<gh_stars>1-10
from __future__ import print_function

import argparse
import csv
import os
import os.path
import shutil
import time

import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.parallel
import torch.optim as optim
import torch.utils.data as data
import torchvision.datasets as datasets
import torchvision.models as models
import torchvision.transforms as transforms
from PIL import Image

model_names = sorted(name for name in models.__dict__ if name.islower() and not name.startswith("__"))

parser = argparse.ArgumentParser(description='PyTorch Cats vs Dogs fine-tuning example')
parser.add_argument('data', metavar='DIR', help='path to dataset')
parser.add_argument(
    '--arch',
    metavar='ARCH',
    default='resnet101',
    choices=model_names,
    help='model architecture: ' + ' | '.join(model_names) + ' (default: resnet101)')
parser.add_argument('--workers', default=4, type=int, metavar='N', help='number of data loading workers (default: 4)')
parser.add_argument('--epochs', default=90, type=int, metavar='N', help='number of total epochs to run')
parser.add_argument('--start-epoch', default=0, type=int, metavar='N', help='manual epoch number (useful on restarts)')
parser.add_argument('-b', '--batch-size', default=256, type=int, metavar='N', help='mini-batch size (default: 256)')
parser.add_argument('--lr', '--learning-rate', default=1e-4, type=float, metavar='LR', help='initial learning rate')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M', help='momentum')
parser.add_argument('--weight-decay', default=1e-4, type=float, metavar='W', help='weight decay')
parser.add_argument('--print-freq', default=1, type=int, metavar='N', help='print frequency')
parser.add_argument('--resume', default='', type=str, metavar='PATH', help='path to latest checkpoint')
parser.add_argument('--evaluate', dest='evaluate', action='store_true', help='evaluate model on validation set')
parser.add_argument('--test', dest='test', action='store_true', help='evaluate model on test set')
parser.add_argument('--pretrained', dest='pretrained', action='store_true', help='use pre-trained model')

best_prec1 = 0


def main():
    global args, best_prec1
    args = parser.parse_args()

    # create model
    if args.pretrained:
        print("=> using pre-trained model '{}'".format(args.arch))
        model = models.__dict__[args.arch](pretrained=True)
        # Don't update non-classifier learned features in the pretrained networks
        for param in model.parameters():
            param.requires_grad = False
        # Replace the last fully-connected layer
        # Parameters of newly constructed modules have requires_grad=True by default
        # Final dense layer needs to replaced with the previous out chans, and number of classes
        # in this case -- resnet 101 - it's 2048 with two classes (cats and dogs)
        if args.arch == 'resnet101':
            model.fc = nn.Linear(2048, 2)
        elif args.arch == 'resnet18':
            model.fc = nn.Linear(512,2)

    else:
        print("=> creating model '{}'".format(args.arch))
        model = models.__dict__[args.arch]()

    if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
        model.features = torch.nn.DataParallel(model.features)
        model.cuda()
    else:
        model = torch.nn.DataParallel(model).cuda()
    # optionally resume from a checkpoint
    if args.resume:
        if os.path.isfile(args.resume):
            print("=> loading checkpoint '{}'".format(args.resume))
            checkpoint = torch.load(args.resume)
            args.start_epoch = checkpoint['epoch']
            best_prec1 = checkpoint['best_prec1']
            model.load_state_dict(checkpoint['state_dict'])
            print("=> loaded checkpoint '{}' (epoch {})".format(args.evaluate, checkpoint['epoch']))
        else:
            print("=> no checkpoint found at '{}'".format(args.resume))

    cudnn.benchmark = True

    # Data loading code
    traindir = os.path.join(args.data, 'train')
    valdir = os.path.join(args.data, 'val')
    print(valdir)
    testdir = os.path.join(args.data, 'test')

