diff --git a/.history/datasets/__init___20250113130146.py b/.history/datasets/__init___20250113130146.py
new file mode 100644
index 0000000000000000000000000000000000000000..1edfb97a0df357e3a97cafe2cc604db03c0e99bc
--- /dev/null
+++ b/.history/datasets/__init___20250113130146.py
@@ -0,0 +1,38 @@
+import torch.utils.data
+import torchvision
+
+from .ytvos import build as build_ytvos
+from .ytvos_ref import build as build_ytvos_ref
+from .davis import build as build_davis
+from .a2d import build as build_a2d
+from .jhmdb import build as build_jhmdb
+from .refexp import build as build_refexp
+from .concat_dataset import build as build_joint
+
+
+def get_coco_api_from_dataset(dataset):
+    for _ in range(10):
+        # if isinstance(dataset, torchvision.datasets.CocoDetection):
+        #     break
+        if isinstance(dataset, torch.utils.data.Subset):
+            dataset = dataset.dataset
+    if isinstance(dataset, torchvision.datasets.CocoDetection):
+        return dataset.coco
+
+
+def build_dataset(dataset_file: str, image_set: str, args):
+    if dataset_file == 'ytvos':
+        return build_ytvos(image_set, args)
+    if dataset_file == 'davis':
+        return build_davis(image_set, args)
+    if dataset_file == 'a2d':
+        return build_a2d(image_set, args)
+    if dataset_file == 'jhmdb':
+        return build_jhmdb(image_set, args)
+    # for pretraining
+    if dataset_file == "refcoco" or dataset_file == "refcoco+" or dataset_file == "refcocog":
+        return build_refexp(dataset_file, image_set, args)
+    # for joint training of refcoco and ytvos
+    if dataset_file == 'joint':
+        return build_joint(image_set, args)
+    raise ValueError(f'dataset {dataset_file} not supported')
diff --git a/.history/datasets/ytvos_ref_20250113161625.py b/.history/datasets/ytvos_ref_20250113161625.py
new file mode 100644
index 0000000000000000000000000000000000000000..38b465ecee3c980aabd733f60bb28c26dd25ad6e
--- /dev/null
+++ b/.history/datasets/ytvos_ref_20250113161625.py
@@ -0,0 +1,243 @@
+"""
+Ref-YoutubeVOS data loader
+"""
+from pathlib import Path
+
+import torch
+from torch.autograd.grad_mode import F
+from torch.utils.data import Dataset
+import datasets.transforms_video as T
+
+import os
+from PIL import Image
+import json
+import numpy as np
+import random
+
+from datasets.categories import ytvos_category_dict as category_dict
+
+
+class YTVOSDataset(Dataset):
+    """
+    A dataset class for the Refer-Youtube-VOS dataset which was first introduced in the paper:
+    "URVOS: Unified Referring Video Object Segmentation Network with a Large-Scale Benchmark"
+    (see https://link.springer.com/content/pdf/10.1007/978-3-030-58555-6_13.pdf).
+    The original release of the dataset contained both 'first-frame' and 'full-video' expressions. However, the first
+    dataset is not publicly available anymore as now only the harder 'full-video' subset is available to download
+    through the Youtube-VOS referring video object segmentation competition page at:
+    https://competitions.codalab.org/competitions/29139
+    Furthermore, for the competition the subset's original validation set, which consists of 507 videos, was split into
+    two competition 'validation' & 'test' subsets, consisting of 202 and 305 videos respectively. Evaluation can
+    currently only be done on the competition 'validation' subset using the competition's server, as
+    annotations were publicly released only for the 'train' subset of the competition.
+
+    """
+    def __init__(self, img_folder: Path, ann_file: Path, transforms, return_masks: bool, 
+                 num_frames: int, max_skip: int):
+        self.img_folder = img_folder     
+        self.ann_file = ann_file         
+        self._transforms = transforms    
+        self.return_masks = return_masks # not used
+        self.num_frames = num_frames     
+        self.max_skip = max_skip
+        # create video meta data
+        self.prepare_metas()       
+
+        print('\n video num: ', len(self.videos), ' clip num: ', len(self.metas))  
+        print('\n')    
+
+    def prepare_metas(self):
+        # read object information
+        with open(os.path.join(str(self.img_folder), 'meta.json'), 'r') as f:
+            subset_metas_by_video = json.load(f)['videos']
+        
+        # read expression data
+        with open(str(self.ann_file), 'r') as f:
+            subset_expressions_by_video = json.load(f)['videos']
+        self.videos = list(subset_expressions_by_video.keys())
+
+        self.metas = []
+        for vid in self.videos:
+            vid_meta = subset_metas_by_video[vid]
+            vid_data = subset_expressions_by_video[vid]
+            vid_frames = sorted(vid_data['frames'])
+            vid_len = len(vid_frames)
+
+
+            for exp_id, exp_dict in vid_data['expressions'].items():
+                # Exclude start_idx (0, 1) and end_idx (vid_len-1, vid_len-2)
+                start_idx , end_idx = 2, vid_len-2
+                bin_size = (end_idx - start_idx) // 4
+
+                bins = []
+                for i in range(4):
+                    bin_start = start_idx + i * bin_size
+                    bin_end = bin_start + bin_size if i < 3 else end_idx
+
+                    bins.append((bin_start, bin_end))
+                
+                for bin_id in range(len(bins)):
+                    start_idx, end_idx = bins[bin_id]
+                    frame_id = random.randint(start_idx, end_idx - 1)
+                
+                    meta = {
+                        'video': vid,
+                        'exp': exp_dict['exp'],
+                        'obj_id': int(exp_dict['obj_id']),
+                        'frames': vid_frames,
+                        'frame_id' : frame_id,
+                        'bins': bins,  
+                        'category': vid_meta['objects'][exp_dict['obj_id']]['category']
+                    }
+                    self.metas.append(meta)
+                
+
+    @staticmethod
+    def bounding_box(img):
+        rows = np.any(img, axis=1)
+        cols = np.any(img, axis=0)
+        rmin, rmax = np.where(rows)[0][[0, -1]]
+        cmin, cmax = np.where(cols)[0][[0, -1]]
+        return rmin, rmax, cmin, cmax # y1, y2, x1, x2 
+        
+    def __len__(self):
+        return len(self.metas)
+        
+    def __getitem__(self, idx):
+        instance_check = False
+        while not instance_check:
+            meta = self.metas[idx]  # dict
+
+
+            video, exp, obj_id, category, frames, bins = \
+                    meta['video'], meta['exp'], meta['obj_id'], meta['category'], meta['frames'], meta['bins']
+                
+    
+            # clean up the caption
+            exp = " ".join(exp.lower().split())
+            category_id = category_dict[category]
+            vid_len = len(frames)
+
+            # num_frames = self.num_frames
+
+            # Random sample one frame from each bin
+            sample_indx = []
+            for start_idx, end_idx in bins:
+                sample_indx.append(random.randint(start_idx, end_idx - 1))
+            sample_indx.sort()  # Ensure indices are in order
+            
+            # read frames and masks
+            imgs, labels, boxes, masks, valid = [], [], [], [], []
+            for frame_indx in sample_indx:
+                frame_name = frames[frame_indx]
+                img_path = os.path.join(str(self.img_folder), 'JPEGImages', video, frame_name + '.jpg')
+                mask_path = os.path.join(str(self.img_folder), 'Annotations', video, frame_name + '.png')
+                img = Image.open(img_path).convert('RGB')
+                mask = Image.open(mask_path).convert('P')
+
+                # create the target
+                label =  torch.tensor(category_id) 
+                mask = np.array(mask)
+                mask = (mask==obj_id).astype(np.float32) # 0,1 binary
+                if (mask > 0).any():
+                    y1, y2, x1, x2 = self.bounding_box(mask)
+                    box = torch.tensor([x1, y1, x2, y2]).to(torch.float)
+                    valid.append(1)
+                else: # some frame didn't contain the instance
+                    box = torch.tensor([0, 0, 0, 0]).to(torch.float) 
+                    valid.append(0)
+                mask = torch.from_numpy(mask)
+
+                # append
+                imgs.append(img)
+                labels.append(label)
+                masks.append(mask)
+                boxes.append(box)
+
+            # transform
+            w, h = img.size
+            labels = torch.stack(labels, dim=0) 
+            boxes = torch.stack(boxes, dim=0) 
+            boxes[:, 0::2].clamp_(min=0, max=w)
+            boxes[:, 1::2].clamp_(min=0, max=h)
+            masks = torch.stack(masks, dim=0) 
+            target = {
+                'frames_idx': torch.tensor(sample_indx), # [T,]
+                'labels': labels,                        # [T,]
+                'boxes': boxes,                          # [T, 4], xyxy
+                'masks': masks,                          # [T, H, W]
+                'valid': torch.tensor(valid),            # [T,]
+                'caption': exp,
+                'orig_size': torch.as_tensor([int(h), int(w)]), 
+                'size': torch.as_tensor([int(h), int(w)])
+            }
+
+            # "boxes" normalize to [0, 1] and transform from xyxy to cxcywh in self._transform
+            if self._transforms:
+                imgs, target = self._transforms(imgs, target)
+                imgs = torch.stack(imgs, dim=0) # [T, 3, H, W]
+            else:
+                imgs = np.array(imgs)
+                imgs = torch.tensor(imgs.transpose(0, 3, 1, 2))
+            
+            
+            # FIXME: handle "valid", since some box may be removed due to random crop
+            if torch.any(target['valid'] == 1):  # at leatst one instance
+                instance_check = True
+            else:
+                idx = random.randint(0, self.__len__() - 1)
+
+        return imgs, target
+
+
+def make_coco_transforms(image_set, max_size=640):
+    normalize = T.Compose([
+        T.ToTensor(),
+        T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+    scales = [288, 320, 352, 392, 416, 448, 480, 512]
+
+    if image_set == 'train':
+        return T.Compose([
+            T.RandomHorizontalFlip(),
+            T.PhotometricDistort(),
+            T.RandomSelect(
+                T.Compose([
+                    T.RandomResize(scales, max_size=max_size),
+                    T.Check(),
+                ]),
+                T.Compose([
+                    T.RandomResize([400, 500, 600]),
+                    T.RandomSizeCrop(384, 600),
+                    T.RandomResize(scales, max_size=max_size),
+                    T.Check(),
+                ])
+            ),
+            normalize,
+        ])
+    
+    # we do not use the 'val' set since the annotations are inaccessible
+    if image_set == 'val':
+        return T.Compose([
+            T.RandomResize([360], max_size=640),
+            normalize,
+        ])
+
+    raise ValueError(f'unknown {image_set}')
+
+
+def build(image_set, args):
+    root = Path(args.ytvos_path)
+    assert root.exists(), f'provided YTVOS path {root} does not exist'
+    PATHS = {
+        "train": (root / "train", root / "meta_expressions" / "train" / "meta_expressions.json"),
+        "val": (root / "valid", root / "meta_expressions" / "valid" / "meta_expressions.json"),    # not used actually
+    }
+    img_folder, ann_file = PATHS[image_set]
+    # dataset = YTVOSDataset(img_folder, ann_file, transforms=make_coco_transforms(image_set, max_size=args.max_size), return_masks=args.masks, 
+    #                        num_frames=args.num_frames, max_skip=args.max_skip)
+    dataset = YTVOSDataset(img_folder, ann_file, transforms=None, return_masks=args.masks, 
+                           num_frames=args.num_frames, max_skip=args.max_skip)
+    return dataset
+
diff --git a/.history/datasets/ytvos_ref_20250113161634.py b/.history/datasets/ytvos_ref_20250113161634.py
new file mode 100644
index 0000000000000000000000000000000000000000..03ab35c3dc537860fc0a98af2594cb0fdfa7bf5e
--- /dev/null
+++ b/.history/datasets/ytvos_ref_20250113161634.py
@@ -0,0 +1,242 @@
+"""
+Ref-YoutubeVOS data loader
+"""
+from pathlib import Path
+
+import torch
+from torch.autograd.grad_mode import F
+from torch.utils.data import Dataset
+import datasets.transforms_video as T
+
+import os
+from PIL import Image
+import json
+import numpy as np
+import random
+
+from datasets.categories import ytvos_category_dict as category_dict
+
+
+class YTVOSDataset(Dataset):
+    """
+    A dataset class for the Refer-Youtube-VOS dataset which was first introduced in the paper:
+    "URVOS: Unified Referring Video Object Segmentation Network with a Large-Scale Benchmark"
+    (see https://link.springer.com/content/pdf/10.1007/978-3-030-58555-6_13.pdf).
+    The original release of the dataset contained both 'first-frame' and 'full-video' expressions. However, the first
+    dataset is not publicly available anymore as now only the harder 'full-video' subset is available to download
+    through the Youtube-VOS referring video object segmentation competition page at:
+    https://competitions.codalab.org/competitions/29139
+    Furthermore, for the competition the subset's original validation set, which consists of 507 videos, was split into
+    two competition 'validation' & 'test' subsets, consisting of 202 and 305 videos respectively. Evaluation can
+    currently only be done on the competition 'validation' subset using the competition's server, as
+    annotations were publicly released only for the 'train' subset of the competition.
+
+    """
+    def __init__(self, img_folder: Path, ann_file: Path, transforms, return_masks: bool, 
+                 num_frames: int, max_skip: int):
+        self.img_folder = img_folder     
+        self.ann_file = ann_file         
+        self._transforms = transforms    
+        self.return_masks = return_masks # not used
+        self.num_frames = num_frames     
+        self.max_skip = max_skip
+        # create video meta data
+        self.prepare_metas()       
+
+        print('\n video num: ', len(self.videos), ' clip num: ', len(self.metas))  
+        print('\n')    
+
+    def prepare_metas(self):
+        # read object information
+        with open(os.path.join(str(self.img_folder), 'meta.json'), 'r') as f:
+            subset_metas_by_video = json.load(f)['videos']
+        
+        # read expression data
+        with open(str(self.ann_file), 'r') as f:
+            subset_expressions_by_video = json.load(f)['videos']
+        self.videos = list(subset_expressions_by_video.keys())
+
+        self.metas = []
+        for vid in self.videos:
+            vid_meta = subset_metas_by_video[vid]
+            vid_data = subset_expressions_by_video[vid]
+            vid_frames = sorted(vid_data['frames'])
+            vid_len = len(vid_frames)
+
+            for exp_id, exp_dict in vid_data['expressions'].items():
+                # Exclude start_idx (0, 1) and end_idx (vid_len-1, vid_len-2)
+                start_idx , end_idx = 2, vid_len-2
+                bin_size = (end_idx - start_idx) // 4
+
+                bins = []
+                for i in range(4):
+                    bin_start = start_idx + i * bin_size
+                    bin_end = bin_start + bin_size if i < 3 else end_idx
+
+                    bins.append((bin_start, bin_end))
+                
+                for bin_id in range(len(bins)):
+                    start_idx, end_idx = bins[bin_id]
+                    frame_id = random.randint(start_idx, end_idx - 1)
+                
+                    meta = {
+                        'video': vid,
+                        'exp': exp_dict['exp'],
+                        'obj_id': int(exp_dict['obj_id']),
+                        'frames': vid_frames,
+                        'frame_id' : frame_id,
+                        'bins': bins,  
+                        'category': vid_meta['objects'][exp_dict['obj_id']]['category']
+                    }
+                    self.metas.append(meta)
+                
+
+    @staticmethod
+    def bounding_box(img):
+        rows = np.any(img, axis=1)
+        cols = np.any(img, axis=0)
+        rmin, rmax = np.where(rows)[0][[0, -1]]
+        cmin, cmax = np.where(cols)[0][[0, -1]]
+        return rmin, rmax, cmin, cmax # y1, y2, x1, x2 
+        
+    def __len__(self):
+        return len(self.metas)
+        
+    def __getitem__(self, idx):
+        instance_check = False
+        while not instance_check:
+            meta = self.metas[idx]  # dict
+
+
+            video, exp, obj_id, category, frames, bins = \
+                    meta['video'], meta['exp'], meta['obj_id'], meta['category'], meta['frames'], meta['bins']
+                
+    
+            # clean up the caption
+            exp = " ".join(exp.lower().split())
+            category_id = category_dict[category]
+            vid_len = len(frames)
+
+            # num_frames = self.num_frames
+
+            # Random sample one frame from each bin
+            sample_indx = []
+            for start_idx, end_idx in bins:
+                sample_indx.append(random.randint(start_idx, end_idx - 1))
+            sample_indx.sort()  # Ensure indices are in order
+            
+            # read frames and masks
+            imgs, labels, boxes, masks, valid = [], [], [], [], []
+            for frame_indx in sample_indx:
+                frame_name = frames[frame_indx]
+                img_path = os.path.join(str(self.img_folder), 'JPEGImages', video, frame_name + '.jpg')
+                mask_path = os.path.join(str(self.img_folder), 'Annotations', video, frame_name + '.png')
+                img = Image.open(img_path).convert('RGB')
+                mask = Image.open(mask_path).convert('P')
+
+                # create the target
+                label =  torch.tensor(category_id) 
+                mask = np.array(mask)
+                mask = (mask==obj_id).astype(np.float32) # 0,1 binary
+                if (mask > 0).any():
+                    y1, y2, x1, x2 = self.bounding_box(mask)
+                    box = torch.tensor([x1, y1, x2, y2]).to(torch.float)
+                    valid.append(1)
+                else: # some frame didn't contain the instance
+                    box = torch.tensor([0, 0, 0, 0]).to(torch.float) 
+                    valid.append(0)
+                mask = torch.from_numpy(mask)
+
+                # append
+                imgs.append(img)
+                labels.append(label)
+                masks.append(mask)
+                boxes.append(box)
+
+            # transform
+            w, h = img.size
+            labels = torch.stack(labels, dim=0) 
+            boxes = torch.stack(boxes, dim=0) 
+            boxes[:, 0::2].clamp_(min=0, max=w)
+            boxes[:, 1::2].clamp_(min=0, max=h)
+            masks = torch.stack(masks, dim=0) 
+            target = {
+                'frames_idx': torch.tensor(sample_indx), # [T,]
+                'labels': labels,                        # [T,]
+                'boxes': boxes,                          # [T, 4], xyxy
+                'masks': masks,                          # [T, H, W]
+                'valid': torch.tensor(valid),            # [T,]
+                'caption': exp,
+                'orig_size': torch.as_tensor([int(h), int(w)]), 
+                'size': torch.as_tensor([int(h), int(w)])
+            }
+
+            # "boxes" normalize to [0, 1] and transform from xyxy to cxcywh in self._transform
+            if self._transforms:
+                imgs, target = self._transforms(imgs, target)
+                imgs = torch.stack(imgs, dim=0) # [T, 3, H, W]
+            else:
+                imgs = np.array(imgs)
+                imgs = torch.tensor(imgs.transpose(0, 3, 1, 2))
+            
+            
+            # FIXME: handle "valid", since some box may be removed due to random crop
+            if torch.any(target['valid'] == 1):  # at leatst one instance
+                instance_check = True
+            else:
+                idx = random.randint(0, self.__len__() - 1)
+
+        return imgs, target
+
+
+def make_coco_transforms(image_set, max_size=640):
+    normalize = T.Compose([
+        T.ToTensor(),
+        T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+    scales = [288, 320, 352, 392, 416, 448, 480, 512]
+
+    if image_set == 'train':
+        return T.Compose([
+            T.RandomHorizontalFlip(),
+            T.PhotometricDistort(),
+            T.RandomSelect(
+                T.Compose([
+                    T.RandomResize(scales, max_size=max_size),
+                    T.Check(),
+                ]),
+                T.Compose([
+                    T.RandomResize([400, 500, 600]),
+                    T.RandomSizeCrop(384, 600),
+                    T.RandomResize(scales, max_size=max_size),
+                    T.Check(),
+                ])
+            ),
+            normalize,
+        ])
+    
+    # we do not use the 'val' set since the annotations are inaccessible
+    if image_set == 'val':
+        return T.Compose([
+            T.RandomResize([360], max_size=640),
+            normalize,
+        ])
+
+    raise ValueError(f'unknown {image_set}')
+
+
+def build(image_set, args):
+    root = Path(args.ytvos_path)
+    assert root.exists(), f'provided YTVOS path {root} does not exist'
+    PATHS = {
+        "train": (root / "train", root / "meta_expressions" / "train" / "meta_expressions.json"),
+        "val": (root / "valid", root / "meta_expressions" / "valid" / "meta_expressions.json"),    # not used actually
+    }
+    img_folder, ann_file = PATHS[image_set]
+    # dataset = YTVOSDataset(img_folder, ann_file, transforms=make_coco_transforms(image_set, max_size=args.max_size), return_masks=args.masks, 
+    #                        num_frames=args.num_frames, max_skip=args.max_skip)
+    dataset = YTVOSDataset(img_folder, ann_file, transforms=None, return_masks=args.masks, 
+                           num_frames=args.num_frames, max_skip=args.max_skip)
+    return dataset
+
diff --git a/.history/datasets/ytvos_ref_20250113162627.py b/.history/datasets/ytvos_ref_20250113162627.py
new file mode 100644
index 0000000000000000000000000000000000000000..9b10e8f0a03e7ea7bcf0fd41c4b678dda9e36936
--- /dev/null
+++ b/.history/datasets/ytvos_ref_20250113162627.py
@@ -0,0 +1,242 @@
+"""
+Ref-YoutubeVOS data loader
+"""
+from pathlib import Path
+
+import torch
+from torch.autograd.grad_mode import F
+from torch.utils.data import Dataset
+import datasets.transforms_video as T
+
+import os
+from PIL import Image
+import json
+import numpy as np
+import random
+
+from datasets.categories import ytvos_category_dict as category_dict
+
+
+class YTVOSDataset(Dataset):
+    """
+    A dataset class for the Refer-Youtube-VOS dataset which was first introduced in the paper:
+    "URVOS: Unified Referring Video Object Segmentation Network with a Large-Scale Benchmark"
+    (see https://link.springer.com/content/pdf/10.1007/978-3-030-58555-6_13.pdf).
+    The original release of the dataset contained both 'first-frame' and 'full-video' expressions. However, the first
+    dataset is not publicly available anymore as now only the harder 'full-video' subset is available to download
+    through the Youtube-VOS referring video object segmentation competition page at:
+    https://competitions.codalab.org/competitions/29139
+    Furthermore, for the competition the subset's original validation set, which consists of 507 videos, was split into
+    two competition 'validation' & 'test' subsets, consisting of 202 and 305 videos respectively. Evaluation can
+    currently only be done on the competition 'validation' subset using the competition's server, as
+    annotations were publicly released only for the 'train' subset of the competition.
+
+    """
+    def __init__(self, img_folder: Path, ann_file: Path, transforms, return_masks: bool, 
+                 num_frames: int, max_skip: int):
+        self.img_folder = img_folder     
+        self.ann_file = ann_file         
+        self._transforms = transforms    
+        self.return_masks = return_masks # not used
+        self.num_frames = num_frames     
+        self.max_skip = max_skip
+        # create video meta data
+        self.prepare_metas()       
+
+        print('\n video num: ', len(self.videos), ' clip num: ', len(self.metas))  
+        print('\n')    
+
+    def prepare_metas(self):
+        # read object information
+        with open(os.path.join(str(self.img_folder), 'meta.json'), 'r') as f:
+            subset_metas_by_video = json.load(f)['videos']
+        
+        # read expression data
+        with open(str(self.ann_file), 'r') as f:
+            subset_expressions_by_video = json.load(f)['videos']
+        self.videos = list(subset_expressions_by_video.keys())
+
+        self.metas = []
+        for vid in self.videos:
+            vid_meta = subset_metas_by_video[vid]
+            vid_data = subset_expressions_by_video[vid]
+            vid_frames = sorted(vid_data['frames'])
+            vid_len = len(vid_frames)
+
+            for exp_id, exp_dict in vid_data['expressions'].items():
+                # Exclude start_idx (0, 1) and end_idx (vid_len-1, vid_len-2)
+                start_idx , end_idx = 2, vid_len-2
+                bin_size = (end_idx - start_idx) // 4
+
+                bins = []
+                for i in range(4):
+                    bin_start = start_idx + i * bin_size
+                    bin_end = bin_start + bin_size if i < 3 else end_idx
+
+                    bins.append((bin_start, bin_end))
+
+                # Random sample one frame from each bin
+                sample_indx = []
+                for start_idx, end_idx in bins:
+                    print(start_idx, end_idx)
+                    sample_indx.append(random.randint(start_idx, end_idx - 1))
+                sample_indx.sort()  # Ensure indices are in order
+
+                
+                for frame_id in sample_indx:              
+                    meta = {
+                        'video': vid,
+                        'exp': exp_dict['exp'],
+                        'obj_id': int(exp_dict['obj_id']),
+                        'frames': vid_frames,
+                        'frame_id' : frame_id,
+                        'sample_frames_id' : sample_indx,
+                        'bins': bins,  
+                        'category': vid_meta['objects'][exp_dict['obj_id']]['category']
+                    }
+                    self.metas.append(meta)
+                
+
+    @staticmethod
+    def bounding_box(img):
+        rows = np.any(img, axis=1)
+        cols = np.any(img, axis=0)
+        rmin, rmax = np.where(rows)[0][[0, -1]]
+        cmin, cmax = np.where(cols)[0][[0, -1]]
+        return rmin, rmax, cmin, cmax # y1, y2, x1, x2 
+        
+    def __len__(self):
+        return len(self.metas)
+        
+    def __getitem__(self, idx):
+        instance_check = False
+        while not instance_check:
+            meta = self.metas[idx]  # dict
+
+
+            video, exp, obj_id, category, frames, frame_id, sample_frames_id, bins = \
+                    meta['video'], meta['exp'], meta['obj_id'], meta['category'], meta['frames'], metas['frame_id'], metas['sample_frames_id'], meta['bins']
+                
+    
+            # clean up the caption
+            exp = " ".join(exp.lower().split())
+            category_id = category_dict[category]
+            vid_len = len(frames)
+
+            # num_frames = self.num_frames
+
+            # read frames and masks
+            imgs, labels, boxes, masks, valid = [], [], [], [], []
+            for frame_indx in sample_frames_id:
+                frame_name = frames[frame_indx]
+                img_path = os.path.join(str(self.img_folder), 'JPEGImages', video, frame_name + '.jpg')
+                mask_path = os.path.join(str(self.img_folder), 'Annotations', video, frame_name + '.png')
+                img = Image.open(img_path).convert('RGB')
+                mask = Image.open(mask_path).convert('P')
+
+                # create the target
+                label =  torch.tensor(category_id) 
+                mask = np.array(mask)
+                mask = (mask==obj_id).astype(np.float32) # 0,1 binary
+                if (mask > 0).any():
+                    y1, y2, x1, x2 = self.bounding_box(mask)
+                    box = torch.tensor([x1, y1, x2, y2]).to(torch.float)
+                    valid.append(1)
+                else: # some frame didn't contain the instance
+                    box = torch.tensor([0, 0, 0, 0]).to(torch.float) 
+                    valid.append(0)
+                mask = torch.from_numpy(mask)
+
+                # append
+                imgs.append(img)
+                labels.append(label)
+                masks.append(mask)
+                boxes.append(box)
+
+            # transform
+            w, h = img.size
+            labels = torch.stack(labels, dim=0) 
+            boxes = torch.stack(boxes, dim=0) 
+            boxes[:, 0::2].clamp_(min=0, max=w)
+            boxes[:, 1::2].clamp_(min=0, max=h)
+            masks = torch.stack(masks, dim=0) 
+            target = {
+                'frames_idx': torch.tensor(sample_frames_id), # [T,]
+                'labels': labels,                        # [T,]
+                'boxes': boxes,                          # [T, 4], xyxy
+                'masks': masks,                          # [T, H, W]
+                'valid': torch.tensor(valid),            # [T,]
+                'caption': exp,
+                'orig_size': torch.as_tensor([int(h), int(w)]), 
+                'size': torch.as_tensor([int(h), int(w)])
+            }
+
+            # "boxes" normalize to [0, 1] and transform from xyxy to cxcywh in self._transform
+            if self._transforms:
+                imgs, target = self._transforms(imgs, target)
+                imgs = torch.stack(imgs, dim=0) # [T, 3, H, W]
+            else:
+                imgs = np.array(imgs)
+                imgs = torch.tensor(imgs.transpose(0, 3, 1, 2))
+            
+            
+            # FIXME: handle "valid", since some box may be removed due to random crop
+            if torch.any(target['valid'] == 1):  # at leatst one instance
+                instance_check = True
+            else:
+                idx = random.randint(0, self.__len__() - 1)
+
+        return imgs, target
+
+
+def make_coco_transforms(image_set, max_size=640):
+    normalize = T.Compose([
+        T.ToTensor(),
+        T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+    scales = [288, 320, 352, 392, 416, 448, 480, 512]
+
+    if image_set == 'train':
+        return T.Compose([
+            T.RandomHorizontalFlip(),
+            T.PhotometricDistort(),
+            T.RandomSelect(
+                T.Compose([
+                    T.RandomResize(scales, max_size=max_size),
+                    T.Check(),
+                ]),
+                T.Compose([
+                    T.RandomResize([400, 500, 600]),
+                    T.RandomSizeCrop(384, 600),
+                    T.RandomResize(scales, max_size=max_size),
+                    T.Check(),
+                ])
+            ),
+            normalize,
+        ])
+    
+    # we do not use the 'val' set since the annotations are inaccessible
+    if image_set == 'val':
+        return T.Compose([
+            T.RandomResize([360], max_size=640),
+            normalize,
+        ])
+
+    raise ValueError(f'unknown {image_set}')
+
+
+def build(image_set, args):
+    root = Path(args.ytvos_path)
+    assert root.exists(), f'provided YTVOS path {root} does not exist'
+    PATHS = {
+        "train": (root / "train", root / "meta_expressions" / "train" / "meta_expressions.json"),
+        "val": (root / "valid", root / "meta_expressions" / "valid" / "meta_expressions.json"),    # not used actually
+    }
+    img_folder, ann_file = PATHS[image_set]
+    # dataset = YTVOSDataset(img_folder, ann_file, transforms=make_coco_transforms(image_set, max_size=args.max_size), return_masks=args.masks, 
+    #                        num_frames=args.num_frames, max_skip=args.max_skip)
+    dataset = YTVOSDataset(img_folder, ann_file, transforms=None, return_masks=args.masks, 
+                           num_frames=args.num_frames, max_skip=args.max_skip)
+    return dataset
+
diff --git a/.history/datasets/ytvos_ref_20250113163106.py b/.history/datasets/ytvos_ref_20250113163106.py
new file mode 100644
index 0000000000000000000000000000000000000000..fe09503ec105ca2c8299052ec2d45560df57c57a
--- /dev/null
+++ b/.history/datasets/ytvos_ref_20250113163106.py
@@ -0,0 +1,244 @@
+"""
+Ref-YoutubeVOS data loader
+"""
+from pathlib import Path
+
+import torch
+from torch.autograd.grad_mode import F
+from torch.utils.data import Dataset
+import datasets.transforms_video as T
+
+import os
+from PIL import Image
+import json
+import numpy as np
+import random
+
+from datasets.categories import ytvos_category_dict as category_dict
+
+
+class YTVOSDataset(Dataset):
+    """
+    A dataset class for the Refer-Youtube-VOS dataset which was first introduced in the paper:
+    "URVOS: Unified Referring Video Object Segmentation Network with a Large-Scale Benchmark"
+    (see https://link.springer.com/content/pdf/10.1007/978-3-030-58555-6_13.pdf).
+    The original release of the dataset contained both 'first-frame' and 'full-video' expressions. However, the first
+    dataset is not publicly available anymore as now only the harder 'full-video' subset is available to download
+    through the Youtube-VOS referring video object segmentation competition page at:
+    https://competitions.codalab.org/competitions/29139
+    Furthermore, for the competition the subset's original validation set, which consists of 507 videos, was split into
+    two competition 'validation' & 'test' subsets, consisting of 202 and 305 videos respectively. Evaluation can
+    currently only be done on the competition 'validation' subset using the competition's server, as
+    annotations were publicly released only for the 'train' subset of the competition.
+
+    """
+    def __init__(self, img_folder: Path, ann_file: Path, transforms, return_masks: bool, 
+                 num_frames: int, max_skip: int):
+        self.img_folder = img_folder     
+        self.ann_file = ann_file         
+        self._transforms = transforms    
+        self.return_masks = return_masks # not used
+        self.num_frames = num_frames     
+        self.max_skip = max_skip
+        # create video meta data
+        self.prepare_metas()       
+
+        print('\n video num: ', len(self.videos), ' clip num: ', len(self.metas))  
+        print('\n')    
+
+    def prepare_metas(self):
+        # read object information
+        with open(os.path.join(str(self.img_folder), 'meta.json'), 'r') as f:
+            subset_metas_by_video = json.load(f)['videos']
+        
+        # read expression data
+        with open(str(self.ann_file), 'r') as f:
+            subset_expressions_by_video = json.load(f)['videos']
+        self.videos = list(subset_expressions_by_video.keys())
+
+        self.metas = []
+        for vid in self.videos:
+            vid_meta = subset_metas_by_video[vid]
+            vid_data = subset_expressions_by_video[vid]
+            vid_frames = sorted(vid_data['frames'])
+            vid_len = len(vid_frames)
+
+            if vid_len < 11:
+                continue
+
+            for exp_id, exp_dict in vid_data['expressions'].items():
+                # Exclude start_idx (0, 1) and end_idx (vid_len-1, vid_len-2)
+                start_idx , end_idx = 2, vid_len-2
+                bin_size = (end_idx - start_idx) // 4
+
+                bins = []
+                for i in range(4):
+                    bin_start = start_idx + i * bin_size
+                    bin_end = bin_start + bin_size if i < 3 else end_idx
+
+                    bins.append((bin_start, bin_end))
+
+                # Random sample one frame from each bin
+                sample_indx = []
+                for start_idx, end_idx in bins:
+                    sample_indx.append(random.randint(start_idx, end_idx - 1))
+                sample_indx.sort()  # Ensure indices are in order
+
+                
+                for frame_id in sample_indx:              
+                    meta = {
+                        'video': vid,
+                        'exp': exp_dict['exp'],
+                        'obj_id': int(exp_dict['obj_id']),
+                        'frames': vid_frames,
+                        'frame_id' : frame_id,
+                        'sample_frames_id' : sample_indx,
+                        'bins': bins,  
+                        'category': vid_meta['objects'][exp_dict['obj_id']]['category']
+                    }
+                    self.metas.append(meta)
+                
+
+    @staticmethod
+    def bounding_box(img):
+        rows = np.any(img, axis=1)
+        cols = np.any(img, axis=0)
+        rmin, rmax = np.where(rows)[0][[0, -1]]
+        cmin, cmax = np.where(cols)[0][[0, -1]]
+        return rmin, rmax, cmin, cmax # y1, y2, x1, x2 
+        
+    def __len__(self):
+        return len(self.metas)
+        
+    def __getitem__(self, idx):
+        instance_check = False
+        while not instance_check:
+            meta = self.metas[idx]  # dict
+
+
+            video, exp, obj_id, category, frames, frame_id, sample_frames_id, bins = \
+                    meta['video'], meta['exp'], meta['obj_id'], meta['category'], meta['frames'], metas['frame_id'], metas['sample_frames_id'], meta['bins']
+                
+    
+            # clean up the caption
+            exp = " ".join(exp.lower().split())
+            category_id = category_dict[category]
+            vid_len = len(frames)
+
+            # num_frames = self.num_frames
+
+            # read frames and masks
+            imgs, labels, boxes, masks, valid = [], [], [], [], []
+            for frame_indx in sample_frames_id:
+                frame_name = frames[frame_indx]
+                img_path = os.path.join(str(self.img_folder), 'JPEGImages', video, frame_name + '.jpg')
+                mask_path = os.path.join(str(self.img_folder), 'Annotations', video, frame_name + '.png')
+                img = Image.open(img_path).convert('RGB')
+                mask = Image.open(mask_path).convert('P')
+
+                # create the target
+                label =  torch.tensor(category_id) 
+                mask = np.array(mask)
+                mask = (mask==obj_id).astype(np.float32) # 0,1 binary
+                if (mask > 0).any():
+                    y1, y2, x1, x2 = self.bounding_box(mask)
+                    box = torch.tensor([x1, y1, x2, y2]).to(torch.float)
+                    valid.append(1)
+                else: # some frame didn't contain the instance
+                    box = torch.tensor([0, 0, 0, 0]).to(torch.float) 
+                    valid.append(0)
+                mask = torch.from_numpy(mask)
+
+                # append
+                imgs.append(img)
+                labels.append(label)
+                masks.append(mask)
+                boxes.append(box)
+
+            # transform
+            w, h = img.size
+            labels = torch.stack(labels, dim=0) 
+            boxes = torch.stack(boxes, dim=0) 
+            boxes[:, 0::2].clamp_(min=0, max=w)
+            boxes[:, 1::2].clamp_(min=0, max=h)
+            masks = torch.stack(masks, dim=0) 
+            target = {
+                'frames_idx': torch.tensor(sample_frames_id), # [T,]
+                'labels': labels,                        # [T,]
+                'boxes': boxes,                          # [T, 4], xyxy
+                'masks': masks,                          # [T, H, W]
+                'valid': torch.tensor(valid),            # [T,]
+                'caption': exp,
+                'orig_size': torch.as_tensor([int(h), int(w)]), 
+                'size': torch.as_tensor([int(h), int(w)])
+            }
+
+            # "boxes" normalize to [0, 1] and transform from xyxy to cxcywh in self._transform
+            if self._transforms:
+                imgs, target = self._transforms(imgs, target)
+                imgs = torch.stack(imgs, dim=0) # [T, 3, H, W]
+            else:
+                imgs = np.array(imgs)
+                imgs = torch.tensor(imgs.transpose(0, 3, 1, 2))
+            
+            
+            # FIXME: handle "valid", since some box may be removed due to random crop
+            if torch.any(target['valid'] == 1):  # at leatst one instance
+                instance_check = True
+            else:
+                idx = random.randint(0, self.__len__() - 1)
+
+        return imgs, target
+
+
+def make_coco_transforms(image_set, max_size=640):
+    normalize = T.Compose([
+        T.ToTensor(),
+        T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+    scales = [288, 320, 352, 392, 416, 448, 480, 512]
+
+    if image_set == 'train':
+        return T.Compose([
+            T.RandomHorizontalFlip(),
+            T.PhotometricDistort(),
+            T.RandomSelect(
+                T.Compose([
+                    T.RandomResize(scales, max_size=max_size),
+                    T.Check(),
+                ]),
+                T.Compose([
+                    T.RandomResize([400, 500, 600]),
+                    T.RandomSizeCrop(384, 600),
+                    T.RandomResize(scales, max_size=max_size),
+                    T.Check(),
+                ])
+            ),
+            normalize,
+        ])
+    
+    # we do not use the 'val' set since the annotations are inaccessible
+    if image_set == 'val':
+        return T.Compose([
+            T.RandomResize([360], max_size=640),
+            normalize,
+        ])
+
+    raise ValueError(f'unknown {image_set}')
+
+
+def build(image_set, args):
+    root = Path(args.ytvos_path)
+    assert root.exists(), f'provided YTVOS path {root} does not exist'
+    PATHS = {
+        "train": (root / "train", root / "meta_expressions" / "train" / "meta_expressions.json"),
+        "val": (root / "valid", root / "meta_expressions" / "valid" / "meta_expressions.json"),    # not used actually
+    }
+    img_folder, ann_file = PATHS[image_set]
+    # dataset = YTVOSDataset(img_folder, ann_file, transforms=make_coco_transforms(image_set, max_size=args.max_size), return_masks=args.masks, 
+    #                        num_frames=args.num_frames, max_skip=args.max_skip)
+    dataset = YTVOSDataset(img_folder, ann_file, transforms=None, return_masks=args.masks, 
+                           num_frames=args.num_frames, max_skip=args.max_skip)
+    return dataset
+
diff --git a/.history/datasets/ytvos_ref_20250113163121.py b/.history/datasets/ytvos_ref_20250113163121.py
new file mode 100644
index 0000000000000000000000000000000000000000..436ff2213d0a1acc45e2804503f493aacfd018a3
--- /dev/null
+++ b/.history/datasets/ytvos_ref_20250113163121.py
@@ -0,0 +1,245 @@
+"""
+Ref-YoutubeVOS data loader
+"""
+from pathlib import Path
+
+import torch
+from torch.autograd.grad_mode import F
+from torch.utils.data import Dataset
+import datasets.transforms_video as T
+
+import os
+from PIL import Image
+import json
+import numpy as np
+import random
+
+from datasets.categories import ytvos_category_dict as category_dict
+
+
+class YTVOSDataset(Dataset):
+    """
+    A dataset class for the Refer-Youtube-VOS dataset which was first introduced in the paper:
+    "URVOS: Unified Referring Video Object Segmentation Network with a Large-Scale Benchmark"
+    (see https://link.springer.com/content/pdf/10.1007/978-3-030-58555-6_13.pdf).
+    The original release of the dataset contained both 'first-frame' and 'full-video' expressions. However, the first
+    dataset is not publicly available anymore as now only the harder 'full-video' subset is available to download
+    through the Youtube-VOS referring video object segmentation competition page at:
+    https://competitions.codalab.org/competitions/29139
+    Furthermore, for the competition the subset's original validation set, which consists of 507 videos, was split into
+    two competition 'validation' & 'test' subsets, consisting of 202 and 305 videos respectively. Evaluation can
+    currently only be done on the competition 'validation' subset using the competition's server, as
+    annotations were publicly released only for the 'train' subset of the competition.
+
+    """
+    def __init__(self, img_folder: Path, ann_file: Path, transforms, return_masks: bool, 
+                 num_frames: int, max_skip: int):
+        self.img_folder = img_folder     
+        self.ann_file = ann_file         
+        self._transforms = transforms    
+        self.return_masks = return_masks # not used
+        self.num_frames = num_frames     
+        self.max_skip = max_skip
+        # create video meta data
+        self.prepare_metas()       
+
+        print('\n video num: ', len(self.videos), ' clip num: ', len(self.metas))  
+        print('\n')    
+
+    def prepare_metas(self):
+        # read object information
+        with open(os.path.join(str(self.img_folder), 'meta.json'), 'r') as f:
+            subset_metas_by_video = json.load(f)['videos']
+        
+        # read expression data
+        with open(str(self.ann_file), 'r') as f:
+            subset_expressions_by_video = json.load(f)['videos']
+        self.videos = list(subset_expressions_by_video.keys())
+
+        self.metas = []
+        for vid in self.videos:
+            vid_meta = subset_metas_by_video[vid]
+            vid_data = subset_expressions_by_video[vid]
+            vid_frames = sorted(vid_data['frames'])
+            vid_len = len(vid_frames)
+
+            if vid_len < 11:
+                print(f"Too short video: {vid}")
+                continue
+
+            for exp_id, exp_dict in vid_data['expressions'].items():
+                # Exclude start_idx (0, 1) and end_idx (vid_len-1, vid_len-2)
+                start_idx , end_idx = 2, vid_len-2
+                bin_size = (end_idx - start_idx) // 4
+
+                bins = []
+                for i in range(4):
+                    bin_start = start_idx + i * bin_size
+                    bin_end = bin_start + bin_size if i < 3 else end_idx
+
+                    bins.append((bin_start, bin_end))
+
+                # Random sample one frame from each bin
+                sample_indx = []
+                for start_idx, end_idx in bins:
+                    sample_indx.append(random.randint(start_idx, end_idx - 1))
+                sample_indx.sort()  # Ensure indices are in order
+
+                
+                for frame_id in sample_indx:              
+                    meta = {
+                        'video': vid,
+                        'exp': exp_dict['exp'],
+                        'obj_id': int(exp_dict['obj_id']),
+                        'frames': vid_frames,
+                        'frame_id' : frame_id,
+                        'sample_frames_id' : sample_indx,
+                        'bins': bins,  
+                        'category': vid_meta['objects'][exp_dict['obj_id']]['category']
+                    }
+                    self.metas.append(meta)
+                
+
+    @staticmethod
+    def bounding_box(img):
+        rows = np.any(img, axis=1)
+        cols = np.any(img, axis=0)
+        rmin, rmax = np.where(rows)[0][[0, -1]]
+        cmin, cmax = np.where(cols)[0][[0, -1]]
+        return rmin, rmax, cmin, cmax # y1, y2, x1, x2 
+        
+    def __len__(self):
+        return len(self.metas)
+        
+    def __getitem__(self, idx):
+        instance_check = False
+        while not instance_check:
+            meta = self.metas[idx]  # dict
+
+
+            video, exp, obj_id, category, frames, frame_id, sample_frames_id, bins = \
+                    meta['video'], meta['exp'], meta['obj_id'], meta['category'], meta['frames'], metas['frame_id'], metas['sample_frames_id'], meta['bins']
+                
+    
+            # clean up the caption
+            exp = " ".join(exp.lower().split())
+            category_id = category_dict[category]
+            vid_len = len(frames)
+
+            # num_frames = self.num_frames
+
+            # read frames and masks
+            imgs, labels, boxes, masks, valid = [], [], [], [], []
+            for frame_indx in sample_frames_id:
+                frame_name = frames[frame_indx]
+                img_path = os.path.join(str(self.img_folder), 'JPEGImages', video, frame_name + '.jpg')
+                mask_path = os.path.join(str(self.img_folder), 'Annotations', video, frame_name + '.png')
+                img = Image.open(img_path).convert('RGB')
+                mask = Image.open(mask_path).convert('P')
+
+                # create the target
+                label =  torch.tensor(category_id) 
+                mask = np.array(mask)
+                mask = (mask==obj_id).astype(np.float32) # 0,1 binary
+                if (mask > 0).any():
+                    y1, y2, x1, x2 = self.bounding_box(mask)
+                    box = torch.tensor([x1, y1, x2, y2]).to(torch.float)
+                    valid.append(1)
+                else: # some frame didn't contain the instance
+                    box = torch.tensor([0, 0, 0, 0]).to(torch.float) 
+                    valid.append(0)
+                mask = torch.from_numpy(mask)
+
+                # append
+                imgs.append(img)
+                labels.append(label)
+                masks.append(mask)
+                boxes.append(box)
+
+            # transform
+            w, h = img.size
+            labels = torch.stack(labels, dim=0) 
+            boxes = torch.stack(boxes, dim=0) 
+            boxes[:, 0::2].clamp_(min=0, max=w)
+            boxes[:, 1::2].clamp_(min=0, max=h)
+            masks = torch.stack(masks, dim=0) 
+            target = {
+                'frames_idx': torch.tensor(sample_frames_id), # [T,]
+                'labels': labels,                        # [T,]
+                'boxes': boxes,                          # [T, 4], xyxy
+                'masks': masks,                          # [T, H, W]
+                'valid': torch.tensor(valid),            # [T,]
+                'caption': exp,
+                'orig_size': torch.as_tensor([int(h), int(w)]), 
+                'size': torch.as_tensor([int(h), int(w)])
+            }
+
+            # "boxes" normalize to [0, 1] and transform from xyxy to cxcywh in self._transform
+            if self._transforms:
+                imgs, target = self._transforms(imgs, target)
+                imgs = torch.stack(imgs, dim=0) # [T, 3, H, W]
+            else:
+                imgs = np.array(imgs)
+                imgs = torch.tensor(imgs.transpose(0, 3, 1, 2))
+            
+            
+            # FIXME: handle "valid", since some box may be removed due to random crop
+            if torch.any(target['valid'] == 1):  # at leatst one instance
+                instance_check = True
+            else:
+                idx = random.randint(0, self.__len__() - 1)
+
+        return imgs, target
+
+
+def make_coco_transforms(image_set, max_size=640):
+    normalize = T.Compose([
+        T.ToTensor(),
+        T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+    scales = [288, 320, 352, 392, 416, 448, 480, 512]
+
+    if image_set == 'train':
+        return T.Compose([
+            T.RandomHorizontalFlip(),
+            T.PhotometricDistort(),
+            T.RandomSelect(
+                T.Compose([
+                    T.RandomResize(scales, max_size=max_size),
+                    T.Check(),
+                ]),
+                T.Compose([
+                    T.RandomResize([400, 500, 600]),
+                    T.RandomSizeCrop(384, 600),
+                    T.RandomResize(scales, max_size=max_size),
+                    T.Check(),
+                ])
+            ),
+            normalize,
+        ])
+    
+    # we do not use the 'val' set since the annotations are inaccessible
+    if image_set == 'val':
+        return T.Compose([
+            T.RandomResize([360], max_size=640),
+            normalize,
+        ])
+
+    raise ValueError(f'unknown {image_set}')
+
+
+def build(image_set, args):
+    root = Path(args.ytvos_path)
+    assert root.exists(), f'provided YTVOS path {root} does not exist'
+    PATHS = {
+        "train": (root / "train", root / "meta_expressions" / "train" / "meta_expressions.json"),
+        "val": (root / "valid", root / "meta_expressions" / "valid" / "meta_expressions.json"),    # not used actually
+    }
+    img_folder, ann_file = PATHS[image_set]
+    # dataset = YTVOSDataset(img_folder, ann_file, transforms=make_coco_transforms(image_set, max_size=args.max_size), return_masks=args.masks, 
+    #                        num_frames=args.num_frames, max_skip=args.max_skip)
+    dataset = YTVOSDataset(img_folder, ann_file, transforms=None, return_masks=args.masks, 
+                           num_frames=args.num_frames, max_skip=args.max_skip)
+    return dataset
+
diff --git a/.history/datasets/ytvos_ref_20250113163340.py b/.history/datasets/ytvos_ref_20250113163340.py
new file mode 100644
index 0000000000000000000000000000000000000000..55e6a1bab44206c74a93efcf1a9c755e31954d0b
--- /dev/null
+++ b/.history/datasets/ytvos_ref_20250113163340.py
@@ -0,0 +1,249 @@
+"""
+Ref-YoutubeVOS data loader
+"""
+from pathlib import Path
+
+import torch
+from torch.autograd.grad_mode import F
+from torch.utils.data import Dataset
+import datasets.transforms_video as T
+
+import os
+from PIL import Image
+import json
+import numpy as np
+import random
+
+from datasets.categories import ytvos_category_dict as category_dict
+
+
+class YTVOSDataset(Dataset):
+    """
+    A dataset class for the Refer-Youtube-VOS dataset which was first introduced in the paper:
+    "URVOS: Unified Referring Video Object Segmentation Network with a Large-Scale Benchmark"
+    (see https://link.springer.com/content/pdf/10.1007/978-3-030-58555-6_13.pdf).
+    The original release of the dataset contained both 'first-frame' and 'full-video' expressions. However, the first
+    dataset is not publicly available anymore as now only the harder 'full-video' subset is available to download
+    through the Youtube-VOS referring video object segmentation competition page at:
+    https://competitions.codalab.org/competitions/29139
+    Furthermore, for the competition the subset's original validation set, which consists of 507 videos, was split into
+    two competition 'validation' & 'test' subsets, consisting of 202 and 305 videos respectively. Evaluation can
+    currently only be done on the competition 'validation' subset using the competition's server, as
+    annotations were publicly released only for the 'train' subset of the competition.
+
+    """
+    def __init__(self, img_folder: Path, ann_file: Path, transforms, return_masks: bool, 
+                 num_frames: int, max_skip: int):
+        self.img_folder = img_folder     
+        self.ann_file = ann_file         
+        self._transforms = transforms    
+        self.return_masks = return_masks # not used
+        self.num_frames = num_frames     
+        self.max_skip = max_skip
+        # create video meta data
+        self.prepare_metas()       
+
+        print('\n video num: ', len(self.videos), ' clip num: ', len(self.metas))  
+        print('\n')    
+
+    def prepare_metas(self):
+        # read object information
+        with open(os.path.join(str(self.img_folder), 'meta.json'), 'r') as f:
+            subset_metas_by_video = json.load(f)['videos']
+        
+        # read expression data
+        with open(str(self.ann_file), 'r') as f:
+            subset_expressions_by_video = json.load(f)['videos']
+        self.videos = list(subset_expressions_by_video.keys())
+
+        self.metas = []
+        skip_vid_count = 0
+
+        for vid in self.videos:
+            vid_meta = subset_metas_by_video[vid]
+            vid_data = subset_expressions_by_video[vid]
+            vid_frames = sorted(vid_data['frames'])
+            vid_len = len(vid_frames)
+
+            if vid_len < 11:
+                print(f"Too short video: {vid} with frame length {vid_len}")
+                skip_vid_count += 1
+                continue
+
+            for exp_id, exp_dict in vid_data['expressions'].items():
+                # Exclude start_idx (0, 1) and end_idx (vid_len-1, vid_len-2)
+                start_idx , end_idx = 2, vid_len-2
+                bin_size = (end_idx - start_idx) // 4
+
+                bins = []
+                for i in range(4):
+                    bin_start = start_idx + i * bin_size
+                    bin_end = bin_start + bin_size if i < 3 else end_idx
+
+                    bins.append((bin_start, bin_end))
+
+                # Random sample one frame from each bin
+                sample_indx = []
+                for start_idx, end_idx in bins:
+                    sample_indx.append(random.randint(start_idx, end_idx - 1))
+                sample_indx.sort()  # Ensure indices are in order
+
+                
+                for frame_id in sample_indx:              
+                    meta = {
+                        'video': vid,
+                        'exp': exp_dict['exp'],
+                        'obj_id': int(exp_dict['obj_id']),
+                        'frames': vid_frames,
+                        'frame_id' : frame_id,
+                        'sample_frames_id' : sample_indx,
+                        'bins': bins,  
+                        'category': vid_meta['objects'][exp_dict['obj_id']]['category']
+                    }
+                    self.metas.append(meta)
+        print(skip_vid_count)
+                
+
+    @staticmethod
+    def bounding_box(img):
+        rows = np.any(img, axis=1)
+        cols = np.any(img, axis=0)
+        rmin, rmax = np.where(rows)[0][[0, -1]]
+        cmin, cmax = np.where(cols)[0][[0, -1]]
+        return rmin, rmax, cmin, cmax # y1, y2, x1, x2 
+        
+    def __len__(self):
+        return len(self.metas)
+        
+    def __getitem__(self, idx):
+        instance_check = False
+        while not instance_check:
+            meta = self.metas[idx]  # dict
+
+
+            video, exp, obj_id, category, frames, frame_id, sample_frames_id, bins = \
+                    meta['video'], meta['exp'], meta['obj_id'], meta['category'], meta['frames'], metas['frame_id'], metas['sample_frames_id'], meta['bins']
+                
+    
+            # clean up the caption
+            exp = " ".join(exp.lower().split())
+            category_id = category_dict[category]
+            vid_len = len(frames)
+
+            # num_frames = self.num_frames
+
+            # read frames and masks
+            imgs, labels, boxes, masks, valid = [], [], [], [], []
+            for frame_indx in sample_frames_id:
+                frame_name = frames[frame_indx]
+                img_path = os.path.join(str(self.img_folder), 'JPEGImages', video, frame_name + '.jpg')
+                mask_path = os.path.join(str(self.img_folder), 'Annotations', video, frame_name + '.png')
+                img = Image.open(img_path).convert('RGB')
+                mask = Image.open(mask_path).convert('P')
+
+                # create the target
+                label =  torch.tensor(category_id) 
+                mask = np.array(mask)
+                mask = (mask==obj_id).astype(np.float32) # 0,1 binary
+                if (mask > 0).any():
+                    y1, y2, x1, x2 = self.bounding_box(mask)
+                    box = torch.tensor([x1, y1, x2, y2]).to(torch.float)
+                    valid.append(1)
+                else: # some frame didn't contain the instance
+                    box = torch.tensor([0, 0, 0, 0]).to(torch.float) 
+                    valid.append(0)
+                mask = torch.from_numpy(mask)
+
+                # append
+                imgs.append(img)
+                labels.append(label)
+                masks.append(mask)
+                boxes.append(box)
+
+            # transform
+            w, h = img.size
+            labels = torch.stack(labels, dim=0) 
+            boxes = torch.stack(boxes, dim=0) 
+            boxes[:, 0::2].clamp_(min=0, max=w)
+            boxes[:, 1::2].clamp_(min=0, max=h)
+            masks = torch.stack(masks, dim=0) 
+            target = {
+                'frames_idx': torch.tensor(sample_frames_id), # [T,]
+                'labels': labels,                        # [T,]
+                'boxes': boxes,                          # [T, 4], xyxy
+                'masks': masks,                          # [T, H, W]
+                'valid': torch.tensor(valid),            # [T,]
+                'caption': exp,
+                'orig_size': torch.as_tensor([int(h), int(w)]), 
+                'size': torch.as_tensor([int(h), int(w)])
+            }
+
+            # "boxes" normalize to [0, 1] and transform from xyxy to cxcywh in self._transform
+            if self._transforms:
+                imgs, target = self._transforms(imgs, target)
+                imgs = torch.stack(imgs, dim=0) # [T, 3, H, W]
+            else:
+                imgs = np.array(imgs)
+                imgs = torch.tensor(imgs.transpose(0, 3, 1, 2))
+            
+            
+            # FIXME: handle "valid", since some box may be removed due to random crop
+            if torch.any(target['valid'] == 1):  # at leatst one instance
+                instance_check = True
+            else:
+                idx = random.randint(0, self.__len__() - 1)
+
+        return imgs, target
+
+
+def make_coco_transforms(image_set, max_size=640):
+    normalize = T.Compose([
+        T.ToTensor(),
+        T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+    scales = [288, 320, 352, 392, 416, 448, 480, 512]
+
+    if image_set == 'train':
+        return T.Compose([
+            T.RandomHorizontalFlip(),
+            T.PhotometricDistort(),
+            T.RandomSelect(
+                T.Compose([
+                    T.RandomResize(scales, max_size=max_size),
+                    T.Check(),
+                ]),
+                T.Compose([
+                    T.RandomResize([400, 500, 600]),
+                    T.RandomSizeCrop(384, 600),
+                    T.RandomResize(scales, max_size=max_size),
+                    T.Check(),
+                ])
+            ),
+            normalize,
+        ])
+    
+    # we do not use the 'val' set since the annotations are inaccessible
+    if image_set == 'val':
+        return T.Compose([
+            T.RandomResize([360], max_size=640),
+            normalize,
+        ])
+
+    raise ValueError(f'unknown {image_set}')
+
+
+def build(image_set, args):
+    root = Path(args.ytvos_path)
+    assert root.exists(), f'provided YTVOS path {root} does not exist'
+    PATHS = {
+        "train": (root / "train", root / "meta_expressions" / "train" / "meta_expressions.json"),
+        "val": (root / "valid", root / "meta_expressions" / "valid" / "meta_expressions.json"),    # not used actually
+    }
+    img_folder, ann_file = PATHS[image_set]
+    # dataset = YTVOSDataset(img_folder, ann_file, transforms=make_coco_transforms(image_set, max_size=args.max_size), return_masks=args.masks, 
+    #                        num_frames=args.num_frames, max_skip=args.max_skip)
+    dataset = YTVOSDataset(img_folder, ann_file, transforms=None, return_masks=args.masks, 
+                           num_frames=args.num_frames, max_skip=args.max_skip)
+    return dataset
+
diff --git a/.history/datasets/ytvos_ref_20250113163347.py b/.history/datasets/ytvos_ref_20250113163347.py
new file mode 100644
index 0000000000000000000000000000000000000000..47b8cf62ff588cdb362f7c14f60677f8e356254a
--- /dev/null
+++ b/.history/datasets/ytvos_ref_20250113163347.py
@@ -0,0 +1,249 @@
+"""
+Ref-YoutubeVOS data loader
+"""
+from pathlib import Path
+
+import torch
+from torch.autograd.grad_mode import F
+from torch.utils.data import Dataset
+import datasets.transforms_video as T
+
+import os
+from PIL import Image
+import json
+import numpy as np
+import random
+
+from datasets.categories import ytvos_category_dict as category_dict
+
+
+class YTVOSDataset(Dataset):
+    """
+    A dataset class for the Refer-Youtube-VOS dataset which was first introduced in the paper:
+    "URVOS: Unified Referring Video Object Segmentation Network with a Large-Scale Benchmark"
+    (see https://link.springer.com/content/pdf/10.1007/978-3-030-58555-6_13.pdf).
+    The original release of the dataset contained both 'first-frame' and 'full-video' expressions. However, the first
+    dataset is not publicly available anymore as now only the harder 'full-video' subset is available to download
+    through the Youtube-VOS referring video object segmentation competition page at:
+    https://competitions.codalab.org/competitions/29139
+    Furthermore, for the competition the subset's original validation set, which consists of 507 videos, was split into
+    two competition 'validation' & 'test' subsets, consisting of 202 and 305 videos respectively. Evaluation can
+    currently only be done on the competition 'validation' subset using the competition's server, as
+    annotations were publicly released only for the 'train' subset of the competition.
+
+    """
+    def __init__(self, img_folder: Path, ann_file: Path, transforms, return_masks: bool, 
+                 num_frames: int, max_skip: int):
+        self.img_folder = img_folder     
+        self.ann_file = ann_file         
+        self._transforms = transforms    
+        self.return_masks = return_masks # not used
+        self.num_frames = num_frames     
+        self.max_skip = max_skip
+        # create video meta data
+        self.prepare_metas()       
+
+        print('\n video num: ', len(self.videos), ' clip num: ', len(self.metas))  
+        print('\n')    
+
+    def prepare_metas(self):
+        # read object information
+        with open(os.path.join(str(self.img_folder), 'meta.json'), 'r') as f:
+            subset_metas_by_video = json.load(f)['videos']
+        
+        # read expression data
+        with open(str(self.ann_file), 'r') as f:
+            subset_expressions_by_video = json.load(f)['videos']
+        self.videos = list(subset_expressions_by_video.keys())
+
+        self.metas = []
+        skip_vid_count = 0
+
+        for vid in self.videos:
+            vid_meta = subset_metas_by_video[vid]
+            vid_data = subset_expressions_by_video[vid]
+            vid_frames = sorted(vid_data['frames'])
+            vid_len = len(vid_frames)
+
+            if vid_len < 11:
+                #print(f"Too short video: {vid} with frame length {vid_len}")
+                skip_vid_count += 1
+                continue
+
+            for exp_id, exp_dict in vid_data['expressions'].items():
+                # Exclude start_idx (0, 1) and end_idx (vid_len-1, vid_len-2)
+                start_idx , end_idx = 2, vid_len-2
+                bin_size = (end_idx - start_idx) // 4
+
+                bins = []
+                for i in range(4):
+                    bin_start = start_idx + i * bin_size
+                    bin_end = bin_start + bin_size if i < 3 else end_idx
+
+                    bins.append((bin_start, bin_end))
+
+                # Random sample one frame from each bin
+                sample_indx = []
+                for start_idx, end_idx in bins:
+                    sample_indx.append(random.randint(start_idx, end_idx - 1))
+                sample_indx.sort()  # Ensure indices are in order
+
+                
+                for frame_id in sample_indx:              
+                    meta = {
+                        'video': vid,
+                        'exp': exp_dict['exp'],
+                        'obj_id': int(exp_dict['obj_id']),
+                        'frames': vid_frames,
+                        'frame_id' : frame_id,
+                        'sample_frames_id' : sample_indx,
+                        'bins': bins,  
+                        'category': vid_meta['objects'][exp_dict['obj_id']]['category']
+                    }
+                    self.metas.append(meta)
+        print(skip_vid_count)
+                
+
+    @staticmethod
+    def bounding_box(img):
+        rows = np.any(img, axis=1)
+        cols = np.any(img, axis=0)
+        rmin, rmax = np.where(rows)[0][[0, -1]]
+        cmin, cmax = np.where(cols)[0][[0, -1]]
+        return rmin, rmax, cmin, cmax # y1, y2, x1, x2 
+        
+    def __len__(self):
+        return len(self.metas)
+        
+    def __getitem__(self, idx):
+        instance_check = False
+        while not instance_check:
+            meta = self.metas[idx]  # dict
+
+
+            video, exp, obj_id, category, frames, frame_id, sample_frames_id, bins = \
+                    meta['video'], meta['exp'], meta['obj_id'], meta['category'], meta['frames'], metas['frame_id'], metas['sample_frames_id'], meta['bins']
+                
+    
+            # clean up the caption
+            exp = " ".join(exp.lower().split())
+            category_id = category_dict[category]
+            vid_len = len(frames)
+
+            # num_frames = self.num_frames
+
+            # read frames and masks
+            imgs, labels, boxes, masks, valid = [], [], [], [], []
+            for frame_indx in sample_frames_id:
+                frame_name = frames[frame_indx]
+                img_path = os.path.join(str(self.img_folder), 'JPEGImages', video, frame_name + '.jpg')
+                mask_path = os.path.join(str(self.img_folder), 'Annotations', video, frame_name + '.png')
+                img = Image.open(img_path).convert('RGB')
+                mask = Image.open(mask_path).convert('P')
+
+                # create the target
+                label =  torch.tensor(category_id) 
+                mask = np.array(mask)
+                mask = (mask==obj_id).astype(np.float32) # 0,1 binary
+                if (mask > 0).any():
+                    y1, y2, x1, x2 = self.bounding_box(mask)
+                    box = torch.tensor([x1, y1, x2, y2]).to(torch.float)
+                    valid.append(1)
+                else: # some frame didn't contain the instance
+                    box = torch.tensor([0, 0, 0, 0]).to(torch.float) 
+                    valid.append(0)
+                mask = torch.from_numpy(mask)
+
+                # append
+                imgs.append(img)
+                labels.append(label)
+                masks.append(mask)
+                boxes.append(box)
+
+            # transform
+            w, h = img.size
+            labels = torch.stack(labels, dim=0) 
+            boxes = torch.stack(boxes, dim=0) 
+            boxes[:, 0::2].clamp_(min=0, max=w)
+            boxes[:, 1::2].clamp_(min=0, max=h)
+            masks = torch.stack(masks, dim=0) 
+            target = {
+                'frames_idx': torch.tensor(sample_frames_id), # [T,]
+                'labels': labels,                        # [T,]
+                'boxes': boxes,                          # [T, 4], xyxy
+                'masks': masks,                          # [T, H, W]
+                'valid': torch.tensor(valid),            # [T,]
+                'caption': exp,
+                'orig_size': torch.as_tensor([int(h), int(w)]), 
+                'size': torch.as_tensor([int(h), int(w)])
+            }
+
+            # "boxes" normalize to [0, 1] and transform from xyxy to cxcywh in self._transform
+            if self._transforms:
+                imgs, target = self._transforms(imgs, target)
+                imgs = torch.stack(imgs, dim=0) # [T, 3, H, W]
+            else:
+                imgs = np.array(imgs)
+                imgs = torch.tensor(imgs.transpose(0, 3, 1, 2))
+            
+            
+            # FIXME: handle "valid", since some box may be removed due to random crop
+            if torch.any(target['valid'] == 1):  # at leatst one instance
+                instance_check = True
+            else:
+                idx = random.randint(0, self.__len__() - 1)
+
+        return imgs, target
+
+
+def make_coco_transforms(image_set, max_size=640):
+    normalize = T.Compose([
+        T.ToTensor(),
+        T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+    scales = [288, 320, 352, 392, 416, 448, 480, 512]
+
+    if image_set == 'train':
+        return T.Compose([
+            T.RandomHorizontalFlip(),
+            T.PhotometricDistort(),
+            T.RandomSelect(
+                T.Compose([
+                    T.RandomResize(scales, max_size=max_size),
+                    T.Check(),
+                ]),
+                T.Compose([
+                    T.RandomResize([400, 500, 600]),
+                    T.RandomSizeCrop(384, 600),
+                    T.RandomResize(scales, max_size=max_size),
+                    T.Check(),
+                ])
+            ),
+            normalize,
+        ])
+    
+    # we do not use the 'val' set since the annotations are inaccessible
+    if image_set == 'val':
+        return T.Compose([
+            T.RandomResize([360], max_size=640),
+            normalize,
+        ])
+
+    raise ValueError(f'unknown {image_set}')
+
+
+def build(image_set, args):
+    root = Path(args.ytvos_path)
+    assert root.exists(), f'provided YTVOS path {root} does not exist'
+    PATHS = {
+        "train": (root / "train", root / "meta_expressions" / "train" / "meta_expressions.json"),
+        "val": (root / "valid", root / "meta_expressions" / "valid" / "meta_expressions.json"),    # not used actually
+    }
+    img_folder, ann_file = PATHS[image_set]
+    # dataset = YTVOSDataset(img_folder, ann_file, transforms=make_coco_transforms(image_set, max_size=args.max_size), return_masks=args.masks, 
+    #                        num_frames=args.num_frames, max_skip=args.max_skip)
+    dataset = YTVOSDataset(img_folder, ann_file, transforms=None, return_masks=args.masks, 
+                           num_frames=args.num_frames, max_skip=args.max_skip)
+    return dataset
+
diff --git a/.history/datasets/ytvos_ref_20250114202456.py b/.history/datasets/ytvos_ref_20250114202456.py
new file mode 100644
index 0000000000000000000000000000000000000000..585cb47bdf393342321af4346b989a3effa08b2c
--- /dev/null
+++ b/.history/datasets/ytvos_ref_20250114202456.py
@@ -0,0 +1,251 @@
+"""
+Ref-YoutubeVOS data loader
+"""
+from pathlib import Path
+
+import torch
+from torch.autograd.grad_mode import F
+from torch.utils.data import Dataset
+import datasets.transforms_video as T
+
+import os
+from PIL import Image
+import json
+import numpy as np
+import random
+
+from datasets.categories import ytvos_category_dict as category_dict
+
+
+class YTVOSDataset(Dataset):
+    """
+    A dataset class for the Refer-Youtube-VOS dataset which was first introduced in the paper:
+    "URVOS: Unified Referring Video Object Segmentation Network with a Large-Scale Benchmark"
+    (see https://link.springer.com/content/pdf/10.1007/978-3-030-58555-6_13.pdf).
+    The original release of the dataset contained both 'first-frame' and 'full-video' expressions. However, the first
+    dataset is not publicly available anymore as now only the harder 'full-video' subset is available to download
+    through the Youtube-VOS referring video object segmentation competition page at:
+    https://competitions.codalab.org/competitions/29139
+    Furthermore, for the competition the subset's original validation set, which consists of 507 videos, was split into
+    two competition 'validation' & 'test' subsets, consisting of 202 and 305 videos respectively. Evaluation can
+    currently only be done on the competition 'validation' subset using the competition's server, as
+    annotations were publicly released only for the 'train' subset of the competition.
