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

utils/datasets.py

@@ -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