    #normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])

    train_loader = data.DataLoader(
        datasets.ImageFolder(traindir,
                             transforms.Compose([
                                 #transforms.RandomSizedCrop(224),
                                 transforms.Resize((218,178)),
                                 transforms.CenterCrop(178),
                                 transforms.Resize((224,224)),
                                 transforms.RandomHorizontalFlip(),
                                 transforms.ToTensor()
                                 #normalize,
                             ])),
        batch_size=args.batch_size,
        shuffle=True,
        num_workers=args.workers,
        pin_memory=True)

    val_loader = data.DataLoader(
        datasets.ImageFolder(valdir,
                             transforms.Compose([
                                 transforms.Scale(256),
                                 transforms.CenterCrop(224),
                                 transforms.ToTensor()
                                 #normalize,
                             ])),
        batch_size=args.batch_size,
        shuffle=True,
        num_workers=args.workers,
        pin_memory=True)

    test_loader = data.DataLoader(
        TestImageFolder(testdir,
                        transforms.Compose([
                            transforms.Scale(256),
                            transforms.CenterCrop(224),
                            transforms.ToTensor()
                            #normalize,
                        ])),
        batch_size=1,
        shuffle=False,
        num_workers=1,
        pin_memory=False)

    if args.test:
        print("Testing the model and generating a output csv for submission")
        test(test_loader, model)
        return
    # define loss function (criterion) and pptimizer
    criterion = nn.CrossEntropyLoss().cuda()

    optimizer = optim.Adam(model.module.fc.parameters(), args.lr, weight_decay=args.weight_decay)

    if args.evaluate:
        validate(val_loader, model, criterion)
        return

    for epoch in range(args.start_epoch, args.epochs):
        adjust_learning_rate(optimizer, epoch)

        # train for one epoch
        train(train_loader, model, criterion, optimizer, epoch)

        # evaluate on validation set
        prec1 = validate(val_loader, model, criterion)

        # remember best Accuracy and save checkpoint
        is_best = prec1 > best_prec1
        best_prec1 = max(prec1, best_prec1)
        save_checkpoint({
            'epoch': epoch + 1,
            'arch': args.arch,
            'state_dict': model.state_dict(),
            'best_prec1': best_prec1,
        }, is_best)


def train(train_loader, model, criterion, optimizer, epoch):
    batch_time = AverageMeter()
    data_time = AverageMeter()
    losses = AverageMeter()
    acc = AverageMeter()

    # switch to train mode
    model.train()

    end = time.time()
    for i, (images, target) in enumerate(train_loader):
        # measure data loading time
        data_time.update(time.time() - end)

        target = target.cuda(async=True)
        image_var = torch.autograd.Variable(images)
        label_var = torch.autograd.Variable(target)

        # compute y_pred
        y_pred = model(image_var)
        loss = criterion(y_pred, label_var)

        # measure accuracy and record loss
        prec1, prec1 = accuracy(y_pred.data, target, topk=(1, 1))
        losses.update(loss.item(), images.size(0))
        acc.update(prec1.item(), images.size(0))

        # compute gradient and do SGD step
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

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

        if i % args.print_freq == 0:
            print('Epoch: [{0}][{1}/{2}]\t'
                  'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                  'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
                  'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
                  'Accuracy {acc.val:.3f} ({acc.avg:.3f})'.format(
                      epoch, i, len(train_loader), batch_time=batch_time, data_time=data_time, loss=losses, acc=acc))


def validate(val_loader, model, criterion):
    batch_time = AverageMeter()
    losses = AverageMeter()
    acc = AverageMeter()

    # switch to evaluate mode
    model.eval()

    end = time.time()
    for i, (images, labels) in enumerate(val_loader):
        labels = labels.cuda(async=True)
        image_var = torch.autograd.Variable(images, volatile=True)
        label_var = torch.autograd.Variable(labels, volatile=True)

