diff --git a/CAGE_expression_inference-apvit/affectnet_annotations/train_set_annotation_without_lnd.csv b/CAGE_expression_inference-apvit/affectnet_annotations/train_set_annotation_without_lnd.csv
new file mode 100644
index 0000000000000000000000000000000000000000..2b543ad7e39559302395fd56b79d61df5bd5f2e2
--- /dev/null
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diff --git a/CAGE_expression_inference-apvit/affectnet_annotations/val_set_annotation_without_lnd.csv b/CAGE_expression_inference-apvit/affectnet_annotations/val_set_annotation_without_lnd.csv
new file mode 100644
index 0000000000000000000000000000000000000000..019d2c663a12c4b3367d001f0820c88cb5754b83
--- /dev/null
+++ b/CAGE_expression_inference-apvit/affectnet_annotations/val_set_annotation_without_lnd.csv
@@ -0,0 +1,4000 @@
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diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/__init__.py b/CAGE_expression_inference-apvit/apvit_mmcls/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8fbf732c80e01fe50d73581adef193135e5eee41
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/__init__.py
@@ -0,0 +1,3 @@
+from .version import __version__
+
+__all__ = ['__version__']
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/apis/__init__.py b/CAGE_expression_inference-apvit/apvit_mmcls/apis/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0035632a76d08e58d4f5c256f26be081f523550c
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/apis/__init__.py
@@ -0,0 +1,8 @@
+from .inference import inference_model, init_model, show_result_pyplot
+from .test import multi_gpu_test, single_gpu_test
+from .train import set_random_seed, train_model
+
+__all__ = [
+    'set_random_seed', 'train_model', 'init_model', 'inference_model',
+    'multi_gpu_test', 'single_gpu_test', 'show_result_pyplot'
+]
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/apis/inference.py b/CAGE_expression_inference-apvit/apvit_mmcls/apis/inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..7e992aa5c216e6b2a8eb657b1d094b9e3eb7b2a4
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/apis/inference.py
@@ -0,0 +1,108 @@
+import warnings
+
+import matplotlib.pyplot as plt
+import mmcv
+import numpy as np
+import torch
+from mmcv.parallel import collate, scatter
+from mmcv.runner import load_checkpoint
+
+from mmcls.datasets.pipelines import Compose
+from mmcls.models import build_classifier
+
+
+def init_model(config, checkpoint=None, device='cuda:0', options=None):
+    """Initialize a classifier from config file.
+
+    Args:
+        config (str or :obj:`mmcv.Config`): Config file path or the config
+            object.
+        checkpoint (str, optional): Checkpoint path. If left as None, the model
+            will not load any weights.
+        options (dict): Options to override some settings in the used config.
+
+    Returns:
+        nn.Module: The constructed classifier.
+    """
+    if isinstance(config, str):
+        config = mmcv.Config.fromfile(config)
+    elif not isinstance(config, mmcv.Config):
+        raise TypeError('config must be a filename or Config object, '
+                        f'but got {type(config)}')
+    if options is not None:
+        config.merge_from_dict(options)
+    config.model.pretrained = None
+    config.model.extractor.pretrained = None
+    config.model.vit.pretrained = None
+    model = build_classifier(config.model)
+    if checkpoint is not None:
+        map_loc = 'cpu' if device == 'cpu' else None
+        checkpoint = load_checkpoint(model, checkpoint, map_location=map_loc)
+        class_loaded = False
+        if 'meta' in checkpoint:
+            if 'CLASSES' in checkpoint['meta']:
+                model.CLASSES = checkpoint['meta']['CLASSES']
+                class_loaded = True
+        if not class_loaded:
+            from mmcls.datasets.raf import FER_CLASSES
+            model.CLASSES = FER_CLASSES
+    model.cfg = config  # save the config in the model for convenience
+    model.to(device)
+    model.eval()
+    return model
+
+
+def inference_model(model, img):
+    """Inference image(s) with the classifier.
+
+    Args:
+        model (nn.Module): The loaded classifier.
+        img (str/ndarray): The image filename or loaded image.
+
+    Returns:
+        result (dict): The classification results that contains
+            `class_name`, `pred_label` and `pred_score`.
+    """
+    cfg = model.cfg
+    device = next(model.parameters()).device  # model device
+    # build the data pipeline
+    if isinstance(img, str):
+        if cfg.data.test.pipeline[0]['type'] != 'LoadImageFromFile':
+            cfg.data.test.pipeline.insert(0, dict(type='LoadImageFromFile'))
+        data = dict(img_info=dict(filename=img), img_prefix=None)
+    else:
+        if cfg.data.test.pipeline[0]['type'] == 'LoadImageFromFile':
+            cfg.data.test.pipeline.pop(0)
+        data = dict(img=img)
+    test_pipeline = Compose(cfg.data.test.pipeline)
+    data = test_pipeline(data)
+    data = collate([data], samples_per_gpu=1)
+    if next(model.parameters()).is_cuda:
+        # scatter to specified GPU
+        data = scatter(data, [device])[0]
+
+    # forward the model
+    with torch.no_grad():
+        scores = model(return_loss=False, **data)
+        pred_score = np.max(scores, axis=1)[0]
+        pred_label = np.argmax(scores, axis=1)[0]
+        result = {'pred_label': pred_label, 'pred_score': float(pred_score)}
+    result['pred_class'] = model.CLASSES[result['pred_label']]
+    return result
+
+
+def show_result_pyplot(model, img, result, fig_size=(15, 10)):
+    """Visualize the classification results on the image.
+
+    Args:
+        model (nn.Module): The loaded classifier.
+        img (str or np.ndarray): Image filename or loaded image.
+        result (list): The classification result.
+        fig_size (tuple): Figure size of the pyplot figure.
+    """
+    if hasattr(model, 'module'):
+        model = model.module
+    img = model.show_result(img, result, show=False)
+    plt.figure(figsize=fig_size)
+    plt.imshow(mmcv.bgr2rgb(img))
+    plt.show()
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/apis/test.py b/CAGE_expression_inference-apvit/apvit_mmcls/apis/test.py
new file mode 100644
index 0000000000000000000000000000000000000000..b59cd75cd0d7cf96dadb72515605ddf047b7b725
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/apis/test.py
@@ -0,0 +1,158 @@
+import os.path as osp
+import pickle
+import shutil
+import tempfile
+import time
+
+import mmcv
+import torch
+import torch.distributed as dist
+from mmcv.runner import get_dist_info
+
+
+def single_gpu_test(model, data_loader, show=False, out_dir=None):
+    model.eval()
+    results = []
+    dataset = data_loader.dataset
+    prog_bar = mmcv.ProgressBar(len(dataset))
+    for i, data in enumerate(data_loader):
+        with torch.no_grad():
+            result = model(return_loss=False, **data)
+        results.append(result)
+
+        if show or out_dir:
+            pass  # TODO
+
+        batch_size = data['img'].size(0)
+        for _ in range(batch_size):
+            prog_bar.update()
+    return results
+
+
+def multi_gpu_test(model, data_loader, tmpdir=None, gpu_collect=False):
+    """Test model with multiple gpus.
+
+    This method tests model with multiple gpus and collects the results
+    under two different modes: gpu and cpu modes. By setting 'gpu_collect=True'
+    it encodes results to gpu tensors and use gpu communication for results
+    collection. On cpu mode it saves the results on different gpus to 'tmpdir'
+    and collects them by the rank 0 worker.
+
+    Args:
+        model (nn.Module): Model to be tested.
+        data_loader (nn.Dataloader): Pytorch data loader.
+        tmpdir (str): Path of directory to save the temporary results from
+            different gpus under cpu mode.
+        gpu_collect (bool): Option to use either gpu or cpu to collect results.
+
+    Returns:
+        list: The prediction results.
+    """
+    model.eval()
+    results = []
+    dataset = data_loader.dataset
+    rank, world_size = get_dist_info()
+    if rank == 0:
+        prog_bar = mmcv.ProgressBar(len(dataset))
+    time.sleep(2)  # This line can prevent deadlock problem in some cases.
+    for i, data in enumerate(data_loader):
+        with torch.no_grad():
+            result = model(return_loss=False, **data)
+        if isinstance(result, list):
+            results.extend(result)
+        else:
+            results.append(result)
+
+        # if rank == 0:
+        #     batch_size = data['img'].size(0)
+        #     for _ in range(batch_size * world_size):
+        #         prog_bar.update()
+
+    # collect results from all ranks
+    if gpu_collect:
+        results = collect_results_gpu(results, len(dataset))
+    else:
+        results = collect_results_cpu(results, len(dataset), tmpdir)
+    return results
+
+
+def collect_results_cpu(result_part, size, tmpdir=None):
+    rank, world_size = get_dist_info()
+    # create a tmp dir if it is not specified
+    if tmpdir is None:
+        MAX_LEN = 512
+        # 32 is whitespace
+        dir_tensor = torch.full((MAX_LEN, ),
+                                32,
+                                dtype=torch.uint8,
+                                device='cuda')
+        if rank == 0:
+            tmpdir = tempfile.mkdtemp()
+            tmpdir = torch.tensor(
+                bytearray(tmpdir.encode()), dtype=torch.uint8, device='cuda')
+            dir_tensor[:len(tmpdir)] = tmpdir
+        dist.broadcast(dir_tensor, 0)
+        tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip()
+    else:
+        mmcv.mkdir_or_exist(tmpdir)
+    # dump the part result to the dir
+    mmcv.dump(result_part, osp.join(tmpdir, f'part_{rank}.pkl'))
+    dist.barrier()
+    # collect all parts
+    if rank != 0:
+        return None
+    else:
+        # load results of all parts from tmp dir
+        part_list = []
+        for i in range(world_size):
+            part_file = osp.join(tmpdir, f'part_{i}.pkl')
+            part_result = mmcv.load(part_file)
+            # When data is severely insufficient, an empty part_result
+            # on a certain gpu could makes the overall outputs empty.
+            if part_result:
+                part_list.append(part_result)
+        # sort the results
+        ordered_results = []
+        for res in zip(*part_list):
+            ordered_results.extend(list(res))
+        # the dataloader may pad some samples
+        ordered_results = ordered_results[:size]
+        # remove tmp dir
+        shutil.rmtree(tmpdir)
+        return ordered_results
+
+
+def collect_results_gpu(result_part, size):
+    rank, world_size = get_dist_info()
+    # dump result part to tensor with pickle
+    part_tensor = torch.tensor(
+        bytearray(pickle.dumps(result_part)), dtype=torch.uint8, device='cuda')
+    # gather all result part tensor shape
+    shape_tensor = torch.tensor(part_tensor.shape, device='cuda')
+    shape_list = [shape_tensor.clone() for _ in range(world_size)]
+    dist.all_gather(shape_list, shape_tensor)
+    # padding result part tensor to max length
+    shape_max = torch.tensor(shape_list).max()
+    part_send = torch.zeros(shape_max, dtype=torch.uint8, device='cuda')
+    part_send[:shape_tensor[0]] = part_tensor
+    part_recv_list = [
+        part_tensor.new_zeros(shape_max) for _ in range(world_size)
+    ]
+    # gather all result part
+    dist.all_gather(part_recv_list, part_send)
+
+    if rank == 0:
+        part_list = []
+        for recv, shape in zip(part_recv_list, shape_list):
+            part_result = pickle.loads(recv[:shape[0]].cpu().numpy().tobytes())
+            # When data is severely insufficient, an empty part_result
+            # on a certain gpu could makes the overall outputs empty.
+            if part_result:
+                part_list.append(part_result)
+        # sort the results
+        ordered_results = []
+        for res in zip(*part_list):
+            ordered_results.extend(list(res))
+        # the dataloader may pad some samples
+        ordered_results = ordered_results[:size]
+        return ordered_results
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/apis/train.py b/CAGE_expression_inference-apvit/apvit_mmcls/apis/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..8710dd9990659afe6ba96cf5c460c1d92691a597
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/apis/train.py
@@ -0,0 +1,140 @@
+import random
+import warnings
+
+import numpy as np
+import torch
+from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
+from mmcv.runner import DistSamplerSeedHook, build_optimizer, build_runner, Fp16OptimizerHook, OptimizerHook
+
+from mmcls.core import (DistEvalHook, EvalHook)
+from mmcls.datasets import build_dataloader, build_dataset
+from mmcls.utils import get_root_logger
+
+
+def set_random_seed(seed, deterministic=False):
+    """Set random seed.
+
+    Args:
+        seed (int): Seed to be used.
+        deterministic (bool): Whether to set the deterministic option for
+            CUDNN backend, i.e., set `torch.backends.cudnn.deterministic`
+            to True and `torch.backends.cudnn.benchmark` to False.
+            Default: False.
+    """
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    if deterministic:
+        torch.backends.cudnn.deterministic = True
+        torch.backends.cudnn.benchmark = False
+
+
+def train_model(model,
+                dataset,
+                cfg,
+                distributed=False,
+                validate=False,
+                timestamp=None,
+                meta=None):
+    logger = get_root_logger(cfg.log_level)
+
+    # prepare data loaders
+    dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
+
+    data_loaders = [
+        build_dataloader(
+            ds,
+            cfg.data.samples_per_gpu,
+            cfg.data.workers_per_gpu,
+            # cfg.gpus will be ignored if distributed
+            num_gpus=len(cfg.gpu_ids),
+            dist=distributed,
+            round_up=True,
+            seed=cfg.seed,
+            # persistent_workers=True
+        ) for ds in dataset
+    ]
+
+    # build runner
+    optimizer = build_optimizer(model, cfg.optimizer)
+
+    # if 'use_fp16' in cfg and cfg.use_fp16:
+    #     import apex
+    #     model, optimizer = apex.amp.initialize(model.cuda(), optimizer, opt_level="O1")
+    #     warnings.warn('**** Initializing mixed precision done. ****')
+
+    # put model on gpus
+    if distributed:
+        find_unused_parameters = cfg.get('find_unused_parameters', False)
+        # Sets the `find_unused_parameters` parameter in
+        # torch.nn.parallel.DistributedDataParallel
+        model = MMDistributedDataParallel(
+            model.cuda(),
+            device_ids=[torch.cuda.current_device()],
+            broadcast_buffers=False,
+            find_unused_parameters=find_unused_parameters)
+    else:
+        model = MMDataParallel(
+            model.cuda(cfg.gpu_ids[0]), device_ids=cfg.gpu_ids)
+
+    if cfg.get('runner') is None:
+        cfg.runner = {
+            'type': 'EpochBasedRunner',
+            'max_epochs': cfg.total_epochs
+        }
+        warnings.warn(
+            'config is now expected to have a `runner` section, '
+            'please set `runner` in your config.', UserWarning)
+
+    runner = build_runner(
+        cfg.runner,
+        default_args=dict(
+            model=model,
+            batch_processor=None,
+            optimizer=optimizer,
+            work_dir=cfg.work_dir,
+            logger=logger,
+            meta=meta))
+
+    # an ugly walkaround to make the .log and .log.json filenames the same
+    runner.timestamp = timestamp
+
+    # fp16 setting
+    fp16_cfg = cfg.get('fp16', None)
+    if fp16_cfg is not None:
+        optimizer_config = Fp16OptimizerHook(
+            **cfg.optimizer_config, **fp16_cfg, distributed=distributed)
+    elif distributed and 'type' not in cfg.optimizer_config:
+        # optimizer_config = DistOptimizerHook(**cfg.optimizer_config)
+        optimizer_config = OptimizerHook(**cfg.optimizer_config)
+    else:
+        optimizer_config = cfg.optimizer_config
+
+    # register hooks
+    runner.register_training_hooks(cfg.lr_config, optimizer_config,
+                                   cfg.checkpoint_config, cfg.log_config,
+                                   cfg.get('momentum_config', None))
+    if distributed and cfg.runner.type=='EpochBasedRunner':
+        runner.register_hook(DistSamplerSeedHook())
+
+    # register eval hooks
+    if validate:
+        val_dataset = build_dataset(cfg.data.val, dict(test_mode=True))
+        val_dataloader = build_dataloader(
+            val_dataset,
+            samples_per_gpu=cfg.data.samples_per_gpu,
+            workers_per_gpu=cfg.data.workers_per_gpu,
+            dist=distributed,
+            shuffle=False,
+            round_up=True)
+        eval_cfg = cfg.get('evaluation', {})
+        eval_cfg['by_epoch'] = cfg.runner['type'] != 'IterBasedRunner'
+        eval_hook = DistEvalHook if distributed else EvalHook
+        runner.register_hook(eval_hook(val_dataloader, **eval_cfg), priority='LOW')
+
+    if cfg.resume_from:
+        runner.resume(cfg.resume_from)
+    elif cfg.load_from:
+        runner.load_checkpoint(cfg.load_from)
+    runner.run(data_loaders, cfg.workflow)
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/core/__init__.py b/CAGE_expression_inference-apvit/apvit_mmcls/core/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ee0dac43fff91b7335718dbc1f8b3273a404511d
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/core/__init__.py
@@ -0,0 +1,3 @@
+from .evaluation import *  # noqa: F401, F403
+from .fp16 import *  # noqa: F401, F403
+from .utils import *  # noqa: F401, F403
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/core/evaluation/__init__.py b/CAGE_expression_inference-apvit/apvit_mmcls/core/evaluation/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0906ff2b046beb291e13ed7ab04bcfea84c8af1a
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/core/evaluation/__init__.py
@@ -0,0 +1,8 @@
+from .eval_hooks import DistEvalHook, EvalHook
+from .mean_ap import average_precision, mAP
+from .multilabel_eval_metrics import average_performance
+
+__all__ = [
+    'DistEvalHook', 'EvalHook', 'average_precision', 'mAP',
+    'average_performance'
+]
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/core/evaluation/eval_hooks.py b/CAGE_expression_inference-apvit/apvit_mmcls/core/evaluation/eval_hooks.py
new file mode 100644
index 0000000000000000000000000000000000000000..7792d36147d06ead9e9e1ca763640f237713a8ce
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/core/evaluation/eval_hooks.py
@@ -0,0 +1,158 @@
+import os.path as osp
+from math import inf
+
+from mmcv.runner import Hook
+from torch.utils.data import DataLoader
+from mmcls.utils import get_root_logger
+
+
+class EvalHook(Hook):
+    """Evaluation hook.
+
+    Args:
+        dataloader (DataLoader): A PyTorch dataloader.
+        interval (int): Evaluation interval (by epochs). Default: 1.
+    """
+
+    def __init__(self, dataloader, interval=1, by_epoch=True, **eval_kwargs):
+        if not isinstance(dataloader, DataLoader):
+            raise TypeError('dataloader must be a pytorch DataLoader, but got'
+                            f' {type(dataloader)}')
+        self.dataloader = dataloader
+        self.interval = interval
+        self.eval_kwargs = eval_kwargs
+        self.by_epoch = by_epoch
+        self.logger = get_root_logger()
+
+    def after_train_epoch(self, runner):
+        if not self.by_epoch or not self.every_n_epochs(runner, self.interval):
+            return
+        from mmcls.apis import single_gpu_test
+        results = single_gpu_test(runner.model, self.dataloader, show=False)
+        self.evaluate(runner, results)
+
+    def after_train_iter(self, runner):
+        if self.by_epoch or not self.every_n_iters(runner, self.interval):
+            return
+        from mmcls.apis import single_gpu_test
+        runner.log_buffer.clear()
+        results = single_gpu_test(runner.model, self.dataloader, show=False)
+        self.evaluate(runner, results)
+
+    def evaluate(self, runner, results):
+        eval_res = self.dataloader.dataset.evaluate(
+            results, logger=runner.logger, **self.eval_kwargs)
+        for name, val in eval_res.items():
+            runner.log_buffer.output[name] = val
+        runner.log_buffer.ready = True
+        return eval_res
+
+
+class DistEvalHook(EvalHook):
+    """Distributed evaluation hook.
+
+    Args:
+        dataloader (DataLoader): A PyTorch dataloader.
+        interval (int): Evaluation interval (by epochs). Default: 1.
+        tmpdir (str | None): Temporary directory to save the results of all
+            processes. Default: None.
+        gpu_collect (bool): Whether to use gpu or cpu to collect results.
+            Default: False.
+    """
+
+    def __init__(self,
+                 dataloader,
+                 interval=1,
+                 gpu_collect=True,
+                 by_epoch=True,
+                 print_best=True,
+                 **eval_kwargs):
+        if not isinstance(dataloader, DataLoader):
+            raise TypeError('dataloader must be a pytorch DataLoader, but got '
+                            f'{type(dataloader)}')
+        super().__init__(dataloader, interval, by_epoch, **eval_kwargs)
+        self.dataloader = dataloader
+        self.interval = interval
+        self.gpu_collect = gpu_collect
+        self.by_epoch = by_epoch
+        self.eval_kwargs = eval_kwargs
+        self.print_best = print_best
+    
+    def before_run(self, runner):
+        if self.print_best is not None:
+            if runner.meta is None:
+                warnings.warn('runner.meta is None. Creating a empty one.')
+                runner.meta = dict()
+            runner.meta.setdefault('hook_msgs', dict())
+    
+    def before_train_epoch(self, runner):
+        return
+        # freeze IRNet
+        # frozen_blocks = 7
+        model = runner.model.module.convert
+        model.eval()
+        for param in model.parameters():
+            param.requires_grad = False
+        return
+
+        if frozen_blocks > 0:
+            print(f'IRSE freeze the first {frozen_blocks} blocks, it has {len(model.body)} blocks ')
+            model.input_layer.eval()
+            print('in freeze', model.input_layer[1].training)
+            for param in model.input_layer.parameters():
+                param.requires_grad = False
+        
+        for i in range(frozen_blocks):
+            m = model.body[i]
+            m.eval()
+            for param in m.parameters():
+                param.requires_grad = False
+
+    def after_train_epoch(self, runner):
+        if not self.by_epoch or not self.every_n_epochs(runner, self.interval):
+            return
+        from mmcls.apis import multi_gpu_test
+        results = multi_gpu_test(
+            runner.model,
+            self.dataloader,
+            tmpdir=osp.join(runner.work_dir, '.eval_hook'),
+            gpu_collect=self.gpu_collect)
+        if runner.rank == 0:
+            print('\n')
+            eval_res = self.evaluate(runner, results)
+            if self.print_best:
+                best_score = runner.meta['hook_msgs'].get('best_score', -inf)
+                if 'top-1' not in eval_res:
+                    return
+                acc = eval_res['top-1']
+                if acc > best_score:
+                    self.logger.info(f'top-1 accuracy improved from {best_score} to {acc}')
+                    runner.meta['hook_msgs']['best_score'] = acc
+                    runner.save_checkpoint(runner.work_dir, save_optimizer=False, filename_tmpl='best.pth', create_symlink=False)
+                else:
+                    self.logger.info(f'top-1 accuracy did not improve from {best_score}')
+
+    def after_train_iter(self, runner):
+        if self.by_epoch or not self.every_n_iters(runner, self.interval):
+            return
+        from mmcls.apis import multi_gpu_test
+        runner.log_buffer.clear()
+        results = multi_gpu_test(
+            runner.model,
+            self.dataloader,
+            tmpdir=osp.join(runner.work_dir, '.eval_hook'),
+            gpu_collect=self.gpu_collect)
+        if runner.rank == 0:
+            print('\n')
+            eval_res = self.evaluate(runner, results)
+            if self.print_best:
+                best_score = runner.meta['hook_msgs'].get('best_score', -inf)
+                if 'top-1' not in eval_res:
+                    return
+                acc = eval_res['top-1']
+                if acc > best_score:
+                    self.logger.info(f'top-1 accuracy improved from {best_score} to {acc}')
+                    runner.meta['hook_msgs']['best_score'] = acc
+                    runner.save_checkpoint(runner.work_dir, save_optimizer=False, filename_tmpl='best.pth', create_symlink=False)
+                else:
+                    self.logger.info(f'top-1 accuracy did not improve from {best_score}')
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/core/evaluation/mean_ap.py b/CAGE_expression_inference-apvit/apvit_mmcls/core/evaluation/mean_ap.py
new file mode 100644
index 0000000000000000000000000000000000000000..06d8e589fb115318f382ddf726a0537ad9709c57
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/core/evaluation/mean_ap.py
@@ -0,0 +1,73 @@
+import numpy as np
+import torch
+
+
+def average_precision(pred, target):
+    """ Calculate the average precision for a single class
+
+    AP summarizes a precision-recall curve as the weighted mean of maximum
+    precisions obtained for any r'>r, where r is the recall:
+
+    ..math::
+        \\text{AP} = \\sum_n (R_n - R_{n-1}) P_n
+
+    Note that no approximation is involved since the curve is piecewise
+    constant.
+
+    Args:
+        pred (np.ndarray): The model prediction with shape (N, ).
+        target (np.ndarray): The target of each prediction with shape (N, ).
+
+    Returns:
+        float: a single float as average precision value.
+    """
+    eps = np.finfo(np.float32).eps
+
+    # sort examples
+    sort_inds = np.argsort(-pred)
+    sort_target = target[sort_inds]
+
+    # count true positive examples
+    pos_inds = sort_target == 1
+    tp = np.cumsum(pos_inds)
+    total_pos = tp[-1]
+
+    # count not difficult examples
+    pn_inds = sort_target != -1
+    pn = np.cumsum(pn_inds)
+
+    tp[np.logical_not(pos_inds)] = 0
+    precision = tp / np.maximum(pn, eps)
+    ap = np.sum(precision) / np.maximum(total_pos, eps)
+    return ap
+
+
+def mAP(pred, target):
+    """ Calculate the mean average precision with respect of classes
+
+    Args:
+        pred (torch.Tensor | np.ndarray): The model prediction with shape
+            (N, C), where C is the number of classes.
+        target (torch.Tensor | np.ndarray): The target of each prediction with
+            shape (N, C), where C is the number of classes. 1 stands for
+            positive examples, 0 stands for negative examples and -1 stands for
+            difficult examples.
+
+    Returns:
+        float: A single float as mAP value.
+    """
+    if isinstance(pred, torch.Tensor) and isinstance(target, torch.Tensor):
+        pred = pred.numpy()
+        target = target.numpy()
+    elif not (isinstance(pred, np.ndarray) and isinstance(target, np.ndarray)):
+        raise TypeError('pred and target should both be torch.Tensor or'
+                        'np.ndarray')
+
+    assert pred.shape == \
+        target.shape, 'pred and target should be in the same shape.'
+    num_classes = pred.shape[1]
+    ap = np.zeros(num_classes)
+    for k in range(num_classes):
+        ap[k] = average_precision(pred[:, k], target[:, k])
+    mean_ap = ap.mean() * 100.0
+    return mean_ap
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/core/evaluation/multilabel_eval_metrics.py b/CAGE_expression_inference-apvit/apvit_mmcls/core/evaluation/multilabel_eval_metrics.py
new file mode 100644
index 0000000000000000000000000000000000000000..6cb9341a4a057d447e6dba3dbfe836d0189f6460
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/core/evaluation/multilabel_eval_metrics.py
@@ -0,0 +1,71 @@
+import warnings
+
+import numpy as np
+import torch
+
+
+def average_performance(pred, target, thr=None, k=None):
+    """Calculate CP, CR, CF1, OP, OR, OF1, where C stands for per-class
+        average, O stands for overall average, P stands for precision, R
+        stands for recall and F1 stands for F1-score
+
+    Args:
+        pred (torch.Tensor | np.ndarray): The model prediction with shape
+            (N, C), where C is the number of classes.
+        target (torch.Tensor | np.ndarray): The target of each prediction with
+            shape (N, C), where C is the number of classes. 1 stands for
+            positive examples, 0 stands for negative examples and -1 stands for
+            difficult examples.
+        thr (float): The confidence threshold. Defaults to None.
+        k (int): Top-k performance. Note that if thr and k are both given, k
+            will be ignored. Defaults to None.
+
+    Returns:
+        tuple: (CP, CR, CF1, OP, OR, OF1)
+    """
+    if isinstance(pred, torch.Tensor) and isinstance(target, torch.Tensor):
+        pred = pred.numpy()
+        target = target.numpy()
+    elif not (isinstance(pred, np.ndarray) and isinstance(target, np.ndarray)):
+        raise TypeError('pred and target should both be torch.Tensor or'
+                        'np.ndarray')
+    if thr is None and k is None:
+        thr = 0.5
+        warnings.warn('Neither thr nor k is given, set thr as 0.5 by '
+                      'default.')
+    elif thr is not None and k is not None:
+        warnings.warn('Both thr and k are given, use threshold in favor of '
+                      'top-k.')
+
+    assert pred.shape == \
+        target.shape, 'pred and target should be in the same shape.'
+
+    eps = np.finfo(np.float32).eps
+    target[target == -1] = 0
+    if thr is not None:
+        # a label is predicted positive if the confidence is no lower than thr
+        pos_inds = pred >= thr
+
+    else:
+        # top-k labels will be predicted positive for any example
+        sort_inds = np.argsort(-pred, axis=1)
+        sort_inds_ = sort_inds[:, :k]
+        inds = np.indices(sort_inds_.shape)
+        pos_inds = np.zeros_like(pred)
+        pos_inds[inds[0], sort_inds_] = 1
+
+    tp = (pos_inds * target) == 1
+    fp = (pos_inds * (1 - target)) == 1
+    fn = ((1 - pos_inds) * target) == 1
+
+    precision_class = tp.sum(axis=0) / np.maximum(
+        tp.sum(axis=0) + fp.sum(axis=0), eps)
+    recall_class = tp.sum(axis=0) / np.maximum(
+        tp.sum(axis=0) + fn.sum(axis=0), eps)
+    CP = precision_class.mean() * 100.0
+    CR = recall_class.mean() * 100.0
+    CF1 = 2 * CP * CR / np.maximum(CP + CR, eps)
+    OP = tp.sum() / np.maximum(tp.sum() + fp.sum(), eps) * 100.0
+    OR = tp.sum() / np.maximum(tp.sum() + fn.sum(), eps) * 100.0
+    OF1 = 2 * OP * OR / np.maximum(OP + OR, eps)
+    return CP, CR, CF1, OP, OR, OF1
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/core/fp16/__init__.py b/CAGE_expression_inference-apvit/apvit_mmcls/core/fp16/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..cc655b7c3a4e9ea1b2cb78fa218233a47f254c1d
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/core/fp16/__init__.py
@@ -0,0 +1,4 @@
+from .decorators import auto_fp16, force_fp32
+from .hooks import Fp16OptimizerHook, wrap_fp16_model
+
+__all__ = ['auto_fp16', 'force_fp32', 'Fp16OptimizerHook', 'wrap_fp16_model']
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/core/fp16/decorators.py b/CAGE_expression_inference-apvit/apvit_mmcls/core/fp16/decorators.py
new file mode 100644
index 0000000000000000000000000000000000000000..10ffbf898734b54fc88f20cd544119d3eed84265
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/core/fp16/decorators.py
@@ -0,0 +1,160 @@
+import functools
+from inspect import getfullargspec
+
+import torch
+
+from .utils import cast_tensor_type
+
+
+def auto_fp16(apply_to=None, out_fp32=False):
+    """Decorator to enable fp16 training automatically.
+
+    This decorator is useful when you write custom modules and want to support
+    mixed precision training. If inputs arguments are fp32 tensors, they will
+    be converted to fp16 automatically. Arguments other than fp32 tensors are
+    ignored.
+
+    Args:
+        apply_to (Iterable, optional): The argument names to be converted.
+            `None` indicates all arguments.
+        out_fp32 (bool): Whether to convert the output back to fp32.
+
+    :Example:
+
+        class MyModule1(nn.Module)
+
+            # Convert x and y to fp16
+            @auto_fp16()
+            def forward(self, x, y):
+                pass
+
+        class MyModule2(nn.Module):
+
+            # convert pred to fp16
+            @auto_fp16(apply_to=('pred', ))
+            def do_something(self, pred, others):
+                pass
+    """
+
+    def auto_fp16_wrapper(old_func):
+
+        @functools.wraps(old_func)
+        def new_func(*args, **kwargs):
+            # check if the module has set the attribute `fp16_enabled`, if not,
+            # just fallback to the original method.
+            if not isinstance(args[0], torch.nn.Module):
+                raise TypeError('@auto_fp16 can only be used to decorate the '
+                                'method of nn.Module')
+            if not (hasattr(args[0], 'fp16_enabled') and args[0].fp16_enabled):
+                return old_func(*args, **kwargs)
+            # get the arg spec of the decorated method
+            args_info = getfullargspec(old_func)
+            # get the argument names to be casted
+            args_to_cast = args_info.args if apply_to is None else apply_to
+            # convert the args that need to be processed
+            new_args = []
+            # NOTE: default args are not taken into consideration
+            if args:
+                arg_names = args_info.args[:len(args)]
+                for i, arg_name in enumerate(arg_names):
+                    if arg_name in args_to_cast:
+                        new_args.append(
+                            cast_tensor_type(args[i], torch.float, torch.half))
+                    else:
+                        new_args.append(args[i])
+            # convert the kwargs that need to be processed
+            new_kwargs = {}
+            if kwargs:
+                for arg_name, arg_value in kwargs.items():
+                    if arg_name in args_to_cast:
+                        new_kwargs[arg_name] = cast_tensor_type(
+                            arg_value, torch.float, torch.half)
+                    else:
+                        new_kwargs[arg_name] = arg_value
+            # apply converted arguments to the decorated method
+            output = old_func(*new_args, **new_kwargs)
+            # cast the results back to fp32 if necessary
+            if out_fp32:
+                output = cast_tensor_type(output, torch.half, torch.float)
+            return output
+
+        return new_func
+
+    return auto_fp16_wrapper
+
+
+def force_fp32(apply_to=None, out_fp16=False):
+    """Decorator to convert input arguments to fp32 in force.
+
+    This decorator is useful when you write custom modules and want to support
+    mixed precision training. If there are some inputs that must be processed
+    in fp32 mode, then this decorator can handle it. If inputs arguments are
+    fp16 tensors, they will be converted to fp32 automatically. Arguments other
+    than fp16 tensors are ignored.
+
+    Args:
+        apply_to (Iterable, optional): The argument names to be converted.
+            `None` indicates all arguments.
+        out_fp16 (bool): Whether to convert the output back to fp16.
+
+    :Example:
+
+        class MyModule1(nn.Module)
+
+            # Convert x and y to fp32
+            @force_fp32()
+            def loss(self, x, y):
+                pass
+
+        class MyModule2(nn.Module):
+
+            # convert pred to fp32
+            @force_fp32(apply_to=('pred', ))
+            def post_process(self, pred, others):
+                pass
+    """
+
+    def force_fp32_wrapper(old_func):
+
+        @functools.wraps(old_func)
+        def new_func(*args, **kwargs):
+            # check if the module has set the attribute `fp16_enabled`, if not,
+            # just fallback to the original method.
+            if not isinstance(args[0], torch.nn.Module):
+                raise TypeError('@force_fp32 can only be used to decorate the '
+                                'method of nn.Module')
+            if not (hasattr(args[0], 'fp16_enabled') and args[0].fp16_enabled):
+                return old_func(*args, **kwargs)
+            # get the arg spec of the decorated method
+            args_info = getfullargspec(old_func)
+            # get the argument names to be casted
+            args_to_cast = args_info.args if apply_to is None else apply_to
+            # convert the args that need to be processed
+            new_args = []
+            if args:
+                arg_names = args_info.args[:len(args)]
+                for i, arg_name in enumerate(arg_names):
+                    if arg_name in args_to_cast:
+                        new_args.append(
+                            cast_tensor_type(args[i], torch.half, torch.float))
+                    else:
+                        new_args.append(args[i])
+            # convert the kwargs that need to be processed
+            new_kwargs = dict()
+            if kwargs:
+                for arg_name, arg_value in kwargs.items():
+                    if arg_name in args_to_cast:
+                        new_kwargs[arg_name] = cast_tensor_type(
+                            arg_value, torch.half, torch.float)
+                    else:
+                        new_kwargs[arg_name] = arg_value
+            # apply converted arguments to the decorated method
+            output = old_func(*new_args, **new_kwargs)
+            # cast the results back to fp32 if necessary
+            if out_fp16:
+                output = cast_tensor_type(output, torch.float, torch.half)
+            return output
+
+        return new_func
+
+    return force_fp32_wrapper
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/core/fp16/hooks.py b/CAGE_expression_inference-apvit/apvit_mmcls/core/fp16/hooks.py
new file mode 100644
index 0000000000000000000000000000000000000000..43e96eaf73373b0e00114c10fd3f16bd190bba7d
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/core/fp16/hooks.py
@@ -0,0 +1,132 @@
+import copy
+
+import torch
+import torch.nn as nn
+from mmcv.runner import OptimizerHook
+from mmcv.utils.parrots_wrapper import _BatchNorm
+
+from ..utils import allreduce_grads
+from .utils import cast_tensor_type
+
+
+class Fp16OptimizerHook(OptimizerHook):
+    """FP16 optimizer hook.
+
+    The steps of fp16 optimizer is as follows.
+    1. Scale the loss value.
+    2. BP in the fp16 model.
+    2. Copy gradients from fp16 model to fp32 weights.
+    3. Update fp32 weights.
+    4. Copy updated parameters from fp32 weights to fp16 model.
+
+    Refer to https://arxiv.org/abs/1710.03740 for more details.
+
+    Args:
+        loss_scale (float): Scale factor multiplied with loss.
+    """
+
+    def __init__(self,
+                 grad_clip=None,
+                 coalesce=True,
+                 bucket_size_mb=-1,
+                 loss_scale=512.,
+                 distributed=True):
+        self.grad_clip = grad_clip
+        self.coalesce = coalesce
+        self.bucket_size_mb = bucket_size_mb
+        self.loss_scale = loss_scale
+        self.distributed = distributed
+
+    def before_run(self, runner):
+        # keep a copy of fp32 weights
+        runner.optimizer.param_groups = copy.deepcopy(
+            runner.optimizer.param_groups)
+        # convert model to fp16
+        wrap_fp16_model(runner.model)
+
+    def copy_grads_to_fp32(self, fp16_net, fp32_weights):
+        """Copy gradients from fp16 model to fp32 weight copy."""
+        for fp32_param, fp16_param in zip(fp32_weights, fp16_net.parameters()):
+            if fp16_param.grad is not None:
+                if fp32_param.grad is None:
+                    fp32_param.grad = fp32_param.data.new(fp32_param.size())
+                fp32_param.grad.copy_(fp16_param.grad)
+
+    def copy_params_to_fp16(self, fp16_net, fp32_weights):
+        """Copy updated params from fp32 weight copy to fp16 model."""
+        for fp16_param, fp32_param in zip(fp16_net.parameters(), fp32_weights):
+            fp16_param.data.copy_(fp32_param.data)
+
+    def after_train_iter(self, runner):
+        # clear grads of last iteration
+        runner.model.zero_grad()
+        runner.optimizer.zero_grad()
+        # scale the loss value
+        scaled_loss = runner.outputs['loss'] * self.loss_scale
+        scaled_loss.backward()
+        # copy fp16 grads in the model to fp32 params in the optimizer
+        fp32_weights = []
+        for param_group in runner.optimizer.param_groups:
+            fp32_weights += param_group['params']
+        self.copy_grads_to_fp32(runner.model, fp32_weights)
+        # allreduce grads
+        if self.distributed:
+            allreduce_grads(fp32_weights, self.coalesce, self.bucket_size_mb)
+        # scale the gradients back
+        for param in fp32_weights:
+            if param.grad is not None:
+                param.grad.div_(self.loss_scale)
+        if self.grad_clip is not None:
+            grad_norm = self.clip_grads(fp32_weights)
+            if grad_norm is not None:
+                # Add grad norm to the logger
+                runner.log_buffer.update({'grad_norm': float(grad_norm)},
+                                            runner.outputs['num_samples'])
+        # update fp32 params
+        runner.optimizer.step()
+        # copy fp32 params to the fp16 model
+        self.copy_params_to_fp16(runner.model, fp32_weights)
+
+
+def wrap_fp16_model(model):
+    # convert model to fp16
+    model.half()
+    # patch the normalization layers to make it work in fp32 mode
+    patch_norm_fp32(model)
+    # set `fp16_enabled` flag
+    for m in model.modules():
+        if hasattr(m, 'fp16_enabled'):
+            m.fp16_enabled = True
+
+
+def patch_norm_fp32(module):
+    if isinstance(module, (_BatchNorm, nn.GroupNorm)):
+        module.float()
+        module.forward = patch_forward_method(module.forward, torch.half,
+                                              torch.float)
+    for child in module.children():
+        patch_norm_fp32(child)
+    return module
+
+
+def patch_forward_method(func, src_type, dst_type, convert_output=True):
+    """Patch the forward method of a module.
+
+    Args:
+        func (callable): The original forward method.
+        src_type (torch.dtype): Type of input arguments to be converted from.
+        dst_type (torch.dtype): Type of input arguments to be converted to.
+        convert_output (bool): Whether to convert the output back to src_type.
+
+    Returns:
+        callable: The patched forward method.
+    """
+
+    def new_forward(*args, **kwargs):
+        output = func(*cast_tensor_type(args, src_type, dst_type),
+                      **cast_tensor_type(kwargs, src_type, dst_type))
+        if convert_output:
+            output = cast_tensor_type(output, dst_type, src_type)
+        return output
+
+    return new_forward
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/core/fp16/utils.py b/CAGE_expression_inference-apvit/apvit_mmcls/core/fp16/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce691c799b78666a6d5ed5b981c158454dcdaf50
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/core/fp16/utils.py
@@ -0,0 +1,23 @@
+from collections import abc
+
+import numpy as np
+import torch
+
+
+def cast_tensor_type(inputs, src_type, dst_type):
+    if isinstance(inputs, torch.Tensor):
+        return inputs.to(dst_type)
+    elif isinstance(inputs, str):
+        return inputs
+    elif isinstance(inputs, np.ndarray):
+        return inputs
+    elif isinstance(inputs, abc.Mapping):
+        return type(inputs)({
+            k: cast_tensor_type(v, src_type, dst_type)
+            for k, v in inputs.items()
+        })
+    elif isinstance(inputs, abc.Iterable):
+        return type(inputs)(
+            cast_tensor_type(item, src_type, dst_type) for item in inputs)
+    else:
+        return inputs
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/core/utils/__init__.py b/CAGE_expression_inference-apvit/apvit_mmcls/core/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0639e6cc837408b02c28161a9c23012b0d8b2e7b
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/core/utils/__init__.py
@@ -0,0 +1,4 @@
+from .dist_utils import DistOptimizerHook, allreduce_grads
+from .misc import multi_apply
+
+__all__ = ['allreduce_grads', 'DistOptimizerHook', 'multi_apply']
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/core/utils/dist_utils.py b/CAGE_expression_inference-apvit/apvit_mmcls/core/utils/dist_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..1c914b22a9be62b3bde00f5ec352fdcf9d4145e1
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/core/utils/dist_utils.py
@@ -0,0 +1,56 @@
+from collections import OrderedDict
+
+import torch.distributed as dist
+from mmcv.runner import OptimizerHook
+from torch._utils import (_flatten_dense_tensors, _take_tensors,
+                          _unflatten_dense_tensors)
+
+
+def _allreduce_coalesced(tensors, world_size, bucket_size_mb=-1):
+    if bucket_size_mb > 0:
+        bucket_size_bytes = bucket_size_mb * 1024 * 1024
+        buckets = _take_tensors(tensors, bucket_size_bytes)
+    else:
+        buckets = OrderedDict()
+        for tensor in tensors:
+            tp = tensor.type()
+            if tp not in buckets:
+                buckets[tp] = []
+            buckets[tp].append(tensor)
+        buckets = buckets.values()
+
+    for bucket in buckets:
+        flat_tensors = _flatten_dense_tensors(bucket)
+        dist.all_reduce(flat_tensors)
+        flat_tensors.div_(world_size)
+        for tensor, synced in zip(
+                bucket, _unflatten_dense_tensors(flat_tensors, bucket)):
+            tensor.copy_(synced)
+
+
+def allreduce_grads(params, coalesce=True, bucket_size_mb=-1):
+    grads = [
+        param.grad.data for param in params
+        if param.requires_grad and param.grad is not None
+    ]
+    world_size = dist.get_world_size()
+    if coalesce:
+        _allreduce_coalesced(grads, world_size, bucket_size_mb)
+    else:
+        for tensor in grads:
+            dist.all_reduce(tensor.div_(world_size))
+
+
+class DistOptimizerHook(OptimizerHook):
+
+    def __init__(self, grad_clip=None, coalesce=True, bucket_size_mb=-1):
+        self.grad_clip = grad_clip
+        self.coalesce = coalesce
+        self.bucket_size_mb = bucket_size_mb
+
+    def after_train_iter(self, runner):
+        runner.optimizer.zero_grad()
+        runner.outputs['loss'].backward()
+        if self.grad_clip is not None:
+            self.clip_grads(runner.model.parameters())
+        runner.optimizer.step()
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/core/utils/misc.py b/CAGE_expression_inference-apvit/apvit_mmcls/core/utils/misc.py
new file mode 100644
index 0000000000000000000000000000000000000000..be6a0b668835f77e9df707d3e1d21106fd6f0f06
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/core/utils/misc.py
@@ -0,0 +1,7 @@
+from functools import partial
+
+
+def multi_apply(func, *args, **kwargs):
+    pfunc = partial(func, **kwargs) if kwargs else func
+    map_results = map(pfunc, *args)
+    return tuple(map(list, zip(*map_results)))
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/datasets/__init__.py b/CAGE_expression_inference-apvit/apvit_mmcls/datasets/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9a79eaee873ae1c32fb624fa58e0c8ae6dbe026c
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/datasets/__init__.py
@@ -0,0 +1,16 @@
+from .base_dataset import BaseDataset
+from .builder import DATASETS, PIPELINES, build_dataloader, build_dataset
+from .cifar import CIFAR10, CIFAR100
+from .dataset_wrappers import (ClassBalancedDataset, ConcatDataset,
+                               RepeatDataset)
+from .imagenet import ImageNet
+from .mnist import MNIST, FashionMNIST
+from .samplers import DistributedSampler
+from .raf import RAF
+
+__all__ = [
+    'BaseDataset', 'ImageNet', 'CIFAR10', 'CIFAR100', 'MNIST', 'FashionMNIST',
+    'build_dataloader', 'build_dataset', 'Compose', 'DistributedSampler',
+    'ConcatDataset', 'RepeatDataset', 'ClassBalancedDataset', 'DATASETS',
+    'PIPELINES', 'RAF', 
+]
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/datasets/base_dataset.py b/CAGE_expression_inference-apvit/apvit_mmcls/datasets/base_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..5c84fac353edf76245d4a6ac587fb244a0e4166a
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/datasets/base_dataset.py
@@ -0,0 +1,185 @@
+import copy
+from abc import ABCMeta, abstractmethod
+import os
+from shutil import copyfile
+
+import mmcv
+import numpy as np
+from torch.utils.data import Dataset
+
+from mmcls.models.losses import accuracy, f1_score, precision, recall
+from mmcls.models.losses.eval_metrics import class_accuracy
+from .pipelines import Compose
+
+
+class BaseDataset(Dataset, metaclass=ABCMeta):
+    """Base dataset.
+
+    Args:
+        data_prefix (str): the prefix of data path
+        pipeline (list): a list of dict, where each element represents
+            a operation defined in `mmcls.datasets.pipelines`
+        ann_file (str | None): the annotation file. When ann_file is str,
+            the subclass is expected to read from the ann_file. When ann_file
+            is None, the subclass is expected to read according to data_prefix
+        test_mode (bool): in train mode or test mode
+    """
+
+    CLASSES = None
+
+    def __init__(self,
+                 data_prefix,
+                 pipeline,
+                 classes=None,
+                 ann_file=None,
+                 test_mode=False):
+        super(BaseDataset, self).__init__()
+
+        self.ann_file = ann_file
+        self.data_prefix = data_prefix
+        self.test_mode = test_mode
+        self.pipeline = Compose(pipeline)
+        self.data_infos = self.load_annotations()
+        # self.data_infos = self.load_exp_and_au_annotations()
+        self.CLASSES = self.get_classes(classes)
+        if os.environ.get('DEBUG_MODE', '0') == '1':
+            self.data_infos = self.data_infos[:30]
+
+    @abstractmethod
+    def load_annotations(self):
+        pass
+
+    @property
+    def class_to_idx(self):
+        """Map mapping class name to class index.
+
+        Returns:
+            dict: mapping from class name to class index.
+        """
+
+        return {_class: i for i, _class in enumerate(self.CLASSES)}
+
+    def get_gt_labels(self):
+        """Get all ground-truth labels (categories).
+
+        Returns:
+            list[int]: categories for all images.
+        """
+
+        gt_labels = np.array([data['gt_label'] for data in self.data_infos])
+        return gt_labels
+    
+    def get_coarse_labels(self):
+        coarse_labels = np.array([data['coarse_label'] for data in self.data_infos])
+        return coarse_labels
+
+    def get_cat_ids(self, idx):
+        """Get category id by index.
+
+        Args:
+            idx (int): Index of data.
+
+        Returns:
+            int: Image category of specified index.
+        """
+
+        return (int(self.data_infos[idx]['gt_label'].astype(np.int)), )
+
+    def prepare_data(self, idx):
+        results = copy.deepcopy(self.data_infos[idx])
+        return self.pipeline(results)
+
+    def __len__(self):
+        return len(self.data_infos)
+
+    def __getitem__(self, idx):
+        return self.prepare_data(idx)
+
+    @classmethod
+    def get_classes(cls, classes=None):
+        """Get class names of current dataset.
+        Args:
+            classes (Sequence[str] | str | None): If classes is None, use
+                default CLASSES defined by builtin dataset. If classes is a
+                string, take it as a file name. The file contains the name of
+                classes where each line contains one class name. If classes is
+                a tuple or list, override the CLASSES defined by the dataset.
+
+        Returns:
+            tuple[str] or list[str]: Names of categories of the dataset.
+        """
+        if classes is None:
+            return cls.CLASSES
+
+        if isinstance(classes, str):
+            # take it as a file path
+            class_names = mmcv.list_from_file(classes)
+        elif isinstance(classes, (tuple, list)):
+            class_names = classes
+        else:
+            raise ValueError(f'Unsupported type {type(classes)} of classes.')
+
+        return class_names
+
+    def evaluate(self,
+                results,
+                metric='accuracy',
+                metric_options={'topk': (1, 2)},
+                logger=None):
+        """Evaluate the dataset.
+
+        Args:
+            results (list): Testing results of the dataset.
+            metric (str | list[str]): Metrics to be evaluated.
+                Default value is `accuracy`.
+            logger (logging.Logger | None | str): Logger used for printing
+                related information during evaluation. Default: None.
+        Returns:
+            dict: evaluation results
+        """
+        if isinstance(metric, str):
+            metrics = [metric]
+        else:
+            metrics = metric
+        allowed_metrics = ['accuracy', 'precision', 'recall', 'f1_score', 'class_accuracy']
+        eval_results = {}
+        for metric in metrics:
+            if metric not in allowed_metrics:
+                raise KeyError(f'metric {metric} is not supported.')
+            results = np.vstack(results)
+            gt_labels = self.get_gt_labels()
+            num_imgs = len(results)
+            assert len(gt_labels) == num_imgs, f'{len(gt_labels)}, {num_imgs}'
+            # convert gt_labels to another dataset format for cross-dataset test
+            # raf2affect_dict = [6, 3, 4, 0, 1, 2, 5] # RAF -> AffectNet实验需要将RAF标签修改为AffectNet的标签
+            # affect2raf_dict = [3, 4, 5, 1, 2, 6, 0]
+            # raf2ferplus_dict = [6,3,0,4,5,2,1,None]
+            # gt_labels = [raf2ferplus_dict[i] for i in gt_labels]
+            # # specific process for RAF -> FERPlus
+            # results = [results[i] for i in range(len(results)) if gt_labels[i] is not None]
+            # gt_labels = [gt_labels[i] for i in range(len(gt_labels)) if gt_labels[i] is not None]
+            # results = np.array(results)
+            # gt_labels = np.array(gt_labels)
+
+            if metric == 'accuracy':
+                topk = metric_options.get('topk')
+                acc = accuracy(results, gt_labels, topk)
+                eval_result = {f'top-{k}': a.item() for k, a in zip(topk, acc)}
+            elif metric == 'precision':
+                precision_value = precision(results, gt_labels)
+                eval_result = {'precision': precision_value}
+            elif metric == 'recall':
+                recall_value = recall(results, gt_labels)
+                eval_result = {'recall': recall_value}
+            elif metric == 'f1_score':
+                f1_score_value = f1_score(results, gt_labels)
+                eval_result = {'f1_score': f1_score_value}
+            elif metric == 'class_accuracy':
+                class_accuracy_value = class_accuracy(results, gt_labels, self.CLASSES)
+                print('\n')
+                for name, val in zip(self.CLASSES, class_accuracy_value):
+                    print(f'{name}: \t{val}')
+                print('\n')
+                eval_result = dict()
+            eval_results.update(eval_result)
+        return eval_results
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/datasets/builder.py b/CAGE_expression_inference-apvit/apvit_mmcls/datasets/builder.py
new file mode 100644
index 0000000000000000000000000000000000000000..cf9490c089f180658949d88af2787be703cac706
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/datasets/builder.py
@@ -0,0 +1,110 @@
+import platform
+import random
+from functools import partial
+
+import numpy as np
+from mmcv.parallel import collate
+from mmcv.runner import get_dist_info
+from mmcv.utils import Registry, build_from_cfg
+from torch.utils.data import DataLoader
+
+from .samplers import DistributedSampler
+
+if platform.system() != 'Windows':
+    # https://github.com/pytorch/pytorch/issues/973
+    import resource
+    rlimit = resource.getrlimit(resource.RLIMIT_NOFILE)
+    hard_limit = rlimit[1]
+    soft_limit = min(4096, hard_limit)
+    resource.setrlimit(resource.RLIMIT_NOFILE, (soft_limit, hard_limit))
+
+DATASETS = Registry('dataset')
+PIPELINES = Registry('pipeline')
+
+
+def build_dataset(cfg, default_args=None):
+    from .dataset_wrappers import (ConcatDataset, RepeatDataset,
+                                   ClassBalancedDataset)
+    cfg = cfg.copy()
+    if isinstance(cfg, (list, tuple)):
+        dataset = ConcatDataset([build_dataset(c, default_args) for c in cfg])
+    elif cfg['type'] == 'RepeatDataset':
+        dataset = RepeatDataset(
+            build_dataset(cfg['dataset'], default_args), cfg['times'])
+    elif cfg['type'] == 'ClassBalancedDataset':
+        ds = build_dataset(cfg['dataset'], default_args)
+        cfg['dataset'] = ds
+        dataset = ClassBalancedDataset(**cfg)
+    else:
+        dataset = build_from_cfg(cfg, DATASETS, default_args)
+
+    return dataset
+
+
+def build_dataloader(dataset,
+                     samples_per_gpu,
+                     workers_per_gpu,
+                     num_gpus=1,
+                     dist=True,
+                     shuffle=True,
+                     round_up=True,
+                     seed=None,
+                     **kwargs):
+    """Build PyTorch DataLoader.
+
+    In distributed training, each GPU/process has a dataloader.
+    In non-distributed training, there is only one dataloader for all GPUs.
+
+    Args:
+        dataset (Dataset): A PyTorch dataset.
+        samples_per_gpu (int): Number of training samples on each GPU, i.e.,
+            batch size of each GPU.
+        workers_per_gpu (int): How many subprocesses to use for data loading
+            for each GPU.
+        num_gpus (int): Number of GPUs. Only used in non-distributed training.
+        dist (bool): Distributed training/test or not. Default: True.
+        shuffle (bool): Whether to shuffle the data at every epoch.
+            Default: True.
+        round_up (bool): Whether to round up the length of dataset by adding
+            extra samples to make it evenly divisible. Default: True.
+        kwargs: any keyword argument to be used to initialize DataLoader
+
+    Returns:
+        DataLoader: A PyTorch dataloader.
+    """
+    rank, world_size = get_dist_info()
+    if dist:
+        sampler = DistributedSampler(
+            dataset, world_size, rank, shuffle=shuffle, round_up=round_up)
+        shuffle = False
+        batch_size = samples_per_gpu
+        num_workers = workers_per_gpu
+    else:
+        sampler = None
+        batch_size = num_gpus * samples_per_gpu
+        num_workers = num_gpus * workers_per_gpu
+
+    init_fn = partial(
+        worker_init_fn, num_workers=num_workers, rank=rank,
+        seed=seed) if seed is not None else None
+
+    data_loader = DataLoader(
+        dataset,
+        batch_size=batch_size,
+        sampler=sampler,
+        num_workers=num_workers,
+        collate_fn=partial(collate, samples_per_gpu=samples_per_gpu),
+        pin_memory=False,
+        shuffle=shuffle,
+        worker_init_fn=init_fn,
+        **kwargs)
+
+    return data_loader
+
+
+def worker_init_fn(worker_id, num_workers, rank, seed):
+    # The seed of each worker equals to
+    # num_worker * rank + worker_id + user_seed
+    worker_seed = num_workers * rank + worker_id + seed
+    np.random.seed(worker_seed)
+    random.seed(worker_seed)
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/datasets/cifar.py b/CAGE_expression_inference-apvit/apvit_mmcls/datasets/cifar.py
new file mode 100644
index 0000000000000000000000000000000000000000..27a5d7476f6c0dbbcbd47fb285705e807520be4e
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/datasets/cifar.py
@@ -0,0 +1,123 @@
+import os
+import os.path
+import pickle
+
+import numpy as np
+
+from .base_dataset import BaseDataset
+from .builder import DATASETS
+from .utils import check_integrity, download_and_extract_archive
+
+
+@DATASETS.register_module()
+class CIFAR10(BaseDataset):
+    """`CIFAR10 <https://www.cs.toronto.edu/~kriz/cifar.html>`_ Dataset.
+
+    This implementation is modified from
+    https://github.com/pytorch/vision/blob/master/torchvision/datasets/cifar.py  # noqa: E501
+    """
+
+    base_folder = 'cifar-10-batches-py'
+    url = 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'
+    filename = 'cifar-10-python.tar.gz'
+    tgz_md5 = 'c58f30108f718f92721af3b95e74349a'
+    train_list = [
+        ['data_batch_1', 'c99cafc152244af753f735de768cd75f'],
+        ['data_batch_2', 'd4bba439e000b95fd0a9bffe97cbabec'],
+        ['data_batch_3', '54ebc095f3ab1f0389bbae665268c751'],
+        ['data_batch_4', '634d18415352ddfa80567beed471001a'],
+        ['data_batch_5', '482c414d41f54cd18b22e5b47cb7c3cb'],
+    ]
+
+    test_list = [
+        ['test_batch', '40351d587109b95175f43aff81a1287e'],
+    ]
+    meta = {
+        'filename': 'batches.meta',
+        'key': 'label_names',
+        'md5': '5ff9c542aee3614f3951f8cda6e48888',
+    }
+
+    def load_annotations(self):
+
+        if not self._check_integrity():
+            download_and_extract_archive(
+                self.url,
+                self.data_prefix,
+                filename=self.filename,
+                md5=self.tgz_md5)
+
+        if not self.test_mode:
+            downloaded_list = self.train_list
+        else:
+            downloaded_list = self.test_list
+
+        self.imgs = []
+        self.gt_labels = []
+
+        # load the picked numpy arrays
+        for file_name, checksum in downloaded_list:
+            file_path = os.path.join(self.data_prefix, self.base_folder,
+                                     file_name)
+            with open(file_path, 'rb') as f:
+                entry = pickle.load(f, encoding='latin1')
+                self.imgs.append(entry['data'])
+                if 'labels' in entry:
+                    self.gt_labels.extend(entry['labels'])
+                else:
+                    self.gt_labels.extend(entry['fine_labels'])
+
+        self.imgs = np.vstack(self.imgs).reshape(-1, 3, 32, 32)
+        self.imgs = self.imgs.transpose((0, 2, 3, 1))  # convert to HWC
+
+        self._load_meta()
+
+        data_infos = []
+        for img, gt_label in zip(self.imgs, self.gt_labels):
+            gt_label = np.array(gt_label, dtype=np.int64)
+            info = {'img': img, 'gt_label': gt_label}
+            data_infos.append(info)
+        return data_infos
+
+    def _load_meta(self):
+        path = os.path.join(self.data_prefix, self.base_folder,
+                            self.meta['filename'])
+        if not check_integrity(path, self.meta['md5']):
+            raise RuntimeError(
+                'Dataset metadata file not found or corrupted.' +
+                ' You can use download=True to download it')
+        with open(path, 'rb') as infile:
+            data = pickle.load(infile, encoding='latin1')
+            self.CLASSES = data[self.meta['key']]
+
+    def _check_integrity(self):
+        root = self.data_prefix
+        for fentry in (self.train_list + self.test_list):
+            filename, md5 = fentry[0], fentry[1]
+            fpath = os.path.join(root, self.base_folder, filename)
+            if not check_integrity(fpath, md5):
+                return False
+        return True
+
+
+@DATASETS.register_module()
+class CIFAR100(CIFAR10):
+    """`CIFAR100 <https://www.cs.toronto.edu/~kriz/cifar.html>`_ Dataset.
+    """
+
+    base_folder = 'cifar-100-python'
+    url = 'https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz'
+    filename = 'cifar-100-python.tar.gz'
+    tgz_md5 = 'eb9058c3a382ffc7106e4002c42a8d85'
+    train_list = [
+        ['train', '16019d7e3df5f24257cddd939b257f8d'],
+    ]
+
+    test_list = [
+        ['test', 'f0ef6b0ae62326f3e7ffdfab6717acfc'],
+    ]
+    meta = {
+        'filename': 'meta',
+        'key': 'fine_label_names',
+        'md5': '7973b15100ade9c7d40fb424638fde48',
+    }
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/datasets/dataset_wrappers.py b/CAGE_expression_inference-apvit/apvit_mmcls/datasets/dataset_wrappers.py
new file mode 100644
index 0000000000000000000000000000000000000000..aea5408e893bc265d60ab788fb28d50e527c892a
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/datasets/dataset_wrappers.py
@@ -0,0 +1,173 @@
+import bisect
+import math
+from collections import defaultdict
+
+import numpy as np
+from torch.utils.data.dataset import ConcatDataset as _ConcatDataset
+
+from .builder import DATASETS
+
+
+@DATASETS.register_module()
+class ConcatDataset(_ConcatDataset):
+    """A wrapper of concatenated dataset.
+
+    Same as :obj:`torch.utils.data.dataset.ConcatDataset`, but
+    add `get_cat_ids` function.
+
+    Args:
+        datasets (list[:obj:`Dataset`]): A list of datasets.
+    """
+
+    def __init__(self, datasets):
+        super(ConcatDataset, self).__init__(datasets)
+        self.CLASSES = datasets[0].CLASSES
+
+    def get_cat_ids(self, idx):
+        if idx < 0:
+            if -idx > len(self):
+                raise ValueError(
+                    'absolute value of index should not exceed dataset length')
+            idx = len(self) + idx
+        dataset_idx = bisect.bisect_right(self.cumulative_sizes, idx)
+        if dataset_idx == 0:
+            sample_idx = idx
+        else:
+            sample_idx = idx - self.cumulative_sizes[dataset_idx - 1]
+        return self.datasets[dataset_idx].get_cat_ids(sample_idx)
+
+
+@DATASETS.register_module()
+class RepeatDataset(object):
+    """A wrapper of repeated dataset.
+
+    The length of repeated dataset will be `times` larger than the original
+    dataset. This is useful when the data loading time is long but the dataset
+    is small. Using RepeatDataset can reduce the data loading time between
+    epochs.
+
+    Args:
+        dataset (:obj:`Dataset`): The dataset to be repeated.
+        times (int): Repeat times.
+    """
+
+    def __init__(self, dataset, times):
+        self.dataset = dataset
+        self.times = times
+        self.CLASSES = dataset.CLASSES
+
+        self._ori_len = len(self.dataset)
+
+    def __getitem__(self, idx):
+        return self.dataset[idx % self._ori_len]
+
+    def get_cat_ids(self, idx):
+        return self.dataset.get_cat_ids(idx % self._ori_len)
+
+    def __len__(self):
+        return self.times * self._ori_len
+
+
+# Modified from https://github.com/facebookresearch/detectron2/blob/41d475b75a230221e21d9cac5d69655e3415e3a4/detectron2/data/samplers/distributed_sampler.py#L57 # noqa
+@DATASETS.register_module()
+class ClassBalancedDataset(object):
+    """A wrapper of repeated dataset with repeat factor.
+
+    Suitable for training on class imbalanced datasets like LVIS. Following
+    the sampling strategy in [1], in each epoch, an image may appear multiple
+    times based on its "repeat factor".
+    The repeat factor for an image is a function of the frequency the rarest
+    category labeled in that image. The "frequency of category c" in [0, 1]
+    is defined by the fraction of images in the training set (without repeats)
+    in which category c appears.
+    The dataset needs to instantiate :func:`self.get_cat_ids(idx)` to support
+    ClassBalancedDataset.
+    The repeat factor is computed as followed.
+    1. For each category c, compute the fraction # of images
+        that contain it: f(c)
+    2. For each category c, compute the category-level repeat factor:
+        r(c) = max(1, sqrt(t/f(c)))
+    3. For each image I and its labels L(I), compute the image-level repeat
+    factor:
+        r(I) = max_{c in L(I)} r(c)
+
+    References:
+        .. [1]  https://arxiv.org/pdf/1908.03195.pdf
+
+    Args:
+        dataset (:obj:`CustomDataset`): The dataset to be repeated.
+        oversample_thr (float): frequency threshold below which data is
+            repeated. For categories with `f_c` >= `oversample_thr`, there is
+            no oversampling. For categories with `f_c` < `oversample_thr`, the
+            degree of oversampling following the square-root inverse frequency
+            heuristic above.
+    """
+
+    def __init__(self, dataset, oversample_thr, method='sqrt', **kwargs):
+        assert(method in ('sqrt', 'reciprocal'))    # reciprocal，倒数，绝对 balance， repeat_factor = f(c) / max( fc )
+        self.method = method
+        self.dataset = dataset
+        self.oversample_thr = oversample_thr
+        self.CLASSES = dataset.CLASSES
+
+        repeat_factors = self._get_repeat_factors(dataset, oversample_thr)
+        repeat_indices = []
+        for dataset_index, repeat_factor in enumerate(repeat_factors):
+            repeat_indices.extend([dataset_index] * math.ceil(repeat_factor))
+        self.repeat_indices = repeat_indices
+
+        flags = []
+        if hasattr(self.dataset, 'flag'):
+            for flag, repeat_factor in zip(self.dataset.flag, repeat_factors):
+                flags.extend([flag] * int(math.ceil(repeat_factor)))
+            assert len(flags) == len(repeat_indices)
+        self.flag = np.asarray(flags, dtype=np.uint8)
+
+    def _get_repeat_factors(self, dataset, repeat_thr):
+        # 1. For each category c, compute the fraction # of images
+        #   that contain it: f(c)
+        category_freq = defaultdict(int)
+        num_images = len(dataset)
+        for idx in range(num_images):
+            cat_ids = set(self.dataset.get_cat_ids(idx))
+            for cat_id in cat_ids:
+                category_freq[cat_id] += 1
+        for k, v in category_freq.items():
+            assert v > 0, f'caterogy {k} does not contain any images'
+            category_freq[k] = v / num_images
+
+        # 2. For each category c, compute the category-level repeat factor:
+        #    r(c) = max(1, sqrt(t/f(c)))
+        if self.method == 'sqrt':
+            category_repeat = {
+                cat_id: max(1.0, math.sqrt(repeat_thr / cat_freq))
+                for cat_id, cat_freq in category_freq.items()
+            }
+        elif self.method == 'reciprocal':
+            cat_freq_max = 0
+            for cat_id, cat_freq in category_freq.items():
+                cat_freq_max = max(cat_freq_max, cat_freq)
+            print('cat_freq_max: ', cat_freq_max)
+            category_repeat = {
+                cat_id: cat_freq_max / cat_freq
+                for cat_id, cat_freq in category_freq.items()
+            }
+        # 3. For each image I and its labels L(I), compute the image-level
+        # repeat factor:
+        #    r(I) = max_{c in L(I)} r(c)
+        repeat_factors = []
+        for idx in range(num_images):
+            cat_ids = set(self.dataset.get_cat_ids(idx))
+            repeat_factor = max(
+                {category_repeat[cat_id]
+                 for cat_id in cat_ids})
+            repeat_factors.append(repeat_factor)
+
+        return repeat_factors
+
+    def __getitem__(self, idx):
+        ori_index = self.repeat_indices[idx]
+        return self.dataset[ori_index]
+
+    def __len__(self):
+        return len(self.repeat_indices)
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/datasets/imagenet.py b/CAGE_expression_inference-apvit/apvit_mmcls/datasets/imagenet.py
new file mode 100644
index 0000000000000000000000000000000000000000..86ac5c5c5dee0cc15d175ba9e4f9a8eb90572bcc
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/datasets/imagenet.py
@@ -0,0 +1,1105 @@
+import os
+
+import numpy as np
+
+from .base_dataset import BaseDataset
+from .builder import DATASETS
+
+
+def has_file_allowed_extension(filename, extensions):
+    """Checks if a file is an allowed extension.
+
+    Args:
+        filename (string): path to a file
+
+    Returns:
+        bool: True if the filename ends with a known image extension
+    """
+    filename_lower = filename.lower()
+    return any(filename_lower.endswith(ext) for ext in extensions)
+
+
+def find_folders(root):
+    """Find classes by folders under a root.
+
+    Args:
+        root (string): root directory of folders
+
+    Returns:
+        folder_to_idx (dict): the map from folder name to class idx
+    """
+    folders = [
+        d for d in os.listdir(root) if os.path.isdir(os.path.join(root, d))
+    ]
+    folders.sort()
+    folder_to_idx = {folders[i]: i for i in range(len(folders))}
+    return folder_to_idx
+
+
+def get_samples(root, folder_to_idx, extensions):
+    """Make dataset by walking all images under a root.
+
+    Args:
+        root (string): root directory of folders
+        folder_to_idx (dict): the map from class name to class idx
+        extensions (tuple): allowed extensions
+
+    Returns:
+        samples (list): a list of tuple where each element is (image, label)
+    """
+    samples = []
+    root = os.path.expanduser(root)
+    for folder_name in sorted(os.listdir(root)):
+        _dir = os.path.join(root, folder_name)
+        if not os.path.isdir(_dir):
+            continue
+
+        for _, _, fns in sorted(os.walk(_dir)):
+            for fn in sorted(fns):
+                if has_file_allowed_extension(fn, extensions):
+                    path = os.path.join(folder_name, fn)
+                    item = (path, folder_to_idx[folder_name])
+                    samples.append(item)
+    return samples
+
+
+@DATASETS.register_module()
+class ImageNet(BaseDataset):
+    """`ImageNet <http://www.image-net.org>`_ Dataset.
+
+    This implementation is modified from
+    https://github.com/pytorch/vision/blob/master/torchvision/datasets/imagenet.py  # noqa: E501
+    """
+
+    IMG_EXTENSIONS = ('.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif')
+    CLASSES = [
+        'tench, Tinca tinca',
+        'goldfish, Carassius auratus',
+        'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias',  # noqa: E501
+        'tiger shark, Galeocerdo cuvieri',
+        'hammerhead, hammerhead shark',
+        'electric ray, crampfish, numbfish, torpedo',
+        'stingray',
+        'cock',
+        'hen',
+        'ostrich, Struthio camelus',
+        'brambling, Fringilla montifringilla',
+        'goldfinch, Carduelis carduelis',
+        'house finch, linnet, Carpodacus mexicanus',
+        'junco, snowbird',
+        'indigo bunting, indigo finch, indigo bird, Passerina cyanea',
+        'robin, American robin, Turdus migratorius',
+        'bulbul',
+        'jay',
+        'magpie',
+        'chickadee',
+        'water ouzel, dipper',
+        'kite',
+        'bald eagle, American eagle, Haliaeetus leucocephalus',
+        'vulture',
+        'great grey owl, great gray owl, Strix nebulosa',
+        'European fire salamander, Salamandra salamandra',
+        'common newt, Triturus vulgaris',
+        'eft',
+        'spotted salamander, Ambystoma maculatum',
+        'axolotl, mud puppy, Ambystoma mexicanum',
+        'bullfrog, Rana catesbeiana',
+        'tree frog, tree-frog',
+        'tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui',
+        'loggerhead, loggerhead turtle, Caretta caretta',
+        'leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea',  # noqa: E501
+        'mud turtle',
+        'terrapin',
+        'box turtle, box tortoise',
+        'banded gecko',
+        'common iguana, iguana, Iguana iguana',
+        'American chameleon, anole, Anolis carolinensis',
+        'whiptail, whiptail lizard',
+        'agama',
+        'frilled lizard, Chlamydosaurus kingi',
+        'alligator lizard',
+        'Gila monster, Heloderma suspectum',
+        'green lizard, Lacerta viridis',
+        'African chameleon, Chamaeleo chamaeleon',
+        'Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis',  # noqa: E501
+        'African crocodile, Nile crocodile, Crocodylus niloticus',
+        'American alligator, Alligator mississipiensis',
+        'triceratops',
+        'thunder snake, worm snake, Carphophis amoenus',
+        'ringneck snake, ring-necked snake, ring snake',
+        'hognose snake, puff adder, sand viper',
+        'green snake, grass snake',
+        'king snake, kingsnake',
+        'garter snake, grass snake',
+        'water snake',
+        'vine snake',
+        'night snake, Hypsiglena torquata',
+        'boa constrictor, Constrictor constrictor',
+        'rock python, rock snake, Python sebae',
+        'Indian cobra, Naja naja',
+        'green mamba',
+        'sea snake',
+        'horned viper, cerastes, sand viper, horned asp, Cerastes cornutus',
+        'diamondback, diamondback rattlesnake, Crotalus adamanteus',
+        'sidewinder, horned rattlesnake, Crotalus cerastes',
+        'trilobite',
+        'harvestman, daddy longlegs, Phalangium opilio',
+        'scorpion',
+        'black and gold garden spider, Argiope aurantia',
+        'barn spider, Araneus cavaticus',
+        'garden spider, Aranea diademata',
+        'black widow, Latrodectus mactans',
+        'tarantula',
+        'wolf spider, hunting spider',
+        'tick',
+        'centipede',
+        'black grouse',
+        'ptarmigan',
+        'ruffed grouse, partridge, Bonasa umbellus',
+        'prairie chicken, prairie grouse, prairie fowl',
+        'peacock',
+        'quail',
+        'partridge',
+        'African grey, African gray, Psittacus erithacus',
+        'macaw',
+        'sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita',
+        'lorikeet',
+        'coucal',
+        'bee eater',
+        'hornbill',
+        'hummingbird',
+        'jacamar',
+        'toucan',
+        'drake',
+        'red-breasted merganser, Mergus serrator',
+        'goose',
+        'black swan, Cygnus atratus',
+        'tusker',
+        'echidna, spiny anteater, anteater',
+        'platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus',  # noqa: E501
+        'wallaby, brush kangaroo',
+        'koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus',  # noqa: E501
+        'wombat',
+        'jellyfish',
+        'sea anemone, anemone',
+        'brain coral',
+        'flatworm, platyhelminth',
+        'nematode, nematode worm, roundworm',
+        'conch',
+        'snail',
+        'slug',
+        'sea slug, nudibranch',
+        'chiton, coat-of-mail shell, sea cradle, polyplacophore',
+        'chambered nautilus, pearly nautilus, nautilus',
+        'Dungeness crab, Cancer magister',
+        'rock crab, Cancer irroratus',
+        'fiddler crab',
+        'king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica',  # noqa: E501
+        'American lobster, Northern lobster, Maine lobster, Homarus americanus',  # noqa: E501
+        'spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish',  # noqa: E501
+        'crayfish, crawfish, crawdad, crawdaddy',
+        'hermit crab',
+        'isopod',
+        'white stork, Ciconia ciconia',
+        'black stork, Ciconia nigra',
+        'spoonbill',
+        'flamingo',
+        'little blue heron, Egretta caerulea',
+        'American egret, great white heron, Egretta albus',
+        'bittern',
+        'crane',
+        'limpkin, Aramus pictus',
+        'European gallinule, Porphyrio porphyrio',
+        'American coot, marsh hen, mud hen, water hen, Fulica americana',
+        'bustard',
+        'ruddy turnstone, Arenaria interpres',
+        'red-backed sandpiper, dunlin, Erolia alpina',
+        'redshank, Tringa totanus',
+        'dowitcher',
+        'oystercatcher, oyster catcher',
+        'pelican',
+        'king penguin, Aptenodytes patagonica',
+        'albatross, mollymawk',
+        'grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus',  # noqa: E501
+        'killer whale, killer, orca, grampus, sea wolf, Orcinus orca',
+        'dugong, Dugong dugon',
+        'sea lion',
+        'Chihuahua',
+        'Japanese spaniel',
+        'Maltese dog, Maltese terrier, Maltese',
+        'Pekinese, Pekingese, Peke',
+        'Shih-Tzu',
+        'Blenheim spaniel',
+        'papillon',
+        'toy terrier',
+        'Rhodesian ridgeback',
+        'Afghan hound, Afghan',
+        'basset, basset hound',
+        'beagle',
+        'bloodhound, sleuthhound',
+        'bluetick',
+        'black-and-tan coonhound',
+        'Walker hound, Walker foxhound',
+        'English foxhound',
+        'redbone',
+        'borzoi, Russian wolfhound',
+        'Irish wolfhound',
+        'Italian greyhound',
+        'whippet',
+        'Ibizan hound, Ibizan Podenco',
+        'Norwegian elkhound, elkhound',
+        'otterhound, otter hound',
+        'Saluki, gazelle hound',
+        'Scottish deerhound, deerhound',
+        'Weimaraner',
+        'Staffordshire bullterrier, Staffordshire bull terrier',
+        'American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier',  # noqa: E501
+        'Bedlington terrier',
+        'Border terrier',
+        'Kerry blue terrier',
+        'Irish terrier',
+        'Norfolk terrier',
+        'Norwich terrier',
+        'Yorkshire terrier',
+        'wire-haired fox terrier',
+        'Lakeland terrier',
+        'Sealyham terrier, Sealyham',
+        'Airedale, Airedale terrier',
+        'cairn, cairn terrier',
+        'Australian terrier',
+        'Dandie Dinmont, Dandie Dinmont terrier',
+        'Boston bull, Boston terrier',
+        'miniature schnauzer',
+        'giant schnauzer',
+        'standard schnauzer',
+        'Scotch terrier, Scottish terrier, Scottie',
+        'Tibetan terrier, chrysanthemum dog',
+        'silky terrier, Sydney silky',
+        'soft-coated wheaten terrier',
+        'West Highland white terrier',
+        'Lhasa, Lhasa apso',
+        'flat-coated retriever',
+        'curly-coated retriever',
+        'golden retriever',
+        'Labrador retriever',
+        'Chesapeake Bay retriever',
+        'German short-haired pointer',
+        'vizsla, Hungarian pointer',
+        'English setter',
+        'Irish setter, red setter',
+        'Gordon setter',
+        'Brittany spaniel',
+        'clumber, clumber spaniel',
+        'English springer, English springer spaniel',
+        'Welsh springer spaniel',
+        'cocker spaniel, English cocker spaniel, cocker',
+        'Sussex spaniel',
+        'Irish water spaniel',
+        'kuvasz',
+        'schipperke',
+        'groenendael',
+        'malinois',
+        'briard',
+        'kelpie',
+        'komondor',
+        'Old English sheepdog, bobtail',
+        'Shetland sheepdog, Shetland sheep dog, Shetland',
+        'collie',
+        'Border collie',
+        'Bouvier des Flandres, Bouviers des Flandres',
+        'Rottweiler',
+        'German shepherd, German shepherd dog, German police dog, alsatian',
+        'Doberman, Doberman pinscher',
+        'miniature pinscher',
+        'Greater Swiss Mountain dog',
+        'Bernese mountain dog',
+        'Appenzeller',
+        'EntleBucher',
+        'boxer',
+        'bull mastiff',
+        'Tibetan mastiff',
+        'French bulldog',
+        'Great Dane',
+        'Saint Bernard, St Bernard',
+        'Eskimo dog, husky',
+        'malamute, malemute, Alaskan malamute',
+        'Siberian husky',
+        'dalmatian, coach dog, carriage dog',
+        'affenpinscher, monkey pinscher, monkey dog',
+        'basenji',
+        'pug, pug-dog',
+        'Leonberg',
+        'Newfoundland, Newfoundland dog',
+        'Great Pyrenees',
+        'Samoyed, Samoyede',
+        'Pomeranian',
+        'chow, chow chow',
+        'keeshond',
+        'Brabancon griffon',
+        'Pembroke, Pembroke Welsh corgi',
+        'Cardigan, Cardigan Welsh corgi',
+        'toy poodle',
+        'miniature poodle',
+        'standard poodle',
+        'Mexican hairless',
+        'timber wolf, grey wolf, gray wolf, Canis lupus',
+        'white wolf, Arctic wolf, Canis lupus tundrarum',
+        'red wolf, maned wolf, Canis rufus, Canis niger',
+        'coyote, prairie wolf, brush wolf, Canis latrans',
+        'dingo, warrigal, warragal, Canis dingo',
+        'dhole, Cuon alpinus',
+        'African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus',
+        'hyena, hyaena',
+        'red fox, Vulpes vulpes',
+        'kit fox, Vulpes macrotis',
+        'Arctic fox, white fox, Alopex lagopus',
+        'grey fox, gray fox, Urocyon cinereoargenteus',
+        'tabby, tabby cat',
+        'tiger cat',
+        'Persian cat',
+        'Siamese cat, Siamese',
+        'Egyptian cat',
+        'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor',  # noqa: E501
+        'lynx, catamount',
+        'leopard, Panthera pardus',
+        'snow leopard, ounce, Panthera uncia',
+        'jaguar, panther, Panthera onca, Felis onca',
+        'lion, king of beasts, Panthera leo',
+        'tiger, Panthera tigris',
+        'cheetah, chetah, Acinonyx jubatus',
+        'brown bear, bruin, Ursus arctos',
+        'American black bear, black bear, Ursus americanus, Euarctos americanus',  # noqa: E501
+        'ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus',
+        'sloth bear, Melursus ursinus, Ursus ursinus',
+        'mongoose',
+        'meerkat, mierkat',
+        'tiger beetle',
+        'ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle',
+        'ground beetle, carabid beetle',
+        'long-horned beetle, longicorn, longicorn beetle',
+        'leaf beetle, chrysomelid',
+        'dung beetle',
+        'rhinoceros beetle',
+        'weevil',
+        'fly',
+        'bee',
+        'ant, emmet, pismire',
+        'grasshopper, hopper',
+        'cricket',
+        'walking stick, walkingstick, stick insect',
+        'cockroach, roach',
+        'mantis, mantid',
+        'cicada, cicala',
+        'leafhopper',
+        'lacewing, lacewing fly',
+        "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk",  # noqa: E501
+        'damselfly',
+        'admiral',
+        'ringlet, ringlet butterfly',
+        'monarch, monarch butterfly, milkweed butterfly, Danaus plexippus',
+        'cabbage butterfly',
+        'sulphur butterfly, sulfur butterfly',
+        'lycaenid, lycaenid butterfly',
+        'starfish, sea star',
+        'sea urchin',
+        'sea cucumber, holothurian',
+        'wood rabbit, cottontail, cottontail rabbit',
+        'hare',
+        'Angora, Angora rabbit',
+        'hamster',
+        'porcupine, hedgehog',
+        'fox squirrel, eastern fox squirrel, Sciurus niger',
+        'marmot',
+        'beaver',
+        'guinea pig, Cavia cobaya',
+        'sorrel',
+        'zebra',
+        'hog, pig, grunter, squealer, Sus scrofa',
+        'wild boar, boar, Sus scrofa',
+        'warthog',
+        'hippopotamus, hippo, river horse, Hippopotamus amphibius',
+        'ox',
+        'water buffalo, water ox, Asiatic buffalo, Bubalus bubalis',
+        'bison',
+        'ram, tup',
+        'bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis',  # noqa: E501
+        'ibex, Capra ibex',
+        'hartebeest',
+        'impala, Aepyceros melampus',
+        'gazelle',
+        'Arabian camel, dromedary, Camelus dromedarius',
+        'llama',
+        'weasel',
+        'mink',
+        'polecat, fitch, foulmart, foumart, Mustela putorius',
+        'black-footed ferret, ferret, Mustela nigripes',
+        'otter',
+        'skunk, polecat, wood pussy',
+        'badger',
+        'armadillo',
+        'three-toed sloth, ai, Bradypus tridactylus',
+        'orangutan, orang, orangutang, Pongo pygmaeus',
+        'gorilla, Gorilla gorilla',
+        'chimpanzee, chimp, Pan troglodytes',
+        'gibbon, Hylobates lar',
+        'siamang, Hylobates syndactylus, Symphalangus syndactylus',
+        'guenon, guenon monkey',
+        'patas, hussar monkey, Erythrocebus patas',
+        'baboon',
+        'macaque',
+        'langur',
+        'colobus, colobus monkey',
+        'proboscis monkey, Nasalis larvatus',
+        'marmoset',
+        'capuchin, ringtail, Cebus capucinus',
+        'howler monkey, howler',
+        'titi, titi monkey',
+        'spider monkey, Ateles geoffroyi',
+        'squirrel monkey, Saimiri sciureus',
+        'Madagascar cat, ring-tailed lemur, Lemur catta',
+        'indri, indris, Indri indri, Indri brevicaudatus',
+        'Indian elephant, Elephas maximus',
+        'African elephant, Loxodonta africana',
+        'lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens',
+        'giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca',
+        'barracouta, snoek',
+        'eel',
+        'coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch',  # noqa: E501
+        'rock beauty, Holocanthus tricolor',
+        'anemone fish',
+        'sturgeon',
+        'gar, garfish, garpike, billfish, Lepisosteus osseus',
+        'lionfish',
+        'puffer, pufferfish, blowfish, globefish',
+        'abacus',
+        'abaya',
+        "academic gown, academic robe, judge's robe",
+        'accordion, piano accordion, squeeze box',
+        'acoustic guitar',
+        'aircraft carrier, carrier, flattop, attack aircraft carrier',
+        'airliner',
+        'airship, dirigible',
+        'altar',
+        'ambulance',
+        'amphibian, amphibious vehicle',
+        'analog clock',
+        'apiary, bee house',
+        'apron',
+        'ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin',  # noqa: E501
+        'assault rifle, assault gun',
+        'backpack, back pack, knapsack, packsack, rucksack, haversack',
+        'bakery, bakeshop, bakehouse',
+        'balance beam, beam',
+        'balloon',
+        'ballpoint, ballpoint pen, ballpen, Biro',
+        'Band Aid',
+        'banjo',
+        'bannister, banister, balustrade, balusters, handrail',
+        'barbell',
+        'barber chair',
+        'barbershop',
+        'barn',
+        'barometer',
+        'barrel, cask',
+        'barrow, garden cart, lawn cart, wheelbarrow',
+        'baseball',
+        'basketball',
+        'bassinet',
+        'bassoon',
+        'bathing cap, swimming cap',
+        'bath towel',
+        'bathtub, bathing tub, bath, tub',
+        'beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon',  # noqa: E501
+        'beacon, lighthouse, beacon light, pharos',
+        'beaker',
+        'bearskin, busby, shako',
+        'beer bottle',
+        'beer glass',
+        'bell cote, bell cot',
+        'bib',
+        'bicycle-built-for-two, tandem bicycle, tandem',
+        'bikini, two-piece',
+        'binder, ring-binder',
+        'binoculars, field glasses, opera glasses',
+        'birdhouse',
+        'boathouse',
+        'bobsled, bobsleigh, bob',
+        'bolo tie, bolo, bola tie, bola',
+        'bonnet, poke bonnet',
+        'bookcase',
+        'bookshop, bookstore, bookstall',
+        'bottlecap',
+        'bow',
+        'bow tie, bow-tie, bowtie',
+        'brass, memorial tablet, plaque',
+        'brassiere, bra, bandeau',
+        'breakwater, groin, groyne, mole, bulwark, seawall, jetty',
+        'breastplate, aegis, egis',
+        'broom',
+        'bucket, pail',
+        'buckle',
+        'bulletproof vest',
+        'bullet train, bullet',
+        'butcher shop, meat market',
+        'cab, hack, taxi, taxicab',
+        'caldron, cauldron',
+        'candle, taper, wax light',
+        'cannon',
+        'canoe',
+        'can opener, tin opener',
+        'cardigan',
+        'car mirror',
+        'carousel, carrousel, merry-go-round, roundabout, whirligig',
+        "carpenter's kit, tool kit",
+        'carton',
+        'car wheel',
+        'cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM',  # noqa: E501
+        'cassette',
+        'cassette player',
+        'castle',
+        'catamaran',
+        'CD player',
+        'cello, violoncello',
+        'cellular telephone, cellular phone, cellphone, cell, mobile phone',
+        'chain',
+        'chainlink fence',
+        'chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour',  # noqa: E501
+        'chain saw, chainsaw',
+        'chest',
+        'chiffonier, commode',
+        'chime, bell, gong',
+        'china cabinet, china closet',
+        'Christmas stocking',
+        'church, church building',
+        'cinema, movie theater, movie theatre, movie house, picture palace',
+        'cleaver, meat cleaver, chopper',
+        'cliff dwelling',
+        'cloak',
+        'clog, geta, patten, sabot',
+        'cocktail shaker',
+        'coffee mug',
+        'coffeepot',
+        'coil, spiral, volute, whorl, helix',
+        'combination lock',
+        'computer keyboard, keypad',
+        'confectionery, confectionary, candy store',
+        'container ship, containership, container vessel',
+        'convertible',
+        'corkscrew, bottle screw',
+        'cornet, horn, trumpet, trump',
+        'cowboy boot',
+        'cowboy hat, ten-gallon hat',
+        'cradle',
+        'crane',
+        'crash helmet',
+        'crate',
+        'crib, cot',
+        'Crock Pot',
+        'croquet ball',
+        'crutch',
+        'cuirass',
+        'dam, dike, dyke',
+        'desk',
+        'desktop computer',
+        'dial telephone, dial phone',
+        'diaper, nappy, napkin',
+        'digital clock',
+        'digital watch',
+        'dining table, board',
+        'dishrag, dishcloth',
+        'dishwasher, dish washer, dishwashing machine',
+        'disk brake, disc brake',
+        'dock, dockage, docking facility',
+        'dogsled, dog sled, dog sleigh',
+        'dome',
+        'doormat, welcome mat',
+        'drilling platform, offshore rig',
+        'drum, membranophone, tympan',
+        'drumstick',
+        'dumbbell',
+        'Dutch oven',
+        'electric fan, blower',
+        'electric guitar',
+        'electric locomotive',
+        'entertainment center',
+        'envelope',
+        'espresso maker',
+        'face powder',
+        'feather boa, boa',
+        'file, file cabinet, filing cabinet',
+        'fireboat',
+        'fire engine, fire truck',
+        'fire screen, fireguard',
+        'flagpole, flagstaff',
+        'flute, transverse flute',
+        'folding chair',
+        'football helmet',
+        'forklift',
+        'fountain',
+        'fountain pen',
+        'four-poster',
+        'freight car',
+        'French horn, horn',
+        'frying pan, frypan, skillet',
+        'fur coat',
+        'garbage truck, dustcart',
+        'gasmask, respirator, gas helmet',
+        'gas pump, gasoline pump, petrol pump, island dispenser',
+        'goblet',
+        'go-kart',
+        'golf ball',
+        'golfcart, golf cart',
+        'gondola',
+        'gong, tam-tam',
+        'gown',
+        'grand piano, grand',
+        'greenhouse, nursery, glasshouse',
+        'grille, radiator grille',
+        'grocery store, grocery, food market, market',
+        'guillotine',
+        'hair slide',
+        'hair spray',
+        'half track',
+        'hammer',
+        'hamper',
+        'hand blower, blow dryer, blow drier, hair dryer, hair drier',
+        'hand-held computer, hand-held microcomputer',
+        'handkerchief, hankie, hanky, hankey',
+        'hard disc, hard disk, fixed disk',
+        'harmonica, mouth organ, harp, mouth harp',
+        'harp',
+        'harvester, reaper',
+        'hatchet',
+        'holster',
+        'home theater, home theatre',
+        'honeycomb',
+        'hook, claw',
+        'hoopskirt, crinoline',
+        'horizontal bar, high bar',
+        'horse cart, horse-cart',
+        'hourglass',
+        'iPod',
+        'iron, smoothing iron',
+        "jack-o'-lantern",
+        'jean, blue jean, denim',
+        'jeep, landrover',
+        'jersey, T-shirt, tee shirt',
+        'jigsaw puzzle',
+        'jinrikisha, ricksha, rickshaw',
+        'joystick',
+        'kimono',
+        'knee pad',
+        'knot',
+        'lab coat, laboratory coat',
+        'ladle',
+        'lampshade, lamp shade',
+        'laptop, laptop computer',
+        'lawn mower, mower',
+        'lens cap, lens cover',
+        'letter opener, paper knife, paperknife',
+        'library',
+        'lifeboat',
+        'lighter, light, igniter, ignitor',
+        'limousine, limo',
+        'liner, ocean liner',
+        'lipstick, lip rouge',
+        'Loafer',
+        'lotion',
+        'loudspeaker, speaker, speaker unit, loudspeaker system, speaker system',  # noqa: E501
+        "loupe, jeweler's loupe",
+        'lumbermill, sawmill',
+        'magnetic compass',
+        'mailbag, postbag',
+        'mailbox, letter box',
+        'maillot',
+        'maillot, tank suit',
+        'manhole cover',
+        'maraca',
+        'marimba, xylophone',
+        'mask',
+        'matchstick',
+        'maypole',
+        'maze, labyrinth',
+        'measuring cup',
+        'medicine chest, medicine cabinet',
+        'megalith, megalithic structure',
+        'microphone, mike',
+        'microwave, microwave oven',
+        'military uniform',
+        'milk can',
+        'minibus',
+        'miniskirt, mini',
+        'minivan',
+        'missile',
+        'mitten',
+        'mixing bowl',
+        'mobile home, manufactured home',
+        'Model T',
+        'modem',
+        'monastery',
+        'monitor',
+        'moped',
+        'mortar',
+        'mortarboard',
+        'mosque',
+        'mosquito net',
+        'motor scooter, scooter',
+        'mountain bike, all-terrain bike, off-roader',
+        'mountain tent',
+        'mouse, computer mouse',
+        'mousetrap',
+        'moving van',
+        'muzzle',
+        'nail',
+        'neck brace',
+        'necklace',
+        'nipple',
+        'notebook, notebook computer',
+        'obelisk',
+        'oboe, hautboy, hautbois',
+        'ocarina, sweet potato',
+        'odometer, hodometer, mileometer, milometer',
+        'oil filter',
+        'organ, pipe organ',
+        'oscilloscope, scope, cathode-ray oscilloscope, CRO',
+        'overskirt',
+        'oxcart',
+        'oxygen mask',
+        'packet',
+        'paddle, boat paddle',
+        'paddlewheel, paddle wheel',
+        'padlock',
+        'paintbrush',
+        "pajama, pyjama, pj's, jammies",
+        'palace',
+        'panpipe, pandean pipe, syrinx',
+        'paper towel',
+        'parachute, chute',
+        'parallel bars, bars',
+        'park bench',
+        'parking meter',
+        'passenger car, coach, carriage',
+        'patio, terrace',
+        'pay-phone, pay-station',
+        'pedestal, plinth, footstall',
+        'pencil box, pencil case',
+        'pencil sharpener',
+        'perfume, essence',
+        'Petri dish',
+        'photocopier',
+        'pick, plectrum, plectron',
+        'pickelhaube',
+        'picket fence, paling',
+        'pickup, pickup truck',
+        'pier',
+        'piggy bank, penny bank',
+        'pill bottle',
+        'pillow',
+        'ping-pong ball',
+        'pinwheel',
+        'pirate, pirate ship',
+        'pitcher, ewer',
+        "plane, carpenter's plane, woodworking plane",
+        'planetarium',
+        'plastic bag',
+        'plate rack',
+        'plow, plough',
+        "plunger, plumber's helper",
+        'Polaroid camera, Polaroid Land camera',
+        'pole',
+        'police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria',  # noqa: E501
+        'poncho',
+        'pool table, billiard table, snooker table',
+        'pop bottle, soda bottle',
+        'pot, flowerpot',
+        "potter's wheel",
+        'power drill',
+        'prayer rug, prayer mat',
+        'printer',
+        'prison, prison house',
+        'projectile, missile',
+        'projector',
+        'puck, hockey puck',
+        'punching bag, punch bag, punching ball, punchball',
+        'purse',
+        'quill, quill pen',
+        'quilt, comforter, comfort, puff',
+        'racer, race car, racing car',
+        'racket, racquet',
+        'radiator',
+        'radio, wireless',
+        'radio telescope, radio reflector',
+        'rain barrel',
+        'recreational vehicle, RV, R.V.',
+        'reel',
+        'reflex camera',
+        'refrigerator, icebox',
+        'remote control, remote',
+        'restaurant, eating house, eating place, eatery',
+        'revolver, six-gun, six-shooter',
+        'rifle',
+        'rocking chair, rocker',
+        'rotisserie',
+        'rubber eraser, rubber, pencil eraser',
+        'rugby ball',
+        'rule, ruler',
+        'running shoe',
+        'safe',
+        'safety pin',
+        'saltshaker, salt shaker',
+        'sandal',
+        'sarong',
+        'sax, saxophone',
+        'scabbard',
+        'scale, weighing machine',
+        'school bus',
+        'schooner',
+        'scoreboard',
+        'screen, CRT screen',
+        'screw',
+        'screwdriver',
+        'seat belt, seatbelt',
+        'sewing machine',
+        'shield, buckler',
+        'shoe shop, shoe-shop, shoe store',
+        'shoji',
+        'shopping basket',
+        'shopping cart',
+        'shovel',
+        'shower cap',
+        'shower curtain',
+        'ski',
+        'ski mask',
+        'sleeping bag',
+        'slide rule, slipstick',
+        'sliding door',
+        'slot, one-armed bandit',
+        'snorkel',
+        'snowmobile',
+        'snowplow, snowplough',
+        'soap dispenser',
+        'soccer ball',
+        'sock',
+        'solar dish, solar collector, solar furnace',
+        'sombrero',
+        'soup bowl',
+        'space bar',
+        'space heater',
+        'space shuttle',
+        'spatula',
+        'speedboat',
+        "spider web, spider's web",
+        'spindle',
+        'sports car, sport car',
+        'spotlight, spot',
+        'stage',
+        'steam locomotive',
+        'steel arch bridge',
+        'steel drum',
+        'stethoscope',
+        'stole',
+        'stone wall',
+        'stopwatch, stop watch',
+        'stove',
+        'strainer',
+        'streetcar, tram, tramcar, trolley, trolley car',
+        'stretcher',
+        'studio couch, day bed',
+        'stupa, tope',
+        'submarine, pigboat, sub, U-boat',
+        'suit, suit of clothes',
+        'sundial',
+        'sunglass',
+        'sunglasses, dark glasses, shades',
+        'sunscreen, sunblock, sun blocker',
+        'suspension bridge',
+        'swab, swob, mop',
+        'sweatshirt',
+        'swimming trunks, bathing trunks',
+        'swing',
+        'switch, electric switch, electrical switch',
+        'syringe',
+        'table lamp',
+        'tank, army tank, armored combat vehicle, armoured combat vehicle',
+        'tape player',
+        'teapot',
+        'teddy, teddy bear',
+        'television, television system',
+        'tennis ball',
+        'thatch, thatched roof',
+        'theater curtain, theatre curtain',
+        'thimble',
+        'thresher, thrasher, threshing machine',
+        'throne',
+        'tile roof',
+        'toaster',
+        'tobacco shop, tobacconist shop, tobacconist',
+        'toilet seat',
+        'torch',
+        'totem pole',
+        'tow truck, tow car, wrecker',
+        'toyshop',
+        'tractor',
+        'trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi',  # noqa: E501
+        'tray',
+        'trench coat',
+        'tricycle, trike, velocipede',
+        'trimaran',
+        'tripod',
+        'triumphal arch',
+        'trolleybus, trolley coach, trackless trolley',
+        'trombone',
+        'tub, vat',
+        'turnstile',
+        'typewriter keyboard',
+        'umbrella',
+        'unicycle, monocycle',
+        'upright, upright piano',
+        'vacuum, vacuum cleaner',
+        'vase',
+        'vault',
+        'velvet',
+        'vending machine',
+        'vestment',
+        'viaduct',
+        'violin, fiddle',
+        'volleyball',
+        'waffle iron',
+        'wall clock',
+        'wallet, billfold, notecase, pocketbook',
+        'wardrobe, closet, press',
+        'warplane, military plane',
+        'washbasin, handbasin, washbowl, lavabo, wash-hand basin',
+        'washer, automatic washer, washing machine',
+        'water bottle',
+        'water jug',
+        'water tower',
+        'whiskey jug',
+        'whistle',
+        'wig',
+        'window screen',
+        'window shade',
+        'Windsor tie',
+        'wine bottle',
+        'wing',
+        'wok',
+        'wooden spoon',
+        'wool, woolen, woollen',
+        'worm fence, snake fence, snake-rail fence, Virginia fence',
+        'wreck',
+        'yawl',
+        'yurt',
+        'web site, website, internet site, site',
+        'comic book',
+        'crossword puzzle, crossword',
+        'street sign',
+        'traffic light, traffic signal, stoplight',
+        'book jacket, dust cover, dust jacket, dust wrapper',
+        'menu',
+        'plate',
+        'guacamole',
+        'consomme',
+        'hot pot, hotpot',
+        'trifle',
+        'ice cream, icecream',
+        'ice lolly, lolly, lollipop, popsicle',
+        'French loaf',
+        'bagel, beigel',
+        'pretzel',
+        'cheeseburger',
+        'hotdog, hot dog, red hot',
+        'mashed potato',
+        'head cabbage',
+        'broccoli',
+        'cauliflower',
+        'zucchini, courgette',
+        'spaghetti squash',
+        'acorn squash',
+        'butternut squash',
+        'cucumber, cuke',
+        'artichoke, globe artichoke',
+        'bell pepper',
+        'cardoon',
+        'mushroom',
+        'Granny Smith',
+        'strawberry',
+        'orange',
+        'lemon',
+        'fig',
+        'pineapple, ananas',
+        'banana',
+        'jackfruit, jak, jack',
+        'custard apple',
+        'pomegranate',
+        'hay',
+        'carbonara',
+        'chocolate sauce, chocolate syrup',
+        'dough',
+        'meat loaf, meatloaf',
+        'pizza, pizza pie',
+        'potpie',
+        'burrito',
+        'red wine',
+        'espresso',
+        'cup',
+        'eggnog',
+        'alp',
+        'bubble',
+        'cliff, drop, drop-off',
+        'coral reef',
+        'geyser',
+        'lakeside, lakeshore',
+        'promontory, headland, head, foreland',
+        'sandbar, sand bar',
+        'seashore, coast, seacoast, sea-coast',
+        'valley, vale',
+        'volcano',
+        'ballplayer, baseball player',
+        'groom, bridegroom',
+        'scuba diver',
+        'rapeseed',
+        'daisy',
+        "yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum",  # noqa: E501
+        'corn',
+        'acorn',
+        'hip, rose hip, rosehip',
+        'buckeye, horse chestnut, conker',
+        'coral fungus',
+        'agaric',
+        'gyromitra',
+        'stinkhorn, carrion fungus',
+        'earthstar',
+        'hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa',  # noqa: E501
+        'bolete',
+        'ear, spike, capitulum',
+        'toilet tissue, toilet paper, bathroom tissue'
+    ]
+
+    def load_annotations(self):
+        if self.ann_file is None:
+            folder_to_idx = find_folders(self.data_prefix)
+            samples = get_samples(
+                self.data_prefix,
+                folder_to_idx,
+                extensions=self.IMG_EXTENSIONS)
+            if len(samples) == 0:
+                raise (RuntimeError('Found 0 files in subfolders of: '
+                                    f'{self.data_prefix}. '
+                                    'Supported extensions are: '
+                                    f'{",".join(self.IMG_EXTENSIONS)}'))
+
+            self.folder_to_idx = folder_to_idx
+        elif isinstance(self.ann_file, str):
+            with open(self.ann_file) as f:
+                samples = [x.strip().split(' ') for x in f.readlines()]
+        else:
+            raise TypeError('ann_file must be a str or None')
+        self.samples = samples
+
+        data_infos = []
+        for filename, gt_label in self.samples:
+            info = {'img_prefix': self.data_prefix}
+            info['img_info'] = {'filename': filename}
+            info['gt_label'] = np.array(gt_label, dtype=np.int64)
+            data_infos.append(info)
+        return data_infos
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/datasets/mnist.py b/CAGE_expression_inference-apvit/apvit_mmcls/datasets/mnist.py
new file mode 100644
index 0000000000000000000000000000000000000000..fadc2f7175c59b8e7757649807d0d6cfa971bac1
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/datasets/mnist.py
@@ -0,0 +1,171 @@
+import codecs
+import os
+import os.path as osp
+
+import numpy as np
+import torch
+
+from .base_dataset import BaseDataset
+from .builder import DATASETS
+from .utils import download_and_extract_archive, rm_suffix
+
+
+@DATASETS.register_module()
+class MNIST(BaseDataset):
+    """`MNIST <http://yann.lecun.com/exdb/mnist/>`_ Dataset.
