diff --git a/tracker/base_tracker.py b/tracker/base_tracker.py
index a544408..71f9f63 100644
--- a/tracker/base_tracker.py
+++ b/tracker/base_tracker.py
@@ -7,14 +7,13 @@ from PIL import Image
 
 import torch
 import yaml
+import torch.nn.functional as F
 from model.network import XMem
 from inference.inference_core import InferenceCore
-from inference.data.mask_mapper import MaskMapper
-
-# for data transormation
+from util.mask_mapper import MaskMapper
 from torchvision import transforms
-from dataset.range_transform import im_normalization
-import torch.nn.functional as F
+from util.range_transform import im_normalization
+
 
 import sys
 sys.path.insert(0, sys.path[0]+"/../")
@@ -39,9 +38,11 @@ class BaseTracker:
             transforms.ToTensor(),
             im_normalization,
         ])
-        self.mapper = MaskMapper()
         self.device = device
 
+        self.mapper = MaskMapper()
+        self.initialised = False
+
     @torch.no_grad()
     def resize_mask(self, mask):
         # mask transform is applied AFTER mapper, so we need to post-process it in eval.py
@@ -51,37 +52,42 @@ class BaseTracker:
                     mode='nearest')
 
     @torch.no_grad()
-    def track(self, frames, first_frame_annotation):
+    def track(self, frame, first_frame_annotation=None):
         """
         Input: 
-        frames: numpy arrays: T, H, W, 3 (T: number of frames)
-        first_frame_annotation: numpy array: H, W
+        frames: numpy arrays (H, W, 3)
+        first_frame_annotation: numpy array (H, W)
 
         Output:
-        masks: numpy arrays: H, W
+        mask: numpy arrays (H, W)
+        prob: numpy arrays, probability map (H, W)
+        painted_image: numpy array (H, W, 3)
         """
-        vid_length = len(frames)
-        masks = []
+        if first_frame_annotation is not None:
+            # initialisation
+            mask, labels = self.mapper.convert_mask(first_frame_annotation)
+            mask = torch.Tensor(mask).to(self.device)
+            self.tracker.set_all_labels(list(self.mapper.remappings.values()))
+        else:
+            mask = None
+            labels = None
 
-        for ti, frame in enumerate(frames):
-            # convert to tensor
-            frame_tensor = self.im_transform(frame).to(self.device)
-            if ti == 0:
-                mask, labels = self.mapper.convert_mask(first_frame_annotation)
-                mask = torch.Tensor(mask).to(self.device)
-                self.tracker.set_all_labels(list(self.mapper.remappings.values()))
-            else:
-                mask = None
-                labels = None
-            
-            # track one frame
-            prob = self.tracker.step(frame_tensor, mask, labels, end=(ti==vid_length-1))
-            # convert to mask
-            out_mask = torch.argmax(prob, dim=0)
-            out_mask = (out_mask.detach().cpu().numpy()).astype(np.uint8)
-            masks.append(out_mask)
+        # prepare inputs
+        frame_tensor = self.im_transform(frame).to(self.device)
+        # track one frame
+        prob = self.tracker.step(frame_tensor, mask, labels)
+        # convert to mask
+        out_mask = torch.argmax(prob, dim=0)
+        out_mask = (out_mask.detach().cpu().numpy()).astype(np.uint8)
+        painted_image = mask_painter(frame, out_mask)
 
-        return np.stack(masks, 0)
+        # mask, _, painted_frame
+        return out_mask, prob, painted_image
+
+    @torch.no_grad()
+    def clear_memory(self):
+        self.tracker.clear_memory()
+        self.mapper.clear_labels()
 
 
 if __name__ == '__main__':
@@ -106,11 +112,40 @@ if __name__ == '__main__':
     tracker = BaseTracker(device, XMEM_checkpoint)
     
     # track anything given in the first frame annotation
-    masks = tracker.track(frames, first_frame_annotation)
-
-    # save
-    for ti, (frame, mask) in enumerate(zip(frames, masks)):
-        painted_image = mask_painter(frame, mask)
+    for ti, frame in enumerate(frames):
+        if ti == 0:
+            mask, prob, painted_image = tracker.track(frame, first_frame_annotation)
+        else:
+            mask, prob, painted_image = tracker.track(frame)
         # save
         painted_image = Image.fromarray(painted_image)
-        painted_image.save(f'/ssd1/gaomingqi/results/TrackA/{ti:05d}.png')
+        painted_image.save(f'/ssd1/gaomingqi/results/TrackA/dance-twirl/{ti:05d}.png')
+
+    # ----------------------------------------------------------
+    # another video
+    # ----------------------------------------------------------
+    # video frames
+    video_path_list = glob.glob(os.path.join('/ssd1/gaomingqi/datasets/davis/JPEGImages/480p/camel', '*.jpg'))
+    video_path_list.sort()
+    # first frame
+    first_frame_path = '/ssd1/gaomingqi/datasets/davis/Annotations/480p/camel/00000.png'
+    # load frames
+    frames = []
+    for video_path in video_path_list:
+        frames.append(np.array(Image.open(video_path).convert('RGB')))
+    frames = np.stack(frames, 0)    # N, H, W, C
+    # load first frame annotation
+    first_frame_annotation = np.array(Image.open(first_frame_path).convert('P'))    # H, W, C
+    
+    print('first video done. clear.')
+
+    tracker.clear_memory()
+    # track anything given in the first frame annotation
+    for ti, frame in enumerate(frames):
+        if ti == 0:
+            mask, prob, painted_image = tracker.track(frame, first_frame_annotation)
+        else:
+            mask, prob, painted_image = tracker.track(frame)
+        # save
+        painted_image = Image.fromarray(painted_image)
+        painted_image.save(f'/ssd1/gaomingqi/results/TrackA/camel/{ti:05d}.png')
diff --git a/tracker/dataset/__init__.py b/tracker/dataset/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/tracker/dataset/reseed.py b/tracker/dataset/reseed.py
deleted file mode 100644
index 600c998..0000000
--- a/tracker/dataset/reseed.py
+++ /dev/null
@@ -1,6 +0,0 @@
-import torch
-import random
-
-def reseed(seed):
-    random.seed(seed)
-    torch.manual_seed(seed)
\ No newline at end of file
diff --git a/tracker/dataset/static_dataset.py b/tracker/dataset/static_dataset.py
deleted file mode 100644
index 5800f5f..0000000
--- a/tracker/dataset/static_dataset.py
+++ /dev/null
@@ -1,179 +0,0 @@
-import os
-from os import path
-
-import torch
-from torch.utils.data.dataset import Dataset
-from torchvision import transforms
-from torchvision.transforms import InterpolationMode
-from PIL import Image
-import numpy as np
-
-from dataset.range_transform import im_normalization, im_mean
-from dataset.tps import random_tps_warp
-from dataset.reseed import reseed
-
-
-class StaticTransformDataset(Dataset):
-    """
-    Generate pseudo VOS data by applying random transforms on static images.
-    Single-object only.
-
-    Method 0 - FSS style (class/1.jpg class/1.png)
-    Method 1 - Others style (XXX.jpg XXX.png)
-    """
-    def __init__(self, parameters, num_frames=3, max_num_obj=1):
-        self.num_frames = num_frames
-        self.max_num_obj = max_num_obj
-
-        self.im_list = []
-        for parameter in parameters:
-            root, method, multiplier = parameter
-            if method == 0:
-                # Get images
-                classes = os.listdir(root)
-                for c in classes:
-                    imgs = os.listdir(path.join(root, c))
-                    jpg_list = [im for im in imgs if 'jpg' in im[-3:].lower()]
-
-                    joint_list = [path.join(root, c, im) for im in jpg_list]
-                    self.im_list.extend(joint_list * multiplier)
-
-            elif method == 1:
-                self.im_list.extend([path.join(root, im) for im in os.listdir(root) if '.jpg' in im] * multiplier)
-
-        print(f'{len(self.im_list)} images found.')
