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modify TIR channel expansion to be w/o augmentation

pull/2109/head
hanoch 2024-10-14 14:02:24 +03:00
parent abdcce0e70
commit e7c36bab68
12 changed files with 260 additions and 23 deletions
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3
data/hyp.tir_od.tiny_aug_no_inversion.yaml
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@ -30,7 +30,8 @@ copy_paste: 0.0 # image copy paste (probability)
paste_in: 0.0 # image copy paste (probability), use 0 for faster training : cutout
loss_ota: 0 #1 # use ComputeLossOTA, use 0 for faster training
inversion: 0 #opposite temperature
tir_channel_expansion: 0.3 #[0, 0.2, 0.5]
img_percentile_removal: 0.3
beta : 0.3
random_perspective : 0
gamma : 80 # percent
gamma_liklihood: 0.0

36
data/hyp.tir_od.tiny_aug_no_inversion_gamma.yaml 100644
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@ -0,0 +1,36 @@
lr0: 0.005 #0.001 # initial learning rate (SGD=1E-2, Adam=1E-3)
lrf: 0.01 # final OneCycleLR learning rate (lr0 * lrf)
momentum: 0.937 # SGD momentum/Adam beta1
weight_decay: 0.005 # optimizer weight decay 5e-4 It resolve mAP of overfitting test
warmup_epochs: 3.0 # warmup epochs (fractions ok)
warmup_momentum: 0.8 # warmup initial momentum
warmup_bias_lr: 0.001 #0.001 # warmup initial bias lr
box: 0.05 # box loss gain
cls: 0.5 # cls loss gain
cls_pw: 1.0 # cls BCELoss positive_weight
obj: 1.0 # obj loss gain (scale with pixels)
obj_pw: 1.0 # obj BCELoss positive_weight
iou_t: 0.60 # like the default in the code was 0.2 IoU training threshold
anchor_t: 4.0 # anchor-multiple threshold
anchors: 2 # anchors per output layer (0 to ignore) @@HK was 3
fl_gamma: 1.5 #1.5 # focal loss gamma (efficientDet default gamma=1.5)
hsv_h: 0.0 # image HSV-Hue augmentation (fraction)
hsv_s: 0.0 # image HSV-Saturation augmentation (fraction)
hsv_v: 0.0 # image HSV-Value augmentation (fraction)
degrees: 0 # image rotation (+/- deg)
translate: 0 # image translation (+/- fraction)
scale: 0 # image scale (+/- gain)
shear: 0.0 # image shear (+/- deg)
perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
flipud: 0.3 # image flip up-down (probability)
fliplr: 0.5 # image flip left-right (probability)
mosaic: 0.5 # image mosaic (probability)
mixup: 0.15 # image mixup (probability)
copy_paste: 0.0 # image copy paste (probability)
paste_in: 0.0 # image copy paste (probability), use 0 for faster training : cutout
loss_ota: 0 #1 # use ComputeLossOTA, use 0 for faster training
inversion: 0 #opposite temperature
img_percentile_removal: 0.3
beta : 0.3
random_perspective : 0
gamma : 80 # percent

36
data/hyp.tir_od.tiny_aug_no_inversion_gamma_nms_iou_0p2.yaml 100644
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@ -0,0 +1,36 @@
lr0: 0.005 #0.001 # initial learning rate (SGD=1E-2, Adam=1E-3)
lrf: 0.01 # final OneCycleLR learning rate (lr0 * lrf)
momentum: 0.937 # SGD momentum/Adam beta1
weight_decay: 0.005 # optimizer weight decay 5e-4 It resolve mAP of overfitting test
warmup_epochs: 3.0 # warmup epochs (fractions ok)
warmup_momentum: 0.8 # warmup initial momentum
warmup_bias_lr: 0.001 #0.001 # warmup initial bias lr
box: 0.05 # box loss gain
cls: 0.5 # cls loss gain
cls_pw: 1.0 # cls BCELoss positive_weight
obj: 1.0 # obj loss gain (scale with pixels)
obj_pw: 1.0 # obj BCELoss positive_weight
iou_t: 0.20 # was 0.2 IoU training threshold
anchor_t: 4.0 # anchor-multiple threshold
anchors: 2 # anchors per output layer (0 to ignore) @@HK was 3
fl_gamma: 1.5 #1.5 # focal loss gamma (efficientDet default gamma=1.5)
hsv_h: 0.0 # image HSV-Hue augmentation (fraction)
hsv_s: 0.0 # image HSV-Saturation augmentation (fraction)
hsv_v: 0.0 # image HSV-Value augmentation (fraction)
degrees: 0 # image rotation (+/- deg)
translate: 0 # image translation (+/- fraction)
scale: 0 # image scale (+/- gain)
shear: 0.0 # image shear (+/- deg)
perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
flipud: 0.3 # image flip up-down (probability)
fliplr: 0.5 # image flip left-right (probability)
mosaic: 0.5 # image mosaic (probability)
mixup: 0.15 # image mixup (probability)
copy_paste: 0.0 # image copy paste (probability)
paste_in: 0.0 # image copy paste (probability), use 0 for faster training : cutout
loss_ota: 0 #1 # use ComputeLossOTA, use 0 for faster training
inversion: 0 #opposite temperature
img_percentile_removal: 0.3
beta : 0.3
random_perspective : 0
gamma : 80 # percent