+
+    This implementation is modified from
+    https://github.com/pytorch/vision/blob/master/torchvision/datasets/mnist.py  # noqa: E501
+    """
+
+    resource_prefix = 'http://yann.lecun.com/exdb/mnist/'
+    resources = {
+        'train_image_file':
+        ('train-images-idx3-ubyte.gz', 'f68b3c2dcbeaaa9fbdd348bbdeb94873'),
+        'train_label_file':
+        ('train-labels-idx1-ubyte.gz', 'd53e105ee54ea40749a09fcbcd1e9432'),
+        'test_image_file':
+        ('t10k-images-idx3-ubyte.gz', '9fb629c4189551a2d022fa330f9573f3'),
+        'test_label_file':
+        ('t10k-labels-idx1-ubyte.gz', 'ec29112dd5afa0611ce80d1b7f02629c')
+    }
+
+    CLASSES = [
+        '0 - zero', '1 - one', '2 - two', '3 - three', '4 - four', '5 - five',
+        '6 - six', '7 - seven', '8 - eight', '9 - nine'
+    ]
+
+    def load_annotations(self):
+        train_image_file = osp.join(
+            self.data_prefix, rm_suffix(self.resources['train_image_file'][0]))
+        train_label_file = osp.join(
+            self.data_prefix, rm_suffix(self.resources['train_label_file'][0]))
+        test_image_file = osp.join(
+            self.data_prefix, rm_suffix(self.resources['test_image_file'][0]))
+        test_label_file = osp.join(
+            self.data_prefix, rm_suffix(self.resources['test_label_file'][0]))
+
+        if not osp.exists(train_image_file) or not osp.exists(
+                train_label_file) or not osp.exists(
+                    test_image_file) or not osp.exists(test_label_file):
+            self.download()
+
+        train_set = (read_image_file(train_image_file),
+                     read_label_file(train_label_file))
+        test_set = (read_image_file(test_image_file),
+                    read_label_file(test_label_file))
+
+        if not self.test_mode:
+            imgs, gt_labels = train_set
+        else:
+            imgs, gt_labels = test_set
+
+        data_infos = []
+        for img, gt_label in zip(imgs, gt_labels):
+            gt_label = np.array(gt_label, dtype=np.int64)
+            info = {'img': img.numpy(), 'gt_label': gt_label}
+            data_infos.append(info)
+        return data_infos
+
+    def download(self):
+        os.makedirs(self.data_prefix, exist_ok=True)
+
+        # download files
+        for url, md5 in self.resources.values():
+            url = osp.join(self.resource_prefix, url)
+            filename = url.rpartition('/')[2]
+            download_and_extract_archive(
+                url,
+                download_root=self.data_prefix,
+                filename=filename,
+                md5=md5)
+
+
+@DATASETS.register_module()
+class FashionMNIST(MNIST):
+    """`Fashion-MNIST <https://github.com/zalandoresearch/fashion-mnist>`_
+        Dataset.
+    """
+
+    resource_prefix = 'http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/'  # noqa: E501
+    resources = {
+        'train_image_file':
+        ('train-images-idx3-ubyte.gz', '8d4fb7e6c68d591d4c3dfef9ec88bf0d'),
+        'train_label_file':
+        ('train-labels-idx1-ubyte.gz', '25c81989df183df01b3e8a0aad5dffbe'),
+        'test_image_file':
+        ('t10k-images-idx3-ubyte.gz', 'bef4ecab320f06d8554ea6380940ec79'),
+        'test_label_file':
+        ('t10k-labels-idx1-ubyte.gz', 'bb300cfdad3c16e7a12a480ee83cd310')
+    }
+    CLASSES = [
+        'T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal',
+        'Shirt', 'Sneaker', 'Bag', 'Ankle boot'
+    ]
+
+
+def get_int(b):
+    return int(codecs.encode(b, 'hex'), 16)
+
+
+def open_maybe_compressed_file(path):
+    """Return a file object that possibly decompresses 'path' on the fly.
+       Decompression occurs when argument `path` is a string
+       and ends with '.gz' or '.xz'.
+    """
+    if not isinstance(path, str):
+        return path
+    if path.endswith('.gz'):
+        import gzip
+        return gzip.open(path, 'rb')
+    if path.endswith('.xz'):
+        import lzma
+        return lzma.open(path, 'rb')
+    return open(path, 'rb')
+
+
+def read_sn3_pascalvincent_tensor(path, strict=True):
+    """Read a SN3 file in "Pascal Vincent" format
+       (Lush file 'libidx/idx-io.lsh').
+       Argument may be a filename, compressed filename, or file object.
+    """
+    # typemap
+    if not hasattr(read_sn3_pascalvincent_tensor, 'typemap'):
+        read_sn3_pascalvincent_tensor.typemap = {
+            8: (torch.uint8, np.uint8, np.uint8),
+            9: (torch.int8, np.int8, np.int8),
+            11: (torch.int16, np.dtype('>i2'), 'i2'),
+            12: (torch.int32, np.dtype('>i4'), 'i4'),
+            13: (torch.float32, np.dtype('>f4'), 'f4'),
+            14: (torch.float64, np.dtype('>f8'), 'f8')
+        }
+    # read
+    with open_maybe_compressed_file(path) as f:
+        data = f.read()
+    # parse
+    magic = get_int(data[0:4])
+    nd = magic % 256
+    ty = magic // 256
+    assert nd >= 1 and nd <= 3
+    assert ty >= 8 and ty <= 14
+    m = read_sn3_pascalvincent_tensor.typemap[ty]
+    s = [get_int(data[4 * (i + 1):4 * (i + 2)]) for i in range(nd)]
+    parsed = np.frombuffer(data, dtype=m[1], offset=(4 * (nd + 1)))
+    assert parsed.shape[0] == np.prod(s) or not strict
+    return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)
+
+
+def read_label_file(path):
+    with open(path, 'rb') as f:
+        x = read_sn3_pascalvincent_tensor(f, strict=False)
+    assert (x.dtype == torch.uint8)
+    assert (x.ndimension() == 1)
+    return x.long()
+
+
+def read_image_file(path):
+    with open(path, 'rb') as f:
+        x = read_sn3_pascalvincent_tensor(f, strict=False)
+    assert (x.dtype == torch.uint8)
+    assert (x.ndimension() == 3)
+    return x
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/datasets/pipelines/__init__.py b/CAGE_expression_inference-apvit/apvit_mmcls/datasets/pipelines/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..286775dfa003576db79ea3404f6b8773c6b604b7
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/datasets/pipelines/__init__.py
@@ -0,0 +1,15 @@
+from .compose import Compose
+from .formating import (Collect, ImageToTensor, ToNumpy, ToPIL, ToTensor,
+                        Transpose, to_tensor)
+from .loading import LoadImageFromFile
+from .transforms import (RandomAppliedTrans, CenterCrop, RandomCrop, RandomFlip, RandomGrayscale,
+                         RandomResizedCrop, Resize, RandomRotate, ColorJitter)
+from .test_time_aug import MultiScaleFlipAug
+
+__all__ = [
+    'Compose', 'to_tensor', 'ToTensor', 'ImageToTensor', 'ToPIL', 'ToNumpy',
+    'Transpose', 'Collect', 'LoadImageFromFile', 'Resize', 'CenterCrop',
+    'RandomFlip', 'Normalize', 'RandomCrop', 'RandomResizedCrop',
+    'RandomGrayscale', 'RandomAppliedTrans', 'RandomRotate', 'ColorJitter',
+    'MultiScaleFlipAug'
+]
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/datasets/pipelines/compose.py b/CAGE_expression_inference-apvit/apvit_mmcls/datasets/pipelines/compose.py
new file mode 100644
index 0000000000000000000000000000000000000000..21960b2225aaaffb342edc90385f85a21d31390a
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/datasets/pipelines/compose.py
@@ -0,0 +1,42 @@
+from collections.abc import Sequence
+
+from mmcv.utils import build_from_cfg
+
+from ..builder import PIPELINES
+
+
+@PIPELINES.register_module()
+class Compose(object):
+    """Compose a data pipeline with a sequence of transforms.
+
+    Args:
+        transforms (list[dict | callable]):
+            Either config dicts of transforms or transform objects.
+    """
+
+    def __init__(self, transforms):
+        assert isinstance(transforms, Sequence)
+        self.transforms = []
+        for transform in transforms:
+            if isinstance(transform, dict):
+                transform = build_from_cfg(transform, PIPELINES)
+                self.transforms.append(transform)
+            elif callable(transform):
+                self.transforms.append(transform)
+            else:
+                raise TypeError('transform must be callable or a dict, but got'
+                                f' {type(transform)}')
+
+    def __call__(self, data):
+        for t in self.transforms:
+            data = t(data)
+            if data is None:
+                return None
+        return data
+
+    def __repr__(self):
+        format_string = self.__class__.__name__ + '('
+        for t in self.transforms:
+            format_string += f'\n    {t}'
+        format_string += '\n)'
+        return format_string
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/datasets/pipelines/formating.py b/CAGE_expression_inference-apvit/apvit_mmcls/datasets/pipelines/formating.py
new file mode 100644
index 0000000000000000000000000000000000000000..e0d78f22079a55f60c80e2e6a9b8eda984e6775c
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/datasets/pipelines/formating.py
@@ -0,0 +1,156 @@
+from collections.abc import Sequence
+
+import mmcv
+import numpy as np
+import torch
+from PIL import Image
+
+from ..builder import PIPELINES
+
+
+def to_tensor(data):
+    """Convert objects of various python types to :obj:`torch.Tensor`.
+
+    Supported types are: :class:`numpy.ndarray`, :class:`torch.Tensor`,
+    :class:`Sequence`, :class:`int` and :class:`float`.
+    """
+    if isinstance(data, torch.Tensor):
+        return data
+    elif isinstance(data, np.ndarray):
+        return torch.from_numpy(data)
+    elif isinstance(data, Sequence) and not mmcv.is_str(data):
+        return torch.tensor(data)
+    elif isinstance(data, int):
+        return torch.LongTensor([data])
+    elif isinstance(data, float):
+        return torch.FloatTensor([data])
+    else:
+        raise TypeError(
+            f'Type {type(data)} cannot be converted to tensor.'
+            'Supported types are: `numpy.ndarray`, `torch.Tensor`, '
+            '`Sequence`, `int` and `float`')
+
+
+@PIPELINES.register_module()
+class ToTensor(object):
+
+    def __init__(self, keys):
+        self.keys = keys
+
+    def __call__(self, results):
+        for key in self.keys:
+            results[key] = to_tensor(results[key])
+        return results
+
+    def __repr__(self):
+        return self.__class__.__name__ + f'(keys={self.keys})'
+
+
+@PIPELINES.register_module()
+class ImageToTensor(object):
+
+    def __init__(self, keys):
+        self.keys = keys
+
+    def __call__(self, results):
+        for key in self.keys:
+            img = results[key]
+            if len(img.shape) < 3:
+                img = np.expand_dims(img, -1)
+            results[key] = to_tensor(img.transpose(2, 0, 1))
+        return results
+
+    def __repr__(self):
+        return self.__class__.__name__ + f'(keys={self.keys})'
+
+
+@PIPELINES.register_module()
+class Transpose(object):
+
+    def __init__(self, keys, order):
+        self.keys = keys
+        self.order = order
+
+    def __call__(self, results):
+        for key in self.keys:
+            results[key] = results[key].transpose(self.order)
+        return results
+
+    def __repr__(self):
+        return self.__class__.__name__ + \
+            f'(keys={self.keys}, order={self.order})'
+
+
+@PIPELINES.register_module()
+class ToPIL(object):
+
+    def __init__(self):
+        pass
+
+    def __call__(self, results):
+        results['img'] = Image.fromarray(results['img'])
+        return results
+
+
+@PIPELINES.register_module()
+class ToNumpy(object):
+
+    def __init__(self):
+        pass
+
+    def __call__(self, results):
+        results['img'] = np.array(results['img'], dtype=np.float32)
+        return results
+
+
+@PIPELINES.register_module()
+class Collect(object):
+    """
+    Collect data from the loader relevant to the specific task.
+
+    This is usually the last stage of the data loader pipeline. Typically keys
+    is set to some subset of "img" and "gt_label".
+    """
+
+    def __init__(self, keys):
+        self.keys = keys
+
+    def __call__(self, results):
+        data = {}
+        for key in self.keys:
+            data[key] = results[key]
+        return data
+
+    def __repr__(self):
+        return self.__class__.__name__ + \
+            f'(keys={self.keys}, meta_keys={self.meta_keys})'
+
+
+@PIPELINES.register_module()
+class WrapFieldsToLists(object):
+    """Wrap fields of the data dictionary into lists for evaluation.
+
+    This class can be used as a last step of a test or validation
+    pipeline for single image evaluation or inference.
+
+    Example:
+        >>> test_pipeline = [
+        >>>    dict(type='LoadImageFromFile'),
+        >>>    dict(type='Normalize',
+                    mean=[123.675, 116.28, 103.53],
+                    std=[58.395, 57.12, 57.375],
+                    to_rgb=True),
+        >>>    dict(type='ImageToTensor', keys=['img']),
+        >>>    dict(type='Collect', keys=['img']),
+        >>>    dict(type='WrapIntoLists')
+        >>> ]
+    """
+
+    def __call__(self, results):
+        # Wrap dict fields into lists
+        for key, val in results.items():
+            results[key] = [val]
+        return results
+
+    def __repr__(self):
+        return f'{self.__class__.__name__}()'
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/datasets/pipelines/loading.py b/CAGE_expression_inference-apvit/apvit_mmcls/datasets/pipelines/loading.py
new file mode 100644
index 0000000000000000000000000000000000000000..f1d977bd6a58fa968eb4cd357d36a00dbdbf220c
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/datasets/pipelines/loading.py
@@ -0,0 +1,67 @@
+import os.path as osp
+
+import mmcv
+import numpy as np
+
+from ..builder import PIPELINES
+
+
+@PIPELINES.register_module()
+class LoadImageFromFile(object):
+    """Load an image from file.
+
+    Required keys are "img_prefix" and "img_info" (a dict that must contain the
+    key "filename"). Added or updated keys are "filename", "img", "img_shape",
+    "ori_shape" (same as `img_shape`) and "img_norm_cfg" (means=0 and stds=1).
+
+    Args:
+        to_float32 (bool): Whether to convert the loaded image to a float32
+            numpy array. If set to False, the loaded image is an uint8 array.
+            Defaults to False.
+        color_type (str): The flag argument for :func:`mmcv.imfrombytes()`.
+            Defaults to 'color'.
+        file_client_args (dict): Arguments to instantiate a FileClient.
+            See :class:`mmcv.fileio.FileClient` for details.
+            Defaults to ``dict(backend='disk')``.
+    """
+
+    def __init__(self,
+                 to_float32=False,
+                 color_type='color',
+                 file_client_args=dict(backend='disk')):
+        self.to_float32 = to_float32
+        self.color_type = color_type
+        self.file_client_args = file_client_args.copy()
+        self.file_client = None
+
+    def __call__(self, results):
+        if self.file_client is None:
+            self.file_client = mmcv.FileClient(**self.file_client_args)
+
+        if results['img_prefix'] is not None:
+            filename = osp.join(results['img_prefix'],
+                                results['img_info']['filename'])
+        else:
+            filename = results['img_info']['filename']
+
+        img_bytes = self.file_client.get(filename)
+        img = mmcv.imfrombytes(img_bytes, flag=self.color_type)
+        if self.to_float32:
+            img = img.astype(np.float32)
+        results['filename'] = filename
+        results['img'] = img
+        results['img_shape'] = img.shape
+        results['ori_shape'] = img.shape
+        num_channels = 1 if len(img.shape) < 3 else img.shape[2]
+        results['img_norm_cfg'] = dict(
+            mean=np.zeros(num_channels, dtype=np.float32),
+            std=np.ones(num_channels, dtype=np.float32),
+            to_rgb=False)
+        return results
+
+    def __repr__(self):
+        repr_str = (f'{self.__class__.__name__}('
+                    f'to_float32={self.to_float32}, '
+                    f"color_type='{self.color_type}', "
+                    f'file_client_args={self.file_client_args})')
+        return repr_str
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/datasets/pipelines/test_time_aug.py b/CAGE_expression_inference-apvit/apvit_mmcls/datasets/pipelines/test_time_aug.py
new file mode 100644
index 0000000000000000000000000000000000000000..7682a6096e1583aae19991dc31aef383403445de
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/datasets/pipelines/test_time_aug.py
@@ -0,0 +1,126 @@
+import warnings
+
+import mmcv
+
+from ..builder import PIPELINES
+from .compose import Compose
+
+
+@PIPELINES.register_module()
+class MultiScaleFlipAug(object):
+    """Test-time augmentation with multiple scales and flipping.
+
+    An example configuration is as followed:
+
+    .. code-block::
+
+        img_scale=(2048, 1024),
+        img_ratios=[0.5, 1.0],
+        flip=True,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ]
+
+    After MultiScaleFLipAug with above configuration, the results are wrapped
+    into lists of the same length as followed:
+
+    .. code-block::
+
+        dict(
+            img=[...],
+            img_shape=[...],
+            scale=[(1024, 512), (1024, 512), (2048, 1024), (2048, 1024)]
+            flip=[False, True, False, True]
+            ...
+        )
+
+    Args:
+        transforms (list[dict]): Transforms to apply in each augmentation.
+        img_scale (tuple | list[tuple]): Images scales for resizing.
+        img_ratios (float | list[float]): Image ratios for resizing
+        flip (bool): Whether apply flip augmentation. Default: False.
+        flip_direction (str | list[str]): Flip augmentation directions,
+            options are "horizontal" and "vertical". If flip_direction is list,
+            multiple flip augmentations will be applied.
+            It has no effect when flip == False. Default: "horizontal".
+    """
+
+    def __init__(self,
+                 transforms,
+                 img_scale,
+                 num=8,
+                 img_ratios=None,
+                 flip=False,
+                 flip_direction='horizontal'):
+        self.transforms = Compose(transforms)
+        self.num = num
+        if img_ratios is not None:
+            # mode 1: given a scale and a range of image ratio
+            img_ratios = img_ratios if isinstance(img_ratios,
+                                                  list) else [img_ratios]
+            assert mmcv.is_list_of(img_ratios, float)
+            assert isinstance(img_scale, tuple) and len(img_scale) == 2
+            self.img_scale = [(int(img_scale[0] * ratio),
+                               int(img_scale[1] * ratio))
+                              for ratio in img_ratios]
+        else:
+            # mode 2: given multiple scales
+            self.img_scale = img_scale if isinstance(img_scale,
+                                                     list) else [img_scale]
+        assert mmcv.is_list_of(self.img_scale, tuple)
+        self.flip = flip
+        self.flip_direction = flip_direction if isinstance(
+            flip_direction, list) else [flip_direction]
+        assert mmcv.is_list_of(self.flip_direction, str)
+        if not self.flip and self.flip_direction != ['horizontal']:
+            warnings.warn(
+                'flip_direction has no effect when flip is set to False')
+        if (self.flip
+                and not any([t['type'] == 'RandomFlip' for t in transforms])):
+            warnings.warn(
+                'flip has no effect when RandomFlip is not in transforms')
+
+    def __call__(self, results):
+        """Call function to apply test time augment transforms on results.
+
+        Args:
+            results (dict): Result dict contains the data to transform.
+
+        Returns:
+           dict[str: list]: The augmented data, where each value is wrapped
+               into a list.
+        """
+
+        aug_data = []
+        # flip_aug = [False, True] if self.flip else [False]
+        # for scale in self.img_scale:
+        #     for flip in flip_aug:
+        #         for direction in self.flip_direction:
+        #             _results = results.copy()
+        #             _results['scale'] = scale
+        #             _results['flip'] = flip
+        #             _results['flip_direction'] = direction
+        #             data = self.transforms(_results)
+        #             aug_data.append(data)
+        for _ in range(self.num):
+            _results = results.copy()
+            data = self.transforms(_results)
+            aug_data.append(data)
+        # list of dict to dict of list
+        aug_data_dict = {key: [] for key in aug_data[0]}
+        for data in aug_data:
+            for key, val in data.items():
+                aug_data_dict[key].append(val)
+        return aug_data_dict
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'(transforms={self.transforms}, '
+        repr_str += f'img_scale={self.img_scale}, flip={self.flip})'
+        repr_str += f'flip_direction={self.flip_direction}'
+        return repr_str
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/datasets/pipelines/transforms.py b/CAGE_expression_inference-apvit/apvit_mmcls/datasets/pipelines/transforms.py
new file mode 100644
index 0000000000000000000000000000000000000000..75a346a3a164520ac5621088dac7bb6c2157a778
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/datasets/pipelines/transforms.py
@@ -0,0 +1,918 @@
+import inspect
+import math
+import random
+import numbers
+import warnings
+from typing import Tuple, List, Optional
+
+import cv2
+import mmcv
+import numpy as np
+from PIL import Image, ImageFilter
+
+import torch
+from torch import Tensor
+from torchvision import transforms as _transforms
+import torchvision.transforms.functional as TVF
+from mmcv.utils import build_from_cfg
+
+from ..builder import PIPELINES
+
+try:
+    import albumentations
+    from albumentations import Compose
+except ImportError:
+    albumentations = None
+    Compose = None
+
+
+@PIPELINES.register_module()
+class RandomAppliedTrans(object):
+    """Randomly applied transformations.
+
+    Args:
+        transforms (list[dict]): List of transformations in dictionaries.
+        p (float): Probability.
+    """
+
+    def __init__(self, transforms, p=0.5):
+        t = [build_from_cfg(t, PIPELINES) for t in transforms]
+        self.trans = _transforms.RandomApply(t, p=p)
+
+    def __call__(self, img):
+        return self.trans(img)
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        return repr_str
+
+
+@PIPELINES.register_module()
+class RandomCrop(object):
+    """Crop the given Image at a random location.
+
+    Args:
+        size (sequence or int): Desired output size of the crop. If size is an
+            int instead of sequence like (h, w), a square crop (size, size) is
+            made.
+        padding (int or sequence, optional): Optional padding on each border
+            of the image. If a sequence of length 4 is provided, it is used to
+            pad left, top, right, bottom borders respectively.  If a sequence
+            of length 2 is provided, it is used to pad left/right, top/bottom
+            borders, respectively. Default: None, which means no padding.
+        pad_if_needed (boolean): It will pad the image if smaller than the
+            desired size to avoid raising an exception. Since cropping is done
+            after padding, the padding seems to be done at a random offset.
+            Default: False.
+        pad_val (Number | Sequence[Number]): Pixel pad_val value for constant
+            fill. If a tuple of length 3, it is used to pad_val R, G, B
+            channels respectively. Default: 0.
+        padding_mode (str): Type of padding. Should be: constant, edge,
+            reflect or symmetric. Default: constant.
+            -constant: Pads with a constant value, this value is specified
+                with pad_val.
+            -edge: pads with the last value at the edge of the image.
+            -reflect: Pads with reflection of image without repeating the
+                last value on the edge. For example, padding [1, 2, 3, 4]
+                with 2 elements on both sides in reflect mode will result
+                in [3, 2, 1, 2, 3, 4, 3, 2].
+            -symmetric: Pads with reflection of image repeating the last
+                value on the edge. For example, padding [1, 2, 3, 4] with
+                2 elements on both sides in symmetric mode will result in
+                [2, 1, 1, 2, 3, 4, 4, 3].
+    """
+
+    def __init__(self,
+                 size,
+                 padding=None,
+                 pad_if_needed=False,
+                 pad_val=0,
+                 padding_mode='constant'):
+        if isinstance(size, (tuple, list)):
+            self.size = size
+        else:
+            self.size = (size, size)
+        # check padding mode
+        assert padding_mode in ['constant', 'edge', 'reflect', 'symmetric']
+        self.padding = padding
+        self.pad_if_needed = pad_if_needed
+        self.pad_val = pad_val
+        self.padding_mode = padding_mode
+
+    @staticmethod
+    def get_params(img, output_size):
+        """Get parameters for ``crop`` for a random crop.
+
+        Args:
+            img (ndarray): Image to be cropped.
+            output_size (tuple): Expected output size of the crop.
+
+        Returns:
+            tuple: Params (xmin, ymin, target_height, target_width) to be
+                passed to ``crop`` for random crop.
+        """
+        height = img.shape[0]
+        width = img.shape[1]
+        target_height, target_width = output_size
+        if width == target_width and height == target_height:
+            return 0, 0, height, width
+
+        xmin = random.randint(0, height - target_height)
+        ymin = random.randint(0, width - target_width)
+        return xmin, ymin, target_height, target_width
+
+    def __call__(self, results):
+        """
+        Args:
+            img (ndarray): Image to be cropped.
+        """
+        for key in results.get('img_fields', ['img']):
+            img = results[key]
+            if self.padding is not None:
+                img = mmcv.impad(
+                    img, padding=self.padding, pad_val=self.pad_val)
+
+            # pad the height if needed
+            if self.pad_if_needed and img.shape[0] < self.size[0]:
+                img = mmcv.impad(
+                    img,
+                    padding=(0, self.size[0] - img.shape[0], 0,
+                             self.size[0] - img.shape[0]),
+                    pad_val=self.pad_val,
+                    padding_mode=self.padding_mode)
+
+            # pad the width if needed
+            if self.pad_if_needed and img.shape[1] < self.size[1]:
+                img = mmcv.impad(
+                    img,
+                    padding=(self.size[1] - img.shape[1], 0,
+                             self.size[1] - img.shape[1], 0),
+                    pad_val=self.pad_val,
+                    padding_mode=self.padding_mode)
+
+            xmin, ymin, height, width = self.get_params(img, self.size)
+            results[key] = mmcv.imcrop(
+                img,
+                np.array([ymin, xmin, ymin + width - 1, xmin + height - 1]))
+        return results
+
+    def __repr__(self):
+        return (self.__class__.__name__ +
+                f'(size={self.size}, padding={self.padding})')
+
+
+@PIPELINES.register_module()
+class RandomResizedCrop(object):
+    """Crop the given image to random size and aspect ratio.
+
+    A crop of random size (default: of 0.08 to 1.0) of the original size and a
+    random aspect ratio (default: of 3/4 to 4/3) of the original aspect ratio
+    is made. This crop is finally resized to given size.
+
+    Args:
+        size (sequence or int): Desired output size of the crop. If size is an
+            int instead of sequence like (h, w), a square crop (size, size) is
+            made.
+        scale (tuple): Range of the random size of the cropped image compared
+            to the original image. Default: (0.08, 1.0).
+        ratio (tuple): Range of the random aspect ratio of the cropped image
+            compared to the original image. Default: (3. / 4., 4. / 3.).
+        interpolation (str): Interpolation method, accepted values are
+            'nearest', 'bilinear', 'bicubic', 'area', 'lanczos'. Default:
+            'bilinear'.
+        backend (str): The image resize backend type, accpeted values are
+            `cv2` and `pillow`. Default: `cv2`.
+    """
+
+    def __init__(self,
+                 size,
+                 scale=(0.08, 1.0),
+                 ratio=(3. / 4., 4. / 3.),
+                 interpolation='bilinear',
+                 backend='cv2'):
+        if isinstance(size, (tuple, list)):
+            self.size = size
+        else:
+            self.size = (size, size)
+        if (scale[0] > scale[1]) or (ratio[0] > ratio[1]):
+            raise ValueError('range should be of kind (min, max). '
+                             f'But received {scale}')
+        if backend not in ['cv2', 'pillow']:
+            raise ValueError(f'backend: {backend} is not supported for resize.'
+                             'Supported backends are "cv2", "pillow"')
+
+        self.interpolation = interpolation
+        self.scale = scale
+        self.ratio = ratio
+        self.backend = backend
+
+    @staticmethod
+    def get_params(img, scale, ratio):
+        """Get parameters for ``crop`` for a random sized crop.
+
+        Args:
+            img (ndarray): Image to be cropped.
+            scale (tuple): Range of the random size of the cropped image
+                compared to the original image size.
+            ratio (tuple): Range of the random aspect ratio of the cropped
+                image compared to the original image area.
+
+        Returns:
+            tuple: Params (xmin, ymin, target_height, target_width) to be
+                passed to ``crop`` for a random sized crop.
+        """
+        height = img.shape[0]
+        width = img.shape[1]
+        area = height * width
+
+        for _ in range(10):
+            target_area = random.uniform(*scale) * area
+            log_ratio = (math.log(ratio[0]), math.log(ratio[1]))
+            aspect_ratio = math.exp(random.uniform(*log_ratio))
+
+            target_width = int(round(math.sqrt(target_area * aspect_ratio)))
+            target_height = int(round(math.sqrt(target_area / aspect_ratio)))
+
+            if 0 < target_width <= width and 0 < target_height <= height:
+                xmin = random.randint(0, height - target_height)
+                ymin = random.randint(0, width - target_width)
+                return xmin, ymin, target_height, target_width
+
+        # Fallback to central crop
+        in_ratio = float(width) / float(height)
+        if in_ratio < min(ratio):
+            target_width = width
+            target_height = int(round(target_width / min(ratio)))
+        elif in_ratio > max(ratio):
+            target_height = height
+            target_width = int(round(target_height * max(ratio)))
+        else:  # whole image
+            target_width = width
+            target_height = height
+        xmin = (height - target_height) // 2
+        ymin = (width - target_width) // 2
+        return xmin, ymin, target_height, target_width
+
+    def __call__(self, results):
+        """
+        Args:
+            img (ndarray): Image to be cropped and resized.
+
+        Returns:
+            ndarray: Randomly cropped and resized image.
+        """
+        for key in results.get('img_fields', ['img']):
+            img = results[key]
+            xmin, ymin, target_height, target_width = self.get_params(
+                img, self.scale, self.ratio)
+            img = mmcv.imcrop(
+                img,
+                np.array([
+                    ymin, xmin, ymin + target_width - 1,
+                    xmin + target_height - 1
+                ]))
+            results[key] = mmcv.imresize(
+                img,
+                tuple(self.size[::-1]),
+                interpolation=self.interpolation,
+                backend=self.backend)
+        return results
+
+    def __repr__(self):
+        format_string = self.__class__.__name__ + f'(size={self.size}'
+        format_string += f', scale={tuple(round(s, 4) for s in self.scale)}'
+        format_string += f', ratio={tuple(round(r, 4) for r in self.ratio)}'
+        format_string += f', interpolation={self.interpolation})'
+        return format_string
+
+
+@PIPELINES.register_module()
+class RandomGrayscale(object):
+    """Randomly convert image to grayscale with a probability of gray_prob.
+
+    Args:
+        gray_prob (float): Probability that image should be converted to
+            grayscale. Default: 0.1.
+
+    Returns:
+        ndarray: Grayscale version of the input image with probability
+            gray_prob and unchanged with probability (1-gray_prob).
+            - If input image is 1 channel: grayscale version is 1 channel.
+            - If input image is 3 channel: grayscale version is 3 channel
+                with r == g == b.
+
+    """
+
+    def __init__(self, gray_prob=0.1):
+        self.gray_prob = gray_prob
+
+    def __call__(self, results):
+        """
+        Args:
+            img (ndarray): Image to be converted to grayscale.
+
+        Returns:
+            ndarray: Randomly grayscaled image.
+        """
+        for key in results.get('img_fields', ['img']):
+            img = results[key]
+            num_output_channels = img.shape[2]
+            if random.random() < self.gray_prob:
+                if num_output_channels > 1:
+                    img = mmcv.rgb2gray(img)[:, :, None]
+                    results[key] = np.dstack(
+                        [img for _ in range(num_output_channels)])
+                    return results
+            results[key] = img
+        return results
+
+    def __repr__(self):
+        return self.__class__.__name__ + f'(gray_prob={self.gray_prob})'
+
+
+@PIPELINES.register_module()
+class RandomFlip(object):
+    """Flip the image randomly.
+
+    Flip the image randomly based on flip probaility and flip direction.
+
+    Args:
+        flip_prob (float): probability of the image being flipped. Default: 0.5
+        direction (str, optional): The flipping direction. Options are
+            'horizontal' and 'vertical'. Default: 'horizontal'.
+    """
+
+    def __init__(self, flip_prob=0.5, direction='horizontal'):
+        assert 0 <= flip_prob <= 1
+        assert direction in ['horizontal', 'vertical']
+        self.flip_prob = flip_prob
+        self.direction = direction
+
+    def __call__(self, results):
+        """Call function to flip image.
+
+        Args:
+            results (dict): Result dict from loading pipeline.
+
+        Returns:
+            dict: Flipped results, 'flip', 'flip_direction' keys are added into
+                result dict.
+        """
+        flip = True if np.random.rand() < self.flip_prob else False
+        results['flip'] = flip
+        results['flip_direction'] = self.direction
+        if results['flip']:
+            # flip image
+            for key in results.get('img_fields', ['img']):
+                results[key] = mmcv.imflip(
+                    results[key], direction=results['flip_direction'])
+        return results
+
+    def __repr__(self):
+        return self.__class__.__name__ + f'(flip_prob={self.flip_prob})'
+
+
+@PIPELINES.register_module()
+class Resize(object):
+    """Resize images.
+
+    Args:
+        size (int | tuple): Images scales for resizing (h, w).
+            When size is int, the default behavior is to resize an image
+            to (size, size). When size is tuple and the second value is -1,
+            the short edge of an image is resized to its first value.
+            For example, when size is 224, the image is resized to 224x224.
+            When size is (224, -1), the short side is resized to 224 and the
+            other side is computed based on the short side, maintaining the
+            aspect ratio.
+        interpolation (str): Interpolation method, accepted values are
+            "nearest", "bilinear", "bicubic", "area", "lanczos".
+            More details can be found in `mmcv.image.geometric`.
+        backend (str): The image resize backend type, accpeted values are
+            `cv2` and `pillow`. Default: `cv2`.
+    """
+
+    def __init__(self, size, interpolation='bilinear', backend='cv2'):
+        assert isinstance(size, int) or (isinstance(size, tuple)
+                                         and len(size) == 2)
+        self.resize_w_short_side = False
+        if isinstance(size, int):
+            assert size > 0
+            size = (size, size)
+        else:
+            assert size[0] > 0 and (size[1] > 0 or size[1] == -1)
+            if size[1] == -1:
+                self.resize_w_short_side = True
+        assert interpolation in ('nearest', 'bilinear', 'bicubic', 'area',
+                                 'lanczos')
+        if backend not in ['cv2', 'pillow']:
+            raise ValueError(f'backend: {backend} is not supported for resize.'
+                             'Supported backends are "cv2", "pillow"')
+
+        self.size = size
+        self.interpolation = interpolation
+        self.backend = backend
+
+    def _resize_img(self, results):
+        for key in results.get('img_fields', ['img']):
+            img = results[key]
+            ignore_resize = False
+            if self.resize_w_short_side:
+                h, w = img.shape[:2]
+                short_side = self.size[0]
+                if (w <= h and w == short_side) or (h <= w
+                                                    and h == short_side):
+                    ignore_resize = True
+                else:
+                    if w < h:
+                        width = short_side
+                        height = int(short_side * h / w)
+                    else:
+                        height = short_side
+                        width = int(short_side * w / h)
+            else:
+                height, width = self.size
+            if not ignore_resize:
+                img = mmcv.imresize(
+                    img,
+                    size=(width, height),
+                    interpolation=self.interpolation,
+                    return_scale=False,
+                    backend=self.backend)
+                results[key] = img
+                results['img_shape'] = img.shape
+
+    def __call__(self, results):
+        self._resize_img(results)
+        return results
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'(size={self.size}, '
+        repr_str += f'interpolation={self.interpolation})'
+        return repr_str
+
+
+@PIPELINES.register_module()
+class CenterCrop(object):
+    """Center crop the image.
+
+    Args:
+        crop_size (int | tuple): Expected size after cropping, (h, w).
+
+    Notes:
+        If the image is smaller than the crop size, return the original image
+    """
+
+    def __init__(self, crop_size):
+        assert isinstance(crop_size, int) or (isinstance(crop_size, tuple)
+                                              and len(crop_size) == 2)
+        if isinstance(crop_size, int):
+            crop_size = (crop_size, crop_size)
+        assert crop_size[0] > 0 and crop_size[1] > 0
+        self.crop_size = crop_size
+
+    def __call__(self, results):
+        crop_height, crop_width = self.crop_size[0], self.crop_size[1]
+        for key in results.get('img_fields', ['img']):
+            img = results[key]
+            # img.shape has length 2 for grayscale, length 3 for color
+            img_height, img_width = img.shape[:2]
+
+            y1 = max(0, int(round((img_height - crop_height) / 2.)))
+            x1 = max(0, int(round((img_width - crop_width) / 2.)))
+            y2 = min(img_height, y1 + crop_height) - 1
+            x2 = min(img_width, x1 + crop_width) - 1
+
+            # crop the image
+            img = mmcv.imcrop(img, bboxes=np.array([x1, y1, x2, y2]))
+            img_shape = img.shape
+            results[key] = img
+        results['img_shape'] = img_shape
+
+        return results
+
+    def __repr__(self):
+        return self.__class__.__name__ + f'(crop_size={self.crop_size})'
+
+
+@PIPELINES.register_module()
+class Normalize(object):
+    """Normalize the image.
+
+    Args:
+        mean (sequence): Mean values of 3 channels.
+        std (sequence): Std values of 3 channels.
+        to_rgb (bool): Whether to convert the image from BGR to RGB,
+            default is true.
+    """
+
+    def __init__(self, mean, std, to_rgb=True):
+        self.mean = np.array(mean, dtype=np.float32)
+        self.std = np.array(std, dtype=np.float32)
+        self.to_rgb = to_rgb
+
+    def __call__(self, results):
+        for key in results.get('img_fields', ['img']):
+            results[key] = mmcv.imnormalize(results[key], self.mean, self.std,
+                                            self.to_rgb)
+        results['img_norm_cfg'] = dict(
+            mean=self.mean, std=self.std, to_rgb=self.to_rgb)
+        return results
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'(mean={list(self.mean)}, '
+        repr_str += f'std={list(self.std)}, '
+        repr_str += f'to_rgb={self.to_rgb})'
+        return repr_str
+
+
+@PIPELINES.register_module()
+class Albu(object):
+    """Albumentation augmentation.
+
+    Adds custom transformations from Albumentations library.
+    Please, visit `https://albumentations.readthedocs.io`
+    to get more information.
+    An example of ``transforms`` is as followed:
+
+    .. code-block::
+        [
+            dict(
+                type='ShiftScaleRotate',
+                shift_limit=0.0625,
+                scale_limit=0.0,
+                rotate_limit=0,
+                interpolation=1,
+                p=0.5),
+            dict(
+                type='RandomBrightnessContrast',
+                brightness_limit=[0.1, 0.3],
+                contrast_limit=[0.1, 0.3],
+                p=0.2),
+            dict(type='ChannelShuffle', p=0.1),
+            dict(
+                type='OneOf',
+                transforms=[
+                    dict(type='Blur', blur_limit=3, p=1.0),
+                    dict(type='MedianBlur', blur_limit=3, p=1.0)
+                ],
+                p=0.1),
+        ]
+
+    Args:
+        transforms (list[dict]): A list of albu transformations
+        keymap (dict): Contains {'input key':'albumentation-style key'}
+    """
+
+    def __init__(self, transforms, keymap=None, update_pad_shape=False):
+        if Compose is None:
+            raise RuntimeError('albumentations is not installed')
+
+        self.transforms = transforms
+        self.filter_lost_elements = False
+        self.update_pad_shape = update_pad_shape
+
+        self.aug = Compose([self.albu_builder(t) for t in self.transforms])
+
+        if not keymap:
+            self.keymap_to_albu = {
+                'img': 'image',
+            }
+        else:
+            self.keymap_to_albu = keymap
+        self.keymap_back = {v: k for k, v in self.keymap_to_albu.items()}
+
+    def albu_builder(self, cfg):
+        """Import a module from albumentations.
+        It inherits some of :func:`build_from_cfg` logic.
+        Args:
+            cfg (dict): Config dict. It should at least contain the key "type".
+        Returns:
+            obj: The constructed object.
+        """
+
+        assert isinstance(cfg, dict) and 'type' in cfg
+        args = cfg.copy()
+
+        obj_type = args.pop('type')
+        if mmcv.is_str(obj_type):
+            if albumentations is None:
+                raise RuntimeError('albumentations is not installed')
+            obj_cls = getattr(albumentations, obj_type)
+        elif inspect.isclass(obj_type):
+            obj_cls = obj_type
+        else:
+            raise TypeError(
+                f'type must be a str or valid type, but got {type(obj_type)}')
+
+        if 'transforms' in args:
+            args['transforms'] = [
+                self.albu_builder(transform)
+                for transform in args['transforms']
+            ]
+
+        return obj_cls(**args)
+
+    @staticmethod
+    def mapper(d, keymap):
+        """Dictionary mapper. Renames keys according to keymap provided.
+        Args:
+            d (dict): old dict
+            keymap (dict): {'old_key':'new_key'}
+        Returns:
+            dict: new dict.
+        """
+
+        updated_dict = {}
+        for k, v in zip(d.keys(), d.values()):
+            new_k = keymap.get(k, k)
+            updated_dict[new_k] = d[k]
+        return updated_dict
+
+    def __call__(self, results):
+        # dict to albumentations format
+        results = self.mapper(results, self.keymap_to_albu)
+
+        results = self.aug(**results)
+
+        if 'gt_labels' in results:
+            if isinstance(results['gt_labels'], list):
+                results['gt_labels'] = np.array(results['gt_labels'])
+            results['gt_labels'] = results['gt_labels'].astype(np.int64)
+
+        # back to the original format
+        results = self.mapper(results, self.keymap_back)
+
+        # update final shape
+        if self.update_pad_shape:
+            results['pad_shape'] = results['img'].shape
+
+        return results
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__ + f'(transforms={self.transforms})'
+        return repr_str
+
+# custom transforms
+@PIPELINES.register_module()
+class GaussianBlur(object):
+
+    def __init__(self, sigma_min, sigma_max):
+        self.sigma_min = sigma_min
+        self.sigma_max = sigma_max
+
+    def __call__(self, results):
+        for key in results.get('img_fields', ['img']):
+            img = results[key]
+            sigma = np.random.uniform(self.sigma_min, self.sigma_max)
+            img = Image.fromarray(img)
+            img = img.filter(ImageFilter.GaussianBlur(radius=sigma))
+            results[key] = np.array(img).astype('float32')
+        return results
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        return repr_str
+
+
+@PIPELINES.register_module()
+class RandomRotate(object):
+    """Rotate the image & seg.
+
+    Args:
+        prob (float): The rotation probability.
+        degree (float, tuple[float]): Range of degrees to select from. If
+            degree is a number instead of tuple like (min, max),
+            the range of degree will be (``-degree``, ``+degree``)
+        pad_val (float, optional): Padding value of image. Default: 0.
+        center (tuple[float], optional): Center point (w, h) of the rotation in
+            the source image. If not specified, the center of the image will be
+            used. Default: None.
+        auto_bound (bool): Whether to adjust the image size to cover the whole
+            rotated image. Default: False
+    """
+
+    def __init__(self,
+                 prob,
+                 degree,
+                 pad_val=0,
+                 center=None,
+                 auto_bound=False):
+        self.prob = prob
+        assert prob >= 0 and prob <= 1
+        if isinstance(degree, (float, int)):
+            assert degree > 0, f'degree {degree} should be positive'
+            self.degree = (-degree, degree)
+        else:
+            self.degree = degree
+        assert len(self.degree) == 2, f'degree {self.degree} should be a ' \
+                                      f'tuple of (min, max)'
+        self.pal_val = pad_val
+        self.center = center
+        self.auto_bound = auto_bound
+
+    def __call__(self, results):
+        """Call function to rotate image.
+
+        Args:
+            results (dict): Result dict from loading pipeline.
+
+        Returns:
+            dict: Rotated results.
+        """
+
+        rotate = True if np.random.rand() < self.prob else False
+        degree = np.random.uniform(min(*self.degree), max(*self.degree))
+        if rotate:
+            # rotate image
+            for key in results.get('img_fields', ['img']):
+                results[key] = mmcv.imrotate(
+                    results[key],
+                    angle=degree,
+                    border_value=self.pal_val,
+                    center=self.center,
+                    auto_bound=self.auto_bound)
+
+        return results
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'(prob={self.prob}, ' \
+                    f'degree={self.degree}, ' \
+                    f'pad_val={self.pal_val}, ' \
+                    f'center={self.center}, ' \
+                    f'auto_bound={self.auto_bound})'
+        return repr_str
+
+
+@PIPELINES.register_module()
+class RandomErasing:
+    """ Randomly selects a rectangle region in an image and erases its pixels.
+    'Random Erasing Data Augmentation' by Zhong et al. See https://arxiv.org/abs/1708.04896
+
+    Args:
+         p: probability that the random erasing operation will be performed.
+         scale: range of proportion of erased area against input image.
+         ratio: range of aspect ratio of erased area.
+         value: erasing value. Default is 0. If a single int, it is used to
+            erase all pixels. If a tuple of length 3, it is used to erase
+            R, G, B channels respectively.
+            If a str of 'random', erasing each pixel with random values.
+         inplace: boolean to make this transform inplace. Default set to False.
+
+    Returns:
+        Erased Image.
+    """
+    def __init__(self, p=0.5, scale=(0.02, 0.33), ratio=(0.3, 3.3), value=0, inplace=False):
+        super().__init__()
+        if not isinstance(value, (numbers.Number, str, tuple, list)):
+            raise TypeError("Argument value should be either a number or str or a sequence")
+        if isinstance(value, str) and value != "random":
+            raise ValueError("If value is str, it should be 'random'")
+        if not isinstance(scale, (tuple, list)):
+            raise TypeError("Scale should be a sequence")
+        if not isinstance(ratio, (tuple, list)):
+            raise TypeError("Ratio should be a sequence")
+        if (scale[0] > scale[1]) or (ratio[0] > ratio[1]):
+            warnings.warn("Scale and ratio should be of kind (min, max)")
+        if scale[0] < 0 or scale[1] > 1:
+            raise ValueError("Scale should be between 0 and 1")
+        if p < 0 or p > 1:
+            raise ValueError("Random erasing probability should be between 0 and 1")
+
+        self.p = p
+        self.scale = scale
+        self.ratio = ratio
+        self.value = value
+        self.inplace = inplace
+
+    @staticmethod
+    def get_params(
+            img: Tensor, scale: Tuple[float, float], ratio: Tuple[float, float], value: Optional[List[float]] = None
+        ) -> Tuple[int, int, int, int, Tensor]:
+        """Get parameters for ``erase`` for a random erasing.
+
+        Args:
+            img (Tensor): Tensor image to be erased.
+            scale (tuple or list): range of proportion of erased area against input image.
+            ratio (tuple or list): range of aspect ratio of erased area.
+            value (list, optional): erasing value. If None, it is interpreted as "random"
+                (erasing each pixel with random values). If ``len(value)`` is 1, it is interpreted as a number,
+                i.e. ``value[0]``.
+
+        Returns:
+            tuple: params (i, j, h, w, v) to be passed to ``erase`` for random erasing.
+        """
+        img_c, img_h, img_w = img.shape[-3], img.shape[-2], img.shape[-1]
+        area = img_h * img_w
+
+        for _ in range(10):
+            erase_area = area * torch.empty(1).uniform_(scale[0], scale[1]).item()
+            aspect_ratio = torch.empty(1).uniform_(ratio[0], ratio[1]).item()
+
+            h = int(round(math.sqrt(erase_area * aspect_ratio)))
+            w = int(round(math.sqrt(erase_area / aspect_ratio)))
+            if not (h < img_h and w < img_w):
+                continue
+
+            if value is None:
+                v = torch.empty([img_c, h, w], dtype=torch.float32).normal_()
+            else:
+                v = torch.tensor(value)[:, None, None]
+
+            i = torch.randint(0, img_h - h + 1, size=(1, )).item()
+            j = torch.randint(0, img_w - w + 1, size=(1, )).item()
+            return i, j, h, w, v
+
+        # Return original image
+        return 0, 0, img_h, img_w, img
+    
+    def __call__(self, results):
+        if torch.rand(1) < self.p:
+            for key in results.get('img_fields', ['img']):
+                img = results[key]
+                img = torch.Tensor(img.transpose([2,0,1]))
+                # the next process need C, H, W
+
+                # cast self.value to script acceptable type
+                if isinstance(self.value, (int, float)):
+                    value = [self.value, ]
+                elif isinstance(self.value, str):
+                    value = None
+                elif isinstance(self.value, tuple):
+                    value = list(self.value)
+                else:
+                    value = self.value
+
+                if value is not None and not (len(value) in (1, img.shape[-3])):
+                    raise ValueError(
+                        "If value is a sequence, it should have either a single value or "
+                        "{} (number of input channels)".format(img.shape[-3])
+                    )
+
+                x, y, h, w, v = self.get_params(img, scale=self.scale, ratio=self.ratio, value=value)
+                results[key] = TVF.erase(img, x, y, h, w, v, self.inplace).numpy().transpose([1,2,0])
+        return results
+
+
+@PIPELINES.register_module()
+class ColorJitter:
+    def __init__(self, brightness=0, contrast=0, saturation=0, hue=0):
+        self.func = _transforms.ColorJitter(brightness, contrast, saturation, hue)
+    
+    def __call__(self, results):
+        for key in results.get('img_fields', ['img']):
+            img = results[key]
+            img = Image.fromarray(img)
+            results[key] = np.array(self.func(img))
+        return results
+    
+    def __repr__(self):
+        return self.func.__repr__()
+
+
+@PIPELINES.register_module()
+class HistogramEqualization:
+
+    def __call__(self, results):
+        for key in results.get('img_fields', ['img']):
+            img = results[key]
+            # convert from RGB color-space to YCrCb
+            ycrcb_img = cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb)
+
+            # equalize the histogram of the Y channel
+            ycrcb_img[:, :, 0] = cv2.equalizeHist(ycrcb_img[:, :, 0])
+
+            # convert back to RGB color-space from YCrCb
+            equalized_img = cv2.cvtColor(ycrcb_img, cv2.COLOR_YCrCb2BGR)
+            results[key] = equalized_img
+        return results
+
+
+@PIPELINES.register_module()
+class TorchNormalize:
+    """
+    Normalize in torchvision
+    """
+    def __init__(self, mean, std, inplace=False):
+        self.mean = mean
+        self.std = std
+        self.inplace = inplace
+
+    def __call__(self, results):
+        """
+        Args:
+            tensor (Tensor): Tensor image of size (C, H, W) to be normalized.
+
+        Returns:
+            Tensor: Normalized Tensor image.
+        """
+        for key in results.get('img_fields', ['img']):
+            img = results[key]
+            results[key] = _transforms.functional.normalize(img, self.mean, self.std, self.inplace)
+        return results
+
+    def __repr__(self):
+        return self.__class__.__name__ + '(mean={0}, std={1})'.format(self.mean, self.std)
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/datasets/raf.py b/CAGE_expression_inference-apvit/apvit_mmcls/datasets/raf.py
new file mode 100644
index 0000000000000000000000000000000000000000..3dc5f506d83d76a33a27c8bfcfd26578f8f43059
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/datasets/raf.py
@@ -0,0 +1,144 @@
+import os
+import random
+from typing import Any, Dict
+
+import numpy as np
+from mmcls.models.losses import accuracy, f1_score, precision, recall
+from mmcls.models.losses.eval_metrics import class_accuracy
+
+from .base_dataset import BaseDataset
+from .builder import DATASETS
+
+
+def has_file_allowed_extension(filename, extensions):
+    """Checks if a file is an allowed extension.
+
+    Args:
+        filename (string): path to a file
+
+    Returns:
+        bool: True if the filename ends with a known image extension
+    """
+    filename_lower = filename.lower()
+    return any(filename_lower.endswith(ext) for ext in extensions)
+
+
+def find_folders(root):
+    """Find classes by folders under a root.
+
+    Args:
+        root (string): root directory of folders
+
+    Returns:
+        folder_to_idx (dict): the map from folder name to class idx
+    """
+    folders = [
+        d for d in os.listdir(root) if os.path.isdir(os.path.join(root, d))
+    ]
+    folders.sort()
+    folder_to_idx = {folders[i]: i for i in range(len(folders))}
+    return folder_to_idx
+
+
+def get_samples(root, folder_to_idx, extensions):
+    """Make dataset by walking all images under a root.
+
+    Args:
+        root (string): root directory of folders
+        folder_to_idx (dict): the map from class name to class idx
+        extensions (tuple): allowed extensions
+
+    Returns:
+        samples (list): a list of tuple where each element is (image, label)
+    """
+    samples = []
+    root = os.path.expanduser(root)
+    for folder_name in sorted(os.listdir(root)):
+        _dir = os.path.join(root, folder_name)
+        if not os.path.isdir(_dir):
+            continue
+
+        for _, _, fns in sorted(os.walk(_dir)):
+            for fn in sorted(fns):
+                if has_file_allowed_extension(fn, extensions):
+                    path = os.path.join(folder_name, fn)
+                    item = (path, folder_to_idx[folder_name])
+                    samples.append(item)
+    return samples
+
+#                  0       1         2          3       4               5           6           7
+FER_CLASSES = ['Anger', 'Disgust', 'Fear', 'Sadness', 'Happiness', 'Surprise', 'Neutral', 'Contempt']
+
+def convert2coarse_label(i:int):
+    """The first four are negative"""
+    if i <= 3:
+        return 0
+    return i - 3
+
+
+def gen_class_map(dataset_class):
+    """
+    generate the convert map from DATASET_CLASSES to FER_CLASSES
+    """
+    convert_map = []
+    for i in dataset_class:
+        convert_map.append(FER_CLASSES.index(i))
+    assert sum(convert_map) == sum([i for i in range(len(dataset_class))])
+    return convert_map
+
+@DATASETS.register_module()
+class RAF(BaseDataset):
+
+    IMG_EXTENSIONS = ('.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif')
+    DATASET_CLASSES = [
+        'Surprise',
+        'Fear',
+        'Disgust',
+        'Happiness',
+        'Sadness',
+        'Anger',
+        'Neutral'
+    ]
+    CLASSES = FER_CLASSES[:7]
+
+    @staticmethod
+    def convert_gt_label(i:int):
+        """# dataset -> FER_CLASSES"""
+        convert_table = (5, 2, 1, 4, 3, 0, 6)
+        assert sum(convert_table) == sum([i for i in range(7)])
+        return convert_table[i]
+
+    def load_annotations(self):
+        if self.ann_file is None:
+            folder_to_idx = find_folders(self.data_prefix)
+            samples = get_samples(
+                self.data_prefix,
+                folder_to_idx,
+                extensions=self.IMG_EXTENSIONS)
+            if len(samples) == 0:
+                raise (RuntimeError('Found 0 files in subfolders of: '
+                                    f'{self.data_prefix}. '
+                                    'Supported extensions are: '
+                                    f'{",".join(self.IMG_EXTENSIONS)}'))
+
+            self.folder_to_idx = folder_to_idx
+        elif isinstance(self.ann_file, str):
+            with open(self.ann_file) as f:
+                samples = [x.strip().split(' ') for x in f.readlines()]
+            samples = [[i[0].replace('_aligned', ''), i[1]] for i in samples]
+        else:
+            raise TypeError('ann_file must be a str or None')
+        self.samples = samples
+
+        data_infos = []
+        for filename, gt_label in self.samples:
+            info = {'img_prefix': self.data_prefix}
+            info['img_info'] = {'filename': filename}
+            gt_label = int(gt_label) - 1
+            gt_label = self.convert_gt_label(gt_label)
+            coarse_label = convert2coarse_label(gt_label)
+            info['gt_label'] = np.array(gt_label, dtype=np.int64)
+            info['coarse_label'] = np.array(coarse_label, dtype=np.int64)
+            data_infos.append(info)
+        return data_infos
+
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/datasets/samplers/__init__.py b/CAGE_expression_inference-apvit/apvit_mmcls/datasets/samplers/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..cffe4dcb0fa663750bd88941e06ad336a43b527f
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/datasets/samplers/__init__.py
@@ -0,0 +1,3 @@
+from .distributed_sampler import DistributedSampler
+
+__all__ = ['DistributedSampler']
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/datasets/samplers/distributed_sampler.py b/CAGE_expression_inference-apvit/apvit_mmcls/datasets/samplers/distributed_sampler.py
new file mode 100644
index 0000000000000000000000000000000000000000..a46a086e7b2e99afbb02004bf9ff03c84e13a1c8
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/datasets/samplers/distributed_sampler.py
@@ -0,0 +1,42 @@
+import torch
+from torch.utils.data import DistributedSampler as _DistributedSampler
+
+
+class DistributedSampler(_DistributedSampler):
+
+    def __init__(self,
+                 dataset,
+                 num_replicas=None,
+                 rank=None,
+                 shuffle=True,
+                 round_up=True):
+        super().__init__(dataset, num_replicas=num_replicas, rank=rank)
+        self.shuffle = shuffle
+        self.round_up = round_up
+        if self.round_up:
+            self.total_size = self.num_samples * self.num_replicas
+        else:
+            self.total_size = len(self.dataset)
+
+    def __iter__(self):
+        # deterministically shuffle based on epoch
+        if self.shuffle:
+            g = torch.Generator()
+            g.manual_seed(self.epoch)
+            indices = torch.randperm(len(self.dataset), generator=g).tolist()
+        else:
+            indices = torch.arange(len(self.dataset)).tolist()
+
+        # add extra samples to make it evenly divisible
+        if self.round_up:
+            indices = (
+                indices *
+                int(self.total_size / len(indices) + 1))[:self.total_size]
+        assert len(indices) == self.total_size
+
+        # subsample
+        indices = indices[self.rank:self.total_size:self.num_replicas]
+        if self.round_up:
+            assert len(indices) == self.num_samples
+
+        return iter(indices)
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/datasets/utils.py b/CAGE_expression_inference-apvit/apvit_mmcls/datasets/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..31dd5645358bf8f85ca3ad1107619ccaae39b088
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/datasets/utils.py
@@ -0,0 +1,152 @@
+import gzip
+import hashlib
+import os
+import os.path
+import shutil
+import tarfile
+import urllib.error
+import urllib.request
+import zipfile
+
+__all__ = ['rm_suffix', 'check_integrity', 'download_and_extract_archive']
+
+
+def rm_suffix(s, suffix=None):
+    if suffix is None:
+        return s[:s.rfind('.')]
+    else:
+        return s[:s.rfind(suffix)]
+
+
+def calculate_md5(fpath, chunk_size=1024 * 1024):
+    md5 = hashlib.md5()
+    with open(fpath, 'rb') as f:
+        for chunk in iter(lambda: f.read(chunk_size), b''):
+            md5.update(chunk)
+    return md5.hexdigest()
+
+
+def check_md5(fpath, md5, **kwargs):
+    return md5 == calculate_md5(fpath, **kwargs)
+
+
+def check_integrity(fpath, md5=None):
+    if not os.path.isfile(fpath):
+        return False
+    if md5 is None:
+        return True
+    return check_md5(fpath, md5)
+
+
+def download_url_to_file(url, fpath):
+    with urllib.request.urlopen(url) as resp, open(fpath, 'wb') as of:
+        shutil.copyfileobj(resp, of)
+
+
+def download_url(url, root, filename=None, md5=None):
+    """Download a file from a url and place it in root.
+
+    Args:
+        url (str): URL to download file from.
+        root (str): Directory to place downloaded file in.
+        filename (str | None): Name to save the file under.
+            If filename is None, use the basename of the URL.
+        md5 (str | None): MD5 checksum of the download.
+            If md5 is None, download without md5 check.
+    """
+    root = os.path.expanduser(root)
+    if not filename:
+        filename = os.path.basename(url)
+    fpath = os.path.join(root, filename)
+
+    os.makedirs(root, exist_ok=True)
+
+    if check_integrity(fpath, md5):
+        print(f'Using downloaded and verified file: {fpath}')
+    else:
+        try:
+            print(f'Downloading {url} to {fpath}')
+            download_url_to_file(url, fpath)
+        except (urllib.error.URLError, IOError) as e:
+            if url[:5] == 'https':
+                url = url.replace('https:', 'http:')
+                print('Failed download. Trying https -> http instead.'
+                      f' Downloading {url} to {fpath}')
+                download_url_to_file(url, fpath)
+            else:
+                raise e
+        # check integrity of downloaded file
+        if not check_integrity(fpath, md5):
+            raise RuntimeError('File not found or corrupted.')
+
+
+def _is_tarxz(filename):
+    return filename.endswith('.tar.xz')
+
+
+def _is_tar(filename):
+    return filename.endswith('.tar')
+
+
+def _is_targz(filename):
+    return filename.endswith('.tar.gz')
+
+
+def _is_tgz(filename):
+    return filename.endswith('.tgz')
+
+
+def _is_gzip(filename):
+    return filename.endswith('.gz') and not filename.endswith('.tar.gz')
+
+
+def _is_zip(filename):
+    return filename.endswith('.zip')
+
+
+def extract_archive(from_path, to_path=None, remove_finished=False):
+    if to_path is None:
+        to_path = os.path.dirname(from_path)
+
+    if _is_tar(from_path):
+        with tarfile.open(from_path, 'r') as tar:
+            tar.extractall(path=to_path)
+    elif _is_targz(from_path) or _is_tgz(from_path):
+        with tarfile.open(from_path, 'r:gz') as tar:
+            tar.extractall(path=to_path)
+    elif _is_tarxz(from_path):
+        with tarfile.open(from_path, 'r:xz') as tar:
+            tar.extractall(path=to_path)
+    elif _is_gzip(from_path):
+        to_path = os.path.join(
+            to_path,
+            os.path.splitext(os.path.basename(from_path))[0])
+        with open(to_path, 'wb') as out_f, gzip.GzipFile(from_path) as zip_f:
+            out_f.write(zip_f.read())
+    elif _is_zip(from_path):
+        with zipfile.ZipFile(from_path, 'r') as z:
+            z.extractall(to_path)
+    else:
+        raise ValueError(f'Extraction of {from_path} not supported')
+
+    if remove_finished:
+        os.remove(from_path)
+
+
+def download_and_extract_archive(url,
+                                 download_root,
+                                 extract_root=None,
+                                 filename=None,
+                                 md5=None,
+                                 remove_finished=False):
+    download_root = os.path.expanduser(download_root)
+    if extract_root is None:
+        extract_root = download_root
+    if not filename:
+        filename = os.path.basename(url)
+
+    download_url(url, download_root, filename, md5)
+
+    archive = os.path.join(download_root, filename)
+    print(f'Extracting {archive} to {extract_root}')
+    extract_archive(archive, extract_root, remove_finished)
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/__init__.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0c954b11915546cc1e84af5ea2fa66934a8610de
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/__init__.py
@@ -0,0 +1,13 @@
+from .backbones import *  # noqa: F401,F403
+from .builder import (BACKBONES, CLASSIFIERS, HEADS, LOSSES, NECKS,
+                      build_backbone, build_classifier, build_head, build_loss,
+                      build_neck)
+from .classifiers import *  # noqa: F401,F403
+from .heads import *  # noqa: F401,F403
+from .losses import *  # noqa: F401,F403
+from .necks import *  # noqa: F401,F403
+
+__all__ = [
+    'BACKBONES', 'HEADS', 'NECKS', 'LOSSES', 'CLASSIFIERS', 'build_backbone',
+    'build_head', 'build_neck', 'build_loss', 'build_classifier'
+]
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/__init__.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d49a1e2ad4e7a082891ce87350a5403799e71ea1
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/__init__.py
@@ -0,0 +1,32 @@
+from .alexnet import AlexNet
+from .lenet import LeNet5
+from .mobilenet_v2 import MobileNetV2
+from .mobilenet_v3 import MobileNetv3
+from .regnet import RegNet
+from .resnest import ResNeSt
+from .resnet import ResNet, ResNetV1d
+from .resnet_cifar import ResNet_CIFAR
+from .resnext import ResNeXt
+from .seresnet import SEResNet
+from .seresnext import SEResNeXt
+from .shufflenet_v1 import ShuffleNetV1
+from .shufflenet_v2 import ShuffleNetV2
+from .vgg import VGG
+from .irse import IRSE
+from .irse_nopadding import IRSENoPadding
+from .mobilefacenet import MobileFaceNet
+from ..vit.vit_origin import VisionTransformerOrigin
+from ..vit.vit_siam_merge import PoolingViT
+from .t2t_vit import T2T_ViT, T2T_ViTPooling
+from .iresnet import IResNet
+from .vtff import VTFF, MViT
+
+__all__ = [
+    'VTFF', 'MViT',
+    'LeNet5', 'AlexNet', 'VGG', 'RegNet', 'ResNet', 'ResNeXt', 'ResNetV1d',
+    'ResNeSt', 'ResNet_CIFAR', 'SEResNet', 'SEResNeXt', 'ShuffleNetV1',
+    'ShuffleNetV2', 'MobileNetV2', 'MobileNetv3', 'IRSE',
+    'MobileFaceNet',
+    'T2T_ViT', 'T2T_ViTPooling', 'VisionTransformerOrigin', 'PoolingViT',
+    'IRSENoPadding', 'IResNet',
+]
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/alexnet.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/alexnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..ee1d2afe05ab601cf094d59b42efcd8184361d3b
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/alexnet.py
@@ -0,0 +1,55 @@
+import torch.nn as nn
+
+from ..builder import BACKBONES
+from .base_backbone import BaseBackbone
+
+
+@BACKBONES.register_module()
+class AlexNet(BaseBackbone):
+    """`AlexNet <https://en.wikipedia.org/wiki/AlexNet>`_ backbone.
+
+    The input for AlexNet is a 224x224 RGB image.
+
+    Args:
+        num_classes (int): number of classes for classification.
+            The default value is -1, which uses the backbone as
+            a feature extractor without the top classifier.
+    """
+
+    def __init__(self, num_classes=-1):
+        super(AlexNet, self).__init__()
+        self.num_classes = num_classes
+        self.features = nn.Sequential(
+            nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=3, stride=2),
+            nn.Conv2d(64, 192, kernel_size=5, padding=2),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=3, stride=2),
+            nn.Conv2d(192, 384, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(384, 256, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(256, 256, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=3, stride=2),
+        )
+        if self.num_classes > 0:
+            self.classifier = nn.Sequential(
+                nn.Dropout(),
+                nn.Linear(256 * 6 * 6, 4096),
+                nn.ReLU(inplace=True),
+                nn.Dropout(),
+                nn.Linear(4096, 4096),
+                nn.ReLU(inplace=True),
+                nn.Linear(4096, num_classes),
+            )
+
+    def forward(self, x):
+
+        x = self.features(x)
+        if self.num_classes > 0:
+            x = x.view(x.size(0), 256 * 6 * 6)
+            x = self.classifier(x)
+
+        return x
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/base_backbone.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/base_backbone.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e4c85cf4564cd954d824200baf429c91e8c31e1
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/base_backbone.py
@@ -0,0 +1,57 @@
+from abc import ABCMeta, abstractmethod
+
+import torch.nn as nn
+from mmcv.runner import load_checkpoint
+from mmcv.utils import get_logger
+# from mmcls.utils import get_root_logger
+
+
+class BaseBackbone(nn.Module, metaclass=ABCMeta):
+    """Base backbone.
+
+    This class defines the basic functions of a backbone.
+    Any backbone that inherits this class should at least
+    define its own `forward` function.
+
+    """
+
+    def __init__(self):
+        super(BaseBackbone, self).__init__()
+
+    def init_weights(self, pretrained=None):
+        """Init backbone weights
+
+        Args:
+            pretrained (str | None): If pretrained is a string, then it
+                initializes backbone weights by loading the pretrained
+                checkpoint. If pretrained is None, then it follows default
+                initializer or customized initializer in subclasses.
+        """
+        if isinstance(pretrained, str):
+            logger = get_logger('mmcv')
+            logger.warning(f'{self.__class__.__name__} load pretrain from {pretrained}')
+            load_checkpoint(self, pretrained, strict=False, logger=logger, map_location='cpu')
+        elif pretrained is None:
+            # use default initializer or customized initializer in subclasses
+            pass
+        else:
+            raise TypeError('pretrained must be a str or None.'
+                            f' But received {type(pretrained)}.')
+
+    @abstractmethod
+    def forward(self, x):
+        """Forward computation
+
+        Args:
+            x (tensor | tuple[tensor]): x could be a Torch.tensor or a tuple of
+                Torch.tensor, containing input data for forward computation.
+        """
+        pass
+
+    def train(self, mode=True):
+        """Set module status before forward computation
+
+        Args:
+            mode (bool): Whether it is train_mode or test_mode
+        """
+        super(BaseBackbone, self).train(mode)
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/iresnet.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/iresnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..ab5da906d7d65db69e6e06b5a56c35ced803e1b7
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/iresnet.py
@@ -0,0 +1,220 @@
+import warnings
+import torch.nn as nn
+from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, ReLU, Sigmoid, Dropout, MaxPool2d, \
+    AdaptiveAvgPool2d, Sequential, Module
+from collections import namedtuple
+import torch
+from mmcv.runner import load_state_dict
+from mmcls.utils import get_root_logger
+
+from ..builder import BACKBONES
+from .base_backbone import BaseBackbone
+
+__all__ = ['iresnet18', 'iresnet34', 'iresnet50', 'iresnet100', 'iresnet200']
+
+
+def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
+    """3x3 convolution with padding"""
+    return nn.Conv2d(in_planes,
+                     out_planes,
+                     kernel_size=3,
+                     stride=stride,
+                     padding=dilation,
+                     groups=groups,
+                     bias=False,
+                     dilation=dilation)
+
+
+def conv1x1(in_planes, out_planes, stride=1):
+    """1x1 convolution"""
+    return nn.Conv2d(in_planes,
+                     out_planes,
+                     kernel_size=1,
+                     stride=stride,
+                     bias=False)
+
+
+class IBasicBlock(nn.Module):
+    expansion = 1
+    def __init__(self, inplanes, planes, stride=1, downsample=None,
+                 groups=1, base_width=64, dilation=1):
+        super(IBasicBlock, self).__init__()
+        if groups != 1 or base_width != 64:
+            raise ValueError('BasicBlock only supports groups=1 and base_width=64')
+        if dilation > 1:
+            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
+        self.bn1 = nn.BatchNorm2d(inplanes, eps=1e-05,)
+        self.conv1 = conv3x3(inplanes, planes)
+        self.bn2 = nn.BatchNorm2d(planes, eps=1e-05,)
+        self.prelu = nn.PReLU(planes)
+        self.conv2 = conv3x3(planes, planes, stride)
+        self.bn3 = nn.BatchNorm2d(planes, eps=1e-05,)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        identity = x
+        out = self.bn1(x)
+        out = self.conv1(out)
+        out = self.bn2(out)
+        out = self.prelu(out)
+        out = self.conv2(out)
+        out = self.bn3(out)
+        if self.downsample is not None:
+            identity = self.downsample(x)
+        out += identity
+        return out
+
+
+layers_map = {
+    18: [2, 2, 2, 2],
+    34: [3, 4, 6, 3],
+    50: [3, 4, 14, 3],
+    100: [3, 13, 30, 3],
+}
+
+
+@BACKBONES.register_module()
+class IResNet(nn.Module):
+    fc_scale = 7 * 7
+    def __init__(self,
+                 depth, block=None, dropout=0, num_features=512, zero_init_residual=False,
+                 groups=1, width_per_group=64, replace_stride_with_dilation=None, fp16=False, pretrained=None):
+        super(IResNet, self).__init__()
+        block = IBasicBlock
+        layers = layers_map[depth]
+        self.fp16 = fp16
+        self.inplanes = 64
+        self.dilation = 1
+        if replace_stride_with_dilation is None:
+            replace_stride_with_dilation = [False, False, False]
+        if len(replace_stride_with_dilation) != 3:
+            raise ValueError("replace_stride_with_dilation should be None "
+                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
+        self.groups = groups
+        self.base_width = width_per_group
+        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(self.inplanes, eps=1e-05)
+        self.prelu = nn.PReLU(self.inplanes)
+        self.layer1 = self._make_layer(block, 64, layers[0], stride=2)
+        self.layer2 = self._make_layer(block,
+                                       128,
+                                       layers[1],
+                                       stride=2,
+                                       dilate=replace_stride_with_dilation[0])
+        self.layer3 = self._make_layer(block,
+                                       256,
+                                       layers[2],
+                                       stride=2,
+                                       dilate=replace_stride_with_dilation[1])
+        # self.layer4 = self._make_layer(block,
+        #                                512,
+        #                                layers[3],
+        #                                stride=2,
+        #                                dilate=replace_stride_with_dilation[2])
+        # self.bn2 = nn.BatchNorm2d(512 * block.expansion, eps=1e-05,)
+        # self.dropout = nn.Dropout(p=dropout, inplace=True)
+        # self.fc = nn.Linear(512 * block.expansion * self.fc_scale, num_features)
+        # self.features = nn.BatchNorm1d(num_features, eps=1e-05)
+        # nn.init.constant_(self.features.weight, 1.0)
+        # self.features.weight.requires_grad = False
+        if pretrained:
+            self.init_weights(pretrained)
+        else:
+            for m in self.modules():
+                if isinstance(m, nn.Conv2d):
+                    nn.init.normal_(m.weight, 0, 0.1)
+                elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
+                    nn.init.constant_(m.weight, 1)
+                    nn.init.constant_(m.bias, 0)
+
+            if zero_init_residual:
+                for m in self.modules():
+                    if isinstance(m, IBasicBlock):
+                        nn.init.constant_(m.bn2.weight, 0)
+
+    def init_weights(self, pretrained):
+        logger = get_root_logger()
+        logger.warning(f'{self.__class__.__name__} load pretrain from {pretrained}')
+        state_dict = torch.load(pretrained, map_location='cpu')
+        if 'state_dict' in state_dict:
+            state_dict = state_dict['state_dict']
+
+        load_state_dict(self, state_dict, strict=False, logger=logger)
+
+
+    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
+        downsample = None
+        previous_dilation = self.dilation
+        if dilate:
+            self.dilation *= stride
+            stride = 1
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                conv1x1(self.inplanes, planes * block.expansion, stride),
+                nn.BatchNorm2d(planes * block.expansion, eps=1e-05, ),
+            )
+        layers = []
+        layers.append(
+            block(self.inplanes, planes, stride, downsample, self.groups,
+                  self.base_width, previous_dilation))
+        self.inplanes = planes * block.expansion
+        for _ in range(1, blocks):
+            layers.append(
+                block(self.inplanes,
+                      planes,
+                      groups=self.groups,
+                      base_width=self.base_width,
+                      dilation=self.dilation))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        # with torch.cuda.amp.autocast(self.fp16):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.prelu(x)
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        return (x, )
+        x = self.layer4(x)
+        return (x, )
+        #     x = self.bn2(x)
+        #     x = torch.flatten(x, 1)
+        #     x = self.dropout(x)
+        # x = self.fc(x.float() if self.fp16 else x)
+        # x = self.features(x)
+        # return x
+
+
+def _iresnet(arch, block, layers, pretrained, progress, **kwargs):
+    model = IResNet(block, layers, **kwargs)
+    if pretrained:
+        raise ValueError()
+    return model
+
+
+def iresnet18(pretrained=False, progress=True, **kwargs):
+    return _iresnet('iresnet18', IBasicBlock, [2, 2, 2, 2], pretrained,
+                    progress, **kwargs)
+
+
+def iresnet34(pretrained=False, progress=True, **kwargs):
+    return _iresnet('iresnet34', IBasicBlock, [3, 4, 6, 3], pretrained,
+                    progress, **kwargs)
+
+
+def iresnet50(pretrained=False, progress=True, **kwargs):
+    return _iresnet('iresnet50', IBasicBlock, [3, 4, 14, 3], pretrained,
+                    progress, **kwargs)
+
+
+def iresnet100(pretrained=False, progress=True, **kwargs):
+    return _iresnet('iresnet100', IBasicBlock, [3, 13, 30, 3], pretrained,
+                    progress, **kwargs)
+
+
+def iresnet200(pretrained=False, progress=True, **kwargs):
+    return _iresnet('iresnet200', IBasicBlock, [6, 26, 60, 6], pretrained,
+                    progress, **kwargs)
\ No newline at end of file
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/irse.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/irse.py
new file mode 100644
index 0000000000000000000000000000000000000000..b55061dbda492ff800f61e573c55658068565dbd
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/irse.py
@@ -0,0 +1,387 @@
+import warnings
+import torch.nn as nn
+from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, ReLU, Sigmoid, Dropout, MaxPool2d, \
+    AdaptiveAvgPool2d, Sequential, Module
+from collections import namedtuple
+import torch
+from mmcv.runner import load_state_dict
+from mmcls.utils import get_root_logger
+
+from ..builder import BACKBONES
+from .base_backbone import BaseBackbone
+
+# Support: ['IR_50', 'IR_101', 'IR_152', 'IR_SE_50', 'IR_SE_101', 'IR_SE_152']
+
+class ABSPool2d(Module):
+    def __init__(self, *args, **kwargs):
+        super().__init__()
+        self.args = args
+        self.kwargs = kwargs
+
+    def forward(self, x):
+        positive = MaxPool2d(*self.args, **self.kwargs)(x)
+        negtive = MaxPool2d(*self.args, **self.kwargs)(-x)
+        result = torch.where(positive>=negtive, positive, negtive)
+        return result
+
+pool_layer = MaxPool2d
+
+class Flatten(Module):
+    def forward(self, input):
+        return input.view(input.size(0), -1)
+
+
+def l2_norm(input, axis=1):
+    norm = torch.norm(input, 2, axis, True)
+    output = torch.div(input, norm)
+
+    return output
+
+
+class SEModule(Module):
+    def __init__(self, channels, reduction):
+        super(SEModule, self).__init__()
+        self.avg_pool = AdaptiveAvgPool2d(1)
+        self.fc1 = Conv2d(
+            channels, channels // reduction, kernel_size=1, padding=0, bias=False)
+
+        nn.init.xavier_uniform_(self.fc1.weight.data)
+
+        self.relu = ReLU(inplace=True)
+        self.fc2 = Conv2d(
+            channels // reduction, channels, kernel_size=1, padding=0, bias=False)
+
+        self.sigmoid = Sigmoid()
+
+    def forward(self, x):
+        module_input = x
+        x = self.avg_pool(x)
+        x = self.fc1(x)
+        x = self.relu(x)
+        x = self.fc2(x)
+        x = self.sigmoid(x)
+
+        return module_input * x
+
+
+class bottleneck_IR(Module):
+    def __init__(self, in_channel, depth, stride):
+        super(bottleneck_IR, self).__init__()
+        if in_channel == depth:
+            self.shortcut_layer = pool_layer(1, stride)
+        else:
+            self.shortcut_layer = Sequential(
+                Conv2d(in_channel, depth, (1, 1), stride, bias=False),
+                BatchNorm2d(depth))
+        self.res_layer = Sequential(
+            BatchNorm2d(in_channel),
+            Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False),
+            PReLU(depth),
+            Conv2d(depth, depth, (3, 3), stride, 1, bias=False),
+            BatchNorm2d(depth))
+
+    def forward(self, x):
+        shortcut = self.shortcut_layer(x)
+        res = self.res_layer(x)
+
+        return res + shortcut
+
+
+class BasicBlockIR(Module):
+    """
+    BasicBlock for IRNet, stolen from TFace
+    https://github.com/Tencent/TFace/blob/d57fd8d9ce9502240921f0998c57f84afa7eaeaa/torchkit/backbone/model_irse.py#L16
+    Add a BatchNorm2d after the first Conv2d
+    """
+    def __init__(self, in_channel, depth, stride):
+        super(BasicBlockIR, self).__init__()
+        if in_channel == depth:
+            self.shortcut_layer = pool_layer(1, stride)
+        else:
+            self.shortcut_layer = Sequential(
+                Conv2d(in_channel, depth, (1, 1), stride, bias=False),
+                BatchNorm2d(depth))
+        self.res_layer = Sequential(
+            BatchNorm2d(in_channel),
+            Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False),
+            BatchNorm2d(depth),
+            PReLU(depth),
+            Conv2d(depth, depth, (3, 3), stride, 1, bias=False),
+            BatchNorm2d(depth))
+
+    def forward(self, x):
+        shortcut = self.shortcut_layer(x)
+        res = self.res_layer(x)
+
+        return res + shortcut
+
+
+class bottleneck_IR_SE(Module):
+    def __init__(self, in_channel, depth, stride):
+        super(bottleneck_IR_SE, self).__init__()
+        if in_channel == depth:
+            self.shortcut_layer = pool_layer(1, stride)
+        else:
+            self.shortcut_layer = Sequential(
+                Conv2d(in_channel, depth, (1, 1), stride, bias=False),
+                BatchNorm2d(depth))
+        self.res_layer = Sequential(
+            BatchNorm2d(in_channel),
+            Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False),
+            PReLU(depth),
+            Conv2d(depth, depth, (3, 3), stride, 1, bias=False),
+            BatchNorm2d(depth),
+            SEModule(depth, 16)
+        )
+
+    def forward(self, x):
+        shortcut = self.shortcut_layer(x)
+        res = self.res_layer(x)
+
+        return res + shortcut
+
+
+class Bottleneck(namedtuple('Block', ['in_channel', 'depth', 'stride'])):
+    '''A named tuple describing a ResNet block.'''
+
+
+def get_block(in_channel, depth, num_units, stride=2):
+
+    return [Bottleneck(in_channel, depth, stride)] + [Bottleneck(depth, depth, 1) for i in range(num_units - 1)]
+
+
+def get_blocks(num_layers):
+
+    if num_layers == 8:
+        blocks = [
+            get_block(in_channel=64, depth=64, num_units=3),
+        ]
+    elif num_layers == 16:
+        blocks = [
+            get_block(in_channel=64, depth=64, num_units=3),
+            get_block(in_channel=64, depth=128, num_units=4),
+        ]
+    elif num_layers == 34:
+        blocks = [
+            get_block(in_channel=64, depth=64, num_units=3),
+            get_block(in_channel=64, depth=128, num_units=4),
+            get_block(in_channel=128, depth=256, num_units=6),
+            get_block(in_channel=256, depth=512, num_units=3)
+        ]
+    elif num_layers == 44:
+        blocks = [
+            get_block(in_channel=64, depth=64, num_units=3),
+            get_block(in_channel=64, depth=128, num_units=4),
+            get_block(in_channel=128, depth=256, num_units=14),
+        ]
+    elif num_layers == 50:
+        blocks = [
+            get_block(in_channel=64, depth=64, num_units=3),    # (B, 64, 56, 56)
+            get_block(in_channel=64, depth=128, num_units=4),   # (B, 128, 28, 28)
+            get_block(in_channel=128, depth=256, num_units=14),   # (B, 256, 14, 14)
+            get_block(in_channel=256, depth=512, num_units=3)     # (B, 512, 7, 7)
+        ]
+    elif num_layers == 100:
+        blocks = [
+            get_block(in_channel=64, depth=64, num_units=3),
+            get_block(in_channel=64, depth=128, num_units=13),
+            get_block(in_channel=128, depth=256, num_units=30),
+            get_block(in_channel=256, depth=512, num_units=3)
+        ]
+    elif num_layers == 152:
+        blocks = [
+            get_block(in_channel=64, depth=64, num_units=3),
+            get_block(in_channel=64, depth=128, num_units=8),
+            get_block(in_channel=128, depth=256, num_units=36),
+            get_block(in_channel=256, depth=512, num_units=3)
+        ]
+
+    return blocks
+
+
+@BACKBONES.register_module()
+class IRSE(BaseBackbone):
+    def __init__(self, input_size, num_layers, mode='ir', with_head=False, pretrained=None, return_index=(0, 1, 2),
+                return_type='Tuple'):
+        super().__init__()
+        assert input_size[0] in [112, 224], "input_size should be [112, 112] or [224, 224]"
+        assert num_layers in [0, 8, 16, 34, 44, 50, 100, 152], "num_layers should be 50, 100 or 152"
+        assert mode in ['ir', 'ir_se'], "mode should be ir or ir_se"
+        assert return_type in ('Tensor', 'Tuple'), 'return_tensor must be Tensor or Tuple'
+        if len(return_index) > 1:
+            assert return_type == 'Tuple'
+        self.num_layers = num_layers
+        self.return_index = return_index
+        self.return_type = return_type
+        if num_layers == 0:
+            return
+        self.with_head = with_head
+        blocks = get_blocks(num_layers)
+        if mode == 'ir':
+            if num_layers == 34:
+                warnings.warn('Using the IR_34 version from TFace')
+                unit_module = BasicBlockIR
+            else:
+                unit_module = bottleneck_IR
+        elif mode == 'ir_se':
+            unit_module = bottleneck_IR_SE
+        self.input_layer = Sequential(Conv2d(3, 64, (3, 3), 1, 1, bias=False),
+                                      BatchNorm2d(64),
+                                      PReLU(64))
+        if with_head:
+            if input_size[0] == 112:
+                self.output_layer = Sequential(BatchNorm2d(512),
+                                            Dropout(),
+                                            Flatten(),
+                                            Linear(512 * 7 * 7, 512),
+                                            BatchNorm1d(512))
+            else:
+                self.output_layer = Sequential(BatchNorm2d(512),
+                                            Dropout(),
+                                            Flatten(),
+                                            Linear(512 * 14 * 14, 512),
+                                            BatchNorm1d(512))
+
+        modules = []
+        max_stage = max(return_index)
+        for block in blocks[:max_stage+1]:
+            block_module = []
+            for bottleneck in block:
+                block_module.append(
+                    unit_module(bottleneck.in_channel,
+                                bottleneck.depth,
+                                bottleneck.stride))
+            modules.append(Sequential(*block_module))
+        # self.body = Sequential(*modules)
+        self.body = nn.ModuleList(modules)
+
+        self._initialize_weights()
+        if pretrained:
+            self.init_weights(pretrained)
+
+    def init_weights(self, pretrained):
+        logger = get_root_logger()
+        logger.warning(f'{self.__class__.__name__} load pretrain from {pretrained}')
+        state_dict = torch.load(pretrained, map_location='cpu')
+        if 'state_dict' in state_dict:
+            state_dict = state_dict['state_dict']
+
+        stage_unit_nums = {
+            34: (3, 4, 6, 3),
+            50: (3, 4, 14, 3),
+        }
+        stage_num = stage_unit_nums[self.num_layers]
+
+        new_state_dict = dict()
+        for k, v in state_dict.items():
+            if k.startswith('body.'):
+                index = int(k.split('.')[1])
+                if 0 <= index < stage_num[0]:
+                    new_key = k.replace('body.', 'body.0.')
+                elif stage_num[0] <= index < sum(stage_num[:2]):
+                    new_key = f"body.1.{index-sum(stage_num[:1])}.{'.'.join(k.split('.')[2:])}"
+                elif sum(stage_num[:2]) <= index < sum(stage_num[:3]):
+                    new_key = f"body.2.{index-sum(stage_num[:2])}.{'.'.join(k.split('.')[2:])}"
+                else:
+                    new_key = k
+            else:
+                new_key = k
+            new_state_dict[new_key] = v
+
+        load_state_dict(self, new_state_dict, strict=False, logger=logger)
+
+    def forward(self, x):
+        if self.num_layers == 0:
+            return x
+        x = self.input_layer(x)
+        output = []
+        return_index = set(self.return_index)
+        for index, m in enumerate(self.body):
+            x = m(x)
+            if index in return_index:
+                output.append(x)        
+        if self.with_head:
+            x = self.output_layer(x)
+        if self.return_type == 'Tensor':
+            return output[0]
+        else:
+            return tuple(output)
+
+    def _initialize_weights(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.xavier_uniform_(m.weight.data)
+                if m.bias is not None:
+                    m.bias.data.zero_()
+            elif isinstance(m, nn.BatchNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+            elif isinstance(m, nn.BatchNorm1d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+            elif isinstance(m, nn.Linear):
+                nn.init.xavier_uniform_(m.weight.data)
+                if m.bias is not None:
+                    m.bias.data.zero_()
+    
+
+    def _freeze_stages(self):
+        if self.frozen_blocks > 0:
+            print(f'IRSE freeze the first {self.frozen_blocks} blocks, it has {len(self.body)} blocks ')
+            self.input_layer.eval()
+            print('in freeze', self.input_layer[1].training)
+            for param in self.input_layer.parameters():
+                param.requires_grad = False
+        
+        for i in range(self.frozen_blocks):
+            m = self.body[i]
+            m.eval()
+            for param in m.parameters():
+                param.requires_grad = False
+
+def IR_50(input_size):
+    """Constructs a ir-50 model.
+    """
+    model = Backbone(input_size, 50, 'ir')
+
+    return model
+
+
+def IR_101(input_size):
+    """Constructs a ir-101 model.
+    """
+    model = Backbone(input_size, 100, 'ir')
+
+    return model
+
+
+def IR_152(input_size):
+    """Constructs a ir-152 model.
+    """
+    model = Backbone(input_size, 152, 'ir')
+
+    return model
+
+
+def IR_SE_50(input_size):
+    """Constructs a ir_se-50 model.
+    """
+    model = Backbone(input_size, 50, 'ir_se')
+
+    return model
+
+
+def IR_SE_101(input_size):
+    """Constructs a ir_se-101 model.
+    """
+    model = Backbone(input_size, 100, 'ir_se')
+
+    return model
+
+
+def IR_SE_152(input_size):
+    """Constructs a ir_se-152 model.
+    """
+    model = Backbone(input_size, 152, 'ir_se')
+
+    return model
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/irse_nopadding.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/irse_nopadding.py
new file mode 100644
index 0000000000000000000000000000000000000000..b9b4193f2b1d5f8902308f282196edbdbc5d67a5
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/irse_nopadding.py
@@ -0,0 +1,366 @@
+import warnings
+import torch.nn as nn
+from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, ReLU, Sigmoid, Dropout, MaxPool2d, \
+    AdaptiveAvgPool2d, Sequential, Module
+from collections import namedtuple
+import torch
+from mmcv.runner import load_state_dict
+from mmcls.utils import get_root_logger
+
+from ..builder import BACKBONES
+from .base_backbone import BaseBackbone
+
+# Support: ['IR_50', 'IR_101', 'IR_152', 'IR_SE_50', 'IR_SE_101', 'IR_SE_152']
+
+class Flatten(Module):
+    def forward(self, input):
+        return input.view(input.size(0), -1)
+
+
+def l2_norm(input, axis=1):
+    norm = torch.norm(input, 2, axis, True)
+    output = torch.div(input, norm)
+
+    return output
+
+
+class SEModule(Module):
+    def __init__(self, channels, reduction):
+        super(SEModule, self).__init__()
+        self.avg_pool = AdaptiveAvgPool2d(1)
+        self.fc1 = Conv2d(
+            channels, channels // reduction, kernel_size=1, padding=0, bias=False)
+
+        nn.init.xavier_uniform_(self.fc1.weight.data)
+
+        self.relu = ReLU(inplace=True)
+        self.fc2 = Conv2d(
+            channels // reduction, channels, kernel_size=1, padding=0, bias=False)
+
+        self.sigmoid = Sigmoid()
+
+    def forward(self, x):
+        module_input = x
+        x = self.avg_pool(x)
+        x = self.fc1(x)
+        x = self.relu(x)
+        x = self.fc2(x)
+        x = self.sigmoid(x)
+
+        return module_input * x
+
+
+class bottleneck_IR(Module):
+    def __init__(self, in_channel, depth, stride):
+        super(bottleneck_IR, self).__init__()
+        if in_channel == depth:
+            self.shortcut_layer = MaxPool2d(1, stride)
+        else:
+            self.shortcut_layer = Sequential(
+                Conv2d(in_channel, depth, (1, 1), stride, bias=False),
+                BatchNorm2d(depth))
+        self.res_layer = Sequential(
+            BatchNorm2d(in_channel),
+            Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False),
+            PReLU(depth),
+            Conv2d(depth, depth, (3, 3), stride, 1, bias=False),
+            BatchNorm2d(depth))
+
+    def forward(self, x):
+        shortcut = self.shortcut_layer(x)
+        res = self.res_layer(x)
+
+        return res + shortcut
+
+
+class BasicBlockIR(Module):
+    """
+    BasicBlock for IRNet, stolen from TFace
+    https://github.com/Tencent/TFace/blob/d57fd8d9ce9502240921f0998c57f84afa7eaeaa/torchkit/backbone/model_irse.py#L16
+    Add a BatchNorm2d after the first Conv2d
+    """
+    def __init__(self, in_channel, depth, stride):
+        super(BasicBlockIR, self).__init__()
+        if in_channel == depth:
+            self.shortcut_layer = MaxPool2d(1, stride)
+        else:
+            self.shortcut_layer = Sequential(
+                Conv2d(in_channel, depth, (1, 1), stride, bias=False),
+                BatchNorm2d(depth))
+        self.res_layer = Sequential(
+            BatchNorm2d(in_channel),
+            Conv2d(in_channel, depth, (3, 3), (1, 1), 0, bias=False),
+            BatchNorm2d(depth),
+            PReLU(depth),
+            Conv2d(depth, depth, (3, 3), stride, 0, bias=False),
+            BatchNorm2d(depth))
+
+    def forward(self, x):
+        shortcut = self.shortcut_layer(x)
+        res = self.res_layer(x)
+
+        return res + shortcut
+
+
+class bottleneck_IR_SE(Module):
+    def __init__(self, in_channel, depth, stride):
+        super(bottleneck_IR_SE, self).__init__()
+        if in_channel == depth:
+            self.shortcut_layer = MaxPool2d(1, stride)
+        else:
+            self.shortcut_layer = Sequential(
+                Conv2d(in_channel, depth, (1, 1), stride, bias=False),
+                BatchNorm2d(depth))
+        self.res_layer = Sequential(
+            BatchNorm2d(in_channel),
+            Conv2d(in_channel, depth, (3, 3), (1, 1), 0, bias=False),
+            PReLU(depth),
+            Conv2d(depth, depth, (3, 3), stride, 0, bias=False),
+            BatchNorm2d(depth),
+            SEModule(depth, 16)
+        )
+
+    def forward(self, x):
+        shortcut = self.shortcut_layer(x)
+        res = self.res_layer(x)
+
+        return res + shortcut
+
+
+class Bottleneck(namedtuple('Block', ['in_channel', 'depth', 'stride'])):
+    '''A named tuple describing a ResNet block.'''
+
+
+def get_block(in_channel, depth, num_units, stride=2):
+
+    return [Bottleneck(in_channel, depth, stride)] + [Bottleneck(depth, depth, 1) for i in range(num_units - 1)]
+
+
+def get_blocks(num_layers):
+
+    if num_layers == 8:
+        blocks = [
+            get_block(in_channel=64, depth=64, num_units=3),
+        ]
+    elif num_layers == 16:
+        blocks = [
+            get_block(in_channel=64, depth=64, num_units=3),
+            get_block(in_channel=64, depth=128, num_units=4),
+        ]
+    elif num_layers == 34:
+        blocks = [
+            get_block(in_channel=64, depth=64, num_units=3),
+            get_block(in_channel=64, depth=128, num_units=4),
+            get_block(in_channel=128, depth=256, num_units=6),
+            get_block(in_channel=256, depth=512, num_units=3)
+        ]
+    elif num_layers == 44:
+        blocks = [
+            get_block(in_channel=64, depth=64, num_units=3),
+            get_block(in_channel=64, depth=128, num_units=4),
+            get_block(in_channel=128, depth=256, num_units=14),
+        ]
+    elif num_layers == 50:
+        blocks = [
+            get_block(in_channel=64, depth=64, num_units=3),    # (B, 64, 56, 56)
+            get_block(in_channel=64, depth=128, num_units=4),   # (B, 128, 28, 28)
+            get_block(in_channel=128, depth=256, num_units=14),   # (B, 256, 14, 14)
+            get_block(in_channel=256, depth=512, num_units=3)     # (B, 512, 7, 7)
+        ]
+    elif num_layers == 100:
+        blocks = [
+            get_block(in_channel=64, depth=64, num_units=3),
+            get_block(in_channel=64, depth=128, num_units=13),
+            get_block(in_channel=128, depth=256, num_units=30),
+            get_block(in_channel=256, depth=512, num_units=3)
+        ]
+    elif num_layers == 152:
+        blocks = [
+            get_block(in_channel=64, depth=64, num_units=3),
+            get_block(in_channel=64, depth=128, num_units=8),
+            get_block(in_channel=128, depth=256, num_units=36),
+            get_block(in_channel=256, depth=512, num_units=3)
+        ]
+
+    return blocks
+
+
+@BACKBONES.register_module()
+class IRSENoPadding(BaseBackbone):
+    def __init__(self, input_size, num_layers, mode='ir', with_head=False, pretrained=None, return_index=(0, 1, 2)):
+        super().__init__()
+        assert input_size[0] in [112, 224], "input_size should be [112, 112] or [224, 224]"
+        assert num_layers in [0, 8, 16, 34, 44, 50, 100, 152], "num_layers should be 50, 100 or 152"
+        assert mode in ['ir', 'ir_se'], "mode should be ir or ir_se"
+        self.num_layers = num_layers
+        self.return_index = return_index
+        if num_layers == 0:
+            return
+        self.with_head = with_head
+        blocks = get_blocks(num_layers)
+        if mode == 'ir':
+            if num_layers == 34:
+                warnings.warn('Using the IR_34 version from TFace')
+                unit_module = BasicBlockIR
+            else:
+                unit_module = bottleneck_IR
+        elif mode == 'ir_se':
+            unit_module = bottleneck_IR_SE
+        self.input_layer = Sequential(Conv2d(3, 64, (3, 3), 1, 0, bias=False),
+                                      BatchNorm2d(64),
+                                      PReLU(64))
+        if with_head:
+            if input_size[0] == 112:
+                self.output_layer = Sequential(BatchNorm2d(512),
+                                            Dropout(),
+                                            Flatten(),
+                                            Linear(512 * 7 * 7, 512),
+                                            BatchNorm1d(512))
+            else:
+                self.output_layer = Sequential(BatchNorm2d(512),
+                                            Dropout(),
+                                            Flatten(),
+                                            Linear(512 * 14 * 14, 512),
+                                            BatchNorm1d(512))
+
+        modules = []
+        max_stage = max(return_index)
+        for block in blocks[:max_stage+1]:
+            block_module = []
+            for bottleneck in block:
+                block_module.append(
+                    unit_module(bottleneck.in_channel,
+                                bottleneck.depth,
+                                bottleneck.stride))
+            modules.append(Sequential(*block_module))
+        # self.body = Sequential(*modules)
+        self.body = nn.ModuleList(modules)
+
+        self._initialize_weights()
+        if pretrained:
+            self.init_weights(pretrained)
+
+    def init_weights(self, pretrained):
+        logger = get_root_logger()
+        logger.warning(f'{self.__class__.__name__} load pretrain from {pretrained}')
+        state_dict = torch.load(pretrained, map_location='cpu')
+        if 'state_dict' in state_dict:
+            state_dict = state_dict['state_dict']
+
+        stage_unit_nums = {
+            34: (3, 4, 6, 3),
+            50: (3, 4, 14, 3),
+        }
+        stage_num = stage_unit_nums[self.num_layers]
+
+        new_state_dict = dict()
+        for k, v in state_dict.items():
+            if k.startswith('body.'):
+                index = int(k.split('.')[1])
+                if 0 <= index < stage_num[0]:
+                    new_key = k.replace('body.', 'body.0.')
