update ImageNetVID dataset

MichiganCOG · Aug 12, 2019 · c837df7 · c837df7
1 parent 54d9bae
commit c837df7
Showing 1 changed file with 0 additions and 86 deletions.
diff --git a/datasets/ImageNetVID.py b/datasets/ImageNetVID.py
@@ -88,7 +88,6 @@ def __getitem__(self, idx):
         ret_dict = dict() 
         ret_dict['data']       = vid_data 
         annot_dict = dict()
-        annot_dict['data'] = vid_data #TODO: Delete once done debugging
         annot_dict['xmin']        = xmin_data
         annot_dict['ymin']        = ymin_data
         annot_dict['xmax']        = xmax_data
@@ -100,88 +99,3 @@ def __getitem__(self, idx):
         ret_dict['annots']     = annot_dict
 
         return ret_dict
-
-
-
-class PreprocessTrain(object):
-    """
-    Container for all transforms used to preprocess clips for training in this dataset.
-    """
-    def __init__(self, **kwargs):
-        crop_type = kwargs['crop_type']
-        self.transforms = []
-
-        self.transforms.append(pt.ResizeClip(**kwargs))
-
-        if crop_type == 'Random':
-            self.transforms.append(pt.RandomCropClip(**kwargs))
-        elif crop_type=='RandomFrame':
-            self.transforms.append(pt.ApplyToClip(transform=torchvision.transforms.RandomCrop(**kwargs)))
-        elif crop_type == 'Center':
-            self.transforms.append(pt.CenterCropClip(**kwargs))
-
-        self.transforms.append(pt.RandomFlipClip(direction='h', p=0.5, **kwargs))
-        #self.transforms.append(pt.RandomRotateClip(**kwargs))
-        self.transforms.append(pt.ToTensorClip(**kwargs))
-
-
-
-    def __call__(self, input_data, bbox_data):
-        """
-        Preprocess the clip and the bbox data accordingly
-        Args:
-            input_data: List of PIL images containing clip frames 
-            bbox_data:  Numpy array containing bbox coordinates per object per frame 
-
-        Return:
-            input_data: Pytorch tensor containing the processed clip data 
-            bbox_data:  Numpy tensor containing the augmented bbox coordinates
-        """
-        for transform in self.transforms:
-            input_data, bbox_data = transform(input_data, bbox_data)
-
-
-
-        return input_data, bbox_data
-
-
-class PreprocessEval(object):
-    """
-    Container for all transforms used to preprocess clips for evaluation in this dataset.
-    """
-    def __init__(self, **kwargs):
-        crop_shape = kwargs['crop_shape']
-        crop_type = kwargs['crop_type']
-        resize_shape = kwargs['resize_shape']
-        self.transforms = []
-
-        if crop_type == 'Random':
-            self.transforms.append(pt.RandomCropClip(*crop_shape))
-        elif crop_type == 'Center':
-            self.transforms.append(pt.CenterCropClip(*crop_shape))
-
-        self.transforms.append(pt.ResizeClip(*resize_shape))
-        self.transforms.append(pt.ToTensorClip())
-
-
-
-    def __call__(self, input_data, bbox_data):
-        """
-        Preprocess the clip and the bbox data accordingly
-        Args:
-            input_data: List of PIL images containing clip frames 
-            bbox_data:  Numpy array containing bbox coordinates per object per frame 
-
-        Return:
-            input_data: Pytorch tensor containing the processed clip data 
-            bbox_data:  Numpy tensor containing the augmented bbox coordinates
-        """
-        for transform in self.transforms:
-            input_data, bbox_data = transform(input_data, bbox_data)
-
-        return input_data, bbox_data
-
-
-
-
-