YOLOv5 AutoCache Update

segment/train.py

@@ -474,7 +474,7 @@ def parse_opt(known=False):
     parser.add_argument('--noplots', action='store_true', help='save no plot files')
     parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations')
     parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
-    parser.add_argument('--cache', type=str, nargs='?', const='ram', help='--cache images in "ram" (default) or "disk"')
+    parser.add_argument('--cache', type=str, nargs='?', const='ram', help='image --cache ram/disk')
     parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training')
     parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
     parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%')

train.py

@@ -444,7 +444,7 @@ def parse_opt(known=False):
     parser.add_argument('--noplots', action='store_true', help='save no plot files')
     parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations')
     parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
-    parser.add_argument('--cache', type=str, nargs='?', const='ram', help='--cache images in "ram" (default) or "disk"')
+    parser.add_argument('--cache', type=str, nargs='?', const='ram', help='image --cache ram/disk')
     parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training')
     parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
     parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%')

utils/__init__.py

@@ -47,7 +47,6 @@ def notebook_init(verbose=True):
     from utils.general import check_font, check_requirements, is_colab
     from utils.torch_utils import select_device  # imports
 
-    check_requirements(('psutil', 'IPython'))
     check_font()
 
     import psutil

utils/dataloaders.py

@@ -19,6 +19,7 @@
 from urllib.parse import urlparse
 
 import numpy as np
+import psutil
 import torch
 import torch.nn.functional as F
 import torchvision
@@ -30,8 +31,8 @@
 from utils.augmentations import (Albumentations, augment_hsv, classify_albumentations, classify_transforms, copy_paste,
                                  cutout, letterbox, mixup, random_perspective)
 from utils.general import (DATASETS_DIR, LOGGER, NUM_THREADS, check_dataset, check_requirements, check_yaml, clean_str,
-                           cv2, is_colab, is_kaggle, segments2boxes, unzip_file, xyn2xy, xywh2xyxy, xywhn2xyxy,
-                           xyxy2xywhn)
+                           colorstr, cv2, is_colab, is_kaggle, segments2boxes, unzip_file, xyn2xy, xywh2xyxy,
+                           xywhn2xyxy, xyxy2xywhn)
 from utils.torch_utils import torch_distributed_zero_first
 
 # Parameters
@@ -564,24 +565,43 @@ def __init__(self,
 
             self.batch_shapes = np.ceil(np.array(shapes) * img_size / stride + pad).astype(int) * stride
 
-        # Cache images into RAM/disk for faster training (WARNING: large datasets may exceed system resources)
+        # Cache images into RAM/disk for faster training
+        if cache_images == 'ram' and not self.check_cache_ram(prefix=prefix):
+            cache_images = False
         self.ims = [None] * n
         self.npy_files = [Path(f).with_suffix('.npy') for f in self.im_files]
         if cache_images:
-            gb = 0  # Gigabytes of cached images
+            b, gb = 0, 1 << 30  # bytes of cached images, bytes per gigabytes
             self.im_hw0, self.im_hw = [None] * n, [None] * n
             fcn = self.cache_images_to_disk if cache_images == 'disk' else self.load_image
             results = ThreadPool(NUM_THREADS).imap(fcn, range(n))
             pbar = tqdm(enumerate(results), total=n, bar_format=BAR_FORMAT, disable=LOCAL_RANK > 0)
             for i, x in pbar:
                 if cache_images == 'disk':
-                    gb += self.npy_files[i].stat().st_size
+                    b += self.npy_files[i].stat().st_size
                 else:  # 'ram'
                     self.ims[i], self.im_hw0[i], self.im_hw[i] = x  # im, hw_orig, hw_resized = load_image(self, i)
-                    gb += self.ims[i].nbytes
-                pbar.desc = f'{prefix}Caching images ({gb / 1E9:.1f}GB {cache_images})'
+                    b += self.ims[i].nbytes
+                pbar.desc = f'{prefix}Caching images ({b / gb:.1f}GB {cache_images})'
             pbar.close()
 
+    def check_cache_ram(self, safety_margin=0.1, prefix=''):
+        # Check image caching requirements vs available memory
+        b, gb = 0, 1 << 30  # bytes of cached images, bytes per gigabytes
+        n = min(self.n, 30)  # extrapolate from 30 random images
+        for _ in range(n):
+            im = cv2.imread(random.choice(self.im_files))  # sample image
+            ratio = self.img_size / max(im.shape[0], im.shape[1])  # max(h, w)  # ratio
+            b += im.nbytes * ratio ** 2
+        mem_required = b * self.n / n  # GB required to cache dataset into RAM
+        mem = psutil.virtual_memory()
+        cache = mem_required * (1 + safety_margin) < mem.available  # to cache or not to cache, that is the question
+        if not cache:
+            LOGGER.info(f"{prefix}{mem_required / gb:.1f}GB RAM required, "
+                        f"{mem.available / gb:.1f}/{mem.total / gb:.1f}GB available, "
+                        f"{'caching images ✅' if cache else 'not caching images ⚠️'}")
+        return cache
+
     def cache_labels(self, path=Path('./labels.cache'), prefix=''):
         # Cache dataset labels, check images and read shapes
         x = {}  # dict

utils/general.py

@@ -374,7 +374,7 @@ def check_requirements(requirements=ROOT / 'requirements.txt', exclude=(), insta
     if s and install and AUTOINSTALL:  # check environment variable
         LOGGER.info(f"{prefix} YOLOv5 requirement{'s' * (n > 1)} {s}not found, attempting AutoUpdate...")
         try:
-            assert check_online(), "AutoUpdate skipped (offline)"
+            # assert check_online(), "AutoUpdate skipped (offline)"
             LOGGER.info(check_output(f'pip install {s} {cmds}', shell=True).decode())
             source = file if 'file' in locals() else requirements
             s = f"{prefix} {n} package{'s' * (n > 1)} updated per {source}\n" \