+                elif stage_num[0] <= index < sum(stage_num[:2]):
+                    new_key = f"body.1.{index-sum(stage_num[:1])}.{'.'.join(k.split('.')[2:])}"
+                elif sum(stage_num[:2]) <= index < sum(stage_num[:3]):
+                    new_key = f"body.2.{index-sum(stage_num[:2])}.{'.'.join(k.split('.')[2:])}"
+                else:
+                    new_key = k
+            else:
+                new_key = k
+            new_state_dict[new_key] = v
+
+        load_state_dict(self, new_state_dict, strict=False, logger=logger)
+
+    def forward(self, x):
+        if self.num_layers == 0:
+            return x
+        x = self.input_layer(x)
+        output = []
+        return_index = set(self.return_index)
+        for index, m in enumerate(self.body):
+            x = m(x)
+            if index in return_index:
+                output.append(x)        
+        if self.with_head:
+            x = self.output_layer(x)
+
+        return output
+
+    def _initialize_weights(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.xavier_uniform_(m.weight.data)
+                if m.bias is not None:
+                    m.bias.data.zero_()
+            elif isinstance(m, nn.BatchNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+            elif isinstance(m, nn.BatchNorm1d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+            elif isinstance(m, nn.Linear):
+                nn.init.xavier_uniform_(m.weight.data)
+                if m.bias is not None:
+                    m.bias.data.zero_()
+    
+
+    def _freeze_stages(self):
+        if self.frozen_blocks > 0:
+            print(f'IRSE freeze the first {self.frozen_blocks} blocks, it has {len(self.body)} blocks ')
+            self.input_layer.eval()
+            print('in freeze', self.input_layer[1].training)
+            for param in self.input_layer.parameters():
+                param.requires_grad = False
+        
+        for i in range(self.frozen_blocks):
+            m = self.body[i]
+            m.eval()
+            for param in m.parameters():
+                param.requires_grad = False
+
+def IR_50(input_size):
+    """Constructs a ir-50 model.
+    """
+    model = Backbone(input_size, 50, 'ir')
+
+    return model
+
+
+def IR_101(input_size):
+    """Constructs a ir-101 model.
+    """
+    model = Backbone(input_size, 100, 'ir')
+
+    return model
+
+
+def IR_152(input_size):
+    """Constructs a ir-152 model.
+    """
+    model = Backbone(input_size, 152, 'ir')
+
+    return model
+
+
+def IR_SE_50(input_size):
+    """Constructs a ir_se-50 model.
+    """
+    model = Backbone(input_size, 50, 'ir_se')
+
+    return model
+
+
+def IR_SE_101(input_size):
+    """Constructs a ir_se-101 model.
+    """
+    model = Backbone(input_size, 100, 'ir_se')
+
+    return model
+
+
+def IR_SE_152(input_size):
+    """Constructs a ir_se-152 model.
+    """
+    model = Backbone(input_size, 152, 'ir_se')
+
+    return model
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/lenet.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/lenet.py
new file mode 100644
index 0000000000000000000000000000000000000000..81b9c59e15e653a0a65b63e3b27353f4a40fc631
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/lenet.py
@@ -0,0 +1,41 @@
+import torch.nn as nn
+
+from ..builder import BACKBONES
+from .base_backbone import BaseBackbone
+
+
+@BACKBONES.register_module()
+class LeNet5(BaseBackbone):
+    """`LeNet5 <https://en.wikipedia.org/wiki/LeNet>`_ backbone.
+
+    The input for LeNet-5 is a 32×32 grayscale image.
+
+    Args:
+        num_classes (int): number of classes for classification.
+            The default value is -1, which uses the backbone as
+            a feature extractor without the top classifier.
+    """
+
+    def __init__(self, num_classes=-1):
+        super(LeNet5, self).__init__()
+        self.num_classes = num_classes
+        self.features = nn.Sequential(
+            nn.Conv2d(1, 6, kernel_size=5, stride=1), nn.Tanh(),
+            nn.AvgPool2d(kernel_size=2),
+            nn.Conv2d(6, 16, kernel_size=5, stride=1), nn.Tanh(),
+            nn.AvgPool2d(kernel_size=2),
+            nn.Conv2d(16, 120, kernel_size=5, stride=1), nn.Tanh())
+        if self.num_classes > 0:
+            self.classifier = nn.Sequential(
+                nn.Linear(120, 84),
+                nn.Tanh(),
+                nn.Linear(84, num_classes),
+            )
+
+    def forward(self, x):
+
+        x = self.features(x)
+        if self.num_classes > 0:
+            x = self.classifier(x.squeeze())
+
+        return x
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/mobilefacenet.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/mobilefacenet.py
new file mode 100644
index 0000000000000000000000000000000000000000..c33ad3778bcd595af6e388eb796e0eac03f71df1
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/mobilefacenet.py
@@ -0,0 +1,133 @@
+import torch
+import torch.nn as nn
+from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, Module, Sequential
+
+from ..builder import BACKBONES
+from .base_backbone import BaseBackbone
+
+
+class Flatten(Module):
+    def forward(self, input):
+        return input.view(input.size(0), -1)
+
+
+def l2_norm(input,axis=1):
+    norm = torch.norm(input,2,axis,True)
+    output = torch.div(input, norm)
+    return output
+
+
+class Conv_block(Module):
+    def __init__(self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=(0, 0), groups=1):
+        super(Conv_block, self).__init__()
+        self.conv = Conv2d(in_c, out_channels=out_c, kernel_size=kernel, groups=groups, stride=stride, padding=padding, bias=False)
+        self.bn = BatchNorm2d(out_c)
+        self.prelu = PReLU(out_c)
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        x = self.prelu(x)
+        return x
+
+class Linear_block(Module):
+    def __init__(self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=(0, 0), groups=1):
+        super(Linear_block, self).__init__()
+        self.conv = Conv2d(in_c, out_channels=out_c, kernel_size=kernel, groups=groups, stride=stride, padding=padding, bias=False)
+        self.bn = BatchNorm2d(out_c)
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        return x
+
+class Depth_Wise(Module):
+     def __init__(self, in_c, out_c, residual = False, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=1):
+        super(Depth_Wise, self).__init__()
+        self.conv = Conv_block(in_c, out_c=groups, kernel=(1, 1), padding=(0, 0), stride=(1, 1))
+        self.conv_dw = Conv_block(groups, groups, groups=groups, kernel=kernel, padding=padding, stride=stride)
+        self.project = Linear_block(groups, out_c, kernel=(1, 1), padding=(0, 0), stride=(1, 1))
+        self.residual = residual
+     def forward(self, x):
+        short_cut = None
+        if self.residual:
+            short_cut = x
+        x = self.conv(x)
+        x = self.conv_dw(x)
+        x = self.project(x)
+        if self.residual:
+            output = short_cut + x
+        else:
+            output = x
+        return output
+
+class Residual(Module):
+    def __init__(self, c, num_block, groups, kernel=(3, 3), stride=(1, 1), padding=(1, 1)):
+        super(Residual, self).__init__()
+        modules = []
+        for _ in range(num_block):
+            modules.append(Depth_Wise(c, c, residual=True, kernel=kernel, padding=padding, stride=stride, groups=groups))
+        self.model = Sequential(*modules)
+    def forward(self, x):
+        return self.model(x)
+
+
+@BACKBONES.register_module()
+class MobileFaceNet(BaseBackbone):
+    """`MobileFaceNet <https://github.com/TreB1eN/InsightFace_Pytorch>`_ backbone.
+
+    The input for MobileFaceNet is a 224x224 RGB image.
+
+    Args:
+        num_classes (int): number of classes for classification.
+            The default value is -1, which uses the backbone as
+            a feature extractor without the top classifier.
+    """
+    def __init__(self, return_stages=[2], pretrained=None):
+        super().__init__()
+        self.conv1 = Conv_block(3, 64, kernel=(3, 3), stride=(2, 2), padding=(1, 1))
+
+        self.conv2_dw = Conv_block(64, 64, kernel=(3, 3), stride=(1, 1), padding=(1, 1), groups=64)
+
+        self.conv_23 = Depth_Wise(64, 64, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=128)
+        self.conv_3 = Residual(64, num_block=4, groups=128, kernel=(3, 3), stride=(1, 1), padding=(1, 1))
+
+        self.conv_34 = Depth_Wise(64, 128, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=256)
+        self.conv_4 = Residual(128, num_block=6, groups=256, kernel=(3, 3), stride=(1, 1), padding=(1, 1))
+
+        # self.conv_45 = Depth_Wise(128, 128, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=512)
+        # self.conv_5 = Residual(128, num_block=2, groups=256, kernel=(3, 3), stride=(1, 1), padding=(1, 1))
+
+        # self.conv_6_sep = Conv_block(128, 512, kernel=(1, 1), stride=(1, 1), padding=(0, 0))
+        # self.conv_6_dw = Linear_block(512, 512, groups=512, kernel=(7,7), stride=(1, 1), padding=(0, 0))
+        # self.conv_6_flatten = Flatten()
+        # self.linear = Linear(512, embedding_size, bias=False)
+        # self.bn = BatchNorm1d(embedding_size)
+        self.return_stages = set(return_stages)
+        if pretrained:
+            self.init_weights(pretrained)
+    
+    def forward(self, x):
+        output = []
+        out = self.conv1(x)
+        out = self.conv2_dw(out)
+        if 0 in self.return_stages:
+            output.append(out)
+        
+        out = self.conv_23(out)
+        out = self.conv_3(out)
+        if 1 in self.return_stages:
+            output.append(out)
+        
+        out = self.conv_34(out)
+        out = self.conv_4(out)
+        if 2 in self.return_stages:
+            output.append(out)
+        # out = self.conv_45(out)
+        # out = self.conv_5(out)
+        # out = self.conv_6_sep(out)
+        # out = self.conv_6_dw(out)
+        # out = self.conv_6_flatten(out)
+        # out = self.linear(out)
+        # out = self.bn(out)
+        # print(out.shape)
+        # return out
+        return output
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/mobilenet_v2.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/mobilenet_v2.py
new file mode 100644
index 0000000000000000000000000000000000000000..1447e6eeb77eb7db9f463e6eec355750eb983c04
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/mobilenet_v2.py
@@ -0,0 +1,273 @@
+import logging
+
+import torch.nn as nn
+import torch.utils.checkpoint as cp
+from mmcv.cnn import ConvModule, constant_init, kaiming_init
+from mmcv.runner import load_checkpoint
+from torch.nn.modules.batchnorm import _BatchNorm
+
+from mmcls.models.utils import make_divisible
+from ..builder import BACKBONES
+from .base_backbone import BaseBackbone
+
+
+class InvertedResidual(nn.Module):
+    """InvertedResidual block for MobileNetV2.
+
+    Args:
+        in_channels (int): The input channels of the InvertedResidual block.
+        out_channels (int): The output channels of the InvertedResidual block.
+        stride (int): Stride of the middle (first) 3x3 convolution.
+        expand_ratio (int): adjusts number of channels of the hidden layer
+            in InvertedResidual by this amount.
+        conv_cfg (dict): Config dict for convolution layer.
+            Default: None, which means using conv2d.
+        norm_cfg (dict): Config dict for normalization layer.
+            Default: dict(type='BN').
+        act_cfg (dict): Config dict for activation layer.
+            Default: dict(type='ReLU6').
+        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
+            memory while slowing down the training speed. Default: False.
+
+    Returns:
+        Tensor: The output tensor
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 stride,
+                 expand_ratio,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN'),
+                 act_cfg=dict(type='ReLU6'),
+                 with_cp=False):
+        super(InvertedResidual, self).__init__()
+        self.stride = stride
+        assert stride in [1, 2], f'stride must in [1, 2]. ' \
+            f'But received {stride}.'
+        self.with_cp = with_cp
+        self.use_res_connect = self.stride == 1 and in_channels == out_channels
+        hidden_dim = int(round(in_channels * expand_ratio))
+
+        layers = []
+        if expand_ratio != 1:
+            layers.append(
+                ConvModule(
+                    in_channels=in_channels,
+                    out_channels=hidden_dim,
+                    kernel_size=1,
+                    conv_cfg=conv_cfg,
+                    norm_cfg=norm_cfg,
+                    act_cfg=act_cfg))
+        layers.extend([
+            ConvModule(
+                in_channels=hidden_dim,
+                out_channels=hidden_dim,
+                kernel_size=3,
+                stride=stride,
+                padding=1,
+                groups=hidden_dim,
+                conv_cfg=conv_cfg,
+                norm_cfg=norm_cfg,
+                act_cfg=act_cfg),
+            ConvModule(
+                in_channels=hidden_dim,
+                out_channels=out_channels,
+                kernel_size=1,
+                conv_cfg=conv_cfg,
+                norm_cfg=norm_cfg,
+                act_cfg=None)
+        ])
+        self.conv = nn.Sequential(*layers)
+
+    def forward(self, x):
+
+        def _inner_forward(x):
+            if self.use_res_connect:
+                return x + self.conv(x)
+            else:
+                return self.conv(x)
+
+        if self.with_cp and x.requires_grad:
+            out = cp.checkpoint(_inner_forward, x)
+        else:
+            out = _inner_forward(x)
+
+        return out
+
+
+@BACKBONES.register_module()
+class MobileNetV2(BaseBackbone):
+    """MobileNetV2 backbone.
+
+    Args:
+        widen_factor (float): Width multiplier, multiply number of
+            channels in each layer by this amount. Default: 1.0.
+        out_indices (None or Sequence[int]): Output from which stages.
+            Default: (7, ).
+        frozen_stages (int): Stages to be frozen (all param fixed).
+            Default: -1, which means not freezing any parameters.
+        conv_cfg (dict): Config dict for convolution layer.
+            Default: None, which means using conv2d.
+        norm_cfg (dict): Config dict for normalization layer.
+            Default: dict(type='BN').
+        act_cfg (dict): Config dict for activation layer.
+            Default: dict(type='ReLU6').
+        norm_eval (bool): Whether to set norm layers to eval mode, namely,
+            freeze running stats (mean and var). Note: Effect on Batch Norm
+            and its variants only. Default: False.
+        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
+            memory while slowing down the training speed. Default: False.
+    """
+
+    # Parameters to build layers. 4 parameters are needed to construct a
+    # layer, from left to right: expand_ratio, channel, num_blocks, stride.
+    arch_settings = [[1, 16, 1, 1], [6, 24, 2, 2], [6, 32, 3, 2],
+                     [6, 64, 4, 2], [6, 96, 3, 1], [6, 160, 3, 2],
+                     [6, 320, 1, 1]]
+
+    def __init__(self,
+                 widen_factor=1.,
+                 out_indices=(7, ),
+                 frozen_stages=-1,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN'),
+                 act_cfg=dict(type='ReLU6'),
+                 norm_eval=False,
+                 with_cp=False):
+        super(MobileNetV2, self).__init__()
+        self.widen_factor = widen_factor
+        self.out_indices = out_indices
+        for index in out_indices:
+            if index not in range(0, 8):
+                raise ValueError('the item in out_indices must in '
+                                 f'range(0, 8). But received {index}')
+
+        if frozen_stages not in range(-1, 8):
+            raise ValueError('frozen_stages must be in range(-1, 8). '
+                             f'But received {frozen_stages}')
+        self.out_indices = out_indices
+        self.frozen_stages = frozen_stages
+        self.conv_cfg = conv_cfg
+        self.norm_cfg = norm_cfg
+        self.act_cfg = act_cfg
+        self.norm_eval = norm_eval
+        self.with_cp = with_cp
+
+        self.in_channels = make_divisible(32 * widen_factor, 8)
+
+        self.conv1 = ConvModule(
+            in_channels=3,
+            out_channels=self.in_channels,
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            conv_cfg=self.conv_cfg,
+            norm_cfg=self.norm_cfg,
+            act_cfg=self.act_cfg)
+
+        self.layers = []
+
+        for i, layer_cfg in enumerate(self.arch_settings):
+            expand_ratio, channel, num_blocks, stride = layer_cfg
+            out_channels = make_divisible(channel * widen_factor, 8)
+            inverted_res_layer = self.make_layer(
+                out_channels=out_channels,
+                num_blocks=num_blocks,
+                stride=stride,
+                expand_ratio=expand_ratio)
+            layer_name = f'layer{i + 1}'
+            self.add_module(layer_name, inverted_res_layer)
+            self.layers.append(layer_name)
+
+        if widen_factor > 1.0:
+            self.out_channel = int(1280 * widen_factor)
+        else:
+            self.out_channel = 1280
+
+        layer = ConvModule(
+            in_channels=self.in_channels,
+            out_channels=self.out_channel,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            conv_cfg=self.conv_cfg,
+            norm_cfg=self.norm_cfg,
+            act_cfg=self.act_cfg)
+        self.add_module('conv2', layer)
+        self.layers.append('conv2')
+
+    def make_layer(self, out_channels, num_blocks, stride, expand_ratio):
+        """ Stack InvertedResidual blocks to build a layer for MobileNetV2.
+
+        Args:
+            out_channels (int): out_channels of block.
+            num_blocks (int): number of blocks.
+            stride (int): stride of the first block. Default: 1
+            expand_ratio (int): Expand the number of channels of the
+                hidden layer in InvertedResidual by this ratio. Default: 6.
+        """
+        layers = []
+        for i in range(num_blocks):
+            if i >= 1:
+                stride = 1
+            layers.append(
+                InvertedResidual(
+                    self.in_channels,
+                    out_channels,
+                    stride,
+                    expand_ratio=expand_ratio,
+                    conv_cfg=self.conv_cfg,
+                    norm_cfg=self.norm_cfg,
+                    act_cfg=self.act_cfg,
+                    with_cp=self.with_cp))
+            self.in_channels = out_channels
+
+        return nn.Sequential(*layers)
+
+    def init_weights(self, pretrained=None):
+        if isinstance(pretrained, str):
+            logger = logging.getLogger()
+            load_checkpoint(self, pretrained, strict=False, logger=logger)
+        elif pretrained is None:
+            for m in self.modules():
+                if isinstance(m, nn.Conv2d):
+                    kaiming_init(m)
+                elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
+                    constant_init(m, 1)
+        else:
+            raise TypeError('pretrained must be a str or None')
+
+    def forward(self, x):
+        x = self.conv1(x)
+
+        outs = []
+        for i, layer_name in enumerate(self.layers):
+            layer = getattr(self, layer_name)
+            x = layer(x)
+            if i in self.out_indices:
+                outs.append(x)
+
+        if len(outs) == 1:
+            return outs[0]
+        else:
+            return tuple(outs)
+
+    def _freeze_stages(self):
+        if self.frozen_stages >= 0:
+            for param in self.conv1.parameters():
+                param.requires_grad = False
+        for i in range(1, self.frozen_stages + 1):
+            layer = getattr(self, f'layer{i}')
+            layer.eval()
+            for param in layer.parameters():
+                param.requires_grad = False
+
+    def train(self, mode=True):
+        super(MobileNetV2, self).train(mode)
+        self._freeze_stages()
+        if mode and self.norm_eval:
+            for m in self.modules():
+                if isinstance(m, _BatchNorm):
+                    m.eval()
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/mobilenet_v3.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/mobilenet_v3.py
new file mode 100644
index 0000000000000000000000000000000000000000..8627735ae0261b9e6139a5bfc7cd41c9d114e6f3
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/mobilenet_v3.py
@@ -0,0 +1,186 @@
+import logging
+
+import torch.nn as nn
+from mmcv.cnn import ConvModule, constant_init, kaiming_init
+from mmcv.runner import load_checkpoint
+from torch.nn.modules.batchnorm import _BatchNorm
+
+from ..builder import BACKBONES
+from ..utils import InvertedResidual
+from .base_backbone import BaseBackbone
+
+
+@BACKBONES.register_module()
+class MobileNetv3(BaseBackbone):
+    """ MobileNetv3 backbone
+
+    Args:
+        arch (str): Architechture of mobilnetv3, from {small, big}.
+            Default: small.
+        conv_cfg (dict): Config dict for convolution layer.
+            Default: None, which means using conv2d.
+        norm_cfg (dict): Config dict for normalization layer.
+            Default: dict(type='BN').
+        out_indices (None or Sequence[int]): Output from which stages.
+            Default: (10, ), which means output tensors from final stage.
+        frozen_stages (int): Stages to be frozen (all param fixed).
+            Defualt: -1, which means not freezing any parameters.
+        norm_eval (bool): Whether to set norm layers to eval mode, namely,
+            freeze running stats (mean and var). Note: Effect on Batch Norm
+            and its variants only. Default: False.
+        with_cp (bool): Use checkpoint or not. Using checkpoint will save
+            some memory while slowing down the training speed.
+            Defualt: False.
+    """
+    # Parameters to build each block:
+    #     [kernel size, mid channels, out channels, with_se, act type, stride]
+    arch_settings = {
+        'small': [[3, 16, 16, True, 'ReLU', 2],
+                  [3, 72, 24, False, 'ReLU', 2],
+                  [3, 88, 24, False, 'ReLU', 1],
+                  [5, 96, 40, True, 'HSwish', 2],
+                  [5, 240, 40, True, 'HSwish', 1],
+                  [5, 240, 40, True, 'HSwish', 1],
+                  [5, 120, 48, True, 'HSwish', 1],
+                  [5, 144, 48, True, 'HSwish', 1],
+                  [5, 288, 96, True, 'HSwish', 2],
+                  [5, 576, 96, True, 'HSwish', 1],
+                  [5, 576, 96, True, 'HSwish', 1]],
+        'big': [[3, 16, 16, False, 'ReLU', 1],
+                [3, 64, 24, False, 'ReLU', 2],
+                [3, 72, 24, False, 'ReLU', 1],
+                [5, 72, 40, True, 'ReLU', 2],
+                [5, 120, 40, True, 'ReLU', 1],
+                [5, 120, 40, True, 'ReLU', 1],
+                [3, 240, 80, False, 'HSwish', 2],
+                [3, 200, 80, False, 'HSwish', 1],
+                [3, 184, 80, False, 'HSwish', 1],
+                [3, 184, 80, False, 'HSwish', 1],
+                [3, 480, 112, True, 'HSwish', 1],
+                [3, 672, 112, True, 'HSwish', 1],
+                [5, 672, 160, True, 'HSwish', 1],
+                [5, 672, 160, True, 'HSwish', 2],
+                [5, 960, 160, True, 'HSwish', 1]]
+    }  # yapf: disable
+
+    def __init__(self,
+                 arch='small',
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN'),
+                 out_indices=(10, ),
+                 frozen_stages=-1,
+                 norm_eval=False,
+                 with_cp=False):
+        super(MobileNetv3, self).__init__()
+        assert arch in self.arch_settings
+        for index in out_indices:
+            if index not in range(0, len(self.arch_settings[arch])):
+                raise ValueError('the item in out_indices must in '
+                                 f'range(0, {len(self.arch_settings[arch])}). '
+                                 f'But received {index}')
+
+        if frozen_stages not in range(-1, len(self.arch_settings[arch])):
+            raise ValueError('frozen_stages must be in range(-1, '
+                             f'{len(self.arch_settings[arch])}). '
+                             f'But received {frozen_stages}')
+        self.out_indices = out_indices
+        self.frozen_stages = frozen_stages
+        self.arch = arch
+        self.conv_cfg = conv_cfg
+        self.norm_cfg = norm_cfg
+        self.out_indices = out_indices
+        self.frozen_stages = frozen_stages
+        self.norm_eval = norm_eval
+        self.with_cp = with_cp
+
+        self.in_channels = 16
+        self.conv1 = ConvModule(
+            in_channels=3,
+            out_channels=self.in_channels,
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            conv_cfg=conv_cfg,
+            norm_cfg=norm_cfg,
+            act_cfg=dict(type='HSwish'))
+
+        self.layers = self._make_layer()
+        self.feat_dim = self.arch_settings[arch][-1][2]
+
+    def _make_layer(self):
+        layers = []
+        layer_setting = self.arch_settings[self.arch]
+        for i, params in enumerate(layer_setting):
+            (kernel_size, mid_channels, out_channels, with_se, act,
+             stride) = params
+            if with_se:
+                se_cfg = dict(
+                    channels=mid_channels,
+                    ratio=4,
+                    act_cfg=(dict(type='ReLU'), dict(type='HSigmoid')))
+            else:
+                se_cfg = None
+
+            layer = InvertedResidual(
+                in_channels=self.in_channels,
+                out_channels=out_channels,
+                mid_channels=mid_channels,
+                kernel_size=kernel_size,
+                stride=stride,
+                se_cfg=se_cfg,
+                with_expand_conv=True,
+                conv_cfg=self.conv_cfg,
+                norm_cfg=self.norm_cfg,
+                act_cfg=dict(type=act),
+                with_cp=self.with_cp)
+            self.in_channels = out_channels
+            layer_name = 'layer{}'.format(i + 1)
+            self.add_module(layer_name, layer)
+            layers.append(layer_name)
+        return layers
+
+    def init_weights(self, pretrained=None):
+        if isinstance(pretrained, str):
+            logger = logging.getLogger()
+            load_checkpoint(self, pretrained, strict=False, logger=logger)
+        elif pretrained is None:
+            for m in self.modules():
+                if isinstance(m, nn.Conv2d):
+                    kaiming_init(m)
+                elif isinstance(m, nn.BatchNorm2d):
+                    constant_init(m, 1)
+        else:
+            raise TypeError('pretrained must be a str or None')
+
+    def forward(self, x):
+        x = self.conv1(x)
+
+        outs = []
+        for i, layer_name in enumerate(self.layers):
+            layer = getattr(self, layer_name)
+            x = layer(x)
+            if i in self.out_indices:
+                outs.append(x)
+
+        if len(outs) == 1:
+            return outs[0]
+        else:
+            return tuple(outs)
+
+    def _freeze_stages(self):
+        if self.frozen_stages >= 0:
+            for param in self.conv1.parameters():
+                param.requires_grad = False
+        for i in range(1, self.frozen_stages + 1):
+            layer = getattr(self, f'layer{i}')
+            layer.eval()
+            for param in layer.parameters():
+                param.requires_grad = False
+
+    def train(self, mode=True):
+        super(MobileNetv3, self).train(mode)
+        self._freeze_stages()
+        if mode and self.norm_eval:
+            for m in self.modules():
+                if isinstance(m, _BatchNorm):
+                    m.eval()
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/modules/t2t.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/modules/t2t.py
new file mode 100644
index 0000000000000000000000000000000000000000..2d35a356488858e2cc3747c60b411d232cab3a71
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/modules/t2t.py
@@ -0,0 +1,57 @@
+import math
+import torch
+from torch import nn
+
+
+class Token_performer(nn.Module):
+    def __init__(self, dim, in_dim, head_cnt=1, kernel_ratio=0.5, dp1=0.1, dp2 = 0.1):
+        super().__init__()
+        self.emb = in_dim * head_cnt # we use 1, so it is no need here
+        self.kqv = nn.Linear(dim, 3 * self.emb)
+        self.dp = nn.Dropout(dp1)
+        self.proj = nn.Linear(self.emb, self.emb)
+        self.head_cnt = head_cnt
+        self.norm1 = nn.LayerNorm(dim)
+        self.norm2 = nn.LayerNorm(self.emb)
+        self.epsilon = 1e-8  # for stable in division
+
+        self.mlp = nn.Sequential(
+            nn.Linear(self.emb, 1 * self.emb),
+            nn.GELU(),
+            nn.Linear(1 * self.emb, self.emb),
+            nn.Dropout(dp2),
+        )
+
+        self.m = int(self.emb * kernel_ratio)
+        self.w = torch.randn(self.m, self.emb)
+        self.w = nn.Parameter(nn.init.orthogonal_(self.w) * math.sqrt(self.m), requires_grad=False)
+
+    def prm_exp(self, x):
+        # part of the function is borrow from https://github.com/lucidrains/performer-pytorch 
+        # and Simo Ryu (https://github.com/cloneofsimo)
+        # ==== positive random features for gaussian kernels ====
+        # x = (B, T, hs)
+        # w = (m, hs)
+        # return : x : B, T, m
+        # SM(x, y) = E_w[exp(w^T x - |x|/2) exp(w^T y - |y|/2)]
+        # therefore return exp(w^Tx - |x|/2)/sqrt(m)
+        xd = ((x * x).sum(dim=-1, keepdim=True)).repeat(1, 1, self.m) / 2
+        wtx = torch.einsum('bti,mi->btm', x.float(), self.w)
+
+        return torch.exp(wtx - xd) / math.sqrt(self.m)
+
+    def single_attn(self, x):
+        k, q, v = torch.split(self.kqv(x), self.emb, dim=-1)
+        kp, qp = self.prm_exp(k), self.prm_exp(q)  # (B, T, m), (B, T, m)
+        D = torch.einsum('bti,bi->bt', qp, kp.sum(dim=1)).unsqueeze(dim=2)  # (B, T, m) * (B, m) -> (B, T, 1)
+        kptv = torch.einsum('bin,bim->bnm', v.float(), kp)  # (B, emb, m)
+        y = torch.einsum('bti,bni->btn', qp, kptv) / (D.repeat(1, 1, self.emb) + self.epsilon)  # (B, T, emb)/Diag
+        # skip connection
+        y = v + self.dp(self.proj(y))  # same as token_transformer in T2T layer, use v as skip connection
+
+        return y
+
+    def forward(self, x):
+        x = self.single_attn(self.norm1(x))
+        x = x + self.mlp(self.norm2(x))
+        return x
\ No newline at end of file
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/modules/vit.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/modules/vit.py
new file mode 100644
index 0000000000000000000000000000000000000000..263d484c7b4783fcc8c98ee554f98737d4f69e25
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/modules/vit.py
@@ -0,0 +1,197 @@
+import torch
+from torch import nn
+from mmcls.models.vit.layers import DropPath
+
+
+class Mlp(nn.Module):
+    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+class Attention(nn.Module):
+    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
+        self.scale = qk_scale or head_dim ** -0.5
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x):
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads)
+
+        qkv = qkv.permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0], qkv[1], qkv[2]   # make torchscript happy (cannot use tensor as tuple)
+
+        attn = (q @ k.transpose(-2, -1)) * self.scale   # [B, head_num, token_num, token_num]
+
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class HeadFusionAttention(nn.Module):
+    """
+    fuse front head output add current head input as current input
+    Origin: head2_output = f(head2_input)
+    Fuse: head2_output = f(head1_output + head2_input)
+    """
+    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
+        self.scale = qk_scale or head_dim ** -0.5
+
+        # self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.group_number = 4 # 每个group内部并行计算
+        self.qkv = nn.ModuleList([nn.Linear(dim//self.group_number, (dim//self.group_number)*3, bias=qkv_bias) for _ in range(self.group_number)])
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x):
+        B, N, C = x.shape
+        # H = self.num_heads
+        # dim = C // self.num_heads
+        g = self.group_number
+        g_dim = C // self.group_number
+        x = x.reshape((B, N, g, g_dim))
+
+        outputs = []
+        head_x = torch.zeros((B, N, g_dim), device=x.device)
+        for i in range(g):
+            # self-attention
+            current_x = x[:, :, i]
+            current_x = current_x + head_x
+            qkv = self.qkv[i](current_x).reshape(B, N, 3, g_dim)
+            qkv = qkv.permute(2, 0, 1, 3)   # [3, B, N, dim]
+            q, k, v = qkv[0], qkv[1], qkv[2] 
+            attn = (q @ k.transpose(-2, -1)) * self.scale   # [B, N, N]
+            attn = attn.softmax(dim=-1)
+            attn = self.attn_drop(attn)
+            head_x = (attn @ v)  # [B, N, d]
+            outputs.append(head_x)
+        x = torch.cat(outputs, dim=-1)
+
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class HeadFusionAttentionV2(nn.Module):
+    """
+    V2:  one branch is origin, onther is modified
+    fuse front head output add current head input as current input
+    Origin: head2_output = f(head2_input)
+    Fuse: head2_output = f(head1_output + head2_input)
+    """
+    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
+        self.scale = qk_scale or head_dim ** -0.5
+
+        # self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.group_number = 2 # 每个group内部并行计算.
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.qkv2 = nn.ModuleList([nn.Linear(dim//self.group_number, (dim//self.group_number)*3, bias=qkv_bias) for _ in range(self.group_number)])
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x):
+        # original        
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads)
+
+        qkv = qkv.permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0], qkv[1], qkv[2]   # make torchscript happy (cannot use tensor as tuple)
+
+        attn = (q @ k.transpose(-2, -1)) * self.scale   # [B, head_num, token_num, token_num]
+
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        origin = (attn @ v)     # [B, H, N, dim]
+
+        # modify
+        g = self.group_number
+        g_dim = C // self.group_number
+        x = x.reshape((B, N, g, g_dim))
+        n = self.num_heads // g  # head number per group
+
+        origin = origin.transpose(1, 2).reshape((B, N, g, g_dim))
+
+        outputs = []
+        head_x = torch.zeros((B, N, g_dim), device=x.device)
+        for i in range(g):
+            # self-attention
+            current_x = x[:, :, i]
+            current_x = current_x + head_x
+            qkv = self.qkv2[i](current_x).reshape(B, N, 3, g_dim)
+            qkv = qkv.permute(2, 0, 1, 3)   # [3, B, N, dim]
+            q, k, v = qkv[0], qkv[1], qkv[2] 
+            attn = (q @ k.transpose(-2, -1)) * self.scale   # [B, N, N]
+            attn = attn.softmax(dim=-1)
+            attn = self.attn_drop(attn)
+            head_x = (attn @ v)  # [B, N, d]
+            outputs.append(head_x + origin[:, :, i])
+        x = torch.cat(outputs, dim=-1)
+
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+
+
+
+class Block(nn.Module):
+    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
+                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, head_fusion=False,
+                 ):
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        if head_fusion:
+            self.attn = HeadFusionAttentionV2(
+                dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
+        else:
+            self.attn = Attention(
+                dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
+        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
+
+    def forward(self, x):
+        feature = self.attn(self.norm1(x))
+        x = x + self.drop_path(feature)
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+        return x
+
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/modules/vit_pooling.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/modules/vit_pooling.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c5e24bd648b5909dc25493f009751ee4bf69d48
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/modules/vit_pooling.py
@@ -0,0 +1,89 @@
+import torch
+from torch import nn
+from .vit import Mlp
+from mmcls.models.utils import top_pool
+from mmcls.models.vit.layers import DropPath
+
+
+class PoolingAttention(nn.Module):
+    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., 
+                 pool_config=None):
+        super().__init__()
+        self.num_heads = num_heads
+        self.dim = dim
+        head_dim = dim // num_heads
+        # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
+        self.scale = qk_scale or head_dim ** -0.5
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+        self.pool_config = pool_config
+
+
+    def forward(self, x):
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads)
+        qkv = qkv.permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0], qkv[1], qkv[2]   # make torchscript happy (cannot use tensor as tuple)
+
+        attn = (q @ k.transpose(-2, -1)) * self.scale   # [B, head_num, token_num, token_num]
+
+        if self.pool_config:
+            attn_method = self.pool_config.get('attn_method')
+            if attn_method == 'SUM_ABS_1':
+                attn_weight = attn[:, :, 0, :].transpose(-1, -2)    # [B, token_num, head_num]
+                attn_weight = torch.sum(torch.abs(attn_weight), dim=-1).unsqueeze(-1)
+            elif attn_method == 'SUM':
+                attn_weight = attn[:, :, 0, :].transpose(-1, -2)    # [B, token_num, head_num]
+                attn_weight = torch.sum(attn_weight, dim=-1).unsqueeze(-1)
+            elif attn_method == 'MAX':
+                attn_weight = attn[:, :, 0, :].transpose(-1, -2)
+                attn_weight = torch.max(attn_weight, dim=-1)[0].unsqueeze(-1)
+            else:
+                raise ValueError('Invalid attn_method: %s' % attn_method)
+
+            # attn_weight = torch.rand(attn_weight.shape, device=attn_weight.device)
+            keep_index = top_pool(attn_weight, dim=self.dim, **self.pool_config)
+        else:
+            keep_index = None
+
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x, keep_index
+
+
+class PoolingBlock(nn.Module):
+
+    def __init__(self, dim=0, num_heads=0, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
+                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, pool_config=None, **kwargs):
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        self.attn = PoolingAttention(
+            dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop, 
+            pool_config=pool_config)
+        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
+
+    def forward(self, x):
+        feature, keep_index = self.attn(self.norm1(x))
+        x = x + self.drop_path(feature)
+        if keep_index is not None:
+            if len(keep_index) != x.shape[1]:
+                x = x.gather(dim=1, index=keep_index)
+                # pooled_x = []
+                # for i in range(keep_index.shape[0]):
+                #     pooled_x.append(x[i, keep_index[i, :, 0]])
+                # x = torch.stack(pooled_x)
+                # assert torch.all(torch.eq(quick_x, x))
+
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+        return x
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/regnet.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/regnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..fe7d118f73db2e85593def55897727ec8f9b3302
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/regnet.py
@@ -0,0 +1,312 @@
+import numpy as np
+import torch.nn as nn
+from mmcv.cnn import build_conv_layer, build_norm_layer
+
+from ..builder import BACKBONES
+from .resnet import ResNet
+from .resnext import Bottleneck
+
+
+@BACKBONES.register_module()
+class RegNet(ResNet):
+    """RegNet backbone.
+
+    More details can be found in `paper <https://arxiv.org/abs/2003.13678>`_ .
+
+    Args:
+        arch (dict): The parameter of RegNets.
+            - w0 (int): initial width
+            - wa (float): slope of width
+            - wm (float): quantization parameter to quantize the width
+            - depth (int): depth of the backbone
+            - group_w (int): width of group
+            - bot_mul (float): bottleneck ratio, i.e. expansion of bottlneck.
+        strides (Sequence[int]): Strides of the first block of each stage.
+        base_channels (int): Base channels after stem layer.
+        in_channels (int): Number of input image channels. Default: 3.
+        dilations (Sequence[int]): Dilation of each stage.
+        out_indices (Sequence[int]): Output from which stages.
+        style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
+            layer is the 3x3 conv layer, otherwise the stride-two layer is
+            the first 1x1 conv layer. Default: "pytorch".
+        frozen_stages (int): Stages to be frozen (all param fixed). -1 means
+            not freezing any parameters. Default: -1.
+        norm_cfg (dict): dictionary to construct and config norm layer.
+            Default: dict(type='BN', requires_grad=True).
+        norm_eval (bool): Whether to set norm layers to eval mode, namely,
+            freeze running stats (mean and var). Note: Effect on Batch Norm
+            and its variants only. Default: False.
+        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
+            memory while slowing down the training speed. Default: False.
+        zero_init_residual (bool): whether to use zero init for last norm layer
+            in resblocks to let them behave as identity. Default: True.
+
+    Example:
+        >>> from mmdet.models import RegNet
+        >>> import torch
+        >>> self = RegNet(
+                arch=dict(
+                    w0=88,
+                    wa=26.31,
+                    wm=2.25,
+                    group_w=48,
+                    depth=25,
+                    bot_mul=1.0))
+        >>> self.eval()
+        >>> inputs = torch.rand(1, 3, 32, 32)
+        >>> level_outputs = self.forward(inputs)
+        >>> for level_out in level_outputs:
+        ...     print(tuple(level_out.shape))
+        (1, 96, 8, 8)
+        (1, 192, 4, 4)
+        (1, 432, 2, 2)
+        (1, 1008, 1, 1)
+    """
+    arch_settings = {
+        'regnetx_400mf':
+        dict(w0=24, wa=24.48, wm=2.54, group_w=16, depth=22, bot_mul=1.0),
+        'regnetx_800mf':
+        dict(w0=56, wa=35.73, wm=2.28, group_w=16, depth=16, bot_mul=1.0),
+        'regnetx_1.6gf':
+        dict(w0=80, wa=34.01, wm=2.25, group_w=24, depth=18, bot_mul=1.0),
+        'regnetx_3.2gf':
+        dict(w0=88, wa=26.31, wm=2.25, group_w=48, depth=25, bot_mul=1.0),
+        'regnetx_4.0gf':
+        dict(w0=96, wa=38.65, wm=2.43, group_w=40, depth=23, bot_mul=1.0),
+        'regnetx_6.4gf':
+        dict(w0=184, wa=60.83, wm=2.07, group_w=56, depth=17, bot_mul=1.0),
+        'regnetx_8.0gf':
+        dict(w0=80, wa=49.56, wm=2.88, group_w=120, depth=23, bot_mul=1.0),
+        'regnetx_12gf':
+        dict(w0=168, wa=73.36, wm=2.37, group_w=112, depth=19, bot_mul=1.0),
+    }
+
+    def __init__(self,
+                 arch,
+                 in_channels=3,
+                 stem_channels=32,
+                 base_channels=32,
+                 strides=(2, 2, 2, 2),
+                 dilations=(1, 1, 1, 1),
+                 out_indices=(3, ),
+                 style='pytorch',
+                 deep_stem=False,
+                 avg_down=False,
+                 frozen_stages=-1,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN', requires_grad=True),
+                 norm_eval=False,
+                 with_cp=False,
+                 zero_init_residual=True):
+        super(ResNet, self).__init__()
+
+        # Generate RegNet parameters first
+        if isinstance(arch, str):
+            assert arch in self.arch_settings, \
+                f'"arch": "{arch}" is not one of the' \
+                ' arch_settings'
+            arch = self.arch_settings[arch]
+        elif not isinstance(arch, dict):
+            raise TypeError('Expect "arch" to be either a string '
+                            f'or a dict, got {type(arch)}')
+
+        widths, num_stages = self.generate_regnet(
+            arch['w0'],
+            arch['wa'],
+            arch['wm'],
+            arch['depth'],
+        )
+        # Convert to per stage format
+        stage_widths, stage_blocks = self.get_stages_from_blocks(widths)
+        # Generate group widths and bot muls
+        group_widths = [arch['group_w'] for _ in range(num_stages)]
+        self.bottleneck_ratio = [arch['bot_mul'] for _ in range(num_stages)]
+        # Adjust the compatibility of stage_widths and group_widths
+        stage_widths, group_widths = self.adjust_width_group(
+            stage_widths, self.bottleneck_ratio, group_widths)
+
+        # Group params by stage
+        self.stage_widths = stage_widths
+        self.group_widths = group_widths
+        self.depth = sum(stage_blocks)
+        self.stem_channels = stem_channels
+        self.base_channels = base_channels
+        self.num_stages = num_stages
+        assert num_stages >= 1 and num_stages <= 4
+        self.strides = strides
+        self.dilations = dilations
+        assert len(strides) == len(dilations) == num_stages
+        self.out_indices = out_indices
+        assert max(out_indices) < num_stages
+        self.style = style
+        self.deep_stem = deep_stem
+        if self.deep_stem:
+            raise NotImplementedError(
+                'deep_stem has not been implemented for RegNet')
+        self.avg_down = avg_down
+        self.frozen_stages = frozen_stages
+        self.conv_cfg = conv_cfg
+        self.norm_cfg = norm_cfg
+        self.with_cp = with_cp
+        self.norm_eval = norm_eval
+        self.zero_init_residual = zero_init_residual
+        self.stage_blocks = stage_blocks[:num_stages]
+
+        self._make_stem_layer(in_channels, stem_channels)
+
+        _in_channels = stem_channels
+        self.res_layers = []
+        for i, num_blocks in enumerate(self.stage_blocks):
+            stride = self.strides[i]
+            dilation = self.dilations[i]
+            group_width = self.group_widths[i]
+            width = int(round(self.stage_widths[i] * self.bottleneck_ratio[i]))
+            stage_groups = width // group_width
+
+            res_layer = self.make_res_layer(
+                block=Bottleneck,
+                num_blocks=num_blocks,
+                in_channels=_in_channels,
+                out_channels=self.stage_widths[i],
+                expansion=1,
+                stride=stride,
+                dilation=dilation,
+                style=self.style,
+                avg_down=self.avg_down,
+                with_cp=self.with_cp,
+                conv_cfg=self.conv_cfg,
+                norm_cfg=self.norm_cfg,
+                base_channels=self.stage_widths[i],
+                groups=stage_groups,
+                width_per_group=group_width)
+            _in_channels = self.stage_widths[i]
+            layer_name = f'layer{i + 1}'
+            self.add_module(layer_name, res_layer)
+            self.res_layers.append(layer_name)
+
+        self._freeze_stages()
+
+        self.feat_dim = stage_widths[-1]
+
+    def _make_stem_layer(self, in_channels, base_channels):
+        self.conv1 = build_conv_layer(
+            self.conv_cfg,
+            in_channels,
+            base_channels,
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            bias=False)
+        self.norm1_name, norm1 = build_norm_layer(
+            self.norm_cfg, base_channels, postfix=1)
+        self.add_module(self.norm1_name, norm1)
+        self.relu = nn.ReLU(inplace=True)
+
+    def generate_regnet(self,
+                        initial_width,
+                        width_slope,
+                        width_parameter,
+                        depth,
+                        divisor=8):
+        """Generates per block width from RegNet parameters.
+
+        Args:
+            initial_width ([int]): Initial width of the backbone
+            width_slope ([float]): Slope of the quantized linear function
+            width_parameter ([int]): Parameter used to quantize the width.
+            depth ([int]): Depth of the backbone.
+            divisor (int, optional): The divisor of channels. Defaults to 8.
+
+        Returns:
+            list, int: return a list of widths of each stage and the number of
+                stages
+        """
+        assert width_slope >= 0
+        assert initial_width > 0
+        assert width_parameter > 1
+        assert initial_width % divisor == 0
+        widths_cont = np.arange(depth) * width_slope + initial_width
+        ks = np.round(
+            np.log(widths_cont / initial_width) / np.log(width_parameter))
+        widths = initial_width * np.power(width_parameter, ks)
+        widths = np.round(np.divide(widths, divisor)) * divisor
+        num_stages = len(np.unique(widths))
+        widths, widths_cont = widths.astype(int).tolist(), widths_cont.tolist()
+        return widths, num_stages
+
+    @staticmethod
+    def quantize_float(number, divisor):
+        """Converts a float to closest non-zero int divisible by divior.
+
+        Args:
+            number (int): Original number to be quantized.
+            divisor (int): Divisor used to quantize the number.
+
+        Returns:
+            int: quantized number that is divisible by devisor.
+        """
+        return int(round(number / divisor) * divisor)
+
+    def adjust_width_group(self, widths, bottleneck_ratio, groups):
+        """Adjusts the compatibility of widths and groups.
+
+        Args:
+            widths (list[int]): Width of each stage.
+            bottleneck_ratio (float): Bottleneck ratio.
+            groups (int): number of groups in each stage
+
+        Returns:
+            tuple(list): The adjusted widths and groups of each stage.
+        """
+        bottleneck_width = [
+            int(w * b) for w, b in zip(widths, bottleneck_ratio)
+        ]
+        groups = [min(g, w_bot) for g, w_bot in zip(groups, bottleneck_width)]
+        bottleneck_width = [
+            self.quantize_float(w_bot, g)
+            for w_bot, g in zip(bottleneck_width, groups)
+        ]
+        widths = [
+            int(w_bot / b)
+            for w_bot, b in zip(bottleneck_width, bottleneck_ratio)
+        ]
+        return widths, groups
+
+    def get_stages_from_blocks(self, widths):
+        """Gets widths/stage_blocks of network at each stage
+
+        Args:
+            widths (list[int]): Width in each stage.
+
+        Returns:
+            tuple(list): width and depth of each stage
+        """
+        width_diff = [
+            width != width_prev
+            for width, width_prev in zip(widths + [0], [0] + widths)
+        ]
+        stage_widths = [
+            width for width, diff in zip(widths, width_diff[:-1]) if diff
+        ]
+        stage_blocks = np.diff([
+            depth for depth, diff in zip(range(len(width_diff)), width_diff)
+            if diff
+        ]).tolist()
+        return stage_widths, stage_blocks
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.norm1(x)
+        x = self.relu(x)
+
+        outs = []
+        for i, layer_name in enumerate(self.res_layers):
+            res_layer = getattr(self, layer_name)
+            x = res_layer(x)
+            if i in self.out_indices:
+                outs.append(x)
+
+        if len(outs) == 1:
+            return outs[0]
+        else:
+            return tuple(outs)
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/resnest.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/resnest.py
new file mode 100644
index 0000000000000000000000000000000000000000..a882a92652bf5f1f3d732a98ced8f933ed27a662
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/resnest.py
@@ -0,0 +1,337 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint as cp
+from mmcv.cnn import build_conv_layer, build_norm_layer
+
+from ..builder import BACKBONES
+from .resnet import Bottleneck as _Bottleneck
+from .resnet import ResLayer, ResNetV1d
+
+
+class RSoftmax(nn.Module):
+    """Radix Softmax module in ``SplitAttentionConv2d``.
+
+    Args:
+        radix (int): Radix of input.
+        groups (int): Groups of input.
+    """
+
+    def __init__(self, radix, groups):
+        super().__init__()
+        self.radix = radix
+        self.groups = groups
+
+    def forward(self, x):
+        batch = x.size(0)
+        if self.radix > 1:
+            x = x.view(batch, self.groups, self.radix, -1).transpose(1, 2)
+            x = F.softmax(x, dim=1)
+            x = x.reshape(batch, -1)
+        else:
+            x = torch.sigmoid(x)
+        return x
+
+
+class SplitAttentionConv2d(nn.Module):
+    """Split-Attention Conv2d.
+
+    Args:
+        in_channels (int): Same as nn.Conv2d.
+        out_channels (int): Same as nn.Conv2d.
+        kernel_size (int | tuple[int]): Same as nn.Conv2d.
+        stride (int | tuple[int]): Same as nn.Conv2d.
+        padding (int | tuple[int]): Same as nn.Conv2d.
+        dilation (int | tuple[int]): Same as nn.Conv2d.
+        groups (int): Same as nn.Conv2d.
+        radix (int): Radix of SpltAtConv2d. Default: 2
+        reduction_factor (int): Reduction factor of SplitAttentionConv2d.
+            Default: 4.
+        conv_cfg (dict): Config dict for convolution layer. Default: None,
+            which means using conv2d.
+        norm_cfg (dict): Config dict for normalization layer. Default: None.
+    """
+
+    def __init__(self,
+                 in_channels,
+                 channels,
+                 kernel_size,
+                 stride=1,
+                 padding=0,
+                 dilation=1,
+                 groups=1,
+                 radix=2,
+                 reduction_factor=4,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN')):
+        super(SplitAttentionConv2d, self).__init__()
+        inter_channels = max(in_channels * radix // reduction_factor, 32)
+        self.radix = radix
+        self.groups = groups
+        self.channels = channels
+        self.conv = build_conv_layer(
+            conv_cfg,
+            in_channels,
+            channels * radix,
+            kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            groups=groups * radix,
+            bias=False)
+        self.norm0_name, norm0 = build_norm_layer(
+            norm_cfg, channels * radix, postfix=0)
+        self.add_module(self.norm0_name, norm0)
+        self.relu = nn.ReLU(inplace=True)
+        self.fc1 = build_conv_layer(
+            None, channels, inter_channels, 1, groups=self.groups)
+        self.norm1_name, norm1 = build_norm_layer(
+            norm_cfg, inter_channels, postfix=1)
+        self.add_module(self.norm1_name, norm1)
+        self.fc2 = build_conv_layer(
+            None, inter_channels, channels * radix, 1, groups=self.groups)
+        self.rsoftmax = RSoftmax(radix, groups)
+
+    @property
+    def norm0(self):
+        return getattr(self, self.norm0_name)
+
+    @property
+    def norm1(self):
+        return getattr(self, self.norm1_name)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.norm0(x)
+        x = self.relu(x)
+
+        batch, rchannel = x.shape[:2]
+        if self.radix > 1:
+            splits = x.view(batch, self.radix, -1, *x.shape[2:])
+            gap = splits.sum(dim=1)
+        else:
+            gap = x
+        gap = F.adaptive_avg_pool2d(gap, 1)
+        gap = self.fc1(gap)
+
+        gap = self.norm1(gap)
+        gap = self.relu(gap)
+
+        atten = self.fc2(gap)
+        atten = self.rsoftmax(atten).view(batch, -1, 1, 1)
+
+        if self.radix > 1:
+            attens = atten.view(batch, self.radix, -1, *atten.shape[2:])
+            out = torch.sum(attens * splits, dim=1)
+        else:
+            out = atten * x
+        return out.contiguous()
+
+
+class Bottleneck(_Bottleneck):
+    """Bottleneck block for ResNeSt.
+
+    Args:
+        in_channels (int): Input channels of this block.
+        out_channels (int): Output channels of this block.
+        groups (int): Groups of conv2.
+        width_per_group (int): Width per group of conv2. 64x4d indicates
+            ``groups=64, width_per_group=4`` and 32x8d indicates
+            ``groups=32, width_per_group=8``.
+        radix (int): Radix of SpltAtConv2d. Default: 2
+        reduction_factor (int): Reduction factor of SplitAttentionConv2d.
+            Default: 4.
+        avg_down_stride (bool): Whether to use average pool for stride in
+            Bottleneck. Default: True.
+        stride (int): stride of the block. Default: 1
+        dilation (int): dilation of convolution. Default: 1
+        downsample (nn.Module): downsample operation on identity branch.
+            Default: None
+        style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
+            layer is the 3x3 conv layer, otherwise the stride-two layer is
+            the first 1x1 conv layer.
+        conv_cfg (dict): dictionary to construct and config conv layer.
+            Default: None
+        norm_cfg (dict): dictionary to construct and config norm layer.
+            Default: dict(type='BN')
+        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
+            memory while slowing down the training speed.
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 groups=1,
+                 width_per_group=4,
+                 base_channels=64,
+                 radix=2,
+                 reduction_factor=4,
+                 avg_down_stride=True,
+                 **kwargs):
+        super(Bottleneck, self).__init__(in_channels, out_channels, **kwargs)
+
+        self.groups = groups
+        self.width_per_group = width_per_group
+
+        # For ResNet bottleneck, middle channels are determined by expansion
+        # and out_channels, but for ResNeXt bottleneck, it is determined by
+        # groups and width_per_group and the stage it is located in.
+        if groups != 1:
+            assert self.mid_channels % base_channels == 0
+            self.mid_channels = (
+                groups * width_per_group * self.mid_channels // base_channels)
+
+        self.avg_down_stride = avg_down_stride and self.conv2_stride > 1
+
+        self.norm1_name, norm1 = build_norm_layer(
+            self.norm_cfg, self.mid_channels, postfix=1)
+        self.norm3_name, norm3 = build_norm_layer(
+            self.norm_cfg, self.out_channels, postfix=3)
+
+        self.conv1 = build_conv_layer(
+            self.conv_cfg,
+            self.in_channels,
+            self.mid_channels,
+            kernel_size=1,
+            stride=self.conv1_stride,
+            bias=False)
+        self.add_module(self.norm1_name, norm1)
+        self.conv2 = SplitAttentionConv2d(
+            self.mid_channels,
+            self.mid_channels,
+            kernel_size=3,
+            stride=1 if self.avg_down_stride else self.conv2_stride,
+            padding=self.dilation,
+            dilation=self.dilation,
+            groups=groups,
+            radix=radix,
+            reduction_factor=reduction_factor,
+            conv_cfg=self.conv_cfg,
+            norm_cfg=self.norm_cfg)
+        delattr(self, self.norm2_name)
+
+        if self.avg_down_stride:
+            self.avd_layer = nn.AvgPool2d(3, self.conv2_stride, padding=1)
+
+        self.conv3 = build_conv_layer(
+            self.conv_cfg,
+            self.mid_channels,
+            self.out_channels,
+            kernel_size=1,
+            bias=False)
+        self.add_module(self.norm3_name, norm3)
+
+    def forward(self, x):
+
+        def _inner_forward(x):
+            identity = x
+
+            out = self.conv1(x)
+            out = self.norm1(out)
+            out = self.relu(out)
+
+            out = self.conv2(out)
+
+            if self.avg_down_stride:
+                out = self.avd_layer(out)
+
+            out = self.conv3(out)
+            out = self.norm3(out)
+
+            if self.downsample is not None:
+                identity = self.downsample(x)
+
+            out += identity
+
+            return out
+
+        if self.with_cp and x.requires_grad:
+            out = cp.checkpoint(_inner_forward, x)
+        else:
+            out = _inner_forward(x)
+
+        out = self.relu(out)
+
+        return out
+
+
+@BACKBONES.register_module()
+class ResNeSt(ResNetV1d):
+    """ResNeSt backbone.
+
+    Please refer to the `paper <https://arxiv.org/pdf/2004.08955.pdf>`_ for
+    details.
+
+    Args:
+        depth (int): Network depth, from {50, 101, 152, 200}.
+        groups (int): Groups of conv2 in Bottleneck. Default: 32.
+        width_per_group (int): Width per group of conv2 in Bottleneck.
+            Default: 4.
+        radix (int): Radix of SpltAtConv2d. Default: 2
+        reduction_factor (int): Reduction factor of SplitAttentionConv2d.
+            Default: 4.
+        avg_down_stride (bool): Whether to use average pool for stride in
+            Bottleneck. Default: True.
+        in_channels (int): Number of input image channels. Default: 3.
+        stem_channels (int): Output channels of the stem layer. Default: 64.
+        num_stages (int): Stages of the network. Default: 4.
+        strides (Sequence[int]): Strides of the first block of each stage.
+            Default: ``(1, 2, 2, 2)``.
+        dilations (Sequence[int]): Dilation of each stage.
+            Default: ``(1, 1, 1, 1)``.
+        out_indices (Sequence[int]): Output from which stages. If only one
+            stage is specified, a single tensor (feature map) is returned,
+            otherwise multiple stages are specified, a tuple of tensors will
+            be returned. Default: ``(3, )``.
+        style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
+            layer is the 3x3 conv layer, otherwise the stride-two layer is
+            the first 1x1 conv layer.
+        deep_stem (bool): Replace 7x7 conv in input stem with 3 3x3 conv.
+            Default: False.
+        avg_down (bool): Use AvgPool instead of stride conv when
+            downsampling in the bottleneck. Default: False.
+        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
+            -1 means not freezing any parameters. Default: -1.
+        conv_cfg (dict | None): The config dict for conv layers. Default: None.
+        norm_cfg (dict): The config dict for norm layers.
+        norm_eval (bool): Whether to set norm layers to eval mode, namely,
+            freeze running stats (mean and var). Note: Effect on Batch Norm
+            and its variants only. Default: False.
+        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
+            memory while slowing down the training speed. Default: False.
+        zero_init_residual (bool): Whether to use zero init for last norm layer
+            in resblocks to let them behave as identity. Default: True.
+    """
+
+    arch_settings = {
+        50: (Bottleneck, (3, 4, 6, 3)),
+        101: (Bottleneck, (3, 4, 23, 3)),
+        152: (Bottleneck, (3, 8, 36, 3)),
+        200: (Bottleneck, (3, 24, 36, 3)),
+        269: (Bottleneck, (3, 30, 48, 8))
+    }
+
+    def __init__(self,
+                 depth,
+                 groups=1,
+                 width_per_group=4,
+                 radix=2,
+                 reduction_factor=4,
+                 avg_down_stride=True,
+                 **kwargs):
+        self.groups = groups
+        self.width_per_group = width_per_group
+        self.radix = radix
+        self.reduction_factor = reduction_factor
+        self.avg_down_stride = avg_down_stride
+        super(ResNeSt, self).__init__(depth=depth, **kwargs)
+
+    def make_res_layer(self, **kwargs):
+        return ResLayer(
+            groups=self.groups,
+            width_per_group=self.width_per_group,
+            base_channels=self.base_channels,
+            radix=self.radix,
+            reduction_factor=self.reduction_factor,
+            avg_down_stride=self.avg_down_stride,
+            **kwargs)
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/resnet.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/resnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..d2259f92f5a0aa24322fe6630717517be95613f5
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/resnet.py
@@ -0,0 +1,676 @@
+import logging
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint as cp
+from mmcv.runner import load_state_dict
+from mmcv.cnn import (ConvModule, build_conv_layer, build_norm_layer,
+                      constant_init, kaiming_init)
+from mmcv.utils.parrots_wrapper import _BatchNorm
+
+from ..builder import BACKBONES
+from .base_backbone import BaseBackbone
+from .irse import ABSPool2d
+
+
+class BasicBlock(nn.Module):
+    """BasicBlock for ResNet.
+
+    Args:
+        in_channels (int): Input channels of this block.
+        out_channels (int): Output channels of this block.
+        expansion (int): The ratio of ``out_channels/mid_channels`` where
+            ``mid_channels`` is the output channels of conv1. This is a
+            reserved argument in BasicBlock and should always be 1. Default: 1.
+        stride (int): stride of the block. Default: 1
+        dilation (int): dilation of convolution. Default: 1
+        downsample (nn.Module): downsample operation on identity branch.
+            Default: None.
+        style (str): `pytorch` or `caffe`. It is unused and reserved for
+            unified API with Bottleneck.
+        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
+            memory while slowing down the training speed.
+        conv_cfg (dict): dictionary to construct and config conv layer.
+            Default: None
+        norm_cfg (dict): dictionary to construct and config norm layer.
+            Default: dict(type='BN')
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 expansion=1,
+                 stride=1,
+                 dilation=1,
+                 downsample=None,
+                 style='pytorch',
+                 with_cp=False,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN')):
+        super(BasicBlock, self).__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.expansion = expansion
+        assert self.expansion == 1
+        assert out_channels % expansion == 0
+        self.mid_channels = out_channels // expansion
+        self.stride = stride
+        self.dilation = dilation
+        self.style = style
+        self.with_cp = with_cp
+        self.conv_cfg = conv_cfg
+        self.norm_cfg = norm_cfg
+
+        self.norm1_name, norm1 = build_norm_layer(
+            norm_cfg, self.mid_channels, postfix=1)
+        self.norm2_name, norm2 = build_norm_layer(
+            norm_cfg, out_channels, postfix=2)
+
+        self.conv1 = build_conv_layer(
+            conv_cfg,
+            in_channels,
+            self.mid_channels,
+            3,
+            stride=stride,
+            padding=dilation,
+            dilation=dilation,
+            bias=False)
+        self.add_module(self.norm1_name, norm1)
+        self.conv2 = build_conv_layer(
+            conv_cfg,
+            self.mid_channels,
+            out_channels,
+            3,
+            padding=1,
+            bias=False)
+        self.add_module(self.norm2_name, norm2)
+
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+
+    @property
+    def norm1(self):
+        return getattr(self, self.norm1_name)
+
+    @property
+    def norm2(self):
+        return getattr(self, self.norm2_name)
+
+    def forward(self, x):
+
+        def _inner_forward(x):
+            identity = x
+
+            out = self.conv1(x)
+            out = self.norm1(out)
+            out = self.relu(out)
+
+            out = self.conv2(out)
+            out = self.norm2(out)
+
+            if self.downsample is not None:
+                identity = self.downsample(x)
+
+            out += identity
+
+            return out
+
+        if self.with_cp and x.requires_grad:
+            out = cp.checkpoint(_inner_forward, x)
+        else:
+            out = _inner_forward(x)
+
+        out = self.relu(out)
+
+        return out
+
+
+class Bottleneck(nn.Module):
+    """Bottleneck block for ResNet.
+
+    Args:
+        in_channels (int): Input channels of this block.
+        out_channels (int): Output channels of this block.
+        expansion (int): The ratio of ``out_channels/mid_channels`` where
+            ``mid_channels`` is the input/output channels of conv2. Default: 4.
+        stride (int): stride of the block. Default: 1
+        dilation (int): dilation of convolution. Default: 1
+        downsample (nn.Module): downsample operation on identity branch.
+            Default: None.
+        style (str): ``"pytorch"`` or ``"caffe"``. If set to "pytorch", the
+            stride-two layer is the 3x3 conv layer, otherwise the stride-two
+            layer is the first 1x1 conv layer. Default: "pytorch".
+        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
+            memory while slowing down the training speed.
+        conv_cfg (dict): dictionary to construct and config conv layer.
+            Default: None
+        norm_cfg (dict): dictionary to construct and config norm layer.
+            Default: dict(type='BN')
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 expansion=4,
+                 stride=1,
+                 dilation=1,
+                 downsample=None,
+                 style='pytorch',
+                 with_cp=False,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN')):
+        super(Bottleneck, self).__init__()
+        assert style in ['pytorch', 'caffe']
+
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.expansion = expansion
+        assert out_channels % expansion == 0
+        self.mid_channels = out_channels // expansion
+        self.stride = stride
+        self.dilation = dilation
+        self.style = style
+        self.with_cp = with_cp
+        self.conv_cfg = conv_cfg
+        self.norm_cfg = norm_cfg
+
+        if self.style == 'pytorch':
+            self.conv1_stride = 1
+            self.conv2_stride = stride
+        else:
+            self.conv1_stride = stride
+            self.conv2_stride = 1
+
+        self.norm1_name, norm1 = build_norm_layer(
+            norm_cfg, self.mid_channels, postfix=1)
+        self.norm2_name, norm2 = build_norm_layer(
+            norm_cfg, self.mid_channels, postfix=2)
+        self.norm3_name, norm3 = build_norm_layer(
+            norm_cfg, out_channels, postfix=3)
+
+        self.conv1 = build_conv_layer(
+            conv_cfg,
+            in_channels,
+            self.mid_channels,
+            kernel_size=1,
+            stride=self.conv1_stride,
+            bias=False)
+        self.add_module(self.norm1_name, norm1)
+        self.conv2 = build_conv_layer(
+            conv_cfg,
+            self.mid_channels,
+            self.mid_channels,
+            kernel_size=3,
+            stride=self.conv2_stride,
+            padding=dilation,
+            dilation=dilation,
+            bias=False)
+
+        self.add_module(self.norm2_name, norm2)
+        self.conv3 = build_conv_layer(
+            conv_cfg,
+            self.mid_channels,
+            out_channels,
+            kernel_size=1,
+            bias=False)
+        self.add_module(self.norm3_name, norm3)
+
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+
+    @property
+    def norm1(self):
+        return getattr(self, self.norm1_name)
+
+    @property
+    def norm2(self):
+        return getattr(self, self.norm2_name)
+
+    @property
+    def norm3(self):
+        return getattr(self, self.norm3_name)
+
+    def forward(self, x):
+
+        def _inner_forward(x):
+            identity = x
+
+            out = self.conv1(x)
+            out = self.norm1(out)
+            out = self.relu(out)
+
+            out = self.conv2(out)
+            out = self.norm2(out)
+            out = self.relu(out)
+
+            out = self.conv3(out)
+            out = self.norm3(out)
+
+            if self.downsample is not None:
+                identity = self.downsample(x)
+
+            out += identity
+
+            return out
+
+        if self.with_cp and x.requires_grad:
+            out = cp.checkpoint(_inner_forward, x)
+        else:
+            out = _inner_forward(x)
+
+        out = self.relu(out)
+
+        return out
+
+
+def get_expansion(block, expansion=None):
+    """Get the expansion of a residual block.
+
+    The block expansion will be obtained by the following order:
+
+    1. If ``expansion`` is given, just return it.
+    2. If ``block`` has the attribute ``expansion``, then return
+       ``block.expansion``.
+    3. Return the default value according the the block type:
+       1 for ``BasicBlock`` and 4 for ``Bottleneck``.
+
+    Args:
+        block (class): The block class.
+        expansion (int | None): The given expansion ratio.
+
+    Returns:
+        int: The expansion of the block.
+    """
+    if isinstance(expansion, int):
+        assert expansion > 0
+    elif expansion is None:
+        if hasattr(block, 'expansion'):
+            expansion = block.expansion
+        elif issubclass(block, BasicBlock):
+            expansion = 1
+        elif issubclass(block, Bottleneck):
+            expansion = 4
+        else:
+            raise TypeError(f'expansion is not specified for {block.__name__}')
+    else:
+        raise TypeError('expansion must be an integer or None')
+
+    return expansion
+
+
+class ResLayer(nn.Sequential):
+    """ResLayer to build ResNet style backbone.
+
+    Args:
+        block (nn.Module): Residual block used to build ResLayer.
+        num_blocks (int): Number of blocks.
+        in_channels (int): Input channels of this block.
+        out_channels (int): Output channels of this block.
+        expansion (int, optional): The expansion for BasicBlock/Bottleneck.
+            If not specified, it will firstly be obtained via
+            ``block.expansion``. If the block has no attribute "expansion",
+            the following default values will be used: 1 for BasicBlock and
+            4 for Bottleneck. Default: None.
+        stride (int): stride of the first block. Default: 1.
+        avg_down (bool): Use AvgPool instead of stride conv when
+            downsampling in the bottleneck. Default: False
+        conv_cfg (dict): dictionary to construct and config conv layer.
+            Default: None
+        norm_cfg (dict): dictionary to construct and config norm layer.
+            Default: dict(type='BN')
+    """
+
+    def __init__(self,
+                 block,
+                 num_blocks,
+                 in_channels,
+                 out_channels,
+                 expansion=None,
+                 stride=1,
+                 avg_down=False,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN'),
+                 **kwargs):
+        self.block = block
+        self.expansion = get_expansion(block, expansion)
+
+        downsample = None
+        if stride != 1 or in_channels != out_channels:
+            downsample = []
+            conv_stride = stride
+            if avg_down and stride != 1:
+                conv_stride = 1
+                downsample.append(
+                    nn.AvgPool2d(
+                        kernel_size=stride,
+                        stride=stride,
+                        ceil_mode=True,
+                        count_include_pad=False))
+            downsample.extend([
+                build_conv_layer(
+                    conv_cfg,
+                    in_channels,
+                    out_channels,
+                    kernel_size=1,
+                    stride=conv_stride,
+                    bias=False),
+                build_norm_layer(norm_cfg, out_channels)[1]
+            ])
+            downsample = nn.Sequential(*downsample)
+
+        layers = []
+        layers.append(
+            block(
+                in_channels=in_channels,
+                out_channels=out_channels,
+                expansion=self.expansion,
+                stride=stride,
+                downsample=downsample,
+                conv_cfg=conv_cfg,
+                norm_cfg=norm_cfg,
+                **kwargs))
+        in_channels = out_channels
+        for i in range(1, num_blocks):
+            layers.append(
+                block(
+                    in_channels=in_channels,
+                    out_channels=out_channels,
+                    expansion=self.expansion,
+                    stride=1,
+                    conv_cfg=conv_cfg,
+                    norm_cfg=norm_cfg,
+                    **kwargs))
+        super(ResLayer, self).__init__(*layers)
+
+
+@BACKBONES.register_module()
+class ResNet(BaseBackbone):
+    """ResNet backbone.
+
+    Please refer to the `paper <https://arxiv.org/abs/1512.03385>`_ for
+    details.
+
+    Args:
+        depth (int): Network depth, from {18, 34, 50, 101, 152}.
+        in_channels (int): Number of input image channels. Default: 3.
+        stem_channels (int): Output channels of the stem layer. Default: 64.
+        base_channels (int): Middle channels of the first stage. Default: 64.
+        num_stages (int): Stages of the network. Default: 4.
+        strides (Sequence[int]): Strides of the first block of each stage.
+            Default: ``(1, 2, 2, 2)``.
+        dilations (Sequence[int]): Dilation of each stage.
+            Default: ``(1, 1, 1, 1)``.
+        out_indices (Sequence[int]): Output from which stages. If only one
+            stage is specified, a single tensor (feature map) is returned,
+            otherwise multiple stages are specified, a tuple of tensors will
+            be returned. Default: ``(3, )``.
+        style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
+            layer is the 3x3 conv layer, otherwise the stride-two layer is
+            the first 1x1 conv layer.
+        deep_stem (bool): Replace 7x7 conv in input stem with 3 3x3 conv.
+            Default: False.
+        avg_down (bool): Use AvgPool instead of stride conv when
+            downsampling in the bottleneck. Default: False.
+        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
+            -1 means not freezing any parameters. Default: -1.
+        conv_cfg (dict | None): The config dict for conv layers. Default: None.
+        norm_cfg (dict): The config dict for norm layers.
+        norm_eval (bool): Whether to set norm layers to eval mode, namely,
+            freeze running stats (mean and var). Note: Effect on Batch Norm
+            and its variants only. Default: False.
+        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
+            memory while slowing down the training speed. Default: False.
+        zero_init_residual (bool): Whether to use zero init for last norm layer
+            in resblocks to let them behave as identity. Default: True.
+
+    Example:
+        >>> from mmcls.models import ResNet
+        >>> import torch
+        >>> self = ResNet(depth=18)
+        >>> self.eval()
+        >>> inputs = torch.rand(1, 3, 32, 32)
+        >>> level_outputs = self.forward(inputs)
+        >>> for level_out in level_outputs:
+        ...     print(tuple(level_out.shape))
+        (1, 64, 8, 8)
+        (1, 128, 4, 4)
+        (1, 256, 2, 2)
+        (1, 512, 1, 1)
+    """
+
+    arch_settings = {
+        18: (BasicBlock, (2, 2, 2, 2)),
+        34: (BasicBlock, (3, 4, 6, 3)),
+        50: (Bottleneck, (3, 4, 6, 3)),
+        101: (Bottleneck, (3, 4, 23, 3)),
+        152: (Bottleneck, (3, 8, 36, 3))
+    }
+
+    def __init__(self,
+                 depth,
+                 in_channels=3,
+                 stem_channels=64,
+                 base_channels=64,
+                 expansion=None,
+                 num_stages=4,
+                 strides=(1, 2, 2, 2),
+                 dilations=(1, 1, 1, 1),
+                 out_indices=(3, ),
+                 style='pytorch',
+                 deep_stem=False,
+                 avg_down=False,
+                 frozen_stages=-1,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN', requires_grad=True),
+                 norm_eval=False,
+                 with_cp=False,
+                 zero_init_residual=True,
+                 pretrained=None,
+                 ):
+        super(ResNet, self).__init__()
+        if depth not in self.arch_settings:
+            raise KeyError(f'invalid depth {depth} for resnet')
+        self.depth = depth
+        self.stem_channels = stem_channels
+        self.base_channels = base_channels
+        self.num_stages = num_stages
+        assert num_stages >= 1 and num_stages <= 4
+        self.strides = strides
+        self.dilations = dilations
+        assert len(strides) == len(dilations) == num_stages
+        self.out_indices = out_indices
+        assert max(out_indices) < num_stages
+        self.style = style
+        self.deep_stem = deep_stem
+        self.avg_down = avg_down
+        self.frozen_stages = frozen_stages
+        self.conv_cfg = conv_cfg
+        self.norm_cfg = norm_cfg
+        self.with_cp = with_cp
+        self.norm_eval = norm_eval
+        self.zero_init_residual = zero_init_residual
+        self.block, stage_blocks = self.arch_settings[depth]
+        self.stage_blocks = stage_blocks[:num_stages]
+        self.expansion = get_expansion(self.block, expansion)
+
+        self._make_stem_layer(in_channels, stem_channels)
+
+        self.res_layers = []
+        _in_channels = stem_channels
+        _out_channels = base_channels * self.expansion
+        for i, num_blocks in enumerate(self.stage_blocks):
+            stride = strides[i]
+            dilation = dilations[i]
+            res_layer = self.make_res_layer(
+                block=self.block,
+                num_blocks=num_blocks,
+                in_channels=_in_channels,
+                out_channels=_out_channels,
+                expansion=self.expansion,
+                stride=stride,
+                dilation=dilation,
+                style=self.style,
+                avg_down=self.avg_down,
+                with_cp=with_cp,
+                conv_cfg=conv_cfg,
+                norm_cfg=norm_cfg)
+            _in_channels = _out_channels
+            _out_channels *= 2
+            layer_name = f'layer{i + 1}'
+            self.add_module(layer_name, res_layer)
+            self.res_layers.append(layer_name)
+
+        self._freeze_stages()
+
+        self.feat_dim = res_layer[-1].out_channels
+        if pretrained:
+            self.init_weights(pretrained)
+
+    def init_weights(self, pretrained):
+        logger = logging.getLogger()
+        logger.warning(f'{self.__class__.__name__} load pretrain from {pretrained}')
+        state_dict = torch.load(pretrained, map_location='cpu')
+        if 'state_dict' in state_dict:
+            state_dict = state_dict['state_dict']
+        new_state_dict = dict()
+        for k, v in state_dict.items():
+            if k.startswith('backbone.'):
+                k = k[9:]
+                # new_state_dict[k] = v
+            if k.startswith('module.'):
+                k = k[7:]
+            new_state_dict[k] = v
+        load_state_dict(self, new_state_dict, strict=False, logger=logger)
+
+    def make_res_layer(self, **kwargs):
+        return ResLayer(**kwargs)
+
+    @property
+    def norm1(self):
+        return getattr(self, self.norm1_name)
+
+    def _make_stem_layer(self, in_channels, stem_channels):
+        if self.deep_stem:
+            self.stem = nn.Sequential(
+                ConvModule(
+                    in_channels,
+                    stem_channels // 2,
+                    kernel_size=3,
+                    stride=2,
+                    padding=1,
+                    conv_cfg=self.conv_cfg,
+                    norm_cfg=self.norm_cfg,
+                    inplace=True),
+                ConvModule(
+                    stem_channels // 2,
+                    stem_channels // 2,
+                    kernel_size=3,
+                    stride=1,
+                    padding=1,
+                    conv_cfg=self.conv_cfg,
+                    norm_cfg=self.norm_cfg,
+                    inplace=True),
+                ConvModule(
+                    stem_channels // 2,
+                    stem_channels,
+                    kernel_size=3,
+                    stride=1,
+                    padding=1,
+                    conv_cfg=self.conv_cfg,
+                    norm_cfg=self.norm_cfg,
+                    inplace=True))
+        else:
+            self.conv1 = build_conv_layer(
+                self.conv_cfg,
+                in_channels,
+                stem_channels,
+                kernel_size=7,
+                stride=2,
+                padding=3,
+                # kernel_size=3,
+                # stride=1,
+                # padding=1,
+                bias=False)
+            self.norm1_name, norm1 = build_norm_layer(
+                self.norm_cfg, stem_channels, postfix=1)
+            self.add_module(self.norm1_name, norm1)
+            self.relu = nn.ReLU(inplace=True)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        # self.maxpool = ABSPool2d(kernel_size=3, stride=2, padding=1)
+
+    def _freeze_stages(self):
+        if self.frozen_stages >= 0:
+            if self.deep_stem:
+                self.stem.eval()
+                for param in self.stem.parameters():
+                    param.requires_grad = False
+            else:
+                self.norm1.eval()
+                for m in [self.conv1, self.norm1]:
+                    for param in m.parameters():
+                        param.requires_grad = False
+
+        for i in range(1, self.frozen_stages + 1):
+            m = getattr(self, f'layer{i}')
+            m.eval()
+            for param in m.parameters():
+                param.requires_grad = False
+
+    # def init_weights(self, pretrained=None):
+    #     super(ResNet, self).init_weights(pretrained)
+    #     if pretrained is None:
+    #         for m in self.modules():
+    #             if isinstance(m, nn.Conv2d):
+    #                 kaiming_init(m)
+    #             elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
+    #                 constant_init(m, 1)
+
+    #         if self.zero_init_residual:
+    #             for m in self.modules():
+    #                 if isinstance(m, Bottleneck):
+    #                     constant_init(m.norm3, 0)
+    #                 elif isinstance(m, BasicBlock):
+    #                     constant_init(m.norm2, 0)
+
+    def forward(self, x):
+        if self.deep_stem:
+            x = self.stem(x)
+        else:
+            x = self.conv1(x)
+            x = self.norm1(x)
+            x = self.relu(x)
+        x = self.maxpool(x)
+        outs = []
+        for i, layer_name in enumerate(self.res_layers):
+            res_layer = getattr(self, layer_name)
+            x = res_layer(x)
+            if i in self.out_indices:
+                outs.append(x)
+        # if len(outs) == 1:
+        #     return outs[0]
+        # else:
+        return tuple(outs)
+
+    def train(self, mode=True):
+        super(ResNet, self).train(mode)
+        self._freeze_stages()
+        if mode and self.norm_eval:
+            for m in self.modules():
+                # trick: eval have effect on BatchNorm only
+                if isinstance(m, _BatchNorm):
+                    m.eval()
+
+
+@BACKBONES.register_module()
+class ResNetV1d(ResNet):
+    """ResNetV1d variant described in
+    `Bag of Tricks <https://arxiv.org/pdf/1812.01187.pdf>`_.
+
+    Compared with default ResNet(ResNetV1b), ResNetV1d replaces the 7x7 conv
+    in the input stem with three 3x3 convs. And in the downsampling block,
+    a 2x2 avg_pool with stride 2 is added before conv, whose stride is
+    changed to 1.
+    """
+
+    def __init__(self, **kwargs):
+        super(ResNetV1d, self).__init__(
+            deep_stem=True, avg_down=True, **kwargs)
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/resnet_cifar.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/resnet_cifar.py
new file mode 100644
index 0000000000000000000000000000000000000000..d07599405675facccd823056326c76c0379c08d1
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/resnet_cifar.py
@@ -0,0 +1,83 @@
+import torch.nn as nn
+from mmcv.cnn import build_conv_layer, build_norm_layer
+
+from ..builder import BACKBONES
+from .resnet import ResNet
+
+
+@BACKBONES.register_module()
+class ResNet_CIFAR(ResNet):
+    """ResNet backbone for CIFAR.
+
+    Compared to standard ResNet, it uses `kernel_size=3` and `stride=1` in
+    conv1, and does not apply MaxPoolinng after stem. It has been proven to
+    be more efficient than standard ResNet in other public codebase, e.g.,
+    `https://github.com/kuangliu/pytorch-cifar/blob/master/models/resnet.py`.
+
+    Args:
+        depth (int): Network depth, from {18, 34, 50, 101, 152}.
+        in_channels (int): Number of input image channels. Default: 3.
+        stem_channels (int): Output channels of the stem layer. Default: 64.
+        base_channels (int): Middle channels of the first stage. Default: 64.
+        num_stages (int): Stages of the network. Default: 4.
+        strides (Sequence[int]): Strides of the first block of each stage.
+            Default: ``(1, 2, 2, 2)``.
+        dilations (Sequence[int]): Dilation of each stage.
+            Default: ``(1, 1, 1, 1)``.
+        out_indices (Sequence[int]): Output from which stages. If only one
+            stage is specified, a single tensor (feature map) is returned,
+            otherwise multiple stages are specified, a tuple of tensors will
+            be returned. Default: ``(3, )``.
+        style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
+            layer is the 3x3 conv layer, otherwise the stride-two layer is
+            the first 1x1 conv layer.
+        deep_stem (bool): This network has specific designed stem, thus it is
+            asserted to be False.
+        avg_down (bool): Use AvgPool instead of stride conv when
+            downsampling in the bottleneck. Default: False.
+        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
+            -1 means not freezing any parameters. Default: -1.
+        conv_cfg (dict | None): The config dict for conv layers. Default: None.
+        norm_cfg (dict): The config dict for norm layers.
+        norm_eval (bool): Whether to set norm layers to eval mode, namely,
+            freeze running stats (mean and var). Note: Effect on Batch Norm
+            and its variants only. Default: False.
+        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
+            memory while slowing down the training speed. Default: False.
+        zero_init_residual (bool): Whether to use zero init for last norm layer
+            in resblocks to let them behave as identity. Default: True.
+    """
+
+    def __init__(self, depth, deep_stem=False, **kwargs):
+        super(ResNet_CIFAR, self).__init__(
+            depth, deep_stem=deep_stem, **kwargs)
+        assert not self.deep_stem, 'ResNet_CIFAR do not support deep_stem'
+
+    def _make_stem_layer(self, in_channels, base_channels):
+        self.conv1 = build_conv_layer(
+            self.conv_cfg,
+            in_channels,
+            base_channels,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            bias=False)
+        self.norm1_name, norm1 = build_norm_layer(
+            self.norm_cfg, base_channels, postfix=1)
+        self.add_module(self.norm1_name, norm1)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.norm1(x)
+        x = self.relu(x)
+        outs = []
+        for i, layer_name in enumerate(self.res_layers):
+            res_layer = getattr(self, layer_name)
+            x = res_layer(x)
+            if i in self.out_indices:
+                outs.append(x)
+        if len(outs) == 1:
+            return outs[0]
+        else:
+            return tuple(outs)
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/resnext.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/resnext.py
new file mode 100644
index 0000000000000000000000000000000000000000..afd28f376464511d85c6f951143d304a89e4893f
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/resnext.py
@@ -0,0 +1,147 @@
+from mmcv.cnn import build_conv_layer, build_norm_layer
+
+from ..builder import BACKBONES
+from .resnet import Bottleneck as _Bottleneck
+from .resnet import ResLayer, ResNet
+
+
+class Bottleneck(_Bottleneck):
+    """Bottleneck block for ResNeXt.
+
+    Args:
+        in_channels (int): Input channels of this block.
+        out_channels (int): Output channels of this block.
+        groups (int): Groups of conv2.
+        width_per_group (int): Width per group of conv2. 64x4d indicates
+            ``groups=64, width_per_group=4`` and 32x8d indicates
+            ``groups=32, width_per_group=8``.
+        stride (int): stride of the block. Default: 1
+        dilation (int): dilation of convolution. Default: 1
+        downsample (nn.Module): downsample operation on identity branch.
+            Default: None
+        style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
+            layer is the 3x3 conv layer, otherwise the stride-two layer is
+            the first 1x1 conv layer.
+        conv_cfg (dict): dictionary to construct and config conv layer.
+            Default: None
+        norm_cfg (dict): dictionary to construct and config norm layer.
+            Default: dict(type='BN')
+        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
+            memory while slowing down the training speed.
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 base_channels=64,
+                 groups=32,
+                 width_per_group=4,
+                 **kwargs):
+        super(Bottleneck, self).__init__(in_channels, out_channels, **kwargs)
+        self.groups = groups
+        self.width_per_group = width_per_group
+
+        # For ResNet bottleneck, middle channels are determined by expansion
+        # and out_channels, but for ResNeXt bottleneck, it is determined by
+        # groups and width_per_group and the stage it is located in.
+        if groups != 1:
+            assert self.mid_channels % base_channels == 0
+            self.mid_channels = (
+                groups * width_per_group * self.mid_channels // base_channels)
+
+        self.norm1_name, norm1 = build_norm_layer(
+            self.norm_cfg, self.mid_channels, postfix=1)
+        self.norm2_name, norm2 = build_norm_layer(
+            self.norm_cfg, self.mid_channels, postfix=2)
+        self.norm3_name, norm3 = build_norm_layer(
+            self.norm_cfg, self.out_channels, postfix=3)
+
+        self.conv1 = build_conv_layer(
+            self.conv_cfg,
+            self.in_channels,
+            self.mid_channels,
+            kernel_size=1,
+            stride=self.conv1_stride,
+            bias=False)
+        self.add_module(self.norm1_name, norm1)
+        self.conv2 = build_conv_layer(
+            self.conv_cfg,
+            self.mid_channels,
+            self.mid_channels,
+            kernel_size=3,
+            stride=self.conv2_stride,
+            padding=self.dilation,
+            dilation=self.dilation,
+            groups=groups,
+            bias=False)
+
+        self.add_module(self.norm2_name, norm2)
+        self.conv3 = build_conv_layer(
+            self.conv_cfg,
+            self.mid_channels,
+            self.out_channels,
+            kernel_size=1,
+            bias=False)
+        self.add_module(self.norm3_name, norm3)
+
+
+@BACKBONES.register_module()
+class ResNeXt(ResNet):
+    """ResNeXt backbone.
+
+    Please refer to the `paper <https://arxiv.org/abs/1611.05431>`_ for
+    details.
+
+    Args:
+        depth (int): Network depth, from {50, 101, 152}.
+        groups (int): Groups of conv2 in Bottleneck. Default: 32.
+        width_per_group (int): Width per group of conv2 in Bottleneck.
+            Default: 4.
+        in_channels (int): Number of input image channels. Default: 3.
+        stem_channels (int): Output channels of the stem layer. Default: 64.
+        num_stages (int): Stages of the network. Default: 4.
+        strides (Sequence[int]): Strides of the first block of each stage.
+            Default: ``(1, 2, 2, 2)``.
+        dilations (Sequence[int]): Dilation of each stage.
+            Default: ``(1, 1, 1, 1)``.
+        out_indices (Sequence[int]): Output from which stages. If only one
+            stage is specified, a single tensor (feature map) is returned,
+            otherwise multiple stages are specified, a tuple of tensors will
+            be returned. Default: ``(3, )``.
+        style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
+            layer is the 3x3 conv layer, otherwise the stride-two layer is
+            the first 1x1 conv layer.
+        deep_stem (bool): Replace 7x7 conv in input stem with 3 3x3 conv.
+            Default: False.
+        avg_down (bool): Use AvgPool instead of stride conv when
+            downsampling in the bottleneck. Default: False.
+        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
+            -1 means not freezing any parameters. Default: -1.
+        conv_cfg (dict | None): The config dict for conv layers. Default: None.
+        norm_cfg (dict): The config dict for norm layers.
+        norm_eval (bool): Whether to set norm layers to eval mode, namely,
+            freeze running stats (mean and var). Note: Effect on Batch Norm
+            and its variants only. Default: False.
+        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
+            memory while slowing down the training speed. Default: False.
+        zero_init_residual (bool): Whether to use zero init for last norm layer
+            in resblocks to let them behave as identity. Default: True.
+    """
+
+    arch_settings = {
+        50: (Bottleneck, (3, 4, 6, 3)),
+        101: (Bottleneck, (3, 4, 23, 3)),
+        152: (Bottleneck, (3, 8, 36, 3))
+    }
+
+    def __init__(self, depth, groups=32, width_per_group=4, **kwargs):
+        self.groups = groups
+        self.width_per_group = width_per_group
+        super(ResNeXt, self).__init__(depth, **kwargs)
+
+    def make_res_layer(self, **kwargs):
+        return ResLayer(
+            groups=self.groups,
+            width_per_group=self.width_per_group,
+            base_channels=self.base_channels,
+            **kwargs)
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/seresnet.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/seresnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..862698f17f94cea383bf4ed0b19aa1e2a0729f40
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/seresnet.py
@@ -0,0 +1,124 @@
+import torch.utils.checkpoint as cp
+
+from ..builder import BACKBONES
+from ..utils.se_layer import SELayer
+from .resnet import Bottleneck, ResLayer, ResNet
+
+
+class SEBottleneck(Bottleneck):
+    """SEBottleneck block for SEResNet.
+
+    Args:
+        in_channels (int): The input channels of the SEBottleneck block.
+        out_channels (int): The output channel of the SEBottleneck block.
+        se_ratio (int): Squeeze ratio in SELayer. Default: 16
+    """
+
+    def __init__(self, in_channels, out_channels, se_ratio=16, **kwargs):
+        super(SEBottleneck, self).__init__(in_channels, out_channels, **kwargs)
+        self.se_layer = SELayer(out_channels, ratio=se_ratio)
+
+    def forward(self, x):
+
+        def _inner_forward(x):
+            identity = x
+
+            out = self.conv1(x)
+            out = self.norm1(out)
+            out = self.relu(out)
+
+            out = self.conv2(out)
+            out = self.norm2(out)
+            out = self.relu(out)
+
+            out = self.conv3(out)
+            out = self.norm3(out)
+
+            out = self.se_layer(out)
+
+            if self.downsample is not None:
+                identity = self.downsample(x)
+
+            out += identity
+
+            return out
+
+        if self.with_cp and x.requires_grad:
+            out = cp.checkpoint(_inner_forward, x)
+        else:
+            out = _inner_forward(x)
+
+        out = self.relu(out)
+
+        return out
+
+
+@BACKBONES.register_module()
+class SEResNet(ResNet):
+    """SEResNet backbone.
+
+    Please refer to the `paper <https://arxiv.org/abs/1709.01507>`_ for
+    details.
+
+    Args:
+        depth (int): Network depth, from {50, 101, 152}.
+        se_ratio (int): Squeeze ratio in SELayer. Default: 16.
+        in_channels (int): Number of input image channels. Default: 3.
+        stem_channels (int): Output channels of the stem layer. Default: 64.
+        num_stages (int): Stages of the network. Default: 4.
+        strides (Sequence[int]): Strides of the first block of each stage.
+            Default: ``(1, 2, 2, 2)``.
+        dilations (Sequence[int]): Dilation of each stage.
+            Default: ``(1, 1, 1, 1)``.
+        out_indices (Sequence[int]): Output from which stages. If only one
+            stage is specified, a single tensor (feature map) is returned,
+            otherwise multiple stages are specified, a tuple of tensors will
+            be returned. Default: ``(3, )``.
+        style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
+            layer is the 3x3 conv layer, otherwise the stride-two layer is
+            the first 1x1 conv layer.
+        deep_stem (bool): Replace 7x7 conv in input stem with 3 3x3 conv.
+            Default: False.
+        avg_down (bool): Use AvgPool instead of stride conv when
+            downsampling in the bottleneck. Default: False.
+        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
+            -1 means not freezing any parameters. Default: -1.
+        conv_cfg (dict | None): The config dict for conv layers. Default: None.
+        norm_cfg (dict): The config dict for norm layers.
+        norm_eval (bool): Whether to set norm layers to eval mode, namely,
+            freeze running stats (mean and var). Note: Effect on Batch Norm
+            and its variants only. Default: False.
+        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
+            memory while slowing down the training speed. Default: False.
+        zero_init_residual (bool): Whether to use zero init for last norm layer
+            in resblocks to let them behave as identity. Default: True.
+
+    Example:
+        >>> from mmcls.models import SEResNet
+        >>> import torch
+        >>> self = SEResNet(depth=50)
+        >>> self.eval()
+        >>> inputs = torch.rand(1, 3, 224, 224)
+        >>> level_outputs = self.forward(inputs)
+        >>> for level_out in level_outputs:
+        ...     print(tuple(level_out.shape))
+        (1, 64, 56, 56)
+        (1, 128, 28, 28)
+        (1, 256, 14, 14)
+        (1, 512, 7, 7)
+    """
+
+    arch_settings = {
+        50: (SEBottleneck, (3, 4, 6, 3)),
+        101: (SEBottleneck, (3, 4, 23, 3)),
+        152: (SEBottleneck, (3, 8, 36, 3))
+    }
+
+    def __init__(self, depth, se_ratio=16, **kwargs):
+        if depth not in self.arch_settings:
+            raise KeyError(f'invalid depth {depth} for SEResNet')
+        self.se_ratio = se_ratio
+        super(SEResNet, self).__init__(depth, **kwargs)
+
+    def make_res_layer(self, **kwargs):
+        return ResLayer(se_ratio=self.se_ratio, **kwargs)
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/seresnext.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/seresnext.py
new file mode 100644
index 0000000000000000000000000000000000000000..3ff1686182f9ffa17714ad26894adfa93efca8c3
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/seresnext.py
@@ -0,0 +1,154 @@
+from mmcv.cnn import build_conv_layer, build_norm_layer
+
+from ..builder import BACKBONES
+from .resnet import ResLayer
+from .seresnet import SEBottleneck as _SEBottleneck
+from .seresnet import SEResNet
+
+
+class SEBottleneck(_SEBottleneck):
+    """SEBottleneck block for SEResNeXt.
+
+    Args:
+        in_channels (int): Input channels of this block.
+        out_channels (int): Output channels of this block.
+        base_channels (int): Middle channels of the first stage. Default: 64.
+        groups (int): Groups of conv2.
+        width_per_group (int): Width per group of conv2. 64x4d indicates
+            ``groups=64, width_per_group=4`` and 32x8d indicates
+            ``groups=32, width_per_group=8``.
+        stride (int): stride of the block. Default: 1
+        dilation (int): dilation of convolution. Default: 1
+        downsample (nn.Module): downsample operation on identity branch.
+            Default: None
+        se_ratio (int): Squeeze ratio in SELayer. Default: 16
+        style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
+            layer is the 3x3 conv layer, otherwise the stride-two layer is
+            the first 1x1 conv layer.
+        conv_cfg (dict): dictionary to construct and config conv layer.
+            Default: None
+        norm_cfg (dict): dictionary to construct and config norm layer.
+            Default: dict(type='BN')
+        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
+            memory while slowing down the training speed.
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 base_channels=64,
+                 groups=32,
+                 width_per_group=4,
+                 se_ratio=16,
+                 **kwargs):
+        super(SEBottleneck, self).__init__(in_channels, out_channels, se_ratio,
+                                           **kwargs)
+        self.groups = groups
+        self.width_per_group = width_per_group
+
+        # We follow the same rational of ResNext to compute mid_channels.
+        # For SEResNet bottleneck, middle channels are determined by expansion
+        # and out_channels, but for SEResNeXt bottleneck, it is determined by
+        # groups and width_per_group and the stage it is located in.
+        if groups != 1:
+            assert self.mid_channels % base_channels == 0
+            self.mid_channels = (
+                groups * width_per_group * self.mid_channels // base_channels)
+
+        self.norm1_name, norm1 = build_norm_layer(
+            self.norm_cfg, self.mid_channels, postfix=1)
+        self.norm2_name, norm2 = build_norm_layer(
+            self.norm_cfg, self.mid_channels, postfix=2)
+        self.norm3_name, norm3 = build_norm_layer(
+            self.norm_cfg, self.out_channels, postfix=3)
+
+        self.conv1 = build_conv_layer(
+            self.conv_cfg,
+            self.in_channels,
+            self.mid_channels,
+            kernel_size=1,
+            stride=self.conv1_stride,
+            bias=False)
+        self.add_module(self.norm1_name, norm1)
+        self.conv2 = build_conv_layer(
+            self.conv_cfg,
+            self.mid_channels,
+            self.mid_channels,
+            kernel_size=3,
+            stride=self.conv2_stride,
+            padding=self.dilation,
+            dilation=self.dilation,
+            groups=groups,
+            bias=False)
+
+        self.add_module(self.norm2_name, norm2)
+        self.conv3 = build_conv_layer(
+            self.conv_cfg,
+            self.mid_channels,
+            self.out_channels,
+            kernel_size=1,
+            bias=False)
+        self.add_module(self.norm3_name, norm3)
+
+
+@BACKBONES.register_module()
+class SEResNeXt(SEResNet):
+    """SEResNeXt backbone.
+
+    Please refer to the `paper <https://arxiv.org/abs/1709.01507>`_ for
+    details.
+
+    Args:
+        depth (int): Network depth, from {50, 101, 152}.
+        groups (int): Groups of conv2 in Bottleneck. Default: 32.
+        width_per_group (int): Width per group of conv2 in Bottleneck.
+            Default: 4.
+        se_ratio (int): Squeeze ratio in SELayer. Default: 16.
+        in_channels (int): Number of input image channels. Default: 3.
+        stem_channels (int): Output channels of the stem layer. Default: 64.
+        num_stages (int): Stages of the network. Default: 4.
+        strides (Sequence[int]): Strides of the first block of each stage.
+            Default: ``(1, 2, 2, 2)``.
+        dilations (Sequence[int]): Dilation of each stage.
+            Default: ``(1, 1, 1, 1)``.
+        out_indices (Sequence[int]): Output from which stages. If only one
+            stage is specified, a single tensor (feature map) is returned,
+            otherwise multiple stages are specified, a tuple of tensors will
+            be returned. Default: ``(3, )``.
+        style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
+            layer is the 3x3 conv layer, otherwise the stride-two layer is
+            the first 1x1 conv layer.
+        deep_stem (bool): Replace 7x7 conv in input stem with 3 3x3 conv.
+            Default: False.
+        avg_down (bool): Use AvgPool instead of stride conv when
+            downsampling in the bottleneck. Default: False.
+        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
+            -1 means not freezing any parameters. Default: -1.
+        conv_cfg (dict | None): The config dict for conv layers. Default: None.
+        norm_cfg (dict): The config dict for norm layers.
+        norm_eval (bool): Whether to set norm layers to eval mode, namely,
+            freeze running stats (mean and var). Note: Effect on Batch Norm
+            and its variants only. Default: False.
+        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
+            memory while slowing down the training speed. Default: False.
+        zero_init_residual (bool): Whether to use zero init for last norm layer
+            in resblocks to let them behave as identity. Default: True.
+    """
+
+    arch_settings = {
+        50: (SEBottleneck, (3, 4, 6, 3)),
+        101: (SEBottleneck, (3, 4, 23, 3)),
+        152: (SEBottleneck, (3, 8, 36, 3))
+    }
+
+    def __init__(self, depth, groups=32, width_per_group=4, **kwargs):
+        self.groups = groups
+        self.width_per_group = width_per_group
+        super(SEResNeXt, self).__init__(depth, **kwargs)
+
+    def make_res_layer(self, **kwargs):
+        return ResLayer(
+            groups=self.groups,
+            width_per_group=self.width_per_group,
+            base_channels=self.base_channels,
+            **kwargs)
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/shufflenet_v1.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/shufflenet_v1.py
new file mode 100644
index 0000000000000000000000000000000000000000..aafc20d851f5d8d3e658e15cf33db77cb7e263bf
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/shufflenet_v1.py
@@ -0,0 +1,323 @@
+import logging
+
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint as cp
+from mmcv.cnn import (ConvModule, build_activation_layer, constant_init,
+                      normal_init)
+from mmcv.runner import load_checkpoint
+from torch.nn.modules.batchnorm import _BatchNorm
+
+from mmcls.models.utils import channel_shuffle, make_divisible
+from ..builder import BACKBONES
+from .base_backbone import BaseBackbone
+
+
+class ShuffleUnit(nn.Module):
+    """ShuffleUnit block.
+
+    ShuffleNet unit with pointwise group convolution (GConv) and channel
+    shuffle.
+
+    Args:
+        in_channels (int): The input channels of the ShuffleUnit.
+        out_channels (int): The output channels of the ShuffleUnit.
+        groups (int, optional): The number of groups to be used in grouped 1x1
+            convolutions in each ShuffleUnit. Default: 3
+        first_block (bool, optional): Whether it is the first ShuffleUnit of a
+            sequential ShuffleUnits. Default: False, which means not using the
+            grouped 1x1 convolution.
+        combine (str, optional): The ways to combine the input and output
+            branches. Default: 'add'.
+        conv_cfg (dict): Config dict for convolution layer. Default: None,
+            which means using conv2d.
+        norm_cfg (dict): Config dict for normalization layer.
+            Default: dict(type='BN').
+        act_cfg (dict): Config dict for activation layer.
+            Default: dict(type='ReLU').
+        with_cp (bool, optional): Use checkpoint or not. Using checkpoint
+            will save some memory while slowing down the training speed.
+            Default: False.
+
+    Returns:
+        Tensor: The output tensor.
