Fix repeated UserWarning and add more flexibility to reference code for segmentation tasks (#2886)

* add a README for training object detection models

* replaced np.asarray with np.array to avoid warning messages

* added data-path for flexibility

* fixed a typo

references/segmentation/README.md

@@ -6,6 +6,12 @@ training and evaluation scripts to quickly bootstrap research.
 
 All models have been trained on 8x V100 GPUs.
 
+You must modify the following flags:
+
+`--data-path=/path/to/dataset`
+
+`--nproc_per_node=<number_of_gpus_available>`
+
 ## fcn_resnet50
 ```
 python -m torch.distributed.launch --nproc_per_node=8 --use_env train.py --lr 0.02 --dataset coco -b 4 --model fcn_resnet50 --aux-loss

references/segmentation/train.py

@@ -12,13 +12,13 @@ import transforms as T
 import utils
 
 
-def get_dataset(name, image_set, transform):
+def get_dataset(dir_path, name, image_set, transform):
     def sbd(*args, **kwargs):
         return torchvision.datasets.SBDataset(*args, mode='segmentation', **kwargs)
     paths = {
-        "voc": ('/datasets01/VOC/060817/', torchvision.datasets.VOCSegmentation, 21),
-        "voc_aug": ('/datasets01/SBDD/072318/', sbd, 21),
-        "coco": ('/datasets01/COCO/022719/', get_coco, 21)
+        "voc": (dir_path, torchvision.datasets.VOCSegmentation, 21),
+        "voc_aug": (dir_path, sbd, 21),
+        "coco": (dir_path, get_coco, 21)
     }
     p, ds_fn, num_classes = paths[name]
 
@@ -101,8 +101,8 @@ def main(args):
 
     device = torch.device(args.device)
 
-    dataset, num_classes = get_dataset(args.dataset, "train", get_transform(train=True))
-    dataset_test, _ = get_dataset(args.dataset, "val", get_transform(train=False))
+    dataset, num_classes = get_dataset(args.data_path, args.dataset, "train", get_transform(train=True))
+    dataset_test, _ = get_dataset(args.data_path, args.dataset, "val", get_transform(train=False))
 
     if args.distributed:
         train_sampler = torch.utils.data.distributed.DistributedSampler(dataset)
@@ -186,7 +186,8 @@ def parse_args():
     import argparse
     parser = argparse.ArgumentParser(description='PyTorch Segmentation Training')
 
-    parser.add_argument('--dataset', default='voc', help='dataset')
+    parser.add_argument('--data-path', default='/datasets01/COCO/022719/', help='dataset path')
+    parser.add_argument('--dataset', default='coco', help='dataset name')
     parser.add_argument('--model', default='fcn_resnet101', help='model')
     parser.add_argument('--aux-loss', action='store_true', help='auxiliar loss')
     parser.add_argument('--device', default='cuda', help='device')

references/segmentation/transforms.py

@@ -78,7 +78,7 @@ class CenterCrop(object):
 class ToTensor(object):
     def __call__(self, image, target):
         image = F.to_tensor(image)
-        target = torch.as_tensor(np.asarray(target), dtype=torch.int64)
+        target = torch.as_tensor(np.array(target), dtype=torch.int64)
         return image, target