+
+    """
+    def __init__(self, img_folder: Path, ann_file: Path, transforms, return_masks: bool, 
+                 num_frames: int, max_skip: int):
+        self.img_folder = img_folder     
+        self.ann_file = ann_file         
+        self._transforms = transforms    
+        self.return_masks = return_masks # not used
+        self.num_frames = num_frames     
+        self.max_skip = max_skip
+        # create video meta data
+        self.prepare_metas()       
+
+        print('\n video num: ', len(self.videos), ' clip num: ', len(self.metas))  
+        print('\n')    
+
+    def prepare_metas(self):
+        # read object information
+        with open(os.path.join(str(self.img_folder), 'meta.json'), 'r') as f:
+            subset_metas_by_video = json.load(f)['videos']
+        
+        # read expression data
+        with open(str(self.ann_file), 'r') as f:
+            subset_expressions_by_video = json.load(f)['videos']
+        self.videos = list(subset_expressions_by_video.keys())
+
+        self.metas = []
+        skip_vid_count = 0
+
+        for vid in self.videos:
+            vid_meta = subset_metas_by_video[vid]
+            vid_data = subset_expressions_by_video[vid]
+            vid_frames = sorted(vid_data['frames'])
+            vid_len = len(vid_frames)
+
+            if vid_len < 11:
+                #print(f"Too short video: {vid} with frame length {vid_len}")
+                skip_vid_count += 1
+                continue
+
+            return vid_meta, vid_data
+            for exp_id, exp_dict in vid_data['expressions'].items():
+                # Exclude start_idx (0, 1) and end_idx (vid_len-1, vid_len-2)
+                start_idx , end_idx = 2, vid_len-2
+                bin_size = (end_idx - start_idx) // 4
+
+                bins = []
+                for i in range(4):
+                    bin_start = start_idx + i * bin_size
+                    bin_end = bin_start + bin_size if i < 3 else end_idx
+
+                    bins.append((bin_start, bin_end))
+
+                # Random sample one frame from each bin
+                sample_indx = []
+                for start_idx, end_idx in bins:
+                    sample_indx.append(random.randint(start_idx, end_idx - 1))
+                sample_indx.sort()  # Ensure indices are in order
+
+                
+                for sample_id in sample_indx:              
+                    meta = {
+                        'video': vid,
+                        'exp': exp_dict['exp'],
+                        'obj_id': int(exp_dict['obj_id']),
+                        'frames': vid_frames,
+                        'sample_id' : sample_id,
+                        'sample_frames_id' : sample_indx,
+                        'bins': bins,  
+                        'category': vid_meta['objects'][exp_dict['obj_id']]['category']
+                    }
+                    self.metas.append(meta)
+
+        print(f"skipped {skip_vid_count} short videos")
+                
+
+    @staticmethod
+    def bounding_box(img):
+        rows = np.any(img, axis=1)
+        cols = np.any(img, axis=0)
+        rmin, rmax = np.where(rows)[0][[0, -1]]
+        cmin, cmax = np.where(cols)[0][[0, -1]]
+        return rmin, rmax, cmin, cmax # y1, y2, x1, x2 
+        
+    def __len__(self):
+        return len(self.metas)
+        
+    def __getitem__(self, idx):
+        instance_check = False
+        while not instance_check:
+            meta = self.metas[idx]  # dict
+
+
+            video, exp, obj_id, category, frames, sample_id, sample_frames_id, bins = \
+                    meta['video'], meta['exp'], meta['obj_id'], meta['category'], meta['frames'], meta['sample_id'], meta['sample_frames_id'], meta['bins']
+                
+    
+            # clean up the caption
+            exp = " ".join(exp.lower().split())
+            category_id = category_dict[category]
+            vid_len = len(frames)
+
+            # num_frames = self.num_frames
+
+            # read frames and masks
+            imgs, labels, boxes, masks, valid = [], [], [], [], []
+            for frame_indx in sample_frames_id:
+                frame_name = frames[frame_indx]
+                img_path = os.path.join(str(self.img_folder), 'JPEGImages', video, frame_name + '.jpg')
+                mask_path = os.path.join(str(self.img_folder), 'Annotations', video, frame_name + '.png')
+                img = Image.open(img_path).convert('RGB')
+                mask = Image.open(mask_path).convert('P')
+
+                # create the target
+                label =  torch.tensor(category_id) 
+                mask = np.array(mask)
+                mask = (mask==obj_id).astype(np.float32) # 0,1 binary
+                if (mask > 0).any():
+                    y1, y2, x1, x2 = self.bounding_box(mask)
+                    box = torch.tensor([x1, y1, x2, y2]).to(torch.float)
+                    valid.append(1)
+                else: # some frame didn't contain the instance
+                    box = torch.tensor([0, 0, 0, 0]).to(torch.float) 
+                    valid.append(0)
+                mask = torch.from_numpy(mask)
+
+                # append
+                imgs.append(img)
+                labels.append(label)
+                masks.append(mask)
+                boxes.append(box)
+
+            # transform
+            w, h = img.size
+            labels = torch.stack(labels, dim=0) 
+            boxes = torch.stack(boxes, dim=0) 
+            boxes[:, 0::2].clamp_(min=0, max=w)
+            boxes[:, 1::2].clamp_(min=0, max=h)
+            masks = torch.stack(masks, dim=0) 
+            target = {
+                'frames_idx': torch.tensor(sample_frames_id), # [T,]
+                'labels': labels,                        # [T,]
+                'boxes': boxes,                          # [T, 4], xyxy
+                'masks': masks,                          # [T, H, W]
+                'valid': torch.tensor(valid),            # [T,]
+                'caption': exp,
+                'orig_size': torch.as_tensor([int(h), int(w)]), 
+                'size': torch.as_tensor([int(h), int(w)])
+            }
+
+            # "boxes" normalize to [0, 1] and transform from xyxy to cxcywh in self._transform
+            if self._transforms:
+                imgs, target = self._transforms(imgs, target)
+                imgs = torch.stack(imgs, dim=0) # [T, 3, H, W]
+            else:
+                imgs = np.array(imgs)
+                imgs = torch.tensor(imgs.transpose(0, 3, 1, 2))
+            
+            
+            # FIXME: handle "valid", since some box may be removed due to random crop
+            if torch.any(target['valid'] == 1):  # at leatst one instance
+                instance_check = True
+            else:
+                idx = random.randint(0, self.__len__() - 1)
+
+        return imgs, target
+
+
+def make_coco_transforms(image_set, max_size=640):
+    normalize = T.Compose([
+        T.ToTensor(),
+        T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+    scales = [288, 320, 352, 392, 416, 448, 480, 512]
+
+    if image_set == 'train':
+        return T.Compose([
+            T.RandomHorizontalFlip(),
+            T.PhotometricDistort(),
+            T.RandomSelect(
+                T.Compose([
+                    T.RandomResize(scales, max_size=max_size),
+                    T.Check(),
+                ]),
+                T.Compose([
+                    T.RandomResize([400, 500, 600]),
+                    T.RandomSizeCrop(384, 600),
+                    T.RandomResize(scales, max_size=max_size),
+                    T.Check(),
+                ])
+            ),
+            normalize,
+        ])
+    
+    # we do not use the 'val' set since the annotations are inaccessible
+    if image_set == 'val':
+        return T.Compose([
+            T.RandomResize([360], max_size=640),
+            normalize,
+        ])
+
+    raise ValueError(f'unknown {image_set}')
+
+
+def build(image_set, args):
+    root = Path(args.ytvos_path)
+    assert root.exists(), f'provided YTVOS path {root} does not exist'
+    PATHS = {
+        "train": (root / "train", root / "meta_expressions" / "train" / "meta_expressions.json"),
+        "val": (root / "valid", root / "meta_expressions" / "valid" / "meta_expressions.json"),    # not used actually
+    }
+    img_folder, ann_file = PATHS[image_set]
+    # dataset = YTVOSDataset(img_folder, ann_file, transforms=make_coco_transforms(image_set, max_size=args.max_size), return_masks=args.masks, 
+    #                        num_frames=args.num_frames, max_skip=args.max_skip)
+    dataset = YTVOSDataset(img_folder, ann_file, transforms=None, return_masks=args.masks, 
+                           num_frames=args.num_frames, max_skip=args.max_skip)
+    return dataset
+
diff --git a/.history/datasets/ytvos_ref_20250114205130.py b/.history/datasets/ytvos_ref_20250114205130.py
new file mode 100644
index 0000000000000000000000000000000000000000..9174bb9ce61fccc70ee309f6b440c0efecfe639c
--- /dev/null
+++ b/.history/datasets/ytvos_ref_20250114205130.py
@@ -0,0 +1,250 @@
+"""
+Ref-YoutubeVOS data loader
+"""
+from pathlib import Path
+
+import torch
+from torch.autograd.grad_mode import F
+from torch.utils.data import Dataset
+import datasets.transforms_video as T
+
+import os
+from PIL import Image
+import json
+import numpy as np
+import random
+
+from datasets.categories import ytvos_category_dict as category_dict
+
+
+class YTVOSDataset(Dataset):
+    """
+    A dataset class for the Refer-Youtube-VOS dataset which was first introduced in the paper:
+    "URVOS: Unified Referring Video Object Segmentation Network with a Large-Scale Benchmark"
+    (see https://link.springer.com/content/pdf/10.1007/978-3-030-58555-6_13.pdf).
+    The original release of the dataset contained both 'first-frame' and 'full-video' expressions. However, the first
+    dataset is not publicly available anymore as now only the harder 'full-video' subset is available to download
+    through the Youtube-VOS referring video object segmentation competition page at:
+    https://competitions.codalab.org/competitions/29139
+    Furthermore, for the competition the subset's original validation set, which consists of 507 videos, was split into
+    two competition 'validation' & 'test' subsets, consisting of 202 and 305 videos respectively. Evaluation can
+    currently only be done on the competition 'validation' subset using the competition's server, as
+    annotations were publicly released only for the 'train' subset of the competition.
+
+    """
+    def __init__(self, img_folder: Path, ann_file: Path, transforms, return_masks: bool, 
+                 num_frames: int, max_skip: int):
+        self.img_folder = img_folder     
+        self.ann_file = ann_file         
+        self._transforms = transforms    
+        self.return_masks = return_masks # not used
+        self.num_frames = num_frames     
+        self.max_skip = max_skip
+        # create video meta data
+        self.prepare_metas()       
+
+        print('\n video num: ', len(self.videos), ' clip num: ', len(self.metas))  
+        print('\n')    
+
+    def prepare_metas(self):
+        # read object information
+        with open(os.path.join(str(self.img_folder), 'meta.json'), 'r') as f:
+            subset_metas_by_video = json.load(f)['videos']
+        
+        # read expression data
+        with open(str(self.ann_file), 'r') as f:
+            subset_expressions_by_video = json.load(f)['videos']
+        self.videos = list(subset_expressions_by_video.keys())
+
+        self.metas = []
+        skip_vid_count = 0
+
+        for vid in self.videos:
+            vid_meta = subset_metas_by_video[vid]
+            vid_data = subset_expressions_by_video[vid]
+            vid_frames = sorted(vid_data['frames'])
+            vid_len = len(vid_frames)
+
+            if vid_len < 11:
+                #print(f"Too short video: {vid} with frame length {vid_len}")
+                skip_vid_count += 1
+                continue
+
+            for exp_id, exp_dict in vid_data['expressions'].items():
+                # Exclude start_idx (0, 1) and end_idx (vid_len-1, vid_len-2)
+                start_idx , end_idx = 2, vid_len-2
+                bin_size = (end_idx - start_idx) // 4
+
+                bins = []
+                for i in range(4):
+                    bin_start = start_idx + i * bin_size
+                    bin_end = bin_start + bin_size if i < 3 else end_idx
+
+                    bins.append((bin_start, bin_end))
+
+                # Random sample one frame from each bin
+                sample_indx = []
+                for start_idx, end_idx in bins:
+                    sample_indx.append(random.randint(start_idx, end_idx - 1))
+                sample_indx.sort()  # Ensure indices are in order
+
+                
+                for sample_id in sample_indx:              
+                    meta = {
+                        'video': vid,
+                        'exp': exp_dict['exp'],
+                        'obj_id': int(exp_dict['obj_id']),
+                        'frames': vid_frames,
+                        'sample_id' : sample_id,
+                        'sample_frames_id' : sample_indx,
+                        'bins': bins,  
+                        'category': vid_meta['objects'][exp_dict['obj_id']]['category']
+                    }
+                    self.metas.append(meta)
+
+        print(f"skipped {skip_vid_count} short videos")
+                
+
+    @staticmethod
+    def bounding_box(img):
+        rows = np.any(img, axis=1)
+        cols = np.any(img, axis=0)
+        rmin, rmax = np.where(rows)[0][[0, -1]]
+        cmin, cmax = np.where(cols)[0][[0, -1]]
+        return rmin, rmax, cmin, cmax # y1, y2, x1, x2 
+        
+    def __len__(self):
+        return len(self.metas)
+        
+    def __getitem__(self, idx):
+        instance_check = False
+        while not instance_check:
+            meta = self.metas[idx]  # dict
+
+
+            video, exp, obj_id, category, frames, sample_id, sample_frames_id, bins = \
+                    meta['video'], meta['exp'], meta['obj_id'], meta['category'], meta['frames'], meta['sample_id'], meta['sample_frames_id'], meta['bins']
+                
+    
+            # clean up the caption
+            exp = " ".join(exp.lower().split())
+            category_id = category_dict[category]
+            vid_len = len(frames)
+
+            # num_frames = self.num_frames
+
+            # read frames and masks
+            imgs, labels, boxes, masks, valid = [], [], [], [], []
+            for frame_indx in sample_frames_id:
+                frame_name = frames[frame_indx]
+                img_path = os.path.join(str(self.img_folder), 'JPEGImages', video, frame_name + '.jpg')
+                mask_path = os.path.join(str(self.img_folder), 'Annotations', video, frame_name + '.png')
+                img = Image.open(img_path).convert('RGB')
+                mask = Image.open(mask_path).convert('P')
+
+                # create the target
+                label =  torch.tensor(category_id) 
+                mask = np.array(mask)
+                mask = (mask==obj_id).astype(np.float32) # 0,1 binary
+                if (mask > 0).any():
+                    y1, y2, x1, x2 = self.bounding_box(mask)
+                    box = torch.tensor([x1, y1, x2, y2]).to(torch.float)
+                    valid.append(1)
+                else: # some frame didn't contain the instance
+                    box = torch.tensor([0, 0, 0, 0]).to(torch.float) 
+                    valid.append(0)
+                mask = torch.from_numpy(mask)
+
+                # append
+                imgs.append(img)
+                labels.append(label)
+                masks.append(mask)
+                boxes.append(box)
+
+            # transform
+            w, h = img.size
+            labels = torch.stack(labels, dim=0) 
+            boxes = torch.stack(boxes, dim=0) 
+            boxes[:, 0::2].clamp_(min=0, max=w)
+            boxes[:, 1::2].clamp_(min=0, max=h)
+            masks = torch.stack(masks, dim=0) 
+            target = {
+                'frames_idx': torch.tensor(sample_frames_id), # [T,]
+                'labels': labels,                        # [T,]
+                'boxes': boxes,                          # [T, 4], xyxy
+                'masks': masks,                          # [T, H, W]
+                'valid': torch.tensor(valid),            # [T,]
+                'caption': exp,
+                'orig_size': torch.as_tensor([int(h), int(w)]), 
+                'size': torch.as_tensor([int(h), int(w)])
+            }
+
+            # "boxes" normalize to [0, 1] and transform from xyxy to cxcywh in self._transform
+            if self._transforms:
+                imgs, target = self._transforms(imgs, target)
+                imgs = torch.stack(imgs, dim=0) # [T, 3, H, W]
+            else:
+                imgs = np.array(imgs)
+                imgs = torch.tensor(imgs.transpose(0, 3, 1, 2))
+            
+            
+            # FIXME: handle "valid", since some box may be removed due to random crop
+            if torch.any(target['valid'] == 1):  # at leatst one instance
+                instance_check = True
+            else:
+                idx = random.randint(0, self.__len__() - 1)
+
+        return imgs, target
+
+
+def make_coco_transforms(image_set, max_size=640):
+    normalize = T.Compose([
+        T.ToTensor(),
+        T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+    scales = [288, 320, 352, 392, 416, 448, 480, 512]
+
+    if image_set == 'train':
+        return T.Compose([
+            T.RandomHorizontalFlip(),
+            T.PhotometricDistort(),
+            T.RandomSelect(
+                T.Compose([
+                    T.RandomResize(scales, max_size=max_size),
+                    T.Check(),
+                ]),
+                T.Compose([
+                    T.RandomResize([400, 500, 600]),
+                    T.RandomSizeCrop(384, 600),
+                    T.RandomResize(scales, max_size=max_size),
+                    T.Check(),
+                ])
+            ),
+            normalize,
+        ])
+    
+    # we do not use the 'val' set since the annotations are inaccessible
+    if image_set == 'val':
+        return T.Compose([
+            T.RandomResize([360], max_size=640),
+            normalize,
+        ])
+
+    raise ValueError(f'unknown {image_set}')
+
+
+def build(image_set, args):
+    root = Path(args.ytvos_path)
+    assert root.exists(), f'provided YTVOS path {root} does not exist'
+    PATHS = {
+        "train": (root / "train", root / "meta_expressions" / "train" / "meta_expressions.json"),
+        "val": (root / "valid", root / "meta_expressions" / "valid" / "meta_expressions.json"),    # not used actually
+    }
+    img_folder, ann_file = PATHS[image_set]
+    # dataset = YTVOSDataset(img_folder, ann_file, transforms=make_coco_transforms(image_set, max_size=args.max_size), return_masks=args.masks, 
+    #                        num_frames=args.num_frames, max_skip=args.max_skip)
+    dataset = YTVOSDataset(img_folder, ann_file, transforms=None, return_masks=args.masks, 
+                           num_frames=args.num_frames, max_skip=args.max_skip)
+    return dataset
+
diff --git a/.history/datasets/ytvos_ref_20250114211235.py b/.history/datasets/ytvos_ref_20250114211235.py
new file mode 100644
index 0000000000000000000000000000000000000000..476a347dd1a81b51464e073348489d0eafab36d0
--- /dev/null
+++ b/.history/datasets/ytvos_ref_20250114211235.py
@@ -0,0 +1,252 @@
+"""
+Ref-YoutubeVOS data loader
+"""
+from pathlib import Path
+
+import torch
+from torch.autograd.grad_mode import F
+from torch.utils.data import Dataset
+import datasets.transforms_video as T
+
+import os
+from PIL import Image
+import json
+import numpy as np
+import random
+
+from datasets.categories import ytvos_category_dict as category_dict
+
+
+class YTVOSDataset(Dataset):
+    """
+    A dataset class for the Refer-Youtube-VOS dataset which was first introduced in the paper:
+    "URVOS: Unified Referring Video Object Segmentation Network with a Large-Scale Benchmark"
+    (see https://link.springer.com/content/pdf/10.1007/978-3-030-58555-6_13.pdf).
+    The original release of the dataset contained both 'first-frame' and 'full-video' expressions. However, the first
+    dataset is not publicly available anymore as now only the harder 'full-video' subset is available to download
+    through the Youtube-VOS referring video object segmentation competition page at:
+    https://competitions.codalab.org/competitions/29139
+    Furthermore, for the competition the subset's original validation set, which consists of 507 videos, was split into
+    two competition 'validation' & 'test' subsets, consisting of 202 and 305 videos respectively. Evaluation can
+    currently only be done on the competition 'validation' subset using the competition's server, as
+    annotations were publicly released only for the 'train' subset of the competition.
+
+    """
+    def __init__(self, img_folder: Path, ann_file: Path, transforms, return_masks: bool, 
+                 num_frames: int, max_skip: int):
+        self.img_folder = img_folder     
+        self.ann_file = ann_file         
+        self._transforms = transforms    
+        self.return_masks = return_masks # not used
+        self.num_frames = num_frames     
+        self.max_skip = max_skip
+        # create video meta data
+        self.prepare_metas()       
+
+        print('\n video num: ', len(self.videos), ' clip num: ', len(self.metas))  
+        print('\n')    
+
+    def prepare_metas(self):
+        # read object information
+        with open(os.path.join(str(self.img_folder), 'meta.json'), 'r') as f:
+            subset_metas_by_video = json.load(f)['videos']
+        
+        # read expression data
+        with open(str(self.ann_file), 'r') as f:
+            subset_expressions_by_video = json.load(f)['videos']
+        self.videos = list(subset_expressions_by_video.keys())
+
+        self.metas = []
+        skip_vid_count = 0
+
+        for vid in self.videos:
+            vid_meta = subset_metas_by_video[vid]
+            vid_data = subset_expressions_by_video[vid]
+            vid_frames = sorted(vid_data['frames'])
+            vid_len = len(vid_frames)
+
+            if vid_len < 11:
+                #print(f"Too short video: {vid} with frame length {vid_len}")
+                skip_vid_count += 1
+                continue
+
+
+            # Exclude start_idx (0, 1) and end_idx (vid_len-1, vid_len-2)
+            start_idx , end_idx = 2, vid_len-2
+            bin_size = (end_idx - start_idx) // 4
+
+            bins = []
+            for i in range(4):
+                bin_start = start_idx + i * bin_size
+                bin_end = bin_start + bin_size if i < 3 else end_idx
+
+                bins.append((bin_start, bin_end))
+
+            # Random sample one frame from each bin
+            sample_indx = []
+            for start_idx, end_idx in bins:
+                sample_indx.append(random.randint(start_idx, end_idx - 1))
+            sample_indx.sort()  # Ensure indices are in order
+
+
+            meta = {
+                'video':vid,
+                'sample_indx':sample_indx,
+                'bins':bins
+            }
+            obj_id_cat = {}
+            for exp_id, exp_dict in vid_data['expressions'].items():      
+                obj_id = exp_dict['obj_id']
+                print(obj_id, type(obj_id))
+                print(vid_meta.keys())
+                if obj_id not in obj_id_cat:
+                    obj_id_cat[obj_id] = vid_meta[obj_id]['category']    
+            meta['obj_id_cat'] = obj_id_cat
+            self.metas.append(meta)
+
+        print(f"skipped {skip_vid_count} short videos")
+                
+
+    @staticmethod
+    def bounding_box(img):
+        rows = np.any(img, axis=1)
+        cols = np.any(img, axis=0)
+        rmin, rmax = np.where(rows)[0][[0, -1]]
+        cmin, cmax = np.where(cols)[0][[0, -1]]
+        return rmin, rmax, cmin, cmax # y1, y2, x1, x2 
+        
+    def __len__(self):
+        return len(self.metas)
+        
+    def __getitem__(self, idx):
+        instance_check = False
+        while not instance_check:
+            meta = self.metas[idx]  # dict
+
+
+            video, exp, obj_id, category, frames, sample_id, sample_frames_id, bins = \
+                    meta['video'], meta['exp'], meta['obj_id'], meta['category'], meta['frames'], meta['sample_id'], meta['sample_frames_id'], meta['bins']
+                
+    
+            # clean up the caption
+            exp = " ".join(exp.lower().split())
+            category_id = category_dict[category]
+            vid_len = len(frames)
+
+            # num_frames = self.num_frames
+
+            # read frames and masks
+            imgs, labels, boxes, masks, valid = [], [], [], [], []
+            for frame_indx in sample_frames_id:
+                frame_name = frames[frame_indx]
+                img_path = os.path.join(str(self.img_folder), 'JPEGImages', video, frame_name + '.jpg')
+                mask_path = os.path.join(str(self.img_folder), 'Annotations', video, frame_name + '.png')
+                img = Image.open(img_path).convert('RGB')
+                mask = Image.open(mask_path).convert('P')
+
+                # create the target
+                label =  torch.tensor(category_id) 
+                mask = np.array(mask)
+                mask = (mask==obj_id).astype(np.float32) # 0,1 binary
+                if (mask > 0).any():
+                    y1, y2, x1, x2 = self.bounding_box(mask)
+                    box = torch.tensor([x1, y1, x2, y2]).to(torch.float)
+                    valid.append(1)
+                else: # some frame didn't contain the instance
+                    box = torch.tensor([0, 0, 0, 0]).to(torch.float) 
+                    valid.append(0)
+                mask = torch.from_numpy(mask)
+
+                # append
+                imgs.append(img)
+                labels.append(label)
+                masks.append(mask)
+                boxes.append(box)
+
+            # transform
+            w, h = img.size
+            labels = torch.stack(labels, dim=0) 
+            boxes = torch.stack(boxes, dim=0) 
+            boxes[:, 0::2].clamp_(min=0, max=w)
+            boxes[:, 1::2].clamp_(min=0, max=h)
+            masks = torch.stack(masks, dim=0) 
+            target = {
+                'frames_idx': torch.tensor(sample_frames_id), # [T,]
+                'labels': labels,                        # [T,]
+                'boxes': boxes,                          # [T, 4], xyxy
+                'masks': masks,                          # [T, H, W]
+                'valid': torch.tensor(valid),            # [T,]
+                'caption': exp,
+                'orig_size': torch.as_tensor([int(h), int(w)]), 
+                'size': torch.as_tensor([int(h), int(w)])
+            }
+
+            # "boxes" normalize to [0, 1] and transform from xyxy to cxcywh in self._transform
+            if self._transforms:
+                imgs, target = self._transforms(imgs, target)
+                imgs = torch.stack(imgs, dim=0) # [T, 3, H, W]
+            else:
+                imgs = np.array(imgs)
+                imgs = torch.tensor(imgs.transpose(0, 3, 1, 2))
+            
+            
+            # FIXME: handle "valid", since some box may be removed due to random crop
+            if torch.any(target['valid'] == 1):  # at leatst one instance
+                instance_check = True
+            else:
+                idx = random.randint(0, self.__len__() - 1)
+
+        return imgs, target
+
+
+def make_coco_transforms(image_set, max_size=640):
+    normalize = T.Compose([
+        T.ToTensor(),
+        T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+    scales = [288, 320, 352, 392, 416, 448, 480, 512]
+
+    if image_set == 'train':
+        return T.Compose([
+            T.RandomHorizontalFlip(),
+            T.PhotometricDistort(),
+            T.RandomSelect(
+                T.Compose([
+                    T.RandomResize(scales, max_size=max_size),
+                    T.Check(),
+                ]),
+                T.Compose([
+                    T.RandomResize([400, 500, 600]),
+                    T.RandomSizeCrop(384, 600),
+                    T.RandomResize(scales, max_size=max_size),
+                    T.Check(),
+                ])
+            ),
+            normalize,
+        ])
+    
+    # we do not use the 'val' set since the annotations are inaccessible
+    if image_set == 'val':
+        return T.Compose([
+            T.RandomResize([360], max_size=640),
+            normalize,
+        ])
+
+    raise ValueError(f'unknown {image_set}')
+
+
+def build(image_set, args):
+    root = Path(args.ytvos_path)
+    assert root.exists(), f'provided YTVOS path {root} does not exist'
+    PATHS = {
+        "train": (root / "train", root / "meta_expressions" / "train" / "meta_expressions.json"),
+        "val": (root / "valid", root / "meta_expressions" / "valid" / "meta_expressions.json"),    # not used actually
+    }
+    img_folder, ann_file = PATHS[image_set]
+    # dataset = YTVOSDataset(img_folder, ann_file, transforms=make_coco_transforms(image_set, max_size=args.max_size), return_masks=args.masks, 
+    #                        num_frames=args.num_frames, max_skip=args.max_skip)
+    dataset = YTVOSDataset(img_folder, ann_file, transforms=None, return_masks=args.masks, 
+                           num_frames=args.num_frames, max_skip=args.max_skip)
+    return dataset
+
diff --git a/.history/datasets/ytvos_ref_20250114211331.py b/.history/datasets/ytvos_ref_20250114211331.py
new file mode 100644
index 0000000000000000000000000000000000000000..12e7b86aedbd3a6ea479c56929d5e5de825ac879
--- /dev/null
+++ b/.history/datasets/ytvos_ref_20250114211331.py
@@ -0,0 +1,250 @@
+"""
+Ref-YoutubeVOS data loader
+"""
+from pathlib import Path
+
+import torch
+from torch.autograd.grad_mode import F
+from torch.utils.data import Dataset
+import datasets.transforms_video as T
+
+import os
+from PIL import Image
+import json
+import numpy as np
+import random
+
+from datasets.categories import ytvos_category_dict as category_dict
+
+
+class YTVOSDataset(Dataset):
+    """
+    A dataset class for the Refer-Youtube-VOS dataset which was first introduced in the paper:
+    "URVOS: Unified Referring Video Object Segmentation Network with a Large-Scale Benchmark"
+    (see https://link.springer.com/content/pdf/10.1007/978-3-030-58555-6_13.pdf).
+    The original release of the dataset contained both 'first-frame' and 'full-video' expressions. However, the first
+    dataset is not publicly available anymore as now only the harder 'full-video' subset is available to download
+    through the Youtube-VOS referring video object segmentation competition page at:
+    https://competitions.codalab.org/competitions/29139
+    Furthermore, for the competition the subset's original validation set, which consists of 507 videos, was split into
+    two competition 'validation' & 'test' subsets, consisting of 202 and 305 videos respectively. Evaluation can
+    currently only be done on the competition 'validation' subset using the competition's server, as
+    annotations were publicly released only for the 'train' subset of the competition.
+
+    """
+    def __init__(self, img_folder: Path, ann_file: Path, transforms, return_masks: bool, 
+                 num_frames: int, max_skip: int):
+        self.img_folder = img_folder     
+        self.ann_file = ann_file         
+        self._transforms = transforms    
+        self.return_masks = return_masks # not used
+        self.num_frames = num_frames     
+        self.max_skip = max_skip
+        # create video meta data
+        self.prepare_metas()       
+
+        print('\n video num: ', len(self.videos), ' clip num: ', len(self.metas))  
+        print('\n')    
+
+    def prepare_metas(self):
+        # read object information
+        with open(os.path.join(str(self.img_folder), 'meta.json'), 'r') as f:
+            subset_metas_by_video = json.load(f)['videos']
+        
+        # read expression data
+        with open(str(self.ann_file), 'r') as f:
+            subset_expressions_by_video = json.load(f)['videos']
+        self.videos = list(subset_expressions_by_video.keys())
+
+        self.metas = []
+        skip_vid_count = 0
+
+        for vid in self.videos:
+            vid_meta = subset_metas_by_video[vid]
+            vid_data = subset_expressions_by_video[vid]
+            vid_frames = sorted(vid_data['frames'])
+            vid_len = len(vid_frames)
+
+            if vid_len < 11:
+                #print(f"Too short video: {vid} with frame length {vid_len}")
+                skip_vid_count += 1
+                continue
+
+
+            # Exclude start_idx (0, 1) and end_idx (vid_len-1, vid_len-2)
+            start_idx , end_idx = 2, vid_len-2
+            bin_size = (end_idx - start_idx) // 4
+
+            bins = []
+            for i in range(4):
+                bin_start = start_idx + i * bin_size
+                bin_end = bin_start + bin_size if i < 3 else end_idx
+
+                bins.append((bin_start, bin_end))
+
+            # Random sample one frame from each bin
+            sample_indx = []
+            for start_idx, end_idx in bins:
+                sample_indx.append(random.randint(start_idx, end_idx - 1))
+            sample_indx.sort()  # Ensure indices are in order
+
+
+            meta = {
+                'video':vid,
+                'sample_indx':sample_indx,
+                'bins':bins
+            }
+            obj_id_cat = {}
+            for exp_id, exp_dict in vid_data['expressions'].items():      
+                obj_id = exp_dict['obj_id']
+                if obj_id not in obj_id_cat:
+                    obj_id_cat[obj_id] = vid_meta['objects'][obj_id]['category']    
+            meta['obj_id_cat'] = obj_id_cat
+            self.metas.append(meta)
+
+        print(f"skipped {skip_vid_count} short videos")
+                
+
+    @staticmethod
+    def bounding_box(img):
+        rows = np.any(img, axis=1)
+        cols = np.any(img, axis=0)
+        rmin, rmax = np.where(rows)[0][[0, -1]]
+        cmin, cmax = np.where(cols)[0][[0, -1]]
+        return rmin, rmax, cmin, cmax # y1, y2, x1, x2 
+        
+    def __len__(self):
+        return len(self.metas)
+        
+    def __getitem__(self, idx):
+        instance_check = False
+        while not instance_check:
+            meta = self.metas[idx]  # dict
+
+
+            video, exp, obj_id, category, frames, sample_id, sample_frames_id, bins = \
+                    meta['video'], meta['exp'], meta['obj_id'], meta['category'], meta['frames'], meta['sample_id'], meta['sample_frames_id'], meta['bins']
+                
+    
+            # clean up the caption
+            exp = " ".join(exp.lower().split())
+            category_id = category_dict[category]
+            vid_len = len(frames)
+
+            # num_frames = self.num_frames
+
+            # read frames and masks
+            imgs, labels, boxes, masks, valid = [], [], [], [], []
+            for frame_indx in sample_frames_id:
+                frame_name = frames[frame_indx]
+                img_path = os.path.join(str(self.img_folder), 'JPEGImages', video, frame_name + '.jpg')
+                mask_path = os.path.join(str(self.img_folder), 'Annotations', video, frame_name + '.png')
+                img = Image.open(img_path).convert('RGB')
+                mask = Image.open(mask_path).convert('P')
+
+                # create the target
+                label =  torch.tensor(category_id) 
+                mask = np.array(mask)
+                mask = (mask==obj_id).astype(np.float32) # 0,1 binary
+                if (mask > 0).any():
+                    y1, y2, x1, x2 = self.bounding_box(mask)
+                    box = torch.tensor([x1, y1, x2, y2]).to(torch.float)
+                    valid.append(1)
+                else: # some frame didn't contain the instance
+                    box = torch.tensor([0, 0, 0, 0]).to(torch.float) 
+                    valid.append(0)
+                mask = torch.from_numpy(mask)
+
+                # append
+                imgs.append(img)
+                labels.append(label)
+                masks.append(mask)
+                boxes.append(box)
+
+            # transform
+            w, h = img.size
+            labels = torch.stack(labels, dim=0) 
+            boxes = torch.stack(boxes, dim=0) 
+            boxes[:, 0::2].clamp_(min=0, max=w)
+            boxes[:, 1::2].clamp_(min=0, max=h)
+            masks = torch.stack(masks, dim=0) 
+            target = {
+                'frames_idx': torch.tensor(sample_frames_id), # [T,]
+                'labels': labels,                        # [T,]
+                'boxes': boxes,                          # [T, 4], xyxy
+                'masks': masks,                          # [T, H, W]
+                'valid': torch.tensor(valid),            # [T,]
+                'caption': exp,
+                'orig_size': torch.as_tensor([int(h), int(w)]), 
+                'size': torch.as_tensor([int(h), int(w)])
+            }
+
+            # "boxes" normalize to [0, 1] and transform from xyxy to cxcywh in self._transform
+            if self._transforms:
+                imgs, target = self._transforms(imgs, target)
+                imgs = torch.stack(imgs, dim=0) # [T, 3, H, W]
+            else:
+                imgs = np.array(imgs)
+                imgs = torch.tensor(imgs.transpose(0, 3, 1, 2))
+            
+            
+            # FIXME: handle "valid", since some box may be removed due to random crop
+            if torch.any(target['valid'] == 1):  # at leatst one instance
+                instance_check = True
+            else:
+                idx = random.randint(0, self.__len__() - 1)
+
+        return imgs, target
+
+
+def make_coco_transforms(image_set, max_size=640):
+    normalize = T.Compose([
+        T.ToTensor(),
+        T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+    scales = [288, 320, 352, 392, 416, 448, 480, 512]
+
+    if image_set == 'train':
+        return T.Compose([
+            T.RandomHorizontalFlip(),
+            T.PhotometricDistort(),
+            T.RandomSelect(
+                T.Compose([
+                    T.RandomResize(scales, max_size=max_size),
+                    T.Check(),
+                ]),
+                T.Compose([
+                    T.RandomResize([400, 500, 600]),
+                    T.RandomSizeCrop(384, 600),
+                    T.RandomResize(scales, max_size=max_size),
+                    T.Check(),
+                ])
+            ),
+            normalize,
+        ])
+    
+    # we do not use the 'val' set since the annotations are inaccessible
+    if image_set == 'val':
+        return T.Compose([
+            T.RandomResize([360], max_size=640),
+            normalize,
+        ])
+
+    raise ValueError(f'unknown {image_set}')
+
+
+def build(image_set, args):
+    root = Path(args.ytvos_path)
+    assert root.exists(), f'provided YTVOS path {root} does not exist'
+    PATHS = {
+        "train": (root / "train", root / "meta_expressions" / "train" / "meta_expressions.json"),
+        "val": (root / "valid", root / "meta_expressions" / "valid" / "meta_expressions.json"),    # not used actually
+    }
+    img_folder, ann_file = PATHS[image_set]
+    # dataset = YTVOSDataset(img_folder, ann_file, transforms=make_coco_transforms(image_set, max_size=args.max_size), return_masks=args.masks, 
+    #                        num_frames=args.num_frames, max_skip=args.max_skip)
+    dataset = YTVOSDataset(img_folder, ann_file, transforms=None, return_masks=args.masks, 
+                           num_frames=args.num_frames, max_skip=args.max_skip)
+    return dataset
+
diff --git a/.history/datasets/ytvos_ref_20250114211640.py b/.history/datasets/ytvos_ref_20250114211640.py
new file mode 100644
index 0000000000000000000000000000000000000000..753afcbd1247d0d07cdb53a8f00e6380d33f8e50
--- /dev/null
+++ b/.history/datasets/ytvos_ref_20250114211640.py
@@ -0,0 +1,242 @@
+"""
+Ref-YoutubeVOS data loader
+"""
+from pathlib import Path
+
+import torch
+from torch.autograd.grad_mode import F
+from torch.utils.data import Dataset
+import datasets.transforms_video as T
+
+import os
+from PIL import Image
+import json
+import numpy as np
+import random
+
+from datasets.categories import ytvos_category_dict as category_dict
+
+
+class YTVOSDataset(Dataset):
+    """
+    A dataset class for the Refer-Youtube-VOS dataset which was first introduced in the paper:
+    "URVOS: Unified Referring Video Object Segmentation Network with a Large-Scale Benchmark"
+    (see https://link.springer.com/content/pdf/10.1007/978-3-030-58555-6_13.pdf).
+    The original release of the dataset contained both 'first-frame' and 'full-video' expressions. However, the first
+    dataset is not publicly available anymore as now only the harder 'full-video' subset is available to download
+    through the Youtube-VOS referring video object segmentation competition page at:
+    https://competitions.codalab.org/competitions/29139
+    Furthermore, for the competition the subset's original validation set, which consists of 507 videos, was split into
+    two competition 'validation' & 'test' subsets, consisting of 202 and 305 videos respectively. Evaluation can
+    currently only be done on the competition 'validation' subset using the competition's server, as
+    annotations were publicly released only for the 'train' subset of the competition.
+
+    """
+    def __init__(self, img_folder: Path, ann_file: Path, transforms, return_masks: bool, 
+                 num_frames: int, max_skip: int):
+        self.img_folder = img_folder     
+        self.ann_file = ann_file         
+        self._transforms = transforms    
+        self.return_masks = return_masks # not used
+        self.num_frames = num_frames     
+        self.max_skip = max_skip
+        # create video meta data
+        self.prepare_metas()       
+
+        print('\n video num: ', len(self.videos), ' clip num: ', len(self.metas))  
+        print('\n')    
+
+    def prepare_metas(self):
+        # read object information
+        with open(os.path.join(str(self.img_folder), 'meta.json'), 'r') as f:
+            subset_metas_by_video = json.load(f)['videos']
+        
+        # read expression data
+        with open(str(self.ann_file), 'r') as f:
+            subset_expressions_by_video = json.load(f)['videos']
+        self.videos = list(subset_expressions_by_video.keys())
+
+        self.metas = []
+        skip_vid_count = 0
+
+        for vid in self.videos:
+            vid_meta = subset_metas_by_video[vid]
+            vid_data = subset_expressions_by_video[vid]
+            vid_frames = sorted(vid_data['frames'])
+            vid_len = len(vid_frames)
+
+            if vid_len < 11:
+                #print(f"Too short video: {vid} with frame length {vid_len}")
+                skip_vid_count += 1
+                continue
+
+
+            # Exclude start_idx (0, 1) and end_idx (vid_len-1, vid_len-2)
+            start_idx , end_idx = 2, vid_len-2
+            bin_size = (end_idx - start_idx) // 4
+
+            bins = []
+            for i in range(4):
+                bin_start = start_idx + i * bin_size
+                bin_end = bin_start + bin_size if i < 3 else end_idx
+
+                bins.append((bin_start, bin_end))
+
+            # Random sample one frame from each bin
+            sample_indx = []
+            for start_idx, end_idx in bins:
+                sample_indx.append(random.randint(start_idx, end_idx - 1))
+            sample_indx.sort()  # Ensure indices are in order
+
+
+            meta = {
+                'video':vid,
+                'sample_indx':sample_indx,
+                'bins':bins,
+                'frames':vid_frames
+            }
+            obj_id_cat = {}
+            for exp_id, exp_dict in vid_data['expressions'].items():      
+                obj_id = exp_dict['obj_id']
+                if obj_id not in obj_id_cat:
+                    obj_id_cat[obj_id] = vid_meta['objects'][obj_id]['category']    
+            meta['obj_id_cat'] = obj_id_cat
+            self.metas.append(meta)
+
+        print(f"skipped {skip_vid_count} short videos")
+                
+
+    @staticmethod
+    def bounding_box(img):
+        rows = np.any(img, axis=1)
+        cols = np.any(img, axis=0)
+        rmin, rmax = np.where(rows)[0][[0, -1]]
+        cmin, cmax = np.where(cols)[0][[0, -1]]
+        return rmin, rmax, cmin, cmax # y1, y2, x1, x2 
+        
+    def __len__(self):
+        return len(self.metas)
+        
+    def __getitem__(self, idx):
+        instance_check = False
+        while not instance_check:
+            meta = self.metas[idx]  # dict
+
+            video, sample_indx, bins, obj_id_cat = \
+                meta['video'], meta['sample_indx'], meta['bins'], meta['obj_id_cat']
+
+            # read frames and masks
+            imgs, labels, boxes, masks, valid = [], [], [], [], []
+            for frame_indx in sample_indx:
+                frame_name = frames[frame_indx]
+                img_path = os.path.join(str(self.img_folder), 'JPEGImages', video, frame_name + '.jpg')
+                mask_path = os.path.join(str(self.img_folder), 'Annotations', video, frame_name + '.png')
+                img = Image.open(img_path).convert('RGB')
+                mask = Image.open(mask_path).convert('P')
+
+                # create the target
+                label =  torch.tensor(category_id) 
+                mask = np.array(mask)
+                mask = (mask==obj_id).astype(np.float32) # 0,1 binary
+                if (mask > 0).any():
+                    y1, y2, x1, x2 = self.bounding_box(mask)
+                    box = torch.tensor([x1, y1, x2, y2]).to(torch.float)
+                    valid.append(1)
+                else: # some frame didn't contain the instance
+                    box = torch.tensor([0, 0, 0, 0]).to(torch.float) 
+                    valid.append(0)
+                mask = torch.from_numpy(mask)
+
+                # append
+                imgs.append(img)
+                labels.append(label)
+                masks.append(mask)
+                boxes.append(box)
+
+            # transform
+            w, h = img.size
+            labels = torch.stack(labels, dim=0) 
+            boxes = torch.stack(boxes, dim=0) 
+            boxes[:, 0::2].clamp_(min=0, max=w)
+            boxes[:, 1::2].clamp_(min=0, max=h)
+            masks = torch.stack(masks, dim=0) 
+            target = {
+                'frames_idx': torch.tensor(sample_frames_id), # [T,]
+                'labels': labels,                        # [T,]
+                'boxes': boxes,                          # [T, 4], xyxy
+                'masks': masks,                          # [T, H, W]
+                'valid': torch.tensor(valid),            # [T,]
+                'caption': exp,
+                'orig_size': torch.as_tensor([int(h), int(w)]), 
+                'size': torch.as_tensor([int(h), int(w)])
+            }
+
+            # "boxes" normalize to [0, 1] and transform from xyxy to cxcywh in self._transform
+            if self._transforms:
+                imgs, target = self._transforms(imgs, target)
+                imgs = torch.stack(imgs, dim=0) # [T, 3, H, W]
+            else:
+                imgs = np.array(imgs)
+                imgs = torch.tensor(imgs.transpose(0, 3, 1, 2))
+            
+            
+            # FIXME: handle "valid", since some box may be removed due to random crop
+            if torch.any(target['valid'] == 1):  # at leatst one instance
+                instance_check = True
+            else:
+                idx = random.randint(0, self.__len__() - 1)
+
+        return imgs, target
+
+
+def make_coco_transforms(image_set, max_size=640):
+    normalize = T.Compose([
+        T.ToTensor(),
+        T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+    scales = [288, 320, 352, 392, 416, 448, 480, 512]
+
+    if image_set == 'train':
+        return T.Compose([
+            T.RandomHorizontalFlip(),
+            T.PhotometricDistort(),
+            T.RandomSelect(
+                T.Compose([
+                    T.RandomResize(scales, max_size=max_size),
+                    T.Check(),
+                ]),
+                T.Compose([
+                    T.RandomResize([400, 500, 600]),
+                    T.RandomSizeCrop(384, 600),
+                    T.RandomResize(scales, max_size=max_size),
+                    T.Check(),
+                ])
+            ),
+            normalize,
+        ])
+    
+    # we do not use the 'val' set since the annotations are inaccessible
+    if image_set == 'val':
+        return T.Compose([
+            T.RandomResize([360], max_size=640),
+            normalize,
+        ])
+
+    raise ValueError(f'unknown {image_set}')
+
+
+def build(image_set, args):
+    root = Path(args.ytvos_path)
+    assert root.exists(), f'provided YTVOS path {root} does not exist'
+    PATHS = {
+        "train": (root / "train", root / "meta_expressions" / "train" / "meta_expressions.json"),
+        "val": (root / "valid", root / "meta_expressions" / "valid" / "meta_expressions.json"),    # not used actually
+    }
+    img_folder, ann_file = PATHS[image_set]
+    # dataset = YTVOSDataset(img_folder, ann_file, transforms=make_coco_transforms(image_set, max_size=args.max_size), return_masks=args.masks, 
+    #                        num_frames=args.num_frames, max_skip=args.max_skip)
+    dataset = YTVOSDataset(img_folder, ann_file, transforms=None, return_masks=args.masks, 
+                           num_frames=args.num_frames, max_skip=args.max_skip)
+    return dataset
+
diff --git a/.history/datasets/ytvos_ref_20250114211841.py b/.history/datasets/ytvos_ref_20250114211841.py
new file mode 100644
index 0000000000000000000000000000000000000000..4c7afe7b0817157e4dc8b6b31fb33359f77e3e62
--- /dev/null
+++ b/.history/datasets/ytvos_ref_20250114211841.py
@@ -0,0 +1,242 @@
+"""
+Ref-YoutubeVOS data loader
+"""
+from pathlib import Path
+
+import torch
+from torch.autograd.grad_mode import F
+from torch.utils.data import Dataset
+import datasets.transforms_video as T
+
+import os
+from PIL import Image
+import json
+import numpy as np
+import random
+
+from datasets.categories import ytvos_category_dict as category_dict
+
+
+class YTVOSDataset(Dataset):
+    """
+    A dataset class for the Refer-Youtube-VOS dataset which was first introduced in the paper:
+    "URVOS: Unified Referring Video Object Segmentation Network with a Large-Scale Benchmark"
+    (see https://link.springer.com/content/pdf/10.1007/978-3-030-58555-6_13.pdf).
+    The original release of the dataset contained both 'first-frame' and 'full-video' expressions. However, the first
+    dataset is not publicly available anymore as now only the harder 'full-video' subset is available to download
+    through the Youtube-VOS referring video object segmentation competition page at:
+    https://competitions.codalab.org/competitions/29139
+    Furthermore, for the competition the subset's original validation set, which consists of 507 videos, was split into
+    two competition 'validation' & 'test' subsets, consisting of 202 and 305 videos respectively. Evaluation can
+    currently only be done on the competition 'validation' subset using the competition's server, as
+    annotations were publicly released only for the 'train' subset of the competition.
+
+    """
+    def __init__(self, img_folder: Path, ann_file: Path, transforms, return_masks: bool, 
+                 num_frames: int, max_skip: int):
+        self.img_folder = img_folder     
+        self.ann_file = ann_file         
+        self._transforms = transforms    
+        self.return_masks = return_masks # not used
+        self.num_frames = num_frames     
+        self.max_skip = max_skip
+        # create video meta data
+        self.prepare_metas()       
+
+        print('\n video num: ', len(self.videos), ' clip num: ', len(self.metas))  
+        print('\n')    
+
+    def prepare_metas(self):
+        # read object information
+        with open(os.path.join(str(self.img_folder), 'meta.json'), 'r') as f:
+            subset_metas_by_video = json.load(f)['videos']
+        
+        # read expression data
+        with open(str(self.ann_file), 'r') as f:
+            subset_expressions_by_video = json.load(f)['videos']
+        self.videos = list(subset_expressions_by_video.keys())
+
+        self.metas = []
+        skip_vid_count = 0
+
+        for vid in self.videos:
+            vid_meta = subset_metas_by_video[vid]
+            vid_data = subset_expressions_by_video[vid]
+            vid_frames = sorted(vid_data['frames'])
+            vid_len = len(vid_frames)
+
+            if vid_len < 11:
+                #print(f"Too short video: {vid} with frame length {vid_len}")
+                skip_vid_count += 1
+                continue
+
+
+            # Exclude start_idx (0, 1) and end_idx (vid_len-1, vid_len-2)
+            start_idx , end_idx = 2, vid_len-2
+            bin_size = (end_idx - start_idx) // 4
+
+            bins = []
+            for i in range(4):
+                bin_start = start_idx + i * bin_size
+                bin_end = bin_start + bin_size if i < 3 else end_idx
+
+                bins.append((bin_start, bin_end))
+
+            # Random sample one frame from each bin
+            sample_indx = []
+            for start_idx, end_idx in bins:
+                sample_indx.append(random.randint(start_idx, end_idx - 1))
+            sample_indx.sort()  # Ensure indices are in order
+
+
+            meta = {
+                'video':vid,
+                'sample_indx':sample_indx,
+                'bins':bins,
+                'frames':vid_frames
+            }
+            obj_id_cat = {}
+            for exp_id, exp_dict in vid_data['expressions'].items():      
+                obj_id = exp_dict['obj_id']
+                if obj_id not in obj_id_cat:
+                    obj_id_cat[obj_id] = vid_meta['objects'][obj_id]['category']    
+            meta['obj_id_cat'] = obj_id_cat
+            self.metas.append(meta)
+
+        print(f"skipped {skip_vid_count} short videos")
+                
+
+    @staticmethod
+    def bounding_box(img):
+        rows = np.any(img, axis=1)
+        cols = np.any(img, axis=0)
+        rmin, rmax = np.where(rows)[0][[0, -1]]
+        cmin, cmax = np.where(cols)[0][[0, -1]]
+        return rmin, rmax, cmin, cmax # y1, y2, x1, x2 
+        
+    def __len__(self):
+        return len(self.metas)
+        
+    def __getitem__(self, idx):
+        instance_check = False
+        while not instance_check:
+            meta = self.metas[idx]  # dict
+
+            video, sample_indx, bins, frames, obj_id_cat = \
+                meta['video'], meta['sample_indx'], meta['bins'], meta['frames'], meta['obj_id_cat']
+
+            # read frames and masks
+            imgs, labels, boxes, masks, valid = [], [], [], [], []
+            for frame_indx in sample_indx:
+                frame_name = frames[frame_indx]
+                img_path = os.path.join(str(self.img_folder), 'JPEGImages', video, frame_name + '.jpg')
+                mask_path = os.path.join(str(self.img_folder), 'Annotations', video, frame_name + '.png')
+                img = Image.open(img_path).convert('RGB')
+                mask = Image.open(mask_path).convert('P')
+
+                # create the target
+                label =  torch.tensor(category_id) 
+                mask = np.array(mask)
+                mask = (mask==obj_id).astype(np.float32) # 0,1 binary
+                if (mask > 0).any():
+                    y1, y2, x1, x2 = self.bounding_box(mask)
+                    box = torch.tensor([x1, y1, x2, y2]).to(torch.float)
+                    valid.append(1)
+                else: # some frame didn't contain the instance
+                    box = torch.tensor([0, 0, 0, 0]).to(torch.float) 
+                    valid.append(0)
+                mask = torch.from_numpy(mask)
+
+                # append
+                imgs.append(img)
+                labels.append(label)
+                masks.append(mask)
+                boxes.append(box)
+
+            # transform
+            w, h = img.size
+            labels = torch.stack(labels, dim=0) 
+            boxes = torch.stack(boxes, dim=0) 
+            boxes[:, 0::2].clamp_(min=0, max=w)
+            boxes[:, 1::2].clamp_(min=0, max=h)
+            masks = torch.stack(masks, dim=0) 
+            target = {
+                'frames_idx': torch.tensor(sample_frames_id), # [T,]
+                'labels': labels,                        # [T,]
+                'boxes': boxes,                          # [T, 4], xyxy
+                'masks': masks,                          # [T, H, W]
+                'valid': torch.tensor(valid),            # [T,]
+                'caption': exp,
+                'orig_size': torch.as_tensor([int(h), int(w)]), 
+                'size': torch.as_tensor([int(h), int(w)])
+            }
+
+            # "boxes" normalize to [0, 1] and transform from xyxy to cxcywh in self._transform
+            if self._transforms:
+                imgs, target = self._transforms(imgs, target)
+                imgs = torch.stack(imgs, dim=0) # [T, 3, H, W]
+            else:
+                imgs = np.array(imgs)
+                imgs = torch.tensor(imgs.transpose(0, 3, 1, 2))
+            
+            
+            # FIXME: handle "valid", since some box may be removed due to random crop
+            if torch.any(target['valid'] == 1):  # at leatst one instance
+                instance_check = True
+            else:
+                idx = random.randint(0, self.__len__() - 1)
+
+        return imgs, target
+
+
+def make_coco_transforms(image_set, max_size=640):
+    normalize = T.Compose([
+        T.ToTensor(),
+        T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+    scales = [288, 320, 352, 392, 416, 448, 480, 512]
+
+    if image_set == 'train':
+        return T.Compose([
+            T.RandomHorizontalFlip(),
+            T.PhotometricDistort(),
+            T.RandomSelect(
+                T.Compose([
+                    T.RandomResize(scales, max_size=max_size),
+                    T.Check(),
+                ]),
+                T.Compose([
+                    T.RandomResize([400, 500, 600]),
+                    T.RandomSizeCrop(384, 600),
+                    T.RandomResize(scales, max_size=max_size),
+                    T.Check(),
+                ])
+            ),
+            normalize,
+        ])
+    
+    # we do not use the 'val' set since the annotations are inaccessible
+    if image_set == 'val':
+        return T.Compose([
+            T.RandomResize([360], max_size=640),
+            normalize,
+        ])
+
+    raise ValueError(f'unknown {image_set}')
+
+
+def build(image_set, args):
+    root = Path(args.ytvos_path)
+    assert root.exists(), f'provided YTVOS path {root} does not exist'
+    PATHS = {
+        "train": (root / "train", root / "meta_expressions" / "train" / "meta_expressions.json"),
+        "val": (root / "valid", root / "meta_expressions" / "valid" / "meta_expressions.json"),    # not used actually
+    }
+    img_folder, ann_file = PATHS[image_set]
+    # dataset = YTVOSDataset(img_folder, ann_file, transforms=make_coco_transforms(image_set, max_size=args.max_size), return_masks=args.masks, 
+    #                        num_frames=args.num_frames, max_skip=args.max_skip)
+    dataset = YTVOSDataset(img_folder, ann_file, transforms=None, return_masks=args.masks, 
+                           num_frames=args.num_frames, max_skip=args.max_skip)
+    return dataset
+
diff --git a/.history/datasets/ytvos_ref_20250114212623.py b/.history/datasets/ytvos_ref_20250114212623.py
new file mode 100644
index 0000000000000000000000000000000000000000..bc8e1142f8e28ada7a3305f57a2a9dcfb21d5566
--- /dev/null
+++ b/.history/datasets/ytvos_ref_20250114212623.py
@@ -0,0 +1,242 @@
+"""
+Ref-YoutubeVOS data loader
+"""
+from pathlib import Path
+
+import torch
+from torch.autograd.grad_mode import F
+from torch.utils.data import Dataset
+import datasets.transforms_video as T
+
+import os
+from PIL import Image
+import json
+import numpy as np
+import random
+
+from datasets.categories import ytvos_category_dict as category_dict
+
+
+class YTVOSDataset(Dataset):
+    """
+    A dataset class for the Refer-Youtube-VOS dataset which was first introduced in the paper:
+    "URVOS: Unified Referring Video Object Segmentation Network with a Large-Scale Benchmark"
+    (see https://link.springer.com/content/pdf/10.1007/978-3-030-58555-6_13.pdf).
+    The original release of the dataset contained both 'first-frame' and 'full-video' expressions. However, the first
+    dataset is not publicly available anymore as now only the harder 'full-video' subset is available to download
+    through the Youtube-VOS referring video object segmentation competition page at:
+    https://competitions.codalab.org/competitions/29139
+    Furthermore, for the competition the subset's original validation set, which consists of 507 videos, was split into
+    two competition 'validation' & 'test' subsets, consisting of 202 and 305 videos respectively. Evaluation can
+    currently only be done on the competition 'validation' subset using the competition's server, as
+    annotations were publicly released only for the 'train' subset of the competition.
+
+    """
+    def __init__(self, img_folder: Path, ann_file: Path, transforms, return_masks: bool, 
+                 num_frames: int, max_skip: int):
+        self.img_folder = img_folder     
+        self.ann_file = ann_file         
+        self._transforms = transforms    
+        self.return_masks = return_masks # not used
+        self.num_frames = num_frames     
+        self.max_skip = max_skip
+        # create video meta data
+        self.prepare_metas()       
+
+        print('\n video num: ', len(self.videos), ' clip num: ', len(self.metas))  
+        print('\n')    
+
+    def prepare_metas(self):
+        # read object information
+        with open(os.path.join(str(self.img_folder), 'meta.json'), 'r') as f:
+            subset_metas_by_video = json.load(f)['videos']
+        
+        # read expression data
+        with open(str(self.ann_file), 'r') as f:
+            subset_expressions_by_video = json.load(f)['videos']
+        self.videos = list(subset_expressions_by_video.keys())
+
+        self.metas = []
+        skip_vid_count = 0
+
+        for vid in self.videos:
+            vid_meta = subset_metas_by_video[vid]
+            vid_data = subset_expressions_by_video[vid]
+            vid_frames = sorted(vid_data['frames'])
+            vid_len = len(vid_frames)
+
+            if vid_len < 11:
+                #print(f"Too short video: {vid} with frame length {vid_len}")
+                skip_vid_count += 1
+                continue
+
+
+            # Exclude start_idx (0, 1) and end_idx (vid_len-1, vid_len-2)
+            start_idx , end_idx = 2, vid_len-2
+            bin_size = (end_idx - start_idx) // 4
+
+            bins = []
+            for i in range(4):
+                bin_start = start_idx + i * bin_size
+                bin_end = bin_start + bin_size if i < 3 else end_idx
+
+                bins.append((bin_start, bin_end))
+
+            # Random sample one frame from each bin
+            sample_indx = []
+            for start_idx, end_idx in bins:
+                sample_indx.append(random.randint(start_idx, end_idx - 1))
+            sample_indx.sort()  # Ensure indices are in order
+
+
+            meta = {
+                'video':vid,
+                'sample_indx':sample_indx,
+                'bins':bins,
+                'frames':vid_frames
+            }
+            obj_id_cat = {}
+            for exp_id, exp_dict in vid_data['expressions'].items():      
+                obj_id = exp_dict['obj_id']
+                if obj_id not in obj_id_cat:
+                    obj_id_cat[obj_id] = vid_meta['objects'][obj_id]['category']    
+            meta['obj_id_cat'] = obj_id_cat
+            self.metas.append(meta)
+
+        print(f"skipped {skip_vid_count} short videos")
+                
+
+    @staticmethod
+    def bounding_box(img):
+        rows = np.any(img, axis=1)
+        cols = np.any(img, axis=0)
+        rmin, rmax = np.where(rows)[0][[0, -1]]
+        cmin, cmax = np.where(cols)[0][[0, -1]]
+        return rmin, rmax, cmin, cmax # y1, y2, x1, x2 
+        
+    def __len__(self):
+        return len(self.metas)
+        
+    def __getitem__(self, idx):
+        instance_check = False
+        while not instance_check:
+            meta = self.metas[idx]  # dict
+
+            video, sample_indx, bins, frames, obj_id_cat = \
+                meta['video'], meta['sample_indx'], meta['bins'], meta['frames'], meta['obj_id_cat']
+
+            # read frames and masks
+            imgs, labels, boxes, masks, valid = [], [], [], [], []
+            for frame_indx in sample_indx:
+                frame_name = frames[frame_indx]
+                img_path = os.path.join(str(self.img_folder), 'JPEGImages', video, frame_name + '.jpg')
+                mask_path = os.path.join(str(self.img_folder), 'Annotations', video, frame_name + '.png')
+                img = Image.open(img_path).convert('RGB')
+                mask = Image.open(mask_path).convert('P')
+                mask = np.array(mask)
+                
+                # create the target
+                for obj_id in list(obj_id_cat.keys()):
+                    obj_mask = (mask==obj_id).astype(np.float32) # 0,1 binary
+                    if (obj_mask > 0).any():
+                        y1, y2, x1, x2 = self.bounding_box(mask)
+                        box = torch.tensor([x1, y1, x2, y2]).to(torch.float)
+                        valid.append(1)
+                    else: # some frame didn't contain the instance
+                        box = torch.tensor([0, 0, 0, 0]).to(torch.float) 
+                        valid.append(0)
+                    obj_mask = torch.from_numpy(obj_mask)
+
+                    # append
+                    imgs.append(img)
+                    masks.append(obj_mask)
+                    boxes.append(box)
+                
+
+            # transform
+            w, h = img.size
+            labels = torch.stack(labels, dim=0) 
+            boxes = torch.stack(boxes, dim=0) 
+            boxes[:, 0::2].clamp_(min=0, max=w)
+            boxes[:, 1::2].clamp_(min=0, max=h)
+            masks = torch.stack(masks, dim=0) 
+            target = {
+                'frames_idx': torch.tensor(sample_frames_id), # [T,]
+                'labels': labels,                        # [T,]
+                'boxes': boxes,                          # [T, 4], xyxy
+                'masks': masks,                          # [T, H, W]
+                'valid': torch.tensor(valid),            # [T,]
+                'caption': exp,
+                'orig_size': torch.as_tensor([int(h), int(w)]), 
+                'size': torch.as_tensor([int(h), int(w)])
+            }
+
+            # "boxes" normalize to [0, 1] and transform from xyxy to cxcywh in self._transform
+            if self._transforms:
+                imgs, target = self._transforms(imgs, target)
+                imgs = torch.stack(imgs, dim=0) # [T, 3, H, W]
+            else:
+                imgs = np.array(imgs)
+                imgs = torch.tensor(imgs.transpose(0, 3, 1, 2))
+            
+            
+            # FIXME: handle "valid", since some box may be removed due to random crop
+            if torch.any(target['valid'] == 1):  # at leatst one instance
+                instance_check = True
+            else:
+                idx = random.randint(0, self.__len__() - 1)
+
+        return imgs, target
+
+
+def make_coco_transforms(image_set, max_size=640):
+    normalize = T.Compose([
+        T.ToTensor(),
+        T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+    scales = [288, 320, 352, 392, 416, 448, 480, 512]
+
+    if image_set == 'train':
+        return T.Compose([
+            T.RandomHorizontalFlip(),
+            T.PhotometricDistort(),
+            T.RandomSelect(
+                T.Compose([
+                    T.RandomResize(scales, max_size=max_size),
+                    T.Check(),
+                ]),
+                T.Compose([
+                    T.RandomResize([400, 500, 600]),
+                    T.RandomSizeCrop(384, 600),
+                    T.RandomResize(scales, max_size=max_size),
+                    T.Check(),
+                ])
+            ),
+            normalize,
+        ])
+    
+    # we do not use the 'val' set since the annotations are inaccessible
+    if image_set == 'val':
+        return T.Compose([
+            T.RandomResize([360], max_size=640),
+            normalize,
+        ])
+
+    raise ValueError(f'unknown {image_set}')
+
+
+def build(image_set, args):
+    root = Path(args.ytvos_path)
+    assert root.exists(), f'provided YTVOS path {root} does not exist'
+    PATHS = {
+        "train": (root / "train", root / "meta_expressions" / "train" / "meta_expressions.json"),
+        "val": (root / "valid", root / "meta_expressions" / "valid" / "meta_expressions.json"),    # not used actually
+    }
+    img_folder, ann_file = PATHS[image_set]
+    # dataset = YTVOSDataset(img_folder, ann_file, transforms=make_coco_transforms(image_set, max_size=args.max_size), return_masks=args.masks, 
+    #                        num_frames=args.num_frames, max_skip=args.max_skip)
+    dataset = YTVOSDataset(img_folder, ann_file, transforms=None, return_masks=args.masks, 
+                           num_frames=args.num_frames, max_skip=args.max_skip)
+    return dataset
+
diff --git a/.history/datasets/ytvos_ref_20250116071135.py b/.history/datasets/ytvos_ref_20250116071135.py
new file mode 100644
index 0000000000000000000000000000000000000000..08de28c1cacbbb201739551a977fec79da2fc8fa
--- /dev/null
+++ b/.history/datasets/ytvos_ref_20250116071135.py
@@ -0,0 +1,240 @@
+"""
+Ref-YoutubeVOS data loader
+"""
+from pathlib import Path
+
+import torch
+from torch.autograd.grad_mode import F
+from torch.utils.data import Dataset
+import datasets.transforms_video as T
+
+import os
+from PIL import Image
+import json
+import numpy as np
+import random
+
+from datasets.categories import ytvos_category_dict as category_dict
+
+
+class YTVOSDataset(Dataset):
+    """
+    A dataset class for the Refer-Youtube-VOS dataset which was first introduced in the paper:
+    "URVOS: Unified Referring Video Object Segmentation Network with a Large-Scale Benchmark"
+    (see https://link.springer.com/content/pdf/10.1007/978-3-030-58555-6_13.pdf).
+    The original release of the dataset contained both 'first-frame' and 'full-video' expressions. However, the first
+    dataset is not publicly available anymore as now only the harder 'full-video' subset is available to download
+    through the Youtube-VOS referring video object segmentation competition page at:
+    https://competitions.codalab.org/competitions/29139
+    Furthermore, for the competition the subset's original validation set, which consists of 507 videos, was split into
+    two competition 'validation' & 'test' subsets, consisting of 202 and 305 videos respectively. Evaluation can
+    currently only be done on the competition 'validation' subset using the competition's server, as
+    annotations were publicly released only for the 'train' subset of the competition.
+
+    """
+    def __init__(self, img_folder: Path, ann_file: Path, transforms, return_masks: bool, 
+                 num_frames: int, max_skip: int):
+        self.img_folder = img_folder     
+        self.ann_file = ann_file         
+        self._transforms = transforms    
+        self.return_masks = return_masks # not used
+        self.num_frames = num_frames     
+        self.max_skip = max_skip
+        # create video meta data
+        self.prepare_metas()       
+
+        print('\n video num: ', len(self.videos), ' clip num: ', len(self.metas))  
+        print('\n')    
+
+    def prepare_metas(self):
+        # read object information
+        with open(os.path.join(str(self.img_folder), 'meta.json'), 'r') as f:
+            subset_metas_by_video = json.load(f)['videos']
+        
+        # read expression data
+        with open(str(self.ann_file), 'r') as f:
+            subset_expressions_by_video = json.load(f)['videos']
+        self.videos = list(subset_expressions_by_video.keys())
+
+        self.metas = []
+        skip_vid_count = 0
+
+        for vid in self.videos:
+            vid_meta = subset_metas_by_video[vid]
+            vid_data = subset_expressions_by_video[vid]
+            vid_frames = sorted(vid_data['frames'])
+            vid_len = len(vid_frames)
+
+            if vid_len < 11:
+                #print(f"Too short video: {vid} with frame length {vid_len}")
+                skip_vid_count += 1
+                continue
+
+
+            # Exclude start_idx (0, 1) and end_idx (vid_len-1, vid_len-2)
+            start_idx , end_idx = 2, vid_len-2
+            bin_size = (end_idx - start_idx) // 4
+
+            bins = []
+            for i in range(4):
+                bin_start = start_idx + i * bin_size
+                bin_end = bin_start + bin_size if i < 3 else end_idx
+
+                bins.append((bin_start, bin_end))
+
+            # Random sample one frame from each bin
+            sample_indx = []
+            for start_idx, end_idx in bins:
+                sample_indx.append(random.randint(start_idx, end_idx - 1))
+            sample_indx.sort()  # Ensure indices are in order
+
+
+            meta = {
+                'video':vid,
+                'sample_indx':sample_indx,
+                'bins':bins,
+                'frames':vid_frames
+            }
+            obj_id_cat = {}
+            for exp_id, exp_dict in vid_data['expressions'].items():      
+                obj_id = exp_dict['obj_id']
+                if obj_id not in obj_id_cat:
+                    obj_id_cat[obj_id] = vid_meta['objects'][obj_id]['category']    
+            meta['obj_id_cat'] = obj_id_cat
+            self.metas.append(meta)
+
+        print(f"skipped {skip_vid_count} short videos")
+                
+
+    @staticmethod
+    def bounding_box(img):
+        rows = np.any(img, axis=1)
+        cols = np.any(img, axis=0)
+        rmin, rmax = np.where(rows)[0][[0, -1]]
+        cmin, cmax = np.where(cols)[0][[0, -1]]
+        return rmin, rmax, cmin, cmax # y1, y2, x1, x2 
+        
+    def __len__(self):
+        return len(self.metas)
+        
+    def __getitem__(self, idx):
+        instance_check = False
+        while not instance_check:
+            meta = self.metas[idx]  # dict
+
+            video, sample_indx, bins, frames, obj_id_cat = \
+                meta['video'], meta['sample_indx'], meta['bins'], meta['frames'], meta['obj_id_cat']
+
+            # read frames and masks
+            imgs, labels, boxes, masks, valid = [], [], [], [], []
+            for frame_indx in sample_indx:
+                frame_name = frames[frame_indx]
+                img_path = os.path.join(str(self.img_folder), 'JPEGImages', video, frame_name + '.jpg')
+                mask_path = os.path.join(str(self.img_folder), 'Annotations', video, frame_name + '.png')
+                img = Image.open(img_path).convert('RGB')
+                mask = Image.open(mask_path).convert('P')
+                mask = np.array(mask)
+                
+                # create the target
+                for obj_id in list(obj_id_cat.keys()):
+                    obj_mask = (mask==obj_id).astype(np.float32) # 0,1 binary
+                    if (obj_mask > 0).any():
+                        y1, y2, x1, x2 = self.bounding_box(mask)
+                        box = torch.tensor([x1, y1, x2, y2]).to(torch.float)
+                        valid.append(1)
+                    else: # some frame didn't contain the instance
+                        box = torch.tensor([0, 0, 0, 0]).to(torch.float) 
+                        valid.append(0)
+                    obj_mask = torch.from_numpy(obj_mask)
+
+                    # append
+                    imgs.append(img)
+                    masks.append(obj_mask)
+                    boxes.append(box)
+                
+
+            # transform
+            w, h = img.size
+            labels = torch.stack(labels, dim=0) 
+            boxes = torch.stack(boxes, dim=0) 
+            boxes[:, 0::2].clamp_(min=0, max=w)
+            boxes[:, 1::2].clamp_(min=0, max=h)
+            masks = torch.stack(masks, dim=0) 
+            target = {
+                'frames_idx': sample_indx, # [T,]
+                'boxes': boxes,                          # [T, 4], xyxy
+                'masks': masks,                          # [T, H, W]
+                'valid': torch.tensor(valid),            # [T,]
+                'orig_size': torch.as_tensor([int(h), int(w)]), 
+                'size': torch.as_tensor([int(h), int(w)])
+            }
+
+            # "boxes" normalize to [0, 1] and transform from xyxy to cxcywh in self._transform
+            if self._transforms:
+                imgs, target = self._transforms(imgs, target)
+                imgs = torch.stack(imgs, dim=0) # [T, 3, H, W]
+            else:
+                imgs = np.array(imgs)
+                imgs = torch.tensor(imgs.transpose(0, 3, 1, 2))
+            
+            
+            # FIXME: handle "valid", since some box may be removed due to random crop
+            if torch.any(target['valid'] == 1):  # at leatst one instance
+                instance_check = True
+            else:
+                idx = random.randint(0, self.__len__() - 1)
+
+        return imgs, target
+
+
+def make_coco_transforms(image_set, max_size=640):
+    normalize = T.Compose([
+        T.ToTensor(),
+        T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+    scales = [288, 320, 352, 392, 416, 448, 480, 512]
+
+    if image_set == 'train':
+        return T.Compose([
+            T.RandomHorizontalFlip(),
+            T.PhotometricDistort(),
+            T.RandomSelect(
+                T.Compose([
+                    T.RandomResize(scales, max_size=max_size),
+                    T.Check(),
+                ]),
+                T.Compose([
+                    T.RandomResize([400, 500, 600]),
+                    T.RandomSizeCrop(384, 600),
+                    T.RandomResize(scales, max_size=max_size),
+                    T.Check(),
+                ])
+            ),
+            normalize,
+        ])
+    
+    # we do not use the 'val' set since the annotations are inaccessible
+    if image_set == 'val':
+        return T.Compose([
+            T.RandomResize([360], max_size=640),
+            normalize,
+        ])
+
+    raise ValueError(f'unknown {image_set}')
+
+
+def build(image_set, args):
+    root = Path(args.ytvos_path)
+    assert root.exists(), f'provided YTVOS path {root} does not exist'
+    PATHS = {
+        "train": (root / "train", root / "meta_expressions" / "train" / "meta_expressions.json"),
+        "val": (root / "valid", root / "meta_expressions" / "valid" / "meta_expressions.json"),    # not used actually
+    }
+    img_folder, ann_file = PATHS[image_set]
+    # dataset = YTVOSDataset(img_folder, ann_file, transforms=make_coco_transforms(image_set, max_size=args.max_size), return_masks=args.masks, 
+    #                        num_frames=args.num_frames, max_skip=args.max_skip)
+    dataset = YTVOSDataset(img_folder, ann_file, transforms=None, return_masks=args.masks, 
+                           num_frames=args.num_frames, max_skip=args.max_skip)
+    return dataset
+
diff --git a/.history/datasets/ytvos_ref_20250116071255.py b/.history/datasets/ytvos_ref_20250116071255.py
new file mode 100644
index 0000000000000000000000000000000000000000..3fc436c63a1529ce59fb0836ce70242344e972f9
--- /dev/null
+++ b/.history/datasets/ytvos_ref_20250116071255.py
@@ -0,0 +1,239 @@
+"""
+Ref-YoutubeVOS data loader
+"""
+from pathlib import Path
+
+import torch
+from torch.autograd.grad_mode import F
+from torch.utils.data import Dataset
+import datasets.transforms_video as T
+
+import os
+from PIL import Image
+import json
+import numpy as np
+import random
+
+from datasets.categories import ytvos_category_dict as category_dict
+
+
+class YTVOSDataset(Dataset):
+    """
+    A dataset class for the Refer-Youtube-VOS dataset which was first introduced in the paper:
+    "URVOS: Unified Referring Video Object Segmentation Network with a Large-Scale Benchmark"
+    (see https://link.springer.com/content/pdf/10.1007/978-3-030-58555-6_13.pdf).
+    The original release of the dataset contained both 'first-frame' and 'full-video' expressions. However, the first
+    dataset is not publicly available anymore as now only the harder 'full-video' subset is available to download
+    through the Youtube-VOS referring video object segmentation competition page at:
+    https://competitions.codalab.org/competitions/29139
+    Furthermore, for the competition the subset's original validation set, which consists of 507 videos, was split into
+    two competition 'validation' & 'test' subsets, consisting of 202 and 305 videos respectively. Evaluation can
+    currently only be done on the competition 'validation' subset using the competition's server, as
+    annotations were publicly released only for the 'train' subset of the competition.
