omniglot_one_shot_task.py

import argparse
import json
import os
import random
import socket
import time
import warnings
from datetime import datetime
from typing import List, Tuple

import torch
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.multiprocessing as mp
import torch.nn.functional
import torch.nn.parallel
import torch.utils.data
import torch.utils.data.distributed
from torch.utils.tensorboard import SummaryWriter

import utils.checkpoint
import utils.meters
import utils.metrics
from data.omniglot_dataset import OmniglotDataset
from functions.autograd_functions import SpikeFunction
from functions.plasticity_functions import InvertedOjaWithSoftUpperBound
from models.network_models import OmniglotOneShot
from models.neuron_models import IafPscDelta
from models.protonet_models import SpikingProtoNet
from utils.distributed_sampler_wrapper import DistributedSamplerWrapper
from utils.episodic_batch_sampler import EpisodicBatchSampler

parser = argparse.ArgumentParser(description='Omniglot one-shot task training')
parser.add_argument('--num_classes', default=5, type=int, metavar='N',
                    help='Number of random classes per sample (default: 5)')
parser.add_argument('--num_time_steps', default=100, type=int, metavar='N',
                    help='Number of time steps for each item in the sequence (default: 100)')

parser.add_argument('--dir', default='./data', type=str, metavar='DIR',
                    help='Path to dataset (default: ./data)')
parser.add_argument('--workers', default=0, type=int, metavar='N',
                    help='Number of data loading workers (default: 0)')
parser.add_argument('--pin_data_to_memory', default=1, choices=[0, 1], type=int, metavar='PIN_DATA_TO_MEMORY',
                    help='Pin data to memory (default: 1)')

parser.add_argument('--memory_size', default=100, type=int, metavar='N',
                    help='Size of the memory matrix (default: 100)')
parser.add_argument('--w_max', default=1.0, type=float, metavar='N',
                    help='Soft maximum of Hebbian weights (default: 1.0)')
parser.add_argument('--gamma_pos', default=0.3, type=float, metavar='N',
                    help='Write factor of Hebbian rule (default: 0.3)')
parser.add_argument('--gamma_neg', default=0.3, type=float, metavar='N',
                    help='Forget factor of Hebbian rule (default: 0.3)')
parser.add_argument('--tau_trace', default=20.0, type=float, metavar='N',
                    help='Time constant of key- and value-trace (default: 20.0)')
parser.add_argument('--readout_delay', default=1, type=int, metavar='N',
                    help='Synaptic delay of the feedback-connections from value-neurons to key-neurons in the '
                         'reading layer (default: 1)')

parser.add_argument('--thr', default=0.1, type=float, metavar='N',
                    help='Spike threshold (default: 0.1)')
parser.add_argument('--perfect_reset', action='store_true',
                    help='Set the membrane potential to zero after a spike')
parser.add_argument('--refractory_time_steps', default=3, type=int, metavar='N',
                    help='The number of time steps the neuron is refractory (default: 3)')
parser.add_argument('--tau_mem', default=20.0, type=float, metavar='N',
                    help='Neuron membrane time constant (default: 20.0)')
parser.add_argument('--dampening_factor', default=1.0, type=float, metavar='N',
                    help='Scale factor for spike pseudo-derivative (default: 1.0)')

parser.add_argument('--epochs', default=200, type=int, metavar='N',
                    help='Number of total epochs to run (default: 200)')
parser.add_argument('--batch_size', default=256, type=int, metavar='N',
                    help='Mini-batch size (default: 256)')
parser.add_argument('--iterations', default=200, type=int, metavar='N',
                    help='Number of episodes per epoch (default: 200)')

