From 3fb6cc81612975d8272d523c3c4535e63af6b3a3 Mon Sep 17 00:00:00 2001
From: Glenn Jocher <glenn.jocher@ultralytics.com>
Date: Sun, 26 Aug 2018 15:40:07 +0200
Subject: [PATCH] updates

---
 .gitignore        |  5 ++---
 train.py          |  2 +-
 utils/datasets.py | 19 ++++++++++---------
 3 files changed, 13 insertions(+), 13 deletions(-)

diff --git a/.gitignore b/.gitignore
index 4f0936fc..2d7e160b 100755
--- a/.gitignore
+++ b/.gitignore
@@ -11,11 +11,10 @@
 *.weights
 *.pt
 *.weights
-*results.txt
 !zidane_result.jpg
-#!coco_training_loss.png
-
+!coco_training_loss.png
 
+results.txt
 temp-plot.html
 
 # MATLAB GitIgnore -----------------------------------------------------------------------------------------------------
diff --git a/train.py b/train.py
index 493a28fd..07939283 100644
--- a/train.py
+++ b/train.py
@@ -178,7 +178,7 @@ def main(opt):
             os.system('cp checkpoints/latest.pt checkpoints/best.pt')
 
         # Save backup checkpoint
-        if (epoch > 0) & (epoch % 100 == 0):
+        if (epoch > 0) & (epoch % 10 == 0):
             os.system('cp checkpoints/latest.pt checkpoints/backup' + str(epoch) + '.pt')
 
     # Save final model
diff --git a/utils/datasets.py b/utils/datasets.py
index d6036d15..b7a7db41 100755
--- a/utils/datasets.py
+++ b/utils/datasets.py
@@ -61,14 +61,15 @@ class ImageFolder():  # for eval-only
 class ListDataset():  # for training
     def __init__(self, path, batch_size=1, img_size=608):
         self.path = path
-        #self.img_files = sorted(glob.glob('%s/*.*' % path))
+        # self.img_files = sorted(glob.glob('%s/*.*' % path))
         with open(path, 'r') as file:
             self.img_files = file.readlines()
 
         if platform == 'darwin':  # macos
-            self.img_files = [path.replace('\n', '').replace('/images','/Users/glennjocher/Downloads/DATA/coco/images') for path in self.img_files]
+            self.img_files = [path.replace('\n', '').replace('/images', '/Users/glennjocher/Downloads/DATA/coco/images')
+                              for path in self.img_files]
         else:
-            self.img_files = [path.replace('\n', '').replace('/images','../coco/images') for path in self.img_files]
+            self.img_files = [path.replace('\n', '').replace('/images', '../coco/images') for path in self.img_files]
 
         self.label_files = [path.replace('images', 'labels').replace('.png', '.txt').replace('.jpg', '.txt') for path in
                             self.img_files]
@@ -77,7 +78,7 @@ class ListDataset():  # for training
         self.nB = math.ceil(self.nF / batch_size)  # number of batches
         self.batch_size = batch_size
 
-        #assert self.nB > 0, 'No images found in path %s' % path
+        # assert self.nB > 0, 'No images found in path %s' % path
         self.height = img_size
 
         # RGB normalization values
@@ -86,8 +87,8 @@ class ListDataset():  # for training
 
     def __iter__(self):
         self.count = -1
-        # self.shuffled_vector = np.random.permutation(self.nF)  # shuffled vector
-        self.shuffled_vector = np.arange(self.nF)
+        self.shuffled_vector = np.random.permutation(self.nF)  # shuffled vector
+        # self.shuffled_vector = np.arange(self.nF)  # not shuffled
         return self
 
     def __next__(self):
@@ -110,7 +111,7 @@ class ListDataset():  # for training
             if img is None:
                 continue
 
-            augment_hsv = True
+            augment_hsv = False
             if augment_hsv:
                 # SV augmentation by 50%
                 fraction = 0.50
@@ -149,7 +150,7 @@ class ListDataset():  # for training
                 labels = np.array([])
 
             # Augment image and labels
-            img, labels, M = random_affine(img, targets=labels, degrees=(-10, 10), translate=(0.2, 0.2), scale=(0.8, 1.2))  # RGB
+            # img, labels, M = random_affine(img, targets=labels, degrees=(-10, 10), translate=(0.2, 0.2), scale=(0.8, 1.2))  # RGB
 
             plotFlag = False
             if plotFlag:
@@ -163,7 +164,7 @@ class ListDataset():  # for training
                 labels[:, 1:5] = xyxy2xywh(labels[:, 1:5].copy()) / height
 
             # random left-right flip
-            lr_flip = True
+            lr_flip = False
             if lr_flip & (random.random() > 0.5):
                 img = np.fliplr(img)
                 if nL > 0: