Melu_L2L_hyperTunning/fast_adapt.py

import torch
import pickle


def fast_adapt(
        learn,
        adaptation_data,
        evaluation_data,
        adaptation_labels,
        evaluation_labels,
        adaptation_steps,
        get_predictions=False):
    for step in range(adaptation_steps):
        temp = learn(adaptation_data, adaptation_labels, training=True)
        train_error = torch.nn.functional.mse_loss(temp.view(-1), adaptation_labels)
        learn.adapt(train_error)

    predictions = learn(evaluation_data, None , training=False)
    # loss = torch.nn.MSELoss(reduction='mean')
    # valid_error = loss(predictions, evaluation_labels)
    valid_error = torch.nn.functional.mse_loss(predictions.view(-1), evaluation_labels)

    if get_predictions:
        return valid_error, predictions
    return valid_error