I have a large dataset (over 200,000 images) that I am using for semantic segmentation. My batch size is 1 due to the size of images and network parameters. I would like to train this model by iterating 4000-5000 images per epoch so I can run validation more often. In Keras, you have an option using fit_generator to assign how many ‘steps_per_epoch’ (batches of samples to be seen per epoch). Is there something similar already implemented in pytorch using the dataset/dataloader classes?
Link to keras’ fit_generator docs: https://keras.io/models/sequential/ 10