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 groups=3,
+                 first_block=True,
+                 combine='add',
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN'),
+                 act_cfg=dict(type='ReLU'),
+                 with_cp=False):
+        super(ShuffleUnit, self).__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.first_block = first_block
+        self.combine = combine
+        self.groups = groups
+        self.bottleneck_channels = self.out_channels // 4
+        self.with_cp = with_cp
+
+        if self.combine == 'add':
+            self.depthwise_stride = 1
+            self._combine_func = self._add
+            assert in_channels == out_channels, (
+                'in_channels must be equal to out_channels when combine '
+                'is add')
+        elif self.combine == 'concat':
+            self.depthwise_stride = 2
+            self._combine_func = self._concat
+            self.out_channels -= self.in_channels
+            self.avgpool = nn.AvgPool2d(kernel_size=3, stride=2, padding=1)
+        else:
+            raise ValueError(f'Cannot combine tensors with {self.combine}. '
+                             'Only "add" and "concat" are supported')
+
+        self.first_1x1_groups = 1 if first_block else self.groups
+        self.g_conv_1x1_compress = ConvModule(
+            in_channels=self.in_channels,
+            out_channels=self.bottleneck_channels,
+            kernel_size=1,
+            groups=self.first_1x1_groups,
+            conv_cfg=conv_cfg,
+            norm_cfg=norm_cfg,
+            act_cfg=act_cfg)
+
+        self.depthwise_conv3x3_bn = ConvModule(
+            in_channels=self.bottleneck_channels,
+            out_channels=self.bottleneck_channels,
+            kernel_size=3,
+            stride=self.depthwise_stride,
+            padding=1,
+            groups=self.bottleneck_channels,
+            conv_cfg=conv_cfg,
+            norm_cfg=norm_cfg,
+            act_cfg=None)
+
+        self.g_conv_1x1_expand = ConvModule(
+            in_channels=self.bottleneck_channels,
+            out_channels=self.out_channels,
+            kernel_size=1,
+            groups=self.groups,
+            conv_cfg=conv_cfg,
+            norm_cfg=norm_cfg,
+            act_cfg=None)
+
+        self.act = build_activation_layer(act_cfg)
+
+    @staticmethod
+    def _add(x, out):
+        # residual connection
+        return x + out
+
+    @staticmethod
+    def _concat(x, out):
+        # concatenate along channel axis
+        return torch.cat((x, out), 1)
+
+    def forward(self, x):
+
+        def _inner_forward(x):
+            residual = x
+
+            out = self.g_conv_1x1_compress(x)
+            out = self.depthwise_conv3x3_bn(out)
+
+            if self.groups > 1:
+                out = channel_shuffle(out, self.groups)
+
+            out = self.g_conv_1x1_expand(out)
+
+            if self.combine == 'concat':
+                residual = self.avgpool(residual)
+                out = self.act(out)
+                out = self._combine_func(residual, out)
+            else:
+                out = self._combine_func(residual, out)
+                out = self.act(out)
+            return out
+
+        if self.with_cp and x.requires_grad:
+            out = cp.checkpoint(_inner_forward, x)
+        else:
+            out = _inner_forward(x)
+
+        return out
+
+
+@BACKBONES.register_module()
+class ShuffleNetV1(BaseBackbone):
+    """ShuffleNetV1 backbone.
+
+    Args:
+        groups (int, optional): The number of groups to be used in grouped 1x1
+            convolutions in each ShuffleUnit. Default: 3.
+        widen_factor (float, optional): Width multiplier - adjusts the number
+            of channels in each layer by this amount. Default: 1.0.
+        out_indices (Sequence[int]): Output from which stages.
+            Default: (2, )
+        frozen_stages (int): Stages to be frozen (all param fixed).
+            Default: -1, which means not freezing any parameters.
+        conv_cfg (dict): Config dict for convolution layer. Default: None,
+            which means using conv2d.
+        norm_cfg (dict): Config dict for normalization layer.
+            Default: dict(type='BN').
+        act_cfg (dict): Config dict for activation layer.
+            Default: dict(type='ReLU').
+        norm_eval (bool): Whether to set norm layers to eval mode, namely,
+            freeze running stats (mean and var). Note: Effect on Batch Norm
+            and its variants only. Default: False.
+        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
+            memory while slowing down the training speed. Default: False.
+    """
+
+    def __init__(self,
+                 groups=3,
+                 widen_factor=1.0,
+                 out_indices=(2, ),
+                 frozen_stages=-1,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN'),
+                 act_cfg=dict(type='ReLU'),
+                 norm_eval=False,
+                 with_cp=False):
+        super(ShuffleNetV1, self).__init__()
+        self.stage_blocks = [4, 8, 4]
+        self.groups = groups
+
+        for index in out_indices:
+            if index not in range(0, 3):
+                raise ValueError('the item in out_indices must in '
+                                 f'range(0, 3). But received {index}')
+
+        if frozen_stages not in range(-1, 3):
+            raise ValueError('frozen_stages must be in range(-1, 3). '
+                             f'But received {frozen_stages}')
+        self.out_indices = out_indices
+        self.frozen_stages = frozen_stages
+        self.conv_cfg = conv_cfg
+        self.norm_cfg = norm_cfg
+        self.act_cfg = act_cfg
+        self.norm_eval = norm_eval
+        self.with_cp = with_cp
+
+        if groups == 1:
+            channels = (144, 288, 576)
+        elif groups == 2:
+            channels = (200, 400, 800)
+        elif groups == 3:
+            channels = (240, 480, 960)
+        elif groups == 4:
+            channels = (272, 544, 1088)
+        elif groups == 8:
+            channels = (384, 768, 1536)
+        else:
+            raise ValueError(f'{groups} groups is not supported for 1x1 '
+                             'Grouped Convolutions')
+
+        channels = [make_divisible(ch * widen_factor, 8) for ch in channels]
+
+        self.in_channels = int(24 * widen_factor)
+
+        self.conv1 = ConvModule(
+            in_channels=3,
+            out_channels=self.in_channels,
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            conv_cfg=conv_cfg,
+            norm_cfg=norm_cfg,
+            act_cfg=act_cfg)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+
+        self.layers = nn.ModuleList()
+        for i, num_blocks in enumerate(self.stage_blocks):
+            first_block = True if i == 0 else False
+            layer = self.make_layer(channels[i], num_blocks, first_block)
+            self.layers.append(layer)
+
+    def _freeze_stages(self):
+        if self.frozen_stages >= 0:
+            for param in self.conv1.parameters():
+                param.requires_grad = False
+        for i in range(self.frozen_stages):
+            layer = self.layers[i]
+            layer.eval()
+            for param in layer.parameters():
+                param.requires_grad = False
+
+    def init_weights(self, pretrained=None):
+        if isinstance(pretrained, str):
+            logger = logging.getLogger()
+            load_checkpoint(self, pretrained, strict=False, logger=logger)
+        elif pretrained is None:
+            for name, m in self.named_modules():
+                if isinstance(m, nn.Conv2d):
+                    if 'conv1' in name:
+                        normal_init(m, mean=0, std=0.01)
+                    else:
+                        normal_init(m, mean=0, std=1.0 / m.weight.shape[1])
+                elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
+                    constant_init(m.weight, val=1, bias=0.0001)
+                    if isinstance(m, _BatchNorm):
+                        if m.running_mean is not None:
+                            nn.init.constant_(m.running_mean, 0)
+        else:
+            raise TypeError('pretrained must be a str or None. But received '
+                            f'{type(pretrained)}')
+
+    def make_layer(self, out_channels, num_blocks, first_block=False):
+        """ Stack ShuffleUnit blocks to make a layer.
+
+        Args:
+            out_channels (int): out_channels of the block.
+            num_blocks (int): Number of blocks.
+            first_block (bool, optional): Whether is the first ShuffleUnit of a
+                sequential ShuffleUnits. Default: False, which means not using
+                the grouped 1x1 convolution.
+        """
+        layers = []
+        for i in range(num_blocks):
+            first_block = first_block if i == 0 else False
+            combine_mode = 'concat' if i == 0 else 'add'
+            layers.append(
+                ShuffleUnit(
+                    self.in_channels,
+                    out_channels,
+                    groups=self.groups,
+                    first_block=first_block,
+                    combine=combine_mode,
+                    conv_cfg=self.conv_cfg,
+                    norm_cfg=self.norm_cfg,
+                    act_cfg=self.act_cfg,
+                    with_cp=self.with_cp))
+            self.in_channels = out_channels
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.maxpool(x)
+
+        outs = []
+        for i, layer in enumerate(self.layers):
+            x = layer(x)
+            if i in self.out_indices:
+                outs.append(x)
+
+        if len(outs) == 1:
+            return outs[0]
+        else:
+            return tuple(outs)
+
+    def train(self, mode=True):
+        super(ShuffleNetV1, self).train(mode)
+        self._freeze_stages()
+        if mode and self.norm_eval:
+            for m in self.modules():
+                if isinstance(m, _BatchNorm):
+                    m.eval()
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/shufflenet_v2.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/shufflenet_v2.py
new file mode 100644
index 0000000000000000000000000000000000000000..367c69f9f9b2587f8fbffcbe73b5235052fc9c43
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/shufflenet_v2.py
@@ -0,0 +1,299 @@
+import logging
+
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint as cp
+from mmcv.cnn import ConvModule, constant_init, normal_init
+from mmcv.runner import load_checkpoint
+from torch.nn.modules.batchnorm import _BatchNorm
+
+from mmcls.models.utils import channel_shuffle
+from ..builder import BACKBONES
+from .base_backbone import BaseBackbone
+
+
+class InvertedResidual(nn.Module):
+    """InvertedResidual block for ShuffleNetV2 backbone.
+
+    Args:
+        in_channels (int): The input channels of the block.
+        out_channels (int): The output channels of the block.
+        stride (int): Stride of the 3x3 convolution layer. Default: 1
+        conv_cfg (dict): Config dict for convolution layer.
+            Default: None, which means using conv2d.
+        norm_cfg (dict): Config dict for normalization layer.
+            Default: dict(type='BN').
+        act_cfg (dict): Config dict for activation layer.
+            Default: dict(type='ReLU').
+        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
+            memory while slowing down the training speed. Default: False.
+
+    Returns:
+        Tensor: The output tensor.
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 stride=1,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN'),
+                 act_cfg=dict(type='ReLU'),
+                 with_cp=False):
+        super(InvertedResidual, self).__init__()
+        self.stride = stride
+        self.with_cp = with_cp
+
+        branch_features = out_channels // 2
+        if self.stride == 1:
+            assert in_channels == branch_features * 2, (
+                f'in_channels ({in_channels}) should equal to '
+                f'branch_features * 2 ({branch_features * 2}) '
+                'when stride is 1')
+
+        if in_channels != branch_features * 2:
+            assert self.stride != 1, (
+                f'stride ({self.stride}) should not equal 1 when '
+                f'in_channels != branch_features * 2')
+
+        if self.stride > 1:
+            self.branch1 = nn.Sequential(
+                ConvModule(
+                    in_channels,
+                    in_channels,
+                    kernel_size=3,
+                    stride=self.stride,
+                    padding=1,
+                    groups=in_channels,
+                    conv_cfg=conv_cfg,
+                    norm_cfg=norm_cfg,
+                    act_cfg=None),
+                ConvModule(
+                    in_channels,
+                    branch_features,
+                    kernel_size=1,
+                    stride=1,
+                    padding=0,
+                    conv_cfg=conv_cfg,
+                    norm_cfg=norm_cfg,
+                    act_cfg=act_cfg),
+            )
+
+        self.branch2 = nn.Sequential(
+            ConvModule(
+                in_channels if (self.stride > 1) else branch_features,
+                branch_features,
+                kernel_size=1,
+                stride=1,
+                padding=0,
+                conv_cfg=conv_cfg,
+                norm_cfg=norm_cfg,
+                act_cfg=act_cfg),
+            ConvModule(
+                branch_features,
+                branch_features,
+                kernel_size=3,
+                stride=self.stride,
+                padding=1,
+                groups=branch_features,
+                conv_cfg=conv_cfg,
+                norm_cfg=norm_cfg,
+                act_cfg=None),
+            ConvModule(
+                branch_features,
+                branch_features,
+                kernel_size=1,
+                stride=1,
+                padding=0,
+                conv_cfg=conv_cfg,
+                norm_cfg=norm_cfg,
+                act_cfg=act_cfg))
+
+    def forward(self, x):
+
+        def _inner_forward(x):
+            if self.stride > 1:
+                out = torch.cat((self.branch1(x), self.branch2(x)), dim=1)
+            else:
+                x1, x2 = x.chunk(2, dim=1)
+                out = torch.cat((x1, self.branch2(x2)), dim=1)
+
+            out = channel_shuffle(out, 2)
+
+            return out
+
+        if self.with_cp and x.requires_grad:
+            out = cp.checkpoint(_inner_forward, x)
+        else:
+            out = _inner_forward(x)
+
+        return out
+
+
+@BACKBONES.register_module()
+class ShuffleNetV2(BaseBackbone):
+    """ShuffleNetV2 backbone.
+
+    Args:
+        widen_factor (float): Width multiplier - adjusts the number of
+            channels in each layer by this amount. Default: 1.0.
+        out_indices (Sequence[int]): Output from which stages.
+            Default: (0, 1, 2, 3).
+        frozen_stages (int): Stages to be frozen (all param fixed).
+            Default: -1, which means not freezing any parameters.
+        conv_cfg (dict): Config dict for convolution layer.
+            Default: None, which means using conv2d.
+        norm_cfg (dict): Config dict for normalization layer.
+            Default: dict(type='BN').
+        act_cfg (dict): Config dict for activation layer.
+            Default: dict(type='ReLU').
+        norm_eval (bool): Whether to set norm layers to eval mode, namely,
+            freeze running stats (mean and var). Note: Effect on Batch Norm
+            and its variants only. Default: False.
+        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
+            memory while slowing down the training speed. Default: False.
+    """
+
+    def __init__(self,
+                 widen_factor=1.0,
+                 out_indices=(3, ),
+                 frozen_stages=-1,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN'),
+                 act_cfg=dict(type='ReLU'),
+                 norm_eval=False,
+                 with_cp=False):
+        super(ShuffleNetV2, self).__init__()
+        self.stage_blocks = [4, 8, 4]
+        for index in out_indices:
+            if index not in range(0, 4):
+                raise ValueError('the item in out_indices must in '
+                                 f'range(0, 4). But received {index}')
+
+        if frozen_stages not in range(-1, 4):
+            raise ValueError('frozen_stages must be in range(-1, 4). '
+                             f'But received {frozen_stages}')
+        self.out_indices = out_indices
+        self.frozen_stages = frozen_stages
+        self.conv_cfg = conv_cfg
+        self.norm_cfg = norm_cfg
+        self.act_cfg = act_cfg
+        self.norm_eval = norm_eval
+        self.with_cp = with_cp
+
+        if widen_factor == 0.5:
+            channels = [48, 96, 192, 1024]
+        elif widen_factor == 1.0:
+            channels = [116, 232, 464, 1024]
+        elif widen_factor == 1.5:
+            channels = [176, 352, 704, 1024]
+        elif widen_factor == 2.0:
+            channels = [244, 488, 976, 2048]
+        else:
+            raise ValueError('widen_factor must be in [0.5, 1.0, 1.5, 2.0]. '
+                             f'But received {widen_factor}')
+
+        self.in_channels = 24
+        self.conv1 = ConvModule(
+            in_channels=3,
+            out_channels=self.in_channels,
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            conv_cfg=conv_cfg,
+            norm_cfg=norm_cfg,
+            act_cfg=act_cfg)
+
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+
+        self.layers = nn.ModuleList()
+        for i, num_blocks in enumerate(self.stage_blocks):
+            layer = self._make_layer(channels[i], num_blocks)
+            self.layers.append(layer)
+
+        output_channels = channels[-1]
+        self.layers.append(
+            ConvModule(
+                in_channels=self.in_channels,
+                out_channels=output_channels,
+                kernel_size=1,
+                conv_cfg=conv_cfg,
+                norm_cfg=norm_cfg,
+                act_cfg=act_cfg))
+
+    def _make_layer(self, out_channels, num_blocks):
+        """ Stack blocks to make a layer.
+
+        Args:
+            out_channels (int): out_channels of the block.
+            num_blocks (int): number of blocks.
+        """
+        layers = []
+        for i in range(num_blocks):
+            stride = 2 if i == 0 else 1
+            layers.append(
+                InvertedResidual(
+                    in_channels=self.in_channels,
+                    out_channels=out_channels,
+                    stride=stride,
+                    conv_cfg=self.conv_cfg,
+                    norm_cfg=self.norm_cfg,
+                    act_cfg=self.act_cfg,
+                    with_cp=self.with_cp))
+            self.in_channels = out_channels
+
+        return nn.Sequential(*layers)
+
+    def _freeze_stages(self):
+        if self.frozen_stages >= 0:
+            for param in self.conv1.parameters():
+                param.requires_grad = False
+
+        for i in range(self.frozen_stages):
+            m = self.layers[i]
+            m.eval()
+            for param in m.parameters():
+                param.requires_grad = False
+
+    def init_weights(self, pretrained=None):
+        if isinstance(pretrained, str):
+            logger = logging.getLogger()
+            load_checkpoint(self, pretrained, strict=False, logger=logger)
+        elif pretrained is None:
+            for name, m in self.named_modules():
+                if isinstance(m, nn.Conv2d):
+                    if 'conv1' in name:
+                        normal_init(m, mean=0, std=0.01)
+                    else:
+                        normal_init(m, mean=0, std=1.0 / m.weight.shape[1])
+                elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
+                    constant_init(m.weight, val=1, bias=0.0001)
+                    if isinstance(m, _BatchNorm):
+                        if m.running_mean is not None:
+                            nn.init.constant_(m.running_mean, 0)
+        else:
+            raise TypeError('pretrained must be a str or None. But received '
+                            f'{type(pretrained)}')
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.maxpool(x)
+
+        outs = []
+        for i, layer in enumerate(self.layers):
+            x = layer(x)
+            if i in self.out_indices:
+                outs.append(x)
+
+        if len(outs) == 1:
+            return outs[0]
+        else:
+            return tuple(outs)
+
+    def train(self, mode=True):
+        super(ShuffleNetV2, self).train(mode)
+        self._freeze_stages()
+        if mode and self.norm_eval:
+            for m in self.modules():
+                if isinstance(m, nn.BatchNorm2d):
+                    m.eval()
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/swin_transformer.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/swin_transformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..c61292890627a61ca198e2b364d86edce1963cda
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/swin_transformer.py
@@ -0,0 +1,582 @@
+import torch
+import torch.nn as nn
+
+from ..builder import BACKBONES
+from .base_backbone import BaseBackbone
+
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint as checkpoint
+from ..vit.layers import DropPath, to_2tuple, trunc_normal_
+
+
+class Mlp(nn.Module):
+    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+def window_partition(x, window_size):
+    """
+    Args:
+        x: (B, H, W, C)
+        window_size (int): window size
+    Returns:
+        windows: (num_windows*B, window_size, window_size, C)
+    """
+    B, H, W, C = x.shape
+    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
+    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+    return windows
+
+
+def window_reverse(windows, window_size, H, W):
+    """
+    Args:
+        windows: (num_windows*B, window_size, window_size, C)
+        window_size (int): Window size
+        H (int): Height of image
+        W (int): Width of image
+    Returns:
+        x: (B, H, W, C)
+    """
+    B = int(windows.shape[0] / (H * W / window_size / window_size))
+    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
+    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
+    return x
+
+
+class WindowAttention(nn.Module):
+    r""" Window based multi-head self attention (W-MSA) module with relative position bias.
+    It supports both of shifted and non-shifted window.
+    Args:
+        dim (int): Number of input channels.
+        window_size (tuple[int]): The height and width of the window.
+        num_heads (int): Number of attention heads.
+        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
+        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
+        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
+    """
+
+    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
+
+        super().__init__()
+        self.dim = dim
+        self.window_size = window_size  # Wh, Ww
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = qk_scale or head_dim ** -0.5
+
+        # define a parameter table of relative position bias
+        self.relative_position_bias_table = nn.Parameter(
+            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH
+
+        # get pair-wise relative position index for each token inside the window
+        coords_h = torch.arange(self.window_size[0])
+        coords_w = torch.arange(self.window_size[1])
+        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
+        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
+        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
+        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
+        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
+        relative_coords[:, :, 1] += self.window_size[1] - 1
+        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
+        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
+        self.register_buffer("relative_position_index", relative_position_index)
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+        trunc_normal_(self.relative_position_bias_table, std=.02)
+        self.softmax = nn.Softmax(dim=-1)
+
+    def forward(self, x, mask=None):
+        """
+        Args:
+            x: input features with shape of (num_windows*B, N, C)
+            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
+        """
+        B_, N, C = x.shape
+        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
+
+        q = q * self.scale
+        attn = (q @ k.transpose(-2, -1))
+
+        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
+            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
+        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
+        attn = attn + relative_position_bias.unsqueeze(0)
+
+        if mask is not None:
+            nW = mask.shape[0]
+            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
+            attn = attn.view(-1, self.num_heads, N, N)
+            attn = self.softmax(attn)
+        else:
+            attn = self.softmax(attn)
+
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+    def extra_repr(self) -> str:
+        return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'
+
+    def flops(self, N):
+        # calculate flops for 1 window with token length of N
+        flops = 0
+        # qkv = self.qkv(x)
+        flops += N * self.dim * 3 * self.dim
+        # attn = (q @ k.transpose(-2, -1))
+        flops += self.num_heads * N * (self.dim // self.num_heads) * N
+        #  x = (attn @ v)
+        flops += self.num_heads * N * N * (self.dim // self.num_heads)
+        # x = self.proj(x)
+        flops += N * self.dim * self.dim
+        return flops
+
+
+class SwinTransformerBlock(nn.Module):
+    r""" Swin Transformer Block.
+    Args:
+        dim (int): Number of input channels.
+        input_resolution (tuple[int]): Input resulotion.
+        num_heads (int): Number of attention heads.
+        window_size (int): Window size.
+        shift_size (int): Shift size for SW-MSA.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float, optional): Stochastic depth rate. Default: 0.0
+        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+    """
+
+    def __init__(self, dim, input_resolution, num_heads, window_size:int=7, shift_size:int=0,
+                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
+                 act_layer=nn.GELU, norm_layer=nn.LayerNorm):
+        super().__init__()
+        self.dim = dim
+        self.input_resolution = input_resolution
+        self.num_heads = num_heads
+        self.window_size = window_size
+        self.shift_size = shift_size
+        self.mlp_ratio = mlp_ratio
+        if min(self.input_resolution) <= self.window_size:
+            # if window size is larger than input resolution, we don't partition windows
+            self.shift_size = 0
+            self.window_size = min(self.input_resolution)
+        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
+
+        self.norm1 = norm_layer(dim)
+        self.attn = WindowAttention(
+            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
+            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
+
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
+
+        if self.shift_size > 0:
+            # calculate attention mask for SW-MSA
+            H, W = self.input_resolution
+            img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
+            h_slices = (slice(0, -self.window_size),
+                        slice(-self.window_size, -self.shift_size),
+                        slice(-self.shift_size, None))
+            w_slices = (slice(0, -self.window_size),
+                        slice(-self.window_size, -self.shift_size),
+                        slice(-self.shift_size, None))
+            cnt = 0
+            for h in h_slices:
+                for w in w_slices:
+                    img_mask[:, h, w, :] = cnt
+                    cnt += 1
+
+            mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
+            mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
+            attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
+            attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
+        else:
+            attn_mask = None
+
+        self.register_buffer("attn_mask", attn_mask)
+
+    def forward(self, x):
+        H, W = self.input_resolution
+        B, L, C = x.shape
+        assert L == H * W, "input feature has wrong size"
+
+        shortcut = x
+        x = self.norm1(x)
+        x = x.view(B, H, W, C)
+
+        # cyclic shift
+        if self.shift_size > 0:
+            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
+        else:
+            shifted_x = x
+
+        # partition windows
+        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
+        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C
+
+        # W-MSA/SW-MSA
+        attn_windows = self.attn(x_windows, mask=self.attn_mask)  # nW*B, window_size*window_size, C
+
+        # merge windows
+        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
+        shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C
+
+        # reverse cyclic shift
+        if self.shift_size > 0:
+            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
+        else:
+            x = shifted_x
+        x = x.view(B, H * W, C)
+
+        # FFN
+        x = shortcut + self.drop_path(x)
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+
+        return x
+
+    def extra_repr(self) -> str:
+        return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
+               f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
+
+    def flops(self):
+        flops = 0
+        H, W = self.input_resolution
+        # norm1
+        flops += self.dim * H * W
+        # W-MSA/SW-MSA
+        nW = H * W / self.window_size / self.window_size
+        flops += nW * self.attn.flops(self.window_size * self.window_size)
+        # mlp
+        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
+        # norm2
+        flops += self.dim * H * W
+        return flops
+
+
+class PatchMerging(nn.Module):
+    r""" Patch Merging Layer.
+    Args:
+        input_resolution (tuple[int]): Resolution of input feature.
+        dim (int): Number of input channels.
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+    """
+
+    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
+        super().__init__()
+        self.input_resolution = input_resolution
+        self.dim = dim
+        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
+        self.norm = norm_layer(4 * dim)
+
+    def forward(self, x):
+        """
+        x: B, H*W, C
+        """
+        H, W = self.input_resolution
+        B, L, C = x.shape
+        assert L == H * W, "input feature has wrong size"
+        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
+
+        x = x.view(B, H, W, C)
+
+        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
+        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
+        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
+        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
+        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
+        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
+
+        x = self.norm(x)
+        x = self.reduction(x)
+
+        return x
+
+    def extra_repr(self) -> str:
+        return f"input_resolution={self.input_resolution}, dim={self.dim}"
+
+    def flops(self):
+        H, W = self.input_resolution
+        flops = H * W * self.dim
+        flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
+        return flops
+
+
+class BasicLayer(nn.Module):
+    """ A basic Swin Transformer layer for one stage.
+    Args:
+        dim (int): Number of input channels.
+        input_resolution (tuple[int]): Input resolution.
+        depth (int): Number of blocks.
+        num_heads (int): Number of attention heads.
+        window_size (int): Local window size.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+    """
+
+    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
+                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
+                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):
+
+        super().__init__()
+        self.dim = dim
+        self.input_resolution = input_resolution
+        self.depth = depth
+        self.use_checkpoint = use_checkpoint
+
+        # build blocks
+        self.blocks = nn.ModuleList([
+            SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
+                                 num_heads=num_heads, window_size=window_size,
+                                 shift_size=0 if (i % 2 == 0) else window_size // 2,
+                                 mlp_ratio=mlp_ratio,
+                                 qkv_bias=qkv_bias, qk_scale=qk_scale,
+                                 drop=drop, attn_drop=attn_drop,
+                                 drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
+                                 norm_layer=norm_layer)
+            for i in range(depth)])
+
+        # patch merging layer
+        if downsample is not None:
+            self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
+        else:
+            self.downsample = None
+
+    def forward(self, x):
+        for blk in self.blocks:
+            if self.use_checkpoint:
+                x = checkpoint.checkpoint(blk, x)
+            else:
+                x = blk(x)
+        if self.downsample is not None:
+            x = self.downsample(x)
+        return x
+
+    def extra_repr(self) -> str:
+        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
+
+    def flops(self):
+        flops = 0
+        for blk in self.blocks:
+            flops += blk.flops()
+        if self.downsample is not None:
+            flops += self.downsample.flops()
+        return flops
+
+
+class PatchEmbed(nn.Module):
+    r""" Image to Patch Embedding
+    Args:
+        img_size (int): Image size.  Default: 224.
+        patch_size (int): Patch token size. Default: 4.
+        in_chans (int): Number of input image channels. Default: 3.
+        embed_dim (int): Number of linear projection output channels. Default: 96.
+        norm_layer (nn.Module, optional): Normalization layer. Default: None
+    """
+
+    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
+        super().__init__()
+        img_size = to_2tuple(img_size)
+        patch_size = to_2tuple(patch_size)
+        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.patches_resolution = patches_resolution
+        self.num_patches = patches_resolution[0] * patches_resolution[1]
+
+        self.in_chans = in_chans
+        self.embed_dim = embed_dim
+
+        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
+        if norm_layer is not None:
+            self.norm = norm_layer(embed_dim)
+        else:
+            self.norm = None
+
+    def forward(self, x):
+        B, C, H, W = x.shape
+        # FIXME look at relaxing size constraints
+        assert H == self.img_size[0] and W == self.img_size[1], \
+            f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
+        x = self.proj(x).flatten(2).transpose(1, 2)  # B Ph*Pw C
+        if self.norm is not None:
+            x = self.norm(x)
+        return x
+
+    def flops(self):
+        Ho, Wo = self.patches_resolution
+        flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
+        if self.norm is not None:
+            flops += Ho * Wo * self.embed_dim
+        return flops
+
+
+@BACKBONES.register_module()
+class SwinTransformer(BaseBackbone):
+    r""" Swin Transformer
+        A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
+          https://arxiv.org/pdf/2103.14030
+    Args:
+        img_size (int | tuple(int)): Input image size. Default 224
+        patch_size (int | tuple(int)): Patch size. Default: 4
+        in_chans (int): Number of input image channels. Default: 3
+        num_classes (int): Number of classes for classification head. Default: 1000
+        embed_dim (int): Patch embedding dimension. Default: 96
+        depths (tuple(int)): Depth of each Swin Transformer layer.
+        num_heads (tuple(int)): Number of attention heads in different layers.
+        window_size (int): Window size. Default: 7
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
+        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
+        drop_rate (float): Dropout rate. Default: 0
+        attn_drop_rate (float): Attention dropout rate. Default: 0
+        drop_path_rate (float): Stochastic depth rate. Default: 0.1
+        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
+        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
+        patch_norm (bool): If True, add normalization after patch embedding. Default: True
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
+    """
+
+    def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes=1000,
+                 embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24],
+                 window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,
+                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
+                 norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
+                 use_checkpoint=False, pretrained=None, **kwargs):
+        super().__init__()
+
+        self.num_classes = num_classes
+        self.num_layers = len(depths)
+        self.embed_dim = embed_dim
+        self.ape = ape
+        self.patch_norm = patch_norm
+        self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
+        self.mlp_ratio = mlp_ratio
+
+        # split image into non-overlapping patches
+        self.patch_embed = PatchEmbed(
+            img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,
+            norm_layer=norm_layer if self.patch_norm else None)
+        num_patches = self.patch_embed.num_patches
+        patches_resolution = self.patch_embed.patches_resolution
+        self.patches_resolution = patches_resolution
+
+        # absolute position embedding
+        if self.ape:
+            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
+            trunc_normal_(self.absolute_pos_embed, std=.02)
+
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        # stochastic depth
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
+
+        # build layers
+        self.layers = nn.ModuleList()
+        for i_layer in range(self.num_layers):
+            layer = BasicLayer(dim=int(embed_dim * 2 ** i_layer),
+                               input_resolution=(patches_resolution[0] // (2 ** i_layer),
+                                                 patches_resolution[1] // (2 ** i_layer)),
+                               depth=depths[i_layer],
+                               num_heads=num_heads[i_layer],
+                               window_size=window_size,
+                               mlp_ratio=self.mlp_ratio,
+                               qkv_bias=qkv_bias, qk_scale=qk_scale,
+                               drop=drop_rate, attn_drop=attn_drop_rate,
+                               drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
+                               norm_layer=norm_layer,
+                               downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
+                               use_checkpoint=use_checkpoint)
+            self.layers.append(layer)
+
+        self.norm = norm_layer(self.num_features)
+        self.avgpool = nn.AdaptiveAvgPool1d(1)
+        # self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
+
+        if pretrained is None:
+            self.apply(self._init_weights)
+        else:
+            self.init_weights(pretrained)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+
+    @torch.jit.ignore
+    def no_weight_decay(self):
+        return {'absolute_pos_embed'}
+
+    @torch.jit.ignore
+    def no_weight_decay_keywords(self):
+        return {'relative_position_bias_table'}
+
+    def forward_features(self, x):
+        x = self.patch_embed(x)
+        if self.ape:
+            x = x + self.absolute_pos_embed
+        x = self.pos_drop(x)
+
+        for layer in self.layers:
+            x = layer(x)
+
+        x = self.norm(x)  # B L C
+        x = self.avgpool(x.transpose(1, 2))  # B C 1
+        x = torch.flatten(x, 1)
+        return x
+
+    def forward(self, x):
+        x = self.forward_features(x)
+        # print('output shape', x.shape)
+        # x = self.head(x)
+        return x
+
+    def flops(self):
+        flops = 0
+        flops += self.patch_embed.flops()
+        for i, layer in enumerate(self.layers):
+            flops += layer.flops()
+        flops += self.num_features * self.patches_resolution[0] * self.patches_resolution[1] // (2 ** self.num_layers)
+        flops += self.num_features * self.num_classes
+        return flops
+
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/t2t_vit.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/t2t_vit.py
new file mode 100644
index 0000000000000000000000000000000000000000..9ac0655560a230e1aac1ae5109ec05642381881b
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/t2t_vit.py
@@ -0,0 +1,445 @@
+# Copyright (c) [2012]-[2021] Shanghai Yitu Technology Co., Ltd.
+#
+# This source code is licensed under the Clear BSD License
+# LICENSE file in the root directory of this file
+# All rights reserved.
+"""
+T2T-ViT
+"""
+import logging
+import numpy as np
+import torch
+import torch.nn as nn
+from mmcv.runner import load_state_dict
+
+from ..builder import BACKBONES
+from ..vit.layers import DropPath, trunc_normal_
+from .modules.vit_pooling import PoolingBlock
+from .modules.t2t import Token_performer
+from .base_backbone import BaseBackbone
+
+# from .token_transformer import Token_transformer
+# from .token_performer import Token_performer
+# from .transformer_block import Block, get_sinusoid_encoding
+
+def _cfg(url='', **kwargs):
+    return {
+        'url': url,
+        'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
+        'crop_pct': .9, 'interpolation': 'bicubic',
+        'mean': (0.485, 0.456, 0.406), 'std': (0.229, 0.224, 0.225),
+        'classifier': 'head',
+        **kwargs
+    }
+
+default_cfgs = {
+    'T2t_vit_7': _cfg(),
+    'T2t_vit_10': _cfg(),
+    'T2t_vit_12': _cfg(),
+    'T2t_vit_14': _cfg(),
+    'T2t_vit_19': _cfg(),
+    'T2t_vit_24': _cfg(),
+    'T2t_vit_t_14': _cfg(),
+    'T2t_vit_t_19': _cfg(),
+    'T2t_vit_t_24': _cfg(),
+    'T2t_vit_14_resnext': _cfg(),
+    'T2t_vit_14_wide': _cfg(),
+}
+
+
+class Mlp(nn.Module):
+    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+class Block(nn.Module):
+
+    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
+                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        self.attn = Attention(  # TODO: is in_dim=dim right?
+            dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
+
+    def forward(self, x):
+        x = x + self.drop_path(self.attn(self.norm1(x)))
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+        return x
+
+
+def get_sinusoid_encoding(n_position, d_hid):
+    ''' Sinusoid position encoding table '''
+
+    def get_position_angle_vec(position):
+        return [position / np.power(10000, 2 * (hid_j // 2) / d_hid) for hid_j in range(d_hid)]
+
+    sinusoid_table = np.array([get_position_angle_vec(pos_i) for pos_i in range(n_position)])
+    sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2])  # dim 2i
+    sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2])  # dim 2i+1
+
+    return torch.FloatTensor(sinusoid_table).unsqueeze(0)
+
+
+class Attention(nn.Module):
+    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+
+        self.scale = qk_scale or head_dim ** -0.5
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x):
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0], qkv[1], qkv[2]
+
+        attn = (q @ k.transpose(-2, -1)) * self.scale
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class TokenAttention(nn.Module):
+    def __init__(self, dim, num_heads=8, in_dim=1, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
+        super().__init__()
+        self.num_heads = num_heads
+        self.in_dim = in_dim
+        head_dim = dim // num_heads
+        self.scale = qk_scale or head_dim ** -0.5
+
+        self.qkv = nn.Linear(dim, in_dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(in_dim, in_dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x):
+        B, N, C = x.shape
+
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, self.in_dim).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0], qkv[1], qkv[2]
+
+        attn = (q * self.scale) @ k.transpose(-2, -1)
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B, N, self.in_dim)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+
+        # skip connection
+        x = v.squeeze(1) + x   # because the original x has different size with current x, use v to do skip connection
+
+        return x
+
+
+class Token_transformer(nn.Module):
+
+    def __init__(self, dim, in_dim, num_heads, mlp_ratio=1., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
+                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        self.attn = TokenAttention(
+            dim, in_dim=in_dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.norm2 = norm_layer(in_dim)
+        self.mlp = Mlp(in_features=in_dim, hidden_features=int(in_dim*mlp_ratio), out_features=in_dim, act_layer=act_layer, drop=drop)
+
+    def forward(self, x):
+        x = self.attn(self.norm1(x))
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+        return x
+
+
+
+class T2T_module(nn.Module):
+    """
+    Tokens-to-Token encoding module
+    """
+    def __init__(self, img_size=224, tokens_type='performer', in_chans=3, embed_dim=768, token_dim=64):
+        super().__init__()
+
+        if tokens_type == 'transformer':
+            print('adopt transformer encoder for tokens-to-token')
+            self.soft_split0 = nn.Unfold(kernel_size=(7, 7), stride=(4, 4), padding=(2, 2))
+            self.soft_split1 = nn.Unfold(kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
+            self.soft_split2 = nn.Unfold(kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
+
+            self.attention1 = Token_transformer(dim=in_chans * 7 * 7, in_dim=token_dim, num_heads=1, mlp_ratio=1.0)
+            self.attention2 = Token_transformer(dim=token_dim * 3 * 3, in_dim=token_dim, num_heads=1, mlp_ratio=1.0)
+            self.project = nn.Linear(token_dim * 3 * 3, embed_dim)
+
+        elif tokens_type == 'performer':
+            print('adopt performer encoder for tokens-to-token')
+            self.soft_split0 = nn.Unfold(kernel_size=(7, 7), stride=(4, 4), padding=(2, 2))
+            self.soft_split1 = nn.Unfold(kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
+            self.soft_split2 = nn.Unfold(kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
+
+            #self.attention1 = Token_performer(dim=token_dim, in_dim=in_chans*7*7, kernel_ratio=0.5)
+            #self.attention2 = Token_performer(dim=token_dim, in_dim=token_dim*3*3, kernel_ratio=0.5)
+            self.attention1 = Token_performer(dim=in_chans*7*7, in_dim=token_dim, kernel_ratio=0.5)
+            self.attention2 = Token_performer(dim=token_dim*3*3, in_dim=token_dim, kernel_ratio=0.5)
+            self.project = nn.Linear(token_dim * 3 * 3, embed_dim)
+
+        elif tokens_type == 'convolution':  # just for comparison with conolution, not our model
+            # for this tokens type, you need change forward as three convolution operation
+            print('adopt convolution layers for tokens-to-token')
+            self.soft_split0 = nn.Conv2d(3, token_dim, kernel_size=(7, 7), stride=(4, 4), padding=(2, 2))  # the 1st convolution
+            self.soft_split1 = nn.Conv2d(token_dim, token_dim, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) # the 2nd convolution
+            self.project = nn.Conv2d(token_dim, embed_dim, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) # the 3rd convolution
+
+        self.num_patches = (img_size // (4 * 2 * 2)) * (img_size // (4 * 2 * 2))  # there are 3 sfot split, stride are 4,2,2 seperately
+
+    def forward(self, x):
+        # step0: soft split
+        x = self.soft_split0(x).transpose(1, 2)
+
+        # iteration1: re-structurization/reconstruction
+        x = self.attention1(x)
+        B, new_HW, C = x.shape
+        x = x.transpose(1,2).reshape(B, C, int(np.sqrt(new_HW)), int(np.sqrt(new_HW)))
+        # iteration1: soft split
+        x = self.soft_split1(x).transpose(1, 2)
+
+        # iteration2: re-structurization/reconstruction
+        x = self.attention2(x)
+        B, new_HW, C = x.shape
+        x = x.transpose(1, 2).reshape(B, C, int(np.sqrt(new_HW)), int(np.sqrt(new_HW)))
+        # iteration2: soft split
+        x = self.soft_split2(x).transpose(1, 2)
+
+        # final tokens
+        x = self.project(x)
+
+        return x
+
+
+@BACKBONES.register_module()
+class T2T_ViT(BaseBackbone):
+    def __init__(self, img_size=224, tokens_type='performer', in_chans=3, num_classes=1000, embed_dim=768, depth=12,
+                 num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0.,
+                 drop_path_rate=0., norm_layer=nn.LayerNorm, token_dim=64, pretrained=None, **kwargs):
+        super().__init__()
+        self.img_size = img_size
+        self.num_classes = num_classes
+        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
+
+        self.tokens_to_token = T2T_module(
+                img_size=img_size, tokens_type=tokens_type, in_chans=in_chans, embed_dim=embed_dim, token_dim=token_dim)
+        num_patches = self.tokens_to_token.num_patches
+
+        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
+        self.pos_embed = nn.Parameter(data=get_sinusoid_encoding(n_position=num_patches + 1, d_hid=embed_dim), requires_grad=False)
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
+        self.blocks = nn.ModuleList([
+            Block(
+                dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
+                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer)
+            for i in range(depth)])
+        self.norm = norm_layer(embed_dim)
+
+        # Classifier head
+        self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
+
+        trunc_normal_(self.cls_token, std=.02)
+        self.apply(self._init_weights)
+        if pretrained:
+            self.init_weights(pretrained)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+    
+    def init_weights(self, pretrained):
+        logger = logging.getLogger()
+        logger.warning(f'{self.__class__.__name__} load pretrain from {pretrained}')
+        state_dict = torch.load(pretrained, map_location='cpu')
+        if 'state_dict' in state_dict:
+            state_dict = state_dict['state_dict']
+        elif 'state_dict_ema' in state_dict:
+            state_dict = state_dict['state_dict_ema']
+
+        load_state_dict(self, state_dict, strict=False, logger=logger)
+
+    @torch.jit.ignore
+    def no_weight_decay(self):
+        return {'cls_token'}
+
+    def get_classifier(self):
+        return self.head
+
+    def reset_classifier(self, num_classes, global_pool=''):
+        self.num_classes = num_classes
+        self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
+
+    def forward_features(self, x):
+        assert x.shape[2] == self.img_size  #[B, C, H, W]
+        B = x.shape[0]
+        x = self.tokens_to_token(x)
+
+        cls_tokens = self.cls_token.expand(B, -1, -1)
+        x = torch.cat((cls_tokens, x), dim=1)
+        x = x + self.pos_embed
+        x = self.pos_drop(x)
+
+        for blk in self.blocks:
+            x = blk(x)
+
+        x = self.norm(x)
+        return x[:, 0]
+
+    def forward(self, x):
+        x = self.forward_features(x)
+        x = self.head(x)
+        return x
+
+@BACKBONES.register_module()
+class T2T_ViTPooling(T2T_ViT):
+    def __init__(self, img_size=224, tokens_type='performer', in_chans=3, num_classes=1000, embed_dim=768, depth=12,
+                 num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0.,
+                 drop_path_rate=0., norm_layer=nn.LayerNorm, token_dim=64, pretrained=None, pool_config=None):
+
+        super().__init__(**{k:v for k, v in locals().items() if k != 'self'})
+        
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
+        if pool_config is not None:
+            assert len(pool_config['keep_rates']) == depth, 'num of keep_rates must be equal to depth'
+            self.blocks = nn.ModuleList([
+                PoolingBlock(
+                    dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
+                    drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, 
+                    pool_config=dict(keep_rate=pool_config['keep_rates'][i], **pool_config),
+                    )
+                for i in range(depth)])
+
+
+def T2t_vit_7(pretrained=False, **kwargs): # adopt performer for tokens to token
+    if pretrained:
+        kwargs.setdefault('qk_scale', 256 ** -0.5)
+    model = T2T_ViT(tokens_type='performer', embed_dim=256, depth=7, num_heads=4, mlp_ratio=2., **kwargs)
+    model.default_cfg = default_cfgs['T2t_vit_7']
+
+    return model
+
+
+def T2t_vit_10(pretrained=False, **kwargs): # adopt performer for tokens to token
+    if pretrained:
+        kwargs.setdefault('qk_scale', 256 ** -0.5)
+    model = T2T_ViT(tokens_type='performer', embed_dim=256, depth=10, num_heads=4, mlp_ratio=2., **kwargs)
+    model.default_cfg = default_cfgs['T2t_vit_10']
+
+    return model
+
+
+def T2t_vit_12(pretrained=False, **kwargs): # adopt performer for tokens to token
+    if pretrained:
+        kwargs.setdefault('qk_scale', 256 ** -0.5)
+    model = T2T_ViT(tokens_type='performer', embed_dim=256, depth=12, num_heads=4, mlp_ratio=2., **kwargs)
+    model.default_cfg = default_cfgs['T2t_vit_12']
+
+    return model
+
+
+def T2t_vit_14(pretrained=False, **kwargs):  # adopt performer for tokens to token
+    if pretrained:
+        kwargs.setdefault('qk_scale', 384 ** -0.5)
+    model = T2T_ViT(tokens_type='performer', embed_dim=384, depth=14, num_heads=6, mlp_ratio=3., **kwargs)
+    model.default_cfg = default_cfgs['T2t_vit_14']
+
+    return model
+
+def T2t_vit_19(pretrained=False, **kwargs): # adopt performer for tokens to token
+    if pretrained:
+        kwargs.setdefault('qk_scale', 448 ** -0.5)
+    model = T2T_ViT(tokens_type='performer', embed_dim=448, depth=19, num_heads=7, mlp_ratio=3., **kwargs)
+    model.default_cfg = default_cfgs['T2t_vit_19']
+
+    return model
+
+
+def T2t_vit_24(pretrained=False, **kwargs): # adopt performer for tokens to token
+    if pretrained:
+        kwargs.setdefault('qk_scale', 512 ** -0.5)
+    model = T2T_ViT(tokens_type='performer', embed_dim=512, depth=24, num_heads=8, mlp_ratio=3., **kwargs)
+    model.default_cfg = default_cfgs['T2t_vit_24']
+
+    return model
+
+
+def T2t_vit_t_14(pretrained=False, **kwargs):  # adopt transformers for tokens to token
+    if pretrained:
+        kwargs.setdefault('qk_scale', 384 ** -0.5)
+    model = T2T_ViT(tokens_type='transformer', embed_dim=384, depth=14, num_heads=6, mlp_ratio=3., **kwargs)
+    model.default_cfg = default_cfgs['T2t_vit_t_14']
+
+    return model
+
+
+def T2t_vit_t_19(pretrained=False, **kwargs):  # adopt transformers for tokens to token
+    if pretrained:
+        kwargs.setdefault('qk_scale', 448 ** -0.5)
+    model = T2T_ViT(tokens_type='transformer', embed_dim=448, depth=19, num_heads=7, mlp_ratio=3., **kwargs)
+    model.default_cfg = default_cfgs['T2t_vit_t_19']
+
+    return model
+
+
+def T2t_vit_t_24(pretrained=False, **kwargs):  # adopt transformers for tokens to token
+    if pretrained:
+        kwargs.setdefault('qk_scale', 512 ** -0.5)
+    model = T2T_ViT(tokens_type='transformer', embed_dim=512, depth=24, num_heads=8, mlp_ratio=3., **kwargs)
+    model.default_cfg = default_cfgs['T2t_vit_t_24']
+
+    return model
+
+
+# rexnext and wide structure
+def T2t_vit_14_resnext(pretrained=False, **kwargs):
+    if pretrained:
+        kwargs.setdefault('qk_scale', 384 ** -0.5)
+    model = T2T_ViT(tokens_type='performer', embed_dim=384, depth=14, num_heads=32, mlp_ratio=3., **kwargs)
+    model.default_cfg = default_cfgs['T2t_vit_14_resnext']
+
+    return model
+
+
+def T2t_vit_14_wide(pretrained=False, **kwargs):
+    if pretrained:
+        kwargs.setdefault('qk_scale', 512 ** -0.5)
+    model = T2T_ViT(tokens_type='performer', embed_dim=768, depth=4, num_heads=12, mlp_ratio=3., **kwargs)
+    model.default_cfg = default_cfgs['T2t_vit_14_wide']
+
+    return model
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/vgg.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/vgg.py
new file mode 100644
index 0000000000000000000000000000000000000000..aaa9eb9ebe02c2d8a4f2b722b903991f58cc0836
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/vgg.py
@@ -0,0 +1,194 @@
+import torch.nn as nn
+from mmcv.cnn import ConvModule, constant_init, kaiming_init, normal_init
+from mmcv.utils.parrots_wrapper import _BatchNorm
+
+from ..builder import BACKBONES
+from .base_backbone import BaseBackbone
+
+
+def make_vgg_layer(in_channels,
+                   out_channels,
+                   num_blocks,
+                   conv_cfg=None,
+                   norm_cfg=None,
+                   act_cfg=dict(type='ReLU'),
+                   dilation=1,
+                   with_norm=False,
+                   ceil_mode=False):
+    layers = []
+    for _ in range(num_blocks):
+        layer = ConvModule(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=3,
+            dilation=dilation,
+            padding=dilation,
+            bias=True,
+            conv_cfg=conv_cfg,
+            norm_cfg=norm_cfg,
+            act_cfg=act_cfg)
+        layers.append(layer)
+        in_channels = out_channels
+    layers.append(nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=ceil_mode))
+
+    return layers
+
+
+@BACKBONES.register_module()
+class VGG(BaseBackbone):
+    """VGG backbone.
+
+    Args:
+        depth (int): Depth of vgg, from {11, 13, 16, 19}.
+        with_norm (bool): Use BatchNorm or not.
+        num_classes (int): number of classes for classification.
+        num_stages (int): VGG stages, normally 5.
+        dilations (Sequence[int]): Dilation of each stage.
+        out_indices (Sequence[int]): Output from which stages. If only one
+            stage is specified, a single tensor (feature map) is returned,
+            otherwise multiple stages are specified, a tuple of tensors will
+            be returned. When it is None, the default behavior depends on
+            whether num_classes is specified. If num_classes <= 0, the default
+            value is (4, ), outputing the last feature map before classifier.
+            If num_classes > 0, the default value is (5, ), outputing the
+            classification score. Default: None.
+        frozen_stages (int): Stages to be frozen (all param fixed). -1 means
+            not freezing any parameters.
+        norm_eval (bool): Whether to set norm layers to eval mode, namely,
+            freeze running stats (mean and var). Note: Effect on Batch Norm
+            and its variants only. Default: False.
+        ceil_mode (bool): Whether to use ceil_mode of MaxPool. Default: False.
+        with_last_pool (bool): Whether to keep the last pooling before
+            classifier. Default: True.
+    """
+
+    # Parameters to build layers. Each element specifies the number of conv in
+    # each stage. For example, VGG11 contains 11 layers with learnable
+    # parameters. 11 is computed as 11 = (1 + 1 + 2 + 2 + 2) + 3,
+    # where 3 indicates the last three fully-connected layers.
+    arch_settings = {
+        11: (1, 1, 2, 2, 2),
+        13: (2, 2, 2, 2, 2),
+        16: (2, 2, 3, 3, 3),
+        19: (2, 2, 4, 4, 4)
+    }
+
+    def __init__(self,
+                 depth,
+                 num_classes=-1,
+                 num_stages=5,
+                 dilations=(1, 1, 1, 1, 1),
+                 out_indices=None,
+                 frozen_stages=-1,
+                 conv_cfg=None,
+                 norm_cfg=None,
+                 act_cfg=dict(type='ReLU'),
+                 norm_eval=False,
+                 ceil_mode=False,
+                 with_last_pool=True):
+        super(VGG, self).__init__()
+        if depth not in self.arch_settings:
+            raise KeyError(f'invalid depth {depth} for vgg')
+        assert num_stages >= 1 and num_stages <= 5
+        stage_blocks = self.arch_settings[depth]
+        self.stage_blocks = stage_blocks[:num_stages]
+        assert len(dilations) == num_stages
+
+        self.num_classes = num_classes
+        self.frozen_stages = frozen_stages
+        self.norm_eval = norm_eval
+        with_norm = norm_cfg is not None
+
+        if out_indices is None:
+            out_indices = (5, ) if num_classes > 0 else (4, )
+        assert max(out_indices) <= num_stages
+        self.out_indices = out_indices
+
+        self.in_channels = 3
+        start_idx = 0
+        vgg_layers = []
+        self.range_sub_modules = []
+        for i, num_blocks in enumerate(self.stage_blocks):
+            num_modules = num_blocks + 1
+            end_idx = start_idx + num_modules
+            dilation = dilations[i]
+            out_channels = 64 * 2**i if i < 4 else 512
+            vgg_layer = make_vgg_layer(
+                self.in_channels,
+                out_channels,
+                num_blocks,
+                conv_cfg=conv_cfg,
+                norm_cfg=norm_cfg,
+                act_cfg=act_cfg,
+                dilation=dilation,
+                with_norm=with_norm,
+                ceil_mode=ceil_mode)
+            vgg_layers.extend(vgg_layer)
+            self.in_channels = out_channels
+            self.range_sub_modules.append([start_idx, end_idx])
+            start_idx = end_idx
+        if not with_last_pool:
+            vgg_layers.pop(-1)
+            self.range_sub_modules[-1][1] -= 1
+        self.module_name = 'features'
+        self.add_module(self.module_name, nn.Sequential(*vgg_layers))
+
+        if self.num_classes > 0:
+            self.classifier = nn.Sequential(
+                nn.Linear(512 * 7 * 7, 4096),
+                nn.ReLU(True),
+                nn.Dropout(),
+                nn.Linear(4096, 4096),
+                nn.ReLU(True),
+                nn.Dropout(),
+                nn.Linear(4096, num_classes),
+            )
+
+    def init_weights(self, pretrained=None):
+        super(VGG, self).init_weights(pretrained)
+        if pretrained is None:
+            for m in self.modules():
+                if isinstance(m, nn.Conv2d):
+                    kaiming_init(m)
+                elif isinstance(m, _BatchNorm):
+                    constant_init(m, 1)
+                elif isinstance(m, nn.Linear):
+                    normal_init(m, std=0.01)
+        else:
+            raise TypeError('pretrained must be a str or None')
+
+    def forward(self, x):
+        outs = []
+        vgg_layers = getattr(self, self.module_name)
+        for i in range(len(self.stage_blocks)):
+            for j in range(*self.range_sub_modules[i]):
+                vgg_layer = vgg_layers[j]
+                x = vgg_layer(x)
+            if i in self.out_indices:
+                outs.append(x)
+        if self.num_classes > 0:
+            x = x.view(x.size(0), -1)
+            x = self.classifier(x)
+            outs.append(x)
+        if len(outs) == 1:
+            return outs[0]
+        else:
+            return tuple(outs)
+
+    def _freeze_stages(self):
+        vgg_layers = getattr(self, self.module_name)
+        for i in range(self.frozen_stages):
+            for j in range(*self.range_sub_modules[i]):
+                m = vgg_layers[j]
+                m.eval()
+                for param in m.parameters():
+                    param.requires_grad = False
+
+    def train(self, mode=True):
+        super(VGG, self).train(mode)
+        self._freeze_stages()
+        if mode and self.norm_eval:
+            for m in self.modules():
+                # trick: eval have effect on BatchNorm only
+                if isinstance(m, _BatchNorm):
+                    m.eval()
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/vtff.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/vtff.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca8b7026ab6b494c8d2a589232889957c8f0efa5
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/backbones/vtff.py
@@ -0,0 +1,137 @@
+import torch
+import torch.nn as nn
+
+from ..builder import BACKBONES, build_backbone
+from .base_backbone import BaseBackbone
+
+
+"""
+Coarse implementation of VTFFF to count FLOPs.
+
+"""
+
+
+class AFS(nn.Module):
+    def __init__(self,):
+        super().__init__()
+        self.Wl = nn.Conv2d(512, 512, 1, 1)
+        self.Wc = nn.Conv2d(512, 512, 1, 1)
+
+        self.relu = nn.ReLU()
+        self.sigmoid = nn.Sigmoid()
+
+        self.convG1 = nn.Conv2d(512, 64, 1, 1)  # 512 // 8
+        self.convG2 = nn.Conv2d(64, 512, 1, 1)
+        self.bnG1 = nn.BatchNorm2d(64)
+        self.bnG2 = nn.BatchNorm2d(512)
+
+        self.convL1 = nn.Conv2d(512, 64, 1, 1)
+        self.convL2 = nn.Conv2d(64, 1, 1, 1)
+        self.bnL1 = nn.BatchNorm2d(64)
+        self.bnL2 = nn.BatchNorm2d(1)
+
+    def forward(self, x1, x2):
+        U = self.Wl(x1) + self.Wc(x2)
+
+        GU = self.sigmoid(self.bnG2(self.convG2(self.sigmoid(self.bnG1(self.convG1(nn.AdaptiveAvgPool2d((1, 1))(U)))))))
+        LU = self.sigmoid(self.bnL2(self.convL2(self.sigmoid(self.bnL1(self.convL1(U))))))
+        GL = GU + LU
+        x = x2 * self.sigmoid(GL) + x1 * self.sigmoid(1 - GL)
+        return x
+
+
+
+@BACKBONES.register_module()
+class VTFF(BaseBackbone):
+    """
+    paper: Facial Expression Recognition with Visual Transformers and Attentional Selective Fusion
+    """
+
+    def __init__(self, ):
+        super().__init__()
+        cnn1 = dict(
+            type='ResNet',
+            depth=18,
+            num_stages=4,
+            out_indices=(3, ),
+            style='pytorch')
+        cnn2 = dict(
+            type='ResNet',
+            depth=18,
+            num_stages=4,
+            out_indices=(3, ),
+            style='pytorch')
+        vit = dict(
+            type='VisionTransformerOrigin',
+            input_type='feature',
+            num_patches=49,
+            depth=8,
+            last_ln=True,
+            img_size=112, patch_size=16, embed_dim=768, num_heads=8, mlp_ratio=3, qkv_bias=False, norm_layer_eps=1e-6
+        )
+
+        self.cnn1 = build_backbone(cnn1)
+        self.cnn2 = build_backbone(cnn2)
+        self.afs = AFS()
+        self.project = nn.Conv2d(512, 768, 1, 1)
+        self.vit = build_backbone(vit)
+
+    def forward(self, x):
+
+        x1 = self.cnn1(x)[0]
+        x2 = self.cnn2(x)[0]
+
+        x_fused = self.afs(x1, x2)
+
+        x = self.project(x_fused)
+        x = x.flatten(2)
+        x = x.transpose(1, 2)  # [B, N, C]
+        x = self.vit(x)
+        
+        return x
+
+
+@BACKBONES.register_module()
+class MViT(BaseBackbone):
+    """
+    paper: MViT: Mask Vision Transformer for Facial Expression Recognition in the wild
+    """
+
+    def __init__(self, ):
+        super().__init__()
+        generator = dict(   # DeiT-S first 6 layers
+            type='VisionTransformerOrigin',
+            input_type='image',
+            # num_patches=49,
+            depth=6,
+            last_ln=True,
+            img_size=224, patch_size=16, embed_dim=384, num_heads=6, mlp_ratio=3, qkv_bias=False, norm_layer_eps=1e-6
+        )
+        mvit = dict(   # DeiT-S
+            type='VisionTransformerOrigin',
+            input_type='image',
+            # num_patches=14 ** 2,
+            depth=12,
+            last_ln=True,
+            img_size=224, patch_size=16, embed_dim=384, num_heads=6, mlp_ratio=3, qkv_bias=False, norm_layer_eps=1e-6
+        )
+        self.generator = build_backbone(generator)
+        self.sigmoid = nn.Sigmoid()
+        self.proj = nn.Linear(384, 768)
+        self.mvit = build_backbone(mvit)
+    
+    def forward(self, img):
+        x = self.generator(img)
+        B, N, C = x.shape
+        output = []
+        for i in range(1, N):
+            output.append(self.sigmoid(self.proj(x[:, i, :])))
+        x = torch.stack(output) # [B, C, N]
+        x = x.reshape((B, 3, 224, 224))
+
+        x = x * img
+
+        x = self.mvit(x)
+        return x[:, 0]
+
+
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/builder.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/builder.py
new file mode 100644
index 0000000000000000000000000000000000000000..9a33913e8b43a0dc26f364c69510a50f7271c6ab
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/builder.py
@@ -0,0 +1,38 @@
+import torch.nn as nn
+from mmcv.utils import Registry, build_from_cfg
+
+BACKBONES = Registry('backbone')
+CLASSIFIERS = Registry('classifier')
+HEADS = Registry('head')
+NECKS = Registry('neck')
+LOSSES = Registry('loss')
+
+
+def build(cfg, registry, default_args=None):
+    if isinstance(cfg, list):
+        modules = [
+            build_from_cfg(cfg_, registry, default_args) for cfg_ in cfg
+        ]
+        return nn.Sequential(*modules)
+    else:
+        return build_from_cfg(cfg, registry, default_args)
+
+
+def build_backbone(cfg) -> nn.Module:
+    return build(cfg, BACKBONES)
+
+
+def build_head(cfg):
+    return build(cfg, HEADS)
+
+
+def build_neck(cfg):
+    return build(cfg, NECKS)
+
+
+def build_loss(cfg):
+    return build(cfg, LOSSES)
+
+
+def build_classifier(cfg):
+    return build(cfg, CLASSIFIERS)
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/classifiers/__init__.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/classifiers/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e55529a5b78f18343b2d0850ff5c03bac1783748
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/classifiers/__init__.py
@@ -0,0 +1,11 @@
+from .base import BaseClassifier
+from .image import ImageClassifier, SiamImageClassifier, SiamImageClassifier
+from ..vit.vit import VisionTransformer
+# from ..vit.vit_vis import VisionTransformerVis
+from .atten_vit import AttentionVitClassifier
+from .pool_vit import PoolingVitClassifier
+
+__all__ = ['BaseClassifier', 'ImageClassifier', 'VisionTransformer', 'SiamImageClassifier',
+# 'VisionTransformerVis',
+'PoolingVitClassifier',
+ 'AttentionVitClassifier']
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/classifiers/atten_vit.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/classifiers/atten_vit.py
new file mode 100644
index 0000000000000000000000000000000000000000..8ce2faf17618a20d45659fe743331fe4b22e06bd
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/classifiers/atten_vit.py
@@ -0,0 +1,213 @@
+import torch
+import torch.nn as nn
+
+from ..builder import CLASSIFIERS, build_backbone, build_head, build_neck
+from .base import BaseClassifier
+
+
+
+def compute_rollout_attention(all_layer_matrices, start_layer=0):
+    # adding residual consideration
+    num_tokens = all_layer_matrices[0].shape[1]
+    batch_size = all_layer_matrices[0].shape[0]
+    eye = torch.eye(num_tokens).expand(batch_size, num_tokens, num_tokens).to(all_layer_matrices[0].device)
+    all_layer_matrices = [all_layer_matrices[i] + eye for i in range(len(all_layer_matrices))]
+    # all_layer_matrices = [all_layer_matrices[i] / all_layer_matrices[i].sum(dim=-1, keepdim=True)
+    #                       for i in range(len(all_layer_matrices))]
+    joint_attention = all_layer_matrices[start_layer]
+    for i in range(start_layer+1, len(all_layer_matrices)):
+        joint_attention = all_layer_matrices[i].bmm(joint_attention)
+    return joint_attention
+
+
+
+
+@CLASSIFIERS.register_module()
+class AttentionVitClassifier(BaseClassifier):
+
+    def __init__(self, extractor, attention, convert, vit, neck=None, head=None, pretrained=None, freeze_backbone=False):
+        super().__init__()
+        if extractor:
+            self.extractor = build_backbone(extractor)
+        if freeze_backbone and extractor:
+            print('freeze backbone: %s' % extractor['type'])
+            self.extractor.eval()
+            for param in self.extractor.parameters():
+                    param.requires_grad = False
+        self.attention = build_neck(attention)
+        self.convert = build_neck(convert)
+        self.vit:nn.Module = build_backbone(vit)
+
+        if neck is not None:
+            self.neck = build_neck(neck)
+
+        if head is not None:
+            self.head = build_head(head)
+
+        self.init_weights(pretrained=pretrained)
+
+    def init_weights(self, pretrained=None):
+        super().init_weights(pretrained)
+        # self.backbone.init_weights(pretrained=pretrained)
+        if self.with_neck:
+            if isinstance(self.neck, nn.Sequential):
+                for m in self.neck:
+                    m.init_weights()
+            else:
+                self.neck.init_weights()
+        if self.with_head:
+            self.head.init_weights()
+
+    def extract_feat(self, img):
+        """Directly extract features from the backbone + neck
+        """
+        aux_loss = dict()
+        if hasattr(self, 'extractor'):
+            x = self.extractor(img)
+        else:
+            x = img
+        # print(x.shape)
+        # exit()
+        x = self.attention(x)
+        aux_loss.update(x['loss'])
+        x = self.convert(img=img, **x)
+        x = self.vit(**x)
+        aux_loss.update(x['loss'])
+        x = x['x']
+        if self.with_neck:
+            x = self.neck(x)
+        return x, aux_loss
+    
+    def extract_la(self, img):
+        if hasattr(self, 'extractor'):
+            x = self.extractor(img)
+        else:
+            x = img
+        x = self.attention(x)
+        return x['la_outs']
+
+    def forward_train(self, img, gt_label, **kwargs):
+        """Forward computation during training.
+
+        Args:
+            img (Tensor): of shape (N, C, H, W) encoding input images.
+                Typically these should be mean centered and std scaled.
+
+            gt_label (Tensor): of shape (N, 1) encoding the ground-truth label
+                of input images.
+
+        Returns:
+            dict[str, Tensor]: a dictionary of loss components
+        """
+        x, aux_loss = self.extract_feat(img)
+
+ 
+        losses = self.head.forward_train(x, gt_label)
+        # losses['ce_loss'] = losses['loss']
+        # losses['loss'] *= 0.
+        # losses['aux_loss'] = aux_loss
+        losses.update(aux_loss)
+
+        return losses
+
+    def simple_test(self, img, **kwargs):
+        """Test without augmentation."""
+        x, _ = self.extract_feat(img)
+        return self.head.simple_test(x)
+    
+    def inference(self, img, **kwargs):
+        x, _ = self.extract_feat(img)
+        x = self.head.extract_feat(x)
+        return x
+    
+    def aug_test(self, imgs, **kwargs): # TODO: pull request: add aug test to mmcls
+        logit = self.inference(imgs[0], **kwargs)
+        for i in range(1, len(imgs)):
+            cur_logit = self.inference(imgs[i])
+            logit += cur_logit
+        logit /= len(imgs)
+        # pred = F.softmax(logit, dim=1)
+        pred = logit
+        pred = pred.cpu().numpy()
+        # unravel batch dim
+        pred = list(pred)
+        return pred
+
+    def relprop(self, cam=None,method="transformer_attribution", is_ablation=False, start_layer=0, **kwargs):
+        # print(kwargs)
+        # print("conservation 1", cam.sum())
+        cam = self.head.relprop(cam, **kwargs)
+        cam = cam.unsqueeze(1)
+        cam = self.vit.pool.relprop(cam, **kwargs)
+        cam = self.vit.norm.relprop(cam, **kwargs)
+        for blk in reversed(self.vit.blocks):
+            cam = blk.relprop(cam, **kwargs)
+
+        # print("conservation 2", cam.sum())
+        # print("min", cam.min())
+
+        if method == "full":
+            (cam, _) = self.vit.add.relprop(cam, **kwargs)
+            cam = cam[:, 1:]
+            cam = self.vit.patch_embed.relprop(cam, **kwargs)
+            # sum on channels
+            cam = cam.sum(dim=1)
+            return cam
+
+        elif method == "rollout":
+            # cam rollout
+            attn_cams = []
+            for blk in self.vit.blocks:
+                attn_heads = blk.attn.get_attn_cam().clamp(min=0)
+                avg_heads = (attn_heads.sum(dim=1) / attn_heads.shape[1]).detach()
+                attn_cams.append(avg_heads)
+            cam = compute_rollout_attention(attn_cams, start_layer=start_layer)
+            cam = cam[:, 0, 1:]
+            return cam
+        
+        # our method, method name grad is legacy
+        elif method == "transformer_attribution" or method == "grad":
+            cams = []
+            for blk in self.vit.blocks:
+                grad = blk.attn.get_attn_gradients()
+                cam = blk.attn.get_attn_cam()
+                cam = cam[0].reshape(-1, cam.shape[-1], cam.shape[-1])
+                grad = grad[0].reshape(-1, grad.shape[-1], grad.shape[-1])
+                cam = grad * cam
+                cam = cam.clamp(min=0).mean(dim=0)
+                cams.append(cam.unsqueeze(0))
+            rollout = compute_rollout_attention(cams, start_layer=start_layer)
+            cam = rollout[:, 0, 1:]
+            # cam = rollout[:, 1:, 0]
+            return cam
+            
+        elif method == "last_layer":
+            cam = self.vit.blocks[-1].attn.get_attn_cam()
+            cam = cam[0].reshape(-1, cam.shape[-1], cam.shape[-1])
+            if is_ablation:
+                grad = self.vit.blocks[-1].attn.get_attn_gradients()
+                grad = grad[0].reshape(-1, grad.shape[-1], grad.shape[-1])
+                cam = grad * cam
+            cam = cam.clamp(min=0).mean(dim=0)
+            cam = cam[0, 1:]
+            return cam
+
+        elif method == "last_layer_attn":
+            cam = self.vit.blocks[-1].attn.get_attn()
+            cam = cam[0].reshape(-1, cam.shape[-1], cam.shape[-1])
+            cam = cam.clamp(min=0).mean(dim=0)
+            cam = cam[0, 1:]
+            return cam
+
+        elif method == "second_layer":
+            cam = self.vit.blocks[1].attn.get_attn_cam()
+            cam = cam[0].reshape(-1, cam.shape[-1], cam.shape[-1])
+            if is_ablation:
+                grad = self.vit.blocks[1].attn.get_attn_gradients()
+                grad = grad[0].reshape(-1, grad.shape[-1], grad.shape[-1])
+                cam = grad * cam
+            cam = cam.clamp(min=0).mean(dim=0)
+            cam = cam[0, 1:]
+            return cam
+
+
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/classifiers/base.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/classifiers/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..5ea4784769a598045328cbd36d1fe9734d6f0638
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/classifiers/base.py
@@ -0,0 +1,228 @@
+import warnings
+from abc import ABCMeta, abstractmethod
+from collections import OrderedDict
+
+import cv2
+import mmcv
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+from mmcv import color_val
+from mmcv.utils import print_log
+from mmcv.runner import auto_fp16
+
+
+class BaseClassifier(nn.Module, metaclass=ABCMeta):
+    """Base class for classifiers"""
+
+    def __init__(self):
+        super(BaseClassifier, self).__init__()
+        self.fp16_enabled = False
+
+    @property
+    def with_neck(self):
+        return hasattr(self, 'neck') and self.neck is not None
+
+    @property
+    def with_head(self):
+        return hasattr(self, 'head') and self.head is not None
+
+    @abstractmethod
+    def extract_feat(self, imgs):
+        pass
+
+    def extract_feats(self, imgs):
+        assert isinstance(imgs, list)
+        for img in imgs:
+            yield self.extract_feat(img)
+
+    @abstractmethod
+    def forward_train(self, imgs, **kwargs):
+        """
+        Args:
+            img (list[Tensor]): List of tensors of shape (1, C, H, W).
+                Typically these should be mean centered and std scaled.
+            kwargs (keyword arguments): Specific to concrete implementation.
+        """
+        pass
+
+    @abstractmethod
+    def simple_test(self, img, **kwargs):
+        pass
+
+    @abstractmethod
+    def aug_test(self, img, **kwargs):
+        pass
+
+    def init_weights(self, pretrained=None):
+        if pretrained is not None:
+            print_log(f'load model from: {pretrained}', logger='root')
+
+    def forward_test(self, imgs, **kwargs):
+        """
+        Args:
+            imgs (List[Tensor]): the outer list indicates test-time
+                augmentations and inner Tensor should have a shape NxCxHxW,
+                which contains all images in the batch.
+        """
+        if isinstance(imgs, torch.Tensor):
+            imgs = [imgs]
+        for var, name in [(imgs, 'imgs')]:
+            if not isinstance(var, list):
+                raise TypeError(f'{name} must be a list, but got {type(var)}')
+
+        if len(imgs) == 1:
+            return self.simple_test(imgs[0], **kwargs)
+        else:
+            return self.aug_test(imgs, **kwargs)
+            # raise NotImplementedError('aug_test has not been implemented')
+
+    @auto_fp16(apply_to=('img', ))
+    def forward(self, img, return_loss=True, **kwargs):
+        """
+        Calls either forward_train or forward_test depending on whether
+        return_loss=True. Note this setting will change the expected inputs.
+        When `return_loss=True`, img and img_meta are single-nested (i.e.
+        Tensor and List[dict]), and when `resturn_loss=False`, img and img_meta
+        should be double nested (i.e.  List[Tensor], List[List[dict]]), with
+        the outer list indicating test time augmentations.
+        """
+        if return_loss:
+            return self.forward_train(img, **kwargs)
+        else:
+            return self.forward_test(img, **kwargs)
+
+    def _parse_losses(self, losses):
+        log_vars = OrderedDict()
+        for loss_name, loss_value in losses.items():
+            if isinstance(loss_value, torch.Tensor):
+                log_vars[loss_name] = loss_value.mean()
+            elif isinstance(loss_value, list):
+                log_vars[loss_name] = sum(_loss.mean() for _loss in loss_value)
+            elif isinstance(loss_value, dict):
+                for name, value in loss_value.items():
+                    log_vars[name] = value
+            elif isinstance(loss_value, float) or isinstance(loss_value, int):
+                log_vars[loss_name] = torch.tensor(loss_value)
+            else:
+                raise TypeError(
+                    f'{loss_name} is not a tensor or list of tensors')
+
+        loss = sum(_value for _key, _value in log_vars.items()
+                   if 'loss' in _key)
+
+        log_vars['loss'] = loss
+        for loss_name, loss_value in log_vars.items():
+            # reduce loss when distributed training
+            if dist.is_available() and dist.is_initialized():
+                loss_value = loss_value.data.clone()
+                dist.all_reduce(loss_value.div_(dist.get_world_size()))
+            log_vars[loss_name] = loss_value.item()
+
+        return loss, log_vars
+
+    def train_step(self, data, optimizer):
+        """The iteration step during training.
+
+        This method defines an iteration step during training, except for the
+        back propagation and optimizer updating, which are done in an optimizer
+        hook. Note that in some complicated cases or models, the whole process
+        including back propagation and optimizer updating are also defined in
+        this method, such as GAN.
+
+        Args:
+            data (dict): The output of dataloader.
+            optimizer (:obj:`torch.optim.Optimizer` | dict): The optimizer of
+                runner is passed to ``train_step()``. This argument is unused
+                and reserved.
+
+        Returns:
+            dict: It should contain at least 3 keys: ``loss``, ``log_vars``,
+                ``num_samples``.
+                ``loss`` is a tensor for back propagation, which can be a
+                weighted sum of multiple losses.
+                ``log_vars`` contains all the variables to be sent to the
+                logger.
+                ``num_samples`` indicates the batch size (when the model is
+                DDP, it means the batch size on each GPU), which is used for
+                averaging the logs.
+        """
+        losses = self(**data)
+        loss, log_vars = self._parse_losses(losses)
+
+        outputs = dict(
+            loss=loss, log_vars=log_vars, num_samples=len(data['img'].data))
+
+        return outputs
+
+    def val_step(self, data, *args, **kwargs):
+        """The iteration step during validation.
+
+        This method shares the same signature as :func:`train_step`, but used
+        during val epochs. Note that the evaluation after training epochs is
+        not implemented with this method, but an evaluation hook.
+        """
+        losses = self(**data)
+        loss, log_vars = self._parse_losses(losses)
+
+        outputs = dict(
+            loss=loss, log_vars=log_vars, num_samples=len(data['img'].data))
+
+        return outputs
+
+    def show_result(self,
+                    img,
+                    result,
+                    text_color='green',
+                    font_scale=0.5,
+                    row_width=20,
+                    show=False,
+                    win_name='',
+                    wait_time=0,
+                    out_file=None):
+        """Draw `result` over `img`.
+
+        Args:
+            img (str or Tensor): The image to be displayed.
+            result (Tensor): The classification results to draw over `img`.
+            text_color (str or tuple or :obj:`Color`): Color of texts.
+            font_scale (float): Font scales of texts.
+            row_width (int): width between each row of results on the image.
+            show (bool): Whether to show the image.
+                Default: False.
+            win_name (str): The window name.
+            wait_time (int): Value of waitKey param.
+                Default: 0.
+            out_file (str or None): The filename to write the image.
+                Default: None.
+
+        Returns:
+            img (Tensor): Only if not `show` or `out_file`
+        """
+        img = mmcv.imread(img)
+        img = img.copy()
+
+        # write results on left-top of the image
+        x, y = 0, row_width
+        text_color = color_val(text_color)
+        for k, v in result.items():
+            if isinstance(v, float):
+                v = f'{v:.2f}'
+            label_text = f'{k}: {v}'
+            cv2.putText(img, label_text, (x, y), cv2.FONT_HERSHEY_COMPLEX,
+                        font_scale, text_color)
+            y += row_width
+
+        # if out_file specified, do not show image in window
+        if out_file is not None:
+            show = False
+
+        if show:
+            mmcv.imshow(img, win_name, wait_time)
+        if out_file is not None:
+            mmcv.imwrite(img, out_file)
+
+        if not (show or out_file):
+            warnings.warn('show==False and out_file is not specified, only '
+                          'result image will be returned')
+            return img
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/classifiers/image.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/classifiers/image.py
new file mode 100644
index 0000000000000000000000000000000000000000..dd870e6b8edfd3593e087b70ebd32245957513b3
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/classifiers/image.py
@@ -0,0 +1,163 @@
+from typing import List
+import torch.nn as nn
+
+from ..builder import CLASSIFIERS, build_backbone, build_head, build_neck
+from .base import BaseClassifier
+
+
+@CLASSIFIERS.register_module()
+class ImageClassifier(BaseClassifier):
+
+    def __init__(self, backbone, neck=None, head=None, pretrained=None):
+        super(ImageClassifier, self).__init__()
+        self.backbone = build_backbone(backbone)
+
+        if neck is not None:
+            self.neck = build_neck(neck)
+
+        if head is not None:
+            self.head = build_head(head)
+
+        if pretrained:
+            self.init_weights(pretrained=pretrained)
+
+    def init_weights(self, pretrained=None):
+        super(ImageClassifier, self).init_weights(pretrained)
+        
+        self.backbone.init_weights(pretrained=pretrained)
+        if self.with_neck:
+            if isinstance(self.neck, nn.Sequential):
+                for m in self.neck:
+                    m.init_weights()
+            else:
+                self.neck.init_weights()
+        if self.with_head:
+            self.head.init_weights()
+
+    def extract_feat(self, img):
+        """Directly extract features from the backbone + neck
+        """
+        x = self.backbone(img)
+        if isinstance(x, dict):
+            loss = x['loss']
+            x = x['x']
+        else:
+            loss = dict()
+        if self.with_neck:
+            x = self.neck(x)
+        return x, loss
+
+    def forward_train(self, img, gt_label, **kwargs):
+        """Forward computation during training.
+
+        Args:
+            img (Tensor): of shape (N, C, H, W) encoding input images.
+                Typically these should be mean centered and std scaled.
+
+            gt_label (Tensor): of shape (N, 1) encoding the ground-truth label
+                of input images.
+
+        Returns:
+            dict[str, Tensor]: a dictionary of loss components
+        """
+        x, losses = self.extract_feat(img)
+
+        loss = self.head.forward_train(x, gt_label=gt_label, **kwargs)
+        losses.update(loss)
+
+        return losses
+
+    def simple_test(self, img, **kwargs):
+        """Test without augmentation."""
+        x, _ = self.extract_feat(img)
+        return self.head.simple_test(x)
+    
+    def inference(self, img):
+        x, _ = self.extract_feat(img)
+        x = self.head.extract_feat(x)
+        return x
+    
+    def aug_test(self, imgs, **kwargs): # TODO: pull request: add aug test to mmcls
+        logit = self.inference(imgs[0], **kwargs)
+        for i in range(1, len(imgs)):
+            cur_logit = self.inference(imgs[i])
+            logit += cur_logit
+        logit /= len(imgs)
+        # pred = F.softmax(logit, dim=1)
+        pred = logit
+        pred = pred.cpu().numpy()
+        # unravel batch dim
+        pred = list(pred)
+        return pred
+
+
+@CLASSIFIERS.register_module()
+class SiamImageClassifier(BaseClassifier):
+
+    def __init__(self, backbone: List[dict], neck=None, head=None, pretrained=None):
+        super().__init__()
+        self.backbone1 = build_backbone(backbone[0])
+        if len(backbone) == 1:
+            self.backbone2 = self.backbon1
+        else:
+            self.backbone2 = build_backbone(backbone[1])
+
+        if neck is not None:
+            self.neck = build_neck(neck)
+
+        if head is not None:
+            self.head = build_head(head)
+
+    def extract_feat(self, img):
+        """Directly extract features from the backbone + neck
+        """
+        x1, x2 = self.backbone1(img), self.backbone2(img)
+        if self.with_neck:
+            x1, x2 = self.neck(x1), self.neck(x2)
+        return x1, x2
+
+    def forward_train(self, img, gt_label, coarse_label, **kwargs):
+        """Forward computation during training.
+
+        Args:
+            img (Tensor): of shape (N, C, H, W) encoding input images.
+                Typically these should be mean centered and std scaled.
+
+            gt_label (Tensor): of shape (N, 1) encoding the ground-truth label
+                of input images.
+
+        Returns:
+            dict[str, Tensor]: a dictionary of loss components
+        """
+        x1, x2 = self.extract_feat(img)
+
+        losses = dict()
+        loss = self.head.forward_train(x1, x2, gt_label, coarse_label)
+        losses.update(loss)
+
+        return losses
+
+    def simple_test(self, img, **kwargs):
+        """Test without augmentation."""
+        x1, x2 = self.extract_feat(img)
+        return self.head.simple_test(x1, x2)
+    
+    def inference(self, img):
+        raise NotImplementedError
+        x = self.extract_feat(img)
+        x = self.head.extract_feat(x)
+        return x
+    
+    def aug_test(self, imgs, **kwargs): # TODO: pull request: add aug test to mmcls
+        raise NotImplementedError
+        logit = self.inference(imgs[0], **kwargs)
+        for i in range(1, len(imgs)):
+            cur_logit = self.inference(imgs[i])
+            logit += cur_logit
+        logit /= len(imgs)
+        # pred = F.softmax(logit, dim=1)
+        pred = logit
+        pred = pred.cpu().numpy()
+        # unravel batch dim
+        pred = list(pred)
+        return pred
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/classifiers/pool_vit.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/classifiers/pool_vit.py
new file mode 100644
index 0000000000000000000000000000000000000000..fb90b5e5ccbc5a9fdd619acb8a2bb54dc9b62288
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/classifiers/pool_vit.py
@@ -0,0 +1,219 @@
+import torch
+import torch.nn as nn
+
+from ..builder import CLASSIFIERS, build_backbone, build_head, build_neck
+from .base import BaseClassifier
+
+
+
+def compute_rollout_attention(all_layer_matrices, start_layer=0):
+    # adding residual consideration
+    num_tokens = all_layer_matrices[0].shape[1]
+    batch_size = all_layer_matrices[0].shape[0]
+    eye = torch.eye(num_tokens).expand(batch_size, num_tokens, num_tokens).to(all_layer_matrices[0].device)
+    all_layer_matrices = [all_layer_matrices[i] + eye for i in range(len(all_layer_matrices))]
+    # all_layer_matrices = [all_layer_matrices[i] / all_layer_matrices[i].sum(dim=-1, keepdim=True)
+    #                       for i in range(len(all_layer_matrices))]
+    joint_attention = all_layer_matrices[start_layer]
+    for i in range(start_layer+1, len(all_layer_matrices)):
+        joint_attention = all_layer_matrices[i].bmm(joint_attention)
+    return joint_attention
+
+
+
+
+@CLASSIFIERS.register_module()
+class PoolingVitClassifier(BaseClassifier):
+
+    def __init__(self, extractor, convert, vit, neck=None, head=None, pretrained=None, freeze_backbone=False):
+        super().__init__()
+        if extractor:
+            self.extractor = build_backbone(extractor)
+        if freeze_backbone and extractor:
+            print('freeze backbone: %s' % extractor['type'])
+            self.extractor.eval()
+            for param in self.extractor.parameters():
+                    param.requires_grad = False
+        if convert:
+            self.convert = build_neck(convert)
+        self.vit:nn.Module = build_backbone(vit)
+
+        if neck is not None:
+            self.neck = build_neck(neck)
+
+        if head is not None:
+            self.head = build_head(head)
+
+        self.init_weights(pretrained=pretrained)
+
+    def init_weights(self, pretrained=None):
+        super().init_weights(pretrained)
+        # self.backbone.init_weights(pretrained=pretrained)
+        if self.with_neck:
+            if isinstance(self.neck, nn.Sequential):
+                for m in self.neck:
+                    m.init_weights()
+            else:
+                self.neck.init_weights()
+        if self.with_head:
+            self.head.init_weights()
+
+    def extract_feat(self, img):
+        """Directly extract features from the backbone + neck
+        """
+        aux_loss = dict()
+        if hasattr(self, 'extractor'):
+            x = self.extractor(img)
+        else:
+            x = img
+        if hasattr(self, 'convert'):
+            x = self.convert(x)
+        else:
+            x = dict(x=x)
+        x = self.vit(**x)
+        if isinstance(x, dict):
+            aux_loss.update(x['loss'])
+            x = x['x']
+        if self.with_neck:
+            x = self.neck(x)
+        return x, aux_loss
+    
+    def extract_attn_map(self, img):
+        if hasattr(self, 'extractor'):
+            x = self.extractor(img)
+        else:
+            x = img
+        if hasattr(self, 'convert'):
+            x = self.convert(x)
+        else:
+            x = dict(x=x)
+        x = self.vit(**x)
+        return x['attn_map']
+
+    def forward_train(self, img, gt_label, au_label=None, **kwargs):
+        """Forward computation during training.
+
+        Args:
+            img (Tensor): of shape (N, C, H, W) encoding input images.
+                Typically these should be mean centered and std scaled.
+
+            gt_label (Tensor): of shape (N, 1) encoding the ground-truth label
+                of input images.
+
+        Returns:
+            dict[str, Tensor]: a dictionary of loss components
+        """
+        x, aux_loss = self.extract_feat(img)
+
+        if au_label is None:
+            losses = self.head.forward_train(x, gt_label)
+        else:
+            losses = self.head.forward_train(x, gt_label, au_label)
+        # losses['ce_loss'] = losses['loss']
+        # losses['loss'] *= 0.
+        # losses['aux_loss'] = aux_loss
+        losses.update(aux_loss)
+
+        return losses
+
+    def simple_test(self, img, **kwargs):
+        """Test without augmentation."""
+        x, _ = self.extract_feat(img)
+        return self.head.simple_test(x)
+    
+    def inference(self, img, **kwargs):
+        x, _ = self.extract_feat(img)
+        x = self.head.extract_feat(x)
+        return x
+    
+    def aug_test(self, imgs, **kwargs): # TODO: pull request: add aug test to mmcls
+        logit = self.inference(imgs[0], **kwargs)
+        for i in range(1, len(imgs)):
+            cur_logit = self.inference(imgs[i])
+            logit += cur_logit
+        logit /= len(imgs)
+        # pred = F.softmax(logit, dim=1)
+        pred = logit
+        pred = pred.cpu().numpy()
+        # unravel batch dim
+        pred = list(pred)
+        return pred
+
+    def relprop(self, cam=None,method="transformer_attribution", is_ablation=False, start_layer=0, **kwargs):
+        # print(kwargs)
+        # print("conservation 1", cam.sum())
+        cam = self.head.relprop(cam, **kwargs)
+        cam = cam.unsqueeze(1)
+        cam = self.vit.pool.relprop(cam, **kwargs)
+        cam = self.vit.norm.relprop(cam, **kwargs)
+        for blk in reversed(self.vit.blocks):
+            cam = blk.relprop(cam, **kwargs)
+
+        # print("conservation 2", cam.sum())
+        # print("min", cam.min())
+
+        if method == "full":
+            (cam, _) = self.vit.add.relprop(cam, **kwargs)
+            cam = cam[:, 1:]
+            cam = self.vit.patch_embed.relprop(cam, **kwargs)
+            # sum on channels
+            cam = cam.sum(dim=1)
+            return cam
+
+        elif method == "rollout":
+            # cam rollout
+            attn_cams = []
+            for blk in self.vit.blocks:
+                attn_heads = blk.attn.get_attn_cam().clamp(min=0)
+                avg_heads = (attn_heads.sum(dim=1) / attn_heads.shape[1]).detach()
+                attn_cams.append(avg_heads)
+            cam = compute_rollout_attention(attn_cams, start_layer=start_layer)
+            cam = cam[:, 0, 1:]
+            return cam
+        
+        # our method, method name grad is legacy
+        elif method == "transformer_attribution" or method == "grad":
+            cams = []
+            for blk in self.vit.blocks:
+                grad = blk.attn.get_attn_gradients()
+                cam = blk.attn.get_attn_cam()
+                cam = cam[0].reshape(-1, cam.shape[-1], cam.shape[-1])
+                grad = grad[0].reshape(-1, grad.shape[-1], grad.shape[-1])
+                cam = grad * cam
+                cam = cam.clamp(min=0).mean(dim=0)
+                cams.append(cam.unsqueeze(0))
+            rollout = compute_rollout_attention(cams, start_layer=start_layer)
+            cam = rollout[:, 0, 1:]
+            # cam = rollout[:, 1:, 0]
+            return cam
+            
+        elif method == "last_layer":
+            cam = self.vit.blocks[-1].attn.get_attn_cam()
+            cam = cam[0].reshape(-1, cam.shape[-1], cam.shape[-1])
+            if is_ablation:
+                grad = self.vit.blocks[-1].attn.get_attn_gradients()
+                grad = grad[0].reshape(-1, grad.shape[-1], grad.shape[-1])
+                cam = grad * cam
+            cam = cam.clamp(min=0).mean(dim=0)
+            cam = cam[0, 1:]
+            return cam
+
+        elif method == "last_layer_attn":
+            cam = self.vit.blocks[-1].attn.get_attn()
+            cam = cam[0].reshape(-1, cam.shape[-1], cam.shape[-1])
+            cam = cam.clamp(min=0).mean(dim=0)
+            cam = cam[0, 1:]
+            return cam
+
+        elif method == "second_layer":
+            cam = self.vit.blocks[1].attn.get_attn_cam()
+            cam = cam[0].reshape(-1, cam.shape[-1], cam.shape[-1])
+            if is_ablation:
+                grad = self.vit.blocks[1].attn.get_attn_gradients()
+                grad = grad[0].reshape(-1, grad.shape[-1], grad.shape[-1])
+                cam = grad * cam
+            cam = cam.clamp(min=0).mean(dim=0)
+            cam = cam[0, 1:]
+            return cam
+
+
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/heads/__init__.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/heads/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..f0c532e3b0eade3787e6f37663f4ffc41ad3a865
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/heads/__init__.py
@@ -0,0 +1,7 @@
+from .cls_head import ClsHead
+from .linear_head import LinearClsHead, MultiLinearClsHead
+from .cf_head import CFHead
+from .cf_siam_head import CFSiamHead, CFTreeHead
+
+__all__ = ['ClsHead', 'LinearClsHead', 'MultiLinearClsHead', 'CFHead', 'CFSiamHead', 'CFTreeHead'
+]
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/heads/base_head.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/heads/base_head.py
new file mode 100644
index 0000000000000000000000000000000000000000..b66d2f10d286fb50958af0da6697b6f8dc702c89
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/heads/base_head.py
@@ -0,0 +1,19 @@
+from abc import ABCMeta, abstractmethod
+
+import torch.nn as nn
+
+
+class BaseHead(nn.Module, metaclass=ABCMeta):
+    """Base head.
+
+    """
+
+    def __init__(self):
+        super(BaseHead, self).__init__()
+
+    def init_weights(self):
+        pass
+
+    @abstractmethod
+    def forward_train(self, x, gt_label, **kwargss):
+        pass
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/heads/cf_head.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/heads/cf_head.py
new file mode 100644
index 0000000000000000000000000000000000000000..6dbf695521a41886025dfbbaffe2525be594225d
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/heads/cf_head.py
@@ -0,0 +1,98 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.cnn import normal_init, constant_init
+
+from mmcls.models.losses import Accuracy
+from ..builder import HEADS, build_loss
+from .base_head import BaseHead
+
+
+@HEADS.register_module()
+class CFHead(BaseHead):
+
+    def __init__(self,
+                 num_classes,
+                 in_channels,
+                coarse_num_classes=4,
+                ada_weight=0.01,
+                coarse_weight=3,
+                fine_weight=5,
+                 topk=(1, )):
+        super().__init__()
+        self.in_channels = in_channels
+        self.num_classes = num_classes
+
+        if self.num_classes <= 0:
+            raise ValueError(
+                f'num_classes={num_classes} must be a positive integer')
+
+        self.topk = topk
+        self.compute_accuracy = Accuracy(topk=self.topk)
+        
+        self.coarse_num_classes = coarse_num_classes
+        # build layers
+        self.fc1f = nn.Linear(self.in_channels, 256)
+        self.bn1f = nn.BatchNorm1d(256)
+        self.fc2f = nn.Linear(256, num_classes)
+
+        self.fc1c = nn.Linear(self.in_channels, 256)
+        self.bn1c = nn.BatchNorm1d(256)
+        self.fc2c = nn.Linear(256, coarse_num_classes)
+        self.bn2c = nn.BatchNorm1d(coarse_num_classes)
+        self.fc3c = nn.Linear(coarse_num_classes, num_classes)
+        self.relu = nn.ReLU()
+        # build losses
+        self.ada_loss = build_loss(dict(type='AdaRegLoss', loss_weight=ada_weight))
+        self.coarse_loss = build_loss(dict(type='CrossEntropyLoss', loss_weight=coarse_weight))
+        self.fine_loss = build_loss(dict(type='CrossEntropyLoss', loss_weight=fine_weight))
+
+    def init_weights(self):
+        # normal_init(self.fc, mean=0, std=0.01, bias=0)
+        constant_init(self.fc, val=0, bias=0)
+    
+    def extract_feat(self, x1, x2):
+        i1 = self.fc1f(x1)
+        x = self.bn1f(i1)
+        x = self.relu(x)
+        k1 = self.fc2f(x)
+
+        i2 = self.fc1c(x2)
+        x = self.bn1c(i2)
+        x = self.relu(x)
+        j2 = self.fc2c(x)
+        x = self.bn2c(j2)
+        x = self.relu(x)
+        k2 = self.fc3c(x)
+        return dict(embedding_256=[i1, i2], coarse_score=j2, fine_score=[k1, k2])
+        
+    def simple_test(self, x1, x2):
+        """Test without augmentation."""
+        r = self.extract_feat(x1, x2)
+        fine_score = (r['fine_score'][0] + r['fine_score'][1]) / 2
+
+        fine_score = list(fine_score.detach().cpu().numpy())
+        coarse_score = list(r['coarse_score'].detach().cpu().numpy())
+        return coarse_score, fine_score
+
+    def forward_train(self, x1, x2, gt_label, coarse_gt_label):
+        r = self.extract_feat(x1, x2)
+        embedding_256 = (r['embedding_256'][0] + r['embedding_256'][1]) / 2
+        ada_loss = self.ada_loss(embedding_256, gt_label)
+        coarse_loss = self.coarse_loss(F.softmax(r['coarse_score'], dim=1), coarse_gt_label)
+
+        fine_score = (r['fine_score'][0] + r['fine_score'][1]) / 2
+        fine_loss = self.fine_loss(F.softmax(fine_score, dim=1), gt_label)
+        losses = dict(ada_loss=ada_loss, coarse_loss=coarse_loss, fine_loss=fine_loss)
+        # compute accuracy
+        coarse_acc = self.compute_accuracy(r['coarse_score'], coarse_gt_label)
+        assert len(coarse_acc) == len(self.topk)
+        losses['coarse_accuracy'] = {f'top-{k}': a for k, a in zip(self.topk, coarse_acc)}
+
+        fine_acc = self.compute_accuracy(fine_score, gt_label)
+        assert len(fine_acc) == len(self.topk)
+        losses['fine_acccuracy'] = {f'top-{k}': a for k, a in zip(self.topk, fine_acc)}
+
+        return losses
+
+
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/heads/cf_siam_head.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/heads/cf_siam_head.py
new file mode 100644
index 0000000000000000000000000000000000000000..b12887de1ba34a1a2a047743f4b6afa5ae2e65f7
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/heads/cf_siam_head.py
@@ -0,0 +1,179 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.cnn import normal_init, constant_init
+import numpy as np
+
+from mmcls.models.losses import Accuracy
+from ..builder import HEADS, build_loss
+from .base_head import BaseHead
+
+
+@HEADS.register_module()
+class CFSiamHead(BaseHead):
+
+    def __init__(self,
+                 num_classes,
+                 in_channels,
+                coarse_num_classes=4,
+                ada_weight=0.01,
+                coarse_weight=3,
+                fine_weight=5,
+                 topk=(1, )):
+        super().__init__()
+        self.in_channels = in_channels
+        self.num_classes = num_classes
+
+        if self.num_classes <= 0:
+            raise ValueError(
+                f'num_classes={num_classes} must be a positive integer')
+
+        self.topk = topk
+        self.compute_accuracy = Accuracy(topk=self.topk)
+        
+        self.coarse_num_classes = coarse_num_classes
+        # build layers
+        # self.fc1f = nn.Linear(self.in_channels, 256)
+        # self.bn1f = nn.BatchNorm1d(256)
+        self.fc2f = nn.Linear(self.in_channels, num_classes)
+
+        # self.fc1c = nn.Linear(self.in_channels, 256)
+        # self.bn1c = nn.BatchNorm1d(256)
+        self.fc2c = nn.Linear(self.in_channels, coarse_num_classes)
+
+        # self.relu = nn.ReLU()
+        # build losses
+        # self.ada_loss = build_loss(dict(type='AdaRegLoss', loss_weight=ada_weight))
+        self.coarse_loss = build_loss(dict(type='CrossEntropyLoss', loss_weight=coarse_weight))
+        self.fine_loss = build_loss(dict(type='CrossEntropyLoss', loss_weight=fine_weight))
+
+    def init_weights(self):
+        # normal_init(self.fc, mean=0, std=0.01, bias=0)
+        constant_init(self.fc, val=0, bias=0)
+    
+    def extract_feat(self, input):
+        # i1 = self.fc1f(input)
+        # x = self.bn1f(i1)
+        # x = self.relu(x)
+        k1 = self.fc2f(input)
+
+        # i2 = self.fc1c(input)
+        # x = self.bn1c(i2)
+        # x = self.relu(x)
+        j2 = self.fc2c(input)
+        return dict(
+            # embedding_256=[i1, i2], 
+            coarse_score=j2, fine_score=k1)
+        
+    def simple_test(self, x):
+        """Test without augmentation."""
+        r = self.extract_feat(x)
+        # fine_score = (r['fine_score'][0] + r['fine_score'][1]) / 2
+        fine_score = r['fine_score']
+        fine_score = list(fine_score.detach().cpu().numpy())
+        coarse_score = list(r['coarse_score'].detach().cpu().numpy())
+        return coarse_score, fine_score
+
+    def forward_train(self, x, gt_label, coarse_label):
+        r = self.extract_feat(x)
+        # embedding_256 = (r['embedding_256'][0] + r['embedding_256'][1]) / 2
+        # ada_loss = self.ada_loss(embedding_256, gt_label)
+        coarse_loss = self.coarse_loss(F.softmax(r['coarse_score'], dim=1), coarse_label)
+
+        fine_score = r['fine_score']
+        fine_loss = self.fine_loss(F.softmax(fine_score, dim=1), gt_label)
+        losses = dict(
+            # ada_loss=ada_loss, 
+            coarse_loss=coarse_loss, fine_loss=fine_loss)
+        # compute accuracy
+        coarse_acc = self.compute_accuracy(r['coarse_score'], coarse_label)
+        assert len(coarse_acc) == len(self.topk)
+        losses['coarse_accuracy'] = {f'coarse_top-{k}': a for k, a in zip(self.topk, coarse_acc)}
+
+        fine_acc = self.compute_accuracy(fine_score, gt_label)
+        assert len(fine_acc) == len(self.topk)
+        losses['fine_acccuracy'] = {f'fine_top-{k}': a for k, a in zip(self.topk, fine_acc)}
+
+        return losses
+
+@HEADS.register_module()
+class CFTreeHead(BaseHead):
+    """
+    The first branch A cls to 4 coarse classes, 
+    the second branch B cls to 4 negative classes.
+    """
+
+    def __init__(self,
+                 in_channels,
+                 neg_num_classes=4,
+                coarse_num_classes=4,
+                neg_weight=0.5,
+                coarse_weight=0.5,
+                 topk=(1, )):
+        super().__init__()
+        self.in_channels = in_channels
+        self.neg_num_classes = neg_num_classes
+        self.coarse_num_classes = coarse_num_classes
+
+
+        if self.neg_num_classes <= 0 or self.coarse_num_classes <= 0:
+            raise ValueError(
+                f'num_classes={neg_num_classes} must be a positive integer')
+
+        self.topk = topk
+        self.compute_accuracy = Accuracy(topk=self.topk)
+        
+        # build layers
+        self.fc1 = nn.Linear(self.in_channels, coarse_num_classes)
+        self.fc2 = nn.Linear(self.in_channels, neg_num_classes)
+
+        self.coarse_loss = build_loss(dict(type='CrossEntropyLoss', loss_weight=coarse_weight))
+        self.neg_loss = build_loss(dict(type='CrossEntropyLoss', loss_weight=neg_weight))
+
+    def init_weights(self):
+        # normal_init(self.fc, mean=0, std=0.01, bias=0)
+        constant_init(self.fc, val=0, bias=0)
+    
+    def extract_feat(self, input):
+
+        k1 = self.fc1(input)
+        j2 = self.fc2(input)
+        return dict(
+            coarse_score=k1, neg_score=j2)
+        
+    def simple_test(self, x):
+        """Test without augmentation."""