36
data/hyp.tir_od.tiny_aug_no_inversion_gamma_nms_iou_0p4.yaml 100644
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@ -0,0 +1,36 @@
lr0: 0.005 #0.001 # initial learning rate (SGD=1E-2, Adam=1E-3)
lrf: 0.01 # final OneCycleLR learning rate (lr0 * lrf)
momentum: 0.937 # SGD momentum/Adam beta1
weight_decay: 0.005 # optimizer weight decay 5e-4 It resolve mAP of overfitting test
warmup_epochs: 3.0 # warmup epochs (fractions ok)
warmup_momentum: 0.8 # warmup initial momentum
warmup_bias_lr: 0.001 #0.001 # warmup initial bias lr
box: 0.05 # box loss gain
cls: 0.5 # cls loss gain
cls_pw: 1.0 # cls BCELoss positive_weight
obj: 1.0 # obj loss gain (scale with pixels)
obj_pw: 1.0 # obj BCELoss positive_weight
iou_t: 0.40 # was 0.2 IoU training threshold
anchor_t: 4.0 # anchor-multiple threshold
anchors: 2 # anchors per output layer (0 to ignore) @@HK was 3
fl_gamma: 1.5 #1.5 # focal loss gamma (efficientDet default gamma=1.5)
hsv_h: 0.0 # image HSV-Hue augmentation (fraction)
hsv_s: 0.0 # image HSV-Saturation augmentation (fraction)
hsv_v: 0.0 # image HSV-Value augmentation (fraction)
degrees: 0 # image rotation (+/- deg)
translate: 0 # image translation (+/- fraction)
scale: 0 # image scale (+/- gain)
shear: 0.0 # image shear (+/- deg)
perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
flipud: 0.3 # image flip up-down (probability)
fliplr: 0.5 # image flip left-right (probability)
mosaic: 0.5 # image mosaic (probability)
mixup: 0.15 # image mixup (probability)
copy_paste: 0.0 # image copy paste (probability)
paste_in: 0.0 # image copy paste (probability), use 0 for faster training : cutout
loss_ota: 0 #1 # use ComputeLossOTA, use 0 for faster training
inversion: 0 #opposite temperature
img_percentile_removal: 0.3
beta : 0.3
random_perspective : 0
gamma : 80 # percent

