Merge pull request #164 from stanstarks/master

Make compatible with torchvision 0.4.0

fcos_core/data/datasets/coco.py

@@ -61,7 +61,7 @@ class COCODataset(torchvision.datasets.coco.CocoDetection):
             v: k for k, v in self.json_category_id_to_contiguous_id.items()
         }
         self.id_to_img_map = {k: v for k, v in enumerate(self.ids)}
-        self.transforms = transforms
+        self._transforms = transforms
 
     def __getitem__(self, idx):
         img, anno = super(COCODataset, self).__getitem__(idx)
@@ -90,8 +90,8 @@ class COCODataset(torchvision.datasets.coco.CocoDetection):
 
         target = target.clip_to_image(remove_empty=True)
 
-        if self.transforms is not None:
-            img, target = self.transforms(img, target)
+        if self._transforms is not None:
+            img, target = self._transforms(img, target)
 
         return img, target, idx
 

fcos_core/data/datasets/evaluation/voc/voc_eval.py

@@ -16,8 +16,6 @@ def do_voc_evaluation(dataset, predictions, output_folder, logger):
     gt_boxlists = []
     for image_id, prediction in enumerate(predictions):
         img_info = dataset.get_img_info(image_id)
-        if len(prediction) == 0:
-            continue
         image_width = img_info["width"]
         image_height = img_info["height"]
         prediction = prediction.resize((image_width, image_height))

fcos_core/data/transforms/transforms.py

@@ -57,9 +57,12 @@ class Resize(object):
     def __call__(self, image, target=None):
         size = self.get_size(image.size)
         image = F.resize(image, size)
-        if target is None:
+        if isinstance(target, list):
+            target = [t.resize(image.size) for t in target]
+        elif target is None:
             return image
-        target = target.resize(image.size)
+        else:
+            target = target.resize(image.size)
         return image, target
 
 

fcos_core/structures/segmentation_mask.py

 import cv2
-
+import copy
 import torch
 import numpy as np
 from fcos_core.layers.misc import interpolate
@@ -195,7 +195,7 @@ class PolygonInstance(object):
             polygons = valid_polygons
 
         elif isinstance(polygons, PolygonInstance):
-            polygons = [p.clone() for p in polygons.polygons]
+            polygons = copy.copy(polygons.polygons)
         else:
             RuntimeError(
                 "Type of argument `polygons` is not allowed:%s" % (type(polygons))
@@ -414,7 +414,8 @@ class PolygonList(object):
         else:
             # advanced indexing on a single dimension
             selected_polygons = []
-            if isinstance(item, torch.Tensor) and item.dtype == torch.uint8:
+            if isinstance(item, torch.Tensor) and \
+               item.dtype == torch.uint8 or item.dtype == torch.bool:
                 item = item.nonzero()
                 item = item.squeeze(1) if item.numel() > 0 else item
                 item = item.tolist()
@@ -522,7 +523,9 @@ class SegmentationMask(object):
             next_segmentation = self.__getitem__(self.iter_idx)
             self.iter_idx += 1
             return next_segmentation
-        raise StopIteration
+        raise StopIteration()
+        
+    next = __next__  # Python 2 compatibility
 
     def __repr__(self):
         s = self.__class__.__name__ + "("