cropping.py 文件源码

def build_augmentation_graph(self):
        num_targets = len(self.dset.targets)

        # Outputs and queue of the data augmentation graph
        train_queue = tf.RandomShuffleQueue(
            self.config["queueing"]["random_queue_size"],
            self.config["queueing"]["min_size"],
            [tf.float32] + [tf.int32] * num_targets,
            shapes=self.input_variables["shapes"]["crops"]
        )
        augmented_op = imaging.augmentations.aument_multiple(
            self.input_variables["labeled_crops"][0],
            self.config["queueing"]["augmentation_workers"]
        )
        train_enqueue_op = train_queue.enqueue_many(
            [augmented_op] +
            self.input_variables["labeled_crops"][1:]
        )
        train_inputs = train_queue.dequeue() #_many(config["training"]["minibatch"])

        self.train_variables = {
            "image_batch":train_inputs[0],
            "queue":train_queue,
            "enqueue_op":train_enqueue_op
        }

        for i in range(num_targets):
            tname = "target_" + str(i)
            tgt = self.dset.targets[i]
            self.train_variables[tname] = tf.one_hot(train_inputs[i+1], tgt.shape[1])

    #################################################
    ## START TRAINING QUEUES
    #################################################