+
+    """
+    def __init__(self, img_folder: Path, ann_file: Path, transforms, return_masks: bool, 
+                 num_frames: int, max_skip: int):
+        self.img_folder = img_folder     
+        self.ann_file = ann_file         
+        self._transforms = transforms    
+        self.return_masks = return_masks # not used
+        self.num_frames = num_frames     
+        self.max_skip = max_skip
+        # create video meta data
+        self.prepare_metas()       
+
+        print('\n video num: ', len(self.videos), ' clip num: ', len(self.metas))  
+        print('\n')    
+
+    def prepare_metas(self):
+        # read object information
+        with open(os.path.join(str(self.img_folder), 'meta.json'), 'r') as f:
+            subset_metas_by_video = json.load(f)['videos']
+        
+        # read expression data
+        with open(str(self.ann_file), 'r') as f:
+            subset_expressions_by_video = json.load(f)['videos']
+        self.videos = list(subset_expressions_by_video.keys())
+
+        self.metas = []
+        skip_vid_count = 0
+
+        for vid in self.videos:
+            vid_meta = subset_metas_by_video[vid]
+            vid_data = subset_expressions_by_video[vid]
+            vid_frames = sorted(vid_data['frames'])
+            vid_len = len(vid_frames)
+
+            if vid_len < 11:
+                #print(f"Too short video: {vid} with frame length {vid_len}")
+                skip_vid_count += 1
+                continue
+
+
+            # Exclude start_idx (0, 1) and end_idx (vid_len-1, vid_len-2)
+            start_idx , end_idx = 2, vid_len-2
+            bin_size = (end_idx - start_idx) // 4
+
+            bins = []
+            for i in range(4):
+                bin_start = start_idx + i * bin_size
+                bin_end = bin_start + bin_size if i < 3 else end_idx
+
+                bins.append((bin_start, bin_end))
+
+            # Random sample one frame from each bin
+            sample_indx = []
+            for start_idx, end_idx in bins:
+                sample_indx.append(random.randint(start_idx, end_idx - 1))
+            sample_indx.sort()  # Ensure indices are in order
+
+
+            meta = {
+                'video':vid,
+                'sample_indx':sample_indx,
+                'bins':bins,
+                'frames':vid_frames
+            }
+            obj_id_cat = {}
+            for exp_id, exp_dict in vid_data['expressions'].items():      
+                obj_id = exp_dict['obj_id']
+                if obj_id not in obj_id_cat:
+                    obj_id_cat[obj_id] = vid_meta['objects'][obj_id]['category']    
+            meta['obj_id_cat'] = obj_id_cat
+            self.metas.append(meta)
+
+        print(f"skipped {skip_vid_count} short videos")
+                
+
+    @staticmethod
+    def bounding_box(img):
+        rows = np.any(img, axis=1)
+        cols = np.any(img, axis=0)
+        rmin, rmax = np.where(rows)[0][[0, -1]]
+        cmin, cmax = np.where(cols)[0][[0, -1]]
+        return rmin, rmax, cmin, cmax # y1, y2, x1, x2 
+        
+    def __len__(self):
+        return len(self.metas)
+        
+    def __getitem__(self, idx):
+        instance_check = False
+        while not instance_check:
+            meta = self.metas[idx]  # dict
+
+            video, sample_indx, bins, frames, obj_id_cat = \
+                meta['video'], meta['sample_indx'], meta['bins'], meta['frames'], meta['obj_id_cat']
+
+            # read frames and masks
+            imgs, labels, boxes, masks, valid = [], [], [], [], []
+            for frame_indx in sample_indx:
+                frame_name = frames[frame_indx]
+                img_path = os.path.join(str(self.img_folder), 'JPEGImages', video, frame_name + '.jpg')
+                mask_path = os.path.join(str(self.img_folder), 'Annotations', video, frame_name + '.png')
+                img = Image.open(img_path).convert('RGB')
+                mask = Image.open(mask_path).convert('P')
+                mask = np.array(mask)
+                
+                # create the target
+                for obj_id in list(obj_id_cat.keys()):
+                    obj_mask = (mask==obj_id).astype(np.float32) # 0,1 binary
+                    if (obj_mask > 0).any():
+                        y1, y2, x1, x2 = self.bounding_box(mask)
+                        box = torch.tensor([x1, y1, x2, y2]).to(torch.float)
+                        valid.append(1)
+                    else: # some frame didn't contain the instance
+                        box = torch.tensor([0, 0, 0, 0]).to(torch.float) 
+                        valid.append(0)
+                    obj_mask = torch.from_numpy(obj_mask)
+
+                    # append
+                    imgs.append(img)
+                    masks.append(obj_mask)
+                    boxes.append(box)
+                
+
+            # transform
+            w, h = img.size
+            boxes = torch.stack(boxes, dim=0) 
+            boxes[:, 0::2].clamp_(min=0, max=w)
+            boxes[:, 1::2].clamp_(min=0, max=h)
+            masks = torch.stack(masks, dim=0) 
+            target = {
+                'frames_idx': sample_indx, # [T,]
+                'boxes': boxes,                          # [T, 4], xyxy
+                'masks': masks,                          # [T, H, W]
+                'valid': torch.tensor(valid),            # [T,]
+                'orig_size': torch.as_tensor([int(h), int(w)]), 
+                'size': torch.as_tensor([int(h), int(w)])
+            }
+
+            # "boxes" normalize to [0, 1] and transform from xyxy to cxcywh in self._transform
+            if self._transforms:
+                imgs, target = self._transforms(imgs, target)
+                imgs = torch.stack(imgs, dim=0) # [T, 3, H, W]
+            else:
+                imgs = np.array(imgs)
+                imgs = torch.tensor(imgs.transpose(0, 3, 1, 2))
+            
+            
+            # FIXME: handle "valid", since some box may be removed due to random crop
+            if torch.any(target['valid'] == 1):  # at leatst one instance
+                instance_check = True
+            else:
+                idx = random.randint(0, self.__len__() - 1)
+
+        return imgs, target
+
+
+def make_coco_transforms(image_set, max_size=640):
+    normalize = T.Compose([
+        T.ToTensor(),
+        T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+    scales = [288, 320, 352, 392, 416, 448, 480, 512]
+
+    if image_set == 'train':
+        return T.Compose([
+            T.RandomHorizontalFlip(),
+            T.PhotometricDistort(),
+            T.RandomSelect(
+                T.Compose([
+                    T.RandomResize(scales, max_size=max_size),
+                    T.Check(),
+                ]),
+                T.Compose([
+                    T.RandomResize([400, 500, 600]),
+                    T.RandomSizeCrop(384, 600),
+                    T.RandomResize(scales, max_size=max_size),
+                    T.Check(),
+                ])
+            ),
+            normalize,
+        ])
+    
+    # we do not use the 'val' set since the annotations are inaccessible
+    if image_set == 'val':
+        return T.Compose([
+            T.RandomResize([360], max_size=640),
+            normalize,
+        ])
+
+    raise ValueError(f'unknown {image_set}')
+
+
+def build(image_set, args):
+    root = Path(args.ytvos_path)
+    assert root.exists(), f'provided YTVOS path {root} does not exist'
+    PATHS = {
+        "train": (root / "train", root / "meta_expressions" / "train" / "meta_expressions.json"),
+        "val": (root / "valid", root / "meta_expressions" / "valid" / "meta_expressions.json"),    # not used actually
+    }
+    img_folder, ann_file = PATHS[image_set]
+    # dataset = YTVOSDataset(img_folder, ann_file, transforms=make_coco_transforms(image_set, max_size=args.max_size), return_masks=args.masks, 
+    #                        num_frames=args.num_frames, max_skip=args.max_skip)
+    dataset = YTVOSDataset(img_folder, ann_file, transforms=None, return_masks=args.masks, 
+                           num_frames=args.num_frames, max_skip=args.max_skip)
+    return dataset
+
diff --git a/.history/datasets/ytvos_ref_20250116071502.py b/.history/datasets/ytvos_ref_20250116071502.py
new file mode 100644
index 0000000000000000000000000000000000000000..944c3040d440661e508de56674cd287a21522d02
--- /dev/null
+++ b/.history/datasets/ytvos_ref_20250116071502.py
@@ -0,0 +1,240 @@
+"""
+Ref-YoutubeVOS data loader
+"""
+from pathlib import Path
+
+import torch
+from torch.autograd.grad_mode import F
+from torch.utils.data import Dataset
+import datasets.transforms_video as T
+
+import os
+from PIL import Image
+import json
+import numpy as np
+import random
+
+from datasets.categories import ytvos_category_dict as category_dict
+
+
+class YTVOSDataset(Dataset):
+    """
+    A dataset class for the Refer-Youtube-VOS dataset which was first introduced in the paper:
+    "URVOS: Unified Referring Video Object Segmentation Network with a Large-Scale Benchmark"
+    (see https://link.springer.com/content/pdf/10.1007/978-3-030-58555-6_13.pdf).
+    The original release of the dataset contained both 'first-frame' and 'full-video' expressions. However, the first
+    dataset is not publicly available anymore as now only the harder 'full-video' subset is available to download
+    through the Youtube-VOS referring video object segmentation competition page at:
+    https://competitions.codalab.org/competitions/29139
+    Furthermore, for the competition the subset's original validation set, which consists of 507 videos, was split into
+    two competition 'validation' & 'test' subsets, consisting of 202 and 305 videos respectively. Evaluation can
+    currently only be done on the competition 'validation' subset using the competition's server, as
+    annotations were publicly released only for the 'train' subset of the competition.
+
+    """
+    def __init__(self, img_folder: Path, ann_file: Path, transforms, return_masks: bool, 
+                 num_frames: int, max_skip: int):
+        self.img_folder = img_folder     
+        self.ann_file = ann_file         
+        self._transforms = transforms    
+        self.return_masks = return_masks # not used
+        self.num_frames = num_frames     
+        self.max_skip = max_skip
+        # create video meta data
+        self.prepare_metas()       
+
+        print('\n video num: ', len(self.videos), ' clip num: ', len(self.metas))  
+        print('\n')    
+
+    def prepare_metas(self):
+        # read object information
+        with open(os.path.join(str(self.img_folder), 'meta.json'), 'r') as f:
+            subset_metas_by_video = json.load(f)['videos']
+        
+        # read expression data
+        with open(str(self.ann_file), 'r') as f:
+            subset_expressions_by_video = json.load(f)['videos']
+        self.videos = list(subset_expressions_by_video.keys())
+
+        self.metas = []
+        skip_vid_count = 0
+
+        for vid in self.videos:
+            vid_meta = subset_metas_by_video[vid]
+            vid_data = subset_expressions_by_video[vid]
+            vid_frames = sorted(vid_data['frames'])
+            vid_len = len(vid_frames)
+
+            if vid_len < 11:
+                #print(f"Too short video: {vid} with frame length {vid_len}")
+                skip_vid_count += 1
+                continue
+
+
+            # Exclude start_idx (0, 1) and end_idx (vid_len-1, vid_len-2)
+            start_idx , end_idx = 2, vid_len-2
+            bin_size = (end_idx - start_idx) // 4
+
+            bins = []
+            for i in range(4):
+                bin_start = start_idx + i * bin_size
+                bin_end = bin_start + bin_size if i < 3 else end_idx
+
+                bins.append((bin_start, bin_end))
+
+            # Random sample one frame from each bin
+            sample_indx = []
+            for start_idx, end_idx in bins:
+                sample_indx.append(random.randint(start_idx, end_idx - 1))
+            sample_indx.sort()  # Ensure indices are in order
+
+
+            meta = {
+                'video':vid,
+                'sample_indx':sample_indx,
+                'bins':bins,
+                'frames':vid_frames
+            }
+            obj_id_cat = {}
+            for exp_id, exp_dict in vid_data['expressions'].items():      
+                obj_id = exp_dict['obj_id']
+                if obj_id not in obj_id_cat:
+                    obj_id_cat[obj_id] = vid_meta['objects'][obj_id]['category']    
+            meta['obj_id_cat'] = obj_id_cat
+            self.metas.append(meta)
+
+        print(f"skipped {skip_vid_count} short videos")
+                
+
+    @staticmethod
+    def bounding_box(img):
+        rows = np.any(img, axis=1)
+        cols = np.any(img, axis=0)
+        rmin, rmax = np.where(rows)[0][[0, -1]]
+        cmin, cmax = np.where(cols)[0][[0, -1]]
+        return rmin, rmax, cmin, cmax # y1, y2, x1, x2 
+        
+    def __len__(self):
+        return len(self.metas)
+        
+    def __getitem__(self, idx):
+        instance_check = False
+        while not instance_check:
+            meta = self.metas[idx]  # dict
+
+            video, sample_indx, bins, frames, obj_id_cat = \
+                meta['video'], meta['sample_indx'], meta['bins'], meta['frames'], meta['obj_id_cat']
+
+            # read frames and masks
+            imgs, labels, boxes, masks, valid = [], [], [], [], []
+            for frame_indx in sample_indx:
+                frame_name = frames[frame_indx]
+                img_path = os.path.join(str(self.img_folder), 'JPEGImages', video, frame_name + '.jpg')
+                mask_path = os.path.join(str(self.img_folder), 'Annotations', video, frame_name + '.png')
+                img = Image.open(img_path).convert('RGB')
+                mask = Image.open(mask_path).convert('P')
+                mask = np.array(mask)
+                
+                # create the target
+                for obj_id in list(obj_id_cat.keys()):
+                    obj_mask = (mask==obj_id).astype(np.float32) # 0,1 binary
+                    if (obj_mask > 0).any():
+                        y1, y2, x1, x2 = self.bounding_box(mask)
+                        box = torch.tensor([x1, y1, x2, y2]).to(torch.float)
+                        valid.append(1)
+                    else: # some frame didn't contain the instance
+                        box = torch.tensor([0, 0, 0, 0]).to(torch.float) 
+                        valid.append(0)
+                    obj_mask = torch.from_numpy(obj_mask)
+
+                    # append
+                    imgs.append(img)
+                    masks.append(obj_mask)
+                    boxes.append(box)
+                
+
+            # transform
+            w, h = img.size
+            boxes = torch.stack(boxes, dim=0) 
+            boxes[:, 0::2].clamp_(min=0, max=w)
+            boxes[:, 1::2].clamp_(min=0, max=h)
+            masks = torch.stack(masks, dim=0) 
+            target = {
+                'frames_idx': sample_indx, # [T,]
+                'boxes': boxes,                          # [T, 4], xyxy
+                'masks': masks,                          # [T, H, W]
+                'valid': torch.tensor(valid),            # [T,]
+                'obj_ids' : list(obj_id_cat.keys())
+                'orig_size': torch.as_tensor([int(h), int(w)]), 
+                'size': torch.as_tensor([int(h), int(w)])
+            }
+
+            # "boxes" normalize to [0, 1] and transform from xyxy to cxcywh in self._transform
+            if self._transforms:
+                imgs, target = self._transforms(imgs, target)
+                imgs = torch.stack(imgs, dim=0) # [T, 3, H, W]
+            else:
+                imgs = np.array(imgs)
+                imgs = torch.tensor(imgs.transpose(0, 3, 1, 2))
+            
+            
+            # FIXME: handle "valid", since some box may be removed due to random crop
+            if torch.any(target['valid'] == 1):  # at leatst one instance
+                instance_check = True
+            else:
+                idx = random.randint(0, self.__len__() - 1)
+
+        return imgs, target
+
+
+def make_coco_transforms(image_set, max_size=640):
+    normalize = T.Compose([
+        T.ToTensor(),
+        T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+    scales = [288, 320, 352, 392, 416, 448, 480, 512]
+
+    if image_set == 'train':
+        return T.Compose([
+            T.RandomHorizontalFlip(),
+            T.PhotometricDistort(),
+            T.RandomSelect(
+                T.Compose([
+                    T.RandomResize(scales, max_size=max_size),
+                    T.Check(),
+                ]),
+                T.Compose([
+                    T.RandomResize([400, 500, 600]),
+                    T.RandomSizeCrop(384, 600),
+                    T.RandomResize(scales, max_size=max_size),
+                    T.Check(),
+                ])
+            ),
+            normalize,
+        ])
+    
+    # we do not use the 'val' set since the annotations are inaccessible
+    if image_set == 'val':
+        return T.Compose([
+            T.RandomResize([360], max_size=640),
+            normalize,
+        ])
+
+    raise ValueError(f'unknown {image_set}')
+
+
+def build(image_set, args):
+    root = Path(args.ytvos_path)
+    assert root.exists(), f'provided YTVOS path {root} does not exist'
+    PATHS = {
+        "train": (root / "train", root / "meta_expressions" / "train" / "meta_expressions.json"),
+        "val": (root / "valid", root / "meta_expressions" / "valid" / "meta_expressions.json"),    # not used actually
+    }
+    img_folder, ann_file = PATHS[image_set]
+    # dataset = YTVOSDataset(img_folder, ann_file, transforms=make_coco_transforms(image_set, max_size=args.max_size), return_masks=args.masks, 
+    #                        num_frames=args.num_frames, max_skip=args.max_skip)
+    dataset = YTVOSDataset(img_folder, ann_file, transforms=None, return_masks=args.masks, 
+                           num_frames=args.num_frames, max_skip=args.max_skip)
+    return dataset
+
diff --git a/.history/datasets/ytvos_ref_20250116071546.py b/.history/datasets/ytvos_ref_20250116071546.py
new file mode 100644
index 0000000000000000000000000000000000000000..499512c28e7ca83c9508c83ff105c4f9f2b63106
--- /dev/null
+++ b/.history/datasets/ytvos_ref_20250116071546.py
@@ -0,0 +1,240 @@
+"""
+Ref-YoutubeVOS data loader
+"""
+from pathlib import Path
+
+import torch
+from torch.autograd.grad_mode import F
+from torch.utils.data import Dataset
+import datasets.transforms_video as T
+
+import os
+from PIL import Image
+import json
+import numpy as np
+import random
+
+from datasets.categories import ytvos_category_dict as category_dict
+
+
+class YTVOSDataset(Dataset):
+    """
+    A dataset class for the Refer-Youtube-VOS dataset which was first introduced in the paper:
+    "URVOS: Unified Referring Video Object Segmentation Network with a Large-Scale Benchmark"
+    (see https://link.springer.com/content/pdf/10.1007/978-3-030-58555-6_13.pdf).
+    The original release of the dataset contained both 'first-frame' and 'full-video' expressions. However, the first
+    dataset is not publicly available anymore as now only the harder 'full-video' subset is available to download
+    through the Youtube-VOS referring video object segmentation competition page at:
+    https://competitions.codalab.org/competitions/29139
+    Furthermore, for the competition the subset's original validation set, which consists of 507 videos, was split into
+    two competition 'validation' & 'test' subsets, consisting of 202 and 305 videos respectively. Evaluation can
+    currently only be done on the competition 'validation' subset using the competition's server, as
+    annotations were publicly released only for the 'train' subset of the competition.
+
+    """
+    def __init__(self, img_folder: Path, ann_file: Path, transforms, return_masks: bool, 
+                 num_frames: int, max_skip: int):
+        self.img_folder = img_folder     
+        self.ann_file = ann_file         
+        self._transforms = transforms    
+        self.return_masks = return_masks # not used
+        self.num_frames = num_frames     
+        self.max_skip = max_skip
+        # create video meta data
+        self.prepare_metas()       
+
+        print('\n video num: ', len(self.videos), ' clip num: ', len(self.metas))  
+        print('\n')    
+
+    def prepare_metas(self):
+        # read object information
+        with open(os.path.join(str(self.img_folder), 'meta.json'), 'r') as f:
+            subset_metas_by_video = json.load(f)['videos']
+        
+        # read expression data
+        with open(str(self.ann_file), 'r') as f:
+            subset_expressions_by_video = json.load(f)['videos']
+        self.videos = list(subset_expressions_by_video.keys())
+
+        self.metas = []
+        skip_vid_count = 0
+
+        for vid in self.videos:
+            vid_meta = subset_metas_by_video[vid]
+            vid_data = subset_expressions_by_video[vid]
+            vid_frames = sorted(vid_data['frames'])
+            vid_len = len(vid_frames)
+
+            if vid_len < 11:
+                #print(f"Too short video: {vid} with frame length {vid_len}")
+                skip_vid_count += 1
+                continue
+
+
+            # Exclude start_idx (0, 1) and end_idx (vid_len-1, vid_len-2)
+            start_idx , end_idx = 2, vid_len-2
+            bin_size = (end_idx - start_idx) // 4
+
+            bins = []
+            for i in range(4):
+                bin_start = start_idx + i * bin_size
+                bin_end = bin_start + bin_size if i < 3 else end_idx
+
+                bins.append((bin_start, bin_end))
+
+            # Random sample one frame from each bin
+            sample_indx = []
+            for start_idx, end_idx in bins:
+                sample_indx.append(random.randint(start_idx, end_idx - 1))
+            sample_indx.sort()  # Ensure indices are in order
+
+
+            meta = {
+                'video':vid,
+                'sample_indx':sample_indx,
+                'bins':bins,
+                'frames':vid_frames
+            }
+            obj_id_cat = {}
+            for exp_id, exp_dict in vid_data['expressions'].items():      
+                obj_id = exp_dict['obj_id']
+                if obj_id not in obj_id_cat:
+                    obj_id_cat[obj_id] = vid_meta['objects'][obj_id]['category']    
+            meta['obj_id_cat'] = obj_id_cat
+            self.metas.append(meta)
+
+        print(f"skipped {skip_vid_count} short videos")
+                
+
+    @staticmethod
+    def bounding_box(img):
+        rows = np.any(img, axis=1)
+        cols = np.any(img, axis=0)
+        rmin, rmax = np.where(rows)[0][[0, -1]]
+        cmin, cmax = np.where(cols)[0][[0, -1]]
+        return rmin, rmax, cmin, cmax # y1, y2, x1, x2 
+        
+    def __len__(self):
+        return len(self.metas)
+        
+    def __getitem__(self, idx):
+        instance_check = False
+        while not instance_check:
+            meta = self.metas[idx]  # dict
+
+            video, sample_indx, bins, frames, obj_id_cat = \
+                meta['video'], meta['sample_indx'], meta['bins'], meta['frames'], meta['obj_id_cat']
+
+            # read frames and masks
+            imgs, labels, boxes, masks, valid = [], [], [], [], []
+            for frame_indx in sample_indx:
+                frame_name = frames[frame_indx]
+                img_path = os.path.join(str(self.img_folder), 'JPEGImages', video, frame_name + '.jpg')
+                mask_path = os.path.join(str(self.img_folder), 'Annotations', video, frame_name + '.png')
+                img = Image.open(img_path).convert('RGB')
+                mask = Image.open(mask_path).convert('P')
+                mask = np.array(mask)
+                
+                # create the target
+                for obj_id in list(obj_id_cat.keys()):
+                    obj_mask = (mask==obj_id).astype(np.float32) # 0,1 binary
+                    if (obj_mask > 0).any():
+                        y1, y2, x1, x2 = self.bounding_box(mask)
+                        box = torch.tensor([x1, y1, x2, y2]).to(torch.float)
+                        valid.append(1)
+                    else: # some frame didn't contain the instance
+                        box = torch.tensor([0, 0, 0, 0]).to(torch.float) 
+                        valid.append(0)
+                    obj_mask = torch.from_numpy(obj_mask)
+
+                    # append
+                    imgs.append(img)
+                    masks.append(obj_mask)
+                    boxes.append(box)
+                
+
+            # transform
+            w, h = img.size
+            boxes = torch.stack(boxes, dim=0) 
+            boxes[:, 0::2].clamp_(min=0, max=w)
+            boxes[:, 1::2].clamp_(min=0, max=h)
+            masks = torch.stack(masks, dim=0) 
+            target = {
+                'frames_idx': sample_indx, # [T,]
+                'boxes': boxes,                          # [T, 4], xyxy
+                'masks': masks,                          # [T, H, W]
+                'valid': torch.tensor(valid),            # [T,]
+                'obj_ids' : list(obj_id_cat.keys()),
+                'orig_size': torch.as_tensor([int(h), int(w)]), 
+                'size': torch.as_tensor([int(h), int(w)])
+            }
+
+            # "boxes" normalize to [0, 1] and transform from xyxy to cxcywh in self._transform
+            if self._transforms:
+                imgs, target = self._transforms(imgs, target)
+                imgs = torch.stack(imgs, dim=0) # [T, 3, H, W]
+            else:
+                imgs = np.array(imgs)
+                imgs = torch.tensor(imgs.transpose(0, 3, 1, 2))
+            
+            
+            # FIXME: handle "valid", since some box may be removed due to random crop
+            if torch.any(target['valid'] == 1):  # at leatst one instance
+                instance_check = True
+            else:
+                idx = random.randint(0, self.__len__() - 1)
+
+        return imgs, target
+
+
+def make_coco_transforms(image_set, max_size=640):
+    normalize = T.Compose([
+        T.ToTensor(),
+        T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+    scales = [288, 320, 352, 392, 416, 448, 480, 512]
+
+    if image_set == 'train':
+        return T.Compose([
+            T.RandomHorizontalFlip(),
+            T.PhotometricDistort(),
+            T.RandomSelect(
+                T.Compose([
+                    T.RandomResize(scales, max_size=max_size),
+                    T.Check(),
+                ]),
+                T.Compose([
+                    T.RandomResize([400, 500, 600]),
+                    T.RandomSizeCrop(384, 600),
+                    T.RandomResize(scales, max_size=max_size),
+                    T.Check(),
+                ])
+            ),
+            normalize,
+        ])
+    
+    # we do not use the 'val' set since the annotations are inaccessible
+    if image_set == 'val':
+        return T.Compose([
+            T.RandomResize([360], max_size=640),
+            normalize,
+        ])
+
+    raise ValueError(f'unknown {image_set}')
+
+
+def build(image_set, args):
+    root = Path(args.ytvos_path)
+    assert root.exists(), f'provided YTVOS path {root} does not exist'
+    PATHS = {
+        "train": (root / "train", root / "meta_expressions" / "train" / "meta_expressions.json"),
+        "val": (root / "valid", root / "meta_expressions" / "valid" / "meta_expressions.json"),    # not used actually
+    }
+    img_folder, ann_file = PATHS[image_set]
+    # dataset = YTVOSDataset(img_folder, ann_file, transforms=make_coco_transforms(image_set, max_size=args.max_size), return_masks=args.masks, 
+    #                        num_frames=args.num_frames, max_skip=args.max_skip)
+    dataset = YTVOSDataset(img_folder, ann_file, transforms=None, return_masks=args.masks, 
+                           num_frames=args.num_frames, max_skip=args.max_skip)
+    return dataset
+
diff --git a/.history/datasets/ytvos_ref_20250116071553.py b/.history/datasets/ytvos_ref_20250116071553.py
new file mode 100644
index 0000000000000000000000000000000000000000..499512c28e7ca83c9508c83ff105c4f9f2b63106
--- /dev/null
+++ b/.history/datasets/ytvos_ref_20250116071553.py
@@ -0,0 +1,240 @@
+"""
+Ref-YoutubeVOS data loader
+"""
+from pathlib import Path
+
+import torch
+from torch.autograd.grad_mode import F
+from torch.utils.data import Dataset
+import datasets.transforms_video as T
+
+import os
+from PIL import Image
+import json
+import numpy as np
+import random
+
+from datasets.categories import ytvos_category_dict as category_dict
+
+
+class YTVOSDataset(Dataset):
+    """
+    A dataset class for the Refer-Youtube-VOS dataset which was first introduced in the paper:
+    "URVOS: Unified Referring Video Object Segmentation Network with a Large-Scale Benchmark"
+    (see https://link.springer.com/content/pdf/10.1007/978-3-030-58555-6_13.pdf).
+    The original release of the dataset contained both 'first-frame' and 'full-video' expressions. However, the first
+    dataset is not publicly available anymore as now only the harder 'full-video' subset is available to download
+    through the Youtube-VOS referring video object segmentation competition page at:
+    https://competitions.codalab.org/competitions/29139
+    Furthermore, for the competition the subset's original validation set, which consists of 507 videos, was split into
+    two competition 'validation' & 'test' subsets, consisting of 202 and 305 videos respectively. Evaluation can
+    currently only be done on the competition 'validation' subset using the competition's server, as
+    annotations were publicly released only for the 'train' subset of the competition.
+
+    """
+    def __init__(self, img_folder: Path, ann_file: Path, transforms, return_masks: bool, 
+                 num_frames: int, max_skip: int):
+        self.img_folder = img_folder     
+        self.ann_file = ann_file         
+        self._transforms = transforms    
+        self.return_masks = return_masks # not used
+        self.num_frames = num_frames     
+        self.max_skip = max_skip
+        # create video meta data
+        self.prepare_metas()       
+
+        print('\n video num: ', len(self.videos), ' clip num: ', len(self.metas))  
+        print('\n')    
+
+    def prepare_metas(self):
+        # read object information
+        with open(os.path.join(str(self.img_folder), 'meta.json'), 'r') as f:
+            subset_metas_by_video = json.load(f)['videos']
+        
+        # read expression data
+        with open(str(self.ann_file), 'r') as f:
+            subset_expressions_by_video = json.load(f)['videos']
+        self.videos = list(subset_expressions_by_video.keys())
+
+        self.metas = []
+        skip_vid_count = 0
+
+        for vid in self.videos:
+            vid_meta = subset_metas_by_video[vid]
+            vid_data = subset_expressions_by_video[vid]
+            vid_frames = sorted(vid_data['frames'])
+            vid_len = len(vid_frames)
+
+            if vid_len < 11:
+                #print(f"Too short video: {vid} with frame length {vid_len}")
+                skip_vid_count += 1
+                continue
+
+
+            # Exclude start_idx (0, 1) and end_idx (vid_len-1, vid_len-2)
+            start_idx , end_idx = 2, vid_len-2
+            bin_size = (end_idx - start_idx) // 4
+
+            bins = []
+            for i in range(4):
+                bin_start = start_idx + i * bin_size
+                bin_end = bin_start + bin_size if i < 3 else end_idx
+
+                bins.append((bin_start, bin_end))
+
+            # Random sample one frame from each bin
+            sample_indx = []
+            for start_idx, end_idx in bins:
+                sample_indx.append(random.randint(start_idx, end_idx - 1))
+            sample_indx.sort()  # Ensure indices are in order
+
+
+            meta = {
+                'video':vid,
+                'sample_indx':sample_indx,
+                'bins':bins,
+                'frames':vid_frames
+            }
+            obj_id_cat = {}
+            for exp_id, exp_dict in vid_data['expressions'].items():      
+                obj_id = exp_dict['obj_id']
+                if obj_id not in obj_id_cat:
+                    obj_id_cat[obj_id] = vid_meta['objects'][obj_id]['category']    
+            meta['obj_id_cat'] = obj_id_cat
+            self.metas.append(meta)
+
+        print(f"skipped {skip_vid_count} short videos")
+                
+
+    @staticmethod
+    def bounding_box(img):
+        rows = np.any(img, axis=1)
+        cols = np.any(img, axis=0)
+        rmin, rmax = np.where(rows)[0][[0, -1]]
+        cmin, cmax = np.where(cols)[0][[0, -1]]
+        return rmin, rmax, cmin, cmax # y1, y2, x1, x2 
+        
+    def __len__(self):
+        return len(self.metas)
+        
+    def __getitem__(self, idx):
+        instance_check = False
+        while not instance_check:
+            meta = self.metas[idx]  # dict
+
+            video, sample_indx, bins, frames, obj_id_cat = \
+                meta['video'], meta['sample_indx'], meta['bins'], meta['frames'], meta['obj_id_cat']
+
+            # read frames and masks
+            imgs, labels, boxes, masks, valid = [], [], [], [], []
+            for frame_indx in sample_indx:
+                frame_name = frames[frame_indx]
+                img_path = os.path.join(str(self.img_folder), 'JPEGImages', video, frame_name + '.jpg')
+                mask_path = os.path.join(str(self.img_folder), 'Annotations', video, frame_name + '.png')
+                img = Image.open(img_path).convert('RGB')
+                mask = Image.open(mask_path).convert('P')
+                mask = np.array(mask)
+                
+                # create the target
+                for obj_id in list(obj_id_cat.keys()):
+                    obj_mask = (mask==obj_id).astype(np.float32) # 0,1 binary
+                    if (obj_mask > 0).any():
+                        y1, y2, x1, x2 = self.bounding_box(mask)
+                        box = torch.tensor([x1, y1, x2, y2]).to(torch.float)
+                        valid.append(1)
+                    else: # some frame didn't contain the instance
+                        box = torch.tensor([0, 0, 0, 0]).to(torch.float) 
+                        valid.append(0)
+                    obj_mask = torch.from_numpy(obj_mask)
+
+                    # append
+                    imgs.append(img)
+                    masks.append(obj_mask)
+                    boxes.append(box)
+                
+
+            # transform
+            w, h = img.size
+            boxes = torch.stack(boxes, dim=0) 
+            boxes[:, 0::2].clamp_(min=0, max=w)
+            boxes[:, 1::2].clamp_(min=0, max=h)
+            masks = torch.stack(masks, dim=0) 
+            target = {
+                'frames_idx': sample_indx, # [T,]
+                'boxes': boxes,                          # [T, 4], xyxy
+                'masks': masks,                          # [T, H, W]
+                'valid': torch.tensor(valid),            # [T,]
+                'obj_ids' : list(obj_id_cat.keys()),
+                'orig_size': torch.as_tensor([int(h), int(w)]), 
+                'size': torch.as_tensor([int(h), int(w)])
+            }
+
+            # "boxes" normalize to [0, 1] and transform from xyxy to cxcywh in self._transform
+            if self._transforms:
+                imgs, target = self._transforms(imgs, target)
+                imgs = torch.stack(imgs, dim=0) # [T, 3, H, W]
+            else:
+                imgs = np.array(imgs)
+                imgs = torch.tensor(imgs.transpose(0, 3, 1, 2))
+            
+            
+            # FIXME: handle "valid", since some box may be removed due to random crop
+            if torch.any(target['valid'] == 1):  # at leatst one instance
+                instance_check = True
+            else:
+                idx = random.randint(0, self.__len__() - 1)
+
+        return imgs, target
+
+
+def make_coco_transforms(image_set, max_size=640):
+    normalize = T.Compose([
+        T.ToTensor(),
+        T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+    scales = [288, 320, 352, 392, 416, 448, 480, 512]
+
+    if image_set == 'train':
+        return T.Compose([
+            T.RandomHorizontalFlip(),
+            T.PhotometricDistort(),
+            T.RandomSelect(
+                T.Compose([
+                    T.RandomResize(scales, max_size=max_size),
+                    T.Check(),
+                ]),
+                T.Compose([
+                    T.RandomResize([400, 500, 600]),
+                    T.RandomSizeCrop(384, 600),
+                    T.RandomResize(scales, max_size=max_size),
+                    T.Check(),
+                ])
+            ),
+            normalize,
+        ])
+    
+    # we do not use the 'val' set since the annotations are inaccessible
+    if image_set == 'val':
+        return T.Compose([
+            T.RandomResize([360], max_size=640),
+            normalize,
+        ])
+
+    raise ValueError(f'unknown {image_set}')
+
+
+def build(image_set, args):
+    root = Path(args.ytvos_path)
+    assert root.exists(), f'provided YTVOS path {root} does not exist'
+    PATHS = {
+        "train": (root / "train", root / "meta_expressions" / "train" / "meta_expressions.json"),
+        "val": (root / "valid", root / "meta_expressions" / "valid" / "meta_expressions.json"),    # not used actually
+    }
+    img_folder, ann_file = PATHS[image_set]
+    # dataset = YTVOSDataset(img_folder, ann_file, transforms=make_coco_transforms(image_set, max_size=args.max_size), return_masks=args.masks, 
+    #                        num_frames=args.num_frames, max_skip=args.max_skip)
+    dataset = YTVOSDataset(img_folder, ann_file, transforms=None, return_masks=args.masks, 
+                           num_frames=args.num_frames, max_skip=args.max_skip)
+    return dataset
+
diff --git a/.history/datasets/ytvos_ref_20250116071841.py b/.history/datasets/ytvos_ref_20250116071841.py
new file mode 100644
index 0000000000000000000000000000000000000000..2de787e8c61d1daba156a2ad67af103ffa52b397
--- /dev/null
+++ b/.history/datasets/ytvos_ref_20250116071841.py
@@ -0,0 +1,239 @@
+"""
+Ref-YoutubeVOS data loader
+"""
+from pathlib import Path
+
+import torch
+from torch.autograd.grad_mode import F
+from torch.utils.data import Dataset
+import datasets.transforms_video as T
+
+import os
+from PIL import Image
+import json
+import numpy as np
+import random
+
+from datasets.categories import ytvos_category_dict as category_dict
+
+
+class YTVOSDataset(Dataset):
+    """
+    A dataset class for the Refer-Youtube-VOS dataset which was first introduced in the paper:
+    "URVOS: Unified Referring Video Object Segmentation Network with a Large-Scale Benchmark"
+    (see https://link.springer.com/content/pdf/10.1007/978-3-030-58555-6_13.pdf).
+    The original release of the dataset contained both 'first-frame' and 'full-video' expressions. However, the first
+    dataset is not publicly available anymore as now only the harder 'full-video' subset is available to download
+    through the Youtube-VOS referring video object segmentation competition page at:
+    https://competitions.codalab.org/competitions/29139
+    Furthermore, for the competition the subset's original validation set, which consists of 507 videos, was split into
+    two competition 'validation' & 'test' subsets, consisting of 202 and 305 videos respectively. Evaluation can
+    currently only be done on the competition 'validation' subset using the competition's server, as
+    annotations were publicly released only for the 'train' subset of the competition.
+
+    """
+    def __init__(self, img_folder: Path, ann_file: Path, transforms, return_masks: bool, 
+                 num_frames: int, max_skip: int):
+        self.img_folder = img_folder     
+        self.ann_file = ann_file         
+        self._transforms = transforms    
+        self.return_masks = return_masks # not used
+        self.num_frames = num_frames     
+        self.max_skip = max_skip
+        # create video meta data
+        self.prepare_metas()       
+
+        print('\n video num: ', len(self.videos), ' clip num: ', len(self.metas))  
+        print('\n')    
+
+    def prepare_metas(self):
+        # read object information
+        with open(os.path.join(str(self.img_folder), 'meta.json'), 'r') as f:
+            subset_metas_by_video = json.load(f)['videos']
+        
+        # read expression data
+        with open(str(self.ann_file), 'r') as f:
+            subset_expressions_by_video = json.load(f)['videos']
+        self.videos = list(subset_expressions_by_video.keys())
+
+        self.metas = []
+        skip_vid_count = 0
+
+        for vid in self.videos:
+            vid_meta = subset_metas_by_video[vid]
+            vid_data = subset_expressions_by_video[vid]
+            vid_frames = sorted(vid_data['frames'])
+            vid_len = len(vid_frames)
+
+            if vid_len < 11:
+                #print(f"Too short video: {vid} with frame length {vid_len}")
+                skip_vid_count += 1
+                continue
+
+
+            # Exclude start_idx (0, 1) and end_idx (vid_len-1, vid_len-2)
+            start_idx , end_idx = 2, vid_len-2
+            bin_size = (end_idx - start_idx) // 4
+
+            bins = []
+            for i in range(4):
+                bin_start = start_idx + i * bin_size
+                bin_end = bin_start + bin_size if i < 3 else end_idx
+
+                bins.append((bin_start, bin_end))
+
+            # Random sample one frame from each bin
+            sample_indx = []
+            for start_idx, end_idx in bins:
+                sample_indx.append(random.randint(start_idx, end_idx - 1))
+            sample_indx.sort()  # Ensure indices are in order
+
+
+            meta = {
+                'video':vid,
+                'sample_indx':sample_indx,
+                'bins':bins,
+                'frames':vid_frames
+            }
+            obj_id_cat = {}
+            for exp_id, exp_dict in vid_data['expressions'].items():      
+                obj_id = exp_dict['obj_id']
+                if obj_id not in obj_id_cat:
+                    obj_id_cat[obj_id] = vid_meta['objects'][obj_id]['category']    
+            meta['obj_id_cat'] = obj_id_cat
+            self.metas.append(meta)
+
+        print(f"skipped {skip_vid_count} short videos")
+                
+
+    @staticmethod
+    def bounding_box(img):
+        rows = np.any(img, axis=1)
+        cols = np.any(img, axis=0)
+        rmin, rmax = np.where(rows)[0][[0, -1]]
+        cmin, cmax = np.where(cols)[0][[0, -1]]
+        return rmin, rmax, cmin, cmax # y1, y2, x1, x2 
+        
+    def __len__(self):
+        return len(self.metas)
+        
+    def __getitem__(self, idx):
+        instance_check = False
+        while not instance_check:
+            meta = self.metas[idx]  # dict
+
+            video, sample_indx, bins, frames, obj_id_cat = \
+                meta['video'], meta['sample_indx'], meta['bins'], meta['frames'], meta['obj_id_cat']
+
+            # read frames and masks
+            imgs, labels, boxes, masks, valid = [], [], [], [], []
+            for frame_indx in sample_indx:
+                frame_name = frames[frame_indx]
+                img_path = os.path.join(str(self.img_folder), 'JPEGImages', video, frame_name + '.jpg')
+                mask_path = os.path.join(str(self.img_folder), 'Annotations', video, frame_name + '.png')
+                img = Image.open(img_path).convert('RGB')
+                mask = Image.open(mask_path).convert('P')
+                mask = np.array(mask)
+                
+                # create the target
+                for obj_id in list(obj_id_cat.keys()):
+                    obj_mask = (mask==obj_id).astype(np.float32) # 0,1 binary
+                    if (obj_mask > 0).any():
+                        y1, y2, x1, x2 = self.bounding_box(mask)
+                        box = torch.tensor([x1, y1, x2, y2]).to(torch.float)
+                        valid.append(1)
+                    else: # some frame didn't contain the instance
+                        box = torch.tensor([0, 0, 0, 0]).to(torch.float) 
+                        valid.append(0)
+                    obj_mask = torch.from_numpy(obj_mask)
+
+                    # append
+                    masks.append(obj_mask)
+                    boxes.append(box)
+                
+
+            # transform
+            w, h = img.size
+            boxes = torch.stack(boxes, dim=0) 
+            boxes[:, 0::2].clamp_(min=0, max=w)
+            boxes[:, 1::2].clamp_(min=0, max=h)
+            masks = torch.stack(masks, dim=0) 
+            target = {
+                'frames_idx': sample_indx, # [T,]
+                'boxes': boxes,                          # [T, 4], xyxy
+                'masks': masks,                          # [T, H, W]
+                'valid': torch.tensor(valid),            # [T,]
+                'obj_ids' : list(obj_id_cat.keys()),
+                'orig_size': torch.as_tensor([int(h), int(w)]), 
+                'size': torch.as_tensor([int(h), int(w)])
+            }
+
+            # "boxes" normalize to [0, 1] and transform from xyxy to cxcywh in self._transform
+            if self._transforms:
+                imgs, target = self._transforms(imgs, target)
+                imgs = torch.stack(imgs, dim=0) # [T, 3, H, W]
+            else:
+                imgs = np.array(imgs)
+                imgs = torch.tensor(imgs.transpose(0, 3, 1, 2))
+            
+            
+            # FIXME: handle "valid", since some box may be removed due to random crop
+            if torch.any(target['valid'] == 1):  # at leatst one instance
+                instance_check = True
+            else:
+                idx = random.randint(0, self.__len__() - 1)
+
+        return imgs, target
+
+
+def make_coco_transforms(image_set, max_size=640):
+    normalize = T.Compose([
+        T.ToTensor(),
+        T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+    scales = [288, 320, 352, 392, 416, 448, 480, 512]
+
+    if image_set == 'train':
+        return T.Compose([
+            T.RandomHorizontalFlip(),
+            T.PhotometricDistort(),
+            T.RandomSelect(
+                T.Compose([
+                    T.RandomResize(scales, max_size=max_size),
+                    T.Check(),
+                ]),
+                T.Compose([
+                    T.RandomResize([400, 500, 600]),
+                    T.RandomSizeCrop(384, 600),
+                    T.RandomResize(scales, max_size=max_size),
+                    T.Check(),
+                ])
+            ),
+            normalize,
+        ])
+    
+    # we do not use the 'val' set since the annotations are inaccessible
+    if image_set == 'val':
+        return T.Compose([
+            T.RandomResize([360], max_size=640),
+            normalize,
+        ])
+
+    raise ValueError(f'unknown {image_set}')
+
+
+def build(image_set, args):
+    root = Path(args.ytvos_path)
+    assert root.exists(), f'provided YTVOS path {root} does not exist'
+    PATHS = {
+        "train": (root / "train", root / "meta_expressions" / "train" / "meta_expressions.json"),
+        "val": (root / "valid", root / "meta_expressions" / "valid" / "meta_expressions.json"),    # not used actually
+    }
+    img_folder, ann_file = PATHS[image_set]
+    # dataset = YTVOSDataset(img_folder, ann_file, transforms=make_coco_transforms(image_set, max_size=args.max_size), return_masks=args.masks, 
+    #                        num_frames=args.num_frames, max_skip=args.max_skip)
+    dataset = YTVOSDataset(img_folder, ann_file, transforms=None, return_masks=args.masks, 
+                           num_frames=args.num_frames, max_skip=args.max_skip)
+    return dataset
+
diff --git a/.history/datasets/ytvos_ref_20250116072442.py b/.history/datasets/ytvos_ref_20250116072442.py
new file mode 100644
index 0000000000000000000000000000000000000000..5a2da136ccc3f4e94a180e9c52046c8343b1c48d
--- /dev/null
+++ b/.history/datasets/ytvos_ref_20250116072442.py
@@ -0,0 +1,241 @@
+"""
+Ref-YoutubeVOS data loader
+"""
+from pathlib import Path
+
+import torch
+from torch.autograd.grad_mode import F
+from torch.utils.data import Dataset
+import datasets.transforms_video as T
+
+import os
+from PIL import Image
+import json
+import numpy as np
+import random
+
+from datasets.categories import ytvos_category_dict as category_dict
+
+
+class YTVOSDataset(Dataset):
+    """
+    A dataset class for the Refer-Youtube-VOS dataset which was first introduced in the paper:
+    "URVOS: Unified Referring Video Object Segmentation Network with a Large-Scale Benchmark"
+    (see https://link.springer.com/content/pdf/10.1007/978-3-030-58555-6_13.pdf).
+    The original release of the dataset contained both 'first-frame' and 'full-video' expressions. However, the first
+    dataset is not publicly available anymore as now only the harder 'full-video' subset is available to download
+    through the Youtube-VOS referring video object segmentation competition page at:
+    https://competitions.codalab.org/competitions/29139
+    Furthermore, for the competition the subset's original validation set, which consists of 507 videos, was split into
+    two competition 'validation' & 'test' subsets, consisting of 202 and 305 videos respectively. Evaluation can
+    currently only be done on the competition 'validation' subset using the competition's server, as
+    annotations were publicly released only for the 'train' subset of the competition.
+
+    """
+    def __init__(self, img_folder: Path, ann_file: Path, transforms, return_masks: bool, 
+                 num_frames: int, max_skip: int):
+        self.img_folder = img_folder     
+        self.ann_file = ann_file         
+        self._transforms = transforms    
+        self.return_masks = return_masks # not used
+        self.num_frames = num_frames     
+        self.max_skip = max_skip
+        # create video meta data
+        self.prepare_metas()       
+
+        print('\n video num: ', len(self.videos), ' clip num: ', len(self.metas))  
+        print('\n')    
+
+    def prepare_metas(self):
+        # read object information
+        with open(os.path.join(str(self.img_folder), 'meta.json'), 'r') as f:
+            subset_metas_by_video = json.load(f)['videos']
+        
+        # read expression data
+        with open(str(self.ann_file), 'r') as f:
+            subset_expressions_by_video = json.load(f)['videos']
+        self.videos = list(subset_expressions_by_video.keys())
+
+        self.metas = []
+        skip_vid_count = 0
+
+        for vid in self.videos:
+            vid_meta = subset_metas_by_video[vid]
+            vid_data = subset_expressions_by_video[vid]
+            vid_frames = sorted(vid_data['frames'])
+            vid_len = len(vid_frames)
+
+            if vid_len < 11:
+                #print(f"Too short video: {vid} with frame length {vid_len}")
+                skip_vid_count += 1
+                continue
+
+
+            # Exclude start_idx (0, 1) and end_idx (vid_len-1, vid_len-2)
+            start_idx , end_idx = 2, vid_len-2
+            bin_size = (end_idx - start_idx) // 4
+
+            bins = []
+            for i in range(4):
+                bin_start = start_idx + i * bin_size
+                bin_end = bin_start + bin_size if i < 3 else end_idx
+
+                bins.append((bin_start, bin_end))
+
+            # Random sample one frame from each bin
+            sample_indx = []
+            for start_idx, end_idx in bins:
+                sample_indx.append(random.randint(start_idx, end_idx - 1))
+            sample_indx.sort()  # Ensure indices are in order
+
+
+            meta = {
+                'video':vid,
+                'sample_indx':sample_indx,
+                'bins':bins,
+                'frames':vid_frames
+            }
+            obj_id_cat = {}
+            for exp_id, exp_dict in vid_data['expressions'].items():      
+                obj_id = exp_dict['obj_id']
+                if obj_id not in obj_id_cat:
+                    obj_id_cat[obj_id] = vid_meta['objects'][obj_id]['category']    
+            meta['obj_id_cat'] = obj_id_cat
+            self.metas.append(meta)
+
+        print(f"skipped {skip_vid_count} short videos")
+                
+
+    @staticmethod
+    def bounding_box(img):
+        rows = np.any(img, axis=1)
+        cols = np.any(img, axis=0)
+        rmin, rmax = np.where(rows)[0][[0, -1]]
+        cmin, cmax = np.where(cols)[0][[0, -1]]
+        return rmin, rmax, cmin, cmax # y1, y2, x1, x2 
+        
+    def __len__(self):
+        return len(self.metas)
+        
+    def __getitem__(self, idx):
+        instance_check = False
+        while not instance_check:
+            meta = self.metas[idx]  # dict
+
+            video, sample_indx, bins, frames, obj_id_cat = \
+                meta['video'], meta['sample_indx'], meta['bins'], meta['frames'], meta['obj_id_cat']
+
+            # read frames and masks
+            imgs, labels, boxes, masks, valid = [], [], [], [], []
+            for frame_indx in sample_indx:
+                frame_name = frames[frame_indx]
+                img_path = os.path.join(str(self.img_folder), 'JPEGImages', video, frame_name + '.jpg')
+                mask_path = os.path.join(str(self.img_folder), 'Annotations', video, frame_name + '.png')
+                img = Image.open(img_path).convert('RGB')
+                imgs.append(img)
+                
+                mask = Image.open(mask_path).convert('P')
+                mask = np.array(mask)
+                
+                # create the target
+                for obj_id in list(obj_id_cat.keys()):
+                    obj_mask = (mask==obj_id).astype(np.float32) # 0,1 binary
+                    if (obj_mask > 0).any():
+                        y1, y2, x1, x2 = self.bounding_box(mask)
+                        box = torch.tensor([x1, y1, x2, y2]).to(torch.float)
+                        valid.append(1)
+                    else: # some frame didn't contain the instance
+                        box = torch.tensor([0, 0, 0, 0]).to(torch.float) 
+                        valid.append(0)
+                    obj_mask = torch.from_numpy(obj_mask)
+
+                    # append
+                    masks.append(obj_mask)
+                    boxes.append(box)
+                
+
+            # transform
+            w, h = img.size
+            boxes = torch.stack(boxes, dim=0) 
+            boxes[:, 0::2].clamp_(min=0, max=w)
+            boxes[:, 1::2].clamp_(min=0, max=h)
+            masks = torch.stack(masks, dim=0) 
+            target = {
+                'frames_idx': sample_indx, # [T,]
+                'boxes': boxes,                          # [T, 4], xyxy
+                'masks': masks,                          # [T, H, W]
+                'valid': torch.tensor(valid),            # [T,]
+                'obj_ids' : list(obj_id_cat.keys()),
+                'orig_size': torch.as_tensor([int(h), int(w)]), 
+                'size': torch.as_tensor([int(h), int(w)])
+            }
+
+            # "boxes" normalize to [0, 1] and transform from xyxy to cxcywh in self._transform
+            if self._transforms:
+                imgs, target = self._transforms(imgs, target)
+                imgs = torch.stack(imgs, dim=0) # [T, 3, H, W]
+            else:
+                imgs = np.array(imgs)
+                imgs = torch.tensor(imgs.transpose(0, 3, 1, 2))
+            
+            
+            # FIXME: handle "valid", since some box may be removed due to random crop
+            if torch.any(target['valid'] == 1):  # at leatst one instance
+                instance_check = True
+            else:
+                idx = random.randint(0, self.__len__() - 1)
+
+        return imgs, target
+
+
+def make_coco_transforms(image_set, max_size=640):
+    normalize = T.Compose([
+        T.ToTensor(),
+        T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+    scales = [288, 320, 352, 392, 416, 448, 480, 512]
+
+    if image_set == 'train':
+        return T.Compose([
+            T.RandomHorizontalFlip(),
+            T.PhotometricDistort(),
+            T.RandomSelect(
+                T.Compose([
+                    T.RandomResize(scales, max_size=max_size),
+                    T.Check(),
+                ]),
+                T.Compose([
+                    T.RandomResize([400, 500, 600]),
+                    T.RandomSizeCrop(384, 600),
+                    T.RandomResize(scales, max_size=max_size),
+                    T.Check(),
+                ])
+            ),
+            normalize,
+        ])
+    
+    # we do not use the 'val' set since the annotations are inaccessible
+    if image_set == 'val':
+        return T.Compose([
+            T.RandomResize([360], max_size=640),
+            normalize,
+        ])
+
+    raise ValueError(f'unknown {image_set}')
+
+
+def build(image_set, args):
+    root = Path(args.ytvos_path)
+    assert root.exists(), f'provided YTVOS path {root} does not exist'
+    PATHS = {
+        "train": (root / "train", root / "meta_expressions" / "train" / "meta_expressions.json"),
+        "val": (root / "valid", root / "meta_expressions" / "valid" / "meta_expressions.json"),    # not used actually
+    }
+    img_folder, ann_file = PATHS[image_set]
+    # dataset = YTVOSDataset(img_folder, ann_file, transforms=make_coco_transforms(image_set, max_size=args.max_size), return_masks=args.masks, 
+    #                        num_frames=args.num_frames, max_skip=args.max_skip)
+    dataset = YTVOSDataset(img_folder, ann_file, transforms=None, return_masks=args.masks, 
+                           num_frames=args.num_frames, max_skip=args.max_skip)
+    return dataset
+
diff --git a/.history/slurm_script/mbench_ref-ytvos_json_20250113182526.sh b/.history/slurm_script/mbench_ref-ytvos_json_20250113182526.sh
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diff --git a/LICENSE b/LICENSE
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+++ b/LICENSE
@@ -0,0 +1,201 @@
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diff --git a/README.md b/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..8f6e9f33500732b26bee560abe7b60f90c183adb
--- /dev/null
+++ b/README.md
@@ -0,0 +1,214 @@
+[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
+[![Framework](https://img.shields.io/badge/PyTorch-%23EE4C2C.svg?&logo=PyTorch&logoColor=white)](https://pytorch.org/)
+
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/language-as-queries-for-referring-video/referring-expression-segmentation-on-refer-1)](https://paperswithcode.com/sota/referring-expression-segmentation-on-refer-1?p=language-as-queries-for-referring-video)
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/language-as-queries-for-referring-video/referring-expression-segmentation-on-a2d)](https://paperswithcode.com/sota/referring-expression-segmentation-on-a2d?p=language-as-queries-for-referring-video)
+
+The official implementation of the **CVPR2022** paper: 
+
+<div align="center">
+<h1>
+<b>
+Language as Queries for Referring <br> Video Object Segmentation
+</b>
+</h1>
+</div>
+
+<p align="center"><img src="docs/network.png" width="800"/></p>
+
+> [**Language as Queries for Referring Video Object Segmentation**](https://arxiv.org/abs/2201.00487)
+>
+> Jiannan Wu, Yi Jiang, Peize Sun, Zehuan Yuan, Ping Luo
+
+### Abstract
+
+In this work, we propose a simple and unified framework built upon Transformer, termed ReferFormer. It views the language as queries and directly attends to the most relevant regions in the video frames. Concretely, we introduce a small set of object queries conditioned on the language as the input to the Transformer. In this manner, all the queries are obligated to find the referred objects only. They are eventually transformed into dynamic kernels which capture the crucial object-level information, and play the role of convolution filters to generate the segmentation masks from feature maps. The object tracking is achieved naturally by linking the corresponding queries across frames. This mechanism greatly simplifies the pipeline and the end-to-end framework is significantly different from the previous methods. Extensive experiments on Ref-Youtube-VOS, Ref-DAVIS17, A2D-Sentences and JHMDB-Sentences show the effectiveness of ReferFormer. 
+
+## Update
+- **(2022/12/19)** We add the results on RefCOCO/+/g validation set.
+- **(2022/07/31)** We upload the files for joint-training. 
+- **(2022/04/04)** We upload the data conversion and main files for pre-training. 
+- **(2022/03/11)** We upload the model on Ref-Youtube-VOS by jointly training Ref-Youtube-VOS and Ref-COCO/+/g, which leads to higher performance.
+- **(2022/03/03)** ReferFormer is accepted by CVPR2022. 👏
+
+## Demo
+
+- Ref-DAVIS17
+
+<img src="docs/davis_demo1.gif" width="400"/><img src="docs/davis_demo2.gif" width="400"/>
+
+- Ref-Youtube-VOS
+
+<img src="docs/ytvos_demo1.gif" width="400"/><img src="docs/ytvos_demo2.gif" width="400"/>
+
+
+
+## Requirements
+
+We test the codes in the following environments, other versions may also be compatible:
+
+- CUDA 11.1
+- Python 3.7
+- Pytorch 1.8.1
+
+
+## Installation
+
+Please refer to [install.md](docs/install.md) for installation.
+
+## Data Preparation
+
+Please refer to [data.md](docs/data.md) for data preparation.
+
+We provide the pretrained model for different visual backbones. You may download them [here]([https://drive.google.com/drive/u/0/folders/11_qps3q75aH41IYHlXToyeIBUKkfdqso](https://connecthkuhk-my.sharepoint.com/:f:/g/personal/wjn922_connect_hku_hk/Et657S8tgGRNguj2hf4azsUBn1UVbMNLAmyjcRWGobs2_A?e=xobQFH)) and put them in the directory `pretrained_weights`.
+
+<!-- For the Swin Transformer and Video Swin Transformer backbones, the weights are intialized using the pretrained model provided in the repo [Swin-Transformer](https://github.com/microsoft/Swin-Transformer) and [Video-Swin-Transformer](https://github.com/SwinTransformer/Video-Swin-Transformer). For your convenience, we upload the pretrained model in the google drives [swin_pretrained](https://drive.google.com/drive/u/0/folders/1QWLayukDJYAxTFk7NPwerfso3Lrx35NL) and [video_swin_pretrained](https://drive.google.com/drive/u/0/folders/19qb9VbKSjuwgxsiPI3uv06XzQkB5brYM). -->
+
+
+After the organization, we expect the directory struture to be the following:
+
+```
+ReferFormer/
+├── data/
+│   ├── ref-youtube-vos/
+│   ├── ref-davis/
+│   ├── a2d_sentences/
+│   ├── jhmdb_sentences/
+├── davis2017/
+├── datasets/
+├── models/
+├── scipts/
+├── tools/
+├── util/
+├── pretrained_weights/
+├── eval_davis.py
+├── main.py
+├── engine.py
+├── inference_ytvos.py
+├── inference_davis.py
+├── opts.py
+...
+```
+
+## Model Zoo
+
+All the models are trained using 8 NVIDIA Tesla V100 GPU. You may change the `--backbone` parameter to use different backbones (see [here](https://github.com/wjn922/ReferFormer/blob/232b4066fb7d10845e4083e6a5a2cc0af5d1757e/opts.py#L31)).
+
+**Note:** If you encounter the `OOM` error, please add the command `--use_checkpoint` (we add this command for Swin-L, Video-Swin-S and Video-Swin-B models).
+
+
+### Ref-Youtube-VOS
+
+To evaluate the results, please upload the zip file to the [competition server](https://competitions.codalab.org/competitions/29139#participate-submit_results).
+
+| Backbone| J&F | CFBI J&F  | Pretrain | Model | Submission | CFBI Submission | 
+| :----: | :----: | :----: | :----: | :----: | :----: | :----: | 
+| ResNet-50 | 55.6 | 59.4 | [weight](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/EepGibYBfyRGt_QedfE9SywBLF3v-bjoxo2R9E9YDqmIcw?e=7J7k1J) | [model](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/EVRsV76e78lKuekbMLHgwlsBdG09pRVafEuBPN_wKXjJ1Q?e=SMeZlS) | [link](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/EZ8tt46rv4xIjoiUkHGGPjwB1Yi6w2H-9BBVTyINOINmgQ?e=yWbDjp) | [link](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/EZp0dd70UCNGvla2g25lTawB2AZyCDPN7QMl_KeESI5dkQ?e=1BfD2W) |
+| ResNet-101 | 57.3 | 60.3 | [weight](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/ESTAK4QCkMdNkVlQz1dd7GoBo3n_i9K4_FK4YLFBAFvBrg?e=Y3PlD5) | [model](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/EaHNEx5MWR9HjTNh__W3IlYBIfhGd-nHKrshJ-MOyvofdw?e=shM4Ok) | [link](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/EbrNhmt-wiNIv2tmQ-gOupgBrSBzhM1OJlNvid0J_8cPJg?e=8Fgets) | [link](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/EWSPiUjgmORMuyaL91ueY1oBl159pO4k7RQYF-9eWrSJ-A?e=81hzDF) |
+| Swin-T | 58.7 | 61.2 | [weight](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/ESdasB6JLydDrs6mf68FrLMBuQBLBF7y_uxdveWl9oK68w?e=H5zeqk) | [model](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/EUxJmp6QYR5LoUK12Wj55E0Bm0o6_9zl3OvOBN5KE9kJkg?e=SRS0qL) | [link](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/EUMveO7cX1VAq48IAk9c6zoBc_Zy5f1kwa5h6C9q4LYt0A?e=iz9uMg) | [link](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/EcnHrx4S5KVPqFYhr9CCARoBftAxdtldaWyGQAougBFnig?e=KG1LDq) |
+| Swin-L | 62.4 | 63.3 | [weight](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/ESngRLeZfV1LtrlZ7x5cVo4BR5_deWfov4Igt28LZGoDew?e=AVAsws) | [model](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/EcCfv66Vl0xDl-rFukByXyQBEFNRTyLeVEKoeWrIvXmjNg?e=GcVTIr) | [link](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/EdI15ujU4UpBilI4wt5lUQQB98JOq6KnMV5GHh77QiAn-w?e=o91ITz) | [link](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/ETmJUpRGgyFHlGdEhcXqzekBDAfbFTExfHtmA4wHKCOkLw?e=l951Ea) |
+| Video-Swin-T* | 56.0 | - | - | [model](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/EYXL3SKYOsRGtfSN-Wr9JCUBDvcXbbp67Sa4hs5dEDplxw?e=g2hGWo) | [link](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/EUosvwAGikhGsyTPEOELMjEBQM-HZOaJ3fqcJjG2SV-5YA?e=vSUD12) | - |
+| Video-Swin-T | 59.4 | - | [weight](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/EUNTvEGXlsdLv3gicAbHfN0Ba23kcyy2-Z15IJTDLXKx_A?e=GqAYxT) | [model](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/EZI2zogC5mtDu3KL5MVIaXIBzG3_3yTthoqyxjfTsGrvzA?e=lT5sVp) | [link](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/EVKtr-5ZK5NIhhTvaUXGdRcBcHEGahAevUh1YCO2nvFfaQ?e=9Am7dc) | - |
+| Video-Swin-S | 60.1 | - | [weight](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/Eb015DXX1LhDpiDoojxJTu8BBQ8ACicpVS8gwFStRJDK1w?e=NC368q) | [model](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/EZI2zogC5mtDu3KL5MVIaXIBzG3_3yTthoqyxjfTsGrvzA?e=QEAdwh) | [link](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/EUUJn8Zu7mlCnxLP8eNSbpIBvoEqz88EOg3y9ftQHhAhCw?e=RnSwxX) | - |
+| Video-Swin-B | 62.9 | - |[weight](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/ETDj4aGm_pRMuz8hLBi9Jy0BEFnsco0Uoz5qQEhWrxdNKQ?e=kKImMX)  | [model](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/EduJ_zS-Vd5Hn1qexxv5_mYBKX_8kRBOeX6dlfhED_GSwg?e=TxTWHb) | [link](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/EZKduAM1fLpJrLK7l762xZ8BesK7zWKBjR0b9dFbCWhbfQ?e=SlAdyg) | - |
+
+\* indicates the model is trained from scratch.
+
+Joint training with Ref-COCO/+/g datasets.
+| Backbone| J&F | J | F | Model | Submission | 
+| :----: | :----: | :----: | :----: | :----: | :----: |
+| ResNet-50 | 58.7 | 57.4 | 60.1 | [model](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/EcxDd8USU4BGo_HlgukKiG4BXLvetkjLdi3_-N-3SpjMvw?e=tAPNFv) | [link](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/EYmORJYVsUJLp8NnbtfnZigBCM-IJ5oomZZrXEbNPhIyww?e=Bh5eYx) |
+| ResNet-101 | 59.3 | 58.1 | 60.4 | [model](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/EShgDd650nBBsfoNEiUbybcB84Ma5NydxOucISeCrZmzHw?e=YOSszd) | [link](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/EcW6Lt67k0RCjr_FT2XOxVcBUcrFSlFJo19-YdFZpBxOsg?e=avszXt) |
+| Swin-L | 64.2 | 62.3 | 66.2 | [model](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/Ec_qxgvukuBPr-GQ_3gNcX0B8VCHCqIUvXX-0ydtk1s7HQ?e=7X99M1) | [link](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/EbNV0kBQ7ZVDrfRafG6B3CwBbpM-yMJtQ9jI01HwEgWXBQ?e=FzoSrT) |
+| Video-Swin-T | 62.6 | 59.9 | 63.3 | [model](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/EdCVQzM4HxxIvdZUBLiNpBwBrcPTLlFEqxHVxOzx0geF3A?e=1ZSZvK) | [link](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/EdAT37_CDDZKkbC1U9MDxTYBkR1DVwTn0zxzqEvgrG-5ig?e=6P065H) |
+| Video-Swin-S | 63.3 | 61.4 | 65.2 | [model](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/EdYbp2xp-xFFuolQopvILNMBYRq88ksNjpcv-zKfGzHxbA?e=NqRzTf) | [link](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/EU6d1rGtkfBFkIoA-xUH2koBwdKW2fPCghYTzzd49KvFLQ?e=FMsJLT) |
+| Video-Swin-B | 64.9 | 62.8 | 67.0 | [model](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/EY3-adM5LptFj--klo5gWgsBhpSDOps91j-C81sBI8i9Hw?e=n19q0w) | [link](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/EcSdF-jsBmZLn7iUzc3zXTUBnlfnXDFxPP7mtRbC1ttJwg?e=0wzR0t) |
+
+
+
+
+### Ref-DAVIS17
+
+As described in the paper, we report the results using the model trained on Ref-Youtube-VOS without finetune.
+
+| Backbone| J&F | J | F | Model | 
+| :----: | :----: | :----: | :----: | :----: |
+| ResNet-50 | 58.5 | 55.8 | 61.3 | [model](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/EVRsV76e78lKuekbMLHgwlsBdG09pRVafEuBPN_wKXjJ1Q?e=SMeZlS) |
+| Swin-L | 60.5 | 57.6 | 63.4 | [model](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/EcCfv66Vl0xDl-rFukByXyQBEFNRTyLeVEKoeWrIvXmjNg?e=GcVTIr) |
+| Video-Swin-B | 61.1 | 58.1 | 64.1 | [model](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/EduJ_zS-Vd5Hn1qexxv5_mYBKX_8kRBOeX6dlfhED_GSwg?e=TxTWHb) |
+
+
+### A2D-Sentences
+
+The pretrained models are the same as those provided for Ref-Youtube-VOS.
+
+| Backbone| Overall IoU | Mean IoU | mAP  | Pretrain | Model |
+| :----: | :----: | :----: | :----: | :----: | :----: |
+| Video-Swin-T* | 72.3 | 64.1 | 48.6 | - | [model](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/EfJs5WPRKfxEvifnIO3impABNgydbiO5qqI_uCF6LYKlCQ?e=mSRLCQ) \| [log](https://connecthkuhk-my.sharepoint.com/:t:/g/personal/wjn922_connect_hku_hk/EVJyHq6zy6ZGuxE--K9nECwB333gFkP9vjXKjh9Mt0otcA?e=Kwnngd) |
+| Video-Swin-T | 77.6 | 69.6 | 52.8 | [weight](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/EUNTvEGXlsdLv3gicAbHfN0Ba23kcyy2-Z15IJTDLXKx_A?e=GqAYxT) | [model](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/Ed3po2mJGQZHivGwMJJg8oMBumXm3Ye94oPH6wfRFK1d8A?e=NG2E9c) \| [log](https://connecthkuhk-my.sharepoint.com/:t:/g/personal/wjn922_connect_hku_hk/EfO50qMduZNGvFcYJdRVKzABIJ8ZHhMiKWWvmDM14K9mnw?e=dgInSK) |
+| Video-Swin-S | 77.7 | 69.8 | 53.9 | [weight](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/Eb015DXX1LhDpiDoojxJTu8BBQ8ACicpVS8gwFStRJDK1w?e=NC368q) | [model](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/EbAiydTvu41KsMYBEFzy_d8B0Nyy1fIf2tWG7Ao-FYD0Ug?e=tmaVAu) \| [log](https://connecthkuhk-my.sharepoint.com/:t:/g/personal/wjn922_connect_hku_hk/EZl6sHhFDTBMgVGKVp18sqwBouTTnwPdirWId4PR6klTfg?e=17lDVV) |
+| Video-Swin-B | 78.6 | 70.3 | 55.0 | [weight](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/ETDj4aGm_pRMuz8hLBi9Jy0BEFnsco0Uoz5qQEhWrxdNKQ?e=kKImMX) | [model](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/EeP1aneDDbBCo9HnMTbNjsgBpMqrgfIzJzF_jVROpZ2GWQ?e=YmkNHC) \| [log](https://connecthkuhk-my.sharepoint.com/:t:/g/personal/wjn922_connect_hku_hk/EUnV-O_IAe5Mkyupsd7NosMBxUg8OjqepmQbpbV0PFB4gQ?e=W14suT) |
+
+\* the model is trained from scratch and set `--num_frames 6`.
+
+
+### JHMDB-Sentences
+
+As described in the paper, we report the results using the model trained on A2D-Sentences without finetune.
+
+| Backbone| Overall IoU | Mean IoU | mAP  | Model | 
+| :----: | :----: | :----: | :----: | :----: | 
+| Video-Swin-T* | 70.0 | 69.3 | 39.1 | [model](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/EfJs5WPRKfxEvifnIO3impABNgydbiO5qqI_uCF6LYKlCQ?e=mSRLCQ) | 
+| Video-Swin-T | 71.9 | 71.0 | 42.2 | [model](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/Ed3po2mJGQZHivGwMJJg8oMBumXm3Ye94oPH6wfRFK1d8A?e=NG2E9c) |
+| Video-Swin-S | 72.8 | 71.5 | 42.4 | [model](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/EbAiydTvu41KsMYBEFzy_d8B0Nyy1fIf2tWG7Ao-FYD0Ug?e=tmaVAu) |
+| Video-Swin-B | 73.0 | 71.8 | 43.7 | [model](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/EeP1aneDDbBCo9HnMTbNjsgBpMqrgfIzJzF_jVROpZ2GWQ?e=YmkNHC) | 
+
+\* the model is trained from scratch and set `--num_frames 6`.
+
+
+### RefCOCO/+/g
+
+We also support evaluate on RefCOCO/+/g validation set by using the pretrained weights (num_frames=1).
+Specifically, we measure the P@0.5 and overall IoU (oIoU) for REC and RIS tasks, respectively.
+
+REC (referring epression understanding):
+
+| Backbone| RefCOCO | RefCOCO+ | RefCOCOg  | Model | 
+| :----: | :----: | :----: | :----: | :----: | 
+| ResNet-50 | 85.0 | 79.2 | 79.0 | [weight](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/EepGibYBfyRGt_QedfE9SywBLF3v-bjoxo2R9E9YDqmIcw?e=7J7k1J) | 
+| ResNet-101 | 85.4 | 75.8 | 79.9 | [weight](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/ESTAK4QCkMdNkVlQz1dd7GoBo3n_i9K4_FK4YLFBAFvBrg?e=Y3PlD5) |
+| Swin-T | 86.7 | 77.2 | 80.6 | [weight](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/ESdasB6JLydDrs6mf68FrLMBuQBLBF7y_uxdveWl9oK68w?e=H5zeqk) |
+| Swin-L | 89.8 | 80.0 | 83.9 | [weight](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/ESngRLeZfV1LtrlZ7x5cVo4BR5_deWfov4Igt28LZGoDew?e=AVAsws) | 
+
+RIS (referring image segmentation):
+
+| Backbone| RefCOCO | RefCOCO+ | RefCOCOg  | Model | 
+| :----: | :----: | :----: | :----: | :----: | 
+| ResNet-50 | 71.1 | 64.1 | 64.1 | [weight](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/EepGibYBfyRGt_QedfE9SywBLF3v-bjoxo2R9E9YDqmIcw?e=7J7k1J) | 
+| ResNet-101 | 71.8 | 61.1 | 64.9 | [weight](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/ESTAK4QCkMdNkVlQz1dd7GoBo3n_i9K4_FK4YLFBAFvBrg?e=Y3PlD5) |
+| Swin-T | 72.9 | 62.4 | 66.1 | [weight](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/ESdasB6JLydDrs6mf68FrLMBuQBLBF7y_uxdveWl9oK68w?e=H5zeqk) |
+| Swin-L | 77.1 | 65.8 | 69.3 | [weight](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wjn922_connect_hku_hk/ESngRLeZfV1LtrlZ7x5cVo4BR5_deWfov4Igt28LZGoDew?e=AVAsws) | 
+
+
+## Get Started
+
+Please see [Ref-Youtube-VOS](docs/Ref-Youtube-VOS.md), [Ref-DAVIS17](docs/Ref-DAVIS17.md), [A2D-Sentences](docs/A2D-Sentences.md) and [JHMDB-Sentences](docs/JHMDB-Sentences.md) for details.
+
+
+
+## Acknowledgement
+
+This repo is based on [Deformable DETR](https://github.com/fundamentalvision/Deformable-DETR) and [VisTR](https://github.com/Epiphqny/VisTR). We also refer to the repositories [MDETR](https://github.com/ashkamath/mdetr) and [MTTR](https://github.com/mttr2021/MTTR). Thanks for their wonderful works.
+
+
+## Citation
+
+```
+@article{wu2022referformer,
+      title={Language as Queries for Referring Video Object Segmentation}, 
+      author={Jiannan Wu and Yi Jiang and Peize Sun and Zehuan Yuan and Ping Luo},
+      journal={arXiv preprint arXiv:2201.00487},
+      year={2022},
+}
+```
+
diff --git a/davis2017/__init__.py b/davis2017/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..2fb263ea94c650a6ac55ef374f2cea1b8c96a1a5
--- /dev/null
+++ b/davis2017/__init__.py
@@ -0,0 +1,3 @@
+from __future__ import absolute_import
+
+__version__ = '0.1.0'
diff --git a/davis2017/evaluation.py b/davis2017/evaluation.py
new file mode 100644
index 0000000000000000000000000000000000000000..7bfb80f86ada4b217995058852b23649c4e87efb
--- /dev/null
+++ b/davis2017/evaluation.py
@@ -0,0 +1,110 @@
+import sys
+from tqdm import tqdm
+import warnings
+warnings.filterwarnings("ignore", category=RuntimeWarning)
+
+import numpy as np
+from davis2017.davis import DAVIS
+from davis2017.metrics import db_eval_boundary, db_eval_iou
+from davis2017 import utils
+from davis2017.results import Results
+from scipy.optimize import linear_sum_assignment
+
+
+class DAVISEvaluation(object):
+    def __init__(self, davis_root, task, gt_set, sequences='all', codalab=False):
+        """
+        Class to evaluate DAVIS sequences from a certain set and for a certain task
+        :param davis_root: Path to the DAVIS folder that contains JPEGImages, Annotations, etc. folders.
+        :param task: Task to compute the evaluation, chose between semi-supervised or unsupervised.