36
data/hyp.tir_od.tiny_aug_no_inversion_gamma_no_warm.yaml 100644
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@ -0,0 +1,36 @@
lr0: 0.005 #0.001 # initial learning rate (SGD=1E-2, Adam=1E-3)
lrf: 0.01 # final OneCycleLR learning rate (lr0 * lrf)
momentum: 0.937 # SGD momentum/Adam beta1
weight_decay: 0.005 # optimizer weight decay 5e-4 It resolve mAP of overfitting test
warmup_epochs: 0.0 # warmup epochs (fractions ok)
warmup_momentum: 0.8 # warmup initial momentum
warmup_bias_lr: 0.001 #0.001 # warmup initial bias lr
box: 0.05 # box loss gain
cls: 0.5 # cls loss gain
cls_pw: 1.0 # cls BCELoss positive_weight
obj: 1.0 # obj loss gain (scale with pixels)
obj_pw: 1.0 # obj BCELoss positive_weight
iou_t: 0.60 # like the default in the code was 0.2 IoU training threshold
anchor_t: 4.0 # anchor-multiple threshold
anchors: 2 # anchors per output layer (0 to ignore) @@HK was 3
fl_gamma: 1.5 #1.5 # focal loss gamma (efficientDet default gamma=1.5)
hsv_h: 0.0 # image HSV-Hue augmentation (fraction)
hsv_s: 0.0 # image HSV-Saturation augmentation (fraction)
hsv_v: 0.0 # image HSV-Value augmentation (fraction)
degrees: 0 # image rotation (+/- deg)
translate: 0 # image translation (+/- fraction)
scale: 0 # image scale (+/- gain)
shear: 0.0 # image shear (+/- deg)
perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
flipud: 0.3 # image flip up-down (probability)
fliplr: 0.5 # image flip left-right (probability)
mosaic: 0.5 # image mosaic (probability)
mixup: 0.15 # image mixup (probability)
copy_paste: 0.0 # image copy paste (probability)
paste_in: 0.0 # image copy paste (probability), use 0 for faster training : cutout
loss_ota: 0 #1 # use ComputeLossOTA, use 0 for faster training
inversion: 0 #opposite temperature
img_percentile_removal: 0.3
beta : 0.3
random_perspective : 0
gamma : 80 # percent

37
data/hyp.tir_od.tiny_aug_no_inversion_no_warm.yaml 100644
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@ -0,0 +1,37 @@
lr0: 0.005 #0.001 # initial learning rate (SGD=1E-2, Adam=1E-3)
lrf: 0.01 # final OneCycleLR learning rate (lr0 * lrf)
momentum: 0.937 # SGD momentum/Adam beta1
weight_decay: 0.005 # optimizer weight decay 5e-4 It resolve mAP of overfitting test
warmup_epochs: 3.0 # warmup epochs (fractions ok)
warmup_momentum: 0.8 # warmup initial momentum
warmup_bias_lr: 0.001 #0.001 # warmup initial bias lr
box: 0.05 # box loss gain
cls: 0.5 # cls loss gain
cls_pw: 1.0 # cls BCELoss positive_weight
obj: 1.0 # obj loss gain (scale with pixels)
obj_pw: 1.0 # obj BCELoss positive_weight
iou_t: 0.20 # IoU training threshold
anchor_t: 4.0 # anchor-multiple threshold
anchors: 2 # anchors per output layer (0 to ignore) @@HK was 3
fl_gamma: 1.5 #1.5 # focal loss gamma (efficientDet default gamma=1.5)
hsv_h: 0.0 # image HSV-Hue augmentation (fraction)
hsv_s: 0.0 # image HSV-Saturation augmentation (fraction)
hsv_v: 0.0 # image HSV-Value augmentation (fraction)
degrees: 0 # image rotation (+/- deg)
translate: 0 # image translation (+/- fraction)
scale: 0 # image scale (+/- gain)
shear: 0.0 # image shear (+/- deg)
perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
flipud: 0.3 # image flip up-down (probability)
fliplr: 0.5 # image flip left-right (probability)
mosaic: 0.5 # image mosaic (probability)
mixup: 0.15 # image mixup (probability)
copy_paste: 0.0 # image copy paste (probability)
paste_in: 0.0 # image copy paste (probability), use 0 for faster training : cutout
loss_ota: 0 #1 # use ComputeLossOTA, use 0 for faster training
inversion: 0 #opposite temperature
img_percentile_removal: 0.3
beta : 0.3
random_perspective : 0
gamma : 80 # percent
gamma_liklihood: 0.0

3
data/hyp.tir_od.tiny_aug_no_inversion_paste_in.yaml
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@ -30,7 +30,8 @@ copy_paste: 0.0 # image copy paste (probability)
paste_in: 0.3 # image copy paste (probability), use 0 for faster training : cutout
loss_ota: 0 #1 # use ComputeLossOTA, use 0 for faster training
inversion: 0 #opposite temperature
tir_channel_expansion: 0.3 #[0, 0.2, 0.5]
img_percentile_removal: 0.3
beta : 0.3
random_perspective : 0
gamma : 80 # percent
gamma_liklihood: 0.25

3
tools/merge_results.py
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@ -2,12 +2,15 @@ import os
import pandas as pd
from argparse import ArgumentParser
import yaml
import warnings
warnings.warn = lambda *args,**kwargs: None
def process_class_stats(file_path):
columns = ['class_name', 'num_files', 'num_objects', 'precision', 'recall', 'map50', 'map']
# Read the text file into a pandas DataFrame
# df = pd.read_csv(file_path, delim_whitespace=True)
warnings.simplefilter# / warnings.catch_warnings
df = pd.read_csv(file_path, delim_whitespace=True, names=columns, header=None)
# Find the index where the last repetition of 'all' starts

