deep-person-reid/torchreid/engine/image/triplet.py

from __future__ import division, print_function, absolute_import
import time
import datetime

from torchreid import metrics
from torchreid.utils import (
    AverageMeter, open_all_layers, open_specified_layers
)
from torchreid.engine import Engine
from torchreid.losses import TripletLoss, CrossEntropyLoss


class ImageTripletEngine(Engine):

    r"""Triplet-loss engine for image-reid.

    Args:
        datamanager (DataManager): an instance of ``torchreid.data.ImageDataManager``
            or ``torchreid.data.VideoDataManager``.
        model (nn.Module): model instance.
        optimizer (Optimizer): an Optimizer.
        margin (float, optional): margin for triplet loss. Default is 0.3.
        weight_t (float, optional): weight for triplet loss. Default is 1.
        weight_x (float, optional): weight for softmax loss. Default is 1.
        scheduler (LRScheduler, optional): if None, no learning rate decay will be performed.
        use_gpu (bool, optional): use gpu. Default is True.
        label_smooth (bool, optional): use label smoothing regularizer. Default is True.

    Examples::
        
        import torchreid
        datamanager = torchreid.data.ImageDataManager(
            root='path/to/reid-data',
            sources='market1501',
            height=256,
            width=128,
            combineall=False,
            batch_size=32,
            num_instances=4,
            train_sampler='RandomIdentitySampler' # this is important
        )
        model = torchreid.models.build_model(
            name='resnet50',
            num_classes=datamanager.num_train_pids,
            loss='triplet'
        )
        model = model.cuda()
        optimizer = torchreid.optim.build_optimizer(
            model, optim='adam', lr=0.0003
        )
        scheduler = torchreid.optim.build_lr_scheduler(
            optimizer,
            lr_scheduler='single_step',
            stepsize=20
        )
        engine = torchreid.engine.ImageTripletEngine(
            datamanager, model, optimizer, margin=0.3,
            weight_t=0.7, weight_x=1, scheduler=scheduler
        )
        engine.run(
            max_epoch=60,
            save_dir='log/resnet50-triplet-market1501',
            print_freq=10
        )
    """

    def __init__(
        self,
        datamanager,
        model,
        optimizer,
        margin=0.3,
        weight_t=1,
        weight_x=1,
        scheduler=None,
        use_gpu=True,
        label_smooth=True
    ):
        super(ImageTripletEngine, self
              ).__init__(datamanager, model, optimizer, scheduler, use_gpu)

        self.weight_t = weight_t
        self.weight_x = weight_x

        self.criterion_t = TripletLoss(margin=margin)
        self.criterion_x = CrossEntropyLoss(
            num_classes=self.datamanager.num_train_pids,
            use_gpu=self.use_gpu,
            label_smooth=label_smooth
        )

    def train(
        self,
        epoch,
        max_epoch,
        writer,
        print_freq=10,
        fixbase_epoch=0,
        open_layers=None
    ):
        losses_t = AverageMeter()
        losses_x = AverageMeter()
        accs = AverageMeter()
        batch_time = AverageMeter()
        data_time = AverageMeter()

        self.model.train()
        if (epoch + 1) <= fixbase_epoch and open_layers is not None:
            print(
                '* Only train {} (epoch: {}/{})'.format(
                    open_layers, epoch + 1, fixbase_epoch
                )
            )
            open_specified_layers(self.model, open_layers)
        else:
            open_all_layers(self.model)

        num_batches = len(self.train_loader)
        end = time.time()
        for batch_idx, data in enumerate(self.train_loader):
            data_time.update(time.time() - end)

            imgs, pids = self._parse_data_for_train(data)
            if self.use_gpu:
                imgs = imgs.cuda()
                pids = pids.cuda()

            self.optimizer.zero_grad()
            outputs, features = self.model(imgs)
            loss_t = self._compute_loss(self.criterion_t, features, pids)
            loss_x = self._compute_loss(self.criterion_x, outputs, pids)
            loss = self.weight_t * loss_t + self.weight_x * loss_x
            loss.backward()
            self.optimizer.step()

            batch_time.update(time.time() - end)

            losses_t.update(loss_t.item(), pids.size(0))
            losses_x.update(loss_x.item(), pids.size(0))
            accs.update(metrics.accuracy(outputs, pids)[0].item())

            if (batch_idx+1) % print_freq == 0:
                # estimate remaining time
                eta_seconds = batch_time.avg * (
                    num_batches - (batch_idx+1) + (max_epoch -
                                                   (epoch+1)) * num_batches
                )
                eta_str = str(datetime.timedelta(seconds=int(eta_seconds)))
                print(
                    'Epoch: [{0}/{1}][{2}/{3}]\t'
                    'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                    'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
                    'Loss_t {loss_t.val:.4f} ({loss_t.avg:.4f})\t'
                    'Loss_x {loss_x.val:.4f} ({loss_x.avg:.4f})\t'
                    'Acc {acc.val:.2f} ({acc.avg:.2f})\t'
                    'Lr {lr:.6f}\t'
                    'eta {eta}'.format(
                        epoch + 1,
                        max_epoch,
                        batch_idx + 1,
                        num_batches,
                        batch_time=batch_time,
                        data_time=data_time,
                        loss_t=losses_t,
                        loss_x=losses_x,
                        acc=accs,
                        lr=self.optimizer.param_groups[0]['lr'],
                        eta=eta_str
                    )
                )

            if writer is not None:
                n_iter = epoch*num_batches + batch_idx
                writer.add_scalar('Train/Time', batch_time.avg, n_iter)
                writer.add_scalar('Train/Data', data_time.avg, n_iter)
                writer.add_scalar('Train/Loss_t', losses_t.avg, n_iter)
                writer.add_scalar('Train/Loss_x', losses_x.avg, n_iter)
                writer.add_scalar('Train/Acc', accs.avg, n_iter)
                writer.add_scalar(
                    'Train/Lr', self.optimizer.param_groups[0]['lr'], n_iter
                )

            end = time.time()

        if self.scheduler is not None:
            self.scheduler.step()