        # compute y_pred
        y_pred = model(image_var)
        loss = criterion(y_pred, label_var)

        # measure accuracy and record loss
        prec1, temp_var = accuracy(y_pred.data, labels, topk=(1, 1))
        #losses.update(loss.data[0], images.size(0))
        losses.update(loss.item(), images.size(0))
        acc.update(prec1.item(), images.size(0))

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

        if i % args.print_freq == 0:
            print('TrainVal: [{0}/{1}]\t'
                  'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                  'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
                  'Accuracy {acc.val:.3f} ({acc.avg:.3f})'.format(
                      i, len(val_loader), batch_time=batch_time, loss=losses, acc=acc))

    print(' * Accuracy {acc.avg:.3f}'.format(acc=acc))

    return acc.avg


def test(test_loader, model):
    csv_map = {}
    # switch to evaluate mode
    model.eval()
    for i, (images, filepath) in enumerate(test_loader):
        # pop extension, treat as id to map
        filepath = os.path.splitext(os.path.basename(filepath[0]))[0]
        filepath = int(filepath)

        image_var = torch.autograd.Variable(images, volatile=True)
        y_pred = model(image_var)
        # get the index of the max log-probability
        smax = nn.Softmax()
        smax_out = smax(y_pred)[0]
        cat_prob = smax_out.data[0]
        dog_prob = smax_out.data[1]
        prob = dog_prob
        if cat_prob > dog_prob:
            prob = 1 - cat_prob
        prob = np.around(prob, decimals=4)
        prob = np.clip(prob, .0001, .999)
        csv_map[filepath] = prob
        # print("{},{}".format(filepath, prob))

    with open(os.path.join(args.data, 'entry.csv'), 'wb') as csvfile:
        fieldnames = ['id', 'label']
        csv_w = csv.writer(csvfile)
        csv_w.writerow(('id', 'label'))
        for row in sorted(csv_map.items()):
            csv_w.writerow(row)

    return


def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
    torch.save(state, filename)
    if is_best:
        shutil.copyfile(filename, 'model_best.pth.tar')


class AverageMeter(object):
    """Computes and stores the average and current value"""

    def __init__(self):
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count


def adjust_learning_rate(optimizer, epoch):
    """Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
    lr = args.lr * (0.1**(epoch // 30))
    for param_group in optimizer.state_dict()['param_groups']:
        param_group['lr'] = lr


def accuracy(y_pred, y_actual, topk=(1, )):
    """Computes the precision@k for the specified values of k"""
    maxk = max(topk)
    batch_size = y_actual.size(0)

    _, pred = y_pred.topk(maxk, 1, True, True)
    pred = pred.t()
    correct = pred.eq(y_actual.view(1, -1).expand_as(pred))

    res = []
    for k in topk:
        correct_k = correct[:k].view(-1).float().sum(0)
        res.append(correct_k.mul_(100.0 / batch_size))

    return res


class TestImageFolder(data.Dataset):
    def __init__(self, root, transform=None):
        images = []
        for filename in os.listdir(root):
            if filename.endswith('jpg'):
                images.append('{}'.format(filename))

        self.root = root
        self.imgs = images
        self.transform = transform

    def __getitem__(self, index):
        filename = self.imgs[index]
        img = Image.open(os.path.join(self.root, filename))
        if self.transform is not None:
            img = self.transform(img)
        return img, filename

    def __len__(self):
        return len(self.imgs)


if __name__ == '__main__':
    main()

from __future__ import print_function

import argparse
import csv
import os
import os.path
import shutil
import time

import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.parallel
import torch.optim as optim
import torch.utils.data as data
import torchvision.datasets as datasets
import torchvision.models as models
import torchvision.transforms as transforms
from PIL import Image

model_names = sorted(name for name in models.__dict__ if name.islower() and not name.startswith("__"))