parser.add_argument('--learning_rate', default=0.001, type=float, metavar='N',
                    help='Initial learning rate (default: 0.001)')
parser.add_argument('--learning_rate_decay', default=0.85, type=float, metavar='N',
                    help='Learning rate decay (default: 0.85)')
parser.add_argument('--decay_learning_rate_every', default=20, type=int, metavar='N',
                    help='Decay the learning rate every N epochs (default: 20)')
parser.add_argument('--max_grad_norm', default=40.0, type=float, metavar='N',
                    help='Gradients with an L2 norm larger than max_grad_norm will be clipped '
                         'to have an L2 norm of max_grad_norm. If None, then the gradient will '
                         'not be clipped. (default: 40.0)')
parser.add_argument('--l2', default=1e-6, type=float, metavar='N',
                    help='L2 rate regularization factor (default: 1e-6)')
parser.add_argument('--target_rate', default=0.0, type=float, metavar='N',
                    help='Target firing rate in Hz for L2 regularization (default: 0.0)')

parser.add_argument('--use_pretrained_protonet', default=1, choices=[0, 1], type=int, metavar='USE_PRETRAINED_PROTONET',
                    help='Use Conv-nets that were pretrained somehow (default: 1)')
parser.add_argument('--protonet_checkpoint_path', default='results/checkpoints/omniglot-one-shot-task-protonet'
                                                          '-checkpoint.pth.tar', type=str, metavar='PATH',
                    help='Path to ProtoNet checkpoint (default: none)')
parser.add_argument('--freeze_protonet', default=0, choices=[0, 1], type=int,
                    help='Freeze pre-trained ProtoNets after conversion (default: 0)')
parser.add_argument('--learn_if_thresholds', action='store_true',
                    help='Learn IF neuron thresholds of teh spiking CNN via threshold balancing (if never done yet)')
parser.add_argument('--fix_cnn_thresholds', action='store_false', help='Do not adjust firing threshold after conversion'
                                                                       '(default: will adjust v_th via a bias)')

parser.add_argument('--start_epoch', default=0, type=int, metavar='N',
                    help='Manual epoch number (useful on restarts, default: 0)')
parser.add_argument('--resume', default='', type=str, metavar='PATH',
                    help='Path to latest checkpoint (default: none)')
parser.add_argument('--evaluate', action='store_true',
                    help='Evaluate the model on the test set')

parser.add_argument('--logging', action='store_true',
                    help='Write tensorboard logs')
parser.add_argument('--print_freq', default=1, type=int, metavar='N',
                    help='Print frequency (default: 1)')
parser.add_argument('--world_size', default=-1, type=int, metavar='N',
                    help='Number of nodes for distributed training (default: -1)')
parser.add_argument('--rank', default=-1, type=int, metavar='N',
                    help='Node rank for distributed training (default: -1)')
parser.add_argument('--dist_url', default='tcp://127.0.0.1:23456', type=str, metavar='DIST_URL',
                    help='URL used to set up distributed training (default: tcp://127.0.0.1:23456)')
parser.add_argument('--dist_backend', default='nccl', choices=['nccl', 'mpi', 'gloo'], type=str, metavar='DIST_BACKEND',
                    help='Distributed backend to use (default: nccl)')
parser.add_argument('--seed', default=None, type=int, metavar='N',
                    help='Seed for initializing training (default: none)')
parser.add_argument('--sampler_seed', default=42, type=int, metavar='N',
                    help='Seed for prototypical batch sampler (default: 42)')
parser.add_argument('--gpu', default=None, type=int, metavar='N',
                    help='GPU id to use (default: none)')
parser.add_argument('--multiprocessing_distributed', action='store_true',
                    help='Use multi-processing distributed training to launch '
                         'N processes per node, which has N GPUs. This is the '
                         'fastest way to use PyTorch for either single node or '
                         'multi node data parallel training')

best_acc = 0
log_dir = ''
writer = None
time_stamp = datetime.now().strftime('%b%d_%H-%M-%S')
suffix = ''

with open('version.txt') as f:
    version = f.readline()


def main():
    args = parser.parse_args()

    args.pin_data_to_memory = True if args.pin_data_to_memory else False

    if args.seed is not None:
        random.seed(args.seed)
        torch.manual_seed(args.seed)
        cudnn.deterministic = True
        warnings.warn('You have chosen to seed training. '
                      'This will turn on the CUDNN deterministic setting, '
                      'which can slow down your training considerably! '
                      'You may see unexpected behavior when restarting '
                      'from checkpoints.')