+        r = self.extract_feat(x)
+        coarse_score = r['coarse_score']
+        neg_score = r['neg_score']
+        coarse_score = coarse_score.detach().cpu().numpy()
+        neg_score = neg_score.detach().cpu().numpy()
+        results = np.array([coarse_score, neg_score]).transpose((1, 0, 2))
+        return list(results)
+
+    def forward_train(self, x, gt_label, coarse_label):
+        r = self.extract_feat(x)
+        coarse_score = r['coarse_score']
+        coarse_loss = self.coarse_loss(F.softmax(coarse_score, dim=1), coarse_label)
+
+        neg_index = coarse_label == 0
+        neg_score = r['neg_score'][neg_index]
+        neg_label = gt_label[neg_index]
+        neg_loss = self.neg_loss(F.softmax(neg_score, dim=1), neg_label)
+
+        losses = dict(
+            coarse_loss=coarse_loss, 
+            neg_loss=neg_loss
+            )
+        # compute accuracy
+        coarse_acc = self.compute_accuracy(coarse_score, coarse_label)
+        assert len(coarse_acc) == len(self.topk)
+        losses['coarse_accuracy'] = {f'coarse_top-{k}': a for k, a in zip(self.topk, coarse_acc)}
+
+        neg_acc = self.compute_accuracy(neg_score, neg_label)
+        assert len(neg_acc) == len(self.topk)
+        losses['neg_acccuracy'] = {f'neg_top-{k}': a for k, a in zip(self.topk, neg_acc)}
+
+        return losses
+
+
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/heads/cls_head.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/heads/cls_head.py
new file mode 100644
index 0000000000000000000000000000000000000000..585e3fc4a71c908fd6a905ab5f33b9fe11d65ccf
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/heads/cls_head.py
@@ -0,0 +1,65 @@
+import torch
+import torch.nn.functional as F
+
+from mmcls.models.losses import Accuracy
+from ..builder import HEADS, build_loss
+from .base_head import BaseHead
+
+
+@HEADS.register_module()
+class ClsHead(BaseHead):
+    """classification head.
+
+    Args:
+        loss (dict | list): Config of classification loss.  # TODO: ClsHead allow multi loss
+        topk (int | tuple): Top-k accuracy.
+    """  # noqa: W605
+
+    def __init__(self,
+                 loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+                 topk=(1, ),
+                 cal_acc=True,
+                 ):
+        super(ClsHead, self).__init__()
+
+        if isinstance(loss, dict):
+            loss = [loss]
+        assert isinstance(topk, (int, tuple))
+        if isinstance(topk, int):
+            topk = (topk, )
+        for _topk in topk:
+            assert _topk > 0, 'Top-k should be larger than 0'
+        self.topk = topk
+
+        self.compute_loss = [build_loss(i) for i in loss]
+        self.cal_acc = cal_acc
+        if cal_acc:
+            self.compute_accuracy = Accuracy(topk=self.topk)
+
+    def loss(self, cls_score, gt_label):
+        num_samples = len(cls_score)
+        losses = dict()
+        # compute loss
+        # TODO: export every loss to losses dict
+        loss = sum([f(cls_score, gt_label, avg_factor=num_samples) for f in self.compute_loss])
+        losses['ce_loss'] = loss
+        if self.cal_acc:
+            # compute accuracy
+            acc = self.compute_accuracy(cls_score, gt_label)
+            assert len(acc) == len(self.topk)
+            losses['accuracy'] = {f'top-{k}': a for k, a in zip(self.topk, acc)}
+        return losses
+
+    def forward_train(self, cls_score, gt_label):
+        losses = self.loss(cls_score, gt_label)
+        return losses
+
+    def simple_test(self, cls_score):
+        """Test without augmentation."""
+        if isinstance(cls_score, list):
+            cls_score = sum(cls_score) / float(len(cls_score))
+        pred = F.softmax(cls_score, dim=1) if cls_score is not None else None
+        if torch.onnx.is_in_onnx_export():
+            return pred
+        pred = list(pred.detach().cpu().numpy())
+        return pred
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/heads/linear_head.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/heads/linear_head.py
new file mode 100644
index 0000000000000000000000000000000000000000..b47e34c74835c32317bb17317d139e706b8ecf11
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/heads/linear_head.py
@@ -0,0 +1,192 @@
+import os
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.cnn import normal_init, constant_init
+
+from ..builder import HEADS, build_head
+from .cls_head import ClsHead
+from .base_head import BaseHead
+
+
+@HEADS.register_module()
+class LinearClsHead(ClsHead):
+    """Linear classifier head.
+
+    Args:
+        num_classes (int): Number of categories excluding the background
+            category.
+        in_channels (int): Number of channels in the input feature map.
+        loss (dict): Config of classification loss.
+    """  # noqa: W605
+
+    def __init__(self,
+                 num_classes,
+                 in_channels,
+                 loss=[dict(type='CrossEntropyLoss', loss_weight=1.0),],
+                 topk=(1, ),
+                 cal_acc=True,
+                 ):
+        super(LinearClsHead, self).__init__(loss=loss, topk=topk, cal_acc=cal_acc)
+        self.in_channels = in_channels
+        self.num_classes = num_classes
+
+        if self.num_classes <= 0:
+            raise ValueError(
+                f'num_classes={num_classes} must be a positive integer')
+
+        self._init_layers()
+
+    def _init_layers(self):
+        self.fc = nn.Linear(self.in_channels, self.num_classes)
+
+    def init_weights(self):
+        # normal_init(self.fc, mean=0, std=0.01, bias=0)
+        constant_init(self.fc, val=0, bias=0)
+    
+    def extract_feat(self, img):
+        cls_score = self.fc(img)
+        if isinstance(cls_score, list):
+            cls_score = sum(cls_score) / float(len(cls_score))
+        return cls_score
+
+    def simple_test(self, img):
+        if isinstance(img, tuple):
+            assert len(img) == 1
+            img = img[0]
+        """Test without augmentation."""
+        cls_score = self.fc(img)
+        if isinstance(cls_score, list):
+            cls_score = sum(cls_score) / float(len(cls_score))
+        pred = F.softmax(cls_score, dim=1) if cls_score is not None else None
+        if torch.onnx.is_in_onnx_export():
+            return pred
+        if os.environ.get('MODEL_VIS', '0') == '0':
+            pred = list(pred.detach().cpu().numpy())
+        return pred
+
+    def forward_train(self, x, gt_label):
+        if isinstance(x, tuple):
+            assert len(x) == 1
+            x = x[0]
+        gt_index = gt_label != 255 if gt_label.dim() == 1 else gt_label[:, 0] != 255
+        x = x[gt_index]
+        gt_label = gt_label[gt_index]
+        cls_score = self.fc(x)
+        losses = self.loss(cls_score, gt_label)
+        return losses
+
+
+@HEADS.register_module()
+class MultiLinearClsHead(ClsHead):
+    """Linear classifier head.
+
+    Args:
+        num_classes (int): Number of categories excluding the background
+            category.
+        in_channels (int): Number of channels in the input feature map.
+        loss (dict): Config of classification loss.
+    """  # noqa: W605
+
+    def __init__(self,
+                 num_classes,
+                 in_channels,
+                 hidden_channels,
+                 loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+                 topk=(1, ),
+                 cal_acc=True):
+        super().__init__(loss=loss, topk=topk, cal_acc=cal_acc)
+        self.in_channels = in_channels
+        self.hidden_channels = hidden_channels
+        self.num_classes = num_classes
+        self.last_score = None
+
+        if self.num_classes <= 0:
+            raise ValueError(
+                f'num_classes={num_classes} must be a positive integer')
+
+        self._init_layers()
+
+    def _init_layers(self):
+        self.fc1 = nn.Linear(self.in_channels, self.hidden_channels)
+        self.bn = nn.BatchNorm1d(self.hidden_channels)
+        self.fc2 = nn.Linear(self.hidden_channels, self.num_classes)
+        self.relu = nn.ReLU()
+
+    def init_weights(self):
+        constant_init(self.fc1, val=0, bias=0)
+        constant_init(self.fc2, val=0, bias=0)        
+    
+    def extract_feat(self, img):
+        x = self.fc1(img)
+        x = self.bn(x)
+        x = self.relu(x)
+        cls_score = self.fc2(x)
+        if isinstance(cls_score, list):
+            cls_score = sum(cls_score) / float(len(cls_score))
+        return cls_score
+
+    def simple_test(self, img):
+        """Test without augmentation."""
+        cls_score = self.extract_feat(img)
+        pred = F.softmax(cls_score, dim=1) if cls_score is not None else None
+        if torch.onnx.is_in_onnx_export():
+            return pred
+        pred = list(pred.detach().cpu().numpy())
+        return pred
+
+    def forward_train(self, x, gt_label):
+        gt_index = gt_label != 255 if gt_label.dim() == 1 else gt_label[:, 0] != 255
+        x = x[gt_index]
+        gt_label = gt_label[gt_index]
+
+        if len(x) >= 1:
+            cls_score = self.extract_feat(x)
+            losses = self.loss(cls_score, gt_label)
+        else:
+            # self.zero_grad()
+            # cls_score = self.extract_feat(torch.zeros([8, self.in_channels], device=x.device)).detach()
+            # losses = dict(ce_loss=torch.sum(cls_score)*0.)
+            losses = dict()
+        return losses
+
+
+@HEADS.register_module()
+class MultiTaskClsHead(BaseHead):
+    """Head for process CE and AU in parallel."""
+    def __init__(self, fc1, fc2, fc3=None):
+        super().__init__()
+        self.fc1 = build_head(fc1)
+        self.fc2 = build_head(fc2)
+        if fc3 is not None:
+            self.fc3 = build_head(fc3)
+    
+    def forward_train(self, x, gt_label, au_label, va_label=None):
+        losses = dict()
+        # gt_index = gt_label!=255
+        # gt_label = gt_label[gt_index]
+        # ce_x = x[gt_index]
+        ce_losses = self.fc1.forward_train(x, gt_label)
+        losses.update(ce_losses)
+
+        # au_index = au_label[:, 0] != 255
+        # au_label = au_label[au_index]
+        # au_x = x[au_index]
+        # if au_label[0][0] != 255:
+        au_losses = self.fc2.forward_train(x, au_label)
+        if 'ce_loss' in au_losses:
+            losses['au_loss'] = au_losses['ce_loss']
+        if va_label is not None:
+            av_loss = self.fc3.forward_train(x, va_label)
+            if 'ce_loss' in av_loss:
+                losses['av_loss'] = av_loss['ce_loss']
+        return losses
+    
+    def simple_test(self, img):
+        return self.fc1.simple_test(img)
+    
+    def aug_test(self, img):
+        pred = self.fc1.extract_feat(img)
+        pred = pred.detach().cpu()
+        return pred
+
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/losses/__init__.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/losses/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..519e44f34e57c4f177922c79638bca206de1c2f1
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/losses/__init__.py
@@ -0,0 +1,16 @@
+from .accuracy import Accuracy, accuracy
+from .cross_entropy_loss import CrossEntropyLoss, cross_entropy
+from .center_loss import CenterLoss
+from .eval_metrics import f1_score, precision, recall
+from .label_smooth_loss import LabelSmoothLoss, label_smooth
+from .utils import reduce_loss, weight_reduce_loss, weighted_loss
+from .ada_reg_loss import AdaRegLoss
+from .diverse import DiverseCosineLoss, DiverseEuclidLoss, SparceLoss
+from .focal_loss import FocalLoss
+
+__all__ = [
+    'accuracy', 'Accuracy', 'cross_entropy', 'CrossEntropyLoss', 'reduce_loss',
+    'weight_reduce_loss', 'label_smooth', 'LabelSmoothLoss', 'weighted_loss',
+    'precision', 'recall', 'f1_score', 'CenterLoss', 'AdaRegLoss',
+    'DiverseCosineLoss', 'DiverseEuclidLoss', 'SparceLoss', 'FocalLoss'
+]
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/losses/accuracy.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/losses/accuracy.py
new file mode 100644
index 0000000000000000000000000000000000000000..d3fa576451719dbfb18d6e64fb8baaf681ee99f3
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/losses/accuracy.py
@@ -0,0 +1,90 @@
+import numpy as np
+import torch
+import torch.nn as nn
+
+
+def accuracy_numpy(pred, target, topk):
+    res = []
+    maxk = max(topk)
+    num = pred.shape[0]
+    pred_label = pred.argsort(axis=1)[:, -maxk:][:, ::-1]
+
+    for k in topk:
+        correct_k = np.logical_or.reduce(
+            pred_label[:, :k] == target.reshape(-1, 1), axis=1)
+        res.append(correct_k.sum() * 100. / num)
+    return res
+
+
+def accuracy_torch(pred, target, topk=1):
+    res = []
+    maxk = max(topk)
+    num = pred.size(0)
+    _, pred_label = pred.topk(maxk, dim=1)
+    pred_label = pred_label.t()
+    correct = pred_label.eq(target.view(1, -1).expand_as(pred_label))
+
+    for k in topk:
+        correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)
+        res.append(correct_k.mul_(100. / num))
+    return res
+
+
+def accuracy(pred, target, topk=1):
+    """Calculate accuracy according to the prediction and target
+
+    Args:
+        pred (torch.Tensor | np.array): The model prediction.
+        target (torch.Tensor | np.array): The target of each prediction
+        topk (int | tuple[int], optional): If the predictions in ``topk``
+            matches the target, the predictions will be regarded as
+            correct ones. Defaults to 1.
+
+    Returns:
+        float | tuple[float]: If the input ``topk`` is a single integer,
+            the function will return a single float as accuracy. If
+            ``topk`` is a tuple containing multiple integers, the
+            function will return a tuple containing accuracies of
+            each ``topk`` number.
+    """
+    assert isinstance(topk, (int, tuple))
+    if isinstance(topk, int):
+        topk = (topk, )
+        return_single = True
+    else:
+        return_single = False
+
+    if isinstance(pred, torch.Tensor) and isinstance(target, torch.Tensor):
+        res = accuracy_torch(pred, target, topk)
+    elif isinstance(pred, np.ndarray) and isinstance(target, np.ndarray):
+        res = accuracy_numpy(pred, target, topk)
+    else:
+        raise TypeError('pred and target should both be'
+                        'torch.Tensor or np.ndarray')
+
+    return res[0] if return_single else res
+
+
+class Accuracy(nn.Module):
+
+    def __init__(self, topk=(1, )):
+        """Module to calculate the accuracy
+
+        Args:
+            topk (tuple, optional): The criterion used to calculate the
+                accuracy. Defaults to (1,).
+        """
+        super().__init__()
+        self.topk = topk
+
+    def forward(self, pred, target):
+        """Forward function to calculate accuracy
+
+        Args:
+            pred (torch.Tensor): Prediction of models.
+            target (torch.Tensor): Target for each prediction.
+
+        Returns:
+            tuple[float]: The accuracies under different topk criterions.
+        """
+        return accuracy(pred, target, self.topk)
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/losses/ada_reg_loss.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/losses/ada_reg_loss.py
new file mode 100644
index 0000000000000000000000000000000000000000..ff648a457e9d26eee82c18859b9a65fb16fd6843
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/losses/ada_reg_loss.py
@@ -0,0 +1,54 @@
+import torch.nn as nn
+import torch.nn.functional as F
+import torch
+
+from ..builder import LOSSES
+from .utils import weight_reduce_loss
+
+
+@LOSSES.register_module()
+class AdaRegLoss(nn.Module):
+    """Center loss.
+    
+    Reference:
+    Wen et al. A Discriminative Feature Learning Approach for Deep Face Recognition. ECCV 2016.
+    
+    Args:
+        num_classes (int): number of classes.
+        feat_dim (int): feature dimension.
+    """
+    def __init__(self, num_classes=10, loss_weight=1.0):
+        super().__init__()
+        self.num_classes = num_classes
+        self.loss_weight = loss_weight
+    
+    def cal_weights(self, gt_labels):
+        value, count = torch.unique(gt_labels, return_counts=True)
+        results = dict()
+        for v, c in zip(value, count):
+            results[v.item()] = c.item()
+        weights = []
+        for i in range(self.num_classes):
+            weights.append(1- results.get(i, 0) / len(gt_labels))
+        return weights
+
+    def forward(self, x, gt_label):
+        """
+        Args:
+            x: averaged 256d feature (batch_size, 256).
+            gt_label: ground truth labels(7 or 8 classes).
+        """
+        dist = torch.nn.functional.pdist(x, p=2)
+        weights = self.cal_weights(gt_label)
+        dist_weight = []
+        for i in range(len(gt_label)):
+            dist_weight +=  [weights[gt_label[i]]] * (len(gt_label) - i - 1)
+        dist_weight = torch.tensor(dist_weight).cuda()
+        dist = torch.mul(dist, dist_weight)
+        loss = 1 / (10 * dist.sum())
+        loss *= self.loss_weight
+
+        return loss
+
+
+
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/losses/center_loss.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/losses/center_loss.py
new file mode 100644
index 0000000000000000000000000000000000000000..e72d113dff8363f2020272c4013b7592f4486ab5
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/losses/center_loss.py
@@ -0,0 +1,56 @@
+import torch.nn as nn
+import torch.nn.functional as F
+import torch
+
+from ..builder import LOSSES
+from .utils import weight_reduce_loss
+
+
+@LOSSES.register_module()
+class CenterLoss(nn.Module):
+    """Center loss.
+    
+    Reference:
+    Wen et al. A Discriminative Feature Learning Approach for Deep Face Recognition. ECCV 2016.
+    
+    Args:
+        num_classes (int): number of classes.
+        feat_dim (int): feature dimension.
+    """
+    def __init__(self, num_classes=10, feat_dim=2, use_gpu=True, loss_weight=1.0):
+        super(CenterLoss, self).__init__()
+        self.num_classes = num_classes
+        self.feat_dim = feat_dim
+        self.use_gpu = use_gpu
+        self.loss_weight = loss_weight
+
+        if self.use_gpu:
+            self.centers = nn.Parameter(torch.randn(self.num_classes, self.feat_dim).cuda())
+        else:
+            self.centers = nn.Parameter(torch.randn(self.num_classes, self.feat_dim))
+
+    def forward(self, x, labels, **kwargs):
+        """
+        Args:
+            x: feature matrix with shape (batch_size, feat_dim).
+            labels: ground truth labels with shape (batch_size).
+        """
+        # print(f'x shape: {x.shape}')
+        # print(f'labels shape: {labels.shape}')
+        batch_size = x.size(0)
+        distmat = torch.pow(x, 2).sum(dim=1, keepdim=True).expand(batch_size, self.num_classes) + \
+                  torch.pow(self.centers, 2).sum(dim=1, keepdim=True).expand(self.num_classes, batch_size).t()
+        # distmat.addmm_(1, -2, x, self.centers.t())
+        # https://github.com/KaiyangZhou/pytorch-center-loss/issues/16
+        distmat.addmm_(x, self.centers.t(), beta=1, alpha=-2)
+
+        classes = torch.arange(self.num_classes).long()
+        if self.use_gpu: classes = classes.cuda()
+        labels = labels.unsqueeze(1).expand(batch_size, self.num_classes)
+        mask = labels.eq(classes.expand(batch_size, self.num_classes))
+
+        dist = distmat * mask.float()
+        loss = dist.clamp(min=1e-12, max=1e+12).sum() / batch_size
+        loss *= self.loss_weight
+
+        return loss
\ No newline at end of file
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/losses/cross_entropy_loss.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/losses/cross_entropy_loss.py
new file mode 100644
index 0000000000000000000000000000000000000000..cb3176a29bf5e23220f9a70aa6dc8884946a8cde
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/losses/cross_entropy_loss.py
@@ -0,0 +1,66 @@
+import torch.nn as nn
+import torch.nn.functional as F
+import torch
+
+from ..builder import LOSSES
+from .utils import weight_reduce_loss
+
+
+def cross_entropy(pred, label, weight=None, reduction='mean', avg_factor=None):
+    # element-wise losses
+    loss = F.cross_entropy(pred, label, reduction='none')
+
+    # apply weights and do the reduction
+    if weight is not None:
+        weight = weight.float()
+    loss = weight_reduce_loss(
+        loss, weight=weight, reduction=reduction, avg_factor=avg_factor)
+
+    return loss
+
+
+@LOSSES.register_module()
+class CrossEntropyLoss(nn.Module):
+
+    def __init__(self, reduction='mean', loss_weight=1.0):
+        super(CrossEntropyLoss, self).__init__()
+        self.reduction = reduction
+        self.loss_weight = loss_weight
+
+        self.cls_criterion = cross_entropy
+
+    def forward(self,
+                cls_score,
+                label,
+                weight=None,
+                avg_factor=None,
+                reduction_override=None,
+                **kwargs):
+        assert reduction_override in (None, 'none', 'mean', 'sum')
+        reduction = (
+            reduction_override if reduction_override else self.reduction)
+        loss_cls = self.loss_weight * self.cls_criterion(
+            cls_score,
+            label,
+            weight,
+            reduction=reduction,
+            avg_factor=avg_factor,
+            **kwargs)
+        return loss_cls
+
+
+@LOSSES.register_module()
+class WeightCrossEntropyLoss(nn.Module):
+
+    def __init__(self, loss_weight=1.0, weight=None):
+        super().__init__()
+        self.loss_weight = loss_weight
+        if weight is not None:
+            weight = torch.Tensor(weight)
+        self.cls_criterion = nn.CrossEntropyLoss(weight=weight)
+    
+    def forward(self,
+                cls_score,
+                label, *args, **kwargs):
+        return self.loss_weight * self.cls_criterion(cls_score, label)
+
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/losses/diverse.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/losses/diverse.py
new file mode 100644
index 0000000000000000000000000000000000000000..60c05d4754c6155b752b25c7d390121fc78c022b
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/losses/diverse.py
@@ -0,0 +1,148 @@
+from typing import List
+import torch.nn as nn
+import torch.nn.functional as F
+import torch
+
+from ..builder import LOSSES
+from .utils import weight_reduce_loss
+
+
+# Varies Diverse Loss
+# input x should be a list of Tensor of [batch, features], features could have any dimension
+
+
+@LOSSES.register_module()
+class DiverseCosineLoss(nn.Module):
+    def __init__(self, loss_weight=1.0, margin=0.2, z_bias=1e-6, temperature=1., simple=True):
+        super().__init__()
+        self.loss_weight = loss_weight
+        self.margin = margin
+        self.z_bias = z_bias
+        self.simple = simple
+        self.temperature = temperature
+        # self.cos = nn.CosineSimilarity(dim=1, eps=1e-6)
+        
+    def wang_forward(self, x):
+        loss = 0.0
+        num = 0
+        for i in range(len(x)):
+            for j in range(i + 1, len(x)):
+                x0 = torch.flatten(x[i], start_dim=1)
+                x1 = torch.flatten(x[j], start_dim=1)
+
+                dot = torch.sum(x0*x1, 1)
+                D1 = torch.sqrt(torch.sum(torch.pow(x0,2), 1))
+                D2 = torch.sqrt(torch.sum(torch.pow(x1,2), 1))
+                
+                t = dot * (D1*D2 + self.z_bias)**(-1)
+                dist = torch.clamp(t, min=self.margin)
+
+                loss = loss + torch.sum(dist) / x0.shape[0]
+                num = num + 1
+        return loss / num * self.loss_weight
+    
+    def xue_forward(self, x):
+        loss_sum = 0.0
+        num = 0
+        if len(x) <= 1:
+            return torch.tensor(0.).cuda()
+        for i in range(len(x)):
+            for j in range(i + 1, len(x)):
+                p = torch.flatten(x[i], start_dim=1)
+                q = torch.flatten(x[j], start_dim=1)
+                p = F.normalize(p, dim=1)  # l2-normalize
+                q = F.normalize(q, dim=1)  # l2-normalize
+
+                loss = p * q
+                loss = torch.exp(loss / self.temperature) - 1
+                
+                # loss = loss.sum(dim=1).mean()
+                loss = torch.clamp(loss, min=self.margin)
+                loss = loss.mean()
+
+                # version2 
+                # loss = torch.log(loss)
+                # loss = F.cosine_embedding_loss(p, q, torch.ones(p.shape[0]).cuda(), margin=self.margin, reduction='mean')
+                # loss = self.cos(p, q)
+
+                loss_sum = loss_sum + loss
+                num += 1
+        return loss_sum / num * self.loss_weight
+
+    def forward(self, x):
+        if self.simple:
+            return self.xue_forward(x)
+        return self.wang_forward(x)
+
+
+@LOSSES.register_module()
+class DiverseEuclidLoss(torch.nn.Module):
+    def __init__(self, margin=1.0, loss_weight=1.0):
+        super().__init__()
+        self.margin = margin
+        self.loss_weight = loss_weight
+
+    def forward(self, x:List):
+        """
+        x: numbers of attention maps, shape(H, W)
+        [Tensor((B, C, H, W)), ]
+        """
+        loss = 0.0
+        num = 0
+        for i in range(len(x)):
+            for j in range(i + 1, len(x)):
+                x0 = torch.squeeze(x[i])
+                x1 = torch.squeeze(x[j])
+                # euclidian distance
+                diff = x0 - x1
+                dist_sq = torch.sum(torch.sum(torch.pow(diff, 2), 1),1)
+                dist = torch.sqrt(dist_sq) / (x0.size()[2] * x0.size()[1])
+
+                mdist = self.margin - dist
+                dist = torch.clamp(mdist, min=0.0)
+                loss += torch.sum(dist) / 2.0 / x0.size()[0]    #TODO: loss function
+                num += 1
+        loss = loss / num
+        return loss * self.loss_weight
+
+
+@LOSSES.register_module()
+class KLDivLoss(nn.Module):
+    def __init__(self, loss_weight=1.0):
+        super().__init__()
+        self.loss_weight = loss_weight
+        self.criterion = nn.KLDivLoss(reduction="batchmean")
+
+    def forward(self, x:List):
+        loss_sum = 0.0
+        num = 0
+        for i in range(len(x)):
+            for j in range(i + 1, len(x)):
+                p = torch.flatten(x[i], start_dim=1)
+                q = torch.flatten(x[j], start_dim=1)
+                p = torch.softmax(p, dim=1)
+                q = torch.softmax(q, dim=1)
+
+                loss = self.criterion(torch.log(p), q)
+                loss_sum -= loss
+                num += 1
+        if num == 0:
+            return torch.tensor(0.).cuda()
+        loss = loss_sum / num
+        return loss * self.loss_weight
+
+
+@LOSSES.register_module()
+class SparceLoss(nn.Module):
+    
+    def __init__(self, loss_weight=1.0):
+        super().__init__()
+        self.loss_weight = loss_weight
+        
+    def forward(self, x:List):
+        loss3 = 0.0
+        for i in range(len(x)):
+            mask = x[i].squeeze()   # [H, W]
+            loss3 += mask.pow(2).sum(1).mean()
+        loss3 = loss3/len(x)
+        return loss3 * self.loss_weight
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/losses/eval_metrics.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/losses/eval_metrics.py
new file mode 100644
index 0000000000000000000000000000000000000000..c3e48cbbe62d4d0fe09e12411ec8f695a5c87a85
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/losses/eval_metrics.py
@@ -0,0 +1,124 @@
+import numpy as np
+import torch
+
+
+def calculate_confusion_matrix(pred, target):
+    if isinstance(pred, np.ndarray) and isinstance(target, np.ndarray):
+        pred = torch.from_numpy(pred)
+        target = torch.from_numpy(target)
+    elif not (isinstance(pred, torch.Tensor)
+              and isinstance(target, torch.Tensor)):
+        raise TypeError('pred and target should both be'
+                        'torch.Tensor or np.ndarray')
+    _, pred_label = pred.topk(1, dim=1)
+    num_classes = pred.size(1)
+    pred_label = pred_label.view(-1)
+    target_label = target.view(-1)
+    assert len(pred_label) == len(target_label)
+    confusion_matrix = torch.zeros(num_classes, num_classes)
+    with torch.no_grad():
+        for t, p in zip(target_label, pred_label):
+            confusion_matrix[t.long(), p.long()] += 1
+    return confusion_matrix
+
+
+def class_accuracy(pred, target, classes=None):
+    confusion_matrix = calculate_confusion_matrix(pred, target)
+    # plot_confusion_matrix(confusion_matrix.type(torch.long), classes, normalize=True, title='Confusion matrix', cmap=plt.cm.Blues)
+    with torch.no_grad():
+        result = []
+        for i in range(confusion_matrix.shape[0]):
+            acc = confusion_matrix[i][i] / confusion_matrix[i].sum()
+            result.append(acc.item() * 100)
+    return result
+
+
+def precision(pred, target):
+    """Calculate macro-averaged precision according to the prediction and target
+
+    Args:
+        pred (torch.Tensor | np.array): The model prediction.
+        target (torch.Tensor | np.array): The target of each prediction.
+
+    Returns:
+        float: The function will return a single float as precision.
+    """
+    confusion_matrix = calculate_confusion_matrix(pred, target)
+    with torch.no_grad():
+        res = confusion_matrix.diag() / torch.clamp(
+            confusion_matrix.sum(0), min=1)
+        res = res.mean().item() * 100
+    return res
+
+
+def recall(pred, target):
+    """Calculate macro-averaged recall according to the prediction and target
+
+    Args:
+        pred (torch.Tensor | np.array): The model prediction.
+        target (torch.Tensor | np.array): The target of each prediction.
+
+    Returns:
+        float: The function will return a single float as recall.
+    """
+    confusion_matrix = calculate_confusion_matrix(pred, target)
+    with torch.no_grad():
+        res = confusion_matrix.diag() / torch.clamp(
+            confusion_matrix.sum(1), min=1)
+        res = res.mean().item() * 100
+    return res
+
+
+def f1_score(pred, target):
+    """Calculate macro-averaged F1 score according to the prediction and target
+
+    Args:
+        pred (torch.Tensor | np.array): The model prediction.
+        target (torch.Tensor | np.array): The target of each prediction.
+
+    Returns:
+        float: The function will return a single float as F1 score.
+    """
+    confusion_matrix = calculate_confusion_matrix(pred, target)
+    with torch.no_grad():
+        precision = confusion_matrix.diag() / torch.clamp(
+            confusion_matrix.sum(1), min=1)
+        recall = confusion_matrix.diag() / torch.clamp(
+            confusion_matrix.sum(0), min=1)
+        res = 2 * precision * recall / torch.clamp(
+            precision + recall, min=1e-20)
+        res = torch.where(torch.isnan(res), torch.full_like(res, 0), res)
+        res = res.mean().item() * 100
+    return res
+
+
+import itertools
+import matplotlib.pyplot as plt
+
+def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):
+    if normalize:
+        cm = cm.type(torch.float32)
+        sum_row = cm.sum(dim=1)
+        cm = cm / sum_row.unsqueeze(1)
+        print("Normalized confusion matrix")
+    else:
+        print('Confusion matrix, without normalization')
+
+    # print(cm)
+    fig = plt.figure()
+    plt.imshow(cm, interpolation='nearest', cmap=cmap)
+    plt.title(title)
+    plt.colorbar()
+    tick_marks = np.arange(len(classes))
+    plt.xticks(tick_marks, classes, rotation=45)
+    plt.yticks(tick_marks, classes)
+
+    fmt = '.2f' if normalize else 'd'
+    thresh = cm.max() / 2.
+    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
+        plt.text(j, i, format(cm[i, j], fmt), horizontalalignment="center", color="white" if cm[i, j] > thresh else "black")
+
+    plt.tight_layout()
+    plt.ylabel('True label')
+    plt.xlabel('Predicted label')
+    fig.savefig('output/confusion_matrix_FERPlus.jpg')
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/losses/focal_loss.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/losses/focal_loss.py
new file mode 100644
index 0000000000000000000000000000000000000000..747397150343fcdb9e25a84c77a9728f942ea8e0
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/losses/focal_loss.py
@@ -0,0 +1,199 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ..builder import LOSSES
+from .utils import weight_reduce_loss
+
+
+def sigmoid_focal_loss(pred,
+                       target,
+                       weight=None,
+                       gamma=2.0,
+                       alpha=0.25,
+                       reduction='mean',
+                       avg_factor=None):
+    r"""Sigmoid focal loss.
+    Args:
+        pred (torch.Tensor): The prediction with shape (N, \*).
+        target (torch.Tensor): The ground truth label of the prediction with
+            shape (N, \*).
+        weight (torch.Tensor, optional): Sample-wise loss weight with shape
+            (N, ). Defaults to None.
+        gamma (float): The gamma for calculating the modulating factor.
+            Defaults to 2.0.
+        alpha (float): A balanced form for Focal Loss. Defaults to 0.25.
+        reduction (str): The method used to reduce the loss.
+            Options are "none", "mean" and "sum". If reduction is 'none' ,
+            loss is same shape as pred and label. Defaults to 'mean'.
+        avg_factor (int, optional): Average factor that is used to average
+            the loss. Defaults to None.
+    Returns:
+        torch.Tensor: Loss.
+    """
+    assert pred.shape == \
+        target.shape, 'pred and target should be in the same shape.'
+    pred_sigmoid = pred.sigmoid()
+    target = target.type_as(pred)
+    pt = (1 - pred_sigmoid) * target + pred_sigmoid * (1 - target)
+    focal_weight = (alpha * target + (1 - alpha) *
+                    (1 - target)) * pt.pow(gamma)
+    loss = F.binary_cross_entropy_with_logits(
+        pred, target, reduction='none') * focal_weight
+    if weight is not None:
+        assert weight.dim() == 1
+        weight = weight.float()
+        if pred.dim() > 1:
+            weight = weight.reshape(-1, 1)
+    loss = weight_reduce_loss(loss, weight, reduction, avg_factor)
+    return loss
+
+
+@LOSSES.register_module()
+class FocalLoss(nn.Module):
+    """Focal loss.
+    Args:
+        gamma (float): Focusing parameter in focal loss.
+            Defaults to 2.0.
+        alpha (float): The parameter in balanced form of focal
+            loss. Defaults to 0.25.
+        reduction (str): The method used to reduce the loss into
+            a scalar. Options are "none" and "mean". Defaults to 'mean'.
+        loss_weight (float): Weight of loss. Defaults to 1.0.
+    """
+
+    def __init__(self,
+                 gamma=2.0,
+                 alpha=0.25,
+                 reduction='mean',
+                 loss_weight=1.0):
+
+        super(FocalLoss, self).__init__()
+        self.gamma = gamma
+        self.alpha = alpha
+        self.reduction = reduction
+        self.loss_weight = loss_weight
+
+    def forward(self,
+                pred,
+                target,
+                weight=None,
+                avg_factor=None,
+                reduction_override=None):
+        r"""Sigmoid focal loss.
+        Args:
+            pred (torch.Tensor): The prediction with shape (N, \*).
+            target (torch.Tensor): The ground truth label of the prediction
+                with shape (N, \*).
+            weight (torch.Tensor, optional): Sample-wise loss weight with shape
+                (N, \*). Defaults to None.
+            avg_factor (int, optional): Average factor that is used to average
+                the loss. Defaults to None.
+            reduction_override (str, optional): The method used to reduce the
+                loss into a scalar. Options are "none", "mean" and "sum".
+                Defaults to None.
+        Returns:
+            torch.Tensor: Loss.
+        """
+        assert reduction_override in (None, 'none', 'mean', 'sum')
+        reduction = (
+            reduction_override if reduction_override else self.reduction)
+        loss_cls = self.loss_weight * sigmoid_focal_loss(
+            pred,
+            target,
+            weight,
+            gamma=self.gamma,
+            alpha=self.alpha,
+            reduction=reduction,
+            avg_factor=avg_factor)
+        return loss_cls
+
+
+@LOSSES.register_module()
+class FocalLossOri(nn.Module):
+    """
+    stolen from: https://github.com/ratkhohieu/ABAW2021/blob/214e96ff95313293a232f11fe4516ae9e468f028/metrics.py
+    This is a implementation of Focal Loss with smooth label cross entropy supported which is proposed in
+    'Focal Loss for Dense Object Detection. (https://arxiv.org/abs/1708.02002)'
+    Focal_Loss= -1*alpha*((1-pt)**gamma)*log(pt)
+    Args:
+        num_class: number of classes
+        alpha: class balance factor
+        gamma:
+        ignore_index:
+        reduction:
+    """
+
+    def __init__(self, num_class, alpha=None, gamma=2.0, ignore_index=255, reduction='mean', loss_weight=1.0):
+        super(FocalLossOri, self).__init__()
+        self.num_class = num_class
+        self.gamma = gamma
+        self.reduction = reduction
+        self.smooth = 1e-4
+        self.ignore_index = ignore_index
+        self.alpha = alpha
+        self.loss_weight = loss_weight
+        if alpha is None:
+            self.alpha = torch.ones(num_class, )
+        elif isinstance(alpha, (int, float)):
+            self.alpha = torch.as_tensor([alpha] * num_class)
+        elif isinstance(alpha, (list, np.ndarray)):
+            self.alpha = torch.as_tensor(alpha)
+        if self.alpha.shape[0] != num_class:
+            raise RuntimeError('the length not equal to number of class')
+
+        # if isinstance(self.alpha, (list, tuple, np.ndarray)):
+        #     assert len(self.alpha) == self.num_class
+        #     self.alpha = torch.Tensor(list(self.alpha))
+        # elif isinstance(self.alpha, (float, int)):
+        #     assert 0 < self.alpha < 1.0, 'alpha should be in `(0,1)`)'
+        #     assert balance_index > -1
+        #     alpha = torch.ones((self.num_class))
+        #     alpha *= 1 - self.alpha
+        #     alpha[balance_index] = self.alpha
+        #     self.alpha = alpha
+        # elif isinstance(self.alpha, torch.Tensor):
+        #     self.alpha = self.alpha
+        # else:
+        #     raise TypeError('Not support alpha type, expect `int|float|list|tuple|torch.Tensor`')
+
+    def forward(self, logit, target,
+                weight=None,
+                avg_factor=None,
+                reduction_override=None):
+        # assert isinstance(self.alpha,torch.Tensor)\
+        N, C = logit.shape[:2]
+        alpha = self.alpha.to(logit.device)
+        prob = F.softmax(logit, dim=1)
+        if prob.dim() > 2:
+            # N,C,d1,d2 -> N,C,m (m=d1*d2*...)
+            prob = prob.view(N, C, -1)
+            prob = prob.transpose(1, 2).contiguous()  # [N,C,d1*d2..] -> [N,d1*d2..,C]
+            prob = prob.view(-1, prob.size(-1))  # [N,d1*d2..,C]-> [N*d1*d2..,C]
+        ori_shp = target.shape
+        target = target.view(-1, 1)  # [N,d1,d2,...]->[N*d1*d2*...,1]
+        valid_mask = None
+        if self.ignore_index is not None:
+            valid_mask = target != self.ignore_index
+            target = target * valid_mask
+
+        # ----------memory saving way--------
+        prob = prob.gather(1, target).view(-1) + self.smooth  # avoid nan
+        logpt = torch.log(prob)
+        # alpha_class = alpha.gather(0, target.view(-1))
+        alpha_class = alpha[target.squeeze().long()]
+        class_weight = -alpha_class * torch.pow(torch.sub(1.0, prob), self.gamma)
+        loss = class_weight * logpt
+        if valid_mask is not None:
+            loss = loss * valid_mask.squeeze()
+
+        if self.reduction == 'mean':
+            loss = loss.mean()
+            if valid_mask is not None:
+                loss = loss.sum() / valid_mask.sum()
+        elif self.reduction == 'none':
+            loss = loss.view(ori_shp)
+
+        loss *= self.loss_weight
+        return loss
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/losses/label_smooth_loss.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/losses/label_smooth_loss.py
new file mode 100644
index 0000000000000000000000000000000000000000..85e6847a04b57b8598b9e30c79829572bdf8c74b
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/losses/label_smooth_loss.py
@@ -0,0 +1,68 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ..builder import LOSSES
+from .utils import weight_reduce_loss
+
+
+def label_smooth(pred,
+                 label,
+                 label_smooth_val,
+                 avg_smooth_val,
+                 weight=None,
+                 reduction='mean',
+                 avg_factor=None):
+    # # element-wise losses
+    one_hot = torch.zeros_like(pred)
+    one_hot.fill_(avg_smooth_val)
+    label = label.view(-1, 1)
+    one_hot.scatter_(1, label, 1 - label_smooth_val + avg_smooth_val)
+
+    loss = -torch.sum(F.log_softmax(pred, 1) * (one_hot.detach()))
+
+    # apply weights and do the reduction
+    if weight is not None:
+        weight = weight.float()
+    loss = weight_reduce_loss(
+        loss, weight=weight, reduction=reduction, avg_factor=avg_factor)
+
+    return loss
+
+
+@LOSSES.register_module()
+class LabelSmoothLoss(nn.Module):
+
+    def __init__(self,
+                 label_smooth_val,
+                 num_classes,
+                 reduction='mean',
+                 loss_weight=1.0):
+        super(LabelSmoothLoss, self).__init__()
+        self.label_smooth_val = label_smooth_val
+        self.avg_smooth_val = self.label_smooth_val / num_classes
+        self.reduction = reduction
+        self.loss_weight = loss_weight
+
+        self.cls_criterion = label_smooth
+
+    def forward(self,
+                cls_score,
+                label,
+                weight=None,
+                avg_factor=None,
+                reduction_override=None,
+                **kwargs):
+        assert reduction_override in (None, 'none', 'mean', 'sum')
+        reduction = (
+            reduction_override if reduction_override else self.reduction)
+        loss_cls = self.loss_weight * self.cls_criterion(
+            cls_score,
+            label,
+            self.label_smooth_val,
+            self.avg_smooth_val,
+            weight,
+            reduction=reduction,
+            avg_factor=avg_factor,
+            **kwargs)
+        return loss_cls
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/losses/utils.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/losses/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..3361c6cad198e8b8e47173bf033b58c894eda106
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/losses/utils.py
@@ -0,0 +1,98 @@
+import functools
+
+import torch.nn.functional as F
+
+
+def reduce_loss(loss, reduction):
+    """Reduce loss as specified.
+
+    Args:
+        loss (Tensor): Elementwise loss tensor.
+        reduction (str): Options are "none", "mean" and "sum".
+
+    Return:
+        Tensor: Reduced loss tensor.
+    """
+    reduction_enum = F._Reduction.get_enum(reduction)
+    # none: 0, elementwise_mean:1, sum: 2
+    if reduction_enum == 0:
+        return loss
+    elif reduction_enum == 1:
+        return loss.mean()
+    elif reduction_enum == 2:
+        return loss.sum()
+
+
+def weight_reduce_loss(loss, weight=None, reduction='mean', avg_factor=None):
+    """Apply element-wise weight and reduce loss.
+
+    Args:
+        loss (Tensor): Element-wise loss.
+        weight (Tensor): Element-wise weights.
+        reduction (str): Same as built-in losses of PyTorch.
+        avg_factor (float): Avarage factor when computing the mean of losses.
+
+    Returns:
+        Tensor: Processed loss values.
+    """
+    # if weight is specified, apply element-wise weight
+    if weight is not None:
+        loss = loss * weight
+
+    # if avg_factor is not specified, just reduce the loss
+    if avg_factor is None:
+        loss = reduce_loss(loss, reduction)
+    else:
+        # if reduction is mean, then average the loss by avg_factor
+        if reduction == 'mean':
+            loss = loss.sum() / avg_factor
+        # if reduction is 'none', then do nothing, otherwise raise an error
+        elif reduction != 'none':
+            raise ValueError('avg_factor can not be used with reduction="sum"')
+    return loss
+
+
+def weighted_loss(loss_func):
+    """Create a weighted version of a given loss function.
+
+    To use this decorator, the loss function must have the signature like
+    `loss_func(pred, target, **kwargs)`. The function only needs to compute
+    element-wise loss without any reduction. This decorator will add weight
+    and reduction arguments to the function. The decorated function will have
+    the signature like `loss_func(pred, target, weight=None, reduction='mean',
+    avg_factor=None, **kwargs)`.
+
+    :Example:
+
+    >>> import torch
+    >>> @weighted_loss
+    >>> def l1_loss(pred, target):
+    >>>     return (pred - target).abs()
+
+    >>> pred = torch.Tensor([0, 2, 3])
+    >>> target = torch.Tensor([1, 1, 1])
+    >>> weight = torch.Tensor([1, 0, 1])
+
+    >>> l1_loss(pred, target)
+    tensor(1.3333)
+    >>> l1_loss(pred, target, weight)
+    tensor(1.)
+    >>> l1_loss(pred, target, reduction='none')
+    tensor([1., 1., 2.])
+    >>> l1_loss(pred, target, weight, avg_factor=2)
+    tensor(1.5000)
+    """
+
+    @functools.wraps(loss_func)
+    def wrapper(pred,
+                target,
+                weight=None,
+                reduction='mean',
+                avg_factor=None,
+                **kwargs):
+        # get element-wise loss
+        loss = loss_func(pred, target, **kwargs)
+        loss = weight_reduce_loss(loss, weight, reduction, avg_factor)
+        return loss
+
+    return wrapper
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/necks/__init__.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/necks/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..83cde49008bc2708a8e180200c2d7c5bc1bddb8b
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/necks/__init__.py
@@ -0,0 +1,5 @@
+from .gap import GlobalAveragePooling
+from .la_max import MultiLANet, MaxHybridFlatten, ImgCropFlatten
+from .attn_pooling import CNNAttentionPooling
+
+# __all__ = ['GlobalAveragePooling', 'MultiLANet', 'ConvFlatten']
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/necks/attn_pooling.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/necks/attn_pooling.py
new file mode 100644
index 0000000000000000000000000000000000000000..6ba4194fd5ba62b0f73cc9bbef7c63ce18c490b0
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/necks/attn_pooling.py
@@ -0,0 +1,95 @@
+import logging
+import random
+from typing import List
+import torch
+from torch import nn, Tensor
+from mmcv.runner import load_state_dict
+# from mmcls.utils import get_root_logger
+
+from ..builder import NECKS, build_loss, build_neck
+from ..utils import top_pool
+from ..vit.layers import resize_pos_embed
+
+@NECKS.register_module()
+class SUMAggregator(nn.Module):
+    def __init__(self, power=1, abs=True):
+        super().__init__()
+        self.power = power
+        self.abs = abs
+    
+    def forward(self, x):
+        if self.abs:
+            x = torch.abs(x)
+        attn = torch.sum(torch.pow(x, self.power), dim=1, keepdim=True)
+        return attn
+
+
+
+
+@NECKS.register_module()
+class CNNAttentionPooling(nn.Module):
+    """
+    Pool the feature map with attention generated by ONE LANet.
+
+    process:
+        for every input:
+            1. project the feature map to a certain dimension.
+            2. generate an attention map by ONE LANet
+            3. pooling the feature map by the attention map
+
+    """
+    def __init__(self,
+        in_channel:int, out_channel:int,
+        # ratios:List[int], 
+        method=dict(type='SUMAggregator'),
+        patch_num=0,
+        pool_config:dict=None,
+        pretrained=None):
+        super().__init__()
+        self.attn_f = build_neck(method)
+        self.proj = nn.Conv2d(in_channel, out_channel, kernel_size=1)
+        self.pos_embeds = nn.Parameter(torch.zeros(1, patch_num, out_channel), requires_grad=True)
+        self.patch_num = patch_num
+        self.pool_config = pool_config
+        if pretrained is not None:
+            self.init_weights(pretrained)
+    
+    def init_weights(self, pretrained=None):
+        logger = get_root_logger()
+        logger.warning(f'{self.__class__.__name__} load pretrain from {pretrained}')
+        state_dict = torch.load(pretrained, map_location='cpu')
+        if 'state_dict' in state_dict:
+            state_dict = state_dict['state_dict']
+        pos_embed = state_dict['pos_embed'][:, 1:, ...]     # [1, 197, 768] for small
+
+        patch_num_new = self.pos_embeds.shape[1]
+        if patch_num_new != pos_embed.shape[1]:
+            logger.warning(f'interpolate pos_embed from {pos_embed.shape[1]} to {patch_num_new}')
+            pos_embed_new = resize_pos_embed(pos_embed, self.pos_embeds.shape, 0)
+        else:
+            pos_embed_new = pos_embed
+        load_state_dict(self, dict(pos_embeds=pos_embed_new), strict=False, logger=logger)
+    
+    def forward(self, input:List[Tensor]):
+        assert len(input) == 1
+        x = input[0]
+        attn2D = self.attn_f(x)
+        x = self.proj(x)
+        x = x.flatten(start_dim=2).transpose(1, 2)
+        x = x + self.pos_embeds
+
+        if self.pool_config is None:
+            return dict(x=x, attn=attn2D)
+        
+        attn = attn2D.flatten(start_dim=2).transpose(1, 2)
+        keep_indexes = top_pool(attn, **self.pool_config)
+        if keep_indexes is None:
+            return dict(x=x, attn=attn2D)
+        results = []
+        B, N, C = x.shape
+        for j in range(B):
+            results.append(x[j, keep_indexes[j], ...])
+        result =  torch.stack(results)
+        return dict(x=result, attn=attn2D)
+    
+
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/necks/gap.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/necks/gap.py
new file mode 100644
index 0000000000000000000000000000000000000000..5f88f7602f345a1dd07ebf73365644cb9ba2a4e4
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/necks/gap.py
@@ -0,0 +1,75 @@
+import torch
+import torch.nn as nn
+
+from ..builder import NECKS
+from ..utils import top_pool
+
+
+@NECKS.register_module()
+class GlobalAveragePooling(nn.Module):
+    """Global Average Pooling neck.
+
+    Note that we use `view` to remove extra channel after pooling.
+    We do not use `squeeze` as it will also remove the batch dimension
+    when the tensor has a batch dimension of size 1, which can lead to
+    unexpected errors.
+    """
+
+    def __init__(self):
+        super(GlobalAveragePooling, self).__init__()
+        self.gap = nn.AdaptiveAvgPool2d((1, 1))
+
+    def init_weights(self):
+        pass
+
+    def forward(self, inputs):
+        if isinstance(inputs, tuple):
+            outs = tuple([self.gap(x) for x in inputs])
+            outs = tuple(
+                [out.view(x.size(0), -1) for out, x in zip(outs, inputs)])
+        elif isinstance(inputs, torch.Tensor):
+            outs = self.gap(inputs)
+            outs = outs.view(inputs.size(0), -1)
+        else:
+            raise TypeError('neck inputs should be tuple or torch.tensor')
+        return outs
+
+
+@NECKS.register_module()
+class GlobalAveragePoolingWithAttention(nn.Module):
+    """Global Average Pooling neck with Attentive Pooling.
+
+    Note that we use `view` to remove extra channel after pooling.
+    We do not use `squeeze` as it will also remove the batch dimension
+    when the tensor has a batch dimension of size 1, which can lead to
+    unexpected errors.
+    """
+
+    def __init__(self, keep_num):
+        super().__init__()
+        self.k = keep_num
+        self.gap = nn.AdaptiveAvgPool2d((1, 1))
+
+    def init_weights(self):
+        pass
+
+    def forward_one(self, x):
+        assert isinstance(x, torch.Tensor)
+        B, C, H, W = x.shape
+        x = x.flatten(2).transpose(2, 1)  # [B, N, C]
+        keep_indexes = top_pool(x, dim=C, keep_num=self.k, exclude_first=False, attn_method='ABS')
+        if keep_indexes is not None:
+            x = x.gather(dim=1, index=keep_indexes)
+        x = x.transpose(1, 2)   # [B, C, k]
+        x = nn.AdaptiveAvgPool1d(1)(x)      # [B, C, 1]
+        return x.view(x.size(0), -1)
+
+
+    def forward(self, inputs):
+        if isinstance(inputs, tuple):
+            outs = tuple([self.forward_one(x) for x in inputs])
+        elif isinstance(inputs, torch.Tensor):
+            outs = self.forward_one(inputs)
+        else:
+            raise TypeError('neck inputs should be tuple or torch.tensor')
+        return outs
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/necks/la_max.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/necks/la_max.py
new file mode 100644
index 0000000000000000000000000000000000000000..dc0155572665c126b53575c0f8241cdc498ad0d7
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/necks/la_max.py
@@ -0,0 +1,565 @@
+# use for Max(multi LA) -> HybridEmbd
+
+import random
+from typing import List
+import torch
+from torch import nn, Tensor
+from mmcv.runner import load_state_dict
+# from mmcls.utils import get_root_logger
+
+from ..builder import NECKS, build_loss
+from ..utils import top_pool
+from ..vit.layers import resize_pos_embed, trunc_normal_
+
+
+class LANet(nn.Module):
+    def __init__(self, channel_num, ratio=16):
+        super().__init__()
+        assert channel_num % ratio == 0, f"input_channel{channel_num} must be exact division by ratio{ratio}"
+        self.channel_num = channel_num
+        self.ratio = ratio
+        self.relu = nn.ReLU(inplace=True)
+
+        self.LA_conv1 = nn.Conv2d(channel_num, int(channel_num / ratio), kernel_size=1)
+        self.bn1 = nn.BatchNorm2d(int(channel_num / ratio))
+        self.LA_conv2 = nn.Conv2d(int(channel_num / ratio), 1, kernel_size=1)
+        self.bn2 = nn.BatchNorm2d(1)
+        self.sigmoid = nn.Sigmoid()
+
+    def forward(self, x):
+        LA = self.LA_conv1(x)
+        LA = self.bn1(LA)
+        LA = self.relu(LA)
+        LA = self.LA_conv2(LA)
+        LA = self.bn2(LA)
+        LA = self.sigmoid(LA)
+        return LA
+        # LA = LA.repeat(1, self.channel_num, 1, 1)
+        # x = x*LA
+
+        # return x
+
+
+@NECKS.register_module()
+class MLPConvert(nn.Module):
+    """
+    One layer MLP to project CNN feature to ViT token
+    """
+    def __init__(self, in_channels, out_channels):
+        super().__init__()
+        assert len(in_channels) == len(out_channels)
+        self.proj = nn.ModuleList([nn.Conv2d(in_channels[i], out_channels[i], 1,) for i in range(len(in_channels))])
+    
+    def forward(self, x:List[Tensor]):
+        assert isinstance(x, (list, tuple))
+        out = [self.proj[i](x[i]) for i in range(len(x))]
+        out = [i.flatten(2).transpose(2, 1) for i in out]
+        return dict(x=out)
+
+
+@NECKS.register_module()
+class MultiLANet(nn.Module):
+    """Multi LANet.
+    from paper: Hierarchical pyramid diverse attention networks for face recognition
+    
+    return: [Tensor(B,1,H,W), ], loss
+    """
+
+    def __init__(self, in_channels, ratio=16, num=5, 
+        loss=dict(type='LANetAttention_div_loss', loss_margin=1.0, loss_weight=0.5)
+        ):
+        super().__init__()
+        # assert num >=2, 'only 1 LANet can not compute loss'
+        self.num = num
+        self.criterion = build_loss(loss)
+        self.las =  nn.ModuleList([LANet(in_channels, ratio) for _ in range(num)])
+
+    def init_weights(self):
+        pass
+
+    def forward(self, inputs):
+        if isinstance(inputs, tuple):
+            raise TypeError('tuple inputs are not suported by MultiLANet now')
+        elif isinstance(inputs, torch.Tensor):
+            if self.num == 0:
+                return dict(feature=inputs, la_outs=None, loss=dict(LADiv_loss=torch.as_tensor([0.,], device='cuda')))
+            la_outs = [f(inputs) for f in self.las]
+            loss = self.criterion(la_outs)
+        else:
+            raise TypeError(f'neck inputs should be tuple or torch.tensor, but get {type(inputs)}')
+        return dict(feature=inputs, la_outs=la_outs, loss=dict(LADiv_loss=loss))
+
+
+@NECKS.register_module()
+class ConvFlatten(nn.Module):
+    """
+    use Conv2D to flatten (C,H,W)
+    input:
+        input_shape: h, w, c
+        x: [ Tensor((B, 1, H, W)), ]
+    
+    process:
+        [reshape_mlp]
+        [bilinear]
+        mlp
+
+    output: Tensor((B, n, c)), n = len(x)
+    """
+    def __init__(self, input_shape, embed_dim, resize_shape=None, resize_method='bilinear', pretrained=None):
+        super().__init__()
+        assert len(input_shape) == 3, "input_shape must have 3 dimensions"
+        assert resize_method in ('bilinear', 'padding')
+        self.resize_shape = resize_shape
+        self.resize_method = resize_method
+
+        if resize_shape and resize_method == 'padding':
+            padding2 = resize_shape[0] - input_shape[0]
+            assert padding2 % 2 == 0
+            padding = padding2 // 2
+        else:
+            padding = 0
+
+        if resize_shape and resize_shape[2] != input_shape[2]:
+            self.reshape_mlp = nn.Conv2d(input_shape[2], resize_shape[2], kernel_size=1)
+
+        in_shape = resize_shape if resize_shape else input_shape
+        self.proj = nn.Conv2d(in_shape[2], embed_dim, kernel_size=in_shape[0], padding=padding)
+        if pretrained:
+            self.init_weights(pretrained)
+
+    def init_weights(self, pretrained):
+        # only load self.mlp
+        logger = get_root_logger()
+        logger.warning(f'{self.__class__.__name__} load pretrain from {pretrained}')
+        old_dict = torch.load(pretrained, map_location='cpu')['state_dict']
+        mlp_weight_key = 'patch_embed.proj'
+        weight_names = ('weight', 'bias')
+        state_dict = dict()
+        for name in weight_names:
+            state_dict[f'proj.{name}'] = old_dict[f'{mlp_weight_key}.{name}']
+        load_state_dict(self, state_dict, strict=False, logger=logger)
+
+    def forward(self, inputs):
+        if isinstance(inputs, list):
+            outs = inputs # fuck this, but I'm lazy
+            # resize
+            if hasattr(self, 'reshape_mlp'):
+                outs = [self.reshape_mlp(x) for x in outs]
+
+            if self.resize_shape and self.resize_method == 'bilinear':
+                outs = [nn.functional.interpolate(x, size=self.resize_shape[:2], mode='bilinear') for x in outs]
+
+            outs = [self.proj(x) for x in outs]   # [ (B, 768, 1, 1)]
+            outs = torch.cat(outs, dim=2)   # (B, C, n, 1)
+            outs = outs.squeeze(3).transpose(2, 1)
+        else:
+            raise TypeError('ConvFlatten inputs should be list')
+        return outs
+
+
+@NECKS.register_module()
+class GlobalAvgPoolingFlatten(nn.Module):
+    """
+    use Conv2D and AvgPooling to flatten (C,H,W)
+    input:
+        input_shape: h, w, c
+        x: [ Tensor((B, 1, H, W)), ]
+    
+    process:
+        Conv2d(kernel=3, stride=2)
+        AveragePooling
+
+    output: Tensor((B, n, c)), n = len(x)
+    """
+    def __init__(self, input_shape, embed_dim):
+        super().__init__()
+        assert len(input_shape) == 3, "input_shape must have 3 dimensions"
+        self.conv = nn.Conv2d(input_shape[2], embed_dim, kernel_size=3, stride=2)
+
+    def forward(self, x):
+        if isinstance(x, list):
+            x = [self.conv(i) for i in x]
+            x = [nn.AdaptiveAvgPool2d(1)(i) for i in x]# [ (B, 768, 1, 1)]
+
+            outs = torch.cat(x, dim=2)   # (B, C, n, 1)
+            outs = outs.squeeze(3).transpose(2, 1)
+        else:
+            raise TypeError('GlobalAvgPoolingFlatten inputs should be list')
+        return outs
+
+
+@NECKS.register_module()
+class MaxHybridFlatten(nn.Module):
+    """
+    input:
+        input_shape: h, w, c
+        x: [ Tensor((B, 1, H, W)), ]
+    
+    process:
+        max(multi LA feature)
+        1x1 Conv
+
+    output: Tensor((B, n, c)), n = len(x)
+    """
+    def __init__(self, input_shape, embed_dim, drop_rate=0, drop_upper=0, adaptive_drop=False, pretrained=None,
+        pool_config=dict(keep_num=10, alpha1=1, alpha2=0, exclude_first=False),
+        ):
+        super().__init__()
+        assert len(input_shape) == 3, "input_shape must have 3 dimensions"
+        assert 0 <= drop_rate <= 1 
+        self.proj = nn.Conv2d(input_shape[2], embed_dim, kernel_size=1)
+        self.drop_rate = drop_rate
+        self.drop_upper = drop_upper
+        self.adaptive_drop = adaptive_drop
+        if pretrained:
+            self.init_weights(pretrained)
+        if adaptive_drop:
+            print('MaxHybridFlatten Adaptive Drop enabled')
+        self.pool_config = pool_config
+
+    def init_weights(self, pretrained):
+        logger = get_root_logger()
+        logger.warning(f'{self.__class__.__name__} load pretrain from {pretrained}')
+        old_dict = torch.load(pretrained, map_location='cpu')
+        if 'state_dict' in old_dict:
+            old_dict = old_dict['state_dict']
+        mlp_weight_key = 'patch_embed.proj'
+        weight_names = ('weight', 'bias')
+        state_dict = dict()
+        for name in weight_names:
+            state_dict[f'proj.{name}'] = old_dict[f'{mlp_weight_key}.{name}']
+        load_state_dict(self, state_dict, strict=False, logger=logger)
+
+    def forward(self, feature, la_outs, **kwargs):
+        keep_index = None
+        if la_outs is None:
+            x = self.proj(feature)    # [ (B, 768, H, W)]
+            outs = torch.flatten(x, start_dim=2)   # (B, 768, HxW)
+            outs = outs.transpose(2, 1)
+        elif isinstance(la_outs, list):
+            x = torch.cat(la_outs, dim=1)  #(B N H W)
+            B, N, _, _ = x.shape
+            # dropout
+            if self.training and self.drop_rate > 0 and self.drop_upper > 0:
+                batch_rand = torch.rand((B, ))
+                batch_mask = batch_rand <= self.drop_rate
+                all_indexs = [i for i in range(N)]
+                if self.adaptive_drop:
+                    weights = nn.AdaptiveAvgPool2d(1)(x).reshape(B, N)
+                    weights = torch.add(torch.sqrt(weights), 1e-12)
+                    weights = torch.div(weights, torch.sum(weights, dim=1).unsqueeze(1))
+                    # weights += weights.min()
+                else:
+                    weights = [None] * B
+
+                for i in range(B):
+                    if batch_mask[i]:
+                        k = random.randint(1, self.drop_upper)
+                        zero_index = random.choices(all_indexs, k=k, weights=weights[i])
+                        x[i, zero_index, ...] = 0
+
+            x, _ = torch.max(x, dim=1, keepdim=True) #(B 1 H W)
+            feature = self.proj(feature)    # [ (B, 768, H, W)]
+            feature = feature * x
+            outs = torch.flatten(feature, start_dim=2)   # (B, 768, HxW)
+            if self.pool_config:
+                atten = torch.flatten(x, start_dim=1).unsqueeze(dim=-1)   # (B, HxW, 1)
+                keep_index = top_pool(atten, **self.pool_config)
+
+            outs = outs.transpose(1, 2)
+        else:
+            raise TypeError('MaxHybridFlatten inputs should be list')
+        return dict(x=outs, keep_index=keep_index)
+
+
+@NECKS.register_module()
+class ImgCropFlatten(nn.Module):
+    """
+    crop image according to LA output, and then resize to same size.
+    input:
+        input_shape: h, w, c
+        x: [ Tensor((B, 1, H, W)), ]
+    
+    process:
+        max(multi LA feature)
+        1x1 Conv
+
+    output: Tensor((B, n, c)), n = len(x)
+    """
+    def __init__(self, input_shape, embed_dim, lower, upper, sum_thr=5, pretrained=None):
+        super().__init__()
+        assert len(input_shape) == 3, "input_shape must have 3 dimensions"
+        self.lower = lower
+        self.upper = upper
+        self.sum_thr = sum_thr
+        self.proj = nn.Conv2d(input_shape[2], embed_dim, kernel_size=16)
+        if pretrained:
+            self.init_weights(pretrained)
+
+    def init_weights(self, pretrained):
+        logger = get_root_logger()
+        logger.warning(f'{self.__class__.__name__} load pretrain from {pretrained}')
+        old_dict = torch.load(pretrained, map_location='cpu')
+        if 'state_dict' in old_dict:
+            old_dict = old_dict['state_dict']
+        mlp_weight_key = 'patch_embed.proj'
+        weight_names = ('weight', 'bias')
+        state_dict = dict()
+        for name in weight_names:
+            state_dict[f'proj.{name}'] = old_dict[f'{mlp_weight_key}.{name}']
+        load_state_dict(self, state_dict, strict=False, logger=logger)
+
+    def get_bbox_by_mask(self, mask):
+        """
+        return: top, left, height, width
+        """
+        assert mask.dim() == 2, f'mask shape should be (H, W), but get {mask.shape}'
+        def get_change_point(row):
+            left, right = 0, len(row)-1
+            for i in range(len(row)):
+                if row[i] == True:
+                    left = i
+                    break
+            for i in range(len(row)-1, -1, -1):
+                if row[i] == True:
+                    right = i
+                    break
+            if right - left <= 3:
+                print(left, right)
+                if left <= 3:
+                    right += 3
+                elif right >= len(row) - 4:
+                    left -= 3
+                else:
+                    left = max(0, left-2)
+                    right = min(len(row)-1, right+2)
+                # return None
+            return left, right
+        width_ = get_change_point(mask.sum(0) >= self.sum_thr)
+        height_ = get_change_point(mask.sum(1) >= self.sum_thr)
+        if width_ and height_:
+            return height_[0], width_[0], height_[1]-height_[0], width_[1]-width_[0]
+        return
+
+    def forward(self, img, la_outs, **kwargs):
+        if isinstance(la_outs, list):
+            B = img.shape[0]
+            # cast la to bool
+            threshold = random.uniform(self.lower, self.upper)
+            masks = [i >= threshold for i in la_outs]   # [(B, 1, H, W)]
+            out_batch = list()
+            for i in range(B):
+                masks_in = [m[i, 0, ...] for m in masks]
+                # im = ToPILImage()(img[i].cpu())
+                im = img[i]
+                # get bbox
+                bboxs = [self.get_bbox_by_mask(i) for i in masks_in]
+                bboxs = [i for i in bboxs if i]
+                # resize
+                # imgs = [torchvision.transforms.functional.crop(im, *bbox) for bbox in bboxs]
+                # imgs = [torchvision.transforms.functional.resize(im, (16, 16)) for im in imgs]    # [B C H W]
+                imgs = [im[:, bbox[0]: bbox[0]+bbox[2], bbox[1]: bbox[1]+bbox[3]] for bbox in bboxs]
+                imgs = [im.unsqueeze(0) for im in imgs]
+                imgs = [torch.nn.functional.interpolate(im, (16, 16), mode='bilinear') for im in imgs]
+                # proj
+                # imgs = [ToTensor()(im).unsqueeze(0).cuda() for im in imgs]
+                x = [self.proj(i) for i in imgs]    # [B 768 1 1]
+                outs = torch.cat(x, dim=2)   # (B, C, n, 1)
+                outs = outs.squeeze(3).transpose(2, 1)
+                out_batch.append(outs)
+            outs = torch.cat(out_batch, dim=0)
+        else:
+            raise TypeError('inputs should be list')
+        return outs
+
+
+
+@NECKS.register_module()
+class LANetAttention(nn.Module):
+    """
+    Pool the feature map with attention generated by ONE LANet.
+
+    process:
+        for every input:
+            1. project the feature map to a certain dimension.
+            2. generate an attention map by ONE LANet
+            3. pooling the feature map by the attention map
+
+    """
+    def __init__(self, in_channels:List[int], out_channel:int, ratios:List[int], patch_nums:List[int],
+        pool_configs=None,
+        pretrained=None):
+        super().__init__()
+        assert len(in_channels) == len(ratios) == len(pool_configs), 'configs must have the same length'
+        self.projs = nn.ModuleList([nn.Conv2d(in_channel, out_channel, kernel_size=1) for in_channel in in_channels])
+        self.las =  nn.ModuleList([LANet(in_channel, ratio) for in_channel, ratio in  zip(in_channels, ratios)])
+        self.pos_embeds = nn.ParameterList([
+            nn.Parameter(torch.zeros(1, patch_num, out_channel), requires_grad=True) for patch_num in patch_nums])
+        # if len(pool_configs) >= 2: # TODO, only add stage pos embed when len >= 2
+        if len(in_channels) == 2:
+            if pool_configs[0]['keep_num'] > 0:
+                self.stage_pos_embeds = nn.Parameter(torch.tensor([0., 1.]), requires_grad=True)
+        elif len(in_channels) == 3:
+            self.stage_pos_embeds = nn.Parameter(torch.tensor([0., 0., 1.]), requires_grad=True)
+
+        self.pool_configs = pool_configs
+        if pretrained is not None:
+            self.init_weights(pretrained)
+    
+    def init_weights(self, pretrained=None):
+        logger = get_root_logger()
+        logger.warning(f'{self.__class__.__name__} load pretrain from {pretrained}')
+        state_dict = torch.load(pretrained, map_location='cpu')
+        if 'state_dict' in state_dict:
+            state_dict = state_dict['state_dict']
+        pos_embed = state_dict['pos_embed'][:, 1:, ...]     # [1, 197, 768] for base
+        pos_embed_params = []
+        for pos_embed_new in self.pos_embeds:
+            patch_num_new = pos_embed_new.shape[1]
+            if patch_num_new != pos_embed.shape[1]:
+                logger.warning(f'interpolate pos_embed from {pos_embed.shape[1]} to {patch_num_new}')
+                pos_embed_new = resize_pos_embed(pos_embed, pos_embed_new, 0)
+                pos_embed_params.append(pos_embed_new)  # skip cls_token
+            else:
+                pos_embed_params.append(pos_embed)
+        load_state_dict(self, dict(pos_embeds=pos_embed_params), strict=False, logger=logger)
+    
+    def forward(self, x:List[Tensor]):
+        assert len(x) == len(self.projs)
+        tokens = [self.projs[i](x[i]) for i in range(len(x))]
+        las_2D = [self.las[i](x[i]) for i in range(len(x))]
+        tokens = [i.flatten(start_dim=2).transpose(1, 2) for i in tokens]
+        las = [i.flatten(start_dim=2).transpose(1, 2) for i in las_2D]
+        tokens = [tokens[i] * las[i] + self.pos_embeds[i] for i in range(len(tokens))]
+        # apply stage position embedding
+        if hasattr(self, 'stage_pos_embeds'):
+            tokens = [tokens[i] * self.stage_pos_embeds[i] for i in range(len(tokens))]
+            if not self.training:
+                print(self.stage_pos_embeds[0].item(), self.stage_pos_embeds[1].item())
+
+        keep_indexes = [
+            top_pool(las[i], **self.pool_configs[i])
+            for i in range(len(tokens))
+        ]
+        results = []
+        for i in range(len(tokens)):
+            if keep_indexes[i] is None:
+                results.append(tokens[i])
+                continue
+            stage_out = []
+            B, N, C = tokens[i].shape
+            for j in range(B):
+                stage_out.append(tokens[i][j, keep_indexes[i][j], ...])
+            results.append(torch.stack(stage_out))
+        # results = torch.cat(results, dim=1)
+        return dict(x=results, la_outs=las_2D)
+
+
+
+@NECKS.register_module()
+class LANetAttentionMaxPooling(nn.Module):
+    """
+    Pool the feature map with attention generated by ONE LANet on S3.
+    The input shoule be the second (S2) and the third stage (S3) of the feature map
+
+    process:
+        generate an attention map by ONE LANet
+        MaxPooling the S2 to have the same size as S3
+        for every input:
+            1. project the feature map to a certain dimension.
+            3. pooling the feature map by the attention map if the score is greater than pool_threshold
+
+    """
+    def __init__(self, in_channels:List[int], out_channel:int, patch_num, la_ratio=16,
+        pool_config=None,
+        pretrained=None):
+        super().__init__()
+        self.projs = nn.ModuleList([nn.Conv2d(in_channel, out_channel, kernel_size=1) for in_channel in in_channels])
+        self.la = LANet(in_channels[-1], la_ratio)
+        self.pos_embeds = nn.Parameter(torch.zeros(1, patch_num, out_channel), requires_grad=True)
+        self.s1_pooling = nn.MaxPool2d(kernel_size=4)
+        self.s2_pooling = nn.MaxPool2d(kernel_size=2)
+
+        self.pool_config = pool_config
+        if pretrained is not None:
+            self.init_weights(pretrained)
+    
+    def init_weights(self, pretrained=None):
+        logger = get_root_logger()
+        logger.warning(f'{self.__class__.__name__} load pretrain from {pretrained}')
+        state_dict = torch.load(pretrained, map_location='cpu')
+        if 'state_dict' in state_dict:
+            state_dict = state_dict['state_dict']
+        pos_embed = state_dict['pos_embed'][:, 1:, ...]     # [1, 197, 768] for small
+
+        patch_num_new = self.pos_embeds.shape[1]
+        if patch_num_new != pos_embed.shape[1]:
+            logger.warning(f'interpolate pos_embed from {pos_embed.shape[1]} to {patch_num_new}')
+            pos_embed_new = resize_pos_embed(pos_embed, self.pos_embeds.shape, 0)
+        else:
+            pos_embed_new = pos_embed
+        load_state_dict(self, dict(pos_embeds=pos_embed_new), strict=False, logger=logger)
+    
+    def forward(self, x:List[Tensor]):
+        assert len(x) == len(self.projs), f'{len(x)}, {len(self.projs)}'
+        B, C, H, W = x[-1].shape
+        if len(x) == 1:
+            pass
+        elif len(x) == 2:
+            x[0] = self.s2_pooling(x[0]).reshape(B, -1, H, W)
+        elif len(x) == 3:
+            x[0] = self.s1_pooling(x[0]).reshape(B, -1, H, W)
+            x[1] = self.s2_pooling(x[0]).reshape(B, -1, H, W)
+
+        la_2D = self.la(x[-1])
+        tokens = [self.projs[i](x[i]) for i in range(len(x))]   # B, 1, H, W
+
+        tokens = [i.flatten(start_dim=2).transpose(1, 2) for i in tokens]
+        la = la_2D.flatten(start_dim=2).transpose(1, 2)
+        tokens = [tokens[i] * la + self.pos_embeds/len(x) for i in range(len(tokens))]
+
+        if self.pool_config is not None:
+            keep_indexes = top_pool(la, **self.pool_config)
+
+            results = []
+            for i in range(len(tokens)):
+                stage_out = []
+                B, N, C = tokens[i].shape
+                for j in range(B):
+                    if keep_indexes is not None:
+                        stage_out.append(tokens[i][j, keep_indexes[j], ...])
+                    else:
+                        stage_out.append(tokens[i][j, ...])
+                results.append(torch.stack(stage_out))
+
+            return dict(x=results, la_outs=[la_2D])
+        else:
+            return dict(x=tokens, la_outs=[la_2D])
+
+
+
+
+@NECKS.register_module()
+class LANetAttentionUnfold(LANetAttentionMaxPooling):
+    """
+    Pool the feature map with attention generated by ONE LANet on S3.
+    The input shoule be the second (S2) and the third stage (S3) of the feature map
+
+    process:
+        generate an attention map by ONE LANet
+        Unfold the S2 to have the same size as S3, and quadruple feature dims
+        for every input:
+            1. project the feature map to a certain dimension.
+            3. pooling the feature map by the attention map if the score is greater than pool_threshold
+
+    """
+    def __init__(self, in_channels:List[int], out_channel:int, patch_num, la_ratio=16,
+        pool_config=None,
+        pretrained=None):
+        super().__init__(in_channels, out_channel, patch_num, la_ratio, pool_config, pretrained)
+        self.s1_pooling = nn.Unfold(kernel_size=4, stride=4)
+        self.s2_pooling = nn.Unfold(kernel_size=2, stride=2)    # Unfold instead of MaxPooling
+
+
+
+
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/utils/__init__.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca32c4b92535d6c3091749ef57050e6cb64d7485
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/utils/__init__.py
@@ -0,0 +1,7 @@
+from .channel_shuffle import channel_shuffle
+from .inverted_residual import InvertedResidual
+from .make_divisible import make_divisible
+from .se_layer import SELayer
+from .top_pool import top_pool
+
+__all__ = ['channel_shuffle', 'make_divisible', 'InvertedResidual', 'SELayer', 'top_pool']
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/utils/channel_shuffle.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/utils/channel_shuffle.py
new file mode 100644
index 0000000000000000000000000000000000000000..51d6d98c9b15816d8b9eda4b67480b536e9fa161
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/utils/channel_shuffle.py
@@ -0,0 +1,28 @@
+import torch
+
+
+def channel_shuffle(x, groups):
+    """Channel Shuffle operation.
+
+    This function enables cross-group information flow for multiple groups
+    convolution layers.
+
+    Args:
+        x (Tensor): The input tensor.
+        groups (int): The number of groups to divide the input tensor
+            in the channel dimension.
+
+    Returns:
+        Tensor: The output tensor after channel shuffle operation.
+    """
+
+    batch_size, num_channels, height, width = x.size()
+    assert (num_channels % groups == 0), ('num_channels should be '
+                                          'divisible by groups')
+    channels_per_group = num_channels // groups
+
+    x = x.view(batch_size, groups, channels_per_group, height, width)
+    x = torch.transpose(x, 1, 2).contiguous()
+    x = x.view(batch_size, -1, height, width)
+
+    return x
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/utils/inverted_residual.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/utils/inverted_residual.py
new file mode 100644
index 0000000000000000000000000000000000000000..261d929edbda4e7d3201b66bf4d531a6b788bf62
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/utils/inverted_residual.py
@@ -0,0 +1,118 @@
+import torch.nn as nn
+import torch.utils.checkpoint as cp
+from mmcv.cnn import ConvModule
+
+from .se_layer import SELayer
+
+
+class InvertedResidual(nn.Module):
+    """Inverted Residual Block
+
+    Args:
+        in_channels (int): The input channels of this Module.
+        out_channels (int): The output channels of this Module.
+        mid_channels (int): The input channels of the depthwise convolution.
+        kernel_size (int): The kernal size of the depthwise convolution.
+            Default: 3.
+        stride (int): The stride of the depthwise convolution. Default: 1.
+        se_cfg (dict): Config dict for se layer. Defaul: None, which means no
+            se layer.
+        with_expand_conv (bool): Use expand conv or not. If set False,
+            mid_channels must be the same with in_channels.
+            Default: True.
+        conv_cfg (dict): Config dict for convolution layer. Default: None,
+            which means using conv2d.
+        norm_cfg (dict): Config dict for normalization layer.
+            Default: dict(type='BN').
+        act_cfg (dict): Config dict for activation layer.
+            Default: dict(type='ReLU').
+        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
+            memory while slowing down the training speed. Default: False.
+
+    Returns:
+        Tensor: The output tensor.
+
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 mid_channels,
+                 kernel_size=3,
+                 stride=1,
+                 se_cfg=None,
+                 with_expand_conv=True,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN'),
+                 act_cfg=dict(type='ReLU'),
+                 with_cp=False):
+        super(InvertedResidual, self).__init__()
+        self.with_res_shortcut = (stride == 1 and in_channels == out_channels)
+        assert stride in [1, 2]
+        self.with_cp = with_cp
+        self.with_se = se_cfg is not None
+        self.with_expand_conv = with_expand_conv
+
+        if self.with_se:
+            assert isinstance(se_cfg, dict)
+        if not self.with_expand_conv:
+            assert mid_channels == in_channels
+
+        if self.with_expand_conv:
+            self.expand_conv = ConvModule(
+                in_channels=in_channels,
+                out_channels=mid_channels,
+                kernel_size=1,
+                stride=1,
+                padding=0,
+                conv_cfg=conv_cfg,
+                norm_cfg=norm_cfg,
+                act_cfg=act_cfg)
+        self.depthwise_conv = ConvModule(
+            in_channels=mid_channels,
+            out_channels=mid_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=kernel_size // 2,
+            groups=mid_channels,
+            conv_cfg=conv_cfg,
+            norm_cfg=norm_cfg,
+            act_cfg=act_cfg)
+        if self.with_se:
+            self.se = SELayer(**se_cfg)
+        self.linear_conv = ConvModule(
+            in_channels=mid_channels,
+            out_channels=out_channels,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            conv_cfg=conv_cfg,
+            norm_cfg=norm_cfg,
+            act_cfg=act_cfg)
+
+    def forward(self, x):
+
+        def _inner_forward(x):
+            out = x
+
+            if self.with_expand_conv:
+                out = self.expand_conv(out)
+
+            out = self.depthwise_conv(out)
+
+            if self.with_se:
+                out = self.se(out)
+
+            out = self.linear_conv(out)
+
+            if self.with_res_shortcut:
+                return x + out
+            else:
+                return out
+
+        if self.with_cp and x.requires_grad:
+            out = cp.checkpoint(_inner_forward, x)
+        else:
+            out = _inner_forward(x)
+
+        return out
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/utils/make_divisible.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/utils/make_divisible.py
new file mode 100644
index 0000000000000000000000000000000000000000..02ee047c50d8b6962042e441ac203903f2ecdb64
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/utils/make_divisible.py
@@ -0,0 +1,24 @@
+def make_divisible(value, divisor, min_value=None, min_ratio=0.9):
+    """Make divisible function.
+
+    This function rounds the channel number down to the nearest value that can
+    be divisible by the divisor.
+
+    Args:
+        value (int): The original channel number.
+        divisor (int): The divisor to fully divide the channel number.
+        min_value (int, optional): The minimum value of the output channel.
+            Default: None, means that the minimum value equal to the divisor.
+        min_ratio (float, optional): The minimum ratio of the rounded channel
+            number to the original channel number. Default: 0.9.
+    Returns:
+        int: The modified output channel number
+    """
+
+    if min_value is None:
+        min_value = divisor
+    new_value = max(min_value, int(value + divisor / 2) // divisor * divisor)
+    # Make sure that round down does not go down by more than (1-min_ratio).
+    if new_value < min_ratio * value:
+        new_value += divisor
+    return new_value
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/utils/se_layer.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/utils/se_layer.py
new file mode 100644
index 0000000000000000000000000000000000000000..334f27656c986173805b60ace91b6d3d048c5c45
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/utils/se_layer.py
@@ -0,0 +1,53 @@
+import mmcv
+import torch.nn as nn
+from mmcv.cnn import ConvModule
+
+
+class SELayer(nn.Module):
+    """Squeeze-and-Excitation Module.
+
+    Args:
+        channels (int): The input (and output) channels of the SE layer.
+        ratio (int): Squeeze ratio in SELayer, the intermediate channel will be
+            ``int(channels/ratio)``. Default: 16.
+        conv_cfg (None or dict): Config dict for convolution layer.
+            Default: None, which means using conv2d.
+        act_cfg (dict or Sequence[dict]): Config dict for activation layer.
+            If act_cfg is a dict, two activation layers will be configurated
+            by this dict. If act_cfg is a sequence of dicts, the first
+            activation layer will be configurated by the first dict and the
+            second activation layer will be configurated by the second dict.
+            Default: (dict(type='ReLU'), dict(type='Sigmoid'))
+    """
+
+    def __init__(self,
+                 channels,
+                 ratio=16,
+                 conv_cfg=None,
+                 act_cfg=(dict(type='ReLU'), dict(type='Sigmoid'))):
+        super(SELayer, self).__init__()
+        if isinstance(act_cfg, dict):
+            act_cfg = (act_cfg, act_cfg)
+        assert len(act_cfg) == 2
+        assert mmcv.is_tuple_of(act_cfg, dict)
+        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
+        self.conv1 = ConvModule(
+            in_channels=channels,
+            out_channels=int(channels / ratio),
+            kernel_size=1,
+            stride=1,
+            conv_cfg=conv_cfg,
+            act_cfg=act_cfg[0])
+        self.conv2 = ConvModule(
+            in_channels=int(channels / ratio),
+            out_channels=channels,
+            kernel_size=1,
+            stride=1,
+            conv_cfg=conv_cfg,
+            act_cfg=act_cfg[1])
+
+    def forward(self, x):
+        out = self.global_avgpool(x)
+        out = self.conv1(out)
+        out = self.conv2(out)
+        return x * out
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/utils/top_pool.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/utils/top_pool.py
new file mode 100644
index 0000000000000000000000000000000000000000..d837c9ee8a1103e4f5a49c72cb684ab2927b3da9
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/utils/top_pool.py
@@ -0,0 +1,56 @@
+import torch
+from torch import Tensor
+
+
+def top_pool(x:Tensor, dim=1, keep_num:int=None, keep_rate=None, alpha1=1, alpha2=0, exclude_first=True, **kwargs):
+    """
+    根据输入x 的值和方差来选择 topk 个元素，并返回其 index, index的shape为[B, keep_num, dim]
+    选择标准为 alpha1 * mean + alpha2 * std
+    args:
+        exclude_first: if set to True, will return the index of the first element and the top k-1 elements
+    """
+    # print('random weight')
+    # x = torch.rand(x.shape, device=x.device)
+    assert x.ndim == 3, 'input x must have 3 dimensions(B, N, C)'
+    assert not (keep_num is not None and keep_rate is not None), 'keep_num and keep_rate can not be assigned on the same time'
+    assert not (keep_num is None and keep_rate is None)
+    B, N, C = x.shape
+    if exclude_first is True:
+        x = x[:, 1:, :]
+        N -= 1
+    if keep_num is None:
+        keep_num = max(int(N * keep_rate), 1)
+    
+    if N == keep_num:
+        return None
+
+    mean_weight = x.mean(dim=-1)
+    if C == 1:
+        std_weight = torch.zeros((B, N)).to(mean_weight.device)
+    else:
+        std_weight = x.std(dim=-1)
+    pool_weight = alpha1 * mean_weight + alpha2 * std_weight
+    pool_weight = pool_weight.unsqueeze(-1).expand(B, N, dim)
+
+    if exclude_first is False:
+        try:
+            _, keep_index = torch.topk(pool_weight, k=keep_num, dim=1, sorted=False)
+        except Exception as e:
+            print(e)
+            print('pool_weight', pool_weight.shape)
+            print('k', keep_num)
+            exit()
+        keep_index, _ = torch.sort(keep_index, dim=1)
+    else:
+        # pool_weight = pool_weight[:, 1:, ...]
+        _, keep_index = torch.topk(pool_weight, k=keep_num, dim=1, sorted=False)
+        keep_index, _ = torch.sort(keep_index, dim=1)
+        keep_index = torch.cat([torch.zeros([B, 1, dim]).type(torch.int16).to(keep_index.device), keep_index + 1], dim=1)
+    return keep_index
+
+
+if __name__ == '__main__':
+    a = torch.tensor([1, 2, 3, 4]).view(1, 4, 1).type(torch.float32)
+    print(a.shape)
+    print(sparse_pool(a, 2, 1, 0, True))
+
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/vit/layers.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/vit/layers.py
new file mode 100644
index 0000000000000000000000000000000000000000..e03364f24f355c029b06604c41e907d20725f137
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/vit/layers.py
@@ -0,0 +1,275 @@
+""" DropBlock, DropPath
+PyTorch implementations of DropBlock and DropPath (Stochastic Depth) regularization layers.
+Papers:
+DropBlock: A regularization method for convolutional networks (https://arxiv.org/abs/1810.12890)
+Deep Networks with Stochastic Depth (https://arxiv.org/abs/1603.09382)
+Code:
+DropBlock impl inspired by two Tensorflow impl that I liked:
+ - https://github.com/tensorflow/tpu/blob/master/models/official/resnet/resnet_model.py#L74
+ - https://github.com/clovaai/assembled-cnn/blob/master/nets/blocks.py
+Hacked together by / Copyright 2020 Ross Wightman
+"""
+from typing import Tuple
+from torch import Tensor
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+import warnings
+from mmcls.utils import get_root_logger
+
+
+def drop_block_2d(
+        x, drop_prob: float = 0.1, block_size: int = 7,  gamma_scale: float = 1.0,
+        with_noise: bool = False, inplace: bool = False, batchwise: bool = False):
+    """ DropBlock. See https://arxiv.org/pdf/1810.12890.pdf
+    DropBlock with an experimental gaussian noise option. This layer has been tested on a few training
+    runs with success, but needs further validation and possibly optimization for lower runtime impact.
+    """
+    B, C, H, W = x.shape
+    total_size = W * H
+    clipped_block_size = min(block_size, min(W, H))
+    # seed_drop_rate, the gamma parameter
+    gamma = gamma_scale * drop_prob * total_size / clipped_block_size ** 2 / (
+        (W - block_size + 1) * (H - block_size + 1))
+
+    # Forces the block to be inside the feature map.
+    w_i, h_i = torch.meshgrid(torch.arange(W).to(x.device), torch.arange(H).to(x.device))
+    valid_block = ((w_i >= clipped_block_size // 2) & (w_i < W - (clipped_block_size - 1) // 2)) & \
+                  ((h_i >= clipped_block_size // 2) & (h_i < H - (clipped_block_size - 1) // 2))
+    valid_block = torch.reshape(valid_block, (1, 1, H, W)).to(dtype=x.dtype)
+
+    if batchwise:
+        # one mask for whole batch, quite a bit faster
+        uniform_noise = torch.rand((1, C, H, W), dtype=x.dtype, device=x.device)
+    else:
+        uniform_noise = torch.rand_like(x)
+    block_mask = ((2 - gamma - valid_block + uniform_noise) >= 1).to(dtype=x.dtype)
+    block_mask = -F.max_pool2d(
+        -block_mask,
+        kernel_size=clipped_block_size,  # block_size,
+        stride=1,
+        padding=clipped_block_size // 2)
+
+    if with_noise:
+        normal_noise = torch.randn((1, C, H, W), dtype=x.dtype, device=x.device) if batchwise else torch.randn_like(x)
+        if inplace:
+            x.mul_(block_mask).add_(normal_noise * (1 - block_mask))
+        else:
+            x = x * block_mask + normal_noise * (1 - block_mask)
+    else:
+        normalize_scale = (block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-7)).to(x.dtype)
+        if inplace:
+            x.mul_(block_mask * normalize_scale)
+        else:
+            x = x * block_mask * normalize_scale
+    return x
+
+
+def drop_block_fast_2d(
+        x: torch.Tensor, drop_prob: float = 0.1, block_size: int = 7,
+        gamma_scale: float = 1.0, with_noise: bool = False, inplace: bool = False, batchwise: bool = False):
+    """ DropBlock. See https://arxiv.org/pdf/1810.12890.pdf
+    DropBlock with an experimental gaussian noise option. Simplied from above without concern for valid
+    block mask at edges.
+    """
+    B, C, H, W = x.shape
+    total_size = W * H
+    clipped_block_size = min(block_size, min(W, H))
+    gamma = gamma_scale * drop_prob * total_size / clipped_block_size ** 2 / (
+            (W - block_size + 1) * (H - block_size + 1))
+
+    if batchwise:
+        # one mask for whole batch, quite a bit faster
+        block_mask = torch.rand((1, C, H, W), dtype=x.dtype, device=x.device) < gamma
+    else:
+        # mask per batch element
+        block_mask = torch.rand_like(x) < gamma
+    block_mask = F.max_pool2d(
+        block_mask.to(x.dtype), kernel_size=clipped_block_size, stride=1, padding=clipped_block_size // 2)
+
+    if with_noise:
+        normal_noise = torch.randn((1, C, H, W), dtype=x.dtype, device=x.device) if batchwise else torch.randn_like(x)
+        if inplace:
+            x.mul_(1. - block_mask).add_(normal_noise * block_mask)
+        else:
+            x = x * (1. - block_mask) + normal_noise * block_mask
+    else:
+        block_mask = 1 - block_mask
+        normalize_scale = (block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-7)).to(dtype=x.dtype)
+        if inplace:
+            x.mul_(block_mask * normalize_scale)
+        else:
+            x = x * block_mask * normalize_scale
+    return x
+
+
+class DropBlock2d(nn.Module):
+    """ DropBlock. See https://arxiv.org/pdf/1810.12890.pdf
+    """
+    def __init__(self,
+                 drop_prob=0.1,
+                 block_size=7,
+                 gamma_scale=1.0,
+                 with_noise=False,
+                 inplace=False,
+                 batchwise=False,
+                 fast=True):
+        super(DropBlock2d, self).__init__()
+        self.drop_prob = drop_prob
+        self.gamma_scale = gamma_scale
+        self.block_size = block_size
+        self.with_noise = with_noise
+        self.inplace = inplace
+        self.batchwise = batchwise
+        self.fast = fast  # FIXME finish comparisons of fast vs not
+
+    def forward(self, x):
+        if not self.training or not self.drop_prob:
+            return x
+        if self.fast:
+            return drop_block_fast_2d(
+                x, self.drop_prob, self.block_size, self.gamma_scale, self.with_noise, self.inplace, self.batchwise)
+        else:
+            return drop_block_2d(
+                x, self.drop_prob, self.block_size, self.gamma_scale, self.with_noise, self.inplace, self.batchwise)
+
+
+def drop_path(x, drop_prob: float = 0., training: bool = False):
+    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
+    This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
+    the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
+    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
+    changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
+    'survival rate' as the argument.
+    """
+    if drop_prob == 0. or not training:
+        return x
+    keep_prob = 1 - drop_prob
+    shape = (x.shape[0],) + (1,) * (x.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
+    random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
+    random_tensor.floor_()  # binarize
+    output = x.div(keep_prob) * random_tensor
+    return output
+
+
+class DropPath(nn.Module):
+    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
+    """
+    def __init__(self, drop_prob=None):
+        super(DropPath, self).__init__()
+        self.drop_prob = drop_prob
+
+    def forward(self, x):
+        return drop_path(x, self.drop_prob, self.training)
+
+
+def _no_grad_trunc_normal_(tensor, mean, std, a, b):
+    # Cut & paste from PyTorch official master until it's in a few official releases - RW
+    # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
+    def norm_cdf(x):
+        # Computes standard normal cumulative distribution function
+        return (1. + math.erf(x / math.sqrt(2.))) / 2.
+
+    if (mean < a - 2 * std) or (mean > b + 2 * std):
+        warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
+                      "The distribution of values may be incorrect.",
+                      stacklevel=2)
+
+    with torch.no_grad():
+        # Values are generated by using a truncated uniform distribution and
+        # then using the inverse CDF for the normal distribution.
+        # Get upper and lower cdf values
+        l = norm_cdf((a - mean) / std)
+        u = norm_cdf((b - mean) / std)
+
+        # Uniformly fill tensor with values from [l, u], then translate to
+        # [2l-1, 2u-1].
+        tensor.uniform_(2 * l - 1, 2 * u - 1)
+
+        # Use inverse cdf transform for normal distribution to get truncated
+        # standard normal
+        tensor.erfinv_()
+
+        # Transform to proper mean, std
+        tensor.mul_(std * math.sqrt(2.))
+        tensor.add_(mean)
+
+        # Clamp to ensure it's in the proper range
+        tensor.clamp_(min=a, max=b)
+        return tensor
+
+
+def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
+    # type: (Tensor, float, float, float, float) -> Tensor
+    r"""Fills the input Tensor with values drawn from a truncated
+    normal distribution. The values are effectively drawn from the
+    normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
+    with values outside :math:`[a, b]` redrawn until they are within
+    the bounds. The method used for generating the random values works
+    best when :math:`a \leq \text{mean} \leq b`.
+    Args:
+        tensor: an n-dimensional `torch.Tensor`
+        mean: the mean of the normal distribution
+        std: the standard deviation of the normal distribution
+        a: the minimum cutoff value
+        b: the maximum cutoff value
+    Examples:
+        >>> w = torch.empty(3, 5)
+        >>> nn.init.trunc_normal_(w)
+    """
+    return _no_grad_trunc_normal_(tensor, mean, std, a, b)
+
+###
+# From PyTorch internals
+from itertools import repeat
+import collections.abc
+def _ntuple(n) :
+    def parse(x) -> Tuple[int]:
+        if isinstance(x, collections.abc.Iterable):
+            return x
+        return tuple(repeat(x, n))
+    return parse
+to_2tuple = _ntuple(2)
+
+
+def resize_pos_embed(posemb, posemb_new, num_tokens=1):
+    # Rescale the grid of position embeddings when loading from state_dict. Adapted from
+    # https://github.com/google-research/vision_transformer/blob/00883dd691c63a6830751563748663526e811cee/vit_jax/checkpoint.py#L224
+    _logger = get_root_logger()
+    _logger.info('Resized position embedding: %s to %s', posemb.shape, posemb_new.shape)
+    ntok_new = posemb_new.shape[1]
+    if num_tokens:
+        posemb_tok, posemb_grid = posemb[:, :num_tokens], posemb[0, num_tokens:]
+        ntok_new -= num_tokens
+    else:
+        posemb_tok, posemb_grid = posemb[:, :0], posemb[0]
+    gs_old = int(math.sqrt(len(posemb_grid)))
+    gs_new = int(math.sqrt(ntok_new))
+    _logger.info('Position embedding grid-size from %s to %s', gs_old, gs_new)
+    posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2)
+    posemb_grid = F.interpolate(posemb_grid, size=(gs_new, gs_new), mode='bilinear')
+    posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_new * gs_new, -1)
+    posemb = torch.cat([posemb_tok, posemb_grid], dim=1)
+    return posemb
+
+
+def resize_pos_embed_v2(posemb:Tensor, token_num_new:int, num_tokens=1):
+    # Rescale the grid of position embeddings when loading from state_dict. Adapted from
+    # https://github.com/google-research/vision_transformer/blob/00883dd691c63a6830751563748663526e811cee/vit_jax/checkpoint.py#L224
+    _logger = get_root_logger()
+    _logger.info('Resized position embedding: %s to %s', posemb.shape, token_num_new)
+    ntok_new = token_num_new
+    if num_tokens:
+        posemb_tok, posemb_grid = posemb[:, :num_tokens], posemb[0, num_tokens:]
+        ntok_new -= num_tokens
+    else:
+        posemb_tok, posemb_grid = posemb[:, :0], posemb[0]
+    gs_old = int(math.sqrt(len(posemb_grid)))
+    gs_new = int(math.sqrt(ntok_new))
+    _logger.info('Position embedding grid-size from %s to %s', gs_old, gs_new)
+    posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2)
+    posemb_grid = F.interpolate(posemb_grid, size=(gs_new, gs_new), mode='bilinear')
+    posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_new * gs_new, -1)
+    posemb = torch.cat([posemb_tok, posemb_grid], dim=1)
+    return posemb
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/vit/vit.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/vit/vit.py
new file mode 100644
index 0000000000000000000000000000000000000000..49e33d95d00cb59d843da3c0e515e0a2e0ff122d
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/vit/vit.py
@@ -0,0 +1,596 @@
+""" Vision Transformer (ViT) in PyTorch
+A PyTorch implement of Vision Transformers as described in
+'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale' - https://arxiv.org/abs/2010.11929
+The official jax code is released and available at https://github.com/google-research/vision_transformer
+Status/TODO:
+* Models updated to be compatible with official impl. Args added to support backward compat for old PyTorch weights.
+* Weights ported from official jax impl for 384x384 base and small models, 16x16 and 32x32 patches.
+* Trained (supervised on ImageNet-1k) my custom 'small' patch model to 77.9, 'base' to 79.4 top-1 with this code.
+* Hopefully find time and GPUs for SSL or unsupervised pretraining on OpenImages w/ ImageNet fine-tune in future.
+Acknowledgments:
+* The paper authors for releasing code and weights, thanks!
+* I fixed my class token impl based on Phil Wang's https://github.com/lucidrains/vit-pytorch ... check it out
+for some einops/einsum fun
+* Simple transformer style inspired by Andrej Karpathy's https://github.com/karpathy/minGPT
+* Bert reference code checks against Huggingface Transformers and Tensorflow Bert
+Hacked together by / Copyright 2020 Ross Wightman
+"""
+import math
+import random
+import logging
+import torch
+from torch import nn
+from torch import Tensor
+import torch.nn.functional as F
+from functools import partial
+
+from mmcv.runner import load_state_dict
+from ..builder import BACKBONES
+from ..backbones.base_backbone import BaseBackbone
+from .layers import DropPath, to_2tuple, trunc_normal_, resize_pos_embed
+
+
+def _cfg(url='', **kwargs):
+    return {
+        'url': url,
+        'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
+        'crop_pct': .9, 'interpolation': 'bicubic',
+        # 'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
+        'first_conv': 'patch_embed.proj', 'classifier': 'head',
+        **kwargs
+    }
+
+
+default_cfgs = {
+    # patch models
+    'vit_small_patch16_224': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/vit_small_p16_224-15ec54c9.pth',
+    ),
+    'vit_base_patch16_224': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_224-80ecf9dd.pth',
+        mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
+    ),
+    'vit_base_patch16_384': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_384-83fb41ba.pth',
+        input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),
+    'vit_base_patch32_384': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p32_384-830016f5.pth',
+        input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),
+    'vit_large_patch16_224': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p16_224-4ee7a4dc.pth',
+        mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
+    'vit_large_patch16_384': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p16_384-b3be5167.pth',
+        input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),
+    'vit_large_patch32_384': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p32_384-9b920ba8.pth',
+        input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),
+    'vit_huge_patch16_224': _cfg(),
+    'vit_huge_patch32_384': _cfg(input_size=(3, 384, 384)),
+    # hybrid models
+    'vit_small_resnet26d_224': _cfg(),
+    'vit_small_resnet50d_s3_224': _cfg(),
+    'vit_base_resnet26d_224': _cfg(),
+    'vit_base_resnet50d_224': _cfg(),
+}
+
+
+class Mlp(nn.Module):
+    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+class HeadDropOut(nn.Module):
+    """
+    1. choose samples in probability p
+    2. random set k head to zero in selected samples
+    """
+    def __init__(self, p: float = 0.5, head_drop_upper: int=1, adaptive_drop=False):
+        super().__init__()
+        if p < 0 or p > 1:
+            raise ValueError("dropout probability has to be between 0 and 1, "
+                             "but got {}".format(p))
+        self.p = p
+        self.upper = head_drop_upper
+        self.adaptive_drop = adaptive_drop
+        if adaptive_drop:
+            print('ViT HeadDropOut Adaptive Drop enabled')
+    
+    def forward(self, x: Tensor) -> Tensor:
+        B, N, c, num, dim = x.shape # c=3, # 8 197 3 8 96
+        if self.training and self.p > 0 and self.upper > 0:
+            batch_rand = torch.rand((B, ))
+            batch_mask = batch_rand <= self.p
+            # random set k head to zero
+            all_indexs = [i for i in range(num)]
+            if self.adaptive_drop:
+                weight_feature = x.reshape((B, N*c, num, dim)).permute((0, 2, 1, 3))
+                # print(B, N, c, num, dim)
+                # print(weight_feature.shape,)
+                weights = nn.AdaptiveAvgPool2d(1)(weight_feature).reshape(B, num)
+                weights += weights.min() + 1
+                weights = torch.add(torch.sqrt(weights), 1e-12)
+                weights = torch.div(weights, torch.sum(weights,dim=1).unsqueeze(1))
+            #     weights = weights.cpu().numpy()
+            else:
+                weights = [None] * B
+
+            for i in range(B):
+                if batch_mask[i]:
+                    k = random.randint(1, self.upper)
+                    zero_index = random.choices(all_indexs, k=k, weights=weights[i])
+                    x[i, zero_index, ...] = 0
+        return x
+
+
+class Attention(nn.Module):
+    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., 
+                 head_drop_type='Dropout', head_drop_rate=0., head_drop_upper=1):
+        super().__init__()
+        assert head_drop_type in (None, 'Dropout', 'HeadDropOut')
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
+        self.scale = qk_scale or head_dim ** -0.5
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        if head_drop_type == 'Dropout':
+            self.head_drop = nn.Dropout(head_drop_rate)
+        elif head_drop_type == 'HeadDropOut':
+            self.head_drop = HeadDropOut(head_drop_rate, head_drop_upper=head_drop_upper)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+        self.center = None
+
+    # def calculate_center_loss(self, features, center):
+    #     """
+    #     args:
+    #         features: [B, Heads, Tokens, Tokens]
+    #         center: [H, TxT]
+    #     output:
+    #         center_loss: scalar
+    #         diff: [H, TxT]
+    #     """
+    #     # features = features.clone().detach()
+    #     features = features.reshape(features.shape[0], features.shape[1], -1)
+
+    #     # center = torch.nn.functional.normalize(center, dim=-1)
+    #     # features = torch.nn.functional.normalize(features, dim=-1)
+    #     diff = features - center     # [B, H, TxT]
+    #     distance = torch.pow(diff, 2)
+    #     distance = torch.sqrt(distance)
+    #     center_loss = torch.mean(distance)
+
+    #     batch_center = torch.mean(features.detach(), dim=0)
+    #     return center_loss, batch_center
+
+    def calculate_center_loss(self, features, center):
+        features = features.reshape(features.shape[0], features.shape[1], -1)
+        features = features.mean(dim=1)  # [B, c]
+        diff =  features.detach() - center
+        distance = torch.pow(features - center, 2)
+        center_loss = torch.mean(distance)
+        diff = torch.mean(diff, dim=0) # mean for batch
+        return center_loss, diff
+
+    def forward(self, x):
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads)
+        if hasattr(self, 'head_drop'):
+            qkv = self.head_drop(qkv)
+        qkv = qkv.permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0], qkv[1], qkv[2]   # make torchscript happy (cannot use tensor as tuple)
+
+        attn = (q @ k.transpose(-2, -1)) * self.scale   # [B, head_num, token_num, token_num]
+
+        first_step = False
+        if self.center is None:
+            first_step = True
+            self.center = attn.clone().detach().mean(dim=1).mean(dim=0).flatten(start_dim=-2)
+        center_loss, batch_center = self.calculate_center_loss(attn, self.center)
+        momentum = 0.8
+        loss_weight = 0.
