diff --git a/backup/cfgs/anet_clip-simop_order_v2_top20_r2_iter3_th2_refine_aug(8,0.02)_top2_2stage_inscap.yml b/backup/cfgs/anet_clip-simop_order_v2_top20_r2_iter3_th2_refine_aug(8,0.02)_top2_2stage_inscap.yml
deleted file mode 100644
index 63d60c15c02ba592a06fc67e09c654d568891054..0000000000000000000000000000000000000000
--- a/backup/cfgs/anet_clip-simop_order_v2_top20_r2_iter3_th2_refine_aug(8,0.02)_top2_2stage_inscap.yml
+++ /dev/null
@@ -1,20 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/anet/anet_ori_(sim_op_order_v2)_CLIP_refine.yml
-
-
-top_frames: 30
-width_ratio: 2 # scale for the width of the network
-iteration: 3
-width_th: 2
-
-pseudo_box_aug_num: 8
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-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs/anet_clip-simop_order_v2_top30_r2_iter3_th2_refine_aug(8,0.02)_top3_1stage_inscap.yml b/backup/cfgs/anet_clip-simop_order_v2_top30_r2_iter3_th2_refine_aug(8,0.02)_top3_1stage_inscap.yml
deleted file mode 100644
index 9569dff52f023f43117ca926bbde3e1f14003fdd..0000000000000000000000000000000000000000
--- a/backup/cfgs/anet_clip-simop_order_v2_top30_r2_iter3_th2_refine_aug(8,0.02)_top3_1stage_inscap.yml
+++ /dev/null
@@ -1,20 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/anet/anet_ori_(sim_op_order_v2)_CLIP_refine.yml
-
-
-top_frames: 30
-width_ratio: 2 # scale for the width of the network
-iteration: 3
-width_th: 2
-
-pseudo_box_aug_num: 8
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-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs/anet_clip-simop_order_v2_top30_r2_iter3_th2_refine_aug(8,0.02)_top3_2stage_inscap.yml b/backup/cfgs/anet_clip-simop_order_v2_top30_r2_iter3_th2_refine_aug(8,0.02)_top3_2stage_inscap.yml
deleted file mode 100644
index 0b28f2bcdbfef92df0153ebf03faaa2bc73158a1..0000000000000000000000000000000000000000
--- a/backup/cfgs/anet_clip-simop_order_v2_top30_r2_iter3_th2_refine_aug(8,0.02)_top3_2stage_inscap.yml
+++ /dev/null
@@ -1,20 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/anet/anet_ori_(sim_op_order_v2)_CLIP_refine.yml
-
-
-top_frames: 30
-width_ratio: 2 # scale for the width of the network
-iteration: 3
-width_th: 2
-
-pseudo_box_aug_num: 8
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-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs/anet_clip-simop_order_v2_top30_r2_iter3_th2_refine_aug(8,0.02)_top3_3stage_inscap.yml b/backup/cfgs/anet_clip-simop_order_v2_top30_r2_iter3_th2_refine_aug(8,0.02)_top3_3stage_inscap.yml
deleted file mode 100644
index c9314eb31f87ff6f0f44cbcf948b2c4224a9eafa..0000000000000000000000000000000000000000
--- a/backup/cfgs/anet_clip-simop_order_v2_top30_r2_iter3_th2_refine_aug(8,0.02)_top3_3stage_inscap.yml
+++ /dev/null
@@ -1,20 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/anet/anet_ori_(sim_op_order_v2)_CLIP_refine.yml
-
-
-top_frames: 30
-width_ratio: 2 # scale for the width of the network
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-width_th: 2
-
-pseudo_box_aug_num: 8
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-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs/anet_clip-simop_order_v2_top30_r2_iter3_th2_refine_aug(8,0.02)_top4_2stage_inscap.yml b/backup/cfgs/anet_clip-simop_order_v2_top30_r2_iter3_th2_refine_aug(8,0.02)_top4_2stage_inscap.yml
deleted file mode 100644
index fa91240b87d388c80f808d8a78858fc60e197ed5..0000000000000000000000000000000000000000
--- a/backup/cfgs/anet_clip-simop_order_v2_top30_r2_iter3_th2_refine_aug(8,0.02)_top4_2stage_inscap.yml
+++ /dev/null
@@ -1,20 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/anet/anet_ori_(sim_op_order_v2)_CLIP_refine.yml
-
-
-top_frames: 30
-width_ratio: 2 # scale for the width of the network
-iteration: 3
-width_th: 2
-
-pseudo_box_aug_num: 8
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-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs/anet_clip-simop_order_v2_top30_r2_iter3_th2_refine_aug(8,0.02)_top5_2stage_inscap.yml b/backup/cfgs/anet_clip-simop_order_v2_top30_r2_iter3_th2_refine_aug(8,0.02)_top5_2stage_inscap.yml
deleted file mode 100644
index 201c1ff4ff577f8ff9d247b699a0118d13adb728..0000000000000000000000000000000000000000
--- a/backup/cfgs/anet_clip-simop_order_v2_top30_r2_iter3_th2_refine_aug(8,0.02)_top5_2stage_inscap.yml
+++ /dev/null
@@ -1,20 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/anet/anet_ori_(sim_op_order_v2)_CLIP_refine.yml
-
-
-top_frames: 30
-width_ratio: 2 # scale for the width of the network
-iteration: 3
-width_th: 2
-
-pseudo_box_aug_num: 8
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-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs/anet_univl-simop_order_v2_top20_r2_iter3_th2_refine_aug(8,0.02)_top2_2stage_inscap.yml b/backup/cfgs/anet_univl-simop_order_v2_top20_r2_iter3_th2_refine_aug(8,0.02)_top2_2stage_inscap.yml
deleted file mode 100644
index 6a34d7ea66b7574d48f980820ae8fd055632c014..0000000000000000000000000000000000000000
--- a/backup/cfgs/anet_univl-simop_order_v2_top20_r2_iter3_th2_refine_aug(8,0.02)_top2_2stage_inscap.yml
+++ /dev/null
@@ -1,20 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/anet/anet_ori_(sim_op_order_v2)_UniVL_refine.yml
-
-
-top_frames: 30
-width_ratio: 2 # scale for the width of the network
-iteration: 3
-width_th: 2
-
-pseudo_box_aug_num: 8
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-pseudo_box_aug_mode: random_range
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-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs/anet_univl-simop_order_v2_top30_r2_iter3_th2_refine_aug(8,0.02)_top3_1stage_inscap.yml b/backup/cfgs/anet_univl-simop_order_v2_top30_r2_iter3_th2_refine_aug(8,0.02)_top3_1stage_inscap.yml
deleted file mode 100644
index 8f7cc5a1f6a8314a0fb47ec38587b39870114639..0000000000000000000000000000000000000000
--- a/backup/cfgs/anet_univl-simop_order_v2_top30_r2_iter3_th2_refine_aug(8,0.02)_top3_1stage_inscap.yml
+++ /dev/null
@@ -1,20 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/anet/anet_ori_(sim_op_order_v2)_UniVL_refine.yml
-
-
-top_frames: 30
-width_ratio: 2 # scale for the width of the network
-iteration: 3
-width_th: 2
-
-pseudo_box_aug_num: 8
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-pseudo_box_aug_mode: random_range
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-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs/anet_univl-simop_order_v2_top30_r2_iter3_th2_refine_aug(8,0.02)_top3_2stage_inscap.yml b/backup/cfgs/anet_univl-simop_order_v2_top30_r2_iter3_th2_refine_aug(8,0.02)_top3_2stage_inscap.yml
deleted file mode 100644
index 423e5ab89a334fa4ddb0234345c578eee20851cd..0000000000000000000000000000000000000000
--- a/backup/cfgs/anet_univl-simop_order_v2_top30_r2_iter3_th2_refine_aug(8,0.02)_top3_2stage_inscap.yml
+++ /dev/null
@@ -1,20 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/anet/anet_ori_(sim_op_order_v2)_UniVL_refine.yml
-
-
-top_frames: 30
-width_ratio: 2 # scale for the width of the network
-iteration: 3
-width_th: 2
-
-pseudo_box_aug_num: 8
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-pseudo_box_aug_mode: random_range
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-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs/anet_univl-simop_order_v2_top30_r2_iter3_th2_refine_aug(8,0.02)_top3_3stage_inscap.yml b/backup/cfgs/anet_univl-simop_order_v2_top30_r2_iter3_th2_refine_aug(8,0.02)_top3_3stage_inscap.yml
deleted file mode 100644
index 4a5acd993bcf0a0f439319aef297c4eaf9ec2b15..0000000000000000000000000000000000000000
--- a/backup/cfgs/anet_univl-simop_order_v2_top30_r2_iter3_th2_refine_aug(8,0.02)_top3_3stage_inscap.yml
+++ /dev/null
@@ -1,20 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/anet/anet_ori_(sim_op_order_v2)_UniVL_refine.yml
-
-
-top_frames: 30
-width_ratio: 2 # scale for the width of the network
-iteration: 3
-width_th: 2
-
-pseudo_box_aug_num: 8
-pseudo_box_aug_ratio: 0.02
-pseudo_box_aug_mode: random_range
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-pseudo_box_type: similarity_op_order_v2
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 20
-mil_loss_coef: 0
-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs/anet_univl-simop_order_v2_top30_r2_iter3_th2_refine_aug(8,0.02)_top4_2stage_inscap.yml b/backup/cfgs/anet_univl-simop_order_v2_top30_r2_iter3_th2_refine_aug(8,0.02)_top4_2stage_inscap.yml
deleted file mode 100644
index 70fa415758e8d88826ce9466a1533dbf91cbcf95..0000000000000000000000000000000000000000
--- a/backup/cfgs/anet_univl-simop_order_v2_top30_r2_iter3_th2_refine_aug(8,0.02)_top4_2stage_inscap.yml
+++ /dev/null
@@ -1,20 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/anet/anet_ori_(sim_op_order_v2)_UniVL_refine.yml
-
-
-top_frames: 30
-width_ratio: 2 # scale for the width of the network
-iteration: 3
-width_th: 2
-
-pseudo_box_aug_num: 8
-pseudo_box_aug_ratio: 0.02
-pseudo_box_aug_mode: random_range
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-gt_proposal_sample_num: 20
-mil_loss_coef: 0
-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs/anet_univl-simop_order_v2_top30_r2_iter3_th2_refine_aug(8,0.02)_top5_2stage_inscap.yml b/backup/cfgs/anet_univl-simop_order_v2_top30_r2_iter3_th2_refine_aug(8,0.02)_top5_2stage_inscap.yml
deleted file mode 100644
index 1bba6ba8d60d7c73b4a8f81a30d3bfafbcc6c1bf..0000000000000000000000000000000000000000
--- a/backup/cfgs/anet_univl-simop_order_v2_top30_r2_iter3_th2_refine_aug(8,0.02)_top5_2stage_inscap.yml
+++ /dev/null
@@ -1,20 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/anet/anet_ori_(sim_op_order_v2)_UniVL_refine.yml
-
-
-top_frames: 30
-width_ratio: 2 # scale for the width of the network
-iteration: 3
-width_th: 2
-
-pseudo_box_aug_num: 8
-pseudo_box_aug_ratio: 0.02
-pseudo_box_aug_mode: random_range
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-mil_loss_coef: 0
-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs/howto-anet_anet_clip_topk20_r1_iter3_th1.yml b/backup/cfgs/howto-anet_anet_clip_topk20_r1_iter3_th1.yml
deleted file mode 100644
index 864c3a0fc0ada3b8ae6d5c81edc5d12586d3123e..0000000000000000000000000000000000000000
--- a/backup/cfgs/howto-anet_anet_clip_topk20_r1_iter3_th1.yml
+++ /dev/null
@@ -1,14 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_CLIP.yml
-
-
-refine_pseudo_box: 0
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-merge_criterion: ins_cap_topk
diff --git a/backup/cfgs/howto-anet_anet_clip_topk20_r1_iter3_th1_puyu.yml b/backup/cfgs/howto-anet_anet_clip_topk20_r1_iter3_th1_puyu.yml
deleted file mode 100644
index 8a34730f54add9830465c52e42fbfc9536b95a29..0000000000000000000000000000000000000000
--- a/backup/cfgs/howto-anet_anet_clip_topk20_r1_iter3_th1_puyu.yml
+++ /dev/null
@@ -1,14 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_CLIP_puyu.yml
-
-
-refine_pseudo_box: 0
-pseudo_box_type: similarity_op_order_v2
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-width_th: 1
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-merge_criterion: ins_cap_topk
diff --git a/backup/cfgs/howto-anet_anet_clip_topk20_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap.yml b/backup/cfgs/howto-anet_anet_clip_topk20_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap.yml
deleted file mode 100644
index de7481364454b43fb87a1655b09d949110b25c5c..0000000000000000000000000000000000000000
--- a/backup/cfgs/howto-anet_anet_clip_topk20_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap.yml
+++ /dev/null
@@ -1,19 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_CLIP_refine.yml
-
-
-pseudo_box_aug_num: 8
-pseudo_box_aug_ratio: 0.02
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-merge_criterion: ins_cap_topk
diff --git a/backup/cfgs/howto-anet_anet_clip_topk20_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap_puyu.yml b/backup/cfgs/howto-anet_anet_clip_topk20_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap_puyu.yml
deleted file mode 100644
index 34eecf5b883e5b9c6f750e1e747313b6202c5291..0000000000000000000000000000000000000000
--- a/backup/cfgs/howto-anet_anet_clip_topk20_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap_puyu.yml
+++ /dev/null
@@ -1,19 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_CLIP_refine_puyu.yml
-
-
-pseudo_box_aug_num: 8
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diff --git a/backup/cfgs/howto-anet_anet_clip_topk20_r1_iter3_th2_refine_aug(8,0.02)_top3_2stage_inscap.yml b/backup/cfgs/howto-anet_anet_clip_topk20_r1_iter3_th2_refine_aug(8,0.02)_top3_2stage_inscap.yml
deleted file mode 100644
index 61d3e52456e1ea6b0d6726c159018cade8a37d34..0000000000000000000000000000000000000000
--- a/backup/cfgs/howto-anet_anet_clip_topk20_r1_iter3_th2_refine_aug(8,0.02)_top3_2stage_inscap.yml
+++ /dev/null
@@ -1,19 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_CLIP_refine.yml
-
-
-pseudo_box_aug_num: 8
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diff --git a/backup/cfgs/howto-anet_anet_clip_topk20_r1_iter3_th2_refine_aug(8,0.02)_top3_2stage_inscap_puyu.yml b/backup/cfgs/howto-anet_anet_clip_topk20_r1_iter3_th2_refine_aug(8,0.02)_top3_2stage_inscap_puyu.yml
deleted file mode 100644
index a07a5868fc11247094f40d8d8350e9088832eb8d..0000000000000000000000000000000000000000
--- a/backup/cfgs/howto-anet_anet_clip_topk20_r1_iter3_th2_refine_aug(8,0.02)_top3_2stage_inscap_puyu.yml
+++ /dev/null
@@ -1,19 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_CLIP_refine_puyu.yml
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-
-pseudo_box_aug_num: 8
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deleted file mode 100644
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deleted file mode 100644
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deleted file mode 100644
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--- a/backup/cfgs/howto-anet_anet_univl_topk20_r1_iter3_th1.yml
+++ /dev/null
@@ -1,14 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_UniVL.yml
-
-
-refine_pseudo_box: 0
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-width_th: 1
-use_query_box_for: 0
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-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs/howto-anet_anet_univl_topk20_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap.yml b/backup/cfgs/howto-anet_anet_univl_topk20_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap.yml
deleted file mode 100644
index c4d48bc7e63e6428984d0bc2129742f2f7dbc262..0000000000000000000000000000000000000000
--- a/backup/cfgs/howto-anet_anet_univl_topk20_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap.yml
+++ /dev/null
@@ -1,19 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_UniVL_refine.yml
-
-
-pseudo_box_aug_num: 8
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-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs/howto-anet_anet_univl_topk30_r1_iter3_th1.yml b/backup/cfgs/howto-anet_anet_univl_topk30_r1_iter3_th1.yml
deleted file mode 100644
index 7555c91df9a6110009920a7b5ac22c155cc59cfe..0000000000000000000000000000000000000000
--- a/backup/cfgs/howto-anet_anet_univl_topk30_r1_iter3_th1.yml
+++ /dev/null
@@ -1,14 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_UniVL.yml
-
-
-refine_pseudo_box: 0
-pseudo_box_type: similarity_op_order_v2
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-width_ratio: 1
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-width_th: 1
-use_query_box_for: 0
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-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs/howto-anet_anet_univl_topk30_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap.yml b/backup/cfgs/howto-anet_anet_univl_topk30_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap.yml
deleted file mode 100644
index 820f13ab195e62fe83b6a5c8d8086ef3ffb62b28..0000000000000000000000000000000000000000
--- a/backup/cfgs/howto-anet_anet_univl_topk30_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap.yml
+++ /dev/null
@@ -1,19 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_UniVL_refine.yml
-
-
-pseudo_box_aug_num: 8
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-pseudo_box_type: similarity_op_order_v2
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-width_ratio: 1
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-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs/howto-anet_anet_univl_topk40_r1_iter3_th1.yml b/backup/cfgs/howto-anet_anet_univl_topk40_r1_iter3_th1.yml
deleted file mode 100644
index 62551ec728f7b1283c495996b80d72abe2302686..0000000000000000000000000000000000000000
--- a/backup/cfgs/howto-anet_anet_univl_topk40_r1_iter3_th1.yml
+++ /dev/null
@@ -1,14 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_UniVL.yml
-
-
-refine_pseudo_box: 0
-pseudo_box_type: similarity_op_order_v2
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-width_ratio: 1
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-width_th: 1
-use_query_box_for: 0
-gt_proposal_sample_num: 20
-mil_loss_coef: 0
-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs/howto-anet_anet_univl_topk40_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap.yml b/backup/cfgs/howto-anet_anet_univl_topk40_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap.yml
deleted file mode 100644
index 3a2cda8b9fa5b1093cd4327dcc407bff408a00e6..0000000000000000000000000000000000000000
--- a/backup/cfgs/howto-anet_anet_univl_topk40_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap.yml
+++ /dev/null
@@ -1,19 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_UniVL_refine.yml
-
-
-pseudo_box_aug_num: 8
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-pseudo_box_aug_mode: random_range
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-pseudo_box_type: similarity_op_order_v2
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-width_th: 1
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-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs/howto-yc2_yc2_clip_topk15_r1_iter3_th1.yml b/backup/cfgs/howto-yc2_yc2_clip_topk15_r1_iter3_th1.yml
deleted file mode 100644
index e270b46619490eec7d96e25950138a4e96238d6a..0000000000000000000000000000000000000000
--- a/backup/cfgs/howto-yc2_yc2_clip_topk15_r1_iter3_th1.yml
+++ /dev/null
@@ -1,14 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_CLIP.yml
-
-
-refine_pseudo_box: 0
-pseudo_box_type: similarity_op_order_v2
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-width_th: 1
-use_query_box_for: 0
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-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs/howto-yc2_yc2_clip_topk15_r1_iter3_th1_puyu.yml b/backup/cfgs/howto-yc2_yc2_clip_topk15_r1_iter3_th1_puyu.yml
deleted file mode 100644
index 65ca7f9d880f365ced5096533819011ad152b1be..0000000000000000000000000000000000000000
--- a/backup/cfgs/howto-yc2_yc2_clip_topk15_r1_iter3_th1_puyu.yml
+++ /dev/null
@@ -1,14 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_CLIP_puyu.yml
-
-
-refine_pseudo_box: 0
-pseudo_box_type: similarity_op_order_v2
-top_frames: 15
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-width_th: 1
-use_query_box_for: 0
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-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs/howto-yc2_yc2_clip_topk15_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap.yml b/backup/cfgs/howto-yc2_yc2_clip_topk15_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap.yml
deleted file mode 100644
index c9d59a3f0a2f985360f987d74142d74c3ad8ce9b..0000000000000000000000000000000000000000
--- a/backup/cfgs/howto-yc2_yc2_clip_topk15_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap.yml
+++ /dev/null
@@ -1,19 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_CLIP_refine.yml
-
-
-pseudo_box_aug_num: 8
-pseudo_box_aug_ratio: 0.02
-pseudo_box_aug_mode: random_range
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-pseudo_box_type: similarity_op_order_v2
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-width_th: 1
-use_query_box_for_refine: 0
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-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs/howto-yc2_yc2_clip_topk15_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap_puyu.yml b/backup/cfgs/howto-yc2_yc2_clip_topk15_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap_puyu.yml
deleted file mode 100644
index 87364862dc54a01ff7835edf337d948ef7aff565..0000000000000000000000000000000000000000
--- a/backup/cfgs/howto-yc2_yc2_clip_topk15_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap_puyu.yml
+++ /dev/null
@@ -1,19 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_CLIP_refine_puyu.yml
-
-
-pseudo_box_aug_num: 8
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-merge_k_boxes: 3
-pseudo_box_type: similarity_op_order_v2
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-width_ratio: 1
-iteration: 3
-width_th: 1
-use_query_box_for_refine: 0
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-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs/howto-yc2_yc2_clip_topk20_r1_iter3_th1.yml b/backup/cfgs/howto-yc2_yc2_clip_topk20_r1_iter3_th1.yml
deleted file mode 100644
index 73c09505c83eae12bc26b6b16f8e4239aa5914d8..0000000000000000000000000000000000000000
--- a/backup/cfgs/howto-yc2_yc2_clip_topk20_r1_iter3_th1.yml
+++ /dev/null
@@ -1,14 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_CLIP.yml
-
-
-refine_pseudo_box: 0
-pseudo_box_type: similarity_op_order_v2
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-width_th: 1
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-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs/howto-yc2_yc2_clip_topk20_r1_iter3_th1_puyu.yml b/backup/cfgs/howto-yc2_yc2_clip_topk20_r1_iter3_th1_puyu.yml
deleted file mode 100644
index ff51f66d82ad89fac2bd3d3340d30d2d2d5c1885..0000000000000000000000000000000000000000
--- a/backup/cfgs/howto-yc2_yc2_clip_topk20_r1_iter3_th1_puyu.yml
+++ /dev/null
@@ -1,14 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_CLIP_puyu.yml
-
-
-refine_pseudo_box: 0
-pseudo_box_type: similarity_op_order_v2
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-use_query_box_for: 0
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-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs/howto-yc2_yc2_clip_topk20_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap.yml b/backup/cfgs/howto-yc2_yc2_clip_topk20_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap.yml
deleted file mode 100644
index a9391a3c8b96f98d601212fe5fee01d56fd73b2f..0000000000000000000000000000000000000000
--- a/backup/cfgs/howto-yc2_yc2_clip_topk20_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap.yml
+++ /dev/null
@@ -1,19 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_CLIP_refine.yml
-
-
-pseudo_box_aug_num: 8
-pseudo_box_aug_ratio: 0.02
-pseudo_box_aug_mode: random_range
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-refine_pseudo_stage_num: 2
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-pseudo_box_type: similarity_op_order_v2
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-width_th: 1
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 20
-mil_loss_coef: 0
-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs/howto-yc2_yc2_clip_topk20_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap_puyu.yml b/backup/cfgs/howto-yc2_yc2_clip_topk20_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap_puyu.yml
deleted file mode 100644
index 756953c9ca6e0fc91efe05cd28cd8d01f18c1700..0000000000000000000000000000000000000000
--- a/backup/cfgs/howto-yc2_yc2_clip_topk20_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap_puyu.yml
+++ /dev/null
@@ -1,19 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_CLIP_refine_puyu.yml
-
-
-pseudo_box_aug_num: 8
-pseudo_box_aug_ratio: 0.02
-pseudo_box_aug_mode: random_range
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-merge_k_boxes: 3
-pseudo_box_type: similarity_op_order_v2
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-width_ratio: 1
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-width_th: 1
-use_query_box_for_refine: 0
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-mil_loss_coef: 0
-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs/howto-yc2_yc2_clip_topk25_r1_iter3_th1.yml b/backup/cfgs/howto-yc2_yc2_clip_topk25_r1_iter3_th1.yml
deleted file mode 100644
index fdc8083bf18fbabbcdc25a93b1f095f6a276a544..0000000000000000000000000000000000000000
--- a/backup/cfgs/howto-yc2_yc2_clip_topk25_r1_iter3_th1.yml
+++ /dev/null
@@ -1,14 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_CLIP.yml
-
-
-refine_pseudo_box: 0
-pseudo_box_type: similarity_op_order_v2
-top_frames: 25
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-width_th: 1
-use_query_box_for: 0
-gt_proposal_sample_num: 20
-mil_loss_coef: 0
-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs/howto-yc2_yc2_clip_topk25_r1_iter3_th1_puyu.yml b/backup/cfgs/howto-yc2_yc2_clip_topk25_r1_iter3_th1_puyu.yml
deleted file mode 100644
index 5c6bbfc78122b62b4c6f8bb4abae8adc706e30ad..0000000000000000000000000000000000000000
--- a/backup/cfgs/howto-yc2_yc2_clip_topk25_r1_iter3_th1_puyu.yml
+++ /dev/null
@@ -1,14 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_CLIP_puyu.yml
-
-
-refine_pseudo_box: 0
-pseudo_box_type: similarity_op_order_v2
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-width_th: 1
-use_query_box_for: 0
-gt_proposal_sample_num: 20
-mil_loss_coef: 0
-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs/howto-yc2_yc2_clip_topk25_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap.yml b/backup/cfgs/howto-yc2_yc2_clip_topk25_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap.yml
deleted file mode 100644
index c8527b0a6296d1a970dca3691f3d31ea3dfa281d..0000000000000000000000000000000000000000
--- a/backup/cfgs/howto-yc2_yc2_clip_topk25_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap.yml
+++ /dev/null
@@ -1,19 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_CLIP_refine.yml
-
-
-pseudo_box_aug_num: 8
-pseudo_box_aug_ratio: 0.02
-pseudo_box_aug_mode: random_range
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-refine_pseudo_stage_num: 2
-merge_k_boxes: 3
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-top_frames: 25
-width_ratio: 1
-iteration: 3
-width_th: 1
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 20
-mil_loss_coef: 0
-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs/howto-yc2_yc2_clip_topk25_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap_puyu.yml b/backup/cfgs/howto-yc2_yc2_clip_topk25_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap_puyu.yml
deleted file mode 100644
index 0d9d74565b11b912a681a1871360ed0bd2385ff9..0000000000000000000000000000000000000000
--- a/backup/cfgs/howto-yc2_yc2_clip_topk25_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap_puyu.yml
+++ /dev/null
@@ -1,19 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_CLIP_refine_puyu.yml
-
-
-pseudo_box_aug_num: 8
-pseudo_box_aug_ratio: 0.02
-pseudo_box_aug_mode: random_range
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-refine_pseudo_stage_num: 2
-merge_k_boxes: 3
-pseudo_box_type: similarity_op_order_v2
-top_frames: 25
-width_ratio: 1
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-width_th: 1
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 20
-mil_loss_coef: 0
-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs/howto-yc2_yc2_univl_topk15_r1_iter3_th1.yml b/backup/cfgs/howto-yc2_yc2_univl_topk15_r1_iter3_th1.yml
deleted file mode 100644
index 57f86183de530604294a363c8387c1c8b49e93af..0000000000000000000000000000000000000000
--- a/backup/cfgs/howto-yc2_yc2_univl_topk15_r1_iter3_th1.yml
+++ /dev/null
@@ -1,14 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_UniVL.yml
-
-
-refine_pseudo_box: 0
-pseudo_box_type: similarity_op_order_v2
-top_frames: 15
-width_ratio: 1
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-width_th: 1
-use_query_box_for: 0
-gt_proposal_sample_num: 20
-mil_loss_coef: 0
-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs/howto-yc2_yc2_univl_topk15_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap.yml b/backup/cfgs/howto-yc2_yc2_univl_topk15_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap.yml
deleted file mode 100644
index de9accc5c444a46a8639dc63b3d2841b6b1fdb28..0000000000000000000000000000000000000000
--- a/backup/cfgs/howto-yc2_yc2_univl_topk15_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap.yml
+++ /dev/null
@@ -1,19 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_UniVL_refine.yml
-
-
-pseudo_box_aug_num: 8
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-pseudo_box_aug_mode: random_range
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-width_th: 1
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-gt_proposal_sample_num: 20
-mil_loss_coef: 0
-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs/howto-yc2_yc2_univl_topk20_r1_iter3_th1.yml b/backup/cfgs/howto-yc2_yc2_univl_topk20_r1_iter3_th1.yml
deleted file mode 100644
index a600901cb9f10422e2ce532cb8c77ac03dc57959..0000000000000000000000000000000000000000
--- a/backup/cfgs/howto-yc2_yc2_univl_topk20_r1_iter3_th1.yml
+++ /dev/null
@@ -1,14 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_UniVL.yml
-
-
-refine_pseudo_box: 0
-pseudo_box_type: similarity_op_order_v2
-top_frames: 20
-width_ratio: 1
-iteration: 3
-width_th: 1
-use_query_box_for: 0
-gt_proposal_sample_num: 20
-mil_loss_coef: 0
-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs/howto-yc2_yc2_univl_topk20_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap.yml b/backup/cfgs/howto-yc2_yc2_univl_topk20_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap.yml
deleted file mode 100644
index 29e6ac298128de00d5eafc256f3cefc35eb26585..0000000000000000000000000000000000000000
--- a/backup/cfgs/howto-yc2_yc2_univl_topk20_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap.yml
+++ /dev/null
@@ -1,19 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_UniVL_refine.yml
-
-
-pseudo_box_aug_num: 8
-pseudo_box_aug_ratio: 0.02
-pseudo_box_aug_mode: random_range
-refine_pseudo_box: 1
-refine_pseudo_stage_num: 2
-merge_k_boxes: 3
-pseudo_box_type: similarity_op_order_v2
-top_frames: 20
-width_ratio: 1
-iteration: 3
-width_th: 1
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 20
-mil_loss_coef: 0
-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs/howto-yc2_yc2_univl_topk25_r1_iter3_th1.yml b/backup/cfgs/howto-yc2_yc2_univl_topk25_r1_iter3_th1.yml
deleted file mode 100644
index dae41f38476b77921cdb2f030d11a1f32076622b..0000000000000000000000000000000000000000
--- a/backup/cfgs/howto-yc2_yc2_univl_topk25_r1_iter3_th1.yml
+++ /dev/null
@@ -1,14 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_UniVL.yml
-
-
-refine_pseudo_box: 0
-pseudo_box_type: similarity_op_order_v2
-top_frames: 25
-width_ratio: 1
-iteration: 3
-width_th: 1
-use_query_box_for: 0
-gt_proposal_sample_num: 20
-mil_loss_coef: 0
-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs/howto-yc2_yc2_univl_topk25_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap.yml b/backup/cfgs/howto-yc2_yc2_univl_topk25_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap.yml
deleted file mode 100644
index ac09bd7b115aac8b96a46053f07ee52d43c4a165..0000000000000000000000000000000000000000
--- a/backup/cfgs/howto-yc2_yc2_univl_topk25_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap.yml
+++ /dev/null
@@ -1,19 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_UniVL_refine.yml
-
-
-pseudo_box_aug_num: 8
-pseudo_box_aug_ratio: 0.02
-pseudo_box_aug_mode: random_range
-refine_pseudo_box: 1
-refine_pseudo_stage_num: 2
-merge_k_boxes: 3
-pseudo_box_type: similarity_op_order_v2
-top_frames: 25
-width_ratio: 1
-iteration: 3
-width_th: 1
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 20
-mil_loss_coef: 0
-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs/yc2_clip-simop_order_v2_top15_r1_iter3_th1_refine_aug(8,0.02)_top2_2stage_inscap.yml b/backup/cfgs/yc2_clip-simop_order_v2_top15_r1_iter3_th1_refine_aug(8,0.02)_top2_2stage_inscap.yml
deleted file mode 100644
index 3741fe96fbe15b96ea12feca4e9fa98e58b4b141..0000000000000000000000000000000000000000
--- a/backup/cfgs/yc2_clip-simop_order_v2_top15_r1_iter3_th1_refine_aug(8,0.02)_top2_2stage_inscap.yml
+++ /dev/null
@@ -1,20 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/yc2/yc2_ori_(sim_op_order_v2)_CLIP_refine.yml
-
-
-top_frames: 15
-width_ratio: 1 # scale for the width of the network
-iteration: 3
-width_th: 1
-
-pseudo_box_aug_num: 8
-pseudo_box_aug_ratio: 0.02
-pseudo_box_aug_mode: random_range
-refine_pseudo_box: 1
-refine_pseudo_stage_num: 2
-merge_k_boxes: 2
-pseudo_box_type: similarity_op_order_v2
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 20
-mil_loss_coef: 0
-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs/yc2_clip-simop_order_v2_top15_r1_iter3_th1_refine_aug(8,0.02)_top3_1stage_inscap.yml b/backup/cfgs/yc2_clip-simop_order_v2_top15_r1_iter3_th1_refine_aug(8,0.02)_top3_1stage_inscap.yml
deleted file mode 100644
index 1ee94d104a9e0878da2aa2e588adeb888ff12355..0000000000000000000000000000000000000000
--- a/backup/cfgs/yc2_clip-simop_order_v2_top15_r1_iter3_th1_refine_aug(8,0.02)_top3_1stage_inscap.yml
+++ /dev/null
@@ -1,20 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/yc2/yc2_ori_(sim_op_order_v2)_CLIP_refine.yml
-
-
-top_frames: 15
-width_ratio: 1 # scale for the width of the network
-iteration: 3
-width_th: 1
-
-pseudo_box_aug_num: 8
-pseudo_box_aug_ratio: 0.02
-pseudo_box_aug_mode: random_range
-refine_pseudo_box: 1
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: similarity_op_order_v2
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 20
-mil_loss_coef: 0
-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs/yc2_clip-simop_order_v2_top15_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap.yml b/backup/cfgs/yc2_clip-simop_order_v2_top15_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap.yml
deleted file mode 100644
index bc15339112fa0ff01c5615b311bebee685e3c089..0000000000000000000000000000000000000000
--- a/backup/cfgs/yc2_clip-simop_order_v2_top15_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap.yml
+++ /dev/null
@@ -1,20 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/yc2/yc2_ori_(sim_op_order_v2)_CLIP_refine.yml
-
-
-top_frames: 15
-width_ratio: 1 # scale for the width of the network
-iteration: 3
-width_th: 1
-
-pseudo_box_aug_num: 8
-pseudo_box_aug_ratio: 0.02
-pseudo_box_aug_mode: random_range
-refine_pseudo_box: 1
-refine_pseudo_stage_num: 2
-merge_k_boxes: 3
-pseudo_box_type: similarity_op_order_v2
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 20
-mil_loss_coef: 0
-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs/yc2_clip-simop_order_v2_top15_r1_iter3_th1_refine_aug(8,0.02)_top3_3stage_inscap.yml b/backup/cfgs/yc2_clip-simop_order_v2_top15_r1_iter3_th1_refine_aug(8,0.02)_top3_3stage_inscap.yml
deleted file mode 100644
index 38f4cd642822a36efd860f20971030093b467b26..0000000000000000000000000000000000000000
--- a/backup/cfgs/yc2_clip-simop_order_v2_top15_r1_iter3_th1_refine_aug(8,0.02)_top3_3stage_inscap.yml
+++ /dev/null
@@ -1,20 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/yc2/yc2_ori_(sim_op_order_v2)_CLIP_refine.yml
-
-
-top_frames: 15
-width_ratio: 1 # scale for the width of the network
-iteration: 3
-width_th: 1
-
-pseudo_box_aug_num: 8
-pseudo_box_aug_ratio: 0.02
-pseudo_box_aug_mode: random_range
-refine_pseudo_box: 1
-refine_pseudo_stage_num: 3
-merge_k_boxes: 3
-pseudo_box_type: similarity_op_order_v2
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 20
-mil_loss_coef: 0
-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs/yc2_clip-simop_order_v2_top15_r1_iter3_th1_refine_aug(8,0.02)_top4_2stage_inscap.yml b/backup/cfgs/yc2_clip-simop_order_v2_top15_r1_iter3_th1_refine_aug(8,0.02)_top4_2stage_inscap.yml
deleted file mode 100644
index c08068e50a1b52db2346ed7d91f994822ecb308a..0000000000000000000000000000000000000000
--- a/backup/cfgs/yc2_clip-simop_order_v2_top15_r1_iter3_th1_refine_aug(8,0.02)_top4_2stage_inscap.yml
+++ /dev/null
@@ -1,20 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/yc2/yc2_ori_(sim_op_order_v2)_CLIP_refine.yml
-
-
-top_frames: 15
-width_ratio: 1 # scale for the width of the network
-iteration: 3
-width_th: 1
-
-pseudo_box_aug_num: 8
-pseudo_box_aug_ratio: 0.02
-pseudo_box_aug_mode: random_range
-refine_pseudo_box: 1
-refine_pseudo_stage_num: 2
-merge_k_boxes: 4
-pseudo_box_type: similarity_op_order_v2
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 20
-mil_loss_coef: 0
-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs/yc2_clip-simop_order_v2_top15_r1_iter3_th1_refine_aug(8,0.02)_top5_2stage_inscap.yml b/backup/cfgs/yc2_clip-simop_order_v2_top15_r1_iter3_th1_refine_aug(8,0.02)_top5_2stage_inscap.yml
deleted file mode 100644
index e3d37e0af57206d3a8ace41cd93cac2d92a99aad..0000000000000000000000000000000000000000
--- a/backup/cfgs/yc2_clip-simop_order_v2_top15_r1_iter3_th1_refine_aug(8,0.02)_top5_2stage_inscap.yml
+++ /dev/null
@@ -1,20 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/yc2/yc2_ori_(sim_op_order_v2)_CLIP_refine.yml
-
-
-top_frames: 15
-width_ratio: 1 # scale for the width of the network
-iteration: 3
-width_th: 1
-
-pseudo_box_aug_num: 8
-pseudo_box_aug_ratio: 0.02
-pseudo_box_aug_mode: random_range
-refine_pseudo_box: 1
-refine_pseudo_stage_num: 2
-merge_k_boxes: 5
-pseudo_box_type: similarity_op_order_v2
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 20
-mil_loss_coef: 0
-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs/yc2_univl-simop_order_v2_top15_r1_iter3_th1_refine_aug(8,0.02)_top2_2stage_inscap.yml b/backup/cfgs/yc2_univl-simop_order_v2_top15_r1_iter3_th1_refine_aug(8,0.02)_top2_2stage_inscap.yml
deleted file mode 100644
index 39fac1ab1f5ba0ef8be9166ab400b0303dab3c55..0000000000000000000000000000000000000000
--- a/backup/cfgs/yc2_univl-simop_order_v2_top15_r1_iter3_th1_refine_aug(8,0.02)_top2_2stage_inscap.yml
+++ /dev/null
@@ -1,20 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/yc2/yc2_ori_(sim_op_order_v2)_UniVL_refine.yml
-
-
-top_frames: 15
-width_ratio: 1 # scale for the width of the network
-iteration: 3
-width_th: 1
-
-pseudo_box_aug_num: 8
-pseudo_box_aug_ratio: 0.02
-pseudo_box_aug_mode: random_range
-refine_pseudo_box: 1
-refine_pseudo_stage_num: 2
-merge_k_boxes: 2
-pseudo_box_type: similarity_op_order_v2
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 20
-mil_loss_coef: 0
-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs/yc2_univl-simop_order_v2_top15_r1_iter3_th1_refine_aug(8,0.02)_top3_1stage_inscap.yml b/backup/cfgs/yc2_univl-simop_order_v2_top15_r1_iter3_th1_refine_aug(8,0.02)_top3_1stage_inscap.yml
deleted file mode 100644
index 2342e4a5a58d9938a847f0bf11ea87de5900dadf..0000000000000000000000000000000000000000
--- a/backup/cfgs/yc2_univl-simop_order_v2_top15_r1_iter3_th1_refine_aug(8,0.02)_top3_1stage_inscap.yml
+++ /dev/null
@@ -1,20 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/yc2/yc2_ori_(sim_op_order_v2)_UniVL_refine.yml
-
-
-top_frames: 15
-width_ratio: 1 # scale for the width of the network
-iteration: 3
-width_th: 1
-
-pseudo_box_aug_num: 8
-pseudo_box_aug_ratio: 0.02
-pseudo_box_aug_mode: random_range
-refine_pseudo_box: 1
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: similarity_op_order_v2
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 20
-mil_loss_coef: 0
-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs/yc2_univl-simop_order_v2_top15_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap.yml b/backup/cfgs/yc2_univl-simop_order_v2_top15_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap.yml
deleted file mode 100644
index d90465e8281858d7557440ae000a2d8030b5f1be..0000000000000000000000000000000000000000
--- a/backup/cfgs/yc2_univl-simop_order_v2_top15_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap.yml
+++ /dev/null
@@ -1,20 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/yc2/yc2_ori_(sim_op_order_v2)_UniVL_refine.yml
-
-
-top_frames: 15
-width_ratio: 1 # scale for the width of the network
-iteration: 3
-width_th: 1
-
-pseudo_box_aug_num: 8
-pseudo_box_aug_ratio: 0.02
-pseudo_box_aug_mode: random_range
-refine_pseudo_box: 1
-refine_pseudo_stage_num: 2
-merge_k_boxes: 3
-pseudo_box_type: similarity_op_order_v2
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 20
-mil_loss_coef: 0
-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs/yc2_univl-simop_order_v2_top15_r1_iter3_th1_refine_aug(8,0.02)_top3_3stage_inscap.yml b/backup/cfgs/yc2_univl-simop_order_v2_top15_r1_iter3_th1_refine_aug(8,0.02)_top3_3stage_inscap.yml
deleted file mode 100644
index ca08a4041f2660b9eef0f6db53a672d88bfaa52e..0000000000000000000000000000000000000000
--- a/backup/cfgs/yc2_univl-simop_order_v2_top15_r1_iter3_th1_refine_aug(8,0.02)_top3_3stage_inscap.yml
+++ /dev/null
@@ -1,20 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/yc2/yc2_ori_(sim_op_order_v2)_UniVL_refine.yml
-
-
-top_frames: 15
-width_ratio: 1 # scale for the width of the network
-iteration: 3
-width_th: 1
-
-pseudo_box_aug_num: 8
-pseudo_box_aug_ratio: 0.02
-pseudo_box_aug_mode: random_range
-refine_pseudo_box: 1
-refine_pseudo_stage_num: 3
-merge_k_boxes: 3
-pseudo_box_type: similarity_op_order_v2
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 20
-mil_loss_coef: 0
-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs/yc2_univl-simop_order_v2_top15_r1_iter3_th1_refine_aug(8,0.02)_top4_2stage_inscap.yml b/backup/cfgs/yc2_univl-simop_order_v2_top15_r1_iter3_th1_refine_aug(8,0.02)_top4_2stage_inscap.yml
deleted file mode 100644
index 18d258a9eff34e7bbbcdebcd0462250746352d21..0000000000000000000000000000000000000000
--- a/backup/cfgs/yc2_univl-simop_order_v2_top15_r1_iter3_th1_refine_aug(8,0.02)_top4_2stage_inscap.yml
+++ /dev/null
@@ -1,20 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/yc2/yc2_ori_(sim_op_order_v2)_UniVL_refine.yml
-
-
-top_frames: 15
-width_ratio: 1 # scale for the width of the network
-iteration: 3
-width_th: 1
-
-pseudo_box_aug_num: 8
-pseudo_box_aug_ratio: 0.02
-pseudo_box_aug_mode: random_range
-refine_pseudo_box: 1
-refine_pseudo_stage_num: 2
-merge_k_boxes: 4
-pseudo_box_type: similarity_op_order_v2
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 20
-mil_loss_coef: 0
-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs/yc2_univl-simop_order_v2_top15_r1_iter3_th1_refine_aug(8,0.02)_top5_2stage_inscap.yml b/backup/cfgs/yc2_univl-simop_order_v2_top15_r1_iter3_th1_refine_aug(8,0.02)_top5_2stage_inscap.yml
deleted file mode 100644
index 739efd5c2d9c526fac569d14c81206c02a677755..0000000000000000000000000000000000000000
--- a/backup/cfgs/yc2_univl-simop_order_v2_top15_r1_iter3_th1_refine_aug(8,0.02)_top5_2stage_inscap.yml
+++ /dev/null
@@ -1,20 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/yc2/yc2_ori_(sim_op_order_v2)_UniVL_refine.yml
-
-
-top_frames: 15
-width_ratio: 1 # scale for the width of the network
-iteration: 3
-width_th: 1
-
-pseudo_box_aug_num: 8
-pseudo_box_aug_ratio: 0.02
-pseudo_box_aug_mode: random_range
-refine_pseudo_box: 1
-refine_pseudo_stage_num: 2
-merge_k_boxes: 5
-pseudo_box_type: similarity_op_order_v2
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 20
-mil_loss_coef: 0
-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs_base/anet/anet_CLIP_pdvc.yml b/backup/cfgs_base/anet/anet_CLIP_pdvc.yml
deleted file mode 100644
index dbc433e0a1e0d5b37361a96e3970c0d720639db4..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_CLIP_pdvc.yml
+++ /dev/null
@@ -1,17 +0,0 @@
-id: base # the results and logs will saved in this folder ./save/id
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-ec_alpha: 1.0
\ No newline at end of file
diff --git a/backup/cfgs_base/anet/anet_UniVL_pdvc.yml b/backup/cfgs_base/anet/anet_UniVL_pdvc.yml
deleted file mode 100644
index d4e2a056537258e7c06849d22d4c26c7b25e223f..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_UniVL_pdvc.yml
+++ /dev/null
@@ -1,17 +0,0 @@
-id: base # the results and logs will saved in this folder ./save/id
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-ec_alpha: 1.0
\ No newline at end of file
diff --git a/backup/cfgs_base/anet/anet_abox_CLIP_pdvc.yml b/backup/cfgs_base/anet/anet_abox_CLIP_pdvc.yml
deleted file mode 100644
index 72849bd96ab440b568774e6ee8a57f6ed6788162..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_abox_CLIP_pdvc.yml
+++ /dev/null
@@ -1,27 +0,0 @@
-id: base # the results and logs will saved in this folder ./save/id
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 0
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.1
-pseudo_box_type: similarity
-use_anchor: 1
-pretrained_language_model: CLIP
-disable_contrastive_projection: 1
-
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-ec_alpha: 1.0
\ No newline at end of file
diff --git a/backup/cfgs_base/anet/anet_abox_UniVL_pdvc.yml b/backup/cfgs_base/anet/anet_abox_UniVL_pdvc.yml
deleted file mode 100644
index fbcd022d8623d2fa4e95c31b1f8f6adef8076c1f..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_abox_UniVL_pdvc.yml
+++ /dev/null
@@ -1,27 +0,0 @@
-id: base # the results and logs will saved in this folder ./save/id
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 0
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.1
-pseudo_box_type: similarity
-use_anchor: 1
-pretrained_language_model: UniVL
-disable_contrastive_projection: 1
-
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-ec_alpha: 1.0
\ No newline at end of file
diff --git a/backup/cfgs_base/anet/anet_anc_(align)_CLIP.yml b/backup/cfgs_base/anet/anet_anc_(align)_CLIP.yml
deleted file mode 100644
index fa5891c2d59ff2e88a6ccbca706f7ca15f539976..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_anc_(align)_CLIP.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 0
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 0
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: align
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 1
-pretrained_language_model: CLIP
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/anet/anet_anc_(align)_CLIP_refine.yml b/backup/cfgs_base/anet/anet_anc_(align)_CLIP_refine.yml
deleted file mode 100644
index 9d3fff7669479f43563fb8f80dc98091b835cd49..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_anc_(align)_CLIP_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 1
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 1
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: align
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 1
-pretrained_language_model: CLIP
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/anet/anet_anc_(align)_UniVL.yml b/backup/cfgs_base/anet/anet_anc_(align)_UniVL.yml
deleted file mode 100644
index d00afb6f6a5f3d979ee0b513299460ed59528d71..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_anc_(align)_UniVL.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 0
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 0
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: align
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 1
-pretrained_language_model: UniVL
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/anet/anet_anc_(align)_UniVL_refine.yml b/backup/cfgs_base/anet/anet_anc_(align)_UniVL_refine.yml
deleted file mode 100644
index b56d4945517c6bd61351fcbef05b48c9b7448d25..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_anc_(align)_UniVL_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 1
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 1
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: align
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 1
-pretrained_language_model: UniVL
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/anet/anet_anc_(sim)_CLIP.yml b/backup/cfgs_base/anet/anet_anc_(sim)_CLIP.yml
deleted file mode 100644
index 5ee0f7cd39031aac12a49fa3febbad874ee84eb3..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_anc_(sim)_CLIP.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 0
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 0
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: similarity
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 1
-pretrained_language_model: CLIP
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/anet/anet_anc_(sim)_CLIP_refine.yml b/backup/cfgs_base/anet/anet_anc_(sim)_CLIP_refine.yml
deleted file mode 100644
index 09ee646dbe8a44dc5bd827e2ff354f954306512e..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_anc_(sim)_CLIP_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 1
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 1
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: similarity
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 1
-pretrained_language_model: CLIP
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/anet/anet_anc_(sim)_UniVL.yml b/backup/cfgs_base/anet/anet_anc_(sim)_UniVL.yml
deleted file mode 100644
index bcc52404af176f94a0d3cecc3fcff26900f73e07..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_anc_(sim)_UniVL.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 0
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 0
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: similarity
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 1
-pretrained_language_model: UniVL
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/anet/anet_anc_(sim)_UniVL_refine.yml b/backup/cfgs_base/anet/anet_anc_(sim)_UniVL_refine.yml
deleted file mode 100644
index c22d10c1f33975e94f1777fc6529b90feb81ba71..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_anc_(sim)_UniVL_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 1
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 1
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: similarity
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 1
-pretrained_language_model: UniVL
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/anet/anet_anc_(sim_op)_CLIP.yml b/backup/cfgs_base/anet/anet_anc_(sim_op)_CLIP.yml
deleted file mode 100644
index 5a4ee8540f6cbd301e407c8fd4795518a729b2a7..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_anc_(sim_op)_CLIP.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 0
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 0
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: similarity_op
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 1
-pretrained_language_model: CLIP
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/anet/anet_anc_(sim_op)_CLIP_refine.yml b/backup/cfgs_base/anet/anet_anc_(sim_op)_CLIP_refine.yml
deleted file mode 100644
index e5cc7747b0917df6ff67aa7af2dcc961853d3643..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_anc_(sim_op)_CLIP_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 1
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 1
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: similarity_op
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 1
-pretrained_language_model: CLIP
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/anet/anet_anc_(sim_op)_UniVL.yml b/backup/cfgs_base/anet/anet_anc_(sim_op)_UniVL.yml
deleted file mode 100644
index a8ec8379c8cfae0384ed5c5a23cdc8ba28b250d8..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_anc_(sim_op)_UniVL.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 0
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 0
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: similarity_op
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 1
-pretrained_language_model: UniVL
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/anet/anet_anc_(sim_op)_UniVL_refine.yml b/backup/cfgs_base/anet/anet_anc_(sim_op)_UniVL_refine.yml
deleted file mode 100644
index 7174b3bc97912d16e2db4f3948daf1d5ccca79d2..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_anc_(sim_op)_UniVL_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['UniVL']
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diff --git a/backup/cfgs_base/anet/anet_anc_(sim_op_order)_CLIP.yml b/backup/cfgs_base/anet/anet_anc_(sim_op_order)_CLIP.yml
deleted file mode 100644
index 047111607ce477a768f570d0c1bdbe1809fd2b27..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_anc_(sim_op_order)_CLIP.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
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diff --git a/backup/cfgs_base/anet/anet_anc_(sim_op_order)_CLIP_refine.yml b/backup/cfgs_base/anet/anet_anc_(sim_op_order)_CLIP_refine.yml
deleted file mode 100644
index ec22f2c3149a6bc2a839c8b7a1e7f95f799a740b..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_anc_(sim_op_order)_CLIP_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
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diff --git a/backup/cfgs_base/anet/anet_anc_(sim_op_order)_UniVL.yml b/backup/cfgs_base/anet/anet_anc_(sim_op_order)_UniVL.yml
deleted file mode 100644
index bcd7b440c4ff76e8b2cfa3324524c6a0fb7f8b3e..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_anc_(sim_op_order)_UniVL.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
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diff --git a/backup/cfgs_base/anet/anet_anc_(sim_op_order)_UniVL_refine.yml b/backup/cfgs_base/anet/anet_anc_(sim_op_order)_UniVL_refine.yml
deleted file mode 100644
index 7ceeeab0f8eddaef985f7f96f30092bf3be1c477..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_anc_(sim_op_order)_UniVL_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
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diff --git a/backup/cfgs_base/anet/anet_anc_(sim_op_order_v1)_CLIP.yml b/backup/cfgs_base/anet/anet_anc_(sim_op_order_v1)_CLIP.yml
deleted file mode 100644
index b9514fabfbb681385913ea0ab7edaedaaa62b628..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_anc_(sim_op_order_v1)_CLIP.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
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diff --git a/backup/cfgs_base/anet/anet_anc_(sim_op_order_v1)_CLIP_refine.yml b/backup/cfgs_base/anet/anet_anc_(sim_op_order_v1)_CLIP_refine.yml
deleted file mode 100644
index 521dc511180da8f9b94569e4ab0a45844266d973..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_anc_(sim_op_order_v1)_CLIP_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
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diff --git a/backup/cfgs_base/anet/anet_anc_(sim_op_order_v1)_UniVL.yml b/backup/cfgs_base/anet/anet_anc_(sim_op_order_v1)_UniVL.yml
deleted file mode 100644
index c59ddd576b3753f4db9a137e8d3cb80dd233e0a4..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_anc_(sim_op_order_v1)_UniVL.yml
+++ /dev/null
@@ -1,38 +0,0 @@
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diff --git a/backup/cfgs_base/anet/anet_anc_(sim_op_order_v1)_UniVL_refine.yml b/backup/cfgs_base/anet/anet_anc_(sim_op_order_v1)_UniVL_refine.yml
deleted file mode 100644
index cbe60a5936fe763099137039a222cd0563ebaea9..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_anc_(sim_op_order_v1)_UniVL_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
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diff --git a/backup/cfgs_base/anet/anet_anc_(sim_op_order_v2)_CLIP.yml b/backup/cfgs_base/anet/anet_anc_(sim_op_order_v2)_CLIP.yml
deleted file mode 100644
index 9960dcaa94683c69f1c63bd6fadef7b17315ebea..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_anc_(sim_op_order_v2)_CLIP.yml
+++ /dev/null
@@ -1,39 +0,0 @@
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diff --git a/backup/cfgs_base/anet/anet_anc_(sim_op_order_v2)_CLIP_refine.yml b/backup/cfgs_base/anet/anet_anc_(sim_op_order_v2)_CLIP_refine.yml
deleted file mode 100644
index e57a1150b7f8eb19b75d32729945101e3f63970a..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_anc_(sim_op_order_v2)_CLIP_refine.yml
+++ /dev/null
@@ -1,39 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
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diff --git a/backup/cfgs_base/anet/anet_anc_(sim_op_order_v2)_UniVL.yml b/backup/cfgs_base/anet/anet_anc_(sim_op_order_v2)_UniVL.yml
deleted file mode 100644
index 5a639c02411b28e7b61485c8306201526a98bb30..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_anc_(sim_op_order_v2)_UniVL.yml
+++ /dev/null
@@ -1,39 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
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diff --git a/backup/cfgs_base/anet/anet_anc_(sim_op_order_v2)_UniVL_refine.yml b/backup/cfgs_base/anet/anet_anc_(sim_op_order_v2)_UniVL_refine.yml
deleted file mode 100644
index 09300cdacf1b21dae6e6d983cc53c0c3674f146c..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_anc_(sim_op_order_v2)_UniVL_refine.yml
+++ /dev/null
@@ -1,39 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
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-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/anet/anet_anc_(weight_index)_CLIP.yml b/backup/cfgs_base/anet/anet_anc_(weight_index)_CLIP.yml
deleted file mode 100644
index d933f4e367996fcd355585308151316fde844160..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_anc_(weight_index)_CLIP.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 0
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 0
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: weight_index
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 1
-pretrained_language_model: CLIP
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/anet/anet_anc_(weight_index)_CLIP_refine.yml b/backup/cfgs_base/anet/anet_anc_(weight_index)_CLIP_refine.yml
deleted file mode 100644
index 9289c03643143a572b64987cf8b733118531b7a2..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_anc_(weight_index)_CLIP_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 1
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 1
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: weight_index
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 1
-pretrained_language_model: CLIP
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/anet/anet_anc_(weight_index)_UniVL.yml b/backup/cfgs_base/anet/anet_anc_(weight_index)_UniVL.yml
deleted file mode 100644
index ecd4bd64bff3f0c9ec4c9a532146ccd657edc907..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_anc_(weight_index)_UniVL.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 0
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 0
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: weight_index
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 1
-pretrained_language_model: UniVL
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/anet/anet_anc_(weight_index)_UniVL_refine.yml b/backup/cfgs_base/anet/anet_anc_(weight_index)_UniVL_refine.yml
deleted file mode 100644
index 0d228dc7e9de40c84d91a90b4e9e3accd41af0d7..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_anc_(weight_index)_UniVL_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 1
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 1
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: weight_index
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 1
-pretrained_language_model: UniVL
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/anet/anet_anc_(weight_sim)_CLIP.yml b/backup/cfgs_base/anet/anet_anc_(weight_sim)_CLIP.yml
deleted file mode 100644
index 6fe87609b78a2e77ad203ca2136882ba13568493..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_anc_(weight_sim)_CLIP.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 0
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 0
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: weight_sim
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 1
-pretrained_language_model: CLIP
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/anet/anet_anc_(weight_sim)_CLIP_refine.yml b/backup/cfgs_base/anet/anet_anc_(weight_sim)_CLIP_refine.yml
deleted file mode 100644
index d21448627979c6757d3299faee2ea5ab4d2d1b09..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_anc_(weight_sim)_CLIP_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 1
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 1
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: weight_sim
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 1
-pretrained_language_model: CLIP
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/anet/anet_anc_(weight_sim)_UniVL.yml b/backup/cfgs_base/anet/anet_anc_(weight_sim)_UniVL.yml
deleted file mode 100644
index bc8c5a5dc77dae477e0f880e515993a46357a8c8..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_anc_(weight_sim)_UniVL.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 0
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 0
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: weight_sim
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 1
-pretrained_language_model: UniVL
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/anet/anet_anc_(weight_sim)_UniVL_refine.yml b/backup/cfgs_base/anet/anet_anc_(weight_sim)_UniVL_refine.yml
deleted file mode 100644
index 665f42194576bed58d657628d916710efa51b514..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_anc_(weight_sim)_UniVL_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 1
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 1
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: weight_sim
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 1
-pretrained_language_model: UniVL
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/anet/anet_anc_GT_CLIP.yml b/backup/cfgs_base/anet/anet_anc_GT_CLIP.yml
deleted file mode 100644
index 4bc8f3897e2e398bf6cedf9c02936553098f2c73..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_anc_GT_CLIP.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 0
-pseudo_box_aug: 0
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 0
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: similarity
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 1
-pretrained_language_model: CLIP
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/anet/anet_anc_GT_UniVL.yml b/backup/cfgs_base/anet/anet_anc_GT_UniVL.yml
deleted file mode 100644
index 5ac2b451a19de13876e1d0dd042878289fdaa195..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_anc_GT_UniVL.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 0
-pseudo_box_aug: 0
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 0
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: similarity
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 1
-pretrained_language_model: UniVL
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/anet/anet_c3d_pdvc.yml b/backup/cfgs_base/anet/anet_c3d_pdvc.yml
deleted file mode 100644
index 935b0ececa15dcf4658c1e10a5ae52b93079b0fc..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_c3d_pdvc.yml
+++ /dev/null
@@ -1,11 +0,0 @@
-id: anet_c3d_pdvc # the results and logs will saved in this folder ./save/id
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-ec_alpha: 1.0
\ No newline at end of file
diff --git a/backup/cfgs_base/anet/anet_c3d_pdvc_gt.yml b/backup/cfgs_base/anet/anet_c3d_pdvc_gt.yml
deleted file mode 100644
index e0db6b87acea5ffa66e35e868e44194a04c39852..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_c3d_pdvc_gt.yml
+++ /dev/null
@@ -1,9 +0,0 @@
-id: anet_c3d_pdvc_gt
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl_gt.yml
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
\ No newline at end of file
diff --git a/backup/cfgs_base/anet/anet_c3d_pdvcl.yml b/backup/cfgs_base/anet/anet_c3d_pdvcl.yml
deleted file mode 100644
index 828311fc71fcc95e9b1a08506d11bb6ab602b665..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_c3d_pdvcl.yml
+++ /dev/null
@@ -1,53 +0,0 @@
-id: anet_c3d_pdvcl
-
-visual_feature_type: c3d
-visual_feature_folder: 'data/anet/features/c3d'
-feature_dim: 500
-invalid_video_json: []
-train_proposal_file: data/generated_proposals/dbg_trainval_top100.json
-eval_proposal_file: data/generated_proposals/dbg_trainval_top100.json
-gt_file_for_eval: ['data/anet/captiondata/val_1.json', 'data/anet/captiondata/val_2.json']
-gt_file_for_para_eval: ['data/anet/captiondata/para/anet_entities_val_1_para.json', 'data/anet/captiondata/para/anet_entities_val_2_para.json']
-
-train_proposal_type: gt
-gt_proposal_sample_num: 30
-sample_method: nearest
-
-batch_size: 1
-lr: 0.00005
-learning_rate_decay_start: 8
-learning_rate_decay_every: 3
-learning_rate_decay_rate: 0.5
-weight_decay: 0.0001
-save_all_checkpoint: 0
-
-num_queries: 100
-dec_layers: 2
-enc_layers: 2
-transformer_ff_dim: 512
-transformer_dropout_prob: 0.1
-frame_embedding_num: 100
-caption_decoder_type: light
-att_hid_size: 0
-
-with_box_refine: 1
-
-fix_xcw: 1
-set_cost_caption: 0
-set_cost_giou: 4
-set_cost_bbox: 0
-set_cost_class: 2
-self_iou_loss_coef: 0
-#cost_alpha: 0.5
-#cost_gamma: 1
-#focal_alpha: 0.5
-#focal_gamma: 1
-caption_loss_coef: 2
-giou_loss_coef: 4
-bbox_loss_coef: 0
-cls_loss_coef: 2
-count_loss_coef: 0.5
-max_eseq_length: 10
-lloss_cross_entropy: 0
-lloss_focal_loss: 0
-lloss_gau_mask: 1
\ No newline at end of file
diff --git a/backup/cfgs_base/anet/anet_c3d_pdvcl_gt.yml b/backup/cfgs_base/anet/anet_c3d_pdvcl_gt.yml
deleted file mode 100644
index 02b38b6f2dbbb53b838d9bfbab8cf268a7c02c62..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_c3d_pdvcl_gt.yml
+++ /dev/null
@@ -1,55 +0,0 @@
-id: anet_c3d_pdvcl_gt
-
-visual_feature_type: c3d
-visual_feature_folder: 'data/anet/features/c3d'
-feature_dim: 500
-invalid_video_json: []
-train_proposal_file: data/generated_proposals/dbg_trainval_top100.json
-eval_proposal_file: data/generated_proposals/dbg_trainval_top100.json
-gt_file_for_eval: ['data/anet/captiondata/val_1.json', 'data/anet/captiondata/val_2.json']
-gt_file_for_para_eval: ['data/anet/captiondata/para/anet_entities_val_1_para.json', 'data/anet/captiondata/para/anet_entities_val_2_para.json']
-
-train_proposal_type: gt
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-batch_size: 1
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-#with_box_refine: 1
-
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-
-#two_stage: 1
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\ No newline at end of file
diff --git a/backup/cfgs_base/anet/anet_c3d_props.yml b/backup/cfgs_base/anet/anet_c3d_props.yml
deleted file mode 100644
index 3d2aa20fce1241e60ad77a69980acf1e3b653ef1..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_c3d_props.yml
+++ /dev/null
@@ -1,51 +0,0 @@
-id: anet_c3d_props
-visual_feature_type: c3d
-visual_feature_folder: 'data/anet/features/c3d'
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-train_proposal_file: data/generated_proposals/dbg_trainval_top100.json
-eval_proposal_file: data/generated_proposals/dbg_trainval_top100.json
-gt_file_for_eval: ['data/anet/captiondata/val_1.json', 'data/anet/captiondata/val_2.json']
-gt_file_for_para_eval: ['data/anet/captiondata/para/anet_entities_val_1_para.json', 'data/anet/captiondata/para/anet_entities_val_2_para.json']
-
-train_proposal_type: gt
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-
-batch_size: 1
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-
-num_queries: 10
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-
-with_box_refine: 1
-
-fix_xcw: 1
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-#focal_alpha: 0.5
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\ No newline at end of file
diff --git a/backup/cfgs_base/anet/anet_i3dvgg_pdvc.yml b/backup/cfgs_base/anet/anet_i3dvgg_pdvc.yml
deleted file mode 100644
index 147d726179a848dabb0367b22575fa2f20de4097..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_i3dvgg_pdvc.yml
+++ /dev/null
@@ -1,6 +0,0 @@
-id: anet_i3dvgg_pdvc
-base_cfg_path: cfgs_base/anet_c3d_pdvc.yml
-visual_feature_type: ['i3d_rgb', 'i3d_flow', 'vggish']
-visual_feature_folder: ['data/anet/features/i3d/', 'data/anet/features/i3d/', 'data/anet/features/vggish/']
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-feature_dim: 2176
\ No newline at end of file
diff --git a/backup/cfgs_base/anet/anet_i3dvgg_pdvc_gt.yml b/backup/cfgs_base/anet/anet_i3dvgg_pdvc_gt.yml
deleted file mode 100644
index 5a6991e551815ec0ac234c30ab3a6d09f1bd75cf..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_i3dvgg_pdvc_gt.yml
+++ /dev/null
@@ -1,6 +0,0 @@
-id: anet_i3dvgg_pdvc_gt
-base_cfg_path: cfgs_base/anet_c3d_pdvc_gt.yml
-visual_feature_type: ['i3d_rgb', 'i3d_flow', 'vggish']
-visual_feature_folder: ['data/anet/features/i3d_25fps_stack64step64_2stream_npy/', 'data/anet/features/i3d_25fps_stack64step64_2stream_npy/', 'data/anet/features/vggish_npy/']
-invalid_video_json: ['data/anet/features/I3D_vggish_invalid_videos.json']
-feature_dim: 2176
\ No newline at end of file
diff --git a/backup/cfgs_base/anet/anet_ori_(align)_CLIP.yml b/backup/cfgs_base/anet/anet_ori_(align)_CLIP.yml
deleted file mode 100644
index f30c2ab5a626538f4dbc2c1a1bc497196ff46f24..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_ori_(align)_CLIP.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/text/']
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-
-use_pseudo_box: 1
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-pseudo_box_type: align
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-
-use_anchor: 0
-pretrained_language_model: CLIP
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-
-caption_decoder_type: standard
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-cap_dec_n_points: 4
-cap_num_feature_levels: 4
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-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/anet/anet_ori_(align)_CLIP_refine.yml b/backup/cfgs_base/anet/anet_ori_(align)_CLIP_refine.yml
deleted file mode 100644
index 2d3b50035fe6a95bb2a5790f8b3611be54fc0fa7..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_ori_(align)_CLIP_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/text/']
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-
-use_pseudo_box: 1
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-pseudo_box_type: align
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-
-use_anchor: 0
-pretrained_language_model: CLIP
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-
-caption_decoder_type: standard
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-cap_num_feature_levels: 4
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-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
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-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/anet/anet_ori_(align)_UniVL.yml b/backup/cfgs_base/anet/anet_ori_(align)_UniVL.yml
deleted file mode 100644
index 270ae993e3f72bc2d9091b4809e8715fc6c86dae..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_ori_(align)_UniVL.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 0
-pseudo_box_aug_num: 5
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-refine_pseudo_box: 0
-refine_pseudo_stage_num: 1
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-pseudo_box_type: align
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 0
-pretrained_language_model: UniVL
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-
-caption_decoder_type: standard
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-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/anet/anet_ori_(align)_UniVL_refine.yml b/backup/cfgs_base/anet/anet_ori_(align)_UniVL_refine.yml
deleted file mode 100644
index 7340a306c59e2ac685e8c59ac2960fde366e9c7b..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_ori_(align)_UniVL_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/text/']
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-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 1
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-refine_pseudo_stage_num: 1
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-pseudo_box_type: align
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-
-use_anchor: 0
-pretrained_language_model: UniVL
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-
-caption_decoder_type: standard
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-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/anet/anet_ori_(sim)_CLIP.yml b/backup/cfgs_base/anet/anet_ori_(sim)_CLIP.yml
deleted file mode 100644
index 16f3082ecb947291ccb4f2226312fcf3fa06d349..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_ori_(sim)_CLIP.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 0
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 0
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: similarity
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 0
-pretrained_language_model: CLIP
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/anet/anet_ori_(sim)_CLIP_refine.yml b/backup/cfgs_base/anet/anet_ori_(sim)_CLIP_refine.yml
deleted file mode 100644
index c167a9b0499503b9eff84d0c1ea1aa42453cf117..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_ori_(sim)_CLIP_refine.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 1
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 1
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: similarity
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-window_size: 2
-top_frames: 35
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 0
-pretrained_language_model: CLIP
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/anet/anet_ori_(sim)_UniVL.yml b/backup/cfgs_base/anet/anet_ori_(sim)_UniVL.yml
deleted file mode 100644
index c48adf788535e24daf8e7ffe16f2e60009118f1f..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_ori_(sim)_UniVL.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 0
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 0
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: similarity
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 0
-pretrained_language_model: UniVL
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/anet/anet_ori_(sim)_UniVL_refine.yml b/backup/cfgs_base/anet/anet_ori_(sim)_UniVL_refine.yml
deleted file mode 100644
index 65d5217788315daae5e6bbf002eb746b010e2bde..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_ori_(sim)_UniVL_refine.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 1
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 1
-refine_pseudo_stage_num: 1
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-pseudo_box_type: similarity
-statistic_mode: mode
-width_ratio: 1
-window_size: 2
-top_frames: 35
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 0
-pretrained_language_model: UniVL
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
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-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/anet/anet_ori_(sim_op)_CLIP.yml b/backup/cfgs_base/anet/anet_ori_(sim_op)_CLIP.yml
deleted file mode 100644
index 9dc19b7dcbc2f364b03abc0014d17eb6375b4a99..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_ori_(sim_op)_CLIP.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/text/']
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-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 0
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-refine_pseudo_box: 0
-refine_pseudo_stage_num: 1
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-pseudo_box_type: similarity_op
-use_query_box_for_refine: 0
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-
-use_anchor: 0
-pretrained_language_model: CLIP
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
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-cap_num_feature_levels: 4
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-att_hid_size: 512
-
-num_queries: 100
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-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/anet/anet_ori_(sim_op)_CLIP_refine.yml b/backup/cfgs_base/anet/anet_ori_(sim_op)_CLIP_refine.yml
deleted file mode 100644
index 042d3f885aff7b261f66fa03dad252aedbf2fcf9..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_ori_(sim_op)_CLIP_refine.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/text/']
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-hidden_dim: 512
-
-use_pseudo_box: 1
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-refine_pseudo_stage_num: 1
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-pseudo_box_type: similarity_op
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-
-use_anchor: 0
-pretrained_language_model: CLIP
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-
-caption_decoder_type: standard
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diff --git a/backup/cfgs_base/anet/anet_ori_(sim_op)_UniVL.yml b/backup/cfgs_base/anet/anet_ori_(sim_op)_UniVL.yml
deleted file mode 100644
index 2f6e44731cf2c826b91b8173148e120b64d04f66..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_ori_(sim_op)_UniVL.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['UniVL']
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diff --git a/backup/cfgs_base/anet/anet_ori_(sim_op)_UniVL_refine.yml b/backup/cfgs_base/anet/anet_ori_(sim_op)_UniVL_refine.yml
deleted file mode 100644
index 4e0f151d036988969e902c436085fa850bb50a4d..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_ori_(sim_op)_UniVL_refine.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['UniVL']
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diff --git a/backup/cfgs_base/anet/anet_ori_(sim_op_order)_CLIP.yml b/backup/cfgs_base/anet/anet_ori_(sim_op_order)_CLIP.yml
deleted file mode 100644
index 8e44527dea994822b026b814df9b354aff082b53..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_ori_(sim_op_order)_CLIP.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
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diff --git a/backup/cfgs_base/anet/anet_ori_(sim_op_order)_CLIP_refine.yml b/backup/cfgs_base/anet/anet_ori_(sim_op_order)_CLIP_refine.yml
deleted file mode 100644
index 08f52e1ac3b1ebca5e2d62c639fd5f6b5752ddf9..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_ori_(sim_op_order)_CLIP_refine.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
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diff --git a/backup/cfgs_base/anet/anet_ori_(sim_op_order)_UniVL.yml b/backup/cfgs_base/anet/anet_ori_(sim_op_order)_UniVL.yml
deleted file mode 100644
index bd202e1359b3d756d903c93acf07e4dad268323e..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_ori_(sim_op_order)_UniVL.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/visual/']
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-ec_alpha: 1.0
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diff --git a/backup/cfgs_base/anet/anet_ori_(sim_op_order)_UniVL_refine.yml b/backup/cfgs_base/anet/anet_ori_(sim_op_order)_UniVL_refine.yml
deleted file mode 100644
index 3fe14dc05390932f58a3f5ce8b3ffa3828296200..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_ori_(sim_op_order)_UniVL_refine.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/visual/']
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diff --git a/backup/cfgs_base/anet/anet_ori_(sim_op_order_v1)_CLIP.yml b/backup/cfgs_base/anet/anet_ori_(sim_op_order_v1)_CLIP.yml
deleted file mode 100644
index 211b0adc17f02c1b64ce3ceff2c0122c7581eada..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_ori_(sim_op_order_v1)_CLIP.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
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diff --git a/backup/cfgs_base/anet/anet_ori_(sim_op_order_v1)_CLIP_refine.yml b/backup/cfgs_base/anet/anet_ori_(sim_op_order_v1)_CLIP_refine.yml
deleted file mode 100644
index 674edd157c6ffe26bd7e9248faffc1a68a997d35..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_ori_(sim_op_order_v1)_CLIP_refine.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
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diff --git a/backup/cfgs_base/anet/anet_ori_(sim_op_order_v1)_UniVL.yml b/backup/cfgs_base/anet/anet_ori_(sim_op_order_v1)_UniVL.yml
deleted file mode 100644
index 20243b5ac8be187c91a7e54eb86cc27db6f21559..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_ori_(sim_op_order_v1)_UniVL.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/visual/']
