DataLoader ( val_set, batch_size = 4, shuffle = False, collate_fn = collate_fn ) DataLoader ( train_set, batch_size = 4, shuffle = False, collate_fn = collate_fn ) val_dataloader = torch. random_split ( dataset, ) # We sample 10% of the images as a validation dataset train_dataloader = torch. stack ( images, dim = 0 ) return images, targets, metadatas dataset = WheatDataset ( "train.csv", "train", transform = transform ) train_size = int ( len ( dataset ) * 0.9 ) val_size = len ( dataset ) - train_size train_set, val_set = torch.
:param batch: an iterable of N sets from _getitem_() :return: a tensor of images, lists of varying-size tensors of bounding boxes, labels, and difficulties """ images = list () targets = list () metadatas = list () for i, t, m in batch : images.
zeros (( 0, 4 ))ĭef collate_fn ( batch ): """ Since each image may have a different number of objects, we need a collate function (to be passed to the DataLoader). empty boxes will be returned" ) return np. split ( " " )]) return boxes except : print ( BoxesString ) print ( "Submission is not well formatted. zeros (( 0, 4 )) else : try : boxes = np. zeros (( 0, 4 )) return image, bboxes, domain def decodeString ( self, BoxesString ): """ Small method to decode the BoxesString """ if BoxesString = "no_box" : return np. transform ( image = image, bboxes = bboxes, class_labels = * len ( bboxes )) #Albumentations can transform images and boxes image = transformed bboxes = transformed if len ( bboxes ) > 0 : bboxes = torch. COLOR_BGR2RGB ) # Opencv open images in BGR mode by default if self. domain_list # We don't use the domain information but you could ! bboxes = self. image_list ) def _getitem_ ( self, idx ): imgp = str ( self. transform = transform def _len_ ( self ): return len ( self. root_dir = Path ( root_dir ) annotations = pd. transform (callable, optional): Optional data augmentation to be applied on a sample. root_dir (string): Directory with all the images. Class WheatDataset ( Dataset ): """A dataset example for GWC 2021 competition.""" def _init_ ( self, csv_file, root_dir, transform = None ): """ Args: csv_file (string): Path to the csv file with annotations.
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