    if args.gpu is not None:
        warnings.warn('You have chosen a specific GPU. This will completely '
                      'disable data parallelism.')

    if args.dist_url == "env://" and args.world_size == -1:
        args.world_size = int(os.environ["WORLD_SIZE"])

    args.distributed = args.world_size > 1 or args.multiprocessing_distributed

    num_gpus_per_node = torch.cuda.device_count()
    if args.multiprocessing_distributed:
        # Since we have num_gpus_per_node processes per node, the total world_size
        # needs to be adjusted accordingly
        args.world_size = num_gpus_per_node * args.world_size
        # Use torch.multiprocessing.spawn to launch distributed processes: the
        # main_worker process function
        mp.spawn(main_worker, nprocs=num_gpus_per_node, args=(num_gpus_per_node, args))  # noqa
    else:
        # Simply call main_worker function
        main_worker(args.gpu, num_gpus_per_node, args)


def main_worker(gpu, num_gpus_per_node, args):
    global best_acc
    global log_dir
    global writer
    global time_stamp
    global version
    global suffix
    args.gpu = gpu

    suffix = f'1-shot-{args.num_classes}-way'

    if args.gpu is not None:
        print("Use GPU: {} for training".format(args.gpu))

    if args.distributed:
        if args.dist_url == "env://" and args.rank == -1:
            args.rank = int(os.environ["RANK"])
        if args.multiprocessing_distributed:
            # For multiprocessing distributed training, rank needs to be the
            # global rank among all the processes
            args.rank = args.rank * num_gpus_per_node + gpu
        dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
                                world_size=args.world_size, rank=args.rank)

    # Data loading code
    train_set = OmniglotDataset(mode='train', root=args.dir)
    val_set = OmniglotDataset(mode='val', root=args.dir)
    test_set = OmniglotDataset(mode='test', root=args.dir)

    if args.use_pretrained_protonet:
        # Load checkpoint of pretrained ProtoNet to be converted to ProtoNetSpiking
        if not os.path.isfile(args.protonet_checkpoint_path):
            raise Exception(f"=> ProtoNet checkpoint file not found. "
                            f"Create a checkpoint by pre-training SpikingProtoNet first. Aborting...")

        print("=> loading ProtoNet checkpoint '{}'".format(args.protonet_checkpoint_path))
        if args.gpu is None:
            protonet_checkpoint = utils.checkpoint.load_checkpoint(args.protonet_checkpoint_path, 'cpu')
        else:
            # Map model to be loaded to specified single gpu
            protonet_checkpoint = utils.checkpoint.load_checkpoint(args.protonet_checkpoint_path,
                                                                   'cuda:{}'.format(args.gpu))

        args.fix_cnn_thresholds = False
        image_embedding_layer = SpikingProtoNet(IafPscDelta(thr=args.thr,
                                                            perfect_reset=args.perfect_reset,
                                                            refractory_time_steps=args.refractory_time_steps,
                                                            tau_mem=args.tau_mem,
                                                            spike_function=SpikeFunction,
                                                            dampening_factor=args.dampening_factor),
                                                weight_dict=protonet_checkpoint['state_dict'],
                                                input_size=784,
                                                num_time_steps=args.num_time_steps,
                                                refractory_time_steps=args.refractory_time_steps,
                                                use_bias=args.fix_cnn_thresholds)

