File size: 1,953 Bytes
377dccd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 | # Copyright 2020-present, Pietro Buzzega, Matteo Boschini, Angelo Porrello, Davide Abati, Simone Calderara.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from models.utils.continual_model import ContinualModel
from utils.args import add_management_args, add_experiment_args, add_rehearsal_args, ArgumentParser
from utils.buffer import Buffer
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
def get_parser() -> ArgumentParser:
parser = ArgumentParser(description='Continual learning via'
' Experience Replay.')
add_management_args(parser)
add_experiment_args(parser)
add_rehearsal_args(parser)
return parser
class Er(ContinualModel):
NAME = 'er'
COMPATIBILITY = ['class-il', 'domain-il', 'task-il', 'general-continual']
def __init__(self, backbone, loss, args, transform):
super(Er, self).__init__(backbone, loss, args, transform)
self.buffer = Buffer(self.args.buffer_size, self.device)
self.task=0
def observe(self, inputs, labels, not_aug_inputs):
real_batch_size = inputs.shape[0]
self.opt.zero_grad()
if not self.buffer.is_empty():
buf_inputs, buf_labels = self.buffer.get_data(
self.args.minibatch_size, transform=self.transform)
inputs = torch.cat((inputs, buf_inputs))
labels = torch.cat((labels, buf_labels))
outputs = self.net(inputs)
loss = self.loss(outputs, labels)
loss.backward()
self.opt.step()
self.buffer.add_data(examples=not_aug_inputs,
labels=labels[:real_batch_size])
return loss.item()
def end_task(self, dataset):
print('\n\n')
self.task+=1
print(self.task)
|