Feature python train.py --cache disk (#4049)

* Add cache-on-disk and cache-directory to cache images on disk

* Fix load_image with cache_on_disk

* Add no_cache flag for load_image

* Revert the parts('logging' and a new line) that do not need to be modified

* Add the assertion for shapes of cached images

* Add a suffix string for cached images

* Fix boundary-error of letterbox for load_mosaic

* Add prefix as cache-key of cache-on-disk

* Update cache-function on disk

* Add psutil in requirements.txt

* Update train.py

* Cleanup1

* Cleanup2

* Skip existing npy

* Include re-space

* Export return character fix

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

export.py

@@ -156,8 +156,8 @@ def run(weights='./yolov5s.pt',  # weights path
 
     # Finish
     print(f'\nExport complete ({time.time() - t:.2f}s)'
-          f"Results saved to {colorstr('bold', file.parent.resolve())}\n"
+          f"\nResults saved to {colorstr('bold', file.parent.resolve())}"
-          f'Visualize with https://netron.app')
+          f'\nVisualize with https://netron.app')
 
 
 def parse_opt():

train.py

@@ -201,7 +201,7 @@ def train(hyp,  # path/to/hyp.yaml or hyp dictionary
 
     # Trainloader
     train_loader, dataset = create_dataloader(train_path, imgsz, batch_size // WORLD_SIZE, gs, single_cls,
-                                              hyp=hyp, augment=True, cache=opt.cache_images, rect=opt.rect, rank=RANK,
+                                              hyp=hyp, augment=True, cache=opt.cache, rect=opt.rect, rank=RANK,
                                               workers=workers, image_weights=opt.image_weights, quad=opt.quad,
                                               prefix=colorstr('train: '))
     mlc = np.concatenate(dataset.labels, 0)[:, 0].max()  # max label class
@@ -211,7 +211,7 @@ def train(hyp,  # path/to/hyp.yaml or hyp dictionary
     # Process 0
     if RANK in [-1, 0]:
         val_loader = create_dataloader(val_path, imgsz, batch_size // WORLD_SIZE * 2, gs, single_cls,
-                                       hyp=hyp, cache=opt.cache_images and not noval, rect=True, rank=-1,
+                                       hyp=hyp, cache=None if noval else opt.cache, rect=True, rank=-1,
                                        workers=workers, pad=0.5,
                                        prefix=colorstr('val: '))[0]
 
@@ -389,7 +389,7 @@ def train(hyp,  # path/to/hyp.yaml or hyp dictionary
         # end epoch ----------------------------------------------------------------------------------------------------
     # end training -----------------------------------------------------------------------------------------------------
     if RANK in [-1, 0]:
-        LOGGER.info(f'{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.\n')
+        LOGGER.info(f'\n{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.')
         if not evolve:
             if is_coco:  # COCO dataset
                 for m in [last, best] if best.exists() else [last]:  # speed, mAP tests
@@ -430,7 +430,7 @@ def parse_opt(known=False):
     parser.add_argument('--noautoanchor', action='store_true', help='disable autoanchor check')
     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-images', action='store_true', help='cache images for faster training')
+    parser.add_argument('--cache', type=str, nargs='?', const='ram', help='--cache images in "ram" (default) or "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/datasets.py

@@ -455,16 +455,25 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
             self.batch_shapes = np.ceil(np.array(shapes) * img_size / stride + pad).astype(np.int) * stride
 
         # Cache images into memory for faster training (WARNING: large datasets may exceed system RAM)
-        self.imgs = [None] * n
+        self.imgs, self.img_npy = [None] * n, [None] * n
         if cache_images:
+            if cache_images == 'disk':
+                self.im_cache_dir = Path(Path(self.img_files[0]).parent.as_posix() + '_npy')
+                self.img_npy = [self.im_cache_dir / Path(f).with_suffix('.npy').name for f in self.img_files]
+                self.im_cache_dir.mkdir(parents=True, exist_ok=True)
             gb = 0  # Gigabytes of cached images
             self.img_hw0, self.img_hw = [None] * n, [None] * n
             results = ThreadPool(NUM_THREADS).imap(lambda x: load_image(*x), zip(repeat(self), range(n)))
             pbar = tqdm(enumerate(results), total=n)
             for i, x in pbar:
-                self.imgs[i], self.img_hw0[i], self.img_hw[i] = x  # img, hw_original, hw_resized = load_image(self, i)
+                if cache_images == 'disk':
-                gb += self.imgs[i].nbytes
+                    if not self.img_npy[i].exists():
-                pbar.desc = f'{prefix}Caching images ({gb / 1E9:.1f}GB)'
+                        np.save(self.img_npy[i].as_posix(), x[0])
+                    gb += self.img_npy[i].stat().st_size
+                else:
+                    self.imgs[i], self.img_hw0[i], self.img_hw[i] = x  # im, hw_orig, hw_resized = load_image(self, i)
+                    gb += self.imgs[i].nbytes
+                pbar.desc = f'{prefix}Caching images ({gb / 1E9:.1f}GB {cache_images})'
             pbar.close()
 
     def cache_labels(self, path=Path('./labels.cache'), prefix=''):
@@ -618,21 +627,25 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
 
 
 # Ancillary functions --------------------------------------------------------------------------------------------------
-def load_image(self, index):
+def load_image(self, i):
-    # loads 1 image from dataset, returns img, original hw, resized hw
+    # loads 1 image from dataset index 'i', returns im, original hw, resized hw
-    img = self.imgs[index]
+    im = self.imgs[i]
-    if img is None:  # not cached
+    if im is None:  # not cached in ram
-        path = self.img_files[index]
+        npy = self.img_npy[i]
-        img = cv2.imread(path)  # BGR
+        if npy and npy.exists():  # load npy
-        assert img is not None, 'Image Not Found ' + path
+            im = np.load(npy)
-        h0, w0 = img.shape[:2]  # orig hw
+        else:  # read image
+            path = self.img_files[i]
+            im = cv2.imread(path)  # BGR
+            assert im is not None, 'Image Not Found ' + path
+        h0, w0 = im.shape[:2]  # orig hw
         r = self.img_size / max(h0, w0)  # ratio
         if r != 1:  # if sizes are not equal
-            img = cv2.resize(img, (int(w0 * r), int(h0 * r)),
+            im = cv2.resize(im, (int(w0 * r), int(h0 * r)),
-                             interpolation=cv2.INTER_AREA if r < 1 and not self.augment else cv2.INTER_LINEAR)
+                            interpolation=cv2.INTER_AREA if r < 1 and not self.augment else cv2.INTER_LINEAR)
-        return img, (h0, w0), img.shape[:2]  # img, hw_original, hw_resized
+        return im, (h0, w0), im.shape[:2]  # im, hw_original, hw_resized
     else:
-        return self.imgs[index], self.img_hw0[index], self.img_hw[index]  # img, hw_original, hw_resized
+        return self.imgs[i], self.img_hw0[i], self.img_hw[i]  # im, hw_original, hw_resized
 
 
 def load_mosaic(self, index):