+        :param gt_set: Set to compute the evaluation
+        :param sequences: Sequences to consider for the evaluation, 'all' to use all the sequences in a set.
+        """
+        self.davis_root = davis_root
+        self.task = task
+        self.dataset = DAVIS(root=davis_root, task=task, subset=gt_set, sequences=sequences, codalab=codalab)
+
+    @staticmethod
+    def _evaluate_semisupervised(all_gt_masks, all_res_masks, all_void_masks, metric):
+        if all_res_masks.shape[0] > all_gt_masks.shape[0]:
+            sys.stdout.write("\nIn your PNG files there is an index higher than the number of objects in the sequence!")
+            sys.exit()
+        elif all_res_masks.shape[0] < all_gt_masks.shape[0]:
+            zero_padding = np.zeros((all_gt_masks.shape[0] - all_res_masks.shape[0], *all_res_masks.shape[1:]))
+            all_res_masks = np.concatenate([all_res_masks, zero_padding], axis=0)
+        j_metrics_res, f_metrics_res = np.zeros(all_gt_masks.shape[:2]), np.zeros(all_gt_masks.shape[:2])
+        for ii in range(all_gt_masks.shape[0]):
+            if 'J' in metric:
+                j_metrics_res[ii, :] = db_eval_iou(all_gt_masks[ii, ...], all_res_masks[ii, ...], all_void_masks)
+            if 'F' in metric:
+                f_metrics_res[ii, :] = db_eval_boundary(all_gt_masks[ii, ...], all_res_masks[ii, ...], all_void_masks)
+        return j_metrics_res, f_metrics_res
+
+    @staticmethod
+    def _evaluate_unsupervised(all_gt_masks, all_res_masks, all_void_masks, metric, max_n_proposals=20):
+        if all_res_masks.shape[0] > max_n_proposals:
+            sys.stdout.write(f"\nIn your PNG files there is an index higher than the maximum number ({max_n_proposals}) of proposals allowed!")
+            sys.exit()
+        elif all_res_masks.shape[0] < all_gt_masks.shape[0]:
+            zero_padding = np.zeros((all_gt_masks.shape[0] - all_res_masks.shape[0], *all_res_masks.shape[1:]))
+            all_res_masks = np.concatenate([all_res_masks, zero_padding], axis=0)
+        j_metrics_res = np.zeros((all_res_masks.shape[0], all_gt_masks.shape[0], all_gt_masks.shape[1]))
+        f_metrics_res = np.zeros((all_res_masks.shape[0], all_gt_masks.shape[0], all_gt_masks.shape[1]))
+        for ii in range(all_gt_masks.shape[0]):
+            for jj in range(all_res_masks.shape[0]):
+                if 'J' in metric:
+                    j_metrics_res[jj, ii, :] = db_eval_iou(all_gt_masks[ii, ...], all_res_masks[jj, ...], all_void_masks)
+                if 'F' in metric:
+                    f_metrics_res[jj, ii, :] = db_eval_boundary(all_gt_masks[ii, ...], all_res_masks[jj, ...], all_void_masks)
+        if 'J' in metric and 'F' in metric:
+            all_metrics = (np.mean(j_metrics_res, axis=2) + np.mean(f_metrics_res, axis=2)) / 2
+        else:
+            all_metrics = np.mean(j_metrics_res, axis=2) if 'J' in metric else np.mean(f_metrics_res, axis=2)
+        row_ind, col_ind = linear_sum_assignment(-all_metrics)
+        return j_metrics_res[row_ind, col_ind, :], f_metrics_res[row_ind, col_ind, :]
+
+    def evaluate(self, res_path, metric=('J', 'F'), debug=False):
+        metric = metric if isinstance(metric, tuple) or isinstance(metric, list) else [metric]
+        if 'T' in metric:
+            raise ValueError('Temporal metric not supported!')
+        if 'J' not in metric and 'F' not in metric:
+            raise ValueError('Metric possible values are J for IoU or F for Boundary')
+
+        # Containers
+        metrics_res = {}
+        if 'J' in metric:
+            metrics_res['J'] = {"M": [], "R": [], "D": [], "M_per_object": {}}
+        if 'F' in metric:
+            metrics_res['F'] = {"M": [], "R": [], "D": [], "M_per_object": {}}
+
+        # Sweep all sequences
+        results = Results(root_dir=res_path)
+        for seq in tqdm(list(self.dataset.get_sequences())):
+            all_gt_masks, all_void_masks, all_masks_id = self.dataset.get_all_masks(seq, True)
+            if self.task == 'semi-supervised':
+                all_gt_masks, all_masks_id = all_gt_masks[:, 1:-1, :, :], all_masks_id[1:-1]
+            all_res_masks = results.read_masks(seq, all_masks_id)
+            if self.task == 'unsupervised':
+                j_metrics_res, f_metrics_res = self._evaluate_unsupervised(all_gt_masks, all_res_masks, all_void_masks, metric)
+            elif self.task == 'semi-supervised':
+                j_metrics_res, f_metrics_res = self._evaluate_semisupervised(all_gt_masks, all_res_masks, None, metric)
+            for ii in range(all_gt_masks.shape[0]):
+                seq_name = f'{seq}_{ii+1}'
+                if 'J' in metric:
+                    [JM, JR, JD] = utils.db_statistics(j_metrics_res[ii])
+                    metrics_res['J']["M"].append(JM)
+                    metrics_res['J']["R"].append(JR)
+                    metrics_res['J']["D"].append(JD)
+                    metrics_res['J']["M_per_object"][seq_name] = JM
+                if 'F' in metric:
+                    [FM, FR, FD] = utils.db_statistics(f_metrics_res[ii])
+                    metrics_res['F']["M"].append(FM)
+                    metrics_res['F']["R"].append(FR)
+                    metrics_res['F']["D"].append(FD)
+                    metrics_res['F']["M_per_object"][seq_name] = FM
+
+            # Show progress
+            if debug:
+                sys.stdout.write(seq + '\n')
+                sys.stdout.flush()
+        return metrics_res
diff --git a/davis2017/metrics.py b/davis2017/metrics.py
new file mode 100644
index 0000000000000000000000000000000000000000..75dc2b015848d345fcec57eec1d7f1133f58e9a0
--- /dev/null
+++ b/davis2017/metrics.py
@@ -0,0 +1,197 @@
+import math
+import numpy as np
+import cv2
+
+
+def db_eval_iou(annotation, segmentation, void_pixels=None):
+    """ Compute region similarity as the Jaccard Index.
+    Arguments:
+        annotation   (ndarray): binary annotation   map.
+        segmentation (ndarray): binary segmentation map.
+        void_pixels  (ndarray): optional mask with void pixels
+
+    Return:
+        jaccard (float): region similarity
+    """
+    assert annotation.shape == segmentation.shape, \
+        f'Annotation({annotation.shape}) and segmentation:{segmentation.shape} dimensions do not match.'
+    annotation = annotation.astype(np.bool)
+    segmentation = segmentation.astype(np.bool)
+
+    if void_pixels is not None:
+        assert annotation.shape == void_pixels.shape, \
+            f'Annotation({annotation.shape}) and void pixels:{void_pixels.shape} dimensions do not match.'
+        void_pixels = void_pixels.astype(np.bool)
+    else:
+        void_pixels = np.zeros_like(segmentation)
+
+    # Intersection between all sets
+    inters = np.sum((segmentation & annotation) & np.logical_not(void_pixels), axis=(-2, -1))
+    union = np.sum((segmentation | annotation) & np.logical_not(void_pixels), axis=(-2, -1))
+
+    j = inters / union
+    if j.ndim == 0:
+        j = 1 if np.isclose(union, 0) else j
+    else:
+        j[np.isclose(union, 0)] = 1
+    return j
+
+
+def db_eval_boundary(annotation, segmentation, void_pixels=None, bound_th=0.008):
+    assert annotation.shape == segmentation.shape
+    if void_pixels is not None:
+        assert annotation.shape == void_pixels.shape
+    if annotation.ndim == 3:
+        n_frames = annotation.shape[0]
+        f_res = np.zeros(n_frames)
+        for frame_id in range(n_frames):
+            void_pixels_frame = None if void_pixels is None else void_pixels[frame_id, :, :, ]
+            f_res[frame_id] = f_measure(segmentation[frame_id, :, :, ], annotation[frame_id, :, :], void_pixels_frame, bound_th=bound_th)
+    elif annotation.ndim == 2:
+        f_res = f_measure(segmentation, annotation, void_pixels, bound_th=bound_th)
+    else:
+        raise ValueError(f'db_eval_boundary does not support tensors with {annotation.ndim} dimensions')
+    return f_res
+
+
+def f_measure(foreground_mask, gt_mask, void_pixels=None, bound_th=0.008):
+    """
+    Compute mean,recall and decay from per-frame evaluation.
+    Calculates precision/recall for boundaries between foreground_mask and
+    gt_mask using morphological operators to speed it up.
+
+    Arguments:
+        foreground_mask (ndarray): binary segmentation image.
+        gt_mask         (ndarray): binary annotated image.
+        void_pixels     (ndarray): optional mask with void pixels
+
+    Returns:
+        F (float): boundaries F-measure
+    """
+    assert np.atleast_3d(foreground_mask).shape[2] == 1
+    if void_pixels is not None:
+        void_pixels = void_pixels.astype(np.bool)
+    else:
+        void_pixels = np.zeros_like(foreground_mask).astype(np.bool)
+
+    bound_pix = bound_th if bound_th >= 1 else \
+        np.ceil(bound_th * np.linalg.norm(foreground_mask.shape))
+
+    # Get the pixel boundaries of both masks
+    fg_boundary = _seg2bmap(foreground_mask * np.logical_not(void_pixels))
+    gt_boundary = _seg2bmap(gt_mask * np.logical_not(void_pixels))
+
+    from skimage.morphology import disk
+
+    # fg_dil = binary_dilation(fg_boundary, disk(bound_pix))
+    fg_dil = cv2.dilate(fg_boundary.astype(np.uint8), disk(bound_pix).astype(np.uint8))
+    # gt_dil = binary_dilation(gt_boundary, disk(bound_pix))
+    gt_dil = cv2.dilate(gt_boundary.astype(np.uint8), disk(bound_pix).astype(np.uint8))
+
+    # Get the intersection
+    gt_match = gt_boundary * fg_dil
+    fg_match = fg_boundary * gt_dil
+
+    # Area of the intersection
+    n_fg = np.sum(fg_boundary)
+    n_gt = np.sum(gt_boundary)
+
+    # % Compute precision and recall
+    if n_fg == 0 and n_gt > 0:
+        precision = 1
+        recall = 0
+    elif n_fg > 0 and n_gt == 0:
+        precision = 0
+        recall = 1
+    elif n_fg == 0 and n_gt == 0:
+        precision = 1
+        recall = 1
+    else:
+        precision = np.sum(fg_match) / float(n_fg)
+        recall = np.sum(gt_match) / float(n_gt)
+
+    # Compute F measure
+    if precision + recall == 0:
+        F = 0
+    else:
+        F = 2 * precision * recall / (precision + recall)
+
+    return F
+
+
+def _seg2bmap(seg, width=None, height=None):
+    """
+    From a segmentation, compute a binary boundary map with 1 pixel wide
+    boundaries.  The boundary pixels are offset by 1/2 pixel towards the
+    origin from the actual segment boundary.
+    Arguments:
+        seg     : Segments labeled from 1..k.
+        width	  :	Width of desired bmap  <= seg.shape[1]
+        height  :	Height of desired bmap <= seg.shape[0]
+    Returns:
+        bmap (ndarray):	Binary boundary map.
+     David Martin <dmartin@eecs.berkeley.edu>
+     January 2003
+    """
+
+    seg = seg.astype(np.bool)
+    seg[seg > 0] = 1
+
+    assert np.atleast_3d(seg).shape[2] == 1
+
+    width = seg.shape[1] if width is None else width
+    height = seg.shape[0] if height is None else height
+
+    h, w = seg.shape[:2]
+
+    ar1 = float(width) / float(height)
+    ar2 = float(w) / float(h)
+
+    assert not (
+        width > w | height > h | abs(ar1 - ar2) > 0.01
+    ), "Can" "t convert %dx%d seg to %dx%d bmap." % (w, h, width, height)
+
+    e = np.zeros_like(seg)
+    s = np.zeros_like(seg)
+    se = np.zeros_like(seg)
+
+    e[:, :-1] = seg[:, 1:]
+    s[:-1, :] = seg[1:, :]
+    se[:-1, :-1] = seg[1:, 1:]
+
+    b = seg ^ e | seg ^ s | seg ^ se
+    b[-1, :] = seg[-1, :] ^ e[-1, :]
+    b[:, -1] = seg[:, -1] ^ s[:, -1]
+    b[-1, -1] = 0
+
+    if w == width and h == height:
+        bmap = b
+    else:
+        bmap = np.zeros((height, width))
+        for x in range(w):
+            for y in range(h):
+                if b[y, x]:
+                    j = 1 + math.floor((y - 1) + height / h)
+                    i = 1 + math.floor((x - 1) + width / h)
+                    bmap[j, i] = 1
+
+    return bmap
+
+
+if __name__ == '__main__':
+    from davis2017.davis import DAVIS
+    from davis2017.results import Results
+
+    dataset = DAVIS(root='input_dir/ref', subset='val', sequences='aerobatics')
+    results = Results(root_dir='examples/osvos')
+    # Test timing F measure
+    for seq in dataset.get_sequences():
+        all_gt_masks, _, all_masks_id = dataset.get_all_masks(seq, True)
+        all_gt_masks, all_masks_id = all_gt_masks[:, 1:-1, :, :], all_masks_id[1:-1]
+        all_res_masks = results.read_masks(seq, all_masks_id)
+        f_metrics_res = np.zeros(all_gt_masks.shape[:2])
+        for ii in range(all_gt_masks.shape[0]):
+            f_metrics_res[ii, :] = db_eval_boundary(all_gt_masks[ii, ...], all_res_masks[ii, ...])
+
+    # Run using to profile code: python -m cProfile -o f_measure.prof metrics.py
+    #                            snakeviz f_measure.prof
diff --git a/docs/A2D-Sentences.md b/docs/A2D-Sentences.md
new file mode 100644
index 0000000000000000000000000000000000000000..b1a94514651e6bd477e9f014aca76fb1a50af1bd
--- /dev/null
+++ b/docs/A2D-Sentences.md
@@ -0,0 +1,55 @@
+## A2D-Sentences
+
+### Model Zoo
+
+The pretrained models are the same as those provided for Ref-Youtube-VOS.
+
+| Backbone| Overall IoU | Mean IoU | mAP  | Pretrain | Model |
+| :----: | :----: | :----: | :----: | :----: | :----: |
+| Video-Swin-T* | 72.3 | 64.1 | 48.6 | - | [model](https://drive.google.com/file/d/1z-HO71IcFOZ9A6KD71wAXkbiQgKDpSp7/view?usp=sharing) \| [log](https://drive.google.com/file/d/1JhsXgcWOYv97u6tpAUnBi9-D3mxcHXzO/view?usp=sharing) |
+| Video-Swin-T | 77.6 | 69.6 | 52.8 | [weight](https://drive.google.com/file/d/1g9Dm1vLdwpwSKVtIZzWKPUk2-zK3IbQa/view?usp=sharing) | [model](https://drive.google.com/file/d/1z-HO71IcFOZ9A6KD71wAXkbiQgKDpSp7/view?usp=sharing) \| [log](https://drive.google.com/file/d/1xjevouL3a1gHZN5KHtA07Cpa07R4T1Qi/view?usp=sharing) |
+| Video-Swin-S | 77.7 | 69.8 | 53.9 | [weight](https://drive.google.com/file/d/1GrhFhsUidsVs7-dhY8NkVgWfBZdeit9C/view?usp=sharing) | [model](https://drive.google.com/file/d/1ng2FAX9J4FyQ7Bq1eeQC9Vvv1W8JZmek/view?usp=sharing) \| [log](https://drive.google.com/file/d/1Uu72THexbtEje4aKXR7Q2Yd4zyPmQsi3/view?usp=sharing) |
+| Video-Swin-B | 78.6 | 70.3 | 55.0 | [weight](https://drive.google.com/file/d/1MJ1362zjqu-uZdXsSQH6pI1QOFqwv5lY/view?usp=sharing) | [model](https://drive.google.com/file/d/1WlNjKS_Li-1KoUzuPM4MRM4b-oK2Ka7c/view?usp=sharing) \| [log](https://drive.google.com/file/d/1tH-f9_U0gY-iNfXm6GRyttJp3uvm5NQw/view?usp=sharing) |
+
+\* the model is trained from scratch and set `--num_frames 6`.
+
+
+### Inference & Evaluation
+
+```
+python3 -m torch.distributed.launch --nproc_per_node=8 --use_env main.py --dataset_file a2d --with_box_refine --freeze_text_encoder --batch_size 2 --resume [/path/to/model_weight] --backbone [backbone]  --eval
+```
+
+For example, evaluating the Video-Swin-Tiny model, run the following command:
+
+```
+python3 -m torch.distributed.launch --nproc_per_node=8 --use_env main.py --dataset_file a2d --with_box_refine --freeze_text_encoder --batch_size 2 --resume a2d_video_swin_tiny.pth --backbone video_swin_t_p4w7  --eval
+```
+
+### Training
+
+- Finetune
+
+```
+./scripts/dist_train_a2d.sh [/path/to/output_dir] [/path/to/pretrained_weight] --backbone [backbone]
+```
+
+For example, training the Video-Swin-Tiny model, run the following command:
+```
+./scripts/dist_train_a2d.sh a2d_dirs/video_swin_tiny pretrained_weights/video_swin_tiny_pretrained.pth --backbone video_swin_t_p4w7
+```
+
+- Train from scratch
+
+```
+python3 -m torch.distributed.launch --nproc_per_node=8 --use_env main.py --dataset_file a2d --with_box_refine --freeze_text_encoder --epochs 12 --lr_drop 8 10 --dropout 0 --weight_decay 1e-4 --output_dir=[/path/to/output_dir] --backbone [backbone] --backbone_pretrained [/path/to/pretrained backbone weight] [other args]
+```
+
+For example, training the Video-Swin-Tiny model from scratch and set window size as 6, run the following command:
+
+```
+python3 -m torch.distributed.launch --nproc_per_node=8 --use_env main.py --dataset_file a2d --with_box_refine --freeze_text_encoder --epochs 12 --lr_drop 8 10 --dropout 0 --weight_decay 1e-4 --output_dir a2d_dirs/video_swin_tiny_scratch_frame6 --backbone video_swin_t_p4w7 --bacbkone_pretrained video_swin_pretrained/swin_tiny_patch244_window877_kinetics400_1k.pth --num_frames 6
+```
+
+
+
diff --git a/docs/JHMDB-Sentences.md b/docs/JHMDB-Sentences.md
new file mode 100644
index 0000000000000000000000000000000000000000..d9edee4fca92fcce61e3d6a1f6a4df03d12010d9
--- /dev/null
+++ b/docs/JHMDB-Sentences.md
@@ -0,0 +1,27 @@
+## JHMDB-Sentences
+
+### Model Zoo
+
+As described in the paper, we report the results using the model trained on A2D-Sentences without finetune.
+
+| Backbone| Overall IoU | Mean IoU | mAP  | Model | 
+| :----: | :----: | :----: | :----: | :----: | 
+| Video-Swin-T* | 70.0 | 69.3 | 39.1 | [model](https://drive.google.com/file/d/1z-HO71IcFOZ9A6KD71wAXkbiQgKDpSp7/view?usp=sharing) | 
+| Video-Swin-T | 71.9 | 71.0 | 42.2 | [model](https://drive.google.com/file/d/1z-HO71IcFOZ9A6KD71wAXkbiQgKDpSp7/view?usp=sharing) |
+| Video-Swin-S | 72.8 | 71.5 | 42.4 | [model](https://drive.google.com/file/d/1ng2FAX9J4FyQ7Bq1eeQC9Vvv1W8JZmek/view?usp=sharing) |
+| Video-Swin-B | 73.0 | 71.8 | 43.7 | [model](https://drive.google.com/file/d/1WlNjKS_Li-1KoUzuPM4MRM4b-oK2Ka7c/view?usp=sharing) | 
+
+\* the model is trained from scratch and set `--num_frames 6`.
+
+
+### Inference & Evaluation
+
+```
+python3 -m torch.distributed.launch --nproc_per_node=8 --use_env main.py --dataset_file jhmdb --with_box_refine --freeze_text_encoder --batch_size 2 --resume [/path/to/model_weight] --backbone [backbone]  --eval
+```
+
+For example, evaluating the Video-Swin-Tiny model, run the following command:
+
+```
+python3 -m torch.distributed.launch --nproc_per_node=8 --use_env main.py --dataset_file jhmdb --with_box_refine --freeze_text_encoder --batch_size 2 --resume a2d_video_swin_tiny.pth --backbone video_swin_t_p4w7  --eval
+```
diff --git a/docs/Ref-DAVIS17.md b/docs/Ref-DAVIS17.md
new file mode 100644
index 0000000000000000000000000000000000000000..15a09893d20cf4c21102a7a9a0d75a82c8af80e9
--- /dev/null
+++ b/docs/Ref-DAVIS17.md
@@ -0,0 +1,24 @@
+## Ref-DAVIS17
+
+### Model Zoo
+
+As described in the paper, we report the results using the model trained on Ref-Youtube-VOS without finetune.
+
+| Backbone| J&F | J | F | Model | 
+| :----: | :----: | :----: | :----: | :----: |
+| ResNet-50 | 58.5 | 55.8 | 61.3 | [model](https://drive.google.com/file/d/1VKYIbd3tiuLyWkh7ajnIiA3HZ3_IdvxV/view?usp=sharing) |
+| Swin-L | 60.5 | 57.6 | 63.4 | [model](https://drive.google.com/file/d/1_uwwlWv8AXhHfE8GVId7YtGraznRebaZ/view?usp=sharing) |
+| Video-Swin-B | 61.1 | 58.1 | 64.1 | [model](https://drive.google.com/file/d/1nw7D3C_RrKTMzwtzjo39snbYLbv73anH/view?usp=sharing) |
+
+
+### Inference & Evaluation
+
+```
+./scripts/dist_test_davis.sh [/path/to/output_dir] [/path/to/model_weight] --backbone [backbone]
+```
+
+For example, evaluating the Swin-Large model, run the following command:
+
+```
+./scripts/dist_test_davis.sh davis_dirs/swin_large ytvos_swin_large.pth --backbone swin_l_p4w7
+```
\ No newline at end of file
diff --git a/docs/Ref-Youtube-VOS.md b/docs/Ref-Youtube-VOS.md
new file mode 100644
index 0000000000000000000000000000000000000000..0e60eaf3759afc9453f479bfcd0dd13d74446e0b
--- /dev/null
+++ b/docs/Ref-Youtube-VOS.md
@@ -0,0 +1,83 @@
+## Ref-Youtube-VOS
+
+### Model Zoo
+
+To evaluate the results, please upload the zip file to the [competition server](https://competitions.codalab.org/competitions/29139#participate-submit_results).
+
+| Backbone| J&F | CFBI J&F  | Pretrain | Model | Submission | CFBI Submission | 
+| :----: | :----: | :----: | :----: | :----: | :----: | :----: | 
+| ResNet-50 | 55.6 | 59.4 | [weight](https://drive.google.com/file/d/1mJd5zBUv4EYLOKQ0H87-NeAuInyrn577/view?usp=sharing) | [model](https://drive.google.com/file/d/1VKYIbd3tiuLyWkh7ajnIiA3HZ3_IdvxV/view?usp=sharing) | [link](https://drive.google.com/file/d/1IXKu8a06ppPAVBvy4Y0UfcKhCat4HRJt/view?usp=sharing) | [link](https://drive.google.com/file/d/1VJAKZ_j7kQFpocv_vDzER47CXWwAAE8h/view?usp=sharing) |
+| ResNet-101 | 57.3 | 60.3 | [weight](https://drive.google.com/file/d/1EMOwwAygdSfTZiVxI4f0UaVd7P6JzmuM/view?usp=sharing) | [model](https://drive.google.com/file/d/1FCHAAMf-HXPhZGTZp748l3pn6FfMyV1L/view?usp=sharing) | [link](https://drive.google.com/file/d/1cFxjVW2RlwjoVYR1M6NlkRpv9L3tPlcZ/view?usp=sharing) | [link](https://drive.google.com/file/d/1RPnFPqf7iiVypc7QbN-ev6s6xfmD-m5c/view?usp=sharing) |
+| Swin-T | 58.7 | 61.2 | [weight](https://drive.google.com/file/d/155sZm6yE7YQ8Y8Ln0ShaVZKLejYORqTQ/view?usp=sharing) | [model](https://drive.google.com/file/d/19jIbjRRUGDhfnI604Pw7hcGP5DqdvVtl/view?usp=sharing) | [link](https://drive.google.com/file/d/1eZZ-2zz0gdCwPrislGP3WKAHk-RnNY7v/view?usp=sharing) | [link](https://drive.google.com/file/d/1O9B35oieBfo7sRjxTpSyFz52J2AAHLce/view?usp=sharing) |
+| Swin-L | 62.4 | 63.3 | [weight](https://drive.google.com/file/d/1eJKNHvk_KcFuT4k6Te7HDuuSXH2DVOY5/view?usp=sharing) | [model](https://drive.google.com/file/d/1_uwwlWv8AXhHfE8GVId7YtGraznRebaZ/view?usp=sharing) | [link](https://drive.google.com/file/d/1uxBwbKdlilaCNt-RbdcPj1LshA-WY9Q6/view?usp=sharing) | [link](https://drive.google.com/file/d/16kVmJzv5oXzk3zGcfMcb2sEiN6HTOCmW/view?usp=sharing) |
+| Video-Swin-T* | 55.8 | - | - | [model](https://drive.google.com/file/d/1vNiQGpKuYfR7F7YKZK7H2HAzljDf9Wuf/view?usp=sharing) | [link](https://drive.google.com/file/d/18G0qIeZndacj3Y0EuyJsZFeFRWJ0_3O_/view?usp=sharing) | - |
+| Video-Swin-T | 59.4 | - | [weight](https://drive.google.com/file/d/1g9Dm1vLdwpwSKVtIZzWKPUk2-zK3IbQa/view?usp=sharing) | [model](https://drive.google.com/file/d/17RL6o_A57giHT-bMuP7ysUGogueT7wYm/view?usp=sharing) | [link](https://drive.google.com/file/d/1nhjvDWgMWufMGAjOKesgyLRB_-Ct6kXP/view?usp=sharing) | - |
+| Video-Swin-S | 60.1 | - | [weight](https://drive.google.com/file/d/1GrhFhsUidsVs7-dhY8NkVgWfBZdeit9C/view?usp=sharing) | [model](https://drive.google.com/file/d/1GrhFhsUidsVs7-dhY8NkVgWfBZdeit9C/view?usp=sharing) | [link](https://drive.google.com/file/d/1mhb0UAaJkTFYmGrwXHHJuaXVp-0BSkgm/view?usp=sharing) | - |
+| Video-Swin-B | 62.9 | - |[weight](https://drive.google.com/file/d/1MJ1362zjqu-uZdXsSQH6pI1QOFqwv5lY/view?usp=sharing)  | [model](https://drive.google.com/file/d/1nw7D3C_RrKTMzwtzjo39snbYLbv73anH/view?usp=sharing) | [link](https://drive.google.com/file/d/1dAQdr2RqCxYUmOVQ4jFE-vv5zavNhz7B/view?usp=sharing) | - |
+
+\* indicates the model is trained from scratch.
+
+
+Joint training with Ref-COCO/+/g datasets.
+| Backbone| J&F | J | F | Model | Submission | 
+| :----: | :----: | :----: | :----: | :----: | :----: |
+| ResNet-50 | 58.7 | 57.4 | 60.1 | [model](https://drive.google.com/file/d/1tXgC_GRmQCvHjhlNoT0uXc_0oQ21d0hk/view?usp=sharing) | [link](https://drive.google.com/file/d/1Vbrl11mBfjwpM-H4DOleyD1i2STCN-SM/view?usp=sharing) |
+| ResNet-101 | 59.3 | 58.1 | 60.4 | [model](https://drive.google.com/file/d/1LUflgRgwZgTpYr5V9qeDKTIlBjLqHOVj/view?usp=sharing) | [link](https://drive.google.com/file/d/1BANQcqY34SebORZ9_PTF4C-QWuCJl2_W/view?usp=sharing) |
+| Swin-L | 64.2 | 62.3 | 66.2 | [model](https://drive.google.com/file/d/1JeppEr8m0O9844xncSfSZrYE_NH8oXb7/view?usp=sharing) | [link](https://drive.google.com/file/d/14klluhPeQhhNKl3EBibtiziChSKfBHU0/view?usp=sharing) |
+| Video-Swin-T | 62.6 | 59.9 | 63.3 | [model](https://drive.google.com/file/d/1rVO2ZC4U4symSh9Ifgg68YGdYBZH00MT/view?usp=sharing) | [link](https://drive.google.com/file/d/1-i67hTmo-qpyICbJ9vbTeQdPaL2VnbXQ/view?usp=sharing) |
+| Video-Swin-S | 63.3 | 61.4 | 65.2 | [model](https://drive.google.com/file/d/15ifI2yd9oDqMB05DgjhNVMe2MGXVvZnj/view?usp=sharing) | [link](https://drive.google.com/file/d/1II1gZl99FGECkS7DR6B8MszxAKadu-9y/view?usp=sharing) |
+| Video-Swin-B | 64.9 | 62.8 | 67.0 | [model](https://drive.google.com/file/d/19XO5VoR6qTE3VNLF-IjYzabL-2tb9E14/view?usp=sharing) | [link](https://drive.google.com/file/d/11FTV-B3MkWfl4azNI-aRmiRqQ9TBXG03/view?usp=sharing) |
+
+### Inference & Evaluation
+
+
+First, inference using the trained model.
+
+```
+python3 inference_ytvos.py --with_box_refine --binary --freeze_text_encoder --output_dir=[/path/to/output_dir] --resume=[/path/to/model_weight] --backbone [backbone] 
+```
+
+```
+python3 inference_ytvos.py --with_box_refine --binary --freeze_text_encoder --output_dir=ytvos_dirs/swin_tiny --resume=ytvos_swin_tiny.pth --backbone swin_t_p4w7
+```
+
+If you want to visualize the predited masks, you may add `--visualize` to the above command.
+
+Then, enter the `output_dir`, rename the folder `valid` as `Annotations`. Use the following command to zip the folder:
+
+```
+zip -q -r submission.zip Annotations
+```
+
+To evaluate the results, please upload the zip file to the [competition server](https://competitions.codalab.org/competitions/29139#participate-submit_results).
+
+### Training
+
+
+- Finetune 
+
+The following command includes the training and inference stages.
+
+```
+./scripts/dist_train_test_ytvos.sh [/path/to/output_dir] [/path/to/pretrained_weight] --backbone [backbone] 
+```
+
+For example, training the Video-Swin-Tiny model, run the following command:
+
+```
+./scripts/dist_train_test_ytvos.sh ytvos_dirs/video_swin_tiny pretrained_weights/video_swin_tiny_pretrained.pth --backbone video_swin_t_p4w7 
+```
+
+- Train from scratch
+
+The following command includes the training and inference stages.
+
+```
+./scripts/dist_train_test_ytvos_scratch.sh [/path/to/output_dir] --backbone [backbone] --backbone_pretrained [/path/to/backbone_pretrained_weight] [other args]
+```
+
+For example, training the Video-Swin-Tiny model, run the following command:
+
+```
+./scripts/dist_train_test_ytvos.sh ytvos_dirs/video_swin_tiny_scratch --backbone video_swin_t_p4w7 --backbone_pretrained video_swin_pretrained/swin_tiny_patch244_window877_kinetics400_1k.pth
+```
diff --git a/docs/data.md b/docs/data.md
new file mode 100644
index 0000000000000000000000000000000000000000..87137dec70508986395220b8f67799f5b6031c47
--- /dev/null
+++ b/docs/data.md
@@ -0,0 +1,127 @@
+# Data Preparation
+
+Create a new directory `data` to store all the datasets.
+
+## Ref-COCO
+
+Download the dataset from the official website [COCO](https://cocodataset.org/#download).   
+RefCOCO/+/g use the COCO2014 train split.
+Download the annotation files from [github](https://github.com/lichengunc/refer).
+
+Convert the annotation files:
+
+```
+python3 tools/data/convert_refexp_to_coco.py
+```
+
+Finally, we expect the directory structure to be the following:
+
+```
+ReferFormer
+├── data
+│   ├── coco
+│   │   ├── train2014
+│   │   ├── refcoco
+│   │   │   ├── instances_refcoco_train.json
+│   │   │   ├── instances_refcoco_val.json
+│   │   ├── refcoco+
+│   │   │   ├── instances_refcoco+_train.json
+│   │   │   ├── instances_refcoco+_val.json
+│   │   ├── refcocog
+│   │   │   ├── instances_refcocog_train.json
+│   │   │   ├── instances_refcocog_val.json
+```
+
+
+## Ref-Youtube-VOS
+
+Download the dataset from the competition's website [here](https://competitions.codalab.org/competitions/29139#participate-get_data).
+Then, extract and organize the file. We expect the directory structure to be the following:
+
+```
+ReferFormer
+├── data
+│   ├── ref-youtube-vos
+│   │   ├── meta_expressions
+│   │   ├── train
+│   │   │   ├── JPEGImages
+│   │   │   ├── Annotations
+│   │   │   ├── meta.json
+│   │   ├── valid
+│   │   │   ├── JPEGImages
+```
+
+## Ref-DAVIS17
+
+Downlaod the DAVIS2017 dataset from the [website](https://davischallenge.org/davis2017/code.html). Note that you only need to download the two zip files `DAVIS-2017-Unsupervised-trainval-480p.zip` and `DAVIS-2017_semantics-480p.zip`.
+Download the text annotations from the [website](https://www.mpi-inf.mpg.de/departments/computer-vision-and-machine-learning/research/video-segmentation/video-object-segmentation-with-language-referring-expressions).
+Then, put the zip files in the directory as follows.
+
+
+```
+ReferFormer
+├── data
+│   ├── ref-davis
+│   │   ├── DAVIS-2017_semantics-480p.zip
+│   │   ├── DAVIS-2017-Unsupervised-trainval-480p.zip
+│   │   ├── davis_text_annotations.zip
+```
+
+Unzip these zip files.
+```
+unzip -o davis_text_annotations.zip
+unzip -o DAVIS-2017_semantics-480p.zip
+unzip -o DAVIS-2017-Unsupervised-trainval-480p.zip
+```
+
+Preprocess the dataset to Ref-Youtube-VOS format. (Make sure you are in the main directory)
+
+```
+python tools/data/convert_davis_to_ytvos.py
+```
+
+Finally, unzip the file `DAVIS-2017-Unsupervised-trainval-480p.zip` again (since we use `mv` in preprocess for efficiency).
+
+```
+unzip -o DAVIS-2017-Unsupervised-trainval-480p.zip
+```
+
+
+
+
+## A2D-Sentences
+
+Follow the instructions and download the dataset from the website [here](https://kgavrilyuk.github.io/publication/actor_action/). 
+Then, extract the files. Additionally, we use the same json annotation files generated by [MTTR](https://github.com/mttr2021/MTTR). Please download these files from [onedrive](https://connecthkuhk-my.sharepoint.com/:f:/g/personal/wjn922_connect_hku_hk/EnvcpWsMsY5NrMF5If3F6DwBseMrqmzQwpTtL8HXoLAChw?e=Vlv1et).
+We expect the directory structure to be the following:
+
+```
+ReferFormer
+├── data
+│   ├── a2d_sentences
+│   │   ├── Release
+│   │   ├── text_annotations
+│   │   │   ├── a2d_annotation_with_instances
+│   │   │   ├── a2d_annotation.txt
+│   │   │   ├── a2d_missed_videos.txt
+│   │   ├── a2d_sentences_single_frame_test_annotations.json
+│   │   ├── a2d_sentences_single_frame_train_annotations.json
+│   │   ├── a2d_sentences_test_annotations_in_coco_format.json
+```
+
+## JHMDB-Sentences
+
+Follow the instructions and download the dataset from the website [here](https://kgavrilyuk.github.io/publication/actor_action/). 
+Then, extract the files. Additionally, we use the same json annotation files generated by [MTTR](https://github.com/mttr2021/MTTR). Please download these files from [onedrive](https://connecthkuhk-my.sharepoint.com/:f:/g/personal/wjn922_connect_hku_hk/EjPyzXq93s5Jm4GU07JrWIMBb6nObY8fEmLyuiGg-0uBtg?e=GsZ6jP).
+We expect the directory structure to be the following:
+
+```
+ReferFormer
+├── data
+│   ├── jhmdb_sentences
+│   │   ├── Rename_Images
+│   │   ├── puppet_mask
+│   │   ├── jhmdb_annotation.txt
+│   │   ├── jhmdb_sentences_samples_metadata.json
+│   │   ├── jhmdb_sentences_gt_annotations_in_coco_format.json
+```
\ No newline at end of file
diff --git a/engine.py b/engine.py
new file mode 100644
index 0000000000000000000000000000000000000000..0dbdd475d19fc712919d5cc2511a0a01c353f0a3
--- /dev/null
+++ b/engine.py
@@ -0,0 +1,253 @@
+"""
+Train and eval functions used in main.py
+Modified from DETR (https://github.com/facebookresearch/detr)
+"""
+import math
+from models import postprocessors
+import os
+import sys
+from typing import Iterable
+
+import torch
+import torch.distributed as dist
+
+import util.misc as utils
+from datasets.coco_eval import CocoEvaluator
+from datasets.refexp_eval import RefExpEvaluator
+
+from pycocotools.coco import COCO
+from pycocotools.cocoeval import COCOeval
+from datasets.a2d_eval import calculate_precision_at_k_and_iou_metrics, calculate_bbox_precision_at_k_and_iou_metrics
+
+def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
+                    data_loader: Iterable, optimizer: torch.optim.Optimizer,
+                    device: torch.device, epoch: int, max_norm: float = 0):
+    model.train()
+    criterion.train()
+    metric_logger = utils.MetricLogger(delimiter="  ")
+    metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
+    header = 'Epoch: [{}]'.format(epoch)
+    print_freq = 10
+    for samples, targets in metric_logger.log_every(data_loader, print_freq, header):
+        samples = samples.to(device)
+        captions = [t["caption"] for t in targets]
+        targets = utils.targets_to(targets, device) 
+
+        outputs = model(samples, captions, targets) 
+        loss_dict = criterion(outputs, targets)
+
+        weight_dict = criterion.weight_dict
+        losses = sum(loss_dict[k] * weight_dict[k] for k in loss_dict.keys() if k in weight_dict)
+
+        # reduce losses over all GPUs for logging purposes
+        loss_dict_reduced = utils.reduce_dict(loss_dict)
+        loss_dict_reduced_unscaled = {f'{k}_unscaled': v
+                                      for k, v in loss_dict_reduced.items()}
+        loss_dict_reduced_scaled = {k: v * weight_dict[k]
+                                    for k, v in loss_dict_reduced.items() if k in weight_dict}
+        losses_reduced_scaled = sum(loss_dict_reduced_scaled.values())
+
+        loss_value = losses_reduced_scaled.item()
+
+        if not math.isfinite(loss_value):
+            print("Loss is {}, stopping training".format(loss_value))
+            print(loss_dict_reduced)
+            sys.exit(1)
+        optimizer.zero_grad()
+        losses.backward()
+        if max_norm > 0:
+            grad_total_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm)
+        else:
+            grad_total_norm = utils.get_total_grad_norm(model.parameters(), max_norm)
+        optimizer.step()
+
+        metric_logger.update(loss=loss_value, **loss_dict_reduced_scaled, **loss_dict_reduced_unscaled)
+        metric_logger.update(lr=optimizer.param_groups[0]["lr"])
+        metric_logger.update(grad_norm=grad_total_norm)
+
+    # gather the stats from all processes
+    metric_logger.synchronize_between_processes()
+    print("Averaged stats:", metric_logger)
+    return {k: meter.global_avg for k, meter in metric_logger.meters.items()}
+
+
+@torch.no_grad()
+def evaluate(model, criterion, postprocessors, data_loader, evaluator_list, device, args):
+    model.eval()
+    criterion.eval()
+
+    metric_logger = utils.MetricLogger(delimiter="  ")
+    header = 'Test:'
+
+    predictions = []
+    for samples, targets in metric_logger.log_every(data_loader, 10, header):
+        dataset_name = targets[0]["dataset_name"]
+        samples = samples.to(device)
+        captions = [t["caption"] for t in targets]
+        targets = utils.targets_to(targets, device)
+
+        outputs = model(samples, captions, targets)
+        loss_dict = criterion(outputs, targets)
+        weight_dict = criterion.weight_dict
+
+        # reduce losses over all GPUs for logging purposes
+        loss_dict_reduced = utils.reduce_dict(loss_dict)
+        loss_dict_reduced_scaled = {k: v * weight_dict[k]
+                                    for k, v in loss_dict_reduced.items() if k in weight_dict}
+        loss_dict_reduced_unscaled = {f'{k}_unscaled': v
+                                      for k, v in loss_dict_reduced.items()}
+        metric_logger.update(loss=sum(loss_dict_reduced_scaled.values()),
+                             **loss_dict_reduced_scaled,
+                             **loss_dict_reduced_unscaled)
+
+        orig_target_sizes = torch.stack([t["orig_size"] for t in targets], dim=0)
+        results = postprocessors['bbox'](outputs, orig_target_sizes)
+        if 'segm' in postprocessors.keys():
+            target_sizes = torch.stack([t["size"] for t in targets], dim=0)
+            results = postprocessors['segm'](results, outputs, orig_target_sizes, target_sizes)
+        res = {target['image_id'].item(): output for target, output in zip(targets, results)}
+        
+        for evaluator in evaluator_list:
+            evaluator.update(res)
+
+        # REC & RES predictions
+        for p, target in zip(results, targets):
+            for s, b, m in zip(p['scores'], p['boxes'], p['rle_masks']):
+                    predictions.append({'image_id': target['image_id'].item(),
+                                        'category_id': 1,  # dummy label, as categories are not predicted in ref-vos
+                                        'bbox': b.tolist(),
+                                        'segmentation': m,
+                                        'score': s.item()})
+
+
+    # gather the stats from all processes
+    metric_logger.synchronize_between_processes()
+    print("Averaged stats:", metric_logger)
+    for evaluator in evaluator_list:
+        evaluator.synchronize_between_processes()
+
+    # accumulate predictions from all images
+    refexp_res = None
+    for evaluator in evaluator_list:
+        if isinstance(evaluator, CocoEvaluator):
+            evaluator.accumulate()
+            evaluator.summarize()
+        elif isinstance(evaluator, RefExpEvaluator):
+            refexp_res = evaluator.summarize()
+
+    stats = {k: meter.global_avg for k, meter in metric_logger.meters.items()}
+
+    # update stats
+    for evaluator in evaluator_list:
+        if isinstance(evaluator, CocoEvaluator):
+            if "bbox" in postprocessors.keys():
+                stats["coco_eval_bbox"] = evaluator.coco_eval["bbox"].stats.tolist()
+            if "segm" in postprocessors.keys():
+                stats["coco_eval_masks"] = evaluator.coco_eval["segm"].stats.tolist()
+    if refexp_res is not None:
+        stats.update(refexp_res)
+
+    # evaluate RES 
+    # gather and merge predictions from all gpus
+    gathered_pred_lists = utils.all_gather(predictions)
+    predictions = [p for p_list in gathered_pred_lists for p in p_list]
+
+    eval_metrics = {}
+    if utils.is_main_process():
+        if dataset_name == 'refcoco':
+            coco_gt = COCO(os.path.join(args.coco_path, 'refcoco/instances_refcoco_val.json'))
+        elif dataset_name == 'refcoco+':
+            coco_gt = COCO(os.path.join(args.coco_path, 'refcoco+/instances_refcoco+_val.json'))
+        elif dataset_name == 'refcocog':
+            coco_gt = COCO(os.path.join(args.coco_path, 'refcocog/instances_refcocog_val.json'))
+        else:
+            raise NotImplementedError
+        coco_pred = coco_gt.loadRes(predictions)
+        coco_eval = COCOeval(coco_gt, coco_pred, iouType='segm')
+        coco_eval.params.useCats = 0  # ignore categories as they are not predicted in ref-vos task
+        coco_eval.evaluate()
+        coco_eval.accumulate()
+        coco_eval.summarize()
+        # ap_labels = ['mAP 0.5:0.95', 'AP 0.5', 'AP 0.75', 'AP 0.5:0.95 S', 'AP 0.5:0.95 M', 'AP 0.5:0.95 L']
+        # ap_metrics = coco_eval.stats[:6]
+        # eval_metrics = {l: m for l, m in zip(ap_labels, ap_metrics)}
+        # Precision and IOU
+        # bbox 
+        precision_at_k, overall_iou, mean_iou = calculate_bbox_precision_at_k_and_iou_metrics(coco_gt, coco_pred)
+        eval_metrics.update({f'bbox P@{k}': m for k, m in zip([0.5, 0.6, 0.7, 0.8, 0.9], precision_at_k)})
+        eval_metrics.update({'bbox overall_iou': overall_iou, 'bbox mean_iou': mean_iou})
+        # mask
+        precision_at_k, overall_iou, mean_iou = calculate_precision_at_k_and_iou_metrics(coco_gt, coco_pred)
+        eval_metrics.update({f'segm P@{k}': m for k, m in zip([0.5, 0.6, 0.7, 0.8, 0.9], precision_at_k)})
+        eval_metrics.update({'segm overall_iou': overall_iou, 'segm mean_iou': mean_iou})
+        print(eval_metrics)
+        stats.update(eval_metrics)
+        
+    return stats
+
+
+@torch.no_grad()
+def evaluate_a2d(model, data_loader, postprocessor, device, args):
+    model.eval()
+    predictions = []
+    metric_logger = utils.MetricLogger(delimiter="  ")
+    header = 'Test:'
+
+    for samples, targets in metric_logger.log_every(data_loader, 10, header):
+        image_ids = [t['image_id'] for t in targets]
+
+        samples = samples.to(device)
+        captions = [t["caption"] for t in targets]
+        targets = utils.targets_to(targets, device)
+
+        outputs = model(samples, captions, targets)
+
+        orig_target_sizes = torch.stack([t["orig_size"] for t in targets], dim=0)
+        target_sizes = torch.stack([t["size"] for t in targets], dim=0)
+        processed_outputs = postprocessor(outputs, orig_target_sizes, target_sizes)
+
+        for p, image_id in zip(processed_outputs, image_ids):
+            for s, m in zip(p['scores'], p['rle_masks']):
+                    predictions.append({'image_id': image_id,
+                                        'category_id': 1,  # dummy label, as categories are not predicted in ref-vos
+                                        'segmentation': m,
+                                        'score': s.item()})
+    
+    # gather and merge predictions from all gpus
+    gathered_pred_lists = utils.all_gather(predictions)
+    predictions = [p for p_list in gathered_pred_lists for p in p_list]
+    # evaluation
+    eval_metrics = {}
+    if utils.is_main_process():
+        if args.dataset_file == 'a2d':
+            coco_gt = COCO(os.path.join(args.a2d_path, 'a2d_sentences_test_annotations_in_coco_format.json'))
+        elif args.dataset_file == 'jhmdb':
+            coco_gt = COCO(os.path.join(args.jhmdb_path, 'jhmdb_sentences_gt_annotations_in_coco_format.json'))
+        else:
+            raise NotImplementedError
+        coco_pred = coco_gt.loadRes(predictions)
+        coco_eval = COCOeval(coco_gt, coco_pred, iouType='segm')
+        coco_eval.params.useCats = 0  # ignore categories as they are not predicted in ref-vos task
+        coco_eval.evaluate()
+        coco_eval.accumulate()
+        coco_eval.summarize()
+        ap_labels = ['mAP 0.5:0.95', 'AP 0.5', 'AP 0.75', 'AP 0.5:0.95 S', 'AP 0.5:0.95 M', 'AP 0.5:0.95 L']
+        ap_metrics = coco_eval.stats[:6]
+        eval_metrics = {l: m for l, m in zip(ap_labels, ap_metrics)}
+        # Precision and IOU
+        precision_at_k, overall_iou, mean_iou = calculate_precision_at_k_and_iou_metrics(coco_gt, coco_pred)
+        eval_metrics.update({f'P@{k}': m for k, m in zip([0.5, 0.6, 0.7, 0.8, 0.9], precision_at_k)})
+        eval_metrics.update({'overall_iou': overall_iou, 'mean_iou': mean_iou})
+        print(eval_metrics)
+
+    # sync all processes before starting a new epoch or exiting
+    dist.barrier()
+    return eval_metrics
+
+
+
+
+
+
+
+
diff --git a/eval_davis.py b/eval_davis.py
new file mode 100644
index 0000000000000000000000000000000000000000..907e9c0c299f8cc581f32502ceeab0cfe9638134
--- /dev/null
+++ b/eval_davis.py
@@ -0,0 +1,68 @@
+#!/usr/bin/env python
+import os
+import sys
+from time import time
+import argparse
+
+import numpy as np
+import pandas as pd
+from davis2017.evaluation import DAVISEvaluation
+
+default_davis_path = 'data/ref-davis/DAVIS'
+
+time_start = time()
+parser = argparse.ArgumentParser()
+parser.add_argument('--davis_path', type=str, help='Path to the DAVIS folder containing the JPEGImages, Annotations, '
+                                                   'ImageSets, Annotations_unsupervised folders',
+                    required=False, default=default_davis_path)
+parser.add_argument('--set', type=str, help='Subset to evaluate the results', default='val') # val subset
+parser.add_argument('--task', type=str, help='Task to evaluate the results', default='unsupervised',
+                    choices=['semi-supervised', 'unsupervised'])
+parser.add_argument('--results_path', type=str, help='Path to the folder containing the sequences folders',
+                    required=True)
+args, _ = parser.parse_known_args()
+csv_name_global = f'global_results-{args.set}.csv'
+csv_name_per_sequence = f'per-sequence_results-{args.set}.csv'
+
+# Check if the method has been evaluated before, if so read the results, otherwise compute the results
+csv_name_global_path = os.path.join(args.results_path, csv_name_global)
+csv_name_per_sequence_path = os.path.join(args.results_path, csv_name_per_sequence)
+if os.path.exists(csv_name_global_path) and os.path.exists(csv_name_per_sequence_path):
+    print('Using precomputed results...')
+    table_g = pd.read_csv(csv_name_global_path)
+    table_seq = pd.read_csv(csv_name_per_sequence_path)
+else:
+    print(f'Evaluating sequences for the {args.task} task...')
+    # Create dataset and evaluate
+    dataset_eval = DAVISEvaluation(davis_root=args.davis_path, task=args.task, gt_set=args.set)
+    metrics_res = dataset_eval.evaluate(args.results_path)
+    J, F = metrics_res['J'], metrics_res['F']
+
+    # Generate dataframe for the general results
+    g_measures = ['J&F-Mean', 'J-Mean', 'J-Recall', 'J-Decay', 'F-Mean', 'F-Recall', 'F-Decay']
+    final_mean = (np.mean(J["M"]) + np.mean(F["M"])) / 2.
+    g_res = np.array([final_mean, np.mean(J["M"]), np.mean(J["R"]), np.mean(J["D"]), np.mean(F["M"]), np.mean(F["R"]),
+                      np.mean(F["D"])])
+    g_res = np.reshape(g_res, [1, len(g_res)])
+    table_g = pd.DataFrame(data=g_res, columns=g_measures)
+    with open(csv_name_global_path, 'w') as f:
+        table_g.to_csv(f, index=False, float_format="%.5f")
+    print(f'Global results saved in {csv_name_global_path}')
+
+    # Generate a dataframe for the per sequence results
+    seq_names = list(J['M_per_object'].keys())
+    seq_measures = ['Sequence', 'J-Mean', 'F-Mean']
+    J_per_object = [J['M_per_object'][x] for x in seq_names]
+    F_per_object = [F['M_per_object'][x] for x in seq_names]
+    table_seq = pd.DataFrame(data=list(zip(seq_names, J_per_object, F_per_object)), columns=seq_measures)
+    with open(csv_name_per_sequence_path, 'w') as f:
+        table_seq.to_csv(f, index=False, float_format="%.5f")
+    print(f'Per-sequence results saved in {csv_name_per_sequence_path}')
+
+# Print the results
+sys.stdout.write(f"--------------------------- Global results for {args.set} ---------------------------\n")
+print(table_g.to_string(index=False))
+sys.stdout.write(f"\n---------- Per sequence results for {args.set} ----------\n")
+print(table_seq.to_string(index=False))
+total_time = time() - time_start
+sys.stdout.write('\nTotal time:' + str(total_time))
diff --git a/jptr_chaeyun.txt b/jptr_chaeyun.txt
new file mode 100644
index 0000000000000000000000000000000000000000..fbb51018193e7c2858204ef8bf4812b10994b366
--- /dev/null
+++ b/jptr_chaeyun.txt
@@ -0,0 +1,179 @@
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+[W 2025-02-06 14:30:16.751 JupyterNotebookApp] 'password' has moved from NotebookApp to ServerApp. This config will be passed to ServerApp. Be sure to update your config before our next release.
+[W 2025-02-06 14:30:16.754 ServerApp] ServerApp.password config is deprecated in 2.0. Use PasswordIdentityProvider.hashed_password.
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+[I 2025-02-06 14:30:18.042 LabApp] JupyterLab extension loaded from /home/chaeyun/.conda/envs/risall/lib/python3.9/site-packages/jupyterlab
+[I 2025-02-06 14:30:18.042 LabApp] JupyterLab application directory is /data/conda_envs/chaeyun/envs/risall/share/jupyter/lab
+[I 2025-02-06 14:30:18.053 LabApp] Extension Manager is 'pypi'.
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+[I 2025-02-06 14:30:18.394 ServerApp] notebook | extension was successfully loaded.
+[I 2025-02-06 14:30:18.395 ServerApp] Serving notebooks from local directory: /data/projects/yejin/VerbCentric_RIS/ReferFormer
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+[I 2025-02-06 14:30:18.395 ServerApp] http://localhost:5727/tree
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+[I 2025-02-06 14:30:18.395 ServerApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
+[I 2025-02-06 14:30:18.574 ServerApp] Skipped non-installed server(s): bash-language-server, dockerfile-language-server-nodejs, javascript-typescript-langserver, jedi-language-server, julia-language-server, pyright, python-language-server, python-lsp-server, r-languageserver, sql-language-server, texlab, typescript-language-server, unified-language-server, vscode-css-languageserver-bin, vscode-html-languageserver-bin, vscode-json-languageserver-bin, yaml-language-server
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+[I 2025-02-06 14:32:05.968 ServerApp] Creating new notebook in 
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+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+	- Avoid using `tokenizers` before the fork if possible
+	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+srun: Job step aborted: Waiting up to 32 seconds for job step to finish.
+slurmstepd-node05: error: *** STEP 7716.0 ON node05 CANCELLED AT 2025-02-06T16:54:16 ***
+slurmstepd-node05: error: *** JOB 7716 ON node05 CANCELLED AT 2025-02-06T16:54:16 ***
+[C 2025-02-06 16:54:16.389 ServerApp] received signal 15, stopping
diff --git a/make_ref-ytvos/annotate_ref_ytvos.py b/make_ref-ytvos/annotate_ref_ytvos.py
new file mode 100644
index 0000000000000000000000000000000000000000..b43add40959f364c081672def6ec08b452069e4f
--- /dev/null
+++ b/make_ref-ytvos/annotate_ref_ytvos.py
@@ -0,0 +1,288 @@
+from datasets import build_dataset
+import argparse
+import opts
+
+import sys
+from pathlib import Path
+from os import path as osp
+import io
+
+import numpy as np
+import pandas as pd
+import regex as re
+import json
+
+import cv2
+from PIL import Image
+import torch
+from torchvision.transforms import functional as F
+
+from skimage import measure                        # (pip install scikit-image)
+from shapely.geometry import Polygon, MultiPolygon # (pip install Shapely)
+
+import matplotlib.pyplot as plt
+from matplotlib.collections import PatchCollection
+from matplotlib.patches import Rectangle
+
+import ipywidgets as widgets
+from IPython.display import display, clear_output
+
+parser = argparse.ArgumentParser('ReferFormer training and evaluation script', parents=[opts.get_args_parser()])
+args = parser.parse_args()
+
+#==================데이터 불러오기===================
+# 전체 데이터셋
+train_dataset = build_dataset('ytvos', image_set = 'train', args = args)
+
+# 전체 데이터셋 메타데이터
+metas = train_dataset.metas
+
+# 필터링한 프레임들
+selected_frames_df = pd.read_json("selected_frames4.jsonl", lines = True)
+
+#==================마스크 만드는 함수들===================
+def prepare_mask_for_pil(mask_tensor):
+    mask_array = mask_tensor.squeeze(0).cpu().numpy() 
+    mask_array = (mask_array * 255).astype(np.uint8)  
+    mask_image = Image.fromarray(mask_array)
+    return mask_image
+
+def create_sub_masks(mask_image):
+    width, height = mask_image.size
+
+    sub_masks = {}
+    for x in range(width):
+        for y in range(height):
+            # Get the RGB values of the pixel
+            pixel = mask_image.getpixel((x, y))
+
+            # If the pixel is not black...
+            if pixel != 0 :
+                # Check to see if we've created a sub-mask...
+                pixel_str = str(pixel)
+                sub_mask = sub_masks.get(pixel_str)
+                if sub_mask is None:
+                   # Create a sub-mask (one bit per pixel) and add to the dictionary
+                    # Note: we add 1 pixel of padding in each direction
+                    # because the contours module doesn't handle cases
+                    # where pixels bleed to the edge of the image
+                    sub_masks[pixel_str] = Image.new('1', (width+2, height+2))
+
+                # Set the pixel value to 1 (default is 0), accounting for padding
+                sub_masks[pixel_str].putpixel((x+1, y+1), 1)
+    return sub_masks
+
+#==================마스크 annotation 만드는 함수===================
+def create_sub_mask_annotation(sub_mask, image_id, annotation_id, is_crowd):
+    # Find contours (boundary lines) around each sub-mask
+    # Note: there could be multiple contours if the object
+    # is partially occluded. (E.g. an elephant behind a tree)
+    contours = measure.find_contours(sub_mask, 0.5, positive_orientation='low')
+
+    segmentations = []
+    polygons = []
+    for contour in contours:
+        # Flip from (row, col) representation to (x, y)
+        # and subtract the padding pixel
+        for i in range(len(contour)):
+            row, col = contour[i]
+            contour[i] = (col - 1, row - 1)
+
+        # Make a polygon and simplify it
+        poly = Polygon(contour)
+        poly = poly.simplify(1.0, preserve_topology=False)
+        polygons.append(poly)
+        segmentation = np.array(poly.exterior.coords).ravel().tolist()
+        segmentations.append(segmentation)
+
+    # Combine the polygons to calculate the bounding box and area
+    multi_poly = MultiPolygon(polygons)
+    x, y, max_x, max_y = multi_poly.bounds
+    width = max_x - x
+    height = max_y - y
+    bbox = (x, y, width, height)
+    area = multi_poly.area
+
+    annotation = {
+        'segmentation': segmentations,
+        'iscrowd': is_crowd,
+        'image_id': image_id,
+        'id': annotation_id,
+        'bbox': bbox,
+        'area': area
+    }
+    return annotation
+
+#==================시각화 함수===================
+# annotation dictionary as input
+def showRef(annotation, image_dir, seg_box='seg'):
+    ax = plt.gca()
+    I = io.imread(osp.join(image_dir, annotation['file_name']))
+    ax.imshow(I)
+    
+        
+    for sid, sent in enumerate(annotation['sentences']):
+        print('%s. %s' % (sid + 1, sent))
+    
+    if seg_box == 'seg':
+        polygons = []
+        color = []
+        c = (np.random.random((1, 3)) * 0.6 + 0.4).tolist()[0]
+        
+        if type(annotation['segmentation'][0]) == list:
+            # polygon used for refcoco*
+            for seg in annotation['segmentation']:
+                poly = np.array(seg).reshape((int(len(seg) / 2), 2))
+                polygons.append(Polygon(poly))
+                color.append(c)
+
+            p = PatchCollection(polygons,
+                                facecolors=(221/255, 160/255, 221/255),  # 연보라색
+                                linewidths=0,
+                                alpha=0.4)
+            ax.add_collection(p)
+            
+            p = PatchCollection(polygons,
+                                facecolors='none',
+                                edgecolors=color,
+                                linewidths=2)
+            ax.add_collection(p)
+        # else:
+        #     # mask used for refclef
+        #     rle = annotation['segmentation']
+        #     m = mask.decode(rle)
+        #     img = np.ones((m.shape[0], m.shape[1], 3))
+        #     color_mask = np.array([2.0, 166.0, 101.0]) / 255
+        #     for i in range(3):
+        #         img[:, :, i] = color_mask[i]
+        #     ax.imshow(np.dstack((img, m * 0.5)))
+    
+    # bounding box 
+    elif seg_box == 'box':
+        bbox = annotation['bbox']
+        box_plot = Rectangle((bbox[0], bbox[1]),
+                            bbox[2],
+                            bbox[3],
+                            fill=False,
+                            edgecolor='green',
+                            linewidth=3)
+        ax.add_patch(box_plot)
+
+#==================모두 종합한 함수===================
+def create_dict_from_selected_images(selected_frames_df):
+    
+    image_id = 0
+    anno_id = 0
+    train_idx = 0
+
+    with open("/home/yejin/data/data/dataset/VRIS/mbench/ytvos/selected_instances2.jsonl", "w") as f:
+
+        for selected_idx in range(len(selected_frames_df)):
+            selected = selected_frames_df.loc[selected_idx]
+            selected_vid_id = selected['video']
+            selected_frame_id = selected['frame_id']
+
+            for obj_id in selected['objects'].keys():
+                
+                selected_exp = selected['objects'][obj_id][0]  #캡션
+                selected_verb = selected['objects'][obj_id][1]  #동사
+            
+                train_idx = next(
+                    idx for idx, meta in enumerate(metas)
+                    if meta['video'] == selected_vid_id
+                    and meta['frame_id'] == selected_frame_id
+                    and meta['obj_id'] == int(obj_id)
+                    and meta['exp'] == selected_exp
+                )
+
+                train_frames, train_info = train_dataset[train_idx]
+                
+                try:
+                    valid_frame_loc = train_info['frames_idx'].tolist().index(selected_frame_id)  #valid한 frame이 있는 index
+                except ValueError:
+                    print(f"selected vid id: {selected_vid_id}, metas['frame_id']: {metas[train_idx]['frame_id']}, selected frame id: {selected_frame_id}, train_info['frames_idx']: {train_info['frames_idx'].tolist()}")
+
+
+                frame = train_frames[valid_frame_loc]  #해당 frame
+                frame = F.to_pil_image(frame)
+                
+                image_file_name = f"{selected_vid_id}_{str(selected_frame_id).rjust(5, '0')}"
+
+                #원래 frame 저장하기
+                save_dir = Path("/home/yejin/data/data/dataset/VRIS/mbench/ytvos/selected_frames")
+                #save_dir.mkdir(exist_ok=True) 
+                save_path = save_dir / f"{image_file_name}.png"
+                #frame.save(save_path)
+
+                #카테고리
+                label = train_info['labels'][valid_frame_loc].item()  #category id
+                category_name = metas[train_idx]['category']  #category name
+
+                #박스 정보
+                box = train_info['boxes'][valid_frame_loc]
+                
+                # Annotation tools ########################################################################
+                mask = train_info['masks'][valid_frame_loc]
+                # print(mask.shape)
+
+                # frame과 mask 맞는지 확인만
+                # plt.imshow(frame.permute(1, 2, 0))
+                # mask_color = np.zeros((*mask.shape, 3), dtype = np.uint8)
+                # mask_color[mask == 1] = [255, 0, 0]
+                # plt.imshow(mask_color, alpha = 0.5)
+                # plt.show()
+
+
+                mask_image = prepare_mask_for_pil(mask)
+                sub_masks = create_sub_masks(mask_image)
+
+                for color, sub_mask in sub_masks.items():
+                    # print(f"Color: {color}, Sub-mask size: {sub_mask.size}")
+                    sub_mask_array = np.array(sub_mask, dtype=np.uint8)
+                    annotation = create_sub_mask_annotation(sub_mask_array, image_id, anno_id, is_crowd = 0)
+                    anno_id += 1
+                image_id += 1
+
+                #파일 경로 추가
+                annotation['file_name'] = f"{image_file_name}.png"
+
+                #불필요한 정보 지우기
+                annotation.pop('iscrowd', None)
+                annotation.pop('image_id', None)
+                annotation.pop('id', None)
+
+                valid = train_info['valid'][valid_frame_loc]
+                orig_size = train_info['orig_size']
+                size = train_info['size']
+                caption = metas[train_idx]['exp']
+
+                #filename, height, width 추가
+                #annotation['file_name'] = save_path
+                annotation['height'] = orig_size[0].item()
+                annotation['width'] = orig_size[1].item()
+
+                # category id,name, sentence dictionary 추가
+                annotation['label'] = label
+                annotation['category_name'] = category_name
+                sentence_dict = {
+                    "tokens" : caption.split(' '),
+                    "raw" : caption,
+                    "sent" : re.sub('[^A-Za-z0-9\s]+', '', caption.lower())
+                }
+                annotation['sentences'] = sentence_dict
+                ############################################################################################
+                # double check for segmentation annotation
+                # orig_img_np = draw_polygon_on_image(frame, annotation['segmentation'])
+                # plt.imshow(orig_img_np)
+                # plt.axis('off')
+                # plt.show()
+
+                # showRef(annotation, save_dir)
+                ############################################################################################
+
+                # 최종
+                f.write(json.dumps(annotation) + "\n")
+                f.flush()
+
+# if __name__ == '__main__':
+#     create_dict_from_selected_images(selected_frames_df)
\ No newline at end of file
diff --git a/make_ref-ytvos/folder2lmdb.py b/make_ref-ytvos/folder2lmdb.py
new file mode 100644
index 0000000000000000000000000000000000000000..6239a19cdc3097e3866b5fd1997298935074e51f
--- /dev/null
+++ b/make_ref-ytvos/folder2lmdb.py
@@ -0,0 +1,109 @@
+import argparse
+import os
+import os.path as osp
+import lmdb
+from PIL import Image
+import pyarrow as pa
+import json
+from tqdm import tqdm
+import warnings
+warnings.filterwarnings("ignore")
+
+
+def loads_pyarrow(buf):
+    """
+    Args:
+        buf: the output of `dumps`.
+    """
+    return pa.deserialize(buf)
+
+
+def raw_reader(path):
+    with open(path, 'rb') as f:
+        bin_data = f.read()
+    return bin_data
+
+
+def dumps_pyarrow(obj):
+    """
+    Serialize an object.
+    Returns:
+        Implementation-dependent bytes-like object
+    """
+    return pa.serialize(obj).to_buffer()
+
+
+def folder2lmdb(json_data, img_dir, mask_dir, output_dir, split, write_frequency=1000):
+    lmdb_path = osp.join(output_dir, "%s.lmdb" % split)
+    isdir = os.path.isdir(lmdb_path)
+
+    print("Generate LMDB to %s" % lmdb_path)
+    db = lmdb.open(lmdb_path, subdir=isdir,
+                   map_size=1099511627776 * 2, readonly=False,
+                   meminit=False, map_async=True)
+
+    txn = db.begin(write=True)
+    tbar = tqdm(json_data)
+    for idx, item in enumerate(tbar):
+        img = raw_reader(osp.join(img_dir, item['file_name']))
+        mask = raw_reader(osp.join(mask_dir, f"{idx}.png"))
+
+        # Pillow로 파일 크기 확인
+        #with Image.open(osp.join(img_dir, item['file_name'])) as im:
+            #print(f"Image size (Pillow): {im.size}")
+        #with Image.open(osp.join(mask_dir, item['file_name'])) as mk:
+            #print(f"Mask size (Pillow): {mk.size}")
+                
+        data = {'img': img, 'mask': mask, 'cat': item['category_name'],
+                'seg_id': idx, 'file_name': item['file_name'],
+                'num_sents': 1, 'sents': item['sentences']['sent']}
+        txn.put(u'{}'.format(idx).encode('ascii'), dumps_pyarrow(data))
+        if idx % write_frequency == 0:
+            # print("[%d/%d]" % (idx, len(data_loader)))
+            txn.commit()
+            txn = db.begin(write=True)
+
+    # finish iterating through dataset
+    txn.commit()
+    keys = [u'{}'.format(k).encode('ascii') for k in range(idx + 1)]
+    with db.begin(write=True) as txn:
+        txn.put(b'__keys__', dumps_pyarrow(keys))
+        txn.put(b'__len__', dumps_pyarrow(len(keys)))
+
+    print("Flushing database ...")
+    db.sync()
+    db.close()
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='COCO Folder to LMDB.')
+    parser.add_argument('-j', '--json-dir', type=str,
+                        default='',
+                        help='the name of json file.')
+    parser.add_argument('-i', '--img-dir', type=str,
+                        default='refcoco+',
+                        help='the folder of images.')
+    parser.add_argument('-m', '--mask-dir', type=str,
+                        default='refcoco+',
+                        help='the folder of masks.')
+    parser.add_argument('-o', '--output-dir', type=str,
+                        default='refcoco+',
+                        help='the folder of output lmdb file.')
+    parser.add_argument('-s', '--split', type=str,
+                        default='train',
+                        help='the split type.')
