def create_coco_loader(*paths): transform = utils.get_transform(config.image_size, config.central_fraction) datasets = [data.CocoImages(path, transform=transform) for path in paths] dataset = data.Composite(*datasets) data_loader = torch.utils.data.DataLoader( dataset, batch_size=config.preprocess_batch_size, num_workers=config.data_workers, shuffle=False, pin_memory=True, ) return data_loader
def create_coco_loader(*paths): transform = utils.get_transform(config.image_size, config.central_fraction) datasets = [data.CocoImages(path, transform=transform) for path in paths] #ipdb.set_trace() ## datasets[0].__getitem__(116591)[0] print the largest coco_id - this is within torch int64 bound! dataset = data.Composite(*datasets) data_loader = torch.utils.data.DataLoader( dataset, batch_size=config.preprocess_batch_size, num_workers=config.data_workers, shuffle=False, pin_memory=True, ) return data_loader
def create_data_loader(self, *paths): """ Create a united PyTorch COCO data loader for every given path in the arguments""" transform = utils.get_transform(self.image_size, self.keep_central_fraction) datasets = [ data.CocoImages(path, transform=transform) for path in paths ] dataset = data.Composite(*datasets) data_loader = torch.utils.data.DataLoader( dataset, batch_size=self.batch_size, num_workers=self.num_threads_to_use, shuffle=False, pin_memory=True, ) features_shape = (len(data_loader.dataset), config.output_features, config.output_size, config.output_size) return data_loader, features_shape