-
-        # These set of transform is the same for im/gt pairs, but different among the 3 sampled frames
-        self.pair_im_lone_transform = transforms.Compose([
-            transforms.ColorJitter(0.1, 0.05, 0.05, 0), # No hue change here as that's not realistic
-        ])
-
-        self.pair_im_dual_transform = transforms.Compose([
-            transforms.RandomAffine(degrees=20, scale=(0.9,1.1), shear=10, interpolation=InterpolationMode.BICUBIC, fill=im_mean),
-            transforms.Resize(384, InterpolationMode.BICUBIC),
-            transforms.RandomCrop((384, 384), pad_if_needed=True, fill=im_mean),
-        ])
-
-        self.pair_gt_dual_transform = transforms.Compose([
-            transforms.RandomAffine(degrees=20, scale=(0.9,1.1), shear=10, interpolation=InterpolationMode.BICUBIC, fill=0),
-            transforms.Resize(384, InterpolationMode.NEAREST),
-            transforms.RandomCrop((384, 384), pad_if_needed=True, fill=0),
-        ])
-
-
-        # These transform are the same for all pairs in the sampled sequence
-        self.all_im_lone_transform = transforms.Compose([
-            transforms.ColorJitter(0.1, 0.05, 0.05, 0.05),
-            transforms.RandomGrayscale(0.05),
-        ])
-
-        self.all_im_dual_transform = transforms.Compose([
-            transforms.RandomAffine(degrees=0, scale=(0.8, 1.5), fill=im_mean),
-            transforms.RandomHorizontalFlip(),
-        ])
-
-        self.all_gt_dual_transform = transforms.Compose([
-            transforms.RandomAffine(degrees=0, scale=(0.8, 1.5), fill=0),
-            transforms.RandomHorizontalFlip(),
-        ])
-
-        # Final transform without randomness
-        self.final_im_transform = transforms.Compose([
-            transforms.ToTensor(),
-            im_normalization,
-        ])
-
-        self.final_gt_transform = transforms.Compose([
-            transforms.ToTensor(),
-        ])
-
-    def _get_sample(self, idx):
-        im = Image.open(self.im_list[idx]).convert('RGB')
-        gt = Image.open(self.im_list[idx][:-3]+'png').convert('L')
-
-        sequence_seed = np.random.randint(2147483647)
-
-        images = []
-        masks = []
-        for _ in range(self.num_frames):
-            reseed(sequence_seed)
-            this_im = self.all_im_dual_transform(im)
-            this_im = self.all_im_lone_transform(this_im)
-            reseed(sequence_seed)
-            this_gt = self.all_gt_dual_transform(gt)
-
-            pairwise_seed = np.random.randint(2147483647)
-            reseed(pairwise_seed)
-            this_im = self.pair_im_dual_transform(this_im)
-            this_im = self.pair_im_lone_transform(this_im)
-            reseed(pairwise_seed)
-            this_gt = self.pair_gt_dual_transform(this_gt)
-
-            # Use TPS only some of the times
-            # Not because TPS is bad -- just that it is too slow and I need to speed up data loading
-            if np.random.rand() < 0.33:
-                this_im, this_gt = random_tps_warp(this_im, this_gt, scale=0.02)
-
-            this_im = self.final_im_transform(this_im)
-            this_gt = self.final_gt_transform(this_gt)
-
-            images.append(this_im)
-            masks.append(this_gt)
-
-        images = torch.stack(images, 0)
-        masks = torch.stack(masks, 0)
-
-        return images, masks.numpy()
-
-    def __getitem__(self, idx):
-        additional_objects = np.random.randint(self.max_num_obj)
-        indices = [idx, *np.random.randint(self.__len__(), size=additional_objects)]
-
-        merged_images = None
-        merged_masks = np.zeros((self.num_frames, 384, 384), dtype=np.int64)
-
-        for i, list_id in enumerate(indices):
-            images, masks = self._get_sample(list_id)
-            if merged_images is None:
-                merged_images = images
-            else:
-                merged_images = merged_images*(1-masks) + images*masks
-            merged_masks[masks[:,0]>0.5] = (i+1)
-
-        masks = merged_masks
-
-        labels = np.unique(masks[0])
-        # Remove background
-        labels = labels[labels!=0]
-        target_objects = labels.tolist()
-
-        # Generate one-hot ground-truth
-        cls_gt = np.zeros((self.num_frames, 384, 384), dtype=np.int64)
-        first_frame_gt = np.zeros((1, self.max_num_obj, 384, 384), dtype=np.int64)
-        for i, l in enumerate(target_objects):
-            this_mask = (masks==l)
-            cls_gt[this_mask] = i+1
-            first_frame_gt[0,i] = (this_mask[0])
-        cls_gt = np.expand_dims(cls_gt, 1)
-
-        info = {}
-        info['name'] = self.im_list[idx]
-        info['num_objects'] = max(1, len(target_objects))
-
-        # 1 if object exist, 0 otherwise
-        selector = [1 if i < info['num_objects'] else 0 for i in range(self.max_num_obj)]
-        selector = torch.FloatTensor(selector)
-
-        data = {
-            'rgb': merged_images,
-            'first_frame_gt': first_frame_gt,
-            'cls_gt': cls_gt,
-            'selector': selector,
-            'info': info
-        }
-
-        return data
-
-
-    def __len__(self):
-        return len(self.im_list)
diff --git a/tracker/dataset/tps.py b/tracker/dataset/tps.py
deleted file mode 100644
index 9ee3747..0000000
--- a/tracker/dataset/tps.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import numpy as np
-from PIL import Image
-import cv2
-import thinplate as tps
-
-cv2.setNumThreads(0)
-
-def pick_random_points(h, w, n_samples):
-    y_idx = np.random.choice(np.arange(h), size=n_samples, replace=False)
-    x_idx = np.random.choice(np.arange(w), size=n_samples, replace=False)
-    return y_idx/h, x_idx/w
-
-
-def warp_dual_cv(img, mask, c_src, c_dst):
-    dshape = img.shape
-    theta = tps.tps_theta_from_points(c_src, c_dst, reduced=True)
-    grid = tps.tps_grid(theta, c_dst, dshape)
-    mapx, mapy = tps.tps_grid_to_remap(grid, img.shape)
-    return cv2.remap(img, mapx, mapy, cv2.INTER_LINEAR), cv2.remap(mask, mapx, mapy, cv2.INTER_NEAREST)
-
-
-def random_tps_warp(img, mask, scale, n_ctrl_pts=12):
-    """
-    Apply a random TPS warp of the input image and mask
-    Uses randomness from numpy
-    """
-    img = np.asarray(img)
-    mask = np.asarray(mask)
-
-    h, w = mask.shape
-    points = pick_random_points(h, w, n_ctrl_pts)
-    c_src = np.stack(points, 1)
-    c_dst = c_src + np.random.normal(scale=scale, size=c_src.shape)
-    warp_im, warp_gt = warp_dual_cv(img, mask, c_src, c_dst)
-
-    return Image.fromarray(warp_im), Image.fromarray(warp_gt)
-
diff --git a/tracker/dataset/util.py b/tracker/dataset/util.py
deleted file mode 100644
index f8e5523..0000000
--- a/tracker/dataset/util.py
+++ /dev/null
@@ -1,13 +0,0 @@
-import numpy as np
-
-
-def all_to_onehot(masks, labels):
-    if len(masks.shape) == 3:
-        Ms = np.zeros((len(labels), masks.shape[0], masks.shape[1], masks.shape[2]), dtype=np.uint8)
-    else:
-        Ms = np.zeros((len(labels), masks.shape[0], masks.shape[1]), dtype=np.uint8)
-
-    for ni, l in enumerate(labels):
-        Ms[ni] = (masks == l).astype(np.uint8)
-        
-    return Ms
diff --git a/tracker/dataset/vos_dataset.py b/tracker/dataset/vos_dataset.py
deleted file mode 100644
index 2b5d365..0000000
--- a/tracker/dataset/vos_dataset.py
+++ /dev/null
@@ -1,216 +0,0 @@
-import os
-from os import path, replace
-
-import torch
-from torch.utils.data.dataset import Dataset
-from torchvision import transforms
-from torchvision.transforms import InterpolationMode
-from PIL import Image
-import numpy as np
-
-from dataset.range_transform import im_normalization, im_mean
-from dataset.reseed import reseed
-
-
-class VOSDataset(Dataset):
-    """
-    Works for DAVIS/YouTubeVOS/BL30K training
-    For each sequence:
-    - Pick three frames
-    - Pick two objects
-    - Apply some random transforms that are the same for all frames
-    - Apply random transform to each of the frame
-    - The distance between frames is controlled
-    """
-    def __init__(self, im_root, gt_root, max_jump, is_bl, subset=None, num_frames=3, max_num_obj=3, finetune=False):
-        self.im_root = im_root
-        self.gt_root = gt_root
-        self.max_jump = max_jump
-        self.is_bl = is_bl
-        self.num_frames = num_frames
-        self.max_num_obj = max_num_obj
-
-        self.videos = []
-        self.frames = {}
-