        if args.learn_if_thresholds:
            print('This may take time as it will go through the whole training set.')
            aux_train_loader = torch.utils.data.DataLoader(train_set, batch_sampler=init_sampler(train_set.y, args))
            with torch.no_grad():
                for i, sample in enumerate(aux_train_loader):
                    _, _, aux_query, _ = parse_sample(sample, args)
                    aux_query = aux_query.cuda(args.gpu, non_blocking=True) if args.gpu is not None else aux_query
                    image_embedding_layer.threshold_balancing(None, aux_query)
                    print(str(i), image_embedding_layer.layer_v_th)
        else:
            image_embedding_layer.threshold_balancing([0.9151, 4.7120, 3.1376, 2.6661])

        if args.freeze_protonet:
            for param in image_embedding_layer.parameters():
                param.requires_grad = False
    else:
        # Create ProtoNet
        image_embedding_layer = SpikingProtoNet(IafPscDelta(thr=args.thr,
                                                            perfect_reset=args.perfect_reset,
                                                            refractory_time_steps=args.refractory_time_steps,
                                                            tau_mem=args.tau_mem,
                                                            spike_function=SpikeFunction,
                                                            dampening_factor=args.dampening_factor),
                                                input_size=784,
                                                num_time_steps=args.num_time_steps,
                                                refractory_time_steps=args.refractory_time_steps,
                                                use_bias=args.fix_cnn_thresholds)

        image_embedding_layer.threshold_balancing([args.thr, args.thr, args.thr, args.thr])

    # Create model
    print("=> creating model '{model_name}'".format(model_name=OmniglotOneShot.__name__))
    model = OmniglotOneShot(
        num_embeddings=args.num_classes,
        output_size=args.num_classes,
        memory_size=args.memory_size,
        num_time_steps=args.num_time_steps,
        readout_delay=args.readout_delay,
        tau_trace=args.tau_trace,
        image_embedding_layer=image_embedding_layer,
        plasticity_rule=InvertedOjaWithSoftUpperBound(w_max=args.w_max,
                                                      gamma_pos=args.gamma_pos,
                                                      gamma_neg=args.gamma_neg),
        dynamics=IafPscDelta(thr=args.thr,
                             perfect_reset=args.perfect_reset,
                             refractory_time_steps=args.refractory_time_steps,
                             tau_mem=args.tau_mem,
                             spike_function=SpikeFunction,
                             dampening_factor=args.dampening_factor))

    if not torch.cuda.is_available():
        print('using CPU, this will be slow')
    elif args.distributed:
        # For multiprocessing distributed, DistributedDataParallel constructor
        # should always set the single device scope, otherwise,
        # DistributedDataParallel will use all available devices.
        if args.gpu is not None:
            torch.cuda.set_device(args.gpu)
            model.cuda(args.gpu)
            # When using a single GPU per process and per
            # DistributedDataParallel, we need to divide the batch size
            # ourselves based on the total number of GPUs we have
            args.batch_size = int(args.batch_size / num_gpus_per_node)
            args.workers = int((args.workers + num_gpus_per_node - 1) / num_gpus_per_node)
            model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
        else:
            model.cuda()
            # DistributedDataParallel will divide and allocate batch_size to all
            # available GPUs if device_ids are not set
            model = torch.nn.parallel.DistributedDataParallel(model)
    elif args.gpu is not None:
        torch.cuda.set_device(args.gpu)
        model = model.cuda(args.gpu)
    else:
        # DataParallel will divide and allocate batch_size to all available GPUs
        model = torch.nn.DataParallel(model).cuda()

    # Define loss function (criterion) and optimizer
    criterion = torch.nn.CrossEntropyLoss().cuda(args.gpu)
    optimizer = torch.optim.Adam(model.parameters(), args.learning_rate)

