt_classes = torch.cat([p.gt_classes for p in proposals])
logits = predictions[0]
data = (logits, gt_classes)
location = os.path.join(self.energy_save_path, shortuuid.uuid() + '.pkl')
torch.save(data, location)
def forward(self, images, features, proposals, targets=None):
"""
See :meth:`ROIHeads.forward`.
@ -466,6 +477,8 @@ class Res5ROIHeads(ROIHeads):
if self.enable_clustering:
self.box_predictor.update_feature_store(input_features, proposals)
del features
if self.compute_energy_flag:
self.compute_energy(predictions, proposals)
losses = self.box_predictor.losses(predictions, proposals, input_features)
if self.mask_on:
proposals, fg_selection_masks = select_foreground_proposals(