我一直在处理属性错误："list"对象没有属性"dtype">这来自coursera作业，coursera jupyter笔记本上的一切都很好，但当我在本地机器上运行它时，我开始出现这个错误，不知道为什么？有人能帮我解决这个问题吗。
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 detectionyad2kmodelskeras_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])
~anaconda3envstensorflow_envlibsite-packagestensorflowpythonutildispatch.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
~anaconda3envstensorflow_envlibsite-packagestensorflowpythonkerasbackend.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```
您对模型的输入类型是什么？您应该先将其强制转换为NumPy数组。
a = [1, 2, 3]
a.dtype
这将引发您的错误：
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-2-b3cdf8323901> in <module>
----> 1 a.dtype
AttributeError: 'list' object has no attribute 'dtype'
在转换为NumPy数组后，它将正常工作：
import numpy as np
a = np.array(a)
a.dtype # dtype('int64')