+        center_loss *= loss_weight
+        if first_step:
+            self.center = batch_center
+        else:
+            self.center = self.center * momentum + batch_center * (1-momentum)
+
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x, center_loss
+
+
+class Block(nn.Module):
+
+    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
+                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, head_drop_type='Dropout', head_drop_rate=0., head_drop_upper=1):
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        self.attn = Attention(
+            dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop, 
+            head_drop_type=head_drop_type, head_drop_rate=head_drop_rate, head_drop_upper=head_drop_upper)
+        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
+
+        # self.center = torch.zeros(dim, device='cuda')
+        self.center = None
+    
+    def calculate_pooling_center_loss(self, features, center, alpha=0.95):
+        """
+        args:
+            features: [B, N, 768], N: token number
+            center: [N, 768]
+        output:
+            center_loss: scalar
+            diff: [N, 768]
+        """
+        # features = features.reshape(features.shape[0], -1)
+        centers_batch = torch.nn.functional.normalize(center, dim=-1)
+        features = features.clone().detach()
+        diff =  (1-alpha)*(features - centers_batch)
+        distance = torch.pow(features - centers_batch, 2)
+        distance = torch.sum(distance, dim=-1)
+        center_loss = torch.mean(distance)
+        diff = torch.mean(diff, dim=0)
+        return center_loss, diff
+
+    def forward(self, x):
+        feature, center_loss = self.attn(self.norm1(x))
+        # B, N, dim = feature.shape
+        # if self.center is None:
+        #     self.center = torch.zeros(feature.shape[1:], device='cuda')
+        # center_loss, diff = self.calculate_pooling_center_loss(feature, self.center)
+        # center_loss *= 0
+        # self.center += diff
+        # center_loss = 0
+        x = x + self.drop_path(feature)
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+        return x, center_loss
+
+
+class PatchEmbed(nn.Module):
+    """ Image to Patch Embedding
+    """
+    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, stride=None, padding=0):
+        super().__init__()
+        if stride is None:
+            stride = patch_size
+        img_size = to_2tuple(img_size)
+        patch_size = to_2tuple(patch_size)
+        # num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
+        num_patches = (img_size[0] + 2 * padding - patch_size[0]) / stride + 1
+        if num_patches % 1 != 0:
+            print('Warning: patch numbser is not int:', num_patches)
+        num_patches = int(num_patches) ** 2
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.num_patches = num_patches
+
+        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=stride, padding=padding)
+
+
+    def forward(self, x):
+        B, C, H, W = x.shape
+        # FIXME look at relaxing size constraints
+        assert H == self.img_size[0] and W == self.img_size[1], \
+            f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
+        x = self.proj(x).flatten(2).transpose(1, 2)
+        return x
+
+
+class HybridEmbed(nn.Module):
+    """ CNN Feature Map Embedding
+    Extract feature map from CNN, flatten, project to embedding dim.
+    """
+    def __init__(self, backbone, img_size=224, feature_size=None, in_chans=3, embed_dim=768):
+        super().__init__()
+        assert isinstance(backbone, nn.Module)
+        img_size = to_2tuple(img_size)
+        self.img_size = img_size
+        self.backbone = backbone
+        if feature_size is None:
+            with torch.no_grad():
+                # FIXME this is hacky, but most reliable way of determining the exact dim of the output feature
+                # map for all networks, the feature metadata has reliable channel and stride info, but using
+                # stride to calc feature dim requires info about padding of each stage that isn't captured.
+                training = backbone.training
+                if training:
+                    backbone.eval()
+                o = self.backbone(torch.zeros(1, in_chans, img_size[0], img_size[1]))[-1]
+                feature_size = o.shape[-2:]
+                feature_dim = o.shape[1]
+                backbone.train(training)
+        else:
+            feature_size = to_2tuple(feature_size)
+            feature_dim = self.backbone.feature_info.channels()[-1]
+        self.num_patches = feature_size[0] * feature_size[1]
+        self.proj = nn.Linear(feature_dim, embed_dim)
+
+    def forward(self, x):
+        x = self.backbone(x)[-1]
+        x = x.flatten(2).transpose(1, 2)
+        x = self.proj(x)
+        return x
+
+
+@BACKBONES.register_module()
+class VisionTransformer(BaseBackbone):
+    """ Vision Transformer with support for patch or hybrid CNN input stage
+    """
+    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, depth=12,
+                 num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0., 
+                 head_drop_type='HeadDropOut', head_drop_rate=0., head_drop_upper=1,
+                 drop_path_rate=0., hybrid_backbone=None, norm_layer_eps=1e-5, patch_stride=None, patch_padding=0, freeze=False,
+                 input_type='image', num_patches=None, pretrained=None, **kwargs):
+        super().__init__()
+        if kwargs:
+            print('Unused kwargs: ')
+            print(kwargs)
+        assert input_type in ('image', 'feature'), 'input_type must be "image" or "feature"'
+        if input_type == 'feature':
+            assert num_patches is not None
+        self.input_type = input_type
+        norm_layer = partial(nn.LayerNorm, eps=norm_layer_eps)
+        # self.num_classes = num_classes
+        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
+
+        if input_type == 'image':
+            if hybrid_backbone is not None:
+                self.patch_embed = HybridEmbed(
+                    hybrid_backbone, img_size=img_size, in_chans=in_chans, embed_dim=embed_dim)
+            else:
+                self.patch_embed = PatchEmbed(
+                    img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim, stride=patch_stride, padding=patch_padding)
+            num_patches = self.patch_embed.num_patches
+
+        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
+        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
+        self.blocks = nn.ModuleList([
+            Block(
+                dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
+                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, 
+                head_drop_type=head_drop_type, head_drop_rate=head_drop_rate, head_drop_upper=head_drop_upper)
+            for i in range(depth)])
+        self.norm = norm_layer(embed_dim)
+
+        # NOTE as per official impl, we could have a pre-logits representation dense layer + tanh here
+        #self.repr = nn.Linear(embed_dim, representation_size)
+        #self.repr_act = nn.Tanh()
+
+        # Classifier head
+        # self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
+        trunc_normal_(self.pos_embed, std=.02)
+        trunc_normal_(self.cls_token, std=.02)
+        self.apply(self._init_weights)
+        if pretrained:
+            self.init_weights(pretrained, num_patches + 1)
+        if freeze:
+            self.apply(self._freeze_weights)
+
+    def init_weights(self, pretrained, patch_num=None):
+        assert isinstance(patch_num, int)
+        logger = logging.getLogger()
+        logger.warning(f'{self.__class__.__name__} load pretrain from {pretrained}')
+        state_dict = torch.load(pretrained, map_location='cpu')
+        if 'state_dict' in state_dict:
+            state_dict = state_dict['state_dict']
+        pos_embed = state_dict['pos_embed']     # [1, 197, 768] for base
+        if patch_num is not None and patch_num != pos_embed.shape[1]:
+            logger.warning(f'interpolate pos_embed from {pos_embed.shape[1]} to {patch_num}')
+            pos_embed = resize_pos_embed(pos_embed, self.pos_embed, 1)
+        state_dict['pos_embed'] = pos_embed
+        load_state_dict(self, state_dict, strict=False, logger=logger)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+    
+    def _freeze_weights(self, m):
+        if isinstance(m, nn.modules.batchnorm._BatchNorm):
+            m.eval()
+        for param in m.parameters():
+            param.requires_grad = False
+
+    @torch.jit.ignore
+    def no_weight_decay(self):
+        return {'pos_embed', 'cls_token'}
+
+    # def get_classifier(self):
+    #     return self.head
+
+    # def reset_classifier(self, num_classes, global_pool=''):
+    #     self.num_classes = num_classes
+    #     self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
+
+    def forward_features(self, x):
+        B = x.shape[0]
+        if self.input_type == 'image':
+            x = self.patch_embed(x)  # (B, N, C) N: patch number
+
+        cls_tokens = self.cls_token.expand(B, -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
+        x = torch.cat((cls_tokens, x), dim=1)
+        x = x + self.pos_embed
+        x = self.pos_drop(x)
+
+        center_loss_sum = 0
+
+        for blk in self.blocks:
+            x, center_loss = blk(x)
+            center_loss_sum = center_loss
+
+        # center_loss = center_loss_sum / len(self.blocks)
+        center_loss = center_loss_sum   # only compute the last block
+
+        x = self.norm(x)    # (B, N, dim)
+        return x[:, 0], center_loss
+        # x = torch.mean(x, dim=1)    # global_avgpool
+        # return x, center_loss
+
+    def forward(self, x):
+        x, center_loss = self.forward_features(x)
+        # x = self.head(x)
+        return x, center_loss
+
+
+def _conv_filter(state_dict, patch_size=16):
+    """ convert patch embedding weight from manual patchify + linear proj to conv"""
+    out_dict = {}
+    for k, v in state_dict.items():
+        if 'patch_embed.proj.weight' in k:
+            v = v.reshape((v.shape[0], 3, patch_size, patch_size))
+        out_dict[k] = v
+    return out_dict
+
+
+def vit_small_patch16_224(pretrained=False, **kwargs):
+    if pretrained:
+        # NOTE my scale was wrong for original weights, leaving this here until I have better ones for this model
+        kwargs.setdefault('qk_scale', 768 ** -0.5)
+    model = VisionTransformer(patch_size=16, embed_dim=768, depth=8, num_heads=8, mlp_ratio=3., **kwargs)
+    model.default_cfg = default_cfgs['vit_small_patch16_224']
+    if pretrained:
+        load_pretrained(
+            model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3), filter_fn=_conv_filter)
+    return model
+
+
+def vit_base_patch16_224(pretrained=False, **kwargs):
+    model = VisionTransformer(
+        patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
+    model.default_cfg = default_cfgs['vit_base_patch16_224']
+    if pretrained:
+        load_pretrained(
+            model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3), filter_fn=_conv_filter)
+    return model
+
+
+def vit_base_patch16_384(pretrained=False, **kwargs):
+    model = VisionTransformer(
+        img_size=384, patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
+    model.default_cfg = default_cfgs['vit_base_patch16_384']
+    if pretrained:
+        load_pretrained(model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3))
+    return model
+
+
+def vit_base_patch32_384(pretrained=False, **kwargs):
+    model = VisionTransformer(
+        img_size=384, patch_size=32, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
+    model.default_cfg = default_cfgs['vit_base_patch32_384']
+    if pretrained:
+        load_pretrained(model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3))
+    return model
+
+
+def vit_large_patch16_224(pretrained=False, **kwargs):
+    model = VisionTransformer(
+        patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, qkv_bias=True,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
+    model.default_cfg = default_cfgs['vit_large_patch16_224']
+    if pretrained:
+        load_pretrained(model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3))
+    return model
+
+
+def vit_large_patch16_384(pretrained=False, **kwargs):
+    model = VisionTransformer(
+        img_size=384, patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4,  qkv_bias=True,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
+    model.default_cfg = default_cfgs['vit_large_patch16_384']
+    if pretrained:
+        load_pretrained(model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3))
+    return model
+
+
+def vit_large_patch32_384(pretrained=False, **kwargs):
+    model = VisionTransformer(
+        img_size=384, patch_size=32, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4,  qkv_bias=True,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
+    model.default_cfg = default_cfgs['vit_large_patch32_384']
+    if pretrained:
+        load_pretrained(model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3))
+    return model
+
+
+def vit_huge_patch16_224(pretrained=False, **kwargs):
+    model = VisionTransformer(patch_size=16, embed_dim=1280, depth=32, num_heads=16, mlp_ratio=4, **kwargs)
+    model.default_cfg = default_cfgs['vit_huge_patch16_224']
+    return model
+
+
+def vit_huge_patch32_384(pretrained=False, **kwargs):
+    model = VisionTransformer(
+        img_size=384, patch_size=32, embed_dim=1280, depth=32, num_heads=16, mlp_ratio=4, **kwargs)
+    model.default_cfg = default_cfgs['vit_huge_patch32_384']
+    return model
+
+
+def vit_small_resnet26d_224(pretrained=False, **kwargs):
+    pretrained_backbone = kwargs.get('pretrained_backbone', True)  # default to True for now, for testing
+    backbone = resnet26d(pretrained=pretrained_backbone, features_only=True, out_indices=[4])
+    model = VisionTransformer(
+        img_size=224, embed_dim=768, depth=8, num_heads=8, mlp_ratio=3, hybrid_backbone=backbone, **kwargs)
+    model.default_cfg = default_cfgs['vit_small_resnet26d_224']
+    return model
+
+
+def vit_small_resnet50d_s3_224(pretrained=False, **kwargs):
+    pretrained_backbone = kwargs.get('pretrained_backbone', True)  # default to True for now, for testing
+    backbone = resnet50d(pretrained=pretrained_backbone, features_only=True, out_indices=[3])
+    model = VisionTransformer(
+        img_size=224, embed_dim=768, depth=8, num_heads=8, mlp_ratio=3, hybrid_backbone=backbone, **kwargs)
+    model.default_cfg = default_cfgs['vit_small_resnet50d_s3_224']
+    return model
+
+
+def vit_base_resnet26d_224(pretrained=False, **kwargs):
+    pretrained_backbone = kwargs.get('pretrained_backbone', True)  # default to True for now, for testing
+    backbone = resnet26d(pretrained=pretrained_backbone, features_only=True, out_indices=[4])
+    model = VisionTransformer(
+        img_size=224, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, hybrid_backbone=backbone, **kwargs)
+    model.default_cfg = default_cfgs['vit_base_resnet26d_224']
+    return model
+
+
+def vit_base_resnet50d_224(pretrained=False, **kwargs):
+    pretrained_backbone = kwargs.get('pretrained_backbone', True)  # default to True for now, for testing
+    backbone = resnet50d(pretrained=pretrained_backbone, features_only=True, out_indices=[4])
+    model = VisionTransformer(
+        img_size=224, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, hybrid_backbone=backbone, **kwargs)
+    model.default_cfg = default_cfgs['vit_base_resnet50d_224']
+    return model
\ No newline at end of file
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/vit/vit_origin.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/vit/vit_origin.py
new file mode 100644
index 0000000000000000000000000000000000000000..883c9015b26fc039c2346d55d1f2f7f2eb4216e7
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/vit/vit_origin.py
@@ -0,0 +1,423 @@
+""" Vision Transformer (ViT) in PyTorch
+A PyTorch implement of Vision Transformers as described in
+'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale' - https://arxiv.org/abs/2010.11929
+The official jax code is released and available at https://github.com/google-research/vision_transformer
+Status/TODO:
+* Models updated to be compatible with official impl. Args added to support backward compat for old PyTorch weights.
+* Weights ported from official jax impl for 384x384 base and small models, 16x16 and 32x32 patches.
+* Trained (supervised on ImageNet-1k) my custom 'small' patch model to 77.9, 'base' to 79.4 top-1 with this code.
+* Hopefully find time and GPUs for SSL or unsupervised pretraining on OpenImages w/ ImageNet fine-tune in future.
+Acknowledgments:
+* The paper authors for releasing code and weights, thanks!
+* I fixed my class token impl based on Phil Wang's https://github.com/lucidrains/vit-pytorch ... check it out
+for some einops/einsum fun
+* Simple transformer style inspired by Andrej Karpathy's https://github.com/karpathy/minGPT
+* Bert reference code checks against Huggingface Transformers and Tensorflow Bert
+Hacked together by / Copyright 2020 Ross Wightman
+"""
+import logging
+import warnings
+import torch
+from torch import nn
+import torch.nn.functional as F
+from functools import partial
+
+
+from mmcv.runner import load_state_dict
+from ..builder import BACKBONES
+from ..backbones.base_backbone import BaseBackbone
+from .layers import DropPath, to_2tuple, trunc_normal_, resize_pos_embed
+from ..backbones.modules.vit import Block
+from ..backbones.modules.vit_pooling import PoolingBlock
+
+
+def _cfg(url='', **kwargs):
+    return {
+        'url': url,
+        'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
+        'crop_pct': .9, 'interpolation': 'bicubic',
+        # 'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
+        'first_conv': 'patch_embed.proj', 'classifier': 'head',
+        **kwargs
+    }
+
+
+default_cfgs = {
+    # patch models
+    'vit_small_patch16_224': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/vit_small_p16_224-15ec54c9.pth',
+    ),
+    'vit_base_patch16_224': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_224-80ecf9dd.pth',
+        mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
+    ),
+    'vit_base_patch16_384': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_384-83fb41ba.pth',
+        input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),
+    'vit_base_patch32_384': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p32_384-830016f5.pth',
+        input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),
+    'vit_large_patch16_224': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p16_224-4ee7a4dc.pth',
+        mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
+    'vit_large_patch16_384': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p16_384-b3be5167.pth',
+        input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),
+    'vit_large_patch32_384': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p32_384-9b920ba8.pth',
+        input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),
+    'vit_huge_patch16_224': _cfg(),
+    'vit_huge_patch32_384': _cfg(input_size=(3, 384, 384)),
+    # hybrid models
+    'vit_small_resnet26d_224': _cfg(),
+    'vit_small_resnet50d_s3_224': _cfg(),
+    'vit_base_resnet26d_224': _cfg(),
+    'vit_base_resnet50d_224': _cfg(),
+}
+
+
+class PatchEmbed(nn.Module):
+    """ Image to Patch Embedding
+    """
+    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, stride=None, padding=0):
+        super().__init__()
+        if stride is None:
+            stride = patch_size
+        img_size = to_2tuple(img_size)
+        patch_size = to_2tuple(patch_size)
+        # num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
+        num_patches = (img_size[0] + 2 * padding - patch_size[0]) / stride + 1
+        if num_patches % 1 != 0:
+            print('Warning: patch numbser is not int:', num_patches)
+        num_patches = int(num_patches) ** 2
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.num_patches = num_patches
+
+        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=stride, padding=padding)
+
+
+    def forward(self, x):
+        B, C, H, W = x.shape
+        # FIXME look at relaxing size constraints
+        assert H == self.img_size[0] and W == self.img_size[1], \
+            f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
+        x = self.proj(x).flatten(2).transpose(1, 2)
+        return x
+
+
+class HybridEmbed(nn.Module):
+    """ CNN Feature Map Embedding
+    Extract feature map from CNN, flatten, project to embedding dim.
+    """
+    def __init__(self, backbone, img_size=224, feature_size=None, in_chans=3, embed_dim=768):
+        super().__init__()
+        assert isinstance(backbone, nn.Module)
+        img_size = to_2tuple(img_size)
+        self.img_size = img_size
+        self.backbone = backbone
+        if feature_size is None:
+            with torch.no_grad():
+                # FIXME this is hacky, but most reliable way of determining the exact dim of the output feature
+                # map for all networks, the feature metadata has reliable channel and stride info, but using
+                # stride to calc feature dim requires info about padding of each stage that isn't captured.
+                training = backbone.training
+                if training:
+                    backbone.eval()
+                o = self.backbone(torch.zeros(1, in_chans, img_size[0], img_size[1]))[-1]
+                feature_size = o.shape[-2:]
+                feature_dim = o.shape[1]
+                backbone.train(training)
+        else:
+            feature_size = to_2tuple(feature_size)
+            feature_dim = self.backbone.feature_info.channels()[-1]
+        self.num_patches = feature_size[0] * feature_size[1]
+        self.proj = nn.Linear(feature_dim, embed_dim)
+
+    def forward(self, x):
+        x = self.backbone(x)[-1]
+        x = x.flatten(2).transpose(1, 2)
+        x = self.proj(x)
+        return x
+
+
+@BACKBONES.register_module()
+class VisionTransformerOrigin(BaseBackbone):
+    """ Vision Transformer with support for patch or hybrid CNN input stage
+    """
+    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, depth=12,
+                 num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0., 
+                 drop_path_rate=0., hybrid_backbone=None, norm_layer_eps=1e-5, patch_stride=None, patch_padding=0, freeze=False,
+                vit_pool_configs=None, last_ln=True,
+                 input_type='image', num_patches=None, pretrained=None, **kwargs):
+        super().__init__()
+        if kwargs:
+            warnings.warn('Unused kwargs: ')
+            warnings.warn(str(kwargs))
+        assert input_type in ('image', 'feature'), 'input_type must be "image" or "feature"'
+        if input_type == 'feature':
+            assert num_patches is not None
+        self.input_type = input_type
+        self.last_ln = last_ln
+        norm_layer = partial(nn.LayerNorm, eps=norm_layer_eps)
+        if last_ln:
+            self.norm = norm_layer(embed_dim)
+        # self.num_classes = num_classes
+        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
+
+        if input_type == 'image':
+            if hybrid_backbone is not None:
+                self.patch_embed = HybridEmbed(
+                    hybrid_backbone, img_size=img_size, in_chans=in_chans, embed_dim=embed_dim)
+            else:
+                self.patch_embed = PatchEmbed(
+                    img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim, stride=patch_stride, padding=patch_padding)
+            num_patches = self.patch_embed.num_patches
+
+        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
+        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
+        if vit_pool_configs is None:
+            self.blocks = nn.ModuleList([
+                Block(
+                    dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
+                    drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer
+                    )
+                for i in range(depth)])
+        else:
+            self.blocks = nn.ModuleList([
+            PoolingBlock(
+                dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
+                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, 
+                pool_config=dict(keep_rate=vit_pool_configs['keep_rates'][i], **vit_pool_configs),
+                )
+            for i in range(depth)])
+
+        # NOTE as per official impl, we could have a pre-logits representation dense layer + tanh here
+        #self.repr = nn.Linear(embed_dim, representation_size)
+        #self.repr_act = nn.Tanh()
+
+        # Classifier head
+        # self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
+        trunc_normal_(self.pos_embed, std=.02)
+        trunc_normal_(self.cls_token, std=.02)
+        self.apply(self._init_weights)
+        if pretrained:
+            self.init_weights(pretrained, num_patches + 1)
+        if freeze:
+            self.apply(self._freeze_weights)
+
+    def init_weights(self, pretrained, patch_num=None):
+        if patch_num is None:
+            patch_num = self.patch_embed.num_patches + 1
+        else:
+            assert isinstance(patch_num, int)
+        logger = logging.getLogger()
+        logger.warning(f'{self.__class__.__name__} load pretrain from {pretrained}')
+        logger.info('model has %s tokens' % patch_num)
+        print(patch_num)
+        state_dict = torch.load(pretrained, map_location='cpu')
+        if 'state_dict' in state_dict:
+            state_dict = state_dict['state_dict']
+        if 'model' in state_dict:
+            state_dict = state_dict['model']
+        # for k in list(state_dict.keys()):
+        #     if k.startswith('backbone.'):
+        #         state_dict[k[9:]] = state_dict[k]
+        #         del state_dict[k]
+        pos_embed = state_dict['pos_embed']     # [1, 197, 768] for base
+        if patch_num is not None and patch_num != pos_embed.shape[1]:
+            logger.warning(f'interpolate pos_embed from {pos_embed.shape[1]} to {patch_num}')
+            pos_embed = resize_pos_embed(pos_embed, self.pos_embed, 1)
+        state_dict['pos_embed'] = pos_embed
+        load_state_dict(self, state_dict, strict=False, logger=logger)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+    
+    def _freeze_weights(self, m):
+        if isinstance(m, nn.modules.batchnorm._BatchNorm):
+            m.eval()
+        for param in m.parameters():
+            param.requires_grad = False
+
+    @torch.jit.ignore
+    def no_weight_decay(self):
+        return {'pos_embed', 'cls_token'}
+
+    # def get_classifier(self):
+    #     return self.head
+
+    # def reset_classifier(self, num_classes, global_pool=''):
+    #     self.num_classes = num_classes
+    #     self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
+
+    def forward_features(self, x):
+        if isinstance(x, list):
+            assert len(x) == 1, 'ViT Origin only accept input with on group of set, but got %s' % len(x)
+            x = x[0]
+        # x = x[0] + x[1]
+        B = x.shape[0]
+        if self.input_type == 'image':
+            x = self.patch_embed(x)  # (B, N, C) N: patch number
+
+        cls_tokens = self.cls_token.expand(B, -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
+        x = torch.cat((cls_tokens, x), dim=1)
+        x = x + self.pos_embed
+        x = self.pos_drop(x)
+
+
+        for blk in self.blocks:
+            x = blk(x)
+        
+        if self.last_ln:
+            x = self.norm(x)    # (B, N, dim)
+        return x[:, 0]
+        # return x
+        # x = torch.mean(x, dim=1)    # global_avgpool
+        # return x, center_loss
+
+    def forward(self, x):
+        x = self.forward_features(x)
+        # x = self.head(x)
+        return x
+
+
+def _conv_filter(state_dict, patch_size=16):
+    """ convert patch embedding weight from manual patchify + linear proj to conv"""
+    out_dict = {}
+    for k, v in state_dict.items():
+        if 'patch_embed.proj.weight' in k:
+            v = v.reshape((v.shape[0], 3, patch_size, patch_size))
+        out_dict[k] = v
+    return out_dict
+
+
+def vit_small_patch16_224(pretrained=False, **kwargs):
+    if pretrained:
+        # NOTE my scale was wrong for original weights, leaving this here until I have better ones for this model
+        kwargs.setdefault('qk_scale', 768 ** -0.5)
+    model = VisionTransformer(patch_size=16, embed_dim=768, depth=8, num_heads=8, mlp_ratio=3., **kwargs)
+    model.default_cfg = default_cfgs['vit_small_patch16_224']
+    if pretrained:
+        load_pretrained(
+            model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3), filter_fn=_conv_filter)
+    return model
+
+
+def vit_base_patch16_224(pretrained=False, **kwargs):
+    model = VisionTransformer(
+        patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
+    model.default_cfg = default_cfgs['vit_base_patch16_224']
+    if pretrained:
+        load_pretrained(
+            model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3), filter_fn=_conv_filter)
+    return model
+
+
+def vit_base_patch16_384(pretrained=False, **kwargs):
+    model = VisionTransformer(
+        img_size=384, patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
+    model.default_cfg = default_cfgs['vit_base_patch16_384']
+    if pretrained:
+        load_pretrained(model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3))
+    return model
+
+
+def vit_base_patch32_384(pretrained=False, **kwargs):
+    model = VisionTransformer(
+        img_size=384, patch_size=32, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
+    model.default_cfg = default_cfgs['vit_base_patch32_384']
+    if pretrained:
+        load_pretrained(model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3))
+    return model
+
+
+def vit_large_patch16_224(pretrained=False, **kwargs):
+    model = VisionTransformer(
+        patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, qkv_bias=True,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
+    model.default_cfg = default_cfgs['vit_large_patch16_224']
+    if pretrained:
+        load_pretrained(model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3))
+    return model
+
+
+def vit_large_patch16_384(pretrained=False, **kwargs):
+    model = VisionTransformer(
+        img_size=384, patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4,  qkv_bias=True,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
+    model.default_cfg = default_cfgs['vit_large_patch16_384']
+    if pretrained:
+        load_pretrained(model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3))
+    return model
+
+
+def vit_large_patch32_384(pretrained=False, **kwargs):
+    model = VisionTransformer(
+        img_size=384, patch_size=32, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4,  qkv_bias=True,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
+    model.default_cfg = default_cfgs['vit_large_patch32_384']
+    if pretrained:
+        load_pretrained(model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3))
+    return model
+
+
+def vit_huge_patch16_224(pretrained=False, **kwargs):
+    model = VisionTransformer(patch_size=16, embed_dim=1280, depth=32, num_heads=16, mlp_ratio=4, **kwargs)
+    model.default_cfg = default_cfgs['vit_huge_patch16_224']
+    return model
+
+
+def vit_huge_patch32_384(pretrained=False, **kwargs):
+    model = VisionTransformer(
+        img_size=384, patch_size=32, embed_dim=1280, depth=32, num_heads=16, mlp_ratio=4, **kwargs)
+    model.default_cfg = default_cfgs['vit_huge_patch32_384']
+    return model
+
+
+def vit_small_resnet26d_224(pretrained=False, **kwargs):
+    pretrained_backbone = kwargs.get('pretrained_backbone', True)  # default to True for now, for testing
+    backbone = resnet26d(pretrained=pretrained_backbone, features_only=True, out_indices=[4])
+    model = VisionTransformer(
+        img_size=224, embed_dim=768, depth=8, num_heads=8, mlp_ratio=3, hybrid_backbone=backbone, **kwargs)
+    model.default_cfg = default_cfgs['vit_small_resnet26d_224']
+    return model
+
+
+def vit_small_resnet50d_s3_224(pretrained=False, **kwargs):
+    pretrained_backbone = kwargs.get('pretrained_backbone', True)  # default to True for now, for testing
+    backbone = resnet50d(pretrained=pretrained_backbone, features_only=True, out_indices=[3])
+    model = VisionTransformer(
+        img_size=224, embed_dim=768, depth=8, num_heads=8, mlp_ratio=3, hybrid_backbone=backbone, **kwargs)
+    model.default_cfg = default_cfgs['vit_small_resnet50d_s3_224']
+    return model
+
+
+def vit_base_resnet26d_224(pretrained=False, **kwargs):
+    pretrained_backbone = kwargs.get('pretrained_backbone', True)  # default to True for now, for testing
+    backbone = resnet26d(pretrained=pretrained_backbone, features_only=True, out_indices=[4])
+    model = VisionTransformer(
+        img_size=224, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, hybrid_backbone=backbone, **kwargs)
+    model.default_cfg = default_cfgs['vit_base_resnet26d_224']
+    return model
+
+
+def vit_base_resnet50d_224(pretrained=False, **kwargs):
+    pretrained_backbone = kwargs.get('pretrained_backbone', True)  # default to True for now, for testing
+    backbone = resnet50d(pretrained=pretrained_backbone, features_only=True, out_indices=[4])
+    model = VisionTransformer(
+        img_size=224, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, hybrid_backbone=backbone, **kwargs)
+    model.default_cfg = default_cfgs['vit_base_resnet50d_224']
+    return model
\ No newline at end of file
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/models/vit/vit_siam_merge.py b/CAGE_expression_inference-apvit/apvit_mmcls/models/vit/vit_siam_merge.py
new file mode 100644
index 0000000000000000000000000000000000000000..cc929cf023c64bdaea8ae67e62191723da1a9a64
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/models/vit/vit_siam_merge.py
@@ -0,0 +1,1295 @@
+"""
+input: 
+    stage2 and stage3 tokens from CNN
+
+forward:
+    s2 and s3 processed by 3 MHSA blocks, seperately
+    the output are concatenated and feed to 3+2 MHSA
+    add a skip connection to last 2 MHSA
+
+"""
+import math
+import os
+import torch
+from torch import nn
+from torch import Tensor
+import torch.nn.functional as F
+from functools import partial
+from typing import List
+
+from mmcls.utils import get_root_logger
+from mmcv.runner import load_state_dict
+from mmcls.models.necks.la_max import LANet
+from ..builder import BACKBONES, build_loss
+from ..backbones.base_backbone import BaseBackbone
+from .layers import DropPath, to_2tuple, trunc_normal_, resize_pos_embed_v2
+from ..utils import top_pool
+from ..backbones.modules.vit import Block
+from ..backbones.modules.vit_pooling import PoolingBlock
+
+
+@BACKBONES.register_module()
+class CNNMultiStageCatOrigin(BaseBackbone):
+    """
+    CNN 不同 stage 的 Concate 在一起
+    """
+    def __init__(self, embed_dim=768, depth=12,
+                 num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0., 
+                 drop_path_rate=0., norm_layer_eps=1e-5, freeze=False,
+                 input_type='image',  pretrained=None, 
+                 in_channels=[], patch_nums=[],
+                 **kwargs):
+        super().__init__()
+        if kwargs:
+            print('Unused kwargs: ')
+            print(kwargs)
+        assert input_type  == 'feature'
+        if input_type == 'feature':
+            assert len(in_channels) == len(patch_nums), 'Input channels must have the same length of patch_nums'
+
+        self.input_type = input_type
+        norm_layer = partial(nn.LayerNorm, eps=norm_layer_eps)
+
+        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
+
+        self.projs = nn.ModuleList([nn.Conv2d(in_channels[i], embed_dim, 1,) for i in range(len(in_channels))])
+
+        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim), requires_grad=True)
+        self.pos_embed = nn.Parameter(torch.zeros(1, sum(patch_nums), embed_dim), requires_grad=True)
+        self.cls_pos_embed = nn.Parameter(torch.zeros(1, 1, embed_dim), requires_grad=True)
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
+        self.blocks = nn.ModuleList([
+            Block(
+                dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
+                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, 
+                )
+            for i in range(depth)])
+        
+        self.norm = norm_layer(embed_dim)
+
+
+        if pretrained:
+            self.init_weights(pretrained, patch_nums)
+        else:
+            trunc_normal_(self.pos_embed, std=.02)
+            trunc_normal_(self.cls_token, std=.02)
+            self.apply(self._init_weights)
+        if freeze:
+            self.apply(self._freeze_weights)
+
+    def init_weights(self, pretrained, patch_nums=[]):
+        logger = get_root_logger()
+        logger.warning(f'{self.__class__.__name__} load pretrain from {pretrained}')
+        state_dict = torch.load(pretrained, map_location='cpu')
+        if 'state_dict' in state_dict:
+            state_dict = state_dict['state_dict']
+        pos_embed = state_dict['pos_embed']     # [1, 197, 768] for base
+        patch_pos_embed = pos_embed[:, 1:, :]
+        pos_embeds = []
+        for patch_num in patch_nums:
+            print('patch_num', patch_num)
+            if patch_num != pos_embed.shape[1] - 1:
+                logger.warning(f'interpolate pos_embed from {patch_pos_embed.shape[1]} to {patch_num}')
+                pos_embed_new = resize_pos_embed_v2(patch_pos_embed, patch_num, 0)
+            else:   # 去掉 cls_token
+                print('does not need to resize')
+                pos_embed_new = patch_pos_embed
+            pos_embeds.append(pos_embed_new)
+        state_dict['pos_embed'] = torch.cat(pos_embeds, dim=1)
+        state_dict['cls_pos_embed'] = pos_embed[:, 0, :].unsqueeze(1)
+        load_state_dict(self, state_dict, strict=False, logger=logger)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+    
+    def _freeze_weights(self, m):
+        if isinstance(m, nn.modules.batchnorm._BatchNorm):
+            m.eval()
+        for param in m.parameters():
+            param.requires_grad = False
+
+    @torch.jit.ignore
+    def no_weight_decay(self):
+        return {'pos_embed', 'cls_token'}
+
+    def forward_features(self, x):
+        x = [self.projs[i](x[i]) for i in range(len(x))]
+        x = [i.flatten(2).transpose(2, 1) for i in x]
+
+        x = torch.cat(x, dim=1)
+        
+        B = x.shape[0]
+
+        cls_tokens = self.cls_token.expand(B, -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
+
+        x = torch.cat((cls_tokens, x), dim=1)
+        x_pos = torch.cat((self.cls_pos_embed, self.pos_embed), dim=1)
+        x = x + x_pos
+   
+        x = self.pos_drop(x)
+
+        for blk in self.blocks:
+            x = blk(x)
+
+        x = self.norm(x)    # (B, N, dim)
+        if self.training is False:
+            print(x.shape)
+        loss = dict()
+        return x[:, 0], loss
+
+
+    def forward(self, x, **kwargs):
+        x, loss = self.forward_features(x)
+        return dict(x=x, loss=dict(VitDiv_loss=loss))
+
+
+@BACKBONES.register_module()
+class CNNMultiStageIntraAdd(BaseBackbone):
+    """ 
+    CNN 不同 stage 的 feature 经过 siam_block 来探索 stage 内的关系，
+    然后 add 在一起探索 stage 间的关系
+    Intra(S2) + Intra(S3)
+    """
+    def __init__(self, embed_dim=768, depth=12,
+                 num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0., 
+                 drop_path_rate=0., norm_layer_eps=1e-5, freeze=False,
+                 input_type='image',  pretrained=None, 
+                 in_channels=[], patch_num=0,
+                 s2_pooling_method='MaxPooling',
+                 siam_depth=1,
+                 **kwargs):
+        super().__init__()
+        if kwargs:
+            print('Unused kwargs: ')
+            print(kwargs)
+        assert input_type  == 'feature'
+
+        self.input_type = input_type
+        norm_layer = partial(nn.LayerNorm, eps=norm_layer_eps)
+
+        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
+
+        self.projs = nn.ModuleList([nn.Conv2d(in_channels[i], embed_dim, 1,) for i in range(len(in_channels))])
+
+        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim), requires_grad=True)
+        self.patch_pos_embed = nn.Parameter(torch.zeros(1, patch_num, embed_dim), requires_grad=True)
+        self.cls_pos_embed = nn.Parameter(torch.zeros(1, 1, embed_dim), requires_grad=True)
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
+        self.blocks = nn.ModuleList([
+            Block(
+                dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
+                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, 
+                )
+            for i in range(depth)])
+        self.siam_blocks = nn.ModuleList([
+            Block(
+                dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
+                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, 
+                )
+            for i in range(siam_depth)])
+        self.norm = norm_layer(embed_dim)
+        if s2_pooling_method == 'MaxPooling':
+            self.s2_pooling = nn.MaxPool2d(kernel_size=2)
+        elif s2_pooling_method == 'Unfold':
+            self.s2_pooling = nn.Unfold(kernel_size=2, stride=2)    # Unfold instead of MaxPooling
+        else:
+            raise ValueError("Unknown pooling method: %s" % s2_pooling_method)
+
+
+        if pretrained:
+            self.init_weights(pretrained, patch_num)
+        else:
+            trunc_normal_(self.patch_pos_embed, std=.02)
+            trunc_normal_(self.cls_token, std=.02)
+            self.apply(self._init_weights)
+        if freeze:
+            self.apply(self._freeze_weights)
+
+    def init_weights(self, pretrained, patch_num=0):
+        logger = get_root_logger()
+        logger.warning(f'{self.__class__.__name__} load pretrain from {pretrained}')
+        state_dict = torch.load(pretrained, map_location='cpu')
+        if 'state_dict' in state_dict:
+            state_dict = state_dict['state_dict']
+        pos_embed = state_dict['pos_embed']     # [1, 197, 768] for base
+        patch_pos_embed = pos_embed[:, 1:, :]
+
+        if patch_num != pos_embed.shape[1] - 1:
+            logger.warning(f'interpolate pos_embed from {patch_pos_embed.shape[1]} to {patch_num}')
+            pos_embed_new = resize_pos_embed_v2(patch_pos_embed, patch_num, 0)
+        else:   # 去掉 cls_token
+            print('does not need to resize')
+            pos_embed_new = patch_pos_embed
+        del state_dict['pos_embed']
+        state_dict['patch_pos_embed'] = pos_embed_new
+        state_dict['cls_pos_embed'] = pos_embed[:, 0, :].unsqueeze(1)
+
+        # copy weights from blocks to siam blocks
+        siam_state_dict = dict()
+        for k, v in state_dict.items():
+            if k.startswith(f'blocks.'):
+                if int(k.split('.')[1]) in list(range(len(self.siam_blocks))):
+                    siam_state_dict[k.replace('blocks.', 'siam_blocks.')] = v
+        state_dict.update(siam_state_dict)
+
+        load_state_dict(self, state_dict, strict=False, logger=logger)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+    
+    def _freeze_weights(self, m):
+        if isinstance(m, nn.modules.batchnorm._BatchNorm):
+            m.eval()
+        for param in m.parameters():
+            param.requires_grad = False
+
+    def forward_features(self, x):
+        assert len(x) == 2, '目前只支持2个 stage'
+        B, C, H, W = x[-1].shape
+        x[0] = self.s2_pooling(x[0]).reshape(B, -1, H, W)
+
+        x2D = [self.projs[i](x[i]) for i in range(len(x))]
+        x = [i.flatten(2).transpose(2, 1) for i in x2D]
+        if len(self.siam_blocks) == 0:
+            x = [x[i] + self.patch_pos_embed/len(x) for i in range(len(x))]
+        else:
+            x = [x[i] + self.patch_pos_embed for i in range(len(x))]
+
+        x0, x1 = x[0], x[1]
+
+        cls_tokens = self.cls_token.expand(B, -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
+        if len(self.siam_blocks) == 0:
+            cls_tokens = cls_tokens + self.cls_pos_embed/len(x)
+        else:
+            cls_tokens = cls_tokens + self.cls_pos_embed
+
+        x0 = torch.cat((cls_tokens, x0), dim=1)
+        x1 = torch.cat((cls_tokens, x1), dim=1)
+
+        x0 = self.pos_drop(x0)
+        x1 = self.pos_drop(x1)
+
+        # Intra
+        for blk in self.siam_blocks:
+            x0 = blk(x0)
+
+        for blk in self.blocks[:len(self.siam_blocks)]:
+            x1 = blk(x1)
+
+        x = x0 + x1
+
+        for blk in self.blocks:
+            x = blk(x)
+
+        x = self.norm(x)    # (B, N, dim)
+        if self.training is False:
+            print(x.shape)
+        loss = dict()
+        return x[:, 0], loss
+
+
+    def forward(self, x, **kwargs):
+        x, loss = self.forward_features(x)
+        return dict(x=x, loss=dict(VitDiv_loss=loss))
+
+
+@BACKBONES.register_module()
+class PoolingViT(BaseBackbone):
+    """ 
+    
+    """
+    def __init__(self, embed_dim=768, depth=12,
+                 num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0.,
+                 drop_path_rate=0., norm_layer_eps=1e-5, freeze=False,
+                 input_type='image',  pretrained=None, 
+                 in_channels=[], patch_num=0,
+                 attn_method='SUM_ABS_1',
+                 cnn_pool_config=None,
+                 vit_pool_configs=None,
+                 multi_head_fusion=False,
+                 sum_batch_mean=False,
+                 **kwargs):
+        super().__init__()
+        if kwargs:
+            print('Unused kwargs: ')
+            print(kwargs)
+        assert input_type  == 'feature', 'Only suit for hybrid model'
+        self.sum_batch_mean = sum_batch_mean
+        if sum_batch_mean:
+            self.alpha = nn.Parameter(torch.tensor(1.0), requires_grad=True)
+        self.multi_head_fusion = multi_head_fusion
+        self.num_heads = num_heads
+        if multi_head_fusion:
+            assert vit_pool_configs is None, 'MultiHeadFusion only support original ViT Block, by now'
+
+        self.input_type = input_type
+        norm_layer = partial(nn.LayerNorm, eps=norm_layer_eps)
+
+        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
+
+        self.projs = nn.ModuleList([nn.Conv2d(in_channels[i], embed_dim, 1,) for i in range(len(in_channels))])
+
+        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim), requires_grad=True)
+        self.patch_pos_embed = nn.Parameter(torch.zeros(1, patch_num, embed_dim), requires_grad=True)
+        self.cls_pos_embed = nn.Parameter(torch.zeros(1, 1, embed_dim), requires_grad=True)
+        self.pos_drop = nn.Dropout(p=drop_rate)
+        self.attn_method = attn_method
+        self.cnn_pool_config = cnn_pool_config
+        if attn_method == 'LA':
+            # self.attn_f = LANet(in_channels[-1], 16)
+            self.attn_f = LANet(embed_dim, 16)
+            
+        elif attn_method == 'SUM':
+            self.attn_f = lambda x: torch.sum(x, dim=1).unsqueeze(1)
+        elif attn_method == 'SUM_ABS_1':
+            self.attn_f = lambda x: torch.sum(torch.abs(x), dim=1).unsqueeze(1)
+        elif attn_method == 'SUM_ABS_2':
+            self.attn_f = lambda x: torch.sum(torch.pow(torch.abs(x), 2), dim=1).unsqueeze(1)
+        elif attn_method == 'MAX':
+            self.attn_f = lambda x: torch.max(x, dim=1)[0].unsqueeze(1)
+        elif attn_method == 'MAX_ABS_1':
+            self.attn_f = lambda x: torch.max(torch.abs(x), dim=1)[0].unsqueeze(1)
+        elif attn_method == 'Random':
+            self.attn_f = lambda x: x[:, torch.randint(high=x.shape[1], size=(1,))[0], ...].unsqueeze(1)
+        else:
+            raise ValueError("Unknown attn_method")
+
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
+        if vit_pool_configs is None:
+
+            self.blocks = nn.ModuleList([
+                Block(
+                    dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
+                    drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, head_fusion=multi_head_fusion,
+                    )
+                for i in range(depth)])
+        else:
+            vit_keep_rates = vit_pool_configs['keep_rates']
+            self.blocks = nn.ModuleList([
+                PoolingBlock(
+                    dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
+                    drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, 
+                    pool_config=dict(keep_rate=vit_keep_rates[i], **vit_pool_configs),
+                    )
+            for i in range(depth)])
+        self.norm = norm_layer(embed_dim)
+
+        self.s2_pooling = nn.MaxPool2d(kernel_size=2)
+
+        if pretrained:
+            self.init_weights(pretrained, patch_num)
+        else:
+            trunc_normal_(self.patch_pos_embed, std=.02)
+            trunc_normal_(self.cls_token, std=.02)
+            self.apply(self._init_weights)
+        if freeze:
+            self.apply(self._freeze_weights)
+
+    def init_weights(self, pretrained, patch_num=0):
+        logger = get_root_logger()
+        logger.warning(f'{self.__class__.__name__} load pretrain from {pretrained}')
+        state_dict = torch.load(pretrained, map_location='cpu')
+        if 'model' in state_dict:
+            state_dict = state_dict['model']
+        if 'state_dict' in state_dict:
+            state_dict = state_dict['state_dict']
+        pos_embed = state_dict['pos_embed']     # [1, 197, 768] for base
+        patch_pos_embed = pos_embed[:, 1:, :]
+
+        if patch_num != pos_embed.shape[1] - 1:
+            logger.warning(f'interpolate pos_embed from {patch_pos_embed.shape[1]} to {patch_num}')
+            pos_embed_new = resize_pos_embed_v2(patch_pos_embed, patch_num, 0)
+        else:   # remove cls_token
+            print('does not need to resize!')
+            pos_embed_new = patch_pos_embed
+        del state_dict['pos_embed']
+        state_dict['patch_pos_embed'] = pos_embed_new
+        state_dict['cls_pos_embed'] = pos_embed[:, 0, :].unsqueeze(1)
+
+        if self.multi_head_fusion:
+            # convert blocks.0.attn.qkv.weight to blocks.0.attn.qkv.0.weight
+            num_groups = self.blocks[0].attn.group_number
+            d = self.embed_dim // num_groups
+            print('d', d)
+            for k in list(state_dict.keys()):
+                if k.startswith('blocks.'):
+                    keys = k.split('.')
+                    if  not (keys[2] == 'attn' and keys[3] == 'qkv'):
+                        continue
+                    for i in range(num_groups):
+                        new_key = f'blocks.{keys[1]}.attn.qkv.{i}.weight'
+                        new_value = state_dict[k][i*3*d:(i+1)*3*d, i*d: i*d+d]
+                        state_dict[new_key] = new_value
+
+                    del state_dict[k]
+
+        for k in ('patch_embed.proj.weight', 'patch_embed.proj.bias', 'head.weight', 'head.bias'):
+            del state_dict[k]
+        load_state_dict(self, state_dict, strict=False, logger=logger)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+    
+    def _freeze_weights(self, m):
+        if isinstance(m, nn.modules.batchnorm._BatchNorm):
+            m.eval()
+        for param in m.parameters():
+            param.requires_grad = False
+
+    def forward_features(self, x):
+        assert len(x) == 1, '目前只支持1个 stage'
+        assert isinstance(x, list) or isinstance(x, tuple)
+        if len(x) == 2: # S2, S3
+            x[0] = self.s2_pooling(x[0])
+        elif len(x) == 3:
+            x[0] = nn.MaxPool2d(kernel_size=4)(x[0])
+            x[1] = self.s2_pooling(x[1])
+        if os.getenv('DEBUG_MODE') == '1':
+            print(x[0].shape)
+
+        x = [self.projs[i](x[i]) for i in range(len(x))]
+        # x = x[0]
+        B, C, H, W = x[-1].shape
+        attn_map = self.attn_f(x[-1]) # [B, 1, H, W]
+        if self.attn_method == 'LA':
+            x[-1] = x[-1] * attn_map    #  to have gradient
+        x = [i.flatten(2).transpose(2, 1) for i in x]
+        # x = self.projs[0](x).flatten(2).transpose(2, 1)
+        # disable the first row and columns
+        # attn_map[:, :, 0, :] = 0.
+        # attn_map[:, :, :, 0] = 0.
+        attn_weight = attn_map.flatten(2).transpose(2, 1)
+
+        # attn_weight = torch.rand(attn_weight.shape, device=attn_weight.device)
+        
+        x = torch.stack(x).sum(dim=0)   # S1 + S2 + S3
+        x = x + self.patch_pos_embed
+        
+        B, N, C = x.shape
+        
+        if self.cnn_pool_config is not None:
+            keep_indexes = top_pool(attn_weight, dim=C, **self.cnn_pool_config)
+            if keep_indexes is not None:
+                x = x.gather(dim=1, index=keep_indexes)
+        cls_tokens = self.cls_token.expand(B, -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
+
+        cls_tokens = cls_tokens + self.cls_pos_embed
+
+        x = torch.cat((cls_tokens, x), dim=1)
+
+        for blk in self.blocks:
+            x = blk(x)
+        x = self.norm(x)    # (B, N, dim)
+        if os.environ.get('DEBUG_MODE', '0') == '1':
+            print('output', x.shape)
+        x = x[:, 0]
+        if self.sum_batch_mean:
+            x = x + x.mean(dim=0) * self.alpha
+        loss = dict()
+        return x, loss, attn_map
+
+    def forward(self, x, **kwargs):
+        x, loss, attn_map = self.forward_features(x)
+        return dict(x=x, loss=dict(VitDiv_loss=loss), attn_map=attn_map)
+
+
+@BACKBONES.register_module()
+class HeadFusionViT(BaseBackbone):
+    """ 
+    
+    """
+    def __init__(self, embed_dim=768, depth=12,
+                 num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0., 
+                 drop_path_rate=0., norm_layer_eps=1e-5, freeze=False,
+                 input_type='image',  pretrained=None, 
+                 in_channels=[], patch_num=0,
+                 attn_method='LA',
+                 cnn_pool_config=None,
+                 vit_pool_configs=None,
+                 multi_head_fusion=False,
+                 **kwargs):
+        super().__init__()
+        if kwargs:
+            print('Unused kwargs: ')
+            print(kwargs)
+        assert input_type  == 'feature'
+        self.multi_head_fusion = multi_head_fusion
+        self.num_heads = num_heads
+        if multi_head_fusion:
+            assert vit_pool_configs is None, 'MultiHeadFusion only support original ViT Block, by now'
+
+        self.input_type = input_type
+        norm_layer = partial(nn.LayerNorm, eps=norm_layer_eps)
+
+        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
+
+        self.projs = nn.ModuleList([nn.Conv2d(in_channels[i], embed_dim, 1,) for i in range(len(in_channels))])
+
+        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim), requires_grad=True)
+        self.patch_pos_embed = nn.Parameter(torch.zeros(1, patch_num, embed_dim), requires_grad=True)
+        self.cls_pos_embed = nn.Parameter(torch.zeros(1, 1, embed_dim), requires_grad=True)
+        self.pos_drop = nn.Dropout(p=drop_rate)
+        self.attn_method = attn_method
+        self.cnn_pool_config = cnn_pool_config
+        if attn_method == 'LA':
+            self.attn_f = LANet(in_channels[-1], 16)
+        elif attn_method == 'SUM':
+            self.attn_f = lambda x: torch.sum(x, dim=1).unsqueeze(1)
+        elif attn_method == 'SUM_ABS_1':
+            self.attn_f = lambda x: torch.sum(torch.abs(x), dim=1).unsqueeze(1)
+        elif attn_method == 'SUM_ABS_2':
+            self.attn_f = lambda x: torch.sum(torch.pow(torch.abs(x), 2), dim=1).unsqueeze(1)
+        elif attn_method == 'MAX':
+            self.attn_f = lambda x: torch.max(x, dim=1)[0].unsqueeze(1)
+        elif attn_method == 'MAX_ABS_1':
+            self.attn_f = lambda x: torch.max(torch.abs(x), dim=1)[0].unsqueeze(1)
+        else:
+            raise ValueError("Unknown attn_method")
+
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
+        if vit_pool_configs is None:
+            self.blocks = nn.ModuleList([
+                Block(
+                    dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
+                    drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, head_fusion=multi_head_fusion,
+                    )
+                for i in range(depth)])
+        else:
+            self.blocks = nn.ModuleList([
+            PoolingBlock(
+                dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
+                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, 
+                pool_config=dict(keep_rate=vit_pool_configs['keep_rates'][i], **vit_pool_configs),
+                )
+            for i in range(depth)])
+        self.norm = norm_layer(embed_dim)
+
+        if pretrained:
+            self.init_weights(pretrained, patch_num)
+        else:
+            trunc_normal_(self.patch_pos_embed, std=.02)
+            trunc_normal_(self.cls_token, std=.02)
+            self.apply(self._init_weights)
+        if freeze:
+            self.apply(self._freeze_weights)
+
+
+    def init_weights(self, pretrained, patch_num=0):
+        logger = get_root_logger()
+        logger.warning(f'{self.__class__.__name__} load pretrain from {pretrained}')
+        state_dict = torch.load(pretrained, map_location='cpu')
+        if 'state_dict' in state_dict:
+            state_dict = state_dict['state_dict']
+        pos_embed = state_dict['pos_embed']     # [1, 197, 768] for base
+        patch_pos_embed = pos_embed[:, 1:, :]
+
+        if patch_num != pos_embed.shape[1] - 1:
+            logger.warning(f'interpolate pos_embed from {patch_pos_embed.shape[1]} to {patch_num}')
+            pos_embed_new = resize_pos_embed_v2(patch_pos_embed, patch_num, 0)
+        else:   # 去掉 cls_token
+            print('does not need to resize')
+            pos_embed_new = patch_pos_embed
+        del state_dict['pos_embed']
+        state_dict['patch_pos_embed'] = pos_embed_new
+        state_dict['cls_pos_embed'] = pos_embed[:, 0, :].unsqueeze(1)
+
+        if self.multi_head_fusion:
+            # convert blocks.0.attn.qkv.weight to blocks.0.attn.qkv.0.weight
+            num_groups = self.blocks[0].attn.group_number
+            d = self.embed_dim // num_groups
+            print('d', d)
+            for k in list(state_dict.keys()):
+                if k.startswith('blocks.'):
+                    keys = k.split('.')
+                    if  not (keys[2] == 'attn' and keys[3] == 'qkv'):
+                        continue
+                    for i in range(num_groups):
+                        new_key = f'blocks.{keys[1]}.attn.qkv2.{i}.weight'
+                        new_value = state_dict[k][i*3*d:(i+1)*3*d, i*d: i*d+d]
+                        state_dict[new_key] = new_value
+
+                    # del state_dict[k]
+
+        for k in ('patch_embed.proj.weight', 'patch_embed.proj.bias', 'head.weight', 'head.bias'):
+            del state_dict[k]
+        load_state_dict(self, state_dict, strict=False, logger=logger)
+
+
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+    
+    def _freeze_weights(self, m):
+        if isinstance(m, nn.modules.batchnorm._BatchNorm):
+            m.eval()
+        for param in m.parameters():
+            param.requires_grad = False
+
+    def forward_features(self, x):
+        assert len(x) == 1, '目前只支持1个 stage'
+        x = x[0]
+        B, C, H, W = x.shape
+        attn_map = self.attn_f(x) # [B, 1, H, W]
+        if self.attn_method == 'LA':
+            x = x * attn_map    #  to have gradient
+        x = self.projs[0](x).flatten(2).transpose(2, 1)
+        attn_weight = attn_map.flatten(2).transpose(2, 1)
+
+        x = x + self.patch_pos_embed
+        
+        B, N, C = x.shape
+        
+        if self.cnn_pool_config is not None:
+            keep_indexes = top_pool(attn_weight, dim=C, **self.cnn_pool_config)
+            if keep_indexes is not None:
+                x = x.gather(dim=1, index=keep_indexes)
+        
+        cls_tokens = self.cls_token.expand(B, -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
+
+        cls_tokens = cls_tokens + self.cls_pos_embed
+
+        x = torch.cat((cls_tokens, x), dim=1)
+
+        for blk in self.blocks:
+            x = blk(x)
+
+        x = self.norm(x)    # (B, N, dim)
+        if os.environ.get('DEBUG_MODE', '0') == '1':
+            print(x.shape)
+        loss = dict()
+        return x[:, 0], loss
+
+    def forward(self, x, **kwargs):
+        x, loss = self.forward_features(x)
+        return dict(x=x, loss=dict(VitDiv_loss=loss))
+
+
+@BACKBONES.register_module()
+class DeFPNViTV2(BaseBackbone):
+    """ 
+    浅层feature经过更多的block，高层经过的block少
+    CNN每个stage再经过个Pooling
+    """
+    def __init__(self, embed_dim=768, depth=12,
+                 num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0., 
+                 drop_path_rate=0., norm_layer_eps=1e-5, freeze=False,
+                 input_type='image',  pretrained=None, 
+                 in_channels=[], patch_num=0,
+                 attn_method='LA',
+                 cnn_pool_config=None,
+                 vit_pool_configs=None,
+                 insert_index=(0, 1, 2),
+                 **kwargs):
+        super().__init__()
+        if kwargs:
+            print('Unused kwargs: ')
+            print(kwargs)
+        assert input_type  == 'feature', 'Only suit for hybrid model'
+        assert len(insert_index) == len(in_channels), 'insert_index must have the same length as in_channels'
+        assert max(insert_index) < depth, 'insert_index must be smaller than depth'
+        self.insert_index = insert_index
+        self.num_heads = num_heads
+        self.alpha = nn.Parameter(torch.tensor(1.0), requires_grad=True)
+
+        self.input_type = input_type
+        norm_layer = partial(nn.LayerNorm, eps=norm_layer_eps)
+
+        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
+
+        self.projs = nn.ModuleList([nn.Conv2d(in_channels[i], embed_dim, 1,) for i in range(len(in_channels))])
+
+        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim), requires_grad=True)
+        self.patch_pos_embed = nn.Parameter(torch.zeros(1, patch_num, embed_dim), requires_grad=True)
+        # self.patch_pos_embed_0 = nn.Parameter(torch.zeros(1, patch_num * 16, embed_dim), requires_grad=True)
+        self.patch_pos_embed_1 = nn.Parameter(torch.zeros(1, patch_num * 4, embed_dim), requires_grad=True)
+        self.cls_pos_embed = nn.Parameter(torch.zeros(1, 1, embed_dim), requires_grad=True)
+        self.pos_drop = nn.Dropout(p=drop_rate)
+        self.attn_method = attn_method
+        self.cnn_pool_config = cnn_pool_config
+        if attn_method == 'LA':
+            self.attn_f = LANet(in_channels[-1], 16)
+        elif attn_method == 'SUM':
+            self.attn_f = lambda x: torch.sum(x, dim=1).unsqueeze(1)
+        elif attn_method == 'SUM_ABS_1':
+            self.attn_f = lambda x: torch.sum(torch.abs(x), dim=1).unsqueeze(1)
+        elif attn_method == 'SUM_ABS_2':
+            self.attn_f = lambda x: torch.sum(torch.pow(torch.abs(x), 2), dim=1).unsqueeze(1)
+        elif attn_method == 'MAX':
+            self.attn_f = lambda x: torch.max(x, dim=1)[0].unsqueeze(1)
+        elif attn_method == 'MAX_ABS_1':
+            self.attn_f = lambda x: torch.max(torch.abs(x), dim=1)[0].unsqueeze(1)
+        else:
+            raise ValueError("Unknown attn_method")
+
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
+        if vit_pool_configs is None:
+            self.blocks = nn.ModuleList([
+                Block(
+                    dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
+                    drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, head_fusion=False,
+                    )
+                for i in range(depth)])
+        else:
+            self.blocks = nn.ModuleList([
+                PoolingBlock(
+                    dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
+                    drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, 
+                    pool_config=dict(keep_rate=vit_pool_configs['keep_rates'][i], **vit_pool_configs),
+                    )
+            for i in range(depth)])
+        # self.norm = norm_layer(embed_dim)
+        self.norm = nn.BatchNorm1d(embed_dim, momentum=0.9, affine=False)
+
+        self.s2_pooling = nn.MaxPool2d(kernel_size=2)
+
+        if pretrained:
+            self.init_weights(pretrained, patch_num)
+        else:
+            trunc_normal_(self.patch_pos_embed, std=.02)
+            trunc_normal_(self.cls_token, std=.02)
+            self.apply(self._init_weights)
+        if freeze:
+            self.apply(self._freeze_weights)
+
+        # self.test_sum = torch.load('output/sum.pth')
+
+    def init_weights(self, pretrained, patch_num=0):
+        logger = get_root_logger()
+        logger.warning(f'{self.__class__.__name__} load pretrain from {pretrained}')
+        state_dict = torch.load(pretrained, map_location='cpu')
+        if 'state_dict' in state_dict:
+            state_dict = state_dict['state_dict']
+        pos_embed = state_dict['pos_embed']     # [1, 197, 768] for base
+        patch_pos_embed = pos_embed[:, 1:, :]
+
+        if patch_num != pos_embed.shape[1] - 1:
+            logger.warning(f'interpolate pos_embed from {patch_pos_embed.shape[1]} to {patch_num}')
+            pos_embed_new = resize_pos_embed_v2(patch_pos_embed, patch_num, 0)
+        else:   # 去掉 cls_token
+            print('does not need to resize')
+            pos_embed_new = patch_pos_embed
+        del state_dict['pos_embed']
+        state_dict['patch_pos_embed'] = pos_embed_new
+        # without maxpooling
+        # pos_embed_new_0 = resize_pos_embed_v2(patch_pos_embed, patch_num*16, 0)
+        # state_dict['patch_pos_embed_0'] = pos_embed_new_0
+        pos_embed_new_1 = resize_pos_embed_v2(patch_pos_embed, patch_num*4, 0)
+        state_dict['patch_pos_embed_1'] = pos_embed_new_1
+
+        state_dict['cls_pos_embed'] = pos_embed[:, 0, :].unsqueeze(1)
+
+        for k in ('patch_embed.proj.weight', 'patch_embed.proj.bias', 'head.weight', 'head.bias'):
+            del state_dict[k]
+        load_state_dict(self, state_dict, strict=False, logger=logger)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+    
+    def _freeze_weights(self, m):
+        if isinstance(m, nn.modules.batchnorm._BatchNorm):
+            m.eval()
+        for param in m.parameters():
+            param.requires_grad = False
+
+    def forward_features(self, x):
+        assert isinstance(x, list)
+        # if len(x) == 2: # S2, S3
+        #     x[0] = self.s2_pooling(x[0])
+        # elif len(x) == 3:
+        #     x[0] = nn.MaxPool2d(kernel_size=4)(x[0])
+        #     x[1] = self.s2_pooling(x[1])
+        
+        x = [self.projs[i](x[i]) for i in range(len(x))]
+
+
+        # x = x[0]
+        B, C, H, W = x[-1].shape
+        attn_map = self.attn_f(x[-1]) # [B, 1, H, W]
+        if self.attn_method == 'LA':
+            x[-1] = x[-1] * attn_map    #  to have gradient
+
+        attn_weights = [self.attn_f(i).flatten(2).transpose(2, 1) for i in x]
+        x = [i.flatten(2).transpose(2, 1) for i in x]
+
+
+        # x = [i + self.patch_pos_embed for i in x]
+        pos_embes = [self.patch_pos_embed_1, self.patch_pos_embed]
+        x = [x[i] + pos_embes[i] for i in range(len(x))]
+
+        if self.cnn_pool_config is not None:
+            # keep_indexes = top_pool(attn_weight, dim=C, **self.cnn_pool_config)
+            keep_indexes = [
+                top_pool(
+                    attn_weights[i], 
+                    dim=C, keep_num=self.cnn_pool_config['keep_num'][i], exclude_first=False
+                ) for i in range(len(x))]
+            x = [x[i].gather(dim=1, index=keep_indexes[i]) if keep_indexes[i] is not None else x[i] for i in range(len(x))]
+
+        # x = x + self.patch_pos_embed
+        
+        # B, N, C = x.shape
+        
+        
+        cls_tokens = self.cls_token.expand(B, -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
+
+        cls_tokens = cls_tokens + self.cls_pos_embed
+
+        for i, blk in enumerate(self.blocks):
+            if i == self.insert_index[0]:
+                flow = torch.cat((cls_tokens, x[0]), dim=1)
+            elif i == self.insert_index[1]:
+                flow[:, 1:, :] = x[1] + flow[:, 1:, :]  # type: ignore  enforce flow to define in index[0],
+            # elif i == self.insert_index[2]:
+            #     flow[:, 1:, :] = x[2] + flow[:, 1:, :]  # type: ignore
+            flow = blk(flow)    # type: ignore
+
+        # for blk in self.blocks:
+        #     x = blk(x)
+        flow = flow[:, 0]
+        x_static = self.norm(flow)    # type: ignore (B, N, dim)
+        x = flow
+        if os.environ.get('DEBUG_MODE', '0') == '1':
+            print(x.shape)
+        loss = dict()
+        # if self.batch_mean is None:
+        #     self.batch_mean =x.mean(dim=0)
+        # x = x + self.batch_mean * self.alpha
+        # if self.test_sum is None:
+        #     self.test_sum = x.sum(dim=0)
+        # else:
+        #     self.test_sum += x.sum(dim=0)
+        # torch.save(self.test_sum, 'output/sum.pth')
+        # x = x + x.mean(dim=0) * self.alpha
+        x = x + self.norm.running_mean * self.alpha
+        # print(self.alpha)
+        # x = x[:, 0]
+        # x = x + self.norm.running_mean * self.alpha
+        return x, loss
+        # return x[:, 0], loss
+
+    def forward(self, x, **kwargs):
+        x, loss = self.forward_features(x)
+        return dict(x=x, loss=dict(VitDiv_loss=loss))
+
+
+@BACKBONES.register_module()
+class DeFPNViTV5(BaseBackbone):
+    """ 
+    浅层feature经过更多的block，高层经过的block少
+    CNN每个stage再经过个Pooling
+    S2的pooling追随S3，ViT中前期不pooling，只相加
+    """
+    def __init__(self, embed_dim=768, depth=12,
+                 num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0., 
+                 drop_path_rate=0., norm_layer_eps=1e-5, freeze=False,
+                 input_type='image',  pretrained=None, 
+                 in_channels=[], patch_num=0,
+                 attn_method='LA',
+                 cnn_pool_config=None,
+                 vit_pool_configs=None,
+                 insert_index=(0, 1, 2),
+                 **kwargs):
+        super().__init__()
+        if kwargs:
+            print('Unused kwargs: ')
+            print(kwargs)
+        assert input_type  == 'feature', 'Only suit for hybrid model'
+        assert len(insert_index) == len(in_channels), 'insert_index must have the same length as in_channels'
+        assert max(insert_index) < depth, 'insert_index must be smaller than depth'
+        self.insert_index = insert_index
+        self.num_heads = num_heads
+        # self.alpha = nn.Parameter(torch.tensor(1.0), requires_grad=True)
+
+        self.input_type = input_type
+        norm_layer = partial(nn.LayerNorm, eps=norm_layer_eps)
+
+        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
+
+        self.projs = nn.ModuleList([nn.Conv2d(in_channels[i], embed_dim, 1,) for i in range(len(in_channels))])
+
+        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim), requires_grad=True)
+        self.patch_pos_embed = nn.Parameter(torch.zeros(1, patch_num, embed_dim), requires_grad=True)
+        # self.patch_pos_embed_0 = nn.Parameter(torch.zeros(1, patch_num * 16, embed_dim), requires_grad=True)
+        # self.patch_pos_embed_1 = nn.Parameter(torch.zeros(1, patch_num * 4, embed_dim), requires_grad=True)
+        self.cls_pos_embed = nn.Parameter(torch.zeros(1, 1, embed_dim), requires_grad=True)
+        self.pos_drop = nn.Dropout(p=drop_rate)
+        self.attn_method = attn_method
+        self.cnn_pool_config = cnn_pool_config
+        if attn_method == 'LA':
+            self.attn_f = LANet(in_channels[-1], 16)
+        elif attn_method == 'SUM':
+            self.attn_f = lambda x: torch.sum(x, dim=1).unsqueeze(1)
+        elif attn_method == 'SUM_ABS_1':
+            self.attn_f = lambda x: torch.sum(torch.abs(x), dim=1).unsqueeze(1)
+        elif attn_method == 'SUM_ABS_2':
+            self.attn_f = lambda x: torch.sum(torch.pow(torch.abs(x), 2), dim=1).unsqueeze(1)
+        elif attn_method == 'MAX':
+            self.attn_f = lambda x: torch.max(x, dim=1)[0].unsqueeze(1)
+        elif attn_method == 'MAX_ABS_1':
+            self.attn_f = lambda x: torch.max(torch.abs(x), dim=1)[0].unsqueeze(1)
+        else:
+            raise ValueError("Unknown attn_method")
+
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
+        if vit_pool_configs is None:
+            self.blocks = nn.ModuleList([
+                Block(
+                    dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
+                    drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, head_fusion=False,
+                    )
+                for i in range(depth)])
+        else:
+            self.blocks = nn.ModuleList([
+                PoolingBlock(
+                    dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
+                    drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, 
+                    pool_config=dict(keep_rate=vit_pool_configs['keep_rates'][i], **vit_pool_configs),
+                    )
+            for i in range(depth)])
+        self.norm = norm_layer(embed_dim)
+        # self.norm = nn.BatchNorm1d(embed_dim, momentum=0.9, affine=False)
+
+        self.s1_pooling = nn.MaxPool2d(kernel_size=4)
+        self.s2_pooling = nn.MaxPool2d(kernel_size=2)
+
+        if pretrained:
+            self.init_weights(pretrained, patch_num)
+        else:
+            trunc_normal_(self.patch_pos_embed, std=.02)
+            trunc_normal_(self.cls_token, std=.02)
+            self.apply(self._init_weights)
+        if freeze:
+            self.apply(self._freeze_weights)
+
+    def init_weights(self, pretrained, patch_num=0):
+        logger = get_root_logger()
+        logger.warning(f'{self.__class__.__name__} load pretrain from {pretrained}')
+        state_dict = torch.load(pretrained, map_location='cpu')
+        if 'state_dict' in state_dict:
+            state_dict = state_dict['state_dict']
+        pos_embed = state_dict['pos_embed']     # [1, 197, 768] for base
+        patch_pos_embed = pos_embed[:, 1:, :]
+
+        if patch_num != pos_embed.shape[1] - 1:
+            logger.warning(f'interpolate pos_embed from {patch_pos_embed.shape[1]} to {patch_num}')
+            pos_embed_new = resize_pos_embed_v2(patch_pos_embed, patch_num, 0)
+        else:   # 去掉 cls_token
+            print('does not need to resize')
+            pos_embed_new = patch_pos_embed
+        del state_dict['pos_embed']
+        state_dict['patch_pos_embed'] = pos_embed_new
+
+        state_dict['cls_pos_embed'] = pos_embed[:, 0, :].unsqueeze(1)
+
+        for k in ('patch_embed.proj.weight', 'patch_embed.proj.bias', 'head.weight', 'head.bias'):
+            del state_dict[k]
+        load_state_dict(self, state_dict, strict=False, logger=logger)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+    
+    def _freeze_weights(self, m):
+        if isinstance(m, nn.modules.batchnorm._BatchNorm):
+            m.eval()
+        for param in m.parameters():
+            param.requires_grad = False
+
+    def forward_features(self, x):
+        assert isinstance(x, list)
+        assert len(x) == 2, 'The SOTA of proposed model should use S2 and S3 ( \
+            MultiHeadFusion or DeFPN)'
+        if len(x) == 2: # S2, S3
+            x[0] = self.s2_pooling(x[0])
+        elif len(x) == 3:
+            x[0] = self.s1_pooling(x[0])
+            x[1] = self.s2_pooling(x[1])
+        
+        x = [self.projs[i](x[i]) for i in range(len(x))]
+
+
+        # x = x[0]
+        B, C, H, W = x[-1].shape
+        attn_map = self.attn_f(x[-1]) # [B, 1, H, W]
+        if self.attn_method == 'LA':
+            x[-1] = x[-1] * attn_map    #  to have gradient
+        
+        attn_weight = attn_map.flatten(2).transpose(2, 1)
+
+        x = [i.flatten(2).transpose(2, 1) for i in x]
+
+
+        x = [i + self.patch_pos_embed for i in x]
+
+        if self.cnn_pool_config is not None:
+            keep_indexes = top_pool(attn_weight, dim=C, **self.cnn_pool_config)
+            x = [x[i].gather(dim=1, index=keep_indexes) if keep_indexes is not None else x[i] for i in range(len(x))]
+
+        # x = x + self.patch_pos_embed
+        
+        # B, N, C = x.shape
+        
+        
+        cls_tokens = self.cls_token.expand(B, -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
+
+        cls_tokens = cls_tokens + self.cls_pos_embed
+
+        for i, blk in enumerate(self.blocks):
+            if i == self.insert_index[0]:
+                flow = torch.cat((cls_tokens, x[0]), dim=1)
+            elif i == self.insert_index[1]:
+                flow[:, 1:, :] = x[1] + flow[:, 1:, :]  # type: ignore  enforce flow to define in index[0],
+            # elif i == self.insert_index[2]:
+            #     flow[:, 1:, :] = x[2] + flow[:, 1:, :]  # type: ignore
+            flow = blk(flow)    # type: ignore
+
+        flow = self.norm(flow)
+
+        if os.environ.get('DEBUG_MODE', '0') == '1':
+            print(flow.shape)
+            # exit()
+        x = flow[:, 0]
+        loss = dict()
+        return x, loss
+        # return x[:, 0], loss
+
+    def forward(self, x, **kwargs):
+        x, loss = self.forward_features(x)
+        return dict(x=x, loss=dict(VitDiv_loss=loss))
+
+
+
+@BACKBONES.register_module()
+class DeFPNViTV7(BaseBackbone):
+    """ 
+    浅层feature经过更多的block，高层经过的block少
+    CNN每个stage再经过个Pooling
+    S2的pooling追随S3，但keep num不同
+    ViT前期就pooling
+    """
+    def __init__(self, embed_dim=768, depth=12,
+                 num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0., 
+                 drop_path_rate=0., norm_layer_eps=1e-5, freeze=False,
+                 input_type='image',  pretrained=None, 
+                 in_channels=[], patch_num=0,
+                 attn_method='SUM_ABS_1',
+                 cnn_pool_config=None,
+                 vit_pool_configs=None,
+                 insert_index=(0, 1, 2),
+                 **kwargs):
+        super().__init__()
+        if kwargs:
+            print('Unused kwargs: ')
+            print(kwargs)
+        assert input_type  == 'feature', 'Only suit for hybrid model'
+        assert len(insert_index) == len(in_channels), 'insert_index must have the same length as in_channels'
+        assert max(insert_index) < depth, 'insert_index must be smaller than depth'
+        self.insert_index = insert_index
+        self.num_heads = num_heads
+        # self.alpha = nn.Parameter(torch.tensor(1.0), requires_grad=True)
+
+        self.input_type = input_type
+        norm_layer = partial(nn.LayerNorm, eps=norm_layer_eps)
+
+        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
+
+        self.projs = nn.ModuleList([nn.Conv2d(in_channels[i], embed_dim, 1,) for i in range(len(in_channels))])
+
+        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim), requires_grad=True)
+        self.patch_pos_embed = nn.Parameter(torch.zeros(1, patch_num, embed_dim), requires_grad=True)
+        # self.patch_pos_embed_0 = nn.Parameter(torch.zeros(1, patch_num * 16, embed_dim), requires_grad=True)
+        # self.patch_pos_embed_1 = nn.Parameter(torch.zeros(1, patch_num * 4, embed_dim), requires_grad=True)
+        self.cls_pos_embed = nn.Parameter(torch.zeros(1, 1, embed_dim), requires_grad=True)
+        self.pos_drop = nn.Dropout(p=drop_rate)
+        self.attn_method = attn_method
+        self.cnn_pool_config = cnn_pool_config
+        if attn_method == 'LA':
+            self.attn_f = LANet(in_channels[-1], 16)
+        elif attn_method == 'SUM':
+            self.attn_f = lambda x: torch.sum(x, dim=1).unsqueeze(1)
+        elif attn_method == 'SUM_ABS_1':
+            self.attn_f = lambda x: torch.sum(torch.abs(x), dim=1).unsqueeze(1)
+        elif attn_method == 'SUM_ABS_2':
+            self.attn_f = lambda x: torch.sum(torch.pow(torch.abs(x), 2), dim=1).unsqueeze(1)
+        elif attn_method == 'MAX':
+            self.attn_f = lambda x: torch.max(x, dim=1)[0].unsqueeze(1)
+        elif attn_method == 'MAX_ABS_1':
+            self.attn_f = lambda x: torch.max(torch.abs(x), dim=1)[0].unsqueeze(1)
+        else:
+            raise ValueError("Unknown attn_method")
+
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
+        if vit_pool_configs is None:
+            self.blocks = nn.ModuleList([
+                Block(
+                    dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
+                    drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, head_fusion=False,
+                    )
+                for i in range(depth)])
+        else:
+            assert len(vit_pool_configs['keep_rates']) == depth, 'Keep rates must have the same length of depth'
+            self.blocks = nn.ModuleList([
+                PoolingBlock(
+                    dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
+                    drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, 
+                    pool_config=dict(keep_rate=vit_pool_configs['keep_rates'][i], **vit_pool_configs),
+                    )
+            for i in range(depth)])
+        self.norm = norm_layer(embed_dim)
+        # self.norm = nn.BatchNorm1d(embed_dim, momentum=0.9, affine=False)
+
+        self.s1_pooling = nn.MaxPool2d(kernel_size=4)
+        self.s2_pooling = nn.MaxPool2d(kernel_size=2)
+
+        if pretrained:
+            self.init_weights(pretrained, patch_num)
+        else:
+            trunc_normal_(self.patch_pos_embed, std=.02)
+            trunc_normal_(self.cls_token, std=.02)
+            self.apply(self._init_weights)
+        if freeze:
+            self.apply(self._freeze_weights)
+
+    def init_weights(self, pretrained, patch_num=0):
+        logger = get_root_logger()
+        logger.warning(f'{self.__class__.__name__} load pretrain from {pretrained}')
+        state_dict = torch.load(pretrained, map_location='cpu')
+        if 'state_dict' in state_dict:
+            state_dict = state_dict['state_dict']
+        pos_embed = state_dict['pos_embed']     # [1, 197, 768] for base
+        patch_pos_embed = pos_embed[:, 1:, :]
+
+        if patch_num != pos_embed.shape[1] - 1:
+            logger.warning(f'interpolate pos_embed from {patch_pos_embed.shape[1]} to {patch_num}')
+            pos_embed_new = resize_pos_embed_v2(patch_pos_embed, patch_num, 0)
+        else:   # 去掉 cls_token
+            print('does not need to resize')
+            pos_embed_new = patch_pos_embed
+        del state_dict['pos_embed']
+        state_dict['patch_pos_embed'] = pos_embed_new
+
+        state_dict['cls_pos_embed'] = pos_embed[:, 0, :].unsqueeze(1)
+
+        for k in ('patch_embed.proj.weight', 'patch_embed.proj.bias', 'head.weight', 'head.bias'):
+            del state_dict[k]
+        load_state_dict(self, state_dict, strict=False, logger=logger)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+    
+    def _freeze_weights(self, m):
+        if isinstance(m, nn.modules.batchnorm._BatchNorm):
+            m.eval()
+        for param in m.parameters():
+            param.requires_grad = False
+
+    def forward_features(self, x):
+        assert isinstance(x, list)
+        if len(x) == 2: # S2, S3
+            x[0] = self.s2_pooling(x[0])
+        elif len(x) == 3:
+            x[0] = self.s1_pooling(x[0])
+            x[1] = self.s2_pooling(x[1])
+        
+        x = [self.projs[i](x[i]) for i in range(len(x))]
+
+
+        # x = x[0]
+        B, C, H, W = x[-1].shape
+        attn_map = self.attn_f(x[-1]) # [B, 1, H, W]
+        if self.attn_method == 'LA':
+            x[-1] = x[-1] * attn_map    #  to have gradient
+        
+        attn_weight = attn_map.flatten(2).transpose(2, 1)
+
+        x = [i.flatten(2).transpose(2, 1) for i in x]
+
+
+        x = [i + self.patch_pos_embed for i in x]
+
+        if self.cnn_pool_config is not None:
+            assert len(self.cnn_pool_config['keep_num']) == len(x), 'x must have the same length of keep_num'
+            # keep_indexes = top_pool(attn_weight, dim=C, **self.cnn_pool_config)
+            keep_indexes = [top_pool(attn_weight, dim=C, keep_num=self.cnn_pool_config['keep_num'][i], exclude_first=False) for i in range(len(x))]
+            x = [x[i].gather(dim=1, index=keep_indexes[i]) if keep_indexes[i] is not None else x[i] for i in range(len(x))]
+
+        # x = x + self.patch_pos_embed
+        
+        # B, N, C = x.shape
+        
+        
+        cls_tokens = self.cls_token.expand(B, -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
+
+        cls_tokens = cls_tokens + self.cls_pos_embed
+
+        for i, blk in enumerate(self.blocks):
+            if i == self.insert_index[0]:
+                flow = torch.cat((cls_tokens, x[0]), dim=1)
+            elif i == self.insert_index[1]:
+                flow[:, 1:, :] = x[1] + flow[:, 1:, :]  # type: ignore  enforce flow to define in index[0],
+            # elif i == self.insert_index[2]:
+            #     flow[:, 1:, :] = x[2] + flow[:, 1:, :]  # type: ignore
+            flow = blk(flow)    # type: ignore
+
+        # for blk in self.blocks:
+        #     x = blk(x)
+        flow = self.norm(flow)
+        if os.environ.get('DEBUG_MODE', '0') == '1':
+            print(flow.shape)
+            exit()
+        x = flow[:, 0]
+
+        loss = dict()
+        return x, loss
+        # return x[:, 0], loss
+
+    def forward(self, x, **kwargs):
+        x, loss = self.forward_features(x)
+        return dict(x=x, loss=dict(VitDiv_loss=loss))
+
+
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/utils/__init__.py b/CAGE_expression_inference-apvit/apvit_mmcls/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6fde58e6123b63cad0f4c3b88d34a5c36bdc1485
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/utils/__init__.py
@@ -0,0 +1,5 @@
+from .collect_env import collect_env
+from .logger import get_root_logger
+from .hooks import VisualDLLoggerHook
+
+__all__ = ['collect_env', 'get_root_logger', 'VisualDLLoggerHook']
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/utils/collect_env.py b/CAGE_expression_inference-apvit/apvit_mmcls/utils/collect_env.py
new file mode 100644
index 0000000000000000000000000000000000000000..f889ecf6852f3d1bcfe9e177562170f4f97efab1
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/utils/collect_env.py
@@ -0,0 +1,16 @@
+from mmcv.utils import collect_env as collect_base_env
+from mmcv.utils import get_git_hash
+
+import mmcls
+
+
+def collect_env():
+    """Collect the information of the running environments."""
+    env_info = collect_base_env()
+    env_info['MMClassification'] = mmcls.__version__ + '+' + get_git_hash()[:7]
+    return env_info
+
+
+if __name__ == '__main__':
+    for name, val in collect_env().items():
+        print(f'{name}: {val}')
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/utils/hooks.py b/CAGE_expression_inference-apvit/apvit_mmcls/utils/hooks.py
new file mode 100644
index 0000000000000000000000000000000000000000..11a309fcbf84c899eafc1779cf31e2a0b56c5435
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/utils/hooks.py
@@ -0,0 +1,48 @@
+import os
+import os.path as osp
+
+from mmcv.runner import master_only, HOOKS, LoggerHook
+
+
+@HOOKS.register_module()
+class VisualDLLoggerHook(LoggerHook):
+
+    def __init__(self,
+                 log_dir=None,
+                 interval=10,
+                 ignore_last=True,
+                 reset_flag=True,
+                 by_epoch=True):
+        super().__init__(interval, ignore_last, reset_flag, by_epoch)
+        afs_path = os.getenv("VDL_LOG_PATH")
+        if afs_path:
+            log_dir = afs_path.replace("afs://PUBLIC_KM_WD_Data:PUBLIC_km_wd_2020@wudang.afs.baidu.com:9902/user/PUBLIC_KM_WD_Data", 'afs')
+        self.log_dir = log_dir
+
+    @master_only
+    def before_run(self, runner):
+        try:
+            from visualdl import LogWriter
+        except ImportError:
+            raise ImportError('Please install visualdl to use '
+                                'VisualDLLoggerHook.')
+
+        if self.log_dir is None:
+            self.log_dir = osp.join(runner.work_dir, 'VisualDl_logs')
+        else:
+            self.log_dir = osp.join(self.log_dir, runner.work_dir.split('/')[-1])
+        print('VisualDL log dir: ', self.log_dir)
+        self.writer = LogWriter(self.log_dir)
+
+    @master_only
+    def log(self, runner):
+        tags = self.get_loggable_tags(runner, allow_text=True)
+        for tag, val in tags.items():
+            if isinstance(val, str):
+                self.writer.add_text(tag, val, self.get_iter(runner))
+            else:
+                self.writer.add_scalar(tag, val, self.get_iter(runner))
+
+    @master_only
+    def after_run(self, runner):
+        self.writer.close()
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/utils/logger.py b/CAGE_expression_inference-apvit/apvit_mmcls/utils/logger.py
new file mode 100644
index 0000000000000000000000000000000000000000..22f9cf500b08f0a81c6ef80e439926f7a7eeec7a
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/utils/logger.py
@@ -0,0 +1,7 @@
+import logging
+
+from mmcv.utils import get_logger
+
+
+def get_root_logger(log_file=None, log_level=logging.INFO):
+    return get_logger('mmcls', log_file, log_level)
diff --git a/CAGE_expression_inference-apvit/apvit_mmcls/version.py b/CAGE_expression_inference-apvit/apvit_mmcls/version.py
new file mode 100644
index 0000000000000000000000000000000000000000..71046347c9e058ea508e4b55373908400c2848e5
--- /dev/null
+++ b/CAGE_expression_inference-apvit/apvit_mmcls/version.py
@@ -0,0 +1,27 @@
+# Copyright (c) Open-MMLab. All rights reserved.
+
+__version__ = '0.7.0'
+
+
+def parse_version_info(version_str):
+    """Parse a version string into a tuple.
+    Args:
+        version_str (str): The version string.
+    Returns:
+        tuple[int | str]: The version info, e.g., "1.3.0" is parsed into
+            (1, 3, 0), and "2.0.0rc1" is parsed into (2, 0, 0, 'rc1').
+    """
+    version_info = []
+    for x in version_str.split('.'):
+        if x.isdigit():
+            version_info.append(int(x))
+        elif x.find('rc') != -1:
+            patch_version = x.split('rc')
+            version_info.append(int(patch_version[0]))
+            version_info.append(f'rc{patch_version[1]}')
+    return tuple(version_info)
+
+
+version_info = parse_version_info(__version__)
+
+__all__ = ['__version__', 'version_info', 'parse_version_info']
diff --git a/CAGE_expression_inference-apvit/models/AffectNet8_Maxvit_Combined/__pycache__/generate_csv.cpython-312.pyc b/CAGE_expression_inference-apvit/models/AffectNet8_Maxvit_Combined/__pycache__/generate_csv.cpython-312.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..207d7175ae7c75fef738bbf06394e642d875dde4
Binary files /dev/null and b/CAGE_expression_inference-apvit/models/AffectNet8_Maxvit_Combined/__pycache__/generate_csv.cpython-312.pyc differ
diff --git a/CAGE_expression_inference-apvit/models/AffectNet8_Maxvit_Combined/__pycache__/train.cpython-312.pyc b/CAGE_expression_inference-apvit/models/AffectNet8_Maxvit_Combined/__pycache__/train.cpython-312.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..e5e1eb348ce7a57b985aea431151f27b0e6f451f
Binary files /dev/null and b/CAGE_expression_inference-apvit/models/AffectNet8_Maxvit_Combined/__pycache__/train.cpython-312.pyc differ
diff --git a/CAGE_expression_inference-apvit/models/AffectNet8_Maxvit_Combined/generate_csv.py b/CAGE_expression_inference-apvit/models/AffectNet8_Maxvit_Combined/generate_csv.py
new file mode 100644
index 0000000000000000000000000000000000000000..5f0de1d871c6b0907386bc093a0e4ec218f4074c
--- /dev/null
+++ b/CAGE_expression_inference-apvit/models/AffectNet8_Maxvit_Combined/generate_csv.py
@@ -0,0 +1,222 @@
+# ── APViT repo path setup (must precede mmcls/mmcv imports) ─────────────────
+import sys
+from pathlib import Path as _Path
+_apvit_path = str(_Path(__file__).resolve().parent.parent.parent / "APViT")
+if _apvit_path not in sys.path:
+    sys.path.insert(0, _apvit_path)
+# ─────────────────────────────────────────────────────────────────────────────
+
+import pandas as pd
+import os
+import torch
+import torchvision.transforms as transforms
+from torch.utils.data import DataLoader, Dataset
+import torch.nn as nn
+from PIL import Image
+from pathlib import Path
+import mmcv
+from mmcls.models import build_classifier
+
+SCRIPT_DIR = Path(__file__).resolve().parent
+MODEL_PATH = os.getenv("MODEL_PATH", str(SCRIPT_DIR / "model.pt"))
+APVIT_REPO = SCRIPT_DIR.parent.parent / "APViT"
+APVIT_CONFIG = APVIT_REPO / "configs" / "apvit" / "RAF.py"
+
+
+def resolve_images_dir(split_name, explicit_env_var, data_root, extract_root):
+    explicit = os.getenv(explicit_env_var, "").strip()
+    candidates = []
+    if explicit:
+        candidates.append(Path(explicit))
+
+    candidates.append(Path(data_root) / f"{split_name}_set" / "images")
+    candidates.append(Path(extract_root) / f"{split_name}_extracted" / "images")
+
+    extract_split_root = Path(extract_root) / f"{split_name}_extracted"
+    if extract_split_root.exists():
+        for p in extract_split_root.rglob("images"):
+            if p.is_dir():
+                candidates.append(p)
+
+    for p in candidates:
+        if p.exists() and p.is_dir():
+            return str(p)
+
+    tried = "\n".join([str(p) for p in candidates])
+    raise FileNotFoundError(
+        f"Could not find images folder for split='{split_name}'. Tried:\n{tried}"
+    )
+
+# Load the annotations for validation from CSV file
+DATA_ROOT = os.getenv("AFFECTNET_ROOT", "/workspace/data_affectnet/AffectNet")
+EXTRACT_ROOT = os.getenv("AFFECTNET_EXTRACT_ROOT", f"{DATA_ROOT}/extracted")
+ANNO_ROOT = os.getenv("AFFECTNET_ANNO_ROOT", "../../affectnet_annotations")
+IMAGE_FOLDER = resolve_images_dir("train", "AFFECTNET_TRAIN_IMAGES", DATA_ROOT, EXTRACT_ROOT)
+IMAGE_FOLDER_TEST = resolve_images_dir("val", "AFFECTNET_VAL_IMAGES", DATA_ROOT, EXTRACT_ROOT)
+valid_annotations_path = os.getenv(
+    "AFFECTNET_VAL_ANNO", f"{ANNO_ROOT}/val_set_annotation_without_lnd.csv"
+)
+valid_annotations_df = pd.read_csv(valid_annotations_path)
+
+# Set parameters
+BATCHSIZE = int(os.getenv("BATCHSIZE", "64"))
+NUM_WORKERS = int(os.getenv("NUM_WORKERS", "0"))
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+print("DATA_ROOT =", DATA_ROOT)
+print("EXTRACT_ROOT =", EXTRACT_ROOT)
+print("AFFECTNET_TRAIN_IMAGES =", IMAGE_FOLDER)
+print("AFFECTNET_VAL_IMAGES =", IMAGE_FOLDER_TEST)
+print("MODEL_PATH =", MODEL_PATH)
+
+
+# **** Create dataset and data loaders ****
+class CustomDataset(Dataset):
+    def __init__(self, dataframe, root_dir, transform=None, balance=False):
+        self.dataframe = dataframe
+        self.transform = transform
+        self.root_dir = root_dir
+        self.balance = balance
+
+        if self.balance:
+            self.dataframe = self.balance_dataset()
+
+    def __len__(self):
+        return len(self.dataframe)
+
+    def __getitem__(self, idx):
+        image_path = os.path.join(
+            self.root_dir, f"{self.dataframe['number'].iloc[idx]}.jpg"
+        )
+        if os.path.exists(image_path):
+            image = Image.open(image_path)
+        else:
+            image = Image.new(
+                "RGB", (112, 112), color="white"
+            )  # Handle missing image file
+
+        classes = torch.tensor(self.dataframe["exp"].iloc[idx], dtype=torch.long)
+        labels = torch.tensor(self.dataframe.iloc[idx, 2:4].values, dtype=torch.float32)
+
+        if self.transform:
+            image = self.transform(image)
+
+        return image, classes, labels
+
+    def balance_dataset(self):
+        balanced_df = self.dataframe.groupby("exp", group_keys=False).apply(
+            lambda x: x.sample(self.dataframe["exp"].value_counts().min())
+        )
+        return balanced_df
+
+
+transform_valid = transforms.Compose(
+    [
+        transforms.Resize(112),  # APViT / IR-50 requires 112x112 input
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+    ]
+)
+
+valid_dataset = CustomDataset(
+    dataframe=valid_annotations_df,
+    root_dir=IMAGE_FOLDER_TEST,
+    transform=transform_valid,
+    balance=False,
+)
+valid_loader = DataLoader(
+    valid_dataset, batch_size=BATCHSIZE, shuffle=False, num_workers=NUM_WORKERS
+)
+
+# ***** Define the model *****
+
+APVIT_EMBED_DIM = 768
+
+
+def _build_apvit_backbone():
+    """Build APViT PoolingVitClassifier (backbone only, no head)."""
+    cfg = mmcv.Config.fromfile(str(APVIT_CONFIG))
+    cfg.model.pretrained = None
+    cfg.model.head = None
+    ir50_w = APVIT_REPO / "weights" / "backbone_ir50_ms1m_epoch120.pth"
+    vit_small_w = APVIT_REPO / "weights" / "vit_small_p16_224-15ec54c9.pth"
+    cfg.model.extractor.pretrained = str(ir50_w) if ir50_w.exists() else None
+    cfg.model.vit.pretrained = str(vit_small_w) if vit_small_w.exists() else None
+    return build_classifier(cfg.model)
+
+
+class APViTWithHead(nn.Module):
+    def __init__(self, num_outputs):
+        super().__init__()
+        self.apvit = _build_apvit_backbone()
+        self.head = nn.Sequential(
+            nn.LayerNorm(APVIT_EMBED_DIM),
+            nn.Linear(APVIT_EMBED_DIM, APVIT_EMBED_DIM),
+            nn.Tanh(),
+            nn.Linear(APVIT_EMBED_DIM, num_outputs, bias=False),
+        )
+
+    def forward(self, x):
+        features, _ = self.apvit.extract_feat(x)  # [B, 768] CLS token
+        return self.head(features)
+
+
+MODEL = APViTWithHead(num_outputs=10)
+MODEL.to(DEVICE)  # Put the model to the GPU
+
+# Set the model to evaluation mode
+if not Path(MODEL_PATH).exists():
+    raise FileNotFoundError(
+        f"Missing Combined checkpoint: {MODEL_PATH}. "
+        "Run train.py first or set MODEL_PATH to a valid checkpoint."