parser = argparse.ArgumentParser(description='PyTorch Cats vs Dogs fine-tuning example')
parser.add_argument('data', metavar='DIR', help='path to dataset')
parser.add_argument(
    '--arch',
    metavar='ARCH',
    default='resnet101',
    choices=model_names,
    help='model architecture: ' + ' | '.join(model_names) + ' (default: resnet101)')
parser.add_argument('--workers', default=4, type=int, metavar='N', help='number of data loading workers (default: 4)')
parser.add_argument('--epochs', default=90, type=int, metavar='N', help='number of total epochs to run')
parser.add_argument('--start-epoch', default=0, type=int, metavar='N', help='manual epoch number (useful on restarts)')
parser.add_argument('-b', '--batch-size', default=256, type=int, metavar='N', help='mini-batch size (default: 256)')
parser.add_argument('--lr', '--learning-rate', default=1e-4, type=float, metavar='LR', help='initial learning rate')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M', help='momentum')
parser.add_argument('--weight-decay', default=1e-4, type=float, metavar='W', help='weight decay')
parser.add_argument('--print-freq', default=1, type=int, metavar='N', help='print frequency')
parser.add_argument('--resume', default='', type=str, metavar='PATH', help='path to latest checkpoint')
parser.add_argument('--evaluate', dest='evaluate', action='store_true', help='evaluate model on validation set')
parser.add_argument('--test', dest='test', action='store_true', help='evaluate model on test set')
parser.add_argument('--pretrained', dest='pretrained', action='store_true', help='use pre-trained model')

best_prec1 = 0


def main():
    global args, best_prec1
    args = parser.parse_args()

    # create model
    if args.pretrained:
        print("=> using pre-trained model '{}'".format(args.arch))
        model = models.__dict__[args.arch](pretrained=True)
        # Don't update non-classifier learned features in the pretrained networks
        for param in model.parameters():
            param.requires_grad = False
        # Replace the last fully-connected layer
        # Parameters of newly constructed modules have requires_grad=True by default
        # Final dense layer needs to replaced with the previous out chans, and number of classes
        # in this case -- resnet 101 - it's 2048 with two classes (cats and dogs)
        if args.arch == 'resnet101':
            model.fc = nn.Linear(2048, 2)
        elif args.arch == 'resnet18':
            model.fc = nn.Linear(512,2)

    else:
        print("=> creating model '{}'".format(args.arch))
        model = models.__dict__[args.arch]()

    if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
        model.features = torch.nn.DataParallel(model.features)
        model.cuda()
    else:
        model = torch.nn.DataParallel(model).cuda()
    # optionally resume from a checkpoint
    if args.resume:
        if os.path.isfile(args.resume):
            print("=> loading checkpoint '{}'".format(args.resume))
            checkpoint = torch.load(args.resume)
            args.start_epoch = checkpoint['epoch']
            best_prec1 = checkpoint['best_prec1']
            model.load_state_dict(checkpoint['state_dict'])
            print("=> loaded checkpoint '{}' (epoch {})".format(args.evaluate, checkpoint['epoch']))
        else:
            print("=> no checkpoint found at '{}'".format(args.resume))

    cudnn.benchmark = True

    # Data loading code
    traindir = os.path.join(args.data, 'train')
    valdir = os.path.join(args.data, 'val')
    print(valdir)
    testdir = os.path.join(args.data, 'test')

    #normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])

    train_loader = data.DataLoader(
        datasets.ImageFolder(traindir,
                             transforms.Compose([
                                 #transforms.RandomSizedCrop(224),
                                 transforms.Resize((218,178)),
                                 transforms.CenterCrop(178),
                                 transforms.Resize((224,224)),
                                 transforms.RandomHorizontalFlip(),
                                 transforms.ToTensor()
                                 #normalize,
                             ])),
        batch_size=args.batch_size,
        shuffle=True,
        num_workers=args.workers,
        pin_memory=True)