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diff --git a/backup/cfgs_base/anet/anet_ori_(sim_op_order_v1)_UniVL_refine.yml b/backup/cfgs_base/anet/anet_ori_(sim_op_order_v1)_UniVL_refine.yml
deleted file mode 100644
index cb675d9549b34cf0ee2258d3e6a107273d1e4ffd..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_ori_(sim_op_order_v1)_UniVL_refine.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['UniVL']
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diff --git a/backup/cfgs_base/anet/anet_ori_(sim_op_order_v2)_CLIP.yml b/backup/cfgs_base/anet/anet_ori_(sim_op_order_v2)_CLIP.yml
deleted file mode 100644
index 586821983af378162d355b298b2788e0c651e0e6..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_ori_(sim_op_order_v2)_CLIP.yml
+++ /dev/null
@@ -1,39 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
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diff --git a/backup/cfgs_base/anet/anet_ori_(sim_op_order_v2)_CLIP_refine.yml b/backup/cfgs_base/anet/anet_ori_(sim_op_order_v2)_CLIP_refine.yml
deleted file mode 100644
index 42476cbf72bb3d8b304e89192a30bda1606046aa..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_ori_(sim_op_order_v2)_CLIP_refine.yml
+++ /dev/null
@@ -1,43 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
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diff --git a/backup/cfgs_base/anet/anet_ori_(sim_op_order_v2)_UniVL.yml b/backup/cfgs_base/anet/anet_ori_(sim_op_order_v2)_UniVL.yml
deleted file mode 100644
index b0a8e97b44c541a99afc965764187cf264bd4268..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_ori_(sim_op_order_v2)_UniVL.yml
+++ /dev/null
@@ -1,39 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 0
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 0
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: similarity_op_order_v2
-width_th: 0.5
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 0
-pretrained_language_model: UniVL
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/anet/anet_ori_(sim_op_order_v2)_UniVL_refine.yml b/backup/cfgs_base/anet/anet_ori_(sim_op_order_v2)_UniVL_refine.yml
deleted file mode 100644
index 9e0ce1a0db8dd453b811f6f0d5609f8ae7648a6d..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_ori_(sim_op_order_v2)_UniVL_refine.yml
+++ /dev/null
@@ -1,43 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 1
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 1
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: similarity_op_order_v2
-width_th: 0.5
-statistic_mode: mode
-width_ratio: 1
-window_size: 2
-top_frames: 35
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 0
-pretrained_language_model: UniVL
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/anet/anet_ori_(weight_index)_CLIP.yml b/backup/cfgs_base/anet/anet_ori_(weight_index)_CLIP.yml
deleted file mode 100644
index c8725e365b44b967e794cdc16423a571c71e33bd..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_ori_(weight_index)_CLIP.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 0
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 0
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: weight_index
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 0
-pretrained_language_model: CLIP
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/anet/anet_ori_(weight_index)_CLIP_refine.yml b/backup/cfgs_base/anet/anet_ori_(weight_index)_CLIP_refine.yml
deleted file mode 100644
index 2fd6450a47d8392d35b67b997ec6173f35b6ee4b..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_ori_(weight_index)_CLIP_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 1
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 1
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: weight_index
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 0
-pretrained_language_model: CLIP
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/anet/anet_ori_(weight_index)_UniVL.yml b/backup/cfgs_base/anet/anet_ori_(weight_index)_UniVL.yml
deleted file mode 100644
index 5cd314fb9fd20fa05b1b5417604b40d115ee008e..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_ori_(weight_index)_UniVL.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 0
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 0
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: weight_index
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 0
-pretrained_language_model: UniVL
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/anet/anet_ori_(weight_index)_UniVL_refine.yml b/backup/cfgs_base/anet/anet_ori_(weight_index)_UniVL_refine.yml
deleted file mode 100644
index 23e3dae52ca63a879083d7eafc8c1ab7e1556d71..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_ori_(weight_index)_UniVL_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 1
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 1
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: weight_index
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 0
-pretrained_language_model: UniVL
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/anet/anet_ori_(weight_sim)_CLIP.yml b/backup/cfgs_base/anet/anet_ori_(weight_sim)_CLIP.yml
deleted file mode 100644
index a4a108d38d2c512864344770d5943e439eb151d5..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_ori_(weight_sim)_CLIP.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 0
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 0
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: weight_sim
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 0
-pretrained_language_model: CLIP
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/anet/anet_ori_(weight_sim)_CLIP_refine.yml b/backup/cfgs_base/anet/anet_ori_(weight_sim)_CLIP_refine.yml
deleted file mode 100644
index c30ad37380e6b9ef2b845061471ab2a4ff293d91..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_ori_(weight_sim)_CLIP_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 1
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 1
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: weight_sim
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 0
-pretrained_language_model: CLIP
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/anet/anet_ori_(weight_sim)_UniVL.yml b/backup/cfgs_base/anet/anet_ori_(weight_sim)_UniVL.yml
deleted file mode 100644
index b140662d119689dca4e409f85da91e882323bc0f..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_ori_(weight_sim)_UniVL.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 0
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 0
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: weight_sim
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 0
-pretrained_language_model: UniVL
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/anet/anet_ori_(weight_sim)_UniVL_refine.yml b/backup/cfgs_base/anet/anet_ori_(weight_sim)_UniVL_refine.yml
deleted file mode 100644
index 5e03028433a6bbc1c25ae936d791096e8e7826b2..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_ori_(weight_sim)_UniVL_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 1
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 1
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: weight_sim
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 0
-pretrained_language_model: UniVL
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/anet/anet_ori_GT_CLIP.yml b/backup/cfgs_base/anet/anet_ori_GT_CLIP.yml
deleted file mode 100644
index ad1160b06416b51bd4e728eef5e6225f023796c0..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_ori_GT_CLIP.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 0
-pseudo_box_aug: 0
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 0
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: similarity
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 0
-pretrained_language_model: CLIP
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/anet/anet_ori_GT_UniVL.yml b/backup/cfgs_base/anet/anet_ori_GT_UniVL.yml
deleted file mode 100644
index f56ac030b287f6c0a806833b546d90a9d8fe9670..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_ori_GT_UniVL.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 0
-pseudo_box_aug: 0
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 0
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: similarity
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 0
-pretrained_language_model: UniVL
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/anet/anet_tsn_pdvc.yml b/backup/cfgs_base/anet/anet_tsn_pdvc.yml
deleted file mode 100644
index 79f9caa36975efda224cb605af412efda721e7dc..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_tsn_pdvc.yml
+++ /dev/null
@@ -1,6 +0,0 @@
-id: anet_tsn_pdvc
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvc.yml
-visual_feature_type: ['resnet', 'bn']
-visual_feature_folder: ['data/anet/features/resnet_bn', 'data/anet/features/resnet_bn']
-invalid_video_json: ['data/anet/features/resnet_bn_invalid_videos.json']
-feature_dim: 3072
\ No newline at end of file
diff --git a/backup/cfgs_base/anet/anet_tsn_pdvc_gt.yml b/backup/cfgs_base/anet/anet_tsn_pdvc_gt.yml
deleted file mode 100644
index c748cd44f1b9ea7607e4482da4af8444347d3f88..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_tsn_pdvc_gt.yml
+++ /dev/null
@@ -1,6 +0,0 @@
-id: anet_tsn_pdvc_gt
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvc_gt.yml
-visual_feature_type: ['resnet', 'bn']
-visual_feature_folder: ['data/anet/features/resnet_bn', 'data/anet/features/resnet_bn']
-invalid_video_json: ['data/anet/features/resnet_bn_invalid_videos.json']
-feature_dim: 3072
\ No newline at end of file
diff --git a/backup/cfgs_base/anet/anet_tsn_pdvcl.yml b/backup/cfgs_base/anet/anet_tsn_pdvcl.yml
deleted file mode 100644
index 5543e4e259942b72d98f1fe16cd4311be93ef3c7..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_tsn_pdvcl.yml
+++ /dev/null
@@ -1,6 +0,0 @@
-id: anet_tsn_pdvcl
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-visual_feature_type: ['resnet', 'bn']
-visual_feature_folder: ['data/anet/features/resnet_bn', 'data/anet/features/resnet_bn']
-invalid_video_json: ['data/anet/features/resnet_bn_invalid_videos.json']
-feature_dim: 3072
\ No newline at end of file
diff --git a/backup/cfgs_base/anet/anet_tsn_pdvcl_gt.yml b/backup/cfgs_base/anet/anet_tsn_pdvcl_gt.yml
deleted file mode 100644
index 9804be364f78a4a8f26e30e0e6923558194edcd9..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_tsn_pdvcl_gt.yml
+++ /dev/null
@@ -1,6 +0,0 @@
-id: anet_tsn_pdvcl_gt
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl_gt.yml
-visual_feature_type: ['resnet', 'bn']
-visual_feature_folder: ['data/anet/features/resnet_bn', 'data/anet/features/resnet_bn']
-invalid_video_json: ['data/anet/features/resnet_bn_invalid_videos.json']
-feature_dim: 3072
\ No newline at end of file
diff --git a/backup/cfgs_base/anet/anet_tsp_pdvc.yml b/backup/cfgs_base/anet/anet_tsp_pdvc.yml
deleted file mode 100644
index 1c4ef82922a7df99f37d1a626d4a89e8c9b95722..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_tsp_pdvc.yml
+++ /dev/null
@@ -1,6 +0,0 @@
-id: anet_tsp_pdvc
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvc.yml
-visual_feature_type: ['tsp']
-visual_feature_folder: ['data/anet/features/tsp']
-invalid_video_json: []
-feature_dim: 512
\ No newline at end of file
diff --git a/backup/cfgs_base/anet/anet_tsp_pdvc_gt.yml b/backup/cfgs_base/anet/anet_tsp_pdvc_gt.yml
deleted file mode 100644
index df92966691ed0fa33bc4b7417f6c0ade5b383869..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_tsp_pdvc_gt.yml
+++ /dev/null
@@ -1,6 +0,0 @@
-id: anet_tsp_pdvc_gt
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvc_gt.yml
-visual_feature_type: ['tsp']
-visual_feature_folder: ['data/anet/features/tsp']
-invalid_video_json: []
-feature_dim: 512
diff --git a/backup/cfgs_base/anet/anet_tsp_pdvcl.yml b/backup/cfgs_base/anet/anet_tsp_pdvcl.yml
deleted file mode 100644
index c5298c707ab8887be611c86d522e855a8a5123a4..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/anet/anet_tsp_pdvcl.yml
+++ /dev/null
@@ -1,6 +0,0 @@
-id: anet_tsp_pdvcl
-base_cfg_path: cfgs_base/anet/anet_c3d_pdvcl.yml
-visual_feature_type: ['tsp']
-visual_feature_folder: ['data/anet/features/tsp']
-invalid_video_json: []
-feature_dim: 512
\ No newline at end of file
diff --git a/backup/cfgs_base/howto/base_howto-anet_anet.yml b/backup/cfgs_base/howto/base_howto-anet_anet.yml
deleted file mode 100644
index d83ae5b6762ddc39bcdab2aedddf47a6ed8571d3..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/base_howto-anet_anet.yml
+++ /dev/null
@@ -1,64 +0,0 @@
-id: anet
-
-visual_feature_type: c3d
-visual_feature_folder: 'data/anet/features/c3d'
-feature_dim: 500
-invalid_video_json: []
-train_proposal_file: data/generated_proposals/dbg_trainval_top100.json
-eval_proposal_file: data/generated_proposals/dbg_trainval_top100.json
-gt_file_for_eval: ['data/anet/captiondata/val_1.json', 'data/anet/captiondata/val_2.json']
-gt_file_for_para_eval: ['data/anet/captiondata/para/anet_entities_val_1_para.json', 'data/anet/captiondata/para/anet_entities_val_2_para.json']
-train_caption_file: ['data/howto/captiondata/howto100m_train.json', 'data/anet/captiondata/train_modified.json']
-val_caption_file: 'data/anet/captiondata/val_1.json'
-
-max_caption_len: 50
-
-dict_file: data/howto/vocabulary_howto_rate2_anet.json
-vocab_size: 16221
-# dict_file_for_sim: data/howto/vocabulary_howto_rate5.json
-# vocab_size: 8531
-
-
-train_proposal_type: gt
-train_proposal_sample_num: 30
-sample_method: nearest
-
-epoch: 10
-batch_size: 1
-lr: 0.00005
-learning_rate_decay_start: 8
-learning_rate_decay_every: 3
-learning_rate_decay_rate: 0.5
-weight_decay: 0.0001
-save_all_checkpoint: 0
-
-num_queries: 100
-dec_layers: 2
-enc_layers: 2
-transformer_ff_dim: 512
-transformer_dropout_prob: 0.1
-frame_embedding_num: 100
-caption_decoder_type: light
-att_hid_size: 0
-
-with_box_refine: 1
-
-fix_xcw: 1
-set_cost_caption: 0
-set_cost_giou: 4
-set_cost_bbox: 0
-set_cost_class: 2
-self_iou_loss_coef: 0
-#cost_alpha: 0.5
-#cost_gamma: 1
-#focal_alpha: 0.5
-#focal_gamma: 1
-caption_loss_coef: 2
-giou_loss_coef: 4
-bbox_loss_coef: 0
-cls_loss_coef: 2
-count_loss_coef: 0.5
-max_eseq_length: 10
-lloss_cross_entropy: 0
-lloss_focal_loss: 0
-lloss_gau_mask: 1
\ No newline at end of file
diff --git a/backup/cfgs_base/howto/base_howto-anet_anet_mixlm.yml b/backup/cfgs_base/howto/base_howto-anet_anet_mixlm.yml
deleted file mode 100644
index 889d543d5b813b8a574700f9ad209fd237144075..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/base_howto-anet_anet_mixlm.yml
+++ /dev/null
@@ -1,64 +0,0 @@
-id: anet
-
-visual_feature_type: c3d
-visual_feature_folder: 'data/anet/features/c3d'
-feature_dim: 500
-invalid_video_json: []
-train_proposal_file: data/generated_proposals/dbg_trainval_top100.json
-eval_proposal_file: data/generated_proposals/dbg_trainval_top100.json
-gt_file_for_eval: ['data/anet/captiondata/val_1.json', 'data/anet/captiondata/val_2.json']
-gt_file_for_para_eval: ['data/anet/captiondata/para/anet_entities_val_1_para.json', 'data/anet/captiondata/para/anet_entities_val_2_para.json']
-train_caption_file: ['data/howto/captiondata/howto100m_train_mixlm.json', 'data/anet/captiondata/train_modified.json']
-val_caption_file: 'data/anet/captiondata/val_1.json'
-
-max_caption_len: 50
-
-dict_file: data/howto/vocabulary_howto_rate2_mixlm_anet.json
-vocab_size: 18884
-# dict_file_for_sim: data/howto/vocabulary_howto_rate5.json
-# vocab_size: 8531
-
-
-train_proposal_type: gt
-train_proposal_sample_num: 30
-sample_method: nearest
-
-epoch: 10
-batch_size: 1
-lr: 0.00005
-learning_rate_decay_start: 8
-learning_rate_decay_every: 3
-learning_rate_decay_rate: 0.5
-weight_decay: 0.0001
-save_all_checkpoint: 0
-
-num_queries: 100
-dec_layers: 2
-enc_layers: 2
-transformer_ff_dim: 512
-transformer_dropout_prob: 0.1
-frame_embedding_num: 100
-caption_decoder_type: light
-att_hid_size: 0
-
-with_box_refine: 1
-
-fix_xcw: 1
-set_cost_caption: 0
-set_cost_giou: 4
-set_cost_bbox: 0
-set_cost_class: 2
-self_iou_loss_coef: 0
-#cost_alpha: 0.5
-#cost_gamma: 1
-#focal_alpha: 0.5
-#focal_gamma: 1
-caption_loss_coef: 2
-giou_loss_coef: 4
-bbox_loss_coef: 0
-cls_loss_coef: 2
-count_loss_coef: 0.5
-max_eseq_length: 10
-lloss_cross_entropy: 0
-lloss_focal_loss: 0
-lloss_gau_mask: 1
\ No newline at end of file
diff --git a/backup/cfgs_base/howto/base_howto-anet_anet_puyu.yml b/backup/cfgs_base/howto/base_howto-anet_anet_puyu.yml
deleted file mode 100644
index a7bf2a745aecc0b05232f717c81a97333ee55af3..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/base_howto-anet_anet_puyu.yml
+++ /dev/null
@@ -1,64 +0,0 @@
-id: anet
-
-visual_feature_type: c3d
-visual_feature_folder: 'data/anet/features/c3d'
-feature_dim: 500
-invalid_video_json: []
-train_proposal_file: data/generated_proposals/dbg_trainval_top100.json
-eval_proposal_file: data/generated_proposals/dbg_trainval_top100.json
-gt_file_for_eval: ['data/anet/captiondata/val_1.json', 'data/anet/captiondata/val_2.json']
-gt_file_for_para_eval: ['data/anet/captiondata/para/anet_entities_val_1_para.json', 'data/anet/captiondata/para/anet_entities_val_2_para.json']
-train_caption_file: ['data/howto/captiondata/howto100m_train_puyu.json', 'data/anet/captiondata/train_modified.json']
-val_caption_file: 'data/anet/captiondata/val_1.json'
-
-max_caption_len: 50
-
-dict_file: data/howto/vocabulary_howto_rate2_puyu_anet.json
-vocab_size: 15249
-# dict_file_for_sim: data/howto/vocabulary_howto_rate5.json
-# vocab_size: 8531
-
-
-train_proposal_type: gt
-train_proposal_sample_num: 30
-sample_method: nearest
-
-epoch: 10
-batch_size: 1
-lr: 0.00005
-learning_rate_decay_start: 8
-learning_rate_decay_every: 3
-learning_rate_decay_rate: 0.5
-weight_decay: 0.0001
-save_all_checkpoint: 0
-
-num_queries: 100
-dec_layers: 2
-enc_layers: 2
-transformer_ff_dim: 512
-transformer_dropout_prob: 0.1
-frame_embedding_num: 100
-caption_decoder_type: light
-att_hid_size: 0
-
-with_box_refine: 1
-
-fix_xcw: 1
-set_cost_caption: 0
-set_cost_giou: 4
-set_cost_bbox: 0
-set_cost_class: 2
-self_iou_loss_coef: 0
-#cost_alpha: 0.5
-#cost_gamma: 1
-#focal_alpha: 0.5
-#focal_gamma: 1
-caption_loss_coef: 2
-giou_loss_coef: 4
-bbox_loss_coef: 0
-cls_loss_coef: 2
-count_loss_coef: 0.5
-max_eseq_length: 10
-lloss_cross_entropy: 0
-lloss_focal_loss: 0
-lloss_gau_mask: 1
\ No newline at end of file
diff --git a/backup/cfgs_base/howto/base_howto-yc2_yc2.yml b/backup/cfgs_base/howto/base_howto-yc2_yc2.yml
deleted file mode 100644
index 17b3bd0263edd713fc329bf1df7b539e2f160b3d..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/base_howto-yc2_yc2.yml
+++ /dev/null
@@ -1,61 +0,0 @@
-id: yc2_tsn_pdvcl
-
-visual_feature_type: ['resnet', 'bn']
-visual_feature_folder: ['data/yc2/features/resnet_bn/', 'data/yc2/features/resnet_bn/']
-feature_dim: 3072
-invalid_video_json: []
-train_caption_file: ['data/howto/captiondata/howto100m_train.json', 'data/yc2/captiondata/yc2_train.json']
-val_caption_file: 'data/yc2/captiondata/yc2_val.json'
-gt_file_for_eval: ['data/yc2/captiondata/yc2_val.json']
-gt_file_for_para_eval: ['data/yc2/captiondata/para/para_yc2_val.json']
-max_caption_len: 50
-
-dict_file: data/howto/vocabulary_howto_rate2_yc2.json
-vocab_size: 14538
-# dict_file_for_sim: data/howto/vocabulary_howto_rate5.json
-# vocab_size: 8531
-
-
-train_proposal_type: gt
-train_proposal_sample_num: 30
-sample_method: nearest
-
-epoch: 10
-batch_size: 1
-lr: 0.00005
-learning_rate_decay_start: 8
-learning_rate_decay_every: 3
-learning_rate_decay_rate: 0.5
-weight_decay: 0.0001
-save_all_checkpoint: 0
-
-num_queries: 100
-dec_layers: 2
-enc_layers: 2
-transformer_ff_dim: 512
-transformer_dropout_prob: 0.1
-frame_embedding_num: 200
-caption_decoder_type: light
-att_hid_size: 0
-
-with_box_refine: 1
-
-fix_xcw: 1
-set_cost_caption: 0
-set_cost_giou: 4
-set_cost_bbox: 0
-set_cost_class: 2
-self_iou_loss_coef: 0
-#cost_alpha: 0.5
-#cost_gamma: 1
-#focal_alpha: 0.5
-#focal_gamma: 1
-caption_loss_coef: 2
-giou_loss_coef: 4
-bbox_loss_coef: 0
-cls_loss_coef: 2
-count_loss_coef: 0.5
-max_eseq_length: 20
-lloss_cross_entropy: 0
-lloss_focal_loss: 0
-lloss_gau_mask: 1
\ No newline at end of file
diff --git a/backup/cfgs_base/howto/base_howto-yc2_yc2_mixlm.yml b/backup/cfgs_base/howto/base_howto-yc2_yc2_mixlm.yml
deleted file mode 100644
index 36d19db653936c2342b12bfc603de32b2295e287..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/base_howto-yc2_yc2_mixlm.yml
+++ /dev/null
@@ -1,61 +0,0 @@
-id: yc2_tsn_pdvcl
-
-visual_feature_type: ['resnet', 'bn']
-visual_feature_folder: ['data/yc2/features/resnet_bn/', 'data/yc2/features/resnet_bn/']
-feature_dim: 3072
-invalid_video_json: []
-train_caption_file: ['data/howto/captiondata/howto100m_train_mixlm.json', 'data/yc2/captiondata/yc2_train.json']
-val_caption_file: 'data/yc2/captiondata/yc2_val.json'
-gt_file_for_eval: ['data/yc2/captiondata/yc2_val.json']
-gt_file_for_para_eval: ['data/yc2/captiondata/para/para_yc2_val.json']
-max_caption_len: 50
-
-dict_file: data/howto/vocabulary_howto_rate2_mixlm_yc2.json
-vocab_size: 17447
-# dict_file_for_sim: data/howto/vocabulary_howto_rate5.json
-# vocab_size: 8531
-
-
-train_proposal_type: gt
-train_proposal_sample_num: 30
-sample_method: nearest
-
-epoch: 10
-batch_size: 1
-lr: 0.00005
-learning_rate_decay_start: 8
-learning_rate_decay_every: 3
-learning_rate_decay_rate: 0.5
-weight_decay: 0.0001
-save_all_checkpoint: 0
-
-num_queries: 100
-dec_layers: 2
-enc_layers: 2
-transformer_ff_dim: 512
-transformer_dropout_prob: 0.1
-frame_embedding_num: 200
-caption_decoder_type: light
-att_hid_size: 0
-
-with_box_refine: 1
-
-fix_xcw: 1
-set_cost_caption: 0
-set_cost_giou: 4
-set_cost_bbox: 0
-set_cost_class: 2
-self_iou_loss_coef: 0
-#cost_alpha: 0.5
-#cost_gamma: 1
-#focal_alpha: 0.5
-#focal_gamma: 1
-caption_loss_coef: 2
-giou_loss_coef: 4
-bbox_loss_coef: 0
-cls_loss_coef: 2
-count_loss_coef: 0.5
-max_eseq_length: 20
-lloss_cross_entropy: 0
-lloss_focal_loss: 0
-lloss_gau_mask: 1
\ No newline at end of file
diff --git a/backup/cfgs_base/howto/base_howto-yc2_yc2_puyu.yml b/backup/cfgs_base/howto/base_howto-yc2_yc2_puyu.yml
deleted file mode 100644
index 0f9ec30bf455a8a9d51bb867bdbc8e4d514c8006..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/base_howto-yc2_yc2_puyu.yml
+++ /dev/null
@@ -1,61 +0,0 @@
-id: yc2_tsn_pdvcl
-
-visual_feature_type: ['resnet', 'bn']
-visual_feature_folder: ['data/yc2/features/resnet_bn/', 'data/yc2/features/resnet_bn/']
-feature_dim: 3072
-invalid_video_json: []
-train_caption_file: ['data/howto/captiondata/howto100m_train_puyu.json', 'data/yc2/captiondata/yc2_train.json']
-val_caption_file: 'data/yc2/captiondata/yc2_val.json'
-gt_file_for_eval: ['data/yc2/captiondata/yc2_val.json']
-gt_file_for_para_eval: ['data/yc2/captiondata/para/para_yc2_val.json']
-max_caption_len: 50
-
-dict_file: data/howto/vocabulary_howto_rate2_yc2_puyu.json
-vocab_size: 13411
-# dict_file_for_sim: data/howto/vocabulary_howto_rate5.json
-# vocab_size: 8531
-
-
-train_proposal_type: gt
-train_proposal_sample_num: 30
-sample_method: nearest
-
-epoch: 10
-batch_size: 1
-lr: 0.00005
-learning_rate_decay_start: 8
-learning_rate_decay_every: 3
-learning_rate_decay_rate: 0.5
-weight_decay: 0.0001
-save_all_checkpoint: 0
-
-num_queries: 100
-dec_layers: 2
-enc_layers: 2
-transformer_ff_dim: 512
-transformer_dropout_prob: 0.1
-frame_embedding_num: 200
-caption_decoder_type: light
-att_hid_size: 0
-
-with_box_refine: 1
-
-fix_xcw: 1
-set_cost_caption: 0
-set_cost_giou: 4
-set_cost_bbox: 0
-set_cost_class: 2
-self_iou_loss_coef: 0
-#cost_alpha: 0.5
-#cost_gamma: 1
-#focal_alpha: 0.5
-#focal_gamma: 1
-caption_loss_coef: 2
-giou_loss_coef: 4
-bbox_loss_coef: 0
-cls_loss_coef: 2
-count_loss_coef: 0.5
-max_eseq_length: 20
-lloss_cross_entropy: 0
-lloss_focal_loss: 0
-lloss_gau_mask: 1
\ No newline at end of file
diff --git a/backup/cfgs_base/howto/base_howto_anet.yml b/backup/cfgs_base/howto/base_howto_anet.yml
deleted file mode 100644
index 3deec04627b419ff129a14bcf6ef5f8382bca7af..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/base_howto_anet.yml
+++ /dev/null
@@ -1,64 +0,0 @@
-id: anet
-
-visual_feature_type: c3d
-visual_feature_folder: 'data/anet/features/c3d'
-feature_dim: 500
-invalid_video_json: []
-train_proposal_file: data/generated_proposals/dbg_trainval_top100.json
-eval_proposal_file: data/generated_proposals/dbg_trainval_top100.json
-gt_file_for_eval: ['data/anet/captiondata/val_1.json', 'data/anet/captiondata/val_2.json']
-gt_file_for_para_eval: ['data/anet/captiondata/para/anet_entities_val_1_para.json', 'data/anet/captiondata/para/anet_entities_val_2_para.json']
-train_caption_file: 'data/howto/captiondata/howto100m_train.json'
-val_caption_file: 'data/anet/captiondata/val_1.json'
-
-max_caption_len: 50
-
-dict_file: data/howto/vocabulary_howto_rate2_anet.json
-vocab_size: 16221
-
-
-
-
-train_proposal_type: gt
-train_proposal_sample_num: 30
-sample_method: nearest
-
-epoch: 10
-batch_size: 1
-lr: 0.00005
-learning_rate_decay_start: 8
-learning_rate_decay_every: 3
-learning_rate_decay_rate: 0.5
-weight_decay: 0.0001
-save_all_checkpoint: 0
-
-num_queries: 100
-dec_layers: 2
-enc_layers: 2
-transformer_ff_dim: 512
-transformer_dropout_prob: 0.1
-frame_embedding_num: 100
-caption_decoder_type: light
-att_hid_size: 0
-
-with_box_refine: 1
-
-fix_xcw: 1
-set_cost_caption: 0
-set_cost_giou: 4
-set_cost_bbox: 0
-set_cost_class: 2
-self_iou_loss_coef: 0
-#cost_alpha: 0.5
-#cost_gamma: 1
-#focal_alpha: 0.5
-#focal_gamma: 1
-caption_loss_coef: 2
-giou_loss_coef: 4
-bbox_loss_coef: 0
-cls_loss_coef: 2
-count_loss_coef: 0.5
-max_eseq_length: 10
-lloss_cross_entropy: 0
-lloss_focal_loss: 0
-lloss_gau_mask: 1
\ No newline at end of file
diff --git a/backup/cfgs_base/howto/base_howto_yc2.yml b/backup/cfgs_base/howto/base_howto_yc2.yml
deleted file mode 100644
index 85343a3924a24e42054f963f220b2a3e93769070..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/base_howto_yc2.yml
+++ /dev/null
@@ -1,62 +0,0 @@
-id: yc2_tsn_pdvcl
-
-visual_feature_type: ['resnet', 'bn']
-visual_feature_folder: ['data/yc2/features/resnet_bn/', 'data/yc2/features/resnet_bn/']
-feature_dim: 3072
-invalid_video_json: []
-train_caption_file: 'data/howto/captiondata/howto100m_train.json'
-val_caption_file: 'data/yc2/captiondata/yc2_val.json'
-gt_file_for_eval: ['data/yc2/captiondata/yc2_val.json']
-gt_file_for_para_eval: ['data/yc2/captiondata/para/para_yc2_val.json']
-max_caption_len: 50
-
-dict_file: data/howto/vocabulary_howto_rate2_yc2.json
-vocab_size: 14538
-# dict_file: data/howto/vocabulary_howto_rate2.json
-# vocab_size: 14432
-
-
-
-train_proposal_type: gt
-train_proposal_sample_num: 30
-sample_method: nearest
-
-epoch: 10
-batch_size: 1
-lr: 0.00005
-learning_rate_decay_start: 8
-learning_rate_decay_every: 3
-learning_rate_decay_rate: 0.5
-weight_decay: 0.0001
-save_all_checkpoint: 0
-
-num_queries: 100
-dec_layers: 2
-enc_layers: 2
-transformer_ff_dim: 512
-transformer_dropout_prob: 0.1
-frame_embedding_num: 200
-caption_decoder_type: light
-att_hid_size: 0
-
-with_box_refine: 1
-
-fix_xcw: 1
-set_cost_caption: 0
-set_cost_giou: 4
-set_cost_bbox: 0
-set_cost_class: 2
-self_iou_loss_coef: 0
-#cost_alpha: 0.5
-#cost_gamma: 1
-#focal_alpha: 0.5
-#focal_gamma: 1
-caption_loss_coef: 2
-giou_loss_coef: 4
-bbox_loss_coef: 0
-cls_loss_coef: 2
-count_loss_coef: 0.5
-max_eseq_length: 20
-lloss_cross_entropy: 0
-lloss_focal_loss: 0
-lloss_gau_mask: 1
\ No newline at end of file
diff --git a/backup/cfgs_base/howto/howto-anet_anet_anc_(sim_op_order_v2)_CLIP.yml b/backup/cfgs_base/howto/howto-anet_anet_anc_(sim_op_order_v2)_CLIP.yml
deleted file mode 100644
index 92c6b64a0b9a276122b86cabe3ad428fa8fd6c8a..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-anet_anet_anc_(sim_op_order_v2)_CLIP.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-anet_anet.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip/visual', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip/text_proj', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/text/']
-visual_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
-text_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 0
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 0
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: similarity_op_order_v2
-iteration: 3
-width_th: 2
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 1
-pretrained_language_model: CLIP
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/howto/howto-anet_anet_anc_(sim_op_order_v2)_CLIP_mixlm.yml b/backup/cfgs_base/howto/howto-anet_anet_anc_(sim_op_order_v2)_CLIP_mixlm.yml
deleted file mode 100644
index 87fd2de7d0e97619b3774084d4de97485ac0eedd..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-anet_anet_anc_(sim_op_order_v2)_CLIP_mixlm.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-anet_anet_mixlm.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip/visual', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
-text_feature_folder: ['/mnt/data/Gvlab/wuhao/code/clip_frame_feature_extraction/features/howto100m/clip', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/text/']
-visual_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
-text_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 0
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 0
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: similarity_op_order_v2
-iteration: 3
-width_th: 2
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 1
-pretrained_language_model: CLIP
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/howto/howto-anet_anet_anc_(sim_op_order_v2)_CLIP_puyu.yml b/backup/cfgs_base/howto/howto-anet_anet_anc_(sim_op_order_v2)_CLIP_puyu.yml
deleted file mode 100644
index dfb930405a3050a89e929c9219635231b546d3cb..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-anet_anet_anc_(sim_op_order_v2)_CLIP_puyu.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-anet_anet_puyu.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip/visual', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
-text_feature_folder: ['/mnt/data/Gvlab/wuhao/code/clip_frame_feature_extraction/features/howto100m/clip', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/text/']
-visual_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
-text_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 0
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 0
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: similarity_op_order_v2
-iteration: 3
-width_th: 2
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 1
-pretrained_language_model: CLIP
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/howto/howto-anet_anet_anc_(sim_op_order_v2)_CLIP_refine.yml b/backup/cfgs_base/howto/howto-anet_anet_anc_(sim_op_order_v2)_CLIP_refine.yml
deleted file mode 100644
index 15f8524313eeff1620c67764a09bb5268d50c249..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-anet_anet_anc_(sim_op_order_v2)_CLIP_refine.yml
+++ /dev/null
@@ -1,46 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-anet_anet.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip/visual', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip/text_proj', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/text/']
-visual_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
-text_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 1
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 1
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: similarity_op_order_v2
-iteration: 3
-width_th: 2
-statistic_mode: mode
-width_ratio: 1
-window_size: 2
-top_frames: 35
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 1
-pretrained_language_model: CLIP
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/howto/howto-anet_anet_anc_(sim_op_order_v2)_CLIP_refine_mixlm.yml b/backup/cfgs_base/howto/howto-anet_anet_anc_(sim_op_order_v2)_CLIP_refine_mixlm.yml
deleted file mode 100644
index da4400435c82abc6bc70c758bdbb8d52b9d68cc2..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-anet_anet_anc_(sim_op_order_v2)_CLIP_refine_mixlm.yml
+++ /dev/null
@@ -1,46 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-anet_anet_mixlm.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip/visual', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
-text_feature_folder: ['/mnt/data/Gvlab/wuhao/code/clip_frame_feature_extraction/features/howto100m/clip', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/text/']
-visual_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
-text_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 1
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 1
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: similarity_op_order_v2
-iteration: 3
-width_th: 2
-statistic_mode: mode
-width_ratio: 1
-window_size: 2
-top_frames: 35
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 1
-pretrained_language_model: CLIP
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/howto/howto-anet_anet_anc_(sim_op_order_v2)_CLIP_refine_puyu.yml b/backup/cfgs_base/howto/howto-anet_anet_anc_(sim_op_order_v2)_CLIP_refine_puyu.yml
deleted file mode 100644
index 1c19e4b987a23b04a75b0eba01abfe0de360783c..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-anet_anet_anc_(sim_op_order_v2)_CLIP_refine_puyu.yml
+++ /dev/null
@@ -1,46 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-anet_anet_puyu.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip/visual', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
-text_feature_folder: ['/mnt/data/Gvlab/wuhao/code/clip_frame_feature_extraction/features/howto100m/clip', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/text/']
-visual_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
-text_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 1
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 1
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: similarity_op_order_v2
-iteration: 3
-width_th: 2
-statistic_mode: mode
-width_ratio: 1
-window_size: 2
-top_frames: 35
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 1
-pretrained_language_model: CLIP
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/howto/howto-anet_anet_anc_(sim_op_order_v2)_UniVL.yml b/backup/cfgs_base/howto/howto-anet_anet_anc_(sim_op_order_v2)_UniVL.yml
deleted file mode 100644
index 6141c44f52d807457f9cf0c759ae34f0ce6c024c..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-anet_anet_anc_(sim_op_order_v2)_UniVL.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-anet_anet.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL/visual', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL/text', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/text/']
-visual_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/visual/']
-text_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 0
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 0
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: similarity_op_order_v2
-iteration: 3
-width_th: 2
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 1
-pretrained_language_model: UniVL
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/howto/howto-anet_anet_anc_(sim_op_order_v2)_UniVL_mixlm.yml b/backup/cfgs_base/howto/howto-anet_anet_anc_(sim_op_order_v2)_UniVL_mixlm.yml
deleted file mode 100644
index 57427fb4f57b5c5b3518de52811ee7d31f799017..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-anet_anet_anc_(sim_op_order_v2)_UniVL_mixlm.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-anet_anet_mixlm.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL/visual', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL/text', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/text/']
-visual_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/visual/']
-text_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 0
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 0
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: similarity_op_order_v2
-iteration: 3
-width_th: 2
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 1
-pretrained_language_model: UniVL
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/howto/howto-anet_anet_anc_(sim_op_order_v2)_UniVL_puyu.yml b/backup/cfgs_base/howto/howto-anet_anet_anc_(sim_op_order_v2)_UniVL_puyu.yml
deleted file mode 100644
index f45381d945d502f5a7d421ac2e2d17a7abcd5d87..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-anet_anet_anc_(sim_op_order_v2)_UniVL_puyu.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-anet_anet_puyu.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL/visual', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL/text', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/text/']
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-num_queries: 100
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diff --git a/backup/cfgs_base/howto/howto-anet_anet_anc_(sim_op_order_v2)_UniVL_refine.yml b/backup/cfgs_base/howto/howto-anet_anet_anc_(sim_op_order_v2)_UniVL_refine.yml
deleted file mode 100644
index 351d2d32411e886fb1dbbe52674c262b39e1ca77..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-anet_anet_anc_(sim_op_order_v2)_UniVL_refine.yml
+++ /dev/null
@@ -1,46 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-anet_anet.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL/visual', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL/text', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/text/']
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-text_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/text/']
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-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/howto/howto-anet_anet_anc_(sim_op_order_v2)_UniVL_refine_mixlm.yml b/backup/cfgs_base/howto/howto-anet_anet_anc_(sim_op_order_v2)_UniVL_refine_mixlm.yml
deleted file mode 100644
index 93a5cabefb35f8f40517cd74d3cee811008d4659..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-anet_anet_anc_(sim_op_order_v2)_UniVL_refine_mixlm.yml
+++ /dev/null
@@ -1,46 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-anet_anet_mixlm.yml
-
-visual_feature_type: ['UniVL']
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-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL/text', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/text/']
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-
-use_pseudo_box: 1
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-width_th: 2
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-num_queries: 100
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-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/howto/howto-anet_anet_anc_(sim_op_order_v2)_UniVL_refine_puyu.yml b/backup/cfgs_base/howto/howto-anet_anet_anc_(sim_op_order_v2)_UniVL_refine_puyu.yml
deleted file mode 100644
index 691b7faef9301ea5d8b205f226ecdbfe7f0618c9..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-anet_anet_anc_(sim_op_order_v2)_UniVL_refine_puyu.yml
+++ /dev/null
@@ -1,46 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-anet_anet_puyu.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL/visual', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL/text', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/text/']
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-num_queries: 100
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-ec_alpha: 1.0
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diff --git a/backup/cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_CLIP.yml b/backup/cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_CLIP.yml
deleted file mode 100644
index 73f5f0634fa1d4b00a2fb49f1793d72e67c16c87..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_CLIP.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-anet_anet.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip/visual', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip/text_proj', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/text/']
-visual_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
-text_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/text/']
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-use_pseudo_box: 1
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-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_CLIP_mixlm.yml b/backup/cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_CLIP_mixlm.yml
deleted file mode 100644
index 6b3775fd1a72294b76078cdc49baf850ed5056ca..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_CLIP_mixlm.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-anet_anet_mixlm.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip/visual', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
-text_feature_folder: ['/mnt/data/Gvlab/wuhao/code/clip_frame_feature_extraction/features/howto100m/clip', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/text/']
-visual_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
-text_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/text/']
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-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_CLIP_puyu.yml b/backup/cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_CLIP_puyu.yml
deleted file mode 100644
index 078379e88f4811421e58a8d7930e932ca6641e24..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_CLIP_puyu.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-anet_anet_puyu.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip/visual', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
-text_feature_folder: ['/mnt/data/Gvlab/wuhao/code/clip_frame_feature_extraction/features/howto100m/clip', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/text/']
-visual_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
-text_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/text/']
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-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_CLIP_refine.yml b/backup/cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_CLIP_refine.yml
deleted file mode 100644
index a98bb3237ea032ebfca52ae34e59d88aa3592ffa..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_CLIP_refine.yml
+++ /dev/null
@@ -1,46 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-anet_anet.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip/visual', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip/text_proj', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/text/']
-visual_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
-text_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/text/']
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-width_th: 2
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-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_CLIP_refine_mixlm.yml b/backup/cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_CLIP_refine_mixlm.yml
deleted file mode 100644
index 48afb4f1afd1ca36d3e0c6689d2e7d562099cbeb..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_CLIP_refine_mixlm.yml
+++ /dev/null
@@ -1,46 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-anet_anet_mixlm.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip/visual', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
-text_feature_folder: ['/mnt/data/Gvlab/wuhao/code/clip_frame_feature_extraction/features/howto100m/clip', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/text/']
-visual_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
-text_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/text/']
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-width_th: 2
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-num_queries: 100
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-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_CLIP_refine_puyu.yml b/backup/cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_CLIP_refine_puyu.yml
deleted file mode 100644
index f3192d4aa5b4456deba35f0f4e404a2b11fa7e00..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_CLIP_refine_puyu.yml
+++ /dev/null
@@ -1,46 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-anet_anet_puyu.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip/visual', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
-text_feature_folder: ['/mnt/data/Gvlab/wuhao/code/clip_frame_feature_extraction/features/howto100m/clip', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/text/']
-visual_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
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diff --git a/backup/cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_UniVL.yml b/backup/cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_UniVL.yml
deleted file mode 100644
index b6b1224667dae251754c76aeaccbf93a63893e54..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_UniVL.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-anet_anet.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL/visual', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/visual/']
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-visual_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/visual/']
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-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_UniVL_mixlm.yml b/backup/cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_UniVL_mixlm.yml
deleted file mode 100644
index 62f4d92634f0885f13107332cc7e23b824ff8596..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_UniVL_mixlm.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-anet_anet_mixlm.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL/visual', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL/text', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/text/']
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-use_anchor: 0
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-num_queries: 100
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-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_UniVL_puyu.yml b/backup/cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_UniVL_puyu.yml
deleted file mode 100644
index 5545fc09291a7589f59c8725f38a17144f43f826..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_UniVL_puyu.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-anet_anet_puyu.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL/visual', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL/text', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/text/']
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-num_queries: 100
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-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_UniVL_refine.yml b/backup/cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_UniVL_refine.yml
deleted file mode 100644
index c202054206ca67c6885cf4a34bf12a2c9eb163ef..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_UniVL_refine.yml
+++ /dev/null
@@ -1,46 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-anet_anet.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL/visual', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL/text', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/text/']
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-text_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/text/']
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-
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-num_queries: 100
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-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_UniVL_refine_mixlm.yml b/backup/cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_UniVL_refine_mixlm.yml
deleted file mode 100644
index 64486f4d57b00de9c7b0c403ff49749c7cccee4a..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_UniVL_refine_mixlm.yml
+++ /dev/null
@@ -1,46 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-anet_anet_mixlm.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL/visual', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL/text', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/text/']
-visual_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/visual/']
-text_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/text/']
-feature_dim: 768
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-
-use_pseudo_box: 1
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-num_queries: 100
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-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_UniVL_refine_puyu.yml b/backup/cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_UniVL_refine_puyu.yml
deleted file mode 100644
index ebd15f0c193ea72d878905401027fd28a330a6e9..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_UniVL_refine_puyu.yml
+++ /dev/null
@@ -1,46 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-anet_anet_puyu.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL/visual', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL/text', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/text/']
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-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/howto/howto-yc2_yc2_anc_(sim_op_order_v2)_CLIP.yml b/backup/cfgs_base/howto/howto-yc2_yc2_anc_(sim_op_order_v2)_CLIP.yml
deleted file mode 100644
index d8dd87d3485fe65777b79be423ffe881796fe879..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-yc2_yc2_anc_(sim_op_order_v2)_CLIP.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-yc2_yc2.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip/visual', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/video/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip/text_proj', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/text_proj/']
-visual_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/video/']
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-ec_alpha: 1.0
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diff --git a/backup/cfgs_base/howto/howto-yc2_yc2_anc_(sim_op_order_v2)_CLIP_mixlm.yml b/backup/cfgs_base/howto/howto-yc2_yc2_anc_(sim_op_order_v2)_CLIP_mixlm.yml
deleted file mode 100644
index 29489663091e3ca5546b6d55f9937dc01ff97a8e..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-yc2_yc2_anc_(sim_op_order_v2)_CLIP_mixlm.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-yc2_yc2_mixlm.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip/visual', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/video/']
-text_feature_folder: ['/mnt/data/Gvlab/wuhao/code/clip_frame_feature_extraction/features/howto100m/clip', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/text_proj/']
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diff --git a/backup/cfgs_base/howto/howto-yc2_yc2_anc_(sim_op_order_v2)_CLIP_puyu.yml b/backup/cfgs_base/howto/howto-yc2_yc2_anc_(sim_op_order_v2)_CLIP_puyu.yml
deleted file mode 100644
index 9bf87a23994a2adb512b6fb1d1a2188f70de82a6..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-yc2_yc2_anc_(sim_op_order_v2)_CLIP_puyu.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-yc2_yc2_puyu.yml
-
-visual_feature_type: ['CLIP']
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diff --git a/backup/cfgs_base/howto/howto-yc2_yc2_anc_(sim_op_order_v2)_CLIP_refine.yml b/backup/cfgs_base/howto/howto-yc2_yc2_anc_(sim_op_order_v2)_CLIP_refine.yml
deleted file mode 100644
index 271b44b03d385f7ad93fdfff959e8b41486451df..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-yc2_yc2_anc_(sim_op_order_v2)_CLIP_refine.yml
+++ /dev/null
@@ -1,46 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-yc2_yc2.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip/visual', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/video/']
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diff --git a/backup/cfgs_base/howto/howto-yc2_yc2_anc_(sim_op_order_v2)_CLIP_refine_mixlm.yml b/backup/cfgs_base/howto/howto-yc2_yc2_anc_(sim_op_order_v2)_CLIP_refine_mixlm.yml
deleted file mode 100644
index bf6ced9cc86151f3c804201b38a127a8cb2f5381..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-yc2_yc2_anc_(sim_op_order_v2)_CLIP_refine_mixlm.yml
+++ /dev/null
@@ -1,46 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-yc2_yc2_mixlm.yml
-
-visual_feature_type: ['CLIP']
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diff --git a/backup/cfgs_base/howto/howto-yc2_yc2_anc_(sim_op_order_v2)_CLIP_refine_puyu.yml b/backup/cfgs_base/howto/howto-yc2_yc2_anc_(sim_op_order_v2)_CLIP_refine_puyu.yml
deleted file mode 100644
index 9779a1898513b16aa1c2aaa57f4eb6255b2f9253..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-yc2_yc2_anc_(sim_op_order_v2)_CLIP_refine_puyu.yml
+++ /dev/null
@@ -1,46 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-yc2_yc2_puyu.yml
-
-visual_feature_type: ['CLIP']
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diff --git a/backup/cfgs_base/howto/howto-yc2_yc2_anc_(sim_op_order_v2)_UniVL.yml b/backup/cfgs_base/howto/howto-yc2_yc2_anc_(sim_op_order_v2)_UniVL.yml
deleted file mode 100644
index 996e5360fc812f1943959c0bc468974117a97a94..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-yc2_yc2_anc_(sim_op_order_v2)_UniVL.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-yc2_yc2.yml
-
-visual_feature_type: ['UniVL']
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diff --git a/backup/cfgs_base/howto/howto-yc2_yc2_anc_(sim_op_order_v2)_UniVL_mixlm.yml b/backup/cfgs_base/howto/howto-yc2_yc2_anc_(sim_op_order_v2)_UniVL_mixlm.yml
deleted file mode 100644
index 472fdf294e8ba71f16708011d3d8529e5b32800c..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-yc2_yc2_anc_(sim_op_order_v2)_UniVL_mixlm.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-yc2_yc2_mixlm.yml
-
-visual_feature_type: ['UniVL']
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diff --git a/backup/cfgs_base/howto/howto-yc2_yc2_anc_(sim_op_order_v2)_UniVL_puyu.yml b/backup/cfgs_base/howto/howto-yc2_yc2_anc_(sim_op_order_v2)_UniVL_puyu.yml
deleted file mode 100644
index c56573da93d2d948eae7d723904a9243498ac484..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-yc2_yc2_anc_(sim_op_order_v2)_UniVL_puyu.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-yc2_yc2_puyu.yml
-
-visual_feature_type: ['UniVL']
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diff --git a/backup/cfgs_base/howto/howto-yc2_yc2_anc_(sim_op_order_v2)_UniVL_refine.yml b/backup/cfgs_base/howto/howto-yc2_yc2_anc_(sim_op_order_v2)_UniVL_refine.yml
deleted file mode 100644
index 4940aa43eec6d5fcb08905bac7892f10b28c7549..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-yc2_yc2_anc_(sim_op_order_v2)_UniVL_refine.yml
+++ /dev/null
@@ -1,46 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-yc2_yc2.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL/visual', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/UniVL_features/UniVL_visual/']
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-ec_alpha: 1.0
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diff --git a/backup/cfgs_base/howto/howto-yc2_yc2_anc_(sim_op_order_v2)_UniVL_refine_mixlm.yml b/backup/cfgs_base/howto/howto-yc2_yc2_anc_(sim_op_order_v2)_UniVL_refine_mixlm.yml
deleted file mode 100644
index 9505834e0b888345ad6b73283848e9717a58ac98..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-yc2_yc2_anc_(sim_op_order_v2)_UniVL_refine_mixlm.yml
+++ /dev/null
@@ -1,46 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-yc2_yc2_mixlm.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL/visual', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/UniVL_features/UniVL_visual/']
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-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/howto/howto-yc2_yc2_anc_(sim_op_order_v2)_UniVL_refine_puyu.yml b/backup/cfgs_base/howto/howto-yc2_yc2_anc_(sim_op_order_v2)_UniVL_refine_puyu.yml
deleted file mode 100644
index 9f373f5e6dfc0a586f104777611c18ad10dcddb9..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-yc2_yc2_anc_(sim_op_order_v2)_UniVL_refine_puyu.yml
+++ /dev/null
@@ -1,46 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-yc2_yc2_puyu.yml
-
-visual_feature_type: ['UniVL']
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-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL/text', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/UniVL_features/UniVL_text/']
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diff --git a/backup/cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_CLIP.yml b/backup/cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_CLIP.yml
deleted file mode 100644
index e28db709d1590f546524626df9b13676034d0489..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_CLIP.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-yc2_yc2.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip/visual', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/video/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip/text_proj', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/text_proj/']
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-ec_alpha: 1.0
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diff --git a/backup/cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_CLIP_mixlm.yml b/backup/cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_CLIP_mixlm.yml
deleted file mode 100644
index 753fdd06b9fbb789f5f5215ce9ae73d2f694bf12..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_CLIP_mixlm.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-yc2_yc2_mixlm.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip/visual', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/video/']
-text_feature_folder: ['/mnt/data/Gvlab/wuhao/code/clip_frame_feature_extraction/features/howto100m/clip', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/text_proj/']
-visual_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/video/']
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diff --git a/backup/cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_CLIP_puyu.yml b/backup/cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_CLIP_puyu.yml
deleted file mode 100644
index f8bf9b58b304ea4bc7becf5368d3025b192e5ea5..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_CLIP_puyu.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-yc2_yc2_puyu.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip/visual', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/video/']
-text_feature_folder: ['/mnt/data/Gvlab/wuhao/code/clip_frame_feature_extraction/features/howto100m/clip', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/text_proj/']
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diff --git a/backup/cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_CLIP_refine.yml b/backup/cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_CLIP_refine.yml
deleted file mode 100644
index 5c56556156aa50810c468bb22a2d00ace2213860..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_CLIP_refine.yml
+++ /dev/null
@@ -1,46 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-yc2_yc2.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip/visual', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/video/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip/text_proj', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/text_proj/']
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-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_CLIP_refine_mixlm.yml b/backup/cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_CLIP_refine_mixlm.yml
deleted file mode 100644
index 09ca13a378e8eacef7f262ae789b9ffa7346c34e..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_CLIP_refine_mixlm.yml
+++ /dev/null
@@ -1,46 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-yc2_yc2_mixlm.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip/visual', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/video/']
-text_feature_folder: ['/mnt/data/Gvlab/wuhao/code/clip_frame_feature_extraction/features/howto100m/clip', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/text_proj/']
-visual_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/video/']
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-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_CLIP_refine_mixlm_v0.yml b/backup/cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_CLIP_refine_mixlm_v0.yml
deleted file mode 100644
index 09ca13a378e8eacef7f262ae789b9ffa7346c34e..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_CLIP_refine_mixlm_v0.yml
+++ /dev/null
@@ -1,46 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-yc2_yc2_mixlm.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip/visual', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/video/']
-text_feature_folder: ['/mnt/data/Gvlab/wuhao/code/clip_frame_feature_extraction/features/howto100m/clip', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/text_proj/']
-visual_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/video/']
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-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_CLIP_refine_puyu.yml b/backup/cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_CLIP_refine_puyu.yml
deleted file mode 100644
index 4bc67bf5e3278f9c16228a024efbf3ffa703b854..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_CLIP_refine_puyu.yml
+++ /dev/null
@@ -1,46 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-yc2_yc2_puyu.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip/visual', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/video/']
-text_feature_folder: ['/mnt/data/Gvlab/wuhao/code/clip_frame_feature_extraction/features/howto100m/clip', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/text_proj/']
-visual_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/video/']
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diff --git a/backup/cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_CLIP_refine_puyu_v0.yml b/backup/cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_CLIP_refine_puyu_v0.yml
deleted file mode 100644
index 4bc67bf5e3278f9c16228a024efbf3ffa703b854..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_CLIP_refine_puyu_v0.yml
+++ /dev/null
@@ -1,46 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-yc2_yc2_puyu.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip/visual', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/video/']
-text_feature_folder: ['/mnt/data/Gvlab/wuhao/code/clip_frame_feature_extraction/features/howto100m/clip', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/text_proj/']
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-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_UniVL.yml b/backup/cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_UniVL.yml
deleted file mode 100644
index 2ac92f31028eece0c85e6cc642876b3fee015063..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_UniVL.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-yc2_yc2.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL/visual', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/UniVL_features/UniVL_visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL/text', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/UniVL_features/UniVL_text/']
-visual_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/UniVL_features/UniVL_visual/']
-text_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/UniVL_features/UniVL_text/']
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-num_queries: 100
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-ec_alpha: 1.0
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diff --git a/backup/cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_UniVL_mixlm.yml b/backup/cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_UniVL_mixlm.yml
deleted file mode 100644
index cae9be1bd91a74c7b263399c84dd4e0ff80849b4..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_UniVL_mixlm.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-yc2_yc2_mixlm.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL/visual', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/UniVL_features/UniVL_visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL/text', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/UniVL_features/UniVL_text/']
-visual_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/UniVL_features/UniVL_visual/']
-text_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/UniVL_features/UniVL_text/']
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-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_UniVL_puyu.yml b/backup/cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_UniVL_puyu.yml
deleted file mode 100644
index 798dc7b939e23386a864ee0ac3f53f2628b7138d..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_UniVL_puyu.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-yc2_yc2_puyu.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL/visual', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/UniVL_features/UniVL_visual/']
-text_feature_folder: ['/mnt/data/pjlab-3090-sport/wuhao/features/howto100m/univl_features/text_puyu', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/UniVL_features/UniVL_text/']
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_UniVL_refine.yml b/backup/cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_UniVL_refine.yml
deleted file mode 100644
index b5ab9e0bdfca3c12d8c932e52a0e0e20bf6e759a..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_UniVL_refine.yml
+++ /dev/null
@@ -1,46 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-yc2_yc2.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL/visual', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/UniVL_features/UniVL_visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL/text', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/UniVL_features/UniVL_text/']
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-ec_alpha: 1.0
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diff --git a/backup/cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_UniVL_refine_mixlm.yml b/backup/cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_UniVL_refine_mixlm.yml
deleted file mode 100644
index 36061ccd68f650aef565bc8ce8a31be53eebf41a..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_UniVL_refine_mixlm.yml
+++ /dev/null
@@ -1,46 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-yc2_yc2_mixlm.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL/visual', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/UniVL_features/UniVL_visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL/text', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/UniVL_features/UniVL_text/']
-visual_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/UniVL_features/UniVL_visual/']
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-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_UniVL_refine_puyu.yml b/backup/cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_UniVL_refine_puyu.yml
deleted file mode 100644
index 54a0ceb172e743011c5a9183cbc31c39d3084019..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_UniVL_refine_puyu.yml
+++ /dev/null
@@ -1,46 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-yc2_yc2_puyu.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL/visual', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/UniVL_features/UniVL_visual/']
-text_feature_folder: ['/mnt/data/pjlab-3090-sport/wuhao/features/howto100m/univl_features/text_puyu', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/UniVL_features/UniVL_text/']
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-ec_alpha: 1.0
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diff --git a/backup/cfgs_base/howto/howto_anet_anc_(sim_op_order_v2)_CLIP.yml b/backup/cfgs_base/howto/howto_anet_anc_(sim_op_order_v2)_CLIP.yml
deleted file mode 100644
index 8e32f2b3092a0f11c35bf4a2ab0ab62172c23815..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto_anet_anc_(sim_op_order_v2)_CLIP.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto_anet.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip/visual']
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-visual_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
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-use_anchor: 1
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-caption_decoder_type: standard
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-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/howto/howto_anet_anc_(sim_op_order_v2)_CLIP_refine.yml b/backup/cfgs_base/howto/howto_anet_anc_(sim_op_order_v2)_CLIP_refine.yml
deleted file mode 100644
index 9205bfd2568d41ec26689b63fcdb82f30f1e0c7b..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto_anet_anc_(sim_op_order_v2)_CLIP_refine.yml
+++ /dev/null
@@ -1,48 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto_anet.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip/visual']
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-visual_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/CLIP_feature/visual/']
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-
-
-use_anchor: 1
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-caption_decoder_type: standard
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-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/howto/howto_anet_anc_(sim_op_order_v2)_UniVL.yml b/backup/cfgs_base/howto/howto_anet_anc_(sim_op_order_v2)_UniVL.yml
deleted file mode 100644
index 2ed0b046f4922fec52231c7c0d5c551cff82f0cc..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto_anet_anc_(sim_op_order_v2)_UniVL.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto_anet.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL/visual']
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-visual_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/visual/']
-text_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/text/']
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-use_pseudo_box: 1
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-use_anchor: 1
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-caption_decoder_type: standard
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-num_queries: 100
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-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/howto/howto_anet_anc_(sim_op_order_v2)_UniVL_refine.yml b/backup/cfgs_base/howto/howto_anet_anc_(sim_op_order_v2)_UniVL_refine.yml
deleted file mode 100644
index b40c179f05dff58d76a601a232e6fada42f29505..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto_anet_anc_(sim_op_order_v2)_UniVL_refine.yml
+++ /dev/null
@@ -1,46 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto_anet.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL/visual']
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-visual_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/visual/']
-text_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet/UniVL_feature/text/']
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-use_pseudo_box: 1
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diff --git a/backup/cfgs_base/howto/howto_anet_ori_(sim_op_order_v2)_CLIP.yml b/backup/cfgs_base/howto/howto_anet_ori_(sim_op_order_v2)_CLIP.yml
deleted file mode 100644
index 32e237518518df60263b914b65d18dba1d0b8f46..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto_anet_ori_(sim_op_order_v2)_CLIP.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto_anet.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip/visual']
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-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/howto/howto_anet_ori_(sim_op_order_v2)_CLIP_refine.yml b/backup/cfgs_base/howto/howto_anet_ori_(sim_op_order_v2)_CLIP_refine.yml
deleted file mode 100644
index 1dcb3b5b547fda7ed525234c4f97976badc60c04..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto_anet_ori_(sim_op_order_v2)_CLIP_refine.yml
+++ /dev/null
@@ -1,46 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto_anet.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip/visual']
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-num_queries: 100
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-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/howto/howto_anet_ori_(sim_op_order_v2)_UniVL.yml b/backup/cfgs_base/howto/howto_anet_ori_(sim_op_order_v2)_UniVL.yml
deleted file mode 100644
index 6f775d16de8d0ca478c89310cf56ae9cc12b6d6e..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto_anet_ori_(sim_op_order_v2)_UniVL.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto_anet.yml
-
-visual_feature_type: ['UniVL']
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-use_anchor: 0
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-caption_decoder_type: standard
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-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/howto/howto_anet_ori_(sim_op_order_v2)_UniVL_refine.yml b/backup/cfgs_base/howto/howto_anet_ori_(sim_op_order_v2)_UniVL_refine.yml
deleted file mode 100644
index 04d7bda3fde5f365542bbd033cee609457c89604..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto_anet_ori_(sim_op_order_v2)_UniVL_refine.yml
+++ /dev/null
@@ -1,46 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto_anet.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL/visual']
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-caption_decoder_type: standard
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-ec_alpha: 1.0
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diff --git a/backup/cfgs_base/howto/howto_yc2_anc_(sim_op_order_v2)_CLIP.yml b/backup/cfgs_base/howto/howto_yc2_anc_(sim_op_order_v2)_CLIP.yml
deleted file mode 100644
index ebb97f21d137b0826c96dfea0a86e234195d39be..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto_yc2_anc_(sim_op_order_v2)_CLIP.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto_yc2.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip/visual']
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-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/howto/howto_yc2_anc_(sim_op_order_v2)_CLIP_refine.yml b/backup/cfgs_base/howto/howto_yc2_anc_(sim_op_order_v2)_CLIP_refine.yml
deleted file mode 100644
index 47aa1ebe8a83f7375281ca97c736b47b312d7806..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto_yc2_anc_(sim_op_order_v2)_CLIP_refine.yml
+++ /dev/null
@@ -1,46 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto_yc2.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip/visual']
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-caption_decoder_type: standard
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/howto/howto_yc2_anc_(sim_op_order_v2)_UniVL.yml b/backup/cfgs_base/howto/howto_yc2_anc_(sim_op_order_v2)_UniVL.yml
deleted file mode 100644
index 86ad0779edfff76fd33862932cd3b534902be794..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto_yc2_anc_(sim_op_order_v2)_UniVL.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto_yc2.yml
-
-visual_feature_type: ['UniVL']
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-use_anchor: 1
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-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/howto/howto_yc2_anc_(sim_op_order_v2)_UniVL_refine.yml b/backup/cfgs_base/howto/howto_yc2_anc_(sim_op_order_v2)_UniVL_refine.yml
deleted file mode 100644
index 11cbc212d5837924bc8afb7d4635427be51ee216..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto_yc2_anc_(sim_op_order_v2)_UniVL_refine.yml
+++ /dev/null
@@ -1,46 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto_yc2.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL/visual']
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-visual_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/UniVL_features/UniVL_visual/']
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-ec_alpha: 1.0
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diff --git a/backup/cfgs_base/howto/howto_yc2_ori_(sim_op_order_v2)_CLIP.yml b/backup/cfgs_base/howto/howto_yc2_ori_(sim_op_order_v2)_CLIP.yml
deleted file mode 100644
index 0dcf153ed24336b039f3e288035937de41f36a94..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto_yc2_ori_(sim_op_order_v2)_CLIP.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto_yc2.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip/visual']
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-visual_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/video/']
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-num_queries: 100
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-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/howto/howto_yc2_ori_(sim_op_order_v2)_CLIP_refine.yml b/backup/cfgs_base/howto/howto_yc2_ori_(sim_op_order_v2)_CLIP_refine.yml
deleted file mode 100644
index 424cf659ab8edb6fc4b81364ce47a2d568f65c07..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto_yc2_ori_(sim_op_order_v2)_CLIP_refine.yml
+++ /dev/null
@@ -1,46 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto_yc2.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip/visual']
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-visual_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/video/']
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diff --git a/backup/cfgs_base/howto/howto_yc2_ori_(sim_op_order_v2)_UniVL.yml b/backup/cfgs_base/howto/howto_yc2_ori_(sim_op_order_v2)_UniVL.yml
deleted file mode 100644
index 53a57713fe15fb61233549dc5ecb309e986a5508..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto_yc2_ori_(sim_op_order_v2)_UniVL.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto_yc2.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL/visual']
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-visual_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/UniVL_features/UniVL_visual/']
-text_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/UniVL_features/UniVL_text/']
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-
-use_pseudo_box: 1
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-
-use_anchor: 0
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-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
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-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/howto/howto_yc2_ori_(sim_op_order_v2)_UniVL_refine.yml b/backup/cfgs_base/howto/howto_yc2_ori_(sim_op_order_v2)_UniVL_refine.yml
deleted file mode 100644
index 13ca9fd266ebd75d23197a8e51ad913227640b06..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/howto/howto_yc2_ori_(sim_op_order_v2)_UniVL_refine.yml
+++ /dev/null
@@ -1,46 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/howto/base_howto-yc2_yc2.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL/visual']
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-visual_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/UniVL_features/UniVL_visual/']
-text_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/UniVL_features/UniVL_text/']
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-
-use_pseudo_box: 1
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-
-use_anchor: 0
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-
-caption_decoder_type: standard
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-cap_num_feature_levels: 4
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-att_hid_size: 512
-
-num_queries: 100
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-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/tasty/tasty_CLIP_pdvc.yml b/backup/cfgs_base/tasty/tasty_CLIP_pdvc.yml
deleted file mode 100644
index f5f9a193a786aad4960447288edf675aecb58129..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/tasty/tasty_CLIP_pdvc.yml
+++ /dev/null
@@ -1,21 +0,0 @@
-id: base
-base_cfg_path: cfgs_base/tasty/tasty_tsn_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/Tasty/features/clip/visual']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/Tasty/features/clip/text']
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-
-use_anchor: 1
-
-caption_decoder_type: standard
