pretraining_imagenet.py

import argparse

import numpy as np
import torch
from torch.nn import functional as F
from tqdm import tqdm

import datasets.datasetfactory as df
import model.learner as learner
import model.modelfactory as mf
import utils
from experiment.experiment import experiment
import logging
import datasets.miniimagenet as imgnet

logger = logging.getLogger('experiment')

def main(args):
    torch.manual_seed(args.seed)
    if torch.cuda.is_available():
        torch.cuda.manual_seed_all(args.seed)

    if torch.cuda.is_available():
        device = torch.device('cuda')
    else:
        device = torch.device('cpu')

    np.random.seed(args.seed)

    my_experiment = experiment(args.name, args, "../results/")

    args.classes = list(range(64))

    # args.traj_classes = list(range(int(64 / 2), 963))

    dataset = imgnet.MiniImagenet(args.dataset_path, mode='train')

    dataset_test = imgnet.MiniImagenet(args.dataset_path, mode='test')

    # Iterators used for evaluation
    iterator_test = torch.utils.data.DataLoader(dataset_test, batch_size=5,
                                                shuffle=True, num_workers=1)

    iterator = torch.utils.data.DataLoader(dataset, batch_size=128,
                                                 shuffle=True, num_workers=1)


    #
    logger.info(str(args))

    config = mf.ModelFactory.get_model("na", args.dataset)

    maml = learner.Learner(config).to(device)

    opt = torch.optim.Adam(maml.parameters(), lr=args.lr)

    for e in range(args.epoch):
        correct = 0
        for img, y in tqdm(iterator):
            if e == 50:
                opt = torch.optim.Adam(maml.parameters(), lr=0.00001)
                logger.info("Changing LR from %f to %f", 0.0001, 0.00001)
            img = img.to(device)
            y = y.to(device)
            pred = maml(img)
            feature = maml(img, feature=True)
            loss_rep = torch.abs(feature).sum()

            opt.zero_grad()
            loss = F.cross_entropy(pred, y)
            # loss_rep.backward(retain_graph=True)
            # logger.info("L1 norm = %s", str(loss_rep.item()))
            loss.backward()
            opt.step()
            correct += (pred.argmax(1) == y).sum().float()/ len(y)
        logger.info("Accuracy at epoch %d = %s", e, str(correct/len(iterator)))

        # correct = 0
        # with torch.no_grad():
        #     for img, y in tqdm(iterator_test):
        #
        #         img = img.to(device)
        #         y = y.to(device)
        #         pred = maml(img)
        #         feature = maml(img, feature=True)
        #         loss_rep = torch.abs(feature).sum()
        #
        #         correct += (pred.argmax(1) == y).sum().float() / len(y)
        #     logger.info("Accuracy Test at epoch %d = %s", e, str(correct / len(iterator_test)))


        torch.save(maml, my_experiment.path + "model.net")


if __name__ == '__main__':
    argparser = argparse.ArgumentParser()
    argparser.add_argument('--epoch', type=int, help='epoch number', default=30)
    argparser.add_argument('--seed', type=int, help='epoch number', default=222)
    argparser.add_argument('--dataset', help='Name of experiment', default="imagenet")
    argparser.add_argument('--dataset-path', help='Name of experiment', default="imagenet")
    argparser.add_argument('--lr', type=float, help='task-level inner update learning rate', default=0.0001)
    argparser.add_argument('--classes', type=int, nargs='+', help='Total classes to use in training',
                           default=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
    argparser.add_argument('--name', help='Name of experiment', default="baseline")

    args = argparser.parse_args()

    args.name = "/".join([args.dataset, "baseline", str(args.epoch).replace(".", "_"), args.name])

    main(args)