1.将模型存储

model.save('my_model.h5')

2.加载模型报错

from nets.centernet_training import focal_loss, reg_l1_loss
from keras.models import load_model
model = load_model('my_model.h5', compile=False, custom_objects={'focal_loss': focal_loss,
'reg_l1_loss': reg_l1_loss})
报错信息如下：
Traceback (most recent call last):
File "load_model_error.py", line 21, in
model = load_model('my_model.h5', compile=False, custom_objects={'focal_loss': focal_loss, 'reg_l1_loss': reg_l1_loss})
File "/home/wisdom/anaconda3/envs/lvbangxwk/lib/python3.6/site-packages/keras/models.py", line 243, in load_model
model = model_from_config(model_config, custom_objects=custom_objects)
File "/home/wisdom/anaconda3/envs/lvbangxwk/lib/python3.6/site-packages/keras/models.py", line 317, in model_from_config
return layer_module.deserialize(config, custom_objects=custom_objects)
File "/home/wisdom/anaconda3/envs/lvbangxwk/lib/python3.6/site-packages/keras/layers/init.py", line 55, in deserialize
printable_module_name='layer')
File "/home/wisdom/anaconda3/envs/lvbangxwk/lib/python3.6/site-packages/keras/utils/generic_utils.py", line 144, in deserialize_keras_object
list(custom_objects.items())))
File "/home/wisdom/anaconda3/envs/lvbangxwk/lib/python3.6/site-packages/keras/engine/topology.py", line 2524, in from_config
process_node(layer, node_data)
File "/home/wisdom/anaconda3/envs/lvbangxwk/lib/python3.6/site-packages/keras/engine/topology.py", line 2481, in process_node
layer(input_tensors[0], **kwargs)
File "/home/wisdom/anaconda3/envs/lvbangxwk/lib/python3.6/site-packages/keras/engine/topology.py", line 619, in call
output = self.call(inputs, **kwargs)
File "/home/wisdom/anaconda3/envs/lvbangxwk/lib/python3.6/site-packages/keras/layers/core.py", line 663, in call
return self.function(inputs, **arguments)
File "C:\Users\wisdom\Anaconda3\envs\keras-centernet\lib\site-packages\tensorflow\python\ops\math_ops.py", line 1530, in reduce_mean_v1
NameError: name 'deprecation' is not defined

ValueError: Shape mismatch in layer #175 (named conv2d_transpose_1) for weight conv2d_transpose_1/kernel:0. Weight expects shape (4, 4, 128, 256). Received saved weight with shape (2048, 256, 4, 4)

def gaussian2D(shape, sigma=1):
m, n = [(ss - 1.) / 2. for ss in shape]
y, x = np.ogrid[-m:m + 1, -n:n + 1]

h = np.exp(-(x * x + y * y) / (2 * sigma * sigma))

h[h < np.finfo(h.dtype).eps * h.max()] = 0
return h

TypeError: float() argument must be a string or a number, not '_NoValueType'
这个h.max()报错

用的TensorFlow版本有点老啊，能更新到2.0+吗？

batch_hms[b, :, :, cls_id] = draw_gaussian(batch_hms[b, :, :, cls_id], ct_int, radius)

batch_hms.all() == 0 为True，没有发生改变

def draw_gaussian(heatmap, center, radius, k=1):
diameter = 2 * radius + 1
gaussian = gaussian2D((diameter, diameter), sigma=diameter / 6)

x, y = int(center[0]), int(center[1])

height, width = heatmap.shape[0:2]

left, right = min(x, radius), min(width - x, radius + 1)
top, bottom = min(y, radius), min(height - y, radius + 1)

masked_heatmap = heatmap[y - top:y + bottom, x - left:x + right]
masked_gaussian = gaussian[radius - top:radius + bottom, radius - left:radius + right]
if min(masked_gaussian.shape) > 0 and min(masked_heatmap.shape) > 0:  # TODO debug
    np.maximum(masked_heatmap, masked_gaussian * k, out=masked_heatmap)

return heatmap

因为要预测的图片长宽比都是2：1，觉得做 letterbox会造成速度慢
程序中的 "input_shape" : [512,512,3] 改成"input_shape" : [640,640,3] 没问题
改成[640，512，3]就不出结果了

self.centernet.load_weights(self.model_path,by_name=True)
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py", line 2209, in load_weights
f, self.layers, skip_mismatch=skip_mismatch)
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/tensorflow/python/keras/saving/hdf5_format.py", line 786, in load_weights_from_hdf5_group_by_name
str(weight_values[i].shape) + '.')
ValueError: Layer #175 (named "conv2d_transpose_1"), weight <tf.Variable 'conv2d_transpose_1/kernel:0' shape=(4, 4, 128, 256) dtype=float32, numpy=
array([[[[ 3.48129659e-03, 2.83368863e-02, -2.63886303e-02, ...,
1.38676679e-02, -4.14527431e-02, 9.28733498e-03],
....
]]]],
dtype=float32)> has shape (4, 4, 128, 256), but the saved weight has shape (2048, 256, 4, 4).

train.py中在model.compile里加了一句metrics=['accuracy']，运行时报错list index out of range，请问这是什么问题导致的？如何解决呢

#请问使用您提供的预训练模型结果如图，不知道其他小伙伴使用是否有这个情况，请问可以解答一下吗