Class imported from a local module cannot find the definition of a variable declared in the same module, Python

Question

I have this piece of code defined in the model_utils.py local module.

import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
os.environ["SM_FRAMEWORK"] = "tf.keras"

import segmentation_models as sm
import matplotlib.pyplot as plt 
import tensorflow_io as tfio
import tensorflow as tf
from tensorflow import keras

keras.backend.clear_session() #uvolneni RAM
BACKBONE   = 'efficientnetb0'
n_classes  = 1 
activation = 'sigmoid' 
model = sm.Unet(BACKBONE, classes=n_classes, activation=activation) 

class DisplayCallback(keras.callbacks.Callback):
    def __init__(self, dataset, epoch_interval=5):
        self.dataset = dataset
        self.epoch_interval = epoch_interval
    
    def display(self, display_list, extra_title=''):
        plt.figure(figsize=(15, 15))
        title = ['Input Image', 'True Mask', 'Predicted Mask']

        if len(display_list) > len(title):
            title.append(extra_title)

        for i in range(len(display_list)):
            plt.subplot(1, len(display_list), i+1)
            plt.title(title[i])
            plt.imshow(display_list[i], cmap='gray')
            plt.axis('off')
        plt.show()
        
    def create_mask(self, pred_mask):
        pred_mask = (pred_mask > 0.5).astype("int32")
        return pred_mask[0]
    
    def show_predictions(self, dataset, num=1):
        for image, mask in dataset.take(num):
            pred_mask = model.predict(image)
            self.display([image[0], mask[0], self.create_mask(pred_mask)])
        
    def on_epoch_end(self, epoch, logs=None):
        if epoch and epoch % self.epoch_interval == 0:
            self.show_predictions(self.dataset)
            print ('\nSample Prediction after epoch {}\n'.format(epoch+1))

I imported it with from model_utils import * into a jupyter notebook stored in the same directory. And used it as a callback for training of my model.

model.compile(optim, bce, metrics)
with tf.device('/gpu:0'):
    model.fit(
        train_dataset, 
        callbacks=[DisplayCallback(train_dataset)], #additionally cp_callback
        epochs=400
    )

Doing so I got this error as soon as the training approached the moment of using the DisplayCallback class.

NameError: name 'model' is not defined

Why is this? How can i resolve it? Please be kind and thanks a lot for any help in advance!

Answer 1

The problem is not with importing, but within DisplayCallback. The class doesn't recognize what model is.

Fixes:

1. Inside __init__, call super().__init__(). This method defines the self.model attribute.

2. Inside your class, every time you refer to model, it must be self.model. I found one mistake in show_predictions but I could've missed a few more.

Here's a reference of how to implement custom callbacks, scroll down to the part where self.model is used.