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-
-num_queries: 100
-
-ec_alpha: 1.0
\ No newline at end of file
diff --git a/backup/cfgs_base/tasty/tasty_UniVL_pdvc.yml b/backup/cfgs_base/tasty/tasty_UniVL_pdvc.yml
deleted file mode 100644
index d8dbabf6d032f5848991215aec06c7fcef0fb711..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/tasty/tasty_UniVL_pdvc.yml
+++ /dev/null
@@ -1,21 +0,0 @@
-id: base
-base_cfg_path: cfgs_base/tasty/tasty_tsn_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Tasty/UniVL_feature/visual/']
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-
-use_anchor: 1
-
-caption_decoder_type: standard
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-
-num_queries: 100
-
-ec_alpha: 1.0
\ No newline at end of file
diff --git a/backup/cfgs_base/tasty/tasty_abox_UniVL_pdvc.yml b/backup/cfgs_base/tasty/tasty_abox_UniVL_pdvc.yml
deleted file mode 100644
index d7a064f9627e6d2f00667c740f27f3564ce16d63..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/tasty/tasty_abox_UniVL_pdvc.yml
+++ /dev/null
@@ -1,32 +0,0 @@
-id: base
-base_cfg_path: cfgs_base/tasty/tasty_tsn_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Tasty/UniVL_feature/visual/']
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-
-use_pseudo_box: 1
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-pretrained_language_model: UniVL
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-
-caption_decoder_type: standard
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-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
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-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/tasty/tasty_anc_(align)_CLIP.yml b/backup/cfgs_base/tasty/tasty_anc_(align)_CLIP.yml
deleted file mode 100644
index ef7c8924196f230fbf7abf4bd2aada4d8c813275..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/tasty/tasty_anc_(align)_CLIP.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/tasty/tasty_tsn_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/Tasty/features/clip/visual']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/Tasty/features/clip/text']
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-
-use_pseudo_box: 1
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-
-use_anchor: 1
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-
-caption_decoder_type: standard
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-cap_num_feature_levels: 4
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-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/tasty/tasty_anc_(align)_CLIP_refine.yml b/backup/cfgs_base/tasty/tasty_anc_(align)_CLIP_refine.yml
deleted file mode 100644
index 355c7d66081451b3513304f8b81126fd1976f918..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/tasty/tasty_anc_(align)_CLIP_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/tasty/tasty_tsn_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/Tasty/features/clip/visual']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/Tasty/features/clip/text']
-feature_dim: 768
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-
-use_pseudo_box: 1
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-
-use_anchor: 1
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-
-caption_decoder_type: standard
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-cap_num_feature_levels: 4
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-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
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-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/tasty/tasty_anc_(align)_UniVL.yml b/backup/cfgs_base/tasty/tasty_anc_(align)_UniVL.yml
deleted file mode 100644
index 5121b6cf76c3b4966c382f4cfca11c71891a721c..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/tasty/tasty_anc_(align)_UniVL.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/tasty/tasty_tsn_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Tasty/UniVL_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Tasty/UniVL_feature/text/']
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-
-use_pseudo_box: 1
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-refine_pseudo_stage_num: 1
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-pseudo_box_type: align
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-gt_proposal_sample_num: 12
-
-use_anchor: 1
-pretrained_language_model: UniVL
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-
-caption_decoder_type: standard
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-cap_num_feature_levels: 4
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-
-num_queries: 100
-
-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/tasty/tasty_anc_(align)_UniVL_refine.yml b/backup/cfgs_base/tasty/tasty_anc_(align)_UniVL_refine.yml
deleted file mode 100644
index a95a0e782daf8c2f799f90d93aa95a6eceb015b2..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/tasty/tasty_anc_(align)_UniVL_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/tasty/tasty_tsn_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Tasty/UniVL_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Tasty/UniVL_feature/text/']
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-
-use_pseudo_box: 1
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-
-use_anchor: 1
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-
-caption_decoder_type: standard
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-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/tasty/tasty_anc_(sim)_CLIP.yml b/backup/cfgs_base/tasty/tasty_anc_(sim)_CLIP.yml
deleted file mode 100644
index 59967b4b1bc17087eda8b17e520c3e83e153e699..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/tasty/tasty_anc_(sim)_CLIP.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/tasty/tasty_tsn_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/Tasty/features/clip/visual']
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-
-use_pseudo_box: 1
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-pseudo_box_type: similarity
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-
-use_anchor: 1
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-
-caption_decoder_type: standard
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-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
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-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/tasty/tasty_anc_(sim)_CLIP_refine.yml b/backup/cfgs_base/tasty/tasty_anc_(sim)_CLIP_refine.yml
deleted file mode 100644
index 178db2ad770c80e3d3d365842e7df6f2a24c0fee..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/tasty/tasty_anc_(sim)_CLIP_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/tasty/tasty_tsn_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/Tasty/features/clip/visual']
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-
-use_pseudo_box: 1
-pseudo_box_aug: 1
-pseudo_box_aug_num: 5
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-refine_pseudo_box: 1
-refine_pseudo_stage_num: 1
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-pseudo_box_type: similarity
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-gt_proposal_sample_num: 12
-
-use_anchor: 1
-pretrained_language_model: CLIP
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-
-caption_decoder_type: standard
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-cap_num_feature_levels: 4
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-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
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-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/tasty/tasty_anc_(sim)_UniVL.yml b/backup/cfgs_base/tasty/tasty_anc_(sim)_UniVL.yml
deleted file mode 100644
index 7555f552813b710ea274c5068b4b4b46b15e00e8..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/tasty/tasty_anc_(sim)_UniVL.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/tasty/tasty_tsn_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Tasty/UniVL_feature/visual/']
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-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 0
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
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-refine_pseudo_stage_num: 1
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-pseudo_box_type: similarity
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-
-use_anchor: 1
-pretrained_language_model: UniVL
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-
-caption_decoder_type: standard
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-cap_num_feature_levels: 4
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-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/tasty/tasty_anc_(sim)_UniVL_refine.yml b/backup/cfgs_base/tasty/tasty_anc_(sim)_UniVL_refine.yml
deleted file mode 100644
index 726c043ff0300b678776e8391d1bb3ed962307cf..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/tasty/tasty_anc_(sim)_UniVL_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/tasty/tasty_tsn_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Tasty/UniVL_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Tasty/UniVL_feature/text/']
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-
-use_pseudo_box: 1
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-gt_proposal_sample_num: 12
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-use_anchor: 1
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-caption_decoder_type: standard
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-cap_num_feature_levels: 4
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-num_queries: 100
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-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/tasty/tasty_anc_(weight_index)_CLIP.yml b/backup/cfgs_base/tasty/tasty_anc_(weight_index)_CLIP.yml
deleted file mode 100644
index 71be5f3eb1240f38e131b7600e58cf1a1b3f7b3d..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/tasty/tasty_anc_(weight_index)_CLIP.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/tasty/tasty_tsn_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/Tasty/features/clip/visual']
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/tasty/tasty_anc_(weight_index)_CLIP_refine.yml b/backup/cfgs_base/tasty/tasty_anc_(weight_index)_CLIP_refine.yml
deleted file mode 100644
index bc03cac62cb816ce7b98e9fcf4ff96167d03b682..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/tasty/tasty_anc_(weight_index)_CLIP_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/tasty/tasty_tsn_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/Tasty/features/clip/visual']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/Tasty/features/clip/text']
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diff --git a/backup/cfgs_base/tasty/tasty_anc_(weight_index)_UniVL.yml b/backup/cfgs_base/tasty/tasty_anc_(weight_index)_UniVL.yml
deleted file mode 100644
index d054ac67dc4a7765def67b554b1c520c04cd18da..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/tasty/tasty_anc_(weight_index)_UniVL.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/tasty/tasty_tsn_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Tasty/UniVL_feature/visual/']
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diff --git a/backup/cfgs_base/tasty/tasty_anc_(weight_index)_UniVL_refine.yml b/backup/cfgs_base/tasty/tasty_anc_(weight_index)_UniVL_refine.yml
deleted file mode 100644
index 27e89d7b820b566c825bf2be4e6164f218664588..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/tasty/tasty_anc_(weight_index)_UniVL_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/tasty/tasty_tsn_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Tasty/UniVL_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Tasty/UniVL_feature/text/']
-feature_dim: 768
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-
-use_pseudo_box: 1
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-use_anchor: 1
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-num_queries: 100
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-ec_alpha: 1.0
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diff --git a/backup/cfgs_base/tasty/tasty_anc_(weight_sim)_CLIP.yml b/backup/cfgs_base/tasty/tasty_anc_(weight_sim)_CLIP.yml
deleted file mode 100644
index 39dfc692b19e2acf130606f314214410f0720990..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/tasty/tasty_anc_(weight_sim)_CLIP.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/tasty/tasty_tsn_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/Tasty/features/clip/visual']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/Tasty/features/clip/text']
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-caption_decoder_type: standard
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-num_queries: 100
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-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/tasty/tasty_anc_(weight_sim)_CLIP_refine.yml b/backup/cfgs_base/tasty/tasty_anc_(weight_sim)_CLIP_refine.yml
deleted file mode 100644
index fe6cba647bb5d104b990a3415706d56971426b1f..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/tasty/tasty_anc_(weight_sim)_CLIP_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/tasty/tasty_tsn_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/Tasty/features/clip/visual']
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-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/tasty/tasty_anc_(weight_sim)_UniVL.yml b/backup/cfgs_base/tasty/tasty_anc_(weight_sim)_UniVL.yml
deleted file mode 100644
index 05c7d7053dd25cb4a3e06340268c29a14d7dc05e..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/tasty/tasty_anc_(weight_sim)_UniVL.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/tasty/tasty_tsn_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Tasty/UniVL_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Tasty/UniVL_feature/text/']
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-use_anchor: 1
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-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/tasty/tasty_anc_(weight_sim)_UniVL_refine.yml b/backup/cfgs_base/tasty/tasty_anc_(weight_sim)_UniVL_refine.yml
deleted file mode 100644
index 40fd330d45cf07a8e34c442d56c1339fa237c5e1..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/tasty/tasty_anc_(weight_sim)_UniVL_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/tasty/tasty_tsn_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Tasty/UniVL_feature/visual/']
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diff --git a/backup/cfgs_base/tasty/tasty_anc_GT_CLIP.yml b/backup/cfgs_base/tasty/tasty_anc_GT_CLIP.yml
deleted file mode 100644
index fd4ead981978c54801c70e6dec9fc4154f6fcf40..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/tasty/tasty_anc_GT_CLIP.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/tasty/tasty_tsn_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/Tasty/features/clip/visual']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/Tasty/features/clip/text']
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-num_queries: 100
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-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/tasty/tasty_anc_GT_UniVL.yml b/backup/cfgs_base/tasty/tasty_anc_GT_UniVL.yml
deleted file mode 100644
index 86b411963f0ab337fa843b01a49b743c55d0f4ba..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/tasty/tasty_anc_GT_UniVL.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/tasty/tasty_tsn_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Tasty/UniVL_feature/visual/']
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-caption_decoder_type: standard
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-ec_alpha: 1.0
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diff --git a/backup/cfgs_base/tasty/tasty_ori_(align)_CLIP.yml b/backup/cfgs_base/tasty/tasty_ori_(align)_CLIP.yml
deleted file mode 100644
index a4b959243136e53599aa026ae7a386d9ae8ce41a..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/tasty/tasty_ori_(align)_CLIP.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/tasty/tasty_tsn_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/Tasty/features/clip/visual']
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-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/tasty/tasty_ori_(align)_CLIP_refine.yml b/backup/cfgs_base/tasty/tasty_ori_(align)_CLIP_refine.yml
deleted file mode 100644
index 259b30520a7d6eb411b14497d97e2b47939933ae..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/tasty/tasty_ori_(align)_CLIP_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/tasty/tasty_tsn_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/Tasty/features/clip/visual']
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-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/tasty/tasty_ori_(align)_UniVL.yml b/backup/cfgs_base/tasty/tasty_ori_(align)_UniVL.yml
deleted file mode 100644
index 59ad8e64a36d46297e604044ab9fa8c0fe8bf3e1..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/tasty/tasty_ori_(align)_UniVL.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/tasty/tasty_tsn_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Tasty/UniVL_feature/visual/']
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-use_pseudo_box: 1
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-use_anchor: 0
-pretrained_language_model: UniVL
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-caption_decoder_type: standard
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-num_queries: 100
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-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/tasty/tasty_ori_(align)_UniVL_refine.yml b/backup/cfgs_base/tasty/tasty_ori_(align)_UniVL_refine.yml
deleted file mode 100644
index 3f8a497a405c767f138ba08ec811b417d62e5674..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/tasty/tasty_ori_(align)_UniVL_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
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-
-visual_feature_type: ['UniVL']
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diff --git a/backup/cfgs_base/tasty/tasty_ori_(sim)_CLIP.yml b/backup/cfgs_base/tasty/tasty_ori_(sim)_CLIP.yml
deleted file mode 100644
index 0aebca8432993dd31f123e618c40a701da8ed968..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/tasty/tasty_ori_(sim)_CLIP.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/tasty/tasty_tsn_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/Tasty/features/clip/visual']
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diff --git a/backup/cfgs_base/tasty/tasty_ori_(sim)_CLIP_refine.yml b/backup/cfgs_base/tasty/tasty_ori_(sim)_CLIP_refine.yml
deleted file mode 100644
index 29a1b716404048c53360c2e95eb69510f03eb1e9..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/tasty/tasty_ori_(sim)_CLIP_refine.yml
+++ /dev/null
@@ -1,61 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/tasty/tasty_tsn_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/Tasty/features/clip/visual']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/Tasty/features/clip/text']
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diff --git a/backup/cfgs_base/tasty/tasty_ori_(sim)_UniVL.yml b/backup/cfgs_base/tasty/tasty_ori_(sim)_UniVL.yml
deleted file mode 100644
index 9c2142a150ea00790b8c18556d0004bfa89afae8..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/tasty/tasty_ori_(sim)_UniVL.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/tasty/tasty_tsn_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Tasty/UniVL_feature/visual/']
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diff --git a/backup/cfgs_base/tasty/tasty_ori_(sim)_UniVL_refine.yml b/backup/cfgs_base/tasty/tasty_ori_(sim)_UniVL_refine.yml
deleted file mode 100644
index 1b624be12c30a6132baf378a696b5d85949754b5..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/tasty/tasty_ori_(sim)_UniVL_refine.yml
+++ /dev/null
@@ -1,49 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/tasty/tasty_tsn_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Tasty/UniVL_feature/visual/']
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-
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diff --git a/backup/cfgs_base/tasty/tasty_ori_(weight_index)_CLIP.yml b/backup/cfgs_base/tasty/tasty_ori_(weight_index)_CLIP.yml
deleted file mode 100644
index 3aedf7494857125480dd5c5dc50bd3a5cf9f4260..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/tasty/tasty_ori_(weight_index)_CLIP.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/tasty/tasty_tsn_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/Tasty/features/clip/visual']
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/tasty/tasty_ori_(weight_index)_CLIP_refine.yml b/backup/cfgs_base/tasty/tasty_ori_(weight_index)_CLIP_refine.yml
deleted file mode 100644
index 5104cde9760d48a7f9fa23cb1a04b825c18f3844..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/tasty/tasty_ori_(weight_index)_CLIP_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/tasty/tasty_tsn_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/Tasty/features/clip/visual']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/Tasty/features/clip/text']
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diff --git a/backup/cfgs_base/tasty/tasty_ori_(weight_index)_UniVL.yml b/backup/cfgs_base/tasty/tasty_ori_(weight_index)_UniVL.yml
deleted file mode 100644
index 8bca42c4f97b02f9f9bb5f73f37c7660a2c17f64..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/tasty/tasty_ori_(weight_index)_UniVL.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/tasty/tasty_tsn_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Tasty/UniVL_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Tasty/UniVL_feature/text/']
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-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/tasty/tasty_ori_(weight_index)_UniVL_refine.yml b/backup/cfgs_base/tasty/tasty_ori_(weight_index)_UniVL_refine.yml
deleted file mode 100644
index f75a379f584c0e1c6937f7382106b41aa13de36b..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/tasty/tasty_ori_(weight_index)_UniVL_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/tasty/tasty_tsn_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Tasty/UniVL_feature/visual/']
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diff --git a/backup/cfgs_base/tasty/tasty_ori_(weight_sim)_CLIP.yml b/backup/cfgs_base/tasty/tasty_ori_(weight_sim)_CLIP.yml
deleted file mode 100644
index 70bb98540afb82ad228fd38386b705eac0186b43..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/tasty/tasty_ori_(weight_sim)_CLIP.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/tasty/tasty_tsn_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/Tasty/features/clip/visual']
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diff --git a/backup/cfgs_base/tasty/tasty_ori_(weight_sim)_CLIP_refine.yml b/backup/cfgs_base/tasty/tasty_ori_(weight_sim)_CLIP_refine.yml
deleted file mode 100644
index 377d0315f95c91a695c3452a623a2178f36f8b5e..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/tasty/tasty_ori_(weight_sim)_CLIP_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/tasty/tasty_tsn_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/Tasty/features/clip/visual']
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-ec_alpha: 1.0
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diff --git a/backup/cfgs_base/tasty/tasty_ori_(weight_sim)_UniVL.yml b/backup/cfgs_base/tasty/tasty_ori_(weight_sim)_UniVL.yml
deleted file mode 100644
index a43f4fbb5db27f501c2e18f01a4ce609982ca832..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/tasty/tasty_ori_(weight_sim)_UniVL.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/tasty/tasty_tsn_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Tasty/UniVL_feature/visual/']
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-ec_alpha: 1.0
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diff --git a/backup/cfgs_base/tasty/tasty_ori_(weight_sim)_UniVL_refine.yml b/backup/cfgs_base/tasty/tasty_ori_(weight_sim)_UniVL_refine.yml
deleted file mode 100644
index edb891d5da98423d456c7b80ab35cc9a50143577..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/tasty/tasty_ori_(weight_sim)_UniVL_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/tasty/tasty_tsn_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Tasty/UniVL_feature/visual/']
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-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/tasty/tasty_ori_GT_CLIP.yml b/backup/cfgs_base/tasty/tasty_ori_GT_CLIP.yml
deleted file mode 100644
index b51463e36ec85d9216a6644801548bae016228fe..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/tasty/tasty_ori_GT_CLIP.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/tasty/tasty_tsn_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/Tasty/features/clip/visual']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/Tasty/features/clip/text']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 0
-pseudo_box_aug: 0
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 0
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: similarity
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 0
-pretrained_language_model: CLIP
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/tasty/tasty_ori_GT_UniVL.yml b/backup/cfgs_base/tasty/tasty_ori_GT_UniVL.yml
deleted file mode 100644
index 349183f42fc665a103e3b1b628b6f055bfbaee2d..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/tasty/tasty_ori_GT_UniVL.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/tasty/tasty_tsn_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Tasty/UniVL_feature/visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Tasty/UniVL_feature/text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 0
-pseudo_box_aug: 0
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 0
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: similarity
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 0
-pretrained_language_model: UniVL
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/tasty/tasty_tsn_pdvcl.yml b/backup/cfgs_base/tasty/tasty_tsn_pdvcl.yml
deleted file mode 100644
index 87138b61dc4b554deebc0245686a152f593825fc..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/tasty/tasty_tsn_pdvcl.yml
+++ /dev/null
@@ -1,57 +0,0 @@
-id: tasty_tsn_pdvcl
-
-visual_feature_type: ['resnet', 'bn']
-visual_feature_folder: ['data/yc2/features/resnet_bn/', 'data/yc2/features/resnet_bn/']
-feature_dim: 3072
-invalid_video_json: []
-train_caption_file: 'data/tasty/captiondata/tasty_train.json'
-val_caption_file: 'data/tasty/captiondata/tasty_test.json'
-gt_file_for_eval: ['data/tasty/captiondata/tasty_test.json']
-gt_file_for_para_eval: ['data/tasty/captiondata/para/tasty_test_para.json']
-dict_file: data/tasty/voc_tasty_14.json
-vocab_size: 14670
-max_caption_len: 50
-
-train_proposal_type: gt
-train_proposal_sample_num: 50
-gt_proposal_sample_num: 50
-sample_method: nearest
-
-batch_size: 1
-lr: 0.00005
-learning_rate_decay_start: 8
-learning_rate_decay_every: 3
-learning_rate_decay_rate: 0.5
-weight_decay: 0.0001
-save_all_checkpoint: 0
-
-num_queries: 100
-dec_layers: 2
-enc_layers: 2
-transformer_ff_dim: 512
-transformer_dropout_prob: 0.1
-frame_embedding_num: 200
-caption_decoder_type: standard
-att_hid_size: 0
-
-with_box_refine: 1
-
-fix_xcw: 1
-set_cost_caption: 0
-set_cost_giou: 4
-set_cost_bbox: 0
-set_cost_class: 2
-self_iou_loss_coef: 0
-#cost_alpha: 0.5
-#cost_gamma: 1
-#focal_alpha: 0.5
-#focal_gamma: 1
-caption_loss_coef: 2
-giou_loss_coef: 4
-bbox_loss_coef: 0
-cls_loss_coef: 2
-count_loss_coef: 0.5
-max_eseq_length: 20 # 42 is the max number of events in tasty
-lloss_cross_entropy: 0
-lloss_focal_loss: 0
-lloss_gau_mask: 1
\ No newline at end of file
diff --git a/backup/cfgs_base/tasty/tasty_tsn_pdvcl_voc30.yml b/backup/cfgs_base/tasty/tasty_tsn_pdvcl_voc30.yml
deleted file mode 100644
index 1a82a7274fb3f956c8545096bdf86e3e1f9c0468..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/tasty/tasty_tsn_pdvcl_voc30.yml
+++ /dev/null
@@ -1,57 +0,0 @@
-id: tasty_tsn_pdvcl
-
-visual_feature_type: ['resnet', 'bn']
-visual_feature_folder: ['data/yc2/features/resnet_bn/', 'data/yc2/features/resnet_bn/']
-feature_dim: 3072
-invalid_video_json: []
-train_caption_file: 'data/tasty/captiondata/tasty_train.json'
-val_caption_file: 'data/tasty/captiondata/tasty_test.json'
-gt_file_for_eval: ['data/tasty/captiondata/tasty_test.json']
-gt_file_for_para_eval: ['data/tasty/captiondata/para/tasty_test_para.json']
-dict_file: data/tasty/vocabulary_tasty.json
-vocab_size: 30171
-max_caption_len: 50
-
-train_proposal_type: gt
-train_proposal_sample_num: 50
-gt_proposal_sample_num: 50
-sample_method: nearest
-
-batch_size: 1
-lr: 0.00005
-learning_rate_decay_start: 8
-learning_rate_decay_every: 3
-learning_rate_decay_rate: 0.5
-weight_decay: 0.0001
-save_all_checkpoint: 0
-
-num_queries: 100
-dec_layers: 2
-enc_layers: 2
-transformer_ff_dim: 512
-transformer_dropout_prob: 0.1
-frame_embedding_num: 200
-caption_decoder_type: standard
-att_hid_size: 0
-
-with_box_refine: 1
-
-fix_xcw: 1
-set_cost_caption: 0
-set_cost_giou: 4
-set_cost_bbox: 0
-set_cost_class: 2
-self_iou_loss_coef: 0
-#cost_alpha: 0.5
-#cost_gamma: 1
-#focal_alpha: 0.5
-#focal_gamma: 1
-caption_loss_coef: 2
-giou_loss_coef: 4
-bbox_loss_coef: 0
-cls_loss_coef: 2
-count_loss_coef: 0.5
-max_eseq_length: 42 # 42 is the max number of events in tasty
-lloss_cross_entropy: 0
-lloss_focal_loss: 0
-lloss_gau_mask: 1
\ No newline at end of file
diff --git a/backup/cfgs_base/vlep/base_vlep-yc2_yc2.yml b/backup/cfgs_base/vlep/base_vlep-yc2_yc2.yml
deleted file mode 100644
index d14b206d40c4f2399400913d1ff15b8659b575b9..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/vlep/base_vlep-yc2_yc2.yml
+++ /dev/null
@@ -1,61 +0,0 @@
-id: yc2_tsn_pdvcl
-
-visual_feature_type: ['resnet', 'bn']
-visual_feature_folder: ['data/yc2/features/resnet_bn/', 'data/yc2/features/resnet_bn/']
-feature_dim: 3072
-invalid_video_json: []
-train_caption_file: ['data/vlep/captiondata/vlep_meta.json', 'data/yc2/captiondata/yc2_train.json']
-val_caption_file: 'data/yc2/captiondata/yc2_val.json'
-gt_file_for_eval: ['data/yc2/captiondata/yc2_val.json']
-gt_file_for_para_eval: ['data/yc2/captiondata/para/para_yc2_val.json']
-max_caption_len: 50
-
-dict_file: data/vlep/vlep_vocabulary_rate2_yc2.json
-vocab_size: 4491
-# dict_file_for_sim: data/howto/vocabulary_howto_rate5.json
-# vocab_size: 8531
-
-
-train_proposal_type: gt
-train_proposal_sample_num: 30
-sample_method: nearest
-
-epoch: 10
-batch_size: 1
-lr: 0.00005
-learning_rate_decay_start: 8
-learning_rate_decay_every: 3
-learning_rate_decay_rate: 0.5
-weight_decay: 0.0001
-save_all_checkpoint: 0
-
-num_queries: 100
-dec_layers: 2
-enc_layers: 2
-transformer_ff_dim: 512
-transformer_dropout_prob: 0.1
-frame_embedding_num: 200
-caption_decoder_type: light
-att_hid_size: 0
-
-with_box_refine: 1
-
-fix_xcw: 1
-set_cost_caption: 0
-set_cost_giou: 4
-set_cost_bbox: 0
-set_cost_class: 2
-self_iou_loss_coef: 0
-#cost_alpha: 0.5
-#cost_gamma: 1
-#focal_alpha: 0.5
-#focal_gamma: 1
-caption_loss_coef: 2
-giou_loss_coef: 4
-bbox_loss_coef: 0
-cls_loss_coef: 2
-count_loss_coef: 0.5
-max_eseq_length: 20
-lloss_cross_entropy: 0
-lloss_focal_loss: 0
-lloss_gau_mask: 1
\ No newline at end of file
diff --git a/backup/cfgs_base/vlep/vlep-yc2_yc2_ori_(sim)_CLIP_refine.yml b/backup/cfgs_base/vlep/vlep-yc2_yc2_ori_(sim)_CLIP_refine.yml
deleted file mode 100644
index cccf2ec29dc7c513d8b4cc90d4a6f6a3fabbc28d..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/vlep/vlep-yc2_yc2_ori_(sim)_CLIP_refine.yml
+++ /dev/null
@@ -1,44 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/vlep/base_vlep-yc2_yc2.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/mnt/data/Gvlab/wuhao/code/clip_frame_feature_extraction/output/vlep_clip_feature', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/video/']
-text_feature_folder: ['/mnt/data/Gvlab/wuhao/code/clip_frame_feature_extraction/features/vlep/clip', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/text_proj/']
-visual_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/video/']
-text_feature_folder_val: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/text_proj/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 1
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 1
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: similarity
-statistic_mode: mode
-width_ratio: 1
-window_size: 3
-top_frames: 10
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 0
-pretrained_language_model: CLIP
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/yc2/yc2_UniVL_pdvc.yml b/backup/cfgs_base/yc2/yc2_UniVL_pdvc.yml
deleted file mode 100644
index 00399b0c2f2a021a7476750b003026045d776cd1..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_UniVL_pdvc.yml
+++ /dev/null
@@ -1,20 +0,0 @@
-id: yc2_UniVL_pdvc
-base_cfg_path: cfgs_base/yc2/yc2_tsn_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/UniVL_features/UniVL_visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/UniVL_features/UniVL_text/']
-
-feature_dim: 768
-hidden_dim: 512
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 50
-
-ec_alpha: 1.0
\ No newline at end of file
diff --git a/backup/cfgs_base/yc2/yc2_ViP_pdvc.yml b/backup/cfgs_base/yc2/yc2_ViP_pdvc.yml
deleted file mode 100644
index ab29c8850e08c4549496f768a65f7ff4d08f33ba..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_ViP_pdvc.yml
+++ /dev/null
@@ -1,19 +0,0 @@
-id: yc2_ViP_pdvc_norm
-base_cfg_path: cfgs_base/yc2_tsn_pdvcl.yml
-
-visual_feature_type: ['CLIP-ViP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/ViP_features/visual_norm/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/ViP_features/text/']
-feature_dim: 512
-hidden_dim: 512
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 50
-
-ec_alpha: 1.0
\ No newline at end of file
diff --git a/backup/cfgs_base/yc2/yc2_abox_ViP_pdvc.yml b/backup/cfgs_base/yc2/yc2_abox_ViP_pdvc.yml
deleted file mode 100644
index c8ecdedb6ef1d7787c74040ef64b644bfb98d956..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_abox_ViP_pdvc.yml
+++ /dev/null
@@ -1,29 +0,0 @@
-id: yc2_abox_ViP_pseudo_similarity
-base_cfg_path: cfgs_base/yc2_tsn_pdvcl.yml
-
-visual_feature_type: ['CLIP-ViP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/ViP_features/visual_norm']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/ViP_features/text']
-feature_dim: 512
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_type: similarity
-use_anchor: 0
-pretrained_language_model: CLIP-ViP
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
\ No newline at end of file
diff --git a/backup/cfgs_base/yc2/yc2_anc_(align)_CLIP.yml b/backup/cfgs_base/yc2/yc2_anc_(align)_CLIP.yml
deleted file mode 100644
index c15a2a7c6e8dea1452f91f26dda6fe9fb6ebe8f7..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_anc_(align)_CLIP.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/yc2/yc2_tsn_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/video/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/text_proj/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 0
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 0
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: align
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 1
-pretrained_language_model: CLIP
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/yc2/yc2_anc_(align)_CLIP_refine.yml b/backup/cfgs_base/yc2/yc2_anc_(align)_CLIP_refine.yml
deleted file mode 100644
index 217693d7afa3593227e2d368fdb2552cd9371369..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_anc_(align)_CLIP_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/yc2/yc2_tsn_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/video/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/text_proj/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 1
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 1
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: align
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 1
-pretrained_language_model: CLIP
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/yc2/yc2_anc_(align)_UniVL.yml b/backup/cfgs_base/yc2/yc2_anc_(align)_UniVL.yml
deleted file mode 100644
index 13eef191acd1177c2d4e7bdc64f2b755e80ab5e5..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_anc_(align)_UniVL.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/yc2/yc2_tsn_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/UniVL_features/UniVL_visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/UniVL_features/UniVL_text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 1
-pseudo_box_aug: 0
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 0
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: align
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 1
-pretrained_language_model: UniVL
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/yc2/yc2_anc_(align)_UniVL_refine.yml b/backup/cfgs_base/yc2/yc2_anc_(align)_UniVL_refine.yml
deleted file mode 100644
index 88bd8b2fec5674da63b5171fa4c7bfeee0426fc9..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_anc_(align)_UniVL_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/yc2/yc2_tsn_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/UniVL_features/UniVL_visual/']
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diff --git a/backup/cfgs_base/yc2/yc2_anc_(sim)_CLIP.yml b/backup/cfgs_base/yc2/yc2_anc_(sim)_CLIP.yml
deleted file mode 100644
index 4846c148573235eb6aa047cb024e6c78a4e1cba2..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_anc_(sim)_CLIP.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/yc2/yc2_tsn_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/video/']
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diff --git a/backup/cfgs_base/yc2/yc2_anc_(sim)_CLIP_refine.yml b/backup/cfgs_base/yc2/yc2_anc_(sim)_CLIP_refine.yml
deleted file mode 100644
index 4b329f14bec0848ba829c1b4048e7fe22fb46e83..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_anc_(sim)_CLIP_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/yc2/yc2_tsn_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/video/']
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diff --git a/backup/cfgs_base/yc2/yc2_anc_(sim)_UniVL.yml b/backup/cfgs_base/yc2/yc2_anc_(sim)_UniVL.yml
deleted file mode 100644
index 8d62fd00faf076f269351f19da5083817b419ff0..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_anc_(sim)_UniVL.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/yc2/yc2_tsn_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/UniVL_features/UniVL_visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/UniVL_features/UniVL_text/']
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-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/yc2/yc2_anc_(sim)_UniVL_refine.yml b/backup/cfgs_base/yc2/yc2_anc_(sim)_UniVL_refine.yml
deleted file mode 100644
index e25901b1bf82142d41e05343f65e1ba59b8d908b..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_anc_(sim)_UniVL_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/yc2/yc2_tsn_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/UniVL_features/UniVL_visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/UniVL_features/UniVL_text/']
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-use_anchor: 1
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-caption_decoder_type: standard
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diff --git a/backup/cfgs_base/yc2/yc2_anc_(sim_op)_CLIP.yml b/backup/cfgs_base/yc2/yc2_anc_(sim_op)_CLIP.yml
deleted file mode 100644
index 77143cc57f11432ea5001da37c0014eb1696acc2..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_anc_(sim_op)_CLIP.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/yc2/yc2_tsn_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/video/']
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-ec_alpha: 1.0
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diff --git a/backup/cfgs_base/yc2/yc2_anc_(sim_op)_CLIP_refine.yml b/backup/cfgs_base/yc2/yc2_anc_(sim_op)_CLIP_refine.yml
deleted file mode 100644
index 87ff91cad79953a42bb1b582e8170779d1e147c7..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_anc_(sim_op)_CLIP_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/yc2/yc2_tsn_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/video/']
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-use_pseudo_box: 1
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-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/yc2/yc2_anc_(sim_op)_UniVL.yml b/backup/cfgs_base/yc2/yc2_anc_(sim_op)_UniVL.yml
deleted file mode 100644
index 388cbc2b2527190f4ccf17c7006ec9adee33ae5e..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_anc_(sim_op)_UniVL.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/yc2/yc2_tsn_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/UniVL_features/UniVL_visual/']
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-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/yc2/yc2_anc_(sim_op)_UniVL_refine.yml b/backup/cfgs_base/yc2/yc2_anc_(sim_op)_UniVL_refine.yml
deleted file mode 100644
index c8b7d7cecca9a90d08a1fe516e115891645fb0b6..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_anc_(sim_op)_UniVL_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/yc2/yc2_tsn_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/UniVL_features/UniVL_visual/']
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-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/yc2/yc2_anc_(sim_op_order)_CLIP.yml b/backup/cfgs_base/yc2/yc2_anc_(sim_op_order)_CLIP.yml
deleted file mode 100644
index c49641fdda1e81d5e713e9b615d3d1186a7fea7d..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_anc_(sim_op_order)_CLIP.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/yc2/yc2_tsn_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/video/']
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-num_queries: 100
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-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/yc2/yc2_anc_(sim_op_order)_CLIP_refine.yml b/backup/cfgs_base/yc2/yc2_anc_(sim_op_order)_CLIP_refine.yml
deleted file mode 100644
index dbb37176adcdc33825abe0ed4a943588ad157f4a..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_anc_(sim_op_order)_CLIP_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/yc2/yc2_tsn_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/video/']
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diff --git a/backup/cfgs_base/yc2/yc2_anc_(sim_op_order)_UniVL.yml b/backup/cfgs_base/yc2/yc2_anc_(sim_op_order)_UniVL.yml
deleted file mode 100644
index b271a74c0f4f3d976410fcc2b607d12056124d08..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_anc_(sim_op_order)_UniVL.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/yc2/yc2_tsn_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/UniVL_features/UniVL_visual/']
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diff --git a/backup/cfgs_base/yc2/yc2_anc_(sim_op_order)_UniVL_refine.yml b/backup/cfgs_base/yc2/yc2_anc_(sim_op_order)_UniVL_refine.yml
deleted file mode 100644
index 5d91010f653fe7c8f52caead5e7737a8ea102fd9..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_anc_(sim_op_order)_UniVL_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/yc2/yc2_tsn_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/UniVL_features/UniVL_visual/']
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diff --git a/backup/cfgs_base/yc2/yc2_anc_(sim_op_order_v1)_CLIP.yml b/backup/cfgs_base/yc2/yc2_anc_(sim_op_order_v1)_CLIP.yml
deleted file mode 100644
index bed129c0b6368d63e430310d7caa0ce4a633e329..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_anc_(sim_op_order_v1)_CLIP.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
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-
-visual_feature_type: ['CLIP']
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diff --git a/backup/cfgs_base/yc2/yc2_anc_(sim_op_order_v1)_CLIP_refine.yml b/backup/cfgs_base/yc2/yc2_anc_(sim_op_order_v1)_CLIP_refine.yml
deleted file mode 100644
index ca26e6a13b9df3b80e91cf34b8668676895d6214..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_anc_(sim_op_order_v1)_CLIP_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/yc2/yc2_tsn_pdvcl.yml
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-visual_feature_type: ['CLIP']
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diff --git a/backup/cfgs_base/yc2/yc2_anc_(sim_op_order_v1)_UniVL.yml b/backup/cfgs_base/yc2/yc2_anc_(sim_op_order_v1)_UniVL.yml
deleted file mode 100644
index c5086a3c1ab5482fd8595d66ebd5cecd3da4502c..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_anc_(sim_op_order_v1)_UniVL.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/yc2/yc2_tsn_pdvcl.yml
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-visual_feature_type: ['UniVL']
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diff --git a/backup/cfgs_base/yc2/yc2_anc_(sim_op_order_v1)_UniVL_refine.yml b/backup/cfgs_base/yc2/yc2_anc_(sim_op_order_v1)_UniVL_refine.yml
deleted file mode 100644
index 7ec5cdeb72ada972c4b0ec906ed3d96060a7e018..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_anc_(sim_op_order_v1)_UniVL_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
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-visual_feature_type: ['UniVL']
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diff --git a/backup/cfgs_base/yc2/yc2_anc_(sim_op_order_v2)_CLIP.yml b/backup/cfgs_base/yc2/yc2_anc_(sim_op_order_v2)_CLIP.yml
deleted file mode 100644
index 9d56829b138e152c73a8513f85f3f086c40ae838..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_anc_(sim_op_order_v2)_CLIP.yml
+++ /dev/null
@@ -1,39 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
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diff --git a/backup/cfgs_base/yc2/yc2_anc_(sim_op_order_v2)_CLIP_refine.yml b/backup/cfgs_base/yc2/yc2_anc_(sim_op_order_v2)_CLIP_refine.yml
deleted file mode 100644
index 042ae96b01a2c741fe1304441db5bd0ca14109b4..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_anc_(sim_op_order_v2)_CLIP_refine.yml
+++ /dev/null
@@ -1,39 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
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-visual_feature_type: ['CLIP']
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diff --git a/backup/cfgs_base/yc2/yc2_anc_(sim_op_order_v2)_UniVL.yml b/backup/cfgs_base/yc2/yc2_anc_(sim_op_order_v2)_UniVL.yml
deleted file mode 100644
index e9f47c5587ae99e197c9e4c2d87ccc4545923143..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_anc_(sim_op_order_v2)_UniVL.yml
+++ /dev/null
@@ -1,39 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
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diff --git a/backup/cfgs_base/yc2/yc2_anc_(sim_op_order_v2)_UniVL_refine.yml b/backup/cfgs_base/yc2/yc2_anc_(sim_op_order_v2)_UniVL_refine.yml
deleted file mode 100644
index 78f0529a85faecb3ca48833e213e1be64072439a..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_anc_(sim_op_order_v2)_UniVL_refine.yml
+++ /dev/null
@@ -1,39 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
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-visual_feature_type: ['UniVL']
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diff --git a/backup/cfgs_base/yc2/yc2_anc_(weight_index)_CLIP.yml b/backup/cfgs_base/yc2/yc2_anc_(weight_index)_CLIP.yml
deleted file mode 100644
index 615d745a91273d92d158ecbd28e9eb7e5ec77640..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_anc_(weight_index)_CLIP.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
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-visual_feature_type: ['CLIP']
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diff --git a/backup/cfgs_base/yc2/yc2_anc_(weight_index)_CLIP_refine.yml b/backup/cfgs_base/yc2/yc2_anc_(weight_index)_CLIP_refine.yml
deleted file mode 100644
index f2fe9580c80c0a6c19ebd865cc9625a7f3b997ec..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_anc_(weight_index)_CLIP_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
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diff --git a/backup/cfgs_base/yc2/yc2_anc_(weight_index)_UniVL.yml b/backup/cfgs_base/yc2/yc2_anc_(weight_index)_UniVL.yml
deleted file mode 100644
index a3866db1b5556c8b1c7a3f37a37686bdc2170a13..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_anc_(weight_index)_UniVL.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
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diff --git a/backup/cfgs_base/yc2/yc2_anc_(weight_index)_UniVL_refine.yml b/backup/cfgs_base/yc2/yc2_anc_(weight_index)_UniVL_refine.yml
deleted file mode 100644
index 707926c6592afdac874f5ba9bafb7c3855e9b18e..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_anc_(weight_index)_UniVL_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
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diff --git a/backup/cfgs_base/yc2/yc2_anc_(weight_sim)_CLIP.yml b/backup/cfgs_base/yc2/yc2_anc_(weight_sim)_CLIP.yml
deleted file mode 100644
index cbc2e0503505aa158c62d8f3687cce87a45ece37..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_anc_(weight_sim)_CLIP.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
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diff --git a/backup/cfgs_base/yc2/yc2_anc_(weight_sim)_CLIP_refine.yml b/backup/cfgs_base/yc2/yc2_anc_(weight_sim)_CLIP_refine.yml
deleted file mode 100644
index 6ff93b47407b09e7af54d101c6dfcab1d359c1d3..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_anc_(weight_sim)_CLIP_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
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diff --git a/backup/cfgs_base/yc2/yc2_anc_(weight_sim)_UniVL.yml b/backup/cfgs_base/yc2/yc2_anc_(weight_sim)_UniVL.yml
deleted file mode 100644
index d8bdddf2857f8baee722f798460eee584a75e07e..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_anc_(weight_sim)_UniVL.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
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-visual_feature_type: ['UniVL']
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diff --git a/backup/cfgs_base/yc2/yc2_anc_(weight_sim)_UniVL_refine.yml b/backup/cfgs_base/yc2/yc2_anc_(weight_sim)_UniVL_refine.yml
deleted file mode 100644
index 35cf1c1bee6180fdd84cf580e3266c24f80bfb2b..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_anc_(weight_sim)_UniVL_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
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diff --git a/backup/cfgs_base/yc2/yc2_anc_GT_CLIP.yml b/backup/cfgs_base/yc2/yc2_anc_GT_CLIP.yml
deleted file mode 100644
index c68cd4381b5ddc5447b4da98c968702fcfad52d2..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_anc_GT_CLIP.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
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-visual_feature_type: ['CLIP']
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diff --git a/backup/cfgs_base/yc2/yc2_anc_GT_UniVL.yml b/backup/cfgs_base/yc2/yc2_anc_GT_UniVL.yml
deleted file mode 100644
index b1dcf629977378040564bf4b9256f66fe8a76282..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_anc_GT_UniVL.yml
+++ /dev/null
@@ -1,38 +0,0 @@
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diff --git a/backup/cfgs_base/yc2/yc2_ori_(align)_CLIP.yml b/backup/cfgs_base/yc2/yc2_ori_(align)_CLIP.yml
deleted file mode 100644
index 6768351e467f9cdcfb4cf503621faee1d28da7d5..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_ori_(align)_CLIP.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
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diff --git a/backup/cfgs_base/yc2/yc2_ori_(align)_CLIP_refine.yml b/backup/cfgs_base/yc2/yc2_ori_(align)_CLIP_refine.yml
deleted file mode 100644
index 48c7d7ff81d64b9b885be7e8c9c114d5b3a177c6..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_ori_(align)_CLIP_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
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diff --git a/backup/cfgs_base/yc2/yc2_ori_(align)_UniVL.yml b/backup/cfgs_base/yc2/yc2_ori_(align)_UniVL.yml
deleted file mode 100644
index a2880d6336436d6d2da1330e3835323340f5ddd7..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_ori_(align)_UniVL.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
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diff --git a/backup/cfgs_base/yc2/yc2_ori_(align)_UniVL_refine.yml b/backup/cfgs_base/yc2/yc2_ori_(align)_UniVL_refine.yml
deleted file mode 100644
index 96d7bc4b0db3ea7647ac39f487d43fe96d1b9846..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_ori_(align)_UniVL_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
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diff --git a/backup/cfgs_base/yc2/yc2_ori_(sim)_CLIP.yml b/backup/cfgs_base/yc2/yc2_ori_(sim)_CLIP.yml
deleted file mode 100644
index 6dd2d07dd9d060137eeb3f135a7e2a420d110fdf..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_ori_(sim)_CLIP.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
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diff --git a/backup/cfgs_base/yc2/yc2_ori_(sim)_CLIP_refine.yml b/backup/cfgs_base/yc2/yc2_ori_(sim)_CLIP_refine.yml
deleted file mode 100644
index 6043b9b8e94f7f09c976fd10fd340ce0370dbc21..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_ori_(sim)_CLIP_refine.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
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diff --git a/backup/cfgs_base/yc2/yc2_ori_(sim)_UniVL.yml b/backup/cfgs_base/yc2/yc2_ori_(sim)_UniVL.yml
deleted file mode 100644
index ef3bd5c6fccf6e8fa460e56fbc89d5cda0ad5a16..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_ori_(sim)_UniVL.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
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diff --git a/backup/cfgs_base/yc2/yc2_ori_(sim)_UniVL_refine.yml b/backup/cfgs_base/yc2/yc2_ori_(sim)_UniVL_refine.yml
deleted file mode 100644
index ba9e35266c5ecce655d98fbda735fffd5505ab3c..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_ori_(sim)_UniVL_refine.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: basic
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diff --git a/backup/cfgs_base/yc2/yc2_ori_(sim_op)_CLIP.yml b/backup/cfgs_base/yc2/yc2_ori_(sim_op)_CLIP.yml
deleted file mode 100644
index 373ef4c276a633c7923d9cdf106d81863573f378..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_ori_(sim_op)_CLIP.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
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-
-visual_feature_type: ['CLIP']
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diff --git a/backup/cfgs_base/yc2/yc2_ori_(sim_op)_CLIP_refine.yml b/backup/cfgs_base/yc2/yc2_ori_(sim_op)_CLIP_refine.yml
deleted file mode 100644
index e6e781eb010da1f9c67a8362776119348f972795..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_ori_(sim_op)_CLIP_refine.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/yc2/yc2_tsn_pdvcl.yml
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diff --git a/backup/cfgs_base/yc2/yc2_ori_(sim_op)_UniVL.yml b/backup/cfgs_base/yc2/yc2_ori_(sim_op)_UniVL.yml
deleted file mode 100644
index c2b90ed1817ba9b5af6f71c7aa48499550fed905..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_ori_(sim_op)_UniVL.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/yc2/yc2_tsn_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/UniVL_features/UniVL_visual/']
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diff --git a/backup/cfgs_base/yc2/yc2_ori_(sim_op)_UniVL_refine.yml b/backup/cfgs_base/yc2/yc2_ori_(sim_op)_UniVL_refine.yml
deleted file mode 100644
index 71cdb3e5f6a305eeefcf987c447ad38ce2c0b9eb..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_ori_(sim_op)_UniVL_refine.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: basic
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-
-visual_feature_type: ['UniVL']
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diff --git a/backup/cfgs_base/yc2/yc2_ori_(sim_op_order)_CLIP.yml b/backup/cfgs_base/yc2/yc2_ori_(sim_op_order)_CLIP.yml
deleted file mode 100644
index 14e4add2192671c62c3c5ecab34be01db392ead2..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_ori_(sim_op_order)_CLIP.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
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-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/video/']
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diff --git a/backup/cfgs_base/yc2/yc2_ori_(sim_op_order)_CLIP_refine.yml b/backup/cfgs_base/yc2/yc2_ori_(sim_op_order)_CLIP_refine.yml
deleted file mode 100644
index 658a48fbf1a31ce7522f65e4f04453965f1a1130..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_ori_(sim_op_order)_CLIP_refine.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
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diff --git a/backup/cfgs_base/yc2/yc2_ori_(sim_op_order)_UniVL.yml b/backup/cfgs_base/yc2/yc2_ori_(sim_op_order)_UniVL.yml
deleted file mode 100644
index 8c2448ad587b99db12d4004259da8ad83c400547..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_ori_(sim_op_order)_UniVL.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
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-visual_feature_type: ['UniVL']
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diff --git a/backup/cfgs_base/yc2/yc2_ori_(sim_op_order)_UniVL_refine.yml b/backup/cfgs_base/yc2/yc2_ori_(sim_op_order)_UniVL_refine.yml
deleted file mode 100644
index ca9efe1df117e81a57d9f4c0b37dccef8ce071a4..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_ori_(sim_op_order)_UniVL_refine.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: basic
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-
-visual_feature_type: ['UniVL']
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diff --git a/backup/cfgs_base/yc2/yc2_ori_(sim_op_order_v1)_CLIP.yml b/backup/cfgs_base/yc2/yc2_ori_(sim_op_order_v1)_CLIP.yml
deleted file mode 100644
index 1ae2b3a1874058268562185df28b4568088518bd..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_ori_(sim_op_order_v1)_CLIP.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/yc2/yc2_tsn_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/video/']
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diff --git a/backup/cfgs_base/yc2/yc2_ori_(sim_op_order_v1)_CLIP_refine.yml b/backup/cfgs_base/yc2/yc2_ori_(sim_op_order_v1)_CLIP_refine.yml
deleted file mode 100644
index 9f416921a9d59d98d643118774215085f50e4adf..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_ori_(sim_op_order_v1)_CLIP_refine.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/yc2/yc2_tsn_pdvcl.yml
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diff --git a/backup/cfgs_base/yc2/yc2_ori_(sim_op_order_v1)_UniVL.yml b/backup/cfgs_base/yc2/yc2_ori_(sim_op_order_v1)_UniVL.yml
deleted file mode 100644
index 8a43d8c6e42cb71191f4db57266b12c2f8ff1df7..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_ori_(sim_op_order_v1)_UniVL.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/yc2/yc2_tsn_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/UniVL_features/UniVL_visual/']
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diff --git a/backup/cfgs_base/yc2/yc2_ori_(sim_op_order_v1)_UniVL_refine.yml b/backup/cfgs_base/yc2/yc2_ori_(sim_op_order_v1)_UniVL_refine.yml
deleted file mode 100644
index 3ce8c21fb91989e7b37c1504989be65eaa5c17c1..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_ori_(sim_op_order_v1)_UniVL_refine.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-id: basic
-base_cfg_path: cfgs_base/yc2/yc2_tsn_pdvcl.yml
-
-visual_feature_type: ['UniVL']
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diff --git a/backup/cfgs_base/yc2/yc2_ori_(sim_op_order_v2)_CLIP.yml b/backup/cfgs_base/yc2/yc2_ori_(sim_op_order_v2)_CLIP.yml
deleted file mode 100644
index 25c75deb4061ea7fe6c59f5d63d26b85c332f8ce..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_ori_(sim_op_order_v2)_CLIP.yml
+++ /dev/null
@@ -1,39 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/yc2/yc2_tsn_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/video/']
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diff --git a/backup/cfgs_base/yc2/yc2_ori_(sim_op_order_v2)_CLIP_refine.yml b/backup/cfgs_base/yc2/yc2_ori_(sim_op_order_v2)_CLIP_refine.yml
deleted file mode 100644
index c1a0e79d75681650dce5753ea1934f39e05931ba..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_ori_(sim_op_order_v2)_CLIP_refine.yml
+++ /dev/null
@@ -1,43 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/yc2/yc2_tsn_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/video/']
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diff --git a/backup/cfgs_base/yc2/yc2_ori_(sim_op_order_v2)_UniVL.yml b/backup/cfgs_base/yc2/yc2_ori_(sim_op_order_v2)_UniVL.yml
deleted file mode 100644
index 7e6f5990069b64bd922d068c9721144ee3ed8467..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_ori_(sim_op_order_v2)_UniVL.yml
+++ /dev/null
@@ -1,39 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/yc2/yc2_tsn_pdvcl.yml
-
-visual_feature_type: ['UniVL']
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diff --git a/backup/cfgs_base/yc2/yc2_ori_(sim_op_order_v2)_UniVL_refine.yml b/backup/cfgs_base/yc2/yc2_ori_(sim_op_order_v2)_UniVL_refine.yml
deleted file mode 100644
index a061bab74963587e8c148e67c47ba3e8d8e7bdc9..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_ori_(sim_op_order_v2)_UniVL_refine.yml
+++ /dev/null
@@ -1,43 +0,0 @@
-id: basic
-base_cfg_path: cfgs_base/yc2/yc2_tsn_pdvcl.yml
-
-visual_feature_type: ['UniVL']
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diff --git a/backup/cfgs_base/yc2/yc2_ori_(weight_index)_CLIP.yml b/backup/cfgs_base/yc2/yc2_ori_(weight_index)_CLIP.yml
deleted file mode 100644
index 6cf3256fca24a4cdaa0c9e8c89a4fed74edd684b..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_ori_(weight_index)_CLIP.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
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-
-visual_feature_type: ['CLIP']
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diff --git a/backup/cfgs_base/yc2/yc2_ori_(weight_index)_CLIP_refine.yml b/backup/cfgs_base/yc2/yc2_ori_(weight_index)_CLIP_refine.yml
deleted file mode 100644
index e13936ee7c32a85bb8553de1bddf8dc85f6acdd7..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_ori_(weight_index)_CLIP_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
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-visual_feature_type: ['CLIP']
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diff --git a/backup/cfgs_base/yc2/yc2_ori_(weight_index)_UniVL.yml b/backup/cfgs_base/yc2/yc2_ori_(weight_index)_UniVL.yml
deleted file mode 100644
index 31d2af3cfa6d15ec3ce1067648dcb89cf410d309..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_ori_(weight_index)_UniVL.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/yc2/yc2_tsn_pdvcl.yml
-
-visual_feature_type: ['UniVL']
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diff --git a/backup/cfgs_base/yc2/yc2_ori_(weight_index)_UniVL_refine.yml b/backup/cfgs_base/yc2/yc2_ori_(weight_index)_UniVL_refine.yml
deleted file mode 100644
index 1d5096ca8903c240faa1e45589e77786800bf4d9..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_ori_(weight_index)_UniVL_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
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-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/UniVL_features/UniVL_visual/']
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diff --git a/backup/cfgs_base/yc2/yc2_ori_(weight_sim)_CLIP.yml b/backup/cfgs_base/yc2/yc2_ori_(weight_sim)_CLIP.yml
deleted file mode 100644
index c04663ab87edcd38ea5a0fa00c17d99f3203fc02..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_ori_(weight_sim)_CLIP.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/yc2/yc2_tsn_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/video/']
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diff --git a/backup/cfgs_base/yc2/yc2_ori_(weight_sim)_CLIP_refine.yml b/backup/cfgs_base/yc2/yc2_ori_(weight_sim)_CLIP_refine.yml
deleted file mode 100644
index ca1d85d39c264126d0cdcad3fb4ef6dcd9d78249..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_ori_(weight_sim)_CLIP_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
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-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/video/']
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-ec_alpha: 1.0
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diff --git a/backup/cfgs_base/yc2/yc2_ori_(weight_sim)_UniVL.yml b/backup/cfgs_base/yc2/yc2_ori_(weight_sim)_UniVL.yml
deleted file mode 100644
index e431de6395e6a745cf9c9e5f560621c1ef911015..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_ori_(weight_sim)_UniVL.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/yc2/yc2_tsn_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/UniVL_features/UniVL_visual/']
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-ec_alpha: 1.0
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-self_iou_loss_coef: 0.0
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diff --git a/backup/cfgs_base/yc2/yc2_ori_(weight_sim)_UniVL_refine.yml b/backup/cfgs_base/yc2/yc2_ori_(weight_sim)_UniVL_refine.yml
deleted file mode 100644
index d4b9dec009027bfad79d96f94545c19465146271..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_ori_(weight_sim)_UniVL_refine.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/yc2/yc2_tsn_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/UniVL_features/UniVL_visual/']
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diff --git a/backup/cfgs_base/yc2/yc2_ori_GT_CLIP.yml b/backup/cfgs_base/yc2/yc2_ori_GT_CLIP.yml
deleted file mode 100644
index e66d364a384fa467573af7703d97baf23098c9a2..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_ori_GT_CLIP.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/yc2/yc2_tsn_pdvcl.yml
-
-visual_feature_type: ['CLIP']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/video/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/text_proj/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 0
-pseudo_box_aug: 0
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 0
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: similarity
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 0
-pretrained_language_model: CLIP
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/yc2/yc2_ori_GT_UniVL.yml b/backup/cfgs_base/yc2/yc2_ori_GT_UniVL.yml
deleted file mode 100644
index 60d558dd0b51cbe8d184681d7227c91e76246540..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_ori_GT_UniVL.yml
+++ /dev/null
@@ -1,38 +0,0 @@
-id: refine_aug(5,0.3)_top3_1stage
-base_cfg_path: cfgs_base/yc2/yc2_tsn_pdvcl.yml
-
-visual_feature_type: ['UniVL']
-visual_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/UniVL_features/UniVL_visual/']
-text_feature_folder: ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/UniVL_features/UniVL_text/']
-feature_dim: 768
-hidden_dim: 512
-
-use_pseudo_box: 0
-pseudo_box_aug: 0
-pseudo_box_aug_num: 5
-pseudo_box_aug_ratio: 0.3
-refine_pseudo_box: 0
-refine_pseudo_stage_num: 1
-merge_k_boxes: 3
-pseudo_box_type: similarity
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 12
-
-use_anchor: 0
-pretrained_language_model: UniVL
-disable_contrastive_projection: 1
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 100
-
-ec_alpha: 1.0
-
-self_iou_loss_coef: 0.0
-ref_rank_loss_coef: 0.0
-contrastive_loss_start_coef: 0.0
diff --git a/backup/cfgs_base/yc2/yc2_tsn_pdvc.yml b/backup/cfgs_base/yc2/yc2_tsn_pdvc.yml
deleted file mode 100644
index fc66b3cbff2550bf0264a79dd43d6b93ab7256a0..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_tsn_pdvc.yml
+++ /dev/null
@@ -1,13 +0,0 @@
-id: yc2_tsn_pdvc
-base_cfg_path: cfgs_base/yc2_tsn_pdvcl.yml
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-
-num_queries: 50
-
-ec_alpha: 1.0
\ No newline at end of file
diff --git a/backup/cfgs_base/yc2/yc2_tsn_pdvc_gt.yml b/backup/cfgs_base/yc2/yc2_tsn_pdvc_gt.yml
deleted file mode 100644
index 9a1c528c5c792081cbb4873983306c4268a23d55..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_tsn_pdvc_gt.yml
+++ /dev/null
@@ -1,9 +0,0 @@
-id: yc2_tsn_pdvc_gt
-base_cfg_path: cfgs_base/yc2_tsn_pdvcl_gt.yml
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
\ No newline at end of file
diff --git a/backup/cfgs_base/yc2/yc2_tsn_pdvc_prior.yml b/backup/cfgs_base/yc2/yc2_tsn_pdvc_prior.yml
deleted file mode 100644
index 79ef87700f600af96cb41f1953b4fb1da336c8ec..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_tsn_pdvc_prior.yml
+++ /dev/null
@@ -1,16 +0,0 @@
-id: yc2_tsn_pdvc_prior
-base_cfg_path: cfgs_base/yc2_tsn_pdvcl.yml
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-num_queries: 50
-
-ec_alpha: 1.0
-
-transformer_input_type: prior_proposals
-
-#dec_layers: 3
\ No newline at end of file
diff --git a/backup/cfgs_base/yc2/yc2_tsn_pdvc_prior_add.yml b/backup/cfgs_base/yc2/yc2_tsn_pdvc_prior_add.yml
deleted file mode 100644
index 14941f50b2699cc25e74ee388bfe086ae0bda74d..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_tsn_pdvc_prior_add.yml
+++ /dev/null
@@ -1,18 +0,0 @@
-id: yc2_tsn_pdvc_prior_add
-base_cfg_path: cfgs_base/yc2_tsn_pdvcl.yml
-
-caption_decoder_type: standard
-cap_nheads: 1
-cap_dec_n_points: 4
-cap_num_feature_levels: 4
-soft_attention: 1
-att_hid_size: 512
-num_queries: 50
-
-prior_manner: add
-
-ec_alpha: 1.0
-
-transformer_input_type: prior_proposals
-
-#dec_layers: 3
\ No newline at end of file
diff --git a/backup/cfgs_base/yc2/yc2_tsn_pdvcl.yml b/backup/cfgs_base/yc2/yc2_tsn_pdvcl.yml
deleted file mode 100644
index 1420f8abf88d8bbdd6c9cf05454f0949a9fb6c44..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_tsn_pdvcl.yml
+++ /dev/null
@@ -1,55 +0,0 @@
-id: yc2_tsn_pdvcl
-
-visual_feature_type: ['resnet', 'bn']
-visual_feature_folder: ['data/yc2/features/resnet_bn/', 'data/yc2/features/resnet_bn/']
-feature_dim: 3072
-invalid_video_json: []
-train_caption_file: 'data/yc2/captiondata/yc2_train.json'
-val_caption_file: 'data/yc2/captiondata/yc2_val.json'
-gt_file_for_eval: ['data/yc2/captiondata/yc2_val.json']
-gt_file_for_para_eval: ['data/yc2/captiondata/para/para_yc2_val.json']
-dict_file: data/yc2/vocabulary_youcook2.json
-vocab_size: 1607
-
-train_proposal_type: gt
-train_proposal_sample_num: 30
-sample_method: nearest
-
-batch_size: 1
-lr: 0.00005
-learning_rate_decay_start: 8
-learning_rate_decay_every: 3
-learning_rate_decay_rate: 0.5
-weight_decay: 0.0001
-save_all_checkpoint: 0
-
-num_queries: 100
-dec_layers: 2
-enc_layers: 2
-transformer_ff_dim: 512
-transformer_dropout_prob: 0.1
-frame_embedding_num: 200
-caption_decoder_type: light
-att_hid_size: 0
-
-with_box_refine: 1
-
-fix_xcw: 1
-set_cost_caption: 0
-set_cost_giou: 4
-set_cost_bbox: 0
-set_cost_class: 2
-self_iou_loss_coef: 0
-#cost_alpha: 0.5
-#cost_gamma: 1
-#focal_alpha: 0.5
-#focal_gamma: 1
-caption_loss_coef: 2
-giou_loss_coef: 4
-bbox_loss_coef: 0
-cls_loss_coef: 2
-count_loss_coef: 0.5
-max_eseq_length: 20
-lloss_cross_entropy: 0
-lloss_focal_loss: 0
-lloss_gau_mask: 1
\ No newline at end of file
diff --git a/backup/cfgs_base/yc2/yc2_tsn_pdvcl_gt.yml b/backup/cfgs_base/yc2/yc2_tsn_pdvcl_gt.yml
deleted file mode 100644
index 435e85fc3946b15c389de755987b34f8bd75d469..0000000000000000000000000000000000000000
--- a/backup/cfgs_base/yc2/yc2_tsn_pdvcl_gt.yml
+++ /dev/null
@@ -1,57 +0,0 @@
-id: yc2_tsn_pdvcl_gt
-
-visual_feature_type: ['resnet', 'bn']
-visual_feature_folder: ['data/yc2/features/resnet_bn/', 'data/yc2/features/resnet_bn/']
-feature_dim: 3072
-invalid_video_json: []
-train_caption_file: 'data/yc2/captiondata/yc2_train.json'
-val_caption_file: 'data/yc2/captiondata/yc2_val.json'
-gt_file_for_eval: ['data/yc2/captiondata/yc2_val.json']
-gt_file_for_para_eval: ['data/yc2/captiondata/para/para_yc2_val.json']
-dict_file: data/yc2/vocabulary_youcook2.json
-vocab_size: 1607
-
-train_proposal_type: gt
-gt_proposal_sample_num: 30
-sample_method: nearest
-
-batch_size: 1
-lr: 0.00005
-learning_rate_decay_start: 8
-learning_rate_decay_every: 3
-learning_rate_decay_rate: 0.5
-weight_decay: 0.0001
-save_all_checkpoint: 0
-
-num_queries: 100
-dec_layers: 2
-enc_layers: 2
-transformer_ff_dim: 512
-transformer_dropout_prob: 0.1
-frame_embedding_num: 200
-caption_decoder_type: light
-att_hid_size: 0
-
-#with_box_refine: 1
-
-fix_xcw: 1
-set_cost_caption: 0
-set_cost_giou: 4
-set_cost_bbox: 0.0001
-set_cost_class: 0
-#cost_alpha: 0.5
-#cost_gamma: 1
-#focal_alpha: 0.5
-#focal_gamma: 1
-caption_loss_coef: 2
-giou_loss_coef: 0
-bbox_loss_coef: 0
-cls_loss_coef: 0
-count_loss_coef: 0
-#max_eseq_length: 10
-#lloss_cross_entropy: 0
-#lloss_focal_loss: 0
-#lloss_gau_mask: 1
-
-#two_stage: 1
-transformer_input_type: gt_proposals
\ No newline at end of file
diff --git a/backup/cfgs_ft_gt/howto-anet_anet_clip_topk30_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap.yml b/backup/cfgs_ft_gt/howto-anet_anet_clip_topk30_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap.yml
deleted file mode 100644
index 01358882bfd7a3c849085a12e2b93b42012add45..0000000000000000000000000000000000000000
--- a/backup/cfgs_ft_gt/howto-anet_anet_clip_topk30_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap.yml
+++ /dev/null
@@ -1,19 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_CLIP_refine.yml
-
-
-pseudo_box_aug_num: 8
-pseudo_box_aug_ratio: 0.02
-pseudo_box_aug_mode: random_range
-refine_pseudo_box: 1
-refine_pseudo_stage_num: 2
-merge_k_boxes: 3
-pseudo_box_type: similarity_op_order_v2
-top_frames: 30
-width_ratio: 1
-iteration: 3
-width_th: 1
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 20
-mil_loss_coef: 0
-merge_criterion: ins_cap_topk
diff --git a/backup/cfgs_ft_gt/howto-anet_anet_clip_topk30_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap_rand2.yml b/backup/cfgs_ft_gt/howto-anet_anet_clip_topk30_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap_rand2.yml
deleted file mode 100644
index 01358882bfd7a3c849085a12e2b93b42012add45..0000000000000000000000000000000000000000
--- a/backup/cfgs_ft_gt/howto-anet_anet_clip_topk30_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap_rand2.yml
+++ /dev/null
@@ -1,19 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/howto/howto-anet_anet_ori_(sim_op_order_v2)_CLIP_refine.yml
-
-
-pseudo_box_aug_num: 8
-pseudo_box_aug_ratio: 0.02
-pseudo_box_aug_mode: random_range
-refine_pseudo_box: 1
-refine_pseudo_stage_num: 2
-merge_k_boxes: 3
-pseudo_box_type: similarity_op_order_v2
-top_frames: 30
-width_ratio: 1
-iteration: 3
-width_th: 1
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 20
-mil_loss_coef: 0
-merge_criterion: ins_cap_topk
diff --git a/backup/cfgs_ft_gt/howto-yc2_yc2_univl_topk25_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap.yml b/backup/cfgs_ft_gt/howto-yc2_yc2_univl_topk25_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap.yml
deleted file mode 100644
index ac09bd7b115aac8b96a46053f07ee52d43c4a165..0000000000000000000000000000000000000000
--- a/backup/cfgs_ft_gt/howto-yc2_yc2_univl_topk25_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap.yml
+++ /dev/null
@@ -1,19 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_UniVL_refine.yml
-
-
-pseudo_box_aug_num: 8
-pseudo_box_aug_ratio: 0.02
-pseudo_box_aug_mode: random_range
-refine_pseudo_box: 1
-refine_pseudo_stage_num: 2
-merge_k_boxes: 3
-pseudo_box_type: similarity_op_order_v2
-top_frames: 25
-width_ratio: 1
-iteration: 3
-width_th: 1
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 20
-mil_loss_coef: 0
-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/cfgs_ft_gt/howto-yc2_yc2_univl_topk25_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap_rand2.yml b/backup/cfgs_ft_gt/howto-yc2_yc2_univl_topk25_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap_rand2.yml
deleted file mode 100644
index ac09bd7b115aac8b96a46053f07ee52d43c4a165..0000000000000000000000000000000000000000
--- a/backup/cfgs_ft_gt/howto-yc2_yc2_univl_topk25_r1_iter3_th1_refine_aug(8,0.02)_top3_2stage_inscap_rand2.yml
+++ /dev/null
@@ -1,19 +0,0 @@
-id: ''
-base_cfg_path: cfgs_base/howto/howto-yc2_yc2_ori_(sim_op_order_v2)_UniVL_refine.yml
-
-
-pseudo_box_aug_num: 8
-pseudo_box_aug_ratio: 0.02
-pseudo_box_aug_mode: random_range
-refine_pseudo_box: 1
-refine_pseudo_stage_num: 2
-merge_k_boxes: 3
-pseudo_box_type: similarity_op_order_v2
-top_frames: 25
-width_ratio: 1
-iteration: 3
-width_th: 1
-use_query_box_for_refine: 0
-gt_proposal_sample_num: 20
-mil_loss_coef: 0
-merge_criterion: ins_cap_topk
\ No newline at end of file
diff --git a/backup/change_config_add.py b/backup/change_config_add.py
deleted file mode 100644
index 4b9ecff04cf568dba78df9a67a4a418abc9edf08..0000000000000000000000000000000000000000
--- a/backup/change_config_add.py
+++ /dev/null
@@ -1,80 +0,0 @@
-import os
-import yaml
-import argparse
-
-# add dryrun option
-parser = argparse.ArgumentParser(description='Change config files')
-parser.add_argument('--dryrun', action='store_true', help='dryrun')
-args = parser.parse_args()
-
-
-
-
-
-# Define the folder containing YAML files
-# folder_path = 'cfgs_ref'
-# folder_path = 'cfgs_base/anet'
-# folder_path = 'cfgs'
-folder_path = 'cfgs_yc2_ft_perc'
-
-file_filter = ''
-
-
-
-# Define the string to find and the replacement string
-# find_string = '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/video'
-# find_string = '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/clip_features/text_proj'
-# find_string = 'data/yc2/captiondata/yc2'
-# find_string = "/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/UniVL_features/UniVL_text"
-find_string = "ft_gt_percent: 0.25"
-# find_string = "pdvc_mode: 0"
-
-# replace_string = '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/Tasty/features/clip/visual'
-# replace_string = '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/Tasty/features/clip/text'
-# replace_string = 'data/tasty/captiondata/tasty'
-# replace_string = "cfgs_base/tasty/tasty_tsn_pdvcl.yml"
-replace_string = "ft_gt_percent: 0.75"
-# replace_string = "pdvc_mode: 1"
-
-old_name = 'perc0.25'
-new_name = 'perc0.75'
-
-def replace_yaml(yaml_file_path, new_file_path, old_string, new_string):
-    # Read the YAML file as text
-    with open(yaml_file_path, 'r') as file:
-        yaml_text = file.read()
-
-    # Replace a string (e.g., 'old_string') with another string (e.g., 'new_string')
-
-    yaml_text = yaml_text.replace(old_string, new_string)
-
-    # Save the modified text back to a YAML file
-    with open(new_file_path, 'w') as file:
-        file.write(yaml_text)
-
-    # # Load the modified YAML data (optional)
-    # modified_yaml_data = yaml.safe_load(yaml_text)
-
-# You can now work with the modified_yaml_data as needed
-
-filelist = os.listdir(folder_path)
-# Iterate over the files in the folder
-for filename in filelist:
-    if not file_filter in filename:
-        continue
-    # breakpoint()
-    if filename.endswith('.yaml') or filename.endswith('.yml') and old_name in filename:
-        # breakpoint()
-        file_path = os.path.join(folder_path, filename)
-        if old_name == '':
-            new_filename = filename.replace('.yml',  '_{}.yml'.format(new_name))
-        else:
-            new_filename = filename.replace(old_name, new_name)
-        new_file_path = os.path.join(folder_path, new_filename)
-
-        if args.dryrun:
-            print("Dryrun: {} -> {}".format(file_path, new_file_path))
-        else:
-            replace_yaml(file_path, new_file_path, find_string, replace_string)
-
-print("String replacement completed.")