    val_loader = data.DataLoader(
        datasets.ImageFolder(valdir,
                             transforms.Compose([
                                 transforms.Scale(256),
                                 transforms.CenterCrop(224),
                                 transforms.ToTensor()
                                 #normalize,
                             ])),
        batch_size=args.batch_size,
        shuffle=True,
        num_workers=args.workers,
        pin_memory=True)

    test_loader = data.DataLoader(
        TestImageFolder(testdir,
                        transforms.Compose([
                            transforms.Scale(256),
                            transforms.CenterCrop(224),
                            transforms.ToTensor()
                            #normalize,
                        ])),
        batch_size=1,
        shuffle=False,
        num_workers=1,
        pin_memory=False)

    if args.test:
        print("Testing the model and generating a output csv for submission")
        test(test_loader, model)
        return
    # define loss function (criterion) and pptimizer
    criterion = nn.CrossEntropyLoss().cuda()

    optimizer = optim.Adam(model.module.fc.parameters(), args.lr, weight_decay=args.weight_decay)

    if args.evaluate:
        validate(val_loader, model, criterion)
        return

    for epoch in range(args.start_epoch, args.epochs):
        adjust_learning_rate(optimizer, epoch)

        # train for one epoch
        train(train_loader, model, criterion, optimizer, epoch)

        # evaluate on validation set
        prec1 = validate(val_loader, model, criterion)

        # remember best Accuracy and save checkpoint
        is_best = prec1 > best_prec1
        best_prec1 = max(prec1, best_prec1)
        save_checkpoint({
            'epoch': epoch + 1,
            'arch': args.arch,
            'state_dict': model.state_dict(),
            'best_prec1': best_prec1,
        }, is_best)


def train(train_loader, model, criterion, optimizer, epoch):
    batch_time = AverageMeter()
    data_time = AverageMeter()
    losses = AverageMeter()
    acc = AverageMeter()

    # switch to train mode
    model.train()

    end = time.time()
    for i, (images, target) in enumerate(train_loader):
        # measure data loading time
        data_time.update(time.time() - end)

        target = target.cuda(async=True)
        image_var = torch.autograd.Variable(images)
        label_var = torch.autograd.Variable(target)

        # compute y_pred
        y_pred = model(image_var)
        loss = criterion(y_pred, label_var)

        # measure accuracy and record loss
        prec1, prec1 = accuracy(y_pred.data, target, topk=(1, 1))
        losses.update(loss.item(), images.size(0))
        acc.update(prec1.item(), images.size(0))

        # compute gradient and do SGD step
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

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

        if i % args.print_freq == 0:
            print('Epoch: [{0}][{1}/{2}]\t'
                  'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                  'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
                  'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
                  'Accuracy {acc.val:.3f} ({acc.avg:.3f})'.format(
                      epoch, i, len(train_loader), batch_time=batch_time, data_time=data_time, loss=losses, acc=acc))


def validate(val_loader, model, criterion):
    batch_time = AverageMeter()
    losses = AverageMeter()
    acc = AverageMeter()

    # switch to evaluate mode
    model.eval()

    end = time.time()
    for i, (images, labels) in enumerate(val_loader):
        labels = labels.cuda(async=True)
        image_var = torch.autograd.Variable(images, volatile=True)
        label_var = torch.autograd.Variable(labels, volatile=True)

        # compute y_pred
        y_pred = model(image_var)
        loss = criterion(y_pred, label_var)

        # measure accuracy and record loss
        prec1, temp_var = accuracy(y_pred.data, labels, topk=(1, 1))
        #losses.update(loss.data[0], images.size(0))
        losses.update(loss.item(), images.size(0))
        acc.update(prec1.item(), images.size(0))