+    args = parser.parse_args()
+    return args
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    args.split = osp.basename(args.json_dir).split(".")[0]
+    os.makedirs(args.output_dir, exist_ok=True)
+
+    json_data = []
+    with open(args.json_dir, 'rb') as f:
+        for line in f:
+            json_data.append(json.loads(line))
+
+    folder2lmdb(json_data, args.img_dir, args.mask_dir, args.output_dir, args.split)
diff --git a/make_ref-ytvos/manual_selected_frames.jsonl b/make_ref-ytvos/manual_selected_frames.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b182f58e66e249c0764013e2668ba699cceeebc1
--- /dev/null
+++ b/make_ref-ytvos/manual_selected_frames.jsonl
@@ -0,0 +1,101 @@
+{"index": 0, "new_sent": ""}
+{"index": 4, "new_sent": "a zebra walking away from camera"}
+{"index": 12, "new_sent": "the panda has his hand on another pandas back"}
+{"index": 13, "new_sent": "the panda is standing on the rocks"}
+{"index": 17, "new_sent": "the panda fell down on his back"}
+{"index": 28, "new_sent": "a sheep lying down"}
+{"index": 31, "new_sent": "a sheep lying down and getting spider legs"}
+{"index": 40, "new_sent": ""}
+{"index": 41, "new_sent": ""}
+{"index": 48, "new_sent": "man using his hands next to an inside tree"}
+{"index": 52, "new_sent": ""}
+{"index": 55, "new_sent": ""}
+{"index": 57, "new_sent": "a monkey hugging another monkey"}
+{"index": 76, "new_sent": "an ape seated and breastfeeding while another ape plays nearby"}
+{"index": 77, "new_sent": "an ape playing near a nursing ape"}
+{"index": 78, "new_sent": "an ape is laying on the chest of another ape sitting on the dirt"}
+{"index": 172, "new_sent": "person standing on stage and using a microphone"}
+{"index": 173, "new_sent": "person sitting on stage playing a piano"}
+{"index": 196, "new_sent": "a monkey eating some fruit"}
+{"index": 197, "new_sent": "a monkey sitting while watching another monkey eat"}
+{"index": 244, "new_sent": ""}
+{"index": 270, "new_sent": "a turtle in water while another follows"}
+{"index": 271, "new_sent": ""}
+{"index": 299, "new_sent": "a duck stretching out its neck"}
+{"index": 326, "new_sent": ""}
+{"index": 327, "new_sent": ""}
+{"index": 388, "new_sent": ""}
+{"index": 389, "new_sent": "a lizard putting its head under a branch"}
+{"index": 409, "new_sent": ""}
+{"index": 410, "new_sent": "a raccoon standing and attacking another raccoon"}
+{"index": 415, "new_sent": ""}
+{"index": 416, "new_sent": ""}
+{"index": 417, "new_sent": "a person taking a picture"}
+{"index": 428, "new_sent": "a panda laying under another panda"}
+{"index": 429, "new_sent": "a panda standing and playing with another panda"}
+{"index": 447, "new_sent": "a panda playing and rolling over on the ground"}
+{"index": 448, "new_sent": "a panda sitting and looking at another panda"}
+{"index": 451, "new_sent": ""}
+{"index": 495, "new_sent": "a lion sitting in front of a lion thats playing with a man"}
+{"index": 509, "new_sent": ""}
+{"index": 510, "new_sent": ""}
+{"index": 517, "new_sent": "a person squatting and looking at a skateboarder perform"}
+{"index": 518, "new_sent": ""}
+{"index": 528, "new_sent": "a person doing a hand stand"}
+{"index": 559, "new_sent": "a dog holding up his head"}
+{"index": 560, "new_sent": "a dog smelling the ground"}
+{"index": 561, "new_sent": ""}
+{"index": 562, "new_sent": ""}
+{"index": 569, "new_sent": ""}
+{"index": 570, "new_sent": ""}
+{"index": 594, "new_sent": "a mouse sitting under a wheel while another runs around"}
+{"index": 595, "new_sent": ""}
+{"index": 617, "new_sent": "a monkey moving underneath another monkey"}
+{"index": 618, "new_sent": "a monkey laying on the ground with its arm over another monkey"}
+{"index": 634, "new_sent": "ape laying under another ape"}
+{"index": 644, "new_sent": "ape reaching out his arms and legs"}
+{"index": 645, "new_sent": ""}
+{"index": 646, "new_sent": "a person standing with his arms crossed in a room with others"}
+{"index": 654, "new_sent": ""}
+{"index": 659, "new_sent": "a giraffe eating hay"}
+{"index": 662, "new_sent": "a penguin laying on its belly playing with another penguin"}
+{"index": 673, "new_sent": "a penguin moving on its belly"}
+{"index": 720, "new_sent": "a person riding a surfboard on a wave in front of other surfer"}
+{"index": 722, "new_sent": "a person laying on surf board"}
+{"index": 725, "new_sent": "person swimming away"}
+{"index": 735, "new_sent": "person mounting a cow"}
+{"index": 738, "new_sent": "person walking towards a cow"}
+{"index": 741, "new_sent": "person riding a cow"}
+{"index": 747, "new_sent": "person holding out his right arm"}
+{"index": 764, "new_sent": "a grey duck facing away"}
+{"index": 765, "new_sent": ""}
+{"index": 766, "new_sent": "a grey duck eating bread"}
+{"index": 816, "new_sent": "a person raising his arms and flying with another person"}
+{"index": 821, "new_sent": "person holding on to the belt and screaming"}
+{"index": 824, "new_sent": "person smiling"}
+{"index": 828, "new_sent": "person holding another persons arms"}
+{"index": 831, "new_sent": ""}
+{"index": 832, "new_sent": ""}
+{"index": 904, "new_sent": ""}
+{"index": 914, "new_sent": "elephant walking away from the camera"}
+{"index": 918, "new_sent": "a person riding a horse"}
+{"index": 919, "new_sent": "a person opening a gate"}
+{"index": 931, "new_sent": "person resting hand on a tree limb"}
+{"index": 932, "new_sent": "person trying to feed a small animal"}
+{"index": 993, "new_sent": "horse leading the way in the water"}
+{"index": 994, "new_sent": "horse following another horse"}
+{"index": 1049, "new_sent": "person fell from a bull"}
+{"index": 1051, "new_sent": "person getting up from the ground"}
+{"index": 1052, "new_sent": "person standing and running away from a bull"}
+{"index": 1054, "new_sent": "person squatting down"}
+{"index": 1096, "new_sent": "an ape crossing his arms"}
+{"index": 1097, "new_sent": ""}
+{"index": 1115, "new_sent": "a man trying to catch a frisbee"}
+{"index": 1137, "new_sent": ""}
+{"index": 1138, "new_sent": "the monkey leaning over the fence facing the bike"}
+{"index": 1139, "new_sent": ""}
+{"index": 1147, "new_sent": "the monkey hanging backwards"}
+{"index": 1173, "new_sent": ""}
+{"index": 1174, "new_sent": "a horse leading the way"}
+{"index": 1184, "new_sent": ""}
+{"index": 1194, "new_sent": "person milking a cow"}
\ No newline at end of file
diff --git a/make_ref-ytvos/review_images.ipynb b/make_ref-ytvos/review_images.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..0d2e2c4fd0d6dc90b0994eb800d34cada9bb69f1
--- /dev/null
+++ b/make_ref-ytvos/review_images.ipynb
@@ -0,0 +1,341 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/data/projects/yejin/VerbCentric_RIS/ReferFormer\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/yejin/.conda/envs/referformer/lib/python3.9/site-packages/IPython/core/magics/osm.py:417: UserWarning: using dhist requires you to install the `pickleshare` library.\n",
+      "  self.shell.db['dhist'] = compress_dhist(dhist)[-100:]\n"
+     ]
+    }
+   ],
+   "source": [
+    "%cd /home/yejin/data/projects/yejin/VerbCentric_RIS/ReferFormer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datasets import build_dataset\n",
+    "import argparse\n",
+    "import opts\n",
+    "\n",
+    "import sys\n",
+    "from pathlib import Path\n",
+    "from os import path as osp\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import regex as re\n",
+    "import json\n",
+    "\n",
+    "import cv2\n",
+    "from PIL import Image\n",
+    "import torch\n",
+    "from torchvision.transforms import functional as F\n",
+    "from skimage import io\n",
+    "from skimage import measure                        # (pip install scikit-image)\n",
+    "from shapely.geometry import Polygon, MultiPolygon # (pip install Shapely)\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.collections import PatchCollection\n",
+    "from matplotlib import patches\n",
+    "\n",
+    "import ipywidgets as widgets\n",
+    "from IPython.display import display, clear_output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#데이터 불러오기\n",
+    "annotations = pd.read_json(\"/home/yejin/data/dataset/VRIS/mbench/ytvos/selected_instances.jsonl\", lines = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def review_images(annotations, image_dir):\n",
+    "    index = 0\n",
+    "\n",
+    "    #시각화 함수\n",
+    "    def showRef(i):\n",
+    "        nonlocal index\n",
+    "        print(index)\n",
+    "\n",
+    "        if index < len(annotations):\n",
+    "            annotation = annotations.loc[index]\n",
+    "            \n",
+    "            ax = plt.gca()\n",
+    "            I = io.imread(osp.join(image_dir, annotation['file_name']))\n",
+    "            ax.imshow(I)\n",
+    "\n",
+    "            file_name = annotation['file_name']\n",
+    "            raw_sent = annotation['sentences']['raw']\n",
+    "\n",
+    "            polygons = []\n",
+    "            color = []\n",
+    "            c = (np.random.random((1, 3)) * 0.6 + 0.4).tolist()[0]\n",
+    "                \n",
+    "            if type(annotation['segmentation'][0]) == list:\n",
+    "                # polygon used for refcoco*\n",
+    "\n",
+    "                for seg in annotation['segmentation']:\n",
+    "                    poly = np.array(seg).reshape((int(len(seg) / 2), 2))\n",
+    "                    polygons.append(Polygon(poly))\n",
+    "                    color.append(c)\n",
+    "\n",
+    "                p = PatchCollection(polygons,\n",
+    "                                    facecolors=(221/255, 160/255, 221/255),  # 연보라색\n",
+    "                                    linewidths=0,\n",
+    "                                    alpha=0.4)\n",
+    "                ax.add_collection(p)\n",
+    "                    \n",
+    "                p = PatchCollection(polygons,\n",
+    "                                    facecolors='none',\n",
+    "                                    edgecolors=color,\n",
+    "                                    linewidths=2)\n",
+    "                ax.add_collection(p)\n",
+    "                \n",
+    "                ax.set_title(file_name)\n",
+    "                fig = plt.gcf()\n",
+    "                fig.text(0.5, 0, raw_sent, ha='center', fontsize=12, color='gray')\n",
+    "\n",
+    "\n",
+    "            \n",
+    "            else:\n",
+    "                print(\"No more images in the dataset\")\n",
+    "                return\n",
+    "\n",
+    "\n",
+    "    #버튼 클릭 시 호출될 함수\n",
+    "    def on_button_click(b):\n",
+    "        nonlocal index\n",
+    "        index+=1\n",
+    "\n",
+    "        clear_output(wait=True)\n",
+    "        showRef(index)\n",
+    "        \n",
+    "        if index < len(annotations):\n",
+    "            display(button)\n",
+    "        \n",
+    "\n",
+    "    #버튼 생성\n",
+    "    button = widgets.Button(description = \"Next image\")\n",
+    "    button.on_click(on_button_click)\n",
+    "\n",
+    "    \n",
+    "    #첫 이미지\n",
+    "    showRef(index)\n",
+    "    display(button)\n",
+    "\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#================segmentation이 2개 이상인 경우 확인=======================\n",
+    "\n",
+    "# def review_images(annotations, image_dir):\n",
+    "#     index = 0\n",
+    "\n",
+    "#     #시각화 함수\n",
+    "#     def showRef(i):\n",
+    "#         nonlocal index\n",
+    "\n",
+    "#         if index < len(annotations):\n",
+    "#             annotation = annotations.loc[index]\n",
+    "            \n",
+    "#             ax = plt.gca()\n",
+    "#             I = io.imread(osp.join(image_dir, annotation['file_name']))\n",
+    "#             ax.imshow(I)\n",
+    "\n",
+    "#             file_name = annotation['file_name']\n",
+    "#             raw_sent = annotation['sentences']['raw']\n",
+    "\n",
+    "            \n",
+    "            \n",
+    "                \n",
+    "#             if type(annotation['segmentation'][0]) == list:\n",
+    "#                 # polygon used for refcoco*\n",
+    "#                 print(len(annotation['segmentation']))\n",
+    "\n",
+    "#                 if len(annotation['segmentation']) > 1:\n",
+    "#                     for seg in annotation['segmentation']:\n",
+    "#                         polygons = []\n",
+    "#                         color = []\n",
+    "#                         c = (np.random.random((1, 3)) * 0.6 + 0.4).tolist()[0]\n",
+    "\n",
+    "#                         poly = np.array(seg).reshape((int(len(seg) / 2), 2))\n",
+    "#                         polygons.append(Polygon(poly))\n",
+    "#                         color.append(c)\n",
+    "\n",
+    "#                         p = PatchCollection(polygons,\n",
+    "#                                             facecolors=(221/255, 160/255, 221/255),  # 연보라색\n",
+    "#                                             linewidths=0,\n",
+    "#                                             alpha=0.4)\n",
+    "#                         ax.add_collection(p)\n",
+    "                            \n",
+    "#                         p = PatchCollection(polygons,\n",
+    "#                                             facecolors='none',\n",
+    "#                                             edgecolors=color,\n",
+    "#                                             linewidths=2)\n",
+    "#                         ax.add_collection(p)\n",
+    "                        \n",
+    "#                         ax.set_title(file_name)\n",
+    "#                         fig = plt.gcf()\n",
+    "#                         fig.text(0.5, 0, raw_sent, ha='center', fontsize=12, color='gray')\n",
+    "#                 else:\n",
+    "#                     polygons = []\n",
+    "#                     color = []\n",
+    "#                     c = (np.random.random((1, 3)) * 0.6 + 0.4).tolist()[0]\n",
+    "\n",
+    "#                     for seg in annotation['segmentation']:\n",
+    "                        \n",
+    "#                         poly = np.array(seg).reshape((int(len(seg) / 2), 2))\n",
+    "#                         polygons.append(Polygon(poly))\n",
+    "#                         color.append(c)\n",
+    "\n",
+    "#                     p = PatchCollection(polygons,\n",
+    "#                                             facecolors=(221/255, 160/255, 221/255),  # 연보라색\n",
+    "#                                             linewidths=0,\n",
+    "#                                             alpha=0.4)\n",
+    "#                     ax.add_collection(p)\n",
+    "                            \n",
+    "#                     p = PatchCollection(polygons,\n",
+    "#                                             facecolors='none',\n",
+    "#                                             edgecolors=color,\n",
+    "#                                             linewidths=2)\n",
+    "#                     ax.add_collection(p)\n",
+    "                        \n",
+    "#                     ax.set_title(file_name)\n",
+    "#                     fig = plt.gcf()\n",
+    "#                     fig.text(0.5, 0, raw_sent, ha='center', fontsize=12, color='gray')\n",
+    "                    \n",
+    "\n",
+    "\n",
+    "#             else:\n",
+    "#                 print(\"No more images in the dataset\")\n",
+    "#                 return\n",
+    "\n",
+    "\n",
+    "#     #버튼 클릭 시 호출될 함수\n",
+    "#     def on_button_click(b):\n",
+    "#         nonlocal index\n",
+    "#         index+=1\n",
+    "\n",
+    "#         clear_output(wait=True)\n",
+    "#         showRef(index)\n",
+    "        \n",
+    "#         if index < len(annotations):\n",
+    "#             display(button)\n",
+    "        \n",
+    "\n",
+    "#     #버튼 생성\n",
+    "#     button = widgets.Button(description = \"Next image\")\n",
+    "#     button.on_click(on_button_click)\n",
+    "\n",
+    "    \n",
+    "#     #첫 이미지\n",
+    "#     showRef(index)\n",
+    "#     display(button)\n",
+    "\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1194\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9b667e40efe04add91f053fb7257f685",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Button(description='Next image', style=ButtonStyle())"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "review_images(annotations, image_dir='/home/yejin/data/dataset/VRIS/mbench/ytvos/selected_frames')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "referformer",
+   "language": "python",
+   "name": "referformer"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.20"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/make_ref-ytvos/revised_frames.jsonl b/make_ref-ytvos/revised_frames.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9f07c52b92b18cbffc6f7c75377f89e56dd23e3d
--- /dev/null
+++ b/make_ref-ytvos/revised_frames.jsonl
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diff --git a/make_ref-ytvos/selected_frames.jsonl b/make_ref-ytvos/selected_frames.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5267d3c3e997870ad6155ff533c51b66dd5ac340
--- /dev/null
+++ b/make_ref-ytvos/selected_frames.jsonl
@@ -0,0 +1,3301 @@
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+{"video": "ff15a5eff6", "frame_id": 25, "objects": {"2": ["a blue colored motorbike chasing another bike in a race", "chase"], "4": ["a bike been driven by a person leading the race", "drive"]}, "frames": ["00000", "00005", "00010", "00015", "00020", "00025", "00030", "00035", "00040", "00045", "00050", "00055", "00060", "00065", "00070", "00075", "00080", "00085", "00090", "00095", "00100", "00105", "00110", "00115", "00120", "00125", "00130", "00135", "00140"], "category": "motorbike"}
+{"video": "ff204daf4b", "frame_id": 0, "objects": {"1": ["a black bear on top of standing with its hands on another in the snow backing the view with people looking on", "stand"], "2": ["a bear sitting on the snow with another touch it while standing", "sit"]}, "frames": ["00000", "00005", "00010", "00015", "00020", "00025", "00030", "00035", "00040", "00045", "00050", "00055", "00060", "00065", "00070", "00075", "00080", "00085", "00090", "00095"], "category": "bear"}
+{"video": "ff204daf4b", "frame_id": 5, "objects": {"1": ["a black bear on top of standing with its hands on another in the snow backing the view with people looking on", "stand"], "2": ["a bear sitting on the snow with another touch it while standing", "sit"]}, "frames": ["00000", "00005", "00010", "00015", "00020", "00025", "00030", "00035", "00040", "00045", "00050", "00055", "00060", "00065", "00070", "00075", "00080", "00085", "00090", "00095"], "category": "bear"}
+{"video": "ff204daf4b", "frame_id": 10, "objects": {"1": ["a black bear on top of standing with its hands on another in the snow backing the view with people looking on", "stand"], "2": ["a bear sitting on the snow with another touch it while standing", "sit"]}, "frames": ["00000", "00005", "00010", "00015", "00020", "00025", "00030", "00035", "00040", "00045", "00050", "00055", "00060", "00065", "00070", "00075", "00080", "00085", "00090", "00095"], "category": "bear"}
+{"video": "ff204daf4b", "frame_id": 15, "objects": {"1": ["a black bear on top of standing with its hands on another in the snow backing the view with people looking on", "stand"], "2": ["a bear sitting on the snow with another touch it while standing", "sit"]}, "frames": ["00000", "00005", "00010", "00015", "00020", "00025", "00030", "00035", "00040", "00045", "00050", "00055", "00060", "00065", "00070", "00075", "00080", "00085", "00090", "00095"], "category": "bear"}
+{"video": "ff66152b25", "frame_id": 0, "objects": {"1": ["the cobra furthest to the left facing a man", "face"], "2": ["the snake is on the right and close to the person sitting on the green floor", "sit"]}, "frames": ["00000", "00005", "00010", "00015", "00020", "00025", "00030", "00035", "00040", "00045", "00050", "00055", "00060", "00065", "00070", "00075", "00080", "00085", "00090", "00095", "00100", "00105", "00110", "00115", "00120", "00125", "00130", "00135", "00140", "00145", "00150", "00155", "00160", "00165", "00170", "00175"], "category": "snake"}
+{"video": "ff66152b25", "frame_id": 5, "objects": {"1": ["the cobra furthest to the left facing a man", "face"], "2": ["the snake is on the right and close to the person sitting on the green floor", "sit"]}, "frames": ["00000", "00005", "00010", "00015", "00020", "00025", "00030", "00035", "00040", "00045", "00050", "00055", "00060", "00065", "00070", "00075", "00080", "00085", "00090", "00095", "00100", "00105", "00110", "00115", "00120", "00125", "00130", "00135", "00140", "00145", "00150", "00155", "00160", "00165", "00170", "00175"], "category": "snake"}
+{"video": "ff66152b25", "frame_id": 10, "objects": {"1": ["the cobra furthest to the left facing a man", "face"], "2": ["the snake is on the right and close to the person sitting on the green floor", "sit"]}, "frames": ["00000", "00005", "00010", "00015", "00020", "00025", "00030", "00035", "00040", "00045", "00050", "00055", "00060", "00065", "00070", "00075", "00080", "00085", "00090", "00095", "00100", "00105", "00110", "00115", "00120", "00125", "00130", "00135", "00140", "00145", "00150", "00155", "00160", "00165", "00170", "00175"], "category": "snake"}
+{"video": "ff66152b25", "frame_id": 15, "objects": {"1": ["the cobra furthest to the left facing a man", "face"], "2": ["the snake is on the right and close to the person sitting on the green floor", "sit"]}, "frames": ["00000", "00005", "00010", "00015", "00020", "00025", "00030", "00035", "00040", "00045", "00050", "00055", "00060", "00065", "00070", "00075", "00080", "00085", "00090", "00095", "00100", "00105", "00110", "00115", "00120", "00125", "00130", "00135", "00140", "00145", "00150", "00155", "00160", "00165", "00170", "00175"], "category": "snake"}
+{"video": "ff66152b25", "frame_id": 20, "objects": {"1": ["the cobra furthest to the left facing a man", "face"], "2": ["the snake is on the right and close to the person sitting on the green floor", "sit"]}, "frames": ["00000", "00005", "00010", "00015", "00020", "00025", "00030", "00035", "00040", "00045", "00050", "00055", "00060", "00065", "00070", "00075", "00080", "00085", "00090", "00095", "00100", "00105", "00110", "00115", "00120", "00125", "00130", "00135", "00140", "00145", "00150", "00155", "00160", "00165", "00170", "00175"], "category": "snake"}
+{"video": "ff66152b25", "frame_id": 25, "objects": {"1": ["the cobra furthest to the left facing a man", "face"], "2": ["the snake is on the right and close to the person sitting on the green floor", "sit"]}, "frames": ["00000", "00005", "00010", "00015", "00020", "00025", "00030", "00035", "00040", "00045", "00050", "00055", "00060", "00065", "00070", "00075", "00080", "00085", "00090", "00095", "00100", "00105", "00110", "00115", "00120", "00125", "00130", "00135", "00140", "00145", "00150", "00155", "00160", "00165", "00170", "00175"], "category": "snake"}
+{"video": "ff66152b25", "frame_id": 30, "objects": {"1": ["the cobra furthest to the left facing a man", "face"], "2": ["the snake is on the right and close to the person sitting on the green floor", "sit"]}, "frames": ["00000", "00005", "00010", "00015", "00020", "00025", "00030", "00035", "00040", "00045", "00050", "00055", "00060", "00065", "00070", "00075", "00080", "00085", "00090", "00095", "00100", "00105", "00110", "00115", "00120", "00125", "00130", "00135", "00140", "00145", "00150", "00155", "00160", "00165", "00170", "00175"], "category": "snake"}
+{"video": "ff66152b25", "frame_id": 35, "objects": {"1": ["the cobra furthest to the left facing a man", "face"], "2": ["the snake is on the right and close to the person sitting on the green floor", "sit"]}, "frames": ["00000", "00005", "00010", "00015", "00020", "00025", "00030", "00035", "00040", "00045", "00050", "00055", "00060", "00065", "00070", "00075", "00080", "00085", "00090", "00095", "00100", "00105", "00110", "00115", "00120", "00125", "00130", "00135", "00140", "00145", "00150", "00155", "00160", "00165", "00170", "00175"], "category": "snake"}
diff --git a/mbench/result.json b/mbench/result.json
new file mode 100644
index 0000000000000000000000000000000000000000..c9031d1952d271b7fae6c07003d0da880fb016c4
--- /dev/null
+++ b/mbench/result.json
@@ -0,0 +1,465 @@
+{
+    "8056117b89": null,
+    "30085a2cc6": {
+        "00020": {
+            "1": {
+                "ref_exp": "The elephant walks on four legs, with its trunk lowered and touching the ground.",
+                "caption": "The image shows two elephants performing distinct actions. \n\n1. The larger elephant walks forward, swinging its trunk side to side as it moves through the grass. \n2. The smaller elephant follows closely behind, walking on four legs, with its trunk lowered and touching the ground. \n\nThe larger elephant also flaps its ears open and closed, creating a visible motion as it progresses. Both elephants are primarily focused on their movement through the grassy area.",
+                "cat_name": "elephant",
+                "file_name": "00020",
+                "isValid": true
+            },
+            "2": {
+                "ref_exp": "The elephant follows closely behind while walking on four legs with its trunk lowered and touching the ground.",
+                "caption": "The image shows two elephants performing distinct actions. \n\n1. The larger elephant walks forward, swinging its trunk side to side as it moves through the grass. \n2. The smaller elephant follows closely behind, walking on four legs, with its trunk lowered and touching the ground. \n\nThe larger elephant also flaps its ears open and closed, creating a visible motion as it progresses. Both elephants are primarily focused on their movement through the grassy area.",
+                "cat_name": "elephant",
+                "file_name": "00020",
+                "isValid": false
+            }
+        },
+        "00035": {
+            "1": {
+                "ref_exp": "The elephant is using its trunk to grasp and pull foliage from the ground.",
+                "caption": "In the image, one elephant is standing upright, using its trunk to grasp and pull foliage from the ground. It appears to be consuming the vegetation. The second elephant, smaller in size, is positioned nearby, walking closely alongside the first, with its trunk lowered toward the ground, possibly interacting with the grasses. The body posture of both elephants indicates movement and engagement with their surroundings.",
+                "cat_name": "elephant",
+                "file_name": "00035",
+                "isValid": false
+            },
+            "2": {
+                "ref_exp": "The elephant is standing and using its trunk to grasp and pull foliage from the ground.",
+                "caption": "In the image, one elephant is standing upright, using its trunk to grasp and pull foliage from the ground. It appears to be consuming the vegetation. The second elephant, smaller in size, is positioned nearby, walking closely alongside the first, with its trunk lowered toward the ground, possibly interacting with the grasses. The body posture of both elephants indicates movement and engagement with their surroundings.",
+                "cat_name": "elephant",
+                "file_name": "00035",
+                "isValid": true
+            }
+        },
+        "00055": {
+            "1": {
+                "ref_exp": "The elephant is walking forward while using its trunk to reach down to the ground.",
+                "caption": "In the image, a larger elephant is walking forward with a slight bend in its trunk, while a smaller elephant follows closely behind. The larger elephant is moving through the tall grass, using its trunk to occasionally reach down to the ground. At the same time, the smaller elephant appears to be moving at a brisk pace and is also navigating through the grass, maintaining proximity to the larger one. The movement of both elephants indicates a coordinated action as they travel in the same direction toward the nearby water.",
+                "cat_name": "elephant",
+                "file_name": "00055",
+                "isValid": false
+            },
+            "2": {
+                "ref_exp": "The elephant is walking forward with its trunk slightly bent, using it to reach down to the ground.",
+                "caption": "In the image, a larger elephant is walking forward with a slight bend in its trunk, while a smaller elephant follows closely behind. The larger elephant is moving through the tall grass, using its trunk to occasionally reach down to the ground. At the same time, the smaller elephant appears to be moving at a brisk pace and is also navigating through the grass, maintaining proximity to the larger one. The movement of both elephants indicates a coordinated action as they travel in the same direction toward the nearby water.",
+                "cat_name": "elephant",
+                "file_name": "00055",
+                "isValid": false
+            }
+        },
+        "00075": {
+            "1": {
+                "ref_exp": "The elephant is moving forward with its trunk extended downward.",
+                "caption": "In the image, several elephants are depicted in motion. \n\n1. The largest elephant is moving forward, its trunk extended downward, and its ears are flapping. \n2. A smaller elephant is walking closely behind, appearing to be following the larger one, with its trunk raised slightly. \n3. A very small elephant is positioned near the middle, walking on a similar path, with its trunk held close to the ground. \n\nThe actions of the elephants are directed towards moving through the grassy area, maintaining a formation where the smaller elephants trail behind the larger one.",
+                "cat_name": "elephant",
+                "file_name": "00075",
+                "isValid": false
+            },
+            "2": {
+                "ref_exp": "The elephant is moving forward with its trunk extended downward.",
+                "caption": "In the image, several elephants are depicted in motion. \n\n1. The largest elephant is moving forward, its trunk extended downward, and its ears are flapping. \n2. A smaller elephant is walking closely behind, appearing to be following the larger one, with its trunk raised slightly. \n3. A very small elephant is positioned near the middle, walking on a similar path, with its trunk held close to the ground. \n\nThe actions of the elephants are directed towards moving through the grassy area, maintaining a formation where the smaller elephants trail behind the larger one.",
+                "cat_name": "elephant",
+                "file_name": "00075",
+                "isValid": false
+            }
+        }
+    },
+    "013099c098": {
+        "00020": {
+            "1": {
+                "ref_exp": "The giant_panda is climbing on the back of another giant_panda.",
+                "caption": "Two giant pandas are engaged in distinct actions. \n\n1. The panda on the left is climbing on the back of the panda on the right, using its front paws to grasp the other panda's shoulders. \n\n2. The panda on the right is seated in a slight stance, with its head lowered and looking towards the ground while water from a small stream splashes around.\n\n3. Water is trickling down from the rocks behind them, creating ripples around the panda on the right as it shifts its weight.\n\n4. The panda on the left is positioned slightly higher, with its body elevated above the other, indicating a playful interaction. \n\nEach action is prominent and clear, showcasing the pandas\u2019 movements distinctly.",
+                "cat_name": "giant_panda",
+                "file_name": "00020",
+                "isValid": false
+            },
+            "2": {
+                "ref_exp": "The giant_panda is climbing on the back of another panda.",
+                "caption": "Two giant pandas are engaged in distinct actions. \n\n1. The panda on the left is climbing on the back of the panda on the right, using its front paws to grasp the other panda's shoulders. \n\n2. The panda on the right is seated in a slight stance, with its head lowered and looking towards the ground while water from a small stream splashes around.\n\n3. Water is trickling down from the rocks behind them, creating ripples around the panda on the right as it shifts its weight.\n\n4. The panda on the left is positioned slightly higher, with its body elevated above the other, indicating a playful interaction. \n\nEach action is prominent and clear, showcasing the pandas\u2019 movements distinctly.",
+                "cat_name": "giant_panda",
+                "file_name": "00020",
+                "isValid": true
+            }
+        },
+        "00030": {
+            "1": {
+                "ref_exp": "The giant panda is splashing water with its paws.",
+                "caption": "In the image, one giant panda is positioned beside a small flow of water, using its paws to splash water playfully. The panda appears to be engaging with the water, causing droplets to scatter into the air. Nearby, another panda is focused on grasping something with its mouth, seemingly nibbling or chewing on it. This panda is low to the ground, with its front paws bracing itself as it maintains balance while interacting with the object in front of it. Both pandas are actively involved in their respective actions, creating a dynamic scene.",
+                "cat_name": "giant_panda",
+                "file_name": "00030",
+                "isValid": false
+            },
+            "2": {
+                "ref_exp": "The giant_panda is using its paws to splash water.",
+                "caption": "In the image, one giant panda is positioned beside a small flow of water, using its paws to splash water playfully. The panda appears to be engaging with the water, causing droplets to scatter into the air. Nearby, another panda is focused on grasping something with its mouth, seemingly nibbling or chewing on it. This panda is low to the ground, with its front paws bracing itself as it maintains balance while interacting with the object in front of it. Both pandas are actively involved in their respective actions, creating a dynamic scene.",
+                "cat_name": "giant_panda",
+                "file_name": "00030",
+                "isValid": true
+            }
+        },
+        "00050": {
+            "1": {
+                "ref_exp": "The giant_panda is leaning forward to make contact with the other panda.",
+                "caption": "The image features two giant pandas engaged in the following actions:\n\n1. One panda is positioned on its hind legs, interacting with the other panda, which is lying on its back. The upright panda leans forward, making direct contact with the other panda.\n\n2. The panda that is lying on its back uses its front paws to swat playfully at the upright panda, displaying an open posture with its limbs extended.\n\n3. Water droplets are visible on the lying panda as it rolls slightly, resulting in splashes, indicating movement in a wet area.\n\n4. The upright panda appears to gently push down on the other panda\u2019s chest with its front paw while maintaining its position on hind legs.\n\n5. The prostrate panda lifts its head, looking towards the upright panda, showcasing an engaged posture in response to the interaction.",
+                "cat_name": "giant_panda",
+                "file_name": "00050",
+                "isValid": true
+            },
+            "2": {
+                "ref_exp": "The giant_panda interacts playfully with another panda.",
+                "caption": "The image features two giant pandas engaged in the following actions:\n\n1. One panda is positioned on its hind legs, interacting with the other panda, which is lying on its back. The upright panda leans forward, making direct contact with the other panda.\n\n2. The panda that is lying on its back uses its front paws to swat playfully at the upright panda, displaying an open posture with its limbs extended.\n\n3. Water droplets are visible on the lying panda as it rolls slightly, resulting in splashes, indicating movement in a wet area.\n\n4. The upright panda appears to gently push down on the other panda\u2019s chest with its front paw while maintaining its position on hind legs.\n\n5. The prostrate panda lifts its head, looking towards the upright panda, showcasing an engaged posture in response to the interaction.",
+                "cat_name": "giant_panda",
+                "file_name": "00050",
+                "isValid": false
+            }
+        },
+        "00070": {
+            "1": {
+                "ref_exp": "The giant_panda is using its front paws to grip another panda while lying on its back.",
+                "caption": "In the image, two giant pandas are engaged in a playful interaction. \n\n1. The first panda is lying on its back and using its front paws to push against the second panda, which is positioned above it.\n2. The second panda is leaning forward, resting its front paws on the first panda's chest, its face directed toward the first panda\u2019s face.\n3. Water droplets are splashing from the fur of both pandas as they move, indicating their active engagement.\n4. The first panda adjusts its position, rolling slightly to one side, while the second panda remains steady on top, maintaining contact through their paws.\n5. Both pandas appear to be using their forelimbs to grip each other playfully. \n\nThese actions illustrate their interaction and physical engagement without any other contextual details.",
+                "cat_name": "giant_panda",
+                "file_name": "00070",
+                "isValid": false
+            },
+            "2": {
+                "ref_exp": "The giant_panda is using its front paws to push against the other giant_panda.",
+                "caption": "In the image, two giant pandas are engaged in a playful interaction. \n\n1. The first panda is lying on its back and using its front paws to push against the second panda, which is positioned above it.\n2. The second panda is leaning forward, resting its front paws on the first panda's chest, its face directed toward the first panda\u2019s face.\n3. Water droplets are splashing from the fur of both pandas as they move, indicating their active engagement.\n4. The first panda adjusts its position, rolling slightly to one side, while the second panda remains steady on top, maintaining contact through their paws.\n5. Both pandas appear to be using their forelimbs to grip each other playfully. \n\nThese actions illustrate their interaction and physical engagement without any other contextual details.",
+                "cat_name": "giant_panda",
+                "file_name": "00070",
+                "isValid": false
+            }
+        }
+    },
+    "863b4049d7": {
+        "00010": null,
+        "00065": {
+            "1": {
+                "ref_exp": "The sheep is grazing.",
+                "caption": "In the image, the sheep exhibit various actions:\n\n1. **Grazing**: Several sheep are positioned near the ground, using their mouths to pull at visible patches of grass or forage in the dirt.\n\n2. **Moving**: A group of sheep is shifting from one spot to another, their legs visibly stepping as they walk in different directions.\n\n3. **Standing**: Some sheep are standing still, facing different orientations, with their bodies upright and stable.\n\n4. **Nudging**: A few sheep are seen nudging each other gently with their heads, engaging in a behavior that involves physical contact.\n\n5. **Vocalizing**: Some sheep are shown with their mouths open, indicating they are making noise, contributing to the collective sound of the group.\n\n6. **Resting**: A handful of sheep are positioned with their bodies lowered, likely resting while remaining alert to their surroundings. \n\nEach action demonstrates a specific behavior that reflects the sheep's daily activities.",
+                "cat_name": "sheep",
+                "file_name": "00065",
+                "isValid": false
+            },
+            "2": {
+                "ref_exp": "The sheep is grazing.",
+                "caption": "In the image, the sheep exhibit various actions:\n\n1. **Grazing**: Several sheep are positioned near the ground, using their mouths to pull at visible patches of grass or forage in the dirt.\n\n2. **Moving**: A group of sheep is shifting from one spot to another, their legs visibly stepping as they walk in different directions.\n\n3. **Standing**: Some sheep are standing still, facing different orientations, with their bodies upright and stable.\n\n4. **Nudging**: A few sheep are seen nudging each other gently with their heads, engaging in a behavior that involves physical contact.\n\n5. **Vocalizing**: Some sheep are shown with their mouths open, indicating they are making noise, contributing to the collective sound of the group.\n\n6. **Resting**: A handful of sheep are positioned with their bodies lowered, likely resting while remaining alert to their surroundings. \n\nEach action demonstrates a specific behavior that reflects the sheep's daily activities.",
+                "cat_name": "sheep",
+                "file_name": "00065",
+                "isValid": false
+            }
+        },
+        "00115": null,
+        "00165": {
+            "1": {
+                "ref_exp": "The sheep is stepping forward with a foot raised off the ground.",
+                "caption": "In the image, several sheep are depicted engaging in distinct actions:\n\n1. One sheep is moving away from the group, stepping to the left with a single foot raised off the ground, suggesting a direction towards the open space.\n2. A cluster of sheep stands closely together, with their heads lowered, indicating they are grazing or examining the ground.\n3. Another sheep is positioned at the back, looking back towards the rest of the group, its head turned to the right.\n4. A few sheep are lined up near a fence, forming a semi-circle, with their bodies angled outward.\n5. Some sheep are standing with their legs apart on the ground, creating a stable posture.\n\nOverall, the actions of the sheep demonstrate various movements and interactions within the group.",
+                "cat_name": "sheep",
+                "file_name": "00165",
+                "isValid": false
+            },
+            "2": {
+                "ref_exp": "The sheep is stepping away from the group.",
+                "caption": "In the image, several sheep are depicted engaging in distinct actions:\n\n1. One sheep is moving away from the group, stepping to the left with a single foot raised off the ground, suggesting a direction towards the open space.\n2. A cluster of sheep stands closely together, with their heads lowered, indicating they are grazing or examining the ground.\n3. Another sheep is positioned at the back, looking back towards the rest of the group, its head turned to the right.\n4. A few sheep are lined up near a fence, forming a semi-circle, with their bodies angled outward.\n5. Some sheep are standing with their legs apart on the ground, creating a stable posture.\n\nOverall, the actions of the sheep demonstrate various movements and interactions within the group.",
+                "cat_name": "sheep",
+                "file_name": "00165",
+                "isValid": false
+            }
+        }
+    },
+    "c36240d96f": {
+        "00035": null,
+        "00045": null,
+        "00095": null,
+        "00120": {
+            "1": {
+                "ref_exp": "The parrot pecks at the floor near the mirror.",
+                "caption": "The image features two parrots interacting with each other and their reflection in a mirror. \n\n1. One parrot approaches the mirror and pauses in front of it, turning its head to look at its reflection.\n2. The second parrot moves closer to the first parrot, tilting its head as it observes the first parrot's actions.\n3. The first parrot begins to peck at the floor near the mirror, using its beak to make contact with the surface.\n4. The second parrot shifts its position slightly, fluttering its wings and then stepping back.\n5. The first parrot flaps its wings in response, appearing to mirror the actions of the second parrot.\n6. Both parrots take turns moving closer to the mirror, occasionally stopping to examine their reflections.",
+                "cat_name": "parrot",
+                "file_name": "00120",
+                "isValid": false
+            },
+            "2": {
+                "ref_exp": "The parrot pecks at the floor near the mirror.",
+                "caption": "The image features two parrots interacting with each other and their reflection in a mirror. \n\n1. One parrot approaches the mirror and pauses in front of it, turning its head to look at its reflection.\n2. The second parrot moves closer to the first parrot, tilting its head as it observes the first parrot's actions.\n3. The first parrot begins to peck at the floor near the mirror, using its beak to make contact with the surface.\n4. The second parrot shifts its position slightly, fluttering its wings and then stepping back.\n5. The first parrot flaps its wings in response, appearing to mirror the actions of the second parrot.\n6. Both parrots take turns moving closer to the mirror, occasionally stopping to examine their reflections.",
+                "cat_name": "parrot",
+                "file_name": "00120",
+                "isValid": true
+            }
+        }
+    },
+    "f66981af4e": null,
+    "ef45ce3035": {
+        "00035": {
+            "1": {
+                "ref_exp": "The earless_seal is resting on a rocky surface.",
+                "caption": "In the image, several earless seals are observed engaged in various actions:\n\n1. One seal is swimming close to the surface, its body largely submerged while its head is above the waterline.\n2. Another seal is diving deeper, with its flippers spread as it propels itself downwards.\n3. Several seals are resting on a rocky surface, positioned on their sides with their bodies mostly still.\n4. One seal is nudging another with its snout while floating, demonstrating interaction.\n5. A seal is splashing water with its flippers while maneuvering in the shallow area near the rocks.\n6. Another seal is emerging from the water, shaking its body to remove droplets as it climbs onto the rocky shoreline.",
+                "cat_name": "earless_seal",
+                "file_name": "00035",
+                "isValid": true
+            },
+            "2": {}
+        },
+        "00065": {
+            "1": {
+                "ref_exp": "The earless_seal is resting on the surface.",
+                "caption": "In the image, the earless seals exhibit several distinct actions:\n\n1. One seal is resting on a rock, positioned against the water, with its body sprawled comfortably on the surface.\n2. Another seal is swimming in the water, partially submerged, with its head and upper body visible as it moves forward.\n3. A group of seals is engaged in a playful interaction, splashing the water around them, causing ripples and waves.\n4. A seal is seen diving below the surface, its streamlined body disappearing as it moves downward.\n5. Several seals are clustered together, appearing to engage in social behavior, while others are scattered in various positions throughout the water.",
+                "cat_name": "earless_seal",
+                "file_name": "00065",
+                "isValid": true
+            },
+            "2": {
+                "ref_exp": "The earless_seal is positioned on the ground with its body partially submerged in the water.",
+                "caption": "In the image, the earless seals exhibit several distinct actions:\n\n1. One seal is resting on a rock, positioned against the water, with its body sprawled comfortably on the surface.\n2. Another seal is swimming in the water, partially submerged, with its head and upper body visible as it moves forward.\n3. A group of seals is engaged in a playful interaction, splashing the water around them, causing ripples and waves.\n4. A seal is seen diving below the surface, its streamlined body disappearing as it moves downward.\n5. Several seals are clustered together, appearing to engage in social behavior, while others are scattered in various positions throughout the water.",
+                "cat_name": "earless_seal",
+                "file_name": "00065",
+                "isValid": true
+            }
+        },
+        "00120": {
+            "1": {
+                "ref_exp": "The earless_seal is climbing onto a rock using its flippers.",
+                "caption": "In the image, several earless seals are engaged in distinct actions:\n\n1. One seal is resting on a smooth rock, positioned upright with its body supported by its flippers.\n2. Another seal is partially submerged in the water, with its head above the surface while its body is mainly underwater.\n3. A seal is swimming, moving through the water with its body streamlined, creating ripples around it.\n4. Two seals are interacting close to each other, appearing to engage in playful behavior, possibly splashing water.\n5. A seal is climbing onto a rock, using its flippers to push itself up and elevate its body from the water.\n6. One seal is yawning, displaying its mouth wide open while remaining on a rock. \n\nEach action is clear and distinct, showcasing the behaviors of the earless seals in their environment.",
+                "cat_name": "earless_seal",
+                "file_name": "00120",
+                "isValid": false
+            },
+            "2": {
+                "ref_exp": "The earless_seal is climbing onto a rock.",
+                "caption": "In the image, several earless seals are engaged in distinct actions:\n\n1. One seal is resting on a smooth rock, positioned upright with its body supported by its flippers.\n2. Another seal is partially submerged in the water, with its head above the surface while its body is mainly underwater.\n3. A seal is swimming, moving through the water with its body streamlined, creating ripples around it.\n4. Two seals are interacting close to each other, appearing to engage in playful behavior, possibly splashing water.\n5. A seal is climbing onto a rock, using its flippers to push itself up and elevate its body from the water.\n6. One seal is yawning, displaying its mouth wide open while remaining on a rock. \n\nEach action is clear and distinct, showcasing the behaviors of the earless seals in their environment.",
+                "cat_name": "earless_seal",
+                "file_name": "00120",
+                "isValid": false
+            }
+        },
+        "00165": {
+            "1": {
+                "ref_exp": "The earless seal interacts with the water, creating ripples as it splashes with its flippers.",
+                "caption": "In the image, several earless seals are engaged in various actions:\n\n1. An earless seal lies on a rock, positioned upright and utilizing its flippers for balance.\n2. Another seal is submerged in the water, with only its head visible above the surface, actively paddling with its fore flippers.\n3. A group of seals swims near the surface, moving in synchrony while occasionally basking in the water.\n4. One seal is climbing onto a rocky ledge, using its body to push against the stone for support.\n5. Another seal is interacting with the water, creating ripples as it splashes with its flippers.\n6. In the background, two seals are positioned adjacent to each other, grooming themselves by scratching at their fur.",
+                "cat_name": "earless_seal",
+                "file_name": "00165",
+                "isValid": true
+            },
+            "2": {
+                "ref_exp": "The earless_seal is lying on a rock and using its flippers for balance.",
+                "caption": "In the image, several earless seals are engaged in various actions:\n\n1. An earless seal lies on a rock, positioned upright and utilizing its flippers for balance.\n2. Another seal is submerged in the water, with only its head visible above the surface, actively paddling with its fore flippers.\n3. A group of seals swims near the surface, moving in synchrony while occasionally basking in the water.\n4. One seal is climbing onto a rocky ledge, using its body to push against the stone for support.\n5. Another seal is interacting with the water, creating ripples as it splashes with its flippers.\n6. In the background, two seals are positioned adjacent to each other, grooming themselves by scratching at their fur.",
+                "cat_name": "earless_seal",
+                "file_name": "00165",
+                "isValid": false
+            }
+        }
+    },
+    "750be4c4d8": {
+        "00065": {
+            "1": {
+                "ref_exp": "The person is walking away from the buses.",
+                "caption": "The image depicts several individuals interacting with buses in a bus yard. \n\n1. One person in a red shirt is pointing at the bus, likely directing attention toward it.\n2. Another person wearing a yellow vest is walking towards the bus with a clipboard in hand.\n3. A third individual is standing still, watching the buses, possibly assessing the situation.\n4. An individual with a backpack is walking away from the buses, moving toward the left side of the image.\n\nThe buses appear to be articulated, connected at their center, and are positioned in the yard.",
+                "cat_name": "person",
+                "file_name": "00065",
+                "isValid": false
+            },
+            "2": {
+                "ref_exp": "The person is pointing at the bus.",
+                "caption": "The image depicts several individuals interacting with buses in a bus yard. \n\n1. One person in a red shirt is pointing at the bus, likely directing attention toward it.\n2. Another person wearing a yellow vest is walking towards the bus with a clipboard in hand.\n3. A third individual is standing still, watching the buses, possibly assessing the situation.\n4. An individual with a backpack is walking away from the buses, moving toward the left side of the image.\n\nThe buses appear to be articulated, connected at their center, and are positioned in the yard.",
+                "cat_name": "person",
+                "file_name": "00065",
+                "isValid": false
+            },
+            "3": {
+                "ref_exp": "The person is pointing at the bus.",
+                "caption": "The image depicts several individuals interacting with buses in a bus yard. \n\n1. One person in a red shirt is pointing at the bus, likely directing attention toward it.\n2. Another person wearing a yellow vest is walking towards the bus with a clipboard in hand.\n3. A third individual is standing still, watching the buses, possibly assessing the situation.\n4. An individual with a backpack is walking away from the buses, moving toward the left side of the image.\n\nThe buses appear to be articulated, connected at their center, and are positioned in the yard.",
+                "cat_name": "person",
+                "file_name": "00065",
+                "isValid": false
+            }
+        },
+        "00090": {
+            "1": {
+                "ref_exp": "A person is standing with a backpack, observing a bus.",
+                "caption": "In the image, multiple actions are taking place:\n\n1. A person is standing with a backpack, facing a yellow and blue bus, observing it.\n2. Several individuals are grouped together, appearing to converse or wait near another bus in the background.\n3. A bus is parked with its doors closed, indicating that it is not currently in use.\n4. A small group of children is nearby, some appearing to play or engage in activity.\n5. Another bus is positioned slightly ahead, and a few individuals are walking around it, possibly preparing to board or disembark.\n6. A larger vehicle, likely a truck, is positioned in the background, not in direct interaction with the people. \n\nEach action clearly reflects movement in relation to the buses within the scene.",
+                "cat_name": "person",
+                "file_name": "00090",
+                "isValid": true
+            },
+            "2": {
+                "ref_exp": "Person standing and observing a bus.",
+                "caption": "In the image, multiple actions are taking place:\n\n1. A person is standing with a backpack, facing a yellow and blue bus, observing it.\n2. Several individuals are grouped together, appearing to converse or wait near another bus in the background.\n3. A bus is parked with its doors closed, indicating that it is not currently in use.\n4. A small group of children is nearby, some appearing to play or engage in activity.\n5. Another bus is positioned slightly ahead, and a few individuals are walking around it, possibly preparing to board or disembark.\n6. A larger vehicle, likely a truck, is positioned in the background, not in direct interaction with the people. \n\nEach action clearly reflects movement in relation to the buses within the scene.",
+                "cat_name": "person",
+                "file_name": "00090",
+                "isValid": true
+            },
+            "3": {
+                "ref_exp": "Person is standing with a backpack and observing a bus.",
+                "caption": "In the image, multiple actions are taking place:\n\n1. A person is standing with a backpack, facing a yellow and blue bus, observing it.\n2. Several individuals are grouped together, appearing to converse or wait near another bus in the background.\n3. A bus is parked with its doors closed, indicating that it is not currently in use.\n4. A small group of children is nearby, some appearing to play or engage in activity.\n5. Another bus is positioned slightly ahead, and a few individuals are walking around it, possibly preparing to board or disembark.\n6. A larger vehicle, likely a truck, is positioned in the background, not in direct interaction with the people. \n\nEach action clearly reflects movement in relation to the buses within the scene.",
+                "cat_name": "person",
+                "file_name": "00090",
+                "isValid": false
+            }
+        },
+        "00115": {
+            "1": {
+                "ref_exp": "Person walking towards the yellow bus.",
+                "caption": "In the image, several actions are taking place:\n\n1. A yellow bus is positioned at the center, with its back doors opened, indicating it's either loading or unloading passengers.\n2. A group of individuals, including a small child, is positioned to the left of the yellow bus, standing and facing towards it, some appearing to engage in conversation.\n3. Another individual is walking towards the yellow bus, carrying a backpack, potentially intending to board.\n4. A person in a red jacket is near a second bus, with their body turned slightly towards it, suggesting they are looking at the bus.\n5. In the background, a few figures are standing near a building, possibly waiting or observing the area.\n\nThese actions are distinct and highlight the interactions around the buses.",
+                "cat_name": "person",
+                "file_name": "00115",
+                "isValid": false
+            },
+            "2": {
+                "ref_exp": "The person is looking at the bus.",
+                "caption": "In the image, several actions are taking place:\n\n1. A yellow bus is positioned at the center, with its back doors opened, indicating it's either loading or unloading passengers.\n2. A group of individuals, including a small child, is positioned to the left of the yellow bus, standing and facing towards it, some appearing to engage in conversation.\n3. Another individual is walking towards the yellow bus, carrying a backpack, potentially intending to board.\n4. A person in a red jacket is near a second bus, with their body turned slightly towards it, suggesting they are looking at the bus.\n5. In the background, a few figures are standing near a building, possibly waiting or observing the area.\n\nThese actions are distinct and highlight the interactions around the buses.",
+                "cat_name": "person",
+                "file_name": "00115",
+                "isValid": false
+            },
+            "3": {
+                "ref_exp": "A person is standing near a second bus.",
+                "caption": "In the image, several actions are taking place:\n\n1. A yellow bus is positioned at the center, with its back doors opened, indicating it's either loading or unloading passengers.\n2. A group of individuals, including a small child, is positioned to the left of the yellow bus, standing and facing towards it, some appearing to engage in conversation.\n3. Another individual is walking towards the yellow bus, carrying a backpack, potentially intending to board.\n4. A person in a red jacket is near a second bus, with their body turned slightly towards it, suggesting they are looking at the bus.\n5. In the background, a few figures are standing near a building, possibly waiting or observing the area.\n\nThese actions are distinct and highlight the interactions around the buses.",
+                "cat_name": "person",
+                "file_name": "00115",
+                "isValid": false
+            }
+        },
+        "00125": {
+            "1": {
+                "ref_exp": "The person is walking away from the bus.",
+                "caption": "In the image, a yellow and blue bus is maneuvering, likely making a turn or preparing to depart. Nearby, a group of people is gathered, with some individuals walking in different directions. One person is carrying a bag and walking away from the bus, while another group appears to be interacting near the bus. A fire truck is parked in the background, with its lights visible. Children can be seen moving around the area, possibly playing or exploring. The setting suggests an active scene with various actions unfolding around the bus.",
+                "cat_name": "person",
+                "file_name": "00125",
+                "isValid": true
+            },
+            "2": {
+                "ref_exp": "The person is standing by the bus.",
+                "caption": "In the image, a yellow and blue bus is maneuvering, likely making a turn or preparing to depart. Nearby, a group of people is gathered, with some individuals walking in different directions. One person is carrying a bag and walking away from the bus, while another group appears to be interacting near the bus. A fire truck is parked in the background, with its lights visible. Children can be seen moving around the area, possibly playing or exploring. The setting suggests an active scene with various actions unfolding around the bus.",
+                "cat_name": "person",
+                "file_name": "00125",
+                "isValid": true
+            },
+            "3": {
+                "ref_exp": "Person is standing and interacting with others.",
+                "caption": "In the image, a yellow and blue bus is maneuvering, likely making a turn or preparing to depart. Nearby, a group of people is gathered, with some individuals walking in different directions. One person is carrying a bag and walking away from the bus, while another group appears to be interacting near the bus. A fire truck is parked in the background, with its lights visible. Children can be seen moving around the area, possibly playing or exploring. The setting suggests an active scene with various actions unfolding around the bus.",
+                "cat_name": "person",
+                "file_name": "00125",
+                "isValid": false
+            }
+        }
+    },
+    "c307f33da2": {
+        "00225": {
+            "1": {
+                "ref_exp": "The giraffe extends its neck towards a person offering food.",
+                "caption": "In the image, one giraffe extends its long neck towards a person holding a piece of green leafy food. The giraffe's mouth opens slightly, indicating an action of reaching or preparing to take the food. Another giraffe, positioned slightly behind the first, appears to be standing still, not actively engaged in the feeding process. The person is also leaning forward with an outstretched arm, clearly directing the food towards the giraffe. In the background, several other individuals are observing the scene, but their actions are not the focus of this description.",
+                "cat_name": "giraffe",
+                "file_name": "00225",
+                "isValid": true
+            },
+            "2": {
+                "ref_exp": "The giraffe reaches towards the person holding food.",
+                "caption": "In the image, one giraffe extends its long neck towards a person holding a piece of green leafy food. The giraffe's mouth opens slightly, indicating an action of reaching or preparing to take the food. Another giraffe, positioned slightly behind the first, appears to be standing still, not actively engaged in the feeding process. The person is also leaning forward with an outstretched arm, clearly directing the food towards the giraffe. In the background, several other individuals are observing the scene, but their actions are not the focus of this description.",
+                "cat_name": "giraffe",
+                "file_name": "00225",
+                "isValid": true
+            },
+            "3": {}
+        },
+        "00245": {
+            "1": {
+                "ref_exp": "The giraffe extends its neck to grasp leaves from a woman's hand.",
+                "caption": "In the image, one giraffe extends its long neck towards a woman holding green leaves, using its tongue to grasp the leaves from her hand. This giraffe seems focused on the leaves. A second giraffe is visible in the background, standing upright with its body facing away from the viewer. The woman's arm is extended while she remains stationary, and she appears to be interacting with the giraffe. The leaves are prominently held in front of the giraffe's mouth as it reaches for them.",
+                "cat_name": "giraffe",
+                "file_name": "00245",
+                "isValid": true
+            },
+            "2": {
+                "ref_exp": "The giraffe extends its neck to grasp leaves from a woman's hand.",
+                "caption": "In the image, one giraffe extends its long neck towards a woman holding green leaves, using its tongue to grasp the leaves from her hand. This giraffe seems focused on the leaves. A second giraffe is visible in the background, standing upright with its body facing away from the viewer. The woman's arm is extended while she remains stationary, and she appears to be interacting with the giraffe. The leaves are prominently held in front of the giraffe's mouth as it reaches for them.",
+                "cat_name": "giraffe",
+                "file_name": "00245",
+                "isValid": true
+            },
+            "3": {}
+        },
+        "00265": {
+            "1": {
+                "ref_exp": "The giraffe extends its neck toward the woman, reaching out its tongue to grasp the greens being offered.",
+                "caption": "The image shows two giraffes engaging in distinct actions. \n\n1. The closest giraffe extends its neck toward a woman holding a cluster of leafy greens. Its head is tilted forward, and its tongue is reaching out, likely to grasp the food being offered.\n\n2. A second giraffe is standing slightly behind, facing away from the camera. It is standing upright, with its legs straight and its posture relaxed, while observing the interaction between the first giraffe and the woman.\n\n3. The woman, holding a child, is extending her arm, presenting the greens in an upward motion towards the giraffe.\n\n4. A child in her arms is looking toward the giraffe, with a curious expression as the animal approaches. \n\nThe scene captures the direct interaction and feeding behavior of giraffes in response to human presence.",
+                "cat_name": "giraffe",
+                "file_name": "00265",
+                "isValid": true
+            },
+            "2": {
+                "ref_exp": "The giraffe extends its neck toward the woman offering food.",
+                "caption": "The image shows two giraffes engaging in distinct actions. \n\n1. The closest giraffe extends its neck toward a woman holding a cluster of leafy greens. Its head is tilted forward, and its tongue is reaching out, likely to grasp the food being offered.\n\n2. A second giraffe is standing slightly behind, facing away from the camera. It is standing upright, with its legs straight and its posture relaxed, while observing the interaction between the first giraffe and the woman.\n\n3. The woman, holding a child, is extending her arm, presenting the greens in an upward motion towards the giraffe.\n\n4. A child in her arms is looking toward the giraffe, with a curious expression as the animal approaches. \n\nThe scene captures the direct interaction and feeding behavior of giraffes in response to human presence.",
+                "cat_name": "giraffe",
+                "file_name": "00265",
+                "isValid": true
+            },
+            "3": {
+                "ref_exp": "The giraffe extends its neck toward a woman offering food.",
+                "caption": "The image shows two giraffes engaging in distinct actions. \n\n1. The closest giraffe extends its neck toward a woman holding a cluster of leafy greens. Its head is tilted forward, and its tongue is reaching out, likely to grasp the food being offered.\n\n2. A second giraffe is standing slightly behind, facing away from the camera. It is standing upright, with its legs straight and its posture relaxed, while observing the interaction between the first giraffe and the woman.\n\n3. The woman, holding a child, is extending her arm, presenting the greens in an upward motion towards the giraffe.\n\n4. A child in her arms is looking toward the giraffe, with a curious expression as the animal approaches. \n\nThe scene captures the direct interaction and feeding behavior of giraffes in response to human presence.",
+                "cat_name": "giraffe",
+                "file_name": "00265",
+                "isValid": true
+            }
+        },
+        "00275": {
+            "1": {
+                "ref_exp": "The giraffe reaches forward to grasp food offered by a person.",
+                "caption": "In the image, one giraffe extends its neck forward, reaching toward a person holding out a piece of food. Its long tongue emerges to grasp the food offered. Another giraffe stands nearby, with its head turned slightly, observing the interaction. The first giraffe's movements are distinct as it leans in to eat, while the second giraffe remains stationary, seemingly uninterested in the immediate food. The person extends their arm, actively presenting the food to the giraffe.",
+                "cat_name": "giraffe",
+                "file_name": "00275",
+                "isValid": true
+            },
+            "2": {
+                "ref_exp": "The giraffe extends its neck forward to grasp the food offered.",
+                "caption": "In the image, one giraffe extends its neck forward, reaching toward a person holding out a piece of food. Its long tongue emerges to grasp the food offered. Another giraffe stands nearby, with its head turned slightly, observing the interaction. The first giraffe's movements are distinct as it leans in to eat, while the second giraffe remains stationary, seemingly uninterested in the immediate food. The person extends their arm, actively presenting the food to the giraffe.",
+                "cat_name": "giraffe",
+                "file_name": "00275",
+                "isValid": true
+            },
+            "3": {
+                "ref_exp": "The giraffe extends its neck to grasp food from a person.",
+                "caption": "In the image, one giraffe extends its neck forward, reaching toward a person holding out a piece of food. Its long tongue emerges to grasp the food offered. Another giraffe stands nearby, with its head turned slightly, observing the interaction. The first giraffe's movements are distinct as it leans in to eat, while the second giraffe remains stationary, seemingly uninterested in the immediate food. The person extends their arm, actively presenting the food to the giraffe.",
+                "cat_name": "giraffe",
+                "file_name": "00275",
+                "isValid": true
+            }
+        }
+    },
+    "9877af5063": {
+        "00040": {
+            "1": {
+                "ref_exp": "The sheep extends its mouth toward the stump.",
+                "caption": "In the image, the black sheep is approached closely to a wooden stump. \n\n1. The black sheep stands next to the stump, leaning its head forward.\n2. The sheep extends its mouth toward the stump, making contact with the surface.\n3. The sheep tilts its head slightly, pulling back after an interaction with the stump.\n4. In the background, another sheep is seen grazing, bending its neck to eat grass.\n\nThese actions highlight the sheep's engagement with its immediate environment, particularly the wooden stump.",
+                "cat_name": "sheep",
+                "file_name": "00040",
+                "isValid": false
+            },
+            "2": {
+                "ref_exp": "The sheep extends its mouth toward the stump.",
+                "caption": "In the image, the black sheep is approached closely to a wooden stump. \n\n1. The black sheep stands next to the stump, leaning its head forward.\n2. The sheep extends its mouth toward the stump, making contact with the surface.\n3. The sheep tilts its head slightly, pulling back after an interaction with the stump.\n4. In the background, another sheep is seen grazing, bending its neck to eat grass.\n\nThese actions highlight the sheep's engagement with its immediate environment, particularly the wooden stump.",
+                "cat_name": "sheep",
+                "file_name": "00040",
+                "isValid": true
+            }
+        },
+        "00055": null,
+        "00090": {
+            "1": {
+                "ref_exp": "The sheep is inspecting a broken tree stump.",
+                "caption": "The image features several sheep engaged in various actions:\n\n1. One black lamb stands near a broken tree stump, inspecting it closely.\n2. The lamb appears to nibble at the exposed wood, using its mouth to pull fibers.\n3. Another sheep, in the background, grazes on the grass, using its head to brush against the ground.\n4. A third sheep is seen moving its head side to side, possibly looking for additional grazing spots. \n5. Occasionally, the black lamb shifts its weight, adjusting its stance while still interacting with the stump. \n\nThese actions are clear and distinct within the scene.",
+                "cat_name": "sheep",
+                "file_name": "00090",
+                "isValid": true
+            },
+            "2": {
+                "ref_exp": "The sheep is inspecting and nibbling at the broken tree stump.",
+                "caption": "The image features several sheep engaged in various actions:\n\n1. One black lamb stands near a broken tree stump, inspecting it closely.\n2. The lamb appears to nibble at the exposed wood, using its mouth to pull fibers.\n3. Another sheep, in the background, grazes on the grass, using its head to brush against the ground.\n4. A third sheep is seen moving its head side to side, possibly looking for additional grazing spots. \n5. Occasionally, the black lamb shifts its weight, adjusting its stance while still interacting with the stump. \n\nThese actions are clear and distinct within the scene.",
+                "cat_name": "sheep",
+                "file_name": "00090",
+                "isValid": true
+            }
+        },
+        "00155": null
+    }
+}
\ No newline at end of file
diff --git a/models/__init__.py b/models/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a76be612ce6abdecc280357e22bd9441f64b1d57
--- /dev/null
+++ b/models/__init__.py
@@ -0,0 +1,5 @@
+from .referformer import build
+
+
+def build_model(args):
+    return build(args)
diff --git a/models/backbone.py b/models/backbone.py
new file mode 100644
index 0000000000000000000000000000000000000000..e83b72a0d0bee9e1b2f5771da324cbc14c829d76
--- /dev/null
+++ b/models/backbone.py
@@ -0,0 +1,132 @@
+"""
+Backbone modules.
+Modified from DETR (https://github.com/facebookresearch/detr)
+"""
+from collections import OrderedDict
+
+import torch
+import torch.nn.functional as F
+import torchvision
+from torch import nn
+from torchvision.models._utils import IntermediateLayerGetter
+from typing import Dict, List
+from einops import rearrange
+
+from util.misc import NestedTensor, is_main_process
+
+from .position_encoding import build_position_encoding
+
+
+class FrozenBatchNorm2d(torch.nn.Module):
+    """
+    BatchNorm2d where the batch statistics and the affine parameters are fixed.
+
+    Copy-paste from torchvision.misc.ops with added eps before rqsrt,
+    without which any other models than torchvision.models.resnet[18,34,50,101]
+    produce nans.
+    """
+
+    def __init__(self, n):
+        super(FrozenBatchNorm2d, self).__init__()
+        self.register_buffer("weight", torch.ones(n))
+        self.register_buffer("bias", torch.zeros(n))
+        self.register_buffer("running_mean", torch.zeros(n))
+        self.register_buffer("running_var", torch.ones(n))
+
+    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
+                              missing_keys, unexpected_keys, error_msgs):
+        num_batches_tracked_key = prefix + 'num_batches_tracked'
+        if num_batches_tracked_key in state_dict:
+            del state_dict[num_batches_tracked_key]
+
+        super(FrozenBatchNorm2d, self)._load_from_state_dict(
+            state_dict, prefix, local_metadata, strict,
+            missing_keys, unexpected_keys, error_msgs)
+
+    def forward(self, x):
+        # move reshapes to the beginning
+        # to make it fuser-friendly
+        w = self.weight.reshape(1, -1, 1, 1)
+        b = self.bias.reshape(1, -1, 1, 1)
+        rv = self.running_var.reshape(1, -1, 1, 1)
+        rm = self.running_mean.reshape(1, -1, 1, 1)
+        eps = 1e-5
+        scale = w * (rv + eps).rsqrt()
+        bias = b - rm * scale
+        return x * scale + bias
+
+
+class BackboneBase(nn.Module):
+
+    def __init__(self, backbone: nn.Module, train_backbone: bool, return_interm_layers: bool):
+        super().__init__()
+        for name, parameter in backbone.named_parameters():
+            if not train_backbone or 'layer2' not in name and 'layer3' not in name and 'layer4' not in name:
+                parameter.requires_grad_(False)
+        if return_interm_layers:
+            return_layers = {"layer1": "0", "layer2": "1", "layer3": "2", "layer4": "3"}
+            # return_layers = {"layer2": "0", "layer3": "1", "layer4": "2"} deformable detr
+            self.strides = [4, 8, 16, 32]
+            self.num_channels = [256, 512, 1024, 2048]
+        else:
+            return_layers = {'layer4': "0"}
+            self.strides = [32]
+            self.num_channels = [2048]
+        self.body = IntermediateLayerGetter(backbone, return_layers=return_layers)
+
+    def forward(self, tensor_list: NestedTensor):
+        xs = self.body(tensor_list.tensors)
+        out: Dict[str, NestedTensor] = {}
+        for name, x in xs.items():
+            m = tensor_list.mask
+            assert m is not None
+            mask = F.interpolate(m[None].float(), size=x.shape[-2:]).to(torch.bool)[0]
+            out[name] = NestedTensor(x, mask)
+        return out
+
+
+class Backbone(BackboneBase):
+    """ResNet backbone with frozen BatchNorm."""
+    def __init__(self, name: str,
+                 train_backbone: bool,
+                 return_interm_layers: bool,
+                 dilation: bool):
+        backbone = getattr(torchvision.models, name)(
+            replace_stride_with_dilation=[False, False, dilation],
+            pretrained=is_main_process(), norm_layer=FrozenBatchNorm2d)
+        assert name not in ('resnet18', 'resnet34'), "number of channels are hard coded"
+        super().__init__(backbone, train_backbone, return_interm_layers)
+        if dilation:
+            self.strides[-1] = self.strides[-1] // 2
+
+
+class Joiner(nn.Sequential):
+    def __init__(self, backbone, position_embedding):
+        super().__init__(backbone, position_embedding)
+        self.strides = backbone.strides
+        self.num_channels = backbone.num_channels
+
+
+    def forward(self, tensor_list: NestedTensor):
+        tensor_list.tensors = rearrange(tensor_list.tensors, 'b t c h w -> (b t) c h w')
+        tensor_list.mask = rearrange(tensor_list.mask, 'b t h w -> (b t) h w')
+
+        xs = self[0](tensor_list)
+        out: List[NestedTensor] = []
+        pos = []
+        for name, x in xs.items():
+            out.append(x)
+            # position encoding
+            pos.append(self[1](x).to(x.tensors.dtype))
+        return out, pos
+
+
+def build_backbone(args):
+    position_embedding = build_position_encoding(args)
+    train_backbone = args.lr_backbone > 0
+    return_interm_layers = args.masks or (args.num)
+    backbone = Backbone(args.backbone, train_backbone, return_interm_layers, args.dilation)
+    model = Joiner(backbone, position_embedding)
+    model.num_channels = backbone.num_channels
+    return model
+
diff --git a/models/criterion.py b/models/criterion.py
new file mode 100644
index 0000000000000000000000000000000000000000..4473f1c81e2afdd114c0650a1cc86230565855bd
--- /dev/null
+++ b/models/criterion.py
@@ -0,0 +1,208 @@
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from util import box_ops
+from util.misc import (NestedTensor, nested_tensor_from_tensor_list,
+                       accuracy, get_world_size, interpolate,
+                       is_dist_avail_and_initialized, inverse_sigmoid)
+
+from .segmentation import (dice_loss, sigmoid_focal_loss)
+
+from einops import rearrange
+
+class SetCriterion(nn.Module):
+    """ This class computes the loss for ReferFormer.
+    The process happens in two steps:
+        1) we compute hungarian assignment between ground truth boxes and the outputs of the model
+        2) we supervise each pair of matched ground-truth / prediction (supervise class and box)
+    """
+    def __init__(self, num_classes, matcher, weight_dict, eos_coef, losses, focal_alpha=0.25):
+        """ Create the criterion.
+        Parameters:
+            num_classes: number of object categories, omitting the special no-object category
+            matcher: module able to compute a matching between targets and proposals
+            weight_dict: dict containing as key the names of the losses and as values their relative weight.
+            eos_coef: relative classification weight applied to the no-object category
+            losses: list of all the losses to be applied. See get_loss for list of available losses.
+        """
+        super().__init__()
+        self.num_classes = num_classes
+        self.matcher = matcher
+        self.weight_dict = weight_dict
+        self.eos_coef = eos_coef
+        self.losses = losses
+        empty_weight = torch.ones(self.num_classes + 1)
+        empty_weight[-1] = self.eos_coef
+        self.register_buffer('empty_weight', empty_weight)
+        self.focal_alpha = focal_alpha
+        self.mask_out_stride = 4
+
+    def loss_labels(self, outputs, targets, indices, num_boxes, log=True):
+        """Classification loss (NLL)
+        targets dicts must contain the key "labels" containing a tensor of dim [nb_target_boxes]
+        """
+        assert 'pred_logits' in outputs
+        src_logits = outputs['pred_logits'] 
+        _, nf, nq = src_logits.shape[:3]
+        src_logits = rearrange(src_logits, 'b t q k -> b (t q) k')
+
+        # judge the valid frames
+        valid_indices = []
+        valids = [target['valid'] for target in targets]
+        for valid, (indice_i, indice_j) in zip(valids, indices): 
+            valid_ind = valid.nonzero().flatten() 
+            valid_i = valid_ind * nq + indice_i
+            valid_j = valid_ind + indice_j * nf
+            valid_indices.append((valid_i, valid_j))
+
+        idx = self._get_src_permutation_idx(valid_indices) # NOTE: use valid indices
+        target_classes_o = torch.cat([t["labels"][J] for t, (_, J) in zip(targets, valid_indices)])
+        target_classes = torch.full(src_logits.shape[:2], self.num_classes,
+                                    dtype=torch.int64, device=src_logits.device) 
+        if self.num_classes == 1: # binary referred
+            target_classes[idx] = 0
+        else:
+            target_classes[idx] = target_classes_o
+
+        target_classes_onehot = torch.zeros([src_logits.shape[0], src_logits.shape[1], src_logits.shape[2] + 1],
+                                            dtype=src_logits.dtype, layout=src_logits.layout, device=src_logits.device)
+        target_classes_onehot.scatter_(2, target_classes.unsqueeze(-1), 1)
+
+        target_classes_onehot = target_classes_onehot[:,:,:-1]
+        loss_ce = sigmoid_focal_loss(src_logits, target_classes_onehot, num_boxes, alpha=self.focal_alpha, gamma=2) * src_logits.shape[1]
+        losses = {'loss_ce': loss_ce}
+
+        if log:
+            # TODO this should probably be a separate loss, not hacked in this one here
+            pass
+        return losses
+
+
+    def loss_boxes(self, outputs, targets, indices, num_boxes):
+        """Compute the losses related to the bounding boxes, the L1 regression loss and the GIoU loss
+           targets dicts must contain the key "boxes" containing a tensor of dim [nb_target_boxes, 4]
+           The target boxes are expected in format (center_x, center_y, w, h), normalized by the image size.
+        """
+        assert 'pred_boxes' in outputs
+        src_boxes = outputs['pred_boxes']  
+        bs, nf, nq = src_boxes.shape[:3]
+        src_boxes = src_boxes.transpose(1, 2)  
+
+        idx = self._get_src_permutation_idx(indices)
+        src_boxes = src_boxes[idx]  
+        src_boxes = src_boxes.flatten(0, 1)  # [b*t, 4]
+
+        target_boxes = torch.cat([t['boxes'] for t in targets], dim=0)  # [b*t, 4]
+
+        loss_bbox = F.l1_loss(src_boxes, target_boxes, reduction='none')
+
+        losses = {}
+        losses['loss_bbox'] = loss_bbox.sum() / num_boxes
+
+        loss_giou = 1 - torch.diag(box_ops.generalized_box_iou(
+            box_ops.box_cxcywh_to_xyxy(src_boxes),
+            box_ops.box_cxcywh_to_xyxy(target_boxes)))
+        losses['loss_giou'] = loss_giou.sum() / num_boxes
+        return losses
+
+
+    def loss_masks(self, outputs, targets, indices, num_boxes):
+        """Compute the losses related to the masks: the focal loss and the dice loss.
+           targets dicts must contain the key "masks" containing a tensor of dim [nb_target_boxes, h, w]
+        """
+        assert "pred_masks" in outputs
+
+        src_idx = self._get_src_permutation_idx(indices)
+        # tgt_idx = self._get_tgt_permutation_idx(indices)
+
+        src_masks = outputs["pred_masks"] 
+        src_masks = src_masks.transpose(1, 2) 
+
+        # TODO use valid to mask invalid areas due to padding in loss
+        target_masks, valid = nested_tensor_from_tensor_list([t["masks"] for t in targets], 
+                                                              size_divisibility=32, split=False).decompose()
+        target_masks = target_masks.to(src_masks) 
+
+        # downsample ground truth masks with ratio mask_out_stride
+        start = int(self.mask_out_stride // 2)
+        im_h, im_w = target_masks.shape[-2:]
+        
+        target_masks = target_masks[:, :, start::self.mask_out_stride, start::self.mask_out_stride] 
+        assert target_masks.size(2) * self.mask_out_stride == im_h
+        assert target_masks.size(3) * self.mask_out_stride == im_w
+
+        src_masks = src_masks[src_idx] 
+        # upsample predictions to the target size
+        # src_masks = interpolate(src_masks, size=target_masks.shape[-2:], mode="bilinear", align_corners=False) 
+        src_masks = src_masks.flatten(1) # [b, thw]
+
+        target_masks = target_masks.flatten(1) # [b, thw]
+
+        losses = {
+            "loss_mask": sigmoid_focal_loss(src_masks, target_masks, num_boxes),
+            "loss_dice": dice_loss(src_masks, target_masks, num_boxes),
+        }
+        return losses
+
+    def _get_src_permutation_idx(self, indices):
+        # permute predictions following indices
+        batch_idx = torch.cat([torch.full_like(src, i) for i, (src, _) in enumerate(indices)])
+        src_idx = torch.cat([src for (src, _) in indices])
+        return batch_idx, src_idx
+
+    def _get_tgt_permutation_idx(self, indices):
+        # permute targets following indices
+        batch_idx = torch.cat([torch.full_like(tgt, i) for i, (_, tgt) in enumerate(indices)])
+        tgt_idx = torch.cat([tgt for (_, tgt) in indices])
+        return batch_idx, tgt_idx
+
+    def get_loss(self, loss, outputs, targets, indices, num_boxes, **kwargs):
+        loss_map = {
+            'labels': self.loss_labels,
+            'boxes': self.loss_boxes,
+            'masks': self.loss_masks
+        }
+        assert loss in loss_map, f'do you really want to compute {loss} loss?'
+        return loss_map[loss](outputs, targets, indices, num_boxes, **kwargs)
+
+    def forward(self, outputs, targets):
+        """ This performs the loss computation.
+        Parameters:
+             outputs: dict of tensors, see the output specification of the model for the format
+             targets: list of dicts, such that len(targets) == batch_size.
+                      The expected keys in each dict depends on the losses applied, see each loss' doc
+        """
+        outputs_without_aux = {k: v for k, v in outputs.items() if k != 'aux_outputs'}
+        # Retrieve the matching between the outputs of the last layer and the targets
+        indices = self.matcher(outputs_without_aux, targets)
+
+        # Compute the average number of target boxes accross all nodes, for normalization purposes
+        target_valid = torch.stack([t["valid"] for t in targets], dim=0).reshape(-1) # [B, T] -> [B*T]
+        num_boxes = target_valid.sum().item() 
+        num_boxes = torch.as_tensor([num_boxes], dtype=torch.float, device=next(iter(outputs.values())).device)
+        if is_dist_avail_and_initialized():
+            torch.distributed.all_reduce(num_boxes)
+        num_boxes = torch.clamp(num_boxes / get_world_size(), min=1).item()
+        
+        # Compute all the requested losses
+        losses = {}
+        for loss in self.losses:
+            losses.update(self.get_loss(loss, outputs, targets, indices, num_boxes))
+
+        # In case of auxiliary losses, we repeat this process with the output of each intermediate layer.