\ No newline at end of file
diff --git a/backup/demo.py b/backup/demo.py
deleted file mode 100644
index b8e3ab4946905f140f377d120a14deff85f4622f..0000000000000000000000000000000000000000
--- a/backup/demo.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import numpy as np
-
-# Example similarity matrix with shape [10, 200]
-similarity_matrix = np.random.rand(10, 200)
-
-# Example range of indices for each step (stored in center and width arrays)
-center = np.random.randint(0, 100, size=(10,))
-width = np.random.randint(10, 20, size=(10,))
-
-# Calculate the start and end indices for each step
-start_indices = np.clip(center - width // 2, 0, similarity_matrix.shape[1])
-end_indices = np.clip(center + width // 2, 0, similarity_matrix.shape[1])
-
-# Generate column indices for each range
-col_indices = np.arange(similarity_matrix.shape[1])
-
-# Get topk values and corresponding indices
-topk = 5
-topk_values = []
-topk_indices = []
-
-for start, end in zip(start_indices, end_indices):
-    # Slice the similarity matrix within the specified range
-    range_values = similarity_matrix[:, start:end]
-
-    # Find the indices of the topk values within the range
-    sorted_indices = np.argsort(range_values, axis=1)[:, -topk:]
-    sorted_indices += start  # Adjust indices to the absolute position
-
-    # Flatten and concatenate the indices
-    row_indices = np.arange(len(sorted_indices))[:, np.newaxis]
-    indices_flat = np.ravel_multi_index((row_indices.flatten(), sorted_indices.flatten()), similarity_matrix.shape)
-    
-    # Append topk values and indices
-    topk_values.append(np.take(similarity_matrix, indices_flat))
-    topk_indices.append(np.column_stack((row_indices.repeat(topk, axis=1).flatten(), sorted_indices.flatten())))
-
-# Convert lists to arrays
-topk_values = np.array(topk_values)
-topk_indices = np.array(topk_indices)
-
-print("Topk values within the specified range:", topk_values)
-print("Topk indices within the specified range:", topk_indices)
-
diff --git a/backup/eval.py b/backup/eval.py
deleted file mode 100644
index a2c59801e0e5a9e72ce22521699e53d796efd49b..0000000000000000000000000000000000000000
--- a/backup/eval.py
+++ /dev/null
@@ -1,146 +0,0 @@
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import argparse
-import json
-import os
-import sys
-import torch
-import numpy as np
-import time
-from os.path import dirname, abspath
-
-pdvc_dir = dirname(abspath(__file__))
-sys.path.insert(0, pdvc_dir)
-sys.path.insert(0, os.path.join(pdvc_dir, 'densevid_eval3'))
-sys.path.insert(0, os.path.join(pdvc_dir, 'densevid_eval3/SODA'))
-# print(sys.path)
-
-from eval_utils import evaluate
-from pdvc.pdvc import build
-from misc.utils import create_logger
-from data.video_dataset import PropSeqDataset, collate_fn
-from torch.utils.data import DataLoader
-from os.path import basename
-import pandas as pd
-
-def create_fake_test_caption_file(metadata_csv_path):
-    out = {}
-    df = pd.read_csv(metadata_csv_path)
-    for i, row in df.iterrows():
-        out[basename(row['filename']).split('.')[0]] = {'duration': row['video-duration'], "timestamps": [[0, 0.5]], "sentences":["None"]}
-    fake_test_json = '.fake_test_json.tmp'
-    json.dump(out, open(fake_test_json, 'w'))
-    return fake_test_json
-
-def main(opt):
-    folder_path = os.path.join(opt.eval_save_dir, opt.eval_folder)
-    if opt.eval_mode == 'test':
-        if not os.path.exists(folder_path):
-            os.makedirs(folder_path)
-    logger = create_logger(folder_path, 'val.log')
-    if opt.eval_model_path:
-        model_path = opt.eval_model_path
-        infos_path = os.path.join('/'.join(opt.eval_model_path.split('/')[:-1]), 'info.json')
-    else:
-        model_path = os.path.join(folder_path, 'model-best.pth')
-        infos_path = os.path.join(folder_path, 'info.json')
-
-    logger.info(vars(opt))
-
-    with open(infos_path, 'rb') as f:
-        logger.info('load info from {}'.format(infos_path))
-        old_opt = json.load(f)['best']['opt']
-
-    for k, v in old_opt.items():
-        if k[:4] != 'eval':
-            vars(opt).update({k: v})
-
-    opt.transformer_input_type = opt.eval_transformer_input_type
-
-    if not torch.cuda.is_available():
-        opt.nthreads = 0
-    # Create the Data Loader instance
-
-    if opt.eval_mode == 'test':
-        opt.eval_caption_file = create_fake_test_caption_file(opt.test_video_meta_data_csv_path)
-        opt.visual_feature_folder = opt.test_video_feature_folder
-
-    val_dataset = PropSeqDataset(opt.eval_caption_file,
-                                 opt.visual_feature_folder, opt.text_feature_folder,
-                                 opt.dict_file, False, opt.eval_proposal_type,
-                                 opt)
-    loader = DataLoader(val_dataset, batch_size=opt.batch_size_for_eval,
-                        shuffle=False, num_workers=opt.nthreads, collate_fn=collate_fn)
-
-
-    model, criterion, contrastive_criterion, postprocessors = build(opt)
-    model.translator = val_dataset.translator
-
-
-
-    while not os.path.exists(model_path):
-        raise AssertionError('File {} does not exist'.format(model_path))
-
-    logger.debug('Loading model from {}'.format(model_path))
-    loaded_pth = torch.load(model_path, map_location=opt.eval_device)
-    epoch = loaded_pth['epoch']
-
-    # loaded_pth = transfer(model, loaded_pth, model_path+'.transfer.pth')
-    model.load_state_dict(loaded_pth['model'], strict=True)
-    model.eval()
-
-    model.to(opt.eval_device)
-
-    if opt.eval_mode == 'test':
-        out_json_path = os.path.join(folder_path, 'dvc_results.json')
-        evaluate(model, criterion, postprocessors, loader, out_json_path,
-                         logger, args=opt, alpha=opt.ec_alpha, dvc_eval_version=opt.eval_tool_version, device=opt.eval_device, debug=False, skip_lang_eval=True)
-
-
-    else:
-        out_json_path = os.path.join(folder_path, '{}_epoch{}_num{}_alpha{}.json'.format(
-            time.strftime("%Y-%m-%d-%H-%M-%S_", time.localtime()) + str(opt.id), epoch, len(loader.dataset),
-            opt.ec_alpha))
-        caption_scores, eval_loss = evaluate(model, criterion, postprocessors, loader, out_json_path,
-                         logger, args=opt, alpha=opt.ec_alpha, dvc_eval_version=opt.eval_tool_version, device=opt.eval_device, debug=False, skip_lang_eval=False)
-        # breakpoint()
-        avg_eval_score = {key: np.array(value).mean() for key, value in caption_scores.items() if key !='tiou'}
-        # avg_eval_score2 = {key: np.array(value).mean() * 4917 / len(loader.dataset) for key, value in caption_scores.items() if key != 'tiou'}
-
-        # logger.info(
-        #     '\nValidation result based on all 4917 val videos:\n {}\n avg_score:\n{}'.format(
-        #                                                                                caption_scores.items(),
-        #                                                                                avg_eval_score))
-
-        logger.info(
-                '\nValidation result based on {} available val videos:\n avg_score:\n{}'.format(len(loader.dataset),
-                                                                                           avg_eval_score))
-
-    logger.info('saving reults json to {}'.format(out_json_path))
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--eval_save_dir', type=str, default='save')
-    parser.add_argument('--eval_mode', type=str, default='eval', choices=['eval', 'test'])
-    parser.add_argument('--test_video_feature_folder', type=str, nargs='+', default=None)
-    parser.add_argument('--test_video_meta_data_csv_path', type=str, default=None)
-    parser.add_argument('--eval_folder', type=str, required=True)
-    parser.add_argument('--eval_model_path', type=str, default='')
-    parser.add_argument('--eval_tool_version', type=str, default='2018', choices=['2018', '2021'])
-    parser.add_argument('--eval_caption_file', type=str, default='data/anet/captiondata/val_1.json')
-    parser.add_argument('--eval_proposal_type', type=str, default='gt')
-    parser.add_argument('--eval_transformer_input_type', type=str, default='queries', choices=['gt_proposals', 'prior_proposals','queries'])
-    parser.add_argument('--gpu_id', type=str, nargs='+', default=['0'])
-    parser.add_argument('--eval_device', type=str, default='cuda')
-    parser.add_argument('--prior_manner', type=str, default='all', choices=['add', 'all'])
-    opt = parser.parse_args()
-
-    #breakpoint()
-
-    os.environ["CUDA_VISIBLE_DEVICES"] = ",".join([str(i) for i in opt.gpu_id])
-    os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'
-    if True:
-        torch.backends.cudnn.enabled = False
-    main(opt)
diff --git a/backup/eval_utils.py b/backup/eval_utils.py
deleted file mode 100644
index f4cd727ecebd0364fe9ad45d94f582fdcb17d54b..0000000000000000000000000000000000000000
--- a/backup/eval_utils.py
+++ /dev/null
@@ -1,241 +0,0 @@
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import os
-import sys
-import collections
-import torch
-import numpy as np
-import json
-from collections import OrderedDict
-from tqdm import tqdm
-from os.path import dirname, abspath
-
-pdvc_dir = dirname(abspath(__file__))
-sys.path.insert(0, pdvc_dir)
-sys.path.insert(0, os.path.join(pdvc_dir, 'densevid_eval3'))
-sys.path.insert(0, os.path.join(pdvc_dir, 'densevid_eval3/SODA'))
-
-
-from densevid_eval3.eval_soda import eval_soda
-from densevid_eval3.eval_para import eval_para
-from densevid_eval3.eval_dvc import eval_dvc
-
-def calculate_avg_proposal_num(json_path):
-    data = json.load(open(json_path))
-    return np.array([len(v) for v in data['results'].values()]).mean()
-
-def convert_tapjson_to_dvcjson(tap_json, dvc_json):
-    data = json.load(open(tap_json, 'r'))
-    data['version'] = "VERSION 1.0"
-    data['external_data'] = {'used:': True, 'details': "C3D pretrained on Sports-1M"}
-
-    all_names = list(data['results'].keys())
-    for video_name in all_names:
-        for p_info in data['results'][video_name]:
-            p_info['timestamp'] = p_info.pop('segment')
-            p_info['proposal_score'] = p_info.pop('score')
-            p_info['sentence_score'] = p_info.pop('sentence_score', 0)
-        data['results']["v_" + video_name] = data['results'].pop(video_name)
-    json.dump(data, open(dvc_json, 'w'))
-
-
-def convert_dvcjson_to_tapjson(dvc_json, tap_json):
-    data = json.load(open(dvc_json, 'r'))['results']
-    out = {}
-    out['version'] = "VERSION 1.0"
-    out['external_data'] = {'used:': True, 'details': "GT proposals"}
-    out['results'] = {}
-
-    all_names = list(data.keys())
-    for video_name in all_names:
-        video_info = []
-        event_num = len(data[video_name])
-        timestamps = [data[video_name][i]['timestamp'] for i in range(event_num)]
-        sentences = [data[video_name][i]['sentence'] for i in range(event_num)]
-        for i, timestamp in enumerate(timestamps):
-            score = data[video_name][i].get('proposal_score', 1.0)
-            video_info.append({'segment': timestamp, 'score': score, 'sentence': sentences[i], 'sentence_score': data[video_name][i].get('sentence_score', 0)})
-        out['results'][video_name[2:]] = video_info
-    json.dump(out, open(tap_json, 'w'))
-
-
-def convert_gtjson_to_tapjson(gt_json, tap_json):
-    data = json.load(open(gt_json, 'r'))
-    out = {}
-    out['version'] = "VERSION 1.0"
-    out['external_data'] = {'used:': True, 'details': "GT proposals"}
-    out['results'] = {}
-
-    all_names = list(data.keys())
-    for video_name in all_names:
-        video_info = []
-        timestamps = data[video_name]['timestamps']
-        sentences = data[video_name]['sentences']
-        for i, timestamp in enumerate(timestamps):
-            video_info.append({'segment': timestamp, 'score': 1., 'sentence': sentences[i]})
-        out['results'][video_name[2:]] = video_info
-    with open(tap_json, 'w') as f:
-        json.dump(out, f)
-
-
-def get_topn_from_dvcjson(dvc_json, out_json, top_n=3, ranking_key='proposal_score', score_thres=-1e8):
-    data = json.load(open(dvc_json, 'r'))['results']
-    out = {}
-    out['version'] = "VERSION 1.0"
-    out['external_data'] = {'used:': True, 'details': "GT proposals"}
-    out['results'] = {}
-    all_names = list(data.keys())
-    num = 0
-    bad_vid = 0
-    for video_name in all_names:
-        info = data[video_name]
-        new_info = sorted(info, key=lambda x: x[ranking_key], reverse=True)
-        new_info = [p for p in new_info if p[ranking_key] > score_thres]
-        new_info = new_info[:top_n]
-        out['results'][video_name] = new_info
-        num += len(new_info)
-        if len(new_info) == 0:
-            bad_vid += 1
-            out['results'].pop(video_name)
-    print('average proosal number: {}'.format(num / len(all_names)))
-    print('bad videos number: {}'.format(bad_vid))
-    print('good videos number: {}'.format(len(out['results'])))
-    with open(out_json, 'w') as f:
-        json.dump(out, f)
-
-
-def eval_metrics(dvc_filename, gt_filenames, para_gt_filenames, alpha=0.3, ranking_key='proposal_score', rerank=False, dvc_eval_version='2018', transformer_input_type='queries'):
-    score = collections.defaultdict(lambda: -1)
-    # top_n = 3
-    # top_n_filename = dvc_filename + '.top{}.json'.format(top_n)
-    # get_topn_from_dvcjson(dvc_filename, top_n_filename, top_n=top_n, ranking_key=ranking_key)
-    # dvc_score = eval_dvc(json_path=top_n_filename, reference=gt_filenames)
-    # dvc_score = {k: sum(v) / len(v) for k, v in dvc_score.items()}
-    # dvc_score.update(eval_soda(top_n_filename, ref_list=gt_filenames))
-    # dvc_score.update(eval_para(top_n_filename, referneces=para_gt_filenames))
-    # for key in dvc_score.keys():
-    #     score[key] = dvc_score[key]
-    if transformer_input_type == 'prior_proposals':
-        dvc_score = eval_para(dvc_filename, referneces=para_gt_filenames)
-        score.update(dvc_score)
-        #breakpoint()
-        return score
-        
-    else:
-        if rerank:
-            dvc_filename = reranking(dvc_filename, alpha=alpha, temperature=2.0)
-        dvc_score = eval_dvc(json_path=dvc_filename, reference=gt_filenames, version=dvc_eval_version)
-        dvc_score = {k: sum(v) / len(v) for k, v in dvc_score.items()}
-        dvc_score.update(eval_soda(dvc_filename, ref_list=gt_filenames))
-        dvc_score.update(eval_para(dvc_filename, referneces=para_gt_filenames))
-        score.update(dvc_score)
-        return score
-
-
-def save_dvc_json(out_json, path):
-    with open(path, 'w') as f:
-        out_json['valid_video_num'] = len(out_json['results'])
-        out_json['avg_proposal_num'] = np.array([len(v) for v in out_json['results'].values()]).mean().item()
-        json.dump(out_json, f)
-
-def reranking(p_src, alpha, temperature):
-    print('alpha: {}, temp: {}'.format(alpha, temperature))
-    d = json.load(open(p_src))
-    d_items = list(d['results'].items())
-    for k,v in d_items:
-        if True:
-            sent_scores = [p['sentence_score'] / (float(len(p['sentence'].split()))**(temperature) + 1e-5) for p in v]
-            prop_score = [p['proposal_score'] for p in v]
-            joint_score = alpha * (np.array(sent_scores)) + (np.array(prop_score))
-        for i,p in enumerate(v):
-            p['joint_score'] = joint_score[i]
-        v = sorted(v, key=lambda x: x['joint_score'], reverse=True)
-        topN = v[0]['pred_event_count']
-        v = v[:topN]
-        v = sorted(v, key=lambda x: x['timestamp'])
-        d['results'][k] = v
-    save_path = p_src+'_rerank_alpha{}_temp{}.json'.format(alpha, temperature)
-    save_dvc_json(d, save_path)
-    return save_path
-
-
-def evaluate(model, criterion, postprocessors, loader, dvc_json_path, logger=None, args=None, score_threshold=0,
-             alpha=0.3, dvc_eval_version='2018', device='cuda', debug=False, skip_lang_eval=False):
-    out_json = {'results': {},
-                'version': "VERSION 1.0",
-                'external_data': {'used:': True, 'details': None}}
-    opt = loader.dataset.opt
-
-    loss_sum = OrderedDict()
-    with torch.set_grad_enabled(False):
-        for dt in tqdm(loader, disable=opt.disable_tqdm):
-            # valid_keys = ["video_tensor", "video_length", "video_mask", "video_key"]
-            # dt = {key: value for key, value in dt.items() if key in valid_keys}
-            dt = {key: _.to(device) if isinstance(_, torch.Tensor) else _ for key, _ in dt.items()}
-            dt = collections.defaultdict(lambda: None, dt)
-
-            dt['video_target'] = [
-                    {key: _.to(device) if isinstance(_, torch.Tensor) else _ for key, _ in vid_info.items()} for vid_info in
-                    dt['video_target']]
-
-            # output, loss = model(dt, criterion, contrastive_criterion=None, eval_mode=True)
-            output, _ = model(dt, criterion, contrastive_criterion=None, eval_mode=True)
-            orig_target_sizes = dt['video_length'][:, 1]
-
-            weight_dict = criterion.weight_dict
-            # Huabin comment this line (anything about 'loss') to avoid reporting losses during evaluation
-            # final_loss = sum(loss[k] * weight_dict[k] for k in loss.keys() if k in weight_dict)
-            
-            # Huabin comment this line to avoid reporting losses during evaluation
-            # for loss_k, loss_v in loss.items():
-            #     loss_sum[loss_k] = loss_sum.get(loss_k, 0) + loss_v.item()
-            # loss_sum['total_loss'] = loss_sum.get('total_loss', 0) + final_loss.item()
-
-            results = postprocessors['bbox'](output, orig_target_sizes, loader)
-
-            batch_json = {}
-            for idx, video_name in enumerate(dt['video_key']):
-                segment = results[idx]['boxes'].cpu().numpy()
-                raw_boxes = results[idx]['raw_boxes'].cpu().numpy()
-                # pdb.set_trace()
-                #breakpoint()
-                batch_json[video_name] = [
-                    {
-                        "timestamp": segment[pid].tolist(),
-                        "raw_box": raw_boxes[pid].tolist(),
-                        "proposal_score": results[idx]['scores'][pid].item(),
-                        "sentence": results[idx]['captions'][pid],
-                        "sentence_score": results[idx]['caption_scores'][pid],
-                        'query_id': results[idx]['query_id'][pid].item(),
-                        'vid_duration': results[idx]['vid_duration'].item(),
-                        'pred_event_count': results[idx]['pred_seq_len'].item(),
-                    }
-                    for pid in range(len(segment)) if results[idx]['scores'][pid].item() > score_threshold]
-            out_json['results'].update(batch_json)
-            if debug and len(out_json['results']) > 5:
-                break
-
-    save_dvc_json(out_json, dvc_json_path)
-
-    if skip_lang_eval:
-        return None, None
-    
-    # Huabin comment this line to avoid reporting losses during evaluation
-    # for k in loss_sum.keys():
-    #     loss_sum[k] = np.round(loss_sum[k] / (len(loader) + 1e-5), 3).item()
-    # logger.info('loss: {}'.format(loss_sum))
-    scores = eval_metrics(dvc_json_path,
-                          gt_filenames=opt.gt_file_for_eval,
-                          para_gt_filenames=opt.gt_file_for_para_eval,
-                          alpha=alpha,
-                          rerank=(opt.count_loss_coef > 0),
-                          dvc_eval_version=dvc_eval_version,
-                          transformer_input_type=opt.transformer_input_type
-                          )
-
-    out_json.update(scores)
-    save_dvc_json(out_json, dvc_json_path)
-    # return scores, loss_sum
-    return scores, []
diff --git a/backup/misc/MIL_loss.py b/backup/misc/MIL_loss.py
deleted file mode 100644
index f8a234e01695ca8871b045a0ba31b13e9e79883a..0000000000000000000000000000000000000000
--- a/backup/misc/MIL_loss.py
+++ /dev/null
@@ -1,95 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from mmdet.models.losses import accuracy
-from mmdet.models.losses.cross_entropy_loss import _expand_onehot_labels
-from .utils import weight_reduce_loss
-
-
-class MILLoss(nn.Module):
-
-    def __init__(self,
-                 # use_binary=True,
-                 # reduction='mean',
-                 binary_ins=False,
-                 loss_weight=1.0, eps=1e-6, loss_type='gfocal_loss'):
-        """
-        Args:
-            use_binary (bool, optional): Whether to the prediction is
-                used for binary cross entopy
-            reduction (str, optional): The method used to reduce the loss into
-                a scalar. Defaults to 'mean'. Options are "none", "mean" and
-                "sum".
-            loss_weight (float, optional): Weight of loss. Defaults to 1.0.
-        """
-        super(MILLoss, self).__init__()
-        # self.use_binary = use_binary
-        # self.reduction = reduction
-        self.loss_weight = loss_weight
-        # if self.use_sigmoid:
-        # self.loss_cls = CrossEntropyLoss(use_sigmoid=True, loss_weight=loss_weight)
-        self.eps = eps
-        self.loss_type = loss_type
-        self.binary_ins = binary_ins
-
-    def gfocal_loss(self, p, q, w=1.0):
-        l1 = (p - q) ** 2
-        l2 = q * (p + self.eps).log() + (1 - q) * (1 - p + self.eps).log()
-        return -(l1 * l2 * w).sum(dim=-1)
-
-    def forward(self, bag_cls_prob, bag_ins_outs, labels, valid, weight=None):
-        """
-            bag_cls_outs: (B, N, C),
-            bag_ins_outs: (B, N, C*2/C)
-            valid: (B, N, 1/C)
-            labels: (B, )
-        Returns:
-        """
-        if self.binary_ins:
-            assert bag_ins_outs.shape[-1] / bag_cls_prob.shape[-1] == 2
-        else:
-            assert bag_ins_outs.shape[-1] == bag_cls_prob.shape[-1]
-
-        B, N, C = bag_cls_prob.shape
-        prob_cls = bag_cls_prob.unsqueeze(dim=-1)  # (B, N, C, 1)
-        prob_ins = bag_ins_outs.reshape(B, N, C, -1)  # (B, N, C, 2/1)
-        prob_ins = prob_ins.softmax(dim=1) * valid.unsqueeze(dim=-1)
-        prob_ins = F.normalize(prob_ins, dim=1, p=1)
-        prob = (prob_cls * prob_ins).sum(dim=1)
-        acc = accuracy(prob[..., 0], labels)
-
-        label_weights = (valid.sum(dim=1) > 0).float()
-        labels = _expand_onehot_labels(labels, None, C)[0].float()
-        num_sample = max(torch.sum(label_weights.sum(dim=-1) > 0).float().item(), 1.)
-
-        if prob.shape[-1] == 1:
-            prob = prob.squeeze(dim=-1)
-        elif prob.shape[-1] == 2:  # with binary ins
-            pos_prob, neg_prob = prob[..., 0], prob[..., 1]
-            prob = torch.cat([pos_prob, neg_prob])
-            neg_labels = labels.new_zeros(labels.shape)
-            labels = torch.cat([labels, neg_labels])
-            label_weights = torch.cat([label_weights, label_weights])
-
-        if self.loss_type == 'gfocal_loss':
-            loss = self.gfocal_loss(prob, labels, label_weights)
-            if weight is not None:
-                # modified by fei ##############################################################3
-                weight=weight.squeeze(-1)
-        elif self.loss_type == 'binary_cross_entropy':
-            # if self.use_sigmoid:
-            # method 1:
-            # loss = self.loss_cls(
-            #     prob,
-            #     labels,
-            #     label_weights,
-            #     avg_factor=avg_factor,
-            #     reduction_override=reduction_override)
-            # method 2
-            prob = prob.clamp(0, 1)
-            # modified by fei ##############################################################3
-            loss = F.binary_cross_entropy(prob, labels.float(), None, reduction="none")
-        else:
-            raise ValueError()
-        loss = weight_reduce_loss(loss, weight, avg_factor=num_sample) * self.loss_weight
-        return loss, acc, num_sample
\ No newline at end of file
diff --git a/backup/misc/__pycache__/utils.cpython-38.pyc b/backup/misc/__pycache__/utils.cpython-38.pyc
deleted file mode 100644
index 5a07b1b2f7c2819d5dcfeb1e5e462ac236b6d940..0000000000000000000000000000000000000000
Binary files a/backup/misc/__pycache__/utils.cpython-38.pyc and /dev/null differ
diff --git a/backup/misc/build_vocab.py b/backup/misc/build_vocab.py
deleted file mode 100644
index 181c9ff27a7edc9d57e16cba107a87857062e24b..0000000000000000000000000000000000000000
--- a/backup/misc/build_vocab.py
+++ /dev/null
@@ -1,66 +0,0 @@
-# coding:utf-8
-import json
-
-# file_path_list = ["data/captiondata/train_modified.json", "data/captiondata/val_1.json", "data/captiondata/val_2.json"]
-file_path_list = ["data/captiondata/yc2/yc2_train.json", "data/captiondata/yc2/yc2_val.json"]
-
-count_threshold = 2 # 4 for anet, 2 for youcook2
-# output_path = './data/vocabulary_activitynet.json'
-output_path = './data/vocabulary_youcook2.json'
-
-mark = [',', ':', '!', '_', ';', '-', '.', '?', '/', '"', '\\n', '\\']
-
-count_vocal = {}
-
-for file_path in file_path_list:
-    data = json.load(open(file_path))
-    video_ids = data.keys()
-    print('video num of ' + file_path.split('/')[-1], len(video_ids))
-    for video_id in video_ids:
-        sentences = data[video_id]["sentences"]
-        for sentence in sentences:
-            for m in mark:
-                if m in sentence:
-                    sentence = sentence.replace(m, " ")
-                sentence = sentence.replace("  ", " ")
-                sentence = sentence.replace("  ", " ")
-                sentence = sentence.replace("  ", " ")
-
-            sentence = sentence.lstrip()
-            sentence = sentence.rstrip()
-            sentence = sentence.lower()
-            sentence = sentence.split(" ")
-            length = len(sentence)
-
-            # print(sentence)
-            for word in sentence:
-                # print(type(word))
-                for m in word:
-                    if m == ' ':
-                        print('warning !')
-                        word = word.replace(m, '')
-                if word == '':
-                    print('warning !')
-                    pass
-                count_vocal[word] = count_vocal.get(word, 0) + 1
-
-print("total word:", sum(count_vocal.values()))
-count_vocal['<bos>'] = 1e10
-count_vocal['<eos>'] = 1e10
-vocab = [word for word, n in count_vocal.items() if n >= count_threshold]
-bad_word = [word for word, n in count_vocal.items() if n < count_threshold]
-bad_count = sum(count_vocal[word] for word in bad_word)
-
-vocab.append('UNK')
-print("number of vocab:", len(vocab))
-print("number of bad word:", len(bad_word))
-print("number of unks:", bad_count)
-
-itow = {i + 1: w for i, w in enumerate(vocab)}
-wtoi = {w: i + 1 for i, w in enumerate(vocab)}
-print(len(itow))
-print(len(wtoi))
-
-json.dump({'ix_to_word': itow,
-           'word_to_ix': wtoi}, open(output_path, 'w'))
-print("saving vocabulary file to {}".format(output_path))
\ No newline at end of file
diff --git a/backup/misc/detr_utils/__pycache__/box_ops.cpython-37.pyc b/backup/misc/detr_utils/__pycache__/box_ops.cpython-37.pyc
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diff --git a/backup/misc/detr_utils/__pycache__/misc.cpython-38.pyc b/backup/misc/detr_utils/__pycache__/misc.cpython-38.pyc
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diff --git a/backup/misc/detr_utils/box_ops.py b/backup/misc/detr_utils/box_ops.py
deleted file mode 100644
index 7d7106ba6c48a3cc3827a4bd923b08c7c61213af..0000000000000000000000000000000000000000
--- a/backup/misc/detr_utils/box_ops.py
+++ /dev/null
@@ -1,48 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-"""
-Utilities for bounding box manipulation and GIoU.
-"""
-import torch
-from torchvision.ops.boxes import box_area
-
-def box_cl_to_xy(x):
-    c, l = x.unbind(-1)
-    b = [c - 0.5 * l, c + 0.5 * l]
-    return torch.stack(b, dim=-1)
-
-def box_xy_to_cl(x):
-    x0, x1 = x.unbind(-1)
-    b = [(x0 + x1) / 2, (x1 - x0)]
-    return torch.stack(b, dim=-1)
-
-# modified from torchvision to also return the union
-def box_iou(boxes1, boxes2):
-    area1 = boxes1[:, 1] - boxes1[:, 0]
-    area2 = boxes2[:, 1] - boxes2[:, 0]
-    lt = torch.max(boxes1[:, None, 0], boxes2[:, 0])  # [N,M,2]
-    rb = torch.min(boxes1[:, None, 1], boxes2[:, 1])  # [N,M,2]
-    inter = (rb - lt).clamp(min=0)  # [N,M,2]
-    union = area1[:, None] + area2 - inter
-    iou = inter / (union + 1e-5)
-    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[:, 1:] >= boxes1[:, :1]).all()
-    assert (boxes2[:, 1:] >= boxes2[:, :1]).all()
-    iou, union = box_iou(boxes1, boxes2)
-    lt = torch.min(boxes1[:, None, 0], boxes2[:, 0])
-    rb = torch.max(boxes1[:, None, 1], boxes2[:, 1])
-    area = (rb - lt).clamp(min=0)  # [N,M,2]
-    giou = iou - (area - union) / (area + 1e-5)
-    return giou
\ No newline at end of file
diff --git a/backup/misc/detr_utils/misc.py b/backup/misc/detr_utils/misc.py
deleted file mode 100644
index 168603606353a959ca9cf6a39fbf2d7f9216e560..0000000000000000000000000000000000000000
--- a/backup/misc/detr_utils/misc.py
+++ /dev/null
@@ -1,989 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-"""
-Misc functions, including distributed helpers.
-
-Mostly copy-paste from torchvision references.
-"""
-import os
-import subprocess
-import time
-from collections import defaultdict, deque
-import datetime
-import pickle
-from typing import Optional, List
-
-import torch
-import torch.distributed as dist
-from torch import Tensor
-
-# 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
-
-
-# ------------------------------------------------------------------------
-# 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
-# ------------------------------------------------------------------------
-
-"""
-Misc functions, including distributed helpers.
-
-Mostly copy-paste from torchvision references.
-"""
-import os
-import subprocess
-import time
-from collections import defaultdict, deque
-import datetime
-import pickle
-from typing import Optional, List
-
-import torch
-import torch.nn as nn
-import torch.distributed as dist
-from torch import Tensor
-
-# needed due to empty tensor bug in pytorch and torchvision 0.5
-import torchvision
-if float(torchvision.__version__[:3]) < 0.5:
-    import math
-    # from torchvision.ops.misc import _NewEmptyTensorOp
-    def _check_size_scale_factor(dim, size, scale_factor):
-        # type: (int, Optional[List[int]], Optional[float]) -> None
-        if size is None and scale_factor is None:
-            raise ValueError("either size or scale_factor should be defined")
-        if size is not None and scale_factor is not None:
-            raise ValueError("only one of size or scale_factor should be defined")
-        if not (scale_factor is not None and len(scale_factor) != dim):
-            raise ValueError(
-                "scale_factor shape must match input shape. "
-                "Input is {}D, scale_factor size is {}".format(dim, len(scale_factor))
-            )
-    def _output_size(dim, input, size, scale_factor):
-        # type: (int, Tensor, Optional[List[int]], Optional[float]) -> List[int]
-        assert dim == 2
-        _check_size_scale_factor(dim, size, scale_factor)
-        if size is not None:
-            return size
-        # if dim is not 2 or scale_factor is iterable use _ntuple instead of concat
-        assert scale_factor is not None and isinstance(scale_factor, (int, float))
-        scale_factors = [scale_factor, scale_factor]
-        # math.floor might return float in py2.7
-        return [
-            int(math.floor(input.size(i + 2) * scale_factors[i])) for i in range(dim)
-        ]
-elif 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)
-
-
-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]
-
-    # serialized to a Tensor
-    buffer = pickle.dumps(data)
-    storage = torch.ByteStorage.from_buffer(buffer)
-    tensor = torch.ByteTensor(storage).to("cuda")
-
-    # obtain Tensor size of each rank
-    local_size = torch.tensor([tensor.numel()], device="cuda")
-    size_list = [torch.tensor([0], device="cuda") for _ in range(world_size)]
-    dist.all_gather(size_list, local_size)
-    size_list = [int(size.item()) for size in size_list]
-    max_size = max(size_list)
-
-    # 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="cuda"))
-    if local_size != max_size:
-        padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device="cuda")
-        tensor = torch.cat((tensor, padding), dim=0)
-    dist.all_gather(tensor_list, tensor)
-
-    data_list = []
-    for size, tensor in zip(size_list, tensor_list):
-        buffer = tensor.cpu().numpy().tobytes()[:size]
-        data_list.append(pickle.loads(buffer))
-
-    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 = list(zip(*batch))
-    batch[0] = nested_tensor_from_tensor_list(batch[0])
-    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:]:
-        for index, item in enumerate(sublist):
-            maxes[index] = max(maxes[index], item)
-    return maxes
-
-
-def nested_tensor_from_tensor_list(tensor_list: List[Tensor]):
-    # TODO make this more general
-    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])
-        # 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
-    else:
-        raise ValueError('not supported')
-    return NestedTensor(tensor, mask)
-
-
-class NestedTensor(object):
-    def __init__(self, tensors, mask: Optional[Tensor], duration=None):
-        self.tensors = tensors
-        self.mask = mask
-        self.duration = duration
-
-    def to(self, device, non_blocking=False):
-        # type: (Device) -> NestedTensor # noqa
-        cast_tensor = self.tensors.to(device, non_blocking=non_blocking)
-        mask = self.mask
-        if mask is not None:
-            assert mask is not None
-            cast_mask = mask.to(device, non_blocking=non_blocking)
-        else:
-            cast_mask = None
-        return NestedTensor(cast_tensor, cast_mask)
-
-    def record_stream(self, *args, **kwargs):
-        self.tensors.record_stream(*args, **kwargs)
-        if self.mask is not None:
-            self.mask.record_stream(*args, **kwargs)
-
-    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 get_local_size():
-    if not is_dist_avail_and_initialized():
-        return 1
-    return int(os.environ['LOCAL_SIZE'])
-
-
-def get_local_rank():
-    if not is_dist_avail_and_initialized():
-        return 0
-    return int(os.environ['LOCAL_RANK'])
-
-
-def is_main_process():
-    return get_rank() == 0
-
-
-def save_on_master(*args, **kwargs):
-    if is_main_process():
-        torch.save(*args, **kwargs)
-
-
-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)
-#         if float(torchvision.__version__[:3]) < 0.5:
-#             return _NewEmptyTensorOp.apply(input, output_shape)
-#         return _new_empty_tensor(input, output_shape)
-#     else:
-#         return torchvision.ops.misc.interpolate(input, size, scale_factor, mode, align_corners)
-
-
-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)
-    return total_norm
-
-
-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)
-
-
-
-# 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)
-#
-#
-# 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]
-#
-#     # serialized to a Tensor
-#     buffer = pickle.dumps(data)
-#     storage = torch.ByteStorage.from_buffer(buffer)
-#     tensor = torch.ByteTensor(storage).to("cuda")
-#
-#     # obtain Tensor size of each rank
-#     local_size = torch.tensor([tensor.numel()], device="cuda")
-#     size_list = [torch.tensor([0], device="cuda") for _ in range(world_size)]
-#     dist.all_gather(size_list, local_size)
-#     size_list = [int(size.item()) for size in size_list]
-#     max_size = max(size_list)
-#
-#     # 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="cuda"))
-#     if local_size != max_size:
-#         padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device="cuda")
-#         tensor = torch.cat((tensor, padding), dim=0)
-#     dist.all_gather(tensor_list, tensor)
-#
-#     data_list = []
-#     for size, tensor in zip(size_list, tensor_list):
-#         buffer = tensor.cpu().numpy().tobytes()[:size]
-#         data_list.append(pickle.loads(buffer))
-#
-#     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 = list(zip(*batch))
-# #     batch[0] = nested_tensor_from_tensor_list(batch[0])
-# #     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:]:
-#         for index, item in enumerate(sublist):
-#             maxes[index] = max(maxes[index], item)
-#     return maxes
-#
-#
-# 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 nested_tensor_from_tensor_list(tensor_list: List[Tensor]):
-# #     # TODO make this more general
-# #     if tensor_list[0].ndim == 3:
-# #         if torchvision._is_tracing():
-# #             # nested_tensor_from_tensor_list() does not export well to ONNX
-# #             # call _onnx_nested_tensor_from_tensor_list() instead
-# #             return _onnx_nested_tensor_from_tensor_list(tensor_list)
-# #
-# #         # TODO make it support different-sized images
-# #         max_size = _max_by_axis([list(img.shape) for img in tensor_list])
-# #         # 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
-# #     else:
-# #         raise ValueError('not supported')
-# #     return NestedTensor(tensor, mask)
-#
-#
-# # _onnx_nested_tensor_from_tensor_list() is an implementation of
-# # nested_tensor_from_tensor_list() that is supported by ONNX tracing.
-# # @torch.jit.unused
-# # def _onnx_nested_tensor_from_tensor_list(tensor_list: List[Tensor]) -> NestedTensor:
-# #     max_size = []
-# #     for i in range(tensor_list[0].dim()):
-# #         max_size_i = torch.max(torch.stack([img.shape[i] for img in tensor_list]).to(torch.float32)).to(torch.int64)
-# #         max_size.append(max_size_i)
-# #     max_size = tuple(max_size)
-# #
-# #     # work around for
-# #     # pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
-# #     # m[: img.shape[1], :img.shape[2]] = False
-# #     # which is not yet supported in onnx
-# #     padded_imgs = []
-# #     padded_masks = []
-# #     for img in tensor_list:
-# #         padding = [(s1 - s2) for s1, s2 in zip(max_size, tuple(img.shape))]
-# #         padded_img = torch.nn.functional.pad(img, (0, padding[2], 0, padding[1], 0, padding[0]))
-# #         padded_imgs.append(padded_img)
-# #
-# #         m = torch.zeros_like(img[0], dtype=torch.int, device=img.device)
-# #         padded_mask = torch.nn.functional.pad(m, (0, padding[2], 0, padding[1]), "constant", 1)
-# #         padded_masks.append(padded_mask.to(torch.bool))
-# #
-# #     tensor = torch.stack(padded_imgs)
-# #     mask = torch.stack(padded_masks)
-# #
-# #     return NestedTensor(tensor, mask=mask)
-#
-#
-# 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)
-#
-#
-# 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'])
-#     elif 'SLURM_PROCID' in os.environ:
-#         args.rank = int(os.environ['SLURM_PROCID'])
-#         args.gpu = args.rank % torch.cuda.device_count()
-#     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)
diff --git a/backup/misc/utils.py b/backup/misc/utils.py
deleted file mode 100644
index 301a498189d0568ce14362b3630f2c89c2a26c6e..0000000000000000000000000000000000000000
--- a/backup/misc/utils.py
+++ /dev/null
@@ -1,357 +0,0 @@
-# coding:utf-8
-# from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import time
-import torch
-import numpy as np
-import glob
-import shutil
-import os
-import colorlog
-import random
-import six
-from six.moves import cPickle
-import matplotlib as mpl
-
-mpl.use('Agg')
-import matplotlib.pyplot as plt
-
-
-def match_name_keywords(n, name_keywords):
-    out = False
-    for b in name_keywords:
-        if b in n:
-            out = True
-            break
-    return out
-
-
-def decide_two_stage(transformer_input_type, dt, criterion):
-    if transformer_input_type == 'gt_proposals':
-        two_stage = True
-        proposals = dt['gt_boxes']
-        proposals_mask = dt['gt_boxes_mask']
-        criterion.matcher.cost_caption = 0
-        for q_k in ['loss_length', 'loss_ce', 'loss_bbox', 'loss_giou']:
-            for key in criterion.weight_dict.keys():
-                if q_k in key:
-                    criterion.weight_dict[key] = 0
-        disable_iterative_refine = True
-    elif transformer_input_type == 'prior_proposals':
-        two_stage = True
-        proposals = dt['gt_boxes']
-        proposals_mask = None
-        criterion.matcher.cost_caption = 0
-        for q_k in ['loss_length', 'loss_ce', 'loss_bbox', 'loss_giou']:
-            for key in criterion.weight_dict.keys():
-                if q_k in key:
-                    criterion.weight_dict[key] = 0
-        disable_iterative_refine = False
-    elif transformer_input_type == 'queries':  #
-        two_stage = False
-        proposals = None
-        proposals_mask = None
-        disable_iterative_refine = False
-    else:
-        raise ValueError('Wrong value of transformer_input_type, got {}'.format(transformer_input_type))
-    return two_stage, disable_iterative_refine, proposals, proposals_mask
-
-
-def pickle_load(f):
-    """ Load a pickle.
-    Parameters
-    ----------
-    f: file-like object
-    """
-    if six.PY3:
-        return cPickle.load(f, encoding='latin-1')
-    else:
-        return cPickle.load(f)
-
-
-def pickle_dump(obj, f):
-    """ Dump a pickle.
-    Parameters
-    ----------
-    obj: pickled object
-    f: file-like object
-    """
-    if six.PY3:
-        return cPickle.dump(obj, f, protocol=2)
-    else:
-        return cPickle.dump(obj, f)
-
-
-def set_seed(seed):
-    random.seed(seed)
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-    torch.cuda.manual_seed(seed)
-    torch.cuda.manual_seed_all(seed)
-    torch.backends.cudnn.deterministic = True
-    torch.backends.cudnn.benchmark = False
-
-    # grid_sampler_2d_backward_cuda does not have a deterministic implementation. try set torch.use_deterministic_algorithms(True, warn_only=True) to see the non-deterministic operation
-    # torch.use_deterministic_algorithms(True, warn_only=True)
-
-
-def update_values(dict_from, dict_to):
-    for key, value in dict_from.items():
-        if key not in dict_to.keys():
-            raise AssertionError('key mismatching: {}'.format(key))
-        if isinstance(value, dict):
-            update_values(dict_from[key], dict_to[key])
-        elif value is not None:
-            dict_to[key] = dict_from[key]
-
-
-def print_opt(opt, model, logger):
-    print_alert_message('All args:', logger)
-    for key, item in opt._get_kwargs():
-        logger.info('{} = {}'.format(key, item))
-    print_alert_message('Model structure:', logger)
-    logger.info(model)
-
-
-def build_folder_name(opt):
-    # The dataset
-    # breakpoint()
-    if len(opt.visual_feature_folder) == 2:
-        if ('youcook2' in opt.visual_feature_folder[1]) or ('yc2' in opt.visual_feature_folder[1]):
-            dataset_name = 'howto-yc2_yc2'
-        elif ('Tasty' in opt.visual_feature_folder[1]) or ('tasty' in opt.visual_feature_folder[1]):
-            dataset_name = 'howto-tasty_tasty'
-        elif ('anet' in opt.visual_feature_folder[1]) or ('Anet' in opt.visual_feature_folder[1]):
-            dataset_name = 'howto-anet_anet'
-        # elif ('vlep' in opt.visual_feature_folder[1]) or ('Vlep' in opt.visual_feature_folder[1]):
-        #     dataset_name = 'howto-vlep_vlep'
-        else:
-            raise ValueError('Wrong dataset name')
-        
-        if 'vlep' in opt.visual_feature_folder[0] or 'Vlep' in opt.visual_feature_folder[0]:
-            dataset_name = dataset_name.replace('howto', 'vlep')
-    else:
-        if ('youcook2' in opt.visual_feature_folder[0]) or ('yc2' in opt.visual_feature_folder[0]):
-            dataset_name = 'yc2'
-        elif ('Anet' in opt.visual_feature_folder[0]) or ('anet' in opt.visual_feature_folder[0]):
-            dataset_name = 'anet'
-        elif ('Tasty' in opt.visual_feature_folder[0]) or ('tasty' in opt.visual_feature_folder[0]):
-            dataset_name = 'tasty'
-        elif ('Howto' in opt.visual_feature_folder[0]) or ('howto' in opt.visual_feature_folder[0]):
-            if ('yc2' in opt.visual_feature_folder_val[0]) or ('youcook2' in opt.visual_feature_folder_val[0]):
-                dataset_name = 'howto_yc2'
-            elif 'tasty' in opt.visual_feature_folder_val[0] or 'Tasty' in opt.visual_feature_folder_val[0]:
-                dataset_name = 'howto_tasty'
-            elif 'anet' in opt.visual_feature_folder_val[0] or 'Anet' in opt.visual_feature_folder_val[0]:
-                dataset_name = 'howto_anet'
-        elif ('vlep' in opt.visual_feature_folder[0]) or ('Vlep' in opt.visual_feature_folder[0]):
-            if ('yc2' in opt.visual_feature_folder_val[0]) or ('youcook2' in opt.visual_feature_folder_val[0]):
-                dataset_name = 'vlep_yc2'
-            elif 'tasty' in opt.visual_feature_folder_val[0] or 'Tasty' in opt.visual_feature_folder_val[0]:
-                dataset_name = 'vlep_tasty'
-            elif 'anet' in opt.visual_feature_folder_val[0] or 'Anet' in opt.visual_feature_folder_val[0]:
-                dataset_name = 'vlep_anet'
-        else:
-            raise ValueError('Wrong dataset name')
-    if 'tasty_14' in opt.dict_file:
-        dataset_name += '_voc14'
-    
-    # The code base
-    if opt.use_anchor:
-        use_anchor = 'anc' # Means learnable anchor is used
-    else:
-        use_anchor = 'ori' # Means original anchor in pdvc is used
-
-    # The state of using pseudo boxes
-    if opt.use_pseudo_box:
-        use_pseudo = 'pbox'
-        if opt.pseudo_box_type == 'similarity':
-            use_pseudo += '(sim)'
-        else:
-            use_pseudo += '({})'.format(opt.pseudo_box_type)
-    else:
-        use_pseudo = 'GT'
-
-    # The viusal-text model used
-    if opt.pretrained_language_model == 'CLIP-ViP':
-        text_model = 'ViP'
-    elif opt.pretrained_language_model == 'UniVL':
-        text_model = 'Uni'
-    else:
-        text_model = opt.pretrained_language_model
-    
-    format_folder_name = '_'.join([dataset_name, use_anchor, use_pseudo, text_model])
-    
-
-
-    return format_folder_name
-
-def build_folder(opt):
-    # breakpoint()
-    if opt.start_from:
-        print('Start training from id:{}'.format(opt.start_from))
-        save_folder = os.path.join(opt.save_dir, opt.start_from)
-        assert os.path.exists(save_folder) and os.path.isdir(save_folder), 'Wrong start_from path: {}'.format(save_folder)
-    else:
-        if not os.path.exists(opt.save_dir):
-            os.mkdir(opt.save_dir)
-        format_folder_name = build_folder_name(opt)
-        # breakpoint()
-        save_foldername = ''
-        if opt.use_pseudo_box:
-            if opt.pseudo_box_type != 'align':
-                if opt.pseudo_box_type == 'similarity_op' or opt.pseudo_box_type == 'similarity_op_order':
-                    save_foldername = '{}_topf{}_beta{}_iter{}_r{}'.format(opt.pseudo_box_type, opt.top_frames, opt.beta, opt.iteration, opt.width_ratio)
-                elif opt.pseudo_box_type == 'similarity_op_order_v2':
-                    save_foldername = '{}_topf{}_iter{}_r{}_th{}'.format(opt.pseudo_box_type, opt.top_frames, opt.iteration, opt.width_ratio, opt.width_th)
-                else:
-                    save_foldername = '{}_topf{}_w{}_{}_r{}'.format(opt.pseudo_box_type, opt.top_frames, opt.window_size, opt.statistic_mode, opt.width_ratio)
-            else:
-                save_folder = 'align'
-        else:
-            save_foldername = 'gtbox'
-
-        if opt.refine_pseudo_box:
-            save_foldername += '_refine_aug({},{})_top{}_{}stage'.format(opt.pseudo_box_aug_num, \
-                                                                        opt.pseudo_box_aug_ratio, \
-                                                                        opt.merge_k_boxes, \
-                                                                        opt.refine_pseudo_stage_num)
-            if opt.pseudo_box_aug_mode == 'uniform':
-                save_foldername += '_uniform'
-            elif opt.pseudo_box_aug_mode == 'random_new':
-                save_foldername += '_random_new'
-            save_foldername += ('_' + opt.merge_criterion)
-            if opt.merge_mode == 'interpolate':
-                save_foldername += '_interpolate'
-            if opt.use_neg_pseudo_box:
-                save_foldername += '_{}neg'.format(opt.num_neg_box)
-            if opt.mil_loss_coef != 1.0:
-                save_foldername += '_mil_coef{}'.format(str(opt.mil_loss_coef))
-            if opt.weighted_mil_loss:
-                save_foldername += '_wMIL'
-            if not opt.focal_mil:
-                save_foldername += '_noFocal'
-            if opt.disable_rematch:
-                save_foldername += '_nomatch'
-            if opt.use_additional_score_layer:
-                save_foldername += '_S-layer'
-            if opt.use_additional_cap_layer:
-                save_foldername += '_C-layer'
-            if 'puyu' in opt.train_caption_file[0]:
-                save_foldername += '_puyu'
-            elif 'mixlm' in opt.train_caption_file[0]:
-                save_foldername += '_mixlm'
-
-        if opt.id != '':
-            save_foldername += '_{}'.format(opt.id)
-        # breakpoint()
-        # basefilename = os.path.basename(opt.cfg_path)
-        # basefilename = os.path.splitext(basefilename)[0]
-        save_folder = os.path.join(opt.save_dir, format_folder_name)
-        save_folder = os.path.join(save_folder, save_foldername)
-        if os.path.exists(save_folder):
-            print('Results folder "{}" already exists, renaming it...'.format(save_folder))
-            i = 1
-            while 1:
-                new_save_folder = save_folder + '_{}'.format(i)
-                if not os.path.exists(new_save_folder):
-                    save_folder = new_save_folder
-                    break
-                i += 1
-            # wait_flag = input('Warning! Path {} already exists, rename it? (Y/N) : '.format(save_folder))
-            # if wait_flag in ['Y', 'y']:
-            #     # opt.id = opt.id + '_{}'.format(time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()))
-            #     # save_folder = os.path.join(opt.save_dir, opt.id)
-            #     # print('Rename opt.id as "{}".'.format(opt.id))
-            #     new_name = input('the new name to be appended :')
-            #     save_folder = save_folder + '_' + new_name
-            # # elif wait_flag in ['N', 'n']:
-            # #     wait_flag_new = input('Are you sure re-write this folder:{}? (Y/N): '.format(save_folder))
-            # #     if wait_flag_new in ['Y', 'y']:
-            # #         return save_folder
-            # #     else:
-            # #         raise AssertionError('Folder {} already exists'.format(save_folder))
-            # else:
-            #     raise AssertionError('Folder {} already exists'.format(save_folder))
-        print('Results folder "{}" does not exist, creating folder...'.format(save_folder))
-        os.makedirs(save_folder)
-        os.makedirs(os.path.join(save_folder, 'prediction'))
-    return save_folder
-
-
-def backup_envir(save_folder, opt):
-    cfg_path = opt.cfg_path
-    dir_path = os.path.dirname(cfg_path)
-    backup_folders = ['cfgs_base', 'cfgs', 'misc', 'pdvc']
-    if dir_path not in backup_folders:
-        backup_folders.append(dir_path)
-
-    backup_files = glob.glob('./*.py')
-    for folder in backup_folders:
-        shutil.copytree(folder, os.path.join(save_folder, 'backup', folder))
-    for file in backup_files:
-        shutil.copyfile(file, os.path.join(save_folder, 'backup', file))
-
-
-def create_logger(folder, filename):
-    log_colors = {
-        'DEBUG': 'blue',
-        'INFO': 'white',
-        'WARNING': 'green',
-        'ERROR': 'red',
-        'CRITICAL': 'yellow',
-    }
-
-    import logging
-    logger = logging.getLogger('DVC')
-    # %(filename)s$RESET:%(lineno)d
-    # LOGFORMAT = "%(log_color)s%(asctime)s [%(log_color)s%(filename)s:%(lineno)d] | %(log_color)s%(message)s%(reset)s |"
-    LOGFORMAT = ""
-    LOG_LEVEL = logging.DEBUG
-    logging.root.setLevel(LOG_LEVEL)
-    stream = logging.StreamHandler()
-    stream.setLevel(LOG_LEVEL)
-    stream.setFormatter(colorlog.ColoredFormatter(LOGFORMAT, datefmt='%d %H:%M', log_colors=log_colors))
-
-    # print to log file
-    hdlr = logging.FileHandler(os.path.join(folder, filename))
-    hdlr.setLevel(LOG_LEVEL)
-    # hdlr.setFormatter(logging.Formatter("[%(asctime)s] %(message)s"))
-    hdlr.setFormatter(logging.Formatter("%(message)s"))
-    logger.addHandler(hdlr)
-    logger.addHandler(stream)
-    return logger
-
-
-def print_alert_message(str, logger=None):
-    msg = '*' * 20 + ' ' + str + ' ' + '*' * (58 - len(str))
-    if logger:
-        logger.info('\n\n' + msg)
-    else:
-        print(msg)
-
-
-def set_lr(optimizer, lr):
-    for group in optimizer.param_groups:
-        group['lr'] = lr
-
-
-def clip_gradient(optimizer, grad_clip):
-    for group in optimizer.param_groups:
-        for i, param in enumerate(group['params']):
-            if param.grad is not None:
-                param.grad.data.clamp_(-grad_clip, grad_clip)
-
-
-if __name__ == '__main__':
-    # import opts
-    #
-    # info = {'opt': vars(opts.parse_opts()),
-    #         'loss': {'tap_loss': 0, 'tap_reg_loss': 0, 'tap_conf_loss': 0, 'lm_loss': 0}}
-    # record_this_run_to_csv(info, 'save/results_all_runs.csv')
-
-    logger = create_logger('./', 'mylogger.log')
-    logger.info('debug')
-    logger.info('test2')
diff --git a/backup/opts.py b/backup/opts.py
deleted file mode 100644
index 8c0abaea05f6aefca9779237b1d3c555f10e45ec..0000000000000000000000000000000000000000
--- a/backup/opts.py
+++ /dev/null
@@ -1,312 +0,0 @@
-import argparse
-import time
-import yaml
-import os
-import numpy as np
-
-def parse_opts():
-    parser = argparse.ArgumentParser()
-
-    # configure of this run
-    parser.add_argument('--cfg_path', type=str, required=True, help='config file')
-    parser.add_argument('--id', type=str, default='', help='id of this run. Results and logs will saved in this folder ./save/id')
-    parser.add_argument('--gpu_id', type=str, nargs='+', default=[])
-    parser.add_argument('--disable_tqdm', action='store_true')
-    parser.add_argument('--seed', type=int, default=777)
-    parser.add_argument('--random_seed',  action='store_true', help='choose a random seed from {1,...,1000}')
-    parser.add_argument('--disable_cudnn', type=int, default=0, help='disable cudnn may solve some unknown bugs')
-    parser.add_argument('--debug', action='store_true', help='using mini-dataset for fast debugging')
-    parser.add_argument('--device', default='cuda', choices=['cpu', 'cuda'], help='device to use for training / testing')
-    parser.add_argument('--map', action='store_true', default=False, help='map a100 data path to 3090 data path')
-    # parser.add_argument('--extra_id', type=str, default='', help='extra config for listed in the folder name')
-
-    #  ***************************** INPUT DATA PATH *****************************
-    parser.add_argument('--train_caption_file', type=str,
-                        default='data/anet/captiondata/train_modified.json', help='')
-    parser.add_argument('--invalid_video_json', type=str, nargs='+', default=[])
-    parser.add_argument('--val_caption_file', type=str, default='data/anet/captiondata/val_1.json')
-    parser.add_argument('--visual_feature_folder', type=str, default='data/anet/resnet_bn')
-    parser.add_argument('--text_feature_folder', type=str, default=None)
-    parser.add_argument('--gt_file_for_auc', type=str, nargs='+', default='data/anet/captiondata/val_all.json')
-    parser.add_argument('--gt_file_for_eval', type=str, nargs='+', default=['data/anet/captiondata/val_1.json', 'data/anet/captiondata/val_2.json'])
-    parser.add_argument('--gt_file_for_para_eval', type=str, nargs='+', default= ['data/anet/captiondata/para/anet_entities_val_1_para.json', 'data/anet/captiondata/para/anet_entities_val_2_para.json'])
-    parser.add_argument('--dict_file', type=str, default='data/anet/vocabulary_activitynet.json', help='')
-    parser.add_argument('--criteria_for_best_ckpt', type=str, default='overall', choices=['dvc', 'pc', 'overall'], help='for dense video captioning, use soda_c + METEOR as the criteria'
-                                                                                                         'for paragraph captioning, choose the best para_METEOR+para_CIDEr+para_BLEU4'
-                                                                                                         'for overall, select BLEU4 + METEOR + F1_score')
-
-    parser.add_argument('--visual_feature_type', type=str, default='c3d', choices=['c3d', 'resnet_bn', 'resnet', 'UniVL', 'CLIP', 'CLIP-ViP'])
-    parser.add_argument('--feature_dim', type=int, default=500, help='dim of frame-level feature vector')
-
-    parser.add_argument('--start_from', type=str, default='', help='id of the run with incompleted training')
-    parser.add_argument('--start_from_mode', type=str, choices=['best', 'last'], default="last")
-    parser.add_argument('--pretrain', type=str, choices=['full', 'encoder', 'decoder'])
-    parser.add_argument('--pretrain_path', type=str, default='', help='path of .pth')
-
-    #  ***************************** DATALOADER OPTION *****************************
-    parser.add_argument('--nthreads', type=int, default=4)
-    parser.add_argument('--data_norm', type=int, default=0)
-    parser.add_argument('--data_rescale', type=int, default=1)
-
-    parser.add_argument('--feature_sample_rate', type=int, default=1)
-    parser.add_argument('--train_proposal_sample_num', type=int,
-                        default=24,
-                        help='number of sampled proposals (or proposal sequence), a bigger value may be better')
-    parser.add_argument('--gt_proposal_sample_num', type=int, default=30)
-    parser.add_argument('--ft_gt_percent', type=float, default=1.0, help='the percentage of gt samples used in pbox+gt setting. 1.0 means using all gt samples in yc2/tasty.')
