diff --git a/configs/selfsup/_base_/datasets/coco_orl.py b/configs/selfsup/_base_/datasets/coco_orl.py
new file mode 100644
index 000000000..6eecee059
--- /dev/null
+++ b/configs/selfsup/_base_/datasets/coco_orl.py
@@ -0,0 +1,57 @@
+import copy
+
+# dataset settings
+dataset_type = 'mmdet.CocoDataset'
+# data_root = 'data/coco/'
+data_root = '../data/coco/'
+file_client_args = dict(backend='disk')
+view_pipeline = [
+    dict(
+        type='RandomResizedCrop',
+        size=224,
+        interpolation='bicubic',
+        backend='pillow'),
+    dict(type='RandomFlip', prob=0.5),
+    dict(
+        type='RandomApply',
+        transforms=[
+            dict(
+                type='ColorJitter',
+                brightness=0.4,
+                contrast=0.4,
+                saturation=0.2,
+                hue=0.1)
+        ],
+        prob=0.8),
+    dict(
+        type='RandomGrayscale',
+        prob=0.2,
+        keep_channels=True,
+        channel_weights=(0.114, 0.587, 0.2989)),
+    dict(type='RandomGaussianBlur', sigma_min=0.1, sigma_max=2.0, prob=1),
+    dict(type='RandomSolarize', prob=0)
+]
+view_pipeline1 = copy.deepcopy(view_pipeline)
+view_pipeline2 = copy.deepcopy(view_pipeline)
+view_pipeline2[4]['prob'] = 0.1  # gaussian blur
+view_pipeline2[5]['prob'] = 0.2  # solarization
+train_pipeline = [
+    dict(type='LoadImageFromFile', file_client_args=file_client_args),
+    dict(
+        type='MultiView',
+        num_views=[1, 1],
+        transforms=[view_pipeline1, view_pipeline2]),
+    dict(type='PackSelfSupInputs', meta_keys=['img_path'])
+]
+train_dataloader = dict(
+    batch_size=64,
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    collate_fn=dict(type='default_collate'),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='annotations/instances_train2017.json',
+        data_prefix=dict(img='train2017/'),
+        pipeline=train_pipeline))
diff --git a/configs/selfsup/orl/stage1/orl_resnet50_8xb64-coslr-800e_coco.py b/configs/selfsup/orl/stage1/orl_resnet50_8xb64-coslr-800e_coco.py
new file mode 100644
index 000000000..eb7f9e35a
--- /dev/null
+++ b/configs/selfsup/orl/stage1/orl_resnet50_8xb64-coslr-800e_coco.py
@@ -0,0 +1,63 @@
+_base_ = [
+    '../../_base_/models/byol.py',
+    '../../_base_/datasets/coco_orl.py',
+    '../../_base_/schedules/sgd_coslr-200e_in1k.py',
+    '../../_base_/default_runtime.py',
+]
+
+# model settings
+model = dict(
+    neck=dict(
+        type='NonLinearNeck',
+        in_channels=2048,
+        hid_channels=4096,
+        out_channels=256,
+        num_layers=2,
+        with_bias=False,
+        with_last_bn=False,
+        with_avg_pool=True),
+    head=dict(
+        type='LatentPredictHead',
+        predictor=dict(
+            type='NonLinearNeck',
+            in_channels=256,
+            hid_channels=4096,
+            out_channels=256,
+            num_layers=2,
+            with_bias=False,
+            with_last_bn=False,
+            with_avg_pool=False)))
+
+update_interval = 1  # interval for accumulate gradient
+# Amp optimizer
+optimizer = dict(type='SGD', lr=0.4, weight_decay=0.0001, momentum=0.9)
+optim_wrapper = dict(
+    type='AmpOptimWrapper',
+    optimizer=optimizer,
+    accumulative_counts=update_interval,
+)
+warmup_epochs = 4
+total_epochs = 800
+# learning policy
+param_scheduler = [
+    # warmup
+    dict(
+        type='LinearLR',
+        start_factor=0.0001,
+        by_epoch=True,
+        end=warmup_epochs,
+        # Update the learning rate after every iters.
+        convert_to_iter_based=True),
+    # ConsineAnnealingLR/StepLR/..