+        if 'aux_outputs' in outputs:
+            for i, aux_outputs in enumerate(outputs['aux_outputs']):
+                indices = self.matcher(aux_outputs, targets)
+                for loss in self.losses:
+                    kwargs = {}
+                    if loss == 'labels':
+                        # Logging is enabled only for the last layer
+                        kwargs = {'log': False}
+                    l_dict = self.get_loss(loss, aux_outputs, targets, indices, num_boxes, **kwargs)
+                    l_dict = {k + f'_{i}': v for k, v in l_dict.items()}
+                    losses.update(l_dict)
+
+        return losses
+
+
diff --git a/models/deformable_transformer.py b/models/deformable_transformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..8fd940a7542f9022c6fefebe0348e0a2e621c866
--- /dev/null
+++ b/models/deformable_transformer.py
@@ -0,0 +1,444 @@
+# ------------------------------------------------------------------------
+# Deformable DETR
+# Copyright (c) 2020 SenseTime. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Modified from DETR (https://github.com/facebookresearch/detr)
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+# ------------------------------------------------------------------------
+
+import copy
+from typing import Optional, List
+import math
+
+import torch
+import torch.nn.functional as F
+from torch import nn, Tensor
+from torch.nn.init import xavier_uniform_, constant_, uniform_, normal_
+
+from util.misc import inverse_sigmoid
+from models.ops.modules import MSDeformAttn
+
+from einops import rearrange
+
+
+class DeformableTransformer(nn.Module):
+    def __init__(self, d_model=256, nhead=8,
+                 num_encoder_layers=6, num_decoder_layers=6, dim_feedforward=1024, dropout=0.1,
+                 activation="relu", return_intermediate_dec=False,
+                 num_feature_levels=4, dec_n_points=4,  enc_n_points=4,
+                 two_stage=False, two_stage_num_proposals=300):
+        super().__init__()
+
+        self.d_model = d_model
+        self.nhead = nhead
+        self.dropout = dropout
+        self.two_stage = two_stage
+        self.two_stage_num_proposals = two_stage_num_proposals
+        self.num_feature_level = num_feature_levels
+
+        encoder_layer = DeformableTransformerEncoderLayer(d_model, dim_feedforward,
+                                                          dropout, activation,
+                                                          num_feature_levels,
+                                                          nhead, enc_n_points)
+        self.encoder = DeformableTransformerEncoder(encoder_layer, num_encoder_layers)
+
+        decoder_layer = DeformableTransformerDecoderLayer(d_model, dim_feedforward,
+                                                          dropout, activation,
+                                                          num_feature_levels, 
+                                                          nhead, dec_n_points)
+        self.decoder = DeformableTransformerDecoder(decoder_layer, num_decoder_layers, return_intermediate_dec)
+
+        self.level_embed = nn.Parameter(torch.Tensor(num_feature_levels, d_model))
+
+        if two_stage:
+            self.enc_output = nn.Linear(d_model, d_model)
+            self.enc_output_norm = nn.LayerNorm(d_model)
+            self.pos_trans = nn.Linear(d_model * 2, d_model * 2)
+            self.pos_trans_norm = nn.LayerNorm(d_model * 2)
+        else:
+            self.reference_points = nn.Linear(d_model, 2) # reference point here (x, y)
+
+        self._reset_parameters()
+
+    def _reset_parameters(self):
+        for p in self.parameters():
+            if p.dim() > 1:
+                nn.init.xavier_uniform_(p)
+        for m in self.modules():
+            if isinstance(m, MSDeformAttn):
+                m._reset_parameters()
+        if not self.two_stage:
+            xavier_uniform_(self.reference_points.weight.data, gain=1.0)
+            constant_(self.reference_points.bias.data, 0.)
+        normal_(self.level_embed)
+
+    def get_proposal_pos_embed(self, proposals):
+        num_pos_feats = 128
+        temperature = 10000
+        scale = 2 * math.pi
+
+        dim_t = torch.arange(num_pos_feats, dtype=torch.float32, device=proposals.device)
+        dim_t = temperature ** (2 * (dim_t // 2) / num_pos_feats)
+        # N, L, 4
+        proposals = proposals.sigmoid() * scale
+        # N, L, 4, 128
+        pos = proposals[:, :, :, None] / dim_t
+        # N, L, 4, 64, 2
+        pos = torch.stack((pos[:, :, :, 0::2].sin(), pos[:, :, :, 1::2].cos()), dim=4).flatten(2)
+        return pos
+
+    def gen_encoder_output_proposals(self, memory, memory_padding_mask, spatial_shapes):
+        N_, S_, C_ = memory.shape
+        base_scale = 4.0
+        proposals = []
+        _cur = 0
+        for lvl, (H_, W_) in enumerate(spatial_shapes):
+            mask_flatten_ = memory_padding_mask[:, _cur:(_cur + H_ * W_)].view(N_, H_, W_, 1)
+            valid_H = torch.sum(~mask_flatten_[:, :, 0, 0], 1)
+            valid_W = torch.sum(~mask_flatten_[:, 0, :, 0], 1)
+
+            grid_y, grid_x = torch.meshgrid(torch.linspace(0, H_ - 1, H_, dtype=torch.float32, device=memory.device),
+                                            torch.linspace(0, W_ - 1, W_, dtype=torch.float32, device=memory.device))
+            grid = torch.cat([grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)], -1)
+
+            scale = torch.cat([valid_W.unsqueeze(-1), valid_H.unsqueeze(-1)], 1).view(N_, 1, 1, 2)
+            grid = (grid.unsqueeze(0).expand(N_, -1, -1, -1) + 0.5) / scale
+            wh = torch.ones_like(grid) * 0.05 * (2.0 ** lvl)
+            proposal = torch.cat((grid, wh), -1).view(N_, -1, 4)
+            proposals.append(proposal)
+            _cur += (H_ * W_)
+        output_proposals = torch.cat(proposals, 1)
+        output_proposals_valid = ((output_proposals > 0.01) & (output_proposals < 0.99)).all(-1, keepdim=True)
+        output_proposals = torch.log(output_proposals / (1 - output_proposals))
+        output_proposals = output_proposals.masked_fill(memory_padding_mask.unsqueeze(-1), float('inf'))
+        output_proposals = output_proposals.masked_fill(~output_proposals_valid, float('inf'))
+
+        output_memory = memory
+        output_memory = output_memory.masked_fill(memory_padding_mask.unsqueeze(-1), float(0))
+        output_memory = output_memory.masked_fill(~output_proposals_valid, float(0))
+        output_memory = self.enc_output_norm(self.enc_output(output_memory))
+        return output_memory, output_proposals
+
+    def get_valid_ratio(self, mask):
+        _, H, W = mask.shape
+        valid_H = torch.sum(~mask[:, :, 0], 1)
+        valid_W = torch.sum(~mask[:, 0, :], 1)
+        valid_ratio_h = valid_H.float() / H
+        valid_ratio_w = valid_W.float() / W
+        valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1)
+        return valid_ratio
+
+    def forward(self, srcs, tgt, masks, pos_embeds, query_embed=None):
+        assert self.two_stage or query_embed is not None
+        """
+        srcs (list[Tensor]): list of tensors num_layers x [batch_size*time, c, hi, wi], input of encoder
+        tgt (Tensor): [batch_size, time, c, num_queries_per_frame]
+        masks (list[Tensor]): list of tensors num_layers x [batch_size*time, hi, wi], the mask of srcs
+        pos_embeds (list[Tensor]): list of tensors num_layers x [batch_size*time, c, hi, wi], position encoding of srcs
+        query_embed (Tensor): [num_queries, c]
+        """
+        # prepare input for encoder
+        src_flatten = []
+        mask_flatten = []
+        lvl_pos_embed_flatten = []
+        spatial_shapes = []
+        for lvl, (src, mask, pos_embed) in enumerate(zip(srcs, masks, pos_embeds)):
+            bs, c, h, w = src.shape
+            spatial_shape = (h, w)
+            spatial_shapes.append(spatial_shape)
+
+            src = src.flatten(2).transpose(1, 2) # [batch_size, hi*wi, c]
+            mask = mask.flatten(1)               # [batch_size, hi*wi]
+            pos_embed = pos_embed.flatten(2).transpose(1, 2) # [batch_size, hi*wi, c]
+            lvl_pos_embed = pos_embed + self.level_embed[lvl].view(1, 1, -1)
+
+            lvl_pos_embed_flatten.append(lvl_pos_embed)
+            src_flatten.append(src)
+            mask_flatten.append(mask)
+
+        # For a clip, concat all the features, first fpn layer size, then frame size
+        src_flatten = torch.cat(src_flatten, 1)     # [bs*t, \sigma(hi*wi), c] 
+        mask_flatten = torch.cat(mask_flatten, 1)   # [bs*t, \sigma(hi*wi)]
+        lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1) 
+        spatial_shapes = torch.as_tensor(spatial_shapes, dtype=torch.long, device=src_flatten.device)
+        level_start_index = torch.cat((spatial_shapes.new_zeros((1, )), spatial_shapes.prod(1).cumsum(0)[:-1]))
+        valid_ratios = torch.stack([self.get_valid_ratio(m) for m in masks], 1)
+
+        # encoder
+        # memory: [bs*t, \sigma(hi*wi), c]
+        memory = self.encoder(src_flatten, spatial_shapes, level_start_index, valid_ratios, lvl_pos_embed_flatten, mask_flatten)
+
+        # prepare input for decoder
+        bs, _, c = memory.shape 
+        if self.two_stage:
+            output_memory, output_proposals = self.gen_encoder_output_proposals(memory, mask_flatten, spatial_shapes)
+
+            # hack implementation for two-stage Deformable DETR
+            enc_outputs_class = self.decoder.class_embed[self.decoder.num_layers](output_memory)
+            enc_outputs_coord_unact = self.decoder.bbox_embed[self.decoder.num_layers](output_memory) + output_proposals
+
+            topk = self.two_stage_num_proposals
+            topk_proposals = torch.topk(enc_outputs_class[..., 0], topk, dim=1)[1]
+            topk_coords_unact = torch.gather(enc_outputs_coord_unact, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4))
+            topk_coords_unact = topk_coords_unact.detach()
+            reference_points = topk_coords_unact.sigmoid()
+            init_reference_out = reference_points
+            pos_trans_out = self.pos_trans_norm(self.pos_trans(self.get_proposal_pos_embed(topk_coords_unact)))
+            query_embed, tgt = torch.split(pos_trans_out, c, dim=2)
+        else:
+            b, t, q, c = tgt.shape
+            tgt = rearrange(tgt, 'b t q c -> (b t) q c')
+            query_embed = query_embed.unsqueeze(0).expand(b*t, -1, -1)      # [batch_size*time, num_queries_per_frame, c]
+            reference_points = self.reference_points(query_embed).sigmoid() # [batch_size*time, num_queries_per_frame, 2]
+            init_reference_out = reference_points
+
+        # decoder
+        hs, inter_references, inter_samples = self.decoder(tgt, reference_points, memory,
+                                            spatial_shapes, level_start_index, valid_ratios, query_embed, mask_flatten)
+
+        inter_references_out = inter_references
+
+        # convert memory to fpn format
+        memory_features = []  # 8x -> 32x
+        spatial_index = 0
+        for lvl in range(self.num_feature_level - 1):
+            h, w = spatial_shapes[lvl]
+            # [bs*t, c, h, w]
+            memory_lvl = memory[:, spatial_index : spatial_index + h * w, :].reshape(bs, h, w, c).permute(0, 3, 1, 2).contiguous()  
+            memory_features.append(memory_lvl)
+            spatial_index += h * w
+
+        if self.two_stage:
+            return hs, memory_features, init_reference_out, inter_references_out, enc_outputs_class, enc_outputs_coord_unact, inter_samples
+        # hs: [l, batch_size*time, num_queries_per_frame, c], where l is number of decoder layers
+        # init_reference_out: [batch_size*time, num_queries_per_frame, 2]
+        # inter_references_out: [l, batch_size*time, num_queries_per_frame, 4]
+        # memory: [batch_size*time, \sigma(hi*wi), c]
+        # memory_features: list[Tensor]
+
+        return hs, memory_features, init_reference_out, inter_references_out, None, None, inter_samples
+
+
+class DeformableTransformerEncoderLayer(nn.Module):
+    def __init__(self,
+                 d_model=256, d_ffn=1024,
+                 dropout=0.1, activation="relu",
+                 n_levels=4, n_heads=8, n_points=4):
+        super().__init__()
+
+        # self attention
+        self.self_attn = MSDeformAttn(d_model, n_levels, n_heads, n_points)
+        self.dropout1 = nn.Dropout(dropout)
+        self.norm1 = nn.LayerNorm(d_model)
+
+        # ffn
+        self.linear1 = nn.Linear(d_model, d_ffn)
+        self.activation = _get_activation_fn(activation)
+        self.dropout2 = nn.Dropout(dropout)
+        self.linear2 = nn.Linear(d_ffn, d_model)
+        self.dropout3 = nn.Dropout(dropout)
+        self.norm2 = nn.LayerNorm(d_model)
+
+    @staticmethod
+    def with_pos_embed(tensor, pos):
+        return tensor if pos is None else tensor + pos
+
+    def forward_ffn(self, src):
+        src2 = self.linear2(self.dropout2(self.activation(self.linear1(src))))
+        src = src + self.dropout3(src2)
+        src = self.norm2(src)
+        return src
+
+    def forward(self, src, pos, reference_points, spatial_shapes, level_start_index, padding_mask=None):
+        # self attention
+        src2, sampling_locations, attention_weights = self.self_attn(self.with_pos_embed(src, pos), reference_points, 
+                                                                src, spatial_shapes, level_start_index, padding_mask)
+        src = src + self.dropout1(src2)
+        src = self.norm1(src)
+
+        # ffn
+        src = self.forward_ffn(src)
+
+        return src
+
+
+class DeformableTransformerEncoder(nn.Module):
+    def __init__(self, encoder_layer, num_layers):
+        super().__init__()
+        self.layers = _get_clones(encoder_layer, num_layers)
+        self.num_layers = num_layers
+
+    @staticmethod
+    def get_reference_points(spatial_shapes, valid_ratios, device):
+        reference_points_list = []
+        for lvl, (H_, W_) in enumerate(spatial_shapes):
+
+            ref_y, ref_x = torch.meshgrid(torch.linspace(0.5, H_ - 0.5, H_, dtype=torch.float32, device=device),
+                                          torch.linspace(0.5, W_ - 0.5, W_, dtype=torch.float32, device=device))
+            ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * H_)
+            ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * W_)
+            ref = torch.stack((ref_x, ref_y), -1)
+            reference_points_list.append(ref)
+        reference_points = torch.cat(reference_points_list, 1)
+        reference_points = reference_points[:, :, None] * valid_ratios[:, None]
+        return reference_points
+
+    def forward(self, src, spatial_shapes, level_start_index, valid_ratios, pos=None, padding_mask=None):
+        output = src
+        reference_points = self.get_reference_points(spatial_shapes, valid_ratios, device=src.device)
+        for _, layer in enumerate(self.layers):
+            output = layer(output, pos, reference_points, spatial_shapes, level_start_index, padding_mask)
+
+        return output
+
+
+class DeformableTransformerDecoderLayer(nn.Module):
+    def __init__(self, d_model=256, d_ffn=1024,
+                 dropout=0.1, activation="relu",
+                 n_levels=4, n_heads=8, n_points=4):
+        super().__init__()
+
+        # cross attention
+        self.cross_attn = MSDeformAttn(d_model, n_levels, n_heads, n_points)
+        self.dropout1 = nn.Dropout(dropout)
+        self.norm1 = nn.LayerNorm(d_model)
+
+        # self attention
+        self.self_attn = nn.MultiheadAttention(d_model, n_heads, dropout=dropout)
+        self.dropout2 = nn.Dropout(dropout)
+        self.norm2 = nn.LayerNorm(d_model)
+
+        # ffn
+        self.linear1 = nn.Linear(d_model, d_ffn)
+        self.activation = _get_activation_fn(activation)
+        self.dropout3 = nn.Dropout(dropout)
+        self.linear2 = nn.Linear(d_ffn, d_model)
+        self.dropout4 = nn.Dropout(dropout)
+        self.norm3 = nn.LayerNorm(d_model)
+
+    @staticmethod
+    def with_pos_embed(tensor, pos):
+        return tensor if pos is None else tensor + pos
+
+    def forward_ffn(self, tgt):
+        tgt2 = self.linear2(self.dropout3(self.activation(self.linear1(tgt))))
+        tgt = tgt + self.dropout4(tgt2)
+        tgt = self.norm3(tgt)
+        return tgt
+
+    def forward(self, tgt, query_pos, reference_points, src, src_spatial_shapes, level_start_index, src_padding_mask=None):
+        # self attention
+        q = k = self.with_pos_embed(tgt, query_pos)
+        tgt2 = self.self_attn(q.transpose(0, 1), k.transpose(0, 1), tgt.transpose(0, 1))[0].transpose(0, 1)
+        tgt = tgt + self.dropout2(tgt2)
+        tgt = self.norm2(tgt)
+
+        # cross attention
+        tgt2, sampling_locations, attention_weights = self.cross_attn(self.with_pos_embed(tgt, query_pos),
+                               reference_points,
+                               src, src_spatial_shapes, level_start_index, src_padding_mask)
+        tgt = tgt + self.dropout1(tgt2)
+        tgt = self.norm1(tgt)
+
+        # ffn
+        tgt = self.forward_ffn(tgt)
+
+        return tgt, sampling_locations, attention_weights
+
+
+
+class DeformableTransformerDecoder(nn.Module):
+    def __init__(self, decoder_layer, num_layers, return_intermediate=False):
+        super().__init__()
+        self.layers = _get_clones(decoder_layer, num_layers)
+        self.num_layers = num_layers
+        self.return_intermediate = return_intermediate
+        # hack implementation for iterative bounding box refinement and two-stage Deformable DETR
+        self.bbox_embed = None
+        self.class_embed = None
+
+    def forward(self, tgt, reference_points, src, src_spatial_shapes, src_level_start_index, src_valid_ratios,
+                query_pos=None, src_padding_mask=None):
+        # we modify here for get the information of sample points
+        output = tgt 
+
+        intermediate = []
+        intermediate_reference_points = []
+        intermediate_samples = [] # sample points
+        for lid, layer in enumerate(self.layers):
+            if reference_points.shape[-1] == 4:
+                reference_points_input = reference_points[:, :, None] \
+                                         * torch.cat([src_valid_ratios, src_valid_ratios], -1)[:, None]
+            else:
+                assert reference_points.shape[-1] == 2
+                reference_points_input = reference_points[:, :, None] * src_valid_ratios[:, None]
+            output, sampling_locations, attention_weights = layer(output, query_pos, reference_points_input, 
+                                                        src, src_spatial_shapes, src_level_start_index, src_padding_mask)
+
+            # sampling_loactions: [N, Len_q, self.n_heads, self.n_levels, self.n_points, 2], 
+            #                     [B, Q, n_head, n_level(num_feature_level*num_frames), n_points, 2]
+            # attention_weights: [B, Q, n_head, n_level(num_feature_level*num_frames), n_points]
+            # src_valid_ratios: [N, self.n_levels, 2]
+            N, Len_q = sampling_locations.shape[:2]
+            sampling_locations = sampling_locations / src_valid_ratios[:, None, None, :, None, :]
+            weights_flat = attention_weights.view(N, Len_q, -1)      # [B, Q, n_head * n_level * n_points]
+            samples_flat = sampling_locations.view(N, Len_q, -1, 2)  # [B, Q, n_head * n_level * n_points, 2]
+            top_weights, top_idx = weights_flat.topk(30, dim=2)      # [B, Q, 30], [B, Q, 30]
+            weights_keep = torch.gather(weights_flat, 2, top_idx)    # [B, Q, 30]
+            samples_keep = torch.gather(samples_flat, 2, top_idx.unsqueeze(-1).repeat(1, 1, 1, 2))  # [B, Q, 30, 2]
+
+            # hack implementation for iterative bounding box refinement
+            if self.bbox_embed is not None:
+                tmp = self.bbox_embed[lid](output)
+                if reference_points.shape[-1] == 4:
+                    new_reference_points = tmp + inverse_sigmoid(reference_points)
+                    new_reference_points = new_reference_points.sigmoid()
+                else:
+                    assert reference_points.shape[-1] == 2
+                    new_reference_points = tmp
+                    new_reference_points[..., :2] = tmp[..., :2] + inverse_sigmoid(reference_points)
+                    new_reference_points = new_reference_points.sigmoid()
+                reference_points = new_reference_points.detach()
+
+            if self.return_intermediate:
+                intermediate.append(output)
+                intermediate_reference_points.append(reference_points)
+                intermediate_samples.append(samples_keep)
+
+        if self.return_intermediate:
+            return torch.stack(intermediate), torch.stack(intermediate_reference_points), torch.stack(intermediate_samples)
+
+        return output, reference_points, samples_keep
+
+
+def _get_clones(module, N):
+    return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
+
+
+def _get_activation_fn(activation):
+    """Return an activation function given a string"""
+    if activation == "relu":
+        return F.relu
+    if activation == "gelu":
+        return F.gelu
+    if activation == "glu":
+        return F.glu
+    raise RuntimeError(F"activation should be relu/gelu, not {activation}.")
+
+
+def build_deforamble_transformer(args):
+    return DeformableTransformer(
+        d_model=args.hidden_dim,
+        nhead=args.nheads,
+        num_encoder_layers=args.enc_layers,
+        num_decoder_layers=args.dec_layers,
+        dim_feedforward=args.dim_feedforward,
+        dropout=args.dropout,
+        activation="relu",
+        return_intermediate_dec=True,
+        num_feature_levels=args.num_feature_levels,
+        dec_n_points=args.dec_n_points,
+        enc_n_points=args.enc_n_points,
+        two_stage=args.two_stage,
+        two_stage_num_proposals=args.num_queries)
diff --git a/models/matcher.py b/models/matcher.py
new file mode 100644
index 0000000000000000000000000000000000000000..5f65bd9b4dd7f9d078596890e0276855811988c6
--- /dev/null
+++ b/models/matcher.py
@@ -0,0 +1,206 @@
+"""
+Instance Sequence Matching
+Modified from DETR (https://github.com/facebookresearch/detr)
+"""
+import torch
+from scipy.optimize import linear_sum_assignment
+from torch import nn
+import torch.nn.functional as F
+
+from util.box_ops import box_cxcywh_to_xyxy, generalized_box_iou, multi_iou
+from util.misc import nested_tensor_from_tensor_list
+
+INF = 100000000
+
+def dice_coef(inputs, targets):
+    inputs = inputs.sigmoid()
+    inputs = inputs.flatten(1).unsqueeze(1) # [N, 1, THW]
+    targets = targets.flatten(1).unsqueeze(0) # [1, M, THW]
+    numerator = 2 * (inputs * targets).sum(2)
+    denominator = inputs.sum(-1) + targets.sum(-1)
+
+    # NOTE coef doesn't be subtracted to 1 as it is not necessary for computing costs
+    coef = (numerator + 1) / (denominator + 1)
+    return coef
+
+def sigmoid_focal_coef(inputs, targets, alpha: float = 0.25, gamma: float = 2):
+    N, M = len(inputs), len(targets)
+    inputs = inputs.flatten(1).unsqueeze(1).expand(-1, M, -1) # [N, M, THW]
+    targets = targets.flatten(1).unsqueeze(0).expand(N, -1, -1) # [N, M, THW]
+
+    prob = inputs.sigmoid()
+    ce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none")
+    p_t = prob * targets + (1 - prob) * (1 - targets)
+    coef = ce_loss * ((1 - p_t) ** gamma)
+
+    if alpha >= 0:
+        alpha_t = alpha * targets + (1 - alpha) * (1 - targets)
+        coef = alpha_t * coef
+
+    return coef.mean(2) # [N, M]
+
+
+class HungarianMatcher(nn.Module):
+    """This class computes an assignment between the targets and the predictions of the network
+
+    For efficiency reasons, the targets don't include the no_object. Because of this, in general,
+    there are more predictions than targets. In this case, we do a 1-to-1 matching of the best predictions,
+    while the others are un-matched (and thus treated as non-objects).
+    """
+
+    def __init__(self, cost_class: float = 1, cost_bbox: float = 1, cost_giou: float = 1,
+                       cost_mask: float = 1, cost_dice: float = 1, num_classes: int = 1):
+        """Creates the matcher
+
+        Params:
+            cost_class: This is the relative weight of the classification error in the matching cost
+            cost_bbox: This is the relative weight of the L1 error of the bounding box coordinates in the matching cost
+            cost_giou: This is the relative weight of the giou loss of the bounding box in the matching cost
+            cost_mask: This is the relative weight of the sigmoid focal loss of the mask in the matching cost
+            cost_dice: This is the relative weight of the dice loss of the mask in the matching cost
+        """
+        super().__init__()
+        self.cost_class = cost_class
+        self.cost_bbox = cost_bbox
+        self.cost_giou = cost_giou
+        self.cost_mask = cost_mask
+        self.cost_dice = cost_dice
+        self.num_classes = num_classes
+        assert cost_class != 0 or cost_bbox != 0 or cost_giou != 0 \
+            or cost_mask != 0 or cost_dice != 0, "all costs cant be 0"
+        self.mask_out_stride = 4
+
+    @torch.no_grad()
+    def forward(self, outputs, targets):
+        """ Performs the matching
+        Params:
+            outputs: This is a dict that contains at least these entries:
+                 "pred_logits": Tensor of dim [batch_size, num_queries_per_frame, num_frames, num_classes] with the classification logits
+                 "pred_boxes": Tensor of dim [batch_size, num_queries_per_frame, num_frames, 4] with the predicted box coordinates
+                 "pred_masks": Tensor of dim [batch_size, num_queries_per_frame, num_frames, h, w], h,w in 4x size
+            targets: This is a list of targets (len(targets) = batch_size), where each target is a dict containing:
+                 NOTE: Since every frame has one object at most
+                 "labels": Tensor of dim [num_frames] (where num_target_boxes is the number of ground-truth
+                           objects in the target) containing the class labels
+                 "boxes": Tensor of dim [num_frames, 4] containing the target box coordinates
+                 "masks": Tensor of dim [num_frames, h, w], h,w in origin size 
+        Returns:
+            A list of size batch_size, containing tuples of (index_i, index_j) where:
+                - index_i is the indices of the selected predictions (in order)
+                - index_j is the indices of the corresponding selected targets (in order)
+            For each batch element, it holds:
+                len(index_i) = len(index_j) = min(num_queries, num_target_boxes)
+        """
+        src_logits = outputs["pred_logits"] 
+        src_boxes = outputs["pred_boxes"]   
+        src_masks = outputs["pred_masks"]   
+
+        bs, nf, nq, h, w = src_masks.shape 
+
+        # handle mask padding issue
+        target_masks, valid = nested_tensor_from_tensor_list([t["masks"] for t in targets], 
+                                                             size_divisibility=32,
+                                                             split=False).decompose()
+        target_masks = target_masks.to(src_masks) # [B, T, H, W]
+
+        # downsample ground truth masks with ratio mask_out_stride
+        start = int(self.mask_out_stride // 2)
+        im_h, im_w = target_masks.shape[-2:]
+        
+        target_masks = target_masks[:, :, start::self.mask_out_stride, start::self.mask_out_stride] 
+        assert target_masks.size(2) * self.mask_out_stride == im_h
+        assert target_masks.size(3) * self.mask_out_stride == im_w
+
+        indices = []
+        for i in range(bs): 
+            out_prob = src_logits[i].sigmoid() 
+            out_bbox = src_boxes[i]            
+            out_mask = src_masks[i]            
+
+            tgt_ids = targets[i]["labels"]     
+            tgt_bbox = targets[i]["boxes"]     
+            tgt_mask = target_masks[i]         
+            tgt_valid = targets[i]["valid"]    
+
+            # class cost
+            # we average the cost on valid frames
+            cost_class = []
+            for t in range(nf):
+                if tgt_valid[t] == 0:
+                    continue
+
+                out_prob_split = out_prob[t]    
+                tgt_ids_split = tgt_ids[t].unsqueeze(0)     
+
+                # Compute the classification cost.
+                alpha = 0.25
+                gamma = 2.0
+                neg_cost_class = (1 - alpha) * (out_prob_split ** gamma) * (-(1 - out_prob_split + 1e-8).log())
+                pos_cost_class = alpha * ((1 - out_prob_split) ** gamma) * (-(out_prob_split + 1e-8).log())
+                if self.num_classes == 1:  # binary referred
+                    cost_class_split = pos_cost_class[:, [0]] - neg_cost_class[:, [0]]
+                else:
+                    cost_class_split = pos_cost_class[:, tgt_ids_split] - neg_cost_class[:, tgt_ids_split] 
+
+                cost_class.append(cost_class_split)
+            cost_class = torch.stack(cost_class, dim=0).mean(0)  # [q, 1]
+
+            # box cost
+            # we average the cost on every frame
+            cost_bbox, cost_giou = [], []
+            for t in range(nf):
+                out_bbox_split = out_bbox[t]    
+                tgt_bbox_split = tgt_bbox[t].unsqueeze(0)  
+
+                # Compute the L1 cost between boxes
+                cost_bbox_split = torch.cdist(out_bbox_split, tgt_bbox_split, p=1)  
+
+                # Compute the giou cost betwen boxes
+                cost_giou_split = -generalized_box_iou(box_cxcywh_to_xyxy(out_bbox_split),
+                                                box_cxcywh_to_xyxy(tgt_bbox_split))
+                
+                cost_bbox.append(cost_bbox_split)
+                cost_giou.append(cost_giou_split)
+            cost_bbox = torch.stack(cost_bbox, dim=0).mean(0)
+            cost_giou = torch.stack(cost_giou, dim=0).mean(0)
+
+            # mask cost
+            # Compute the focal loss between masks
+            cost_mask = sigmoid_focal_coef(out_mask.transpose(0, 1), tgt_mask.unsqueeze(0))
+
+            # Compute the dice loss betwen masks
+            cost_dice = -dice_coef(out_mask.transpose(0, 1), tgt_mask.unsqueeze(0))
+
+            # Final cost matrix
+            C = self.cost_class * cost_class + self.cost_bbox * cost_bbox + self.cost_giou * cost_giou + \
+                self.cost_mask * cost_mask + self.cost_dice * cost_dice  # [q, 1]
+
+            # Only has one tgt, MinCost Matcher
+            _, src_ind = torch.min(C, dim=0)
+            tgt_ind = torch.arange(1).to(src_ind)  
+            indices.append((src_ind.long(), tgt_ind.long()))
+            
+        # list[tuple], length is batch_size
+        return indices
+                
+
+def build_matcher(args):
+    if args.binary:
+        num_classes = 1
+    else:
+        if args.dataset_file == 'ytvos':
+            num_classes = 65 
+        elif args.dataset_file == 'davis':
+            num_classes = 78
+        elif args.dataset_file == 'a2d' or args.dataset_file == 'jhmdb':
+            num_classes = 1
+        else: 
+            num_classes = 91  # for coco
+    return HungarianMatcher(cost_class=args.set_cost_class, 
+                            cost_bbox=args.set_cost_bbox, 
+                            cost_giou=args.set_cost_giou,
+                            cost_mask=args.set_cost_mask,
+                            cost_dice=args.set_cost_dice,
+                            num_classes=num_classes)
+
+
diff --git a/models/ops/make.sh b/models/ops/make.sh
new file mode 100644
index 0000000000000000000000000000000000000000..106b685722bc6ed70a06bf04309e75e62f73a430
--- /dev/null
+++ b/models/ops/make.sh
@@ -0,0 +1,10 @@
+#!/usr/bin/env bash
+# ------------------------------------------------------------------------------------------------
+# Deformable DETR
+# Copyright (c) 2020 SenseTime. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------------------------------
+# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+# ------------------------------------------------------------------------------------------------
+
+python setup.py build install
diff --git a/models/ops/modules/__init__.py b/models/ops/modules/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..f82cb1ad9d634a87b54ba6a71b58a230bcade5fe
--- /dev/null
+++ b/models/ops/modules/__init__.py
@@ -0,0 +1,9 @@
+# ------------------------------------------------------------------------------------------------
+# Deformable DETR
+# Copyright (c) 2020 SenseTime. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------------------------------
+# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+# ------------------------------------------------------------------------------------------------
+
+from .ms_deform_attn import MSDeformAttn
diff --git a/models/ops/src/cpu/ms_deform_attn_cpu.cpp b/models/ops/src/cpu/ms_deform_attn_cpu.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..e1bf854de1f3860d20b6fef5c1a17817c268e70a
--- /dev/null
+++ b/models/ops/src/cpu/ms_deform_attn_cpu.cpp
@@ -0,0 +1,41 @@
+/*!
+**************************************************************************************************
+* Deformable DETR
+* Copyright (c) 2020 SenseTime. All Rights Reserved.
+* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+**************************************************************************************************
+* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+**************************************************************************************************
+*/
+
+#include <vector>
+
+#include <ATen/ATen.h>
+#include <ATen/cuda/CUDAContext.h>
+
+
+at::Tensor
+ms_deform_attn_cpu_forward(
+    const at::Tensor &value, 
+    const at::Tensor &spatial_shapes,
+    const at::Tensor &level_start_index,
+    const at::Tensor &sampling_loc,
+    const at::Tensor &attn_weight,
+    const int im2col_step)
+{
+    AT_ERROR("Not implement on cpu");
+}
+
+std::vector<at::Tensor>
+ms_deform_attn_cpu_backward(
+    const at::Tensor &value, 
+    const at::Tensor &spatial_shapes,
+    const at::Tensor &level_start_index,
+    const at::Tensor &sampling_loc,
+    const at::Tensor &attn_weight,
+    const at::Tensor &grad_output,
+    const int im2col_step)
+{
+    AT_ERROR("Not implement on cpu");
+}
+
diff --git a/models/ops/src/cuda/ms_deform_attn_cuda.cu b/models/ops/src/cuda/ms_deform_attn_cuda.cu
new file mode 100644
index 0000000000000000000000000000000000000000..d6d583647cce987196d5ad1968a8a365a379e774
--- /dev/null
+++ b/models/ops/src/cuda/ms_deform_attn_cuda.cu
@@ -0,0 +1,153 @@
+/*!
+**************************************************************************************************
+* Deformable DETR
+* Copyright (c) 2020 SenseTime. All Rights Reserved.
+* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+**************************************************************************************************
+* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+**************************************************************************************************
+*/
+
+#include <vector>
+#include "cuda/ms_deform_im2col_cuda.cuh"
+
+#include <ATen/ATen.h>
+#include <ATen/cuda/CUDAContext.h>
+#include <cuda.h>
+#include <cuda_runtime.h>
+
+
+at::Tensor ms_deform_attn_cuda_forward(
+    const at::Tensor &value, 
+    const at::Tensor &spatial_shapes,
+    const at::Tensor &level_start_index,
+    const at::Tensor &sampling_loc,
+    const at::Tensor &attn_weight,
+    const int im2col_step)
+{
+    AT_ASSERTM(value.is_contiguous(), "value tensor has to be contiguous");
+    AT_ASSERTM(spatial_shapes.is_contiguous(), "spatial_shapes tensor has to be contiguous");
+    AT_ASSERTM(level_start_index.is_contiguous(), "level_start_index tensor has to be contiguous");
+    AT_ASSERTM(sampling_loc.is_contiguous(), "sampling_loc tensor has to be contiguous");
+    AT_ASSERTM(attn_weight.is_contiguous(), "attn_weight tensor has to be contiguous");
+
+    AT_ASSERTM(value.type().is_cuda(), "value must be a CUDA tensor");
+    AT_ASSERTM(spatial_shapes.type().is_cuda(), "spatial_shapes must be a CUDA tensor");
+    AT_ASSERTM(level_start_index.type().is_cuda(), "level_start_index must be a CUDA tensor");
+    AT_ASSERTM(sampling_loc.type().is_cuda(), "sampling_loc must be a CUDA tensor");
+    AT_ASSERTM(attn_weight.type().is_cuda(), "attn_weight must be a CUDA tensor");
+
+    const int batch = value.size(0);
+    const int spatial_size = value.size(1);
+    const int num_heads = value.size(2);
+    const int channels = value.size(3);
+
+    const int num_levels = spatial_shapes.size(0);
+
+    const int num_query = sampling_loc.size(1);
+    const int num_point = sampling_loc.size(4);
+
+    const int im2col_step_ = std::min(batch, im2col_step);
+
+    AT_ASSERTM(batch % im2col_step_ == 0, "batch(%d) must divide im2col_step(%d)", batch, im2col_step_);
+    
+    auto output = at::zeros({batch, num_query, num_heads, channels}, value.options());
+
+    const int batch_n = im2col_step_;
+    auto output_n = output.view({batch/im2col_step_, batch_n, num_query, num_heads, channels});
+    auto per_value_size = spatial_size * num_heads * channels;
+    auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2;
+    auto per_attn_weight_size = num_query * num_heads * num_levels * num_point;
+    for (int n = 0; n < batch/im2col_step_; ++n)
+    {
+        auto columns = output_n.select(0, n);
+        AT_DISPATCH_FLOATING_TYPES(value.type(), "ms_deform_attn_forward_cuda", ([&] {
+            ms_deformable_im2col_cuda(at::cuda::getCurrentCUDAStream(),
+                value.data<scalar_t>() + n * im2col_step_ * per_value_size,
+                spatial_shapes.data<int64_t>(),
+                level_start_index.data<int64_t>(),
+                sampling_loc.data<scalar_t>() + n * im2col_step_ * per_sample_loc_size,
+                attn_weight.data<scalar_t>() + n * im2col_step_ * per_attn_weight_size,
+                batch_n, spatial_size, num_heads, channels, num_levels, num_query, num_point,
+                columns.data<scalar_t>());
+
+        }));
+    }
+
+    output = output.view({batch, num_query, num_heads*channels});
+
+    return output;
+}
+
+
+std::vector<at::Tensor> ms_deform_attn_cuda_backward(
+    const at::Tensor &value, 
+    const at::Tensor &spatial_shapes,
+    const at::Tensor &level_start_index,
+    const at::Tensor &sampling_loc,
+    const at::Tensor &attn_weight,
+    const at::Tensor &grad_output,
+    const int im2col_step)
+{
+
+    AT_ASSERTM(value.is_contiguous(), "value tensor has to be contiguous");
+    AT_ASSERTM(spatial_shapes.is_contiguous(), "spatial_shapes tensor has to be contiguous");
+    AT_ASSERTM(level_start_index.is_contiguous(), "level_start_index tensor has to be contiguous");
+    AT_ASSERTM(sampling_loc.is_contiguous(), "sampling_loc tensor has to be contiguous");
+    AT_ASSERTM(attn_weight.is_contiguous(), "attn_weight tensor has to be contiguous");
+    AT_ASSERTM(grad_output.is_contiguous(), "grad_output tensor has to be contiguous");
+
+    AT_ASSERTM(value.type().is_cuda(), "value must be a CUDA tensor");
+    AT_ASSERTM(spatial_shapes.type().is_cuda(), "spatial_shapes must be a CUDA tensor");
+    AT_ASSERTM(level_start_index.type().is_cuda(), "level_start_index must be a CUDA tensor");
+    AT_ASSERTM(sampling_loc.type().is_cuda(), "sampling_loc must be a CUDA tensor");
+    AT_ASSERTM(attn_weight.type().is_cuda(), "attn_weight must be a CUDA tensor");
+    AT_ASSERTM(grad_output.type().is_cuda(), "grad_output must be a CUDA tensor");
+
+    const int batch = value.size(0);
+    const int spatial_size = value.size(1);
+    const int num_heads = value.size(2);
+    const int channels = value.size(3);
+
+    const int num_levels = spatial_shapes.size(0);
+
+    const int num_query = sampling_loc.size(1);
+    const int num_point = sampling_loc.size(4);
+
+    const int im2col_step_ = std::min(batch, im2col_step);
+
+    AT_ASSERTM(batch % im2col_step_ == 0, "batch(%d) must divide im2col_step(%d)", batch, im2col_step_);
+
+    auto grad_value = at::zeros_like(value);
+    auto grad_sampling_loc = at::zeros_like(sampling_loc);
+    auto grad_attn_weight = at::zeros_like(attn_weight);
+
+    const int batch_n = im2col_step_;
+    auto per_value_size = spatial_size * num_heads * channels;
+    auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2;
+    auto per_attn_weight_size = num_query * num_heads * num_levels * num_point;
+    auto grad_output_n = grad_output.view({batch/im2col_step_, batch_n, num_query, num_heads, channels});
+    
+    for (int n = 0; n < batch/im2col_step_; ++n)
+    {
+        auto grad_output_g = grad_output_n.select(0, n);
+        AT_DISPATCH_FLOATING_TYPES(value.type(), "ms_deform_attn_backward_cuda", ([&] {
+            ms_deformable_col2im_cuda(at::cuda::getCurrentCUDAStream(),
+                                    grad_output_g.data<scalar_t>(),
+                                    value.data<scalar_t>() + n * im2col_step_ * per_value_size,
+                                    spatial_shapes.data<int64_t>(),
+                                    level_start_index.data<int64_t>(),
+                                    sampling_loc.data<scalar_t>() + n * im2col_step_ * per_sample_loc_size,
+                                    attn_weight.data<scalar_t>() + n * im2col_step_ * per_attn_weight_size,
+                                    batch_n, spatial_size, num_heads, channels, num_levels, num_query, num_point,
+                                    grad_value.data<scalar_t>() +  n * im2col_step_ * per_value_size,
+                                    grad_sampling_loc.data<scalar_t>() + n * im2col_step_ * per_sample_loc_size,
+                                    grad_attn_weight.data<scalar_t>() + n * im2col_step_ * per_attn_weight_size);
+
+        }));
+    }
+
+    return {
+        grad_value, grad_sampling_loc, grad_attn_weight
+    };
+}
\ No newline at end of file
diff --git a/models/ops/src/cuda/ms_deform_im2col_cuda.cuh b/models/ops/src/cuda/ms_deform_im2col_cuda.cuh
new file mode 100644
index 0000000000000000000000000000000000000000..6bc2acb7aea0eab2e9e91e769a16861e1652c284
--- /dev/null
+++ b/models/ops/src/cuda/ms_deform_im2col_cuda.cuh
@@ -0,0 +1,1327 @@
+/*!
+**************************************************************************
+* Deformable DETR
+* Copyright (c) 2020 SenseTime. All Rights Reserved.
+* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+**************************************************************************
+* Modified from DCN (https://github.com/msracver/Deformable-ConvNets)
+* Copyright (c) 2018 Microsoft
+**************************************************************************
+*/
+
+#include <cstdio>
+#include <algorithm>
+#include <cstring>
+
+#include <ATen/ATen.h>
+#include <ATen/cuda/CUDAContext.h>
+
+#include <THC/THCAtomics.cuh>
+
+#define CUDA_KERNEL_LOOP(i, n)                          \
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x;   \
+      i < (n);                                          \
+      i += blockDim.x * gridDim.x)
+
+const int CUDA_NUM_THREADS = 1024;
+inline int GET_BLOCKS(const int N, const int num_threads)
+{
+  return (N + num_threads - 1) / num_threads;
+}
+
+
+template <typename scalar_t>
+__device__ scalar_t ms_deform_attn_im2col_bilinear(const scalar_t* &bottom_data, 
+                                                   const int &height, const int &width, const int &nheads, const int &channels,
+                                                   const scalar_t &h, const scalar_t &w, const int &m, const int &c)
+{
+  const int h_low = floor(h);
+  const int w_low = floor(w);
+  const int h_high = h_low + 1;
+  const int w_high = w_low + 1;
+
+  const scalar_t lh = h - h_low;
+  const scalar_t lw = w - w_low;
+  const scalar_t hh = 1 - lh, hw = 1 - lw;
+
+  const int w_stride = nheads * channels;
+  const int h_stride = width * w_stride;
+  const int h_low_ptr_offset = h_low * h_stride;
+  const int h_high_ptr_offset = h_low_ptr_offset + h_stride;
+  const int w_low_ptr_offset = w_low * w_stride;
+  const int w_high_ptr_offset = w_low_ptr_offset + w_stride;
+  const int base_ptr = m * channels + c;
+
+  scalar_t v1 = 0;
+  if (h_low >= 0 && w_low >= 0)
+  {
+    const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr;
+    v1 = bottom_data[ptr1];
+  }
+  scalar_t v2 = 0;
+  if (h_low >= 0 && w_high <= width - 1)
+  {
+    const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr;
+    v2 = bottom_data[ptr2];
+  }
+  scalar_t v3 = 0;
+  if (h_high <= height - 1 && w_low >= 0)
+  {
+    const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr;
+    v3 = bottom_data[ptr3];
+  }
+  scalar_t v4 = 0;
+  if (h_high <= height - 1 && w_high <= width - 1)
+  {
+    const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr;
+    v4 = bottom_data[ptr4];
+  }
+
+  const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw;
+
+  const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);
+  return val;
+}
+
+
+template <typename scalar_t>
+__device__ void ms_deform_attn_col2im_bilinear(const scalar_t* &bottom_data, 
+                                                   const int &height, const int &width, const int &nheads, const int &channels,
+                                                   const scalar_t &h, const scalar_t &w, const int &m, const int &c,
+                                                   const scalar_t &top_grad,
+                                                   const scalar_t &attn_weight,
+                                                   scalar_t* &grad_value, 
+                                                   scalar_t* grad_sampling_loc,
+                                                   scalar_t* grad_attn_weight)
+{
+  const int h_low = floor(h);
+  const int w_low = floor(w);
+  const int h_high = h_low + 1;
+  const int w_high = w_low + 1;
+
+  const scalar_t lh = h - h_low;
+  const scalar_t lw = w - w_low;
+  const scalar_t hh = 1 - lh, hw = 1 - lw;
+
+  const int w_stride = nheads * channels;
+  const int h_stride = width * w_stride;
+  const int h_low_ptr_offset = h_low * h_stride;
+  const int h_high_ptr_offset = h_low_ptr_offset + h_stride;
+  const int w_low_ptr_offset = w_low * w_stride;
+  const int w_high_ptr_offset = w_low_ptr_offset + w_stride;
+  const int base_ptr = m * channels + c;
+
+  const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw;
+  const scalar_t top_grad_value = top_grad * attn_weight;
+  scalar_t grad_h_weight = 0, grad_w_weight = 0;
+
+  scalar_t v1 = 0;
+  if (h_low >= 0 && w_low >= 0)
+  {
+    const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr;
+    v1 = bottom_data[ptr1];
+    grad_h_weight -= hw * v1;
+    grad_w_weight -= hh * v1;
+    atomicAdd(grad_value+ptr1, w1*top_grad_value);
+  }
+  scalar_t v2 = 0;
+  if (h_low >= 0 && w_high <= width - 1)
+  {
+    const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr;
+    v2 = bottom_data[ptr2];
+    grad_h_weight -= lw * v2;
+    grad_w_weight += hh * v2;
+    atomicAdd(grad_value+ptr2, w2*top_grad_value);
+  }
+  scalar_t v3 = 0;
+  if (h_high <= height - 1 && w_low >= 0)
+  {
+    const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr;
+    v3 = bottom_data[ptr3];
+    grad_h_weight += hw * v3;
+    grad_w_weight -= lh * v3;
+    atomicAdd(grad_value+ptr3, w3*top_grad_value); 
+  }
+  scalar_t v4 = 0;
+  if (h_high <= height - 1 && w_high <= width - 1)
+  {
+    const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr;
+    v4 = bottom_data[ptr4];
+    grad_h_weight += lw * v4;
+    grad_w_weight += lh * v4;
+    atomicAdd(grad_value+ptr4, w4*top_grad_value);
+  }
+
+  const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);
+  *grad_attn_weight = top_grad * val;
+  *grad_sampling_loc = width * grad_w_weight * top_grad_value;
+  *(grad_sampling_loc + 1) = height * grad_h_weight * top_grad_value;
+}
+
+
+template <typename scalar_t>
+__device__ void ms_deform_attn_col2im_bilinear_gm(const scalar_t* &bottom_data, 
+                                                   const int &height, const int &width, const int &nheads, const int &channels,
+                                                   const scalar_t &h, const scalar_t &w, const int &m, const int &c,
+                                                   const scalar_t &top_grad,
+                                                   const scalar_t &attn_weight,
+                                                   scalar_t* &grad_value, 
+                                                   scalar_t* grad_sampling_loc,
+                                                   scalar_t* grad_attn_weight)
+{
+  const int h_low = floor(h);
+  const int w_low = floor(w);
+  const int h_high = h_low + 1;
+  const int w_high = w_low + 1;
+
+  const scalar_t lh = h - h_low;
+  const scalar_t lw = w - w_low;
+  const scalar_t hh = 1 - lh, hw = 1 - lw;
+
+  const int w_stride = nheads * channels;
+  const int h_stride = width * w_stride;
+  const int h_low_ptr_offset = h_low * h_stride;
+  const int h_high_ptr_offset = h_low_ptr_offset + h_stride;
+  const int w_low_ptr_offset = w_low * w_stride;
+  const int w_high_ptr_offset = w_low_ptr_offset + w_stride;
+  const int base_ptr = m * channels + c;
+
+  const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw;
+  const scalar_t top_grad_value = top_grad * attn_weight;
+  scalar_t grad_h_weight = 0, grad_w_weight = 0;
+
+  scalar_t v1 = 0;
+  if (h_low >= 0 && w_low >= 0)
+  {
+    const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr;
+    v1 = bottom_data[ptr1];
+    grad_h_weight -= hw * v1;
+    grad_w_weight -= hh * v1;
+    atomicAdd(grad_value+ptr1, w1*top_grad_value);
+  }
+  scalar_t v2 = 0;
+  if (h_low >= 0 && w_high <= width - 1)
+  {
+    const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr;
+    v2 = bottom_data[ptr2];
+    grad_h_weight -= lw * v2;
+    grad_w_weight += hh * v2;
+    atomicAdd(grad_value+ptr2, w2*top_grad_value);
+  }
+  scalar_t v3 = 0;
+  if (h_high <= height - 1 && w_low >= 0)
+  {
+    const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr;
+    v3 = bottom_data[ptr3];
+    grad_h_weight += hw * v3;
+    grad_w_weight -= lh * v3;
+    atomicAdd(grad_value+ptr3, w3*top_grad_value); 
+  }
+  scalar_t v4 = 0;
+  if (h_high <= height - 1 && w_high <= width - 1)
+  {
+    const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr;
+    v4 = bottom_data[ptr4];
+    grad_h_weight += lw * v4;
+    grad_w_weight += lh * v4;
+    atomicAdd(grad_value+ptr4, w4*top_grad_value);
+  }
+
+  const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);
+  atomicAdd(grad_attn_weight, top_grad * val); 
+  atomicAdd(grad_sampling_loc, width * grad_w_weight * top_grad_value);
+  atomicAdd(grad_sampling_loc + 1, height * grad_h_weight * top_grad_value);
+}
+
+
+template <typename scalar_t>
+__global__ void ms_deformable_im2col_gpu_kernel(const int n,
+                                                const scalar_t *data_value, 
+                                                const int64_t *data_spatial_shapes,
+                                                const int64_t *data_level_start_index, 
+                                                const scalar_t *data_sampling_loc,
+                                                const scalar_t *data_attn_weight,
+                                                const int batch_size, 
+                                                const int spatial_size, 
+                                                const int num_heads,
+                                                const int channels, 
+                                                const int num_levels,
+                                                const int num_query,
+                                                const int num_point,
+                                                scalar_t *data_col)
+{
+  CUDA_KERNEL_LOOP(index, n)
+  {
+    int _temp = index;
+    const int c_col = _temp % channels;
+    _temp /= channels;
+    const int sampling_index = _temp; 
+    const int m_col = _temp % num_heads;
+    _temp /= num_heads;
+    const int q_col = _temp % num_query;
+    _temp /= num_query;
+    const int b_col = _temp;
+
+    scalar_t *data_col_ptr = data_col + index;
+    int data_weight_ptr = sampling_index * num_levels * num_point;
+    int data_loc_w_ptr = data_weight_ptr << 1;
+    const int qid_stride = num_heads * channels;
+    const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
+    scalar_t col = 0;
+    
+    for (int l_col=0; l_col < num_levels; ++l_col)
+    {
+      const int level_start_id = data_level_start_index[l_col];
+      const int spatial_h_ptr = l_col << 1;
+      const int spatial_h = data_spatial_shapes[spatial_h_ptr];
+      const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
+      const scalar_t *data_value_ptr = data_value + (data_value_ptr_init_offset + level_start_id * qid_stride);
+      for (int p_col=0; p_col < num_point; ++p_col)
+      {
+        const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
+        const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
+        const scalar_t weight = data_attn_weight[data_weight_ptr];
+
+        const scalar_t h_im = loc_h * spatial_h - 0.5;
+        const scalar_t w_im = loc_w * spatial_w - 0.5;
+
+        if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)
+        {
+          col += ms_deform_attn_im2col_bilinear(data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col) * weight;
+        }
+
+        data_weight_ptr += 1;
+        data_loc_w_ptr += 2;
+      }
+    }
+    *data_col_ptr = col;
+  }
+}
+
+template <typename scalar_t, unsigned int blockSize>
+__global__ void ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1(const int n,
+                                                const scalar_t *grad_col,
+                                                const scalar_t *data_value,
+                                                const int64_t *data_spatial_shapes,
+                                                const int64_t *data_level_start_index, 
+                                                const scalar_t *data_sampling_loc,
+                                                const scalar_t *data_attn_weight,
+                                                const int batch_size, 
+                                                const int spatial_size, 
+                                                const int num_heads,
+                                                const int channels, 
+                                                const int num_levels,
+                                                const int num_query,
+                                                const int num_point,
+                                                scalar_t *grad_value,
+                                                scalar_t *grad_sampling_loc,
+                                                scalar_t *grad_attn_weight)
+{
+  CUDA_KERNEL_LOOP(index, n)
+  {
+    __shared__ scalar_t cache_grad_sampling_loc[blockSize * 2];
+    __shared__ scalar_t cache_grad_attn_weight[blockSize];
+    unsigned int tid = threadIdx.x;
+    int _temp = index;
+    const int c_col = _temp % channels;
+    _temp /= channels;
+    const int sampling_index = _temp; 
+    const int m_col = _temp % num_heads;
+    _temp /= num_heads;
+    const int q_col = _temp % num_query;
+    _temp /= num_query;
+    const int b_col = _temp;
+
+    const scalar_t top_grad = grad_col[index];
+
+    int data_weight_ptr = sampling_index * num_levels * num_point;
+    int data_loc_w_ptr = data_weight_ptr << 1;
+    const int grad_sampling_ptr = data_weight_ptr;
+    grad_sampling_loc += grad_sampling_ptr << 1;
+    grad_attn_weight += grad_sampling_ptr;
+    const int grad_weight_stride = 1;
+    const int grad_loc_stride = 2;
+    const int qid_stride = num_heads * channels;
+    const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
+
+    for (int l_col=0; l_col < num_levels; ++l_col)
+    {
+      const int level_start_id = data_level_start_index[l_col];
+      const int spatial_h_ptr = l_col << 1;
+      const int spatial_h = data_spatial_shapes[spatial_h_ptr];
+      const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
+      const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;
+      const scalar_t *data_value_ptr = data_value + value_ptr_offset;
+      scalar_t *grad_value_ptr = grad_value + value_ptr_offset;
+
+      for (int p_col=0; p_col < num_point; ++p_col)
+      {
+        const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
+        const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
+        const scalar_t weight = data_attn_weight[data_weight_ptr];
+
+        const scalar_t h_im = loc_h * spatial_h - 0.5;
+        const scalar_t w_im = loc_w * spatial_w - 0.5;
+        *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0;
+        *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0;
+        *(cache_grad_attn_weight+threadIdx.x)=0;
+        if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)
+        {
+          ms_deform_attn_col2im_bilinear(
+            data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,
+            top_grad, weight, grad_value_ptr, 
+            cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x);
+        }
+        
+        __syncthreads();
+        if (tid == 0)
+        {
+          scalar_t _grad_w=cache_grad_sampling_loc[0], _grad_h=cache_grad_sampling_loc[1], _grad_a=cache_grad_attn_weight[0];
+          int sid=2;
+          for (unsigned int tid = 1; tid < blockSize; ++tid)
+          {
+            _grad_w += cache_grad_sampling_loc[sid];
+            _grad_h += cache_grad_sampling_loc[sid + 1];
+            _grad_a += cache_grad_attn_weight[tid];
+            sid += 2;
+          }
+          
+          
+          *grad_sampling_loc = _grad_w;
+          *(grad_sampling_loc + 1) = _grad_h;
+          *grad_attn_weight = _grad_a;
+        }
+        __syncthreads();
+
+        data_weight_ptr += 1;
+        data_loc_w_ptr += 2;
+        grad_attn_weight += grad_weight_stride;
+        grad_sampling_loc += grad_loc_stride;
+      }
+    }
+  }
+}
+
+
+template <typename scalar_t, unsigned int blockSize>
+__global__ void ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2(const int n,
+                                                const scalar_t *grad_col,
+                                                const scalar_t *data_value,
+                                                const int64_t *data_spatial_shapes,
+                                                const int64_t *data_level_start_index, 
+                                                const scalar_t *data_sampling_loc,
+                                                const scalar_t *data_attn_weight,
+                                                const int batch_size, 
+                                                const int spatial_size, 
+                                                const int num_heads,
+                                                const int channels, 
+                                                const int num_levels,
+                                                const int num_query,
+                                                const int num_point,
+                                                scalar_t *grad_value,
+                                                scalar_t *grad_sampling_loc,
+                                                scalar_t *grad_attn_weight)
+{
+  CUDA_KERNEL_LOOP(index, n)
+  {
+    __shared__ scalar_t cache_grad_sampling_loc[blockSize * 2];
+    __shared__ scalar_t cache_grad_attn_weight[blockSize];
+    unsigned int tid = threadIdx.x;
+    int _temp = index;
+    const int c_col = _temp % channels;
+    _temp /= channels;
+    const int sampling_index = _temp; 
+    const int m_col = _temp % num_heads;
+    _temp /= num_heads;
+    const int q_col = _temp % num_query;
+    _temp /= num_query;
+    const int b_col = _temp;
+
+    const scalar_t top_grad = grad_col[index];
+
+    int data_weight_ptr = sampling_index * num_levels * num_point;
+    int data_loc_w_ptr = data_weight_ptr << 1;
+    const int grad_sampling_ptr = data_weight_ptr;
+    grad_sampling_loc += grad_sampling_ptr << 1;
+    grad_attn_weight += grad_sampling_ptr;
+    const int grad_weight_stride = 1;
+    const int grad_loc_stride = 2;
+    const int qid_stride = num_heads * channels;
+    const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
+
+    for (int l_col=0; l_col < num_levels; ++l_col)
+    {
+      const int level_start_id = data_level_start_index[l_col];
+      const int spatial_h_ptr = l_col << 1;
+      const int spatial_h = data_spatial_shapes[spatial_h_ptr];
+      const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
+      const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;
+      const scalar_t *data_value_ptr = data_value + value_ptr_offset;
+      scalar_t *grad_value_ptr = grad_value + value_ptr_offset;
+
+      for (int p_col=0; p_col < num_point; ++p_col)
+      {
+        const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
+        const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
+        const scalar_t weight = data_attn_weight[data_weight_ptr];
+
+        const scalar_t h_im = loc_h * spatial_h - 0.5;
+        const scalar_t w_im = loc_w * spatial_w - 0.5;
+        *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0;
+        *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0;
+        *(cache_grad_attn_weight+threadIdx.x)=0;
+        if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)
+        {
+          ms_deform_attn_col2im_bilinear(
+            data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,
+            top_grad, weight, grad_value_ptr, 
+            cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x);
+        }
+        
+        __syncthreads();
+
+        for (unsigned int s=blockSize/2; s>0; s>>=1)
+        {
+          if (tid < s) {
+            const unsigned int xid1 = tid << 1;
+            const unsigned int xid2 = (tid + s) << 1;
+            cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s];
+            cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2];
+            cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1];
+          }
+          __syncthreads();
+        }
+
+        if (tid == 0)
+        { 
+          *grad_sampling_loc = cache_grad_sampling_loc[0];
+          *(grad_sampling_loc + 1) = cache_grad_sampling_loc[1];
+          *grad_attn_weight = cache_grad_attn_weight[0];
+        }
+        __syncthreads();
+
+        data_weight_ptr += 1;
+        data_loc_w_ptr += 2;
+        grad_attn_weight += grad_weight_stride;
+        grad_sampling_loc += grad_loc_stride;
+      }
+    }
+  }
+}
+
+
+template <typename scalar_t>
+__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v1(const int n,
+                                                const scalar_t *grad_col,
+                                                const scalar_t *data_value,
+                                                const int64_t *data_spatial_shapes,
+                                                const int64_t *data_level_start_index, 
+                                                const scalar_t *data_sampling_loc,
+                                                const scalar_t *data_attn_weight,
+                                                const int batch_size, 
+                                                const int spatial_size, 
+                                                const int num_heads,
+                                                const int channels, 
+                                                const int num_levels,
+                                                const int num_query,
+                                                const int num_point,
+                                                scalar_t *grad_value,
+                                                scalar_t *grad_sampling_loc,
+                                                scalar_t *grad_attn_weight)
+{
+  CUDA_KERNEL_LOOP(index, n)
+  {
+    extern __shared__ int _s[];
+    scalar_t* cache_grad_sampling_loc = (scalar_t*)_s;
+    scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x;
+    unsigned int tid = threadIdx.x;
+    int _temp = index;
+    const int c_col = _temp % channels;
+    _temp /= channels;
+    const int sampling_index = _temp; 
+    const int m_col = _temp % num_heads;
+    _temp /= num_heads;
+    const int q_col = _temp % num_query;
+    _temp /= num_query;
+    const int b_col = _temp;
+
+    const scalar_t top_grad = grad_col[index];
+
+    int data_weight_ptr = sampling_index * num_levels * num_point;
+    int data_loc_w_ptr = data_weight_ptr << 1;
+    const int grad_sampling_ptr = data_weight_ptr;
+    grad_sampling_loc += grad_sampling_ptr << 1;
+    grad_attn_weight += grad_sampling_ptr;
+    const int grad_weight_stride = 1;
+    const int grad_loc_stride = 2;
+    const int qid_stride = num_heads * channels;
+    const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
+
+    for (int l_col=0; l_col < num_levels; ++l_col)
+    {
+      const int level_start_id = data_level_start_index[l_col];
+      const int spatial_h_ptr = l_col << 1;
+      const int spatial_h = data_spatial_shapes[spatial_h_ptr];
+      const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
+      const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;
+      const scalar_t *data_value_ptr = data_value + value_ptr_offset;
+      scalar_t *grad_value_ptr = grad_value + value_ptr_offset;
+
+      for (int p_col=0; p_col < num_point; ++p_col)
+      {
+        const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
+        const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
+        const scalar_t weight = data_attn_weight[data_weight_ptr];
+
+        const scalar_t h_im = loc_h * spatial_h - 0.5;
+        const scalar_t w_im = loc_w * spatial_w - 0.5;
+        *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0;
+        *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0;
+        *(cache_grad_attn_weight+threadIdx.x)=0;
+        if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)
+        {
+          ms_deform_attn_col2im_bilinear(
+            data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,
+            top_grad, weight, grad_value_ptr, 
+            cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x);
+        }
+        
+        __syncthreads();
+        if (tid == 0)
+        {
+          scalar_t _grad_w=cache_grad_sampling_loc[0], _grad_h=cache_grad_sampling_loc[1], _grad_a=cache_grad_attn_weight[0];
+          int sid=2;
+          for (unsigned int tid = 1; tid < blockDim.x; ++tid)
+          {
+            _grad_w += cache_grad_sampling_loc[sid];
+            _grad_h += cache_grad_sampling_loc[sid + 1];
+            _grad_a += cache_grad_attn_weight[tid];
+            sid += 2;
+          }
+          
+          
+          *grad_sampling_loc = _grad_w;
+          *(grad_sampling_loc + 1) = _grad_h;
+          *grad_attn_weight = _grad_a;
+        }
+        __syncthreads();
+
+        data_weight_ptr += 1;
+        data_loc_w_ptr += 2;
+        grad_attn_weight += grad_weight_stride;
+        grad_sampling_loc += grad_loc_stride;
+      }
+    }
+  }
+}
+
+template <typename scalar_t>
+__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v2(const int n,
+                                                const scalar_t *grad_col,
+                                                const scalar_t *data_value,
+                                                const int64_t *data_spatial_shapes,
+                                                const int64_t *data_level_start_index, 
+                                                const scalar_t *data_sampling_loc,
+                                                const scalar_t *data_attn_weight,
+                                                const int batch_size, 
+                                                const int spatial_size, 
+                                                const int num_heads,
+                                                const int channels, 
+                                                const int num_levels,
+                                                const int num_query,
+                                                const int num_point,
+                                                scalar_t *grad_value,
+                                                scalar_t *grad_sampling_loc,
+                                                scalar_t *grad_attn_weight)
+{
+  CUDA_KERNEL_LOOP(index, n)
+  {
+    extern __shared__ int _s[];
+    scalar_t* cache_grad_sampling_loc = (scalar_t*)_s;
+    scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x;
+    unsigned int tid = threadIdx.x;
+    int _temp = index;
+    const int c_col = _temp % channels;
+    _temp /= channels;
+    const int sampling_index = _temp; 
+    const int m_col = _temp % num_heads;
+    _temp /= num_heads;
+    const int q_col = _temp % num_query;
+    _temp /= num_query;
+    const int b_col = _temp;
+
+    const scalar_t top_grad = grad_col[index];
+
+    int data_weight_ptr = sampling_index * num_levels * num_point;
+    int data_loc_w_ptr = data_weight_ptr << 1;
+    const int grad_sampling_ptr = data_weight_ptr;
+    grad_sampling_loc += grad_sampling_ptr << 1;
+    grad_attn_weight += grad_sampling_ptr;
+    const int grad_weight_stride = 1;
+    const int grad_loc_stride = 2;
+    const int qid_stride = num_heads * channels;
+    const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
+
+    for (int l_col=0; l_col < num_levels; ++l_col)
+    {
+      const int level_start_id = data_level_start_index[l_col];
+      const int spatial_h_ptr = l_col << 1;
+      const int spatial_h = data_spatial_shapes[spatial_h_ptr];
+      const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
+      const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;
+      const scalar_t *data_value_ptr = data_value + value_ptr_offset;
+      scalar_t *grad_value_ptr = grad_value + value_ptr_offset;
+
+      for (int p_col=0; p_col < num_point; ++p_col)
+      {
+        const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
+        const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
+        const scalar_t weight = data_attn_weight[data_weight_ptr];
+
+        const scalar_t h_im = loc_h * spatial_h - 0.5;
+        const scalar_t w_im = loc_w * spatial_w - 0.5;
+        *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0;
+        *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0;
+        *(cache_grad_attn_weight+threadIdx.x)=0;
+        if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)
+        {
+          ms_deform_attn_col2im_bilinear(
+            data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,
+            top_grad, weight, grad_value_ptr, 
+            cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x);
+        }
+        
+        __syncthreads();
+
+        for (unsigned int s=blockDim.x/2, spre=blockDim.x; s>0; s>>=1, spre>>=1)
+        {
+          if (tid < s) {
+            const unsigned int xid1 = tid << 1;
+            const unsigned int xid2 = (tid + s) << 1;
+            cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s];
+            cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2];
+            cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1];
+            if (tid + (s << 1) < spre)
+            {
+              cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + (s << 1)];
+              cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2 + (s << 1)];
+              cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1 + (s << 1)];
+            } 
+          }
+          __syncthreads();
+        }
+
+        if (tid == 0)
+        {
+          *grad_sampling_loc = cache_grad_sampling_loc[0];
+          *(grad_sampling_loc + 1) = cache_grad_sampling_loc[1];
+          *grad_attn_weight = cache_grad_attn_weight[0];
+        }
+        __syncthreads();
+
+        data_weight_ptr += 1;
+        data_loc_w_ptr += 2;
+        grad_attn_weight += grad_weight_stride;
+        grad_sampling_loc += grad_loc_stride;
+      }
+    }
+  }
+}
+
+template <typename scalar_t>
+__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v2_multi_blocks(const int n,
+                                                const scalar_t *grad_col,
+                                                const scalar_t *data_value,
+                                                const int64_t *data_spatial_shapes,
+                                                const int64_t *data_level_start_index, 
+                                                const scalar_t *data_sampling_loc,
+                                                const scalar_t *data_attn_weight,
+                                                const int batch_size, 
+                                                const int spatial_size, 
+                                                const int num_heads,
+                                                const int channels, 
+                                                const int num_levels,
+                                                const int num_query,
+                                                const int num_point,
+                                                scalar_t *grad_value,
+                                                scalar_t *grad_sampling_loc,
+                                                scalar_t *grad_attn_weight)
+{
+  CUDA_KERNEL_LOOP(index, n)
+  {
+    extern __shared__ int _s[];
+    scalar_t* cache_grad_sampling_loc = (scalar_t*)_s;
+    scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x;
+    unsigned int tid = threadIdx.x;
+    int _temp = index;
+    const int c_col = _temp % channels;
+    _temp /= channels;
+    const int sampling_index = _temp; 
+    const int m_col = _temp % num_heads;
+    _temp /= num_heads;
+    const int q_col = _temp % num_query;
+    _temp /= num_query;
+    const int b_col = _temp;
+
+    const scalar_t top_grad = grad_col[index];
+
+    int data_weight_ptr = sampling_index * num_levels * num_point;
+    int data_loc_w_ptr = data_weight_ptr << 1;
+    const int grad_sampling_ptr = data_weight_ptr;
+    grad_sampling_loc += grad_sampling_ptr << 1;
+    grad_attn_weight += grad_sampling_ptr;
+    const int grad_weight_stride = 1;
+    const int grad_loc_stride = 2;
+    const int qid_stride = num_heads * channels;
+    const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
+
+    for (int l_col=0; l_col < num_levels; ++l_col)
+    {
+      const int level_start_id = data_level_start_index[l_col];
+      const int spatial_h_ptr = l_col << 1;
+      const int spatial_h = data_spatial_shapes[spatial_h_ptr];
+      const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
+      const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;
+      const scalar_t *data_value_ptr = data_value + value_ptr_offset;
+      scalar_t *grad_value_ptr = grad_value + value_ptr_offset;
+
+      for (int p_col=0; p_col < num_point; ++p_col)
+      {
+        const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
+        const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
+        const scalar_t weight = data_attn_weight[data_weight_ptr];
+
+        const scalar_t h_im = loc_h * spatial_h - 0.5;
+        const scalar_t w_im = loc_w * spatial_w - 0.5;
+        *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0;
+        *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0;
+        *(cache_grad_attn_weight+threadIdx.x)=0;
+        if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)
+        {
+          ms_deform_attn_col2im_bilinear(
+            data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,
+            top_grad, weight, grad_value_ptr, 
+            cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x);
+        }
+        
+        __syncthreads();
+
+        for (unsigned int s=blockDim.x/2, spre=blockDim.x; s>0; s>>=1, spre>>=1)
+        {
+          if (tid < s) {
+            const unsigned int xid1 = tid << 1;
+            const unsigned int xid2 = (tid + s) << 1;
+            cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s];
+            cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2];
+            cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1];
+            if (tid + (s << 1) < spre)
+            {
+              cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + (s << 1)];
+              cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2 + (s << 1)];
+              cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1 + (s << 1)];
+            }
+          }
+          __syncthreads();
+        }
+
+        if (tid == 0)
+        {
+          atomicAdd(grad_sampling_loc, cache_grad_sampling_loc[0]);
+          atomicAdd(grad_sampling_loc + 1, cache_grad_sampling_loc[1]);
+          atomicAdd(grad_attn_weight, cache_grad_attn_weight[0]);
+        }
+        __syncthreads();
+
+        data_weight_ptr += 1;
+        data_loc_w_ptr += 2;
+        grad_attn_weight += grad_weight_stride;
+        grad_sampling_loc += grad_loc_stride;
+      }
+    }
+  }
+}
+
+
+template <typename scalar_t>
+__global__ void ms_deformable_col2im_gpu_kernel_gm(const int n,
+                                                const scalar_t *grad_col,
+                                                const scalar_t *data_value,
+                                                const int64_t *data_spatial_shapes,
+                                                const int64_t *data_level_start_index, 
+                                                const scalar_t *data_sampling_loc,
+                                                const scalar_t *data_attn_weight,
+                                                const int batch_size, 
+                                                const int spatial_size, 
+                                                const int num_heads,
+                                                const int channels, 
+                                                const int num_levels,
+                                                const int num_query,
+                                                const int num_point,
+                                                scalar_t *grad_value,
+                                                scalar_t *grad_sampling_loc,
+                                                scalar_t *grad_attn_weight)
+{
+  CUDA_KERNEL_LOOP(index, n)
+  {
+    int _temp = index;
+    const int c_col = _temp % channels;
+    _temp /= channels;
+    const int sampling_index = _temp; 
+    const int m_col = _temp % num_heads;
+    _temp /= num_heads;
+    const int q_col = _temp % num_query;
+    _temp /= num_query;
+    const int b_col = _temp;
+
+    const scalar_t top_grad = grad_col[index];
+
+    int data_weight_ptr = sampling_index * num_levels * num_point;
+    int data_loc_w_ptr = data_weight_ptr << 1;
+    const int grad_sampling_ptr = data_weight_ptr;
+    grad_sampling_loc += grad_sampling_ptr << 1;
+    grad_attn_weight += grad_sampling_ptr;
+    const int grad_weight_stride = 1;
+    const int grad_loc_stride = 2;
+    const int qid_stride = num_heads * channels;
+    const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
+
+    for (int l_col=0; l_col < num_levels; ++l_col)
+    {
+      const int level_start_id = data_level_start_index[l_col];
+      const int spatial_h_ptr = l_col << 1;
+      const int spatial_h = data_spatial_shapes[spatial_h_ptr];
+      const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
+      const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;
+      const scalar_t *data_value_ptr = data_value + value_ptr_offset;
+      scalar_t *grad_value_ptr = grad_value + value_ptr_offset;
+
+      for (int p_col=0; p_col < num_point; ++p_col)
+      {
+        const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
+        const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
+        const scalar_t weight = data_attn_weight[data_weight_ptr];
+
+        const scalar_t h_im = loc_h * spatial_h - 0.5;
+        const scalar_t w_im = loc_w * spatial_w - 0.5;
+        if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)
+        {
+          ms_deform_attn_col2im_bilinear_gm(
+            data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,
+            top_grad, weight, grad_value_ptr, 
+            grad_sampling_loc, grad_attn_weight);
+        }
+        data_weight_ptr += 1;
+        data_loc_w_ptr += 2;
+        grad_attn_weight += grad_weight_stride;
+        grad_sampling_loc += grad_loc_stride;
+      }
+    }
+  }
+}
+
+
+template <typename scalar_t>
+void ms_deformable_im2col_cuda(cudaStream_t stream,
+                              const scalar_t* data_value,
+                              const int64_t* data_spatial_shapes, 
+                              const int64_t* data_level_start_index, 
+                              const scalar_t* data_sampling_loc,
+                              const scalar_t* data_attn_weight,
+                              const int batch_size,
+                              const int spatial_size, 
+                              const int num_heads, 
+                              const int channels, 
+                              const int num_levels, 
+                              const int num_query,
+                              const int num_point,
+                              scalar_t* data_col)
+{
+  const int num_kernels = batch_size * num_query * num_heads * channels;
+  const int num_actual_kernels = batch_size * num_query * num_heads * channels;
+  const int num_threads = CUDA_NUM_THREADS;
+  ms_deformable_im2col_gpu_kernel<scalar_t>
+      <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+          0, stream>>>(
+      num_kernels, data_value, data_spatial_shapes, data_level_start_index, data_sampling_loc, data_attn_weight, 
+      batch_size, spatial_size, num_heads, channels, num_levels, num_query, num_point, data_col);
+  
+  cudaError_t err = cudaGetLastError();
+  if (err != cudaSuccess)
+  {
+    printf("error in ms_deformable_im2col_cuda: %s\n", cudaGetErrorString(err));
+  }
+
+}
+
+template <typename scalar_t>
+void ms_deformable_col2im_cuda(cudaStream_t stream,
+                              const scalar_t* grad_col,
+                              const scalar_t* data_value,
+                              const int64_t * data_spatial_shapes,
+                              const int64_t * data_level_start_index,
+                              const scalar_t * data_sampling_loc,
+                              const scalar_t * data_attn_weight,
+                              const int batch_size, 
+                              const int spatial_size, 
+                              const int num_heads,
+                              const int channels, 
+                              const int num_levels,
+                              const int num_query,
+                              const int num_point, 
+                              scalar_t* grad_value,
+                              scalar_t* grad_sampling_loc,
+                              scalar_t* grad_attn_weight)
+{
+  const int num_threads = (channels > CUDA_NUM_THREADS)?CUDA_NUM_THREADS:channels;
+  const int num_kernels = batch_size * num_query * num_heads * channels;
+  const int num_actual_kernels = batch_size * num_query * num_heads * channels;
+  if (channels > 1024)
+  {
+    if ((channels & 1023) == 0)
+    {
+      ms_deformable_col2im_gpu_kernel_shm_reduce_v2_multi_blocks<scalar_t>
+          <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+              num_threads*3*sizeof(scalar_t), stream>>>(
+                        num_kernels, 
+                        grad_col,
+                        data_value,
+                        data_spatial_shapes,
+                        data_level_start_index, 
+                        data_sampling_loc,
+                        data_attn_weight,
+                        batch_size, 
+                        spatial_size, 
+                        num_heads,
+                        channels, 
+                        num_levels,
+                        num_query,
+                        num_point,
+                        grad_value,
+                        grad_sampling_loc,
+                        grad_attn_weight);
+    }
+    else
+    {
+      ms_deformable_col2im_gpu_kernel_gm<scalar_t>
+        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+            0, stream>>>(
+                      num_kernels, 
+                      grad_col,
+                      data_value,
+                      data_spatial_shapes,
+                      data_level_start_index, 
+                      data_sampling_loc,
+                      data_attn_weight,
+                      batch_size, 
+                      spatial_size, 
+                      num_heads,
+                      channels, 
+                      num_levels,
+                      num_query,
+                      num_point,
+                      grad_value,
+                      grad_sampling_loc,
+                      grad_attn_weight);
+    }
+  }
+  else{
+    switch(channels)
+    {
+      case 1:
+        ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 1>
+        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+            0, stream>>>(
+                      num_kernels, 
+                      grad_col,
+                      data_value,
+                      data_spatial_shapes,
+                      data_level_start_index, 
+                      data_sampling_loc,
+                      data_attn_weight,
+                      batch_size, 
+                      spatial_size, 
+                      num_heads,
+                      channels, 
+                      num_levels,
+                      num_query,
+                      num_point,
+                      grad_value,
+                      grad_sampling_loc,
+                      grad_attn_weight);
+        break;
+      case 2:
+        ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 2>
+        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+            0, stream>>>(
+                      num_kernels, 
+                      grad_col,
+                      data_value,
+                      data_spatial_shapes,
+                      data_level_start_index, 
+                      data_sampling_loc,
+                      data_attn_weight,
+                      batch_size, 
+                      spatial_size, 
+                      num_heads,
+                      channels, 
+                      num_levels,
+                      num_query,
+                      num_point,
+                      grad_value,
+                      grad_sampling_loc,
+                      grad_attn_weight);
+        break;
+      case 4:
+        ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 4>
+        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+            0, stream>>>(
+                      num_kernels, 
+                      grad_col,
+                      data_value,
+                      data_spatial_shapes,
+                      data_level_start_index, 
+                      data_sampling_loc,
+                      data_attn_weight,
+                      batch_size, 
+                      spatial_size, 
+                      num_heads,
+                      channels, 
+                      num_levels,
+                      num_query,
+                      num_point,
+                      grad_value,
+                      grad_sampling_loc,
+                      grad_attn_weight);
+        break;
+      case 8:
+        ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 8>
+        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+            0, stream>>>(
+                      num_kernels, 
+                      grad_col,
+                      data_value,
+                      data_spatial_shapes,
+                      data_level_start_index, 
+                      data_sampling_loc,
+                      data_attn_weight,
+                      batch_size, 
+                      spatial_size, 
+                      num_heads,
+                      channels, 
+                      num_levels,
+                      num_query,
+                      num_point,
+                      grad_value,
+                      grad_sampling_loc,
+                      grad_attn_weight);
+        break;
+      case 16:
+        ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 16>
+        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+            0, stream>>>(
+                      num_kernels, 
+                      grad_col,
+                      data_value,
+                      data_spatial_shapes,
+                      data_level_start_index, 
+                      data_sampling_loc,
+                      data_attn_weight,
+                      batch_size, 
+                      spatial_size, 
+                      num_heads,
+                      channels, 
+                      num_levels,
+                      num_query,
+                      num_point,
+                      grad_value,
+                      grad_sampling_loc,
+                      grad_attn_weight);
+        break;
+      case 32:
+        ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 32>
+        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+            0, stream>>>(
+                      num_kernels, 
+                      grad_col,
+                      data_value,
+                      data_spatial_shapes,
+                      data_level_start_index, 
+                      data_sampling_loc,
+                      data_attn_weight,
+                      batch_size, 
+                      spatial_size, 
+                      num_heads,
+                      channels, 
+                      num_levels,
+                      num_query,
+                      num_point,
+                      grad_value,
+                      grad_sampling_loc,
+                      grad_attn_weight);
+        break;
+      case 64:
+        ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2<scalar_t, 64>
+        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+            0, stream>>>(
+                      num_kernels, 
+                      grad_col,
+                      data_value,
+                      data_spatial_shapes,
+                      data_level_start_index, 
+                      data_sampling_loc,
+                      data_attn_weight,
+                      batch_size, 
+                      spatial_size, 
+                      num_heads,
+                      channels, 
+                      num_levels,
+                      num_query,
+                      num_point,
+                      grad_value,
+                      grad_sampling_loc,
+                      grad_attn_weight);
+        break;
+      case 128:
+        ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2<scalar_t, 128>
+        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+            0, stream>>>(
+                      num_kernels, 
+                      grad_col,
+                      data_value,
+                      data_spatial_shapes,
+                      data_level_start_index, 
+                      data_sampling_loc,
+                      data_attn_weight,
+                      batch_size, 
+                      spatial_size, 
+                      num_heads,
+                      channels, 
+                      num_levels,
+                      num_query,
+                      num_point,
+                      grad_value,
+                      grad_sampling_loc,
+                      grad_attn_weight);
+        break;
+      case 256:
+        ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2<scalar_t, 256>
+        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+            0, stream>>>(
+                      num_kernels, 
+                      grad_col,
+                      data_value,
+                      data_spatial_shapes,
+                      data_level_start_index, 
+                      data_sampling_loc,
+                      data_attn_weight,
+                      batch_size, 
+                      spatial_size, 
+                      num_heads,
+                      channels, 
+                      num_levels,
+                      num_query,
+                      num_point,
+                      grad_value,
+                      grad_sampling_loc,
+                      grad_attn_weight);
+        break;
+      case 512:
+        ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2<scalar_t, 512>
+        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+            0, stream>>>(
+                      num_kernels, 
+                      grad_col,
+                      data_value,
+                      data_spatial_shapes,
+                      data_level_start_index, 
+                      data_sampling_loc,
+                      data_attn_weight,
+                      batch_size, 
+                      spatial_size, 
+                      num_heads,
+                      channels, 
+                      num_levels,
+                      num_query,
+                      num_point,
+                      grad_value,
+                      grad_sampling_loc,
+                      grad_attn_weight);
+        break;
+      case 1024:
+        ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2<scalar_t, 1024>
+        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+            0, stream>>>(
+                      num_kernels, 
+                      grad_col,
+                      data_value,
+                      data_spatial_shapes,
+                      data_level_start_index, 
+                      data_sampling_loc,
+                      data_attn_weight,
+                      batch_size, 
+                      spatial_size, 
+                      num_heads,
+                      channels, 
+                      num_levels,
+                      num_query,
+                      num_point,
+                      grad_value,
+                      grad_sampling_loc,
+                      grad_attn_weight);
+        break;
+      default:
+        if (channels < 64)
+        {
+          ms_deformable_col2im_gpu_kernel_shm_reduce_v1<scalar_t>
+          <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+              num_threads*3*sizeof(scalar_t), stream>>>(
+                        num_kernels, 
+                        grad_col,
+                        data_value,
+                        data_spatial_shapes,
+                        data_level_start_index, 
+                        data_sampling_loc,
+                        data_attn_weight,
+                        batch_size, 
+                        spatial_size, 
+                        num_heads,
+                        channels, 
+                        num_levels,
+                        num_query,
+                        num_point,
+                        grad_value,
+                        grad_sampling_loc,
+                        grad_attn_weight);
+        }
+        else
+        {
+          ms_deformable_col2im_gpu_kernel_shm_reduce_v2<scalar_t>
+          <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+              num_threads*3*sizeof(scalar_t), stream>>>(
+                        num_kernels, 
+                        grad_col,
+                        data_value,
+                        data_spatial_shapes,
+                        data_level_start_index, 
+                        data_sampling_loc,
+                        data_attn_weight,
+                        batch_size, 
+                        spatial_size, 
+                        num_heads,
+                        channels, 
+                        num_levels,
+                        num_query,
+                        num_point,
+                        grad_value,
+                        grad_sampling_loc,
+                        grad_attn_weight);
+        }
+    }
+  }
+  cudaError_t err = cudaGetLastError();
+  if (err != cudaSuccess)
+  {
+    printf("error in ms_deformable_col2im_cuda: %s\n", cudaGetErrorString(err));
+  }
+
+}
\ No newline at end of file
diff --git a/models/position_encoding.py b/models/position_encoding.py
new file mode 100644
index 0000000000000000000000000000000000000000..88f8915cb6fff6ea80a3648a6c25bff6931faebc
--- /dev/null
+++ b/models/position_encoding.py
@@ -0,0 +1,145 @@
+"""
+Various positional encodings for the transformer.