-    parser.add_argument('--pre_percent', type=float, default=1.0, help='the percentage of gt samples used in pbox+gt setting. 1.0 means using all gt samples in yc2/tasty.')
-
-
-    #  ***************************** Caption Decoder  *****************************
-    parser.add_argument('--vocab_size', type=int, default=5747)
-    parser.add_argument('--wordRNN_input_feats_type', type=str, default='C', choices=['C', 'E', 'C+E'],
-                        help='C:clip-level features, E: event-level features, C+E: both')
-    parser.add_argument('--caption_decoder_type', type=str, default="light",
-                        choices=['none','light', 'standard'])
-    parser.add_argument('--rnn_size', type=int, default=512,
-                        help='size of the rnn in number of hidden nodes in each layer')
-    parser.add_argument('--num_layers', type=int, default=1, help='number of layers in the RNN')
-    parser.add_argument('--input_encoding_size', type=int, default=512,
-                        help='the encoding size of each token in the vocabulary')
-    parser.add_argument('--att_hid_size', type=int, default=512, help='the hidden size of the attention MLP')
-    parser.add_argument('--drop_prob', type=float, default=0.5, help='strength of dropout in the Language Model RNN')
-    parser.add_argument('--max_caption_len', type=int, default=30, help='')
-
-    #  ***************************** Transformer  *****************************
-    parser.add_argument('--hidden_dim', type=int, default=512)
-    parser.add_argument('--num_queries', type=int, default=100)
-    parser.add_argument('--hidden_dropout_prob', type=float, default=0.5)
-    parser.add_argument('--layer_norm_eps', type=float, default=1e-12)
-    parser.add_argument('--caption_cost_type', type=str, default='loss')
-    parser.add_argument('--set_cost_caption', type=float, default=0)
-    parser.add_argument('--set_cost_class', type=float, default=1)
-    parser.add_argument('--set_cost_bbox', type=float, default=5)
-    parser.add_argument('--set_cost_giou', type=float, default=2)
-    parser.add_argument('--cost_alpha', type=float, default=0.25)
-    parser.add_argument('--cost_gamma', type=float, default=2)
-
-    parser.add_argument('--bbox_loss_coef', default=5, type=float)
-    parser.add_argument('--giou_loss_coef', default=2, type=float)
-    parser.add_argument('--count_loss_coef', default=0, type=float)
-    parser.add_argument('--caption_loss_coef', default=0, type=float)
-    parser.add_argument('--eos_coef', default=0.1, type=float,
-                        help="Relative classification weight of the no-object class")
-    parser.add_argument('--num_classes', type=int, default=1)
-    parser.add_argument('--dec_layers', type=int, default=6)
-    parser.add_argument('--enc_layers', type=int, default=6)
-    parser.add_argument('--transformer_ff_dim', type=int, default=2048)
-    parser.add_argument('--transformer_dropout_prob', type=float, default=0.1)
-    parser.add_argument('--frame_embedding_num', type=int, default = 100)
-    parser.add_argument('--sample_method', type=str, default = 'nearest', choices=['nearest', 'linear'])
-    parser.add_argument('--fix_xcw', type=int, default=0)
-
-    # ***************************** Learnable anchor *****************************
-    parser.add_argument('--use_anchor', default=False, action='store_true')
-    parser.add_argument('--random_anchor_init', default=True, action='store_false')
-    parser.add_argument('--prior_anchor_duration_init', default=True, action='store_false')
-
-    # ***************************** Text-query alignment *****************************
-    parser.add_argument('--matcher_type', type=str, default='default', choices=['default', 'DTW', 'Sim'])
-    # === For Text encoder ===
-    parser.add_argument('--pretrained_language_model', type=str, default='UniVL', \
-                        choices=['UniVL', 'CLIP', 'CLIP-ViP'], help='Pretrained hugging face model')
-    parser.add_argument('--text_hidden_dim', type=int, default=768, help='hidden dim of text encoder')
-    parser.add_argument('--max_text_input_len', type=int, default=32, help='')
-    parser.add_argument('--max_pos_num', type=int, default=500)
-    parser.add_argument('--huggingface_cache_dir', type=str, default='.cache')
-    parser.add_argument('--text_encoder_learning_strategy', type=str, default='frozen',choices=('frozen'))
-
-    # === For generate_pesudo_bbox ===
-    parser.add_argument('--use_pseudo_box', default=False, action='store_true')
-    parser.add_argument('--pseudo_box_type', type=str, default='similarity', choices=['align', 'similarity', 'weight_sim', 'weight_index', 'modeframe'])
-
-    # 1) For different ways of generating pseudo box
-    parser.add_argument('--top_frames', type=int, default=15)
-    parser.add_argument('--window_size', type=int, default=2)
-    parser.add_argument('--statistic_mode', type=str, default='median', choices=['mode', 'median'])
-    parser.add_argument('--width_ratio', type=float, default=-1)
-    parser.add_argument('--beta', type=float, default=1, help="weight for overlap loss")
-    parser.add_argument('--width_th', type=float, default=0.5, help="threshold for width")
-    parser.add_argument('--iteration', type=int, default=3, help="iteration for pseudo box generation")
-    # 2) For box refinement
-    parser.add_argument('--pseudo_box_aug', default=False, action='store_true')
-    parser.add_argument('--pseudo_box_aug_num', type=int, default=5)
-    parser.add_argument('--pseudo_box_aug_ratio', type=float, default=0.1)
-    parser.add_argument('--pseudo_box_aug_mode', default='random', choices=['random', 'uniform'])
-    parser.add_argument('--refine_pseudo_box', default=False, action='store_true')
-    parser.add_argument('--use_additional_score_layer', default=False, action='store_true')
-    parser.add_argument('--use_additional_cap_layer', default=False, action='store_true')
-    parser.add_argument('--merge_k_boxes', type=int, default=3)
-    parser.add_argument('--merge_criterion', type=str, choices=['cap_topk', 'ins_topk', 'ins_cap_topk'], default='cap_topk')
-    parser.add_argument('--merge_mode', type=str, choices=['weighted_sum, interpolate'], default='weighted_sum')
-    parser.add_argument('--refine_pseudo_stage_num', type=int, default=2)
-    parser.add_argument('--use_query_box_for_refine', default=False, action='store_true')
-    parser.add_argument('--norm_ins_score', default='sigmoid', choices=['sigmoid', 'softmax'])
-    parser.add_argument('--cap_prob_clip', default=False, action='store_true')
-    parser.add_argument('--use_neg_pseudo_box', default=False, action='store_true')
-    parser.add_argument('--num_neg_box', default=10, type=int)
-    parser.add_argument('--weighted_mil_loss', default=False, action='store_true')
-    parser.add_argument('--focal_mil', default=False, action='store_true')
-    parser.add_argument('--disable_rematch', default=False, action='store_true')
-    parser.add_argument('--start_refine_epoch', default=-1, type=int)
-    
-
-    # === For DTW ===
-    parser.add_argument('--align_keep_percentile', type=float, default=0.1)
-    parser.add_argument('--align_top_band_size', type=int, default=0)
-    parser.add_argument('--align_drop_z', type=int, default=0)
-    parser.add_argument('--align_one_to_many', default=False, action='store_true')
-    parser.add_argument('--align_many_to_one', default=False, action='store_true')
-    parser.add_argument('--align_contiguous', default=False, action='store_true')
-    
-    # === For Sim matcher
-    parser.add_argument('--set_cost_sim', type=float, default=1.0)
-
-    # === For contrastive ===
-    parser.add_argument('--enable_contrastive', default=False, action='store_true', help='enable contrastive learning')
-    parser.add_argument('--disable_contrastive_projection', default=False, action='store_true', help='disable contrastive projection layers')
-    parser.add_argument('--contrastive_hidden_size', type=int, default=128, help='Contrastive hidden size')
-    parser.add_argument('--contrastive_loss_start_coef', type=float, default=0.1, help='Weight of contrastive loss')
-    parser.add_argument('--contrastive_loss_temperature', type=float, default=0.1, help='Temperature of cl temperature')
-    parser.add_argument('--enable_cross_video_cl', type=bool, default=True, help='Enable cross video contrastive loss')
-    parser.add_argument('--enable_e2t_cl', default=True, action='store_true', help=' enable event-to-text contrastive')
-    parser.add_argument('--enable_bg_for_cl', default=True, action='store_true', help=' add a class for background events')
-    parser.add_argument('--set_cost_cl', type=float, default=0.0)
-    parser.add_argument('--cl_schedule_val', type=float, nargs='+', default=[0, 0.1])
-    parser.add_argument('--cl_schedule_time', type=int, nargs='+', default=[0, 2])
-
-
-
-    # ***************************** Prior *****************************
-    parser.add_argument('--prior_manner', type=str, default='all', choices=['add', 'all'])
-
-    #  ***************************** OPTIMIZER *****************************
-    parser.add_argument('--training_scheme', type=str, default='all', choices=['cap_head_only', 'no_cap_head', 'all'])
-    parser.add_argument('--epoch', type=int, default=25)
-    parser.add_argument('--batch_size', type=int, default=1, help='batch_size')
-    parser.add_argument('--batch_size_for_eval', type=int, default=1, help='')
-    parser.add_argument('--grad_clip', type=float, default=100., help='clip gradients at this value')
-    parser.add_argument('--optimizer_type', type=str, default='adam')
-    parser.add_argument('--weight_decay', type=float, default=0, help='weight_decay')
-
-    parser.add_argument('--lr', type=float, default=1e-4, help='1e-4 for resnet feature and 5e-5 for C3D feature')
-    parser.add_argument('--learning_rate_decay_start', type=float, default=8)
-    parser.add_argument('--learning_rate_decay_every', type=float, default=3)
-    parser.add_argument('--learning_rate_decay_rate', type=float, default=0.5)
-
-    #  ***************************** SAVING AND LOGGING *****************************
-    parser.add_argument('--min_epoch_when_save', type=int, default=-1)
-    parser.add_argument('--save_checkpoint_every', type=int, default=1)
-    parser.add_argument('--save_all_checkpoint', action='store_true')
-    parser.add_argument('--save_dir', type=str, default='/mnt/data/pjlab-3090-sport/wuhao/logs/dibs', help='directory to store checkpointed models')
-
-    #  ***************************** For Deformable DETR *************************************
-    parser.add_argument('--lr_backbone_names', default=["None"], type=str, nargs='+')
-    parser.add_argument('--lr_backbone', default=2e-5, type=float)
-    parser.add_argument('--lr_proj', default=0, type=int)
-    parser.add_argument('--lr_linear_proj_names', default=['reference_points', 'sampling_offsets'], type=str, nargs='+')
-    parser.add_argument('--lr_linear_proj_mult', default=0.1, type=float)
-
-    # Variants of Deformable DETR
-    parser.add_argument('--with_box_refine', default=False, action='store_true')
-    parser.add_argument('--transformer_input_type', default='queries', choices=['gt_proposals', 'prior_proposals', 'learnt_proposals', 'queries'])
-
-    # * Backbone
-    parser.add_argument('--backbone', default=None, type=str,
-                        help="Name of the convolutional backbone to use")
-    parser.add_argument('--dilation', action='store_true',
-                        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('--position_embedding_scale', default=2 * np.pi, type=float,
-                        help="position / size * scale")
-    parser.add_argument('--num_feature_levels', default=4, type=int, help='number of feature levels')
-
-    # * Transformer
-
-    parser.add_argument('--nheads', default=8, type=int,
-                        help="Number of attention heads inside the transformer's attentions")
-    parser.add_argument('--dec_n_points', default=4, type=int)
-    parser.add_argument('--enc_n_points', default=4, type=int)
-
-    parser.add_argument('--share_caption_head', type = int ,default=1)
-
-    parser.add_argument('--cap_nheads', default=8, type=int)
-    parser.add_argument('--cap_dec_n_points', default=4, type=int)
-    parser.add_argument('--cap_num_feature_levels', default=4, type=int)
-    parser.add_argument('--disable_mid_caption_heads', action='store_true')
-
-    # Loss
-    parser.add_argument('--no_aux_loss', dest='aux_loss', action='store_false',
-                        help="Disables auxiliary decoding losses (loss at each layer)")
-
-
-    # * Loss coefficients
-
-    parser.add_argument('--cls_loss_coef', default=2, type=float)
-    parser.add_argument('--self_iou_loss_coef', default=0.0, type=float)
-    parser.add_argument('--ref_rank_loss_coef', default=0.1, type=float)
-    parser.add_argument('--mil_loss_coef', default=1.0, type=float)
-    parser.add_argument('--focal_alpha', default=0.25, type=float)
-    parser.add_argument('--focal_gamma', default=2., type=float)
-
-
-    #***************************** Event counter *****************************
-    parser.add_argument('--max_eseq_length', default=10, type=int)
-    parser.add_argument('--lloss_gau_mask', default=1, type=int)
-    parser.add_argument('--lloss_beta', default=1, type=float)
-
-    # scheduled sampling
-    parser.add_argument('--scheduled_sampling_start', type=int, default=-1,
-                        help='at what iteration to start decay gt probability')
-    parser.add_argument('--basic_ss_prob', type=float, default=0, help='initial ss prob')
-    parser.add_argument('--scheduled_sampling_increase_every', type=int, default=2,
-                        help='every how many iterations thereafter to gt probability')
-    parser.add_argument('--scheduled_sampling_increase_prob', type=float, default=0.05,
-                        help='How much to update the prob')
-    parser.add_argument('--scheduled_sampling_max_prob', type=float, default=0.25,
-                        help='Maximum scheduled sampling prob.')
-
-    # reranking
-    parser.add_argument('--ec_alpha', type=float, default=0.3)
-    parser.add_argument('--test', action='store_true', default=False)
-    args = parser.parse_args()
-
-    if args.cfg_path:
-        import_cfg(args.cfg_path, vars(args))
-
-    if args.random_seed:
-        import random
-        seed = int(random.random() * 1000)
-        new_id = args.id + '_seed{}'.format(seed)
-        save_folder = os.path.join(args.save_dir, new_id)
-        while os.path.exists(save_folder):
-            seed = int(random.random() * 1000)
-            new_id = args.id + '_seed{}'.format(seed)
-            save_folder = os.path.join(args.save_dir, new_id)
-        args.id = new_id
-        args.seed = seed
-
-    if args.debug:
-        args.id = 'debug_' + time.strftime("%Y-%m-%d_%H-%M-%S", time.localtime())
-        args.save_checkpoint_every = 1
-        args.shuffle = 0
-
-    if args.caption_decoder_type == 'none':
-        assert args.caption_loss_coef == 0
-        assert args.set_cost_caption == 0
-
-    print("args.id: {}".format(args.id))
-    return args
-
-def import_cfg(cfg_path, args):
-    with open(cfg_path, 'r') as handle:
-        yml = yaml.load(handle, Loader=yaml.FullLoader)
-        if 'base_cfg_path' in yml:
-            base_cfg_path = yml['base_cfg_path']
-            import_cfg(base_cfg_path, args)
-        args.update(yml)
-    pass
-if __name__ == '__main__':
-    opt = parse_opts()
-    print(opt)
\ No newline at end of file
diff --git a/backup/pdvc/CaptioningHead/LSTM.py b/backup/pdvc/CaptioningHead/LSTM.py
deleted file mode 100644
index 4b44fae2e15520e0c09c298d233e686c9b45d36e..0000000000000000000000000000000000000000
--- a/backup/pdvc/CaptioningHead/LSTM.py
+++ /dev/null
@@ -1,174 +0,0 @@
-# This file contains ShowAttendTell and AllImg model
-
-# ShowAttendTell is from Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
-# https://arxiv.org/abs/1502.03044
-
-# AllImg is a model where
-# img feature is concatenated with word embedding at every time step as the input of lstm
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import pdb
-
-import numpy
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.autograd import *
-
-class Captioner(nn.Module):
-    def __init__(self, opt):
-        super(Captioner, self).__init__()
-        self.opt = opt
-
-        self.vocab_size = opt.vocab_size
-        self.input_encoding_size = opt.input_encoding_size
-        self.rnn_size = opt.rnn_size
-        self.num_layers = opt.num_layers
-        self.drop_prob_lm = opt.drop_prob
-        self.max_caption_len = opt.max_caption_len
-
-        self.ss_prob = 0.0 # Schedule sampling probability
-        self.embed = nn.Embedding(self.vocab_size + 1, self.input_encoding_size)
-
-        self.logit = nn.Linear(self.rnn_size, self.vocab_size + 1)
-        self.dropout = nn.Dropout(self.drop_prob_lm)
-
-        self.init_weights()
-
-    def init_weights(self):
-        initrange = 0.1
-        self.embed.weight.data.uniform_(-initrange, initrange)
-        self.logit.bias.data.fill_(0)
-        self.logit.weight.data.uniform_(-initrange, initrange)
-
-    def init_hidden(self, batch_size):
-        weight = next(self.parameters()).data
-        return (weight.new(self.num_layers, batch_size, self.rnn_size).zero_(),
-                weight.new(self.num_layers, batch_size, self.rnn_size).zero_())  # (h0, c0)
-
-    def build_loss(self, input, target, mask):
-        one_hot = torch.nn.functional.one_hot(target, self.opt.vocab_size+1)
-        max_len = input.shape[1]
-        output = - (one_hot[:, :max_len] * input * mask[:, :max_len, None]).sum(2).sum(1) / (mask.sum(1) + 1e-6)
-        return output
-
-    def forward(self, event, clip, clip_mask, seq):
-        batch_size = clip.shape[0]
-
-        state = self.init_hidden(batch_size)
-        outputs = []
-        seq = seq.long()
-
-        for i in range(seq.size(1) - 1):
-            if self.training and i >= 1 and self.ss_prob > 0.0: # otherwiste no need to sample
-                sample_prob = clip.data.new(batch_size).uniform_(0, 1)
-                sample_mask = sample_prob < self.ss_prob
-                if sample_mask.sum() == 0:
-                    it = seq[:, i].clone()
-                else:
-                    sample_ind = sample_mask.nonzero().view(-1)
-                    it = seq[:, i].data.clone()
-                    prob_prev = torch.exp(outputs[-1].data) # fetch prev distribution: shape Nx(M+1)
-                    it.index_copy_(0, sample_ind, torch.multinomial(prob_prev, 1).view(-1).index_select(0, sample_ind))
-                    it = Variable(it, requires_grad=False)
-            else:
-                it = seq[:, i].clone()
-                # break if all the sequences end
-            if i >= 1 and seq[:, i].data.sum() == 0:
-                break
-
-            output, state = self.get_logprobs_state(it, event, clip, clip_mask, state)
-            outputs.append(output)
-
-        return torch.cat([_.unsqueeze(1) for _ in outputs], 1)
-
-
-    def get_logprobs_state(self, it, event , clip, clip_mask, state):
-        xt = self.embed(it)
-        output, state = self.core(xt, event , clip, clip_mask, state)
-        logprobs = F.log_softmax(self.logit(self.dropout(output)), dim=1)
-        return logprobs, state
-
-    def sample(self, event , clip, clip_mask, opt={}):
-
-        sample_max = opt.get('sample_max', 1)
-        beam_size = opt.get('beam_size', 1)
-        temperature = opt.get('temperature', 1.0)
-
-        batch_size = clip.shape[0]
-
-        state = self.init_hidden(batch_size)
-
-        seq = []
-        seqLogprobs = []
-
-        for t in range(self.max_caption_len + 1):
-            if t == 0: # input <bos>
-                it = clip.data.new(batch_size).long().zero_()
-            elif sample_max:
-                sampleLogprobs, it = torch.max(logprobs.data, 1)
-                it = it.view(-1).long()
-            else:
-                if temperature == 1.0:
-                    prob_prev = torch.exp(logprobs.data) # fetch prev distribution: shape Nx(M+1)
-                else:
-                    # scale logprobs by temperature
-                    prob_prev = torch.exp(torch.div(logprobs.data, temperature))
-                it = torch.multinomial(prob_prev, 1)
-                sampleLogprobs = logprobs.gather(1, it) # gather the logprobs at sampled positions
-                it = it.view(-1).long() # and flatten indices for downstream processing
-
-            logprobs, state = self.get_logprobs_state(it, event , clip, clip_mask, state)
-
-            if t >= 1:
-                # stop when all finished
-                if t == 1:
-                    unfinished = it > 0
-                else:
-                    unfinished = unfinished & (it > 0)
-                if unfinished.sum() == 0:
-                    break
-                it = it * unfinished.type_as(it)
-                seq.append(it) #seq[t] the input of t+2 time step
-                seqLogprobs.append(sampleLogprobs.view(-1))
-
-        if seq==[] or len(seq)==0:
-            return [],[]
-        return torch.cat([_.unsqueeze(1) for _ in seq], 1), torch.cat([_.unsqueeze(1) for _ in seqLogprobs], 1)
-
-class AllImgCore(nn.Module):
-    def __init__(self, opt):
-        super(AllImgCore, self).__init__()
-        self.input_encoding_size = opt.input_encoding_size
-        self.rnn_size = opt.rnn_size
-        self.num_layers = opt.num_layers
-        self.drop_prob_lm = opt.drop_prob
-        self.att_feat_size = opt.clip_context_dim
-
-        self.opt = opt
-        self.wordRNN_input_feats_type = opt.wordRNN_input_feats_type
-        self.input_dim = self.decide_input_feats_dim()
-        self.rnn = nn.LSTM(self.input_encoding_size + self.input_dim,
-                           self.rnn_size, self.num_layers, bias=False, dropout=self.drop_prob_lm)
-        assert self.wordRNN_input_feats_type == 'C'
-
-    def decide_input_feats_dim(self):
-        dim = 0
-        if 'E' in self.wordRNN_input_feats_type:
-            dim += self.opt.event_context_dim
-        if 'C' in self.wordRNN_input_feats_type:
-            dim += self.opt.clip_context_dim
-        return dim
-
-    def forward(self, xt, event, clip, clip_mask, state):
-        input_feats = (clip * clip_mask.unsqueeze(2)).sum(1) / (clip_mask.sum(1, keepdims=True) + 1e-5)
-        output, state = self.rnn(torch.cat([xt, input_feats], 1).unsqueeze(0), state)
-        return output.squeeze(0), state
-
-
-class LightCaptioner(Captioner):
-    def __init__(self, opt):
-        super(LightCaptioner, self).__init__(opt)
-        self.core = AllImgCore(opt)
diff --git a/backup/pdvc/CaptioningHead/LSTM_DSA.py b/backup/pdvc/CaptioningHead/LSTM_DSA.py
deleted file mode 100644
index 918fb0ccf89416929b4cee8c1deadd7c99d586ae..0000000000000000000000000000000000000000
--- a/backup/pdvc/CaptioningHead/LSTM_DSA.py
+++ /dev/null
@@ -1,289 +0,0 @@
-# This file contains ShowAttendTell and AllImg model
-
-# ShowAttendTell(Soft attention) is from Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
-# https://arxiv.org/abs/1502.03044
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.autograd import *
-
-from pdvc.ops.modules import MSDeformAttnCap
-
-class Captioner(nn.Module):
-    def __init__(self, opt):
-        super(Captioner, self).__init__()
-        self.opt = opt
-
-        self.vocab_size = opt.vocab_size
-        self.input_encoding_size = opt.input_encoding_size
-        self.rnn_size = opt.rnn_size
-        self.num_layers = opt.num_layers
-        self.drop_prob_lm = opt.drop_prob
-        self.max_caption_len = opt.max_caption_len
-
-        self.ss_prob = 0.0 # Schedule sampling probability
-        self.embed = nn.Embedding(self.vocab_size + 1, self.input_encoding_size)
-
-        self.logit = nn.Linear(self.rnn_size, self.vocab_size + 1)
-        self.dropout = nn.Dropout(self.drop_prob_lm)
-
-        self.init_weights()
-
-    def init_weights(self):
-        initrange = 0.1
-        self.embed.weight.data.uniform_(-initrange, initrange)
-        self.logit.bias.data.fill_(0)
-        self.logit.weight.data.uniform_(-initrange, initrange)
-
-    def init_hidden(self, batch_size):
-        weight = next(self.parameters()).data
-        return (weight.new(self.num_layers, batch_size, self.rnn_size).zero_(),
-                weight.new(self.num_layers, batch_size, self.rnn_size).zero_())  # (h0, c0)
-
-    def build_loss(self, input, target, mask):
-        one_hot = torch.nn.functional.one_hot(target, self.opt.vocab_size+1)
-        max_len = input.shape[1]
-        output = - (one_hot[:, :max_len] * input * mask[:, :max_len, None]).sum(2).sum(1) / (mask.sum(1) + 1e-6)
-        return output
-
-    def build_prob(self, input, target, mask):
-        '''
-        Calculate the sentence-level predicted prob for each GT sentence of each query
-        input: [num_sentence, max_length, num_words_voc]
-        '''
-        # breakpoint()
-        one_hot = torch.nn.functional.one_hot(target, self.opt.vocab_size+1) # [num_sentence, max_length, num_words_voc]
-        max_len = input.shape[1]
-        # output = (one_hot[:, :max_len] * input * mask[:, :max_len, None]).sum(-1).sum(-1) / (mask.sum(1) + 1e-6)
-        output = (one_hot[:, :max_len] * input * mask[:, :max_len, None]).sum(-1).sum(-1) / (mask.sum(1) + 1e-6)
-        return output
-
-    def forward(self,hs, reference, others, cap_tensor):
-        seq = cap_tensor
-        vid_num, query_num, _ = hs.shape
-        assert vid_num == 1
-
-        reference_points = reference
-        input_flatten = others['memory']
-        input_spatial_shapes = others['spatial_shapes']
-        input_level_start_index = others['level_start_index']
-        input_padding_mask = others['mask_flatten']
-        if reference_points.shape[-1] == 2:
-            reference_points = reference_points[:, :, None] \
-                                     * torch.stack([others['valid_ratios']]*2, -1)[:, None]
-        elif reference_points.shape[-1] == 1: 
-            reference_points = reference_points[:, :, None] * others['valid_ratios'][:, None, :, None]
-
-        query = hs
-        batch_size = query.shape[1]
-        state = self.init_hidden(batch_size)
-        outputs = []
-        raw_probs = []
-        seq = seq.long()
-
-        n_levels = self.core.n_levels
-        if n_levels < self.core.opt.num_feature_levels:
-            input_spatial_shapes = input_spatial_shapes[:n_levels]
-            input_level_start_index = input_level_start_index[:n_levels]
-            total_input_len = torch.prod(input_spatial_shapes, dim=1).sum()
-            input_flatten = input_flatten[:, :total_input_len]
-            input_padding_mask = input_padding_mask[:, :total_input_len]
-            reference_points = reference_points[:, :, :n_levels]
-            pass
-
-        for i in range(seq.size(1) - 1):
-            if self.training and i >= 1 and self.ss_prob > 0.0: # otherwiste no need to sample
-                sample_prob = hs.new_zeros(batch_size).uniform_(0, 1)
-                sample_mask = sample_prob < self.ss_prob
-                if sample_mask.sum() == 0:
-                    it = seq[:, i].clone()
-                else:
-                    sample_ind = sample_mask.nonzero().view(-1)
-                    it = seq[:, i].data.clone()
-                    prob_prev = torch.exp(outputs[-1].data) # fetch prev distribution: shape Nx(M+1)
-                    it.index_copy_(0, sample_ind, torch.multinomial(prob_prev, 1).view(-1).index_select(0, sample_ind))
-                    it = Variable(it, requires_grad=False)
-            else:
-                it = seq[:, i].clone()
-                # break if all the sequences end
-            if i >= 1 and seq[:, i].data.sum() == 0:
-                break
-
-            output, state, raw_prob = self.get_logprobs_state(it, state, query, reference_points, input_flatten, input_spatial_shapes, input_level_start_index, input_padding_mask)
-            outputs.append(output)
-            raw_probs.append(raw_prob)
-
-        if self.opt.refine_pseudo_box and self.training:
-            return torch.cat([_.unsqueeze(1) for _ in outputs], 1), torch.cat([_.unsqueeze(1) for _ in raw_probs], 1)
-
-        return torch.cat([_.unsqueeze(1) for _ in outputs], 1)
-
-
-    def get_logprobs_state(self, it, state, query, reference_points, input_flatten, input_spatial_shapes, input_level_start_index, mask):
-        xt = self.embed(it)
-        output, state = self.core(xt, state, query, reference_points, input_flatten, input_spatial_shapes, input_level_start_index, mask)
-        logprobs = F.log_softmax(self.logit(self.dropout(output)), dim=1)
-        softmax_probs = F.softmax(self.logit(self.dropout(output)), dim=1)
-        raw_probs = self.logit(self.dropout(output))
-        # raw_probs: [max_num_word, vocab_size+1]
-        return logprobs, state, raw_probs
-
-    def sample(self,hs, reference, others, opt={}):
-
-        vid_num, query_num, _ = hs.shape
-        assert vid_num == 1
-        batch_size = vid_num * query_num
-        sample_max = opt.get('sample_max', 1)
-        beam_size = opt.get('beam_size', 1)
-        temperature = opt.get('temperature', 1.0)
-
-        reference_points = reference
-        input_flatten = others['memory']
-        input_spatial_shapes = others['spatial_shapes']
-        input_level_start_index = others['level_start_index']
-        input_padding_mask = others['mask_flatten']
-        if reference_points.shape[-1] == 2:
-            reference_points = reference_points[:, :, None] \
-                                     * torch.stack([others['valid_ratios']]*2, -1)[:, None]
-        elif reference_points.shape[-1] == 1:
-            reference_points = reference_points[:, :, None] * others['valid_ratios'][:, None,:, None]
-        query = hs
-
-        n_levels = self.core.n_levels
-        if n_levels < self.core.opt.num_feature_levels:
-            input_spatial_shapes = input_spatial_shapes[:n_levels]
-            input_level_start_index = input_level_start_index[:n_levels]
-            total_input_len = torch.prod(input_spatial_shapes, dim=1).sum()
-            input_flatten = input_flatten[:, :total_input_len]
-            input_padding_mask = input_padding_mask[:, :total_input_len]
-            reference_points = reference_points[:, :, :n_levels]
-            pass
-
-        state = self.init_hidden(batch_size)
-
-        seq = []
-        seqLogprobs = []
-        #breakpoint()
-
-        for t in range(self.max_caption_len + 1):
-            if t == 0: # input <bos>
-                it = hs.data.new(batch_size).long().zero_()
-            elif sample_max:
-                sampleLogprobs, it = torch.max(logprobs.data, 1)
-                it = it.view(-1).long()
-            else:
-                if temperature == 1.0:
-                    prob_prev = torch.exp(logprobs.data) # fetch prev distribution: shape Nx(M+1)
-                else:
-                    # scale logprobs by temperature
-                    prob_prev = torch.exp(torch.div(logprobs.data, temperature))
-                it = torch.multinomial(prob_prev, 1)
-                sampleLogprobs = logprobs.gather(1, it) # gather the logprobs at sampled positions
-                it = it.view(-1).long() # and flatten indices for downstream processing
-
-            logprobs, state, softmax_prob = self.get_logprobs_state(it, state, query, reference_points, input_flatten, input_spatial_shapes, input_level_start_index, input_padding_mask)
-
-            if t >= 1:
-                # stop when all finished
-                if t == 1:
-                    unfinished = it > 0
-                else:
-                    unfinished = unfinished & (it > 0)
-                if unfinished.sum() == 0:
-                    break
-                it = it * unfinished.type_as(it)
-                seq.append(it) #seq[t] the input of t+2 time step
-                seqLogprobs.append(sampleLogprobs.view(-1))
-
-        if seq==[] or len(seq)==0:
-            return [],[]
-        return torch.cat([_.unsqueeze(1) for _ in seq], 1), torch.cat([_.unsqueeze(1) for _ in seqLogprobs], 1)
-
-
-class ShowAttendTellCore(nn.Module):
-
-    def __init__(self, opt):
-        super(ShowAttendTellCore, self).__init__()
-        self.input_encoding_size = opt.input_encoding_size
-
-        self.rnn_size = opt.rnn_size
-        self.num_layers = opt.num_layers
-        self.drop_prob_lm = opt.drop_prob
-        #self.fc_feat_size = opt.fc_feat_size
-        self.att_feat_size = int(opt.clip_context_dim / opt.cap_nheads)
-        self.att_hid_size = opt.att_hid_size
-
-        self.opt = opt
-        self.wordRNN_input_feats_type = opt.wordRNN_input_feats_type
-        self.input_dim = opt.hidden_dim * 2
-
-        self.rnn = nn.LSTM(self.input_encoding_size + self.input_dim ,
-                                                      self.rnn_size, self.num_layers, bias=False, dropout=self.drop_prob_lm)
-        self.att_drop = nn.Dropout(0.5)
-
-        d_model = opt.hidden_dim
-        self.n_levels = opt.cap_num_feature_levels
-        self.n_heads = opt.cap_nheads
-        self.n_points = opt.cap_dec_n_points
-
-        self.deformable_att = MSDeformAttnCap(d_model, self.n_levels, self.n_heads, self.n_points)
-
-        if self.att_hid_size > 0:
-            self.ctx2att = nn.Linear(self.att_feat_size, self.att_hid_size)
-            self.h2att = nn.Linear(self.rnn_size, self.att_hid_size)
-            self.alpha_net = nn.Linear(self.att_hid_size, 1)
-
-    def get_input_feats(self, event, att_clip):
-        input_feats = []
-        if 'E' in self.wordRNN_input_feats_type:
-            input_feats.append(event)
-        if 'C' in self.wordRNN_input_feats_type:
-            input_feats.append(att_clip)
-        input_feats = torch.cat(input_feats,1)
-        return input_feats
-
-    def forward(self,xt, state, query, reference_points, input_flatten, input_spatial_shapes, input_level_start_index, input_padding_mask):
-
-        joint_query = torch.cat((state[0][-1].unsqueeze(0), query), 2)
-        # (N_, N_q, C)
-
-        N_, Lq_, L_, _ = reference_points.shape
-
-        # (N_ * M_, D_, Lq_, L_* P_)
-        clip = self.deformable_att(joint_query, reference_points, input_flatten, input_spatial_shapes,
-                                       input_level_start_index, input_padding_mask)
-        clip = clip.reshape(N_, self.n_heads, -1, Lq_, self.n_levels * self.n_points).permute(0, 3, 1, 4, 2)
-        clip = clip.reshape(N_ * Lq_, self.n_heads, self.n_levels * self.n_points, self.att_feat_size)
-        att_size = self.n_levels * self.n_points
-
-        att = self.ctx2att(clip)                             # (batch * att_size) * att_hid_size
-        att = att.view(-1, self.n_heads, att_size, self.att_hid_size)     # batch * att_size * att_hid_size
-        att_h = self.h2att(state[0][-1])                    # batch * att_hid_size
-        att_h = att_h.unsqueeze(1).unsqueeze(1).expand_as(att)           # batch * att_size * att_hid_size
-        dot = att + att_h                                   # batch * att_size * att_hid_size
-        dot = torch.tanh(dot)  # batch * att_size * att_hid_size
-        dot = dot.view(-1, self.att_hid_size)               # (batch * att_size) * att_hid_size
-        dot = self.alpha_net(dot)                           # (batch * att_size) * 1
-        dot = dot.view(-1, att_size)                        # batch * att_size
-
-        weight = F.softmax(dot, dim=1)
-        att_feats_ = clip.reshape(-1, att_size, self.att_feat_size) # batch * att_size * att_feat_size
-        att_res = torch.bmm(weight.unsqueeze(1), att_feats_).squeeze(1) # batch * att_feat_size
-        att_res = att_res.reshape(N_ * Lq_, self.n_heads, self.att_feat_size).flatten(1)
-        input_feats = torch.cat((att_res.unsqueeze(0), query), 2)
-        # print(xt.shape, input_feats.shape, query.shape, reference_points.shape)
-        output, state = self.rnn(torch.cat([xt.unsqueeze(0), input_feats], 2), state)
-
-        return output.squeeze(0), state
-
-
-class LSTMDSACaptioner(Captioner):
-    def __init__(self, opt):
-        super(LSTMDSACaptioner, self).__init__(opt)
-        self.core = ShowAttendTellCore(opt)
-
diff --git a/backup/pdvc/CaptioningHead/Puppet.py b/backup/pdvc/CaptioningHead/Puppet.py
deleted file mode 100644
index 3051b3d3de863fefc196e08740e7d6d05474adfd..0000000000000000000000000000000000000000
--- a/backup/pdvc/CaptioningHead/Puppet.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import torch
-import torch.nn as nn
-
-
-class PuppetCaptionModel(nn.Module):
-    def __init__(self, opt):
-        super(PuppetCaptionModel, self).__init__()
-        self.vocab_size = opt.vocab_size
-        self.opt = opt
-        self.puppet_layer= nn.Linear(1,1)
-
-    def forward(self, event, clip, clip_mask, seq):
-        N, L = seq.shape
-        output = torch.zeros((N, L-1, self.vocab_size + 1), device=seq.device)
-        return output
-
-    def sample(self, event, clip, clip_mask, opt={}):
-        N, _, C = clip.shape
-        output = torch.zeros((N, 3), device=clip.device)
-        prob = torch.zeros((N, 3), device=clip.device)
-        return output, prob
-
-    def build_loss(self, input, target, mask):
-        one_hot = torch.nn.functional.one_hot(target, self.opt.vocab_size+1)
-        output = - (one_hot * input * mask[..., None]).sum(2).sum(1) / (mask.sum(1) + 1e-6)
-        return output
\ No newline at end of file
diff --git a/backup/pdvc/CaptioningHead/__init__.py b/backup/pdvc/CaptioningHead/__init__.py
deleted file mode 100644
index 55abd1cc8681971b0e498d5db23771053240029f..0000000000000000000000000000000000000000
--- a/backup/pdvc/CaptioningHead/__init__.py
+++ /dev/null
@@ -1,22 +0,0 @@
-from .LSTM import LightCaptioner
-from .Puppet import PuppetCaptionModel
-from .LSTM_DSA import LSTMDSACaptioner
-
-def build_captioner(opt):
-    if opt.caption_decoder_type == 'none':
-        caption_embed = PuppetCaptionModel(opt)
-
-    elif opt.caption_decoder_type == 'light':
-        opt.event_context_dim = None
-        opt.clip_context_dim = opt.hidden_dim
-        caption_embed = LightCaptioner(opt)
-
-    elif opt.caption_decoder_type == 'standard':
-        opt.event_context_dim = None
-        opt.clip_context_dim = opt.hidden_dim
-        caption_embed = LSTMDSACaptioner(opt)
-
-    else:
-        raise ValueError('caption decoder type is invalid')
-    return caption_embed
-
diff --git a/backup/pdvc/CaptioningHead/__pycache__/LSTM.cpython-37.pyc b/backup/pdvc/CaptioningHead/__pycache__/LSTM.cpython-37.pyc
deleted file mode 100644
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deleted file mode 100644
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diff --git a/backup/pdvc/UniVL.py b/backup/pdvc/UniVL.py
deleted file mode 100644
index c5a8bcf7f019968d8751bbbab0537295c77ebfdd..0000000000000000000000000000000000000000
--- a/backup/pdvc/UniVL.py
+++ /dev/null
@@ -1,238 +0,0 @@
-
-import os
-import random
-import numpy as np
-from pathlib import Path
-from pdvc.modules.modeling import UniVL
-from pdvc.modules.tokenization import BertTokenizer
-from transformers import AutoTokenizer, BertForPreTraining
-import torch
-import argparse
-
-PYTORCH_PRETRAINED_BERT_CACHE = Path(os.getenv('PYTORCH_PRETRAINED_BERT_CACHE',
-                                               Path.home() / '.pytorch_pretrained_bert'))
-
-class UniVL_args(object):
-    def __init__(self) -> None:
-        self.do_pretrain = False
-        self.do_train = False
-        self.do_eval = True
-        self.train_csv = 'data/youcookii_singlef_train.csv'
-        self.val_csv = 'data/youcookii_singlef_val.csv'
-        self.data_path = 'data/youcookii_caption.pickle'
-        self.features_path = 'data/youcookii_videos_feature.pickle'
-        self.num_thread_reader = 1
-        self.lr = 0.0001
-        self.epochs = 20
-        self.batch_size = 256
-        self.batch_size_val = 3500
-        self.lr_decay = 0.9
-        self.n_display = 100
-        self.video_dim = 1024
-        self.seed = 42
-        self.max_words = 48
-        self.max_frames = 100
-        self.feature_framerate = 1
-        self.margin = 0.1
-        self.hard_negative_rate = 0.5
-        self.negative_weighting = 1
-        self.n_pair = 1
-        self.output_dir = None
-        self.bert_model = "bert-base-uncased"
-        self.visual_model = "visual-base"
-        self.cross_model = "cross-base"
-        self.decoder_model = "decoder-base"
-        self.init_model = None
-        self.do_lower_case = True
-        self.warmup_proportion = 0.1
-        self.gradient_accumulation_steps = 1
-        self.n_gpu = 1
-        self.cache_dir = ""
-        self.fp16 = False
-        self.fp16_opt_level = 'O1'
-        self.task_type = "retrieval"
-        self.datatype = "youcook"
-        self.world_size = 0
-        self.local_rank = 0
-        self.coef_lr = 0.1
-        self.use_mil = False
-        self.sampled_use_mil = False
-        self.text_num_hidden_layers = 12
-        self.visual_num_hidden_layers = 6
-        self.cross_num_hidden_layers = 2
-        self.decoder_num_hidden_layers = 3
-        self.train_sim_after_cross = False
-        self.expand_msrvtt_sentences = False
-        self.batch_size = int(self.batch_size / self.gradient_accumulation_steps)
-
-    def __repr__(self) -> str:
-        return str(self.__dict__)
-
-
-
-
-# def get_args(description='UniVL on Retrieval Task'):
-#     parser = argparse.ArgumentParser(description=description)
-#     parser.add_argument("--do_pretrain", action='store_true', help="Whether to run training.")
-#     parser.add_argument("--do_train", action='store_true', help="Whether to run training.")
-#     parser.add_argument("--do_eval", action='store_true', default=True, help="Whether to run eval on the dev set.")
-
-#     parser.add_argument('--train_csv', type=str, default='data/youcookii_singlef_train.csv', help='')
-#     parser.add_argument('--val_csv', type=str, default='data/youcookii_singlef_val.csv', help='')
-#     parser.add_argument('--data_path', type=str, default='data/youcookii_caption.pickle', help='data pickle file path')
-#     parser.add_argument('--features_path', type=str, default='data/youcookii_videos_feature.pickle', help='feature path')
-
-#     parser.add_argument('--num_thread_reader', type=int, default=1, help='')
-#     parser.add_argument('--lr', type=float, default=0.0001, help='initial learning rate')
-#     parser.add_argument('--epochs', type=int, default=20, help='upper epoch limit')
-#     parser.add_argument('--batch_size', type=int, default=256, help='batch size')
-#     parser.add_argument('--batch_size_val', type=int, default=3500, help='batch size eval')
-#     parser.add_argument('--lr_decay', type=float, default=0.9, help='Learning rate exp epoch decay')
-#     parser.add_argument('--n_display', type=int, default=100, help='Information display frequence')
-#     parser.add_argument('--video_dim', type=int, default=1024, help='video feature dimension')
-#     parser.add_argument('--seed', type=int, default=42, help='random seed')
-#     parser.add_argument('--max_words', type=int, default=20, help='')
-#     parser.add_argument('--max_frames', type=int, default=100, help='')
-#     parser.add_argument('--feature_framerate', type=int, default=1, help='')
-#     parser.add_argument('--margin', type=float, default=0.1, help='margin for loss')
-#     parser.add_argument('--hard_negative_rate', type=float, default=0.5, help='rate of intra negative sample')
-#     parser.add_argument('--negative_weighting', type=int, default=1, help='Weight the loss for intra negative')
-#     parser.add_argument('--n_pair', type=int, default=1, help='Num of pair to output from data loader')
-
-#     parser.add_argument("--output_dir", default=None, type=str,
-#                         help="The output directory where the model predictions and checkpoints will be written.")
-#     parser.add_argument("--bert_model", default="bert-base-uncased", type=str,
-#                         help="Bert pre-trained model")
-#     parser.add_argument("--visual_model", default="visual-base", type=str, required=False, help="Visual module")
-#     parser.add_argument("--cross_model", default="cross-base", type=str, required=False, help="Cross module")
-#     parser.add_argument("--decoder_model", default="decoder-base", type=str, required=False, help="Decoder module")
-#     parser.add_argument("--init_model", default=None, type=str, required=False, help="Initial model.")
-#     parser.add_argument("--do_lower_case", action='store_true', help="Set this flag if you are using an uncased model.")
-#     parser.add_argument("--warmup_proportion", default=0.1, type=float,
-#                         help="Proportion of training to perform linear learning rate warmup for. E.g., 0.1 = 10%% of training.")
-#     parser.add_argument('--gradient_accumulation_steps', type=int, default=1,
-#                         help="Number of updates steps to accumulate before performing a backward/update pass.")
-#     parser.add_argument('--n_gpu', type=int, default=1, help="Changed in the execute process.")
-
-#     parser.add_argument("--cache_dir", default="", type=str,
-#                         help="Where do you want to store the pre-trained models downloaded from s3")
-
-#     parser.add_argument('--fp16', action='store_true',
-#                         help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit")
-#     parser.add_argument('--fp16_opt_level', type=str, default='O1',
-#                         help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
-#                              "See details at https://nvidia.github.io/apex/amp.html")
-
-#     parser.add_argument("--task_type", default="retrieval", type=str, help="Point the task `retrieval` to finetune.")
-#     parser.add_argument("--datatype", default="youcook", type=str, help="Point the dataset `youcook` to finetune.")
-
-#     parser.add_argument("--world_size", default=0, type=int, help="distribted training")
-#     parser.add_argument("--local_rank", default=0, type=int, help="distribted training")
-#     parser.add_argument('--coef_lr', type=float, default=0.1, help='coefficient for bert branch.')
-#     parser.add_argument('--use_mil', action='store_true', help="Whether use MIL as Miech et. al. (2020).")
-#     parser.add_argument('--sampled_use_mil', action='store_true', help="Whether MIL, has a high priority than use_mil.")
-
-#     parser.add_argument('--text_num_hidden_layers', type=int, default=12, help="Layer NO. of text.")
-#     parser.add_argument('--visual_num_hidden_layers', type=int, default=6, help="Layer NO. of visual.")
-#     parser.add_argument('--cross_num_hidden_layers', type=int, default=2, help="Layer NO. of cross.")
-#     parser.add_argument('--decoder_num_hidden_layers', type=int, default=3, help="Layer NO. of decoder.")
-
-#     parser.add_argument('--train_sim_after_cross', action='store_true', help="Test retrieval after cross encoder.")
-#     parser.add_argument('--expand_msrvtt_sentences', action='store_true', help="")
-
-#     args = parser.parse_args()
-
-#     # Check paramenters
-#     if args.gradient_accumulation_steps < 1:
-#         raise ValueError("Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format(
-#             args.gradient_accumulation_steps))
-#     if not args.do_train and not args.do_eval:
-#         raise ValueError("At least one of `do_train` or `do_eval` must be True.")
-
-#     args.batch_size = int(args.batch_size / args.gradient_accumulation_steps)
-
-#     return args
-
-def set_seed_logger(args):
-    # predefining random initial seeds
-    random.seed(args.seed)
-    os.environ['PYTHONHASHSEED'] = str(args.seed)
-    np.random.seed(args.seed)
-    torch.manual_seed(args.seed)
-    torch.cuda.manual_seed(args.seed)
-    torch.cuda.manual_seed_all(args.seed)  # if you are using multi-GPU.
-    torch.backends.cudnn.benchmark = False
-    torch.backends.cudnn.deterministic = True
-
-    # world_size = torch.distributed.get_world_size()
-    # torch.cuda.set_device(args.local_rank)
-    # args.world_size = world_size
-
-    # if not os.path.exists(args.output_dir):
-    #     os.makedirs(args.output_dir, exist_ok=True)
-
-    return args
-
-def load_pretrained_UniVL(return_visual_encoder=False):
-
-    args = UniVL_args()
-    args = set_seed_logger(args)
-    device, n_gpu = 'cuda', 1
-
-    init_model = '/cpfs01/user/liuhuabin/PDVC/pdvc/modules/univl.pretrained.bin'
-    model_state_dict = torch.load(init_model, map_location='cpu')
-
-    # Prepare model
-    cache_dir = os.path.join(str(PYTORCH_PRETRAINED_BERT_CACHE), 'distributed')
-    model = UniVL.from_pretrained('bert-base-uncased', 'visual-base', 'cross-base', 'decoder-base',
-                                   cache_dir=cache_dir, state_dict=model_state_dict, task_config=args)
-
-    model.to(device)
-    if return_visual_encoder:
-        return model.bert, model.visual, model.normalize_video
-    else:
-        return model.bert
-
-def build_UniVL_tokenizer():
-    return BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True)
-
-# if __name__ == '__main__':
-#     device, n_gpu = 'cuda', 1
-#     captions = ['I love you', 'you believe me']
-
-#     tokenizer_hg = AutoTokenizer.from_pretrained("bert-base-uncased")
-#     text_encoder_hg = tokenizer_hg(captions, return_tensors='pt', truncation=True, padding=True, max_length=20)
-#     text_encoder_hg = {key: _.to(device) if isinstance(_, torch.Tensor) else _ for key, _ in text_encoder_hg.items()}
-#     attention_mask = text_encoder_hg['attention_mask']
-
-#     args = UniVL_args()
-#     args = set_seed_logger(args)
-#     args.init_model = 'modules/univl.pretrained.bin'
-#     # tokenizer = build_UniVL_tokenizer()
-#     # input_ids = []
-#     # for sent in captions:
-#     #     sent = tokenizer.tokenize(sent)
-#     #     sent = ['[CLS]'] + sent + ['[SEP]']
-#     #     input_ids += tokenizer.convert_tokens_to_ids(sent)
-#     model = load_pretrained_UniVL(args, device, n_gpu, args.local_rank, args.init_model)
-#     text_embed = model(**text_encoder_hg, output_all_encoded_layers=True)[0][-1]
-#     breakpoint()
-
-if __name__ == '__main__':
-    device, n_gpu = 'cuda', 1
-    args = UniVL_args()
-    args = set_seed_logger(args)
-    args.init_model = 'modules/univl.pretrained.bin'
-    # tokenizer = build_UniVL_tokenizer()
-    # input_ids = []
-    # for sent in captions:
-    #     sent = tokenizer.tokenize(sent)
-    #     sent = ['[CLS]'] + sent + ['[SEP]']
-    #     input_ids += tokenizer.convert_tokens_to_ids(sent)
-    model_bert, model_visual, video_normalizer = load_pretrained_UniVL(args, device, n_gpu, args.local_rank, args.init_model)
-    inputs = torch.rand(2,215,1024)
-    video_mask = torch.ones(2,215)
-    inputs = video_normalizer(inputs)
-    visual_embed = model_visual(inputs, video_mask, output_all_encoded_layers=True)[0][-1]
-    
-    breakpoint()
\ No newline at end of file
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diff --git a/backup/pdvc/base_encoder.py b/backup/pdvc/base_encoder.py
deleted file mode 100644
index 6cb150a62dbb709589ec5271fe1b11ec16adf8f8..0000000000000000000000000000000000000000
--- a/backup/pdvc/base_encoder.py
+++ /dev/null
@@ -1,86 +0,0 @@
-# ------------------------------------------------------------------------
-# PDVC
-# ------------------------------------------------------------------------
-# Modified from 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
-# ------------------------------------------------------------------------
-
-"""
-Base Encoder to create multi-level conv features and positional embedding.
-"""
-
-import torch
-import torch.nn.functional as F
-from torch import nn
-from misc.detr_utils.misc import NestedTensor
-from .position_encoding import PositionEmbeddingSine
-
-
-class BaseEncoder(nn.Module):
-    def __init__(self, num_feature_levels, vf_dim, hidden_dim):
-        super(BaseEncoder, self).__init__()
-        self.pos_embed = PositionEmbeddingSine(hidden_dim//2, normalize=True)
-        self.num_feature_levels = num_feature_levels
-        self.hidden_dim = hidden_dim
-
-        if num_feature_levels > 1:
-            input_proj_list = []
-            in_channels = vf_dim
-            input_proj_list.append(nn.Sequential(
-                nn.Conv1d(in_channels, hidden_dim, kernel_size=1),
-                nn.GroupNorm(32, hidden_dim),
-            ))
-            for _ in range(num_feature_levels - 1):
-                input_proj_list.append(nn.Sequential(
-                    nn.Conv1d(in_channels, hidden_dim, kernel_size=3, stride=2, padding=1),
-                    nn.GroupNorm(32, hidden_dim),
-                ))
-                in_channels = hidden_dim
-            self.input_proj = nn.ModuleList(input_proj_list)
-        else:
-            self.input_proj = nn.ModuleList([
-                nn.Sequential(
-                    nn.Conv2d(vf_dim, hidden_dim, kernel_size=1),
-                    nn.GroupNorm(32, hidden_dim),
-                )])
-
-        for proj in self.input_proj:
-            nn.init.xavier_uniform_(proj[0].weight, gain=1)
-            nn.init.constant_(proj[0].bias, 0)
-
-    def forward(self, vf, mask, duration):
-        # vf: (N, L, C), mask: (N, L),  duration: (N)
-        vf = vf.transpose(1, 2)  # (N, L, C) --> (N, C, L)
-        vf_nt = NestedTensor(vf, mask, duration)
-        pos0 = self.pos_embed(vf_nt)
-
-        srcs = []
-        masks = []
-        poses = []
-
-        src0, mask0 = vf_nt.decompose()
-        srcs.append(self.input_proj[0](src0))
-        masks.append(mask0)
-        poses.append(pos0)
-        assert mask is not None
-
-        for l in range(1, self.num_feature_levels):
-            if l == 1:
-                src = self.input_proj[l](vf_nt.tensors)
-            else:
-                src = self.input_proj[l](srcs[-1])
-            m = vf_nt.mask
-            mask = F.interpolate(m[None].float(), size=src.shape[-1:]).to(torch.bool)[0]
-            pos_l = self.pos_embed(NestedTensor(src, mask, duration)).to(src.dtype)
-            srcs.append(src)
-            masks.append(mask)
-            poses.append(pos_l)
-        return srcs, masks, poses
-
-def build_base_encoder(args):
-    base_encoder = BaseEncoder(args.num_feature_levels, args.feature_dim, args.hidden_dim)
-    return base_encoder
diff --git a/backup/pdvc/criterion.py b/backup/pdvc/criterion.py
deleted file mode 100644
index d47eb41a6711be9904ad6c55d502572261ff73c9..0000000000000000000000000000000000000000
--- a/backup/pdvc/criterion.py
+++ /dev/null
@@ -1,726 +0,0 @@
-# ------------------------------------------------------------------------
-# Modified from DETR (https://github.com/facebookresearch/detr)
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-# ------------------------------------------------------------------------
-import copy
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from misc.detr_utils import box_ops
-from misc.detr_utils.misc import (accuracy, get_world_size,
-                         is_dist_avail_and_initialized)
-
-class SetCriterion(nn.Module):
-    """ This class computes the loss for DETR.
-    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, losses, focal_alpha=0.25, focal_gamma=2, opt={}):
-        """ 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.
-            losses: list of all the losses to be applied. See get_loss for list of available losses.
-            focal_alpha: alpha in Focal Loss
-        """
-        super().__init__()
-        self.num_classes = num_classes
-        self.matcher = matcher
-        self.weight_dict = weight_dict
-        self.losses = losses
-        self.focal_alpha = focal_alpha
-        self.focal_gamma = focal_gamma
-        self.opt = opt
-        self.pseudo_box_aug = opt.pseudo_box_aug
-        self.refine_pseudo_box = opt.refine_pseudo_box
-        if ('Tasty' in opt.visual_feature_folder[0]) or ('tasty' in opt.visual_feature_folder[0]):
-            counter_class_rate  =[0.0, 0.012703673018503175, 0.04915769124551229, 0.06489919911626622, 0.0740127036730185, 0.07346037006351837, 0.08064070698702017,
-            0.07069870201601768, 0.07870753935376967, 0.07097486882076774, 0.06766086716376692, 0.0579950289975145, 0.05247169290251312, 0.03783485225075946,
-            0.03534935100800884, 0.03203534935100801, 0.026788180060756697, 0.02236951118475559, 0.01988400994200497, 0.016570008285004142, 0.013256006628003313,
-            0.00856117094725214, 0.006904170118751726, 0.005523336095001381, 0.004694835680751174, 0.0038663352665009665, 0.0027616680475006906, 0.0027616680475006906,
-            0.0016570008285004142, 0.0016570008285004142, 0.0005523336095001381, 0.0008285004142502071, 0.0, 0.00027616680475006904, 0.0, 0.0, 0.00027616680475006904,
-            0.0011046672190002762, 0.0, 0.0005523336095001381, 0.0, 0.0, 0.0005523336095001381]
-        else:
-            counter_class_rate = [0.00000000e+00, 0.00000000e+00, 1.93425917e-01, 4.12129084e-01,
-       1.88929963e-01, 7.81296833e-02, 5.09541413e-02, 3.12718553e-02,
-       1.84833650e-02, 8.39244680e-03, 6.59406534e-03, 4.49595364e-03,
-       2.19802178e-03, 1.79838146e-03, 5.99460486e-04, 4.99550405e-04,
-       4.99550405e-04, 1.99820162e-04, 2.99730243e-04, 3.99640324e-04,
-       2.99730243e-04, 0.00000000e+00, 1.99820162e-04, 0.00000000e+00,
-       0.00000000e+00, 0.00000000e+00, 9.99100809e-05, 9.99100809e-05]
-        self.counter_class_rate = torch.tensor(counter_class_rate)
-
-    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]
-        """
-        indices, many2one_indices = indices
-        assert 'pred_logits' in outputs
-        src_logits = outputs['pred_logits']
-        idx = self._get_src_permutation_idx(indices)
-        target_classes_o = torch.cat([t["labels"][J] for t, (_, J) in zip(targets, indices)])
-        target_classes = torch.full(src_logits.shape[:2], self.num_classes,
-                                    dtype=torch.int64, device=src_logits.device)
-        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=self.focal_gamma) * src_logits.shape[1]
-        losses = {'loss_ce': loss_ce}
-        pred_count = outputs['pred_count']
-        max_length = pred_count.shape[1] - 1
-        counter_target = [len(target['boxes']) if len(target['boxes']) < max_length  else max_length for target in targets]
-        counter_target = torch.tensor(counter_target, device=src_logits.device, dtype=torch.long)
-        counter_target_onehot = torch.zeros_like(pred_count)
-        counter_target_onehot.scatter_(1, counter_target.unsqueeze(-1), 1)
-        weight = self.counter_class_rate[:max_length + 1].to(src_logits.device)
-
-        counter_loss = cross_entropy_with_gaussian_mask(pred_count, counter_target_onehot, self.opt, weight)
-        losses['loss_counter'] = counter_loss
-
-        return losses
-
-    @torch.no_grad()
-    def loss_cardinality(self, outputs, targets, indices, num_boxes):
-        """ Compute the cardinality error, ie the absolute error in the number of predicted non-empty boxes
-        This is not really a loss, it is intended for logging purposes only. It doesn't propagate gradients
-        """
-        pred_logits = outputs['pred_logits']
-        device = pred_logits.device
-        tgt_lengths = torch.as_tensor([len(v["labels"]) for v in targets], device=device)
-        # Count the number of predictions that are NOT "no-object" (which is the last class)
-        card_pred = (pred_logits.argmax(-1) != pred_logits.shape[-1] - 1).sum(1)
-        card_err = F.l1_loss(card_pred.float(), tgt_lengths.float())
-        losses = {'cardinality_error': card_err}
-        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, 2]
-           The target boxes are expected in format (center, length), normalized by the image size.
