张量流:如何从预测张量中检索信息?

我发现了一种用于语义分割的神经网络。网络工作得很好,我提供我的培训、验证和测试数据,然后得到输出(不同颜色的分割部分)。直到这里,一切都好。我在TensorFlow 1.7.0中使用Keras,启用了GPU。python版本是3.5

不过,我想实现的是访问像素组(段),以便获得它们的边界图像坐标,即形成预测图像中以绿色显示的段x边界的点数组。

怎么做?很明显,我不能把整个代码放在这里,但这里有一个片段,我应该修改它来实现我想要的:

我在我的 评价函数 以下内容:

    def evaluate(model_file):
    net = load_model(model_file, custom_objects={'iou_metric': create_iou_metric(1 + len(PART_NAMES)),
                                                 'acc_metric': create_accuracy_metric(1 + len(PART_NAMES), output_mode='pixelwise_mean')})

    img_size = net.input_shape[1]
    image_filename = lambda fp: fp + '.jpg'
    d_test_x = TensorResize((img_size, img_size))(ImageSource(TEST_DATA, image_filename=image_filename))
    d_test_x = PixelwiseSubstract([103.93, 116.78, 123.68], use_lane_names=['X'])(d_test_x)
    d_test_pred = Predict(net)(d_test_x)
    d_test_pred.metadata['properties'] = ['background'] + PART_NAMES

    d_x, d_y = process_data(VALIDATION_DATA, img_size)
    d_x = PixelwiseSubstract([103.93, 116.78, 123.68], use_lane_names=['X'])(d_x)
    d_y = AddBackgroundMap(use_lane_names=['Y'])(d_y)

    d_train = Join()([d_x, d_y])
    print('losses:', net.evaluate_generator(d_train.batch_array_tuple_generator(batch_size=3), 3))

    # the tensor which needs to be modified
    pred_y = Predict(net)(d_x)
    Visualize(('slices', 'labels'))(Join()([d_test_x, d_test_pred]))
    Visualize(('slices', 'labels', 'labels'))(Join()([d_x, pred_y, d_y]))

对于predict函数,下面是片段:

或者,我发现通过使用以下方法,可以访问张量:

#    for sample_img, in d_x.batch_array_tuple_generator(batch_size=3, n_samples=5):
#        aa = net.predict(sample_img)
#        indexes = np.argmax(aa,axis=3)
#        print(indexes)
#        import pdb
#        pdb.set_trace()

但我不知道这是怎么回事,我从来没用过PDB,所以不知道。

如果有人想看 训练功能 ,这里是:

def train(model_name='refine_res', k=3, recompute=False, img_size=224,
        epochs=10, train_decoder_only=False, augmentation_boost=2, learning_rate=0.001,
        opt='rmsprop'):

    print("Traning on: " + str(PART_NAMES))
    print("In Total: " + str(1 + len(PART_NAMES)) + " parts.")

    metrics = [create_iou_metric(1 + len(PART_NAMES)),
               create_accuracy_metric(1 + len(PART_NAMES), output_mode='pixelwise_mean')]

    if model_name == 'dummy':
        net = build_dummy((224, 224, 3), 1 + len(PART_NAMES))  # 1+ because background class
    elif model_name == 'refine_res':
        net = build_resnet50_upconv_refine((img_size, img_size, 3), 1 + len(PART_NAMES), k=k, optimizer=opt, learning_rate=learning_rate, softmax_top=True,
                                           objective_function=categorical_crossentropy,
                                           metrics=metrics, train_full=not train_decoder_only)
    elif model_name == 'vgg_upconv':
        net = build_vgg_upconv((img_size, img_size, 3), 1 + len(PART_NAMES), k=k, optimizer=opt, learning_rate=learning_rate, softmax_top=True,
                               objective_function=categorical_crossentropy,metrics=metrics, train_full=not train_decoder_only)
    else:
        net = load_model(model_name)

    d_x, d_y = process_data(TRAINING_DATA, img_size, recompute=recompute, ignore_cache=False)
    d = Join()([d_x, d_y])

    # create more samples by rotating top view images and translating
    images_to_be_rotated = {}
    factor = 5
    for root, dirs, files in os.walk(TRAINING_DATA, topdown=False):
        for name in dirs:
            format = str(name + '/' + name)  # construct the format of foldername/foldername
            images_to_be_rotated.update({format: factor})

    d_aug = ImageAugmentation(factor_per_filepath_prefix=images_to_be_rotated, rotation_variance=90, recalc_base_seed=True)(d)
    d_aug = ImageAugmentation(factor=3 * augmentation_boost, color_interval=0.03, shift_interval=0.1, contrast=0.4,  recalc_base_seed=True, use_lane_names=['X'])(d_aug)
    d_aug = ImageAugmentation(factor=2, rotation_variance=20, recalc_base_seed=True)(d_aug)
    d_aug = ImageAugmentation(factor=7 * augmentation_boost, rotation_variance=10, translation=35, mirror=True, recalc_base_seed=True)(d_aug)

    # apply augmentation on the images of the training dataset only

    d_aug = AddBackgroundMap(use_lane_names=['Y'])(d_aug)
    d_aug.metadata['properties'] = ['background'] + PART_NAMES

    # substract mean and shuffle
    d_aug = Shuffle()(d_aug)
    d_aug, d_val = RandomSplit(0.8)(d_aug)
    d_aug = PixelwiseSubstract([103.93, 116.78, 123.68], use_lane_names=['X'])(d_aug)
    d_val = PixelwiseSubstract([103.93, 116.78, 123.68], use_lane_names=['X'])(d_val)

    # Visualize()(d_aug)

    d_aug.configure()
    d_val.configure()
    print('training size:', d_aug.size())
    batch_size = 4

    callbacks = []
    #callbacks += [EarlyStopping(patience=10)]
    callbacks += [ModelCheckpoint(filepath="trained_models/"+model_name + '.hdf5', monitor='val_iou_metric', mode='max',
                                  verbose=1, save_best_only=True)]
    callbacks += [CSVLogger('logs/'+model_name + '.csv')]
    history = History()
    callbacks += [history]

    # sess = K.get_session()
    # sess.run(tf.initialize_local_variables())

    net.fit_generator(d_aug.batch_array_tuple_generator(batch_size=batch_size, shuffle_samples=True), steps_per_epoch=d_aug.size() // batch_size,
                      validation_data=d_val.batch_array_tuple_generator(batch_size=batch_size), validation_steps=d_val.size() // batch_size,
                      callbacks=callbacks, epochs=epochs)

    return {k: (max(history.history[k]), min(history.history[k])) for k in history.history.keys()}