Develop Reference

Converting PyTorch to CoreML gives a TypeError: 'dict' object is not callable

I've been following Apple's coremltools docs for converting PyTorch segmentation models to CoreML.
While it works fine when we're loading a remote PyTorch model, I'm yet to figure out a working Python script to perform conversions with local/already-downloaded PyTorch models.
The following piece of code throws a TypeError: 'dict' object is not callable
#This works fine: model = torch.hub.load('pytorch/vision:v0.6.0', 'deeplabv3_resnet101',pretrained=True).eval()
model = torch.load('local_model_file.pth')
input_tensor = preprocess(input_image)
input_batch = input_tensor.unsqueeze(0)
with torch.no_grad():
output = model(input_batch)['out'][0] #error here
torch_predictions = output.argmax(0)
There is a SO answer that offers a solution by initialising the model class and loading the state_dict, but I wonder what's the concrete solution when we don't have access to the PyTorch model?

In your code, model is a state dict, which is a dictionary from parameter names to the parameter tensor values. As the linked answer stated, the right way to load a state dict is by (a) creating the model object that the state dict belongs to and then (b) use nn.Module.load_state_dict to load the state dict. To do (a), you need access to the model's class definition. If you don't have that access, then unfortunately I don't see any reliable way to load the state dict.
You might be able to guess what the class' __init__ look like by looking at the parameter names in the state dict (e.g., 'module.stage1.rebnconvin.conv_s1.weight' looks like a convolution). However, even if the guess is correct and the state dict can be loaded, you still need to define the forward method because the state dict only stores the parameters.

TransformerWrapper' object has no attribute '_memory_transform

I used PyCaret 3.0-rc. I developed model and saved them. after almost 3 weeks, now I loaded model. But when I want to use them :
train_predictions = predict_model(model, data=train)
I got the following error:
AttributeError: 'TransformerWrapper' object has no attribute '_memory_transform'
Can any one help me solve it?

How to fit a list or a dictionary of models in python?

I have a some models and I am trying to fit them all.
At the moment I have tried with a dictionary and fit them:
dictionary_of_models = {'catboost':CatBoostClassifier(random_state=0,), 'logistic_regression':LogisticRegression(random_state=0)}
for model in dictionary_of_models.keys():
print(model)
dictionary_of_models[model]=model.fit(X_train, y_train)
But, even the model is printed out, I receive this error:
model.fit(X_train, y_train)
AttributeError: 'str' object has no attribute 'fit'
What's wrong with the code?
I think that a string is going passed to the fit function instead of a model object, but I don't know I can create a model from a dictionary, except for way I tried.

The problem is that you tried to apply fit to the name you gave the model. YOu have to fit the model not its name.
dictionary_of_models = {'catboost':CatBoostClassifier(random_state=0,),
'logistic_regression':LogisticRegression(random_state=0)}
for name, model in dictionary_of_models.items():
print(name)
dictionary_of_models[model]=model.fit(X_train, y_train)

Complementing Prune's answer, I believe you can avoid items. dictionary_of_model[model] would generate a KeyError given you are not passing the Key of the dictionary, but the value itself.
Please try:
for model in dictionary_of_models:
print(model)
dictionary_of_models[model] = dictionary_of_models[model].fit(X_train, y_train)

AttributeError: 'numpy.ndarray' object has no attribute 'predict'

i am trying to find a predicted Y value (output is numerical) with x inputs using strings (eg. Business type, Department and Region). After using this :
print(model.predict([['Finance and Control'], ['EMEA'], ['Professional Services']]))
it returned this error : AttributeError: 'numpy.ndarray' object has no attribute 'predict'
import pickle
model = pickle.load(open('model3.pkl','rb'))
print(model.predict([['Finance and Control'], ['EMEA'], ['Professional Services']]))
Sample array after OHE

I ran into a similar issue as this, though without additional context, I'm not sure our errors derive from the same problem. However, the resolution I arrived at may help someone in the future as they go down the SO rabbit hole.
Using sklearn-0.20,
import joblib
model = joblib.load('model.pkl')
model.predict(previously_loaded_data)
Resulted in
AttributeError: 'numpy.ndarray' object has no attribute 'predict'
However, the following allowed me to load the actual model and use its predict method:
from sklearn.externals import joblib
model = joblib.load('model.pkl')
model.predict(previously_loaded_data)
sklearn.externals.joblib is deprecated since sklearn-0.23+, but my use case required sklearn-0.20.

Save a model with keras

I use the method save_model and load_mode but it don't work.
I have an error : AttributeError: 'GridSearchCV' object has no attribute 'get_config'
I don't know if I use correctly this method. I show my code for take an example:
gridSearch = GridSearchCV(estimator = classifier,
param_grid = parameters,
scoring = "accuracy",
cv = 10)
gridSearch.fit(X_train, y_train)
save_model(gridSearch, filepath = 'monModele.h5')
The result is the error attribute Error. Can you help me to find a solution for this problem or to find an other method to save and load a keras model.

That is because GridSearchCV is not a Keras model, but a module from sklearn that also has a fit function with a similar API.
In order to use save_model and load_model you need the actual Keras model, my guess is it is your classifier. Specifically, an instance of the Model class from Keras.