update segment training (#57)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: ayush chaurasia <ayush.chaurarsia@gmail.com>

ultralytics/yolo/data/augment.py

@@ -578,8 +578,8 @@ class Albumentations:
             # TODO: add supports of segments and keypoints
             if self.transform and random.random() < self.p:
                 new = self.transform(image=im, bboxes=bboxes, class_labels=cls)  # transformed
-            labels["img"] = new["image"]
-            labels["cls"] = np.array(new["class_labels"])
+                labels["img"] = new["image"]
+                labels["cls"] = np.array(new["class_labels"])
             labels["instances"].update(bboxes=bboxes)
         return labels
 
@@ -635,7 +635,7 @@ class Format:
     def _format_img(self, img):
         if len(img.shape) < 3:
             img = np.expand_dims(img, -1)
-        img = np.ascontiguousarray(img.transpose(2, 0, 1))
+        img = np.ascontiguousarray(img.transpose(2, 0, 1)[::-1])
         img = torch.from_numpy(img)
         return img
 

ultralytics/yolo/data/base.py

@@ -151,7 +151,7 @@ class BaseDataset(Dataset):
         bi = np.floor(np.arange(self.ni) / self.batch_size).astype(int)  # batch index
         nb = bi[-1] + 1  # number of batches
 
-        s = np.array([x["shape"] for x in self.labels])  # hw
+        s = np.array([x.pop("shape") for x in self.labels])  # hw
         ar = s[:, 0] / s[:, 1]  # aspect ratio
         irect = ar.argsort()
         self.im_files = [self.im_files[i] for i in irect]

ultralytics/yolo/data/build.py

@@ -5,7 +5,7 @@ import numpy as np
 import torch
 from torch.utils.data import DataLoader, dataloader, distributed
 
-from ..utils import LOGGER
+from ..utils import LOGGER, colorstr
 from ..utils.torch_utils import torch_distributed_zero_first
 from .dataset import ClassificationDataset, YOLODataset
 from .utils import PIN_MEMORY, RANK
@@ -52,53 +52,36 @@ def seed_worker(worker_id):
     random.seed(worker_seed)
 
 
-# TODO: we can inject most args from a config file
-def build_dataloader(
-    img_path,
-    img_size,  #
-    batch_size,  #
-    single_cls=False,  #
-    hyp=None,  #
-    augment=False,
-    cache=False,  #
-    image_weights=False,  #
-    stride=32,
-    label_path=None,
-    pad=0.0,
-    rect=False,
-    rank=-1,
-    workers=8,
-    prefix="",
-    shuffle=False,
-    use_segments=False,
-    use_keypoints=False,
-):
-    if rect and shuffle:
+def build_dataloader(cfg, batch_size, img_path, stride=32, label_path=None, rank=-1, mode="train"):
+    assert mode in ["train", "val"]
+    shuffle = mode == "train"
+    if cfg.rect and shuffle:
         LOGGER.warning("WARNING ⚠️ --rect is incompatible with DataLoader shuffle, setting shuffle=False")
         shuffle = False
     with torch_distributed_zero_first(rank):  # init dataset *.cache only once if DDP
         dataset = YOLODataset(
             img_path=img_path,
-            img_size=img_size,
-            batch_size=batch_size,
             label_path=label_path,
-            augment=augment,  # augmentation
-            hyp=hyp,
-            rect=rect,  # rectangular batches
-            cache=cache,
-            single_cls=single_cls,
+            img_size=cfg.img_size,
+            batch_size=batch_size,
+            augment=True if mode == "train" else False,  # augmentation
+            hyp=cfg.get("augment_hyp", None),
+            rect=cfg.rect if mode == "train" else True,  # rectangular batches
+            cache=None if cfg.noval else cfg.get("cache", None),
+            single_cls=cfg.get("single_cls", False),
             stride=int(stride),
-            pad=pad,
-            prefix=prefix,
-            use_segments=use_segments,
-            use_keypoints=use_keypoints,
+            pad=0.0 if mode == "train" else 0.5,
+            prefix=colorstr(f"{mode}: "),
+            use_segments=cfg.task == "segment",
+            use_keypoints=cfg.task == "keypoint",
         )
 
     batch_size = min(batch_size, len(dataset))
     nd = torch.cuda.device_count()  # number of CUDA devices
+    workers = cfg.workers if mode == "train" else cfg.workers * 2
     nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers])  # number of workers
     sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle)
-    loader = DataLoader if image_weights else InfiniteDataLoader  # only DataLoader allows for attribute updates
+    loader = DataLoader if cfg.image_weights else InfiniteDataLoader  # only DataLoader allows for attribute updates
     generator = torch.Generator()
     generator.manual_seed(6148914691236517205 + RANK)
     return (
@@ -118,6 +101,7 @@ def build_dataloader(
 
 
 # build classification
+# TODO: using cfg like `build_dataloader`
 def build_classification_dataloader(path,
                                     imgsz=224,
                                     batch_size=16,