    # Optionally resume from a checkpoint
    if args.resume:
        if os.path.isfile(args.resume):
            print("=> loading checkpoint '{}'".format(args.resume))
            if args.gpu is None:
                checkpoint = utils.checkpoint.load_checkpoint(args.resume)
            else:
                # Map model to be loaded to specified single gpu.
                checkpoint = utils.checkpoint.load_checkpoint(args.resume, 'cuda:{}'.format(args.gpu))
            args.start_epoch = checkpoint['epoch']
            best_acc = checkpoint['best_acc']
            log_dir = checkpoint['log_dir']
            time_stamp = checkpoint['time_stamp']

            # Checkpoint parameters have to match current parameters. If not, abort.
            ignore_keys = ['workers', 'prefetch_factor', 'pin_data_to_memory', 'epochs', 'start_epoch', 'resume',
                           'evaluate', 'logging', 'print_freq', 'world_size', 'rank', 'dist_url', 'dist_backend',
                           'seed', 'gpu', 'multiprocessing_distributed', 'distributed', 'dir']
            if args.evaluate:
                ignore_keys.append('batch_size')

            for key, val in vars(checkpoint['params']).items():
                if key not in ignore_keys:
                    if vars(args)[key] != val:
                        raise Exception("=> You tried to restart training of a model that was trained with different "
                                        "parameters as you requested now. Aborting...")

            if args.gpu is not None:
                # best_acc may be from a checkpoint from a different GPU
                best_acc = best_acc.to(args.gpu)
            model.load_state_dict(checkpoint['state_dict'])
            optimizer.load_state_dict(checkpoint['optimizer'])
            print("=> loaded checkpoint '{}' (epoch {})".format(args.resume, checkpoint['epoch']))
        else:
            print("=> no checkpoint found at '{}'".format(args.resume))

    cudnn.benchmark = True

    if args.distributed:
        train_sampler = DistributedSamplerWrapper(init_sampler(train_set.y, args))
        val_sampler = DistributedSamplerWrapper(init_sampler(val_set.y, args))
    else:
        train_sampler = init_sampler(train_set.y, args)
        val_sampler = init_sampler(val_set.y, args)
    test_sampler = init_sampler(test_set.y, args)

    train_loader = torch.utils.data.DataLoader(train_set, batch_sampler=train_sampler,
                                               num_workers=args.workers, pin_memory=args.pin_data_to_memory)
    val_loader = torch.utils.data.DataLoader(val_set, batch_sampler=val_sampler,
                                             num_workers=args.workers, pin_memory=args.pin_data_to_memory)
    test_loader = torch.utils.data.DataLoader(test_set, batch_sampler=test_sampler,
                                              num_workers=args.workers, pin_memory=args.pin_data_to_memory)

    if args.evaluate:
        validate(test_loader, model, criterion, args, prefix='Test: ')
        return

    if not args.multiprocessing_distributed or (args.multiprocessing_distributed and
                                                args.rank % num_gpus_per_node == 0):
        if log_dir and args.logging:
            # Use the directory that is stored in checkpoint if we resume training
            writer = SummaryWriter(log_dir=log_dir)
        elif args.logging:
            log_dir = os.path.join('results', 'runs', time_stamp +
                                   '_' + socket.gethostname() +
                                   f'_version-{version}-{suffix}_omniglot_one-shot_task')
            writer = SummaryWriter(log_dir=log_dir)

            def pretty_json(hp):
                json_hp = json.dumps(hp, indent=2, sort_keys=False)
                return "".join('\t' + line for line in json_hp.splitlines(True))

            writer.add_text('Info/params', pretty_json(vars(args)))

    for epoch in range(args.start_epoch, args.epochs):
        if args.distributed:
            train_sampler.set_epoch(epoch)
        current_lr = adjust_learning_rate(optimizer, epoch, args)

        # Train for one epoch
        train_loss, reg_loss, train_acc = train(train_loader, model, criterion, optimizer, epoch, args)