-        """
-        indices, many2one_indices = indices
-        N = len(indices[-1][0])
-        assert 'pred_boxes' in outputs
-        idx, idx2 = self._get_src_permutation_idx2(indices)
-        src_boxes = outputs['pred_boxes'][idx]
-        if self.opt.use_pseudo_box and self.training:
-            # print('use pseudo box')
-            target_boxes = torch.cat([t['boxes_pseudo'][i] for t, (_, i) in zip(targets, indices)], dim=0)
-        else:
-            # print('use gt box')
-            target_boxes = torch.cat([t['boxes'][i] for t, (_, i) in zip(targets, indices)], dim=0)
-        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_cl_to_xy(src_boxes),
-            box_ops.box_cl_to_xy(target_boxes)))
-        losses['loss_giou'] = loss_giou.sum() / num_boxes
-        # print(src_boxes)
-        self_iou = torch.triu(box_ops.box_iou(box_ops.box_cl_to_xy(src_boxes),
-                                              box_ops.box_cl_to_xy(src_boxes))[0], diagonal=1)
-        sizes = [len(v[0]) for v in indices]
-        if sizes == [1]:
-            losses['loss_self_iou'] = self_iou
-            return losses
-        self_iou_split = 0
-        for i, c in enumerate(self_iou.split(sizes, -1)):
-            cc = c.split(sizes, -2)[i]
-            self_iou_split += cc.sum() / (0.5 * (sizes[i]) * (sizes[i]-1))
-        has_nan = False if torch.all(~torch.isnan(self_iou_split)) else True  
-        has_inf = False if torch.all(torch.isfinite(self_iou_split)) else True
-        if has_nan or has_inf:
-            breakpoint()
-        losses['loss_self_iou'] = self_iou_split
-
-        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_src_permutation_idx2(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])
-        src_idx2 = torch.cat([src for (_, src) in indices])
-        return (batch_idx, src_idx), src_idx2
-
-    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_jittered_box(self, box, box_jitter, box_aug_num=5, mode='random'):
-        # breakpoint()
-        box = box.unsqueeze(0) # (1,2)
-        if mode == 'random':
-            scale_c = torch.empty((1000, 1), dtype=box.dtype, device=box.device).uniform_(1-box_jitter, 1+box_jitter)
-            scale_d = torch.empty((1000, 1), dtype=box.dtype, device=box.device).uniform_(1-box_jitter, 1+box_jitter)
-            scale = torch.cat([scale_c, scale_d], dim=1)
-            scale_box = box * scale
-            scale_box = scale_box.clamp(min=0., max=1.)
-            iou, _ = box_ops.box_iou(box_ops.box_cl_to_xy(scale_box), box_ops.box_cl_to_xy(box))
-            keep_idx = torch.where(iou.reshape(-1) > 0.1)[0]
-            min_keep_cnt = (box_aug_num-1) if (box_aug_num-1) < keep_idx.numel() else keep_idx.numel()
-            box_repeat = box.repeat(box_aug_num, 1)
-            box_repeat[:min_keep_cnt] = scale_box[keep_idx[:min_keep_cnt]]
-        elif mode == 'random_new':
-            scale_c = torch.empty((1000, 1), dtype=box.dtype, device=box.device).uniform_(1-box_jitter, 1+box_jitter)
-            scale_d = torch.empty((1000, 1), dtype=box.dtype, device=box.device).uniform_(1-box_jitter, 1+box_jitter)
-            scale = torch.cat([scale_c, scale_d], dim=1)
-            scale_box = box * scale
-            scale_box = scale_box.clamp(min=0., max=1.)
-            iou, _ = box_ops.box_iou(box_ops.box_cl_to_xy(scale_box), box_ops.box_cl_to_xy(box))
-            keep_idx = torch.where(iou.reshape(-1) > 0.1)[0]
-            min_keep_cnt = (box_aug_num-1) if (box_aug_num-1) < keep_idx.numel() else keep_idx.numel()
-            box_repeat = box.repeat(box_aug_num, 1)
-            box_repeat[:min_keep_cnt] = scale_box[keep_idx[:min_keep_cnt]]
-        elif mode == 'uniform':
-            ratio_c = box_jitter
-            ratio_d = 0.048 / 2
-            scale_c = torch.tensor([-ratio_c, -ratio_c/2, -ratio_c/4, ratio_c/4, ratio_c/2, ratio_c])
-            scale_d = torch.tensor([-ratio_d, -ratio_d/2, ratio_d/2, ratio_d])
-            scale = torch.cartesian_prod(scale_c, scale_d).to(device=box.device)
-            breakpoint()
-            scale_box = box + scale
-            scale_box = scale_box.clamp(min=0., max=1.)
-            iou, _ = box_ops.box_iou(box_ops.box_cl_to_xy(scale_box), box_ops.box_cl_to_xy(box))
-            keep_idx = torch.where(iou.reshape(-1) > 0.1)[0]
-            unkeep_idx = torch.where(iou.reshape(-1) <= 0.1)[0]
-            if keep_idx.numel() < (box_aug_num-1):
-                box_repeat = box.repeat(box_aug_num, 1)
-                box_repeat[:keep_idx.numel()] = scale_box[keep_idx]
-                random_indices = torch.randperm(unkeep_idx.size(0))[:(box_aug_num-1-keep_idx.numel())]
-                box_repeat[keep_idx.numel():(box_aug_num-1)] = scale_box[unkeep_idx[random_indices]]
-            else:
-                box_repeat = box.repeat(box_aug_num, 1)
-                random_indices = torch.randperm(keep_idx.numel())[:(box_aug_num-1)]
-                box_repeat[:box_aug_num-1] = scale_box[keep_idx[random_indices]]
-        elif mode == 'uniform_old':
-            # Conduct augment using pre-defined ratio
-            ratio_c = box_jitter
-            ratio_d = box_jitter
-            scale_c = torch.linspace(1-ratio_c, 1+ratio_c, 4)
-            scale_d = torch.linspace(1-ratio_d, 1+ratio_d, 2)
-            scale = torch.cartesian_prod(scale_c, scale_d).to(device=box.device) # 16 augmented boxes in total
-            scale_box = box * scale
-            scale_box = scale_box.clamp(min=0., max=1.)
-            iou, _ = box_ops.box_iou(box_ops.box_cl_to_xy(scale_box), box_ops.box_cl_to_xy(box))
-            # keep_idx = torch.where(iou.reshape(-1) > 0.1)[0]
-            box_repeat = box.repeat(box_aug_num, 1)
-            random_indices = torch.randperm(scale_box.size(0))[:(box_aug_num-1)]
-            box_repeat[:(box_aug_num-1)] = scale_box[random_indices]
-        elif mode == 'random_range':
-            def batch_randomize_boxes(boxes, max_vary_range, num_samples=1):
-                # Get the centers and widths from the input boxes
-                centers = boxes[:, 0]
-                widths = boxes[:, 1]
-                # breakpoint()
-                # Generate random values for the left and right boundaries for each box
-
-                left_boundaries = centers - (widths / 2) - torch.empty(centers.size(0), num_samples, device=boxes.device).uniform_(0, max_vary_range)
-                right_boundaries = centers + (widths / 2) + torch.empty(centers.size(0), num_samples, device=boxes.device).uniform_(0, max_vary_range)
-
-                # Ensure that the boundaries stay within the [0, 1] range
-                left_boundaries = left_boundaries.clamp(0, 1)
-                right_boundaries = right_boundaries.clamp(0, 1)
-
-
-                # Calculate the new centers and widths
-                new_centers = (left_boundaries + right_boundaries) / 2
-                new_widths = right_boundaries - left_boundaries
-
-                # Ensure that the widths are non-negative and revert to the original boxes if needed
-                is_negative = new_widths <= 0
-                new_widths = torch.where(is_negative, widths, new_widths)
-                new_centers = torch.where(is_negative, centers, new_centers)
-
-                # Create and return the new boxes tensor
-                new_boxes = torch.stack((new_centers, new_widths), dim=2)
-                return new_boxes.squeeze(0)
-            box_repeat = batch_randomize_boxes(box, box_jitter, box_aug_num)
-            if torch.isnan(box_repeat).any():
-                breakpoint()
-        elif mode == 'augment_width': # original width is 0.5 \sigma range
-            import random
-            def augment_boxes_with_scale(boxes, scale, num_augments):
-                augmented_boxes = []
-                for _ in range(num_augments):
-                    center, width = boxes[0]
-                    # Generate a random scale factor with a more uniform distribution
-                    random_scale = scale ** random.uniform(-1, 1)
-                    new_width = width * random_scale
-                    if center + new_width / 2 > 1 or center - new_width / 2 < 0:
-                        new_width = width
-                    augmented_boxes.append([center, new_width])
-                augmented_boxes = torch.tensor(augmented_boxes, device=boxes.device)
-                return augmented_boxes
-            box_repeat = augment_boxes_with_scale(box, box_jitter, box_aug_num)
-            # breakpoint()
-
-        else:
-            raise NotImplementedError('Not support box augmentation mode: {}'.format(mode))      
-        return box_repeat
-
-    def get_loss(self, loss, outputs, targets, indices, num_boxes, **kwargs):
-        loss_map = {
-            'labels': self.loss_labels,
-            'cardinality': self.loss_cardinality,
-            'boxes': self.loss_boxes,
-        }
-        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, others=None, aug_num=None, aug_ratio=None):
-        """ 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' and k != 'enc_outputs'}
-        if self.training and self.pseudo_box_aug:
-            targets_cp = copy.deepcopy(targets)
-            assert self.opt.use_pseudo_box
-            for i in range((len(targets_cp))):
-                boxes_aug = []
-                for j in range(len(targets_cp[i]['labels'])):
-                    try: 
-                        pseudo_box = targets_cp[i]['boxes_pseudo'][j]
-                    except:
-                        breakpoint()
-                    peseudo_box_aug = self.get_jittered_box(pseudo_box, aug_ratio, aug_num, self.opt.pseudo_box_aug_mode)
-                    boxes_aug.append(peseudo_box_aug)
-                targets_cp[i]['boxes_pseudo'] = torch.cat(boxes_aug, dim=0)
-                targets_cp[i]['labels'] = targets_cp[i]['labels'].unsqueeze(dim=1).repeat(1, aug_num).reshape(-1,)
-                targets[i]['box_pseudo_aug'] = torch.cat(boxes_aug, dim=0)
-        # Retrieve the matching between the outputs of the last layer and the targets
-            last_indices = self.matcher(outputs_without_aux, targets_cp)
-        else:
-            targets_cp = targets
-            last_indices = self.matcher(outputs_without_aux, targets)
-        outputs['matched_indices'] = last_indices
-
-        num_boxes = sum(len(t["labels"]) for t in targets_cp)
-        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:
-            kwargs = {}
-            losses.update(self.get_loss(loss, outputs, targets_cp, last_indices, num_boxes, **kwargs))
-
-        # In case of auxiliary losses, we repeat this process with the output of each intermediate layer.
-        if 'aux_outputs' in outputs:
-            aux_indices = []
-            for i, aux_outputs in enumerate(outputs['aux_outputs']):
-                indices = self.matcher(aux_outputs, targets_cp)
-                aux_indices.append(indices)
-                for loss in self.losses:
-                    if loss == 'masks':
-                        # Intermediate masks losses are too costly to compute, we ignore them.
-                        continue
-                    kwargs = {}
-                    if loss == 'labels':
-                        # Logging is enabled only for the last layer
-                        kwargs['log'] = False
-                    l_dict = self.get_loss(loss, aux_outputs, targets_cp, indices, num_boxes, **kwargs)
-                    l_dict = {k + f'_{i}': v for k, v in l_dict.items()}
-                    losses.update(l_dict)
-
-            return losses, last_indices, aux_indices
-        return losses, last_indices
-
-class AlignCriterion(nn.Module):
-    """ This class computes the loss for DETR.
-    The process happens in two steps:
-        1) we compute DTW assignment between ground truth captions and the outputs object queries
-        2) we supervise each pair of matched ground-truth / prediction (supervise class)
-    """
-    def __init__(self, num_classes, matcher, weight_dict, losses, focal_alpha=0.25, focal_gamma=2, opt={}):
-        """ 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.
-            losses: list of all the losses to be applied. See get_loss for list of available losses.
-            focal_alpha: alpha in Focal Loss
-        """
-        super().__init__()
-        self.num_classes = num_classes
-        self.matcher = matcher
-        self.weight_dict = weight_dict
-        self.losses = losses
-        self.focal_alpha = focal_alpha
-        self.focal_gamma = focal_gamma
-        self.opt = opt
-        counter_class_rate = [0.00000000e+00, 0.00000000e+00, 1.93425917e-01, 4.12129084e-01,
-       1.88929963e-01, 7.81296833e-02, 5.09541413e-02, 3.12718553e-02,
-       1.84833650e-02, 8.39244680e-03, 6.59406534e-03, 4.49595364e-03,
-       2.19802178e-03, 1.79838146e-03, 5.99460486e-04, 4.99550405e-04,
-       4.99550405e-04, 1.99820162e-04, 2.99730243e-04, 3.99640324e-04,
-       2.99730243e-04, 0.00000000e+00, 1.99820162e-04, 0.00000000e+00,
-       0.00000000e+00, 0.00000000e+00, 9.99100809e-05, 9.99100809e-05]
-        self.counter_class_rate = torch.tensor(counter_class_rate)
-
-    def loss_labels(self, outputs, targets, indices, num_boxes, log=True):
-        """Classification loss (NLL)
-        Compute the classification loss and counter loss
-        targets dicts must contain the key "labels" containing a tensor of dim [nb_target_boxes]
-        """
-        indices, many2one_indices = indices
-        assert 'pred_logits' in outputs
-        src_logits = outputs['pred_logits']
-        idx = self._get_src_permutation_idx(indices)
-        target_classes_o = torch.cat([t["labels"][J] for t, (_, J) in zip(targets, indices)])
-        target_classes = torch.full(src_logits.shape[:2], self.num_classes,
-                                    dtype=torch.int64, device=src_logits.device)
-        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=self.focal_gamma) * src_logits.shape[1]
-        losses = {'loss_ce': loss_ce}
-
-        pred_count = outputs['pred_count']
-        max_length = pred_count.shape[1] - 1
-        counter_target = [len(target['boxes']) if len(target['boxes']) < max_length  else max_length for target in targets]
-        counter_target = torch.tensor(counter_target, device=src_logits.device, dtype=torch.long)
-        counter_target_onehot = torch.zeros_like(pred_count)
-        counter_target_onehot.scatter_(1, counter_target.unsqueeze(-1), 1)
-        weight = self.counter_class_rate[:max_length + 1].to(src_logits.device)
-        # breakpoint()
-        counter_loss = cross_entropy_with_gaussian_mask(pred_count, counter_target_onehot, self.opt, weight)
-        losses['loss_counter'] = counter_loss
-
-        return losses
-    
-    def loss_boxes(self, outputs, targets, indices, num_boxes):
-        # Compute temporal IOU loss among given predicted N temporal boundaries, which encourages the temporal boundaries to be more diverse and no overlap
-        # outputs: (bsz, num_query, 2)
-        # breakpoint()
-        # breakpoint()
-        indices, many2one_indices = indices
-        idx, idx2 = self._get_src_permutation_idx2(indices)
-        src_boxes = outputs['pred_boxes'][idx] # num_boxes, 2
-        avg_duration = torch.mean(src_boxes[:, 1])
-        center_point = src_boxes[:,0]
-        N = len(indices[-1][0])
-
-        losses = {}
-
-        if self.opt.use_pseudo_box and self.training:
-            # If generate peseudo ground truth boxes from alignment, use the alignment boxes as the target boxes
-            target_boxes = torch.cat([t['boxes_pseudo'][i] for t, (_, i) in zip(targets, indices)], dim=0)
-            loss_bbox = F.l1_loss(src_boxes, target_boxes, reduction='none')
-            losses['loss_bbox'] = loss_bbox.sum() / num_boxes
-
-            loss_giou = 1 - torch.diag(box_ops.generalized_box_iou(
-            box_ops.box_cl_to_xy(src_boxes),
-            box_ops.box_cl_to_xy(target_boxes)))
-            losses['loss_giou'] = loss_giou.sum() / num_boxes
-
-        if not self.opt.use_pseudo_box:
-            ## Squence Ordering loss
-            rank_margin = 0.01
-            pairs = torch.combinations(torch.arange(center_point.size(0)), 2)
-            rank_dist = center_point[pairs[:, 0]] - center_point[pairs[:, 1]] + rank_margin
-            # Make sure that the center points are ordered
-            rank_loss = torch.relu(rank_margin + rank_dist).mean()
-
-            losses['loss_ref_rank']  = rank_loss
-
-            ## Self IOU loss
-            prior_duration = 0.06
-            self_iou = torch.triu(box_ops.box_iou(box_ops.box_cl_to_xy(src_boxes),
-                                                box_ops.box_cl_to_xy(src_boxes))[0], diagonal=1)
-            sizes = [len(v[0]) for v in indices]
-            self_iou_split = 0
-            for i, c in enumerate(self_iou.split(sizes, -1)):
-                cc = c.split(sizes, -2)[i]
-                self_iou_split += cc.sum() / (0.5 * (sizes[i]) * (sizes[i]-1))
-            duration_constraint = torch.abs(prior_duration/(avg_duration + 1e-6) - 1)
-            self_iou_split += duration_constraint
-            
-            
-            losses['loss_self_iou'] = self_iou_split
-
-        return losses
-
-    @torch.no_grad()
-    def loss_cardinality(self, outputs, targets, indices, num_boxes):
-        """ Compute the cardinality error, ie the absolute error in the number of predicted non-empty boxes
-        This is not really a loss, it is intended for logging purposes only. It doesn't propagate gradients
-        """
-        pred_logits = outputs['pred_logits']
-        device = pred_logits.device
-        tgt_lengths = torch.as_tensor([len(v["labels"]) for v in targets], device=device)
-        # Count the number of predictions that are NOT "no-object" (which is the last class)
-        card_pred = (pred_logits.argmax(-1) != pred_logits.shape[-1] - 1).sum(1)
-        card_err = F.l1_loss(card_pred.float(), tgt_lengths.float())
-        losses = {'cardinality_error': card_err}
-        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_src_permutation_idx2(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])
-        src_idx2 = torch.cat([src for (_, src) in indices])
-        return (batch_idx, src_idx), src_idx2
-
-    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,
-            'cardinality': self.loss_cardinality,
-        }
-        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, others):
-        """ 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
-        """
-        text_embed = others['text_embed'] # num_dec_layers, num_sentence, dim
-        event_embed = others['event_embed'] # num_dec_layers, num_query, dim
-        dim = event_embed.shape[-1]
-
-        # Retrieve the matching between the outputs of the last layer and the targets
-        # if self.opt.matcher_type == 'DTW':
-        #     last_indices = self.matcher(text_embed[-1], event_embed[-1].reshape(-1, dim))
-        # elif self.opt.matcher_type == 'Sim':
-        #     last_indices = self.matcher(outputs, targets, text_embed[-1], event_embed[-1].reshape(-1, dim))
-        # else:
-        #     raise NotImplementedError('Align Criterion does not support:{}'.format(self.opt.matcher_type))
-        #breakpoint()
-        last_indices = self.matcher(outputs, targets, text_embed[-1], event_embed[-1].reshape(-1, dim))
-        outputs['matched_indices'] = last_indices
-
-        num_boxes = sum(len(t["labels"]) for t in targets)
-        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:
-            kwargs = {}
-            losses.update(self.get_loss(loss, outputs, targets, last_indices, num_boxes, **kwargs))
-
-        # In case of auxiliary losses, we repeat this process with the output of each intermediate layer.
-        if 'aux_outputs' in outputs:
-            aux_indices = []
-            for i, aux_outputs in enumerate(outputs['aux_outputs']):
-                indices = self.matcher(outputs, targets, text_embed[-1], event_embed[-1].reshape(-1, dim))
-                aux_indices.append(indices)
-                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, last_indices, aux_indices
-        return losses, last_indices
-
-class ContrastiveCriterion(nn.Module):
-    '''
-    Contrastive loss between event feature and caption feature
-    '''
-
-    def __init__(self, temperature=0.1, enable_cross_video_cl=False, enable_e2t_cl=False, enable_bg_for_cl=False):
-        super().__init__()
-        self.temperature = temperature
-        self.enable_cross_video_cl = enable_cross_video_cl
-        self.enable_e2t_cl = enable_e2t_cl
-        self.enable_bg_for_cl = enable_bg_for_cl
-
-    def forward_logits(self, text_embed, event_embed, bg_embed=None):
-        normalized_text_emb = F.normalize(text_embed, p=2, dim=1)
-        normalized_event_emb = F.normalize(event_embed, p=2, dim=1)
-        logits = torch.mm(normalized_text_emb, normalized_event_emb.t())
-        if bg_embed is not None:
-            bg_logits = torch.sum(normalized_event_emb * F.normalize(bg_embed, p=2), dim=1)
-            logits = torch.cat((logits, bg_logits.unsqueeze(0)), dim=0)
-        return logits
-
-
-    def forward(self, text_embed, event_embed, matching_indices, return_logits=False, bg_embed=None):
-
-        '''
-        :param text_embed: [(event_num, contrastive_hidden_size)], len = batch size
-                            total_event_number = sum of event number of each item in current batch
-        :param event_embed: (bsz, max_event_num, contrastive_hiddent_size), which need to be
-                            expand in this function
-        :param matching_indices: (bsz, event_num)
-        '''
-        batch_size, max_event_num, _ = event_embed.shape
-        event_embed, text_embed, gt_labels, gt_event_num = self._preprocess(event_embed, [text_embed], matching_indices)
-        raw_logits = self.forward_logits(text_embed, event_embed)
-        logits = raw_logits / self.temperature
-
-        if self.enable_cross_video_cl:
-            t2e_loss = F.cross_entropy(logits, gt_labels)
-            if self.enable_e2t_cl:
-                gt_label_matrix = torch.zeros(len(text_embed) + 1, len(event_embed), device=text_embed.device)
-                gt_label_matrix[torch.arange(len(gt_labels)), gt_labels] = 1
-                event_mask = gt_label_matrix.sum(dim=0) == 0
-                gt_label_matrix[-1, event_mask] = 1
-                e2t_gt_label = gt_label_matrix.max(dim=0)[1]
-                bg_logits = torch.sum(F.normalize(event_embed, p=2) * F.normalize(bg_embed, p=2), dim=1)
-                e2t_logits = torch.cat((logits, bg_logits.unsqueeze(0) / self.temperature), dim=0)
-                if self.enable_bg_for_cl:
-                    e2t_loss = F.cross_entropy(e2t_logits.t(), e2t_gt_label)
-                else:
-                    e2t_loss = F.cross_entropy(e2t_logits.t()[~event_mask], e2t_gt_label[~event_mask])
-                loss = 0.5 * (t2e_loss + e2t_loss)
-            else:
-                loss = t2e_loss
-        else:
-            loss = 0; base = 0
-            for i in range(batch_size):
-                current_gt_event_num = gt_event_num[i]
-                current_logits = logits[base: base + current_gt_event_num, i * max_event_num: (i + 1) * max_event_num]
-                current_gt_labels = gt_labels[base: base + current_gt_event_num]
-                t2e_loss = F.cross_entropy(current_logits, current_gt_labels)
-                if self.enable_e2t_cl:
-                    gt_label_matrix = torch.zeros(gt_event_num[i] + 1, max_event_num, device=text_embed.device)
-                    gt_label_matrix[torch.arange(current_gt_labels), current_gt_labels] = 1
-                    event_mask = gt_label_matrix.sum(dim=0) == 0
-                    e2t_gt_label = gt_label_matrix.max(dim=0)[1]
-                    bg_logits = torch.sum(F.normalize(event_embed, p=2) * F.normalize(bg_embed, p=2), dim=1)
-                    e2t_logits = torch.cat((current_logits, bg_logits.unsqueeze(0) / self.temperature), dim=0)
-                    if self.enable_bg_for_cl:
-                        e2t_loss = F.cross_entropy(e2t_logits.t(), e2t_gt_label)
-                    else:
-                        e2t_loss = F.cross_entropy(e2t_logits.t(), e2t_gt_label, ignore_index=len(text_embed), reduction='sum') / (1e-5 + sum(~event_mask))
-                    loss += 0.5 * (t2e_loss + e2t_loss)
-                else:
-                    loss += t2e_loss
-                base += current_gt_event_num
-            loss = loss / batch_size
-        # pdb.set_trace()
-        if return_logits:
-            return loss, raw_logits
-        return loss
-
-
-    def _preprocess(self, event_embed, text_embed, matching_indices):
-        '''
-        Flatten event_embed of a batch, get gt label
-
-        :param matching_indices: [(event_num, )]  len = bsz
-        '''
-        batch_size, max_event_num, f_dim = event_embed.shape
-        gt_labels = []
-        text_features = []
-        gt_event_num = []
-        event_features = event_embed.view(-1, f_dim)
-        for i in range(batch_size):
-            base = i * max_event_num if self.enable_cross_video_cl else 0
-            feat_ids, cap_ids = matching_indices[i]
-            gt_event_num.append(len(feat_ids))
-            text_features.append(text_embed[i][cap_ids])
-            gt_labels.append(feat_ids + base)
-        text_features = torch.cat(text_features, dim=0)
-        gt_labels = torch.cat(gt_labels, dim=0)
-        gt_labels = gt_labels.to(event_embed.device)
-            
-        return event_features, text_features, gt_labels, gt_event_num
-
-def cross_entropy_with_gaussian_mask(inputs, targets, opt, weight):
-    gau_mask = opt.lloss_gau_mask
-    beta = opt.lloss_beta
-
-    N_, max_seq_len = targets.shape
-    gassian_mu = torch.arange(max_seq_len, device=inputs.device).unsqueeze(0).expand(max_seq_len,
-                                                                                     max_seq_len).float()
-    x = gassian_mu.transpose(0, 1)
-    gassian_sigma = 2
-    mask_dict = torch.exp(-(x - gassian_mu) ** 2 / (2 * gassian_sigma ** 2))
-    _, ind = targets.max(dim=1)
-    mask = mask_dict[ind]
-
-    loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none", weight= 1 - weight)
-    if gau_mask:
-        coef = targets + ((1 - mask) ** beta) * (1 - targets)
-    else:
-        coef = targets + (1 - targets)
-    loss = loss * coef
-    loss = loss.mean(1)
-    return loss.mean()
-
-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") # with_logits func calculates sigmoid and CE jointly
-    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 regression_loss(inputs, targets, opt, weight):
-    inputs = F.relu(inputs) + 2
-    max_id = torch.argmax(targets, dim=1)
-    if opt.regression_loss_type == 'l1':
-        loss = nn.L1Loss()(inputs[:, 0], max_id.float())
-    elif opt.regression_loss_type == 'l2':
-        loss = nn.MSELoss()(inputs[:, 0], max_id.float())
-    return loss
\ No newline at end of file
diff --git a/backup/pdvc/deformable_transformer.py b/backup/pdvc/deformable_transformer.py
deleted file mode 100644
index 5e9b742061b166e0badc41db80f5423b0e46a746..0000000000000000000000000000000000000000
--- a/backup/pdvc/deformable_transformer.py
+++ /dev/null
@@ -1,496 +0,0 @@
-# ------------------------------------------------------------------------
-# 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
-import math
-
-import torch
-import torch.nn.functional as F
-from torch import nn
-from torch.nn.init import xavier_uniform_, constant_, normal_
-
-from misc.detr_utils.misc import  inverse_sigmoid
-from pdvc.ops.modules import MSDeformAttn
-
-
-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, use_anchor=False):
-        super().__init__()
-
-        self.d_model = d_model
-        self.nhead = nhead
-        self.use_anchor = use_anchor
-
-        self.no_encoder = (num_encoder_layers == 0)
-        self.num_feature_levels = 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, d_model, use_anchor)
-
-        self.level_embed = nn.Parameter(torch.Tensor(num_feature_levels, d_model))
-
-        self.pos_trans = nn.Linear(d_model, d_model * 2)
-        self.pos_trans_norm = nn.LayerNorm(d_model * 2)
-        self.reference_points = nn.Linear(d_model, 1)
-
-        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.use_anchor:
-        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 = 256
-        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, 2
-        proposals = proposals.sigmoid() * scale
-        # N, L, 2, 256
-        pos = proposals[:, :, :, None] / dim_t
-        # N, L, 2, 128, 2
-        pos = torch.stack((pos[:, :, :, 0::2].sin(), pos[:, :, :, 1::2].cos()), dim=4).flatten(2)
-        return pos
-    
-    def get_proposal_pos_embed_1d(self, proposals):
-        num_pos_feats = 512
-        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
-        proposals = proposals.sigmoid() * scale
-        # N, L, 512
-        pos = proposals[:, None] / dim_t 
-
-        pos = torch.stack((pos[:, 0::2].sin(), pos[:, 1::2].cos()), dim=2).flatten(1) 
-        return pos 
-
-    def get_valid_ratio(self, mask):
-        valid_ratio_L = torch.sum(~mask, 1).float() / mask.shape[1]
-        return valid_ratio_L
-
-    def prepare_encoder_inputs(self, srcs, masks, pos_embeds):
-        # prepare input for encoder
-        src_flatten = []
-        mask_flatten = []
-        lvl_pos_embed_flatten = []
-        temporal_shapes = []
-        for lvl, (src, mask, pos_embed) in enumerate(zip(srcs, masks, pos_embeds)):
-            """
-            lvl: (bs, )
-            src: (bs, c, L )
-            mask: (bs, L)
-            pos_embed: (bs, d_m, L)
-            """
-            bs, c, L = src.shape
-            temporal_shapes.append(L)
-            src = src.transpose(1, 2)  # （bs, L, c）
-            pos_embed = pos_embed.transpose(1, 2)  # #（bs, L, d_m）
-            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)
-        src_flatten = torch.cat(src_flatten, 1)  # (lvl_num, bs, wh, c)
-        mask_flatten = torch.cat(mask_flatten, 1)  # (lvl_num, bs, wh)
-        lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1)  # (lvl_num, bs, wh, d_m)
-        temporal_shapes = torch.as_tensor(temporal_shapes, dtype=torch.long, device=src_flatten.device)  # (lvl_num, 2)
-        level_start_index = torch.cat((temporal_shapes.new_zeros((1,)), temporal_shapes.cumsum(0)[
-                                                                       :-1]))  # prod: [w0h0, w0h0+w1h1, w0h0+w1h1+w2h2, ...]
-        valid_ratios = torch.stack([self.get_valid_ratio(m) for m in masks],
-                                   1)  # (bs, lvl_num, 2), where 2 means (h_rate, and w_rate)， all values <= 1
-
-        return src_flatten, temporal_shapes, level_start_index, valid_ratios, lvl_pos_embed_flatten, mask_flatten
-
-    def forward_encoder(self, src_flatten, temporal_shapes, level_start_index, valid_ratios, lvl_pos_embed_flatten,
-                        mask_flatten):
-        # encoder
-        if self.no_encoder:
-            memory = src_flatten
-        else:
-            memory = self.encoder(src_flatten, temporal_shapes, level_start_index, valid_ratios, lvl_pos_embed_flatten,
-                                  mask_flatten)
-
-        return memory
-
-    def prepare_decoder_input_query(self, memory, query_embed):
-        bs, _, _ = memory.shape
-        query_embed, tgt = torch.chunk(query_embed, 2, dim=1)
-        query_embed = query_embed.unsqueeze(0).expand(bs, -1, -1)
-        tgt = tgt.unsqueeze(0).expand(bs, -1, -1)
-        reference_points = self.reference_points(query_embed).sigmoid() # (bs, object_query, 1)
-        init_reference_out = reference_points  # (bs, object_query, 1)
-        return init_reference_out, tgt, reference_points, query_embed
-    
-    def prepare_init_anchor_and_query(self, anchor_embed, hidden_dim, random_anchor_init=False, prior_anchor_duration_init=False, prior_duration=0.048):
-        num_queries = anchor_embed.weight.shape[0]
-        # query_embed = nn.Embedding(num_queries, hidden_dim)
-        if random_anchor_init:
-            anchor_embed.weight.data[:, :1] = torch.linspace(0, 1, num_queries).unsqueeze(1)
-            anchor_embed.weight.data[:, :1] = inverse_sigmoid(anchor_embed.weight.data[:, :1])
-            print('Initilize the anchor center point with uniform distribution')
-            #self.anchor_embed.weight.data[:, :1].requires_grad = False # DAB-anchor set this to be False
-            anchor_embed.weight.data[:, :1].requires_grad = True # I set it to be True
-            # breakpoint()
-        if prior_anchor_duration_init:
-            # TODO: add prior anchor duration initialization, the below implementation is not correct
-            torch.nn.init.constant_(anchor_embed.weight.data[:, 1:], prior_duration)
-            anchor_embed.weight.data[:, 1:] = inverse_sigmoid(anchor_embed.weight.data[:, 1:])
-            anchor_embed.weight.data[:, 1:].requires_grad = True
-            print('Initilize the anchor duration point with: {}'.format(prior_duration))
-        reference_points = anchor_embed.weight.data.detach().clone().sigmoid().unsqueeze(0).expand(1, -1, -1) 
-        topk_coords_unact = inverse_sigmoid(reference_points[0, :, 0])
-        query_embed = self.pos_trans_norm(self.pos_trans(self.get_proposal_pos_embed_1d(topk_coords_unact))) # Position embedding receives non-sigmoided coordinates
-        # breakpoint()
-        return query_embed
-
-    def prepare_decoder_input_anchor(self, memory, query_anchor):
-        bs, _, _ = memory.shape
-        query_embed, anchor = query_anchor
-        position_embedding, tgt = torch.chunk(query_embed, 2, dim=1)
-        position_embedding = position_embedding.unsqueeze(0).expand(bs, -1, -1)
-        tgt = tgt.unsqueeze(0).expand(bs, -1, -1)
-        reference_points = anchor.sigmoid().unsqueeze(0).expand(bs, -1, -1) # (bs, num_queries, 2)
-        # tgt = query_embed[..., :self.d_model]
-        # tgt = tgt.unsqueeze(0).expand(bs, -1, -1) # (bs, num_queries, query_dim)
-        init_reference_out = reference_points
-
-        # topk_coords_unact = inverse_sigmoid(reference_points)
-        # position_embeding = self.pos_trans_norm(self.pos_trans(self.get_proposal_pos_embed_1d(topk_coords_unact)))
-        return init_reference_out, tgt, reference_points, position_embedding
-
-    def prepare_decoder_input_prior(self, proposals, num_queries=100):
-        '''
-        :param proposals: (batch, num_sentence, 2)
-        '''
-        bs,_,_ = proposals.shape
-        # Uniformly generate normalized coordinates according to number of sentences
-        reference_points_list = []
-        for i in range(bs):
-        # Generate N-1 points from 0~1 for each sentence uniformly
-            ns = proposals[i].shape[0] # number of sentences
-            reference_points_c = torch.linspace(0,1, 2*ns+1, dtype=torch.float32, device=proposals.device)
-            reference_points_c = reference_points_c[1:-1:2] # (num_sentence,)
-            reference_points_d = torch.Tensor([1.0/ns]).to(proposals.device).repeat(ns) # (num_sentence,)
-            reference_points = torch.stack([reference_points_c, reference_points_d], -1) # (num_sentence, 2)
-            # Padding the reference point to the same length
-            
-            num_query_per_sentence = num_queries // ns
-            reference_points = reference_points.repeat(1, num_query_per_sentence).reshape(-1,2)  # (num_queries, 2)
-            if num_queries % ns != 0: # Padding with zeros
-                num_padding = num_queries - num_query_per_sentence * ns
-                padding = torch.Tensor([[1.0, 1.0/ns]]).to(proposals.device).repeat(num_padding, 1)
-                reference_points = torch.cat([reference_points, padding], 0)
-            reference_points_list.append(reference_points)
-        reference_points = torch.stack(reference_points_list, 0) # (batch, num_queries, 2)
-        init_reference_out = reference_points[:,:,:1]
-        topk_coords_unact = inverse_sigmoid(reference_points)
-        pos_trans_out = self.pos_trans_norm(self.pos_trans(self.get_proposal_pos_embed(topk_coords_unact))) # (bs, num_sentence, 2*hidden_dim)
-        query_embed, tgt = torch.chunk(pos_trans_out, 2, dim=2)
-        return init_reference_out, tgt, reference_points[:,:,:1], query_embed
-
-    def prepare_decoder_input_proposal(self, gt_reference_points):
-        '''
-        :param gt_reference_points: (batch, num_sentence, 2)
-        '''
-        #breakpoint()
-        topk_coords_unact = inverse_sigmoid(gt_reference_points)
-        reference_points = gt_reference_points
-        init_reference_out = reference_points
-        pos_trans_out = self.pos_trans_norm(self.pos_trans(self.get_proposal_pos_embed(topk_coords_unact))) # (bs, num_sentence, 2*hidden_dim)
-        query_embed, tgt = torch.chunk(pos_trans_out, 2, dim=2) # Split to query_embed and position_embed (bs, num_sentence, hidden_dim, 2)
-        return init_reference_out, tgt, reference_points, query_embed
-
-    def forward_decoder(self, *kargs):
-        hs, inter_references_out = self.decoder(*kargs)
-        return hs, inter_references_out
-
-
-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, temporal_shapes, level_start_index, padding_mask=None):
-        # self attention
-        src2 = self.self_attn(self.with_pos_embed(src, pos), reference_points, src, temporal_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(temporal_shapes, valid_ratios, device):
-        reference_points_list = []
-        for lvl, (L_) in enumerate(temporal_shapes):
-            ref = torch.linspace(0.5, L_ - 0.5, L_, dtype=torch.float32, device=device)
-            ref = ref.reshape(-1)[None] / (valid_ratios[:, None, lvl] * L_)
-            reference_points_list.append(ref)
-        reference_points = torch.cat(reference_points_list, 1)
-        reference_points = reference_points[:, :, None] * valid_ratios[:, None]
-        reference_points = reference_points[:,:,:,None]
-        return reference_points
-
-    def forward(self, src, temporal_shapes, level_start_index, valid_ratios, pos=None, padding_mask=None):
-        output = src
-        reference_points = self.get_reference_points(temporal_shapes, valid_ratios, device=src.device)
-        for _, layer in enumerate(self.layers):
-            output = layer(output, pos, reference_points, temporal_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_temporal_shapes, level_start_index,
-                src_padding_mask=None, query_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), key_padding_mask=~query_mask)[
-            0].transpose(0, 1)
-        tgt = tgt + self.dropout2(tgt2)
-        tgt = self.norm2(tgt)
-
-        # cross attention
-        tgt2 = self.cross_attn(self.with_pos_embed(tgt, query_pos),
-                               reference_points,
-                               src, src_temporal_shapes, level_start_index, src_padding_mask)
-        tgt = tgt + self.dropout1(tgt2)
-        tgt = self.norm1(tgt)
-
-        # ffn
-        tgt = self.forward_ffn(tgt)
-        return tgt
-
-
-class DeformableTransformerDecoder(nn.Module):
-    def __init__(self, decoder_layer, num_layers, return_intermediate=False, d_model=256, use_anchor=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_head = None
-        self.use_anchor = use_anchor
-        self.d_model = d_model
-        # if use_anchor:
-        #     self.anchor_head = MLP(d_model, d_model, d_model, 2)
-        #     self.scale_head = MLP(d_model, d_model, d_model, 2)
-
-
-    def forward(self, tgt, reference_points, src, src_temporal_shapes, src_level_start_index, src_valid_ratios,
-                query_pos=None, src_padding_mask=None, query_padding_mask=None, disable_iterative_refine=False):
-        output = tgt
-
-        intermediate = []
-        intermediate_reference_points = []
-        bs = tgt.shape[0]
-        for lid, layer in enumerate(self.layers):
-            if reference_points.shape[-1] == 2:
-                reference_points_input = reference_points[:, :, None] \
-                                         * torch.stack([src_valid_ratios, src_valid_ratios], -1)[:, None]
-            else:
-                assert reference_points.shape[-1] == 1
-                reference_points_input = reference_points[:, :, None] * src_valid_ratios[:, None, :, None]
-            # if self.use_anchor:
-                # query_sine_embed = gen_sineembed_for_position(reference_points_input[:,:,0,:], self.d_model)
-                # raw_query_pos = self.anchor_head(query_sine_embed) # num_query, bs, 256
-                # query_scale_embed = self.scale_head(output) if lid != 0 else 1
-                # query_pos = query_scale_embed * raw_query_pos
-            output = layer(output, query_pos, reference_points_input, src, src_temporal_shapes, src_level_start_index,
-                           src_padding_mask, query_padding_mask)
-
-            if self.use_anchor:
-                assert reference_points.shape[-1] == 2
-                
-            # hack implementation for iterative bounding box refinement
-            if disable_iterative_refine:
-                reference_points = reference_points
-            else:
-                if (self.bbox_head is not None):
-                    tmp = self.bbox_head[lid](output)
-                    if reference_points.shape[-1] == 2:
-                        new_reference_points = tmp + inverse_sigmoid(reference_points)
-                        new_reference_points = new_reference_points.sigmoid()
-                    else:
-                        assert reference_points.shape[-1] == 1
-                        new_reference_points = tmp
-                        new_reference_points[..., :1] = tmp[..., :1] + inverse_sigmoid(reference_points)
-                        new_reference_points = new_reference_points.sigmoid()
-                    reference_points = new_reference_points.detach()
-                else:
-                    reference_points = reference_points
-
-            if self.return_intermediate:
-                intermediate.append(output)
-                intermediate_reference_points.append(reference_points)
-        # breakpoint()
-
-        if self.return_intermediate:
-            return torch.stack(intermediate), torch.stack(intermediate_reference_points)
-
-        return output, reference_points
-
-
-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 gen_sineembed_for_position(pos_tensor, d_model):
-    # n_query, bs, _ = pos_tensor.size()
-    # sineembed_tensor = torch.zeros(n_query, bs, 256)
-    hidden_dim = d_model // 2
-    scale = 2 * math.pi
-    dim_t = torch.arange(hidden_dim, dtype=torch.float32, device=pos_tensor.device)
-    dim_t = 10000 ** (2 * (dim_t // 2) / hidden_dim)
-    x_embed = pos_tensor[:, :, 0] * scale
-    pos_x = x_embed[:, :, None] / dim_t
-    pos_x = torch.stack((pos_x[:, :, 0::2].sin(), pos_x[:, :, 1::2].cos()), dim=3).flatten(2)
-    if pos_tensor.size(-1) == 1:
-        pos = pos_x
-    elif pos_tensor.size(-1) == 2:
-        w_embed = pos_tensor[:, :, 1] * scale
-        pos_w = w_embed[:, :, None] / dim_t
-        pos_w = torch.stack((pos_w[:, :, 0::2].sin(), pos_w[:, :, 1::2].cos()), dim=3).flatten(2)
-
-        pos = torch.cat((pos_x, pos_w), dim=2)
-    else:
-        raise ValueError("Unknown pos_tensor shape(-1):{}".format(pos_tensor.size(-1)))
-    return pos
-
-class MLP(nn.Module):
-    """ Very simple multi-layer perceptron (also called FFN)"""
-
-    def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
-        super().__init__()
-        self.num_layers = num_layers
-        h = [hidden_dim] * (num_layers - 1)
-        self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))
-
-    def forward(self, x):
-        for i, layer in enumerate(self.layers):
-            x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
-        return x
-
-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.transformer_ff_dim,
-        dropout=args.transformer_dropout_prob,
-        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,
-        use_anchor=args.use_anchor)
diff --git a/backup/pdvc/dp/CFSA.py b/backup/pdvc/dp/CFSA.py
deleted file mode 100644
index 135defd0c1a48435405a27e2cc12532d86b5d79a..0000000000000000000000000000000000000000
--- a/backup/pdvc/dp/CFSA.py
+++ /dev/null
@@ -1,327 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from einops import rearrange, repeat
-from torch.nn import CrossEntropyLoss
-import math
-
-def compute_cls_loss(pred, labels, use_cosface=False):
-    if use_cosface:
-        # CosFace Loss
-        s, m = 30.0, 0.4
-        cos_value = torch.diagonal(pred.transpose(0, 1)[labels])
-        numerator = s * (cos_value - m)
-        excl = torch.cat([torch.cat((pred[i, :y], pred[i, y + 1:])).unsqueeze(0) for i, y in enumerate(labels)], dim=0)
-        denominator = torch.exp(numerator) + torch.sum(torch.exp(s * excl), dim=1)
-        L = numerator - torch.log(denominator)
-        loss = -torch.mean(L)
-    else:
-        # Softmax Loss
-        criterion = CrossEntropyLoss().cuda()
-        loss = criterion(pred, labels)
-
-    return loss
-
-
-def frame_blank_align_loss(seq_features1, seq_features2, step_num):
-    seq_features1 = seq_features1[:, 1:]
-    blank2 = seq_features2[:, :1]
-    seq_features2 = seq_features2[:, 1:]
-    (B, T, C), device = seq_features1.shape, seq_features1.device
-    
-    K = 2 * step_num + 1
-    sparse_seq_features2 = torch.cat((blank2, seq_features2[:, [5, 7, 8, 9, 11, 12, 13, 14], :]), dim=1)
-    pred = (torch.einsum('bic,bjc->bij', seq_features1, sparse_seq_features2) / math.sqrt(C)).log_softmax(-1)
-
-    D_pre = torch.full((B, K), fill_value=float('-99999999'), device=device)
-    D_pre[:, 0] = pred[:, 0, 0]
-    D_pre[:, 1] = pred[:, 0, 1]
-    
-    for t in range(1, T):
-        D_cur = torch.full((B, K), fill_value=float('-99999999'), device=device)
-        D_cur[:, 0] = D_pre[:, 0] + pred[:, t, 0]
-        D_cur[:, 1] = torch.logsumexp(torch.stack([D_pre[:, 0], D_pre[:, 1]]), dim=0) + pred[:, t, 1]
-        
-        # blank term
-        blank_pre_ind = torch.arange(1, K, 2)[None, :].repeat(B, 1)
-        blank_pre = D_pre[torch.arange(B, device=device).unsqueeze(-1), blank_pre_ind]
-        
-        blank_cur_ind = torch.arange(2, K, 2)[None, :].repeat(B, 1)
-        blank_cur = D_pre[torch.arange(B, device=device).unsqueeze(-1), blank_cur_ind]
-        
-        blank_log_prob = torch.logsumexp(torch.stack([blank_pre, blank_cur]), dim=0)
-        D_cur[:, 2:][:, ::2] = blank_log_prob + pred[:, t, 0][:, None].repeat(1, blank_log_prob.shape[-1])
-        
-        # step term
-        step_prepre_ind = torch.arange(1, K, 2)[None, :-1].repeat(B, 1)
-        step_prepre = D_pre[torch.arange(B, device=device).unsqueeze(-1), step_prepre_ind]
-        
-        step_pre_ind = torch.arange(2, K, 2)[None, :-1].repeat(B, 1)
-        step_pre = D_pre[torch.arange(B, device=device).unsqueeze(-1), step_pre_ind]
-        
-        step_cur_ind = torch.arange(3, K, 2)[None, :].repeat(B, 1)
-        step_cur = D_pre[torch.arange(B, device=device).unsqueeze(-1), step_cur_ind]
-        
-        step_log_prob = torch.logsumexp(torch.stack([step_prepre, step_pre, step_cur]), dim=0)
-        D_cur[:, 2:][:, 1::2] = step_log_prob + pred[:, t, 2:]
-        D_pre = D_cur
-
-    fsa_distance = -torch.logsumexp(D_cur[:, -2:], dim=-1) / 13
-    loss = fsa_distance.mean(0)
-    
-    return loss
-
-
-def consist_step_mining(seq_features1, seq_features2, step_num):
-    (B, T, C), device = seq_features1.shape, seq_features1.device
-    
-    pred = (torch.einsum('bic,bjc->bij', seq_features1, seq_features2) / math.sqrt(C)).softmax(-1)
-    # pred = torch.cosine_similarity(seq_features1.unsqueeze(2), seq_features2.unsqueeze(1), dim=-1)
-    pred = pred.cumsum(-2).cumsum(-1)
-    
-    D = torch.zeros((B, T, T, T), device=device)
-    D_ind = torch.zeros((B, T, T, T), dtype=torch.long, device=device)
-    
-    D[:, 0] = pred / torch.ones_like(pred).cumsum(-2).cumsum(-1)
-    
-    area = torch.ones_like(pred).cumsum(-2).cumsum(-1)
-    area = (area[:, :, :, None, None] - area[:, :, None, None, :] - area.transpose(1,2)[:, None, :, :, None] + area[:, None, None, :, :])
-    block_mat = (pred[:, :, :, None, None] - pred[:, :, None, None, :] - pred.transpose(1,2)[:, None, :, :, None] + pred[:, None, None, :, :])
-    
-    top, left, bottom, right = torch.meshgrid(*[torch.arange(T, device=device)]*4)
-    area = area.clamp_min(1).sqrt()
-
-    block_mat = block_mat.masked_fill(((bottom >= top) | (right >= left)).unsqueeze(0), float('-inf')) / area
-    
-    for k in range(1, T):
-        tmp = ((D[:, k-1, None, None, :, :] * k) + block_mat) / (k+1)
-        D[:, k] = torch.max(tmp.flatten(3), -1).values
-        D_ind[:, k] = torch.max(tmp.flatten(3), -1).indices
-    
-    segment1, segment2 = [torch.full((B, 1), T, dtype=torch.long, device=device)]*2
-    k = step_num - 1
-    i, j, a, b = [torch.full((B, 1), T-1, dtype=torch.long, device=device)]*4
-    
-    while k >= 0:
-        ind = D_ind[range(B), k, i.squeeze(), j.squeeze()][:, None]
-        a = ind // T
-        b = ind % T
-        segment1 = torch.cat([a, segment1], dim=-1)
-        segment2 = torch.cat([b, segment2], dim=-1)
-        i, j, k = a, b, k-1
-    
-    repeat_times1 = (segment1[:, 1:] - segment1[:, :-1]).flatten()
-    repeat_target1 = torch.arange(step_num, device=device).repeat((B, ))
-    step_index1 = repeat_target1.repeat_interleave(repeat_times1).reshape(B, T)
-    
-    repeat_times2 = (segment2[:, 1:] - segment2[:, :-1]).flatten()
-    repeat_target2 = torch.arange(step_num, device=device).repeat((B, ))
-    step_index2 = repeat_target2.repeat_interleave(repeat_times2).reshape(B, T)
-    
-    div_term = torch.exp(torch.arange(0, C, 2, device=device) * -(math.log(10000.0) / C))
-    
-    pos_emb1 = torch.zeros(B, T, C, device=device)
-    pos_emb1[:, :, 0::2] = torch.sin(step_index1.unsqueeze(-1) * div_term)
-    pos_emb1[:, :, 1::2] = torch.cos(step_index1.unsqueeze(-1) * div_term)
-    
-    pos_emb2 = torch.zeros(B, T, C, device=device)
-    pos_emb2[:, :, 0::2] = torch.sin(step_index2.unsqueeze(-1) * div_term)
-    pos_emb2[:, :, 1::2] = torch.cos(step_index2.unsqueeze(-1) * div_term)
-    
-    return pos_emb1, pos_emb2, segment1[:, :-1]+1, segment2[:, :-1]+1
-
-
-
-def consist_step_mining_train(seq_features1, seq_features2, step_num, pair_labels):
-    # seq_features1 = seq_features1[:, 1:]
-    # seq_features2 = seq_features2[:, 1:]
-    (B, T, C), device = seq_features1.shape, seq_features1.device
-    
-    pred = (torch.einsum('bic,bjc->bij', seq_features1, seq_features2) / math.sqrt(C)).softmax(-1)
-    pred = pred.cumsum(-2).cumsum(-1)
-    
-    D = torch.zeros((B, T, T, T), device=device)
-    D_ind = torch.zeros((B, T, T, T), dtype=torch.long, device=device)
-    
-    D[:, 0] = pred / torch.ones_like(pred).cumsum(-2).cumsum(-1)
-    
-    area = torch.ones_like(pred).cumsum(-2).cumsum(-1)
-    area = (area[:, :, :, None, None] - area[:, :, None, None, :] \
-            - area.transpose(1,2)[:, None, :, :, None] + area[:, None, None, :, :])
-    
-    block_mat = (pred[:, :, :, None, None] - pred[:, :, None, None, :] \
-                - pred.transpose(1,2)[:, None, :, :, None] + pred[:, None, None, :, :])
-    
-    top, left, bottom, right = torch.meshgrid(*[torch.arange(T, device=device)]*4)
-    area = area.clamp_min(1)
-
-    block_mat = block_mat.masked_fill(((bottom >= top) | (right >= left)).unsqueeze(0), float('-inf')) / area
-
-    for k in range(1, T):
-        tmp = D[:, k-1, None, None, :, :] + block_mat
-        D[:, k] = tmp.flatten(3).max(-1).values
-        D_ind[:, k] = tmp.flatten(3).max(-1).indices
-    
-    segment1, segment2 = [torch.full((B, 1), T, dtype=torch.long, device=device)]*2
-    k = step_num
-    i, j, a, b = [torch.full((B, 1), T-1, dtype=torch.long, device=device)]*4
-    
-    while k > 0:
-        ind = D_ind[range(B), k, i.squeeze(), j.squeeze()][:, None]
-        a = ind // T
-        b = ind % T
-        segment1 = torch.cat([a, segment1], dim=-1)
-        segment2 = torch.cat([b, segment2], dim=-1)
-        i, j, k = a, b, k-1
-        
-    final_result = D[:, :, T-1, T-1]
-    
-    video_seg1 = segment1[:, :-1] + 1
-    video_seg2 = segment2[:, :-1] + 1
-    
-    # loss_step = (-(pair_labels * final_result.max(dim=-1).values)).sum()
-    loss_step = -(pair_labels * final_result.max(dim=-1).values).mean()
-    
-    return loss_step, video_seg1, video_seg2
-
-
-
-def consist_step_mining_inference(seq_features1, seq_features2, step_num):
-    seq_features1 = seq_features1[:, 1:]
-    seq_features2 = seq_features2[:, 1:]
-    (B, T, C), device = seq_features1.shape, seq_features1.device
-    
-    # pred = (torch.einsum('bic,bjc->bij', seq_features1, seq_features2) / math.sqrt(C)).softmax(-1)
-    pred = torch.cosine_similarity(seq_features1.unsqueeze(2), seq_features2.unsqueeze(1), dim=-1)
-    pred = pred.cumsum(-2).cumsum(-1)
-    
-    D = torch.zeros((B, T, T, T), device=device)
-    D_ind = torch.zeros((B, T, T, T), dtype=torch.long, device=device)
-    
-    D[:, 0] = pred / torch.ones_like(pred).cumsum(-2).cumsum(-1)
-    
-    area = torch.ones_like(pred).cumsum(-2).cumsum(-1)
-    area = (area[:, :, :, None, None] - area[:, :, None, None, :] \
-            - area.transpose(1,2)[:, None, :, :, None] + area[:, None, None, :, :])
-    
-    block_mat = (pred[:, :, :, None, None] - pred[:, :, None, None, :] \
-                - pred.transpose(1,2)[:, None, :, :, None] + pred[:, None, None, :, :])
-    
-    top, left, bottom, right = torch.meshgrid(*[torch.arange(T, device=device)]*4)
-    area = area.clamp_min(1).sqrt()
-
-    block_mat = block_mat.masked_fill(((bottom >= top) | (right >= left)).unsqueeze(0), float('-inf')) / area
-    
-    for k in range(1, T):
-        tmp = ((D[:, k-1, None, None, :, :] * k) + block_mat) / (k+1)
-        D[:, k] = torch.max(tmp.flatten(3), -1).values
-        D_ind[:, k] = torch.max(tmp.flatten(3), -1).indices
-    
-    segment1, segment2 = [torch.full((B, 1), T, dtype=torch.long, device=device)]*2
-    k = step_num
-    i, j, a, b = [torch.full((B, 1), T-1, dtype=torch.long, device=device)]*4
-    
-    while k > 0:
-        ind = D_ind[range(B), k, i.squeeze(), j.squeeze()][:, None]
-        a = ind // T
-        b = ind % T
-        segment1 = torch.cat([a, segment1], dim=-1)
-        segment2 = torch.cat([b, segment2], dim=-1)
-        i, j, k = a, b, k-1
-        
-    return segment1[:, :-1] + 1, segment2[:, :-1] + 1
-
-
-def step_align_loss(seq_features1, seq_features2):
-    B, T, C = seq_features1.shape
-    # the similarity matrix: 16 * 16
-    pred = (torch.einsum('bic,bjc->bij', seq_features1, seq_features2) / math.sqrt(C)).softmax(-1)
-    # pred = torch.cosine_similarity(seq_features1.unsqueeze(2), seq_features2.unsqueeze(1), dim=-1)
-    pred = pred.cumsum(-2).cumsum(-1)
-    
-    D = torch.zeros((B, T, T, T), device=seq_features1.device)
-    D_ind = torch.zeros((B, T, T, T), dtype=torch.long, device=pred.device)
-    
-    D[:, 0] = pred / torch.ones_like(pred).cumsum(-2).cumsum(-1)
-    
-    area = torch.ones_like(pred).cumsum(-2).cumsum(-1)
-    area = (area[:, :, :, None, None] - area[:, :, None, None, :] - area.transpose(1,2)[:, None, :, :, None] + area[:, None, None, :, :])
-    block_mat = (pred[:, :, :, None, None] - pred[:, :, None, None, :] - pred.transpose(1,2)[:, None, :, :, None] + pred[:, None, None, :, :])
-    
-    i, j, a, b = torch.meshgrid(*[torch.arange(T, device=seq_features1.device)]*4)
-    area = area.clamp_min(1).sqrt()
-
-    block_mat = block_mat.masked_fill(((a >= i) | (b >= j)).unsqueeze(0), float('-inf')) / area
-    
-    for k in range(1, T):
-        # tmp = ((D[:, k-1, None, None, :, :] * k) + block_mat) / (k+1)
-        tmp = D[:, k-1, None, None, :, :] + block_mat
-        D[:, k] = torch.max(tmp.flatten(3), -1).values
-        D_ind[:, k] = torch.max(tmp.flatten(3), -1).indices
-    
-    final_result = D[:, :, T-1, T-1]
-    return -(final_result.max(dim=-1).values).mean(), final_result.max(dim=-1).indices, D_ind
-
-
-def single_align_loss(seq_features1, seq_features2):
-    device = seq_features1.device
-    T, C = seq_features1.shape
-    pred = (torch.einsum('ic,jc->ij', seq_features1, seq_features2) / math.sqrt(C)).log_softmax(-1)
-    
-    ZERO_PAD = torch.zeros((1), device=device)
-    ONE_PAD = torch.ones((1), device=device)
-    S = seq_features2.shape[0]
-
-    target = (torch.arange(S, device=device))
-    
-    D_TABLE = ONE_PAD.log()
-    for t in range(T):
-        D_VEC_1 = torch.logsumexp(torch.stack([D_TABLE[1:t+1], D_TABLE[:-1][:t]]), 0) + pred[t, target[:t]]
-        D_VEC_2 = D_TABLE[t:t+1] + pred[t, target[t:t+1]]
-        D_TABLE = torch.cat([ZERO_PAD.log(), D_VEC_1, D_VEC_2], dim=-1)
-    # changed by hotel: remove " / s"
-    ctc_distance = -D_TABLE[S]
-    return ctc_distance
-
-
-def frame2varstep_loss(seq_features1, seq_features2, video_seg):
-    B, T, C = seq_features1.shape
-    losses = []
-    for batch in range(B):
-        seq_feature1 = seq_features1[batch]
-        
-        cur_seg = video_seg[batch]
-        cur_seg = cur_seg[:-1] + 1
-        sparse_feature2 = seq_features2[batch, cur_seg, :]
-        frame_loss = single_align_loss(seq_feature1, sparse_feature2)
-        losses.append(frame_loss)
-        
-    return torch.stack(losses, dim=-1).mean(-1)
-
-
-def frame2varstep_dist(seq_features1, seq_features2, video_seg):
-    B, T, C = seq_features1.shape
-    losses = []
-    for batch in range(B):
-        seq_feature1 = seq_features1[batch]
-        
-        cur_seg = video_seg[batch]
-        cur_seg = cur_seg[:-1] + 1
-        sparse_feature2 = seq_features2[batch, cur_seg, :]
-        frame_loss = single_align_loss(seq_feature1, sparse_feature2)
-        losses.append(frame_loss)
-        
-    return torch.stack(losses, dim=-1)
-
-
-def frame2learnedstep_dist(frame_feats1, step_feats2):
-    B, T, C = frame_feats1.shape
-    losses = []
-    for batch in range(B):
-        frame_feat1 = frame_feats1[batch]
-        step_feat2 = step_feats2[batch]
-        # step_feat2 = step_feat2[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]
-        frame_loss = single_align_loss(frame_feat1, step_feat2)
-        losses.append(frame_loss)
-        
-    return torch.stack(losses, dim=-1)
diff --git a/backup/pdvc/dp/__init__.py b/backup/pdvc/dp/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/backup/pdvc/dp/__pycache__/__init__.cpython-37.pyc b/backup/pdvc/dp/__pycache__/__init__.cpython-37.pyc
deleted file mode 100644
index dd28dccf2f11d713b40d4e237cb5a055bf54ca5d..0000000000000000000000000000000000000000
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deleted file mode 100644
index f1dcdb6e6cb0385b1862aff36c779cdda89cf563..0000000000000000000000000000000000000000
--- a/backup/pdvc/dp/dp_utils.py
+++ /dev/null
@@ -1,402 +0,0 @@
-import numpy as np
-import torch
-import math
-
-from itertools import product
-from torch import log, exp
-import torch.nn.functional as F
-
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-
-def compute_all_costs(
-    z_features,
-    x_features,
-    gamma_xz,
-    drop_cost_type,
-    keep_percentile,
-    l2_normalize=False,
-    given_baseline_logits=None,
-    return_baseline=False,
-):
-    """This function computes pairwise match and individual drop costs used in Drop-DTW
-
-    Parameters
-    __________
-
-    sample: dict
-        sample dictionary
-    distractor: torch.tensor of size [d] or None
-        Background class prototype. Only used if the drop cost is learnable.
-    drop_cost_type: str
-        The type of drop cost definition, i.g., learnable or logits percentile.
-    keep_percentile: float in [0, 1]
-        if drop_cost_type == 'logit', defines drop (keep) cost threshold as logits percentile
-    l2_normalize: bool
-        wheather to normalize clip and step features before computing the costs
-    """
-
-    if l2_normalize:
-        x_features = F.normalize(x_features, p=2, dim=1)
-        z_features = F.normalize(z_features, p=2, dim=1)
-
-    sim = z_features @ x_features.T
-
-    if drop_cost_type == "logit":
-        if keep_percentile > 1:
-            baseline_logit = sim.min().detach() - 1
-        else:
-            k = max([1, int(torch.numel(sim) * keep_percentile)])
-            baseline_logit = torch.topk(sim.reshape([-1]), k).values[-1].detach()
-        baseline_logits = baseline_logit.repeat([1, sim.shape[1]])  # making it of shape [1, N]
-        sims_ext = torch.cat([sim, baseline_logits], dim=0)
-    else:
-        assert False, f"No such drop mode {drop_cost_type}"
-
-    softmax_sims = torch.nn.functional.softmax(sims_ext / gamma_xz, dim=0)
-    matching_probs, drop_probs = softmax_sims[:-1], softmax_sims[-1]
-    zx_costs = -torch.log(matching_probs + 1e-5)
-    drop_costs = -torch.log(drop_probs + 1e-5)
-    return zx_costs, drop_costs, drop_probs
-
-
-def compute_double_costs(
-    z_features,
-    x_features,
-    gamma_xz,
-    drop_cost_type,
-    keep_percentile,
-    l2_normalize=False,
-    return_baseline=False,
-):
-    """This function computes pairwise match and individual drop costs used in Drop-DTW
-
-    Parameters
-    __________
-
-    sample: dict
-        sample dictionary
-    distractor: torch.tensor of size [d] or None
-        Background class prototype. Only used if the drop cost is learnable.
-    drop_cost_type: str
-        The type of drop cost definition, i.g., learnable or logits percentile.
-    keep_percentile: float in [0, 1]
-        if drop_cost_type == 'logit', defines drop (keep) cost threshold as logits percentile
-    l2_normalize: bool
-        wheather to normalize clip and step features before computing the costs
-    """
-
-    z_features, frame_features = z_features, x_features
-    if l2_normalize:
-        x_features = F.normalize(frame_features, p=2, dim=1)
-        z_features = F.normalize(z_features, p=2, dim=1)
-    sim = z_features @ x_features.T
-
-    if drop_cost_type == "logit":
-        k = max([1, int(torch.numel(sim) * keep_percentile)])
-        baseline_logit = torch.topk(sim.reshape([-1]), k).values[-1].detach()
-    else:
-        assert False, f"No such drop mode {drop_cost_type}"
-    sim_ext = F.pad(sim, (0, 1, 0, 1), value=baseline_logit)
-
-    softmax_sims = torch.nn.functional.softmax(sim_ext.reshape(-1) / gamma_xz, dim=0).reshape(sim_ext.shape)
-    matching_probs, x_drop_probs, z_drop_probs = softmax_sims[:-1, :-1], softmax_sims[-1, :-1], softmax_sims[:-1, -1]
-    zx_costs = -torch.log(matching_probs + 1e-5)
-    x_drop_costs = -torch.log(x_drop_probs + 1e-5)
-    z_drop_costs = -torch.log(z_drop_probs + 1e-5)
-    return zx_costs, x_drop_costs, z_drop_costs
-
-
-class VarTable:
-    def __init__(self, dims, dtype=torch.float, device=device):
-        self.dims = dims
-        d1, d2, d_rest = dims[0], dims[1], dims[2:]
-
-        self.vars = []
-        for i in range(d1):
-            self.vars.append([])
-            for j in range(d2):
-                var = torch.zeros(d_rest).to(dtype).to(device)
-                self.vars[i].append(var)
-
-    def __getitem__(self, pos):
-        i, j = pos
-        return self.vars[i][j]
-
-    def __setitem__(self, pos, new_val):
-        i, j = pos
-        if self.vars[i][j].sum() != 0:
-            assert False, "This cell has already been assigned. There must be a bug somwhere."
