hyperparameter expansion to flips, perspective, mixup

train.py

@@ -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