        # Evaluate on validation set
        val_loss, val_acc = validate(val_loader, model, criterion, args)

        # Remember best acc@1 and save checkpoint
        is_best = val_acc > best_acc
        best_acc = max(val_acc, best_acc)

        if not args.multiprocessing_distributed or (args.multiprocessing_distributed and
                                                    args.rank % num_gpus_per_node == 0):
            if args.logging:
                writer.add_scalar('Loss/train', train_loss, epoch)
                writer.add_scalar('Acc/train', train_acc, epoch)
                writer.add_scalar('Loss/val', val_loss, epoch)
                writer.add_scalar('Acc/val', val_acc, epoch)
                writer.add_scalar('Misc/lr', current_lr, epoch)
                writer.add_scalar('Misc/reg_loss', reg_loss, epoch)
                if epoch + 1 == args.epochs:
                    writer.flush()

            utils.checkpoint.save_checkpoint({
                'epoch': epoch + 1,
                'state_dict': model.state_dict(),
                'best_acc': best_acc,
                'optimizer': optimizer.state_dict(),
                'log_dir': writer.get_logdir() if args.logging else '',
                'time_stamp': time_stamp,
                'params': args
            }, is_best, filename=os.path.join(
                'results', 'checkpoints', time_stamp +
                '_' + socket.gethostname() + f'_version-{version}-{suffix}_omniglot_one-shot_task'))


def train(train_loader, model, criterion, optimizer, epoch, args):
    batch_time = utils.meters.AverageMeter('Time', ':6.3f')
    data_time = utils.meters.AverageMeter('Data', ':6.3f')
    losses = utils.meters.AverageMeter('Loss', ':.4e')
    reg_losses = utils.meters.AverageMeter('RegLoss', ':.4e')
    top1 = utils.meters.AverageMeter('Acc', ':6.2f')
    progress = utils.meters.ProgressMeter(
        len(train_loader),
        [batch_time, data_time, losses, top1],
        prefix="Epoch: [{}]".format(epoch))

    # Switch to train mode
    model.train()

    end = time.time()
    for i, sample in enumerate(train_loader):
        # Measure data loading time
        data_time.update(time.time() - end)

        facts, labels, query, answer = parse_sample(sample, args)

        if args.gpu is not None:
            facts = facts.cuda(args.gpu, non_blocking=True)
            labels = labels.cuda(args.gpu, non_blocking=True)
            query = query.cuda(args.gpu, non_blocking=True)
        if torch.cuda.is_available():
            answer = answer.cuda(args.gpu, non_blocking=True)

        # Compute output
        output, encoding_outputs, writing_outputs, reading_outputs = model(facts, labels, query)
        loss = criterion(output, answer)

        # Regularization
        def compute_l2_loss(x, target, weight):
            if isinstance(weight, torch.Tensor):
                mean = torch.mean(torch.sum(x, dim=1) / weight.unsqueeze(1), dim=0)
            else:
                mean = torch.mean(torch.sum(x, dim=1) / weight, dim=0)

            return torch.mean((mean - target) ** 2)

        l2_act_reg_loss = 0
        weight_query = 1e-3 * args.num_time_steps
        weight_facts = 1e-3 * args.num_time_steps * args.num_classes
        l2_act_reg_loss += compute_l2_loss(encoding_outputs[1], args.target_rate, weight=weight_facts)
        if not args.freeze_protonet:
            l2_act_reg_loss += compute_l2_loss(encoding_outputs[0], args.target_rate, weight=weight_facts)
            l2_act_reg_loss += compute_l2_loss(encoding_outputs[2], args.target_rate, weight=weight_query)

        l2_act_reg_loss += compute_l2_loss(writing_outputs[1], args.target_rate, weight=weight_facts)
        l2_act_reg_loss += compute_l2_loss(writing_outputs[2], args.target_rate, weight=weight_facts)

        l2_act_reg_loss += compute_l2_loss(reading_outputs[0], args.target_rate, weight=weight_query)
        l2_act_reg_loss += compute_l2_loss(reading_outputs[1], args.target_rate, weight=weight_query)

        act_reg_loss = args.l2 * l2_act_reg_loss

        if epoch > 0:
            # Start regularizing after an initial training period
            loss += act_reg_loss