+    )
+MODEL.load_state_dict(torch.load(MODEL_PATH, map_location=DEVICE))
+MODEL.to(DEVICE)
+MODEL.eval()
+
+all_labels_cls = []
+all_predicted_cls = []
+
+all_true_val = []
+all_pred_val = []
+all_true_aro = []
+all_pred_aro = []
+
+# Start inference on test set
+with torch.no_grad():
+    for images, classes, labels in iter(valid_loader):
+        images, classes, labels = (
+            images.to(DEVICE),
+            classes.to(DEVICE),
+            labels.to(DEVICE),
+        )
+
+        outputs = MODEL(images)
+        outputs_cls = outputs[:, :8]
+        outputs_reg = outputs[:, 8:]
+        val_pred = outputs_reg[:, 0]
+        aro_pred = outputs_reg[:, 1]
+
+        _, predicted_cls = torch.max(outputs_cls, 1)
+
+        all_labels_cls.extend(classes.cpu().numpy())
+        all_predicted_cls.extend(predicted_cls.cpu().numpy())
+        val_true = labels[:, 0]
+        aro_true = labels[:, 1]
+
+        all_true_val.extend(val_true.cpu().numpy())
+        all_true_aro.extend(aro_true.cpu().numpy())
+        all_pred_val.extend(val_pred.cpu().numpy())
+        all_pred_aro.extend(aro_pred.cpu().numpy())
+
+df = pd.DataFrame(
+    {
+        "cat_pred": all_predicted_cls,
+        "cat_true": all_labels_cls,
+        "val_pred": all_pred_val,
+        "val_true": all_true_val,
+        "aro_pred": all_pred_aro,
+        "aro_true": all_true_aro,
+    }
+)
+df.to_csv("inference.csv", index=False)
diff --git a/CAGE_expression_inference-apvit/models/AffectNet8_Maxvit_Combined/inference.csv b/CAGE_expression_inference-apvit/models/AffectNet8_Maxvit_Combined/inference.csv
new file mode 100644
index 0000000000000000000000000000000000000000..af7288eeaa0b4f75e982f5272937c761a1e125f8
--- /dev/null
+++ b/CAGE_expression_inference-apvit/models/AffectNet8_Maxvit_Combined/inference.csv
@@ -0,0 +1,4000 @@
+cat_pred,cat_true,val_pred,val_true,aro_pred,aro_true
+2,0,-0.29929897,-0.176846,0.07077715,-0.0776398
+6,0,-0.2761099,-0.367789,0.16029069,0.183895
+4,4,-0.0490036,-0.0629113,0.6618632,0.532327
+0,0,-0.006628599,0.00483933,-0.0035724165,-0.00483933
+2,2,-0.26438874,-0.642857,-0.11601092,-0.507936
+5,5,-0.639429,-0.597729,0.51877266,0.483164
+6,6,-0.39771467,-0.309524,0.5786384,0.285714
+2,0,-0.5043792,-0.147493,-0.23100542,-0.379267
+7,7,-0.030931132,-0.648471,0.13727744,0.658149
+7,7,0.32828382,-0.626984,-0.056825183,0.611111
+6,6,-0.4247722,-0.539683,0.15089789,0.357143
+1,1,0.67812186,0.537295,0.053361222,0.115887
+4,4,-0.13729183,-0.246032,0.90733564,0.793651
+6,2,-0.51876503,-0.793651,0.37234682,-0.373016
+7,7,0.56775427,-0.643631,-0.03553333,0.65331
+4,4,-0.20606029,-0.159698,0.71802974,0.938831
+6,2,-0.29893085,-0.571429,0.178082,-0.222222
+0,4,-0.016281115,-0.238095,-0.06070441,0.753968
+0,2,-0.0911766,-0.565045,-0.021538362,-0.176846
+2,2,-0.85428023,-0.854767,-0.14782478,-0.234996
+7,7,0.5647081,-0.18254,-0.009311907,0.563492
+3,3,-0.04507007,0.0689021,0.2075399,0.750268
+0,0,-0.25679812,-0.316056,0.03057703,-0.15276
+7,7,0.46229517,-0.300039,0.011583259,0.333914
+7,1,0.49911425,0.611111,0.026048146,0.0873016
+3,3,0.21276489,0.224293,0.87157714,0.733264
+2,2,-0.6998066,-0.769841,-0.24410875,-0.388889
+5,5,-0.34493712,-0.698413,0.7383124,0.47619
+7,7,0.43888915,-0.643631,0.0008866899,0.648471
+2,2,-0.60573745,-0.844784,-0.020707173,-0.324086
+0,7,0.19980833,-0.237127,-0.027258731,0.237127
+3,3,0.08797427,0.187567,0.7904415,0.803858
+6,6,-0.41680998,-0.348675,0.52888393,0.632596
+2,0,-0.23859505,-0.0919473,-0.06573003,-0.0677507
+2,2,-0.42774528,-0.556523,-0.2269187,-0.348432
+2,2,-0.5749501,-0.738784,-0.07032293,-0.202879
+1,1,0.6978604,0.922565,0.062352072,0.0885835
+7,7,0.33548003,-0.174603,0.07956343,0.547619
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diff --git a/CAGE_expression_inference-apvit/models/AffectNet8_Maxvit_Combined/model.pt b/CAGE_expression_inference-apvit/models/AffectNet8_Maxvit_Combined/model.pt
new file mode 100644
index 0000000000000000000000000000000000000000..2d339ecafa39bb7c43310fb97a90def5fda8291a
--- /dev/null
+++ b/CAGE_expression_inference-apvit/models/AffectNet8_Maxvit_Combined/model.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a0e343475ac3722bdc807472d637d55efec217ba6488bfc038c60631228a7c48
+size 122482514
diff --git a/CAGE_expression_inference-apvit/models/AffectNet8_Maxvit_Combined/train.py b/CAGE_expression_inference-apvit/models/AffectNet8_Maxvit_Combined/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..2ea0c791263a0c9e53d4dc8230f4db6626faea8c
--- /dev/null
+++ b/CAGE_expression_inference-apvit/models/AffectNet8_Maxvit_Combined/train.py
@@ -0,0 +1,360 @@
+# ── APViT repo path setup (must precede mmcls/mmcv imports) ─────────────────
+import sys
+from pathlib import Path as _Path
+_apvit_path = str(_Path(__file__).resolve().parent.parent.parent / "APViT")
+if _apvit_path not in sys.path:
+    sys.path.insert(0, _apvit_path)
+# ─────────────────────────────────────────────────────────────────────────────
+
+import pandas as pd
+import os
+import numpy as np
+import torch
+import torchvision.transforms as transforms
+from torch.utils.data import DataLoader, Dataset
+import torch.nn as nn
+import torch.optim as optim
+from PIL import Image
+from torch.optim import lr_scheduler
+from tqdm import tqdm
+from pathlib import Path
+import mmcv
+from mmcls.models import build_classifier
+
+SCRIPT_DIR = Path(__file__).resolve().parent
+MODEL_PATH = os.getenv("MODEL_PATH", str(SCRIPT_DIR / "model.pt"))
+APVIT_REPO = SCRIPT_DIR.parent.parent / "APViT"
+APVIT_CONFIG = APVIT_REPO / "configs" / "apvit" / "RAF.py"
+
+
+def resolve_images_dir(split_name, explicit_env_var, data_root, extract_root):
+    explicit = os.getenv(explicit_env_var, "").strip()
+    candidates = []
+    if explicit:
+        candidates.append(Path(explicit))
+
+    candidates.append(Path(data_root) / f"{split_name}_set" / "images")
+    candidates.append(Path(extract_root) / f"{split_name}_extracted" / "images")
+
+    extract_split_root = Path(extract_root) / f"{split_name}_extracted"
+    if extract_split_root.exists():
+        for p in extract_split_root.rglob("images"):
+            if p.is_dir():
+                candidates.append(p)
+
+    for p in candidates:
+        if p.exists() and p.is_dir():
+            return str(p)
+
+    tried = "\n".join([str(p) for p in candidates])
+    raise FileNotFoundError(
+        f"Could not find images folder for split='{split_name}'. Tried:\n{tried}"
+    )
+
+# Load the annotations for training and validation from separate CSV files
+DATA_ROOT = os.getenv("AFFECTNET_ROOT", "/workspace/data_affectnet/AffectNet")
+EXTRACT_ROOT = os.getenv("AFFECTNET_EXTRACT_ROOT", f"{DATA_ROOT}/extracted")
+ANNO_ROOT = os.getenv("AFFECTNET_ANNO_ROOT", "../../affectnet_annotations")
+IMAGE_FOLDER = resolve_images_dir("train", "AFFECTNET_TRAIN_IMAGES", DATA_ROOT, EXTRACT_ROOT)
+IMAGE_FOLDER_TEST = resolve_images_dir("val", "AFFECTNET_VAL_IMAGES", DATA_ROOT, EXTRACT_ROOT)
+train_annotations_path = os.getenv(
+    "AFFECTNET_TRAIN_ANNO", f"{ANNO_ROOT}/train_set_annotation_without_lnd.csv"
+)
+valid_annotations_path = os.getenv(
+    "AFFECTNET_VAL_ANNO", f"{ANNO_ROOT}/val_set_annotation_without_lnd.csv"
+)
+train_annotations_df = pd.read_csv(train_annotations_path)
+valid_annotations_df = pd.read_csv(valid_annotations_path)
+
+
+# Set parameters
+BATCHSIZE = int(os.getenv("BATCHSIZE", "64"))
+NUM_EPOCHS = 20
+LR = 4e-5
+NUM_WORKERS = int(os.getenv("NUM_WORKERS", "0"))
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+print("DATA_ROOT =", DATA_ROOT)
+print("EXTRACT_ROOT =", EXTRACT_ROOT)
+print("AFFECTNET_TRAIN_IMAGES =", IMAGE_FOLDER)
+print("AFFECTNET_VAL_IMAGES =", IMAGE_FOLDER_TEST)
+print("MODEL_PATH =", MODEL_PATH)
+
+
+# **** Create dataset and data loaders ****
+class CustomDataset(Dataset):
+    def __init__(self, dataframe, root_dir, transform=None, balance=False):
+        self.dataframe = dataframe
+        self.transform = transform
+        self.root_dir = root_dir
+        self.balance = balance
+
+        if self.balance:
+            self.dataframe = self.balance_dataset()
+
+    def __len__(self):
+        return len(self.dataframe)
+
+    def __getitem__(self, idx):
+        image_path = os.path.join(
+            self.root_dir, f"{self.dataframe['number'].iloc[idx]}.jpg"
+        )
+        if os.path.exists(image_path):
+            image = Image.open(image_path)
+        else:
+            image = Image.new(
+                "RGB", (224, 224), color="white"
+            )  # Handle missing image file
+
+        classes = torch.tensor(self.dataframe["exp"].iloc[idx], dtype=torch.long)
+        labels = torch.tensor(self.dataframe.iloc[idx, 2:4].values, dtype=torch.float32)
+
+        if self.transform:
+            image = self.transform(image)
+
+        return image, classes, labels
+
+    def balance_dataset(self):
+        balanced_df = self.dataframe.groupby("exp", group_keys=False).apply(
+            lambda x: x.sample(self.dataframe["exp"].value_counts().min())
+        )
+        return balanced_df
+
+
+transform = transforms.Compose(
+    [
+        transforms.Resize(112),  # APViT / IR-50 requires 112x112 input
+        transforms.RandomHorizontalFlip(0.5),
+        transforms.RandomGrayscale(0.01),
+        transforms.RandomRotation(10),
+        transforms.ColorJitter(
+            brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1
+        ),  # model more robust to changes in lighting conditions.
+        transforms.RandomPerspective(
+            distortion_scale=0.2, p=0.5
+        ),  # can be helpful if your images might have varying perspectives.
+        transforms.ToTensor(),  # saves image as tensor (automatically divides by 255)
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+        transforms.RandomErasing(
+            p=0.5, scale=(0.02, 0.2), ratio=(0.3, 3.3), value="random"
+        ),  # TEST: Should help overfitting
+    ]
+)
+
+transform_valid = transforms.Compose(
+    [
+        transforms.Resize(112),  # APViT / IR-50 requires 112x112 input
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+    ]
+)
+
+train_dataset = CustomDataset(
+    dataframe=train_annotations_df,
+    root_dir=IMAGE_FOLDER,
+    transform=transform,
+    balance=False,
+)
+valid_dataset = CustomDataset(
+    dataframe=valid_annotations_df,
+    root_dir=IMAGE_FOLDER_TEST,
+    transform=transform_valid,
+    balance=False,
+)
+train_loader = DataLoader(
+    train_dataset, batch_size=BATCHSIZE, shuffle=True, num_workers=NUM_WORKERS
+)
+valid_loader = DataLoader(
+    valid_dataset, batch_size=BATCHSIZE, shuffle=False, num_workers=NUM_WORKERS
+)
+
+# ***** Define the model *****
+
+APVIT_EMBED_DIM = 768
+
+
+def _build_apvit_backbone():
+    """Build APViT PoolingVitClassifier (backbone only, no head)."""
+    cfg = mmcv.Config.fromfile(str(APVIT_CONFIG))
+    cfg.model.pretrained = None
+    cfg.model.head = None  # replaced by our custom head below
+    ir50_w = APVIT_REPO / "weights" / "backbone_ir50_ms1m_epoch120.pth"
+    vit_small_w = APVIT_REPO / "weights" / "vit_small_p16_224-15ec54c9.pth"
+    cfg.model.extractor.pretrained = str(ir50_w) if ir50_w.exists() else None
+    cfg.model.vit.pretrained = str(vit_small_w) if vit_small_w.exists() else None
+    if cfg.model.extractor.pretrained is None:
+        print("[WARNING] IR-50 weights not found at", ir50_w, "— training from random init.")
+    if cfg.model.vit.pretrained is None:
+        print("[WARNING] ViT-Small weights not found at", vit_small_w, "— training from random init.")
+    return build_classifier(cfg.model)
+
+
+class APViTWithHead(nn.Module):
+    def __init__(self, num_outputs):
+        super().__init__()
+        self.apvit = _build_apvit_backbone()  # IR-50 + PoolingViT (no head)
+        self.head = nn.Sequential(
+            nn.LayerNorm(APVIT_EMBED_DIM),
+            nn.Linear(APVIT_EMBED_DIM, APVIT_EMBED_DIM),
+            nn.Tanh(),
+            nn.Linear(APVIT_EMBED_DIM, num_outputs, bias=False),
+        )
+
+    def forward(self, x):
+        features, _ = self.apvit.extract_feat(x)  # [B, 768] CLS token
+        return self.head(features)
+
+
+MODEL = APViTWithHead(num_outputs=10)
+MODEL.to(DEVICE)  # Put the model to the GPU
+
+# Define (weighted) loss function
+weights = torch.tensor(
+    [0.015605, 0.008709, 0.046078, 0.083078, 0.185434, 0.305953, 0.046934, 0.30821]
+)
+criterion_cls = nn.CrossEntropyLoss(weights.to(DEVICE))
+criterion_cls_val = (
+    nn.CrossEntropyLoss()
+)  # Use two loss functions, as the validation dataset is balanced
+criterion_reg = nn.MSELoss()
+
+optimizer = optim.AdamW(MODEL.parameters(), lr=LR)
+lr_scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=BATCHSIZE * NUM_EPOCHS)
+
+
+def ccc_numpy_1d(x, y, eps=1e-8):
+    x = np.asarray(x, dtype=np.float32).reshape(-1)
+    y = np.asarray(y, dtype=np.float32).reshape(-1)
+    x_mean, y_mean = x.mean(), y.mean()
+    x_var, y_var = x.var(), y.var()
+    cov = ((x - x_mean) * (y - y_mean)).mean()
+    return (2.0 * cov) / (x_var + y_var + (x_mean - y_mean) ** 2 + eps)
+
+
+# ***** Train the model *****
+print("--- Start training ---")
+scaler = torch.cuda.amp.GradScaler()
+best_valid_loss = float("inf")
+best_epoch = -1
+history = {
+    "epoch": [], "train_loss": [], "valid_loss": [],
+    "train_acc": [], "valid_acc": [],
+    "ccc_v": [], "ccc_a": [], "ccc_mean": [], "lr": [],
+}
+
+for epoch in range(NUM_EPOCHS):
+    MODEL.train()
+    total_train_loss = 0.0
+    total_train_correct = 0
+    total_train_samples = 0
+    for images, classes, labels in tqdm(
+        train_loader, desc="Epoch train_loader progress"
+    ):
+        images, classes, labels = (
+            images.to(DEVICE),
+            classes.to(DEVICE),
+            labels.to(DEVICE),
+        )
+        optimizer.zero_grad()
+        with torch.autocast(device_type="cuda", dtype=torch.float16):
+            outputs = MODEL(images)
+            outputs_cls = outputs[:, :8]
+            outputs_reg = outputs[:, 8:]
+            loss = criterion_cls(
+                outputs_cls.cuda(), classes.cuda()
+            ) + 5 * criterion_reg(outputs_reg.cuda(), labels.cuda())
+            total_train_loss += loss.item()
+            scaler.scale(loss).backward()
+            scaler.step(optimizer)
+            scaler.update()
+            lr_scheduler.step()
+            current_lr = optimizer.param_groups[0]["lr"]
+
+        _, train_predicted = torch.max(outputs_cls, 1)
+        total_train_samples += classes.size(0)
+        total_train_correct += (train_predicted == classes).sum().item()
+
+    train_loss = total_train_loss / max(1, len(train_loader))
+    train_accuracy = (total_train_correct / total_train_samples) * 100
+
+    MODEL.eval()
+    valid_loss = 0.0
+    correct = 0
+    total = 0
+    val_true_all, val_pred_all = [], []
+    aro_true_all, aro_pred_all = [], []
+    with torch.no_grad():
+        for images, classes, labels in valid_loader:
+            images, classes, labels = (
+                images.to(DEVICE),
+                classes.to(DEVICE),
+                labels.to(DEVICE),
+            )
+            outputs = MODEL(images)
+            outputs_cls = outputs[:, :8]
+            outputs_reg = outputs[:, 8:]
+            loss = criterion_cls_val(
+                outputs_cls.cuda(), classes.cuda()
+            ) + 5 * criterion_reg(outputs_reg.cuda(), labels.cuda())
+            valid_loss += loss.item()
+            _, predicted = torch.max(outputs_cls, 1)
+            total += classes.size(0)
+            correct += (predicted == classes).sum().item()
+            val_true_all.extend(labels[:, 0].cpu().float().numpy())
+            val_pred_all.extend(outputs_reg[:, 0].cpu().float().numpy())
+            aro_true_all.extend(labels[:, 1].cpu().float().numpy())
+            aro_pred_all.extend(outputs_reg[:, 1].cpu().float().numpy())
+
+    valid_loss_mean = valid_loss / max(1, len(valid_loader))
+    valid_acc = (correct / total) * 100
+    ccc_v = ccc_numpy_1d(val_pred_all, val_true_all)
+    ccc_a = ccc_numpy_1d(aro_pred_all, aro_true_all)
+    ccc_mean = (ccc_v + ccc_a) / 2.0
+    current_lr = optimizer.param_groups[0]["lr"]
+
+    history["epoch"].append(epoch + 1)
+    history["train_loss"].append(train_loss)
+    history["valid_loss"].append(valid_loss_mean)
+    history["train_acc"].append(train_accuracy)
+    history["valid_acc"].append(valid_acc)
+    history["ccc_v"].append(ccc_v)
+    history["ccc_a"].append(ccc_a)
+    history["ccc_mean"].append(ccc_mean)
+    history["lr"].append(current_lr)
+
+    print(
+        f"Epoch [{epoch+1}/{NUM_EPOCHS}] - "
+        f"Train Loss: {train_loss:.4f}, "
+        f"Valid Loss: {valid_loss_mean:.4f}, "
+        f"Train Acc: {train_accuracy:.2f}%, "
+        f"Valid Acc: {valid_acc:.2f}%, "
+        f"Valid CCC_v: {ccc_v:.4f}, "
+        f"Valid CCC_a: {ccc_a:.4f}, "
+        f"Valid CCC_mean: {ccc_mean:.4f}, "
+        f"LR: {current_lr:.8f}"
+    )
+
+    if valid_loss_mean < best_valid_loss:
+        best_valid_loss = valid_loss_mean
+        best_epoch = epoch + 1
+        print(f"Saving model at epoch {best_epoch}")
+        torch.save(MODEL.state_dict(), MODEL_PATH)  # Save the best model
+
+# ── End-of-training summary ──────────────────────────────────────────────────
+best_idx = best_epoch - 1
+print("\n" + "=" * 60)
+print("TRAINING COMPLETE")
+print("=" * 60)
+print(f"Best epoch : {best_epoch}/{NUM_EPOCHS}")
+print(f"  Valid Loss  : {history['valid_loss'][best_idx]:.4f}")
+print(f"  Valid Acc   : {history['valid_acc'][best_idx]:.2f}%")
+print(f"  Valid CCC_v : {history['ccc_v'][best_idx]:.4f}")
+print(f"  Valid CCC_a : {history['ccc_a'][best_idx]:.4f}")
+print(f"  Valid CCC_mean: {history['ccc_mean'][best_idx]:.4f}")
+print("\nAverage across all epochs:")
+print(f"  Avg Valid Loss  : {sum(history['valid_loss'])/NUM_EPOCHS:.4f}")
+print(f"  Avg Valid Acc   : {sum(history['valid_acc'])/NUM_EPOCHS:.2f}%")
+print(f"  Avg CCC_v       : {sum(history['ccc_v'])/NUM_EPOCHS:.4f}")
+print(f"  Avg CCC_a       : {sum(history['ccc_a'])/NUM_EPOCHS:.4f}")
+print(f"  Avg CCC_mean    : {sum(history['ccc_mean'])/NUM_EPOCHS:.4f}")
+print("=" * 60)
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diff --git a/CAGE_expression_inference-apvit/models/AffectNet8_Maxvit_VA/generate_csv.py b/CAGE_expression_inference-apvit/models/AffectNet8_Maxvit_VA/generate_csv.py
new file mode 100644
index 0000000000000000000000000000000000000000..51fabeef74b1ef9b43a0d814fb99547f457ec316
--- /dev/null
+++ b/CAGE_expression_inference-apvit/models/AffectNet8_Maxvit_VA/generate_csv.py
@@ -0,0 +1,200 @@
+# ── APViT repo path setup (must precede mmcls/mmcv imports) ─────────────────
+import sys
+from pathlib import Path as _Path
+_apvit_path = str(_Path(__file__).resolve().parent.parent.parent / "APViT")
+if _apvit_path not in sys.path:
+    sys.path.insert(0, _apvit_path)
+# ─────────────────────────────────────────────────────────────────────────────
+
+import pandas as pd
+import os
+import torch
+import torchvision.transforms as transforms
+from torch.utils.data import DataLoader, Dataset
+import torch.nn as nn
+from PIL import Image
+from pathlib import Path
+import mmcv
+from mmcls.models import build_classifier
+
+
+def resolve_images_dir(split_name, explicit_env_var, data_root, extract_root):
+    explicit = os.getenv(explicit_env_var, "").strip()
+    candidates = []
+    if explicit:
+        candidates.append(Path(explicit))
+
+    candidates.append(Path(data_root) / f"{split_name}_set" / "images")
+    candidates.append(Path(extract_root) / f"{split_name}_extracted" / "images")
+
+    extract_split_root = Path(extract_root) / f"{split_name}_extracted"
+    if extract_split_root.exists():
+        for p in extract_split_root.rglob("images"):
+            if p.is_dir():
+                candidates.append(p)
+
+    for p in candidates:
+        if p.exists() and p.is_dir():
+            return str(p)
+
+    tried = "\n".join([str(p) for p in candidates])
+    raise FileNotFoundError(
+        f"Could not find images folder for split='{split_name}'. Tried:\n{tried}"
+    )
+
+# Load the annotations for validation from CSV file
+DATA_ROOT = os.getenv("AFFECTNET_ROOT", "/workspace/data_affectnet/AffectNet")
+EXTRACT_ROOT = os.getenv("AFFECTNET_EXTRACT_ROOT", f"{DATA_ROOT}/extracted")
+ANNO_ROOT = os.getenv("AFFECTNET_ANNO_ROOT", "../../affectnet_annotations")
+IMAGE_FOLDER = resolve_images_dir("train", "AFFECTNET_TRAIN_IMAGES", DATA_ROOT, EXTRACT_ROOT)
+IMAGE_FOLDER_TEST = resolve_images_dir("val", "AFFECTNET_VAL_IMAGES", DATA_ROOT, EXTRACT_ROOT)
+valid_annotations_path = os.getenv(
+    "AFFECTNET_VAL_ANNO", f"{ANNO_ROOT}/val_set_annotation_without_lnd.csv"
+)
+valid_annotations_df = pd.read_csv(valid_annotations_path)
+
+# Set parameters
+BATCHSIZE = int(os.getenv("BATCHSIZE", "64"))
+NUM_WORKERS = int(os.getenv("NUM_WORKERS", "0"))
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+print("DATA_ROOT =", DATA_ROOT)
+print("EXTRACT_ROOT =", EXTRACT_ROOT)
+print("AFFECTNET_TRAIN_IMAGES =", IMAGE_FOLDER)
+print("AFFECTNET_VAL_IMAGES =", IMAGE_FOLDER_TEST)
+
+
+# **** Create dataset and data loaders ****
+class CustomDataset(Dataset):
+    def __init__(self, dataframe, root_dir, transform=None, balance=False):
+        self.dataframe = dataframe
+        self.transform = transform
+        self.root_dir = root_dir
+        self.balance = balance
+
+        if self.balance:
+            self.dataframe = self.balance_dataset()
+
+    def __len__(self):
+        return len(self.dataframe)
+
+    def __getitem__(self, idx):
+        image_path = os.path.join(
+            self.root_dir, f"{self.dataframe['number'].iloc[idx]}.jpg"
+        )
+        image = Image.open(image_path)
+
+        classes = torch.tensor(self.dataframe.iloc[idx, 1], dtype=torch.int8)
+        valence = torch.tensor(self.dataframe.iloc[idx, 2], dtype=torch.float16)
+        arousal = torch.tensor(self.dataframe.iloc[idx, 3], dtype=torch.float16)
+
+        if self.transform:
+            image = self.transform(image)
+
+        return image, classes, valence, arousal
+
+    def balance_dataset(self):
+        balanced_df = self.dataframe.groupby("exp", group_keys=False).apply(
+            lambda x: x.sample(self.dataframe["exp"].value_counts().min())
+        )
+        return balanced_df
+
+
+transform_valid = transforms.Compose(
+    [
+        transforms.Resize(112),  # APViT / IR-50 requires 112x112 input
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+    ]
+)
+
+valid_dataset = CustomDataset(
+    dataframe=valid_annotations_df,
+    root_dir=IMAGE_FOLDER_TEST,
+    transform=transform_valid,
+    balance=False,
+)
+valid_loader = DataLoader(
+    valid_dataset, batch_size=BATCHSIZE, shuffle=False, num_workers=NUM_WORKERS
+)
+
+# ***** Define the model *****
+
+APVIT_EMBED_DIM = 768
+
+
+def _build_apvit_backbone():
+    cfg = mmcv.Config.fromfile(str(APVIT_CONFIG))
+    cfg.model.pretrained = None
+    cfg.model.head = None
+    ir50_w = APVIT_REPO / "weights" / "backbone_ir50_ms1m_epoch120.pth"
+    vit_small_w = APVIT_REPO / "weights" / "vit_small_p16_224-15ec54c9.pth"
+    cfg.model.extractor.pretrained = str(ir50_w) if ir50_w.exists() else None
+    cfg.model.vit.pretrained = str(vit_small_w) if vit_small_w.exists() else None
+    return build_classifier(cfg.model)
+
+
+class APViTVAModel(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.apvit = _build_apvit_backbone()
+        self.head = nn.Sequential(
+            nn.LayerNorm(APVIT_EMBED_DIM),
+            nn.Linear(APVIT_EMBED_DIM, APVIT_EMBED_DIM),
+            nn.Tanh(),
+            nn.Dropout(0.3),
+            nn.Linear(APVIT_EMBED_DIM, 2, bias=False),
+        )
+
+    def forward(self, x):
+        features, _ = self.apvit.extract_feat(x)  # [B, 768] CLS token
+        return self.head(features)
+
+
+# Initialize the model
+MODEL = APViTVAModel()
+MODEL.to(DEVICE)
+
+# **** Test the model performance for classification ****
+
+# Set the model to evaluation mode
+MODEL.load_state_dict(torch.load("model.pt", map_location=DEVICE))
+MODEL.to(DEVICE)
+MODEL.eval()
+
+all_val_true_values = []
+all_val_predicted_values = []
+all_aro_true_values = []
+all_aro_predicted_values = []
+
+# Start inference on test set
+with torch.no_grad():
+    for images, _, val_true, aro_true in valid_loader:
+        images, val_true, aro_true = (
+            images.to(DEVICE),
+            val_true.to(DEVICE),
+            aro_true.to(DEVICE),
+        )
+
+        outputs = MODEL(images)
+        val_pred = outputs[:, 0]
+        aro_pred = outputs[:, 1]
+
+        # Append to the lists --> Regression
+        true_val_values = val_true.cpu().numpy()
+        true_aro_values = aro_true.cpu().numpy()
+        pred_val_values = val_pred.cpu().numpy()
+        pred_aro_values = aro_pred.cpu().numpy()
+        all_val_true_values.extend(true_val_values)
+        all_aro_true_values.extend(true_aro_values)
+        all_val_predicted_values.extend(pred_val_values)
+        all_aro_predicted_values.extend(pred_aro_values)
+df = pd.DataFrame(
+    {
+        "val_pred": all_val_predicted_values,
+        "val_true": all_val_true_values,
+        "aro_pred": all_aro_predicted_values,
+        "aro_true": all_aro_true_values,
+    }
+)
+df.to_csv("inference.csv", index=False)
diff --git a/CAGE_expression_inference-apvit/models/AffectNet8_Maxvit_VA/inference.csv b/CAGE_expression_inference-apvit/models/AffectNet8_Maxvit_VA/inference.csv
new file mode 100644
index 0000000000000000000000000000000000000000..970f6091ef7a332cbbb3e1b02b72309456a46636
--- /dev/null
+++ b/CAGE_expression_inference-apvit/models/AffectNet8_Maxvit_VA/inference.csv
@@ -0,0 +1,4000 @@
+val_pred,val_true,aro_pred,aro_true
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diff --git a/CAGE_expression_inference-apvit/models/AffectNet8_Maxvit_VA/model.pt b/CAGE_expression_inference-apvit/models/AffectNet8_Maxvit_VA/model.pt
new file mode 100644
index 0000000000000000000000000000000000000000..a552fb7c91e4a5e4d51e8f497394984a836d8ab1
--- /dev/null
+++ b/CAGE_expression_inference-apvit/models/AffectNet8_Maxvit_VA/model.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f328f7277cff775f4e459343b9d86b5925f1a993d9b8bab4a5dfe6c2071a050e
+size 122466194
diff --git a/CAGE_expression_inference-apvit/models/AffectNet8_Maxvit_VA/train.py b/CAGE_expression_inference-apvit/models/AffectNet8_Maxvit_VA/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..d00ba67dbe123a2f634d006b6e885086c454e1bc
--- /dev/null
+++ b/CAGE_expression_inference-apvit/models/AffectNet8_Maxvit_VA/train.py
@@ -0,0 +1,1078 @@
+# import pandas as pd
+# import os
+# import torch
+# import torchvision.transforms as transforms
+# import torchvision.models as models
+# from torch.utils.data import DataLoader, Dataset
+# import torch.nn as nn
+# import torch.optim as optim
+# from PIL import Image
+# from torch.optim import lr_scheduler
+# from tqdm import tqdm
+# from pathlib import Path
+
+# SCRIPT_DIR = Path(__file__).resolve().parent
+# MODEL_PATH = os.getenv("MODEL_PATH", str(SCRIPT_DIR / "model.pt"))
+
+
+# def resolve_images_dir(split_name, explicit_env_var, data_root, extract_root):
+#     explicit = os.getenv(explicit_env_var, "").strip()
+#     candidates = []
+#     if explicit:
+#         candidates.append(Path(explicit))
+
+#     candidates.append(Path(data_root) / f"{split_name}_set" / "images")
+#     candidates.append(Path(extract_root) / f"{split_name}_extracted" / "images")
+
+#     extract_split_root = Path(extract_root) / f"{split_name}_extracted"
+#     if extract_split_root.exists():
+#         for p in extract_split_root.rglob("images"):
+#             if p.is_dir():
+#                 candidates.append(p)
+
+#     for p in candidates:
+#         if p.exists() and p.is_dir():
+#             return str(p)
+
+#     tried = "\n".join([str(p) for p in candidates])
+#     raise FileNotFoundError(
+#         f"Could not find images folder for split='{split_name}'. Tried:\n{tried}"
+#     )
+
+# # Load the annotations for training and validation from separate CSV files
+# DATA_ROOT = os.getenv("AFFECTNET_ROOT", "/workspace/data_affectnet/AffectNet")
+# EXTRACT_ROOT = os.getenv("AFFECTNET_EXTRACT_ROOT", f"{DATA_ROOT}/extracted")
+# ANNO_ROOT = os.getenv("AFFECTNET_ANNO_ROOT", "../../affectnet_annotations")
+# IMAGE_FOLDER = resolve_images_dir("train", "AFFECTNET_TRAIN_IMAGES", DATA_ROOT, EXTRACT_ROOT)
+# IMAGE_FOLDER_TEST = resolve_images_dir("val", "AFFECTNET_VAL_IMAGES", DATA_ROOT, EXTRACT_ROOT)
+# train_annotations_path = os.getenv(
+#     "AFFECTNET_TRAIN_ANNO", f"{ANNO_ROOT}/train_set_annotation_without_lnd.csv"
+# )
+# valid_annotations_path = os.getenv(
+#     "AFFECTNET_VAL_ANNO", f"{ANNO_ROOT}/val_set_annotation_without_lnd.csv"
+# )
+# train_annotations_df = pd.read_csv(train_annotations_path)
+# valid_annotations_df = pd.read_csv(valid_annotations_path)
+
+
+# # Set parameters
+# BATCHSIZE = int(os.getenv("BATCHSIZE", "64"))
+# NUM_EPOCHS = 20
+# LR = 4e-5
+# NUM_WORKERS = int(os.getenv("NUM_WORKERS", "0"))
+# DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# print("DATA_ROOT =", DATA_ROOT)
+# print("EXTRACT_ROOT =", EXTRACT_ROOT)
+# print("AFFECTNET_TRAIN_IMAGES =", IMAGE_FOLDER)
+# print("AFFECTNET_VAL_IMAGES =", IMAGE_FOLDER_TEST)
+# print("MODEL_PATH =", MODEL_PATH)
+
+
+# # **** Create dataset and data loaders ****
+# class CustomDataset(Dataset):
+#     def __init__(self, dataframe, root_dir, transform=None, balance=False):
+#         self.dataframe = dataframe
+#         self.transform = transform
+#         self.root_dir = root_dir
+#         self.balance = balance
+
+#         if self.balance:
+#             self.dataframe = self.balance_dataset()
+
+#     def __len__(self):
+#         return len(self.dataframe)
+
+#     def __getitem__(self, idx):
+#         image_path = os.path.join(
+#             self.root_dir, f"{self.dataframe['number'].iloc[idx]}.jpg"
+#         )
+#         image = Image.open(image_path)
+
+#         classes = torch.tensor(self.dataframe.iloc[idx, 1], dtype=torch.int8)
+#         valence = torch.tensor(self.dataframe.iloc[idx, 2], dtype=torch.float16)
+#         arousal = torch.tensor(self.dataframe.iloc[idx, 3], dtype=torch.float16)
+
+#         if self.transform:
+#             image = self.transform(image)
+
+#         return image, classes, valence, arousal
+
+#     def balance_dataset(self):
+#         balanced_df = self.dataframe.groupby("exp", group_keys=False).apply(
+#             lambda x: x.sample(self.dataframe["exp"].value_counts().min())
+#         )
+#         return balanced_df
+
+
+# transform = transforms.Compose(
+#     [
+#         transforms.RandomHorizontalFlip(0.5),
+#         transforms.RandomGrayscale(0.01),
+#         transforms.RandomRotation(10),
+#         transforms.ColorJitter(
+#             brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1
+#         ),  # model more robust to changes in lighting conditions.
+#         transforms.RandomPerspective(
+#             distortion_scale=0.2, p=0.5
+#         ),  # can be helpful if your images might have varying perspectives.
+#         transforms.ToTensor(),  # saves image as tensor (automatically divides by 255)
+#         transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+#         transforms.RandomErasing(
+#             p=0.5, scale=(0.02, 0.2), ratio=(0.3, 3.3), value="random"
+#         ),  # Should help overfitting
+#     ]
+# )
+
+# transform_valid = transforms.Compose(
+#     [
+#         transforms.ToTensor(),
+#         transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+#     ]
+# )
+
+# train_dataset = CustomDataset(
+#     dataframe=train_annotations_df,
+#     root_dir=IMAGE_FOLDER,
+#     transform=transform,
+#     balance=True,
+# )
+# valid_dataset = CustomDataset(
+#     dataframe=valid_annotations_df,
+#     root_dir=IMAGE_FOLDER_TEST,
+#     transform=transform_valid,
+#     balance=False,
+# )
+# train_loader = DataLoader(
+#     train_dataset, batch_size=BATCHSIZE, shuffle=True, num_workers=NUM_WORKERS
+# )
+# valid_loader = DataLoader(
+#     valid_dataset, batch_size=BATCHSIZE, shuffle=False, num_workers=NUM_WORKERS
+# )
+
+# # ***** Define the model *****
+
+# # Initialize the model
+# MODEL = models.maxvit_t(weights="DEFAULT")
+# block_channels = MODEL.classifier[3].in_features
+# MODEL.classifier = nn.Sequential(
+#     nn.AdaptiveAvgPool2d(1),
+#     nn.Flatten(),
+#     nn.LayerNorm(block_channels),
+#     nn.Linear(block_channels, block_channels),
+#     nn.Tanh(),
+#     nn.Linear(block_channels, 10, bias=False),
+# )
+# MODEL.to(DEVICE)
+# COMBINED_MODEL_PATH = os.getenv(
+#     "COMBINED_MODEL_PATH",
+#     str((SCRIPT_DIR / "../AffectNet8_Maxvit_Combined/model.pt").resolve()),
+# )
+# MODEL.load_state_dict(torch.load(COMBINED_MODEL_PATH, map_location=DEVICE))
+# MODEL.classifier = nn.Sequential(
+#     nn.AdaptiveAvgPool2d(1),
+#     nn.Flatten(),
+#     nn.LayerNorm(block_channels),
+#     nn.Linear(block_channels, block_channels),
+#     nn.Tanh(),
+#     nn.Dropout(0.3),
+#     nn.Linear(block_channels, 2, bias=False),
+# )
+# MODEL.to(DEVICE)
+
+
+# def CCCLoss(x, y):
+#     # Compute means
+#     x_mean = torch.mean(x, dim=0)
+#     y_mean = torch.mean(y, dim=0)
+#     # Compute variances
+#     x_var = torch.var(x, dim=0)
+#     y_var = torch.var(y, dim=0)
+#     # Compute covariance matrix
+#     cov_matrix = torch.matmul(
+#         (x - x_mean).permute(*torch.arange(x.dim() - 1, -1, -1)), y - y_mean
+#     ) / (x.size(0) - 1)
+#     # Compute CCC
+#     numerator = 2 * cov_matrix
+#     denominator = x_var + y_var + torch.pow((x_mean - y_mean), 2)
+#     ccc = torch.mean(numerator / denominator)
+#     return -ccc
+
+
+# val_loss = nn.MSELoss()
+# aro_loss = nn.MSELoss()
+
+# optimizer = optim.AdamW(MODEL.parameters(), lr=LR)
+# lr_scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=BATCHSIZE * NUM_EPOCHS)
+
+# # ***** Train the model *****
+# print("--- Start training ---")
+# scaler = torch.cuda.amp.GradScaler()
+# best_valid_loss = float("inf")
+# l2_lambda = 0.00001  # L1 Regularization
+# l1_lambda = 0.00001  # L2 Regularization
+
+# for epoch in range(NUM_EPOCHS):
+#     MODEL.train()
+#     total_train_correct = 0
+#     total_train_samples = 0
+#     current_lr = optimizer.param_groups[0]["lr"]
+#     for images, _, val_true, aro_true in tqdm(
+#         train_loader, desc="Epoch train_loader progress"
+#     ):
+#         images, val_true, aro_true = (
+#             images.to(DEVICE),
+#             val_true.to(DEVICE),
+#             aro_true.to(DEVICE),
+#         )
+#         optimizer.zero_grad()
+#         train_loss = 0
+#         l2_reg = 0
+#         l1_reg = 0
+#         with torch.autocast(device_type="cuda", dtype=torch.float16):
+#             outputs = MODEL(images)
+#             val_pred = outputs[:, 0]
+#             aro_pred = outputs[:, 1]
+#             for param in MODEL.parameters():
+#                 l2_reg += torch.norm(param, 2)  # **2
+#                 l1_reg += torch.norm(param, 1)
+#             loss = (
+#                 3 * val_loss(val_pred.cuda(), val_true.cuda())
+#                 + 3 * aro_loss(aro_pred.cuda(), aro_true.cuda())
+#                 + CCCLoss(val_pred.cuda(), val_true.cuda())
+#                 + CCCLoss(aro_pred.cuda(), aro_true.cuda())
+#             )
+#             # + l2_lambda * l2_reg + l1_lambda * l1_reg
+#             train_loss += loss.item()
+#             scaler.scale(loss).backward()
+#             scaler.step(optimizer)
+#             scaler.update()
+#             lr_scheduler.step()
+
+#     MODEL.eval()
+#     valid_loss = 0.0
+#     total_valid_correct = 0
+#     total_valid_samples = 0
+#     with torch.no_grad():
+#         for images, _, val_true, aro_true in valid_loader:
+#             images, val_true, aro_true = (
+#                 images.to(DEVICE),
+#                 val_true.to(DEVICE),
+#                 aro_true.to(DEVICE),
+#             )
+#             with torch.autocast(device_type="cuda", dtype=torch.float16):
+#                 outputs = MODEL(images)
+#                 val_pred = outputs[:, 0]
+#                 aro_pred = outputs[:, 1]
+#                 loss = (
+#                     3 * val_loss(val_pred.cuda(), val_true.cuda())
+#                     + 3 * aro_loss(aro_pred.cuda(), aro_true.cuda())
+#                     + CCCLoss(val_pred.cuda(), val_true.cuda())
+#                     + CCCLoss(aro_pred.cuda(), aro_true.cuda())
+#                 )
+#                 valid_loss += loss.item()
+
+#     print(
+#         f"Epoch [{epoch+1}/{NUM_EPOCHS}] - "
+#         f"Training Loss: {train_loss/len(train_loader):.4f}, "
+#         f"Validation Loss: {valid_loss/len(valid_loader):.4f}, "
+#         f"Learning Rate: {current_lr:.8f}, "
+#     )
+
+#     valid_loss_mean = valid_loss / max(1, len(valid_loader))
+
+#     if valid_loss_mean < best_valid_loss:
+#         best_valid_loss = valid_loss_mean
+#         print(f"Saving model at epoch {epoch+1}")
+#         torch.save(MODEL.state_dict(), MODEL_PATH)  # Save the best model
+
+
+# ── APViT repo path setup (must precede mmcls/mmcv imports) ─────────────────
+import sys
+from pathlib import Path as _Path
+_apvit_path = str(_Path(__file__).resolve().parent.parent.parent / "APViT")
+if _apvit_path not in sys.path:
+    sys.path.insert(0, _apvit_path)
+# ─────────────────────────────────────────────────────────────────────────────
+
+import pandas as pd
+import os
+import numpy as np
+import torch
+import torchvision.transforms as transforms
+from torch.utils.data import DataLoader, Dataset
+import torch.nn as nn
+import torch.optim as optim
+from PIL import Image
+from torch.optim import lr_scheduler
+from tqdm import tqdm
+from pathlib import Path
+from sklearn.model_selection import StratifiedKFold
+import mmcv
+from mmcls.models import build_classifier
+
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+
+
+SCRIPT_DIR = Path(__file__).resolve().parent
+MODEL_PATH = os.getenv("MODEL_PATH", str(SCRIPT_DIR / "model.pt"))
+PLOTS_DIR = SCRIPT_DIR / "plots"
+PLOTS_DIR.mkdir(parents=True, exist_ok=True)
+APVIT_REPO = SCRIPT_DIR.parent.parent / "APViT"
+APVIT_CONFIG = APVIT_REPO / "configs" / "apvit" / "RAF.py"
+
+
+def resolve_images_dir(split_name, explicit_env_var, data_root, extract_root):
+    explicit = os.getenv(explicit_env_var, "").strip()
+    candidates = []
+    if explicit:
+        candidates.append(Path(explicit))
+
+    candidates.append(Path(data_root) / f"{split_name}_set" / "images")
+    candidates.append(Path(extract_root) / f"{split_name}_extracted" / "images")
+
+    extract_split_root = Path(extract_root) / f"{split_name}_extracted"
+    if extract_split_root.exists():
+        for p in extract_split_root.rglob("images"):
+            if p.is_dir():
+                candidates.append(p)
+
+    for p in candidates:
+        if p.exists() and p.is_dir():
+            return str(p)
+
+    tried = "\n".join([str(p) for p in candidates])
+    raise FileNotFoundError(
+        f"Could not find images folder for split='{split_name}'. Tried:\n{tried}"
+    )
+
+
+# Load the annotations for training and validation from separate CSV files
+DATA_ROOT = os.getenv("AFFECTNET_ROOT", "/workspace/data_affectnet/AffectNet")
+EXTRACT_ROOT = os.getenv("AFFECTNET_EXTRACT_ROOT", f"{DATA_ROOT}/extracted")
+ANNO_ROOT = os.getenv("AFFECTNET_ANNO_ROOT", "../../affectnet_annotations")
+IMAGE_FOLDER = resolve_images_dir("train", "AFFECTNET_TRAIN_IMAGES", DATA_ROOT, EXTRACT_ROOT)
+IMAGE_FOLDER_TEST = resolve_images_dir("val", "AFFECTNET_VAL_IMAGES", DATA_ROOT, EXTRACT_ROOT)
+train_annotations_path = os.getenv(
+    "AFFECTNET_TRAIN_ANNO", f"{ANNO_ROOT}/train_set_annotation_without_lnd.csv"
+)
+valid_annotations_path = os.getenv(
+    "AFFECTNET_VAL_ANNO", f"{ANNO_ROOT}/val_set_annotation_without_lnd.csv"
+)
+train_annotations_df = pd.read_csv(train_annotations_path)
+valid_annotations_df = pd.read_csv(valid_annotations_path)
+
+
+# Set parameters
+BATCHSIZE = int(os.getenv("BATCHSIZE", "64"))
+NUM_EPOCHS = 20
+LR = 4e-5
+NUM_WORKERS = int(os.getenv("NUM_WORKERS", "0"))
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+print("DATA_ROOT =", DATA_ROOT)
+print("EXTRACT_ROOT =", EXTRACT_ROOT)
+print("AFFECTNET_TRAIN_IMAGES =", IMAGE_FOLDER)
+print("AFFECTNET_VAL_IMAGES =", IMAGE_FOLDER_TEST)
+print("MODEL_PATH =", MODEL_PATH)
+print("PLOTS_DIR =", PLOTS_DIR)
+print("Train set size =", len(train_annotations_df))
+print("Official val_set size (final test only) =", len(valid_annotations_df))
+print("StratifiedKFold splits = 5 (each fold train/val = 8:2)")
+
+
+# =========================
+# Plot helpers
+# =========================
+def save_line_plot(x, ys, labels, title, xlabel, ylabel, save_path):
+    plt.figure(figsize=(10, 6))
+    for y, label in zip(ys, labels):
+        plt.plot(x, y, marker="o", linewidth=2, markersize=4, label=label)
+    plt.title(title)
+    plt.xlabel(xlabel)
+    plt.ylabel(ylabel)
+    plt.grid(True, alpha=0.3)
+    plt.legend()
+    plt.tight_layout()
+    plt.savefig(save_path, dpi=200)
+    plt.close()
+
+
+def save_bar_plot(labels, values, title, xlabel, ylabel, save_path, annotate=True):
+    plt.figure(figsize=(10, 6))
+    bars = plt.bar(labels, values)
+    plt.title(title)
+    plt.xlabel(xlabel)
+    plt.ylabel(ylabel)
+    plt.grid(True, axis="y", alpha=0.3)
+    if annotate:
+        for bar, val in zip(bars, values):
+            plt.text(
+                bar.get_x() + bar.get_width() / 2,
+                bar.get_height(),
+                f"{val:.4f}",
+                ha="center",
+                va="bottom",
+                fontsize=9,
+            )
+    plt.tight_layout()
+    plt.savefig(save_path, dpi=200)
+    plt.close()
+
+
+def save_boxplot(data_list, labels, title, ylabel, save_path):
+    plt.figure(figsize=(10, 6))
+    plt.boxplot(data_list, labels=labels, vert=True)
+    plt.title(title)
+    plt.ylabel(ylabel)
+    plt.grid(True, axis="y", alpha=0.3)
+    plt.tight_layout()
+    plt.savefig(save_path, dpi=200)
+    plt.close()
+
+
+def save_histogram(values, bins, title, xlabel, ylabel, save_path):
+    plt.figure(figsize=(10, 6))
+    plt.hist(values, bins=bins)
+    plt.title(title)
+    plt.xlabel(xlabel)
+    plt.ylabel(ylabel)
+    plt.grid(True, alpha=0.3)
+    plt.tight_layout()
+    plt.savefig(save_path, dpi=200)
+    plt.close()
+
+
+def save_scatter(x, y, title, xlabel, ylabel, save_path, annotate_labels=None):
+    plt.figure(figsize=(10, 6))
+    plt.scatter(x, y)
+    if annotate_labels is not None:
+        for xi, yi, txt in zip(x, y, annotate_labels):
+            plt.annotate(str(txt), (xi, yi), fontsize=9)
+    plt.title(title)
+    plt.xlabel(xlabel)
+    plt.ylabel(ylabel)
+    plt.grid(True, alpha=0.3)
+    plt.tight_layout()
+    plt.savefig(save_path, dpi=200)
+    plt.close()
+
+
+def save_fold_plots(history_df, fold_idx, seed, fold_dir):
+    epochs = history_df["epoch"].tolist()
+
+    save_line_plot(
+        epochs,
+        [history_df["train_loss"].tolist()],
+        ["Train Loss"],
+        f"Fold {fold_idx} Seed {seed} - Train Loss",
+        "Epoch",
+        "Loss",
+        fold_dir / f"fold_{fold_idx}_train_loss.png",
+    )
+
+    save_line_plot(
+        epochs,
+        [history_df["valid_loss"].tolist()],
+        ["Validation Loss"],
+        f"Fold {fold_idx} Seed {seed} - Validation Loss",
+        "Epoch",
+        "Loss",
+        fold_dir / f"fold_{fold_idx}_valid_loss.png",
+    )
+
+    save_line_plot(
+        epochs,
+        [history_df["train_loss"].tolist(), history_df["valid_loss"].tolist()],
+        ["Train Loss", "Validation Loss"],
+        f"Fold {fold_idx} Seed {seed} - Train vs Validation Loss",
+        "Epoch",
+        "Loss",
+        fold_dir / f"fold_{fold_idx}_train_valid_loss.png",
+    )
+
+    save_line_plot(
+        epochs,
+        [history_df["ccc_v"].tolist()],
+        ["CCC_v"],
+        f"Fold {fold_idx} Seed {seed} - CCC_v",
+        "Epoch",
+        "CCC",
+        fold_dir / f"fold_{fold_idx}_ccc_v.png",
+    )
+
+    save_line_plot(
+        epochs,
+        [history_df["ccc_a"].tolist()],
+        ["CCC_a"],
+        f"Fold {fold_idx} Seed {seed} - CCC_a",
+        "Epoch",
+        "CCC",
+        fold_dir / f"fold_{fold_idx}_ccc_a.png",
+    )
+
+    save_line_plot(
+        epochs,
+        [history_df["ccc_mean"].tolist()],
+        ["CCC_mean"],
+        f"Fold {fold_idx} Seed {seed} - CCC_mean",
+        "Epoch",
+        "CCC",
+        fold_dir / f"fold_{fold_idx}_ccc_mean.png",
+    )
+
+    save_line_plot(
+        epochs,
+        [
+            history_df["ccc_v"].tolist(),
+            history_df["ccc_a"].tolist(),
+            history_df["ccc_mean"].tolist(),
+        ],
+        ["CCC_v", "CCC_a", "CCC_mean"],
+        f"Fold {fold_idx} Seed {seed} - Combined CCC Curves",
+        "Epoch",
+        "CCC",
+        fold_dir / f"fold_{fold_idx}_ccc_all.png",
+    )
+
+    save_line_plot(
+        epochs,
+        [history_df["lr"].tolist()],
+        ["Learning Rate"],
+        f"Fold {fold_idx} Seed {seed} - Learning Rate",
+        "Epoch",
+        "LR",
+        fold_dir / f"fold_{fold_idx}_lr.png",
+    )
+
+    save_scatter(
+        history_df["train_loss"].tolist(),
+        history_df["valid_loss"].tolist(),
+        f"Fold {fold_idx} Seed {seed} - Train Loss vs Valid Loss",
+        "Train Loss",
+        "Validation Loss",
+        fold_dir / f"fold_{fold_idx}_train_vs_valid_scatter.png",
+        annotate_labels=epochs,
+    )
+
+    save_scatter(
+        history_df["ccc_v"].tolist(),
+        history_df["ccc_a"].tolist(),
+        f"Fold {fold_idx} Seed {seed} - CCC_v vs CCC_a",
+        "CCC_v",
+        "CCC_a",
+        fold_dir / f"fold_{fold_idx}_ccc_v_vs_ccc_a.png",
+        annotate_labels=epochs,
+    )
+
+
+def save_summary_plots(summary_df, save_dir):
+    fold_labels = [f"Fold {x}" for x in summary_df["fold"].tolist()]
+
+    save_bar_plot(
+        fold_labels,
+        summary_df["test_loss"].tolist(),
+        "Final Test Loss by Fold",
+        "Fold",
+        "Loss",
+        save_dir / "summary_test_loss_by_fold.png",
+    )
+
+    save_bar_plot(
+        fold_labels,
+        summary_df["test_ccc_v"].tolist(),
+        "Final Test CCC_v by Fold",
+        "Fold",
+        "CCC_v",
+        save_dir / "summary_test_ccc_v_by_fold.png",
+    )
+
+    save_bar_plot(
+        fold_labels,
+        summary_df["test_ccc_a"].tolist(),
+        "Final Test CCC_a by Fold",
+        "Fold",
+        "CCC_a",
+        save_dir / "summary_test_ccc_a_by_fold.png",
+    )
+
+    save_bar_plot(
+        fold_labels,
+        summary_df["test_ccc_mean"].tolist(),
+        "Final Test CCC_mean by Fold",
+        "Fold",
+        "CCC_mean",
+        save_dir / "summary_test_ccc_mean_by_fold.png",
+    )
+
+    save_bar_plot(
+        fold_labels,
+        summary_df["best_epoch"].tolist(),
+        "Best Epoch by Fold",
+        "Fold",
+        "Best Epoch",
+        save_dir / "summary_best_epoch_by_fold.png",
+        annotate=True,
+    )
+
+    save_boxplot(
+        [
+            summary_df["test_ccc_v"].tolist(),
+            summary_df["test_ccc_a"].tolist(),
+            summary_df["test_ccc_mean"].tolist(),
+        ],
+        ["CCC_v", "CCC_a", "CCC_mean"],
+        "Distribution of Final CCC Across 5 Folds",
+        "CCC",
+        save_dir / "summary_ccc_boxplot.png",
+    )
+
+    save_histogram(
+        summary_df["test_ccc_mean"].tolist(),
+        bins=min(5, len(summary_df)),
+        title="Histogram of Final CCC_mean",
+        xlabel="CCC_mean",
+        ylabel="Frequency",
+        save_path=save_dir / "summary_ccc_mean_histogram.png",
+    )
+
+    save_scatter(
+        summary_df["best_epoch"].tolist(),
+        summary_df["test_ccc_mean"].tolist(),
+        "Best Epoch vs Final CCC_mean",
+        "Best Epoch",
+        "Final CCC_mean",
+        save_dir / "summary_best_epoch_vs_ccc_mean.png",
+        annotate_labels=summary_df["fold"].tolist(),
+    )
+
+
+# **** Create dataset and data loaders ****
+class CustomDataset(Dataset):
+    def __init__(self, dataframe, root_dir, transform=None, balance=False):
+        self.dataframe = dataframe
+        self.transform = transform
+        self.root_dir = root_dir
+        self.balance = balance
+
+        if self.balance:
+            self.dataframe = self.balance_dataset()
+
+    def __len__(self):
+        return len(self.dataframe)
+
+    def __getitem__(self, idx):
+        img_id = int(float(self.dataframe["number"].iloc[idx]))
+        image_path = os.path.join(self.root_dir, f"{img_id}.jpg")
+        image = Image.open(image_path)
+
+        classes = torch.tensor(self.dataframe.iloc[idx, 1], dtype=torch.int8)
+        valence = torch.tensor(self.dataframe.iloc[idx, 2], dtype=torch.float16)
+        arousal = torch.tensor(self.dataframe.iloc[idx, 3], dtype=torch.float16)
+
+        if self.transform:
+            image = self.transform(image)
+
+        return image, classes, valence, arousal
+
+    def balance_dataset(self):
+        balanced_df = self.dataframe.groupby("exp", group_keys=False).apply(
+            lambda x: x.sample(self.dataframe["exp"].value_counts().min())
+        )
+        return balanced_df
+
+
+transform = transforms.Compose(
+    [
+        transforms.Resize(112),  # APViT / IR-50 requires 112x112 input
+        transforms.RandomHorizontalFlip(0.5),
+        transforms.RandomGrayscale(0.01),
+        transforms.RandomRotation(10),
+        transforms.ColorJitter(
+            brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1
+        ),
+        transforms.RandomPerspective(
+            distortion_scale=0.2, p=0.5
+        ),
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+        transforms.RandomErasing(
+            p=0.5, scale=(0.02, 0.2), ratio=(0.3, 3.3), value="random"
+        ),
+    ]
+)
+
+transform_valid = transforms.Compose(
+    [
+        transforms.Resize(112),  # APViT / IR-50 requires 112x112 input
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+    ]
+)
+
+test_dataset = CustomDataset(
+    dataframe=valid_annotations_df,
+    root_dir=IMAGE_FOLDER_TEST,
+    transform=transform_valid,
+    balance=False,
+)
+test_loader = DataLoader(
+    test_dataset, batch_size=BATCHSIZE, shuffle=False, num_workers=NUM_WORKERS
+)
+
+
+APVIT_EMBED_DIM = 768
+
+
+def _build_apvit_backbone():
+    """Build APViT PoolingVitClassifier (backbone only, no head)."""
+    cfg = mmcv.Config.fromfile(str(APVIT_CONFIG))
+    cfg.model.pretrained = None
+    cfg.model.head = None
+    ir50_w = APVIT_REPO / "weights" / "backbone_ir50_ms1m_epoch120.pth"
+    vit_small_w = APVIT_REPO / "weights" / "vit_small_p16_224-15ec54c9.pth"
+    cfg.model.extractor.pretrained = str(ir50_w) if ir50_w.exists() else None
+    cfg.model.vit.pretrained = str(vit_small_w) if vit_small_w.exists() else None
+    if cfg.model.extractor.pretrained is None:
+        print("[WARNING] IR-50 weights not found at", ir50_w, "— training from random init.")
+    if cfg.model.vit.pretrained is None:
+        print("[WARNING] ViT-Small weights not found at", vit_small_w, "— training from random init.")
+    return build_classifier(cfg.model)
+
+
+class APViTWithHead(nn.Module):
+    def __init__(self, num_outputs):
+        super().__init__()
+        self.apvit = _build_apvit_backbone()
+        self.head = nn.Sequential(
+            nn.LayerNorm(APVIT_EMBED_DIM),
+            nn.Linear(APVIT_EMBED_DIM, APVIT_EMBED_DIM),
+            nn.Tanh(),
+            nn.Linear(APVIT_EMBED_DIM, num_outputs, bias=False),
+        )
+
+    def forward(self, x):
+        features, _ = self.apvit.extract_feat(x)  # [B, 768] CLS token
+        return self.head(features)
+
+
+COMBINED_MODEL_PATH = os.getenv(
+    "COMBINED_MODEL_PATH",
+    str((SCRIPT_DIR / "../AffectNet8_Maxvit_Combined/model.pt").resolve()),
+)
+
+
+def build_model():
+    model = APViTWithHead(num_outputs=10)  # Match Combined checkpoint structure
+    model.to(DEVICE)
+    model.load_state_dict(torch.load(COMBINED_MODEL_PATH, map_location=DEVICE))
+    # Replace head with VA regression head (2 outputs)
+    model.head = nn.Sequential(
+        nn.LayerNorm(APVIT_EMBED_DIM),
+        nn.Linear(APVIT_EMBED_DIM, APVIT_EMBED_DIM),
+        nn.Tanh(),
+        nn.Dropout(0.3),
+        nn.Linear(APVIT_EMBED_DIM, 2, bias=False),
+    )
+    model.to(DEVICE)
+    return model
+
+
+def CCCLoss(x, y):
+    x_mean = torch.mean(x, dim=0)
+    y_mean = torch.mean(y, dim=0)
+    x_var = torch.var(x, dim=0)
+    y_var = torch.var(y, dim=0)
+    cov_matrix = torch.matmul(
+        (x - x_mean).permute(*torch.arange(x.dim() - 1, -1, -1)), y - y_mean
+    ) / (x.size(0) - 1)
+    numerator = 2 * cov_matrix
+    denominator = x_var + y_var + torch.pow((x_mean - y_mean), 2)
+    ccc = torch.mean(numerator / denominator)
+    return -ccc
+
+
+def ccc_numpy_1d(x, y, eps=1e-8):
+    x = np.asarray(x).reshape(-1)
+    y = np.asarray(y).reshape(-1)
+    x_mean = x.mean()
+    y_mean = y.mean()
+    x_var = x.var()
+    y_var = y.var()
+    cov = ((x - x_mean) * (y - y_mean)).mean()
+    return (2.0 * cov) / (x_var + y_var + (x_mean - y_mean) ** 2 + eps)
+
+
+def evaluate_loader(model, loader):
+    model.eval()
+    eval_loss = 0.0
+    eval_val_true = []
+    eval_val_pred = []
+    eval_aro_true = []
+    eval_aro_pred = []
+    with torch.no_grad():
+        for images, _, val_true, aro_true in loader:
+            images, val_true, aro_true = (
+                images.to(DEVICE),
+                val_true.to(DEVICE),
+                aro_true.to(DEVICE),
+            )
+            with torch.autocast(device_type="cuda", dtype=torch.float16):
+                outputs = model(images)
+                val_pred = outputs[:, 0]
+                aro_pred = outputs[:, 1]
+                loss = (
+                    3 * val_loss(val_pred.cuda(), val_true.cuda())
+                    + 3 * aro_loss(aro_pred.cuda(), aro_true.cuda())
+                    + CCCLoss(val_pred.cuda(), val_true.cuda())
+                    + CCCLoss(aro_pred.cuda(), aro_true.cuda())
+                )
+                eval_loss += loss.item()
+
+            eval_val_true.extend(val_true.detach().cpu().float().numpy())
+            eval_val_pred.extend(val_pred.detach().cpu().float().numpy())
+            eval_aro_true.extend(aro_true.detach().cpu().float().numpy())
+            eval_aro_pred.extend(aro_pred.detach().cpu().float().numpy())
+
+    ccc_v = ccc_numpy_1d(eval_val_pred, eval_val_true)
+    ccc_a = ccc_numpy_1d(eval_aro_pred, eval_aro_true)
+    ccc_mean = (ccc_v + ccc_a) / 2.0
+    return eval_loss / max(1, len(loader)), ccc_v, ccc_a, ccc_mean
+
+
+val_loss = nn.MSELoss()
+aro_loss = nn.MSELoss()
+
+# ***** Train the model *****
+print("--- Start training on full train_set, validate on val_set ---")
+l2_lambda = 0.00001
+l1_lambda = 0.00001
+
+train_dataset = CustomDataset(
+    dataframe=train_annotations_df,
+    root_dir=IMAGE_FOLDER,
+    transform=transform,
+    balance=True,
+)
+valid_dataset = CustomDataset(
+    dataframe=valid_annotations_df,
+    root_dir=IMAGE_FOLDER_TEST,
+    transform=transform_valid,
+    balance=False,
+)
+train_loader = DataLoader(
+    train_dataset, batch_size=BATCHSIZE, shuffle=True, num_workers=NUM_WORKERS
+)
+valid_loader = DataLoader(
+    valid_dataset, batch_size=BATCHSIZE, shuffle=False, num_workers=NUM_WORKERS
+)
+
+model = build_model()
+optimizer = optim.AdamW(model.parameters(), lr=LR)
+scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=BATCHSIZE * NUM_EPOCHS)
+scaler = torch.cuda.amp.GradScaler()
+best_valid_loss = float("inf")
+best_epoch = -1
+history = {
+    "epoch": [],
+    "train_loss": [],
+    "valid_loss": [],
+    "ccc_v": [],
+    "ccc_a": [],
+    "ccc_mean": [],
+    "lr": [],
+}
+
+for epoch in range(NUM_EPOCHS):
+    model.train()
+    train_loss = 0.0
+    train_val_true = []
+    train_val_pred = []
+    train_aro_true = []
+    train_aro_pred = []
+    current_lr = optimizer.param_groups[0]["lr"]
+    for images, _, val_true, aro_true in tqdm(train_loader, desc=f"Epoch {epoch+1} train_loader progress"):
+        images, val_true, aro_true = images.to(DEVICE), val_true.to(DEVICE), aro_true.to(DEVICE)
+        optimizer.zero_grad()
+        l2_reg = 0
+        l1_reg = 0
+        with torch.autocast(device_type="cuda", dtype=torch.float16):
+            outputs = model(images)
+            val_pred = outputs[:, 0]
+            aro_pred = outputs[:, 1]
+            for param in model.parameters():
+                l2_reg += torch.norm(param, 2)
+                l1_reg += torch.norm(param, 1)
+            loss = (
+                3 * val_loss(val_pred, val_true)
+                + 3 * aro_loss(aro_pred, aro_true)
+                + CCCLoss(val_pred, val_true)
+                + CCCLoss(aro_pred, aro_true)
+            )
+            train_loss += loss.item()
+            scaler.scale(loss).backward()
+            scaler.step(optimizer)
+            scaler.update()
+            scheduler.step()
+            train_val_true.extend(val_true.detach().cpu().numpy())
+            train_val_pred.extend(val_pred.detach().cpu().numpy())
+            train_aro_true.extend(aro_true.detach().cpu().numpy())
+            train_aro_pred.extend(aro_pred.detach().cpu().numpy())
+
+    train_loss /= max(1, len(train_loader))
+    train_ccc_v = ccc_numpy_1d(train_val_pred, train_val_true)
+    train_ccc_a = ccc_numpy_1d(train_aro_pred, train_aro_true)
+    train_ccc_mean = (train_ccc_v + train_ccc_a) / 2.0
+
+    model.eval()
+    valid_loss = 0.0
+    valid_val_true = []
+    valid_val_pred = []
+    valid_aro_true = []
+    valid_aro_pred = []
+    with torch.no_grad():
+        for images, _, val_true, aro_true in valid_loader:
+            images, val_true, aro_true = images.to(DEVICE), val_true.to(DEVICE), aro_true.to(DEVICE)
+            with torch.autocast(device_type="cuda", dtype=torch.float16):
+                outputs = model(images)
+                val_pred = outputs[:, 0]
+                aro_pred = outputs[:, 1]
+                loss = (
+                    3 * val_loss(val_pred, val_true)
+                    + 3 * aro_loss(aro_pred, aro_true)
+                    + CCCLoss(val_pred, val_true)
+                    + CCCLoss(aro_pred, aro_true)
+                )
+                valid_loss += loss.item()
+                valid_val_true.extend(val_true.detach().cpu().numpy())
+                valid_val_pred.extend(val_pred.detach().cpu().numpy())
+                valid_aro_true.extend(aro_true.detach().cpu().numpy())
+                valid_aro_pred.extend(aro_pred.detach().cpu().numpy())
+
+    valid_loss /= max(1, len(valid_loader))
+    valid_ccc_v = ccc_numpy_1d(valid_val_pred, valid_val_true)
+    valid_ccc_a = ccc_numpy_1d(valid_aro_pred, valid_aro_true)
+    valid_ccc_mean = (valid_ccc_v + valid_ccc_a) / 2.0
+
+    history["epoch"].append(epoch + 1)
+    history["train_loss"].append(train_loss)
+    history["valid_loss"].append(valid_loss)
+    history["ccc_v"].append(valid_ccc_v)
+    history["ccc_a"].append(valid_ccc_a)
+    history["ccc_mean"].append(valid_ccc_mean)
+    history["lr"].append(current_lr)
+
+    print(
+        f"Epoch [{epoch+1}/{NUM_EPOCHS}] - "
+        f"Train Loss: {train_loss:.4f}, "
+        f"Valid Loss: {valid_loss:.4f}, "
+        f"Valid CCC_v: {valid_ccc_v:.4f}, "
+        f"Valid CCC_a: {valid_ccc_a:.4f}, "
+        f"Valid CCC_mean: {valid_ccc_mean:.4f}, "
+        f"Learning Rate: {current_lr:.8f}"
+    )
+
+    if valid_loss < best_valid_loss:
+        best_valid_loss = valid_loss
+        best_epoch = epoch + 1
+        print(f"Saving best model at epoch {best_epoch}")
+        torch.save(model.state_dict(), MODEL_PATH)
+
+# Save training history and plot if needed
+import pandas as pd
+history_df = pd.DataFrame(history)
+history_df.to_csv(PLOTS_DIR / "train_history.csv", index=False)
+
+# Plotting (reuse your plot helpers, but only plot what makes sense)
+save_line_plot(
+    history_df["epoch"].tolist(),
+    [history_df["train_loss"].tolist()],
+    ["Train Loss"],
+    "Train Loss",
+    "Epoch",
+    "Loss",
+    PLOTS_DIR / "train_loss.png",
+)
+save_line_plot(
+    history_df["epoch"].tolist(),
+    [history_df["valid_loss"].tolist()],
+    ["Validation Loss"],
+    "Validation Loss",
+    "Epoch",
+    "Loss",
+    PLOTS_DIR / "valid_loss.png",
+)
+save_line_plot(
+    history_df["epoch"].tolist(),
+    [history_df["train_loss"].tolist(), history_df["valid_loss"].tolist()],
+    ["Train Loss", "Validation Loss"],
+    "Train vs Validation Loss",
+    "Epoch",
+    "Loss",
+    PLOTS_DIR / "train_vs_valid_loss.png",
+)
+save_line_plot(
+    history_df["epoch"].tolist(),
+    [history_df["ccc_v"].tolist()],
+    ["CCC_v"],
+    "Validation CCC_v",
+    "Epoch",
+    "CCC",
+    PLOTS_DIR / "valid_ccc_v.png",
+)
+save_line_plot(
+    history_df["epoch"].tolist(),
+    [history_df["ccc_a"].tolist()],
+    ["CCC_a"],
+    "Validation CCC_a",
+    "Epoch",
+    "CCC",
+    PLOTS_DIR / "valid_ccc_a.png",
+)
+save_line_plot(
+    history_df["epoch"].tolist(),
+    [history_df["ccc_mean"].tolist()],
+    ["CCC_mean"],
+    "Validation CCC_mean",
+    "Epoch",
+    "CCC",
+    PLOTS_DIR / "valid_ccc_mean.png",
+)
+save_line_plot(
+    history_df["epoch"].tolist(),
+    [history_df["lr"].tolist()],
+    ["Learning Rate"],
+    "Learning Rate",
+    "Epoch",
+    "LR",
+    PLOTS_DIR / "lr.png",
+)
+
+
+# In thông tin của epoch tốt nhất
+best_idx = best_epoch - 1
+print("=== Best Epoch Metrics ===")
+print(f"Best epoch: {best_epoch}")
+print(f"Train Loss: {history_df['train_loss'][best_idx]:.4f}")
+print(f"Valid Loss: {history_df['valid_loss'][best_idx]:.4f}")
+print(f"Valid CCC_v: {history_df['ccc_v'][best_idx]:.4f}")
+print(f"Valid CCC_a: {history_df['ccc_a'][best_idx]:.4f}")
+print(f"Valid CCC_mean: {history_df['ccc_mean'][best_idx]:.4f}")
+
+n_epochs_run = len(history_df["ccc_v"])
+avg_ccc_v = sum(history_df["ccc_v"]) / n_epochs_run
+avg_ccc_a = sum(history_df["ccc_a"]) / n_epochs_run
+avg_ccc_mean = sum(history_df["ccc_mean"]) / n_epochs_run
+avg_valid_loss = sum(history_df["valid_loss"]) / n_epochs_run
+print("\n=== Average Across All Epochs ===")
+print(f"Avg Valid Loss  : {avg_valid_loss:.4f}")
+print(f"Avg CCC_v       : {avg_ccc_v:.4f}")
+print(f"Avg CCC_a       : {avg_ccc_a:.4f}")
+print(f"Avg CCC_mean    : {avg_ccc_mean:.4f}")
+print("=================================")
+
+print(f"--- Training complete. Best epoch: {best_epoch}, Best val loss: {best_valid_loss:.4f} ---")
+print(f"Best model saved to: {MODEL_PATH}")
+print(f"All plots and CSV logs saved to: {PLOTS_DIR}")
\ No newline at end of file
diff --git a/CAGE_expression_inference-apvit/models/AffectNet8_Maxvit_VA/train2.py b/CAGE_expression_inference-apvit/models/AffectNet8_Maxvit_VA/train2.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d3836fadefa0caad238314822a54498b253cade
--- /dev/null
+++ b/CAGE_expression_inference-apvit/models/AffectNet8_Maxvit_VA/train2.py
@@ -0,0 +1,790 @@
+"""
+train2.py — Ablation study: 6 experiments (backbone comparison)
+
+Pipeline IDENTICAL to train.py (train on full train_set, validate on official
+val_set, save best model by lowest val loss, 20 epochs).
+Only the model architecture differs across experiments.
+
+All models use ImageNet pretrained weights + standard VA regression head + CCC loss.
+
+Experiments:
+  1) VGG-16           + CCC only
+  2) MobileNetV2      + CCC only
+  3) ResNet-18        + CCC only
+  4) EfficientNetV2-S + CCC only
+  5) Swin-T           + CCC only
+  6) ConvNeXt-Tiny    + CCC only
+
+Output per experiment:
+  experiments/<exp_name>/
+    ├── model.pt
+    ├── train_history.csv
+    └── *.png (10 plots)
+
+Summary:
+  experiments/
+    ├── all_experiments_summary.csv
+    └── summary_*.png
+"""
+
+import pandas as pd
+import os
+import numpy as np
+import torch
+import torchvision.transforms as transforms
+import torchvision.models as models
+from torch.utils.data import DataLoader, Dataset
+import torch.nn as nn
+import torch.optim as optim
+from PIL import Image
+from torch.optim import lr_scheduler
+from tqdm import tqdm
+from pathlib import Path
+
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+
+
+# ============================================================================
+# Paths (identical to train.py)
+# ============================================================================
+SCRIPT_DIR = Path(__file__).resolve().parent
+EXPERIMENTS_DIR = SCRIPT_DIR / "experiments"
+EXPERIMENTS_DIR.mkdir(parents=True, exist_ok=True)
+
+
+def resolve_images_dir(split_name, explicit_env_var, data_root, extract_root):
+    explicit = os.getenv(explicit_env_var, "").strip()
+    candidates = []
+    if explicit:
+        candidates.append(Path(explicit))
+
+    candidates.append(Path(data_root) / f"{split_name}_set" / "images")
+    candidates.append(Path(extract_root) / f"{split_name}_extracted" / "images")
+
+    extract_split_root = Path(extract_root) / f"{split_name}_extracted"
+    if extract_split_root.exists():
+        for p in extract_split_root.rglob("images"):
+            if p.is_dir():
+                candidates.append(p)
+
+    for p in candidates:
+        if p.exists() and p.is_dir():
+            return str(p)
+
+    tried = "\n".join([str(p) for p in candidates])
+    raise FileNotFoundError(
+        f"Could not find images folder for split='{split_name}'. Tried:\n{tried}"
+    )
+
+
+# ============================================================================
+# Data paths (identical to train.py)
+# ============================================================================
+DATA_ROOT = os.getenv("AFFECTNET_ROOT", "/workspace/data_affectnet/AffectNet")
+EXTRACT_ROOT = os.getenv("AFFECTNET_EXTRACT_ROOT", f"{DATA_ROOT}/extracted")
+ANNO_ROOT = os.getenv("AFFECTNET_ANNO_ROOT", "../../affectnet_annotations")
+IMAGE_FOLDER = resolve_images_dir("train", "AFFECTNET_TRAIN_IMAGES", DATA_ROOT, EXTRACT_ROOT)
+IMAGE_FOLDER_TEST = resolve_images_dir("val", "AFFECTNET_VAL_IMAGES", DATA_ROOT, EXTRACT_ROOT)
+train_annotations_path = os.getenv(
+    "AFFECTNET_TRAIN_ANNO", f"{ANNO_ROOT}/train_set_annotation_without_lnd.csv"
+)
+valid_annotations_path = os.getenv(
+    "AFFECTNET_VAL_ANNO", f"{ANNO_ROOT}/val_set_annotation_without_lnd.csv"
+)
+train_annotations_df = pd.read_csv(train_annotations_path)
+valid_annotations_df = pd.read_csv(valid_annotations_path)
+
+
+# ============================================================================
+# Parameters (identical to train.py)
+# ============================================================================
+BATCHSIZE = int(os.getenv("BATCHSIZE", "64"))
+NUM_EPOCHS = 20
+LR = 4e-5
+NUM_WORKERS = int(os.getenv("NUM_WORKERS", "0"))
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+print("DATA_ROOT              =", DATA_ROOT)
+print("EXTRACT_ROOT           =", EXTRACT_ROOT)
+print("AFFECTNET_TRAIN_IMAGES =", IMAGE_FOLDER)
+print("AFFECTNET_VAL_IMAGES   =", IMAGE_FOLDER_TEST)
+print("EXPERIMENTS_DIR        =", EXPERIMENTS_DIR)
+print("Train set size         =", len(train_annotations_df))
+print("Val set size           =", len(valid_annotations_df))
+print("DEVICE                 =", DEVICE)
+print("BATCH_SIZE             =", BATCHSIZE)
+print("NUM_EPOCHS             =", NUM_EPOCHS)
+print("LR                     =", LR)
+
+
+# ============================================================================
+# 6 Experiment configurations (backbone comparison, all CCC loss)
+# ============================================================================
+EXPERIMENTS = [
+    {
+        "name": "exp1_vgg16_ccc",
+        "desc": "VGG-16 + ImageNet + CCC only",
+        "model_type": "vgg16",
+        "loss_type": "ccc_only",
+    },
+    {
+        "name": "exp2_mobilenetv2_ccc",
+        "desc": "MobileNetV2 + ImageNet + CCC only",
+        "model_type": "mobilenetv2",
+        "loss_type": "ccc_only",
+    },
+    {
+        "name": "exp3_resnet18_ccc",
+        "desc": "ResNet-18 + ImageNet + CCC only",
+        "model_type": "resnet18",
+        "loss_type": "ccc_only",
+    },
+    {
+        "name": "exp4_efficientnetv2_ccc",
+        "desc": "EfficientNetV2-S + ImageNet + CCC only",
+        "model_type": "efficientnetv2",
+        "loss_type": "ccc_only",
+    },
+    {
+        "name": "exp5_swin_ccc",
+        "desc": "Swin-T + ImageNet + CCC only",
+        "model_type": "swin",
+        "loss_type": "ccc_only",
+    },
+    {
+        "name": "exp6_convnext_ccc",
+        "desc": "ConvNeXt-Tiny + ImageNet + CCC only",
+        "model_type": "convnext",
+        "loss_type": "ccc_only",
+    },
+]
+
+
+# ============================================================================
+# Plot helpers (from train.py)
+# ============================================================================
+def save_line_plot(x, ys, labels, title, xlabel, ylabel, save_path):
+    plt.figure(figsize=(10, 6))
+    for y, label in zip(ys, labels):
+        plt.plot(x, y, marker="o", linewidth=2, markersize=4, label=label)
+    plt.title(title)
+    plt.xlabel(xlabel)
+    plt.ylabel(ylabel)
+    plt.grid(True, alpha=0.3)
+    plt.legend()
+    plt.tight_layout()
+    plt.savefig(save_path, dpi=200)
+    plt.close()
+
+
+def save_bar_plot(labels, values, title, xlabel, ylabel, save_path, annotate=True):
+    plt.figure(figsize=(12, 6))
+    bars = plt.bar(labels, values)
+    plt.title(title)
+    plt.xlabel(xlabel)
+    plt.ylabel(ylabel)
+    plt.grid(True, axis="y", alpha=0.3)
+    if annotate:
+        for bar, val in zip(bars, values):
+            plt.text(
+                bar.get_x() + bar.get_width() / 2,
+                bar.get_height(),
+                f"{val:.4f}",
+                ha="center",
+                va="bottom",
+                fontsize=8,
+            )
+    plt.xticks(rotation=30, ha="right")
+    plt.tight_layout()
+    plt.savefig(save_path, dpi=200)
+    plt.close()
+
+
+def save_scatter(x, y, title, xlabel, ylabel, save_path, annotate_labels=None):
+    plt.figure(figsize=(10, 6))
+    plt.scatter(x, y)
+    if annotate_labels is not None:
+        for xi, yi, txt in zip(x, y, annotate_labels):
+            plt.annotate(str(txt), (xi, yi), fontsize=9)
+    plt.title(title)
+    plt.xlabel(xlabel)
+    plt.ylabel(ylabel)
+    plt.grid(True, alpha=0.3)
+    plt.tight_layout()
+    plt.savefig(save_path, dpi=200)
+    plt.close()
+
+
+# ============================================================================
+# Dataset & transforms (identical to train.py)
+# ============================================================================
+class CustomDataset(Dataset):
+    def __init__(self, dataframe, root_dir, transform=None, balance=False):
+        self.dataframe = dataframe
+        self.transform = transform
+        self.root_dir = root_dir
+        self.balance = balance
+
+        if self.balance:
+            self.dataframe = self.balance_dataset()
+
+    def __len__(self):
+        return len(self.dataframe)
+
+    def __getitem__(self, idx):
+        img_id = int(float(self.dataframe["number"].iloc[idx]))
+        image_path = os.path.join(self.root_dir, f"{img_id}.jpg")
+        image = Image.open(image_path)
+
+        classes = torch.tensor(self.dataframe.iloc[idx, 1], dtype=torch.int8)
+        valence = torch.tensor(self.dataframe.iloc[idx, 2], dtype=torch.float16)
+        arousal = torch.tensor(self.dataframe.iloc[idx, 3], dtype=torch.float16)
+
+        if self.transform:
+            image = self.transform(image)
+
+        return image, classes, valence, arousal
+
+    def balance_dataset(self):
+        balanced_df = self.dataframe.groupby("exp", group_keys=False).apply(
+            lambda x: x.sample(self.dataframe["exp"].value_counts().min())
+        )
+        return balanced_df
+
+
+transform = transforms.Compose(
+    [
+        transforms.RandomHorizontalFlip(0.5),
+        transforms.RandomGrayscale(0.01),
+        transforms.RandomRotation(10),
+        transforms.ColorJitter(
+            brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1
+        ),
+        transforms.RandomPerspective(
+            distortion_scale=0.2, p=0.5
+        ),
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+        transforms.RandomErasing(
+            p=0.5, scale=(0.02, 0.2), ratio=(0.3, 3.3), value="random"
+        ),
+    ]
+)
+
+transform_valid = transforms.Compose(
+    [
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+    ]
+)
+
+
+# ============================================================================
+# Model builders — all ImageNet pretrained, standard VA regression heads
+# ============================================================================
+def _make_head(in_features, dropout=0.3):
+    """Standard VA regression head used by all models."""
+    return nn.Sequential(
+        nn.LayerNorm(in_features),
+        nn.Linear(in_features, in_features),
+        nn.Tanh(),
+        nn.Dropout(dropout),
+        nn.Linear(in_features, 2, bias=False),
+    )
+
+
+def build_model(exp_config):
+    """
+    Build model based on experiment config.
+    All models use ImageNet pretrained weights + standard VA regression head.
+    """
+    model_type = exp_config["model_type"]
+
+    if model_type == "vgg16":
+        # VGG-16
+        model = models.vgg16(weights="DEFAULT")
+        in_feat = model.classifier[6].in_features  # 4096
+        model.classifier[6] = _make_head(in_feat)
+
+    elif model_type == "mobilenetv2":
+        # MobileNetV2
+        model = models.mobilenet_v2(weights="DEFAULT")
+        in_feat = model.classifier[1].in_features  # 1280
+        model.classifier = _make_head(in_feat)
+
+    elif model_type == "resnet18":
+        # ResNet-18
+        model = models.resnet18(weights="DEFAULT")
+        in_feat = model.fc.in_features  # 512
+        model.fc = _make_head(in_feat)
+
+    elif model_type == "efficientnetv2":
+        # EfficientNetV2-S
+        model = models.efficientnet_v2_s(weights="DEFAULT")
+        in_feat = model.classifier[1].in_features  # 1280
+        model.classifier = _make_head(in_feat)
+
+    elif model_type == "swin":
+        # Swin Transformer Tiny
+        model = models.swin_t(weights="DEFAULT")
+        in_feat = model.head.in_features  # 768
+        model.head = _make_head(in_feat)
+
+    elif model_type == "convnext":
+        # ConvNeXt Tiny
+        model = models.convnext_tiny(weights="DEFAULT")
+        in_feat = model.classifier[2].in_features  # 768
+        model.classifier[2] = _make_head(in_feat)
+
+    else:
+        raise ValueError(f"Unknown model_type: {model_type}")
+
+    model.to(DEVICE)
+    return model
+
+
+# ============================================================================
+# Loss functions & metrics (from train.py)
+# ============================================================================
+val_loss_fn = nn.MSELoss()
+aro_loss_fn = nn.MSELoss()
+
+
+def CCCLoss(x, y):
+    """CCC-based loss (returns -CCC). Identical to train.py."""
+    x_mean = torch.mean(x, dim=0)
+    y_mean = torch.mean(y, dim=0)
+    x_var = torch.var(x, dim=0)
+    y_var = torch.var(y, dim=0)
+    cov_matrix = torch.matmul(
+        (x - x_mean).permute(*torch.arange(x.dim() - 1, -1, -1)), y - y_mean
+    ) / (x.size(0) - 1)
+    numerator = 2 * cov_matrix
+    denominator = x_var + y_var + torch.pow((x_mean - y_mean), 2)
+    ccc = torch.mean(numerator / denominator)
+    return -ccc
+
+
+def compute_loss(val_pred, aro_pred, val_true, aro_true, loss_type):
+    """Compute training/validation loss based on experiment type."""
+    if loss_type == "mse_ccc":
+        return (
+            3 * val_loss_fn(val_pred, val_true)
+            + 3 * aro_loss_fn(aro_pred, aro_true)
+            + CCCLoss(val_pred, val_true)
+            + CCCLoss(aro_pred, aro_true)
+        )
+    else:  # ccc_only
+        return (
+            CCCLoss(val_pred, val_true)
+            + CCCLoss(aro_pred, aro_true)
+        )
+
+
+def ccc_numpy_1d(x, y, eps=1e-8):
+    """CCC metric (numpy). Identical to train.py."""
+    x = np.asarray(x).reshape(-1)
+    y = np.asarray(y).reshape(-1)
+    x_mean = x.mean()
+    y_mean = y.mean()
+    x_var = x.var()
+    y_var = y.var()
+    cov = ((x - x_mean) * (y - y_mean)).mean()
+    return (2.0 * cov) / (x_var + y_var + (x_mean - y_mean) ** 2 + eps)
+
+
+# ============================================================================
+# Per-experiment plots (same plots as train.py)
+# ============================================================================
+def save_experiment_plots(history_df, exp_name, exp_dir):
+    """Save the same set of plots as train.py for each experiment."""
+    epochs = history_df["epoch"].tolist()
+
+    # 1. Train Loss
+    save_line_plot(
+        epochs, [history_df["train_loss"].tolist()], ["Train Loss"],
+        f"{exp_name} — Train Loss", "Epoch", "Loss",
+        exp_dir / "train_loss.png",
+    )
+    # 2. Validation Loss
+    save_line_plot(
+        epochs, [history_df["valid_loss"].tolist()], ["Validation Loss"],
+        f"{exp_name} — Validation Loss", "Epoch", "Loss",
+        exp_dir / "valid_loss.png",
+    )
+    # 3. Train vs Validation Loss
+    save_line_plot(
+        epochs,
+        [history_df["train_loss"].tolist(), history_df["valid_loss"].tolist()],
+        ["Train Loss", "Validation Loss"],
+        f"{exp_name} — Train vs Validation Loss", "Epoch", "Loss",
+        exp_dir / "train_vs_valid_loss.png",
+    )
+    # 4. CCC_v
+    save_line_plot(
+        epochs, [history_df["ccc_v"].tolist()], ["CCC_v"],
+        f"{exp_name} — Validation CCC_v", "Epoch", "CCC",
+        exp_dir / "valid_ccc_v.png",
+    )
+    # 5. CCC_a
+    save_line_plot(
+        epochs, [history_df["ccc_a"].tolist()], ["CCC_a"],
+        f"{exp_name} — Validation CCC_a", "Epoch", "CCC",
+        exp_dir / "valid_ccc_a.png",
+    )
+    # 6. CCC_mean
+    save_line_plot(
+        epochs, [history_df["ccc_mean"].tolist()], ["CCC_mean"],
+        f"{exp_name} — Validation CCC_mean", "Epoch", "CCC",
+        exp_dir / "valid_ccc_mean.png",
+    )
+    # 7. All CCC on one plot
+    save_line_plot(
+        epochs,
+        [
+            history_df["ccc_v"].tolist(),
+            history_df["ccc_a"].tolist(),
+            history_df["ccc_mean"].tolist(),
+        ],
+        ["CCC_v", "CCC_a", "CCC_mean"],
+        f"{exp_name} — All CCC Curves", "Epoch", "CCC",
+        exp_dir / "valid_ccc_all.png",
+    )
+    # 8. Learning Rate
+    save_line_plot(
+        epochs, [history_df["lr"].tolist()], ["Learning Rate"],
+        f"{exp_name} — Learning Rate", "Epoch", "LR",
+        exp_dir / "lr.png",
+    )
+    # 9. Train Loss vs Valid Loss scatter
+    save_scatter(
+        history_df["train_loss"].tolist(),
+        history_df["valid_loss"].tolist(),
+        f"{exp_name} — Train Loss vs Valid Loss",
+        "Train Loss", "Validation Loss",
+        exp_dir / "train_vs_valid_scatter.png",
+        annotate_labels=epochs,
+    )
+    # 10. CCC_v vs CCC_a scatter
+    save_scatter(
+        history_df["ccc_v"].tolist(),
+        history_df["ccc_a"].tolist(),
+        f"{exp_name} — CCC_v vs CCC_a",
+        "CCC_v", "CCC_a",
+        exp_dir / "ccc_v_vs_ccc_a_scatter.png",
+        annotate_labels=epochs,
+    )
+
+
+# ============================================================================
+# Summary plots across all experiments
+# ============================================================================
+def save_summary_plots(summary_df, save_dir):
+    """Save comparison plots across all experiments."""
+    names = summary_df["name"].tolist()
+
+    # Bar: Best CCC_v
+    save_bar_plot(
+        names, summary_df["best_ccc_v"].tolist(),
+        "Best CCC_v by Experiment", "Experiment", "CCC_v",
+        save_dir / "summary_best_ccc_v.png",
+    )
+    # Bar: Best CCC_a
+    save_bar_plot(
+        names, summary_df["best_ccc_a"].tolist(),
+        "Best CCC_a by Experiment", "Experiment", "CCC_a",
+        save_dir / "summary_best_ccc_a.png",
+    )
+    # Bar: Best CCC_mean
+    save_bar_plot(
+        names, summary_df["best_ccc_mean"].tolist(),
+        "Best CCC_mean by Experiment", "Experiment", "CCC_mean",
+        save_dir / "summary_best_ccc_mean.png",
+    )
+    # Bar: Best Validation Loss
+    save_bar_plot(
+        names, summary_df["best_valid_loss"].tolist(),
+        "Best Validation Loss by Experiment", "Experiment", "Loss",
+        save_dir / "summary_best_valid_loss.png",
+    )
+    # Bar: Best Epoch
+    save_bar_plot(
+        names, summary_df["best_epoch"].tolist(),
+        "Best Epoch by Experiment", "Experiment", "Epoch",
+        save_dir / "summary_best_epoch.png",
+    )
+
+    # Overlay: CCC_mean curves from all experiments
+    plt.figure(figsize=(12, 6))
+    for _, row in summary_df.iterrows():
+        hist_csv = save_dir / row["name"] / "train_history.csv"
+        if hist_csv.exists():
+            h = pd.read_csv(hist_csv)
+            plt.plot(
+                h["epoch"], h["ccc_mean"],
+                marker="o", linewidth=2, markersize=3,
+                label=row["name"],
+            )
+    plt.title("Validation CCC_mean — All Experiments")
+    plt.xlabel("Epoch")
+    plt.ylabel("CCC_mean")
+    plt.grid(True, alpha=0.3)
+    plt.legend(fontsize=8)
+    plt.tight_layout()
+    plt.savefig(save_dir / "summary_ccc_mean_overlay.png", dpi=200)
+    plt.close()
+
+    # Overlay: Validation Loss curves from all experiments
+    plt.figure(figsize=(12, 6))
+    for _, row in summary_df.iterrows():
+        hist_csv = save_dir / row["name"] / "train_history.csv"
+        if hist_csv.exists():
+            h = pd.read_csv(hist_csv)
+            plt.plot(
+                h["epoch"], h["valid_loss"],
+                marker="o", linewidth=2, markersize=3,
+                label=row["name"],
+            )
+    plt.title("Validation Loss — All Experiments")
+    plt.xlabel("Epoch")
+    plt.ylabel("Loss")
+    plt.grid(True, alpha=0.3)
+    plt.legend(fontsize=8)
+    plt.tight_layout()
+    plt.savefig(save_dir / "summary_valid_loss_overlay.png", dpi=200)
+    plt.close()
+
+    # Overlay: Train Loss curves from all experiments
+    plt.figure(figsize=(12, 6))
+    for _, row in summary_df.iterrows():
+        hist_csv = save_dir / row["name"] / "train_history.csv"
+        if hist_csv.exists():
+            h = pd.read_csv(hist_csv)
+            plt.plot(
+                h["epoch"], h["train_loss"],
+                marker="o", linewidth=2, markersize=3,
+                label=row["name"],
+            )
+    plt.title("Train Loss — All Experiments")
+    plt.xlabel("Epoch")
+    plt.ylabel("Loss")
+    plt.grid(True, alpha=0.3)
+    plt.legend(fontsize=8)
+    plt.tight_layout()
+    plt.savefig(save_dir / "summary_train_loss_overlay.png", dpi=200)
+    plt.close()
+
+
+# ============================================================================
+# Training function — mirrors train.py's training loop EXACTLY
+# ============================================================================
+def run_experiment(exp_config, train_loader, valid_loader):
+    """
+    Run one experiment. Training loop structure is identical to train.py:
+    - AdamW optimizer, CosineAnnealingLR(T_max=BATCHSIZE*NUM_EPOCHS)
+    - AMP GradScaler + autocast
+    - l2_reg/l1_reg computed per batch (not added to loss, kept for parity)
+    - scheduler.step() called per batch (inside autocast, same as train.py)
+    - Best model selected by minimum validation loss
+    """
+    name = exp_config["name"]
+    desc = exp_config["desc"]
+    loss_type = exp_config["loss_type"]
+
+    exp_dir = EXPERIMENTS_DIR / name
+    exp_dir.mkdir(parents=True, exist_ok=True)
+    exp_model_path = str(exp_dir / "model.pt")
+
+    print(f"\n{'='*70}")
+    print(f"EXPERIMENT: {name}")
+    print(f"  {desc}")
+    print(f"  model_type={exp_config['model_type']}, "
+          f"loss_type={loss_type}")
+    print(f"  output: {exp_dir}")
+    print(f"{'='*70}")
+
+    # Build model
+    model = build_model(exp_config)
+
+    # Optimizer, scheduler, scaler — identical to train.py
+    optimizer = optim.AdamW(model.parameters(), lr=LR)
+    scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=BATCHSIZE * NUM_EPOCHS)
+    scaler = torch.cuda.amp.GradScaler()
+
+    best_valid_loss = float("inf")
+    best_epoch = -1
+    l2_lambda = 0.00001  # kept for parity with train.py (not added to loss)
+    l1_lambda = 0.00001
+
+    history = {
+        "epoch": [],
+        "train_loss": [],
+        "valid_loss": [],
+        "ccc_v": [],
+        "ccc_a": [],
+        "ccc_mean": [],
+        "lr": [],
+    }
+
+    for epoch in range(NUM_EPOCHS):
+        # ── Train (identical structure to train.py) ──────────────────────
+        model.train()
+        train_loss = 0.0
+        current_lr = optimizer.param_groups[0]["lr"]
+
+        for images, _, val_true, aro_true in tqdm(
+            train_loader, desc=f"[{name}] Epoch {epoch+1}/{NUM_EPOCHS}"
+        ):
+            images, val_true, aro_true = (
+                images.to(DEVICE), val_true.to(DEVICE), aro_true.to(DEVICE)
+            )
+            optimizer.zero_grad()
+            l2_reg = 0
+            l1_reg = 0
+            with torch.autocast(device_type="cuda", dtype=torch.float16):
+                outputs = model(images)
+                val_pred = outputs[:, 0]
+                aro_pred = outputs[:, 1]
+                for param in model.parameters():
+                    l2_reg += torch.norm(param, 2)
+                    l1_reg += torch.norm(param, 1)
+                loss = compute_loss(val_pred, aro_pred, val_true, aro_true, loss_type)
+                train_loss += loss.item()
+                scaler.scale(loss).backward()
+                scaler.step(optimizer)
+                scaler.update()
+                scheduler.step()
+
+        train_loss /= max(1, len(train_loader))
+
+        # ── Validate (identical structure to train.py) ───────────────────
+        model.eval()
+        valid_loss_sum = 0.0
+        valid_val_true = []
+        valid_val_pred = []
+        valid_aro_true = []
+        valid_aro_pred = []
+
+        with torch.no_grad():
+            for images, _, val_true, aro_true in valid_loader:
+                images, val_true, aro_true = (
+                    images.to(DEVICE), val_true.to(DEVICE), aro_true.to(DEVICE)
+                )
+                with torch.autocast(device_type="cuda", dtype=torch.float16):
+                    outputs = model(images)
+                    val_pred = outputs[:, 0]
+                    aro_pred = outputs[:, 1]
+                    loss = compute_loss(
+                        val_pred, aro_pred, val_true, aro_true, loss_type
+                    )
+                    valid_loss_sum += loss.item()
+
+                valid_val_true.extend(val_true.detach().cpu().float().numpy())
+                valid_val_pred.extend(val_pred.detach().cpu().float().numpy())
+                valid_aro_true.extend(aro_true.detach().cpu().float().numpy())
+                valid_aro_pred.extend(aro_pred.detach().cpu().float().numpy())
+
+        valid_loss_avg = valid_loss_sum / max(1, len(valid_loader))
+        valid_ccc_v = ccc_numpy_1d(valid_val_pred, valid_val_true)
+        valid_ccc_a = ccc_numpy_1d(valid_aro_pred, valid_aro_true)
+        valid_ccc_mean = (valid_ccc_v + valid_ccc_a) / 2.0
+
+        history["epoch"].append(epoch + 1)
+        history["train_loss"].append(train_loss)
+        history["valid_loss"].append(valid_loss_avg)
+        history["ccc_v"].append(valid_ccc_v)
+        history["ccc_a"].append(valid_ccc_a)
+        history["ccc_mean"].append(valid_ccc_mean)
+        history["lr"].append(current_lr)
+
+        print(
+            f"[{name}] Epoch [{epoch+1}/{NUM_EPOCHS}] - "
+            f"Training Loss: {train_loss:.4f}, "
+            f"Validation Loss: {valid_loss_avg:.4f}, "
+            f"CCC_v: {valid_ccc_v:.4f}, "
+            f"CCC_a: {valid_ccc_a:.4f}, "
+            f"CCC_mean: {valid_ccc_mean:.4f}, "
+            f"Learning Rate: {current_lr:.8f}"
+        )
+
+        if valid_loss_avg < best_valid_loss:
+            best_valid_loss = valid_loss_avg
+            best_epoch = epoch + 1
+            print(f"  -> Saving best model at epoch {best_epoch}")
+            torch.save(model.state_dict(), exp_model_path)
+
+    # ── Save CSV + plots ─────────────────────────────────────────────────
+    history_df = pd.DataFrame(history)
+    history_df.to_csv(exp_dir / "train_history.csv", index=False)
+    save_experiment_plots(history_df, name, exp_dir)
+
+    # Print best epoch metrics
+    best_idx = best_epoch - 1
+    print(f"\n=== [{name}] Best Epoch Metrics ===")
+    print(f"Best epoch:     {best_epoch}")
+    print(f"Train Loss:     {history_df['train_loss'][best_idx]:.4f}")
+    print(f"Valid Loss:     {history_df['valid_loss'][best_idx]:.4f}")
+    print(f"Valid CCC_v:    {history_df['ccc_v'][best_idx]:.4f}")
+    print(f"Valid CCC_a:    {history_df['ccc_a'][best_idx]:.4f}")
+    print(f"Valid CCC_mean: {history_df['ccc_mean'][best_idx]:.4f}")
+
+    return {
+        "name": name,
+        "desc": desc,
+        "model_type": exp_config["model_type"],
+        "loss_type": exp_config["loss_type"],
+        "best_epoch": best_epoch,
+        "best_valid_loss": float(history_df["valid_loss"][best_idx]),
+        "best_ccc_v": float(history_df["ccc_v"][best_idx]),
+        "best_ccc_a": float(history_df["ccc_a"][best_idx]),
+        "best_ccc_mean": float(history_df["ccc_mean"][best_idx]),
+    }
+
+
+# ============================================================================
+# Main
+# ============================================================================
+print("\n--- Creating datasets (shared across all experiments) ---")
+
+train_dataset = CustomDataset(
+    dataframe=train_annotations_df,
+    root_dir=IMAGE_FOLDER,
+    transform=transform,
+    balance=True,
+)
+valid_dataset = CustomDataset(
+    dataframe=valid_annotations_df,
+    root_dir=IMAGE_FOLDER_TEST,
+    transform=transform_valid,
+    balance=False,
+)
+train_loader = DataLoader(
+    train_dataset, batch_size=BATCHSIZE, shuffle=True, num_workers=NUM_WORKERS
+)
+valid_loader = DataLoader(
+    valid_dataset, batch_size=BATCHSIZE, shuffle=False, num_workers=NUM_WORKERS
+)
+
+print(f"Train dataset size (balanced): {len(train_dataset)}")
+print(f"Valid dataset size:            {len(valid_dataset)}")
+print(f"\n--- Running {len(EXPERIMENTS)} experiments ---")
+
+all_results = []
+
+for exp_config in EXPERIMENTS:
+    result = run_experiment(exp_config, train_loader, valid_loader)
+    all_results.append(result)
+
+# ── Summary ──────────────────────────────────────────────────────────────
+summary_df = pd.DataFrame(all_results)
+summary_df.to_csv(EXPERIMENTS_DIR / "all_experiments_summary.csv", index=False)
+save_summary_plots(summary_df, EXPERIMENTS_DIR)
+
+print("\n" + "=" * 70)
+print("ALL 6 EXPERIMENTS COMPLETE")
+print("=" * 70)
+print(summary_df.to_string(index=False))
+print(f"\nSummary CSV: {EXPERIMENTS_DIR / 'all_experiments_summary.csv'}")
+print(f"All output:  {EXPERIMENTS_DIR}")
diff --git a/CAGE_expression_inference-apvit/models/AffectNet8_Maxvit_VA/train2_old.py b/CAGE_expression_inference-apvit/models/AffectNet8_Maxvit_VA/train2_old.py
new file mode 100644
index 0000000000000000000000000000000000000000..ba649a2d379f3d8cd7d7e45c22a7dce7d50c07b1
--- /dev/null
+++ b/CAGE_expression_inference-apvit/models/AffectNet8_Maxvit_VA/train2_old.py
@@ -0,0 +1,733 @@
+"""
+train2.py – Ablation study: 6 experiments (3 models × 2 losses)
+
+Models:
+  1) MaxViT-T + AttentivePoolingHead + load Combined weights   (attn_combined)
+  2) MaxViT-T + AttentivePoolingHead + NO Combined weights     (attn_nocombined)
+  3) MaxViT-T + Standard head        + NO Combined weights     (std_nocombined)
+
+Losses:
+  A) 3*MSE_val + 3*MSE_aro + CCC_val + CCC_aro                (mse_ccc)
+  B) CCC_val + CCC_aro                                         (ccc_only)
+
+Same pipeline as train.py for each experiment. Each saves its own
+folder with per-epoch CSV, plots, and model checkpoint.