1
tools/merge_run_summary_csv.sh
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@ -1,5 +1,6 @@
#!/bin/bash
source /home/hanoch/.virtualenvs/tir_od/bin/activate
cp -rp /home/hanoch/projects/tir_od/runs/train/* /mnt/Data/hanoch/runs/train/
if [ -z $1 ] ; then
python -u /home/hanoch/projects/tir_od/yolov7/tools/merge_results.py
else

1
train.py
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@ -56,6 +56,7 @@ task = Task.init(
task_name="train yolov7 with dummy test"
)
task.set_base_docker(docker_image="nvcr.io/nvidia/pytorch:24.09-py3")
gradient_clip_value = 100.0
def find_clipped_gradient_within_layer(model, gradient_clip_value):
margin_from_sum_abs = 1 / 3

89
utils/datasets.py
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@ -7,6 +7,7 @@ import os
import random
import shutil
import time
import warnings
from itertools import repeat
from multiprocessing.pool import ThreadPool
from pathlib import Path
@ -19,6 +20,9 @@ import torch.nn.functional as F
from PIL import Image, ExifTags
from torch.utils.data import Dataset
from tqdm import tqdm
# from torchvision.transforms.functional import adjust_gamma
from skimage.exposure import adjust_gamma
import albumentations as A
import pickle
from copy import deepcopy
@ -110,6 +114,11 @@ def scaling_image(img, scaling_type, percentile=0.03, beta=0.3):
def create_dataloader(path, imgsz, batch_size, stride, opt, hyp=None, augment=False, cache=False, pad=0.0, rect=False,
rank=-1, world_size=1, workers=8, image_weights=False, quad=False, prefix='',rel_path_images='', num_cls=-1):
# Make sure only the first process in DDP process the dataset first, and the following others can use the cache
if opt.gamma_aug_prob > 0:
hyp['gamma_liklihood'] = opt.gamma_aug_prob
print("", 100 * '==')
print('gamma_liklihood was overriden by optional value ', opt.gamma_aug_prob)
with torch_distributed_zero_first(rank):
dataset = LoadImagesAndLabels(path, imgsz, batch_size,
augment=augment, # augment images
@ -459,6 +468,9 @@ class LoadImagesAndLabels(Dataset): # for training/testing
self.is_tir_signal = not (no_tir_signal)
#self.albumentations = Albumentations() if augment else None
self.albumentations_gamma_contrast = Albumentations_gamma_contrast(alb_prob=hyp['gamma_liklihood'],
gamma_limit=[hyp['gamma'],
100 + 100-hyp['gamma']])
try:
f = [] # image files
@ -474,7 +486,7 @@ class LoadImagesAndLabels(Dataset): # for training/testing
if bool(rel_path_images):
f += [os.path.join(rel_path_images, x.replace('./', '')).rstrip() if x.startswith('./') else x for x in t] # local to global path
else:
f += [x.replace('./', parent) if x.startswith('./') else x for x in t] # local to global path
f += [x.replace('./', parent).rstrip() if x.startswith('./') else x for x in t] # local to global path
# f += [p.parent / x.lstrip(os.sep) for x in t] # local to global path (pathlib)
else:
@ -639,7 +651,7 @@ class LoadImagesAndLabels(Dataset): # for training/testing
index = self.indices[index] # linear, shuffled, or image_weights
hyp = self.hyp
mosaic = self.mosaic and random.random() < hyp['mosaic']
mosaic = self.mosaic and random.random() < hyp['mosaic'] and not(self.tir_channel_expansion)
if mosaic:
# Load mosaic
if random.random() < 0.8:
@ -671,6 +683,24 @@ class LoadImagesAndLabels(Dataset): # for training/testing
if labels.size: # normalized xywh to pixel xyxy format
labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1])
if self.tir_channel_expansion: # HK @@ according to the paper this CE is a sort of augmentation hence no need to preliminary augment. One of the channels are inversion hence avoid channel inversion aug
img = np.repeat(img[np.newaxis, :, :], 3, axis=0) # convert GL to RGB by replication