-        else:
-            self.vars[i][j] = self.vars[i][j] + new_val
-
-    def show(self):
-        device, dtype = self[0, 0].device, self[0, 0].dtype
-        mat = torch.zeros((self.d1, self.d2, self.d3)).to().to(dtype).to(device)
-        for dims in product([range(d) for d in self.dims]):
-            i, j, rest = dims[0], dims[1], dims[2:]
-            mat[dims] = self[i, j][rest]
-        return mat
-
-
-def minGamma(inputs, gamma=1, keepdim=True):
-    """continuous relaxation of min defined in the D3TW paper"""
-    if type(inputs) == list:
-        if inputs[0].shape[0] == 1:
-            inputs = torch.cat(inputs)
-        else:
-            inputs = torch.stack(inputs, dim=0)
-
-    if gamma == 0:
-        minG = inputs.min(dim=0, keepdim=keepdim)
-    else:
-        # log-sum-exp stabilization trick
-        zi = -inputs / gamma
-        max_zi = zi.max()
-        log_sum_G = max_zi + log(exp(zi - max_zi).sum(dim=0, keepdim=keepdim) + 1e-5)
-        minG = -gamma * log_sum_G
-    return minG
-
-
-def minProb(inputs, gamma=1, keepdim=True):
-    if type(inputs) == list:
-        if inputs[0].shape[0] == 1:
-            inputs = torch.cat(inputs)
-        else:
-            inputs = torch.stack(inputs, dim=0)
-
-    if gamma == 0:
-        minP = inputs.min(dim=0, keepdim=keepdim)
-    else:
-        probs = F.softmax(-inputs / gamma, dim=0)
-        minP = (probs * inputs).sum(dim=0, keepdim=keepdim)
-    return minP
-
-
-def prob_min(values, gamma_min, logits=None):
-    logits = values if logits is None else logits
-    assert len(logits) == len(values), "Values and prob logits are of different length"
-
-    if len(values) > 1:
-        values = torch.cat(values, dim=-1)
-        logits = torch.cat(logits, dim=-1)
-    else:
-        values = values[0]
-        logits = logits[0]
-
-    if gamma_min > 0:
-        probs = F.softmax(-logits / gamma_min, dim=-1)
-    else:
-        probs = F.one_hot(logits.argmin(), logits.size(-1))
-
-    if values.dim() > probs.dim():
-        probs = probs[..., None, :]
-
-    out = (values * probs).sum(-1).to(values.dtype)
-    return out
-
-
-def list_min(values, keys=None):
-    keys = values if keys is None else keys
-    assert len(keys) == len(values), "Values and prob logits are of different length"
-
-    if values[0].dim() == keys[0].dim() + 1:
-        dim = -2
-    else:
-        dim = -1
-
-    if len(values) > 1:
-        values = torch.cat(values, dim=dim)
-        keys = torch.cat(keys, dim=-1)
-    else:
-        values = values[0]
-        keys = keys[0]
-
-    onehot = F.one_hot(keys.argmin(-1), keys.size(-1))
-    if values.dim() > keys.dim():
-        onehot = onehot[..., None]
-    out = (values * onehot).sum(dim).to(values.dtype)
-    return out
-
-
-def traceback(D):
-    i, j = np.array(D.shape) - 2
-    p, q = [i], [j]
-    while (i > 0) or (j > 0):
-        tb = np.argmin((D[i, j], D[i, j + 1], D[i + 1, j]))
-        if tb == 0:
-            i -= 1
-            j -= 1
-        elif tb == 1:
-            i -= 1
-        else:  # (tb == 2):
-            j -= 1
-        p.insert(0, i)
-        q.insert(0, j)
-    return np.array(p), np.array(q)
-
-
-def diag_to_mat(diags, K, N):
-    mat = np.zeros([K, N]) - 123
-    for d in range(len(diags)):
-        for r, v in enumerate(diags[d]):
-            j = min(d, N - 1) - r
-            i = d - j
-            mat[i, j] = v if v < 1e8 else np.inf
-    return mat
-
-
-def pad_costs(zx_costs_list, drop_costs_list):
-    B = len(zx_costs_list)
-    Ns, Ks = [], []
-    for i in range(B):
-        Ki, Ni = zx_costs_list[i].shape
-        if Ki >= Ni:
-            # in case the number of steps is greater than the number of frames,
-            # duplicate every frame and let the drops do the job.
-            mult = math.ceil(Ki / Ni)
-            zx_costs_list[i] = torch.stack([zx_costs_list[i]] * mult, dim=-1).reshape([Ki, -1])
-            drop_costs_list[i] = torch.stack([drop_costs_list[i]] * mult, dim=-1).reshape([-1])
-            Ni *= mult
-        Ns.append(Ni)
-        Ks.append(Ki)
-    N, K = max(Ns), max(Ks)
-
-    # preparing padded tables
-    padded_cum_drop_costs, padded_drop_costs, padded_zx_costs = [], [], []
-    for i in range(B):
-        zx_costs = zx_costs_list[i]
-        drop_costs = drop_costs_list[i]
-        cum_drop_costs = torch.cumsum(drop_costs, dim=0)
-
-        # padding everything to the size of the largest N and K
-        row_pad = torch.zeros([N - Ns[i]]).to(zx_costs.device)
-        padded_cum_drop_costs.append(torch.cat([cum_drop_costs, row_pad]))
-        padded_drop_costs.append(torch.cat([drop_costs, row_pad]))
-        multirow_pad = torch.stack([row_pad + 9999999999] * Ks[i], dim=0)
-        padded_table = torch.cat([zx_costs, multirow_pad], dim=1)
-        rest_pad = torch.zeros([K - Ks[i], N]).to(zx_costs.device) + 9999999999
-        padded_table = torch.cat([padded_table, rest_pad], dim=0)
-        padded_zx_costs.append(padded_table)
-    return padded_cum_drop_costs, padded_drop_costs, padded_zx_costs, Ns, Ks
-
-
-def get_diag_coord_grid(B, d_len, num_states, d_idx):
-    """
-    B - batch size
-    d - num_elements in the diagonal
-    num_states - number of states in DP table
-    d_idx - idx of the diagonal , used for marking
-    """
-    r = torch.arange(d_len)
-    s = torch.arange(num_states)
-    d = torch.ones(d_len, num_states) * d_idx
-    mg = torch.stack([d, *torch.meshgrid(r, s)], dim=-1)[None, ...].repeat([B, 1, 1, 1])
-    return mg
-
-
-def diag_traceback(pointer, N, paths):
-    # getting rid of unnecessary elements in the batch
-    pointer = [int(l.item()) for l in pointer]
-    d, r, s = pointer
-    traceback = [pointer]
-    while d > 0:
-        new_pointer = [int(l.item()) for l in paths[d][r, s]]
-        traceback.append(new_pointer)
-        d, r, s = new_pointer
-
-    # transform to rectangular coordinates
-    rectangular_traceback = []
-    for d, r, s in traceback:
-        i = r + max(0, d - N + 1)
-        j = d - i
-        if i > 0 and j > 0:
-            rectangular_traceback.append((i, j, s))
-
-    return traceback, rectangular_traceback
-
-
-def nw_diag_traceback(d, r, N, paths):
-    d, r = int(d.item()), int(r.item())
-    traceback = []
-    while d > 0:
-        d_1, s_1, s = [int(l.item()) for l in paths[d][r, 0]]
-        traceback.append((d, r, s))
-        d, r = d_1, s_1
-
-    # transform to rectangular coordinates
-    rectangular_traceback = []
-    for d, r, s in traceback:
-        i = r + max(0, d - N + 1)
-        j = d - i
-        if i > 0 and j > 0:
-            rectangular_traceback.append((i, j, s))
-
-    return traceback, rectangular_traceback
-
-
-def compute_symmetric_cost(sim, keep_percentile=0.3):
-    k = max([1, int(torch.numel(sim) * keep_percentile)])
-    baseline_logit = torch.topk(sim.reshape([-1]), k).values[-1].detach()
-    baseline_logits = baseline_logit.repeat([1, sim.shape[1]])  # making it of shape [1, N]
-    zx_costs = -sim
-    x_drop_costs = -baseline_logits.squeeze()
-    z_drop_costs = -baseline_logit.repeat([1, sim.shape[0]]).squeeze()
-    return zx_costs, x_drop_costs, z_drop_costs
-
-
-#============ Hach from model_utilis.py in StepFormer ============#
-
-
-
-def unique_softmax(sim, labels, gamma=1, dim=0):
-    assert sim.shape[0] == labels.shape[0]
-    labels = labels.detach().cpu().numpy()
-    _, unique_index, unique_inverse_index = np.unique(labels, return_index=True, return_inverse=True)
-    unique_sim = sim[unique_index]
-    unique_softmax_sim = torch.nn.functional.softmax(unique_sim / gamma, dim=dim)
-    softmax_sim = unique_softmax_sim[unique_inverse_index]
-    return softmax_sim
-
-def compute_masked_sims(z, x, z_pad_mask, x_pad_mask, l2_normalize=False, softmax_dim=None, gamma=None):
-    # z ~ [B, K, d], x ~ [B, N, d]
-    if l2_normalize:
-        z, x = F.normalize(z, dim=-1), F.normalize(x, dim=-1)
-    pad_sims = torch.einsum("bkd,bnd->bkn", z, x)
-    masked_sims = []
-    for i in range(x.shape[0]):
-        masked_sim = pad_sims[i]
-        masked_sim = masked_sim if z_pad_mask is None else masked_sim[~z_pad_mask[i], :]
-        masked_sim = masked_sim if x_pad_mask is None else masked_sim[:, ~x_pad_mask[i]]
-        if softmax_dim is not None:
-            masked_sim = F.softmax(masked_sim / gamma, dim=softmax_dim)
-        masked_sims.append(masked_sim)
-    return masked_sims  
-
-def compute_sim(z, x, l2_norm):
-    if l2_norm:
-        return F.normalize(z, dim=1) @ F.normalize(x, dim=1).T
-    else:
-        return z @ x.T
-
-
-def cosine_sim(x, z):
-    cos_sim_fn = torch.nn.CosineSimilarity(dim=1)
-    return cos_sim_fn(x[..., None], z.T[None, ...])
-
-
-def cos_dist(x, z):
-    cos_sim_fn = torch.nn.CosineSimilarity(dim=1)
-    return (1 - cos_sim_fn(x[..., None], z.T[None, ...])) / 2
-
-
-def l2_dist(x, z):
-    dist_squared = (x**2).sum() + (z**2).sum() - 2 * x @ z.T
-    return torch.clamp(dist_squared, min=0).sqrt()
-
-
-def cos_loglikelihood(x, z, gamma=0.1, z_dim=1):
-    cos_sim = cosine_sim(x, z)
-    probs = F.softmax(cos_sim / gamma, dim=z_dim)
-    return torch.log(probs)
\ No newline at end of file
diff --git a/backup/pdvc/dp/exact_dp.py b/backup/pdvc/dp/exact_dp.py
deleted file mode 100644
index ada874b89a60799af867aab82357c8d7b442348d..0000000000000000000000000000000000000000
--- a/backup/pdvc/dp/exact_dp.py
+++ /dev/null
@@ -1,1123 +0,0 @@
-import torch
-import numpy as np
-import torch.nn.functional as F
-from functools import partial
-from copy import copy
-
-# from dp.dp_utils import get_diag_coord_grid, diag_traceback, nw_diag_traceback, list_min
-from pdvc.dp.dp_utils import get_diag_coord_grid, diag_traceback, nw_diag_traceback, list_min
-
-
-def crosstask_dp(cost_matrix, exactly_one=True, bg_cost=0):
-    "Algorithm used in Cross-Task to calculate Recall"
-
-    def get_step(k):
-        return 0 if k % 2 == 0 else int((k + 1) / 2)
-
-    T = cost_matrix.shape[0]
-    K = cost_matrix.shape[1]
-    K_ext = int(2 * K + 1)
-
-    L = -np.ones([T + 1, K_ext], dtype=float)
-    P = -np.ones([T + 1, K_ext], dtype=float)
-    L[0, 0] = 0
-    P[0, 0] = 0
-
-    for t in range(1, T + 1):
-        Lt = L[t - 1, :]
-        Pt = P[t - 1, :]
-        for k in range(K_ext):
-            s = get_step(k)
-            opt_label = -1
-
-            j = k
-            if (opt_label == -1 or opt_value > Lt[j]) and Pt[j] != -1 and (s == 0 or not exactly_one):
-                opt_label = j
-                opt_value = Lt[j]
-
-            j = k - 1
-            if j >= 0 and (opt_label == -1 or opt_value > Lt[j]) and Pt[j] != -1:
-                opt_label = j
-                opt_value = L[t - 1][j]
-
-            if s != 0:
-                j = k - 2
-                if j >= 0 and (opt_label == -1 or opt_value > Lt[j]) and Pt[j] != -1:
-                    opt_label = j
-                    opt_value = Lt[j]
-
-            if s != 0:
-                L[t, k] = opt_value + cost_matrix[t - 1][s - 1]
-            else:
-                L[t, k] = opt_value + bg_cost
-            P[t, k] = opt_label
-
-    labels = np.zeros_like(cost_matrix)
-    if L[T, K_ext - 1] < L[T, K_ext - 2] or (P[T, K_ext - 2] == -1):
-        k = K_ext - 1
-    else:
-        k = K_ext - 2
-    for t in range(T, 0, -1):
-        s = get_step(k)
-        if s > 0:
-            labels[t - 1, s - 1] = 1
-        k = P[t, k].astype(int)
-    return labels
-
-
-def iou_based_matching(pred_seg, gt_seg, pred_step_ids, gt_step_ids, ignore_class=True):
-    """Performs the matching of predicted and gt sequence segments"""
-    pred_segments = torch.stack([pred_seg == idx for idx in pred_step_ids], 0)  # [N_pred, T]
-    gt_segments = torch.stack([gt_seg == idx for idx in gt_step_ids], 0)  # [N_gt, T]
-    intersection = (
-        torch.logical_and(pred_segments.unsqueeze(1), gt_segments.unsqueeze(0)).to(int).sum(-1)
-    )  # [N_pred, N_gt]
-    union = torch.logical_or(pred_segments.unsqueeze(1), gt_segments.unsqueeze(0)).to(int).sum(-1)  # [N_pred, N_gt]
-    iou = intersection / (union + 1e-5)  # [N_pred, N_gt]
-
-    C = -iou.detach().cpu().numpy().T  # [N_gt, N_pred]
-    if not ignore_class:
-        print("Not ignoring class")
-        is_same_step_id = pred_step_ids.unsqueeze(1) == gt_step_ids.unsqueeze(0)  # [N_pred, N_gt]
-        if is_same_step_id.shape == (1, 1):
-            C[0, 0] += 9999 * (~is_same_step_id[0, 0])
-        else:
-            C[~is_same_step_id] = 9999
-
-    x_drop, z_drop = np.zeros(C.shape[1]), np.zeros(C.shape[0])
-    labels = double_drop_dtw(C, x_drop, z_drop, one_to_many=False, many_to_one=False, return_labels=True) - 1
-    indices = (np.arange(len(labels))[labels > -1], labels[labels > -1])
-    return [torch.as_tensor(i, dtype=torch.int64) for i in indices]
-
-
-def drop_dtw(zx_costs, drop_costs, exclusive=True, contiguous=True, one_to_one=False, return_labels=False):
-    """Drop-DTW algorithm that allows drop only from one (video) side. See Algorithm 1 in the paper.
-
-    Parameters
-    ----------
-    zx_costs: np.ndarray [K, N]
-        pairwise match costs between K steps and N video clips
-    drop_costs: np.ndarray [N]
-        drop costs for each clip
-    exclusive: bool
-        If True any clip can be matched with only one step, not many.
-    contiguous: bool
-        if True, can only match a contiguous sequence of clips to a step
-        (i.e. no drops in between the clips)
-    return_label: bool
-        if True, returns output directly useful for segmentation computation (made for convenience)
-    """
-    K, N = zx_costs.shape
-
-    # D: the dynamic programming table, which records the intermediate costs
-    # P: the path tracking table, which records the previous location and state (zi, xi, prev_state)
-
-    # initialize solutin matrices
-    D = np.zeros([K + 1, N + 1, 2])  # the 2 last dimensions correspond to different states.
-    # State (dim) 0 - x is matched; State 1 - x is dropped
-    D[1:, 0, :] = np.inf  # no drops in z in any state
-    D[0, 1:, 0] = np.inf  # no drops in x in state 0, i.e. state where x is matched
-    D[0, 1:, 1] = np.cumsum(drop_costs)  # drop costs initizlization in state 1
-
-    # initialize path tracking info for each state
-    P = np.zeros([K + 1, N + 1, 2, 3], dtype=int) # the last dimension records the previous location and state (zi, xi, prev_state)
-    for xi in range(1, N + 1):
-        P[0, xi, 1] = 0, xi - 1, 1
-    # filling in the dynamic tables
-    for zi in range(1, K + 1):
-        for xi in range(1, N + 1):
-            # define frequently met neighbors here
-            diag_neigh_states = [0, 1]
-            diag_neigh_coords = [(zi - 1, xi - 1) for _ in diag_neigh_states]
-            diag_neigh_costs = [D[zi - 1, xi - 1, s] for s in diag_neigh_states]
-
-            left_neigh_states = [0, 1]
-            left_neigh_coords = [(zi, xi - 1) for _ in left_neigh_states]
-            left_neigh_costs = [D[zi, xi - 1, s] for s in left_neigh_states]
-
-            left_pos_neigh_states = [0] if contiguous else left_neigh_states
-            left_pos_neigh_coords = [(zi, xi - 1) for _ in left_pos_neigh_states]
-            left_pos_neigh_costs = [D[zi, xi - 1, s] for s in left_pos_neigh_states] # Drop between clips is not allowed when setting `contiguous==True` (one step to sparse clips is not allowed)
-
-            top_pos_neigh_states = [0]
-            top_pos_neigh_coords = [(zi - 1, xi) for _ in top_pos_neigh_states]
-            top_pos_neigh_costs = [D[zi - 1, xi, s] for s in top_pos_neigh_states]
-
-            z_cost_ind, x_cost_ind = zi - 1, xi - 1  # indexind in costs is shifted by 1
-
-            # state 0: matching x to z
-            neigh_states_pos = diag_neigh_states
-            neigh_coords_pos = diag_neigh_coords
-            neigh_costs_pos = diag_neigh_costs
-            if not one_to_one:
-                neigh_states_pos = neigh_states_pos + left_pos_neigh_states
-                neigh_coords_pos = neigh_coords_pos + left_pos_neigh_coords
-                neigh_costs_pos = neigh_costs_pos + left_pos_neigh_costs
-            if not exclusive: # exclusive=True indicates any clip can be matched with only one step, that is, path from top is not allowed
-                neigh_states_pos = neigh_states_pos + top_pos_neigh_states
-                neigh_coords_pos = neigh_coords_pos + top_pos_neigh_coords
-                neigh_costs_pos = neigh_costs_pos + left_pos_neigh_costs + top_pos_neigh_costs
-
-            costs_pos = np.array(neigh_costs_pos) + zx_costs[z_cost_ind, x_cost_ind] # calculate cumulative cost in current step
-            opt_ind_pos = np.argmin(costs_pos)
-            P[zi, xi, 0] = *neigh_coords_pos[opt_ind_pos], neigh_states_pos[opt_ind_pos] # Records the last step's position (zi,xi) and state (0 or 1)
-            D[zi, xi, 0] = costs_pos[opt_ind_pos]  # Update the minimal cumulative cost of selected path
-
-            # state 1: x is dropped
-            costs_neg = np.array(left_neigh_costs) + drop_costs[x_cost_ind]
-            opt_ind_neg = np.argmin(costs_neg)
-            P[zi, xi, 1] = *left_neigh_coords[opt_ind_neg], left_neigh_states[opt_ind_neg]
-            D[zi, xi, 1] = costs_neg[opt_ind_neg]
-
-    cur_state = D[K, N, :].argmin()
-    min_cost = D[K, N, cur_state]
-    #breakpoint()
-
-    # backtracking the solution
-    zi, xi = K, N
-    path, labels = [], np.zeros(N)
-    x_dropped = [] if cur_state == 1 else [N]
-    while not (zi == 0 and xi == 0):
-        path.append((zi, xi))
-        zi_prev, xi_prev, prev_state = P[zi, xi, cur_state]
-        if xi > 0:
-            labels[xi - 1] = zi * (cur_state == 0)  # either zi or 0
-        if prev_state == 1:
-            x_dropped.append(xi_prev)
-        zi, xi, cur_state = zi_prev, xi_prev, prev_state
-
-    if not return_labels:
-        return min_cost, D, path, x_dropped
-    else:
-        return labels
-
-
-def double_drop_dtw(
-    pairwise_zx_costs,
-    x_drop_costs,
-    z_drop_costs,
-    contiguous=True,
-    one_to_many=True,
-    many_to_one=True,
-    return_labels=False,
-):
-    """Drop-DTW algorithm that allows drops from both sequences. See Algorithm 1 in Appendix.
-
-    Parameters
-    ----------
-    pairwise_zx_costs: np.ndarray [K, N]
-        pairwise match costs between K steps and N video clips
-    x_drop_costs: np.ndarray [N]
-        drop costs for each clip
-    z_drop_costs: np.ndarray [N]
-        drop costs for each step
-    contiguous: bool
-        if True, can only match a contiguous sequence of clips to a step
-        (i.e. no drops in between the clips)
-    """
-    K, N = pairwise_zx_costs.shape
-
-    # initialize solution matrices
-    D = np.zeros([K + 1, N + 1, 4])  # the 4 dimensions are the following states: zx, z-, -x, --
-    # no drops allowed in zx DP. Setting the same for all DPs to change later here.
-    D[1:, 0, :] = 99999999
-    D[0, 1:, :] = 99999999
-    D[0, 0, 1:] = 99999999
-    # Allow to drop x in z- and --
-    D[0, 1:, 1], D[0, 1:, 3] = np.cumsum(x_drop_costs), np.cumsum(x_drop_costs)
-    # Allow to drop z in -x and --
-    D[1:, 0, 2], D[1:, 0, 3] = np.cumsum(z_drop_costs), np.cumsum(z_drop_costs)
-
-    # initialize path tracking info for each of the 4 DP tables:
-    P = np.zeros([K + 1, N + 1, 4, 3], dtype=int)  # (zi, xi, prev_state)
-    for zi in range(1, K + 1):
-        P[zi, 0, 2], P[zi, 0, 3] = (zi - 1, 0, 2), (zi - 1, 0, 3)
-    for xi in range(1, N + 1):
-        P[0, xi, 1], P[0, xi, 3] = (0, xi - 1, 1), (0, xi - 1, 3)
-
-    # filling in the dynamic tables
-    for zi in range(1, K + 1):
-        for xi in range(1, N + 1):
-            # define frequently met neighbors here
-            diag_neigh_states = [0, 1, 2, 3]  # zx, z-, -x, --
-            diag_neigh_coords = [(zi - 1, xi - 1) for _ in diag_neigh_states]
-            diag_neigh_costs = [D[zi - 1, xi - 1, s] for s in diag_neigh_states]
-
-            left_pos_neigh_states = [0, 1]  # zx and z-
-            left_pos_neigh_coords = [(zi, xi - 1) for _ in left_pos_neigh_states]
-            left_pos_neigh_costs = [D[zi, xi - 1, s] for s in left_pos_neigh_states]
-
-            top_pos_neigh_states = [0, 2]  # zx and -x
-            top_pos_neigh_coords = [(zi - 1, xi) for _ in top_pos_neigh_states]
-            top_pos_neigh_costs = [D[zi - 1, xi, s] for s in top_pos_neigh_states]
-
-            left_neg_neigh_states = [2, 3]  # -x and --
-            left_neg_neigh_coords = [(zi, xi - 1) for _ in left_neg_neigh_states]
-            left_neg_neigh_costs = [D[zi, xi - 1, s] for s in left_neg_neigh_states]
-
-            top_neg_neigh_states = [1, 3]  # z- and --
-            top_neg_neigh_coords = [(zi - 1, xi) for _ in top_neg_neigh_states]
-            top_neg_neigh_costs = [D[zi - 1, xi, s] for s in top_neg_neigh_states]
-
-            z_cost_ind, x_cost_ind = zi - 1, xi - 1  # indexind in costs is shifted by 1
-
-            # DP 0: coming to zx
-            neigh_states_zx = diag_neigh_states
-            neigh_coords_zx = diag_neigh_coords
-            neigh_costs_zx = diag_neigh_costs
-            if one_to_many:
-                if contiguous:
-                    neigh_states_zx.extend(left_pos_neigh_states[0:1])
-                    neigh_coords_zx.extend(left_pos_neigh_coords[0:1])
-                    neigh_costs_zx.extend(left_pos_neigh_costs[0:1])
-                else:
-                    neigh_states_zx.extend(left_pos_neigh_states)
-                    neigh_coords_zx.extend(left_pos_neigh_coords)
-                    neigh_costs_zx.extend(left_pos_neigh_costs)
-            if many_to_one:
-                neigh_states_zx.extend(top_pos_neigh_states)
-                neigh_coords_zx.extend(top_pos_neigh_coords)
-                neigh_costs_zx.extend(top_pos_neigh_costs)
-
-            costs_zx = np.array(neigh_costs_zx) + pairwise_zx_costs[z_cost_ind, x_cost_ind]
-            opt_ind_zx = np.argmin(costs_zx)
-            P[zi, xi, 0] = *neigh_coords_zx[opt_ind_zx], neigh_states_zx[opt_ind_zx]
-            D[zi, xi, 0] = costs_zx[opt_ind_zx]
-
-            # DP 1: coming to z-
-            neigh_states_z_ = left_pos_neigh_states
-            neigh_coords_z_ = left_pos_neigh_coords
-            neigh_costs_z_ = left_pos_neigh_costs
-            costs_z_ = np.array(neigh_costs_z_) + x_drop_costs[x_cost_ind]
-            opt_ind_z_ = np.argmin(costs_z_)
-            P[zi, xi, 1] = *neigh_coords_z_[opt_ind_z_], neigh_states_z_[opt_ind_z_]
-            D[zi, xi, 1] = costs_z_[opt_ind_z_]
-
-            # DP 2: coming to -x
-            neigh_states__x = top_pos_neigh_states
-            neigh_coords__x = top_pos_neigh_coords
-            neigh_costs__x = top_pos_neigh_costs
-            costs__x = np.array(neigh_costs__x) + z_drop_costs[z_cost_ind]
-            opt_ind__x = np.argmin(costs__x)
-            P[zi, xi, 2] = *neigh_coords__x[opt_ind__x], neigh_states__x[opt_ind__x]
-            D[zi, xi, 2] = costs__x[opt_ind__x]
-
-            # DP 3: coming to --
-            neigh_states___ = np.array(left_neg_neigh_states + top_neg_neigh_states)
-            # neigh_states___ = np.array(left_neg_neigh_states + top_neg_neigh_states + diag_neigh_states)
-            # adding negative left and top neighbors
-            neigh_coords___ = np.array(left_neg_neigh_coords + top_neg_neigh_coords)
-            # neigh_coords___ = np.array(left_neg_neigh_coords + top_neg_neigh_coords + diag_neigh_coords)
-            costs___ = np.concatenate(
-                [
-                    left_neg_neigh_costs + x_drop_costs[x_cost_ind],
-                    top_neg_neigh_costs + z_drop_costs[z_cost_ind],
-                    # diag_neigh_costs + z_drop_costs[z_cost_ind] + x_drop_costs[x_cost_ind],
-                ],
-                0,
-            )
-
-            opt_ind___ = costs___.argmin()
-            P[zi, xi, 3] = *neigh_coords___[opt_ind___], neigh_states___[opt_ind___]
-            D[zi, xi, 3] = costs___[opt_ind___]
-
-    cur_state = D[K, N, :].argmin()
-    min_cost = D[K, N, cur_state]
-
-    # unroll path
-    path = []
-    zi, xi = K, N
-    x_dropped = [N] if cur_state in [1, 3] else []
-    z_dropped = [K] if cur_state in [2, 3] else []
-    while not (zi == 0 and xi == 0):
-        path.append((zi, xi))
-        zi_prev, xi_prev, prev_state = P[zi, xi, cur_state]
-        if prev_state in [1, 3]:
-            x_dropped.append(xi_prev)
-        if prev_state in [2, 3]:
-            z_dropped.append(zi_prev)
-        zi, xi, cur_state = zi_prev, xi_prev, prev_state
-
-    if return_labels:
-        labels = np.zeros(N)
-        for zi, xi in path:
-            if zi not in z_dropped and xi not in x_dropped:
-                labels[xi - 1] = zi
-        return labels
-    else:
-        return min_cost, path, x_dropped, z_dropped
-
-
-def batch_double_drop_dtw_machine(
-    zx_costs_list, x_drop_costs_list, z_drop_costs_list, many_to_one=False, one_to_many=False, contiguous=True
-):
-    # many_to_one is the same as not exclusive, i.e. multiple z match to one x
-    # one_to_many was always true by default before, i.e. multiple x match to one z
-    dev, dtype = zx_costs_list[0].device, zx_costs_list[0].dtype
-    inf = torch.tensor([9999999999], device=dev, dtype=dtype)
-    B = len(zx_costs_list)
-
-    shapes = [t.shape for t in zx_costs_list]
-    Ks, Ns = [s[0] for s in shapes], [s[1] for s in shapes]
-    N, K = max(Ns), max(Ks)
-
-    # transform endpoints into diagonal coordinates
-    Ds, Rs = torch.zeros(B).to(dev).to(int), torch.zeros(B).to(dev).to(int)
-    for i, (Ki, Ni) in enumerate(zip(Ks, Ns)):
-        Ds[i] = Ki + Ni - 2
-        Rs[i] = min(Ds[i] + 2, N) - Ni
-    Ds_orig, Rs_orig = copy(Ds), copy(Rs)
-
-    # special padding of costs to ensure that the path goest through the endpoint
-    all_zx_costs = []
-    for i, c in enumerate(zx_costs_list):
-        c_inf_frame = F.pad(c, [0, 1, 0, 1], value=inf.item())
-        mask = torch.ones_like(c_inf_frame)
-        mask[-1, -1] = 0
-        c_pad = F.pad(c_inf_frame * mask, [0, N - c.shape[1] - 1, 0, K - c.shape[0] - 1])
-        all_zx_costs.append(c_pad)
-    all_zx_costs = torch.stack(all_zx_costs, 0)
-
-    all_x_drop_costs = torch.stack([F.pad(c, [0, N - c.shape[0]], value=inf.item()) for c in x_drop_costs_list], 0)
-    all_cum_x_drop_costs = torch.stack(
-        [F.pad(torch.cumsum(c, -1), [0, N - c.shape[0]], value=inf.item()) for c in x_drop_costs_list], 0
-    )
-    all_z_drop_costs = torch.stack([F.pad(c, [0, K - c.shape[0]], value=inf.item()) for c in z_drop_costs_list], 0)
-    all_cum_z_drop_costs = torch.stack(
-        [F.pad(torch.cumsum(c, -1), [0, K - c.shape[0]], value=inf.item()) for c in z_drop_costs_list], 0
-    )
-    flipped_costs = torch.flip(all_zx_costs, [1])  # flip the cost matrix upside down
-
-    """Rules for the diagonals:
-        dim1: batch dimension
-        dim2: the diagonal itself. The first element along this dim corresponds
-              to the top right element on the diagonal. The movement is from top right
-              to bottom left, like that /
-        dim3: Keep and Drop dimensions of the DP table. The dimensions are as follows:
-              {0: zx, 1: z-, 2: -x, 3: --}
-     """
-    # initialize first two contr diagonals
-    batch_inf = torch.stack([inf] * B, 0)
-    diag_pp = torch.zeros([B, 1, 4], device=dev)  # diag at i-2
-    x1_dropcost, z1_dropcost = all_cum_x_drop_costs[:, [0]], all_cum_z_drop_costs[:, [0]]
-    diag_p_row = torch.stack([batch_inf, x1_dropcost, batch_inf, x1_dropcost], -1)
-    diag_p_col = torch.stack([batch_inf, batch_inf, z1_dropcost, z1_dropcost], -1)
-    diag_p = torch.cat([diag_p_row, diag_p_col], 1)  # diag at i-1
-
-    # The path is also a diagonal representation that carries the optimal pathlength to each point
-    path_pp = torch.zeros([B, 1, 4, 3], device=dev, dtype=int)
-    path_p = torch.zeros([B, 2, 4, 3], device=dev, dtype=int)
-    all_paths = [path_pp, path_p]  # going to store all the intermediate paths diagonals for the backtrack
-
-    # Coords is also a diagonal representation that carries the current coordinates in [d, r] for each point
-    # the last dimension is 3 because it's [d, r, s], where d is a diagonal, r is element's order in the diagonal
-    # and s is statet (one of the 4)
-    coord_pp = get_diag_coord_grid(B, 1, 4, 0).to(dev)
-    coord_p = get_diag_coord_grid(B, 2, 4, 1).to(dev)
-
-    min_costs = torch.zeros(B).to(dtype=dtype).to(device=dev)  # for storing the solution for each element
-    tracebacks = [None for _ in range(B)]  # going to store all the intermediate paths diagonals for the backtrack
-
-    for d in range(K + N - 1):
-        size = diag_p.size(1) - 1
-        pp_start = 0 if d < N else 1
-        neigh_up, neigh_left, neigh_diag = diag_p[:, :-1], diag_p[:, 1:], diag_pp[:, pp_start : (pp_start + size)]
-        neigh_left_pos, neigh_left_neg = neigh_left[..., [0, 1]], neigh_left[..., [2, 3]]
-        neigh_up_pos, neigh_up_neg = neigh_up[..., [0, 2]], neigh_up[..., [1, 3]]
-
-        coord_up, coord_left, coord_diag = coord_p[:, :-1], coord_p[:, 1:], coord_pp[:, pp_start : (pp_start + size)]
-        coord_left_pos, coord_left_neg = coord_left[..., [0, 1], :], coord_left[..., [2, 3], :]
-        coord_up_pos, coord_up_neg = coord_up[..., [0, 2], :], coord_up[..., [1, 3], :]
-
-        # define match and drop cost vectors
-        match_costs_diag = torch.stack(
-            [torch.flip(torch.diag(flipped_costs[j], d + 1 - K), [-1]) for j in range(flipped_costs.size(0))], 0
-        )
-
-        x_d_start, x_d_end = max(d + 1 - K, 0), min(d, N - 1) + 1
-        x_drop_costs_diag = torch.flip(all_x_drop_costs[:, x_d_start:x_d_end], [-1])
-        z_d_start, z_d_end = max(d + 1 - N, 0), min(d, K - 1) + 1
-        z_drop_costs_diag = all_z_drop_costs[:, z_d_start:z_d_end]
-
-        # update positive and negative tables -> compute new diagonal
-
-        # DP 0: coming to zx
-        neighbors_zx = [neigh_diag]
-        coordinates_zx = [coord_diag]
-        if one_to_many:
-            neighbors_zx.append(neigh_left_pos[..., [0]] if contiguous else neigh_left)
-            coordinates_zx.append(coord_left_pos[..., [0], :] if contiguous else coord_left)
-        if many_to_one:
-            neighbors_zx.append(neigh_up_pos)
-            coordinates_zx.append(coord_up_pos)
-        diag_zx = list_min(neighbors_zx) + match_costs_diag
-        path_zx = list_min(coordinates_zx, keys=neighbors_zx)
-
-        # DP 1: coming to z-
-        neighbors_z_ = [neigh_left_pos]
-        coordinates_z_ = [coord_left_pos]
-        diag_z_ = list_min(neighbors_z_) + x_drop_costs_diag
-        path_z_ = list_min(coordinates_z_, keys=neighbors_z_)
-
-        # DP 2: coming to -x
-        neighbors__x = [neigh_up_pos]
-        coordinates__x = [coord_up_pos]
-        diag__x = list_min(neighbors__x) + z_drop_costs_diag
-        path__x = list_min(coordinates__x, keys=neighbors__x)
-
-        # DP 3: coming to --
-        neighbors___ = [neigh_left_neg + x_drop_costs_diag[..., None], neigh_up_neg + z_drop_costs_diag[..., None]]
-        coordinates___ = [coord_left_neg, coord_up_neg]
-        diag___ = list_min(neighbors___)
-        path___ = list_min(coordinates___, neighbors___)
-
-        # Aggregating all the dimensions of DP together
-        diag = torch.stack([diag_zx, diag_z_, diag__x, diag___], -1)
-        path = torch.stack([path_zx, path_z_, path__x, path___], -2)
-
-        # Haven't done below
-        # add the initialization values on the ends of diagonal if needed
-        effective_d = d + 2  # effective count of d is actually d + 2, since started with 2
-        if d < N - 1:
-            # fill in 0th row of cost matrix with [inf, x_drop_cost, inf, x_drop_cost]
-            x_drop_cost = all_cum_x_drop_costs[:, [d + 1]]
-            cost_pad = torch.stack([batch_inf, x_drop_cost, batch_inf, x_drop_cost], -1)
-            diag = torch.cat([cost_pad, diag], dim=1)
-
-            # fill in 0th row of path matrix with the right pointers
-            left_pointer = torch.stack(
-                [torch.ones(4) * (effective_d - 1), torch.zeros(4), torch.arange(4)], dim=-1
-            )  # [4, 3]
-            left_pointer = (
-                left_pointer[None, None, ...].repeat([diag.size(0), 1, 1, 1]).to(dev).to(dtype)
-            )  # [B, 1, 4, 3]
-            path = torch.cat([left_pointer, path], 1)
-        if d < K - 1:
-            # fill in 0th col of cost matrix with [inf, inf, z_drop_cost, z_drop_cost]
-            z_drop_cost = all_cum_z_drop_costs[:, [d + 1]]
-            pad = torch.stack([batch_inf, batch_inf, z_drop_cost, z_drop_cost], -1)
-            diag = torch.cat([diag, pad], dim=1)
-
-            # fill in 0th col of path matrix with the right pointers
-
-            # the number of elements in the prev diagonal. Refers to 0th element of the column
-            last_r_p = diag_p.size(1)
-            up_pointer = torch.stack(
-                [torch.ones(4) * (effective_d - 1), torch.ones(4) * (last_r_p - 1), torch.arange(4)],
-                dim=-1,
-            )  # [4, 3]
-            up_pointer = up_pointer[None, None, ...].repeat([diag.size(0), 1, 1, 1]).to(dev).to(dtype)  # [B, 1, 4, 3]
-            path = torch.cat([path, up_pointer], dim=1)
-
-        all_paths.append(path)
-
-        diag_pp = diag_p
-        diag_p = diag
-
-        coord_pp = coord_p
-        coord_p = get_diag_coord_grid(diag.size(0), diag.size(1), 4, effective_d).to(dev)
-
-        # process answers
-        if (Ds == d).any():
-            mask, orig_mask = Ds == d, Ds_orig == d
-            original_bs = torch.nonzero(orig_mask, as_tuple=False)[:, 0]
-            bs, rs = torch.nonzero(mask, as_tuple=False)[:, 0], Rs[mask]
-            min_costs[orig_mask] = min_costs[orig_mask] + list_min([diag[bs, rs]])
-            for orig_b, b, r in zip(original_bs, bs, rs):
-                # min_costs[orig_b] = min_costs[orig_b] + list_min([diag[b, r]])
-                best_pointer = list_min([coord_p[b, r]], keys=[diag[b, r]])
-                this_paths = [p[b.item()] for p in all_paths]
-                # current_N = Ns[orig_b.item()] + 1
-                current_N = N + 1
-                tracebacks[orig_b.item()] = diag_traceback(best_pointer, current_N, this_paths)[1]
-
-            # filtering out already processed elements
-            diag, diag_p, diag_pp, coord_p, coord_pp, path, Ds, Rs, flipped_costs = [
-                t[~mask] for t in [diag, diag_p, diag_pp, coord_p, coord_pp, path, Ds, Rs, flipped_costs]
-            ]
-            all_x_drop_costs, all_z_drop_costs, all_cum_x_drop_costs, all_cum_z_drop_costs, batch_inf = [
-                t[~mask]
-                for t in [all_x_drop_costs, all_z_drop_costs, all_cum_x_drop_costs, all_cum_z_drop_costs, batch_inf]
-            ]
-            all_paths = [p[~mask] for p in all_paths]
-
-            if torch.numel(Ds) == 0:
-                break
-
-    return min_costs, tracebacks
-
-
-def batch_NW_machine(zx_costs_list, x_drop_costs_list, z_drop_costs_list):
-    # many_to_one is the same as not exclusive, i.e. multiple z match to one x
-    # one_to_many was always true by default before, i.e. multiple x match to one z
-    dev, dtype = zx_costs_list[0].device, zx_costs_list[0].dtype
-    inf = torch.tensor([9999999999], device=dev, dtype=dtype)
-    B = len(zx_costs_list)
-
-    shapes = [t.shape for t in zx_costs_list]
-    Ks, Ns = [s[0] for s in shapes], [s[1] for s in shapes]
-    N, K = max(Ns), max(Ks)
-
-    # transform endpoints into diagonal coordinates
-    Ds, Rs = torch.zeros(B).to(dev).to(int), torch.zeros(B).to(dev).to(int)
-    for i, (Ki, Ni) in enumerate(zip(Ks, Ns)):
-        Ds[i] = Ki + Ni - 2
-        Rs[i] = min(Ds[i] + 2, N) - Ni
-    Ds_orig, Rs_orig = copy(Ds), copy(Rs)
-
-    # special padding of costs to ensure that the path goest through the endpoint
-    all_zx_costs = []
-    for i, c in enumerate(zx_costs_list):
-        c_inf_frame = F.pad(c, [0, 1, 0, 1], value=inf.item())
-        mask = torch.ones_like(c_inf_frame)
-        mask[-1, -1] = 0
-        c_pad = F.pad(c_inf_frame * mask, [0, N - c.shape[1] - 1, 0, K - c.shape[0] - 1])
-        all_zx_costs.append(c_pad)
-    all_zx_costs = torch.stack(all_zx_costs, 0)
-
-    all_x_drop_costs = torch.stack([F.pad(c, [0, N - c.shape[0]], value=inf.item()) for c in x_drop_costs_list], 0)
-    all_cum_x_drop_costs = torch.stack(
-        [F.pad(torch.cumsum(c, -1), [0, N - c.shape[0]], value=inf.item()) for c in x_drop_costs_list], 0
-    )
-    all_z_drop_costs = torch.stack([F.pad(c, [0, K - c.shape[0]], value=inf.item()) for c in z_drop_costs_list], 0)
-    all_cum_z_drop_costs = torch.stack(
-        [F.pad(torch.cumsum(c, -1), [0, K - c.shape[0]], value=inf.item()) for c in z_drop_costs_list], 0
-    )
-    flipped_costs = torch.flip(all_zx_costs, [1])  # flip the cost matrix upside down
-
-    """Rules for the diagonals:
-        dim1: batch dimension
-        dim2: the diagonal itself. The first element along this dim corresponds
-              to the top right element on the diagonal. The movement is from top right
-              to bottom left, like that /
-        dim3: Keep and Drop dimensions of the DP table. The dimensions are as follows:
-              {0: zx, 1: z-, 2: -x, 3: --}
-     """
-    # initialize first two contr diagonals
-    batch_inf = torch.stack([inf] * B, 0)
-    diag_pp = torch.zeros([B, 1, 1], device=dev)  # diag at i-2
-    x1_dropcost, z1_dropcost = all_cum_x_drop_costs[:, [0]], all_cum_z_drop_costs[:, [0]]
-    diag_p_row = x1_dropcost[..., None]
-    diag_p_col = z1_dropcost[..., None]
-    diag_p = torch.cat([diag_p_row, diag_p_col], 1)  # diag at i-1
-
-    # The path is also a diagonal representation that carries the optimal pathlength to each point
-    path_pp = torch.zeros([B, 1, 1, 3], device=dev, dtype=int)
-    path_p = torch.zeros([B, 2, 1, 3], device=dev, dtype=int)
-    all_paths = [path_pp, path_p]  # going to store all the intermediate paths diagonals for the backtrack
-
-    # Coords is also a diagonal representation that carries the current coordinates in [d, r] for each point
-    # the last dimension is 3 because it's [d, r, s], where d is a diagonal, r is element's order in the diagonal
-    # and s is statet (one of the 4)
-    coord_pp = get_diag_coord_grid(B, 1, 1, 0).to(dev)
-    coord_p = get_diag_coord_grid(B, 2, 1, 1).to(dev)
-
-    min_costs = torch.zeros(B).to(dtype=dtype).to(device=dev)  # for storing the solution for each element
-    tracebacks = [None for _ in range(B)]  # going to store all the intermediate paths diagonals for the backtrack
-
-    for d in range(K + N - 1):
-        size = diag_p.size(1) - 1
-        pp_start = 0 if d < N else 1
-        neigh_up, neigh_left, neigh_diag = diag_p[:, :-1], diag_p[:, 1:], diag_pp[:, pp_start : (pp_start + size)]
-
-        coord_up, coord_left, coord_diag = (
-            coord_p[:, :-1].clone(),
-            coord_p[:, 1:].clone(),
-            coord_pp[:, pp_start : (pp_start + size)].clone(),
-        )
-        # assign the right state to coordinates
-        coord_diag[..., 2] = 0
-        coord_left[..., 2] = 1
-        coord_up[..., 2] = 2
-
-        # define match and drop cost vectors
-        match_costs_diag = torch.stack(
-            [torch.flip(torch.diag(flipped_costs[j], d + 1 - K), [-1]) for j in range(flipped_costs.size(0))], 0
-        )
-
-        x_d_start, x_d_end = max(d + 1 - K, 0), min(d, N - 1) + 1
-        x_drop_costs_diag = torch.flip(all_x_drop_costs[:, x_d_start:x_d_end], [-1])
-        z_d_start, z_d_end = max(d + 1 - N, 0), min(d, K - 1) + 1
-        z_drop_costs_diag = all_z_drop_costs[:, z_d_start:z_d_end]
-
-        # update positive and negative tables -> compute new diagonal
-
-        # DP 0: coming to zx
-        neighbors = [
-            neigh_diag + match_costs_diag[..., None],
-            neigh_left + x_drop_costs_diag[..., None],
-            neigh_up + z_drop_costs_diag[..., None],
-        ]
-        coordinates = [coord_diag, coord_left, coord_up]
-        diag = list_min(neighbors)[..., None]
-        path = (list_min(coordinates, keys=neighbors))[..., None, :]
-
-        # Haven't done below
-        # add the initialization values on the ends of diagonal if needed
-        effective_d = d + 2  # effective count of d is actually d + 2, since started with 2
-        if d < N - 1:
-            # fill in 0th row of cost matrix with [inf, x_drop_cost, inf, x_drop_cost]
-            x_drop_cost = all_cum_x_drop_costs[:, [d + 1]]
-            cost_pad = x_drop_cost[..., None]
-            diag = torch.cat([cost_pad, diag], dim=1)
-
-            # fill in 0th row of path matrix with the right pointers
-            left_pointer = torch.stack(
-                [torch.ones(1) * (effective_d - 1), torch.zeros(1), torch.ones(1) * 1], dim=-1
-            )  # [1, 3]
-            left_pointer = (
-                left_pointer[None, None, ...].repeat([diag.size(0), 1, 1, 1]).to(dev).to(dtype)
-            )  # [B, 1, 1, 3]
-            path = torch.cat([left_pointer, path], 1)
-        if d < K - 1:
-            # fill in 0th col of cost matrix with [inf, inf, z_drop_cost, z_drop_cost]
-            z_drop_cost = all_cum_z_drop_costs[:, [d + 1]]
-            pad = z_drop_cost[..., None]
-            diag = torch.cat([diag, pad], dim=1)
-
-            # fill in 0th col of path matrix with the right pointers
-
-            # the number of elements in the prev diagonal. Refers to 0th element of the column
-            last_r_p = diag_p.size(1)
-            up_pointer = torch.stack(
-                [torch.ones(1) * (effective_d - 1), torch.ones(1) * (last_r_p - 1), torch.ones(1) * 2],
-                dim=-1,
-            )  # [1, 3]
-            up_pointer = up_pointer[None, None, ...].repeat([diag.size(0), 1, 1, 1]).to(dev).to(dtype)  # [B, 1, 1, 3]
-            path = torch.cat([path, up_pointer], dim=1)
-
-        all_paths.append(path)
-
-        diag_pp = diag_p
-        diag_p = diag
-
-        coord_pp = coord_p
-        coord_p = get_diag_coord_grid(diag.size(0), diag.size(1), 1, effective_d).to(dev)
-
-        # process answers
-        if (Ds == d).any():
-            mask, orig_mask = Ds == d, Ds_orig == d
-            original_bs = torch.nonzero(orig_mask, as_tuple=False)[:, 0]
-            bs, rs = torch.nonzero(mask, as_tuple=False)[:, 0], Rs[mask]
-            min_costs[orig_mask] = min_costs[orig_mask] + list_min([diag[bs, rs]])
-            for orig_b, b, r in zip(original_bs, bs, rs):
-                this_paths = [p[b.item()] for p in all_paths]
-                current_N = N + 1
-                dc, rc, _ = coord_p[b, r][0]
-                tracebacks[orig_b.item()] = nw_diag_traceback(dc, rc, current_N, this_paths)[1]
-
-            # filtering out already processed elements
-            diag, diag_p, diag_pp, coord_p, coord_pp, path, Ds, Rs, flipped_costs = [
-                t[~mask] for t in [diag, diag_p, diag_pp, coord_p, coord_pp, path, Ds, Rs, flipped_costs]
-            ]
-            all_x_drop_costs, all_z_drop_costs, all_cum_x_drop_costs, all_cum_z_drop_costs, batch_inf = [
-                t[~mask]
-                for t in [all_x_drop_costs, all_z_drop_costs, all_cum_x_drop_costs, all_cum_z_drop_costs, batch_inf]
-            ]
-            all_paths = [p[~mask] for p in all_paths]
-
-            if torch.numel(Ds) == 0:
-                break
-
-    return min_costs, tracebacks
-
-
-def batch_drop_dtw_machine(zx_costs_list, x_drop_costs_list, many_to_one=False, one_to_many=False, contiguous=True):
-    # many_to_one is the same as not exclusive, i.e. multiple z match to one x
-    # one_to_many was always true by default before, i.e. multiple x match to one z
-    dev, dtype = zx_costs_list[0].device, zx_costs_list[0].dtype
-    inf = torch.tensor([9999999999], device=dev, dtype=dtype)
-    B = len(zx_costs_list)
-
-    shapes = [t.shape for t in zx_costs_list]
-    Ks, Ns = [s[0] for s in shapes], [s[1] for s in shapes]
-    N, K = max(Ns), max(Ks)
-
-    # transform endpoints into diagonal coordinates
-    Ds, Rs = torch.zeros(B).to(dev).to(int), torch.zeros(B).to(dev).to(int)
-    for i, (Ki, Ni) in enumerate(zip(Ks, Ns)):
-        Ds[i] = Ki + Ni - 2
-        Rs[i] = min(Ds[i] + 2, N) - Ni
-    Ds_orig, Rs_orig = copy(Ds), copy(Rs)
-
-    # special padding of costs to ensure that the path goest through the endpoint
-    all_zx_costs = []
-    for i, c in enumerate(zx_costs_list):
-        c_inf_frame = F.pad(c, [0, 1, 0, 1], value=inf.item())
-        mask = torch.ones_like(c_inf_frame)
-        mask[-1, -1] = 0
-        c_pad = F.pad(c_inf_frame * mask, [0, N - c.shape[1] - 1, 0, K - c.shape[0] - 1])
-        all_zx_costs.append(c_pad)
-    all_zx_costs = torch.stack(all_zx_costs, 0)
-
-    all_x_drop_costs = torch.stack([F.pad(c, [0, N - c.shape[0]], value=inf.item()) for c in x_drop_costs_list], 0)
-    all_cum_x_drop_costs = torch.stack(
-        [F.pad(torch.cumsum(c, -1), [0, N - c.shape[0]], value=inf.item()) for c in x_drop_costs_list], 0
-    )
-    flipped_costs = torch.flip(all_zx_costs, [1])  # flip the cost matrix upside down
-
-    """Rules for the diagonals:
-        dim1: batch dimension
-        dim2: the diagonal itself. The first element along this dim corresponds
-              to the top right element on the diagonal. The movement is from top right
-              to bottom left, like that /
-        dim3: Keep and Drop dimensions of the DP table. The dimensions are as follows:
-              {0: zx, 1: z-, 2: -x, 3: --}
-     """
-    # initialize first two contr diagonals
-    batch_inf = torch.stack([inf] * B, 0)
-    diag_pp = torch.zeros([B, 1, 2], device=dev)  # diag at i-2
-    x1_dropcost = all_cum_x_drop_costs[:, [0]]
-    diag_p_row = torch.stack([batch_inf, x1_dropcost], -1)
-    diag_p_col = torch.stack([batch_inf, batch_inf], -1)
-    diag_p = torch.cat([diag_p_row, diag_p_col], 1)  # diag at i-1
-
-    # The path is also a diagonal representation that carries the optimal pathlength to each point
-    path_pp = torch.zeros([B, 1, 2, 3], device=dev, dtype=int)
-    path_p = torch.zeros([B, 2, 2, 3], device=dev, dtype=int)
-    all_paths = [path_pp, path_p]  # going to store all the intermediate paths diagonals for the backtrack
-
-    # Coords is also a diagonal representation that carries the current coordinates in [d, r] for each point
-    # the last dimension is 3 because it's [d, r, s], where d is a diagonal, r is element's order in the diagonal
-    # and s is statet (one of the 4)
-    coord_pp = get_diag_coord_grid(B, 1, 2, 0).to(dev)
-    coord_p = get_diag_coord_grid(B, 2, 2, 1).to(dev)
-
-    min_costs = torch.zeros(B).to(dtype=dtype).to(device=dev)  # for storing the solution for each element
-    tracebacks = [None for _ in range(B)]  # going to store all the intermediate paths diagonals for the backtrack
-
-    for d in range(K + N - 1):
-        size = diag_p.size(1) - 1
-        pp_start = 0 if d < N else 1
-        neigh_up, neigh_left, neigh_diag = diag_p[:, :-1], diag_p[:, 1:], diag_pp[:, pp_start : (pp_start + size)]
-        neigh_up_pos, neigh_left_pos = neigh_up[..., [0]], neigh_left[..., [0]]
-
-        coord_up, coord_left, coord_diag = coord_p[:, :-1], coord_p[:, 1:], coord_pp[:, pp_start : (pp_start + size)]
-        coord_up_pos, coord_left_pos = coord_up[..., [0], :], coord_left[..., [0], :]
-
-        # define match and drop cost vectors
-        match_costs_diag = torch.stack(
-            [torch.flip(torch.diag(flipped_costs[j], d + 1 - K), [-1]) for j in range(flipped_costs.size(0))], 0
-        )
-
-        x_d_start, x_d_end = max(d + 1 - K, 0), min(d, N - 1) + 1
-        x_drop_costs_diag = torch.flip(all_x_drop_costs[:, x_d_start:x_d_end], [-1])
-
-        # update positive and negative tables -> compute new diagonal
-
-        # DP 0: coming to zx
-        pos_neighbors = [neigh_diag]
-        pos_coordinates = [coord_diag]
-        if one_to_many:
-            pos_neighbors.append(neigh_left_pos if contiguous else neigh_left)
-            pos_coordinates.append(coord_left_pos if contiguous else coord_left)
-        if many_to_one:
-            pos_neighbors.append(neigh_up)
-            pos_coordinates.append(coord_up)
-        diag_pos = list_min(pos_neighbors) + match_costs_diag
-        path_pos = list_min(pos_coordinates, keys=pos_neighbors)
-
-        neg_neighbors = [neigh_left]
-        neg_coordinates = [coord_left]
-        diag_neg = list_min(neg_neighbors) + x_drop_costs_diag
-        path_neg = list_min(neg_coordinates, keys=neg_neighbors)
-
-        diag = torch.stack([diag_pos, diag_neg], -1)
-        path = torch.stack([path_pos, path_neg], -2)
-
-        # Haven't done below
-        # add the initialization values on the ends of diagonal if needed
-        effective_d = d + 2  # effective count of d is actually d + 2, since started with 2
-        if d < N - 1:
-            # fill in 0th row of cost matrix with [inf, x_drop_cost, inf, x_drop_cost]
-            x_drop_cost = all_cum_x_drop_costs[:, [d + 1]]
-            cost_pad = torch.stack([batch_inf, x_drop_cost], -1)
-            diag = torch.cat([cost_pad, diag], dim=1)
-
-            # fill in 0th row of path matrix with the right pointers
-            left_pointer = torch.stack(
-                [torch.ones(2) * (effective_d - 1), torch.zeros(2), torch.arange(2)], dim=-1
-            )  # [2, 3]
-            left_pointer = (
-                left_pointer[None, None, ...].repeat([diag.size(0), 1, 1, 1]).to(dev).to(dtype)
-            )  # [B, 1, 2, 3]
-            path = torch.cat([left_pointer, path], 1)
-        if d < K - 1:
-            # fill in 0th col of cost matrix with [inf, inf, z_drop_cost, z_drop_cost]
-            pad = torch.stack([batch_inf, batch_inf], -1)
-            diag = torch.cat([diag, pad], dim=1)
-
-            # fill in 0th col of path matrix with the right pointers
-
-            # the number of elements in the prev diagonal. Refers to 0th element of the column
-            last_r_p = diag_p.size(1)
-            up_pointer = torch.stack(
-                [torch.ones(2) * (effective_d - 1), torch.ones(2) * (last_r_p - 1), torch.arange(2)],
-                dim=-1,
-            )  # [2, 3]
-            up_pointer = up_pointer[None, None, ...].repeat([diag.size(0), 1, 1, 1]).to(dev).to(dtype)  # [B, 1, 4, 3]
-            path = torch.cat([path, up_pointer], dim=1)
-
-        all_paths.append(path)
-
-        diag_pp = diag_p
-        diag_p = diag
-
-        coord_pp = coord_p
-        coord_p = get_diag_coord_grid(diag.size(0), diag.size(1), 2, effective_d).to(dev)
-
-        # process answers
-        if (Ds == d).any():
-            mask, orig_mask = Ds == d, Ds_orig == d
-            original_bs = torch.nonzero(orig_mask, as_tuple=False)[:, 0]
-            bs, rs = torch.nonzero(mask, as_tuple=False)[:, 0], Rs[mask]
-            min_costs[orig_mask] = min_costs[orig_mask] + list_min([diag[bs, rs]])
-            for orig_b, b, r in zip(original_bs, bs, rs):
-                best_pointer = list_min([coord_p[b, r]], keys=[diag[b, r]])
-                this_paths = [p[b.item()] for p in all_paths]
-                current_N = N + 1
-                tracebacks[orig_b.item()] = diag_traceback(best_pointer, current_N, this_paths)[1]
-
-            # filtering out already processed elements
-            diag, diag_p, diag_pp, coord_p, coord_pp, path, Ds, Rs, flipped_costs = [
-                t[~mask] for t in [diag, diag_p, diag_pp, coord_p, coord_pp, path, Ds, Rs, flipped_costs]
-            ]
-            all_x_drop_costs, all_cum_x_drop_costs, batch_inf = [
-                t[~mask] for t in [all_x_drop_costs, all_cum_x_drop_costs, batch_inf]
-            ]
-            all_paths = [p[~mask] for p in all_paths]
-
-            if torch.numel(Ds) == 0:
-                break
-
-    return min_costs, tracebacks
-
-
-def fast_batch_double_drop_dtw_machine(
-    zx_costs_list, x_drop_costs_list, z_drop_costs_list, many_to_one=False, one_to_many=False, contiguous=True
-):
-    # many_to_one is the same as not exclusive, i.e. multiple z match to one x
-    # one_to_many was always true by default before, i.e. multiple x match to one z
-    dev, dtype = zx_costs_list[0].device, zx_costs_list[0].dtype
-    inf = torch.tensor([9999999999], device=dev, dtype=dtype)
-    B = len(zx_costs_list)
-
-    shapes = [t.shape for t in zx_costs_list]
-    Ks, Ns = [s[0] for s in shapes], [s[1] for s in shapes]
-    N, K = max(Ns), max(Ks)
-
-    # transform endpoints into diagonal coordinates
-    Ds, Rs = torch.zeros(B).to(dev).to(int), torch.zeros(B).to(dev).to(int)
-    for i, (Ki, Ni) in enumerate(zip(Ks, Ns)):
-        Ds[i] = Ki + Ni - 2
-        Rs[i] = min(Ds[i] + 2, N) - Ni
-    Ds_orig, Rs_orig = copy(Ds), copy(Rs)
-
-    # special padding of costs to ensure that the path goest through the endpoint
-    all_zx_costs = []
-    for i, c in enumerate(zx_costs_list):
-        c_inf_frame = F.pad(c, [0, 1, 0, 1], value=inf.item())
-        mask = torch.ones_like(c_inf_frame)
-        mask[-1, -1] = 0
-        c_pad = F.pad(c_inf_frame * mask, [0, N - c.shape[1] - 1, 0, K - c.shape[0] - 1])
-        all_zx_costs.append(c_pad)
-    all_zx_costs = torch.stack(all_zx_costs, 0)
-
-    all_x_drop_costs = torch.stack([F.pad(c, [0, N - c.shape[0]], value=inf.item()) for c in x_drop_costs_list], 0)
-    all_cum_x_drop_costs = torch.stack(
-        [F.pad(torch.cumsum(c, -1), [0, N - c.shape[0]], value=inf.item()) for c in x_drop_costs_list], 0
-    )
-    all_z_drop_costs = torch.stack([F.pad(c, [0, K - c.shape[0]], value=inf.item()) for c in z_drop_costs_list], 0)
-    all_cum_z_drop_costs = torch.stack(
-        [F.pad(torch.cumsum(c, -1), [0, K - c.shape[0]], value=inf.item()) for c in z_drop_costs_list], 0
-    )
-    flipped_costs = torch.flip(all_zx_costs, [1])  # flip the cost matrix upside down
-
-    """Rules for the diagonals:
-        dim1: batch dimension
-        dim2: the diagonal itself. The first element along this dim corresponds
-              to the top right element on the diagonal. The movement is from top right
-              to bottom left, like that /
-        dim3: Keep and Drop dimensions of the DP table. The dimensions are as follows:
-              {0: zx, 1: z-, 2: -x, 3: --}
-     """
-
-    # create routing masks for selection
-    # 4x3 corresponds to 4 states (zx, z-, -x, --) and 3 neighbors (l, d, u)
-    zx_mask = torch.zeros((4, 3))
-    zx_mask[:, 1] = 1
-    if one_to_many:
-        zx_mask[0, 0] = 1
-        if not contiguous:
-            zx_mask[1, 0] = 1
-    if many_to_one:
-        zx_mask[[0, 2], 2] = 1
-
-    z__mask = torch.zeros((4, 3))
-    z__mask[[0, 1], 0] = 1
-
-    _x_mask = torch.zeros((4, 3))
-    _x_mask[[0, 2], 2] = 1
-
-    ___mask = torch.zeros((4, 3))
-    ___mask[[2, 3], 0] = 1
-    ___mask[[1, 3], 2] = 1
-
-    mask = torch.stack([zx_mask, z__mask, _x_mask, ___mask], dim=-1).to(dev).to(dtype)  # [4, 3, 4]
-
-    def transition(
-        neigh_left, neigh_diag, neigh_up, coord_left, coord_diag, coord_up, match_costs, x_drop_costs, z_drop_costs
-    ):
-        all_neigh = torch.stack([neigh_left, neigh_diag, neigh_up], dim=-1)  # [B, d, 4, 3]
-        all_coords = torch.stack([coord_left, coord_diag, coord_up], dim=-1).permute(
-            [0, 1, 3, 2, 4]
-        )  # [B, d, 3, 4, 3], the first 3 is the spatial dimension of coordinates
-        additions_zx = match_costs[..., None].repeat([1, 1, 3])  # [B, d, 3]
-        additions_z_ = x_drop_costs[..., None].repeat([1, 1, 3])
-        additions__x = z_drop_costs[..., None].repeat([1, 1, 3])
-        additions___ = torch.stack([x_drop_costs, match_costs, z_drop_costs], dim=-1)
-        additions = torch.stack([additions_zx, additions_z_, additions__x, additions___], dim=-1)  # [B, d, 3, 4]
-
-        inverse_mask = (~(mask[None, None, ...].to(bool))).to(dtype)
-        filtered_costs = all_neigh[..., None] * mask[None, None, ...] + inverse_mask * inf[0]  #  [B, d, 4, 3, 4]
-        full_costs = filtered_costs + additions[:, :, None, :, :] * mask[None, None, ...]
