coursera(deep specialization course): CNN week-3 assignment car detection with YOLO

Question

我一直在努力解决 属性错误：“list”对象没有属性“dtype”。 这是来自 coursera 作业，在 coursera jupyter notebook 中一切正常，但是当我在本地机器上运行它时，我开始收到这个错误，不知道为什么？谁能帮我解决这个问题。

在下面的代码中，我们将模型的输出转换为可用的边界框张量。

yolo_outputs = yolo_head(yolo_model.output, anchors, len(class_names))

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-90-d69bb71a2d56> in <module>
----> 1 yolo_outputs = yolo_head(yolo_model.output, anchors, len(class_names))

~\object detection\yad2k\models\keras_yolo.py in yolo_head(feats, anchors, num_classes)
    107     conv_index = K.transpose(K.stack([conv_height_index, conv_width_index]))
    108     conv_index = K.reshape(conv_index, [1, conv_dims[0], conv_dims[1], 1, 2])
--> 109     conv_index = K.cast(conv_index, K.dtype(feats))
    110 
    111     feats = K.reshape(feats, [-1, conv_dims[0], conv_dims[1], num_anchors, num_classes + 5])

~\anaconda3\envs\tensorflow_env\lib\site-packages\tensorflow\python\util\dispatch.py in wrapper(*args, **kwargs)
    199     """Call target, and fall back on dispatchers if there is a TypeError."""
    200     try:
--> 201       return target(*args, **kwargs)
    202     except (TypeError, ValueError):
    203       # Note: convert_to_eager_tensor currently raises a ValueError, not a

~\anaconda3\envs\tensorflow_env\lib\site-packages\tensorflow\python\keras\backend.py in dtype(x)
   1369 
   1370   """
-> 1371   return x.dtype.base_dtype.name
   1372 
   1373 

AttributeError: 'list' object has no attribute 'dtype'

def yolo_head(feats, anchors, num_classes):
    """Convert final layer features to bounding box parameters.

    Parameters
    ----------
    feats : tensor
        Final convolutional layer features.
    anchors : array-like
        Anchor box widths and heights.
    num_classes : int
        Number of target classes.

    Returns
    -------
    box_xy : tensor
        x, y box predictions adjusted by spatial location in conv layer.
    box_wh : tensor
        w, h box predictions adjusted by anchors and conv spatial resolution.
    box_conf : tensor
        Probability estimate for whether each box contains any object.
    box_class_pred : tensor
        Probability distribution estimate for each box over class labels.
    """
    num_anchors = len(anchors)
    # Reshape to batch, height, width, num_anchors, box_params.
    anchors_tensor = K.reshape(K.variable(anchors), [1, 1, 1, num_anchors, 2])
    # Static implementation for fixed models.
    # TODO: Remove or add option for static implementation.
    # _, conv_height, conv_width, _ = K.int_shape(feats)
    # conv_dims = K.variable([conv_width, conv_height])

    # Dynamic implementation of conv dims for fully convolutional model.
    conv_dims = K.shape(feats)[1:3]  # assuming channels last
    # In YOLO the height index is the inner most iteration.
    conv_height_index = K.arange(0, stop=conv_dims[0])
    conv_width_index = K.arange(0, stop=conv_dims[1])
    conv_height_index = K.tile(conv_height_index, [conv_dims[1]])

    # TODO: Repeat_elements and tf.split doesn't support dynamic splits.
    # conv_width_index = K.repeat_elements(conv_width_index, conv_dims[1], axis=0)
    conv_width_index = K.tile(K.expand_dims(conv_width_index, 0), [conv_dims[0], 1])
    conv_width_index = K.flatten(K.transpose(conv_width_index))
    conv_index = K.transpose(K.stack([conv_height_index, conv_width_index]))
    conv_index = K.reshape(conv_index, [1, conv_dims[0], conv_dims[1], 1, 2])
    conv_index = K.cast(conv_index, K.dtype(feats))
    
    feats = K.reshape(feats, [-1, conv_dims[0], conv_dims[1], num_anchors, num_classes + 5])
    conv_dims = K.cast(K.reshape(conv_dims, [1, 1, 1, 1, 2]), K.dtype(feats))

    # Static generation of conv_index:
    # conv_index = np.array([_ for _ in np.ndindex(conv_width, conv_height)])
    # conv_index = conv_index[:, [1, 0]]  # swap columns for YOLO ordering.
    # conv_index = K.variable(
    #     conv_index.reshape(1, conv_height, conv_width, 1, 2))
    # feats = Reshape(
    #     (conv_dims[0], conv_dims[1], num_anchors, num_classes + 5))(feats)

    box_confidence = K.sigmoid(feats[..., 4:5])
    box_xy = K.sigmoid(feats[..., :2])
    box_wh = K.exp(feats[..., 2:4])
    box_class_probs = K.softmax(feats[..., 5:])

    # Adjust preditions to each spatial grid point and anchor size.
    # Note: YOLO iterates over height index before width index.
    box_xy = (box_xy + conv_index) / conv_dims
    box_wh = box_wh * anchors_tensor / conv_dims

    return box_confidence, box_xy, box_wh, box_class_probs```

Answer 1

您对模型的输入是什么类型的？您应该先将其转换为 NumPy 数组。

a = [1, 2, 3]
a.dtype

这会引发你的错误：

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-2-b3cdf8323901> in <module>
----> 1 a.dtype

AttributeError: 'list' object has no attribute 'dtype'

转换为 NumPy 数组后，它将正常工作：

import numpy as np
a = np.array(a)
a.dtype # dtype('int64')