-        vid_list = sorted(os.listdir(self.im_root))
-        # Pre-filtering
-        for vid in vid_list:
-            if subset is not None:
-                if vid not in subset:
-                    continue
-            frames = sorted(os.listdir(os.path.join(self.im_root, vid)))
-            if len(frames) < num_frames:
-                continue
-            self.frames[vid] = frames
-            self.videos.append(vid)
-
-        print('%d out of %d videos accepted in %s.' % (len(self.videos), len(vid_list), im_root))
-
-        # These set of transform is the same for im/gt pairs, but different among the 3 sampled frames
-        self.pair_im_lone_transform = transforms.Compose([
-            transforms.ColorJitter(0.01, 0.01, 0.01, 0),
-        ])
-
-        self.pair_im_dual_transform = transforms.Compose([
-            transforms.RandomAffine(degrees=0 if finetune or self.is_bl else 15, shear=0 if finetune or self.is_bl else 10, interpolation=InterpolationMode.BILINEAR, fill=im_mean),
-        ])
-
-        self.pair_gt_dual_transform = transforms.Compose([
-            transforms.RandomAffine(degrees=0 if finetune or self.is_bl else 15, shear=0 if finetune or self.is_bl else 10, interpolation=InterpolationMode.NEAREST, fill=0),
-        ])
-
-        # These transform are the same for all pairs in the sampled sequence
-        self.all_im_lone_transform = transforms.Compose([
-            transforms.ColorJitter(0.1, 0.03, 0.03, 0),
-            transforms.RandomGrayscale(0.05),
-        ])
-
-        if self.is_bl:
-            # Use a different cropping scheme for the blender dataset because the image size is different
-            self.all_im_dual_transform = transforms.Compose([
-                transforms.RandomHorizontalFlip(),
-                transforms.RandomResizedCrop((384, 384), scale=(0.25, 1.00), interpolation=InterpolationMode.BILINEAR)
-            ])
-
-            self.all_gt_dual_transform = transforms.Compose([
-                transforms.RandomHorizontalFlip(),
-                transforms.RandomResizedCrop((384, 384), scale=(0.25, 1.00), interpolation=InterpolationMode.NEAREST)
-            ])
-        else:
-            self.all_im_dual_transform = transforms.Compose([
-                transforms.RandomHorizontalFlip(),
-                transforms.RandomResizedCrop((384, 384), scale=(0.36,1.00), interpolation=InterpolationMode.BILINEAR)
-            ])
-
-            self.all_gt_dual_transform = transforms.Compose([
-                transforms.RandomHorizontalFlip(),
-                transforms.RandomResizedCrop((384, 384), scale=(0.36,1.00), interpolation=InterpolationMode.NEAREST)
-            ])
-
-        # Final transform without randomness
-        self.final_im_transform = transforms.Compose([
-            transforms.ToTensor(),
-            im_normalization,
-        ])
-
-    def __getitem__(self, idx):
-        video = self.videos[idx]
-        info = {}
-        info['name'] = video
-
-        vid_im_path = path.join(self.im_root, video)
-        vid_gt_path = path.join(self.gt_root, video)
-        frames = self.frames[video]
-
-        trials = 0
-        while trials < 5:
-            info['frames'] = [] # Appended with actual frames
-
-            num_frames = self.num_frames
-            length = len(frames)
-            this_max_jump = min(len(frames), self.max_jump)
-
-            # iterative sampling
-            frames_idx = [np.random.randint(length)]
-            acceptable_set = set(range(max(0, frames_idx[-1]-this_max_jump), min(length, frames_idx[-1]+this_max_jump+1))).difference(set(frames_idx))
-            while(len(frames_idx) < num_frames):
-                idx = np.random.choice(list(acceptable_set))
-                frames_idx.append(idx)
-                new_set = set(range(max(0, frames_idx[-1]-this_max_jump), min(length, frames_idx[-1]+this_max_jump+1)))
-                acceptable_set = acceptable_set.union(new_set).difference(set(frames_idx))
-
-            frames_idx = sorted(frames_idx)
-            if np.random.rand() < 0.5:
-                # Reverse time
-                frames_idx = frames_idx[::-1]
-
-            sequence_seed = np.random.randint(2147483647)
-            images = []
-            masks = []
-            target_objects = []
-            for f_idx in frames_idx:
-                jpg_name = frames[f_idx][:-4] + '.jpg'
-                png_name = frames[f_idx][:-4] + '.png'
-                info['frames'].append(jpg_name)
-
-                reseed(sequence_seed)
-                this_im = Image.open(path.join(vid_im_path, jpg_name)).convert('RGB')
-                this_im = self.all_im_dual_transform(this_im)
-                this_im = self.all_im_lone_transform(this_im)
-                reseed(sequence_seed)
-                this_gt = Image.open(path.join(vid_gt_path, png_name)).convert('P')
-                this_gt = self.all_gt_dual_transform(this_gt)
-
-                pairwise_seed = np.random.randint(2147483647)
-                reseed(pairwise_seed)
-                this_im = self.pair_im_dual_transform(this_im)
-                this_im = self.pair_im_lone_transform(this_im)
-                reseed(pairwise_seed)
-                this_gt = self.pair_gt_dual_transform(this_gt)
-
-                this_im = self.final_im_transform(this_im)
-                this_gt = np.array(this_gt)
-
-                images.append(this_im)
-                masks.append(this_gt)
-
-            images = torch.stack(images, 0)
-
-            labels = np.unique(masks[0])
-            # Remove background
-            labels = labels[labels!=0]
-
-            if self.is_bl:
-                # Find large enough labels
-                good_lables = []
-                for l in labels:
-                    pixel_sum = (masks[0]==l).sum()
-                    if pixel_sum > 10*10:
-                        # OK if the object is always this small
-                        # Not OK if it is actually much bigger
-                        if pixel_sum > 30*30:
-                            good_lables.append(l)
-                        elif max((masks[1]==l).sum(), (masks[2]==l).sum()) < 20*20:
-                            good_lables.append(l)
-                labels = np.array(good_lables, dtype=np.uint8)
-            
-            if len(labels) == 0:
-                target_objects = []
-                trials += 1
-            else:
-                target_objects = labels.tolist()
-                break
-
-        if len(target_objects) > self.max_num_obj:
-            target_objects = np.random.choice(target_objects, size=self.max_num_obj, replace=False)
-
-        info['num_objects'] = max(1, len(target_objects))
-
-        masks = np.stack(masks, 0)
-
-        # Generate one-hot ground-truth
-        cls_gt = np.zeros((self.num_frames, 384, 384), dtype=np.int64)
-        first_frame_gt = np.zeros((1, self.max_num_obj, 384, 384), dtype=np.int64)
-        for i, l in enumerate(target_objects):
-            this_mask = (masks==l)
-            cls_gt[this_mask] = i+1
-            first_frame_gt[0,i] = (this_mask[0])
-        cls_gt = np.expand_dims(cls_gt, 1)
-
-        # 1 if object exist, 0 otherwise
-        selector = [1 if i < info['num_objects'] else 0 for i in range(self.max_num_obj)]
-        selector = torch.FloatTensor(selector)
-
-        data = {
-            'rgb': images,
-            'first_frame_gt': first_frame_gt,
-            'cls_gt': cls_gt,
-            'selector': selector,
-            'info': info,
-        }
-
-        return data
-
-    def __len__(self):
-        return len(self.videos)
diff --git a/tracker/eval.py b/tracker/eval.py
deleted file mode 100644
index 8d4e186..0000000
--- a/tracker/eval.py
+++ /dev/null
@@ -1,257 +0,0 @@
-import os
-from os import path
-from argparse import ArgumentParser
-import shutil
-
-import torch
-import torch.nn.functional as F
-from torch.utils.data import DataLoader
-import numpy as np
-from PIL import Image
-
-from inference.data.test_datasets import LongTestDataset, DAVISTestDataset, YouTubeVOSTestDataset
-from inference.data.mask_mapper import MaskMapper
-from model.network import XMem
-from inference.inference_core import InferenceCore
-
-from progressbar import progressbar
-
-try:
-    import hickle as hkl
-except ImportError:
-    print('Failed to import hickle. Fine if not using multi-scale testing.')