img_ce = np.zeros_like(img).astype('float64')
# CH1 hist equalization
img_chan = scaling_image(img[0, :, :], scaling_type=self.scaling_type,
percentile=0, beta=self.beta)
img_ce[0, :, :] = img_chan.astype('float64')
img_chan = scaling_image(img[1, :, :], scaling_type=self.scaling_type,
percentile=self.percentile, beta=self.beta)
img_ce[1, :, :] = img_chan.astype('float64')
img_chan = inversion_aug(img_ce[1, :, :]) # invert the DRC one
img_ce[2, :, :] = img_chan.astype('float64')
img = img_ce
if self.augment:
# Augment imagespace
if not mosaic:
@ -688,6 +718,8 @@ class LoadImagesAndLabels(Dataset): # for training/testing
#img, labels = self.albumentations(img, labels)
img = self.albumentations_gamma_contrast(img)
if hyp['hsv_h'] >0 or hyp['hsv_s'] >0 or hyp['hsv_v'] >0 :
# Augment colorspace
augment_hsv(img, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v'])
@ -727,29 +759,20 @@ class LoadImagesAndLabels(Dataset): # for training/testing
if nL:
labels[:, 1] = 1 - labels[:, 1]
# if random.random() < hyp['gamma_liklihood']:
# # if img.dtype == np.uint16 or img.dtype == np.uint8:
# # img = img/np.iinfo(img.dtype).max
# # if img.max() > 1.0:
# # warnings.warn("gamma correction operates over standartized images [0-1]!!!")
#
# img = adjust_gamma(img, hyp['gamma'], gain=1)
labels_out = torch.zeros((nL, 6))
if nL:
labels_out[:, 1:] = torch.from_numpy(labels)
if self.tir_channel_expansion:
img = np.repeat(img[np.newaxis, :, :], 3, axis=0) # convert GL to RGB by replication
img_ce = np.zeros_like(img).astype('float64')
# CH1 hist equalization
img_chan = scaling_image(img[0, :, :], scaling_type=self.scaling_type,
percentile=0, beta=self.beta)
img_ce[0, :, :] = img_chan.astype('float64')
img_chan = scaling_image(img[1, :, :], scaling_type=self.scaling_type,
percentile=self.percentile, beta=self.beta)
img_ce[1, :, :] = img_chan.astype('float64')
img_chan = inversion_aug(img_ce[1, :, :]) # invert the DRC one
img_ce[2, :, :] = img_chan.astype('float64')
img = img_ce
# tifffile.imwrite(os.path.join('/home/hanoch/projects/tir_od', 'img_ce.tiff'), 255*img.transpose(1,2,0).astype('uint8'))
else:
if not self.tir_channel_expansion:
if self.is_tir_signal:
img = np.repeat(img[np.newaxis, :, :], self.input_channels, axis=0) #convert GL to RGB by replication
else:
@ -1395,6 +1418,32 @@ def pastein(image, labels, sample_labels, sample_images, sample_masks):
return labels
import albumentations as A
class Albumentations_gamma_contrast:
# YOLOv5 Albumentations class (optional, only used if package is installed)
def __init__(self, alb_prob=0.01, gamma_limit=[80, 120]):
self.transform = None
self.transform = A.Compose([
# A.CLAHE(p=0.01),
A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2, p=alb_prob),
A.RandomGamma(gamma_limit=gamma_limit, p=alb_prob)])
# A.Blur(p=0.01),
# A.MedianBlur(p=0.01),
# A.ToGray(p=0.01),
# A.ImageCompression(quality_lower=75, p=0.01),],
# bbox_params=A.BboxParams(format='pascal_voc', label_fields=['class_labels']))
#logging.info(colorstr('albumentations: ') + ', '.join(f'{x}' for x in self.transform.transforms if x.p))
def __call__(self, im, p=1.0):
if self.transform and random.random() < p:
new = self.transform(image=im) # transformed
im = new['image']
return im
class Albumentations:
# YOLOv5 Albumentations class (optional, only used if package is installed)
def __init__(self):

2
utils/google_utils.py
View File

@ -18,7 +18,7 @@ def gsutil_getsize(url=''):
def attempt_download(file, repo='WongKinYiu/yolov7'):
# Attempt file download if does not exist
file = Path(str(file).strip().replace("'", '').lower())
file = Path(str(file).strip().replace("'", ''))
if not file.exists():
try: