How to train Propos on STL-10 dataset

[Regan-Zhang]

Propos is a charming work, and thanks to your code. I want to ask how to train Propos on STL-10 cos I wonder if the unlabeled images are used for training. Hope that you could show the code. Thanks.

[Hzzone]

Hi, plz refer to #5 (comment)

There are little changes in my codebase, you can take the code below as a reference:

    def set_loader(self):
        opt = self.opt
        dataset_name = opt.dataset

        if dataset_name not in dataset_dict.keys():
            mean, std = (0.485, 0.456, 0.406), (0.229, 0.224, 0.225)
            self.logger.msg_str(f'Dataset does {dataset_name} not exist in dataset_dict,'
                                f' use default normalizations: mean {str(mean)}, std {str(std)}.')
        else:
            mean, std = dataset_dict[dataset_name]

        normalize = transforms.Normalize(mean=mean, std=std, inplace=True)

        train_transform = self.train_transform(normalize)
        self.logger.msg_str('set transforms...')
        self.logger.msg_str(train_transform)

        self.logger.msg_str('set train and unlabeled dataloaders...')
        train_loader, labels, train_sampler = self.build_dataloader(
            transform=train_transform,
            batch_size=opt.batch_size,
            shuffle=True,
            drop_last=True,
            sampler=True,
            train=True)
        unlabeled_loader, _, unlabeled_sampler = self.build_dataloader(
            transform=train_transform,
            batch_size=opt.batch_size,
            shuffle=True,
            drop_last=True,
            sampler=True,
            train=False)

        test_transform = []
        if 'imagenet' in dataset_name:
            test_transform += [transforms.Resize(256), transforms.CenterCrop(224)]
        test_transform += [
            transforms.Resize(opt.img_size),
            transforms.ToTensor(),
            normalize
        ]

        test_transform = transforms.Compose(test_transform)

        # self.logger.msg_str('set test dataloaders...')
        # test_loader = self.build_dataloader(test_transform, train=False, batch_size=opt.batch_size)[0]
        self.logger.msg_str('set memory dataloaders...')
        memory_loader = self.build_dataloader(test_transform, train=True, batch_size=opt.batch_size, sampler=True)[0]

        # self.test_loader = test_loader
        self.train_loader = train_loader
        self.memory_loader = memory_loader
        self.unlabeled_loader = unlabeled_loader
        self.train_sampler = train_sampler
        self.unlabeled_sampler = unlabeled_sampler

        self.iter_per_epoch = len(train_loader) + len(unlabeled_loader)
        self.num_classes = len(np.unique(labels))
        self.num_samples = len(labels)
        self.indices = torch.zeros(len(self.train_sampler), dtype=torch.long).cuda()
        self.num_cluster = self.num_classes if opt.num_cluster is None else opt.num_cluster
        self.psedo_labels = torch.zeros((self.num_samples,)).long().cuda()

        self.logger.msg_str('load {} images...'.format(self.num_samples))

    def fit(self):
        opt = self.opt
        # training routine
        self.progress_bar = tqdm.tqdm(total=self.iter_per_epoch * opt.epochs, disable=(self.rank != 0))

        n_iter = self.iter_per_epoch * opt.resume_epoch + 1
        self.progress_bar.update(n_iter)
        max_iter = opt.epochs * self.iter_per_epoch

        while True:
            epoch = int(n_iter // self.iter_per_epoch + 1)
            self.train_sampler.set_epoch(epoch)
            self.unlabeled_sampler.set_epoch(epoch)

            for inputs in self.unlabeled_loader:
                inputs = convert_to_cuda(inputs)
                self.train_unlabeled(inputs, n_iter)
                self.progress_bar.refresh()
                self.progress_bar.update()
                n_iter += 1

            apply_kmeans = epoch % opt.reassign == 0
            if apply_kmeans:
                self.psedo_labeling(n_iter)

            self.indices.copy_(torch.Tensor(list(iter(self.train_sampler))))
            for inputs in self.train_loader:
                inputs = convert_to_cuda(inputs)
                self.adjust_learning_rate(n_iter)
                self.train(inputs, n_iter)
                self.progress_bar.refresh()
                self.progress_bar.update()
                n_iter += 1
            # if epoch % opt.save_freq == 0:
            # self.logger.checkpoints(int(epcoch))
            # self.test(n_iter)

            if n_iter > max_iter:
                break

def train(self, inputs, n_iter):
        opt = self.opt

        images, labels = inputs
        self.byol.train()

        im_q, im_k = images

        _start = ((n_iter - 1) % self.iter_per_epoch - len(self.unlabeled_loader)) * opt.batch_size
        indices = self.indices[_start: _start + opt.batch_size]
        psedo_labels = self.psedo_labels[indices]

        # compute loss
        contrastive_loss, cluster_loss_batch, q = self.byol(
            im_q, im_k, psedo_labels)

        loss = contrastive_loss
        if ((n_iter - 1) / self.iter_per_epoch) > opt.warmup_epochs:
            loss += cluster_loss_batch * opt.cluster_loss_weight

        self.optimizer.zero_grad()
        # SGD
        loss.backward()
        self.optimizer.step()

        with torch.no_grad():
            q_std = torch.std(q.detach(), dim=0).mean()

        outputs = [contrastive_loss, cluster_loss_batch, q_std]
        self.logger.msg(outputs, n_iter)

    def train_unlabeled(self, inputs, n_iter):
        opt = self.opt

        images, labels = inputs
        self.byol.train()

        im_q, im_k = images

        # compute loss
        unlabeled_contrastive_loss, _, q = self.byol(im_q, im_k, None)

        self.optimizer.zero_grad()
        # SGD
        unlabeled_contrastive_loss.backward()
        self.optimizer.step()

        with torch.no_grad():
            unlabeled_q_std = torch.std(q.detach(), dim=0).mean()

        outputs = [unlabeled_contrastive_loss, unlabeled_q_std]
        self.logger.msg(outputs, n_iter)