-
-
-"""
-Arguments loading
-"""
-parser = ArgumentParser()
-parser.add_argument('--model', default='/ssd1/gaomingqi/checkpoints/XMem-s012.pth')
-
-# Data options
-parser.add_argument('--d16_path', default='../DAVIS/2016')
-parser.add_argument('--d17_path', default='../DAVIS/2017')
-parser.add_argument('--y18_path', default='/ssd1/gaomingqi/datasets/youtube-vos/2018')
-parser.add_argument('--y19_path', default='../YouTube')
-parser.add_argument('--lv_path', default='../long_video_set')
-# For generic (G) evaluation, point to a folder that contains "JPEGImages" and "Annotations"
-parser.add_argument('--generic_path')
-
-parser.add_argument('--dataset', help='D16/D17/Y18/Y19/LV1/LV3/G', default='D17')
-parser.add_argument('--split', help='val/test', default='val')
-parser.add_argument('--output', default=None)
-parser.add_argument('--save_all', action='store_true', 
-            help='Save all frames. Useful only in YouTubeVOS/long-time video', )
-
-parser.add_argument('--benchmark', action='store_true', help='enable to disable amp for FPS benchmarking')
-        
-# Long-term memory options
-parser.add_argument('--disable_long_term', action='store_true')
-parser.add_argument('--max_mid_term_frames', help='T_max in paper, decrease to save memory', type=int, default=10)
-parser.add_argument('--min_mid_term_frames', help='T_min in paper, decrease to save memory', type=int, default=5)
-parser.add_argument('--max_long_term_elements', help='LT_max in paper, increase if objects disappear for a long time', 
-                                                type=int, default=10000)
-parser.add_argument('--num_prototypes', help='P in paper', type=int, default=128)
-
-parser.add_argument('--top_k', type=int, default=30)
-parser.add_argument('--mem_every', help='r in paper. Increase to improve running speed.', type=int, default=5)
-parser.add_argument('--deep_update_every', help='Leave -1 normally to synchronize with mem_every', type=int, default=-1)
-
-# Multi-scale options
-parser.add_argument('--save_scores', action='store_true')
-parser.add_argument('--flip', action='store_true')
-parser.add_argument('--size', default=480, type=int, 
-            help='Resize the shorter side to this size. -1 to use original resolution. ')
-
-args = parser.parse_args()
-config = vars(args)
-config['enable_long_term'] = not config['disable_long_term']
-
-if args.output is None:
-    args.output = f'../output/{args.dataset}_{args.split}'
-    print(f'Output path not provided. Defaulting to {args.output}')
-
-"""
-Data preparation
-"""
-is_youtube = args.dataset.startswith('Y')
-is_davis = args.dataset.startswith('D')
-is_lv = args.dataset.startswith('LV')
-
-if is_youtube or args.save_scores:
-    out_path = path.join(args.output, 'Annotations')
-else:
-    out_path = args.output
-
-if is_youtube:
-    if args.dataset == 'Y18':
-        yv_path = args.y18_path
-    elif args.dataset == 'Y19':
-        yv_path = args.y19_path
-
-    if args.split == 'val':
-        args.split = 'valid'
-        meta_dataset = YouTubeVOSTestDataset(data_root=yv_path, split='valid', size=args.size)
-    elif args.split == 'test':
-        meta_dataset = YouTubeVOSTestDataset(data_root=yv_path, split='test', size=args.size)
-    else:
-        raise NotImplementedError
-
-elif is_davis:
-    if args.dataset == 'D16':
-        if args.split == 'val':
-            # Set up Dataset, a small hack to use the image set in the 2017 folder because the 2016 one is of a different format
-            meta_dataset = DAVISTestDataset(args.d16_path, imset='../../2017/trainval/ImageSets/2016/val.txt', size=args.size)
-        else:
-            raise NotImplementedError
-        palette = None
-    elif args.dataset == 'D17':
-        if args.split == 'val':
-            meta_dataset = DAVISTestDataset(path.join(args.d17_path, 'trainval'), imset='2017/val.txt', size=args.size)
-        elif args.split == 'test':
-            meta_dataset = DAVISTestDataset(path.join(args.d17_path, 'test-dev'), imset='2017/test-dev.txt', size=args.size)
-        else:
-            raise NotImplementedError
-
-elif is_lv:
-    if args.dataset == 'LV1':
-        meta_dataset = LongTestDataset(path.join(args.lv_path, 'long_video'))
-    elif args.dataset == 'LV3':
-        meta_dataset = LongTestDataset(path.join(args.lv_path, 'long_video_x3'))
-    else:
-        raise NotImplementedError
-elif args.dataset == 'G':
-    meta_dataset = LongTestDataset(path.join(args.generic_path), size=args.size)
-    if not args.save_all:
-        args.save_all = True
-        print('save_all is forced to be true in generic evaluation mode.')
-else:
-    raise NotImplementedError
-
-torch.autograd.set_grad_enabled(False)
-
-# Set up loader
-meta_loader = meta_dataset.get_datasets()
-
-# Load our checkpoint
-network = XMem(config, args.model).cuda().eval()
-if args.model is not None:
-    model_weights = torch.load(args.model)
-    network.load_weights(model_weights, init_as_zero_if_needed=True)
-else:
-    print('No model loaded.')
-
-total_process_time = 0
-total_frames = 0
-
-# Start eval
-for vid_reader in progressbar(meta_loader, max_value=len(meta_dataset), redirect_stdout=True):
-
-    loader = DataLoader(vid_reader, batch_size=1, shuffle=False, num_workers=2)
-    vid_name = vid_reader.vid_name
-    vid_length = len(loader)
-    # no need to count usage for LT if the video is not that long anyway
-    config['enable_long_term_count_usage'] = (
-        config['enable_long_term'] and
-        (vid_length
-            / (config['max_mid_term_frames']-config['min_mid_term_frames'])
-            * config['num_prototypes'])
-        >= config['max_long_term_elements']
-    )
-
-    mapper = MaskMapper()
-    processor = InferenceCore(network, config=config)
-    first_mask_loaded = False
-
-    for ti, data in enumerate(loader):
-        with torch.cuda.amp.autocast(enabled=not args.benchmark):
-            rgb = data['rgb'].cuda()[0]
-            msk = data.get('mask')
-            info = data['info']
-            frame = info['frame'][0]
-            shape = info['shape']
-            need_resize = info['need_resize'][0]
-
-            """
-            For timing see https://discuss.pytorch.org/t/how-to-measure-time-in-pytorch/26964
-            Seems to be very similar in testing as my previous timing method 
-            with two cuda sync + time.time() in STCN though 
-            """
-            start = torch.cuda.Event(enable_timing=True)
-            end = torch.cuda.Event(enable_timing=True)
-            start.record()
-
-            if not first_mask_loaded:
-                if msk is not None:
-                    first_mask_loaded = True
-                else:
-                    # no point to do anything without a mask
-                    continue
-
-            if args.flip:
-                rgb = torch.flip(rgb, dims=[-1])
-                msk = torch.flip(msk, dims=[-1]) if msk is not None else None
-
-            # Map possibly non-continuous labels to continuous ones
-            if msk is not None:
-                msk, labels = mapper.convert_mask(msk[0].numpy())
-                msk = torch.Tensor(msk).cuda()
-                if need_resize:
-                    msk = vid_reader.resize_mask(msk.unsqueeze(0))[0]
-                processor.set_all_labels(list(mapper.remappings.values()))
-            else:
-                labels = None
-
-            # Run the model on this frame
-            prob = processor.step(rgb, msk, labels, end=(ti==vid_length-1)) # 0, background, >0, objects
-
-            # Upsample to original size if needed
-            if need_resize:
-                prob = F.interpolate(prob.unsqueeze(1), shape, mode='bilinear', align_corners=False)[:,0]
-
-            end.record()
-            torch.cuda.synchronize()
-            total_process_time += (start.elapsed_time(end)/1000)
-            total_frames += 1
-
-            if args.flip:
-                prob = torch.flip(prob, dims=[-1])
-
-            # Probability mask -> index mask
-            out_mask = torch.argmax(prob, dim=0)
-            out_mask = (out_mask.detach().cpu().numpy()).astype(np.uint8)
-
-            if args.save_scores:
-                prob = (prob.detach().cpu().numpy()*255).astype(np.uint8)
-
-            # Save the mask
-            if args.save_all or info['save'][0]:
-                this_out_path = path.join(out_path, vid_name)
-                os.makedirs(this_out_path, exist_ok=True)
-                out_mask = mapper.remap_index_mask(out_mask)
-                out_img = Image.fromarray(out_mask)
-                if vid_reader.get_palette() is not None:
-                    out_img.putpalette(vid_reader.get_palette())
-                out_img.save(os.path.join(this_out_path, frame[:-4]+'.png'))
-
-            if args.save_scores:
-                np_path = path.join(args.output, 'Scores', vid_name)
-                os.makedirs(np_path, exist_ok=True)
-                if ti==len(loader)-1:
-                    hkl.dump(mapper.remappings, path.join(np_path, f'backward.hkl'), mode='w')
-                if args.save_all or info['save'][0]:
-                    hkl.dump(prob, path.join(np_path, f'{frame[:-4]}.hkl'), mode='w', compression='lzf')
-
-
-print(f'Total processing time: {total_process_time}')
-print(f'Total processed frames: {total_frames}')
-print(f'FPS: {total_frames / total_process_time}')
-print(f'Max allocated memory (MB): {torch.cuda.max_memory_allocated() / (2**20)}')
-
-if not args.save_scores:
-    if is_youtube:
-        print('Making zip for YouTubeVOS...')
