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hyperparameter expansion to flips, perspective, mixup

pull/498/head
Glenn Jocher 2020-08-01 13:47:54 -07:00
parent 6f08e8bcce
commit 127cbeb3f5
2 changed files with 32 additions and 32 deletions
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23
train.py
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@ -16,25 +16,29 @@ from utils.datasets import *
from utils.utils import *
# Hyperparameters
hyp = {'optimizer': 'SGD', # ['adam', 'SGD', None] if none, default is SGD
hyp = {'optimizer': 'SGD', # ['Adam', 'SGD', ...] from torch.optim
'lr0': 0.01, # initial learning rate (SGD=1E-2, Adam=1E-3)
'momentum': 0.937, # SGD momentum/Adam beta1
'weight_decay': 5e-4, # optimizer weight decay
'giou': 0.05, # giou loss gain
'giou': 0.05, # GIoU loss gain
'cls': 0.5, # cls loss gain
'cls_pw': 1.0, # cls BCELoss positive_weight
'obj': 1.0, # obj loss gain (*=img_size/320 if img_size != 320)
'obj': 1.0, # obj loss gain (scale with pixels)
'obj_pw': 1.0, # obj BCELoss positive_weight
'iou_t': 0.20, # iou training threshold
'iou_t': 0.20, # IoU training threshold
'anchor_t': 4.0, # anchor-multiple threshold
'fl_gamma': 0.0, # focal loss gamma (efficientDet default is gamma=1.5)
'fl_gamma': 0.0, # focal loss gamma (efficientDet default gamma=1.5)
'hsv_h': 0.015, # image HSV-Hue augmentation (fraction)
'hsv_s': 0.7, # image HSV-Saturation augmentation (fraction)
'hsv_v': 0.4, # image HSV-Value augmentation (fraction)
'degrees': 0.0, # image rotation (+/- deg)
'translate': 0.5, # image translation (+/- fraction)
'scale': 0.5, # image scale (+/- gain)
'shear': 0.0} # image shear (+/- deg)
'shear': 0.0, # image shear (+/- deg)
'perspective': 0.0, # image perspective (+/- fraction), range 0-0.001
'flipud': 0.0, # image flip up-down (probability)
'fliplr': 0.5, # image flip left-right (probability)
'mixup': 0.0} # image mixup (probability)
def train(hyp, tb_writer, opt, device):
@ -47,8 +51,7 @@ def train(hyp, tb_writer, opt, device):
results_file = log_dir + os.sep + 'results.txt'
epochs, batch_size, total_batch_size, weights, rank = \
opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.local_rank
# TODO: Init DDP logging. Only the first process is allowed to log.
# Since I see lots of print here, the logging configuration is skipped here. We may see repeated outputs.
# TODO: Use DDP logging. Only the first process is allowed to log.
# Save run settings
with open(Path(log_dir) / 'hyp.yaml', 'w') as f:
@ -99,7 +102,7 @@ def train(hyp, tb_writer, opt, device):
else:
pg0.append(v) # all else
if hyp['optimizer'] == 'adam': # https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR
if hyp['optimizer'] == 'Adam':
optimizer = optim.Adam(pg0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999)) # adjust beta1 to momentum
else:
optimizer = optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)
@ -110,9 +113,9 @@ def train(hyp, tb_writer, opt, device):
del pg0, pg1, pg2
# Scheduler https://arxiv.org/pdf/1812.01187.pdf
# https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR
lf = lambda x: (((1 + math.cos(x * math.pi / epochs)) / 2) ** 1.0) * 0.8 + 0.2 # cosine
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
# https://discuss.pytorch.org/t/a-problem-occured-when-resuming-an-optimizer/28822
# plot_lr_scheduler(optimizer, scheduler, epochs)
# Load Model

41
utils/datasets.py
View File

@ -484,11 +484,11 @@ class LoadImagesAndLabels(Dataset): # for training/testing
shapes = None
# MixUp https://arxiv.org/pdf/1710.09412.pdf
# if random.random() < 0.5:
# img2, labels2 = load_mosaic(self, random.randint(0, len(self.labels) - 1))
# r = np.random.beta(0.3, 0.3) # mixup ratio, alpha=beta=0.3
# img = (img * r + img2 * (1 - r)).astype(np.uint8)
# labels = np.concatenate((labels, labels2), 0)
if random.random() < hyp['mixup']:
img2, labels2 = load_mosaic(self, random.randint(0, len(self.labels) - 1))
r = np.random.beta(8.0, 8.0) # mixup ratio, alpha=beta=8.0
img = (img * r + img2 * (1 - r)).astype(np.uint8)
labels = np.concatenate((labels, labels2), 0)
else:
# Load image
@ -517,7 +517,8 @@ class LoadImagesAndLabels(Dataset): # for training/testing
degrees=hyp['degrees'],
translate=hyp['translate'],
scale=hyp['scale'],
shear=hyp['shear'])
shear=hyp['shear'],
perspective=hyp['perspective'])
# Augment colorspace
augment_hsv(img, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v'])
@ -528,28 +529,23 @@ class LoadImagesAndLabels(Dataset): # for training/testing
nL = len(labels) # number of labels
if nL:
# convert xyxy to xywh
labels[:, 1:5] = xyxy2xywh(labels[:, 1:5])
# Normalize coordinates 0 - 1
labels[:, [2, 4]] /= img.shape[0] # height
labels[:, [1, 3]] /= img.shape[1] # width
labels[:, 1:5] = xyxy2xywh(labels[:, 1:5]) # convert xyxy to xywh
labels[:, [2, 4]] /= img.shape[0] # normalized height 0-1
labels[:, [1, 3]] /= img.shape[1] # normalized width 0-1
if self.augment:
# random left-right flip
lr_flip = True
if lr_flip and random.random() < 0.5:
img = np.fliplr(img)
if nL:
labels[:, 1] = 1 - labels[:, 1]
# random up-down flip
ud_flip = False
if ud_flip and random.random() < 0.5:
# flip up-down
if random.random() < hyp['flipud']:
img = np.flipud(img)
if nL:
labels[:, 2] = 1 - labels[:, 2]
# flip left-right
if random.random() < hyp['fliplr']:
img = np.fliplr(img)
if nL:
labels[:, 1] = 1 - labels[:, 1]
labels_out = torch.zeros((nL, 6))
if nL:
labels_out[:, 1:] = torch.from_numpy(labels)
@ -661,6 +657,7 @@ def load_mosaic(self, index):
translate=self.hyp['translate'],
scale=self.hyp['scale'],
shear=self.hyp['shear'],
perspective=self.hyp['perspective'],
border=self.mosaic_border) # border to remove
return img4, labels4