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tinyObjectsValidation.py
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tinyObjectsValidation.py
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import os
import re
import numpy as np
import pandas as pd
from pathlib import Path
import torch
from torch import Tensor
from torch.utils.data import DataLoader
from opts import Opts
from lib.data import getMyDataset
from lib.trainer import Inferencer
from lib.params_init import segOptInit, initSegModelbyCSV
from lib.metrics import manageMetricMM, computeMaskRegion, measureEAM, computeAE
from lib.utils import (
getImgPath, getSetPath, getExpPath, getOnlyFileDirs,
getKeepTinyIds, averageUniqueLog, writeCsv,
)
@torch.inference_mode()
def initAndRunTinyValer(opt, FirstKeepIds, FolderPath, Split, ValSplit):
opt = initSegModelbyCSV(opt, FolderPath, ValSplit)
opt = segOptInit(opt)
Engine = Inferencer(opt, Split)
# dataset attribute should not be set after DataLoader is initialized
KeepTestSet = [Engine.TestSet[i] for i in FirstKeepIds]
TestImgs = getMyDataset(opt, KeepTestSet, IsTraining=False)
Engine.InferDL = DataLoader(
TestImgs, Engine.LoaderBatch, num_workers=Engine.NumWorkers, shuffle=False,
pin_memory=Engine.PinMemory, collate_fn=Engine.CollateFn, drop_last=opt.drop_last
)
Target, Prediction = Engine.run()
return Target, Prediction, KeepTestSet
@torch.inference_mode()
def computeMetrics(Ratio, Prediction: Tensor, Target: Tensor, KeepIdx, Eps):
"""
Compute the calculated metrics restricted by in a size ratio.
It can also be used to inference metrics by giving ratio 100.
"""
Scale = 100.0
TinyPrediction = Prediction[KeepIdx]
TinyTarget = Target[KeepIdx]
AreaPred, AreaInter, AreaUnion, UnionCout = computeMaskRegion(TinyPrediction, TinyTarget)
IoU = AreaInter / AreaUnion
Dice = (2 * AreaInter) / (AreaUnion + AreaInter)
Precision = AreaInter / (AreaPred + Eps)
Recall = AreaInter / (AreaUnion - AreaPred + AreaInter + Eps)
F2Score = 5 *(Precision * Recall) / (4 * Precision + Recall + Eps)
print("Tiny_%s IoU: " % str(Ratio), IoU) # r can be float number
# print("Tiny_%s Dice: " % str(Ratio), Dice)
mIoU = manageMetricMM(IoU, UnionCout, Scale=Scale)
mDice = manageMetricMM(Dice, UnionCout, Scale=Scale)
AvgPrecision = manageMetricMM(Precision, UnionCout, Scale=Scale)
AvgRecall = manageMetricMM(Recall, UnionCout, Scale=Scale)
AvgF2Score = manageMetricMM(F2Score, UnionCout, Scale=Scale)
mEM = measureEAM(TinyPrediction, TinyTarget) / len(KeepIdx) * Scale
mAE = computeAE(TinyPrediction, TinyTarget) / len(KeepIdx)
return mIoU, mDice, AvgPrecision, AvgRecall, AvgF2Score, mEM, mAE
@torch.inference_mode()
def traversalTinyRatio(
opt, TinyRatios, AreaRatios,
Exp, Prediction, Target, KeepTestSet,
OutFilePath, Eps, KeepThred=False,
):
for r in TinyRatios:
# keep only tiny object and those with 0 area
KeepIdx = np.where(AreaRatios < r)[0] if r < 100 or not KeepThred \
else np.arange(len(AreaRatios))
if not KeepIdx.any():
continue
mIoU, mDice, AvgPrecision, AvgRecall, AvgF2Score, mEM, mAE = \
computeMetrics(r, Prediction, Target, KeepIdx, Eps)
# write into csv
LogField = [
"exp", "SegModel", "SegHead", "Weight", "TinyRatio",
"FeatureGuide", "SvAttn", "FGViT", "FGNoStage5", "FGLink",
"mIoU", "mDice", "mEM", "mAE", "AvgRecall", "AvgPrecision", "AvgF2Score",
]
LogInfo = [
re.findall(r"\d+", Exp)[0], opt.seg_model_name, opt.seg_head_name, Path(opt.pretrained_weight).stem, r,
opt.seg_feature_guide, opt.fg_svattn, opt.fg_vit, opt.fg_nostage5, opt.fg_link,
mIoU, mDice, mEM, mAE, AvgRecall, AvgPrecision, AvgF2Score,
]
writeCsv(OutFilePath, LogField, LogInfo)
# break loop if reaches maximum length of dataset
if len(KeepTestSet) == len(KeepIdx):
break
@torch.inference_mode()
def computeRatioRangeMetrics(
opt, TinyRatios, AreaRatios,
FirstKeepIds, Exp, ExpPath, Split=0,
OutFilePath="tiny_metrics.csv", Eps=1e-7,
):
"""
Compute the calculated metrics only in tiny objects (use ratio).