-        shutil.make_archive(path.join(args.output, path.basename(args.output)), 'zip', args.output, 'Annotations')
-    elif is_davis and args.split == 'test':
-        print('Making zip for DAVIS test-dev...')
-        shutil.make_archive(args.output, 'zip', args.output)
diff --git a/tracker/inference/data/__init__.py b/tracker/inference/data/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/tracker/inference/data/test_datasets.py b/tracker/inference/data/test_datasets.py
deleted file mode 100644
index 3a4446e..0000000
--- a/tracker/inference/data/test_datasets.py
+++ /dev/null
@@ -1,96 +0,0 @@
-import os
-from os import path
-import json
-
-from inference.data.video_reader import VideoReader
-
-
-class LongTestDataset:
-    def __init__(self, data_root, size=-1):
-        self.image_dir = path.join(data_root, 'JPEGImages')
-        self.mask_dir = path.join(data_root, 'Annotations')
-        self.size = size
-
-        self.vid_list = sorted(os.listdir(self.image_dir))
-
-    def get_datasets(self):
-        for video in self.vid_list:
-            yield VideoReader(video, 
-                path.join(self.image_dir, video), 
-                path.join(self.mask_dir, video),
-                to_save = [
-                    name[:-4] for name in os.listdir(path.join(self.mask_dir, video))
-                ],
-                size=self.size,
-            )
-
-    def __len__(self):
-        return len(self.vid_list)
-
-
-class DAVISTestDataset:
-    def __init__(self, data_root, imset='2017/val.txt', size=-1):
-        if size != 480:
-            self.image_dir = path.join(data_root, 'JPEGImages', 'Full-Resolution')
-            self.mask_dir = path.join(data_root, 'Annotations', 'Full-Resolution')
-            if not path.exists(self.image_dir):
-                print(f'{self.image_dir} not found. Look at other options.')
-                self.image_dir = path.join(data_root, 'JPEGImages', '1080p')
-                self.mask_dir = path.join(data_root, 'Annotations', '1080p')
-            assert path.exists(self.image_dir), 'path not found'
-        else:
-            self.image_dir = path.join(data_root, 'JPEGImages', '480p')
-            self.mask_dir = path.join(data_root, 'Annotations', '480p')
-        self.size_dir = path.join(data_root, 'JPEGImages', '480p')
-        self.size = size
-
-        with open(path.join(data_root, 'ImageSets', imset)) as f:
-            self.vid_list = sorted([line.strip() for line in f])
-
-    def get_datasets(self):
-        for video in self.vid_list:
-            yield VideoReader(video, 
-                path.join(self.image_dir, video), 
-                path.join(self.mask_dir, video),
-                size=self.size,
-                size_dir=path.join(self.size_dir, video),
-            )
-
-    def __len__(self):
-        return len(self.vid_list)
-
-
-class YouTubeVOSTestDataset:
-    def __init__(self, data_root, split, size=480):
-        self.image_dir = path.join(data_root, 'all_frames', split+'_all_frames', 'JPEGImages')
-        self.mask_dir = path.join(data_root, split, 'Annotations')
-        self.size = size
-
-        self.vid_list = sorted(os.listdir(self.image_dir))
-        self.req_frame_list = {}
-
-        with open(path.join(data_root, split, 'meta.json')) as f:
-            # read meta.json to know which frame is required for evaluation
-            meta = json.load(f)['videos']
-
-            for vid in self.vid_list:
-                req_frames = []
-                objects = meta[vid]['objects']
-                for value in objects.values():
-                    req_frames.extend(value['frames'])
-
-                req_frames = list(set(req_frames))
-                self.req_frame_list[vid] = req_frames
-
-    def get_datasets(self):
-        for video in self.vid_list:
-            yield VideoReader(video, 
-                path.join(self.image_dir, video), 
-                path.join(self.mask_dir, video),
-                size=self.size,
-                to_save=self.req_frame_list[video], 
-                use_all_mask=True
-            )
-
-    def __len__(self):
-        return len(self.vid_list)
diff --git a/tracker/inference/data/video_reader.py b/tracker/inference/data/video_reader.py
deleted file mode 100644
index 28cc4c6..0000000
--- a/tracker/inference/data/video_reader.py
+++ /dev/null
@@ -1,100 +0,0 @@
-import os
-from os import path
-
-from torch.utils.data.dataset import Dataset
-from torchvision import transforms
-from torchvision.transforms import InterpolationMode
-import torch.nn.functional as F
-from PIL import Image
-import numpy as np
-
-from dataset.range_transform import im_normalization
-
-
-class VideoReader(Dataset):
-    """
-    This class is used to read a video, one frame at a time
-    """
-    def __init__(self, vid_name, image_dir, mask_dir, size=-1, to_save=None, use_all_mask=False, size_dir=None):
-        """
-        image_dir - points to a directory of jpg images
-        mask_dir - points to a directory of png masks
-        size - resize min. side to size. Does nothing if <0.
-        to_save - optionally contains a list of file names without extensions 
-            where the segmentation mask is required
-        use_all_mask - when true, read all available mask in mask_dir.
-            Default false. Set to true for YouTubeVOS validation.