+Modified from DETR (https://github.com/facebookresearch/detr)
+"""
+import math
+import torch
+from torch import nn
+
+from util.misc import NestedTensor
+
+# dimension == 1
+class PositionEmbeddingSine1D(nn.Module):
+    """
+    This is a more standard version of the position embedding, very similar to the one
+    used by the Attention is all you need paper, generalized to work on images.
+    """
+    def __init__(self, num_pos_feats=256, temperature=10000, normalize=False, scale=None):
+        super().__init__()
+        self.num_pos_feats = num_pos_feats
+        self.temperature = temperature
+        self.normalize = normalize
+        if scale is not None and normalize is False:
+            raise ValueError("normalize should be True if scale is passed")
+        if scale is None:
+            scale = 2 * math.pi
+        self.scale = scale
+
+    def forward(self, tensor_list: NestedTensor):
+        x = tensor_list.tensors # [B, C, T]
+        mask = tensor_list.mask # [B, T]
+        assert mask is not None
+        not_mask = ~mask
+        x_embed = not_mask.cumsum(1, dtype=torch.float32)  # [B, T]
+        if self.normalize:
+            eps = 1e-6
+            x_embed = x_embed / (x_embed[:, -1:] + eps) * self.scale
+
+        dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
+        dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
+
+        pos_x = x_embed[:, :, None] / dim_t  # [B, T, C]
+        # n,c,t
+        pos_x = torch.stack((pos_x[:, :, 0::2].sin(), pos_x[:, :, 1::2].cos()), dim=3).flatten(2)
+        pos = pos_x.permute(0, 2, 1)    # [B, C, T]
+        return pos
+
+# dimension == 2
+class PositionEmbeddingSine2D(nn.Module):
+    """
+    This is a more standard version of the position embedding, very similar to the one
+    used by the Attention is all you need paper, generalized to work on images.
+    """
+    def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):
+        super().__init__()
+        self.num_pos_feats = num_pos_feats
+        self.temperature = temperature
+        self.normalize = normalize
+        if scale is not None and normalize is False:
+            raise ValueError("normalize should be True if scale is passed")
+        if scale is None:
+            scale = 2 * math.pi
+        self.scale = scale
+
+    def forward(self, tensor_list: NestedTensor):
+        x = tensor_list.tensors  # [B, C, H, W]
+        mask = tensor_list.mask  # [B, H, W]
+        assert mask is not None
+        not_mask = ~mask
+        y_embed = not_mask.cumsum(1, dtype=torch.float32)
+        x_embed = not_mask.cumsum(2, dtype=torch.float32)
+        if self.normalize:
+            eps = 1e-6
+            y_embed = (y_embed - 0.5) / (y_embed[:, -1:, :] + eps) * self.scale
+            x_embed = (x_embed - 0.5) / (x_embed[:, :, -1:] + eps) * self.scale
+
+        dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
+        dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
+
+        pos_x = x_embed[:, :, :, None] / dim_t
+        pos_y = y_embed[:, :, :, None] / dim_t
+        pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3)
+        pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3)
+        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
+        return pos  # [B, C, H, W]
+
+
+# dimension == 3
+class PositionEmbeddingSine3D(nn.Module):
+    """
+    This is a more standard version of the position embedding, very similar to the one
+    used by the Attention is all you need paper, generalized to work on images.
+    """
+    def __init__(self, num_pos_feats=64, num_frames=36, temperature=10000, normalize=False, scale=None):
+        super().__init__()
+        self.num_pos_feats = num_pos_feats
+        self.temperature = temperature
+        self.normalize = normalize
+        self.frames = num_frames
+        if scale is not None and normalize is False:
+            raise ValueError("normalize should be True if scale is passed")
+        if scale is None:
+            scale = 2 * math.pi
+        self.scale = scale
+
+    def forward(self, tensor_list: NestedTensor):
+        x = tensor_list.tensors # [B*T, C, H, W]
+        mask = tensor_list.mask # [B*T, H, W]
+        n,h,w = mask.shape
+        mask = mask.reshape(n//self.frames, self.frames,h,w) # [B, T, H, W]
+        assert mask is not None
+        not_mask = ~mask
+        z_embed = not_mask.cumsum(1, dtype=torch.float32) # [B, T, H, W]
+        y_embed = not_mask.cumsum(2, dtype=torch.float32) # [B, T, H, W]
+        x_embed = not_mask.cumsum(3, dtype=torch.float32) # [B, T, H, W]
+        if self.normalize:
+            eps = 1e-6
+            z_embed = z_embed / (z_embed[:, -1:, :, :] + eps) * self.scale
+            y_embed = y_embed / (y_embed[:, :, -1:, :] + eps) * self.scale
+            x_embed = x_embed / (x_embed[:, :, :, -1:] + eps) * self.scale
+
+        dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) # 
+        dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
+
+        pos_x = x_embed[:, :, :, :, None] / dim_t # [B, T, H, W, c]
+        pos_y = y_embed[:, :, :, :, None] / dim_t
+        pos_z = z_embed[:, :, :, :, None] / dim_t
+        pos_x = torch.stack((pos_x[:, :, :, :, 0::2].sin(), pos_x[:, :, :, :, 1::2].cos()), dim=5).flatten(4) # [B, T, H, W, c]
+        pos_y = torch.stack((pos_y[:, :, :, :, 0::2].sin(), pos_y[:, :, :, :, 1::2].cos()), dim=5).flatten(4)
+        pos_z = torch.stack((pos_z[:, :, :, :, 0::2].sin(), pos_z[:, :, :, :, 1::2].cos()), dim=5).flatten(4)
+        pos = torch.cat((pos_z, pos_y, pos_x), dim=4).permute(0, 1, 4, 2, 3) # [B, T, C, H, W]
+        return pos
+
+
+
+def build_position_encoding(args):
+    # build 2D position encoding
+    N_steps = args.hidden_dim // 2  # 256 / 2 = 128
+    if args.position_embedding in ('v2', 'sine'):
+        # TODO find a better way of exposing other arguments
+        position_embedding = PositionEmbeddingSine2D(N_steps, normalize=True)
+    else:
+        raise ValueError(f"not supported {args.position_embedding}")
+
+    return position_embedding
+
diff --git a/models/postprocessors.py b/models/postprocessors.py
new file mode 100644
index 0000000000000000000000000000000000000000..5986631bddf317bdda71fac09e98ca1ec8b00ca7
--- /dev/null
+++ b/models/postprocessors.py
@@ -0,0 +1,168 @@
+# Copyright (c) Aishwarya Kamath & Nicolas Carion. Licensed under the Apache License 2.0. All Rights Reserved
+"""Postprocessors class to transform MDETR output according to the downstream task"""
+from typing import Dict
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch import nn
+import pycocotools.mask as mask_util
+
+from util import box_ops
+
+
+class A2DSentencesPostProcess(nn.Module):
+    """
+    This module converts the model's output into the format expected by the coco api for the given task
+    """
+    def __init__(self, threshold=0.5):
+        super().__init__()
+        self.threshold = threshold
+
+    @torch.no_grad()
+    def forward(self, outputs, orig_target_sizes, max_target_sizes):
+        """ Perform the computation
+        Parameters:
+            outputs: raw outputs of the model
+            orig_target_sizes: original size of the samples (no augmentations or padding)
+            max_target_sizes: size of samples (input to model) after size augmentation.
+            NOTE: the max_padding_size is 4x out_masks.shape[-2:]
+        """
+        assert len(orig_target_sizes) == len(max_target_sizes)
+        
+        # there is only one valid frames, thus T=1
+        out_logits = outputs['pred_logits'][:, 0, :, 0]  # [B, T, N, 1] -> [B, N]
+        out_masks = outputs['pred_masks'][:, 0, :, :, :] # [B, T, N, out_h, out_w] -> [B, N, out_h, out_w]
+        out_h, out_w = out_masks.shape[-2:]
+
+        scores = out_logits.sigmoid()
+        pred_masks = F.interpolate(out_masks, size=(out_h*4, out_w*4), mode="bilinear", align_corners=False) # [B, N, H, W]
+        pred_masks = (pred_masks.sigmoid() > 0.5) # [B, N, H, W]
+        processed_pred_masks, rle_masks = [], []
+        # for each batch
+        for f_pred_masks, resized_size, orig_size in zip(pred_masks, max_target_sizes, orig_target_sizes):
+            f_mask_h, f_mask_w = resized_size  # resized shape without padding
+            f_pred_masks_no_pad = f_pred_masks[:, :f_mask_h, :f_mask_w].unsqueeze(1)  # remove the samples' padding, [:, 1, h, w]
+            # resize the samples back to their original dataset (target) size for evaluation
+            f_pred_masks_processed = F.interpolate(f_pred_masks_no_pad.float(), size=tuple(orig_size.tolist()), mode="nearest") # origin size, [:, 1, h, w]
+            f_pred_rle_masks = [mask_util.encode(np.array(mask[0, :, :, np.newaxis], dtype=np.uint8, order="F"))[0]
+                                for mask in f_pred_masks_processed.cpu()]
+            processed_pred_masks.append(f_pred_masks_processed)
+            rle_masks.append(f_pred_rle_masks)
+        predictions = [{'scores': s, 'masks': m, 'rle_masks': rle}
+                       for s, m, rle in zip(scores, processed_pred_masks, rle_masks)]
+        return predictions
+
+
+# PostProcess for pretraining
+class PostProcess(nn.Module):
+    """ This module converts the model's output into the format expected by the coco api"""
+
+    @torch.no_grad()
+    def forward(self, outputs, target_sizes):
+        """Perform the computation
+        Parameters:
+            outputs: raw outputs of the model
+            target_sizes: tensor of dimension [batch_size x 2] containing the size of each images of the batch
+                          For evaluation, this must be the original image size (before any data augmentation)
+                          For visualization, this should be the image size after data augment, but before padding
+        Returns:
+
+        """
+        out_logits, out_bbox = outputs["pred_logits"], outputs["pred_boxes"]
+
+        assert len(out_logits) == len(target_sizes)
+        assert target_sizes.shape[1] == 2
+        
+        # coco, num_frames=1
+        out_logits = outputs["pred_logits"].flatten(1, 2)
+        out_boxes = outputs["pred_boxes"].flatten(1, 2)
+        bs, num_queries = out_logits.shape[:2]
+
+        prob = out_logits.sigmoid() # [bs, num_queries, num_classes]
+        topk_values, topk_indexes = torch.topk(prob.view(out_logits.shape[0], -1), k=num_queries, dim=1, sorted=True) 
+        scores = topk_values # [bs, num_queries]
+        topk_boxes = topk_indexes // out_logits.shape[2] # [bs, num_queries]
+        labels = topk_indexes % out_logits.shape[2] # [bs, num_queries]
+
+        boxes = box_ops.box_cxcywh_to_xyxy(out_boxes) # [bs, num_queries, 4]
+        boxes = torch.gather(boxes, 1, topk_boxes.unsqueeze(-1).repeat(1,1,4))
+
+        # and from relative [0, 1] to absolute [0, height] coordinates
+        img_h, img_w = target_sizes.unbind(1)
+        scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1)
+        boxes = boxes * scale_fct[:, None, :] # [bs, num_queries, 4]
+
+        assert len(scores) == len(labels) == len(boxes)
+        # binary for the pretraining
+        results = [{"scores": s, "labels": torch.ones_like(l), "boxes": b} for s, l, b in zip(scores, labels, boxes)]
+
+        return results
+
+
+class PostProcessSegm(nn.Module):
+    """Similar to PostProcess but for segmentation masks.
+    This processor is to be called sequentially after PostProcess.
+    Args:
+        threshold: threshold that will be applied to binarize the segmentation masks.
+    """
+
+    def __init__(self, threshold=0.5):
+        super().__init__()
+        self.threshold = threshold
+
+    @torch.no_grad()
+    def forward(self, results, outputs, orig_target_sizes, max_target_sizes):
+        """Perform the computation
+        Parameters:
+            results: already pre-processed boxes (output of PostProcess) NOTE here
+            outputs: raw outputs of the model
+            orig_target_sizes: tensor of dimension [batch_size x 2] containing the size of each images of the batch
+                          For evaluation, this must be the original image size (before any data augmentation)
+                          For visualization, this should be the image size after data augment, but before padding
+            max_target_sizes: tensor of dimension [batch_size x 2] containing the size of each images of the batch
+                              after data augmentation.
+        """
+        assert len(orig_target_sizes) == len(max_target_sizes)
+
+        out_logits = outputs["pred_logits"].flatten(1, 2)
+        out_masks = outputs["pred_masks"].flatten(1, 2)
+        bs, num_queries = out_logits.shape[:2]
+
+        prob = out_logits.sigmoid()
+        topk_values, topk_indexes = torch.topk(prob.view(out_logits.shape[0], -1), k=num_queries, dim=1, sorted=True) 
+        scores = topk_values # [bs, num_queries]
+        topk_boxes = topk_indexes // out_logits.shape[2] # [bs, num_queries]
+        labels = topk_indexes % out_logits.shape[2] # [bs, num_queries]
+
+        outputs_masks = [out_m[topk_boxes[i]].unsqueeze(0) for i, out_m, in enumerate(out_masks)] # list[Tensor]
+        outputs_masks = torch.cat(outputs_masks, dim=0) # [bs, num_queries, H, W]
+        out_h, out_w = outputs_masks.shape[-2:]
+
+        # max_h, max_w = max_target_sizes.max(0)[0].tolist() 
+        # outputs_masks = F.interpolate(outputs_masks, size=(max_h, max_w), mode="bilinear", align_corners=False)
+        outputs_masks = F.interpolate(outputs_masks, size=(out_h*4, out_w*4), mode="bilinear", align_corners=False)
+        outputs_masks = (outputs_masks.sigmoid() > self.threshold).cpu()
+
+        for i, (cur_mask, t, tt) in enumerate(zip(outputs_masks, max_target_sizes, orig_target_sizes)):
+            img_h, img_w = t[0], t[1]
+            results[i]["masks"] = cur_mask[:, :img_h, :img_w].unsqueeze(1) # [:, 1, h, w]
+            results[i]["masks"] = F.interpolate(
+                results[i]["masks"].float(), size=tuple(tt.tolist()), mode="nearest"
+            ).byte()
+            results[i]["rle_masks"] = [mask_util.encode(np.array(mask[0, :, :, np.newaxis], dtype=np.uint8, order="F"))[0]
+                    for mask in results[i]["masks"].cpu()]
+
+        return results
+
+
+
+def build_postprocessors(args, dataset_name):
+    if dataset_name == 'a2d' or dataset_name == 'jhmdb':
+        postprocessors = A2DSentencesPostProcess(threshold=args.threshold)
+    else:
+        # for coco pretrain postprocessor
+        postprocessors: Dict[str, nn.Module] = {"bbox": PostProcess()}
+        if args.masks:
+            postprocessors["segm"] = PostProcessSegm(threshold=args.threshold)
+    return postprocessors
diff --git a/models/segmentation.py b/models/segmentation.py
new file mode 100644
index 0000000000000000000000000000000000000000..4f0fca03c9765d687732da89e5e4184a1f70cd6c
--- /dev/null
+++ b/models/segmentation.py
@@ -0,0 +1,486 @@
+"""
+Segmentaion Part 
+Modified from DETR (https://github.com/facebookresearch/detr)
+"""
+from collections import defaultdict
+from typing import List, Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch import Tensor
+from PIL import Image
+
+from einops import rearrange, repeat
+
+try:
+    from panopticapi.utils import id2rgb, rgb2id
+except ImportError:
+    pass
+
+import fvcore.nn.weight_init as weight_init
+
+from .position_encoding import PositionEmbeddingSine1D
+
+BN_MOMENTUM = 0.1
+
+def get_norm(norm, out_channels): # only support GN or LN
+    """
+    Args:
+        norm (str or callable): either one of BN, SyncBN, FrozenBN, GN;
+            or a callable that takes a channel number and returns
+            the normalization layer as a nn.Module.
+
+    Returns:
+        nn.Module or None: the normalization layer
+    """
+    if norm is None:
+        return None
+    if isinstance(norm, str):
+        if len(norm) == 0:
+            return None
+        norm = {
+            "GN": lambda channels: nn.GroupNorm(8, channels),
+            "LN": lambda channels: nn.LayerNorm(channels)
+        }[norm]
+    return norm(out_channels)
+
+class Conv2d(torch.nn.Conv2d):
+    """
+    A wrapper around :class:`torch.nn.Conv2d` to support empty inputs and more features.
+    """
+
+    def __init__(self, *args, **kwargs):
+        """
+        Extra keyword arguments supported in addition to those in `torch.nn.Conv2d`:
+
+        Args:
+            norm (nn.Module, optional): a normalization layer
+            activation (callable(Tensor) -> Tensor): a callable activation function
+
+        It assumes that norm layer is used before activation.
+        """
+        norm = kwargs.pop("norm", None)
+        activation = kwargs.pop("activation", None)
+        super().__init__(*args, **kwargs)
+
+        self.norm = norm
+        self.activation = activation
+
+    def forward(self, x):
+        # torchscript does not support SyncBatchNorm yet
+        # https://github.com/pytorch/pytorch/issues/40507
+        # and we skip these codes in torchscript since:
+        # 1. currently we only support torchscript in evaluation mode
+        # 2. features needed by exporting module to torchscript are added in PyTorch 1.6 or
+        # later version, `Conv2d` in these PyTorch versions has already supported empty inputs.
+        if not torch.jit.is_scripting():
+            if x.numel() == 0 and self.training:
+                # https://github.com/pytorch/pytorch/issues/12013
+                assert not isinstance(
+                    self.norm, torch.nn.SyncBatchNorm
+                ), "SyncBatchNorm does not support empty inputs!"
+
+        x = F.conv2d(
+            x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups
+        )
+        if self.norm is not None:
+            x = self.norm(x)
+        if self.activation is not None:
+            x = self.activation(x)
+        return x
+
+# FPN structure
+class CrossModalFPNDecoder(nn.Module):
+    def __init__(self, feature_channels: List, conv_dim: int, mask_dim: int, dim_feedforward: int = 2048, norm=None):
+        """
+        Args:
+            feature_channels: list of fpn feature channel numbers.
+            conv_dim: number of output channels for the intermediate conv layers.
+            mask_dim: number of output channels for the final conv layer.
+            dim_feedforward: number of vision-language fusion module ffn channel numbers.
+            norm (str or callable): normalization for all conv layers
+        """
+        super().__init__()
+
+        self.feature_channels = feature_channels
+
+        lateral_convs = []
+        output_convs = []
+
+        use_bias = norm == ""
+        for idx, in_channels in enumerate(feature_channels):
+            # in_channels: 4x -> 32x
+            lateral_norm = get_norm(norm, conv_dim)
+            output_norm = get_norm(norm, conv_dim)
+
+            lateral_conv = Conv2d(
+                    in_channels, conv_dim, kernel_size=1, bias=use_bias, norm=lateral_norm
+                )
+            output_conv = Conv2d(
+                conv_dim,
+                conv_dim,
+                kernel_size=3,
+                stride=1,
+                padding=1,
+                bias=use_bias,
+                norm=output_norm,
+                activation=F.relu,
+            )
+            weight_init.c2_xavier_fill(lateral_conv)
+            weight_init.c2_xavier_fill(output_conv)
+            stage = idx+1
+            self.add_module("adapter_{}".format(stage), lateral_conv)
+            self.add_module("layer_{}".format(stage), output_conv)
+
+            lateral_convs.append(lateral_conv)
+            output_convs.append(output_conv)
+            
+        # Place convs into top-down order (from low to high resolution)
+        # to make the top-down computation in forward clearer.
+        self.lateral_convs = lateral_convs[::-1]
+        self.output_convs = output_convs[::-1]
+
+        self.mask_dim = mask_dim
+        self.mask_features = Conv2d(
+            conv_dim,
+            mask_dim,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+        )
+        weight_init.c2_xavier_fill(self.mask_features)
+
+        # vision-language cross-modal fusion
+        self.text_pos = PositionEmbeddingSine1D(conv_dim, normalize=True)
+        sr_ratios = [8, 4, 2, 1]
+        cross_attns = []
+        for idx in range(len(feature_channels)): # res2 -> res5
+            cross_attn = VisionLanguageBlock(conv_dim, dim_feedforward=dim_feedforward,
+                                             nhead=8, sr_ratio=sr_ratios[idx])
+            for p in cross_attn.parameters():
+                if p.dim() > 1:
+                    nn.init.xavier_uniform_(p)
+            stage = int(idx + 1)
+            self.add_module("cross_attn_{}".format(stage), cross_attn)
+            cross_attns.append(cross_attn)
+        # place cross-attn in top-down order (from low to high resolution)
+        self.cross_attns = cross_attns[::-1]
+
+
+    def forward_features(self, features, text_features, poses, memory, nf):
+        # nf: num_frames
+        text_pos = self.text_pos(text_features).permute(2, 0, 1)   # [length, batch_size, c]  
+        text_features, text_masks = text_features.decompose()      
+        text_features = text_features.permute(1, 0, 2)   
+
+        for idx, (mem, f, pos) in enumerate(zip(memory[::-1], features[1:][::-1], poses[1:][::-1])): # 32x -> 8x
+            lateral_conv = self.lateral_convs[idx]
+            output_conv = self.output_convs[idx]
+            cross_attn = self.cross_attns[idx]
+            
+            _, x_mask = f.decompose()
+            n, c, h, w = pos.shape
+            b = n // nf
+            t = nf
+
+            # NOTE: here the (h, w) is the size for current fpn layer
+            vision_features = lateral_conv(mem)  # [b*t, c, h, w]
+            vision_features = rearrange(vision_features, '(b t) c h w -> (t h w) b c', b=b, t=t)
+            vision_pos = rearrange(pos, '(b t) c h w -> (t h w) b c', b=b, t=t)
+            vision_masks = rearrange(x_mask, '(b t) h w -> b (t h w)', b=b, t=t)
+
+            cur_fpn = cross_attn(tgt=vision_features,
+                                 memory=text_features,
+                                 t=t, h=h, w=w,
+                                 tgt_key_padding_mask=vision_masks,
+                                 memory_key_padding_mask=text_masks,
+                                 pos=text_pos,
+                                 query_pos=vision_pos
+            ) # [t*h*w, b, c]
+            cur_fpn = rearrange(cur_fpn, '(t h w) b c -> (b t) c h w', t=t, h=h, w=w)
+
+            # upsample
+            if idx == 0: # top layer
+                y = output_conv(cur_fpn)
+            else:
+                # Following FPN implementation, we use nearest upsampling here
+                y = cur_fpn + F.interpolate(y, size=cur_fpn.shape[-2:], mode="nearest")
+                y = output_conv(y)
+        
+        # 4x level
+        lateral_conv = self.lateral_convs[-1]
+        output_conv = self.output_convs[-1]
+        cross_attn = self.cross_attns[-1]
+        
+        x, x_mask = features[0].decompose()
+        pos = poses[0]
+        n, c, h, w = pos.shape
+        b = n // nf
+        t = nf
+
+        vision_features = lateral_conv(x)  # [b*t, c, h, w]
+        vision_features = rearrange(vision_features, '(b t) c h w -> (t h w) b c', b=b, t=t)
+        vision_pos = rearrange(pos, '(b t) c h w -> (t h w) b c', b=b, t=t)
+        vision_masks = rearrange(x_mask, '(b t) h w -> b (t h w)', b=b, t=t)
+
+        cur_fpn = cross_attn(tgt=vision_features,
+                             memory=text_features,
+                             t=t, h=h, w=w,
+                             tgt_key_padding_mask=vision_masks,
+                             memory_key_padding_mask=text_masks,
+                             pos=text_pos,
+                             query_pos=vision_pos
+        ) # [t*h*w, b, c]
+        cur_fpn = rearrange(cur_fpn, '(t h w) b c -> (b t) c h w', t=t, h=h, w=w)
+        # Following FPN implementation, we use nearest upsampling here
+        y = cur_fpn + F.interpolate(y, size=cur_fpn.shape[-2:], mode="nearest")
+        y = output_conv(y)
+        return y   # [b*t, c, h, w], the spatial stride is 4x
+
+    def forward(self, features, text_features, pos, memory, nf):
+        """The forward function receives the vision and language features, 
+            and outputs the mask features with the spatial stride of 4x.
+
+        Args:
+            features (list[NestedTensor]): backbone features (vision), length is number of FPN layers
+                tensors: [b*t, ci, hi, wi], mask: [b*t, hi, wi]
+            text_features (NestedTensor): text features (language)
+                tensors: [b, length, c], mask: [b, length]
+            pos (list[Tensor]): position encoding of vision features, length is number of FPN layers
+                tensors: [b*t, c, hi, wi]
+            memory (list[Tensor]): features from encoder output. from 8x -> 32x
+            NOTE: the layer orders of both features and pos are res2 -> res5
+
+        Returns:
+            mask_features (Tensor): [b*t, mask_dim, h, w], with the spatial stride of 4x.
+        """
+        y = self.forward_features(features, text_features, pos, memory, nf)
+        return self.mask_features(y)
+
+
+class VisionLanguageBlock(nn.Module):
+    def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1,
+                 activation="relu", normalize_before=False, sr_ratio=1):
+        super().__init__()
+        self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
+        self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
+        # Implementation of Feedforward model
+        self.linear1 = nn.Linear(d_model, dim_feedforward)
+        self.dropout = nn.Dropout(dropout)
+        self.linear2 = nn.Linear(dim_feedforward, d_model)
+
+        self.norm1 = nn.LayerNorm(d_model)
+        self.norm2 = nn.LayerNorm(d_model)
+        self.norm3 = nn.LayerNorm(d_model)
+        self.dropout1 = nn.Dropout(dropout)
+        self.dropout2 = nn.Dropout(dropout)
+        self.dropout3 = nn.Dropout(dropout)
+
+        self.activation = _get_activation_fn(activation)
+        self.normalize_before = normalize_before
+
+        # for downsample
+        self.sr_ratio = sr_ratio
+
+    def with_pos_embed(self, tensor, pos: Optional[Tensor]):
+        return tensor if pos is None else tensor + pos
+
+    def forward_post(self, tgt, memory, t, h, w,
+                     tgt_key_padding_mask: Optional[Tensor] = None,
+                     memory_key_padding_mask: Optional[Tensor] = None,
+                     pos: Optional[Tensor] = None,
+                     query_pos: Optional[Tensor] = None):
+        b = tgt.size(1)
+        # self attn
+        q = k = self.with_pos_embed(tgt, query_pos)
+        if self.sr_ratio > 1: # downsample
+            q = rearrange(q, '(t h w) b c -> (b t) c h w', t=t, h=h, w=w)
+            k = rearrange(k, '(t h w) b c -> (b t) c h w', t=t, h=h, w=w)
+            v = rearrange(tgt, '(t h w) b c -> (b t) c h w', t=t, h=h, w=w)
+            # downsample
+            new_h = int(h * 1./self.sr_ratio)
+            new_w = int(w * 1./self.sr_ratio)
+            size = (new_h, new_w)
+            q = F.interpolate(q, size=size, mode='nearest')
+            k = F.interpolate(k, size=size, mode='nearest')
+            v = F.interpolate(v, size=size, mode='nearest')
+            # shape for transformer
+            q = rearrange(q, '(b t) c h w -> (t h w) b c', t=t)
+            k = rearrange(k, '(b t) c h w -> (t h w) b c', t=t)
+            v = rearrange(v, '(b t) c h w -> (t h w) b c', t=t)
+            # downsample mask
+            tgt_key_padding_mask = tgt_key_padding_mask.reshape(b*t, h, w)
+            tgt_key_padding_mask = F.interpolate(tgt_key_padding_mask[None].float(), size=(new_h, new_w), mode='nearest').bool()[0] 
+            tgt_key_padding_mask = tgt_key_padding_mask.reshape(b, t, new_h, new_w).flatten(1)
+        else:
+            v = tgt
+        tgt2 = self.self_attn(q, k, value=v, attn_mask=None,
+                              key_padding_mask=tgt_key_padding_mask)[0] # [H*W, B*T, C]
+        if self.sr_ratio > 1:
+            tgt2 = rearrange(tgt2, '(t h w) b c -> (b t) c h w', t=t, h=new_h, w=new_w)
+            size = (h, w)  # recover to origin size
+            tgt2 = F.interpolate(tgt2, size=size, mode='bilinear', align_corners=False) # [B*T, C, H, W]
+            tgt2 = rearrange(tgt2, '(b t) c h w -> (t h w) b c', t=t)
+        tgt = tgt + self.dropout1(tgt2)
+        tgt = self.norm1(tgt)
+
+        # cross attn
+        tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt, query_pos),
+                                   key=self.with_pos_embed(memory, pos),
+                                   value=memory, attn_mask=None,
+                                   key_padding_mask=memory_key_padding_mask)[0]
+        tgt = tgt + self.dropout2(tgt2)
+        tgt = self.norm2(tgt)
+
+        # ffn
+        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))
+        tgt = tgt + self.dropout3(tgt2)
+        tgt = self.norm3(tgt)
+        return tgt
+
+    def forward_pre(self, tgt, memory, t, h, w,
+                    tgt_key_padding_mask: Optional[Tensor] = None,
+                    memory_key_padding_mask: Optional[Tensor] = None,
+                    pos: Optional[Tensor] = None,
+                    query_pos: Optional[Tensor] = None):
+        b = tgt.size(1)
+        # self attn
+        tgt2 = self.norm1(tgt)
+        q = k = self.with_pos_embed(tgt2, query_pos)
+        if self.sr_ratio > 1: # downsample
+            q = rearrange(q, '(t h w) b c -> (b t) c h w', t=t, h=h, w=w)
+            k = rearrange(k, '(t h w) b c -> (b t) c h w', t=t, h=h, w=w)
+            v = rearrange(tgt, '(t h w) b c -> (b t) c h w', t=t, h=h, w=w)
+            # downsample
+            new_h = int(h * 1./self.sr_ratio)
+            new_w = int(w * 1./self.sr_ratio)
+            size = (new_h, new_w)
+            q = F.interpolate(q, size=size, mode='nearest')
+            k = F.interpolate(k, size=size, mode='nearest')
+            v = F.interpolate(v, size=size, mode='nearest')
+            # shape for transformer
+            q = rearrange(q, '(b t) c h w -> (t h w) b c', t=t)
+            k = rearrange(k, '(b t) c h w -> (t h w) b c', t=t)
+            v = rearrange(v, '(b t) c h w -> (t h w) b c', t=t)
+            # downsample mask
+            tgt_key_padding_mask = tgt_key_padding_mask.reshape(b*t, h, w)
+            tgt_key_padding_mask = F.interpolate(tgt_key_padding_mask[None].float(), size=(new_h, new_w), mode='nearest').bool()[0] 
+            tgt_key_padding_mask = tgt_key_padding_mask.reshape(b, t, new_h, new_w).flatten(1)
+        else:
+            v = tgt2
+        tgt2 = self.self_attn(q, k, value=v, attn_mask=None,
+                              key_padding_mask=tgt_key_padding_mask)[0] # [T*H*W, B, C]
+        if self.sr_ratio > 1:
+            tgt2 = rearrange(tgt2, '(t h w) b c -> (b t) c h w', t=t, h=new_h, w=new_w)
+            size = (h, w)  # recover to origin size
+            tgt2 = F.interpolate(tgt2, size=size, mode='bilinear', align_corners=False) # [B*T, C, H, W]
+            tgt2 = rearrange(tgt2, '(b t) c h w -> (t h w) b c', t=t)
+        tgt = tgt + self.dropout1(tgt2)
+
+        # cross attn
+        tgt2 = self.norm2(tgt)
+        tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt2, query_pos),
+                                   key=self.with_pos_embed(memory, pos),
+                                   value=memory, attn_mask=None,
+                                   key_padding_mask=memory_key_padding_mask)[0]
+        tgt = tgt + self.dropout2(tgt2)
+
+        # ffn
+        tgt2 = self.norm3(tgt)
+        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
+        tgt = tgt + self.dropout3(tgt2)
+        return tgt
+
+    def forward(self, tgt, memory, t, h, w,
+                tgt_key_padding_mask: Optional[Tensor] = None,
+                memory_key_padding_mask: Optional[Tensor] = None,
+                pos: Optional[Tensor] = None,
+                query_pos: Optional[Tensor] = None):
+        if self.normalize_before:
+            return self.forward_pre(tgt, memory, t, h, w,
+                                    tgt_key_padding_mask, memory_key_padding_mask, 
+                                    pos, query_pos)
+        return self.forward_post(tgt, memory, t, h, w,
+                                 tgt_key_padding_mask, memory_key_padding_mask, 
+                                 pos, query_pos)
+
+
+
+class VisionLanguageFusionModule(nn.Module):
+    def __init__(self, d_model, nhead, dropout=0.0):
+        super().__init__()
+        self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
+
+    def with_pos_embed(self, tensor, pos: Optional[Tensor]):
+        return tensor if pos is None else tensor + pos
+
+    def forward(self, tgt, memory,
+                memory_key_padding_mask: Optional[Tensor] = None,
+                pos: Optional[Tensor] = None,
+                query_pos: Optional[Tensor] = None):
+        tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt, query_pos),
+                                   key=self.with_pos_embed(memory, pos),
+                                   value=memory, attn_mask=None,
+                                   key_padding_mask=memory_key_padding_mask)[0]
+        tgt = tgt * tgt2
+        return tgt
+
+
+def dice_loss(inputs, targets, num_boxes):
+    """
+    Compute the DICE loss, similar to generalized IOU for masks
+    Args:
+        inputs: A float tensor of arbitrary shape.
+                The predictions for each example.
+        targets: A float tensor with the same shape as inputs. Stores the binary
+                 classification label for each element in inputs
+                (0 for the negative class and 1 for the positive class).
+    """
+    inputs = inputs.sigmoid()
+    inputs = inputs.flatten(1)
+    numerator = 2 * (inputs * targets).sum(1)
+    denominator = inputs.sum(-1) + targets.sum(-1)
+    loss = 1 - (numerator + 1) / (denominator + 1)
+    return loss.sum() / num_boxes
+
+
+def sigmoid_focal_loss(inputs, targets, num_boxes, alpha: float = 0.25, gamma: float = 2):
+    """
+    Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002.
+    Args:
+        inputs: A float tensor of arbitrary shape.
+                The predictions for each example.
+        targets: A float tensor with the same shape as inputs. Stores the binary
+                 classification label for each element in inputs
+                (0 for the negative class and 1 for the positive class).
+        alpha: (optional) Weighting factor in range (0,1) to balance
+                positive vs negative examples. Default = -1 (no weighting).
+        gamma: Exponent of the modulating factor (1 - p_t) to
+               balance easy vs hard examples.
+    Returns:
+        Loss tensor
+    """
+    prob = inputs.sigmoid()
+    ce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none")
+    p_t = prob * targets + (1 - prob) * (1 - targets)
+    loss = ce_loss * ((1 - p_t) ** gamma)
+
+    if alpha >= 0:
+        alpha_t = alpha * targets + (1 - alpha) * (1 - targets)
+        loss = alpha_t * loss
+
+    return loss.mean(1).sum() / num_boxes
+
+
+def _get_activation_fn(activation):
+    """Return an activation function given a string"""
+    if activation == "relu":
+        return F.relu
+    if activation == "gelu":
+        return F.gelu
+    if activation == "glu":
+        return F.glu
+    raise RuntimeError(F"activation should be relu/gelu, not {activation}.")
+
+
diff --git a/models/swin_transformer.py b/models/swin_transformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..a569b0bede91454680e9b1082571c7170ba71a11
--- /dev/null
+++ b/models/swin_transformer.py
@@ -0,0 +1,759 @@
+# --------------------------------------------------------
+# Modified from Swin Transformer (https://github.com/microsoft/Swin-Transformer) 
+# Copyright (c) 2021 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ze Liu, Yutong Lin, Yixuan Wei
+# --------------------------------------------------------
+"""
+Backbone modules.
+"""
+import os
+from collections import OrderedDict
+import functools
+print = functools.partial(print, flush=True)
+import torch
+import torch.nn.functional as F
+import torchvision
+from torch import nn
+from torchvision.models._utils import IntermediateLayerGetter
+from typing import Dict, List
+from einops import rearrange
+
+from util.misc import NestedTensor, is_main_process
+import torch.utils.checkpoint as checkpoint
+import numpy as np
+from timm.models.layers import DropPath, to_2tuple, trunc_normal_
+from .position_encoding import build_position_encoding
+
+
+class Mlp(nn.Module):
+    """ Multilayer perceptron."""
+
+    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):
+    """ 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):
+        """ Forward function.
+        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
+
+
+class SwinTransformerBlock(nn.Module):
+    """ Swin Transformer Block.
+    Args:
+        dim (int): Number of input channels.
+        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, num_heads, window_size=7, shift_size=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.num_heads = num_heads
+        self.window_size = window_size
+        self.shift_size = shift_size
+        self.mlp_ratio = mlp_ratio
+        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)
+
+        self.H = None
+        self.W = None
+
+    def forward(self, x, mask_matrix):
+        """ Forward function.
+        Args:
+            x: Input feature, tensor size (B, H*W, C).
+            H, W: Spatial resolution of the input feature.
+            mask_matrix: Attention mask for cyclic shift.
+        """
+        B, L, C = x.shape
+        H, W = self.H, self.W
+        assert L == H * W, "input feature has wrong size"
+
+        shortcut = x
+        x = self.norm1(x)
+        x = x.view(B, H, W, C)
+
+        # pad feature maps to multiples of window size
+        pad_l = pad_t = 0
+        pad_r = (self.window_size - W % self.window_size) % self.window_size
+        pad_b = (self.window_size - H % self.window_size) % self.window_size
+        x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
+        _, Hp, Wp, _ = x.shape
+
+        # cyclic shift
+        if self.shift_size > 0:
+            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
+            attn_mask = mask_matrix
+        else:
+            shifted_x = x
+            attn_mask = None
+
+        # 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=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, Hp, Wp)  # 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
+
+        if pad_r > 0 or pad_b > 0:
+            x = x[:, :H, :W, :].contiguous()
+
+        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
+
+
+class PatchMerging(nn.Module):
+    """ Patch Merging Layer
+    Args:
+        dim (int): Number of input channels.
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+    """
+    def __init__(self, dim, norm_layer=nn.LayerNorm):
+        super().__init__()
+        self.dim = dim
+        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
+        self.norm = norm_layer(4 * dim)
+
+    def forward(self, x, H, W):
+        """ Forward function.
+        Args:
+            x: Input feature, tensor size (B, H*W, C).
+            H, W: Spatial resolution of the input feature.
+        """
+        B, L, C = x.shape
+        assert L == H * W, "input feature has wrong size"
+
+        x = x.view(B, H, W, C)
+
+        # padding
+        pad_input = (H % 2 == 1) or (W % 2 == 1)
+        if pad_input:
+            x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
+
+        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
+
+
+class BasicLayer(nn.Module):
+    """ A basic Swin Transformer layer for one stage.
+    Args:
+        dim (int): Number of feature channels
+        depth (int): Depths of this stage.
+        num_heads (int): Number of attention head.
+        window_size (int): Local window size. Default: 7.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
+        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,
+                 depth,
+                 num_heads,
+                 window_size=7,
+                 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.window_size = window_size
+        self.shift_size = window_size // 2
+        self.depth = depth
+        self.use_checkpoint = use_checkpoint
+
+        # build blocks
+        self.blocks = nn.ModuleList([
+            SwinTransformerBlock(
+                dim=dim,
+                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(dim=dim, norm_layer=norm_layer)
+        else:
+            self.downsample = None
+
+    def forward(self, x, H, W):
+        """ Forward function.
+        Args:
+            x: Input feature, tensor size (B, H*W, C).
+            H, W: Spatial resolution of the input feature.
+        """
+
+        # calculate attention mask for SW-MSA
+        Hp = int(np.ceil(H / self.window_size)) * self.window_size
+        Wp = int(np.ceil(W / self.window_size)) * self.window_size
+        img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device)  # 1 Hp Wp 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))
+
+        for blk in self.blocks:
+            blk.H, blk.W = H, W
+            if self.use_checkpoint:
+                x = checkpoint.checkpoint(blk, x, attn_mask)
+            else:
+                x = blk(x, attn_mask)
+        if self.downsample is not None:
+            x_down = self.downsample(x, H, W)
+            Wh, Ww = (H + 1) // 2, (W + 1) // 2
+            return x, H, W, x_down, Wh, Ww
+        else:
+            return x, H, W, x, H, W
+
+
+class PatchEmbed(nn.Module):
+    """ Image to Patch Embedding
+    Args:
+        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, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
+        super().__init__()
+        patch_size = to_2tuple(patch_size)
+        self.patch_size = patch_size
+
+        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):
+        """Forward function."""
+        # padding
+        _, _, H, W = x.size()
+        if W % self.patch_size[1] != 0:
+            x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
+        if H % self.patch_size[0] != 0:
+            x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
+
+        x = self.proj(x)  # B C Wh Ww
+        if self.norm is not None:
+            Wh, Ww = x.size(2), x.size(3)
+            x = x.flatten(2).transpose(1, 2)
+            x = self.norm(x)
+            x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)
+
+        return x
+
+
+class SwinTransformer(nn.Module):
+    """ Swin Transformer backbone.
+        A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
+          https://arxiv.org/pdf/2103.14030
+    Args:
+        pretrain_img_size (int): Input image size for training the pretrained model,
+            used in absolute postion embedding. Default 224.
+        patch_size (int | tuple(int)): Patch size. Default: 4.
+        in_chans (int): Number of input image channels. Default: 3.
+        embed_dim (int): Number of linear projection output channels. Default: 96.
+        depths (tuple[int]): Depths of each Swin Transformer stage.
+        num_heads (tuple[int]): Number of attention head of each stage.
+        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.
+        drop_rate (float): Dropout rate.
+        attn_drop_rate (float): Attention dropout rate. Default: 0.
+        drop_path_rate (float): Stochastic depth rate. Default: 0.2.
+        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.
+        out_indices (Sequence[int]): Output from which stages.
+        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
+            -1 means not freezing any parameters.
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+    """
+
+    def __init__(self,
+                 pretrain_img_size=224,
+                 patch_size=4,
+                 in_chans=3,
+                 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=0.2,
+                 norm_layer=nn.LayerNorm,
+                 ape=False,
+                 patch_norm=True,
+                 out_indices=(0, 1, 2, 3),
+                 frozen_stages=-1,
+                 use_checkpoint=False):
+        super().__init__()
+
+        self.pretrain_img_size = pretrain_img_size
+        self.num_layers = len(depths)
+        self.embed_dim = embed_dim
+        self.ape = ape
+        self.patch_norm = patch_norm
+        self.out_indices = out_indices
+        self.frozen_stages = frozen_stages
+
+        # split image into non-overlapping patches
+        self.patch_embed = PatchEmbed(
+            patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,
+            norm_layer=norm_layer if self.patch_norm else None)
+
+        # absolute position embedding
+        if self.ape:
+            pretrain_img_size = to_2tuple(pretrain_img_size)
+            patch_size = to_2tuple(patch_size)
+            patches_resolution = [pretrain_img_size[0] // patch_size[0], pretrain_img_size[1] // patch_size[1]]
+
+            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1]))
+            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),
+                depth=depths[i_layer],
+                num_heads=num_heads[i_layer],
+                window_size=window_size,
+                mlp_ratio=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)
+
+        num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]
+        self.num_features = num_features
+
+        # add a norm layer for each output
+        for i_layer in out_indices:
+            layer = norm_layer(num_features[i_layer])
+            layer_name = f'norm{i_layer}'
+            self.add_module(layer_name, layer)
+
+        self._freeze_stages()
+
+    def _freeze_stages(self):
+        if self.frozen_stages >= 0:
+            self.patch_embed.eval()
+            for param in self.patch_embed.parameters():
+                param.requires_grad = False
+
+        if self.frozen_stages >= 1 and self.ape:
+            self.absolute_pos_embed.requires_grad = False
+
+        if self.frozen_stages >= 2:
+            self.pos_drop.eval()
+            for i in range(0, self.frozen_stages - 1):
+                m = self.layers[i]
+                m.eval()
+                for param in m.parameters():
+                    param.requires_grad = False
+
+    def init_weights(self, pretrained=None):
+        """Initialize the weights in backbone.
+        Args:
+            pretrained (str, optional): Path to pre-trained weights.
+                Defaults to None.
+        """
+        def _init_weights(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)
+
+        if isinstance(pretrained, str):
+            self.apply(_init_weights)
+            checkpoint = torch.load(pretrained, map_location='cpu')
+            print(f'load from {pretrained}.') 
+            self.load_state_dict(checkpoint['model'], strict=False)
+        elif pretrained is None:
+            self.apply(_init_weights)
+        else:
+            raise TypeError('pretrained must be a str or None')
+
+    def forward(self, x):
+        """Forward function."""
+        x = self.patch_embed(x)
+        Wh, Ww = x.size(2), x.size(3)
+        if self.ape:
+            # interpolate the position embedding to the corresponding size
+            absolute_pos_embed = F.interpolate(self.absolute_pos_embed, size=(Wh, Ww), mode='bicubic')
+            x = (x + absolute_pos_embed).flatten(2).transpose(1, 2)  # B Wh*Ww C
+        else:
+            x = x.flatten(2).transpose(1, 2)
+        x = self.pos_drop(x)
+
+        outs = []
+        for i in range(self.num_layers):
+            layer = self.layers[i]
+            x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
+            if i in self.out_indices:
+                norm_layer = getattr(self, f'norm{i}')
+                x_out = norm_layer(x_out)
+                out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
+                outs.append(out)
+        rets = {str(u): v for (u,v) in enumerate(outs)}
+        return rets
+
+    def train(self, mode=True):
+        """Convert the model into training mode while keep layers freezed."""
+        super(SwinTransformer, self).train(mode)
+        self._freeze_stages()
+
+
+class BackboneBase(nn.Module):
+    def __init__(self, backbone: nn.Module, strides=[4, 8, 16, 32], num_channels=[96, 192, 384, 768]):
+        super().__init__()
+        self.strides = strides
+        self.num_channels = num_channels
+        self.body = backbone
+
+    def forward(self, tensor_list: NestedTensor):
+        xs = self.body(tensor_list.tensors)
+        out: Dict[str, NestedTensor] = {}
+        for name, x in xs.items():
+            m = tensor_list.mask
+            assert m is not None
+            mask = F.interpolate(m[None].float(), size=x.shape[-2:]).to(torch.bool)[0]
+            out[name] = NestedTensor(x, mask)
+        return out
+
+
+class Backbone(BackboneBase):
+    """ResNet backbone with frozen BatchNorm."""
+    def __init__(self, name: str,
+                 checkpoint: bool = False,
+                 pretrained: str = None):
+        assert name in ['swin_t_p4w7', 'swin_s_p4w7', 'swin_b_p4w7', 'swin_l_p4w7']
+        cfgs = configs[name]
+        cfgs.update({'use_checkpoint': checkpoint})
+        out_indices = cfgs['out_indices']
+        strides = [int(2**(i+2)) for i in out_indices]
+        num_channels = [int(cfgs['embed_dim'] * 2**i) for i in out_indices]
+        backbone = SwinTransformer(**cfgs)
+        backbone.init_weights(pretrained)
+        super().__init__(backbone, strides, num_channels)
+
+
+class Joiner(nn.Sequential):
+    def __init__(self, backbone, position_embedding):
+        super().__init__(backbone, position_embedding)
+        self.strides = backbone.strides
+        self.num_channels = backbone.num_channels
+
+    def forward(self, tensor_list: NestedTensor):
+        tensor_list.tensors = rearrange(tensor_list.tensors, 'b t c h w -> (b t) c h w')
+        tensor_list.mask = rearrange(tensor_list.mask, 'b t h w -> (b t) h w')
+
+        xs = self[0](tensor_list)
+        out: List[NestedTensor] = []
+        pos = []
+        for name, x in sorted(xs.items()):
+            out.append(x)
+        # position encoding
+        for x in out:
+            pos.append(self[1](x).to(x.tensors.dtype))
+        return out, pos
+
+    
+def build_swin_backbone(args):
+    position_embedding = build_position_encoding(args)
+    backbone = Backbone(args.backbone, args.use_checkpoint, args.backbone_pretrained)
+    model = Joiner(backbone, position_embedding)
+    return model
+ 
+
+configs = {
+    'swin_t_p4w7': dict(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=0.2,
+                    ape=False,
+                    patch_norm=True,
+                    out_indices=(0, 1, 2, 3),
+                    use_checkpoint=False),
+    'swin_s_p4w7': dict(embed_dim=96,
+                        depths=[2, 2, 18, 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=0.2,
+                        ape=False,
+                        patch_norm=True,
+                        out_indices=(0, 1, 2, 3),
+                        use_checkpoint=False),
+    'swin_b_p4w7': dict(embed_dim=128,
+                        depths=[2, 2, 18, 2],
+                        num_heads=[4, 8, 16, 32],
+                        window_size=7,
+                        mlp_ratio=4.,
+                        qkv_bias=True,
+                        qk_scale=None,
+                        drop_rate=0.,
+                        attn_drop_rate=0.,
+                        drop_path_rate=0.3,
+                        ape=False,
+                        patch_norm=True,
+                        out_indices=(0, 1, 2, 3),
+                        use_checkpoint=False),
+    'swin_l_p4w7': dict(embed_dim=192,
+                        depths=[2, 2, 18, 2],
+                        num_heads=[6, 12, 24, 48],
+                        window_size=7,
+                        mlp_ratio=4.,
+                        qkv_bias=True,
+                        qk_scale=None,
+                        drop_rate=0.,
+                        attn_drop_rate=0.,
+                        drop_path_rate=0.3,
+                        ape=False,
+                        patch_norm=True,
+                        out_indices=(0, 1, 2, 3),
+                        use_checkpoint=False),
+        
+}
+
+
+if __name__ == '__main__':
+    model = SwinTransformer().cuda()
+    inputs = torch.randn(2,3,384,224).cuda()
+    import ipdb; ipdb.set_trace()
+    # outs
+    # 0: (2, 96, 96, 56)
+    # 1: (2, 192, 48, 28)
+    # 2: (2, 384, 24, 14)
+    # 3: (2, 768, 12, 7)
+    out = model(inputs)
+
+    
diff --git a/models/video_swin_transformer.py b/models/video_swin_transformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..988a32481c4753cb55b5624bd70ae5c31ce13200
--- /dev/null
+++ b/models/video_swin_transformer.py
@@ -0,0 +1,820 @@
+"""
+Modified from Video-Swin-Transformer https://github.com/SwinTransformer/Video-Swin-Transformer
+"""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint as checkpoint
+import numpy as np
+from timm.models.layers import DropPath, trunc_normal_
+from functools import reduce, lru_cache
+from operator import mul
+from einops import rearrange
+from typing import Dict, List
+
+from util.misc import NestedTensor
+from .position_encoding import build_position_encoding
+
+class Mlp(nn.Module):
+    """ Multilayer perceptron."""
+
+    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, D, H, W, C)
+        window_size (tuple[int]): window size
+
+    Returns:
+        windows: (B*num_windows, window_size*window_size, C)
+    """
+    B, D, H, W, C = x.shape
+    x = x.view(B, D // window_size[0], window_size[0], H // window_size[1], window_size[1], W // window_size[2], window_size[2], C)
+    windows = x.permute(0, 1, 3, 5, 2, 4, 6, 7).contiguous().view(-1, reduce(mul, window_size), C)
+    return windows
+
+
+def window_reverse(windows, window_size, B, D, H, W):
+    """
+    Args:
+        windows: (B*num_windows, window_size, window_size, C)
+        window_size (tuple[int]): Window size
+        H (int): Height of image
+        W (int): Width of image
+
+    Returns:
+        x: (B, D, H, W, C)
+    """
+    x = windows.view(B, D // window_size[0], H // window_size[1], W // window_size[2], window_size[0], window_size[1], window_size[2], -1)
+    x = x.permute(0, 1, 4, 2, 5, 3, 6, 7).contiguous().view(B, D, H, W, -1)
+    return x
+
+
+def get_window_size(x_size, window_size, shift_size=None):
+    use_window_size = list(window_size)
+    if shift_size is not None:
+        use_shift_size = list(shift_size)
+    for i in range(len(x_size)):
+        if x_size[i] <= window_size[i]:
+            use_window_size[i] = x_size[i]
+            if shift_size is not None:
+                use_shift_size[i] = 0
+
+    if shift_size is None:
+        return tuple(use_window_size)
+    else:
+        return tuple(use_window_size), tuple(use_shift_size)
+
+
+class WindowAttention3D(nn.Module):
+    """ 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 temporal length, 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=False, qk_scale=None, attn_drop=0., proj_drop=0.):
+
+        super().__init__()
+        self.dim = dim
+        self.window_size = window_size  # Wd, 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) * (2 * window_size[2] - 1), num_heads))  # 2*Wd-1 * 2*Wh-1 * 2*Ww-1, nH
+
+        # get pair-wise relative position index for each token inside the window
+        coords_d = torch.arange(self.window_size[0])
+        coords_h = torch.arange(self.window_size[1])
+        coords_w = torch.arange(self.window_size[2])
+        coords = torch.stack(torch.meshgrid(coords_d, coords_h, coords_w))  # 3, Wd, Wh, Ww
+        coords_flatten = torch.flatten(coords, 1)  # 3, Wd*Wh*Ww
+        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 3, Wd*Wh*Ww, Wd*Wh*Ww
+        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wd*Wh*Ww, Wd*Wh*Ww, 3
+        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
+        relative_coords[:, :, 1] += self.window_size[1] - 1
+        relative_coords[:, :, 2] += self.window_size[2] - 1
+
+        relative_coords[:, :, 0] *= (2 * self.window_size[1] - 1) * (2 * self.window_size[2] - 1)
+        relative_coords[:, :, 1] *= (2 * self.window_size[2] - 1)
+        relative_position_index = relative_coords.sum(-1)  # Wd*Wh*Ww, Wd*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):
+        """ Forward function.
+        Args:
+            x: input features with shape of (num_windows*B, N, C)
+            mask: (0/-inf) mask with shape of (num_windows, N, N) 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]  # B_, nH, N, C
+
+        q = q * self.scale
+        attn = q @ k.transpose(-2, -1)
+
+        relative_position_bias = self.relative_position_bias_table[self.relative_position_index[:N, :N].reshape(-1)].reshape(
+            N, N, -1)  # Wd*Wh*Ww,Wd*Wh*Ww,nH
+        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wd*Wh*Ww, Wd*Wh*Ww
+        attn = attn + relative_position_bias.unsqueeze(0) # B_, nH, N, N
+
+        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
+
+
+class SwinTransformerBlock3D(nn.Module):
+    """ Swin Transformer Block.
+
+    Args:
+        dim (int): Number of input channels.
+        num_heads (int): Number of attention heads.
+        window_size (tuple[int]): Window size.
+        shift_size (tuple[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, num_heads, window_size=(2,7,7), shift_size=(0,0,0),
+                 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, use_checkpoint=False):
+        super().__init__()
+        self.dim = dim
+        self.num_heads = num_heads
+        self.window_size = window_size
+        self.shift_size = shift_size
+        self.mlp_ratio = mlp_ratio
+        self.use_checkpoint=use_checkpoint
+
+        assert 0 <= self.shift_size[0] < self.window_size[0], "shift_size must in 0-window_size"
+        assert 0 <= self.shift_size[1] < self.window_size[1], "shift_size must in 0-window_size"
+        assert 0 <= self.shift_size[2] < self.window_size[2], "shift_size must in 0-window_size"
+
+        self.norm1 = norm_layer(dim)
+        self.attn = WindowAttention3D(
+            dim, window_size=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)
+
+    def forward_part1(self, x, mask_matrix):
+        B, D, H, W, C = x.shape
+        window_size, shift_size = get_window_size((D, H, W), self.window_size, self.shift_size)
+
+        x = self.norm1(x)
+        # pad feature maps to multiples of window size
+        pad_l = pad_t = pad_d0 = 0
+        pad_d1 = (window_size[0] - D % window_size[0]) % window_size[0]
+        pad_b = (window_size[1] - H % window_size[1]) % window_size[1]
+        pad_r = (window_size[2] - W % window_size[2]) % window_size[2]
+        x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b, pad_d0, pad_d1))
+        _, Dp, Hp, Wp, _ = x.shape
+        # cyclic shift
+        if any(i > 0 for i in shift_size):
+            shifted_x = torch.roll(x, shifts=(-shift_size[0], -shift_size[1], -shift_size[2]), dims=(1, 2, 3))
+            attn_mask = mask_matrix
+        else:
+            shifted_x = x
+            attn_mask = None
+        # partition windows
+        x_windows = window_partition(shifted_x, window_size)  # B*nW, Wd*Wh*Ww, C
+        # W-MSA/SW-MSA
+        attn_windows = self.attn(x_windows, mask=attn_mask)  # B*nW, Wd*Wh*Ww, C
+        # merge windows
+        attn_windows = attn_windows.view(-1, *(window_size+(C,)))
+        shifted_x = window_reverse(attn_windows, window_size, B, Dp, Hp, Wp)  # B D' H' W' C
+        # reverse cyclic shift
+        if any(i > 0 for i in shift_size):
+            x = torch.roll(shifted_x, shifts=(shift_size[0], shift_size[1], shift_size[2]), dims=(1, 2, 3))
+        else:
+            x = shifted_x
+
+        if pad_d1 >0 or pad_r > 0 or pad_b > 0:
+            x = x[:, :D, :H, :W, :].contiguous()
+        return x
+
+    def forward_part2(self, x):
+        return self.drop_path(self.mlp(self.norm2(x)))
+
+    def forward(self, x, mask_matrix):
+        """ Forward function.
+
+        Args:
+            x: Input feature, tensor size (B, D, H, W, C).
+            mask_matrix: Attention mask for cyclic shift.
+        """
+
+        shortcut = x
+        if self.use_checkpoint:
+            x = checkpoint.checkpoint(self.forward_part1, x, mask_matrix)
+        else:
+            x = self.forward_part1(x, mask_matrix)
+        x = shortcut + self.drop_path(x)
+
+        if self.use_checkpoint:
+            x = x + checkpoint.checkpoint(self.forward_part2, x)
+        else:
+            x = x + self.forward_part2(x)
+
+        return x
+
+
+class PatchMerging(nn.Module):
+    """ Patch Merging Layer
+
+    Args:
+        dim (int): Number of input channels.
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+    """
+    def __init__(self, dim, norm_layer=nn.LayerNorm):
+        super().__init__()
+        self.dim = dim
+        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
+        self.norm = norm_layer(4 * dim)
+
+    def forward(self, x):
+        """ Forward function.
+
+        Args:
+            x: Input feature, tensor size (B, D, H, W, C).
+        """
+        B, D, H, W, C = x.shape
+
+        # padding
+        pad_input = (H % 2 == 1) or (W % 2 == 1)
+        if pad_input:
+            x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
+
+        x0 = x[:, :, 0::2, 0::2, :]  # B D H/2 W/2 C
+        x1 = x[:, :, 1::2, 0::2, :]  # B D H/2 W/2 C
+        x2 = x[:, :, 0::2, 1::2, :]  # B D H/2 W/2 C
+        x3 = x[:, :, 1::2, 1::2, :]  # B D H/2 W/2 C
+        x = torch.cat([x0, x1, x2, x3], -1)  # B D H/2 W/2 4*C
+
+        x = self.norm(x)
+        x = self.reduction(x)
+
+        return x
+
+
+# cache each stage results
+@lru_cache()
+def compute_mask(D, H, W, window_size, shift_size, device):
+    img_mask = torch.zeros((1, D, H, W, 1), device=device)  # 1 Dp Hp Wp 1
+    cnt = 0
+    for d in slice(-window_size[0]), slice(-window_size[0], -shift_size[0]), slice(-shift_size[0],None):
+        for h in slice(-window_size[1]), slice(-window_size[1], -shift_size[1]), slice(-shift_size[1],None):
+            for w in slice(-window_size[2]), slice(-window_size[2], -shift_size[2]), slice(-shift_size[2],None):
+                img_mask[:, d, h, w, :] = cnt
+                cnt += 1
+    mask_windows = window_partition(img_mask, window_size)  # nW, ws[0]*ws[1]*ws[2], 1
+    mask_windows = mask_windows.squeeze(-1)  # nW, ws[0]*ws[1]*ws[2]
+    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))
+    return attn_mask
+
+
+class BasicLayer(nn.Module):
+    """ A basic Swin Transformer layer for one stage.
+
+    Args:
+        dim (int): Number of feature channels
+        depth (int): Depths of this stage.
+        num_heads (int): Number of attention head.
+        window_size (tuple[int]): Local window size. Default: (1,7,7).
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
+        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
+    """
+
+    def __init__(self,
+                 dim,
+                 depth,
+                 num_heads,
+                 window_size=(1,7,7),
+                 mlp_ratio=4.,
+                 qkv_bias=False,
+                 qk_scale=None,
+                 drop=0.,
+                 attn_drop=0.,
+                 drop_path=0.,
+                 norm_layer=nn.LayerNorm,
+                 downsample=None,
+                 use_checkpoint=False):
+        super().__init__()
+        self.window_size = window_size
+        self.shift_size = tuple(i // 2 for i in window_size)
+        self.depth = depth
+        self.use_checkpoint = use_checkpoint
+
+        # build blocks
+        self.blocks = nn.ModuleList([
+            SwinTransformerBlock3D(
+                dim=dim,
+                num_heads=num_heads,
+                window_size=window_size,
+                shift_size=(0,0,0) if (i % 2 == 0) else self.shift_size,
+                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,
+                use_checkpoint=use_checkpoint,
+            )
+            for i in range(depth)])
+        
+        self.downsample = downsample
+        if self.downsample is not None:
+            self.downsample = downsample(dim=dim, norm_layer=norm_layer)
+
+    def forward(self, x):
+        """ Forward function.
+
+        Args:
+            x: Input feature, tensor size (B, C, D, H, W).
+        """
+        # calculate attention mask for SW-MSA
+        B, C, D, H, W = x.shape
+        window_size, shift_size = get_window_size((D,H,W), self.window_size, self.shift_size)
+        x = rearrange(x, 'b c d h w -> b d h w c')
+        Dp = int(np.ceil(D / window_size[0])) * window_size[0]
+        Hp = int(np.ceil(H / window_size[1])) * window_size[1]
+        Wp = int(np.ceil(W / window_size[2])) * window_size[2]
+        attn_mask = compute_mask(Dp, Hp, Wp, window_size, shift_size, x.device)
+        for blk in self.blocks:
+            x = blk(x, attn_mask)
+        x = x.view(B, D, H, W, -1)
+
+        if self.downsample is not None:
+            x = self.downsample(x)
+        x = rearrange(x, 'b d h w c -> b c d h w')
+        return x
+
+
+class PatchEmbed3D(nn.Module):
+    """ Video to Patch Embedding.
+
+    Args:
+        patch_size (int): Patch token size. Default: (2,4,4).
+        in_chans (int): Number of input video 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, patch_size=(2,4,4), in_chans=3, embed_dim=96, norm_layer=None):
+        super().__init__()
+        self.patch_size = patch_size
+
+        self.in_chans = in_chans
+        self.embed_dim = embed_dim
+
+        self.proj = nn.Conv3d(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):
+        """Forward function."""