It can also be used to inference metrics by giving 100 ratio.
"""
if not FirstKeepIds.any():
return
AreaRatios = AreaRatios[FirstKeepIds]
# get all valrpts
FolderPath = "%s/%s" % (ExpPath, Exp)
CSVPath = FolderPath + "/best_metrics.csv"
MetricCsv = pd.read_csv(CSVPath) # read best_metrics.csv
KFolds = MetricCsv["K-Fold"].tolist()
ValSplits = KFolds.copy()
if "Average" in ValSplits:
ValSplits.remove("Average")
ValSplits = [int(re.findall(r"valrpt_(\d+)", s)[0]) for s in ValSplits]
# append tiny metric values in metric csv file
for s in ValSplits:
Target, Prediction, KeepTestSet = \
initAndRunTinyValer(opt, FirstKeepIds, FolderPath, Split, s)
traversalTinyRatio(
opt, TinyRatios, AreaRatios,
Exp, Prediction, Target, KeepTestSet,
OutFilePath.replace(".csv", "_valrpt%d.csv" % s), Eps,
)
return ValSplits
if __name__ == "__main__":
opt = Opts().parse()
opt.gpus = "0" # "-1"
SetNames = [
"FIVES", "ISIC2018T1", "PolypGen",
"ATLAS", "KiTS23", "TissueNet",
"SpermHealth",
]
TinyRatios = [0.5, 1, 2, 3, 5, 10, 100]
RootPath = "exp/segmentation"
for n in SetNames:
opt.setname = n
# start printing
ExpPath, Exps = getExpPath(opt, RootPath)
if ExpPath is None:
continue
# remove exsited tiny_metrics
CurrentFiles = getOnlyFileDirs(ExpPath)
RemoveFiles = [f for f in CurrentFiles if "tiny_metrics" in f]
for f in RemoveFiles:
print("Remove %s" % f)
os.remove(f)
# output file
OutFilePath = "%s/tiny_metrics.csv" % ExpPath
if os.path.isfile(OutFilePath):
os.remove(OutFilePath)
# dataset
opt = segOptInit(opt) # init to get set path
TrainSet, TestSet = getImgPath(opt.dataset_path, opt.num_split, Mode=opt.get_path_mode)
TrainSet, TestSet = getSetPath(TrainSet, TestSet, Split=0) # train set can also be inferenced
# get only val/test mask ratios
AreaRatios, FirstKeepIds = getKeepTinyIds(TestSet, TinyRatios)
# inferencing
for i, e in enumerate(Exps):
ValSplits = computeRatioRangeMetrics(
opt, TinyRatios, AreaRatios, FirstKeepIds, e,
ExpPath=ExpPath, OutFilePath=OutFilePath,
)
if len(ValSplits) > 0:
# average all models' tiny metric into the same csv
MetadataGroup = []
for s in ValSplits:
Metadata = OutFilePath.replace(".csv", "_valrpt%d.csv" % s)
MetaCsv = pd.read_csv(Metadata)
MetadataGroup.append(MetaCsv.drop(columns=["Weight"]))
averageUniqueLog(MetadataGroup, ExpPath, "tiny_metrics_avg")