+"""
+
+import pandas as pd
+import os
+import numpy as np
+import torch
+import torchvision.transforms as transforms
+import torchvision.models as models
+from torch.utils.data import DataLoader, Dataset
+import torch.nn as nn
+import torch.optim as optim
+from PIL import Image
+from torch.optim import lr_scheduler
+from tqdm import tqdm
+from pathlib import Path
+
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+
+
+# =========================
+# Paths
+# =========================
+SCRIPT_DIR = Path(__file__).resolve().parent
+MODEL_PATH = os.getenv("MODEL_PATH", str(SCRIPT_DIR / "model.pt"))
+EXPERIMENTS_DIR = SCRIPT_DIR / "experiments"
+EXPERIMENTS_DIR.mkdir(parents=True, exist_ok=True)
+
+
+def resolve_images_dir(split_name, explicit_env_var, data_root, extract_root):
+    explicit = os.getenv(explicit_env_var, "").strip()
+    candidates = []
+    if explicit:
+        candidates.append(Path(explicit))
+
+    candidates.append(Path(data_root) / f"{split_name}_set" / "images")
+    candidates.append(Path(extract_root) / f"{split_name}_extracted" / "images")
+
+    extract_split_root = Path(extract_root) / f"{split_name}_extracted"
+    if extract_split_root.exists():
+        for p in extract_split_root.rglob("images"):
+            if p.is_dir():
+                candidates.append(p)
+
+    for p in candidates:
+        if p.exists() and p.is_dir():
+            return str(p)
+
+    tried = "\n".join([str(p) for p in candidates])
+    raise FileNotFoundError(
+        f"Could not find images folder for split='{split_name}'. Tried:\n{tried}"
+    )
+
+
+# =========================
+# Data
+# =========================
+DATA_ROOT = os.getenv("AFFECTNET_ROOT", "/workspace/data_affectnet/AffectNet")
+EXTRACT_ROOT = os.getenv("AFFECTNET_EXTRACT_ROOT", f"{DATA_ROOT}/extracted")
+ANNO_ROOT = os.getenv("AFFECTNET_ANNO_ROOT", "../../affectnet_annotations")
+IMAGE_FOLDER = resolve_images_dir("train", "AFFECTNET_TRAIN_IMAGES", DATA_ROOT, EXTRACT_ROOT)
+IMAGE_FOLDER_TEST = resolve_images_dir("val", "AFFECTNET_VAL_IMAGES", DATA_ROOT, EXTRACT_ROOT)
+train_annotations_path = os.getenv(
+    "AFFECTNET_TRAIN_ANNO", f"{ANNO_ROOT}/train_set_annotation_without_lnd.csv"
+)
+valid_annotations_path = os.getenv(
+    "AFFECTNET_VAL_ANNO", f"{ANNO_ROOT}/val_set_annotation_without_lnd.csv"
+)
+train_annotations_df = pd.read_csv(train_annotations_path)
+valid_annotations_df = pd.read_csv(valid_annotations_path)
+
+
+# =========================
+# Parameters
+# =========================
+BATCHSIZE = int(os.getenv("BATCHSIZE", "64"))
+NUM_EPOCHS = 20
+LR = 4e-5
+NUM_WORKERS = int(os.getenv("NUM_WORKERS", "0"))
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+COMBINED_MODEL_PATH = os.getenv(
+    "COMBINED_MODEL_PATH",
+    str((SCRIPT_DIR / "../AffectNet8_Maxvit_Combined/model.pt").resolve()),
+)
+
+print("DATA_ROOT          =", DATA_ROOT)
+print("EXTRACT_ROOT       =", EXTRACT_ROOT)
+print("AFFECTNET_TRAIN_IMAGES =", IMAGE_FOLDER)
+print("AFFECTNET_VAL_IMAGES   =", IMAGE_FOLDER_TEST)
+print("MODEL_PATH         =", MODEL_PATH)
+print("COMBINED_MODEL_PATH=", COMBINED_MODEL_PATH)
+print("EXPERIMENTS_DIR    =", EXPERIMENTS_DIR)
+print("Train set size     =", len(train_annotations_df))
+print("Val set size       =", len(valid_annotations_df))
+print("DEVICE             =", DEVICE)
+print("BATCH_SIZE         =", BATCHSIZE)
+print("NUM_EPOCHS         =", NUM_EPOCHS)
+print("LR                 =", LR)
+
+
+# =========================
+# Experiment configurations
+# =========================
+EXPERIMENTS = [
+    {
+        "name": "exp1_attn_combined_mse_ccc",
+        "desc": "MaxViT-T + Attention + Combined weights + MSE+CCC",
+        "use_attention": True,
+        "load_combined": True,
+        "loss_type": "mse_ccc",
+    },
+    {
+        "name": "exp2_attn_nocombined_mse_ccc",
+        "desc": "MaxViT-T + Attention + ImageNet only + MSE+CCC",
+        "use_attention": True,
+        "load_combined": False,
+        "loss_type": "mse_ccc",
+    },
+    {
+        "name": "exp3_standard_nocombined_mse_ccc",
+        "desc": "MaxViT-T + Standard head + ImageNet only + MSE+CCC",
+        "use_attention": False,
+        "load_combined": False,
+        "loss_type": "mse_ccc",
+    },
+    {
+        "name": "exp4_attn_combined_ccc_only",
+        "desc": "MaxViT-T + Attention + Combined weights + CCC only",
+        "use_attention": True,
+        "load_combined": True,
+        "loss_type": "ccc_only",
+    },
+    {
+        "name": "exp5_attn_nocombined_ccc_only",
+        "desc": "MaxViT-T + Attention + ImageNet only + CCC only",
+        "use_attention": True,
+        "load_combined": False,
+        "loss_type": "ccc_only",
+    },
+    {
+        "name": "exp6_standard_nocombined_ccc_only",
+        "desc": "MaxViT-T + Standard head + ImageNet only + CCC only",
+        "use_attention": False,
+        "load_combined": False,
+        "loss_type": "ccc_only",
+    },
+]
+
+
+# =========================
+# Plot helpers
+# =========================
+def save_line_plot(x, ys, labels, title, xlabel, ylabel, save_path):
+    plt.figure(figsize=(10, 6))
+    for y, label in zip(ys, labels):
+        plt.plot(x, y, marker="o", linewidth=2, markersize=4, label=label)
+    plt.title(title)
+    plt.xlabel(xlabel)
+    plt.ylabel(ylabel)
+    plt.grid(True, alpha=0.3)
+    plt.legend()
+    plt.tight_layout()
+    plt.savefig(save_path, dpi=200)
+    plt.close()
+
+
+def save_bar_plot(labels, values, title, xlabel, ylabel, save_path, annotate=True):
+    plt.figure(figsize=(12, 6))
+    bars = plt.bar(labels, values)
+    plt.title(title)
+    plt.xlabel(xlabel)
+    plt.ylabel(ylabel)
+    plt.grid(True, axis="y", alpha=0.3)
+    if annotate:
+        for bar, val in zip(bars, values):
+            plt.text(
+                bar.get_x() + bar.get_width() / 2,
+                bar.get_height(),
+                f"{val:.4f}",
+                ha="center",
+                va="bottom",
+                fontsize=8,
+            )
+    plt.xticks(rotation=30, ha="right")
+    plt.tight_layout()
+    plt.savefig(save_path, dpi=200)
+    plt.close()
+
+
+# =========================
+# Dataset & transforms
+# =========================
+class CustomDataset(Dataset):
+    def __init__(self, dataframe, root_dir, transform=None, balance=False):
+        self.dataframe = dataframe
+        self.transform = transform
+        self.root_dir = root_dir
+        self.balance = balance
+
+        if self.balance:
+            self.dataframe = self.balance_dataset()
+
+    def __len__(self):
+        return len(self.dataframe)
+
+    def __getitem__(self, idx):
+        img_id = int(float(self.dataframe["number"].iloc[idx]))
+        image_path = os.path.join(self.root_dir, f"{img_id}.jpg")
+        image = Image.open(image_path)
+
+        classes = torch.tensor(self.dataframe.iloc[idx, 1], dtype=torch.int8)
+        valence = torch.tensor(self.dataframe.iloc[idx, 2], dtype=torch.float16)
+        arousal = torch.tensor(self.dataframe.iloc[idx, 3], dtype=torch.float16)
+
+        if self.transform:
+            image = self.transform(image)
+
+        return image, classes, valence, arousal
+
+    def balance_dataset(self):
+        balanced_df = self.dataframe.groupby("exp", group_keys=False).apply(
+            lambda x: x.sample(self.dataframe["exp"].value_counts().min())
+        )
+        return balanced_df
+
+
+transform = transforms.Compose(
+    [
+        transforms.RandomHorizontalFlip(0.5),
+        transforms.RandomGrayscale(0.01),
+        transforms.RandomRotation(10),
+        transforms.ColorJitter(
+            brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1
+        ),
+        transforms.RandomPerspective(distortion_scale=0.2, p=0.5),
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+        transforms.RandomErasing(
+            p=0.5, scale=(0.02, 0.2), ratio=(0.3, 3.3), value="random"
+        ),
+    ]
+)
+
+transform_valid = transforms.Compose(
+    [
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+    ]
+)
+
+
+# =========================
+# Model components
+# =========================
+class AttentivePoolingHead(nn.Module):
+    def __init__(self, feat_dim, hidden_dim, dropout=0.3):
+        super().__init__()
+        self.attn_proj = nn.Linear(feat_dim, 1)
+        self.mlp = nn.Sequential(
+            nn.LayerNorm(feat_dim),
+            nn.Dropout(dropout),
+            nn.Linear(feat_dim, hidden_dim),
+            nn.GELU(),
+            nn.Dropout(dropout),
+            nn.Linear(hidden_dim, 2, bias=False),
+        )
+
+    def forward(self, x):
+        tokens = x.flatten(2).transpose(1, 2)  # [B, HW, C]
+        weights = torch.softmax(self.attn_proj(tokens), dim=1)  # [B, HW, 1]
+        pooled = (tokens * weights).sum(dim=1)  # [B, C]
+        return torch.tanh(self.mlp(pooled))
+
+
+def build_model(use_attention, load_combined):
+    model = models.maxvit_t(weights="DEFAULT")
+    block_channels = model.classifier[3].in_features
+
+    if load_combined:
+        # Temporarily set classifier to match Combined model architecture
+        model.classifier = nn.Sequential(
+            nn.AdaptiveAvgPool2d(1),
+            nn.Flatten(),
+            nn.LayerNorm(block_channels),
+            nn.Linear(block_channels, block_channels),
+            nn.Tanh(),
+            nn.Linear(block_channels, 10, bias=False),
+        )
+        model.to(DEVICE)
+        model.load_state_dict(torch.load(COMBINED_MODEL_PATH, map_location=DEVICE))
+
+    # Now set the final head
+    if use_attention:
+        model.classifier = AttentivePoolingHead(
+            feat_dim=block_channels,
+            hidden_dim=block_channels,
+            dropout=0.3,
+        )
+    else:
+        model.classifier = nn.Sequential(
+            nn.AdaptiveAvgPool2d(1),
+            nn.Flatten(),
+            nn.LayerNorm(block_channels),
+            nn.Linear(block_channels, block_channels),
+            nn.Tanh(),
+            nn.Dropout(0.3),
+            nn.Linear(block_channels, 2, bias=False),
+        )
+
+    model.to(DEVICE)
+    return model
+
+
+# =========================
+# Loss & metrics
+# =========================
+mse_criterion_val = nn.MSELoss()
+mse_criterion_aro = nn.MSELoss()
+
+
+def CCCLoss(x, y):
+    x_mean = torch.mean(x, dim=0)
+    y_mean = torch.mean(y, dim=0)
+    x_var = torch.var(x, dim=0)
+    y_var = torch.var(y, dim=0)
+    cov_matrix = torch.matmul(
+        (x - x_mean).permute(*torch.arange(x.dim() - 1, -1, -1)), y - y_mean
+    ) / (x.size(0) - 1)
+    numerator = 2 * cov_matrix
+    denominator = x_var + y_var + torch.pow((x_mean - y_mean), 2)
+    ccc = torch.mean(numerator / denominator)
+    return -ccc
+
+
+def compute_loss(val_pred, aro_pred, val_true, aro_true, loss_type):
+    if loss_type == "mse_ccc":
+        return (
+            3 * mse_criterion_val(val_pred, val_true)
+            + 3 * mse_criterion_aro(aro_pred, aro_true)
+            + CCCLoss(val_pred, val_true)
+            + CCCLoss(aro_pred, aro_true)
+        )
+    else:  # ccc_only
+        return CCCLoss(val_pred, val_true) + CCCLoss(aro_pred, aro_true)
+
+
+def ccc_numpy_1d(x, y, eps=1e-8):
+    x = np.asarray(x).reshape(-1)
+    y = np.asarray(y).reshape(-1)
+    x_mean = x.mean()
+    y_mean = y.mean()
+    x_var = x.var()
+    y_var = y.var()
+    cov = ((x - x_mean) * (y - y_mean)).mean()
+    return (2.0 * cov) / (x_var + y_var + (x_mean - y_mean) ** 2 + eps)
+
+
+# =========================
+# Per-experiment plots
+# =========================
+def save_experiment_plots(history_df, exp_name, exp_dir):
+    epochs = history_df["epoch"].tolist()
+
+    save_line_plot(
+        epochs,
+        [history_df["train_loss"].tolist()],
+        ["Train Loss"],
+        f"{exp_name} - Train Loss",
+        "Epoch", "Loss",
+        exp_dir / "train_loss.png",
+    )
+    save_line_plot(
+        epochs,
+        [history_df["valid_loss"].tolist()],
+        ["Validation Loss"],
+        f"{exp_name} - Validation Loss",
+        "Epoch", "Loss",
+        exp_dir / "valid_loss.png",
+    )
+    save_line_plot(
+        epochs,
+        [history_df["train_loss"].tolist(), history_df["valid_loss"].tolist()],
+        ["Train Loss", "Validation Loss"],
+        f"{exp_name} - Train vs Validation Loss",
+        "Epoch", "Loss",
+        exp_dir / "train_vs_valid_loss.png",
+    )
+    save_line_plot(
+        epochs,
+        [history_df["ccc_v"].tolist()],
+        ["CCC_v"],
+        f"{exp_name} - Validation CCC_v",
+        "Epoch", "CCC",
+        exp_dir / "valid_ccc_v.png",
+    )
+    save_line_plot(
+        epochs,
+        [history_df["ccc_a"].tolist()],
+        ["CCC_a"],
+        f"{exp_name} - Validation CCC_a",
+        "Epoch", "CCC",
+        exp_dir / "valid_ccc_a.png",
+    )
+    save_line_plot(
+        epochs,
+        [history_df["ccc_mean"].tolist()],
+        ["CCC_mean"],
+        f"{exp_name} - Validation CCC_mean",
+        "Epoch", "CCC",
+        exp_dir / "valid_ccc_mean.png",
+    )
+    save_line_plot(
+        epochs,
+        [
+            history_df["ccc_v"].tolist(),
+            history_df["ccc_a"].tolist(),
+            history_df["ccc_mean"].tolist(),
+        ],
+        ["CCC_v", "CCC_a", "CCC_mean"],
+        f"{exp_name} - All CCC Curves",
+        "Epoch", "CCC",
+        exp_dir / "valid_ccc_all.png",
+    )
+    save_line_plot(
+        epochs,
+        [history_df["lr"].tolist()],
+        ["Learning Rate"],
+        f"{exp_name} - Learning Rate",
+        "Epoch", "LR",
+        exp_dir / "lr.png",
+    )
+
+
+# =========================
+# Summary plots across experiments
+# =========================
+def save_summary_plots(summary_df, save_dir):
+    names = summary_df["name"].tolist()
+
+    save_bar_plot(
+        names,
+        summary_df["best_ccc_v"].tolist(),
+        "Best CCC_v by Experiment",
+        "Experiment", "CCC_v",
+        save_dir / "summary_best_ccc_v.png",
+    )
+    save_bar_plot(
+        names,
+        summary_df["best_ccc_a"].tolist(),
+        "Best CCC_a by Experiment",
+        "Experiment", "CCC_a",
+        save_dir / "summary_best_ccc_a.png",
+    )
+    save_bar_plot(
+        names,
+        summary_df["best_ccc_mean"].tolist(),
+        "Best CCC_mean by Experiment",
+        "Experiment", "CCC_mean",
+        save_dir / "summary_best_ccc_mean.png",
+    )
+    save_bar_plot(
+        names,
+        summary_df["best_valid_loss"].tolist(),
+        "Best Validation Loss by Experiment",
+        "Experiment", "Loss",
+        save_dir / "summary_best_valid_loss.png",
+    )
+    save_bar_plot(
+        names,
+        summary_df["best_epoch"].tolist(),
+        "Best Epoch by Experiment",
+        "Experiment", "Epoch",
+        save_dir / "summary_best_epoch.png",
+    )
+
+    # Overlay CCC_mean curves from all experiments
+    plt.figure(figsize=(12, 6))
+    for _, row in summary_df.iterrows():
+        exp_dir = save_dir / row["name"]
+        hist_csv = exp_dir / "train_history.csv"
+        if hist_csv.exists():
+            h = pd.read_csv(hist_csv)
+            plt.plot(
+                h["epoch"], h["ccc_mean"],
+                marker="o", linewidth=2, markersize=3,
+                label=row["name"],
+            )
+    plt.title("Validation CCC_mean - All Experiments")
+    plt.xlabel("Epoch")
+    plt.ylabel("CCC_mean")
+    plt.grid(True, alpha=0.3)
+    plt.legend(fontsize=8)
+    plt.tight_layout()
+    plt.savefig(save_dir / "summary_ccc_mean_overlay.png", dpi=200)
+    plt.close()
+
+    # Overlay valid loss curves from all experiments
+    plt.figure(figsize=(12, 6))
+    for _, row in summary_df.iterrows():
+        exp_dir = save_dir / row["name"]
+        hist_csv = exp_dir / "train_history.csv"
+        if hist_csv.exists():
+            h = pd.read_csv(hist_csv)
+            plt.plot(
+                h["epoch"], h["valid_loss"],
+                marker="o", linewidth=2, markersize=3,
+                label=row["name"],
+            )
+    plt.title("Validation Loss - All Experiments")
+    plt.xlabel("Epoch")
+    plt.ylabel("Loss")
+    plt.grid(True, alpha=0.3)
+    plt.legend(fontsize=8)
+    plt.tight_layout()
+    plt.savefig(save_dir / "summary_valid_loss_overlay.png", dpi=200)
+    plt.close()
+
+
+# =========================
+# Training function
+# =========================
+def run_experiment(exp_config, train_loader, valid_loader):
+    name = exp_config["name"]
+    desc = exp_config["desc"]
+    loss_type = exp_config["loss_type"]
+
+    exp_dir = EXPERIMENTS_DIR / name
+    exp_dir.mkdir(parents=True, exist_ok=True)
+    exp_model_path = str(SCRIPT_DIR / f"model_{name}.pt")
+
+    print(f"\n{'='*60}")
+    print(f"EXPERIMENT: {name}")
+    print(f"  {desc}")
+    print(f"  use_attention={exp_config['use_attention']}, "
+          f"load_combined={exp_config['load_combined']}, "
+          f"loss_type={loss_type}")
+    print(f"  model_path={exp_model_path}")
+    print(f"  plots_dir={exp_dir}")
+    print(f"{'='*60}")
+
+    model = build_model(exp_config["use_attention"], exp_config["load_combined"])
+    optimizer = optim.AdamW(model.parameters(), lr=LR)
+    scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=BATCHSIZE * NUM_EPOCHS)
+    scaler = torch.cuda.amp.GradScaler()
+    best_valid_loss = float("inf")
+    best_epoch = -1
+
+    history = {
+        "epoch": [],
+        "train_loss": [],
+        "valid_loss": [],
+        "ccc_v": [],
+        "ccc_a": [],
+        "ccc_mean": [],
+        "lr": [],
+    }
+
+    for epoch in range(NUM_EPOCHS):
+        # ── Train ──────────────────────────────────────────────
+        model.train()
+        train_loss = 0.0
+        current_lr = optimizer.param_groups[0]["lr"]
+
+        for images, _, val_true, aro_true in tqdm(
+            train_loader, desc=f"[{name}] Epoch {epoch+1} train"
+        ):
+            images = images.to(DEVICE)
+            val_true = val_true.to(DEVICE)
+            aro_true = aro_true.to(DEVICE)
+
+            optimizer.zero_grad()
+            with torch.autocast(device_type="cuda", dtype=torch.float16):
+                outputs = model(images)
+                val_pred = outputs[:, 0]
+                aro_pred = outputs[:, 1]
+                loss = compute_loss(val_pred, aro_pred, val_true, aro_true, loss_type)
+
+            train_loss += loss.item()
+            scaler.scale(loss).backward()
+            scaler.step(optimizer)
+            scaler.update()
+            scheduler.step()
+
+        train_loss /= max(1, len(train_loader))
+
+        # ── Validate ───────────────────────────────────────────
+        model.eval()
+        valid_loss_sum = 0.0
+        valid_val_true = []
+        valid_val_pred = []
+        valid_aro_true = []
+        valid_aro_pred = []
+
+        with torch.no_grad():
+            for images, _, val_true, aro_true in valid_loader:
+                images = images.to(DEVICE)
+                val_true = val_true.to(DEVICE)
+                aro_true = aro_true.to(DEVICE)
+
+                with torch.autocast(device_type="cuda", dtype=torch.float16):
+                    outputs = model(images)
+                    val_pred = outputs[:, 0]
+                    aro_pred = outputs[:, 1]
+                    loss = compute_loss(
+                        val_pred, aro_pred, val_true, aro_true, loss_type
+                    )
+                    valid_loss_sum += loss.item()
+
+                valid_val_true.extend(val_true.detach().cpu().float().numpy())
+                valid_val_pred.extend(val_pred.detach().cpu().float().numpy())
+                valid_aro_true.extend(aro_true.detach().cpu().float().numpy())
+                valid_aro_pred.extend(aro_pred.detach().cpu().float().numpy())
+
+        valid_loss = valid_loss_sum / max(1, len(valid_loader))
+        valid_ccc_v = ccc_numpy_1d(valid_val_pred, valid_val_true)
+        valid_ccc_a = ccc_numpy_1d(valid_aro_pred, valid_aro_true)
+        valid_ccc_mean = (valid_ccc_v + valid_ccc_a) / 2.0
+
+        history["epoch"].append(epoch + 1)
+        history["train_loss"].append(train_loss)
+        history["valid_loss"].append(valid_loss)
+        history["ccc_v"].append(valid_ccc_v)
+        history["ccc_a"].append(valid_ccc_a)
+        history["ccc_mean"].append(valid_ccc_mean)
+        history["lr"].append(current_lr)
+
+        print(
+            f"[{name}] Epoch [{epoch+1}/{NUM_EPOCHS}] - "
+            f"Train Loss: {train_loss:.4f}, "
+            f"Valid Loss: {valid_loss:.4f}, "
+            f"CCC_v: {valid_ccc_v:.4f}, "
+            f"CCC_a: {valid_ccc_a:.4f}, "
+            f"CCC_mean: {valid_ccc_mean:.4f}, "
+            f"LR: {current_lr:.8f}"
+        )
+
+        if valid_loss < best_valid_loss:
+            best_valid_loss = valid_loss
+            best_epoch = epoch + 1
+            print(f"  -> Saving best model at epoch {best_epoch}")
+            torch.save(model.state_dict(), exp_model_path)
+
+    # ── Save CSV & plots ───────────────────────────────────────
+    history_df = pd.DataFrame(history)
+    history_df.to_csv(exp_dir / "train_history.csv", index=False)
+    save_experiment_plots(history_df, name, exp_dir)
+
+    # Print best epoch info
+    best_idx = best_epoch - 1
+    print(f"\n=== [{name}] Best Epoch Metrics ===")
+    print(f"Best epoch:     {best_epoch}")
+    print(f"Train Loss:     {history_df['train_loss'][best_idx]:.4f}")
+    print(f"Valid Loss:     {history_df['valid_loss'][best_idx]:.4f}")
+    print(f"Valid CCC_v:    {history_df['ccc_v'][best_idx]:.4f}")
+    print(f"Valid CCC_a:    {history_df['ccc_a'][best_idx]:.4f}")
+    print(f"Valid CCC_mean: {history_df['ccc_mean'][best_idx]:.4f}")
+
+    return {
+        "name": name,
+        "desc": desc,
+        "use_attention": exp_config["use_attention"],
+        "load_combined": exp_config["load_combined"],
+        "loss_type": exp_config["loss_type"],
+        "best_epoch": best_epoch,
+        "best_valid_loss": float(history_df["valid_loss"][best_idx]),
+        "best_ccc_v": float(history_df["ccc_v"][best_idx]),
+        "best_ccc_a": float(history_df["ccc_a"][best_idx]),
+        "best_ccc_mean": float(history_df["ccc_mean"][best_idx]),
+    }
+
+
+# =========================
+# Main
+# =========================
+print("\n--- Creating datasets (shared across all experiments) ---")
+
+train_dataset = CustomDataset(
+    dataframe=train_annotations_df,
+    root_dir=IMAGE_FOLDER,
+    transform=transform,
+    balance=True,
+)
+valid_dataset = CustomDataset(
+    dataframe=valid_annotations_df,
+    root_dir=IMAGE_FOLDER_TEST,
+    transform=transform_valid,
+    balance=False,
+)
+train_loader = DataLoader(
+    train_dataset, batch_size=BATCHSIZE, shuffle=True, num_workers=NUM_WORKERS
+)
+valid_loader = DataLoader(
+    valid_dataset, batch_size=BATCHSIZE, shuffle=False, num_workers=NUM_WORKERS
+)
+
+print(f"Train dataset size (balanced): {len(train_dataset)}")
+print(f"Valid dataset size:            {len(valid_dataset)}")
+
+all_results = []
+
+for exp_config in EXPERIMENTS:
+    result = run_experiment(exp_config, train_loader, valid_loader)
+    all_results.append(result)
+
+# =========================
+# Summary
+# =========================
+summary_df = pd.DataFrame(all_results)
+summary_df.to_csv(EXPERIMENTS_DIR / "all_experiments_summary.csv", index=False)
+save_summary_plots(summary_df, EXPERIMENTS_DIR)
+
+print("\n" + "=" * 60)
+print("ALL EXPERIMENTS COMPLETE")
+print("=" * 60)
+print(summary_df.to_string(index=False))
+print(f"\nSummary CSV: {EXPERIMENTS_DIR / 'all_experiments_summary.csv'}")
+print(f"All plots:   {EXPERIMENTS_DIR}")
diff --git a/CAGE_expression_inference-apvit/models/AffectNet8_Maxvit_VA/train2_old2.py b/CAGE_expression_inference-apvit/models/AffectNet8_Maxvit_VA/train2_old2.py
new file mode 100644
index 0000000000000000000000000000000000000000..e5333d853706c01b267a6aadc5d257b6f7524cd5
--- /dev/null
+++ b/CAGE_expression_inference-apvit/models/AffectNet8_Maxvit_VA/train2_old2.py
@@ -0,0 +1,819 @@
+"""
+train2.py — Ablation study: 6 experiments (3 models × 2 losses)
+
+Pipeline IDENTICAL to train.py (train on full train_set, validate on official
+val_set, save best model by lowest val loss, 20 epochs).
+Only the model architecture and loss function differ across experiments.
+
+Models:
+  1) MaxViT-T + AttentivePoolingHead + Combined weights   ("Attention + Combined")
+  2) MaxViT-T + AttentivePoolingHead + ImageNet only       ("Attention + ImageNet")
+  3) MaxViT-T + Standard head        + ImageNet only       ("Standard + ImageNet")
+
+Losses:
+  A) 3*MSE_val + 3*MSE_aro + CCC_val + CCC_aro             ("MSE+CCC")
+  B) CCC_val + CCC_aro                                      ("CCC only")
+
+Output per experiment:
+  experiments/<exp_name>/
+    ├── model.pt
+    ├── train_history.csv
+    └── *.png (10 plots)
+
+Summary:
+  experiments/
+    ├── all_experiments_summary.csv
+    └── summary_*.png
+"""
+
+import pandas as pd
+import os
+import numpy as np
+import torch
+import torchvision.transforms as transforms
+import torchvision.models as models
+from torch.utils.data import DataLoader, Dataset
+import torch.nn as nn
+import torch.optim as optim
+from PIL import Image
+from torch.optim import lr_scheduler
+from tqdm import tqdm
+from pathlib import Path
+
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+
+
+# ============================================================================
+# Paths (identical to train.py)
+# ============================================================================
+SCRIPT_DIR = Path(__file__).resolve().parent
+EXPERIMENTS_DIR = SCRIPT_DIR / "experiments"
+EXPERIMENTS_DIR.mkdir(parents=True, exist_ok=True)
+
+
+def resolve_images_dir(split_name, explicit_env_var, data_root, extract_root):
+    explicit = os.getenv(explicit_env_var, "").strip()
+    candidates = []
+    if explicit:
+        candidates.append(Path(explicit))
+
+    candidates.append(Path(data_root) / f"{split_name}_set" / "images")
+    candidates.append(Path(extract_root) / f"{split_name}_extracted" / "images")
+
+    extract_split_root = Path(extract_root) / f"{split_name}_extracted"
+    if extract_split_root.exists():
+        for p in extract_split_root.rglob("images"):
+            if p.is_dir():
+                candidates.append(p)
+
+    for p in candidates:
+        if p.exists() and p.is_dir():
+            return str(p)
+
+    tried = "\n".join([str(p) for p in candidates])
+    raise FileNotFoundError(
+        f"Could not find images folder for split='{split_name}'. Tried:\n{tried}"
+    )
+
+
+# ============================================================================
+# Data paths (identical to train.py)
+# ============================================================================
+DATA_ROOT = os.getenv("AFFECTNET_ROOT", "/workspace/data_affectnet/AffectNet")
+EXTRACT_ROOT = os.getenv("AFFECTNET_EXTRACT_ROOT", f"{DATA_ROOT}/extracted")
+ANNO_ROOT = os.getenv("AFFECTNET_ANNO_ROOT", "../../affectnet_annotations")
+IMAGE_FOLDER = resolve_images_dir("train", "AFFECTNET_TRAIN_IMAGES", DATA_ROOT, EXTRACT_ROOT)
+IMAGE_FOLDER_TEST = resolve_images_dir("val", "AFFECTNET_VAL_IMAGES", DATA_ROOT, EXTRACT_ROOT)
+train_annotations_path = os.getenv(
+    "AFFECTNET_TRAIN_ANNO", f"{ANNO_ROOT}/train_set_annotation_without_lnd.csv"
+)
+valid_annotations_path = os.getenv(
+    "AFFECTNET_VAL_ANNO", f"{ANNO_ROOT}/val_set_annotation_without_lnd.csv"
+)
+train_annotations_df = pd.read_csv(train_annotations_path)
+valid_annotations_df = pd.read_csv(valid_annotations_path)
+
+
+# ============================================================================
+# Parameters (identical to train.py)
+# ============================================================================
+BATCHSIZE = int(os.getenv("BATCHSIZE", "64"))
+NUM_EPOCHS = 20
+LR = 4e-5
+NUM_WORKERS = int(os.getenv("NUM_WORKERS", "0"))
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+COMBINED_MODEL_PATH = os.getenv(
+    "COMBINED_MODEL_PATH",
+    str((SCRIPT_DIR / "../AffectNet8_Maxvit_Combined/model.pt").resolve()),
+)
+
+print("DATA_ROOT              =", DATA_ROOT)
+print("EXTRACT_ROOT           =", EXTRACT_ROOT)
+print("AFFECTNET_TRAIN_IMAGES =", IMAGE_FOLDER)
+print("AFFECTNET_VAL_IMAGES   =", IMAGE_FOLDER_TEST)
+print("COMBINED_MODEL_PATH    =", COMBINED_MODEL_PATH)
+print("EXPERIMENTS_DIR        =", EXPERIMENTS_DIR)
+print("Train set size         =", len(train_annotations_df))
+print("Val set size           =", len(valid_annotations_df))
+print("DEVICE                 =", DEVICE)
+print("BATCH_SIZE             =", BATCHSIZE)
+print("NUM_EPOCHS             =", NUM_EPOCHS)
+print("LR                     =", LR)
+
+
+# ============================================================================
+# 6 Experiment configurations (3 models × 2 losses)
+# ============================================================================
+EXPERIMENTS = [
+    # ── MSE + CCC loss ──
+    {
+        "name": "exp1_attn_combined_mse_ccc",
+        "desc": "MaxViT-T + AttentivePoolingHead + Combined weights + MSE+CCC",
+        "use_attention": True,
+        "load_combined": True,
+        "loss_type": "mse_ccc",
+    },
+    {
+        "name": "exp2_attn_imagenet_mse_ccc",
+        "desc": "MaxViT-T + AttentivePoolingHead + ImageNet only + MSE+CCC",
+        "use_attention": True,
+        "load_combined": False,
+        "loss_type": "mse_ccc",
+    },
+    {
+        "name": "exp3_standard_imagenet_mse_ccc",
+        "desc": "MaxViT-T + Standard head + ImageNet only + MSE+CCC",
+        "use_attention": False,
+        "load_combined": False,
+        "loss_type": "mse_ccc",
+    },
+    # ── CCC only loss ──
+    {
+        "name": "exp4_attn_combined_ccc_only",
+        "desc": "MaxViT-T + AttentivePoolingHead + Combined weights + CCC only",
+        "use_attention": True,
+        "load_combined": True,
+        "loss_type": "ccc_only",
+    },
+    {
+        "name": "exp5_attn_imagenet_ccc_only",
+        "desc": "MaxViT-T + AttentivePoolingHead + ImageNet only + CCC only",
+        "use_attention": True,
+        "load_combined": False,
+        "loss_type": "ccc_only",
+    },
+    {
+        "name": "exp6_standard_imagenet_ccc_only",
+        "desc": "MaxViT-T + Standard head + ImageNet only + CCC only",
+        "use_attention": False,
+        "load_combined": False,
+        "loss_type": "ccc_only",
+    },
+]
+
+
+# ============================================================================
+# Plot helpers (from train.py)
+# ============================================================================
+def save_line_plot(x, ys, labels, title, xlabel, ylabel, save_path):
+    plt.figure(figsize=(10, 6))
+    for y, label in zip(ys, labels):
+        plt.plot(x, y, marker="o", linewidth=2, markersize=4, label=label)
+    plt.title(title)
+    plt.xlabel(xlabel)
+    plt.ylabel(ylabel)
+    plt.grid(True, alpha=0.3)
+    plt.legend()
+    plt.tight_layout()
+    plt.savefig(save_path, dpi=200)
+    plt.close()
+
+
+def save_bar_plot(labels, values, title, xlabel, ylabel, save_path, annotate=True):
+    plt.figure(figsize=(12, 6))
+    bars = plt.bar(labels, values)
+    plt.title(title)
+    plt.xlabel(xlabel)
+    plt.ylabel(ylabel)
+    plt.grid(True, axis="y", alpha=0.3)
+    if annotate:
+        for bar, val in zip(bars, values):
+            plt.text(
+                bar.get_x() + bar.get_width() / 2,
+                bar.get_height(),
+                f"{val:.4f}",
+                ha="center",
+                va="bottom",
+                fontsize=8,
+            )
+    plt.xticks(rotation=30, ha="right")
+    plt.tight_layout()
+    plt.savefig(save_path, dpi=200)
+    plt.close()
+
+
+def save_scatter(x, y, title, xlabel, ylabel, save_path, annotate_labels=None):
+    plt.figure(figsize=(10, 6))
+    plt.scatter(x, y)
+    if annotate_labels is not None:
+        for xi, yi, txt in zip(x, y, annotate_labels):
+            plt.annotate(str(txt), (xi, yi), fontsize=9)
+    plt.title(title)
+    plt.xlabel(xlabel)
+    plt.ylabel(ylabel)
+    plt.grid(True, alpha=0.3)
+    plt.tight_layout()
+    plt.savefig(save_path, dpi=200)
+    plt.close()
+
+
+# ============================================================================
+# Dataset & transforms (identical to train.py)
+# ============================================================================
+class CustomDataset(Dataset):
+    def __init__(self, dataframe, root_dir, transform=None, balance=False):
+        self.dataframe = dataframe
+        self.transform = transform
+        self.root_dir = root_dir
+        self.balance = balance
+
+        if self.balance:
+            self.dataframe = self.balance_dataset()
+
+    def __len__(self):
+        return len(self.dataframe)
+
+    def __getitem__(self, idx):
+        img_id = int(float(self.dataframe["number"].iloc[idx]))
+        image_path = os.path.join(self.root_dir, f"{img_id}.jpg")
+        image = Image.open(image_path)
+
+        classes = torch.tensor(self.dataframe.iloc[idx, 1], dtype=torch.int8)
+        valence = torch.tensor(self.dataframe.iloc[idx, 2], dtype=torch.float16)
+        arousal = torch.tensor(self.dataframe.iloc[idx, 3], dtype=torch.float16)
+
+        if self.transform:
+            image = self.transform(image)
+
+        return image, classes, valence, arousal
+
+    def balance_dataset(self):
+        balanced_df = self.dataframe.groupby("exp", group_keys=False).apply(
+            lambda x: x.sample(self.dataframe["exp"].value_counts().min())
+        )
+        return balanced_df
+
+
+transform = transforms.Compose(
+    [
+        transforms.RandomHorizontalFlip(0.5),
+        transforms.RandomGrayscale(0.01),
+        transforms.RandomRotation(10),
+        transforms.ColorJitter(
+            brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1
+        ),
+        transforms.RandomPerspective(
+            distortion_scale=0.2, p=0.5
+        ),
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+        transforms.RandomErasing(
+            p=0.5, scale=(0.02, 0.2), ratio=(0.3, 3.3), value="random"
+        ),
+    ]
+)
+
+transform_valid = transforms.Compose(
+    [
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+    ]
+)
+
+
+# ============================================================================
+# Model components (from train.py)
+# ============================================================================
+class AttentivePoolingHead(nn.Module):
+    """Attention-based pooling head — identical to train.py."""
+    def __init__(self, feat_dim, hidden_dim, dropout=0.3):
+        super().__init__()
+        self.attn_proj = nn.Linear(feat_dim, 1)
+        self.mlp = nn.Sequential(
+            nn.LayerNorm(feat_dim),
+            nn.Dropout(dropout),
+            nn.Linear(feat_dim, hidden_dim),
+            nn.GELU(),
+            nn.Dropout(dropout),
+            nn.Linear(hidden_dim, 2, bias=False),
+        )
+
+    def forward(self, x):
+        # x: [B, C, H, W] from MaxViT features before pooling
+        tokens = x.flatten(2).transpose(1, 2)  # [B, HW, C]
+        weights = torch.softmax(self.attn_proj(tokens), dim=1)  # [B, HW, 1]
+        pooled = (tokens * weights).sum(dim=1)  # [B, C]
+        return torch.tanh(self.mlp(pooled))
+
+
+def build_model(use_attention, load_combined):
+    """
+    Build MaxViT-T model with specified head and weight initialization.
+
+    Model 1 (use_attention=True,  load_combined=True):
+        MaxViT-T + AttentivePoolingHead + Combined weights
+        = identical to train.py build_model()
+
+    Model 2 (use_attention=True,  load_combined=False):
+        MaxViT-T + AttentivePoolingHead + ImageNet only
+
+    Model 3 (use_attention=False, load_combined=False):
+        MaxViT-T + Standard head + ImageNet only
+    """
+    model = models.maxvit_t(weights="DEFAULT")
+    block_channels = model.classifier[3].in_features
+
+    if load_combined:
+        # Step 1: set 10-class classifier to match Combined model architecture
+        model.classifier = nn.Sequential(
+            nn.AdaptiveAvgPool2d(1),
+            nn.Flatten(),
+            nn.LayerNorm(block_channels),
+            nn.Linear(block_channels, block_channels),
+            nn.Tanh(),
+            nn.Linear(block_channels, 10, bias=False),
+        )
+        model.to(DEVICE)
+        model.load_state_dict(torch.load(COMBINED_MODEL_PATH, map_location=DEVICE))
+
+    # Step 2: set final VA regression head
+    if use_attention:
+        model.classifier = AttentivePoolingHead(
+            feat_dim=block_channels,
+            hidden_dim=block_channels,
+            dropout=0.3,
+        )
+    else:
+        # Standard head — same architecture as original CAGE model
+        model.classifier = nn.Sequential(
+            nn.AdaptiveAvgPool2d(1),
+            nn.Flatten(),
+            nn.LayerNorm(block_channels),
+            nn.Linear(block_channels, block_channels),
+            nn.Tanh(),
+            nn.Dropout(0.3),
+            nn.Linear(block_channels, 2, bias=False),
+        )
+
+    model.to(DEVICE)
+    return model
+
+
+# ============================================================================
+# Loss functions & metrics (from train.py)
+# ============================================================================
+val_loss = nn.MSELoss()
+aro_loss = nn.MSELoss()
+
+
+def CCCLoss(x, y):
+    """CCC-based loss (returns -CCC). Identical to train.py."""
+    x_mean = torch.mean(x, dim=0)
+    y_mean = torch.mean(y, dim=0)
+    x_var = torch.var(x, dim=0)
+    y_var = torch.var(y, dim=0)
+    cov_matrix = torch.matmul(
+        (x - x_mean).permute(*torch.arange(x.dim() - 1, -1, -1)), y - y_mean
+    ) / (x.size(0) - 1)
+    numerator = 2 * cov_matrix
+    denominator = x_var + y_var + torch.pow((x_mean - y_mean), 2)
+    ccc = torch.mean(numerator / denominator)
+    return -ccc
+
+
+def compute_loss(val_pred, aro_pred, val_true, aro_true, loss_type):
+    """Compute training/validation loss based on experiment type."""
+    if loss_type == "mse_ccc":
+        return (
+            3 * val_loss(val_pred, val_true)
+            + 3 * aro_loss(aro_pred, aro_true)
+            + CCCLoss(val_pred, val_true)
+            + CCCLoss(aro_pred, aro_true)
+        )
+    else:  # ccc_only
+        return (
+            CCCLoss(val_pred, val_true)
+            + CCCLoss(aro_pred, aro_true)
+        )
+
+
+def ccc_numpy_1d(x, y, eps=1e-8):
+    """CCC metric (numpy). Identical to train.py."""
+    x = np.asarray(x).reshape(-1)
+    y = np.asarray(y).reshape(-1)
+    x_mean = x.mean()
+    y_mean = y.mean()
+    x_var = x.var()
+    y_var = y.var()
+    cov = ((x - x_mean) * (y - y_mean)).mean()
+    return (2.0 * cov) / (x_var + y_var + (x_mean - y_mean) ** 2 + eps)
+
+
+# ============================================================================
+# Per-experiment plots (same plots as train.py)
+# ============================================================================
+def save_experiment_plots(history_df, exp_name, exp_dir):
+    """Save the same set of plots as train.py for each experiment."""
+    epochs = history_df["epoch"].tolist()
+
+    # 1. Train Loss
+    save_line_plot(
+        epochs, [history_df["train_loss"].tolist()], ["Train Loss"],
+        f"{exp_name} — Train Loss", "Epoch", "Loss",
+        exp_dir / "train_loss.png",
+    )
+    # 2. Validation Loss
+    save_line_plot(
+        epochs, [history_df["valid_loss"].tolist()], ["Validation Loss"],
+        f"{exp_name} — Validation Loss", "Epoch", "Loss",
+        exp_dir / "valid_loss.png",
+    )
+    # 3. Train vs Validation Loss
+    save_line_plot(
+        epochs,
+        [history_df["train_loss"].tolist(), history_df["valid_loss"].tolist()],
+        ["Train Loss", "Validation Loss"],
+        f"{exp_name} — Train vs Validation Loss", "Epoch", "Loss",
+        exp_dir / "train_vs_valid_loss.png",
+    )
+    # 4. CCC_v
+    save_line_plot(
+        epochs, [history_df["ccc_v"].tolist()], ["CCC_v"],
+        f"{exp_name} — Validation CCC_v", "Epoch", "CCC",
+        exp_dir / "valid_ccc_v.png",
+    )
+    # 5. CCC_a
+    save_line_plot(
+        epochs, [history_df["ccc_a"].tolist()], ["CCC_a"],
+        f"{exp_name} — Validation CCC_a", "Epoch", "CCC",
+        exp_dir / "valid_ccc_a.png",
+    )
+    # 6. CCC_mean
+    save_line_plot(
+        epochs, [history_df["ccc_mean"].tolist()], ["CCC_mean"],
+        f"{exp_name} — Validation CCC_mean", "Epoch", "CCC",
+        exp_dir / "valid_ccc_mean.png",
+    )
+    # 7. All CCC on one plot
+    save_line_plot(
+        epochs,
+        [
+            history_df["ccc_v"].tolist(),
+            history_df["ccc_a"].tolist(),
+            history_df["ccc_mean"].tolist(),
+        ],
+        ["CCC_v", "CCC_a", "CCC_mean"],
+        f"{exp_name} — All CCC Curves", "Epoch", "CCC",
+        exp_dir / "valid_ccc_all.png",
+    )
+    # 8. Learning Rate
+    save_line_plot(
+        epochs, [history_df["lr"].tolist()], ["Learning Rate"],
+        f"{exp_name} — Learning Rate", "Epoch", "LR",
+        exp_dir / "lr.png",
+    )
+    # 9. Train Loss vs Valid Loss scatter
+    save_scatter(
+        history_df["train_loss"].tolist(),
+        history_df["valid_loss"].tolist(),
+        f"{exp_name} — Train Loss vs Valid Loss",
+        "Train Loss", "Validation Loss",
+        exp_dir / "train_vs_valid_scatter.png",
+        annotate_labels=epochs,
+    )
+    # 10. CCC_v vs CCC_a scatter
+    save_scatter(
+        history_df["ccc_v"].tolist(),
+        history_df["ccc_a"].tolist(),
+        f"{exp_name} — CCC_v vs CCC_a",
+        "CCC_v", "CCC_a",
+        exp_dir / "ccc_v_vs_ccc_a_scatter.png",
+        annotate_labels=epochs,
+    )
+
+
+# ============================================================================
+# Summary plots across all 6 experiments
+# ============================================================================
+def save_summary_plots(summary_df, save_dir):
+    """Save comparison plots across all 6 experiments."""
+    names = summary_df["name"].tolist()
+
+    # Bar: Best CCC_v
+    save_bar_plot(
+        names, summary_df["best_ccc_v"].tolist(),
+        "Best CCC_v by Experiment", "Experiment", "CCC_v",
+        save_dir / "summary_best_ccc_v.png",
+    )
+    # Bar: Best CCC_a
+    save_bar_plot(
+        names, summary_df["best_ccc_a"].tolist(),
+        "Best CCC_a by Experiment", "Experiment", "CCC_a",
+        save_dir / "summary_best_ccc_a.png",
+    )
+    # Bar: Best CCC_mean
+    save_bar_plot(
+        names, summary_df["best_ccc_mean"].tolist(),
+        "Best CCC_mean by Experiment", "Experiment", "CCC_mean",
+        save_dir / "summary_best_ccc_mean.png",
+    )
+    # Bar: Best Validation Loss
+    save_bar_plot(
+        names, summary_df["best_valid_loss"].tolist(),
+        "Best Validation Loss by Experiment", "Experiment", "Loss",
+        save_dir / "summary_best_valid_loss.png",
+    )
+    # Bar: Best Epoch
+    save_bar_plot(
+        names, summary_df["best_epoch"].tolist(),
+        "Best Epoch by Experiment", "Experiment", "Epoch",
+        save_dir / "summary_best_epoch.png",
+    )
+
+    # Overlay: CCC_mean curves from all experiments
+    plt.figure(figsize=(12, 6))
+    for _, row in summary_df.iterrows():
+        hist_csv = save_dir / row["name"] / "train_history.csv"
+        if hist_csv.exists():
+            h = pd.read_csv(hist_csv)
+            plt.plot(
+                h["epoch"], h["ccc_mean"],
+                marker="o", linewidth=2, markersize=3,
+                label=row["name"],
+            )
+    plt.title("Validation CCC_mean — All Experiments")
+    plt.xlabel("Epoch")
+    plt.ylabel("CCC_mean")
+    plt.grid(True, alpha=0.3)
+    plt.legend(fontsize=8)
+    plt.tight_layout()
+    plt.savefig(save_dir / "summary_ccc_mean_overlay.png", dpi=200)
+    plt.close()
+
+    # Overlay: Validation Loss curves from all experiments
+    plt.figure(figsize=(12, 6))
+    for _, row in summary_df.iterrows():
+        hist_csv = save_dir / row["name"] / "train_history.csv"
+        if hist_csv.exists():
+            h = pd.read_csv(hist_csv)
+            plt.plot(
+                h["epoch"], h["valid_loss"],
+                marker="o", linewidth=2, markersize=3,
+                label=row["name"],
+            )
+    plt.title("Validation Loss — All Experiments")
+    plt.xlabel("Epoch")
+    plt.ylabel("Loss")
+    plt.grid(True, alpha=0.3)
+    plt.legend(fontsize=8)
+    plt.tight_layout()
+    plt.savefig(save_dir / "summary_valid_loss_overlay.png", dpi=200)
+    plt.close()
+
+    # Overlay: Train Loss curves from all experiments
+    plt.figure(figsize=(12, 6))
+    for _, row in summary_df.iterrows():
+        hist_csv = save_dir / row["name"] / "train_history.csv"
+        if hist_csv.exists():
+            h = pd.read_csv(hist_csv)
+            plt.plot(
+                h["epoch"], h["train_loss"],
+                marker="o", linewidth=2, markersize=3,
+                label=row["name"],
+            )
+    plt.title("Train Loss — All Experiments")
+    plt.xlabel("Epoch")
+    plt.ylabel("Loss")
+    plt.grid(True, alpha=0.3)
+    plt.legend(fontsize=8)
+    plt.tight_layout()
+    plt.savefig(save_dir / "summary_train_loss_overlay.png", dpi=200)
+    plt.close()
+
+
+# ============================================================================
+# Training function — mirrors train.py's training loop EXACTLY
+# ============================================================================
+def run_experiment(exp_config, train_loader, valid_loader):
+    """
+    Run one experiment. Training loop structure is identical to train.py:
+    - AdamW optimizer, CosineAnnealingLR(T_max=BATCHSIZE*NUM_EPOCHS)
+    - AMP GradScaler + autocast
+    - l2_reg/l1_reg computed per batch (not added to loss, kept for parity)
+    - scheduler.step() called per batch (inside autocast, same as train.py)
+    - Best model selected by minimum validation loss
+    """
+    name = exp_config["name"]
+    desc = exp_config["desc"]
+    loss_type = exp_config["loss_type"]
+
+    exp_dir = EXPERIMENTS_DIR / name
+    exp_dir.mkdir(parents=True, exist_ok=True)
+    exp_model_path = str(exp_dir / "model.pt")
+
+    print(f"\n{'='*70}")
+    print(f"EXPERIMENT: {name}")
+    print(f"  {desc}")
+    print(f"  use_attention={exp_config['use_attention']}, "
+          f"load_combined={exp_config['load_combined']}, "
+          f"loss_type={loss_type}")
+    print(f"  output: {exp_dir}")
+    print(f"{'='*70}")
+
+    # Build model
+    model = build_model(exp_config["use_attention"], exp_config["load_combined"])
+
+    # Optimizer, scheduler, scaler — identical to train.py
+    optimizer = optim.AdamW(model.parameters(), lr=LR)
+    scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=BATCHSIZE * NUM_EPOCHS)
+    scaler = torch.cuda.amp.GradScaler()
+
+    best_valid_loss = float("inf")
+    best_epoch = -1
+    l2_lambda = 0.00001  # kept for parity with train.py (not added to loss)
+    l1_lambda = 0.00001
+
+    history = {
+        "epoch": [],
+        "train_loss": [],
+        "valid_loss": [],
+        "ccc_v": [],
+        "ccc_a": [],
+        "ccc_mean": [],
+        "lr": [],
+    }
+
+    for epoch in range(NUM_EPOCHS):
+        # ── Train (identical structure to train.py) ──────────────────────
+        model.train()
+        train_loss = 0.0
+        current_lr = optimizer.param_groups[0]["lr"]
+
+        for images, _, val_true, aro_true in tqdm(
+            train_loader, desc=f"[{name}] Epoch {epoch+1}/{NUM_EPOCHS}"
+        ):
+            images, val_true, aro_true = (
+                images.to(DEVICE), val_true.to(DEVICE), aro_true.to(DEVICE)
+            )
+            optimizer.zero_grad()
+            l2_reg = 0
+            l1_reg = 0
+            with torch.autocast(device_type="cuda", dtype=torch.float16):
+                outputs = model(images)
+                val_pred = outputs[:, 0]
+                aro_pred = outputs[:, 1]
+                for param in model.parameters():
+                    l2_reg += torch.norm(param, 2)
+                    l1_reg += torch.norm(param, 1)
+                loss = compute_loss(val_pred, aro_pred, val_true, aro_true, loss_type)
+                train_loss += loss.item()
+                scaler.scale(loss).backward()
+                scaler.step(optimizer)
+                scaler.update()
+                scheduler.step()
+
+        train_loss /= max(1, len(train_loader))
+
+        # ── Validate (identical structure to train.py) ───────────────────
+        model.eval()
+        valid_loss_sum = 0.0
+        valid_val_true = []
+        valid_val_pred = []
+        valid_aro_true = []
+        valid_aro_pred = []
+
+        with torch.no_grad():
+            for images, _, val_true, aro_true in valid_loader:
+                images, val_true, aro_true = (
+                    images.to(DEVICE), val_true.to(DEVICE), aro_true.to(DEVICE)
+                )
+                with torch.autocast(device_type="cuda", dtype=torch.float16):
+                    outputs = model(images)
+                    val_pred = outputs[:, 0]
+                    aro_pred = outputs[:, 1]
+                    loss = compute_loss(
+                        val_pred, aro_pred, val_true, aro_true, loss_type
+                    )
+                    valid_loss_sum += loss.item()
+
+                valid_val_true.extend(val_true.detach().cpu().float().numpy())
+                valid_val_pred.extend(val_pred.detach().cpu().float().numpy())
+                valid_aro_true.extend(aro_true.detach().cpu().float().numpy())
+                valid_aro_pred.extend(aro_pred.detach().cpu().float().numpy())
+
+        valid_loss_avg = valid_loss_sum / max(1, len(valid_loader))
+        valid_ccc_v = ccc_numpy_1d(valid_val_pred, valid_val_true)
+        valid_ccc_a = ccc_numpy_1d(valid_aro_pred, valid_aro_true)
+        valid_ccc_mean = (valid_ccc_v + valid_ccc_a) / 2.0
+
+        history["epoch"].append(epoch + 1)
+        history["train_loss"].append(train_loss)
+        history["valid_loss"].append(valid_loss_avg)
+        history["ccc_v"].append(valid_ccc_v)
+        history["ccc_a"].append(valid_ccc_a)
+        history["ccc_mean"].append(valid_ccc_mean)
+        history["lr"].append(current_lr)
+
+        print(
+            f"[{name}] Epoch [{epoch+1}/{NUM_EPOCHS}] - "
+            f"Training Loss: {train_loss:.4f}, "
+            f"Validation Loss: {valid_loss_avg:.4f}, "
+            f"CCC_v: {valid_ccc_v:.4f}, "
+            f"CCC_a: {valid_ccc_a:.4f}, "
+            f"CCC_mean: {valid_ccc_mean:.4f}, "
+            f"Learning Rate: {current_lr:.8f}"
+        )
+
+        if valid_loss_avg < best_valid_loss:
+            best_valid_loss = valid_loss_avg
+            best_epoch = epoch + 1
+            print(f"  -> Saving best model at epoch {best_epoch}")
+            torch.save(model.state_dict(), exp_model_path)
+
+    # ── Save CSV + plots ─────────────────────────────────────────────────
+    history_df = pd.DataFrame(history)
+    history_df.to_csv(exp_dir / "train_history.csv", index=False)
+    save_experiment_plots(history_df, name, exp_dir)
+
+    # Print best epoch metrics
+    best_idx = best_epoch - 1
+    print(f"\n=== [{name}] Best Epoch Metrics ===")
+    print(f"Best epoch:     {best_epoch}")
+    print(f"Train Loss:     {history_df['train_loss'][best_idx]:.4f}")
+    print(f"Valid Loss:     {history_df['valid_loss'][best_idx]:.4f}")
+    print(f"Valid CCC_v:    {history_df['ccc_v'][best_idx]:.4f}")
+    print(f"Valid CCC_a:    {history_df['ccc_a'][best_idx]:.4f}")
+    print(f"Valid CCC_mean: {history_df['ccc_mean'][best_idx]:.4f}")
+
+    return {
+        "name": name,
+        "desc": desc,
+        "use_attention": exp_config["use_attention"],
+        "load_combined": exp_config["load_combined"],
+        "loss_type": exp_config["loss_type"],
+        "best_epoch": best_epoch,
+        "best_valid_loss": float(history_df["valid_loss"][best_idx]),
+        "best_ccc_v": float(history_df["ccc_v"][best_idx]),
+        "best_ccc_a": float(history_df["ccc_a"][best_idx]),
+        "best_ccc_mean": float(history_df["ccc_mean"][best_idx]),
+    }
+
+
+# ============================================================================
+# Main
+# ============================================================================
+print("\n--- Creating datasets (shared across all experiments) ---")
+
+train_dataset = CustomDataset(
+    dataframe=train_annotations_df,
+    root_dir=IMAGE_FOLDER,
+    transform=transform,
+    balance=True,
+)
+valid_dataset = CustomDataset(
+    dataframe=valid_annotations_df,
+    root_dir=IMAGE_FOLDER_TEST,
+    transform=transform_valid,
+    balance=False,
+)
+train_loader = DataLoader(
+    train_dataset, batch_size=BATCHSIZE, shuffle=True, num_workers=NUM_WORKERS
+)
+valid_loader = DataLoader(
+    valid_dataset, batch_size=BATCHSIZE, shuffle=False, num_workers=NUM_WORKERS
+)
+
+print(f"Train dataset size (balanced): {len(train_dataset)}")
+print(f"Valid dataset size:            {len(valid_dataset)}")
+print(f"\n--- Running {len(EXPERIMENTS)} experiments ---")
+
+all_results = []
+
+for exp_config in EXPERIMENTS:
+    result = run_experiment(exp_config, train_loader, valid_loader)
+    all_results.append(result)
+
+# ── Summary ──────────────────────────────────────────────────────────────
+summary_df = pd.DataFrame(all_results)
+summary_df.to_csv(EXPERIMENTS_DIR / "all_experiments_summary.csv", index=False)
+save_summary_plots(summary_df, EXPERIMENTS_DIR)
+
+print("\n" + "=" * 70)
+print("ALL 6 EXPERIMENTS COMPLETE")
+print("=" * 70)
+print(summary_df.to_string(index=False))
+print(f"\nSummary CSV: {EXPERIMENTS_DIR / 'all_experiments_summary.csv'}")
+print(f"All output:  {EXPERIMENTS_DIR}")
diff --git a/CAGE_expression_inference-apvit/models/AffectNet8_Maxvit_VA/train3.py b/CAGE_expression_inference-apvit/models/AffectNet8_Maxvit_VA/train3.py
new file mode 100644
index 0000000000000000000000000000000000000000..c552dd68ce4a58f6c830c3c7126647bc4aec67a6
--- /dev/null
+++ b/CAGE_expression_inference-apvit/models/AffectNet8_Maxvit_VA/train3.py
@@ -0,0 +1,718 @@
+"""
+train3.py – CAGE pipeline: 5-Fold StratifiedKFold (1 seed per fold)
+
+Pipeline:
+  - train_set is split into 80% train / 20% val via StratifiedKFold
+  - Official val_set is used as test set (evaluated after each fold)
+  - Model: MaxViT-T + Standard head + Combined weights
+  - Loss:  3*MSE_val + 3*MSE_aro + CCC_val + CCC_aro
+"""
+
+import pandas as pd
+import os
+import numpy as np
+import torch
+import torchvision.transforms as transforms
+import torchvision.models as models
+from torch.utils.data import DataLoader, Dataset
+import torch.nn as nn
+import torch.optim as optim
+from PIL import Image
+from torch.optim import lr_scheduler
+from tqdm import tqdm
+from pathlib import Path
+from sklearn.model_selection import StratifiedKFold
+
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+
+
+SCRIPT_DIR = Path(__file__).resolve().parent
+MODEL_PATH = os.getenv("MODEL_PATH", str(SCRIPT_DIR / "model.pt"))
+PLOTS_DIR = SCRIPT_DIR / "plots_cage"
+PLOTS_DIR.mkdir(parents=True, exist_ok=True)
+
+
+def resolve_images_dir(split_name, explicit_env_var, data_root, extract_root):
+    explicit = os.getenv(explicit_env_var, "").strip()
+    candidates = []
+    if explicit:
+        candidates.append(Path(explicit))
+
+    candidates.append(Path(data_root) / f"{split_name}_set" / "images")
+    candidates.append(Path(extract_root) / f"{split_name}_extracted" / "images")
+
+    extract_split_root = Path(extract_root) / f"{split_name}_extracted"
+    if extract_split_root.exists():
+        for p in extract_split_root.rglob("images"):
+            if p.is_dir():
+                candidates.append(p)
+
+    for p in candidates:
+        if p.exists() and p.is_dir():
+            return str(p)
+
+    tried = "\n".join([str(p) for p in candidates])
+    raise FileNotFoundError(
+        f"Could not find images folder for split='{split_name}'. Tried:\n{tried}"
+    )
+
+
+# Load the annotations for training and validation from separate CSV files
+DATA_ROOT = os.getenv("AFFECTNET_ROOT", "/workspace/data_affectnet/AffectNet")
+EXTRACT_ROOT = os.getenv("AFFECTNET_EXTRACT_ROOT", f"{DATA_ROOT}/extracted")
+ANNO_ROOT = os.getenv("AFFECTNET_ANNO_ROOT", "../../affectnet_annotations")
+IMAGE_FOLDER = resolve_images_dir("train", "AFFECTNET_TRAIN_IMAGES", DATA_ROOT, EXTRACT_ROOT)
+IMAGE_FOLDER_TEST = resolve_images_dir("val", "AFFECTNET_VAL_IMAGES", DATA_ROOT, EXTRACT_ROOT)
+train_annotations_path = os.getenv(
+    "AFFECTNET_TRAIN_ANNO", f"{ANNO_ROOT}/train_set_annotation_without_lnd.csv"
+)
+valid_annotations_path = os.getenv(
+    "AFFECTNET_VAL_ANNO", f"{ANNO_ROOT}/val_set_annotation_without_lnd.csv"
+)
+train_annotations_df = pd.read_csv(train_annotations_path)
+valid_annotations_df = pd.read_csv(valid_annotations_path)
+
+
+# Set parameters
+BATCHSIZE = int(os.getenv("BATCHSIZE", "64"))
+NUM_EPOCHS = 20
+LR = 4e-5
+NUM_WORKERS = int(os.getenv("NUM_WORKERS", "0"))
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+print("DATA_ROOT =", DATA_ROOT)
+print("EXTRACT_ROOT =", EXTRACT_ROOT)
+print("AFFECTNET_TRAIN_IMAGES =", IMAGE_FOLDER)
+print("AFFECTNET_VAL_IMAGES =", IMAGE_FOLDER_TEST)
+print("MODEL_PATH =", MODEL_PATH)
+print("PLOTS_DIR =", PLOTS_DIR)
+print("Train set size =", len(train_annotations_df))
+print("Official val_set size (final test only) =", len(valid_annotations_df))
+
+
+# =========================
+# Plot helpers
+# =========================
+def save_line_plot(x, ys, labels, title, xlabel, ylabel, save_path):
+    plt.figure(figsize=(10, 6))
+    for y, label in zip(ys, labels):
+        plt.plot(x, y, marker="o", linewidth=2, markersize=4, label=label)
+    plt.title(title)
+    plt.xlabel(xlabel)
+    plt.ylabel(ylabel)
+    plt.grid(True, alpha=0.3)
+    plt.legend()
+    plt.tight_layout()
+    plt.savefig(save_path, dpi=200)
+    plt.close()
+
+
+def save_bar_plot(labels, values, title, xlabel, ylabel, save_path, annotate=True):
+    plt.figure(figsize=(10, 6))
+    bars = plt.bar(labels, values)
+    plt.title(title)
+    plt.xlabel(xlabel)
+    plt.ylabel(ylabel)
+    plt.grid(True, axis="y", alpha=0.3)
+    if annotate:
+        for bar, val in zip(bars, values):
+            plt.text(
+                bar.get_x() + bar.get_width() / 2,
+                bar.get_height(),
+                f"{val:.4f}",
+                ha="center",
+                va="bottom",
+                fontsize=9,
+            )
+    plt.tight_layout()
+    plt.savefig(save_path, dpi=200)
+    plt.close()
+
+
+def save_boxplot(data_list, labels, title, ylabel, save_path):
+    plt.figure(figsize=(10, 6))
+    plt.boxplot(data_list, labels=labels, vert=True)
+    plt.title(title)
+    plt.ylabel(ylabel)
+    plt.grid(True, axis="y", alpha=0.3)
+    plt.tight_layout()
+    plt.savefig(save_path, dpi=200)
+    plt.close()
+
+
+def save_histogram(values, bins, title, xlabel, ylabel, save_path):
+    plt.figure(figsize=(10, 6))
+    plt.hist(values, bins=bins)
+    plt.title(title)
+    plt.xlabel(xlabel)
+    plt.ylabel(ylabel)
+    plt.grid(True, alpha=0.3)
+    plt.tight_layout()
+    plt.savefig(save_path, dpi=200)
+    plt.close()
+
+
+def save_scatter(x, y, title, xlabel, ylabel, save_path, annotate_labels=None):
+    plt.figure(figsize=(10, 6))
+    plt.scatter(x, y)
+    if annotate_labels is not None:
+        for xi, yi, txt in zip(x, y, annotate_labels):
+            plt.annotate(str(txt), (xi, yi), fontsize=9)
+    plt.title(title)
+    plt.xlabel(xlabel)
+    plt.ylabel(ylabel)
+    plt.grid(True, alpha=0.3)
+    plt.tight_layout()
+    plt.savefig(save_path, dpi=200)
+    plt.close()
+
+
+def save_fold_plots(history_df, fold_idx, seed, fold_dir):
+    epochs = history_df["epoch"].tolist()
+
+    save_line_plot(
+        epochs,
+        [history_df["train_loss"].tolist()],
+        ["Train Loss"],
+        f"CAGE Fold {fold_idx} Seed {seed} - Train Loss",
+        "Epoch",
+        "Loss",
+        fold_dir / f"fold_{fold_idx}_train_loss.png",
+    )
+
+    save_line_plot(
+        epochs,
+        [history_df["valid_loss"].tolist()],
+        ["Validation Loss"],
+        f"CAGE Fold {fold_idx} Seed {seed} - Validation Loss",
+        "Epoch",
+        "Loss",
+        fold_dir / f"fold_{fold_idx}_valid_loss.png",
+    )
+
+    save_line_plot(
+        epochs,
+        [history_df["train_loss"].tolist(), history_df["valid_loss"].tolist()],
+        ["Train Loss", "Validation Loss"],
+        f"CAGE Fold {fold_idx} Seed {seed} - Train vs Validation Loss",
+        "Epoch",
+        "Loss",
+        fold_dir / f"fold_{fold_idx}_train_valid_loss.png",
+    )
+
+    save_line_plot(
+        epochs,
+        [history_df["ccc_v"].tolist()],
+        ["CCC_v"],
+        f"CAGE Fold {fold_idx} Seed {seed} - CCC_v",
+        "Epoch",
+        "CCC",
+        fold_dir / f"fold_{fold_idx}_ccc_v.png",
+    )
+
+    save_line_plot(
+        epochs,
+        [history_df["ccc_a"].tolist()],
+        ["CCC_a"],
+        f"CAGE Fold {fold_idx} Seed {seed} - CCC_a",
+        "Epoch",
+        "CCC",
+        fold_dir / f"fold_{fold_idx}_ccc_a.png",
+    )
+
+    save_line_plot(
+        epochs,
+        [history_df["ccc_mean"].tolist()],
+        ["CCC_mean"],
+        f"CAGE Fold {fold_idx} Seed {seed} - CCC_mean",
+        "Epoch",
+        "CCC",
+        fold_dir / f"fold_{fold_idx}_ccc_mean.png",
+    )
+
+    save_line_plot(
+        epochs,
+        [
+            history_df["ccc_v"].tolist(),
+            history_df["ccc_a"].tolist(),
+            history_df["ccc_mean"].tolist(),
+        ],
+        ["CCC_v", "CCC_a", "CCC_mean"],
+        f"CAGE Fold {fold_idx} Seed {seed} - Combined CCC Curves",
+        "Epoch",
+        "CCC",
+        fold_dir / f"fold_{fold_idx}_ccc_all.png",
+    )
+
+    save_line_plot(
+        epochs,
+        [history_df["lr"].tolist()],
+        ["Learning Rate"],
+        f"CAGE Fold {fold_idx} Seed {seed} - Learning Rate",
+        "Epoch",
+        "LR",
+        fold_dir / f"fold_{fold_idx}_lr.png",
+    )
+
+    save_scatter(
+        history_df["train_loss"].tolist(),
+        history_df["valid_loss"].tolist(),
+        f"CAGE Fold {fold_idx} Seed {seed} - Train Loss vs Valid Loss",
+        "Train Loss",
+        "Validation Loss",
+        fold_dir / f"fold_{fold_idx}_train_vs_valid_scatter.png",
+        annotate_labels=epochs,
+    )
+
+    save_scatter(
+        history_df["ccc_v"].tolist(),
+        history_df["ccc_a"].tolist(),
+        f"CAGE Fold {fold_idx} Seed {seed} - CCC_v vs CCC_a",
+        "CCC_v",
+        "CCC_a",
+        fold_dir / f"fold_{fold_idx}_ccc_v_vs_ccc_a.png",
+        annotate_labels=epochs,
+    )
+
+
+def save_summary_plots(summary_df, save_dir):
+    fold_labels = [f"Fold {x}" for x in summary_df["fold"].tolist()]
+
+    save_bar_plot(
+        fold_labels,
+        summary_df["test_loss"].tolist(),
+        "CAGE Final Test Loss by Fold",
+        "Fold",
+        "Loss",
+        save_dir / "summary_test_loss_by_fold.png",
+    )
+
+    save_bar_plot(
+        fold_labels,
+        summary_df["test_ccc_v"].tolist(),
+        "CAGE Final Test CCC_v by Fold",
+        "Fold",
+        "CCC_v",
+        save_dir / "summary_test_ccc_v_by_fold.png",
+    )
+
+    save_bar_plot(
+        fold_labels,
+        summary_df["test_ccc_a"].tolist(),
+        "CAGE Final Test CCC_a by Fold",
+        "Fold",
+        "CCC_a",
+        save_dir / "summary_test_ccc_a_by_fold.png",
+    )
+
+    save_bar_plot(
+        fold_labels,
+        summary_df["test_ccc_mean"].tolist(),
+        "CAGE Final Test CCC_mean by Fold",
+        "Fold",
+        "CCC_mean",
+        save_dir / "summary_test_ccc_mean_by_fold.png",
+    )
+
+    save_bar_plot(
+        fold_labels,
+        summary_df["best_epoch"].tolist(),
+        "CAGE Best Epoch by Fold",
+        "Fold",
+        "Best Epoch",
+        save_dir / "summary_best_epoch_by_fold.png",
+        annotate=True,
+    )
+
+    save_boxplot(
+        [
+            summary_df["test_ccc_v"].tolist(),
+            summary_df["test_ccc_a"].tolist(),
+            summary_df["test_ccc_mean"].tolist(),
+        ],
+        ["CCC_v", "CCC_a", "CCC_mean"],
+        "CAGE Distribution of Final CCC Across 5 Folds",
+        "CCC",
+        save_dir / "summary_ccc_boxplot.png",
+    )
+
+    save_histogram(
+        summary_df["test_ccc_mean"].tolist(),
+        bins=min(5, len(summary_df)),
+        title="CAGE Histogram of Final CCC_mean",
+        xlabel="CCC_mean",
+        ylabel="Frequency",
+        save_path=save_dir / "summary_ccc_mean_histogram.png",
+    )
+
+    save_scatter(
+        summary_df["best_epoch"].tolist(),
+        summary_df["test_ccc_mean"].tolist(),
+        "CAGE Best Epoch vs Final CCC_mean",
+        "Best Epoch",
+        "Final CCC_mean",
+        save_dir / "summary_best_epoch_vs_ccc_mean.png",
+        annotate_labels=summary_df["fold"].tolist(),
+    )
+
+
+# **** Create dataset and data loaders ****
+class CustomDataset(Dataset):
+    def __init__(self, dataframe, root_dir, transform=None, balance=False):
+        self.dataframe = dataframe
+        self.transform = transform
+        self.root_dir = root_dir
+        self.balance = balance
+
+        if self.balance:
+            self.dataframe = self.balance_dataset()
+
+    def __len__(self):
+        return len(self.dataframe)
+
+    def __getitem__(self, idx):
+        img_id = int(float(self.dataframe["number"].iloc[idx]))
+        image_path = os.path.join(self.root_dir, f"{img_id}.jpg")
+        image = Image.open(image_path)
+
+        classes = torch.tensor(self.dataframe.iloc[idx, 1], dtype=torch.int8)
+        valence = torch.tensor(self.dataframe.iloc[idx, 2], dtype=torch.float16)
+        arousal = torch.tensor(self.dataframe.iloc[idx, 3], dtype=torch.float16)
+
+        if self.transform:
+            image = self.transform(image)
+
+        return image, classes, valence, arousal
+
+    def balance_dataset(self):
+        balanced_df = self.dataframe.groupby("exp", group_keys=False).apply(
+            lambda x: x.sample(self.dataframe["exp"].value_counts().min())
+        )
+        return balanced_df
+
+
+transform = transforms.Compose(
+    [
+        transforms.RandomHorizontalFlip(0.5),
+        transforms.RandomGrayscale(0.01),
+        transforms.RandomRotation(10),
+        transforms.ColorJitter(
+            brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1
+        ),
+        transforms.RandomPerspective(
+            distortion_scale=0.2, p=0.5
+        ),
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+        transforms.RandomErasing(
+            p=0.5, scale=(0.02, 0.2), ratio=(0.3, 3.3), value="random"
+        ),
+    ]
+)
+
+transform_valid = transforms.Compose(
+    [
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+    ]
+)
+
+test_dataset = CustomDataset(
+    dataframe=valid_annotations_df,
+    root_dir=IMAGE_FOLDER_TEST,
+    transform=transform_valid,
+    balance=False,
+)
+test_loader = DataLoader(
+    test_dataset, batch_size=BATCHSIZE, shuffle=False, num_workers=NUM_WORKERS
+)
+
+
+# ***** Define the model *****
+def build_model():
+    MODEL = models.maxvit_t(weights="DEFAULT")
+    block_channels = MODEL.classifier[3].in_features
+    MODEL.classifier = nn.Sequential(
+        nn.AdaptiveAvgPool2d(1),
+        nn.Flatten(),
+        nn.LayerNorm(block_channels),
+        nn.Linear(block_channels, block_channels),
+        nn.Tanh(),
+        nn.Linear(block_channels, 10, bias=False),
+    )
+    MODEL.to(DEVICE)
+    COMBINED_MODEL_PATH = os.getenv(
+        "COMBINED_MODEL_PATH",
+        str((SCRIPT_DIR / "../AffectNet8_Maxvit_Combined/model.pt").resolve()),
+    )
+    MODEL.load_state_dict(torch.load(COMBINED_MODEL_PATH, map_location=DEVICE))
+    MODEL.classifier = nn.Sequential(
+        nn.AdaptiveAvgPool2d(1),
+        nn.Flatten(),
+        nn.LayerNorm(block_channels),
+        nn.Linear(block_channels, block_channels),
+        nn.Tanh(),
+        nn.Dropout(0.3),
+        nn.Linear(block_channels, 2, bias=False),
+    )
+    MODEL.to(DEVICE)
+    return MODEL
+
+
+def CCCLoss(x, y):
+    x_mean = torch.mean(x, dim=0)
+    y_mean = torch.mean(y, dim=0)
+    x_var = torch.var(x, dim=0)
+    y_var = torch.var(y, dim=0)
+    cov_matrix = torch.matmul(
+        (x - x_mean).permute(*torch.arange(x.dim() - 1, -1, -1)), y - y_mean
+    ) / (x.size(0) - 1)
+    numerator = 2 * cov_matrix
+    denominator = x_var + y_var + torch.pow((x_mean - y_mean), 2)
+    ccc = torch.mean(numerator / denominator)
+    return -ccc
+
+
+def ccc_numpy_1d(x, y, eps=1e-8):
+    x = np.asarray(x).reshape(-1)
+    y = np.asarray(y).reshape(-1)
+    x_mean = x.mean()
+    y_mean = y.mean()
+    x_var = x.var()
+    y_var = y.var()
+    cov = ((x - x_mean) * (y - y_mean)).mean()
+    return (2.0 * cov) / (x_var + y_var + (x_mean - y_mean) ** 2 + eps)
+
+
+def evaluate_loader(model, loader):
+    model.eval()
+    eval_loss = 0.0
+    eval_val_true = []
+    eval_val_pred = []
+    eval_aro_true = []
+    eval_aro_pred = []
+
+    with torch.no_grad():
+        for images, _, val_true, aro_true in loader:
+            images, val_true, aro_true = (
+                images.to(DEVICE),
+                val_true.to(DEVICE),
+                aro_true.to(DEVICE),
+            )
+            with torch.autocast(device_type="cuda", dtype=torch.float16):
+                outputs = model(images)
+                val_pred = outputs[:, 0]
+                aro_pred = outputs[:, 1]
+                loss = (
+                    3 * val_loss(val_pred.cuda(), val_true.cuda())
+                    + 3 * aro_loss(aro_pred.cuda(), aro_true.cuda())
+                    + CCCLoss(val_pred.cuda(), val_true.cuda())
+                    + CCCLoss(aro_pred.cuda(), aro_true.cuda())
+                )
+                eval_loss += loss.item()
+
+            eval_val_true.extend(val_true.detach().cpu().float().numpy())
+            eval_val_pred.extend(val_pred.detach().cpu().float().numpy())
+            eval_aro_true.extend(aro_true.detach().cpu().float().numpy())
+            eval_aro_pred.extend(aro_pred.detach().cpu().float().numpy())
+
+    ccc_v = ccc_numpy_1d(eval_val_pred, eval_val_true)
+    ccc_a = ccc_numpy_1d(eval_aro_pred, eval_aro_true)
+    ccc_mean = (ccc_v + ccc_a) / 2.0
+    return eval_loss / max(1, len(loader)), ccc_v, ccc_a, ccc_mean
+
+
+val_loss = nn.MSELoss()
+aro_loss = nn.MSELoss()
+
+# ***** Train the model *****
+print("--- Start CAGE Stratified 5-Fold training (1 fold = 1 seed) ---")
+seeds = [11, 32, 57, 23, 42]
+fold_test_metrics = []
+global_best_valid_loss = float("inf")
+global_best_model_path = MODEL_PATH
+l2_lambda = 0.00001
+l1_lambda = 0.00001
+
+for fold_idx, seed in enumerate(seeds, start=1):
+    print(f"--- Fold {fold_idx}/5 with seed {seed} ---")
+    fold_dir = PLOTS_DIR / f"fold_{fold_idx}_seed_{seed}"
+    fold_dir.mkdir(parents=True, exist_ok=True)
+
+    skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=seed)
+    split_list = list(skf.split(train_annotations_df, train_annotations_df["exp"]))
+    train_idx, val_idx = split_list[fold_idx - 1]
+
+    fold_train_df = train_annotations_df.iloc[train_idx].reset_index(drop=True)
+    fold_valid_df = train_annotations_df.iloc[val_idx].reset_index(drop=True)
+    print(f"Fold train size = {len(fold_train_df)}, fold val size = {len(fold_valid_df)}")
+
+    fold_train_df.to_csv(fold_dir / f"fold_{fold_idx}_train_split.csv", index=False)
+    fold_valid_df.to_csv(fold_dir / f"fold_{fold_idx}_valid_split.csv", index=False)
+
+    train_dataset = CustomDataset(
+        dataframe=fold_train_df,
+        root_dir=IMAGE_FOLDER,
+        transform=transform,
+        balance=True,
+    )
+    valid_dataset = CustomDataset(
+        dataframe=fold_valid_df,
+        root_dir=IMAGE_FOLDER,
+        transform=transform_valid,
+        balance=False,
+    )
+    train_loader = DataLoader(
+        train_dataset, batch_size=BATCHSIZE, shuffle=True, num_workers=NUM_WORKERS
+    )
+    valid_loader = DataLoader(
+        valid_dataset, batch_size=BATCHSIZE, shuffle=False, num_workers=NUM_WORKERS
+    )
+
+    MODEL = build_model()
+    optimizer = optim.AdamW(MODEL.parameters(), lr=LR)
+    cosine_scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=BATCHSIZE * NUM_EPOCHS)
+    scaler = torch.cuda.amp.GradScaler()
+    fold_best_valid_loss = float("inf")
+    fold_best_epoch = -1
+    fold_model_path = str(SCRIPT_DIR / f"model_s{seed}_fold{fold_idx}.pt")
+
+    history = {
+        "epoch": [],
+        "train_loss": [],
+        "valid_loss": [],
+        "ccc_v": [],
+        "ccc_a": [],
+        "ccc_mean": [],
+        "lr": [],
+    }
+
+    for epoch in range(NUM_EPOCHS):
+        MODEL.train()
+        train_loss = 0.0
+        current_lr = optimizer.param_groups[0]["lr"]
+
+        for images, _, val_true, aro_true in tqdm(
+            train_loader, desc=f"CAGE Fold {fold_idx} train progress"
+        ):
+            images, val_true, aro_true = (
+                images.to(DEVICE),
+                val_true.to(DEVICE),
+                aro_true.to(DEVICE),
+            )
+            optimizer.zero_grad()
+            l2_reg = 0
+            l1_reg = 0
+            with torch.autocast(device_type="cuda", dtype=torch.float16):
+                outputs = MODEL(images)
+                val_pred = outputs[:, 0]
+                aro_pred = outputs[:, 1]
+                for param in MODEL.parameters():
+                    l2_reg += torch.norm(param, 2)
+                    l1_reg += torch.norm(param, 1)
+                loss = (
+                    3 * val_loss(val_pred.cuda(), val_true.cuda())
+                    + 3 * aro_loss(aro_pred.cuda(), aro_true.cuda())
+                    + CCCLoss(val_pred.cuda(), val_true.cuda())
+                    + CCCLoss(aro_pred.cuda(), aro_true.cuda())
+                )
+                train_loss += loss.item()
+                scaler.scale(loss).backward()
+                scaler.step(optimizer)
+                scaler.update()
+                cosine_scheduler.step()
+
+        train_loss_mean = train_loss / len(train_loader)
+        valid_loss_mean, ccc_v, ccc_a, ccc_mean = evaluate_loader(MODEL, valid_loader)
+
+        history["epoch"].append(epoch + 1)
+        history["train_loss"].append(train_loss_mean)
+        history["valid_loss"].append(valid_loss_mean)
+        history["ccc_v"].append(ccc_v)
+        history["ccc_a"].append(ccc_a)
+        history["ccc_mean"].append(ccc_mean)
+        history["lr"].append(current_lr)
+
+        print(
+            f"Fold [{fold_idx}/5] Epoch [{epoch+1}/{NUM_EPOCHS}] - "
+            f"Training Loss: {train_loss_mean:.4f}, "
+            f"Validation Loss (fold 20%): {valid_loss_mean:.4f}, "
+            f"CCC_v: {ccc_v:.4f}, "
+            f"CCC_a: {ccc_a:.4f}, "
+            f"CCC_mean: {ccc_mean:.4f}, "
+            f"Learning Rate: {current_lr:.8f}, "
+        )
+
+        if valid_loss_mean < fold_best_valid_loss:
+            fold_best_valid_loss = valid_loss_mean
+            fold_best_epoch = epoch + 1
+            torch.save(MODEL.state_dict(), fold_model_path)
+            print(f"Saved best fold model: {fold_model_path}")
+
+        if fold_best_valid_loss < global_best_valid_loss:
+            global_best_valid_loss = fold_best_valid_loss
+            global_best_model_path = fold_model_path
+
+    history_df = pd.DataFrame(history)
+    history_df.to_csv(fold_dir / f"fold_{fold_idx}_epoch_history.csv", index=False)
+    save_fold_plots(history_df, fold_idx, seed, fold_dir)
+
+    MODEL.load_state_dict(torch.load(fold_model_path, map_location=DEVICE))
+    test_loss_mean, test_ccc_v, test_ccc_a, test_ccc_mean = evaluate_loader(MODEL, test_loader)
+
+    fold_test_metrics.append(
+        (
+            seed,
+            fold_idx,
+            fold_best_epoch,
+            fold_best_valid_loss,
+            test_loss_mean,
+            test_ccc_v,
+            test_ccc_a,
+            test_ccc_mean,
+        )
+    )
+
+    print(
+        f"Fold [{fold_idx}/5] Final Test (official val_set) - "
+        f"Loss: {test_loss_mean:.4f}, "
+        f"CCC_v: {test_ccc_v:.4f}, "
+        f"CCC_a: {test_ccc_a:.4f}, "
+        f"CCC_mean: {test_ccc_mean:.4f}"
+    )
+
+summary_df = pd.DataFrame(
+    fold_test_metrics,
+    columns=[
+        "seed",
+        "fold",
+        "best_epoch",
+        "best_valid_loss",
+        "test_loss",
+        "test_ccc_v",
+        "test_ccc_a",
+        "test_ccc_mean",
+    ],
+)
+
+summary_df.to_csv(PLOTS_DIR / "fold_summary.csv", index=False)
+save_summary_plots(summary_df, PLOTS_DIR)
+
+print("--- Final summary over runs on official val_set ---")
+test_losses = summary_df["test_loss"].to_numpy()
+test_ccc_vs = summary_df["test_ccc_v"].to_numpy()
+test_ccc_as = summary_df["test_ccc_a"].to_numpy()
+test_ccc_means = summary_df["test_ccc_mean"].to_numpy()
+
+print(
+    f"Runs: {len(fold_test_metrics)}; Mean Loss: {np.mean(test_losses):.4f} (std {np.std(test_losses):.4f}), "
+    f"Mean CCC_v: {np.mean(test_ccc_vs):.4f} (std {np.std(test_ccc_vs):.4f}), "
+    f"Mean CCC_a: {np.mean(test_ccc_as):.4f} (std {np.std(test_ccc_as):.4f}), "
+    f"Mean CCC_mean: {np.mean(test_ccc_means):.4f} (std {np.std(test_ccc_means):.4f})"
+)
+
+torch.save(torch.load(global_best_model_path, map_location=DEVICE), MODEL_PATH)
+print(f"Best fold model copied to MODEL_PATH: {MODEL_PATH}")
+print(f"All plots and CSV logs saved to: {PLOTS_DIR}")
diff --git a/CAGE_expression_inference-apvit/models/__pycache__/evaluation.cpython-312.pyc b/CAGE_expression_inference-apvit/models/__pycache__/evaluation.cpython-312.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..59771b0f86f169f6f6c25778fcf62311bead00d1
Binary files /dev/null and b/CAGE_expression_inference-apvit/models/__pycache__/evaluation.cpython-312.pyc differ
diff --git a/CAGE_expression_inference-apvit/models/evaluation.py b/CAGE_expression_inference-apvit/models/evaluation.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e81ce7be313394e6e39669c5859f5b315822720
--- /dev/null
+++ b/CAGE_expression_inference-apvit/models/evaluation.py
@@ -0,0 +1,132 @@
+import pandas as pd
+import numpy as np
+from sklearn.metrics import (
+    mean_absolute_error,
+    mean_squared_error,
+    root_mean_squared_error,
+    classification_report,
+)
+import os
+import subprocess
+
+ONLY_INFERENCE = False
+root_dir = "."
+command = "echo test"
+command = "python3 generate_csv.py"
+
+label_mapping = {
+    "Neutral": 0,
+    "Happy": 1,
+    "Sad": 2,
+    "Surprise": 3,
+    "Fear": 4,
+    "Disgust": 5,
+    "Anger": 6,
+    "Contempt": 7,
+}
+
+
+def get_subdirectories(directory):
+    subdirs = []
+    for item in os.listdir(directory):
+        full_path = os.path.abspath(os.path.join(directory, item))
+        if os.path.isdir(full_path):
+            subdirs.append(full_path)
+    return subdirs
+
+
+def get_files_in_directory(directory):
+    files = []
+    # Iterate over each item in the directory
+    for item in os.listdir(directory):
+        # Check if it's a file
+        if os.path.isfile(os.path.join(directory, item)):
+            files.append(item)
+    return files
+
+
+def concordance_correlation_coefficient(true_values, pred_values):
+    mean_true = np.mean(true_values)
+    mean_pred = np.mean(pred_values)
+
+    num = 2 * np.cov(true_values, pred_values)[0, 1]
+    den = np.var(true_values) + np.var(pred_values) + (mean_true - mean_pred) ** 2
+    return num / den
+
+
+def print_discrete(true_labels, pred_labels):
+    if max(true_labels) == 7:
+        class_names = [
+            "Anger",
+            "Disgust",
+            "Fear",
+            "Happy",
+            "Sad",
+            "Surprise",
+            "Neutral",
+            "Contempt",
+        ]
+    else:
+        class_names = [
+            "Anger",
+            "Disgust",
+            "Fear",
+            "Happy",
+            "Sad",
+            "Surprise",
+            "Neutral",
+        ]
+
+    mapped_labels = [label_mapping[name] for name in class_names]
+    map = classification_report(
+        true_labels,
+        pred_labels,
+        labels=mapped_labels,
+        target_names=class_names,
+        zero_division=0.0,
+        digits=3,
+        output_dict=True,
+    )
+    precision = map["weighted avg"]["precision"]
+    recall = map["weighted avg"]["recall"]
+    f1 = map["weighted avg"]["f1-score"]
+    print(f"Precision: {precision:.3f}")
+    print(f"Recall: {recall:.3f}")
+    print(f"F1: {f1:.3f}")
+
+
+def evaluate(path: str):
+    df = pd.read_csv(path)
+    discrete = "cat_pred" in df.columns
+    va = "val_pred" in df.columns
+    if va:
+        true_values = list(df["val_true"]) + list(df["aro_true"])
+        pred_values = list(df["val_pred"]) + list(df["aro_pred"])
+    if va:
+        mse = mean_squared_error(true_values, pred_values)
+        mae = mean_absolute_error(true_values, pred_values)
+        rmse = root_mean_squared_error(true_values, pred_values)
+        ccc = concordance_correlation_coefficient(true_values, pred_values)
+    print(path)
+    if discrete:
+        print_discrete(df["cat_true"], df["cat_pred"])
+    if va:
+        print(f"Mean Squared Error (MSE): {mse:.4f}")
+        print(f"Mean Absolute Error (MAE): {mae:.4f}")
+        print(f"Root Mean Squared Error (RMSE): {rmse:.4f}")
+        print(f"Concordance Correlation Coefficient (CCC): {ccc:.4f}")
+
+
+for subdir in get_subdirectories("."):
+    files = get_files_in_directory(subdir)
+    if ONLY_INFERENCE is False:
+        if "model.pt" in files:
+            result = subprocess.run(
+                command, shell=True, cwd=subdir, capture_output=True, text=True
+            )
+    files = get_files_in_directory(subdir)
+    if "inference.csv" in files:
+        evaluate(os.path.join(subdir, "inference.csv"))
+        print("\n")
+        print(50 * "-")
+        print("\n")
diff --git a/CAGE_expression_inference-apvit/requirements.txt b/CAGE_expression_inference-apvit/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..52af1c2fef07ab0232006a7ea1a58b13aeaf106d
--- /dev/null
+++ b/CAGE_expression_inference-apvit/requirements.txt
@@ -0,0 +1,69 @@
+asttokens==2.4.1
+comm==0.2.2
+contourpy==1.2.0
+cycler==0.12.1
+debugpy==1.8.1
+decorator==5.1.1
+exceptiongroup==1.2.0
+executing==2.0.1
+filelock==3.13.1
+fonttools==4.50.0
+fsspec==2024.2.0
+ipykernel==6.29.3
+ipython==8.22.2
+jedi==0.19.1
+Jinja2==3.1.3
+joblib==1.3.2
+jupyter_client==8.6.1
+jupyter_core==5.7.2
+kiwisolver==1.4.5
+MarkupSafe==2.1.5
+matplotlib==3.8.3
+matplotlib-inline==0.1.6
+mpmath==1.3.0
+nest-asyncio==1.6.0
+networkx==3.2.1
+numpy==1.26.4
+nvidia-cublas-cu12==12.1.3.1
+nvidia-cuda-cupti-cu12==12.1.105
+nvidia-cuda-nvrtc-cu12==12.1.105
+nvidia-cuda-runtime-cu12==12.1.105
+nvidia-cudnn-cu12==8.9.2.26
+nvidia-cufft-cu12==11.0.2.54
+nvidia-curand-cu12==10.3.2.106
+nvidia-cusolver-cu12==11.4.5.107
+nvidia-cusparse-cu12==12.1.0.106
+nvidia-nccl-cu12==2.19.3
+nvidia-nvjitlink-cu12==12.4.99
+nvidia-nvtx-cu12==12.1.105
+packaging==24.0
+pandas==2.2.1
+parso==0.8.3
+pexpect==4.9.0
+pillow==10.2.0
+platformdirs==4.2.0
+prompt-toolkit==3.0.43
+psutil==5.9.8
+ptyprocess==0.7.0
+pure-eval==0.2.2
+Pygments==2.17.2
+pyparsing==3.1.2
+python-dateutil==2.9.0.post0
+pytz==2024.1
+pyzmq==25.1.2
+scikit-learn==1.4.1.post1
+scipy==1.12.0
+six==1.16.0
+stack-data==0.6.3
+sympy==1.12
+threadpoolctl==3.3.0
+torch==2.2.1
+torchvision==0.17.1
+tornado==6.4
+tqdm==4.66.2
+traitlets==5.14.2
+triton==2.2.0
+typing_extensions==4.10.0
+tzdata==2024.1
+wcwidth==0.2.13
+
diff --git a/CAGE_expression_inference-apvit/run_once.sh b/CAGE_expression_inference-apvit/run_once.sh
new file mode 100644
index 0000000000000000000000000000000000000000..4c28f8ac8d31a2c68cf52c874b90085c1f458443
--- /dev/null
+++ b/CAGE_expression_inference-apvit/run_once.sh
@@ -0,0 +1,187 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+# One-shot pipeline:
+# 1) Train Combined
+# 2) Generate Combined inference CSV
+# 3) Train VA (initialized from Combined checkpoint)
+# 4) Generate VA inference CSV
+# 5) Evaluate all available inference CSV files
+
+SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
+cd "$SCRIPT_DIR"
+
+# Defaults can be overridden by environment variables before running this script.
+export AFFECTNET_ROOT="${AFFECTNET_ROOT:-/workspace/data_affectnet/AffectNet}"
+export AFFECTNET_EXTRACT_ROOT="${AFFECTNET_EXTRACT_ROOT:-${AFFECTNET_ROOT}/extracted}"
+export AFFECTNET_ANNO_ROOT="${AFFECTNET_ANNO_ROOT:-$SCRIPT_DIR/affectnet_annotations}"
+export NUM_WORKERS="${NUM_WORKERS:-0}"
+export BATCHSIZE="${BATCHSIZE:-64}"
+export TRAIN_ARCHIVE="${TRAIN_ARCHIVE:-${AFFECTNET_ROOT}/train_set.tar}"
+export VAL_ARCHIVE="${VAL_ARCHIVE:-${AFFECTNET_ROOT}/val_set.zip}"
+export EXTRACT_ROOT="${EXTRACT_ROOT:-${AFFECTNET_EXTRACT_ROOT}}"
+export TRAIN_EXTRACT_DIR="${TRAIN_EXTRACT_DIR:-${EXTRACT_ROOT}/train_extracted}"
+export VAL_EXTRACT_DIR="${VAL_EXTRACT_DIR:-${EXTRACT_ROOT}/val_extracted}"
+export AUTO_EXTRACT="${AUTO_EXTRACT:-1}"
+
+PY_BIN="${PY_BIN:-python3}"
+SKIP_PIP_INSTALL="${SKIP_PIP_INSTALL:-0}"
+
+echo "[INFO] SCRIPT_DIR=$SCRIPT_DIR"
+echo "[INFO] AFFECTNET_ROOT=$AFFECTNET_ROOT"
+echo "[INFO] AFFECTNET_EXTRACT_ROOT=$AFFECTNET_EXTRACT_ROOT"
+echo "[INFO] TRAIN_ARCHIVE=$TRAIN_ARCHIVE"
+echo "[INFO] VAL_ARCHIVE=$VAL_ARCHIVE"
+echo "[INFO] EXTRACT_ROOT=$EXTRACT_ROOT"
+echo "[INFO] TRAIN_EXTRACT_DIR=$TRAIN_EXTRACT_DIR"
+echo "[INFO] VAL_EXTRACT_DIR=$VAL_EXTRACT_DIR"
+echo "[INFO] AUTO_EXTRACT=$AUTO_EXTRACT"
+echo "[INFO] AFFECTNET_ANNO_ROOT=$AFFECTNET_ANNO_ROOT"
+echo "[INFO] NUM_WORKERS=$NUM_WORKERS"
+echo "[INFO] BATCHSIZE=$BATCHSIZE"
+echo "[INFO] PY_BIN=$PY_BIN"
+
+has_dataset_layout() {
+  local root="$1"
+  if [[ ! -d "$root" ]]; then
+    return 1
+  fi
+  if [[ -d "$root/images" && -d "$root/annotations" ]]; then
+    return 0
+  fi
+  if find "$root" -type d -name images 2>/dev/null | grep -q .; then
+    if find "$root" -type d -name annotations 2>/dev/null | grep -q .; then
+      return 0
+    fi
+  fi
+  return 1
+}
+
+extract_tar_if_needed() {
+  local tar_path="$1"
+  local out_dir="$2"
+  local marker="$out_dir/.extracted_done"
+  mkdir -p "$out_dir"
+  if [[ -f "$marker" ]]; then
+    echo "[SKIP] already extracted: $tar_path"
+    return
+  fi
+  echo "[EXTRACT] $tar_path -> $out_dir"
+  tar -xf "$tar_path" -C "$out_dir"
+  touch "$marker"
+  echo "[DONE] train extracted"
+}
+
+extract_zip_if_needed() {
+  local zip_path="$1"
+  local out_dir="$2"
+  local marker="$out_dir/.extracted_done"
+  mkdir -p "$out_dir"
+  if [[ -f "$marker" ]]; then
+    echo "[SKIP] already extracted: $zip_path"
+    return
+  fi
+  echo "[EXTRACT] $zip_path -> $out_dir"
+  unzip -q "$zip_path" -d "$out_dir"
+  touch "$marker"
+  echo "[DONE] val extracted"
+}
+
+if [[ "$SKIP_PIP_INSTALL" != "1" ]]; then
+  echo "[STEP] Installing dependencies"
+  "$PY_BIN" -m pip install -r requirements.txt
+  echo "[STEP] Installing mmcv (required by APViT)"
+  "$PY_BIN" -m pip install mmcv
+else
+  echo "[STEP] Skipping dependency install (SKIP_PIP_INSTALL=1)"
+fi
+
+# ── APViT repo setup ──────────────────────────────────────────────────────────
+APVIT_PATH="$SCRIPT_DIR/APViT"
+if [[ ! -d "$APVIT_PATH/.git" ]]; then
+  echo "[STEP] Cloning APViT repo"
+  git clone https://github.com/youqingxiaozhua/APViT.git --depth=1 "$APVIT_PATH"
+else
+  echo "[SKIP] APViT repo already present at $APVIT_PATH"
+fi
+
+WEIGHTS_DIR="$APVIT_PATH/weights"
+mkdir -p "$WEIGHTS_DIR"
+
+VIT_SMALL_W="$WEIGHTS_DIR/vit_small_p16_224-15ec54c9.pth"
+if [[ ! -f "$VIT_SMALL_W" ]]; then
+  echo "[STEP] Downloading ViT-Small pretrained weights"
+  wget -q -O "$VIT_SMALL_W" \
+    "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/vit_small_p16_224-15ec54c9.pth"
+else
+  echo "[SKIP] ViT-Small weights already present"
+fi
+
+IR50_W="$WEIGHTS_DIR/backbone_ir50_ms1m_epoch120.pth"
+if [[ ! -f "$IR50_W" ]]; then
+  echo "[WARNING] IR-50 pretrained weights not found at $IR50_W"
+  echo "[WARNING] Download backbone_ir50_ms1m_epoch120.pth from face.evoLVe model zoo"
+  echo "[WARNING] (https://github.com/ZhaoJ9014/face.evoLVe/#model-zoo) and place it at $IR50_W"
+  echo "[WARNING] Training will proceed with random init for the IR-50 extractor."
+fi
+# ─────────────────────────────────────────────────────────────────────────────
+
+if [[ ! -f "$AFFECTNET_ANNO_ROOT/train_set_annotation_without_lnd.csv" ]]; then
+  echo "[ERROR] Missing train annotation CSV in $AFFECTNET_ANNO_ROOT"
+  exit 1
+fi
+if [[ ! -f "$AFFECTNET_ANNO_ROOT/val_set_annotation_without_lnd.csv" ]]; then
+  echo "[ERROR] Missing val annotation CSV in $AFFECTNET_ANNO_ROOT"
+  exit 1
+fi
+
+train_ready=0
+val_ready=0
+if has_dataset_layout "$TRAIN_EXTRACT_DIR"; then
+  train_ready=1
+fi
+if has_dataset_layout "$VAL_EXTRACT_DIR"; then
+  val_ready=1
+fi
+
+if [[ "$train_ready" -eq 1 && "$val_ready" -eq 1 ]]; then
+  echo "[SKIP] Found existing extracted train/val folders"
+else
+  if [[ "$AUTO_EXTRACT" != "1" ]]; then
+    echo "[ERROR] Extracted folders not found and AUTO_EXTRACT is disabled"
+    echo "[HINT] Set AUTO_EXTRACT=1 or point TRAIN_EXTRACT_DIR / VAL_EXTRACT_DIR to existing extracted data"
+    exit 1
+  fi
+
+  if [[ ! -f "$TRAIN_ARCHIVE" ]]; then
+    echo "[ERROR] Missing TRAIN_ARCHIVE: $TRAIN_ARCHIVE"
+    exit 1
+  fi
+  if [[ ! -f "$VAL_ARCHIVE" ]]; then
+    echo "[ERROR] Missing VAL_ARCHIVE: $VAL_ARCHIVE"
+    exit 1
+  fi
+
+  extract_tar_if_needed "$TRAIN_ARCHIVE" "$TRAIN_EXTRACT_DIR"
+  extract_zip_if_needed "$VAL_ARCHIVE" "$VAL_EXTRACT_DIR"
+fi
+
+echo "[STEP] Train Combined"
+cd "$SCRIPT_DIR/models/AffectNet8_Maxvit_Combined"
+"$PY_BIN" train.py
+
+echo "[STEP] Generate Combined inference CSV"
+"$PY_BIN" generate_csv.py
+
+echo "[STEP] Train VA"
+cd "$SCRIPT_DIR/models/AffectNet8_Maxvit_VA"
+"$PY_BIN" train.py
+
+echo "[STEP] Generate VA inference CSV"
+"$PY_BIN" generate_csv.py
+
+echo "[STEP] Evaluate"
+cd "$SCRIPT_DIR/models"
+"$PY_BIN" evaluation.py
+
+echo "[DONE] Full pipeline completed successfully."