+        # padding
+        _, _, D, H, W = x.size()
+        if W % self.patch_size[2] != 0:
+            x = F.pad(x, (0, self.patch_size[2] - W % self.patch_size[2]))
+        if H % self.patch_size[1] != 0:
+            x = F.pad(x, (0, 0, 0, self.patch_size[1] - H % self.patch_size[1]))
+        if D % self.patch_size[0] != 0:
+            x = F.pad(x, (0, 0, 0, 0, 0, self.patch_size[0] - D % self.patch_size[0]))
+
+        x = self.proj(x)  # B C D Wh Ww
+        if self.norm is not None:
+            D, Wh, Ww = x.size(2), x.size(3), x.size(4)
+            x = x.flatten(2).transpose(1, 2)
+            x = self.norm(x)
+            x = x.transpose(1, 2).view(-1, self.embed_dim, D, Wh, Ww)
+
+        return x
+
+
+class SwinTransformer3D(nn.Module):
+    """ Swin Transformer backbone.
+        A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
+          https://arxiv.org/pdf/2103.14030
+
+    Args:
+        patch_size (int | tuple(int)): Patch size. Default: (4,4,4).
+        in_chans (int): Number of input image channels. Default: 3.
+        embed_dim (int): Number of linear projection output channels. Default: 96.
+        depths (tuple[int]): Depths of each Swin Transformer stage.
+        num_heads (tuple[int]): Number of attention head of each stage.
+        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: Truee
+        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
+        drop_rate (float): Dropout rate.
+        attn_drop_rate (float): Attention dropout rate. Default: 0.
+        drop_path_rate (float): Stochastic depth rate. Default: 0.2.
+        norm_layer: Normalization layer. Default: nn.LayerNorm.
+        patch_norm (bool): If True, add normalization after patch embedding. Default: False.
+        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
+            -1 means not freezing any parameters.
+    """
+
+    def __init__(self,
+                 pretrained=None,
+                 pretrained2d=True,
+                 patch_size=(4,4,4),
+                 in_chans=3,
+                 embed_dim=96,
+                 depths=[2, 2, 6, 2],
+                 num_heads=[3, 6, 12, 24],
+                 window_size=(2,7,7),
+                 mlp_ratio=4.,
+                 qkv_bias=True,
+                 qk_scale=None,
+                 drop_rate=0.,
+                 attn_drop_rate=0.,
+                 drop_path_rate=0.2,
+                 norm_layer=nn.LayerNorm,
+                 patch_norm=False,
+                 frozen_stages=-1,
+                 use_checkpoint=False):
+        super().__init__()
+
+        self.pretrained = pretrained
+        self.pretrained2d = pretrained2d
+        self.num_layers = len(depths)
+        self.embed_dim = embed_dim
+        self.patch_norm = patch_norm
+        self.frozen_stages = frozen_stages
+        self.window_size = window_size
+        self.patch_size = patch_size
+
+        # split image into non-overlapping patches
+        self.patch_embed = PatchEmbed3D(
+            patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,
+            norm_layer=norm_layer if self.patch_norm else None)
+
+        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),
+                depth=depths[i_layer],
+                num_heads=num_heads[i_layer],
+                window_size=window_size,
+                mlp_ratio=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.num_features = int(embed_dim * 2**(self.num_layers-1))
+
+        # add a norm layer for each output
+        self.norm = norm_layer(self.num_features)
+
+        self._freeze_stages()
+
+    def _freeze_stages(self):
+        if self.frozen_stages >= 0:
+            self.patch_embed.eval()
+            for param in self.patch_embed.parameters():
+                param.requires_grad = False
+
+        if self.frozen_stages >= 1:
+            self.pos_drop.eval()
+            for i in range(0, self.frozen_stages):
+                m = self.layers[i]
+                m.eval()
+                for param in m.parameters():
+                    param.requires_grad = False
+
+    def inflate_weights(self, logger):
+        """Inflate the swin2d parameters to swin3d.
+
+        The differences between swin3d and swin2d mainly lie in an extra
+        axis. To utilize the pretrained parameters in 2d model,
+        the weight of swin2d models should be inflated to fit in the shapes of
+        the 3d counterpart.
+
+        Args:
+            logger (logging.Logger): The logger used to print
+                debugging infomation.
+        """
+        checkpoint = torch.load(self.pretrained, map_location='cpu')
+        state_dict = checkpoint['model']
+
+        # delete relative_position_index since we always re-init it
+        relative_position_index_keys = [k for k in state_dict.keys() if "relative_position_index" in k]
+        for k in relative_position_index_keys:
+            del state_dict[k]
+
+        # delete attn_mask since we always re-init it
+        attn_mask_keys = [k for k in state_dict.keys() if "attn_mask" in k]
+        for k in attn_mask_keys:
+            del state_dict[k]
+
+        state_dict['patch_embed.proj.weight'] = state_dict['patch_embed.proj.weight'].unsqueeze(2).repeat(1,1,self.patch_size[0],1,1) / self.patch_size[0]
+
+        # bicubic interpolate relative_position_bias_table if not match
+        relative_position_bias_table_keys = [k for k in state_dict.keys() if "relative_position_bias_table" in k]
+        for k in relative_position_bias_table_keys:
+            relative_position_bias_table_pretrained = state_dict[k]
+            relative_position_bias_table_current = self.state_dict()[k]
+            L1, nH1 = relative_position_bias_table_pretrained.size()
+            L2, nH2 = relative_position_bias_table_current.size()
+            L2 = (2*self.window_size[1]-1) * (2*self.window_size[2]-1)
+            wd = self.window_size[0]
+            if nH1 != nH2:
+                logger.warning(f"Error in loading {k}, passing")
+            else:
+                if L1 != L2:
+                    S1 = int(L1 ** 0.5)
+                    relative_position_bias_table_pretrained_resized = torch.nn.functional.interpolate(
+                        relative_position_bias_table_pretrained.permute(1, 0).view(1, nH1, S1, S1), size=(2*self.window_size[1]-1, 2*self.window_size[2]-1),
+                        mode='bicubic')
+                    relative_position_bias_table_pretrained = relative_position_bias_table_pretrained_resized.view(nH2, L2).permute(1, 0)
+            state_dict[k] = relative_position_bias_table_pretrained.repeat(2*wd-1,1)
+
+        msg = self.load_state_dict(state_dict, strict=False)
+        logger.info(msg)
+        logger.info(f"=> loaded successfully '{self.pretrained}'")
+        del checkpoint
+        torch.cuda.empty_cache()
+
+    def forward(self, x):
+        """Forward function."""
+        x = self.patch_embed(x)
+
+        x = self.pos_drop(x)
+
+        for layer in self.layers:
+            x = layer(x.contiguous())
+
+        x = rearrange(x, 'n c d h w -> n d h w c')
+        x = self.norm(x)
+        x = rearrange(x, 'n d h w c -> n c d h w')
+
+        return x  # [B, C, T, H, W], T does not downsample so we can get the per-frame feature
+
+    def train(self, mode=True):
+        """Convert the model into training mode while keep layers freezed."""
+        super(SwinTransformer3D, self).train(mode)
+        self._freeze_stages()
+
+
+
+class VideoSwinTransformerBackbone(nn.Module):
+    """
+    A wrapper which allows using Video-Swin Transformer as a temporal encoder for MTTR.
+    Check out video-swin's original paper at: https://arxiv.org/abs/2106.13230 for more info about this architecture.
+    Only the 'tiny' version of video swin was tested and is currently supported in our project.
+    Additionally, we slightly modify video-swin to make it output per-frame embeddings as required by MTTR (check our
+    paper's supplementary for more details), and completely discard of its 4th block.
+    """
+    def __init__(self, backbone_pretrained: bool, backbone_pretrained_path, train_backbone: bool, **kwargs):
+        super(VideoSwinTransformerBackbone, self).__init__()
+        # patch_size is (1, 4, 4) instead of the original (2, 4, 4).
+        # this prevents swinT's original temporal downsampling so we can get per-frame features.
+        swin_backbone = SwinTransformer3D(**kwargs)
+        if backbone_pretrained and isinstance(backbone_pretrained_path, str):
+            state_dict = torch.load(backbone_pretrained_path)['state_dict']
+            # extract swinT's kinetics-400 pretrained weights and ignore the rest (prediction head etc.)
+            state_dict = {k[9:]: v for k, v in state_dict.items() if 'backbone.' in k}
+
+            # sum over the patch embedding weight temporal dim  [96, 3, 2, 4, 4] --> [96, 3, 1, 4, 4]
+            patch_embed_weight = state_dict['patch_embed.proj.weight']
+            patch_embed_weight = patch_embed_weight.sum(dim=2, keepdims=True)
+            state_dict['patch_embed.proj.weight'] = patch_embed_weight
+            print(f'load from {backbone_pretrained_path}.') 
+            swin_backbone.load_state_dict(state_dict)
+
+        self.patch_embed = swin_backbone.patch_embed
+        self.pos_drop = swin_backbone.pos_drop
+        self.layers = swin_backbone.layers
+        self.downsamples = nn.ModuleList()
+        for layer in self.layers:
+            self.downsamples.append(layer.downsample)
+            layer.downsample = None
+        self.downsamples[-1] = None  # downsampling after the last layer is not necessary
+
+        self.layer_output_channels = [swin_backbone.embed_dim * 2 ** i for i in range(len(self.layers))]
+        self.train_backbone = train_backbone
+        if not train_backbone:
+            for parameter in self.parameters():
+                parameter.requires_grad_(False)
+
+    def forward(self, samples: torch.Tensor, num_frames):
+        # num_frames is needed, because we put time in batch dimension.
+        # samples: [B*T, 3, H, W]
+        n, c, h, w = samples.shape
+        samples = rearrange(samples, '(b t) c h w -> b c t h w', b=n//num_frames, t=num_frames)
+        vid_embeds = self.patch_embed(samples)
+        vid_embeds = self.pos_drop(vid_embeds)
+
+        out = {} # layer outputs before downsampling
+        for idx, (layer, downsample) in enumerate(zip(self.layers, self.downsamples)):
+            vid_embeds = layer(vid_embeds.contiguous())
+            out[str(idx)] = vid_embeds
+            if downsample:
+                vid_embeds = rearrange(vid_embeds, 'b c t h w -> b t h w c')
+                vid_embeds = downsample(vid_embeds)
+                vid_embeds = rearrange(vid_embeds, 'b t h w c -> b c t h w')
+        # rearrange
+        for idx, o in out.items():
+            out[idx] = rearrange(o, 'b c t h w -> (b t) c h w')
+        return out
+
+
+
+class BackboneBase(nn.Module):
+    def __init__(self, backbone: nn.Module, strides=[4, 8, 16, 32], num_channels=[96, 192, 384, 768]):
+        super().__init__()
+        self.strides = strides
+        self.num_channels = num_channels
+        self.body = backbone
+
+    def forward(self, tensor_list: NestedTensor, num_frames: int):
+        xs = self.body(tensor_list.tensors, num_frames)
+        out: Dict[str, NestedTensor] = {}
+        for name, x in xs.items():
+            m = tensor_list.mask
+            assert m is not None
+            mask = F.interpolate(m[None].float(), size=x.shape[-2:]).to(torch.bool)[0]
+            out[name] = NestedTensor(x, mask)
+        return out
+
+class Backbone(BackboneBase):
+    """ResNet backbone with frozen BatchNorm."""
+    def __init__(self, name: str,
+                 checkpoint: bool = False,
+                 pretrained: str = None):
+        assert name in ['video_swin_t_p4w7', 'video_swin_s_p4w7', 'video_swin_b_p4w7']
+        cfgs = configs[name]
+        cfgs.update({'use_checkpoint': checkpoint})
+        out_indices = (0, 1, 2, 3)
+        strides = [int(2**(i+2)) for i in out_indices]
+        num_channels = [int(cfgs['embed_dim'] * 2**i) for i in out_indices]
+        backbone = VideoSwinTransformerBackbone(True, pretrained, True, **cfgs)
+        super().__init__(backbone, strides, num_channels)
+
+
+configs = {
+    'video_swin_t_p4w7': 
+                   dict(patch_size=(1,4,4),
+                        embed_dim=96,
+                        depths=[2, 2, 6, 2],
+                        num_heads=[3, 6, 12, 24],
+                        window_size=(8,7,7),
+                        mlp_ratio=4.,
+                        qkv_bias=True,
+                        qk_scale=None,
+                        drop_rate=0.,
+                        attn_drop_rate=0.,
+                        drop_path_rate=0.2,
+                        patch_norm=True,
+                        use_checkpoint=False
+                        ),
+    'video_swin_s_p4w7': 
+                   dict(patch_size=(1,4,4),
+                        embed_dim=96,
+                        depths=[2, 2, 18, 2],
+                        num_heads=[3, 6, 12, 24],
+                        window_size=(8,7,7),
+                        mlp_ratio=4.,
+                        qkv_bias=True,
+                        qk_scale=None,
+                        drop_rate=0.,
+                        attn_drop_rate=0.,
+                        drop_path_rate=0.2,
+                        patch_norm=True,
+                        use_checkpoint=False
+                        ),
+    'video_swin_b_p4w7': 
+                   dict(patch_size=(1,4,4),
+                        embed_dim=128,
+                        depths=[2, 2, 18, 2],
+                        num_heads=[4, 8, 16, 32],
+                        window_size=(8,7,7),
+                        mlp_ratio=4.,
+                        qkv_bias=True,
+                        qk_scale=None,
+                        drop_rate=0.,
+                        attn_drop_rate=0.,
+                        drop_path_rate=0.2,
+                        patch_norm=True,
+                        use_checkpoint=False
+                        )
+}
+
+class Joiner(nn.Sequential):
+    def __init__(self, backbone, position_embedding):
+        super().__init__(backbone, position_embedding)
+        self.strides = backbone.strides
+        self.num_channels = backbone.num_channels
+
+    def forward(self, tensor_list: NestedTensor):
+        _, t = tensor_list.tensors.shape[:2]
+        tensor_list.tensors = rearrange(tensor_list.tensors, 'b t c h w -> (b t) c h w')
+        tensor_list.mask = rearrange(tensor_list.mask, 'b t h w -> (b t) h w')
+
+        xs = self[0](tensor_list, num_frames=t)
+        out: List[NestedTensor] = []
+        pos = []
+        for name, x in sorted(xs.items()):
+            out.append(x)
+        # position encoding
+        for x in out:
+            pos.append(self[1](x).to(x.tensors.dtype))
+        return out, pos
+
+    
+def build_video_swin_backbone(args):
+    position_embedding = build_position_encoding(args)
+    backbone = Backbone(args.backbone, args.use_checkpoint, args.backbone_pretrained)
+    model = Joiner(backbone, position_embedding)
+    return model
+    
+
+if __name__ == '__main__':
+    cfgs = configs['video_swin_t_p4w7']
+    model = VideoSwinTransformerBackbone(True, 'video_swin_pretrained/swin_tiny_patch244_window877_kinetics400_1k.pth', True, **cfgs).cuda()
+    inputs = torch.randn(10, 3,384,224).cuda() # 10 = 2 x 5
+    import ipdb; ipdb.set_trace()
+    # outs
+    # 0: (10, 96, 96, 56)
+    # 1: (10, 192, 48, 28)
+    # 2: (10, 384, 24, 14)
+    # 3: (10, 768, 12, 7)
+    out = model(inputs, num_frames=5)
\ No newline at end of file
diff --git a/opts.py b/opts.py
new file mode 100644
index 0000000000000000000000000000000000000000..e903524d0cfe2fe3d31fd25aa0ef931d54ca4a86
--- /dev/null
+++ b/opts.py
@@ -0,0 +1,139 @@
+import argparse
+
+def get_args_parser():
+    parser = argparse.ArgumentParser('ReferFormer training and inference scripts.', add_help=False)
+    parser.add_argument('--lr', default=1e-4, type=float)
+    parser.add_argument('--lr_backbone', default=5e-5, type=float)
+    parser.add_argument('--lr_backbone_names', default=['backbone.0'], type=str, nargs='+')
+    parser.add_argument('--lr_text_encoder', default=1e-5, type=float)
+    parser.add_argument('--lr_text_encoder_names', default=['text_encoder'], type=str, nargs='+')
+    parser.add_argument('--lr_linear_proj_names', default=['reference_points', 'sampling_offsets'], type=str, nargs='+')
+    parser.add_argument('--lr_linear_proj_mult', default=1.0, type=float)
+    parser.add_argument('--batch_size', default=1, type=int)
+    parser.add_argument('--weight_decay', default=5e-4, type=float)
+    parser.add_argument('--epochs', default=10, type=int)
+    parser.add_argument('--lr_drop', default=[6, 8], type=int, nargs='+')
+    parser.add_argument('--clip_max_norm', default=0.1, type=float,
+                        help='gradient clipping max norm')
+
+    # Model parameters
+    # load the pretrained weights
+    parser.add_argument('--pretrained_weights', type=str, default=None,
+                        help="Path to the pretrained model.") 
+
+    # Variants of Deformable DETR
+    parser.add_argument('--with_box_refine', default=False, action='store_true')
+    parser.add_argument('--two_stage', default=False, action='store_true') # NOTE: must be false
+
+    # * Backbone
+    # ["resnet50", "resnet101", "swin_t_p4w7", "swin_s_p4w7", "swin_b_p4w7", "swin_l_p4w7"]
+    # ["video_swin_t_p4w7", "video_swin_s_p4w7", "video_swin_b_p4w7"]
+    parser.add_argument('--backbone', default='resnet50', type=str, 
+                        help="Name of the convolutional backbone to use")
+    parser.add_argument('--backbone_pretrained', default=None, type=str, 
+                        help="if use swin backbone and train from scratch, the path to the pretrained weights")
+    parser.add_argument('--use_checkpoint', action='store_true', help='whether use checkpoint for swin/video swin backbone')
+    parser.add_argument('--dilation', action='store_true', # DC5
+                        help="If true, we replace stride with dilation in the last convolutional block (DC5)")
+    parser.add_argument('--position_embedding', default='sine', type=str, choices=('sine', 'learned'),
+                        help="Type of positional embedding to use on top of the image features")
+    parser.add_argument('--num_feature_levels', default=4, type=int, help='number of feature levels')
+
+    # * Transformer
+    parser.add_argument('--enc_layers', default=4, type=int,
+                        help="Number of encoding layers in the transformer")
+    parser.add_argument('--dec_layers', default=4, type=int,
+                        help="Number of decoding layers in the transformer")
+    parser.add_argument('--dim_feedforward', default=2048, type=int,
+                        help="Intermediate size of the feedforward layers in the transformer blocks")
+    parser.add_argument('--hidden_dim', default=256, type=int, 
+                        help="Size of the embeddings (dimension of the transformer)")
+    parser.add_argument('--dropout', default=0.1, type=float,
+                        help="Dropout applied in the transformer")
+    parser.add_argument('--nheads', default=8, type=int,
+                        help="Number of attention heads inside the transformer's attentions")
+    parser.add_argument('--num_frames', default=5, type=int,
+                        help="Number of clip frames for training")
+    parser.add_argument('--num_queries', default=5, type=int,
+                        help="Number of query slots, all frames share the same queries") 
+    parser.add_argument('--dec_n_points', default=4, type=int)
+    parser.add_argument('--enc_n_points', default=4, type=int)
+    parser.add_argument('--pre_norm', action='store_true')
+    # for text
+    parser.add_argument('--freeze_text_encoder', action='store_true') # default: False
+
+    # * Segmentation
+    parser.add_argument('--masks', action='store_true',
+                        help="Train segmentation head if the flag is provided")
+    parser.add_argument('--mask_dim', default=256, type=int, 
+                        help="Size of the mask embeddings (dimension of the dynamic mask conv)")
+    parser.add_argument('--controller_layers', default=3, type=int, 
+                        help="Dynamic conv layer number")
+    parser.add_argument('--dynamic_mask_channels', default=8, type=int, 
+                        help="Dynamic conv final channel number")
+    parser.add_argument('--no_rel_coord', dest='rel_coord', action='store_false',
+                        help="Disables relative coordinates")
+    
+    # Loss
+    parser.add_argument('--no_aux_loss', dest='aux_loss', action='store_false',
+                        help="Disables auxiliary decoding losses (loss at each layer)")
+    # * Matcher
+    parser.add_argument('--set_cost_class', default=2, type=float,
+                        help="Class coefficient in the matching cost")
+    parser.add_argument('--set_cost_bbox', default=5, type=float,
+                        help="L1 box coefficient in the matching cost")
+    parser.add_argument('--set_cost_giou', default=2, type=float,
+                        help="giou box coefficient in the matching cost")
+    parser.add_argument('--set_cost_mask', default=2, type=float,
+                        help="mask coefficient in the matching cost")
+    parser.add_argument('--set_cost_dice', default=5, type=float,
+                        help="mask coefficient in the matching cost")
+    # * Loss coefficients
+    parser.add_argument('--mask_loss_coef', default=2, type=float)
+    parser.add_argument('--dice_loss_coef', default=5, type=float)
+    parser.add_argument('--cls_loss_coef', default=2, type=float)
+    parser.add_argument('--bbox_loss_coef', default=5, type=float)
+    parser.add_argument('--giou_loss_coef', default=2, type=float)
+    parser.add_argument('--eos_coef', default=0.1, type=float,
+                        help="Relative classification weight of the no-object class")
+    parser.add_argument('--focal_alpha', default=0.25, type=float)
+
+    # dataset parameters
+    # ['ytvos', 'davis', 'a2d', 'jhmdb', 'refcoco', 'refcoco+', 'refcocog', 'all']
+    # 'all': using the three ref datasets for pretraining
+    parser.add_argument('--dataset_file', default='ytvos', help='Dataset name') 
+    parser.add_argument('--coco_path', type=str, default='data/coco')
+    parser.add_argument('--ytvos_path', type=str, default='data/ref-youtube-vos')
+    parser.add_argument('--davis_path', type=str, default='data/ref-davis')
+    parser.add_argument('--a2d_path', type=str, default='data/a2d_sentences')
+    parser.add_argument('--jhmdb_path', type=str, default='data/jhmdb_sentences')
+    parser.add_argument('--max_skip', default=3, type=int, help="max skip frame number")
+    parser.add_argument('--max_size', default=640, type=int, help="max size for the frame")
+    parser.add_argument('--binary', action='store_true')
+    parser.add_argument('--remove_difficult', action='store_true')
+
+    parser.add_argument('--output_dir', default='output',
+                        help='path where to save, empty for no saving')
+    parser.add_argument('--device', default='cuda',
+                        help='device to use for training / testing')
+    parser.add_argument('--seed', default=42, type=int)
+    parser.add_argument('--resume', default='', help='resume from checkpoint')
+    parser.add_argument('--start_epoch', default=0, type=int, metavar='N',
+                        help='start epoch')
+    parser.add_argument('--eval', action='store_true')
+    parser.add_argument('--num_workers', default=4, type=int)
+
+    # test setting
+    parser.add_argument('--threshold', default=0.5, type=float) # binary threshold for mask
+    parser.add_argument('--ngpu', default=8, type=int, help='gpu number when inference for ref-ytvos and ref-davis')
+    parser.add_argument('--split', default='valid', type=str, choices=['valid', 'test'])
+    parser.add_argument('--visualize', action='store_true', help='whether visualize the masks during inference')
+
+    # distributed training parameters
+    parser.add_argument('--world_size', default=1, type=int,
+                        help='number of distributed processes')
+    parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training')
+    parser.add_argument('--cache_mode', default=False, action='store_true', help='whether to cache images on memory')
+    return parser
+
+
diff --git a/refer/__pycache__/refer.cpython-38.pyc b/refer/__pycache__/refer.cpython-38.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..aa17139e716095765a5fe6c454bcb8745c54833d
Binary files /dev/null and b/refer/__pycache__/refer.cpython-38.pyc differ
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..42312ebb8ad9abbbadf1600e3ccd3a1c89e31620
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,12 @@
+transformers
+cython 
+scipy
+opencv-python
+pillow
+scikit-image
+timm
+einops
+pandas
+imgaug
+h5py
+av
\ No newline at end of file
diff --git a/scripts/dist_joint_train_ytvos.sh b/scripts/dist_joint_train_ytvos.sh
new file mode 100644
index 0000000000000000000000000000000000000000..baa992ae438fc338ee0932c6b8322015c80e41c6
--- /dev/null
+++ b/scripts/dist_joint_train_ytvos.sh
@@ -0,0 +1,32 @@
+#!/usr/bin/env bash
+set -x
+
+GPUS=${GPUS:-8}
+PORT=${PORT:-29500}
+if [ $GPUS -lt 8 ]; then
+    GPUS_PER_NODE=${GPUS_PER_NODE:-$GPUS}
+else
+    GPUS_PER_NODE=${GPUS_PER_NODE:-8}
+fi
+CPUS_PER_TASK=${CPUS_PER_TASK:-5}
+
+OUTPUT_DIR=$1
+PY_ARGS=${@:2}  # Any arguments from the forth one are captured by this
+
+
+# train
+PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
+python3 -m torch.distributed.launch --nproc_per_node=${GPUS_PER_NODE} --master_port=${PORT} --use_env \
+main_joint.py --with_box_refine --binary --freeze_text_encoder \
+--epochs 12 --lr_drop 8 10 \
+--output_dir=${OUTPUT_DIR}  ${PY_ARGS} 
+# --backbone [backbone]
+
+# inference
+CHECKPOINT=${OUTPUT_DIR}/checkpoint.pth
+python3 inference_ytvos.py --with_box_refine --binary --freeze_text_encoder \
+--output_dir=${OUTPUT_DIR} --resume=${CHECKPOINT}  ${PY_ARGS}
+# --backbone [backbone]
+
+echo "Working path is: ${OUTPUT_DIR}"
+
diff --git a/scripts/dist_pretrain.sh b/scripts/dist_pretrain.sh
new file mode 100644
index 0000000000000000000000000000000000000000..985ea59a29c73b2ff3a76d8676b93535803484f5
--- /dev/null
+++ b/scripts/dist_pretrain.sh
@@ -0,0 +1,27 @@
+#!/usr/bin/env bash
+set -x
+
+GPUS=${GPUS:-8}
+PORT=${PORT:-29500}
+if [ $GPUS -lt 8 ]; then
+    GPUS_PER_NODE=${GPUS_PER_NODE:-$GPUS}
+else
+    GPUS_PER_NODE=${GPUS_PER_NODE:-8}
+fi
+CPUS_PER_TASK=${CPUS_PER_TASK:-5}
+
+OUTPUT_DIR=$1
+PY_ARGS=${@:2}  # Any arguments from the forth one are captured by this
+
+
+# train
+PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
+python3 -m torch.distributed.launch --nproc_per_node=${GPUS_PER_NODE} --master_port=${PORT} --use_env \
+main_pretrain.py --with_box_refine --dataset_file all  --binary \  # do not freeze text encoder
+--batch_size 2 --num_frames 1 \
+--epochs 12 --lr_drop 8 10 \
+--output_dir=${OUTPUT_DIR} ${PY_ARGS}
+# --backbone [backbone]
+
+echo "Working path is: ${OUTPUT_DIR}"
+
diff --git a/slurm_script/convert_jsonl2lmdb.sh b/slurm_script/convert_jsonl2lmdb.sh
new file mode 100644
index 0000000000000000000000000000000000000000..ecbff9c086a8c63654445b66251249aa7fb0e61c
--- /dev/null
+++ b/slurm_script/convert_jsonl2lmdb.sh
@@ -0,0 +1,18 @@
+#!/bin/bash
+
+#SBATCH --job-name=convert_jsonl2lmdb
+#SBATCH --partition=a4000
+#SBATCH --nodelist=node05
+#SBATCH --gres=gpu:1
+#SBATCH --time=0-06:00:00
+#SBATCH --mem=5G
+#SBATCH --cpus-per-task=4
+#SBATCH --output=/home/yejin/data/projects/yejin/VerbCentric_RIS/ReferFormer/slurm_log/convert_jsonl2lmdb.out
+ cd /home/yejin/data/projects/yejin/VerbCentric_RIS/ReferFormer
+
+ ml purge
+ ml load cuda/12.1
+ eval "$(conda shell.bash hook)"
+ conda activate VerbCentric_RIS
+
+ python3 folder2lmdb.py -j revised_frames.jsonl -i /home/yejin/data/dataset/VRIS/mbench/ytvos/selected_frames -m '/home/yejin/data/dataset/VRIS/mbench/ytvos/filtered_masks_segid' -o '/home/yejin/data/dataset/VRIS/mbench/ytvos/filtered_lmdb_segid' -s 'test'
\ No newline at end of file
diff --git a/slurm_script/jupyter.sh b/slurm_script/jupyter.sh
new file mode 100644
index 0000000000000000000000000000000000000000..8016d1cd5bbbde20ce08b458be6636042329d45a
--- /dev/null
+++ b/slurm_script/jupyter.sh
@@ -0,0 +1,16 @@
+#!/bin/bash
+
+#SBATCH --job-name=jupyter
+#SBATCH --partition=a4000
+#SBATCH --nodelist=node05
+#SBATCH --gres=gpu:1
+#SBATCH --time=14-00:00:00
+#SBATCH --mem=5G
+#SBATCH --cpus-per-task=4
+#SBATCH --output=/home/yejin/data/projects/yejin/VerbCentric_RIS/ReferFormer/slurm_log/jupyter.out
+
+ ml purge
+ ml load cuda/12.1
+ eval "$(conda shell.bash hook)"
+ conda activate referformer
+ srun jupyter notebook --no-browser --port=7890
diff --git a/slurm_script/mbench_gpt_ref-ytvos-revised.sh b/slurm_script/mbench_gpt_ref-ytvos-revised.sh
new file mode 100644
index 0000000000000000000000000000000000000000..ebc3e3eb87ce0237841b3d0e21bea3399918ffaa
--- /dev/null
+++ b/slurm_script/mbench_gpt_ref-ytvos-revised.sh
@@ -0,0 +1,18 @@
+#!/bin/bash
+
+#SBATCH --job-name=mbench_gpt_ref-ytvos_revised50
+#SBATCH --partition=a5000
+#SBATCH --nodelist=node04
+#SBATCH --gres=gpu:1
+#SBATCH --time=14-00:00:00
+#SBATCH --mem=5G
+#SBATCH --cpus-per-task=4
+#SBATCH --output=/home/yejin/data/projects/yejin/VerbCentric_RIS/ReferFormer/slurm_log/mbench_gpt_ref-ytvos_revised50.out
+ cd /home/yejin/data/projects/yejin/VerbCentric_RIS/ReferFormer
+
+ ml purge
+ ml load cuda/12.1
+ eval "$(conda shell.bash hook)"
+ conda activate referformer
+
+ python3 mbench/gpt_ref-ytvos-revised.py
\ No newline at end of file
diff --git a/slurm_script/mbench_gpt_ref-ytvos.sh b/slurm_script/mbench_gpt_ref-ytvos.sh
new file mode 100644
index 0000000000000000000000000000000000000000..5f508bfcaa6330ddfe61012d5cd8f8968f58eee7
--- /dev/null
+++ b/slurm_script/mbench_gpt_ref-ytvos.sh
@@ -0,0 +1,18 @@
+#!/bin/bash
+
+#SBATCH --job-name=mbench_gpt_ref-ytvos
+#SBATCH --partition=a4000
+#SBATCH --nodelist=node05
+#SBATCH --gres=gpu:1
+#SBATCH --time=14-00:00:00
+#SBATCH --mem=5G
+#SBATCH --cpus-per-task=4
+#SBATCH --output=/home/yejin/data/projects/yejin/VerbCentric_RIS/ReferFormer/slurm_log/mbench_gpt_ref-ytvos.out
+ cd /home/yejin/data/projects/yejin/VerbCentric_RIS/ReferFormer
+
+ ml purge
+ ml load cuda/12.1
+ eval "$(conda shell.bash hook)"
+ conda activate referformer
+
+ python3 mbench/gpt_ref-ytvos.py
\ No newline at end of file
diff --git a/tools/data/convert_davis_to_ytvos.py b/tools/data/convert_davis_to_ytvos.py
new file mode 100644
index 0000000000000000000000000000000000000000..f1982c6dee3543b12c095a577005928bbb3bce1c
--- /dev/null
+++ b/tools/data/convert_davis_to_ytvos.py
@@ -0,0 +1,284 @@
+import os
+import json
+
+"""
+ytvos structure
+- train
+    - Annotations
+        - video1
+        - video2
+    - JPEGImages
+        - video1 
+        -video2
+    meta.json
+- valid
+    - Annotations
+    - JPEGImages
+    meta.json
+- meta_expressions
+    - train
+        meta_expressions.json
+    - valid
+        meta_expressions.json
+"""
+
+def read_split_set(data_root='data/ref-davis'):
+    set_split_path = os.path.join(data_root, "DAVIS/ImageSets/2017")
+    # train set
+    with open(os.path.join(set_split_path, "train.txt"), "r") as f:
+        train_set = f.readlines()
+    train_set = [x.strip() for x in train_set] # 60 videos
+    # val set
+    with open(os.path.join(set_split_path, "val.txt"), "r") as f:
+        val_set = f.readlines()
+    val_set = [x.strip() for x in val_set] # 30 videos
+    return train_set, val_set # List
+
+
+def mv_images_to_folder(data_root='data/ref-davis', output_root='data/ref-davis'):
+    train_img_path = os.path.join(output_root, "train/JPEGImages")
+    train_anno_path = os.path.join(output_root, "train/Annotations")
+    val_img_path = os.path.join(output_root, "valid/JPEGImages")
+    val_anno_path = os.path.join(output_root, "valid/Annotations")
+    meta_train_path = os.path.join(output_root, "meta_expressions/train")
+    meta_val_path = os.path.join(output_root, "meta_expressions/valid")
+    paths = [train_img_path, train_anno_path, val_img_path, val_anno_path,
+             meta_train_path, meta_val_path]
+    for path in paths:
+        if not os.path.exists(path):
+            os.makedirs(path)
+
+    # 1. read the train/val split
+    train_set, val_set = read_split_set(data_root)
+
+    # 2. move images and annotations
+    # train set
+    for video in train_set:
+        # move images
+        base_img_path = os.path.join(data_root, "DAVIS/JPEGImages/480p", video)
+        mv_cmd = f"mv {base_img_path} {train_img_path}"
+        os.system(mv_cmd)
+        # move annotations
+        base_anno_path = os.path.join(data_root, "DAVIS/Annotations_unsupervised/480p", video)
+        mv_cmd = f"mv {base_anno_path} {train_anno_path}"
+        os.system(mv_cmd)
+    # val set
+    for video in val_set:
+        # move images
+        base_img_path = os.path.join(data_root, "DAVIS/JPEGImages/480p", video)
+        mv_cmd = f"mv {base_img_path} {val_img_path}"
+        os.system(mv_cmd)
+        # move annotations
+        base_anno_path = os.path.join(data_root, "DAVIS/Annotations_unsupervised/480p", video)
+        mv_cmd = f"mv {base_anno_path} {val_anno_path}"
+        os.system(mv_cmd)
+
+def create_meta_expressions(data_root='data/ref-davis', output_root='data/ref-davis'):
+    """
+    NOTE: expressions odd: first anno, even: full anno
+    meta_expression.json format
+    {
+        "videos": {
+            "video1: {
+                "expressions": {
+                    "0": {
+                        "exp": "xxxxx",
+                        "obj_id": "1" (start from 1)
+                    }
+                    "1": {
+                        "exp": "xxxxx",
+                        "obj_id": "1"
+                    }
+                }
+                "frames": [
+                    "00000",
+                    "00001",
+                    ...
+                ]
+            }
+        }
+    }
+    """
+    train_img_path = os.path.join(output_root, "train/JPEGImages")
+    val_img_path = os.path.join(output_root, "valid/JPEGImages")
+    meta_train_path = os.path.join(output_root, "meta_expressions/train")
+    meta_val_path = os.path.join(output_root, "meta_expressions/valid")
+
+    # 1. read the train/val split
+    train_set, val_set = read_split_set(data_root)
+
+    # 2. create meta_expression.json
+    # NOTE: there are two annotators, and each annotator have first anno and full anno, respectively
+    def read_expressions_from_txt(file_path, encoding='utf-8'):
+        """
+        videos["video1"] = [
+            {"obj_id": 1, "exp": "xxxxx"},
+            {"obj_id": 2, "exp": "xxxxx"},
+            {"obj_id": 3, "exp": "xxxxx"},
+        ]
+        """
+        videos = {}
+        with open(file_path, "r", encoding=encoding) as f:
+            for idx, line in enumerate(f.readlines()):
+                line = line.strip()
+                video_name, obj_id = line.split()[:2]
+                exp = ' '.join(line.split()[2:])[1:-1]
+                # handle bad case
+                if video_name == "clasic-car":
+                    video_name = "classic-car"
+                elif video_name == "dog-scale":
+                    video_name = "dogs-scale"
+                elif video_name == "motor-bike":
+                    video_name = "motorbike"
+
+                
+                if not video_name in videos.keys():
+                    videos[video_name] = []
+                exp_dict = {
+                    "exp": exp,
+                    "obj_id": obj_id
+                }
+                videos[video_name].append(exp_dict)
+
+        # sort the order of expressions in each video
+        for key, value in videos.items():
+            value = sorted(value, key = lambda e:e.__getitem__('obj_id'))
+            videos[key] = value
+        return videos
+
+    anno1_first_path = os.path.join(data_root, "davis_text_annotations/Davis17_annot1.txt")
+    anno1_full_path = os.path.join(data_root, "davis_text_annotations/Davis17_annot1_full_video.txt")
+    anno2_first_path = os.path.join(data_root, "davis_text_annotations/Davis17_annot2.txt")
+    anno2_full_path = os.path.join(data_root, "davis_text_annotations/Davis17_annot2_full_video.txt")
+    # all videos information
+    anno1_first = read_expressions_from_txt(anno1_first_path, encoding='utf-8')
+    anno1_full = read_expressions_from_txt(anno1_full_path, encoding='utf-8')
+    anno2_first = read_expressions_from_txt(anno2_first_path, encoding='latin-1')
+    anno2_full = read_expressions_from_txt(anno2_full_path, encoding='latin-1')
+
+    # 2(1). train
+    train_videos = {}  # {"video1": {}, "video2": {}, ...}, the final results to dump
+    for video in train_set: # 60 videos
+        video_dict = {} # for each video
+
+        # store the information of video
+        expressions = {}
+        exp_id = 0 # start from 0
+        for anno1_first_video, anno1_full_video, anno2_first_video, anno2_full_video in zip(
+                                anno1_first[video], anno1_full[video], anno2_first[video], anno2_full[video]):
+            expressions[str(exp_id)] = anno1_first_video
+            exp_id += 1
+            expressions[str(exp_id)] = anno1_full_video
+            exp_id += 1
+            expressions[str(exp_id)] = anno2_first_video
+            exp_id += 1
+            expressions[str(exp_id)] = anno2_full_video
+            exp_id += 1
+        video_dict["expressions"] = expressions
+        # read frame names for each video
+        video_frames = os.listdir(os.path.join(train_img_path, video))
+        video_frames = [x.split(".")[0] for x in video_frames] # remove ".jpg"
+        video_frames.sort()
+        video_dict["frames"] = video_frames
+
+        train_videos[video] = video_dict
+    
+    # 2(2). val
+    val_videos = {}
+    for video in val_set:
+        video_dict = {} # for each video
+
+        # store the information of video
+        expressions = {}
+        exp_id = 0 # start from 0
+        for anno1_first_video, anno1_full_video, anno2_first_video, anno2_full_video in zip(
+                                anno1_first[video], anno1_full[video], anno2_first[video], anno2_full[video]):
+            expressions[str(exp_id)] = anno1_first_video
+            exp_id += 1
+            expressions[str(exp_id)] = anno1_full_video
+            exp_id += 1
+            expressions[str(exp_id)] = anno2_first_video
+            exp_id += 1
+            expressions[str(exp_id)] = anno2_full_video
+            exp_id += 1
+        video_dict["expressions"] = expressions
+        # read frame names for each video
+        video_frames = os.listdir(os.path.join(val_img_path, video))
+        video_frames = [x.split(".")[0] for x in video_frames] # remove ".jpg"
+        video_frames.sort()
+        video_dict["frames"] = video_frames
+
+        val_videos[video] = video_dict
+
+    # 3. store the meta_expressions.json
+    # train
+    train_meta = {"videos": train_videos}
+    with open(os.path.join(meta_train_path, "meta_expressions.json"), "w") as out:
+        json.dump(train_meta, out)
+    # val 
+    val_meta = {"videos": val_videos}
+    with open(os.path.join(meta_val_path, "meta_expressions.json"), "w") as out:
+        json.dump(val_meta, out)
+
+def create_meta_annotaions(data_root='data/ref-davis', output_root='data/ref-davis'):
+    """
+    NOTE: frame names are not stored compared with ytvos
+    meta.json format
+    {
+        "videos": {
+            "video1: {
+                "objects": {
+                    "1": {"category": "bike"},
+                    "2": {"category": "person"}
+                }
+            }
+        }
+    }
+    """
+    out_train_path = os.path.join(output_root, "train")
+    out_val_path = os.path.join(output_root, "valid")
+
+    # read the semantic information
+    with open(os.path.join(data_root, "DAVIS/davis_semantics.json")) as f:
+        davis_semantics = json.load(f)
+
+    # 1. read the train/val split
+    train_set, val_set = read_split_set(data_root)
+
+    # 2. create meta.json
+    # train
+    train_videos = {}
+    for video in train_set:
+        video_dict = {} # for each video
+        video_dict["objects"] = {}
+        num_obj = len(davis_semantics[video].keys())
+        for obj_id in range(1, num_obj+1): # start from 1
+            video_dict["objects"][str(obj_id)] = {"category": davis_semantics[video][str(obj_id)]}
+        train_videos[video] = video_dict
+
+    # val
+    val_videos = {}
+    for video in val_set:
+        video_dict = {}
+        video_dict["objects"] = {}
+        num_obj = len(davis_semantics[video].keys())
+        for obj_id in range(1, num_obj+1): # start from 1
+            video_dict["objects"][str(obj_id)] = {"category": davis_semantics[video][str(obj_id)]}
+        val_videos[video] = video_dict
+    
+    # store the meta.json file
+    train_meta = {"videos": train_videos}
+    with open(os.path.join(out_train_path, "meta.json"), "w") as out:
+        json.dump(train_meta, out)
+    val_meta = {"videos": val_videos}
+    with open(os.path.join(out_val_path, "meta.json"), "w") as out:
+        json.dump(val_meta, out)
+
+if __name__ == '__main__':
+    data_root = "data/ref-davis"
+    output_root = "data/ref-davis"
+    print("Converting ref-davis to ref-youtube-vos format....")
+    mv_images_to_folder(data_root, output_root)
+    create_meta_expressions(data_root, output_root)
+    create_meta_annotaions(data_root, output_root)
+
diff --git a/tools/data/convert_refexp_to_coco.py b/tools/data/convert_refexp_to_coco.py
new file mode 100644
index 0000000000000000000000000000000000000000..fc53afc15feef6b9aca1aad27b59f65f55d95b67
--- /dev/null
+++ b/tools/data/convert_refexp_to_coco.py
@@ -0,0 +1,153 @@
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).resolve().parents[2]))
+
+import numpy as np
+import os
+from datasets.refer import REFER
+import cv2  
+from tqdm import tqdm
+import json
+import pickle
+import json
+
+
+def convert_to_coco(data_root='data/coco', output_root='data/coco', dataset='refcoco', dataset_split='unc'):
+    dataset_dir = os.path.join(data_root, dataset)
+    output_dir = os.path.join(output_root, dataset) # .json save path
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+
+    # read REFER
+    refer = REFER(data_root, dataset, dataset_split)
+    refs = refer.Refs
+    anns = refer.Anns
+    imgs = refer.Imgs
+    cats = refer.Cats
+    sents = refer.Sents
+    """
+    # create sets of mapping
+        # 1)  Refs: 	 	{ref_id: ref}
+        # 2)  Anns: 	 	{ann_id: ann}
+        # 3)  Imgs:		 	{image_id: image}
+        # 4)  Cats: 	 	{category_id: category_name}
+        # 5)  Sents:     	{sent_id: sent}
+        # 6)  imgToRefs: 	{image_id: refs}
+        # 7)  imgToAnns: 	{image_id: anns}
+        # 8)  refToAnn:  	{ref_id: ann}
+        # 9)  annToRef:  	{ann_id: ref}
+        # 10) catToRefs: 	{category_id: refs}
+        # 11) sentToRef: 	{sent_id: ref}
+        # 12) sentToTokens: {sent_id: tokens}
+
+    Refs: List[Dict], "sent_ids", "file_name", "ann_id", "ref_id", "image_id", "category_id", "split", "sentences"
+                      "sentences": List[Dict], "tokens"(List), "raw", "sent_id", "sent"
+    Anns: List[Dict], "segmentation", "area", "iscrowd", "image_id", "bbox", "category_id", "id"
+    Imgs: List[Dict], "license", "file_name", "coco_url", "height", "width", "date_captured", "flickr_url", "id"
+    Cats: List[Dict], "supercategory", "name", "id"
+    Sents: List[Dict], "tokens"(List), "raw", "sent_id", "sent", here the "sent_id" is consistent
+    """
+    print('Dataset [%s_%s] contains: ' % (dataset, dataset_split))
+    ref_ids = refer.getRefIds()
+    image_ids = refer.getImgIds()
+    print('There are %s expressions for %s refereed objects in %s images.' % (len(refer.Sents), len(ref_ids), len(image_ids)))
+
+    print('\nAmong them:')
+    if dataset == 'refcoco':
+        splits = ['train', 'val', 'testA', 'testB']
+    elif dataset == 'refcoco+':
+        splits = ['train', 'val',  'testA', 'testB']
+    elif dataset == 'refcocog':
+        splits = ['train', 'val', 'test']  # we don't have test split for refcocog right now.
+
+    for split in splits:
+        ref_ids = refer.getRefIds(split=split)
+        print('     %s referred objects are in split [%s].' % (len(ref_ids), split))
+
+    with open(os.path.join(dataset_dir, "instances.json"), "r") as f:
+        ann_json = json.load(f)
+
+
+    # 1. for each split: train, val...
+    for split in splits:
+        max_length = 0 # max length of a sentence
+
+        coco_ann = {
+            "info": "",
+            "licenses": "",
+            "images": [],   # each caption is a image sample
+            "annotations": [],
+            "categories": []
+        }
+        coco_ann['info'], coco_ann['licenses'], coco_ann['categories'] = \
+                                    ann_json['info'], ann_json['licenses'], ann_json['categories']
+        
+        num_images = 0 # each caption is a sample, create a "images" and a "annotations", since each image has one box
+        ref_ids = refer.getRefIds(split=split)
+        # 2. for each referred object
+        for i in tqdm(ref_ids): 
+            ref = refs[i]
+            # "sent_ids", "file_name", "ann_id", "ref_id", "image_id", "category_id", "split", "sentences"
+            #             "sentences": List[Dict], "tokens"(List), "raw", "sent_id", "sent"
+            img = imgs[ref["image_id"]]
+            ann = anns[ref["ann_id"]]
+
+            # 3. for each sentence, which is a sample
+            for sentence in ref["sentences"]: 
+                num_images += 1
+                # append image info
+                image_info = {
+                    "file_name": img["file_name"],
+                    "height": img["height"],
+                    "width": img["width"],
+                    "original_id": img["id"],
+                    "id": num_images,
+                    "caption": sentence["sent"],
+                    "dataset_name": dataset
+                }
+                coco_ann["images"].append(image_info)
+
+                # append annotation info
+                ann_info = {
+                    "segmentation": ann["segmentation"],
+                    "area": ann["area"],
+                    "iscrowd": ann["iscrowd"],
+                    "bbox": ann["bbox"],
+                    "image_id": num_images,
+                    "category_id": ann["category_id"],
+                    "id": num_images,
+                    "original_id": ann["id"]
+                }
+                coco_ann["annotations"].append(ann_info)
+
+                max_length = max(max_length, len(sentence["tokens"]))
+        
+        print("Total expression: {} in split {}".format(num_images, split))
+        print("Max sentence length of the split: ", max_length)
+        # save the json file
+        save_file = "instances_{}_{}.json".format(dataset, split)
+        with open(os.path.join(output_dir, save_file), 'w') as f:
+            json.dump(coco_ann, f)
+
+if __name__ == '__main__':
+    datasets = ["refcoco", "refcoco+", "refcocog"]
+    datasets_split = ["unc", "unc", "umd"]
+    for (dataset, dataset_split) in zip(datasets, datasets_split):
+        convert_to_coco(dataset=dataset, dataset_split=dataset_split)
+        print("")
+
+
+"""
+# original mapping 
+{'person': 1, 'bicycle': 2, 'car': 3, 'motorcycle': 4, 'airplane': 5, 'bus': 6, 'train': 7, 'truck': 8, 'boat': 9, 
+'traffic light': 10, 'fire hydrant': 11, 'stop sign': 13, 'parking meter': 14, 'bench': 15, 'bird': 16, 'cat': 17, 
+'dog': 18, 'horse': 19, 'sheep': 20, 'cow': 21, 'elephant': 22, 'bear': 23, 'zebra': 24, 'giraffe': 25, 'backpack': 27, 
+'umbrella': 28, 'handbag': 31, 'tie': 32, 'suitcase': 33, 'frisbee': 34, 'skis': 35, 'snowboard': 36, 'sports ball': 37, 
+'kite': 38, 'baseball bat': 39, 'baseball glove': 40, 'skateboard': 41, 'surfboard': 42, 'tennis racket': 43, 'bottle': 44, 
+'wine glass': 46, 'cup': 47, 'fork': 48, 'knife': 49, 'spoon': 50, 'bowl': 51, 'banana': 52, 'apple': 53, 'sandwich': 54, 
+'orange': 55, 'broccoli': 56, 'carrot': 57, 'hot dog': 58, 'pizza': 59, 'donut': 60, 'cake': 61, 'chair': 62, 'couch': 63, 
+'potted plant': 64, 'bed': 65, 'dining table': 67, 'toilet': 70, 'tv': 72, 'laptop': 73, 'mouse': 74, 'remote': 75, 
+'keyboard': 76, 'cell phone': 77, 'microwave': 78, 'oven': 79, 'toaster': 80, 'sink': 81, 'refrigerator': 82, 'book': 84, 
+'clock': 85, 'vase': 86, 'scissors': 87, 'teddy bear': 88, 'hair drier': 89, 'toothbrush': 90}
+
+"""
diff --git a/util/__init__.py b/util/__init__.py
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diff --git a/util/box_ops.py b/util/box_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..b2e519ff0447f6fcc6bd5586690c27d993d49da4
--- /dev/null
+++ b/util/box_ops.py
@@ -0,0 +1,108 @@
+"""
+Utilities for bounding box manipulation and GIoU.
+"""
+import torch
+from torchvision.ops.boxes import box_area
+
+def clip_iou(boxes1,boxes2):
+    area1 = box_area(boxes1)
+    area2 = box_area(boxes2)
+    lt = torch.max(boxes1[:, :2], boxes2[:, :2])
+    rb = torch.min(boxes1[:, 2:], boxes2[:, 2:])
+    wh = (rb - lt).clamp(min=0)
+    inter = wh[:,0] * wh[:,1]
+    union = area1 + area2 - inter
+    iou = (inter + 1e-6) / (union+1e-6)
+    return iou
+
+def multi_iou(boxes1, boxes2):
+    lt = torch.max(boxes1[...,:2], boxes2[...,:2])
+    rb = torch.min(boxes1[...,2:], boxes2[...,2:])
+    wh = (rb - lt).clamp(min=0)
+    wh_1 = boxes1[...,2:] - boxes1[...,:2]
+    wh_2 = boxes2[...,2:] - boxes2[...,:2]
+    inter = wh[...,0] * wh[...,1]
+    union = wh_1[...,0] * wh_1[...,1] + wh_2[...,0] * wh_2[...,1] - inter
+    iou = (inter + 1e-6) / (union + 1e-6)
+    return iou
+
+def box_cxcywh_to_xyxy(x):
+    x_c, y_c, w, h = x.unbind(-1)
+    b = [(x_c - 0.5 * w), (y_c - 0.5 * h),
+         (x_c + 0.5 * w), (y_c + 0.5 * h)]
+    return torch.stack(b, dim=-1)
+
+
+def box_xyxy_to_cxcywh(x):
+    x0, y0, x1, y1 = x.unbind(-1)
+    b = [(x0 + x1) / 2, (y0 + y1) / 2,
+         (x1 - x0), (y1 - y0)]
+    return torch.stack(b, dim=-1)
+
+
+# modified from torchvision to also return the union
+def box_iou(boxes1, boxes2):
+    area1 = box_area(boxes1)
+    area2 = box_area(boxes2)
+
+    lt = torch.max(boxes1[:, None, :2], boxes2[:, :2])  # [N,M,2]
+    rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:])  # [N,M,2]
+
+    wh = (rb - lt).clamp(min=0)  # [N,M,2]
+    inter = wh[:, :, 0] * wh[:, :, 1]  # [N,M]
+
+    union = area1[:, None] + area2 - inter
+
+    iou = (inter+1e-6) / (union+1e-6)
+    return iou, union
+
+
+def generalized_box_iou(boxes1, boxes2):
+    """
+    Generalized IoU from https://giou.stanford.edu/
+
+    The boxes should be in [x0, y0, x1, y1] format
+
+    Returns a [N, M] pairwise matrix, where N = len(boxes1)
+    and M = len(boxes2)
+    """
+    # degenerate boxes gives inf / nan results
+    # so do an early check
+    assert (boxes1[:, 2:] >= boxes1[:, :2]).all()
+    assert (boxes2[:, 2:] >= boxes2[:, :2]).all()
+    iou, union = box_iou(boxes1, boxes2)
+
+    lt = torch.min(boxes1[:, None, :2], boxes2[:, :2])
+    rb = torch.max(boxes1[:, None, 2:], boxes2[:, 2:])
+
+    wh = (rb - lt).clamp(min=0)  # [N,M,2]
+    area = wh[:, :, 0] * wh[:, :, 1]
+
+    return iou - ((area - union) + 1e-6) / (area + 1e-6)
+
+
+def masks_to_boxes(masks):
+    """Compute the bounding boxes around the provided masks
+
+    The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions.
+
+    Returns a [N, 4] tensors, with the boxes in xyxy format
+    """
+    if masks.numel() == 0:
+        return torch.zeros((0, 4), device=masks.device)
+
+    h, w = masks.shape[-2:]
+
+    y = torch.arange(0, h, dtype=torch.float)
+    x = torch.arange(0, w, dtype=torch.float)
+    y, x = torch.meshgrid(y, x)
+
+    x_mask = (masks * x.unsqueeze(0))
+    x_max = x_mask.flatten(1).max(-1)[0]
+    x_min = x_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]
+
+    y_mask = (masks * y.unsqueeze(0))
+    y_max = y_mask.flatten(1).max(-1)[0]
+    y_min = y_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]
+
+    return torch.stack([x_min, y_min, x_max, y_max], 1)
diff --git a/util/misc.py b/util/misc.py
new file mode 100644
index 0000000000000000000000000000000000000000..6fd9ecfe5022c446e8527eea5941f09c38d94d9d
--- /dev/null
+++ b/util/misc.py
@@ -0,0 +1,561 @@
+"""
+Misc functions, including distributed helpers.
+Mostly copy-paste from torchvision references.
+"""
+import os
+import io
+import subprocess
+import time
+from collections import defaultdict, deque
+import datetime
+from typing import Optional, List, Dict, Any
+
+import torch
+import torch.distributed as dist
+from torch import Tensor
+import functools
+
+
+_LOCAL_PROCESS_GROUP = None
+
+@functools.lru_cache()
+def _get_global_gloo_group():
+    """
+    Return a process group based on gloo backend, containing all the ranks
+    The result is cached.
+    """
+
+    if dist.get_backend() == "nccl":
+        return dist.new_group(backend="gloo")
+
+    return dist.group.WORLD
+
+
+# needed due to empty tensor bug in pytorch and torchvision 0.5
+import torchvision
+# if float(torchvision.__version__[:3]) < 0.7:
+#     from torchvision.ops import _new_empty_tensor
+#     from torchvision.ops.misc import _output_size
+
+
+class SmoothedValue(object):
+    """Track a series of values and provide access to smoothed values over a
+    window or the global series average.
+    """
+
+    def __init__(self, window_size=20, fmt=None):
+        if fmt is None:
+            fmt = "{median:.4f} ({global_avg:.4f})"
+        self.deque = deque(maxlen=window_size)
+        self.total = 0.0
+        self.count = 0
+        self.fmt = fmt
+
+    def update(self, value, n=1):
+        self.deque.append(value)
+        self.count += n
+        self.total += value * n
+
+    def synchronize_between_processes(self):
+        """
+        Warning: does not synchronize the deque!
+        """
+        if not is_dist_avail_and_initialized():
+            return
+        t = torch.tensor([self.count, self.total], dtype=torch.float64, device='cuda')
+        dist.barrier()
+        dist.all_reduce(t)
+        t = t.tolist()
+        self.count = int(t[0])
+        self.total = t[1]
+
+    @property
+    def median(self):
+        d = torch.tensor(list(self.deque))
+        return d.median().item()
+
+    @property
+    def avg(self):
+        d = torch.tensor(list(self.deque), dtype=torch.float32)
+        return d.mean().item()
+
+    @property
+    def global_avg(self):
+        return self.total / self.count
+
+    @property
+    def max(self):
+        return max(self.deque)
+
+    @property
+    def value(self):
+        return self.deque[-1]
+
+    def __str__(self):
+        return self.fmt.format(
+            median=self.median,
+            avg=self.avg,
+            global_avg=self.global_avg,
+            max=self.max,
+            value=self.value)
+
+# copy-paste from mdetr: https://github.com/ashkamath/mdetr/blob/main/util/dist.py
+def all_gather(data):
+    """
+    Run all_gather on arbitrary picklable data (not necessarily tensors)
+    Args:
+        data: any picklable object
+    Returns:
+        list[data]: list of data gathered from each rank
+    """
+
+    world_size = get_world_size()
+    if world_size == 1:
+        return [data]
+
+    cpu_group = None
+    if os.getenv("MDETR_CPU_REDUCE") == "1":
+        cpu_group = _get_global_gloo_group()
+
+    buffer = io.BytesIO()
+    torch.save(data, buffer)
+    data_view = buffer.getbuffer()
+    device = "cuda" if cpu_group is None else "cpu"
+    tensor = torch.ByteTensor(data_view).to(device)
+
+    # obtain Tensor size of each rank
+    local_size = torch.tensor([tensor.numel()], device=device, dtype=torch.long)
+    size_list = [torch.tensor([0], device=device, dtype=torch.long) for _ in range(world_size)]
+    if cpu_group is None:
+        dist.all_gather(size_list, local_size)
+    else:
+        print("gathering on cpu")
+        dist.all_gather(size_list, local_size, group=cpu_group)
+    size_list = [int(size.item()) for size in size_list]
+    max_size = max(size_list)
+    assert isinstance(local_size.item(), int)
+    local_size = int(local_size.item())
+
+    # receiving Tensor from all ranks
+    # we pad the tensor because torch all_gather does not support
+    # gathering tensors of different shapes
+    tensor_list = []
+    for _ in size_list:
+        tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device=device))
+    if local_size != max_size:
+        padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device=device)
+        tensor = torch.cat((tensor, padding), dim=0)
+    if cpu_group is None:
+        dist.all_gather(tensor_list, tensor)
+    else:
+        dist.all_gather(tensor_list, tensor, group=cpu_group)
+
+    data_list = []
+    for size, tensor in zip(size_list, tensor_list):
+        tensor = torch.split(tensor, [size, max_size - size], dim=0)[0]
+        buffer = io.BytesIO(tensor.cpu().numpy())
+        obj = torch.load(buffer)
+        data_list.append(obj)
+
+    return data_list
+
+def reduce_dict(input_dict, average=True):
+    """
+    Args:
+        input_dict (dict): all the values will be reduced
+        average (bool): whether to do average or sum
+    Reduce the values in the dictionary from all processes so that all processes
+    have the averaged results. Returns a dict with the same fields as
+    input_dict, after reduction.
+    """
+    world_size = get_world_size()
+    if world_size < 2:
+        return input_dict
+    with torch.no_grad():
+        names = []
+        values = []
+        # sort the keys so that they are consistent across processes
+        for k in sorted(input_dict.keys()):
+            names.append(k)
+            values.append(input_dict[k])
+        values = torch.stack(values, dim=0)
+        dist.all_reduce(values)
+        if average:
+            values /= world_size
+        reduced_dict = {k: v for k, v in zip(names, values)}
+    return reduced_dict
+
+
+class MetricLogger(object):
+    def __init__(self, delimiter="\t"):
+        self.meters = defaultdict(SmoothedValue)
+        self.delimiter = delimiter
+
+    def update(self, **kwargs):
+        for k, v in kwargs.items():
+            if isinstance(v, torch.Tensor):
+                v = v.item()
+            assert isinstance(v, (float, int))
+            self.meters[k].update(v)
+
+    def __getattr__(self, attr):
+        if attr in self.meters:
+            return self.meters[attr]
+        if attr in self.__dict__:
+            return self.__dict__[attr]
+        raise AttributeError("'{}' object has no attribute '{}'".format(
+            type(self).__name__, attr))
+
+    def __str__(self):
+        loss_str = []
+        for name, meter in self.meters.items():
+            loss_str.append(
+                "{}: {}".format(name, str(meter))
+            )
+        return self.delimiter.join(loss_str)
+
+    def synchronize_between_processes(self):
+        for meter in self.meters.values():
+            meter.synchronize_between_processes()
+
+    def add_meter(self, name, meter):
+        self.meters[name] = meter
+
+    def log_every(self, iterable, print_freq, header=None):
+        i = 0
+        if not header:
+            header = ''
+        start_time = time.time()
+        end = time.time()
+        iter_time = SmoothedValue(fmt='{avg:.4f}')
+        data_time = SmoothedValue(fmt='{avg:.4f}')
+        space_fmt = ':' + str(len(str(len(iterable)))) + 'd'
+        if torch.cuda.is_available():
+            log_msg = self.delimiter.join([
+                header,
+                '[{0' + space_fmt + '}/{1}]',
+                'eta: {eta}',
+                '{meters}',
+                'time: {time}',
+                'data: {data}',
+                'max mem: {memory:.0f}'
+            ])
+        else:
+            log_msg = self.delimiter.join([
+                header,
+                '[{0' + space_fmt + '}/{1}]',
+                'eta: {eta}',
+                '{meters}',
+                'time: {time}',
+                'data: {data}'
+            ])
+        MB = 1024.0 * 1024.0
+        for obj in iterable:
+            data_time.update(time.time() - end)
+            yield obj
+            iter_time.update(time.time() - end)
+            if i % print_freq == 0 or i == len(iterable) - 1:
+                eta_seconds = iter_time.global_avg * (len(iterable) - i)
+                eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
+                if torch.cuda.is_available():
+                    print(log_msg.format(
+                        i, len(iterable), eta=eta_string,
+                        meters=str(self),
+                        time=str(iter_time), data=str(data_time),
+                        memory=torch.cuda.max_memory_allocated() / MB))
+                else:
+                    print(log_msg.format(
+                        i, len(iterable), eta=eta_string,
+                        meters=str(self),
+                        time=str(iter_time), data=str(data_time)))
+            i += 1
+            end = time.time()
+        total_time = time.time() - start_time
+        total_time_str = str(datetime.timedelta(seconds=int(total_time)))
+        print('{} Total time: {} ({:.4f} s / it)'.format(
+            header, total_time_str, total_time / len(iterable)))
+
+
+def get_sha():
+    cwd = os.path.dirname(os.path.abspath(__file__))
+
+    def _run(command):
+        return subprocess.check_output(command, cwd=cwd).decode('ascii').strip()
+    sha = 'N/A'
+    diff = "clean"
+    branch = 'N/A'
+    try:
+        sha = _run(['git', 'rev-parse', 'HEAD'])
+        subprocess.check_output(['git', 'diff'], cwd=cwd)
+        diff = _run(['git', 'diff-index', 'HEAD'])
+        diff = "has uncommited changes" if diff else "clean"
+        branch = _run(['git', 'rev-parse', '--abbrev-ref', 'HEAD'])
+    except Exception:
+        pass
+    message = f"sha: {sha}, status: {diff}, branch: {branch}"
+    return message
+
+
+def collate_fn(batch):
+    # batch: imgs, targets
+    batch = list(zip(*batch)) 
+    batch[0] = nested_tensor_from_videos_list(batch[0], size_divisibility=32) 
+    # batch[0]: samples: NestedTensor(tensor, mask)
+    #           tensor: [B, T, C, H, W], mask: [B, T, H, W]
+    # batch[1]: targets: list[dict]
+    return tuple(batch) 
+
+
+def _max_by_axis(the_list):
+    # type: (List[List[int]]) -> List[int]
+    maxes = the_list[0]
+    for sublist in the_list[1:]: # (C, H, W)
+        for index, item in enumerate(sublist):
+            maxes[index] = max(maxes[index], item)
+    return maxes
+
+
+def nested_tensor_from_tensor_list(tensor_list: List[Tensor], size_divisibility=1, split=True):
+    """
+    This function receives a list of image tensors and returns a NestedTensor of the padded images, along with their
+    padding masks (true for padding areas, false otherwise).
+    """
+    # TODO make this more general
+    # if image tensor is stacked as [T*3, H, W], then use split
+    if split:
+        tensor_list = [tensor.split(3,dim=0) for tensor in tensor_list] 
+        tensor_list = [item for sublist in tensor_list for item in sublist] 
+        # list[tensor], length = batch_size x time
+
+    if tensor_list[0].ndim == 3: 
+        # TODO make it support different-sized images
+        max_size = _max_by_axis([list(img.shape) for img in tensor_list]) 
+
+        if size_divisibility > 1: # so that the mask dowmsample can be matched
+            stride = size_divisibility
+            # the last two dims are [H, W], both subject to divisibility requirement
+            max_size[-2] = (max_size[-2] + (stride - 1)) // stride * stride
+            max_size[-1] = (max_size[-1] + (stride - 1)) // stride * stride
+
+        # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list]))
+        batch_shape = [len(tensor_list)] + max_size 
+        b, c, h, w = batch_shape 
+        dtype = tensor_list[0].dtype
+        device = tensor_list[0].device
+        tensor = torch.zeros(batch_shape, dtype=dtype, device=device) 
+        mask = torch.ones((b, h, w), dtype=torch.bool, device=device)
+        for img, pad_img, m in zip(tensor_list, tensor, mask):
+            pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
+            m[: img.shape[1], :img.shape[2]] = False # valid locations
+    else:
+        raise ValueError('not supported')
+    return NestedTensor(tensor, mask)
+
+def nested_tensor_from_videos_list(videos_list: List[Tensor], size_divisibility=1):
+    """
+    This function receives a list of videos (each of shape [T, C, H, W]) and returns a NestedTensor of the padded
+    videos (shape [B, T, C, PH, PW], along with their padding masks (true for padding areas, false otherwise, of shape
+    [B, T, PH, PW].
+    """
+    max_size = _max_by_axis([list(img.shape) for img in videos_list])
+
+    if size_divisibility > 1: # so that the mask dowmsample can be matched
+        stride = size_divisibility
+        # the last two dims are [H, W], both subject to divisibility requirement
+        max_size[-2] = (max_size[-2] + (stride - 1)) // stride * stride
+        max_size[-1] = (max_size[-1] + (stride - 1)) // stride * stride
+
+    padded_batch_shape = [len(videos_list)] + max_size
+    b, t, c, h, w = padded_batch_shape
+    dtype = videos_list[0].dtype
+    device = videos_list[0].device
+    padded_videos = torch.zeros(padded_batch_shape, dtype=dtype, device=device)
+    videos_pad_masks = torch.ones((b, t, h, w), dtype=torch.bool, device=device)
+    for vid_frames, pad_vid_frames, vid_pad_m in zip(videos_list, padded_videos, videos_pad_masks):
+        pad_vid_frames[:vid_frames.shape[0], :, :vid_frames.shape[2], :vid_frames.shape[3]].copy_(vid_frames)
+        vid_pad_m[:vid_frames.shape[0], :vid_frames.shape[2], :vid_frames.shape[3]] = False
+    return NestedTensor(padded_videos, videos_pad_masks)
+
+
+class NestedTensor(object):
+    def __init__(self, tensors, mask: Optional[Tensor]):
+        self.tensors = tensors
+        self.mask = mask
+
+    def to(self, device):
+        # type: (Device) -> NestedTensor # noqa
+        cast_tensor = self.tensors.to(device)
+        mask = self.mask
+        if mask is not None:
+            assert mask is not None
+            cast_mask = mask.to(device)
+        else:
+            cast_mask = None
+        return NestedTensor(cast_tensor, cast_mask)
+
+    def decompose(self):
+        return self.tensors, self.mask
+
+    def __repr__(self):
+        return str(self.tensors)
+
+
+def setup_for_distributed(is_master):
+    """
+    This function disables printing when not in master process
+    """
+    import builtins as __builtin__
+    builtin_print = __builtin__.print
+
+    def print(*args, **kwargs):
+        force = kwargs.pop('force', False)
+        if is_master or force:
+            builtin_print(*args, **kwargs)
+
+    __builtin__.print = print
+
+
+def is_dist_avail_and_initialized():
+    if not dist.is_available():
+        return False
+    if not dist.is_initialized():
+        return False
+    return True
+
+
+def get_world_size():
+    if not is_dist_avail_and_initialized():
+        return 1
+    return dist.get_world_size()
+
+
+def get_rank():
+    if not is_dist_avail_and_initialized():
+        return 0
+    return dist.get_rank()
+
+
+def is_main_process():
+    return get_rank() == 0
+
+
+def save_on_master(*args, **kwargs):
+    if is_main_process():
+        torch.save(*args, **kwargs, _use_new_zipfile_serialization=False)
+
+
+def init_distributed_mode(args):
+    if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ:
+        args.rank = int(os.environ["RANK"])
+        args.world_size = int(os.environ['WORLD_SIZE'])
+        args.gpu = int(os.environ['LOCAL_RANK'])
+        args.dist_url = 'env://'
+        os.environ['LOCAL_SIZE'] = str(torch.cuda.device_count())
+    elif 'SLURM_PROCID' in os.environ:
+        proc_id = int(os.environ['SLURM_PROCID'])
+        ntasks = int(os.environ['SLURM_NTASKS'])
+        node_list = os.environ['SLURM_NODELIST']
+        num_gpus = torch.cuda.device_count()
+        addr = subprocess.getoutput(
+            'scontrol show hostname {} | head -n1'.format(node_list))
+        os.environ['MASTER_PORT'] = os.environ.get('MASTER_PORT', '29500')
+        os.environ['MASTER_ADDR'] = addr
+        os.environ['WORLD_SIZE'] = str(ntasks)
+        os.environ['RANK'] = str(proc_id)
+        os.environ['LOCAL_RANK'] = str(proc_id % num_gpus)
+        os.environ['LOCAL_SIZE'] = str(num_gpus)
+        args.dist_url = 'env://'
+        args.world_size = ntasks
+        args.rank = proc_id
+        args.gpu = proc_id % num_gpus
+    else:
+        print('Not using distributed mode')
+        args.distributed = False
+        return
+
+    args.distributed = True
+
+    torch.cuda.set_device(args.gpu)
+    args.dist_backend = 'nccl'
+    print('| distributed init (rank {}): {}'.format(
+        args.rank, args.dist_url), flush=True)
+    torch.distributed.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
+                                         world_size=args.world_size, rank=args.rank)
+    torch.distributed.barrier()
+    setup_for_distributed(args.rank == 0)
+
+
+@torch.no_grad()
+def accuracy(output, target, topk=(1,)):
+    """Computes the precision@k for the specified values of k"""
+    if target.numel() == 0:
+        return [torch.zeros([], device=output.device)]
+    maxk = max(topk)
+    batch_size = target.size(0)
+
+    _, pred = output.topk(maxk, 1, True, True)
+    pred = pred.t()
+    correct = pred.eq(target.view(1, -1).expand_as(pred))
+
+    res = []
+    for k in topk:
+        correct_k = correct[:k].view(-1).float().sum(0)
+        res.append(correct_k.mul_(100.0 / batch_size))
+    return res
+
+
+def interpolate(input, size=None, scale_factor=None, mode="nearest", align_corners=None):
+    # type: (Tensor, Optional[List[int]], Optional[float], str, Optional[bool]) -> Tensor
+    """
+    Equivalent to nn.functional.interpolate, but with support for empty batch sizes.
+    This will eventually be supported natively by PyTorch, and this
+    class can go away.
+    """
+    # if float(torchvision.__version__[:3]) < 0.7:
+    #     if input.numel() > 0:
+    #         return torch.nn.functional.interpolate(
+    #             input, size, scale_factor, mode, align_corners
+    #         )
+
+    #     output_shape = _output_size(2, input, size, scale_factor)
+    #     output_shape = list(input.shape[:-2]) + list(output_shape)
+    #     return _new_empty_tensor(input, output_shape)
+    # else:
+    #     return torchvision.ops.misc.interpolate(input, size, scale_factor, mode, align_corners)
+    return torchvision.ops.misc.interpolate(input, size, scale_factor, mode, align_corners)
+
+
+def targets_to(targets: List[Dict[str, Any]], device):
+    """Moves the target dicts to the given device.
+    excluded_keys = [
+        "questionId",
+        "tokens_positive",
+        "tokens",
+        "dataset_name",
+        "sentence_id",
+        "original_img_id",
+        "nb_eval",
+        "task_id",
+        "original_id",
+    ]
+    """
+    if "dataset_name" in targets[0]:
+        # for ["refcoco", "refcoco+", "refcocog"] evaluation
+        return [{k: v.to(device)  for k, v in t.items() if k not in ["caption", "dataset_name", "original_id"]} for t in targets]
+    else:
+        return [{k: v.to(device)  for k, v in t.items() if k not in ["caption", "dataset_name", "original_id", "image_id"]} for t in targets]
+
+def get_total_grad_norm(parameters, norm_type=2):
+    parameters = list(filter(lambda p: p.grad is not None, parameters))
+    norm_type = float(norm_type)
+    device = parameters[0].grad.device
+    total_norm = torch.norm(torch.stack([torch.norm(p.grad.detach(), norm_type).to(device) for p in parameters]),
+                            norm_type)
+
+
+
+def inverse_sigmoid(x, eps=1e-5):
+    x = x.clamp(min=0, max=1)
+    x1 = x.clamp(min=eps)
+    x2 = (1 - x).clamp(min=eps)
+    return torch.log(x1/x2)