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

        if i % args.print_freq == 0:
            print('TrainVal: [{0}/{1}]\t'
                  'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                  'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
                  'Accuracy {acc.val:.3f} ({acc.avg:.3f})'.format(
                      i, len(val_loader), batch_time=batch_time, loss=losses, acc=acc))

    print(' * Accuracy {acc.avg:.3f}'.format(acc=acc))

    return acc.avg


def test(test_loader, model):
    csv_map = {}
    # switch to evaluate mode
    model.eval()
    for i, (images, filepath) in enumerate(test_loader):
        # pop extension, treat as id to map
        filepath = os.path.splitext(os.path.basename(filepath[0]))[0]
        filepath = int(filepath)

        image_var = torch.autograd.Variable(images, volatile=True)
        y_pred = model(image_var)
        # get the index of the max log-probability
        smax = nn.Softmax()
        smax_out = smax(y_pred)[0]
        cat_prob = smax_out.data[0]
        dog_prob = smax_out.data[1]
        prob = dog_prob
        if cat_prob > dog_prob:
            prob = 1 - cat_prob
        prob = np.around(prob, decimals=4)
        prob = np.clip(prob, .0001, .999)
        csv_map[filepath] = prob
        # print("{},{}".format(filepath, prob))

    with open(os.path.join(args.data, 'entry.csv'), 'wb') as csvfile:
        fieldnames = ['id', 'label']
        csv_w = csv.writer(csvfile)
        csv_w.writerow(('id', 'label'))
        for row in sorted(csv_map.items()):
            csv_w.writerow(row)

    return


def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
    torch.save(state, filename)
    if is_best:
        shutil.copyfile(filename, 'model_best.pth.tar')


class AverageMeter(object):
    """Computes and stores the average and current value"""

    def __init__(self):
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count


def adjust_learning_rate(optimizer, epoch):
    """Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
    lr = args.lr * (0.1**(epoch // 30))
    for param_group in optimizer.state_dict()['param_groups']:
        param_group['lr'] = lr


def accuracy(y_pred, y_actual, topk=(1, )):
    """Computes the precision@k for the specified values of k"""
    maxk = max(topk)
    batch_size = y_actual.size(0)

    _, pred = y_pred.topk(maxk, 1, True, True)
    pred = pred.t()
    correct = pred.eq(y_actual.view(1, -1).expand_as(pred))

    res = []
    for k in topk:
        correct_k = correct[:k].view(-1).float().sum(0)
        res.append(correct_k.mul_(100.0 / batch_size))

    return res


class TestImageFolder(data.Dataset):
    def __init__(self, root, transform=None):
        images = []
        for filename in os.listdir(root):
            if filename.endswith('jpg'):
                images.append('{}'.format(filename))

        self.root = root
        self.imgs = images
        self.transform = transform

    def __getitem__(self, index):
        filename = self.imgs[index]
        img = Image.open(os.path.join(self.root, filename))
        if self.transform is not None:
            img = self.transform(img)
        return img, filename

    def __len__(self):
        return len(self.imgs)


if __name__ == '__main__':
    main()
# create model # Don't update non-classifier learned features in the pretrained networks # Replace the last fully-connected layer # Parameters of newly constructed modules have requires_grad=True by default # Final dense layer needs to replaced with the previous out chans, and number of classes # in this case -- resnet 101 - it's 2048 with two classes (cats and dogs) # optionally resume from a checkpoint # Data loading code #normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) #transforms.RandomSizedCrop(224), #normalize, #normalize, #normalize, # define loss function (criterion) and pptimizer # train for one epoch # evaluate on validation set # remember best Accuracy and save checkpoint # switch to train mode # measure data loading time # compute y_pred # measure accuracy and record loss # compute gradient and do SGD step # measure elapsed time # switch to evaluate mode # compute y_pred # measure accuracy and record loss #losses.update(loss.data[0], images.size(0)) # measure elapsed time # switch to evaluate mode # pop extension, treat as id to map # get the index of the max log-probability # print("{},{}".format(filepath, prob)) Computes and stores the average and current value Sets the learning rate to the initial LR decayed by 10 every 30 epochs Computes the precision@k for the specified values of k