unable to load the model after adding instance_normalization

[prb2307]

@lim-anggun I have read your previous comment on adding the instance_nornalization.py .But I am still getting the error I have added this line of code as mentioned by you. from FgSegNet.instance_normalization import InstanceNormalization model = load_model(mdl_path, custom_objects={'MyUpSampling2D': MyUpSampling2D, 'InstanceNormalization': InstanceNormalization})

Error :

`-----------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-26-69f03b7b76fd> in <module>
      4 mdl_path = 'FgSegNet_M/CDnet/models50/baseline/mdl_pedestrians.h5'
      5 from FgSegNet.instance_normalization import InstanceNormalization
----> 6 model = load_model(mdl_path, custom_objects={'MyUpSampling2D': MyUpSampling2D, 'InstanceNormalization': InstanceNormalization})
      7 #from FgSegNet_v2_module.py import loss2, acc2
      8 #model = load_model(mdl_path, custom_objects={'MyUpSampling2D': MyUpSampling2D, 'InstanceNormalization': InstanceNormalization})

~/anaconda3/envs/p3/lib/python3.6/site-packages/keras/models.py in load_model(filepath, custom_objects, compile)
    262                       metrics=metrics,
    263                       loss_weights=loss_weights,
--> 264                       sample_weight_mode=sample_weight_mode)
    265 
    266         # Set optimizer weights.

~/anaconda3/envs/p3/lib/python3.6/site-packages/keras/engine/training.py in compile(self, optimizer, loss, metrics, loss_weights, sample_weight_mode, **kwargs)
    679             loss_functions = [losses.get(l) for l in loss]
    680         else:
--> 681             loss_function = losses.get(loss)
    682             loss_functions = [loss_function for _ in range(len(self.outputs))]
    683         self.loss_functions = loss_functions

~/anaconda3/envs/p3/lib/python3.6/site-packages/keras/losses.py in get(identifier)
    100     if isinstance(identifier, six.string_types):
    101         identifier = str(identifier)
--> 102         return deserialize(identifier)
    103     elif callable(identifier):
    104         return identifier

~/anaconda3/envs/p3/lib/python3.6/site-packages/keras/losses.py in deserialize(name, custom_objects)
     92                                     module_objects=globals(),
     93                                     custom_objects=custom_objects,
---> 94                                     printable_module_name='loss function')
     95 
     96 

~/anaconda3/envs/p3/lib/python3.6/site-packages/keras/utils/generic_utils.py in deserialize_keras_object(identifier, module_objects, custom_objects, printable_module_name)
    157             if fn is None:
    158                 raise ValueError('Unknown ' + printable_module_name +
--> 159                                  ':' + function_name)
    160         return fn
    161     else:

ValueError: Unknown loss function:loss

I would appreciate your advice on this. Thank you.

[lim-anggun]

Hi @prashant-bansod , You may find this Jupyter Notebook helpful.

You need to add extra parameters to the load_modelfunction as follows:

from instance_normalization import InstanceNormalization
from my_upsampling_2d import MyUpSampling2D
from FgSegNet_v2_module import loss, acc, loss2, acc2

model = load_model(model_path, custom_objects={'MyUpSampling2D': MyUpSampling2D, 'InstanceNormalization': InstanceNormalization, 'loss':loss, 'acc':acc, 'loss2':loss2, 'acc2':acc2})

[prb2307]

@lim-anggun Thank you very much. I made the required corrections. Thanks a ton for your reply. I had one more question, I tried the pedestrian model but the silhouettes I got are noisy. Does the background has an effect on the extracted silhouettes?

What do you think would be the best approach to extract human silhouettes?

[Tomingz]

thanks a lot for your great work, but how to make my own data like the CDnet2014? and how to train it?