        # Measure accuracy and record loss
        acc = utils.metrics.accuracy(output, answer, top_k=(1,))

        losses.update(loss.item(), facts.size()[0])
        reg_losses.update(act_reg_loss, facts.size()[0])
        top1.update(acc[0], facts.size()[0])

        # Compute gradient
        optimizer.zero_grad(set_to_none=True)
        loss.backward()

        if args.max_grad_norm is not None:
            # Clip L2 norm of gradient to max_grad_norm
            torch.nn.utils.clip_grad_norm_(parameters=model.parameters(), max_norm=args.max_grad_norm)

        # Do SGD step
        optimizer.step()

        # Measure elapsed time
        batch_time.update(time.time() - end)
        end = time.time()

        if i % args.print_freq == 0:
            progress.display(i)

    return losses.avg, reg_losses.avg, top1.avg


def validate(data_loader, model, criterion, args, prefix="Val: "):
    batch_time = utils.meters.AverageMeter('Time', ':6.3f')
    losses = utils.meters.AverageMeter('Loss', ':.4e')
    top1 = utils.meters.AverageMeter('Acc', ':6.2f')
    progress = utils.meters.ProgressMeter(
        len(data_loader),
        [batch_time, losses, top1],
        prefix=prefix)

    # Switch to evaluate mode
    model.eval()

    with torch.no_grad():
        end = time.time()
        for i, sample in enumerate(data_loader):

            facts, labels, query, answer = parse_sample(sample, args)

            if args.gpu is not None:
                facts = facts.cuda(args.gpu, non_blocking=True)
                labels = labels.cuda(args.gpu, non_blocking=True)
                query = query.cuda(args.gpu, non_blocking=True)
            if torch.cuda.is_available():
                answer = answer.cuda(args.gpu, non_blocking=True)

            # Compute output
            output, *_ = model(facts, labels, query)
            loss = criterion(output, answer)

            # Measure accuracy and record loss
            acc = utils.metrics.accuracy(output, answer, top_k=(1,))
            losses.update(loss.item(), facts.size()[0])
            top1.update(acc[0], facts.size()[0])

            # Measure elapsed time
            batch_time.update(time.time() - end)
            end = time.time()

            if i % args.print_freq == 0:
                progress.display(i)

        print(' * Acc {top1.avg:.3f}'.format(top1=top1))

    return losses.avg, top1.avg


def adjust_learning_rate(optimizer, epoch, args):
    """Sets the learning rate to the initial LR decayed by X% every N epochs"""
    lr = args.learning_rate * (args.learning_rate_decay ** (epoch // args.decay_learning_rate_every))
    for param_group in optimizer.param_groups:
        param_group['lr'] = lr

    return lr


def init_sampler(labels: List[int], args: argparse.Namespace) -> EpisodicBatchSampler:

    return EpisodicBatchSampler(labels=labels,
                                num_classes=args.num_classes,
                                batch_size=args.batch_size,
                                iterations=args.iterations,
                                seed=args.sampler_seed)


def parse_sample(sample: Tuple[torch.Tensor, torch.Tensor], args: argparse.Namespace) -> Tuple[
        torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
    x, y = sample
    x = x.view(args.batch_size, -1, *x.size()[1:])
    y = y.view(args.batch_size, -1)

    facts = x[:, :args.num_classes]
    query = x[:, -1]

    labels = torch.arange(0, args.num_classes)
    labels = labels.expand(args.batch_size, args.num_classes).long()

    answer = labels[y[:, :-1] == y[:, -1].view(args.batch_size, -1)]

    return facts.contiguous(), labels.contiguous(), query.contiguous(), answer.contiguous()


if __name__ == '__main__':
    main()