-        """
-        self.vid_name = vid_name
-        self.image_dir = image_dir
-        self.mask_dir = mask_dir
-        self.to_save = to_save
-        self.use_all_mask = use_all_mask
-        if size_dir is None:
-            self.size_dir = self.image_dir
-        else:
-            self.size_dir = size_dir
-
-        self.frames = sorted(os.listdir(self.image_dir))
-        self.palette = Image.open(path.join(mask_dir, sorted(os.listdir(mask_dir))[0])).getpalette()
-        self.first_gt_path = path.join(self.mask_dir, sorted(os.listdir(self.mask_dir))[0])
-
-        if size < 0:
-            self.im_transform = transforms.Compose([
-                transforms.ToTensor(),
-                im_normalization,
-            ])
-        else:
-            self.im_transform = transforms.Compose([
-                transforms.ToTensor(),
-                im_normalization,
-                transforms.Resize(size, interpolation=InterpolationMode.BILINEAR),
-            ])
-        self.size = size
-
-
-    def __getitem__(self, idx):
-        frame = self.frames[idx]
-        info = {}
-        data = {}
-        info['frame'] = frame
-        info['save'] = (self.to_save is None) or (frame[:-4] in self.to_save)
-
-        im_path = path.join(self.image_dir, frame)
-        img = Image.open(im_path).convert('RGB')
-
-        if self.image_dir == self.size_dir:
-            shape = np.array(img).shape[:2]
-        else:
-            size_path = path.join(self.size_dir, frame)
-            size_im = Image.open(size_path).convert('RGB')
-            shape = np.array(size_im).shape[:2]
-
-        gt_path = path.join(self.mask_dir, frame[:-4]+'.png')
-        img = self.im_transform(img)
-
-        load_mask = self.use_all_mask or (gt_path == self.first_gt_path)
-        if load_mask and path.exists(gt_path):
-            mask = Image.open(gt_path).convert('P')
-            mask = np.array(mask, dtype=np.uint8)
-            data['mask'] = mask
-
-        info['shape'] = shape
-        info['need_resize'] = not (self.size < 0)
-        data['rgb'] = img
-        data['info'] = info
-
-        return data
-
-    def resize_mask(self, mask):
-        # mask transform is applied AFTER mapper, so we need to post-process it in eval.py
-        h, w = mask.shape[-2:]
-        min_hw = min(h, w)
-        return F.interpolate(mask, (int(h/min_hw*self.size), int(w/min_hw*self.size)), 
-                    mode='nearest')
-
-    def get_palette(self):
-        return self.palette
-
-    def __len__(self):
-        return len(self.frames)
\ No newline at end of file
diff --git a/tracker/inference/inference_core.py b/tracker/inference/inference_core.py
index c4cbffd..8061f51 100644
--- a/tracker/inference/inference_core.py
+++ b/tracker/inference/inference_core.py
@@ -63,8 +63,6 @@ class InferenceCore:
         if need_segment:
             memory_readout = self.memory.match_memory(key, selection).unsqueeze(0)
             
-            
-            
             hidden, _, pred_prob_with_bg = self.network.segment(multi_scale_features, memory_readout, 
                                     self.memory.get_hidden(), h_out=is_normal_update, strip_bg=False)
             # remove batch dim
diff --git a/tracker/util/configuration.py b/tracker/util/configuration.py
deleted file mode 100644
index ba7d9e8..0000000
--- a/tracker/util/configuration.py
+++ /dev/null
@@ -1,138 +0,0 @@
-from argparse import ArgumentParser
-
-
-def none_or_default(x, default):
-    return x if x is not None else default
-
-class Configuration():
-    def parse(self, unknown_arg_ok=False):
-        parser = ArgumentParser()
-
-        # Enable torch.backends.cudnn.benchmark -- Faster in some cases, test in your own environment
-        parser.add_argument('--benchmark', action='store_true')
-        parser.add_argument('--no_amp', action='store_true')
-
-        # Save paths
-        parser.add_argument('--save_path', default='/ssd1/gaomingqi/output/xmem-sam')
-
-        # Data parameters
-        parser.add_argument('--static_root', help='Static training data root', default='/ssd1/gaomingqi/datasets/static')
-        parser.add_argument('--bl_root', help='Blender training data root', default='../BL30K')
-        parser.add_argument('--yv_root', help='YouTubeVOS data root', default='/ssd1/gaomingqi/datasets/youtube-vos/2018')
-        parser.add_argument('--davis_root', help='DAVIS data root', default='/ssd1/gaomingqi/datasets/davis')
-        parser.add_argument('--num_workers', help='Total number of dataloader workers across all GPUs processes', type=int, default=16)
-
-        parser.add_argument('--key_dim', default=64, type=int)
-        parser.add_argument('--value_dim', default=512, type=int)
-        parser.add_argument('--hidden_dim', default=64, help='Set to =0 to disable', type=int)
-
-        parser.add_argument('--deep_update_prob', default=0.2, type=float)
-
-        parser.add_argument('--stages', help='Training stage (0-static images, 1-Blender dataset, 2-DAVIS+YouTubeVOS)', default='02')
-
-        """
-        Stage-specific learning parameters
-        Batch sizes are effective -- you don't have to scale them when you scale the number processes
-        """
-        # Stage 0, static images
-        parser.add_argument('--s0_batch_size', default=16, type=int)
-        parser.add_argument('--s0_iterations', default=150000, type=int)
-        parser.add_argument('--s0_finetune', default=0, type=int)
-        parser.add_argument('--s0_steps', nargs="*", default=[], type=int)
-        parser.add_argument('--s0_lr', help='Initial learning rate', default=1e-5, type=float)
-        parser.add_argument('--s0_num_ref_frames', default=2, type=int)
-        parser.add_argument('--s0_num_frames', default=3, type=int)
-        parser.add_argument('--s0_start_warm', default=20000, type=int)
-        parser.add_argument('--s0_end_warm', default=70000, type=int)
-
-        # Stage 1, BL30K
-        parser.add_argument('--s1_batch_size', default=8, type=int)
-        parser.add_argument('--s1_iterations', default=250000, type=int)
-        # fine-tune means fewer augmentations to train the sensory memory
-        parser.add_argument('--s1_finetune', default=0, type=int)
-        parser.add_argument('--s1_steps', nargs="*", default=[200000], type=int)
-        parser.add_argument('--s1_lr', help='Initial learning rate', default=1e-5, type=float)
-        parser.add_argument('--s1_num_ref_frames', default=3, type=int)
-        parser.add_argument('--s1_num_frames', default=8, type=int)
-        parser.add_argument('--s1_start_warm', default=20000, type=int)
-        parser.add_argument('--s1_end_warm', default=70000, type=int)
-
-        # Stage 2, DAVIS+YoutubeVOS, longer
-        parser.add_argument('--s2_batch_size', default=8, type=int)
-        parser.add_argument('--s2_iterations', default=150000, type=int)
-        # fine-tune means fewer augmentations to train the sensory memory
-        parser.add_argument('--s2_finetune', default=10000, type=int)
-        parser.add_argument('--s2_steps', nargs="*", default=[120000], type=int)
-        parser.add_argument('--s2_lr', help='Initial learning rate', default=1e-5, type=float)
-        parser.add_argument('--s2_num_ref_frames', default=3, type=int)
-        parser.add_argument('--s2_num_frames', default=8, type=int)
-        parser.add_argument('--s2_start_warm', default=20000, type=int)
-        parser.add_argument('--s2_end_warm', default=70000, type=int)
-
-        # Stage 3, DAVIS+YoutubeVOS, shorter
-        parser.add_argument('--s3_batch_size', default=8, type=int)
-        parser.add_argument('--s3_iterations', default=100000, type=int)
-        # fine-tune means fewer augmentations to train the sensory memory
-        parser.add_argument('--s3_finetune', default=10000, type=int)
-        parser.add_argument('--s3_steps', nargs="*", default=[80000], type=int)
-        parser.add_argument('--s3_lr', help='Initial learning rate', default=1e-5, type=float)
-        parser.add_argument('--s3_num_ref_frames', default=3, type=int)
-        parser.add_argument('--s3_num_frames', default=8, type=int)
-        parser.add_argument('--s3_start_warm', default=20000, type=int)