+    dict(
+        type='CosineAnnealingLR',
+        eta_min=0.,
+        T_max=total_epochs,
+        by_epoch=True,
+        begin=warmup_epochs,
+        end=total_epochs)
+]
+
+# runtime settings
+default_hooks = dict(checkpoint=dict(interval=100))
+train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=total_epochs)
diff --git a/configs/selfsup/orl/stage1/orl_resnet50_8xb64-coslr-800e_coco_extractor.py b/configs/selfsup/orl/stage1/orl_resnet50_8xb64-coslr-800e_coco_extractor.py
new file mode 100644
index 000000000..7c4ea9ab6
--- /dev/null
+++ b/configs/selfsup/orl/stage1/orl_resnet50_8xb64-coslr-800e_coco_extractor.py
@@ -0,0 +1,85 @@
+_base_ = [
+    '../../_base_/models/byol.py',
+    '../../_base_/datasets/coco_orl.py',
+    '../../_base_/schedules/sgd_coslr-200e_in1k.py',
+    '../../_base_/default_runtime.py',
+]
+# model settings
+model = dict(
+    neck=dict(
+        type='NonLinearNeck',
+        in_channels=2048,
+        hid_channels=4096,
+        out_channels=256,
+        num_layers=2,
+        with_bias=False,
+        with_last_bn=False,
+        with_avg_pool=True),
+    head=dict(
+        type='LatentPredictHead',
+        predictor=dict(
+            type='NonLinearNeck',
+            in_channels=256,
+            hid_channels=4096,
+            out_channels=256,
+            num_layers=2,
+            with_bias=False,
+            with_last_bn=False,
+            with_avg_pool=False)))
+
+update_interval = 1  # interval for accumulate gradient
+# Amp optimizer
+optimizer = dict(type='SGD', lr=0.4, weight_decay=0.0001, momentum=0.9)
+optim_wrapper = dict(
+    type='AmpOptimWrapper',
+    optimizer=optimizer,
+    accumulative_counts=update_interval,
+)
+warmup_epochs = 4
+total_epochs = 5
+# learning policy
+param_scheduler = [
+    # warmup
+    dict(
+        type='LinearLR',
+        start_factor=0.0001,
+        by_epoch=True,
+        end=warmup_epochs,
+        # Update the learning rate after every iters.
+        convert_to_iter_based=True),
+    # ConsineAnnealingLR/StepLR/..
+    dict(
+        type='CosineAnnealingLR',
+        eta_min=0.,
+        T_max=total_epochs,
+        by_epoch=True,
+        begin=warmup_epochs,
+        end=total_epochs)
+]
+
+# "mmselfsup/configs/selfsup/orl/stage1/
+# orl_resnet50_8xb64-coslr-800e_coco_extractor.py"
+# runtime settings
+default_hooks = dict(checkpoint=dict(interval=100))
+train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=total_epochs)
+# load_from = './work_dirs/selfsup/orl/stage1/
+# orl_resnet50_8xb64-coslr-800e_coco/epoch_100.pth'
+# resume=True
+custom_hooks = [
+    dict(
+        type='ExtractorHook',
+        keys=10,
+        extract_dataloader=dict(
+            batch_size=512,
+            num_workers=6,
+            persistent_workers=False,
+            sampler=dict(type='DefaultSampler', shuffle=False, round_up=True),
+            collate_fn=dict(type='default_collate'),
+            dataset=dict(
+                type={{_base_.dataset_type}},
+                data_root={{_base_.data_root}},
+                ann_file='annotations/instances_train2017.json',
+                data_prefix=dict(img='train2017/'),
+                pipeline={{_base_.train_pipeline}})),
+        normalize=True),
+]
diff --git a/mmselfsup/engine/hooks/__init__.py b/mmselfsup/engine/hooks/__init__.py
