如何更改 VGG16 中的批量大小？

Question

如何更改 VGG16 中的批量大小？ 我試圖通過這樣做來解決超過 memory 約束 10% 的問題。

錯誤：

2021-12-03 16:17:07.263665: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 4888553472 exceeds 10% of free system memory.

這是我的代碼：

def labelObjectFromImage(image_path, directory_filename):
    img = cv2.imread(image_path+directory_filename)
    height = img.shape[0]
    width = img.shape[1]
    channels = img.shape[2]
    img = load_img(image_path+directory_filename, target_size=(height, width))
    model = VGG16(weights="imagenet", include_top = False, input_shape = (height, width, channels))
    img = img_to_array(img)
    img = img.reshape((1, img.shape[0], img.shape[1], img.shape[2]))
    img = preprocess_input(img)
    yhat = model.predict(img)
    label = decode_predictions(yhat)
    label = label[0][0]
    print(label)

我嘗試將 model.predict 更改為：

    yhat = model.predict(img, batch_size=1)

但它似乎對嘗試解決問題沒有任何影響

我嘗試使用：

from tensorflow.keras import backend as K

K.clear_session()

但這並沒有幫助

我跑了

pip3 uninstall tensorflow-gpu

然后通過安裝正常的 tensorflow

pip3 install tensorflow

但這並沒有幫助

僅供參考，到目前為止，我在所有這些嘗試中都遇到了同樣的錯誤。

正如建議的那樣，我嘗試了：

    img_resized = tf.image.resize(img, (height, width))

但我現在收到以下錯誤：

Traceback (most recent call last):
  File "organizeSpreadsheet.py", line 105, in <module>
    main()
  File "organizeSpreadsheet.py", line 86, in main
    objects_from_image = labelObjectFromImage(path_to_images, directory_filename)
  File "organizeSpreadsheet.py", line 53, in labelObjectFromImage
    img = img_resized.reshape((1, height, width, channels))
  File "/home/jr/.local/lib/python3.8/site-packages/tensorflow/python/framework/ops.py", line 437, in __getattr__
    raise AttributeError("""
AttributeError: 
        'EagerTensor' object has no attribute 'reshape'.
        If you are looking for numpy-related methods, please run the following:
        from tensorflow.python.ops.numpy_ops import np_config
        np_config.enable_numpy_behavior()

我知道我沒有完全按照指示做，但它拋出了一個錯誤，所以我遵循了這個建議

我通過進行以下更改更正了錯誤：

def labelObjectFromImage(image_path, directory_filename):
    scale = 60

    img = cv2.imread(image_path+directory_filename)
    height = int(img.shape[0] * scale / 100)
    width = int(img.shape[1] * scale / 100)
    channels = img.shape[2]
    #img_resized = cv2.resize(img, dim, interpolation=cv2.INTER_AREA)
    #img_resized = tf.image.resize(img, (height, width))
    tf.image.resize(img, (height, width))
    #img = load_img(image_path+directory_filename, target_size=(height, width))
    model = VGG16(weights="imagenet", include_top = False, input_shape = (height, width, channels))
    #img = img_to_array(img)
    #img = img.reshape((1, img.shape[0], img.shape[1], img.shape[2]))
    img = img.reshape((1, height, width, channels))
    img = preprocess_input(img)
    yhat = model.predict(img, batch_size=1)
    label = decode_predictions(yhat)
    label = label[0][0]
    print(label)

但現在我得到了錯誤：

ValueError: cannot reshape array of size 63483840 into shape (1,3384,2251,3)

我認為這可以通過嘗試多個尺度來解決，對嗎？

Answer 1

您已經在使用batch_size = 1。

1. 通過在導入 tensorflow 時檢查日志來檢查您是否正在使用 gpu。
2. 在使用tf.image.resize(image, [small_height,small_width,N_channels])進行預測之前嘗試調整圖像大小