-        parser.add_argument('--s3_end_warm', default=70000, type=int)
-
-        parser.add_argument('--gamma', help='LR := LR*gamma at every decay step', default=0.1, type=float)
-        parser.add_argument('--weight_decay', default=0.05, type=float)
-
-        # Loading
-        parser.add_argument('--load_network', help='Path to pretrained network weight only')
-        parser.add_argument('--load_checkpoint', help='Path to the checkpoint file, including network, optimizer and such')
-
-        # Logging information
-        parser.add_argument('--log_text_interval', default=100, type=int)
-        parser.add_argument('--log_image_interval', default=1000, type=int)
-        parser.add_argument('--save_network_interval', default=25000, type=int)
-        parser.add_argument('--save_checkpoint_interval', default=50000, type=int)
-        parser.add_argument('--exp_id', help='Experiment UNIQUE id, use NULL to disable logging to tensorboard', default='NULL')
-        parser.add_argument('--debug', help='Debug mode which logs information more often', action='store_true')
-
-        # # Multiprocessing parameters, not set by users
-        # parser.add_argument('--local_rank', default=0, type=int, help='Local rank of this process')
-
-        if unknown_arg_ok:
-            args, _ = parser.parse_known_args()
-            self.args = vars(args)
-        else:
-            self.args = vars(parser.parse_args())
-
-        self.args['amp'] = not self.args['no_amp']
-
-        # check if the stages are valid
-        stage_to_perform = list(self.args['stages'])
-        for s in stage_to_perform:
-            if s not in ['0', '1', '2', '3']:
-                raise NotImplementedError
-
-    def get_stage_parameters(self, stage):
-        parameters = {
-            'batch_size': self.args['s%s_batch_size'%stage],
-            'iterations': self.args['s%s_iterations'%stage],
-            'finetune': self.args['s%s_finetune'%stage],
-            'steps': self.args['s%s_steps'%stage],
-            'lr': self.args['s%s_lr'%stage],
-            'num_ref_frames': self.args['s%s_num_ref_frames'%stage],
-            'num_frames': self.args['s%s_num_frames'%stage],
-            'start_warm': self.args['s%s_start_warm'%stage],
-            'end_warm': self.args['s%s_end_warm'%stage],
-        }
-
-        return parameters
-
-    def __getitem__(self, key):
-        return self.args[key]
-
-    def __setitem__(self, key, value):
-        self.args[key] = value
-
-    def __str__(self):
-        return str(self.args)
diff --git a/tracker/util/davis_subset.txt b/tracker/util/davis_subset.txt
deleted file mode 100644
index 875c240..0000000
--- a/tracker/util/davis_subset.txt
+++ /dev/null
@@ -1,60 +0,0 @@
-bear
-bmx-bumps
-boat
-boxing-fisheye
-breakdance-flare
-bus
-car-turn
-cat-girl
-classic-car
-color-run
-crossing
-dance-jump
-dancing
-disc-jockey
-dog-agility
-dog-gooses
-dogs-scale
-drift-turn
-drone
-elephant
-flamingo
-hike
-hockey
-horsejump-low
-kid-football
-kite-walk
-koala
-lady-running
-lindy-hop
-longboard
-lucia
-mallard-fly
-mallard-water
-miami-surf
-motocross-bumps
-motorbike
-night-race
-paragliding
-planes-water
-rallye
-rhino
-rollerblade
-schoolgirls
-scooter-board
-scooter-gray
-sheep
-skate-park
-snowboard
-soccerball
-stroller
-stunt
-surf
-swing
-tennis
-tractor-sand
-train
-tuk-tuk
-upside-down
-varanus-cage
-walking
\ No newline at end of file
diff --git a/tracker/util/image_saver.py b/tracker/util/image_saver.py
deleted file mode 100644
index c43d9de..0000000
--- a/tracker/util/image_saver.py
+++ /dev/null
@@ -1,136 +0,0 @@
-import cv2
-import numpy as np
-
-import torch
-from dataset.range_transform import inv_im_trans
-from collections import defaultdict
-
-def tensor_to_numpy(image):
-    image_np = (image.numpy() * 255).astype('uint8')
-    return image_np
-
-def tensor_to_np_float(image):
-    image_np = image.numpy().astype('float32')
-    return image_np
-
-def detach_to_cpu(x):
-    return x.detach().cpu()
-
-def transpose_np(x):
-    return np.transpose(x, [1,2,0])
-
-def tensor_to_gray_im(x):
-    x = detach_to_cpu(x)
-    x = tensor_to_numpy(x)
-    x = transpose_np(x)
-    return x
-
-def tensor_to_im(x):
-    x = detach_to_cpu(x)
-    x = inv_im_trans(x).clamp(0, 1)
-    x = tensor_to_numpy(x)
-    x = transpose_np(x)
-    return x
-
-# Predefined key <-> caption dict
-key_captions = {
-    'im': 'Image', 
-    'gt': 'GT', 
-}
-
-"""
-Return an image array with captions
-keys in dictionary will be used as caption if not provided
-values should contain lists of cv2 images
-"""
-def get_image_array(images, grid_shape, captions={}):
-    h, w = grid_shape
-    cate_counts = len(images)
-    rows_counts = len(next(iter(images.values())))
-
-    font = cv2.FONT_HERSHEY_SIMPLEX
-
-    output_image = np.zeros([w*cate_counts, h*(rows_counts+1), 3], dtype=np.uint8)
-    col_cnt = 0
-    for k, v in images.items():
-
-        # Default as key value itself
-        caption = captions.get(k, k)
-
-        # Handles new line character
-        dy = 40
-        for i, line in enumerate(caption.split('\n')):
-            cv2.putText(output_image, line, (10, col_cnt*w+100+i*dy),
-                     font, 0.8, (255,255,255), 2, cv2.LINE_AA)
-
-        # Put images
-        for row_cnt, img in enumerate(v):
-            im_shape = img.shape
-            if len(im_shape) == 2:
-                img = img[..., np.newaxis]
-
-            img = (img * 255).astype('uint8')
-
-            output_image[(col_cnt+0)*w:(col_cnt+1)*w,
-                         (row_cnt+1)*h:(row_cnt+2)*h, :] = img
-            
-        col_cnt += 1
-
-    return output_image
-
-def base_transform(im, size):
-        im = tensor_to_np_float(im)
-        if len(im.shape) == 3:
-            im = im.transpose((1, 2, 0))
-        else:
-            im = im[:, :, None]
-
-        # Resize
-        if im.shape[1] != size:
-            im = cv2.resize(im, size, interpolation=cv2.INTER_NEAREST)
-
-        return im.clip(0, 1)
-
-def im_transform(im, size):
-        return base_transform(inv_im_trans(detach_to_cpu(im)), size=size)
-
-def mask_transform(mask, size):
-    return base_transform(detach_to_cpu(mask), size=size)
-
-def out_transform(mask, size):
-    return base_transform(detach_to_cpu(torch.sigmoid(mask)), size=size)
-
-def pool_pairs(images, size, num_objects):
-    req_images = defaultdict(list)
-
-    b, t = images['rgb'].shape[:2]
-
-    # limit the number of images saved
-    b = min(2, b)
-
-    # find max num objects
-    max_num_objects = max(num_objects[:b])
-
-    GT_suffix = ''
-    for bi in range(b):
-        GT_suffix += ' \n%s' % images['info']['name'][bi][-25:-4]
-
-    for bi in range(b):
-        for ti in range(t):
-            req_images['RGB'].append(im_transform(images['rgb'][bi,ti], size))
-            for oi in range(max_num_objects):
-                if ti == 0 or oi >= num_objects[bi]:
-                    req_images['Mask_%d'%oi].append(mask_transform(images['first_frame_gt'][bi][0,oi], size))
-                    # req_images['Mask_X8_%d'%oi].append(mask_transform(images['first_frame_gt'][bi][0,oi], size))
-                    # req_images['Mask_X16_%d'%oi].append(mask_transform(images['first_frame_gt'][bi][0,oi], size))
-                else:
-                    req_images['Mask_%d'%oi].append(mask_transform(images['masks_%d'%ti][bi][oi], size))
-                    # req_images['Mask_%d'%oi].append(mask_transform(images['masks_%d'%ti][bi][oi][2], size))
-                    # req_images['Mask_X8_%d'%oi].append(mask_transform(images['masks_%d'%ti][bi][oi][1], size))
-                    # req_images['Mask_X16_%d'%oi].append(mask_transform(images['masks_%d'%ti][bi][oi][0], size))
-                req_images['GT_%d_%s'%(oi, GT_suffix)].append(mask_transform(images['cls_gt'][bi,ti,0]==(oi+1), size))
-                # print((images['cls_gt'][bi,ti,0]==(oi+1)).shape)
-                # print(mask_transform(images['cls_gt'][bi,ti,0]==(oi+1), size).shape)
-
-
-    return get_image_array(req_images, size, key_captions)
\ No newline at end of file
diff --git a/tracker/util/load_subset.py b/tracker/util/load_subset.py
deleted file mode 100644
index 3191f4f..0000000
--- a/tracker/util/load_subset.py
+++ /dev/null
@@ -1,16 +0,0 @@