index 147d254f5..0f2e42df3 100644
--- a/mmselfsup/engine/hooks/__init__.py
+++ b/mmselfsup/engine/hooks/__init__.py
@@ -1,10 +1,12 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from .deepcluster_hook import DeepClusterHook
 from .densecl_hook import DenseCLHook
+from .extractor_hook import ExtractorHook
 from .odc_hook import ODCHook
 from .simsiam_hook import SimSiamHook
 from .swav_hook import SwAVHook
 
 __all__ = [
-    'DeepClusterHook', 'DenseCLHook', 'ODCHook', 'SimSiamHook', 'SwAVHook'
+    'DeepClusterHook', 'DenseCLHook', 'ODCHook', 'SimSiamHook', 'SwAVHook',
+    'ExtractorHook'
 ]
diff --git a/mmselfsup/engine/hooks/deepcluster_hook.py b/mmselfsup/engine/hooks/deepcluster_hook.py
index 902127fde..d6c862c1b 100644
--- a/mmselfsup/engine/hooks/deepcluster_hook.py
+++ b/mmselfsup/engine/hooks/deepcluster_hook.py
@@ -75,6 +75,7 @@ def deepcluster(self, runner) -> None:
         # step 1: get features
         runner.model.eval()
         features = self.extractor(runner.model.module)
+
         runner.model.train()
 
         # step 2: get labels
diff --git a/mmselfsup/engine/hooks/extractor_hook.py b/mmselfsup/engine/hooks/extractor_hook.py
new file mode 100644
index 000000000..299366c7b
--- /dev/null
+++ b/mmselfsup/engine/hooks/extractor_hook.py
@@ -0,0 +1,172 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import json
+import os
+import time
+from typing import Optional
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torchvision.transforms as T
+from mmengine.dist import is_distributed
+from mmengine.hooks import Hook
+from mmengine.logging import print_log
+from mmengine.model import BaseModel
+from torch.nn import functional as F
+
+from mmselfsup.models.utils import Extractor
+from mmselfsup.registry import HOOKS
+
+
+# forward global image for knn retrieval
+def global_forward(img: list, model: BaseModel):
+    assert torch.is_floating_point(img[0]), 'image type mismatch'
+    x = torch.stack(img).cuda()
+    with torch.no_grad():
+        x = model.backbone(x)
+        feats = model.neck(x)[0]
+        feats_norm = F.normalize(feats, dim=1)
+    return feats_norm.detach()
+
+
+class Trans(object):
+
+    def __init__(self):
+        global_trans_list = [T.Resize(256), T.CenterCrop(224)]
+        self.global_transform = T.Compose(global_trans_list)
+        self.img_transform = T.Compose([
+            T.ToTensor(),
+            T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+        ])
+
+
+@HOOKS.register_module()
+class ExtractorHook(Hook):
+    """feature extractor hook.
+
+    This hook includes the global clustering process in DC.
+
+    Args:
+        extractor (dict): Config dict for feature extraction.
+        clustering (dict): Config dict that specifies the clustering algorithm.
+        unif_sampling (bool): Whether to apply uniform sampling.
+        reweight (bool): Whether to apply loss re-weighting.
+        reweight_pow (float): The power of re-weighting.
+        init_memory (bool): Whether to initialize memory banks used in ODC.
+            Defaults to False.
+        initial (bool): Whether to call the hook initially. Defaults to True.
+        interval (int): Frequency of epochs to call the hook. Defaults to 1.
+        seed (int, optional): Random seed. Defaults to None.
+    """
+
+    def __init__(self,
+                 keys: int,
+                 extract_dataloader: dict,
+                 normalize=True,
+                 seed: Optional[int] = None) -> None:
+
+        self.dist_mode = is_distributed()
+        self.keys = keys
+        self.dataset = extract_dataloader['dataset']
+        self.extractor = Extractor(
+            extract_dataloader=extract_dataloader,
+            seed=seed,
+            dist_mode=self.dist_mode,
+            pool_cfg=None)
+        self.normalize = normalize
+
+    def retrieve_knn(self, features: torch.Tensor):
+        # load data
+        data_root = self.dataset['data_root']
+        data_ann = self.dataset['ann_file']
+        data_prefix = self.dataset['data_prefix']['img']
+        train_json = data_root + data_ann
+        train_root = data_root + data_prefix
+        # train_json = '../data/coco/annotations/instances_train2017.json'
+        # train_root = '../data/coco/train2017/'
+        with open(train_json, 'r') as json_file:
+            data = json.load(json_file)
+
+        train_fns = [train_root + item['file_name'] for item in data['images']]
+        imgids = [item['id'] for item in data['images']]
+        knn_imgids = []
+        # batch processing
+        # trans = Trans()
+        batch = 512
+        keys = self.keys
+        # feat_bank = features
+
+        # feats_bank = torch.from_numpy(np.load(feat_bank_npy)).cuda()
+        feat_bank = features
+        for i in range(0, len(train_fns), batch):
+            print('[INFO] processing batch: {}'.format(i + 1))
+            start = time.time()
+            if (i + batch) < len(train_fns):
+                query_feats = feat_bank[i:i + batch, :]
+            else:
+                query_feats = feat_bank[i:len(train_fns), :]
+            similarity = torch.mm(query_feats, feat_bank.T)
+            I_knn = torch.topk(similarity, keys + 1, dim=1)[1].cpu()
+            I_knn = I_knn[:, 1:]  # exclude itself (i.e., 1st nn)
+            knn_list = I_knn.numpy().tolist()
+            [knn_imgids.append(knn) for knn in knn_list]
+            end = time.time()
+            print('[INFO] batch {} took {:.4f} seconds'.format(
+                i + 1, end - start))
+
+        # 118287 for coco, 241690 for coco+
+        num_image = len(train_fns)
+        save_dir = data_root + '/meta/'
+        save_path = save_dir + 'train2017_{}nn_instance.json'.format(keys)
+        if not os.path.exists(save_dir):
+            os.makedirs(save_dir)
+        assert len(imgids) == len(knn_imgids) == len(train_fns) == num_image, \
+            f'Mismatch number of training images, got: {len(knn_imgids)}'
+        # dict
+        data_new = {}
+        info = {}
+        image_info = {}
+        pseudo_anno = {}
+        info['knn_image_num'] = keys
+        print(data.keys())
+        image_info['file_name'] = [
+            item['file_name'] for item in data['images']
+        ]
+        image_info['id'] = [item['id'] for item in data['images']]
+        pseudo_anno['image_id'] = imgids
+        pseudo_anno['knn_image_id'] = knn_imgids
+        data_new['info'] = info
+        data_new['images'] = image_info
+        data_new['pseudo_annotations'] = pseudo_anno
+        with open(save_path, 'w') as f:
+            json.dump(data_new, f)
+        print('[INFO] image-level knn json file has been saved to {}'.format(
+            save_path))
+
+    def after_run(self, runner):
+        self._extract_func(runner)
+
+    def _extract_func(self, runner):
+        # step 1: get features
+        runner.model.eval()
+        features = self.extractor(runner.model.module)['feat']
+        if self.normalize:
+            features = nn.functional.normalize(
+                torch.from_numpy(features), dim=1)
+
+        # step 2: save features
+        if not self.dist_mode or (self.dist_mode and runner.rank == 0):
+            np.save(
+                '{}/feature_epoch_{}.npy'.format(runner.work_dir,
+                                                 runner.epoch),
+                features.numpy())
+            print_log(
+                'Feature extraction done!!! total features: {}\t\
+                feature dimension: {}'.format(
+                    features.size(0), features.size(1)),
+                logger='current')
+        # features = torch.from_numpy(np.load(feat_bank_npy)).cuda()
+        # step3: retrieval knn
+        if runner.rank == 0:
+            self.retrieve_knn(features)
+            # self.retrieve_knn(features, runner, runner.model.module)
diff --git a/mmselfsup/utils/collect.py b/mmselfsup/utils/collect.py
index 3022eeb1f..6aa35ad40 100644
--- a/mmselfsup/utils/collect.py
+++ b/mmselfsup/utils/collect.py
@@ -53,6 +53,7 @@ def dist_forward_collect(func: object, data_loader: DataLoader,
     rank, world_size = get_dist_info()
     results = []
     if rank == 0:
+
         prog_bar = mmengine.ProgressBar(len(data_loader))
     for _, data in enumerate(data_loader):
         with torch.no_grad():
diff --git a/tools/slurm_train.sh b/tools/slurm_train.sh
index ac36d5082..5b56251dc 100644
--- a/tools/slurm_train.sh
+++ b/tools/slurm_train.sh
@@ -1,7 +1,7 @@
 #!/usr/bin/env bash
 
 set -x
-
+source activate idea
 PARTITION=$1
 JOB_NAME=$2
 CONFIG=$3