-"""
-load_subset.py - Presents a subset of data
-DAVIS - only the training set
-YouTubeVOS - I manually filtered some erroneous ones out but I haven't checked all
-"""
-
-
-def load_sub_davis(path='util/davis_subset.txt'):
-    with open(path, mode='r') as f:
-        subset = set(f.read().splitlines())
-    return subset
-
-def load_sub_yv(path='util/yv_subset.txt'):
-    with open(path, mode='r') as f:
-        subset = set(f.read().splitlines())
-    return subset
diff --git a/tracker/util/log_integrator.py b/tracker/util/log_integrator.py
deleted file mode 100644
index e4b26d5..0000000
--- a/tracker/util/log_integrator.py
+++ /dev/null
@@ -1,80 +0,0 @@
-"""
-Integrate numerical values for some iterations
-Typically used for loss computation / logging to tensorboard
-Call finalize and create a new Integrator when you want to display/log
-"""
-
-import torch
-
-
-class Integrator:
-    def __init__(self, logger, distributed=True, local_rank=0, world_size=1):
-        self.values = {}
-        self.counts = {}
-        self.hooks  = [] # List is used here to maintain insertion order
-
-        self.logger = logger
-
-        self.distributed = distributed
-        self.local_rank = local_rank
-        self.world_size = world_size
-
-    def add_tensor(self, key, tensor):
-        if key not in self.values:
-            self.counts[key] = 1
-            if type(tensor) == float or type(tensor) == int:
-                self.values[key] = tensor
-            else:
-                self.values[key] = tensor.mean().item()
-        else:
-            self.counts[key] += 1
-            if type(tensor) == float or type(tensor) == int:
-                self.values[key] += tensor
-            else:
-                self.values[key] += tensor.mean().item()
-
-    def add_dict(self, tensor_dict):
-        for k, v in tensor_dict.items():
-            self.add_tensor(k, v)
-
-    def add_hook(self, hook):
-        """
-        Adds a custom hook, i.e. compute new metrics using values in the dict
-        The hook takes the dict as argument, and returns a (k, v) tuple
-        e.g. for computing IoU
-        """
-        if type(hook) == list:
-            self.hooks.extend(hook)
-        else:
-            self.hooks.append(hook)
-
-    def reset_except_hooks(self):
-        self.values = {}
-        self.counts = {}
-
-    # Average and output the metrics
-    def finalize(self, prefix, it, f=None):
-
-        for hook in self.hooks:
-            k, v = hook(self.values)
-            self.add_tensor(k, v)
-
-        for k, v in self.values.items():
-
-            if k[:4] == 'hide':
-                continue
-
-            avg = v / self.counts[k]
-
-            if self.distributed:
-                # Inplace operation
-                avg = torch.tensor(avg).cuda()
-                torch.distributed.reduce(avg, dst=0)
-
-                if self.local_rank == 0:
-                    avg = (avg/self.world_size).cpu().item()
-                    self.logger.log_metrics(prefix, k, avg, it, f)
-            else:
-                # Simple does it
-                self.logger.log_metrics(prefix, k, avg, it, f)
-
diff --git a/tracker/util/logger.py b/tracker/util/logger.py
deleted file mode 100644
index c3a0d3c..0000000
--- a/tracker/util/logger.py
+++ /dev/null
@@ -1,101 +0,0 @@
-"""
-Dumps things to tensorboard and console
-"""
-
-import os
-import warnings
-
-import torchvision.transforms as transforms
-from torch.utils.tensorboard import SummaryWriter
-
-
-def tensor_to_numpy(image):
-    image_np = (image.numpy() * 255).astype('uint8')
-    return image_np
-
-def detach_to_cpu(x):
-    return x.detach().cpu()
-
-def fix_width_trunc(x):
-    return ('{:.9s}'.format('{:0.9f}'.format(x)))
-
-class TensorboardLogger:
-    def __init__(self, short_id, id, git_info):
-        self.short_id = short_id
-        if self.short_id == 'NULL':
-            self.short_id = 'DEBUG'
-
-        if id is None:
-            self.no_log = True
-            warnings.warn('Logging has been disbaled.')
-        else:
-            self.no_log = False
-
-            self.inv_im_trans = transforms.Normalize(
-                mean=[-0.485/0.229, -0.456/0.224, -0.406/0.225],
-                std=[1/0.229, 1/0.224, 1/0.225])
-
-            self.inv_seg_trans = transforms.Normalize(
-                mean=[-0.5/0.5],
-                std=[1/0.5])
-
-            log_path = os.path.join('.', 'logs', '%s' % id)
-            self.logger = SummaryWriter(log_path)
-
-        self.log_string('git', git_info)
-
-    def log_scalar(self, tag, x, step):
-        if self.no_log:
-            warnings.warn('Logging has been disabled.')
-            return
-        self.logger.add_scalar(tag, x, step)
-
-    def log_metrics(self, l1_tag, l2_tag, val, step, f=None):
-        tag = l1_tag + '/' + l2_tag
-        text = '{:s} - It {:6d} [{:5s}] [{:13}]: {:s}'.format(self.short_id, step, l1_tag.upper(), l2_tag, fix_width_trunc(val))
-        print(text)
-        if f is not None:
-            f.write(text + '\n')
-            f.flush()
-        self.log_scalar(tag, val, step)
-
-    def log_im(self, tag, x, step):
-        if self.no_log:
-            warnings.warn('Logging has been disabled.')
-            return
-        x = detach_to_cpu(x)
-        x = self.inv_im_trans(x)
-        x = tensor_to_numpy(x)
-        self.logger.add_image(tag, x, step)
-
-    def log_cv2(self, tag, x, step):
-        if self.no_log:
-            warnings.warn('Logging has been disabled.')
-            return
-        x = x.transpose((2, 0, 1))
-        self.logger.add_image(tag, x, step)
-
-    def log_seg(self, tag, x, step):
-        if self.no_log:
-            warnings.warn('Logging has been disabled.')
-            return
-        x = detach_to_cpu(x)
-        x = self.inv_seg_trans(x)
-        x = tensor_to_numpy(x)
-        self.logger.add_image(tag, x, step)
-
-    def log_gray(self, tag, x, step):
-        if self.no_log:
-            warnings.warn('Logging has been disabled.')
-            return
-        x = detach_to_cpu(x)
-        x = tensor_to_numpy(x)
-        self.logger.add_image(tag, x, step)
-
-    def log_string(self, tag, x):
-        print(tag, x)
-        if self.no_log:
-            warnings.warn('Logging has been disabled.')
-            return
-        self.logger.add_text(tag, x)
-        
\ No newline at end of file
diff --git a/tracker/inference/data/mask_mapper.py b/tracker/util/mask_mapper.py
similarity index 80%
rename from tracker/inference/data/mask_mapper.py
rename to tracker/util/mask_mapper.py
index 8e5b38d..815807b 100644
--- a/tracker/inference/data/mask_mapper.py
+++ b/tracker/util/mask_mapper.py
@@ -1,8 +1,16 @@
 import numpy as np
 import torch
 
-from dataset.util import all_to_onehot
+def all_to_onehot(masks, labels):
+    if len(masks.shape) == 3:
+        Ms = np.zeros((len(labels), masks.shape[0], masks.shape[1], masks.shape[2]), dtype=np.uint8)
+    else:
+        Ms = np.zeros((len(labels), masks.shape[0], masks.shape[1]), dtype=np.uint8)
 
+    for ni, l in enumerate(labels):
+        Ms[ni] = (masks == l).astype(np.uint8)
+        
+    return Ms
 
 class MaskMapper:
     """
@@ -23,6 +31,12 @@ class MaskMapper:
         # if coherent, no mapping is required
         self.coherent = True
 
+    def clear_labels(self):
+        self.labels = []
+        self.remappings = {}
+        # if coherent, no mapping is required
+        self.coherent = True
+
     def convert_mask(self, mask, exhaustive=False):
         # mask is in index representation, H*W numpy array
         labels = np.unique(mask).astype(np.uint8)
diff --git a/tracker/util/palette.py b/tracker/util/palette.py
deleted file mode 100644
index d254165..0000000
--- a/tracker/util/palette.py
+++ /dev/null
@@ -1,3 +0,0 @@
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diff --git a/tracker/dataset/range_transform.py b/tracker/util/range_transform.py
similarity index 100%
rename from tracker/dataset/range_transform.py
rename to tracker/util/range_transform.py
diff --git a/tracker/util/yv_subset.txt b/tracker/util/yv_subset.txt
deleted file mode 100644
index a26e50a..0000000
--- a/tracker/util/yv_subset.txt
+++ /dev/null
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