-        B, d = full_costs.shape[:2]
-        the_min = full_costs.reshape([B, d, -1, 4]).min(dim=2)
-        new_diag = the_min.values
-
-        all_coords = all_coords[..., None].repeat([1, 1, 1, 1, 1, 4]).reshape([B, d, 3, -1, 4])
-        argmins = the_min.indices[:, :, None, None, :].repeat([1, 1, 3, 1, 1])
-        pointers = torch.gather(all_coords, index=argmins, dim=-2)
-        pointers = pointers[:, :, :, 0, :].permute([0, 1, 3, 2])
-        return new_diag, pointers
-
-    # initialize first two contr diagonals
-    batch_inf = torch.stack([inf] * B, 0)
-    diag_pp = torch.zeros([B, 1, 4], device=dev)  # diag at i-2
-    x1_dropcost, z1_dropcost = all_cum_x_drop_costs[:, [0]], all_cum_z_drop_costs[:, [0]]
-    diag_p_row = torch.stack([batch_inf, x1_dropcost, batch_inf, x1_dropcost], -1)
-    diag_p_col = torch.stack([batch_inf, batch_inf, z1_dropcost, z1_dropcost], -1)
-    diag_p = torch.cat([diag_p_row, diag_p_col], 1)  # diag at i-1
-
-    # The path is also a diagonal representation that carries the optimal pathlength to each point
-    path_pp = torch.zeros([B, 1, 4, 3], device=dev, dtype=int)
-    path_p = torch.zeros([B, 2, 4, 3], device=dev, dtype=int)
-    all_paths = [path_pp, path_p]  # going to store all the intermediate paths diagonals for the backtrack
-
-    # Coords is also a diagonal representation that carries the current coordinates in [d, r] for each point
-    # the last dimension is 3 because it's [d, r, s], where d is a diagonal, r is element's order in the diagonal
-    # and s is statet (one of the 4)
-    coord_pp = get_diag_coord_grid(B, 1, 4, 0).to(dev)
-    coord_p = get_diag_coord_grid(B, 2, 4, 1).to(dev)
-
-    min_costs = torch.zeros(B).to(dtype=dtype).to(device=dev)  # for storing the solution for each element
-    tracebacks = [None for _ in range(B)]  # going to store all the intermediate paths diagonals for the backtrack
-
-    for d in range(K + N - 1):
-        size = diag_p.size(1) - 1
-        pp_start = 0 if d < N else 1
-        neigh_up, neigh_left, neigh_diag = diag_p[:, :-1], diag_p[:, 1:], diag_pp[:, pp_start : (pp_start + size)]
-        coord_up, coord_left, coord_diag = coord_p[:, :-1], coord_p[:, 1:], coord_pp[:, pp_start : (pp_start + size)]
-
-        # define match and drop cost vectors
-        match_costs_diag = torch.stack(
-            [torch.flip(torch.diag(flipped_costs[j], d + 1 - K), [-1]) for j in range(flipped_costs.size(0))], 0
-        )
-
-        x_d_start, x_d_end = max(d + 1 - K, 0), min(d, N - 1) + 1
-        x_drop_costs_diag = torch.flip(all_x_drop_costs[:, x_d_start:x_d_end], [-1])
-        z_d_start, z_d_end = max(d + 1 - N, 0), min(d, K - 1) + 1
-        z_drop_costs_diag = all_z_drop_costs[:, z_d_start:z_d_end]
-
-        # update positive and negative tables -> compute new diagonal
-
-        diag, path = transition(
-            neigh_left,
-            neigh_diag,
-            neigh_up,
-            coord_left,
-            coord_diag,
-            coord_up,
-            match_costs_diag,
-            x_drop_costs_diag,
-            z_drop_costs_diag,
-        )
-
-        # Haven't done below
-        # add the initialization values on the ends of diagonal if needed
-        effective_d = d + 2  # effective count of d is actually d + 2, since started with 2
-        if d < N - 1:
-            # fill in 0th row of cost matrix with [inf, x_drop_cost, inf, x_drop_cost]
-            x_drop_cost = all_cum_x_drop_costs[:, [d + 1]]
-            cost_pad = torch.stack([batch_inf, x_drop_cost, batch_inf, x_drop_cost], -1)
-            diag = torch.cat([cost_pad, diag], dim=1)
-
-            # fill in 0th row of path matrix with the right pointers
-            left_pointer = torch.stack(
-                [torch.ones(4) * (effective_d - 1), torch.zeros(4), torch.arange(4)], dim=-1
-            )  # [4, 3]
-            left_pointer = (
-                left_pointer[None, None, ...].repeat([diag.size(0), 1, 1, 1]).to(dev).to(dtype)
-            )  # [B, 1, 4, 3]
-            path = torch.cat([left_pointer, path], 1)
-        if d < K - 1:
-            # fill in 0th col of cost matrix with [inf, inf, z_drop_cost, z_drop_cost]
-            z_drop_cost = all_cum_z_drop_costs[:, [d + 1]]
-            pad = torch.stack([batch_inf, batch_inf, z_drop_cost, z_drop_cost], -1)
-            diag = torch.cat([diag, pad], dim=1)
-
-            # fill in 0th col of path matrix with the right pointers
-
-            # the number of elements in the prev diagonal. Refers to 0th element of the column
-            last_r_p = diag_p.size(1)
-            up_pointer = torch.stack(
-                [torch.ones(4) * (effective_d - 1), torch.ones(4) * (last_r_p - 1), torch.arange(4)],
-                dim=-1,
-            )  # [4, 3]
-            up_pointer = up_pointer[None, None, ...].repeat([diag.size(0), 1, 1, 1]).to(dev).to(dtype)  # [B, 1, 4, 3]
-            path = torch.cat([path, up_pointer], dim=1)
-
-        all_paths.append(path)
-
-        diag_pp = diag_p
-        diag_p = diag
-
-        coord_pp = coord_p
-        coord_p = get_diag_coord_grid(diag.size(0), diag.size(1), 4, effective_d).to(dev)
-
-        # process answers
-        if (Ds == d).any():
-            local_mask, orig_mask = Ds == d, Ds_orig == d
-            original_bs = torch.nonzero(orig_mask, as_tuple=False)[:, 0]
-            bs, rs = torch.nonzero(local_mask, as_tuple=False)[:, 0], Rs[local_mask]
-            min_costs[orig_mask] = min_costs[orig_mask] + list_min([diag[bs, rs]])
-            for orig_b, b, r in zip(original_bs, bs, rs):
-                # min_costs[orig_b] = min_costs[orig_b] + list_min([diag[b, r]])
-                best_pointer = list_min([coord_p[b, r]], keys=[diag[b, r]])
-                this_paths = [p[b.item()] for p in all_paths]
-                # current_N = Ns[orig_b.item()] + 1
-                current_N = N + 1
-                tracebacks[orig_b.item()] = diag_traceback(best_pointer, current_N, this_paths)[1]
-
-            # filtering out already processed elements
-            diag, diag_p, diag_pp, coord_p, coord_pp, path, Ds, Rs, flipped_costs = [
-                t[~local_mask] for t in [diag, diag_p, diag_pp, coord_p, coord_pp, path, Ds, Rs, flipped_costs]
-            ]
-            all_x_drop_costs, all_z_drop_costs, all_cum_x_drop_costs, all_cum_z_drop_costs, batch_inf = [
-                t[~local_mask]
-                for t in [all_x_drop_costs, all_z_drop_costs, all_cum_x_drop_costs, all_cum_z_drop_costs, batch_inf]
-            ]
-            all_paths = [p[~local_mask] for p in all_paths]
-
-            if torch.numel(Ds) == 0:
-                break
-
-    return min_costs, tracebacks
-
-
-if __name__ == '__main__':
-    zx_costs = np.random.rand(3, 4) # K=3 steps, N=4 clips
-    # zx_costs = np.array([[1.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 1.0]])
-    drop_costs = np.random.rand(4)
-    align = drop_dtw(zx_costs, drop_costs)
-    #breakpoint()
diff --git a/backup/pdvc/dp/soft_dp.py b/backup/pdvc/dp/soft_dp.py
deleted file mode 100644
index 9d5c17e5d5eeff50254dc7b8d31f6d43b253e388..0000000000000000000000000000000000000000
--- a/backup/pdvc/dp/soft_dp.py
+++ /dev/null
@@ -1,617 +0,0 @@
-import numpy as np
-import torch
-import math
-from torch import log, exp
-import torch.nn.functional as F
-from copy import copy
-
-from pdvc.dp.dp_utils import VarTable, minGamma, minProb, pad_costs, prob_min, unique_softmax, cosine_sim
-
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-
-def softDTW(
-    step_features,
-    frame_features,
-    labels,
-    dist_type="inner",
-    softning="prob",
-    gamma_min=0.1,
-    gamma_xz=0.1,
-    step_normalize=True,
-):
-    """function to obtain a soft (differentiable) version of DTW
-    embs1, embs2: embedding of size N*D and M*D (N and M : number of video frames
-    and D: dimensionality of of the embedding vector)
-    """
-    # defining the function
-    _min_fn = minProb if softning == "prob" else minGamma
-    min_fn = lambda x: _min_fn(x, gamma=gamma_min)
-
-    # first get a pairwise distance matrix
-    if dist_type == "inner":
-        dist = step_features @ frame_features.T
-    else:
-        dist = cosine_sim(step_features, frame_features)
-    if step_normalize:
-        if labels is not None:
-            norm_dist = unique_softmax(dist, labels, gamma_xz)
-        else:
-            norm_dist = torch.softmax(dist / gamma_xz, 0)
-        dist = -log(norm_dist)
-
-    # initialize soft-DTW table
-    nrows, ncols = dist.shape
-    # sdtw = torch.zeros((nrows+1,ncols+1)).to(torch.float).to(device)
-    sdtw = VarTable((nrows + 1, ncols + 1))
-    for i in range(1, nrows + 1):
-        sdtw[i, 0] = 9999999999
-    for j in range(1, ncols + 1):
-        sdtw[0, j] = 9999999999
-
-    # obtain dtw table using min_gamma or softMin relaxation
-    for i in range(1, nrows + 1):
-        for j in range(1, ncols + 1):
-            neighbors = torch.stack([sdtw[i, j - 1], sdtw[i - 1, j - 1], sdtw[i - 1, j]])
-            di, dj = i - 1, j - 1  # in the distance matrix indices are shifted by one
-            new_val = dist[di, dj] + min_fn(neighbors)
-            sdtw[i, j] = torch.squeeze(new_val, 0)
-    sdtw_loss = sdtw[nrows, ncols] / step_features.shape[0]
-    return sdtw_loss, sdtw, dist
-
-
-def dropDTW(zx_costs, drop_costs, softning="prob", exclusive=True, contiguous=True, gamma_min=1):
-    """function to obtain a soft (differentiable version of DTW)
-    embs1, embs2: embedding of size N*D and M*D (N and M : number of video frames
-    and D: dimensionality of of the embedding vector)
-    """
-    # defining the min function
-    min_fn = minProb if softning == "prob" else minGamma
-    inf = 9999999999
-    K, N = zx_costs.shape
-    exclusive = exclusive if K <= N else False
-    cum_drop_costs = torch.cumsum(drop_costs, dim=0)
-
-    # Creating and initializing DP tables
-    D = VarTable((K + 1, N + 1, 3))  # This corresponds to B 3-dim DP tables
-    for zi in range(1, K + 1):
-        D[zi, 0] = torch.zeros_like(D[zi, 0]) + inf
-    for xi in range(1, N + 1):
-        D[0, xi] = torch.zeros_like(D[0, xi]) + cum_drop_costs[xi - 1]
-
-    # obtain dtw table using min_gamma or softMin relaxation
-    for zi in range(1, K + 1):
-        for xi in range(1, N + 1):
-            z_cost_ind, x_cost_ind = zi - 1, xi - 1  # indexind in costs is shifted by 1
-
-            d_diag, d_left = D[zi - 1, xi - 1][0:1], D[zi, xi - 1][0:1]
-            dp_left, dp_up = D[zi, xi - 1][2:3], D[zi - 1, xi][2:3]
-
-            # positive transition, i.e. matching x_i to z_j
-            if contiguous:
-                pos_neighbors = [d_diag, dp_left]
-            else:
-                pos_neighbors = [d_diag, d_left]
-            if not exclusive:
-                pos_neighbors.append(dp_up)
-
-            Dp = min_fn(pos_neighbors, gamma=gamma_min) + zx_costs[z_cost_ind, x_cost_ind]
-
-            # negative transition, i.e. dropping xi
-            Dm = d_left + drop_costs[x_cost_ind]
-
-            # update final solution matrix
-            D_final = min_fn([Dm, Dp], gamma=gamma_min)
-            D[zi, xi] = torch.cat([D_final, Dm, Dp], dim=0)
-
-    # Computing the final min cost for the whole batch
-    min_cost = D[K, N][0]
-    return min_cost, D
-
-
-def batch_dropDTW(
-    zx_costs_list, drop_costs_list, softning="prob", exclusive=True, contiguous=True, drop_mode="DropDTW", gamma_min=1
-):
-    """function to obtain a soft (differentiable version of DTW)
-    embs1, embs2: embedding of size N*D and M*D (N and M : number of video frames
-    and D: dimensionality of of the embedding vector)
-    """
-    # defining the min function
-    min_fn = minProb if softning == "prob" else minGamma
-    inf = 9999999999
-
-    # pre-processing
-    B = len(zx_costs_list)
-    padded_cum_drop_costs, padded_drop_costs, padded_zx_costs, Ns, Ks = pad_costs(zx_costs_list, drop_costs_list)
-    all_zx_costs = torch.stack(padded_zx_costs, dim=-1)
-    all_cum_drop_costs = torch.stack(padded_cum_drop_costs, dim=-1)
-    all_drop_costs = torch.stack(padded_drop_costs, dim=-1)
-    N, K = max(Ns), max(Ks)
-
-    # preparing padded tables
-    padded_cum_drop_costs, padded_drop_costs, padded_zx_costs = [], [], []
-    for i in range(B):
-        zx_costs = zx_costs_list[i]
-        drop_costs = drop_costs_list[i]
-        cum_drop_costs = torch.cumsum(drop_costs, dim=0)
-
-        # padding everything to the size of the largest N and K
-        row_pad = torch.zeros([N - Ns[i]]).to(zx_costs.device)
-        padded_cum_drop_costs.append(torch.cat([cum_drop_costs, row_pad]))
-        padded_drop_costs.append(torch.cat([drop_costs, row_pad]))
-        multirow_pad = torch.stack([row_pad + inf] * Ks[i], dim=0)
-        padded_table = torch.cat([zx_costs, multirow_pad], dim=1)
-        rest_pad = torch.zeros([K - Ks[i], N]).to(zx_costs.device) + inf
-        padded_table = torch.cat([padded_table, rest_pad], dim=0)
-        padded_zx_costs.append(padded_table)
-
-    all_zx_costs = torch.stack(padded_zx_costs, dim=-1)
-    all_cum_drop_costs = torch.stack(padded_cum_drop_costs, dim=-1)
-    all_drop_costs = torch.stack(padded_drop_costs, dim=-1)
-
-    # Creating and initializing DP tables
-    D = VarTable((K + 1, N + 1, 3, B))  # This corresponds to B 3-dim DP tables
-    for zi in range(1, K + 1):
-        D[zi, 0] = torch.zeros_like(D[zi, 0]) + inf
-    for xi in range(1, N + 1):
-        if drop_mode == "DropDTW":
-            D[0, xi] = torch.zeros_like(D[0, xi]) + all_cum_drop_costs[(xi - 1) : xi]
-        elif drop_mode == "OTAM":
-            D[0, xi] = torch.zeros_like(D[0, xi])
-        else:  # drop_mode == 'DTW'
-            D[0, xi] = torch.zeros_like(D[0, xi]) + inf
-
-    # obtain dtw table using min_gamma or softMin relaxation
-    for zi in range(1, K + 1):
-        for xi in range(1, N + 1):
-            z_cost_ind, x_cost_ind = zi - 1, xi - 1  # indexind in costs is shifted by 1
-
-            d_diag, d_left = D[zi - 1, xi - 1][0:1], D[zi, xi - 1][0:1]
-            dp_left, dp_up = D[zi, xi - 1][2:3], D[zi - 1, xi][2:3]
-
-            if drop_mode == "DropDTW":
-                # positive transition, i.e. matching x_i to z_j
-                if contiguous:
-                    pos_neighbors = [d_diag, dp_left]
-                else:
-                    pos_neighbors = [d_diag, d_left]
-                if not exclusive:
-                    pos_neighbors.append(dp_up)
-
-                Dp = min_fn(pos_neighbors, gamma=gamma_min) + all_zx_costs[z_cost_ind, x_cost_ind]
-
-                # negative transition, i.e. dropping xi
-                Dm = d_left + all_drop_costs[x_cost_ind]
-
-                # update final solution matrix
-                D_final = min_fn([Dm, Dp], gamma=gamma_min)
-            else:
-                d_right = D[zi - 1, xi][0:1]
-                D_final = Dm = Dp = (
-                    min_fn([d_diag, d_left, d_right], gamma=gamma_min) + all_zx_costs[z_cost_ind, x_cost_ind]
-                )
-            D[zi, xi] = torch.cat([D_final, Dm, Dp], dim=0)
-
-    # Computing the final min cost for the whole batch
-    min_costs = []
-    for i in range(B):
-        Ni, Ki = Ns[i], Ks[i]
-        min_cost_i = D[Ki, Ni][0, i]
-        min_costs.append(min_cost_i / Ni)
-
-    return min_costs, D
-
-
-def batch_double_dropDTW(zx_costs_list, drop_costs_list, gamma_min=1):
-    """function to obtain a soft (differentiable version of DTW)
-    embs1, embs2: embedding of size N*D and M*D (N and M : number of video frames
-    and D: dimensionality of of the embedding vector)
-    """
-    min_fn = lambda x: minProb(x, gamma=gamma_min)
-    dev, dtype = zx_costs_list[0].device, zx_costs_list[0].dtype
-
-    # assuming sequences are the same length
-    B = len(zx_costs_list)
-    padded_cum_drop_costs, padded_drop_costs, padded_zx_costs, Ns, Ks = pad_costs(zx_costs_list, drop_costs_list)
-    all_zx_costs = torch.stack(padded_zx_costs, dim=-1)
-    all_cum_drop_costs = torch.stack(padded_cum_drop_costs, dim=-1)
-    all_drop_costs = torch.stack(padded_drop_costs, dim=-1)
-    N, K = max(Ns), max(Ks)
-
-    # Creating and initializing DP tables
-    D = VarTable((K + 1, N + 1, 4, B), dtype, dev)  # This corresponds to B 4-dim DP tables
-    for zi in range(1, K + 1):
-        D[zi, 0] = torch.zeros_like(D[zi, 0]) + all_cum_drop_costs[(zi - 1) : zi]
-    for xi in range(1, N + 1):
-        D[0, xi] = torch.zeros_like(D[0, xi]) + all_cum_drop_costs[(xi - 1) : xi]
-
-    for zi in range(1, K + 1):
-        for xi in range(1, N + 1):
-            # define frequently met neighbors here
-            diag_neigh_states = [0, 1, 2, 3]  # zx, z-, -x, --
-            diag_neigh_costs = [D[zi - 1, xi - 1][s] for s in diag_neigh_states]
-
-            left_neigh_states = [0, 1]  # zx and z-
-            left_neigh_costs = [D[zi, xi - 1][s] for s in left_neigh_states]
-
-            upper_neigh_states = [0, 2]  # zx and -x
-            upper_neigh_costs = [D[zi - 1, xi][s] for s in upper_neigh_states]
-
-            z_cost_ind, x_cost_ind = zi - 1, xi - 1  # indexind in costs is shifted by 1
-
-            # DP 0: coming to zx
-            neigh_costs_zx = diag_neigh_costs + upper_neigh_costs + left_neigh_costs
-            D0 = min_fn(neigh_costs_zx) + all_zx_costs[z_cost_ind, x_cost_ind]
-
-            # DP 1: coming to z-
-            neigh_costs_z_ = left_neigh_costs
-            D1 = min_fn(neigh_costs_z_) + all_drop_costs[x_cost_ind]
-
-            # DP 2: coming to -x
-            neigh_costs__x = upper_neigh_costs
-            D2 = min_fn(neigh_costs__x) + all_drop_costs[z_cost_ind]
-
-            # DP 3: coming to --
-            costs___ = [d + all_drop_costs[z_cost_ind] * 2 for d in diag_neigh_costs] + [
-                D[zi, xi - 1][3] + all_drop_costs[x_cost_ind],
-                D[zi - 1, xi][3] + all_drop_costs[z_cost_ind],
-            ]
-            D3 = min_fn(costs___)
-
-            D[zi, xi] = torch.cat([D0, D1, D2, D3], dim=0)
-
-    # Computing the final min cost for the whole batch
-    min_costs = []
-    for i in range(B):
-        min_cost_i = min_fn(D[K, N][:, i])
-        min_costs.append(min_cost_i / N)
-    return min_costs, D
-
-
-def drop_dtw_machine(zx_costs, drop_costs, gamma_min=1, exclusive=True, contiguous=True):
-    K, N = zx_costs.shape
-    dev = zx_costs.device
-    flipped_costs = torch.flip(zx_costs, [0])  # flip the cost matrix upside down
-    cum_drop_costs = torch.cumsum(drop_costs, dim=-1)
-
-    # initialize first two contr diagonals
-    inf = torch.tensor([9999999999], device=dev, dtype=zx_costs.dtype)
-    diag_pp = torch.zeros([1, 2], device=dev)  # diag at i-2
-    diag_p_col = torch.ones([1, 2], device=dev) * inf
-    diag_p_row = torch.stack([inf, cum_drop_costs[[0]]], -1)
-    diag_p = torch.cat([diag_p_row, diag_p_col], 0)  # diag at i-1
-
-    for i in range(K + N - 1):
-        size = diag_p.size(0) - 1
-        pp_start = max(0, diag_pp.size(0) - diag_p.size(0))
-        neigh_up, neigh_left, neigh_diag = diag_p[:-1], diag_p[1:], diag_pp[pp_start : (pp_start + size)]
-        neigh_up_pos, neigh_left_pos = neigh_up[:, [0]], neigh_left[:, [0]]
-
-        # define match and drop cost vectors
-        match_costs_diag = torch.flip(torch.diag(flipped_costs, i + 1 - K), [-1])
-        d_start, d_end = max(1 - K + i, 0), min(i, N - 1) + 1
-        drop_costs_diag = torch.flip(drop_costs[d_start:d_end], [-1])
-
-        # update positive and negative tables -> compute new diagonal
-        pos_neighbors = [neigh_diag, neigh_left_pos] if contiguous else [neigh_diag, neigh_left]
-        if not exclusive:
-            pos_neighbors.append(neigh_up_pos)
-        diag_pos = prob_min(pos_neighbors, gamma_min) + match_costs_diag
-        diag_neg = prob_min([neigh_left], gamma_min) + drop_costs_diag
-        diag = torch.stack([diag_pos, diag_neg], -1)
-
-        # add the initialization values on the ends of diagonal if needed
-        if i < N - 1:
-            # fill in 0th row with [drop_cost, inf]
-            pad = torch.stack([inf, cum_drop_costs[[i + 1]]], -1)
-            diag = torch.cat([pad, diag])
-        if i < K - 1:
-            # fill in 0th col with [inf, inf]
-            pad = torch.stack([inf, inf], -1)
-            diag = torch.cat([diag, pad])
-
-        diag_pp = diag_p
-        diag_p = diag
-    assert (diag.size(0) == 1) and (diag.size(1) == 2), f"Last diag shape is {diag.shape} instead of [1, 2]"
-
-    cost = prob_min(diag, gamma_min)
-    return cost
-
-
-def batch_drop_dtw_machine(zx_costs_list, drop_costs_list, gamma_min=1, exclusive=True, contiguous=True):
-    dev, dtype = zx_costs_list[0].device, zx_costs_list[0].dtype
-    inf = torch.tensor([9999999999], device=dev, dtype=dtype)
-    B = len(zx_costs_list)
-
-    # For samples where K > N, exclusive computation is not possible
-    shapes = [t.shape for t in zx_costs_list]
-    Ks, Ns = [s[0] for s in shapes], [s[1] for s in shapes]
-    N, K = max(Ns), max(Ks)
-    persample_exclusive = torch.tensor([Ni >= Ki for Ki, Ni in shapes]).to(dev)
-
-    # transform endpoints into diagonal coordinates
-    Ds, Rs = torch.zeros(B).to(dev).to(int), torch.zeros(B).to(dev).to(int)
-    for i, (Ki, Ni) in enumerate(zip(Ks, Ns)):
-        Ds[i] = Ki + Ni - 2
-        Rs[i] = min(Ds[i] + 2, N) - Ni
-    Ds_orig, Rs_orig = copy(Ds), copy(Rs)
-
-    # define costs in tensors
-    all_zx_costs = [F.pad(c, [0, N - c.shape[1], 0, K - c.shape[0]]) for c in zx_costs_list]
-    all_zx_costs = torch.stack(all_zx_costs, 0)
-
-    all_drop_costs = torch.stack([F.pad(c, [0, N - c.shape[0]], value=inf.item()) for c in drop_costs_list], 0)
-    all_cum_drop_costs = torch.stack(
-        [F.pad(torch.cumsum(c, -1), [0, N - c.shape[0]], value=inf.item()) for c in drop_costs_list], 0
-    )
-    flipped_costs = torch.flip(all_zx_costs, [1])  # flip the cost matrix upside down
-
-    """Rules for the diagonals:
-        dim1: batch dimension
-        dim2: the diagonal itself. The first element along this dim corresponds
-              to the top right element on the diagonal. The movement is from top right
-              to bottom left, like that /
-        dim3: Keep and Drop dimensions of the DP table. Here, 0 is keep, 1 is drop.
-     """
-    # initialize first two contr diagonals
-    batch_inf, batch_ones = torch.stack([inf] * B, 0), torch.ones([B, 1], device=dev, dtype=dtype)
-    diag_pp = torch.zeros([B, 1, 2], device=dev)  # diag at i-2
-    diag_p_col = torch.ones([B, 1, 2], device=dev) * batch_inf[..., None]
-    diag_p_row = torch.stack([batch_inf, all_cum_drop_costs[:, [0]]], -1)
-    diag_p = torch.cat([diag_p_row, diag_p_col], 1)  # diag at i-1
-
-    # The pathlength path is also a diagonal representation that carries the optimal pathlength to each point
-    with torch.no_grad():
-        path_pp = torch.zeros([B, 1, 2], device=dev, dtype=dtype)
-        path_p = torch.ones([B, 2, 2], device=dev, dtype=dtype)
-
-    min_costs = torch.zeros(B).to(dtype=dtype).to(device=dev)
-    path_lens = torch.zeros(B).to(dtype=dtype).to(device=dev)
-    for d in range(K + N - 1):
-        size = diag_p.size(1) - 1
-        pp_start = 0 if d < N else 1
-        neigh_up, neigh_left, neigh_diag = diag_p[:, :-1], diag_p[:, 1:], diag_pp[:, pp_start : (pp_start + size)]
-        neigh_up_pos, neigh_left_pos = neigh_up[..., [0]], neigh_left[..., [0]]
-
-        neigh_path_up, neigh_path_left, neigh_path_diag = (
-            path_p[:, :-1],
-            path_p[:, 1:],
-            path_pp[:, pp_start : (pp_start + size)],
-        )
-        neigh_path_up_pos, neigh_path_left_pos = neigh_path_up[..., [0]], neigh_path_left[..., [0]]
-
-        # define match and drop cost vectors
-        match_costs_diag = torch.stack(
-            [torch.flip(torch.diag(flipped_costs[j], d + 1 - K), [-1]) for j in range(flipped_costs.size(0))], 0
-        )
-
-        d_start, d_end = max(1 - K + d, 0), min(d, N - 1) + 1
-        drop_costs_diag = torch.flip(all_drop_costs[:, d_start:d_end], [-1])
-
-        # update positive and negative tables -> compute new diagonal
-        pos_neighbors = [neigh_diag, neigh_left_pos] if contiguous else [neigh_diag, neigh_left]
-        pos_path_neighbors = (
-            [neigh_path_diag, neigh_path_left_pos] if contiguous else [neigh_path_diag, neigh_path_left]
-        )
-        if exclusive and (~persample_exclusive).any():
-            # apply non-exclusive rule for some batch elements, via masing out the exclusive elements with inf
-            masked_neigh_up_pos = neigh_up_pos + persample_exclusive[:, None, None] * batch_inf[:, None]
-            pos_neighbors.append(masked_neigh_up_pos)
-
-            pos_path_neighbors.append(neigh_path_up_pos * (~persample_exclusive[:, None, None]))
-        elif not exclusive:
-            # apply standard non-exclusive rule to all batch elements
-            pos_neighbors.append(neigh_up_pos)
-            pos_path_neighbors.append(neigh_path_up_pos)
-
-        # DP Table update
-        diag_pos = prob_min(pos_neighbors, gamma_min) + match_costs_diag
-        diag_neg = prob_min([neigh_left], gamma_min) + drop_costs_diag
-        diag = torch.stack([diag_pos, diag_neg], -1)
-
-        # Path Table Update
-        with torch.no_grad():
-            path_pos = prob_min(pos_path_neighbors, gamma_min, pos_neighbors) + 1
-            path_neg = prob_min([neigh_path_left], gamma_min, [neigh_left]) + 1
-            path = torch.stack([path_pos, path_neg], -1)
-
-        # add the initialization values on the ends of diagonal if needed
-        if d < N - 1:
-            # fill in DP table's 0th row with [drop_cost, inf]
-            pad_d = torch.stack([batch_inf, all_cum_drop_costs[:, [d + 1]]], -1)
-            diag = torch.cat([pad_d, diag], 1)
-
-            # fill in Path table's 0th row with [d, inf]
-            pad_p = torch.stack([batch_inf, torch.zeros_like(batch_inf) + d], -1)
-            path = torch.cat([pad_p, path], 1)
-
-        if d < K - 1:
-            # fill in DP table's 0th col with [inf, inf]
-            pad_d = torch.stack([batch_inf, batch_inf], -1)
-            diag = torch.cat([diag, pad_d], 1)
-
-            # fill in Path table's 0th row with [d, inf]
-            pad_p = pad_d
-            path = torch.cat([path, pad_p], 1)
-
-        diag_pp = diag_p
-        diag_p = diag
-
-        path_pp = path_p
-        path_p = path
-
-        # process answers
-        if (Ds == d).any():
-            mask, orig_mask = Ds == d, Ds_orig == d
-            bs, rs = torch.nonzero(mask, as_tuple=False)[:, 0], Rs[mask]
-            min_costs[orig_mask] = min_costs[orig_mask] + prob_min([diag[bs, rs]], gamma_min)
-            path_lens[orig_mask] = path_lens[orig_mask] + prob_min([path[bs, rs]], gamma_min, [diag[bs, rs]])
-
-            diag, diag_p, diag_pp, path, path_p, path_pp, Ds, Rs, flipped_costs = [
-                t[~mask] for t in [diag, diag_p, diag_pp, path, path_p, path_pp, Ds, Rs, flipped_costs]
-            ]
-            all_drop_costs, all_cum_drop_costs, batch_inf, persample_exclusive = [
-                t[~mask] for t in [all_drop_costs, all_cum_drop_costs, batch_inf, persample_exclusive]
-            ]
-            if torch.numel(Ds) == 0:
-                break
-
-    # costs = prob_min([diag], gamma_min)
-    costs_norm = min_costs / path_lens
-    return min_costs, path_lens
-
-
-def batch_double_drop_dtw_machine(
-    zx_costs_list, x_drop_costs_list, z_drop_costs_list, gamma_min=1, exclusive=True, contiguous=True
-):
-    dev, dtype = zx_costs_list[0].device, zx_costs_list[0].dtype
-    inf = torch.tensor([9999999999], device=dev, dtype=dtype)
-    B = len(zx_costs_list)
-
-    Ns, Ks = [], []
-    for i in range(B):
-        Ki, Ni = zx_costs_list[i].shape
-        if exclusive and Ki >= Ni:
-            # in case the number of steps is greater than the number of frames,
-            # duplicate every frame and let the drops do the job.
-            mult = math.ceil(Ki / Ni)
-            zx_costs_list[i] = torch.stack([zx_costs_list[i]] * mult, dim=-1).reshape([Ki, -1])
-            x_drop_costs_list[i] = torch.stack([x_drop_costs_list[i]] * mult, dim=-1).reshape([-1])
-            Ni *= mult
-        Ns.append(Ni)
-        Ks.append(Ki)
-    N, K = max(Ns), max(Ks)
-
-    # transform endpoints into diagonal coordinates
-    Ds, Rs = torch.zeros(B).to(dev).to(int), torch.zeros(B).to(dev).to(int)
-    for i, (Ki, Ni) in enumerate(zip(Ks, Ns)):
-        Ds[i] = Ki + Ni - 2
-        Rs[i] = min(Ds[i] + 2, N) - Ni
-    Ds_orig, Rs_orig = copy(Ds), copy(Rs)
-
-    # special padding of costs to ensure that the path goest through the endpoint
-    all_zx_costs = []
-    for i, c in enumerate(zx_costs_list):
-        c_inf_frame = F.pad(c, [0, 1, 0, 1], value=inf.item())
-        mask = torch.ones_like(c_inf_frame)
-        mask[-1, -1] = 0
-        c_pad = F.pad(c_inf_frame * mask, [0, N - c.shape[1] - 1, 0, K - c.shape[0] - 1])
-        all_zx_costs.append(c_pad)
-    all_zx_costs = torch.stack(all_zx_costs, 0)
-
-    all_x_drop_costs = torch.stack([F.pad(c, [0, N - c.shape[0]], value=inf.item()) for c in x_drop_costs_list], 0)
-    all_cum_x_drop_costs = torch.stack(
-        [F.pad(torch.cumsum(c, -1), [0, N - c.shape[0]], value=inf.item()) for c in x_drop_costs_list], 0
-    )
-    all_z_drop_costs = torch.stack([F.pad(c, [0, K - c.shape[0]], value=inf.item()) for c in z_drop_costs_list], 0)
-    all_cum_z_drop_costs = torch.stack(
-        [F.pad(torch.cumsum(c, -1), [0, K - c.shape[0]], value=inf.item()) for c in z_drop_costs_list], 0
-    )
-    flipped_costs = torch.flip(all_zx_costs, [1])  # flip the cost matrix upside down
-
-    """Rules for the diagonals:
-        dim1: batch dimension
-        dim2: the diagonal itself. The first element along this dim corresponds
-              to the top right element on the diagonal. The movement is from top right
-              to bottom left, like that /
-        dim3: Keep and Drop dimensions of the DP table. The dimensions are as follows:
-              {0: zx, 1: z-, 2: -x, 3: --}
-     """
-    # initialize first two contr diagonals
-    batch_inf = torch.stack([inf] * B, 0)
-    diag_pp = torch.zeros([B, 1, 4], device=dev)  # diag at i-2
-    x1_dropcost, z1_dropcost = all_cum_x_drop_costs[:, [0]], all_cum_z_drop_costs[:, [0]]
-    diag_p_row = torch.stack([batch_inf, x1_dropcost, batch_inf, x1_dropcost], -1)
-    diag_p_col = torch.stack([batch_inf, batch_inf, z1_dropcost, z1_dropcost], -1)
-    diag_p = torch.cat([diag_p_row, diag_p_col], 1)  # diag at i-1
-
-    min_costs = torch.zeros(B).to(dtype=dtype).to(device=dev)  # for storing the solution for each element
-    for d in range(K + N - 1):
-        size = diag_p.size(1) - 1
-        pp_start = 0 if d < N else 1
-        neigh_up, neigh_left, neigh_diag = diag_p[:, :-1], diag_p[:, 1:], diag_pp[:, pp_start : (pp_start + size)]
-        neigh_left_pos, neigh_left_neg = neigh_left[..., [0, 1]], neigh_left[..., [2, 3]]
-        neigh_up_pos, neigh_up_neg = neigh_up[..., [0, 2]], neigh_up[..., [1, 3]]
-
-        # define match and drop cost vectors
-        match_costs_diag = torch.stack(
-            [torch.flip(torch.diag(flipped_costs[j], d + 1 - K), [-1]) for j in range(flipped_costs.size(0))], 0
-        )
-
-        x_d_start, x_d_end = max(d + 1 - K, 0), min(d, N - 1) + 1
-        x_drop_costs_diag = torch.flip(all_x_drop_costs[:, x_d_start:x_d_end], [-1])
-        z_d_start, z_d_end = max(d + 1 - N, 0), min(d, K - 1) + 1
-        z_drop_costs_diag = all_z_drop_costs[:, z_d_start:z_d_end]
-
-        # update positive and negative tables -> compute new diagonal
-
-        # DP 0: coming to zx
-        neighbors_zx = [neigh_diag, neigh_left_pos[..., [0]]] if contiguous else [neigh_diag, neigh_left_pos]
-        if not exclusive:
-            neighbors_zx.append(neigh_up_pos)
-        diag_zx = prob_min(neighbors_zx, gamma_min) + match_costs_diag
-
-        # DP 1: coming to z-
-        neighbors_z_ = [neigh_left_pos]
-        diag_z_ = prob_min(neighbors_z_, gamma_min) + x_drop_costs_diag
-
-        # DP 2: coming to -x
-        neighbors__x = [neigh_up_pos]
-        diag__x = prob_min(neighbors__x, gamma_min) + z_drop_costs_diag
-
-        # DP 3: coming to --
-        neighbors___ = [neigh_left_neg + x_drop_costs_diag[..., None], neigh_up_neg + z_drop_costs_diag[..., None]]
-        diag___ = prob_min(neighbors___, gamma_min)
-
-        # Aggregating all the dimensions of DP together
-        diag = torch.stack([diag_zx, diag_z_, diag__x, diag___], -1)
-
-        # Haven't done below
-        # add the initialization values on the ends of diagonal if needed
-        if d < N - 1:
-            # fill in 0th row with [drop_cost, inf]
-            x_drop_cost = all_cum_x_drop_costs[:, [d + 1]]
-            pad = torch.stack([batch_inf, x_drop_cost, batch_inf, x_drop_cost], -1)
-            diag = torch.cat([pad, diag], 1)
-        if d < K - 1:
-            # fill in 0th col with [inf, inf]
-            z_drop_cost = all_cum_z_drop_costs[:, [d + 1]]
-            pad = torch.stack([batch_inf, batch_inf, z_drop_cost, z_drop_cost], -1)
-            diag = torch.cat([diag, pad], 1)
-
-        diag_pp = diag_p
-        diag_p = diag
-
-        # process answers
-        if (Ds == d).any():
-            mask, orig_mask = Ds == d, Ds_orig == d
-            bs, rs = torch.nonzero(mask, as_tuple=False)[:, 0], Rs[mask]
-            min_costs[orig_mask] = min_costs[orig_mask] + prob_min([diag[bs, rs]], gamma_min)
-
-            # filtering out already processed elements
-            diag, diag_p, diag_pp, Ds, Rs, flipped_costs = [
-                t[~mask] for t in [diag, diag_p, diag_pp, Ds, Rs, flipped_costs]
-            ]
-            all_x_drop_costs, all_z_drop_costs, all_cum_x_drop_costs, all_cum_z_drop_costs = [
-                t[~mask] for t in [all_x_drop_costs, all_z_drop_costs, all_cum_x_drop_costs, all_cum_z_drop_costs]
-            ]
-
-            if torch.numel(Ds) == 0:
-                break
-
-    costs_norm = min_costs / torch.tensor(Ns).to(dev)
-    return costs_norm
-
-
-if __name__ == "__main__":
-    from exact_dp import double_drop_dtw
-
-    K, N = 7, 15
-    zx_costs = torch.normal(torch.ones([K, N]))
-    x_drop_costs = zx_costs.mean(0)
-    z_drop_costs = zx_costs.mean(1)
-
-    min_cost, *_ = double_drop_dtw(zx_costs.numpy(), x_drop_costs.numpy(), z_drop_costs.numpy())
-    my_costs = batch_double_drop_dtw_machine([zx_costs], [x_drop_costs], [z_drop_costs], gamma_min=0)
-    print(my_costs * N, min_cost)
diff --git a/backup/pdvc/dp/visualization.py b/backup/pdvc/dp/visualization.py
deleted file mode 100644
index fed631a8979532253408fb402672eee0cc4a9a64..0000000000000000000000000000000000000000
--- a/backup/pdvc/dp/visualization.py
+++ /dev/null
@@ -1,179 +0,0 @@
-import io
-import numpy as np
-from matplotlib import pyplot as plt
-from matplotlib.pyplot import figure
-from PIL import Image
-
-
-# defining the colors and shapes
-color_code = [
-    "blue",
-    "orange",
-    "green",
-    "red",
-    "purple",
-    "brown",
-    "pink",
-    "grey",
-    "olive",
-    "cyan",
-    "lime",
-    "grey",
-    "firebrick",
-    "coral",
-    "chocolate",
-    "saddlebrown",
-    "bisque",
-    "goldenrod",
-    "gold",
-    "khaki",
-    "darkolivegreen",
-    "greenyellow",
-    "palegreen",
-    "springgreen",
-    "aquamarine",
-    "teal",
-    "deepskyblue",
-    "navy",
-    "mediumslateblue",
-    "royalblue",
-    "indigo",
-    "magenta",
-    "deeppink",
-    "crimson",
-    "violet",
-    "snow",
-    "lightgrey",
-    "wheat",
-    "dodgerblue",
-    "darkseagreen",
-]
-color_code = color_code * 10
-shape_code = ["o", "s", "P", "*", "h", ">", "X", "d", "D", "v", "<", "p"]
-shape_code = shape_code * int(len(color_code) / len(shape_code) + 1)
-
-color_values = []
-for color in color_code:
-    _ = plt.fill([0, 0, 1, 1, 0], [0, 1, 1, 0, 0], color)
-    buf = io.BytesIO()
-    _ = plt.savefig(buf, format="png")
-    _ = plt.close()
-    buf.seek(0)
-    img = np.array(Image.open(buf).convert("RGB"))
-    color_values.append(img[100, 300])
-
-color_code_hex = []
-for color_value in color_values:
-    step_color_rgb = tuple([s.item() for s in color_value])
-    color_code_hex.append("#%02x%02x%02x" % step_color_rgb)
-
-
-def plot_alignment(
-    step_ids, frame_labels, step_colors, step_shapes, size=(15, 2), name="all_step_to_video", to_np=True, grid_on=True
-):
-    N_steps = len(frame_labels)
-
-    plt.rcParams["figure.figsize"] = (size[0], size[1])
-    ax = plt.subplot(1, 1, 1)
-    _ = ax.set_title(name)
-
-    tick_freq = 50 if N_steps > 1500 else 20
-    _ = plt.xticks(np.arange(0, N_steps, tick_freq))
-    _ = plt.xlim(0, N_steps)
-    _ = plt.tick_params(bottom=True, top=False, left=True, right=True, labelright=True)
-
-    if grid_on:
-        _ = plt.grid()
-    else:
-        plt.plot(np.arange(len(frame_labels)), [1] * len(frame_labels), color="grey")
-
-    for si, step_id in enumerate(step_ids):
-        time, val = [], []
-        for i in range(N_steps):
-            if si + 1 == frame_labels[i]:
-                time.append(i)
-                val.append(1)
-        time, val = np.array(time), np.array(val)
-        _ = plt.plot(time, val, step_shapes[step_id], color=step_colors[step_id])
-
-    if to_np:
-        buf = io.BytesIO()
-        plt.savefig(buf, format="png")
-        plt.close()
-        buf.seek(0)
-        img = np.array(Image.open(buf).convert("RGB"))
-        return img
-    else:
-        return plt
-
-
-def plot_step_to_video_alignment(corresp_mat, size=(15, 2)):
-    """corresp_mat is of shape [K, N], where K is num_steps, and N is video_len"""
-    step_ids = np.arange(corresp_mat.size(0)) + 1
-    labels = corresp_mat.to(float).argmax(0) + 1 * corresp_mat.to(bool).any(0)
-
-    K_present = corresp_mat.to(bool).any(1).to(int).sum().item()
-    name = f"Video Segmentation | {K_present} steps present"
-    return plot_alignment(step_ids, labels, color_code, shape_code, name=name, size=size)
-
-
-def plot_similarities(
-    sim,
-    drop_line=None,
-    colors=None,
-    select=None,
-    color_offset=0,
-    do_legend=True,
-    name="",
-    size=(15, 2),
-    grid_on=True,
-    to_np=True,
-    linewidth=1,
-):
-    colors = colors if colors is not None else color_code
-    K, N = sim.shape
-    select = select if select is not None else np.arange(K)
-
-    plt.rcParams["figure.figsize"] = (size[0], size[1])
-    ax = plt.subplot(1, 1, 1)
-    _ = ax.set_title(name)
-
-    _ = plt.xticks(np.arange(0, N, 20))
-    _ = plt.xlim(0, N)
-    _ = plt.tick_params(bottom=True, top=False, left=True, right=True, labelright=True)
-    if grid_on:
-        _ = plt.grid()
-
-    for i in range(K):
-        if i in select:
-            _ = plt.plot(np.arange(N), sim[i], color=colors[i + color_offset], label=str(i), linewidth=linewidth)
-
-    if drop_line is not None:
-        _ = plt.plot(np.arange(N), drop_line * np.ones(N), "--")
-
-    if do_legend:
-        _ = plt.xlim(0, N + int(0.10 * N))
-        plt.legend()
-
-    if to_np:
-        buf = io.BytesIO()
-        plt.savefig(buf, format="png")
-        plt.close()
-        buf.seek(0)
-        img = np.array(Image.open(buf).convert("RGB"))
-        return img
-    else:
-        return plt
-
-
-def plot_gt_seg(N, starts, ends, colors=None, shapes=None, name="GT Seg", clip_len=1, size=(15, 2), grid_on=True):
-    colors = colors if colors is not None else color_code
-    shapes = shapes if shapes is not None else shape_code
-
-    K = len(starts)
-    labels = -np.ones(N)
-    for i in range(K):
-        s, e = int(starts[i]), int(ends[i])
-        labels[s : e + 1] = i
-    step_ids = np.arange(K)
-    return plot_alignment(step_ids, labels, colors, shapes, to_np=False, name=name, size=size, grid_on=grid_on)
diff --git a/backup/pdvc/matcher.py b/backup/pdvc/matcher.py
deleted file mode 100644
index 3311680756df6cf1efeed2bbe2ab55350525b4ce..0000000000000000000000000000000000000000
--- a/backup/pdvc/matcher.py
+++ /dev/null
@@ -1,446 +0,0 @@
-# ------------------------------------------------------------------------
-# 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
-# ------------------------------------------------------------------------
-
-"""
-Modules to compute the matching cost and solve the corresponding LSAP.
-"""
-import torch
-from scipy.optimize import linear_sum_assignment
-from torch import nn
-import torch.nn.functional as F
-from torch import log, exp
-import numpy as np
-
-from misc.detr_utils.box_ops import box_cl_to_xy, generalized_box_iou
-
-# For matcher_align
-from pdvc.dp.soft_dp import batch_drop_dtw_machine, batch_double_drop_dtw_machine
-from pdvc.dp.exact_dp import batch_double_drop_dtw_machine as exact_batch_double_drop_dtw_machine
-from pdvc.dp.exact_dp import batch_drop_dtw_machine as exact_batch_drop_dtw_machine
-from pdvc.dp.exact_dp import fast_batch_double_drop_dtw_machine, batch_NW_machine
-# from dp.gpu_nw import gpu_nw
-from pdvc.dp.dp_utils import compute_all_costs, compute_double_costs
-
-
-def compute_sim(z, x, l2_norm):
-    if l2_norm:
-        return F.normalize(z, dim=1) @ F.normalize(x, dim=1).T
-    else:
-        return z @ x.T
-
-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_alpha = 0.25,
-                 cost_gamma = 2,
-                 use_pseudo_box = False):
-        """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
-        """
-        super().__init__()
-        self.cost_class = cost_class
-        self.cost_bbox = cost_bbox
-        self.cost_giou = cost_giou
-        # self.cost_caption = cost_caption
-        self.cost_alpha = cost_alpha
-        self.cost_gamma = cost_gamma
-        self.use_pseudo_box = use_pseudo_box
-
-        assert cost_class != 0 or cost_bbox != 0 or cost_giou != 0 # or cost_caption!=0, "all costs cant be 0"
-        # breakpoint()
-
-    def forward(self, outputs, targets, verbose=False, many_to_one=False):
-        """ Performs the matching
-
-        Params:
-            outputs: This is a dict that contains at least these entries:
-                 "pred_logits": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits
-                 "pred_boxes": Tensor of dim [batch_size, num_queries, 4] with the predicted box coordinates
-
-            targets: This is a list of targets (len(targets) = batch_size), where each target is a dict containing:
-                 "labels": Tensor of dim [num_target_boxes] (where num_target_boxes is the number of ground-truth
-                           objects in the target) containing the class labels
-                 "boxes": Tensor of dim [num_target_boxes, 4] containing the target box coordinates
-
-        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)
-        """
-        with torch.no_grad():
-            bs, num_queries = outputs["pred_logits"].shape[:2]
-            # We flatten to compute the cost matrices in a batch
-            out_prob = outputs["pred_logits"].flatten(0, 1).sigmoid()
-            out_bbox = outputs["pred_boxes"].flatten(0, 1)  # [batch_size * num_queries, 4]
-
-            # Also concat the target labels and boxes
-            tgt_ids = torch.cat([v["labels"] for v in targets])
-            if self.use_pseudo_box and self.training:
-                # print('use pseudo box')
-                tgt_bbox = torch.cat([v["boxes_pseudo"] for v in targets])
-            else:
-                tgt_bbox = torch.cat([v["boxes"] for v in targets])
-                # print('use gt box')
-
-            # Compute the classification cost.
-            # alpha = 0.25
-            alpha = self.cost_alpha
-            gamma = self.cost_gamma
-            neg_cost_class = (1 - alpha) * (out_prob ** gamma) * (-(1 - out_prob + 1e-8).log())
-            pos_cost_class = alpha * ((1 - out_prob) ** gamma) * (-(out_prob + 1e-8).log())
-            cost_class = pos_cost_class[:, tgt_ids] - neg_cost_class[:, tgt_ids]
-
-            # Compute the L1 cost between boxes
-            cost_bbox = torch.cdist(out_bbox, tgt_bbox, p=1)
-            # breakpoint()
-
-            # Compute the giou cost betwen boxes
-            try:
-                cost_giou = -generalized_box_iou(box_cl_to_xy(out_bbox),
-                                        box_cl_to_xy(tgt_bbox))
-            except:
-                print('out_bbox', out_bbox)
-                print('tgt_bbox', tgt_bbox)
-                breakpoint()
-
-            # cost_caption = outputs['caption_costs'].flatten(0, 1)
-
-            # Final cost matrix
-            # breakpoint()
-            try: # [100, 10], [100, 11], [100, 10]
-                C = self.cost_bbox * cost_bbox + self.cost_class * cost_class + self.cost_giou * cost_giou
-            except:
-                breakpoint()
-
-            costs = {'cost_bbox': cost_bbox,
-                     'cost_class': cost_class,
-                     'cost_giou': cost_giou,
-                     # 'cost_caption': cost_caption,
-                     'out_bbox': out_bbox[:, 0::2]}
-
-            if verbose:
-                print('\n')
-                print(self.cost_bbox, cost_bbox.var(dim=0), cost_bbox.max(dim=0)[0] - cost_bbox.min(dim=0)[0])
-                print(self.cost_class, cost_class.var(dim=0), cost_class.max(dim=0)[0] - cost_class.min(dim=0)[0])
-                print(self.cost_giou, cost_giou.var(dim=0), cost_giou.max(dim=0)[0] - cost_giou.min(dim=0)[0])
-                # print(self.cost_caption, cost_caption.var(dim=0), cost_caption.max(dim=0)[0] - cost_caption.min(dim=0)[0])
-
-            C = C.view(bs, num_queries, -1).cpu()
-
-        
-            sizes = [len(v["boxes_pseudo"]) for v in targets] if self.use_pseudo_box else [len(v["boxes"]) for v in targets]
-            # pdb.set_trace()
-            indices = [linear_sum_assignment(c[i]) for i, c in enumerate(C.split(sizes, -1))]
-            m2o_rate = 4
-            rl_indices = [linear_sum_assignment(torch.cat([c[i]]*m2o_rate, -1)) for i, c in enumerate(C.split(sizes, -1))]
-            rl_indices = [(torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j%sizes[ii], dtype=torch.int64)) for ii,(i, j) in
-                       enumerate(rl_indices)]
-
-            indices = [(torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices]
-
-            if verbose:
-                print('------matching results:')
-                print(indices)
-                for indice in indices:
-                    for i, j in zip(*indice):
-                        print(out_bbox[i][0::2], tgt_bbox[j][0::2])
-                print('-----topK scores:')
-                topk_indices = out_prob.topk(10, dim=0)
-                print(topk_indices)
-                for i,(v,ids) in enumerate(zip(*topk_indices)):
-                    print('top {}'.format(i))
-                    s= ''
-                    for name,cost in costs.items():
-                        s += name + ':{} '.format(cost[ids])
-                    print(s)
-
-            return indices, rl_indices
-
-class DTWMatcher(nn.Module):
-    '''
-    Drop_z: if True, then we drop both the x axis (query) and z axis (text)
-    One_to_many: multiple x match to one z
-    Many_to_one: multiple z match to one x 
-    '''
-    def __init__(self,
-                keep_percentile,
-                top_band_size=0,
-                given_droplines=None,
-                drop_z=True,
-                one_to_many=False,
-                many_to_one=False,
-                contiguous=False):
-        super().__init__()
-        self.keep_percentile = keep_percentile
-        self.top_band_size = top_band_size
-        self.given_droplines = given_droplines
-        self.drop_z = drop_z
-        self.one_to_many = one_to_many
-        self.many_to_one = many_to_one
-        self.contiguous = contiguous
-
-    def forward(self, ouputs, targets, text_embed, event_embed):
-        # computing alignments (without gradients)
-        orig_device = event_embed[0].device
-        # embarisingly, this is faster on CPU than on GPU!
-        sims = compute_sim(text_embed, event_embed, l2_norm=True)
-        #sims = [s.cpu() for s in sims]
-        sims = [sims.cpu()]
-        # TODO: Add the classification cost the the alignment cost
-        self.given_droplines = None if self.given_droplines is None else [s.cpu() for s in self.given_droplines]
-        with torch.no_grad():
-            zx_costs_list = []
-            x_drop_costs_list = []
-            z_drop_costs_list = []
-            for i, sim in enumerate(sims):
-                # computing the baseline logit
-                top_sim = sim
-                if self.given_droplines is None:
-                    if self.top_band_size > 0 and self.top_band_size < sim.shape[1]:
-                        top_sim = sim.topk(self.top_band_size, dim=1).values
-
-                    if self.keep_percentile > 1:
-                        dropline = top_sim.min() - 5
-                    else:
-                        k = max([1, int(torch.numel(top_sim) * self.keep_percentile)])
-                        dropline = torch.topk(top_sim.reshape([-1]), k).values[-1].detach()
-                else:
-                    dropline = self.given_droplines[i]
-
-                # shift the costs by the drop logits, so I can set drop costs to 0 instead
-                zx_costs_list.append(dropline.reshape([1, 1]) - sim)
-                z_drop_cost = torch.zeros([sim.size(0)]).to(sim.device)
-                x_drop_cost = torch.zeros([sim.size(1)]).to(sim.device)
-                z_drop_costs_list.append(z_drop_cost)
-                x_drop_costs_list.append(x_drop_cost)
-
-            # TODO figure out if one_to_many and many_to_one should be on
-            align_paths, corresp_mats = None, None
-            if self.drop_z:
-                if not (self.one_to_many or self.many_to_one):
-                    _, align_paths = batch_NW_machine(zx_costs_list, x_drop_costs_list, z_drop_costs_list)
-                    # corresp_mats = gpu_nw(zx_costs_list, x_drop_costs_list, z_drop_costs_list)
-                else:
-                    _, align_paths = exact_batch_double_drop_dtw_machine(
-                        # _, align_paths = fast_batch_double_drop_dtw_machine(
-                        zx_costs_list,
-                        x_drop_costs_list,
-                        z_drop_costs_list,
-                        one_to_many=self.one_to_many,
-                        many_to_one=self.many_to_one,
-                        contiguous=self.contiguous,
-                    )
-            else:
-                _, align_paths = exact_batch_drop_dtw_machine(
-                    zx_costs_list,
-                    x_drop_costs_list,
-                    one_to_many=self.one_to_many,
-                    many_to_one=self.many_to_one,
-                    contiguous=self.contiguous,
-                )
-
-            if corresp_mats is None:
-                corresp_matrices = []
-                for b_id, sim in enumerate(sims):
-                    corresp_matrix = torch.zeros_like(sim)
-                    for i, j, s in align_paths[b_id]:
-                        if s == 0:
-                            corresp_matrix[i - 1, j - 1] = 1
-                    corresp_matrices.append(corresp_matrix.to(orig_device))
-                    # corresp_matrices.append(corresp_matrix)
-            text_indices = torch.stack([(torch.as_tensor(i-1, dtype=torch.int64)) for i, _, k in align_paths[-1] if k == 0])
-            query_indices = torch.stack([(torch.as_tensor(j-1, dtype=torch.int64)) for _, j, k in align_paths[-1] if k == 0])
-            text_indices, rearrange = torch.sort(text_indices)
-            query_indices = query_indices[rearrange]
-            indices = [(query_indices, text_indices)]
-        #return align_paths, corresp_matrices
-        return indices, []
-
-class SimMatcher(nn.Module):
-    """This class computes an assignment between the targets and the predictions of the network
-    based on the similarity bewteen text embedding and query embedding
-    """
-    def __init__(self,
-                 cost_class: float = 1,
-                 cost_sim: float = 1,
-                 cost_bbox: float = 1,
-                 cost_giou: float = 1,
-                 cost_alpha = 0.25,
-                 cost_gamma = 2,
-                 use_pseudo_box = False):
-        """Creates the matcher
-
-        Params:
-            cost_class: This is the relative weight of the classification error in the matching cost
-            cost_giou: This is the relative weight of the giou loss of the bounding box in the matching cost
-        """
-        super().__init__()
-        self.cost_class = cost_class
-        self.cost_sim = cost_sim
-        self.cost_bbox = cost_bbox
-        self.cost_giou = cost_giou
-        # self.cost_caption = cost_caption
-        self.cost_alpha = cost_alpha
-        self.cost_gamma = cost_gamma
-        self.use_pseudo_box = use_pseudo_box
-
-        assert cost_class != 0 or cost_sim!=0, "all costs cannot be 0"
-        # breakpoint()
-
-    def forward(self, outputs, targets, text_embed, event_embed, verbose=False, many_to_one=False):
-        """ Performs the matching
-
-        Params:
-            outputs: This is a dict that contains at least these entries:
-                 "pred_logits": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits
-                 "pred_boxes": Tensor of dim [batch_size, num_queries, 4] with the predicted box coordinates
-
-            targets: This is a list of targets (len(targets) = batch_size), where each target is a dict containing:
-                 "labels": Tensor of dim [num_target_boxes] (where num_target_boxes is the number of ground-truth
-                           objects in the target) containing the class labels
-                 "boxes": Tensor of dim [num_target_boxes, 4] containing the target box coordinates
-
-        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)
-        """
-        with torch.no_grad():
-            bs, num_queries = outputs["pred_logits"].shape[:2]
-
-            # We flatten to compute the cost matrices in a batch
-            out_prob = outputs["pred_logits"].flatten(0, 1).sigmoid()
-            out_bbox = outputs["pred_boxes"].flatten(0, 1)  # [batch_size * num_queries, 4]
-
-            tgt_ids = torch.cat([v["labels"] for v in targets])
-            alpha = self.cost_alpha
-            gamma = self.cost_gamma
-            neg_cost_class = (1 - alpha) * (out_prob ** gamma) * (-(1 - out_prob + 1e-8).log())
-            pos_cost_class = alpha * ((1 - out_prob) ** gamma) * (-(out_prob + 1e-8).log())
-            cost_class = pos_cost_class[:, tgt_ids] - neg_cost_class[:, tgt_ids]
-
-            # Also concat the target labels and boxes
-            # breakpoint()
-            if self.use_pseudo_box:
-                tgt_bbox = torch.cat([v["boxes_pseudo"] for v in targets])
-                # Compute the L1 cost between boxes
-                cost_bbox = torch.cdist(out_bbox, tgt_bbox, p=1)
-
-                # Compute the giou cost betwen boxes
-                cost_giou = -generalized_box_iou(box_cl_to_xy(out_bbox),
-                                                box_cl_to_xy(tgt_bbox))
-            else:
-                cost_bbox = torch.zeros_like(cost_class)
-                cost_giou = torch.zeros_like(cost_class)
-
-            # Compute the classification cost.
-            # alpha = 0.25
-            alpha = self.cost_alpha
-            gamma = self.cost_gamma
-            neg_cost_class = (1 - alpha) * (out_prob ** gamma) * (-(1 - out_prob + 1e-8).log())
-            pos_cost_class = alpha * ((1 - out_prob) ** gamma) * (-(out_prob + 1e-8).log())
-            cost_class = pos_cost_class[:, tgt_ids] - neg_cost_class[:, tgt_ids]
-            # breakpoint()
-            # Compute the similarity cost
-            cost_sim = compute_sim(text_embed, event_embed, l2_norm=True).permute(1,0)
-            cost_sim = torch.ones_like(cost_sim) - cost_sim
-            # breakpoint()
-
-            # cost_caption = outputs['caption_costs'].flatten(0, 1)
-
-            # Final cost matrix
-            C = self.cost_bbox * cost_bbox + self.cost_class * cost_class + self.cost_giou * cost_giou + self.cost_sim * cost_sim
-
-            costs = {'cost_bbox': cost_bbox,
-                     'cost_class': cost_class,
-                     'cost_giou': cost_giou,
-                     'cost_sim': cost_sim,
-                     # 'cost_caption': cost_caption,
-                     'out_bbox': out_bbox[:, 0::2],
-                     }
-
-            if verbose:
-                print('\n')
-                print(self.cost_bbox, cost_bbox.var(dim=0), cost_bbox.max(dim=0)[0] - cost_bbox.min(dim=0)[0])
-                print(self.cost_class, cost_class.var(dim=0), cost_class.max(dim=0)[0] - cost_class.min(dim=0)[0])
-                print(self.cost_giou, cost_giou.var(dim=0), cost_giou.max(dim=0)[0] - cost_giou.min(dim=0)[0])
-                print(self.cost_sim, cost_sim.var(dim=0), cost_sim.max(dim=0)[0] - cost_sim.min(dim=0)[0])
-                # print(self.cost_caption, cost_caption.var(dim=0), cost_caption.max(dim=0)[0] - cost_caption.min(dim=0)[0])
-
-            C = C.view(bs, num_queries, -1).cpu()
-
-            sizes = [text_embed.size(0)]
-            # pdb.set_trace()
-            indices = [linear_sum_assignment(c[i]) for i, c in enumerate(C.split(sizes, -1))]
-            m2o_rate = 4
-            rl_indices = [linear_sum_assignment(torch.cat([c[i]]*m2o_rate, -1)) for i, c in enumerate(C.split(sizes, -1))]
-            rl_indices = [(torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j%sizes[ii], dtype=torch.int64)) for ii,(i, j) in
-                       enumerate(rl_indices)]
-
-            indices = [(torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices]
-
-
-            return indices, rl_indices    
-
-def build_matcher(args):
-    if args.matcher_type == 'DTW':
-        return DTWMatcher(keep_percentile=args.align_keep_percentile,
-                    top_band_size=args.align_top_band_size,
-                    given_droplines=None,
-                    drop_z=args.align_drop_z,
-                    one_to_many=args.align_one_to_many,
-                    many_to_one=args.align_many_to_one,
-                    contiguous=args.align_contiguous)
-    elif args.matcher_type == 'Sim':
-        return SimMatcher(cost_class=args.set_cost_class,
-                                cost_sim=args.set_cost_sim,
-                                cost_bbox=args.set_cost_bbox,
-                                cost_giou=args.set_cost_giou,
-                                cost_alpha = args.cost_alpha,
-                                cost_gamma = args.cost_gamma,
-                                use_pseudo_box = args.use_pseudo_box
-                                )
-    else:
-        return HungarianMatcher(cost_class=args.set_cost_class,
-                                cost_bbox=args.set_cost_bbox,
-                                cost_giou=args.set_cost_giou,
-                                cost_alpha = args.cost_alpha,
-                                cost_gamma = args.cost_gamma,
-                                use_pseudo_box = args.use_pseudo_box
-                                )
-
-
-def build_matcher_simple():
-    #return DTWMatcher(keep_percentile=0.5)
-    return SimMatcher()
-
-if __name__ == '__main__':
-    text_embed = torch.rand(5, 128)
-    event_embed = torch.rand(15, 128)
-    #sim = torch.eye(3, 4)
-    aligner = build_matcher_simple()
-    indices, matrices = aligner(text_embed, event_embed)
-    breakpoint()
\ No newline at end of file
diff --git a/backup/pdvc/matcher_align.py b/backup/pdvc/matcher_align.py
deleted file mode 100644
index e9b93dce7e9ff252230fbb8f8bc2861ce3a16605..0000000000000000000000000000000000000000
--- a/backup/pdvc/matcher_align.py
+++ /dev/null
@@ -1,154 +0,0 @@
-# ------------------------------------------------------------------------
-# 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
-# ------------------------------------------------------------------------
-
-"""
-Modules to compute the matching cost and solve the corresponding LSAP.
-"""
-import torch
-import torch.nn.functional as F
-from torch import log, exp
-import numpy as np
-from torch import nn
-from scipy.optimize import linear_sum_assignment
-# from misc.detr_utils.box_ops import box_cl_to_xy, generalized_box_iou
-
-# For matcher_align
-from dp.soft_dp import batch_drop_dtw_machine, batch_double_drop_dtw_machine
-from dp.exact_dp import batch_double_drop_dtw_machine as exact_batch_double_drop_dtw_machine
-from dp.exact_dp import batch_drop_dtw_machine as exact_batch_drop_dtw_machine
-from dp.exact_dp import fast_batch_double_drop_dtw_machine, batch_NW_machine
-# from dp.gpu_nw import gpu_nw
-from dp.dp_utils import compute_all_costs, compute_double_costs
-
-
-def compute_sim(z, x, l2_norm):
-    if l2_norm:
-        return F.normalize(z, dim=1) @ F.normalize(x, dim=1).T
-    else:
-        return z @ x.T
-
-class DTWMatcher(nn.Module):
-    '''
-    Drop_z: if True, then we drop both the x axis (query) and z axis (text)
-    One_to_many: multiple x match to one z
-    Many_to_one: multiple z match to one x 
-    '''
-    def __init__(self,
-                keep_percentile,
-                top_band_size=0,
-                given_droplines=None,
-                drop_z=False,
-                one_to_many=False,
-                many_to_one=False,
-                contiguous=False):
-        super().__init__()
-        self.keep_percentile = keep_percentile
-        self.top_band_size = top_band_size
-        self.given_droplines = given_droplines
-        self.drop_z = drop_z
-        self.one_to_many = one_to_many
-        self.many_to_one = many_to_one
-        self.contiguous = contiguous
-
-    def forward(self, text_embed, event_embed):
-        # computing alignments (without gradients)
-        orig_device = event_embed.device
-        # embarisingly, this is faster on CPU than on GPU!
-        sims = compute_sim(text_embed, event_embed, l2_norm=True)
-        #sims = [s.cpu() for s in sims]
-        sims = [sims.cpu()]
-        self.given_droplines = None if self.given_droplines is None else [s.cpu() for s in self.given_droplines]
-        with torch.no_grad():
-            zx_costs_list = []
-            x_drop_costs_list = []
-            z_drop_costs_list = []
-            for i, sim in enumerate(sims):
-                # computing the baseline logit
-                top_sim = sim
-                if self.given_droplines is None:
-                    if self.top_band_size > 0 and self.top_band_size < sim.shape[1]:
-                        top_sim = sim.topk(self.top_band_size, dim=1).values
-
-                    if self.keep_percentile > 1:
-                        dropline = top_sim.min() - 5
-                    else:
-                        k = max([1, int(torch.numel(top_sim) * self.keep_percentile)])
-                        dropline = torch.topk(top_sim.reshape([-1]), k).values[-1].detach()
-                else:
-                    dropline = self.given_droplines[i]
-
-                # shift the costs by the drop logits, so I can set drop costs to 0 instead
-                zx_costs_list.append(dropline.reshape([1, 1]) - sim)
-                z_drop_cost = torch.zeros([sim.size(0)]).to(sim.device)
-                x_drop_cost = torch.zeros([sim.size(1)]).to(sim.device)
-                z_drop_costs_list.append(z_drop_cost)
-                x_drop_costs_list.append(x_drop_cost)
-
-            # TODO figure out if one_to_many and many_to_one should be on
-            align_paths, corresp_mats = None, None
-            if self.drop_z:
-                if not (self.one_to_many or self.many_to_one):
-                    _, align_paths = batch_NW_machine(zx_costs_list, x_drop_costs_list, z_drop_costs_list)
-                    # corresp_mats = gpu_nw(zx_costs_list, x_drop_costs_list, z_drop_costs_list)
-                else:
-                    _, align_paths = exact_batch_double_drop_dtw_machine(
-                        # _, align_paths = fast_batch_double_drop_dtw_machine(
-                        zx_costs_list,
-                        x_drop_costs_list,
-                        z_drop_costs_list,
-                        one_to_many=self.one_to_many,
-                        many_to_one=self.many_to_one,
-                        contiguous=self.contiguous,
-                    )
-            else:
-                _, align_paths = exact_batch_drop_dtw_machine(
-                    zx_costs_list,
-                    x_drop_costs_list,
-                    one_to_many=self.one_to_many,
-                    many_to_one=self.many_to_one,
-                    contiguous=self.contiguous,
-                )
-
-            if corresp_mats is None:
-                corresp_matrices = []
-                for b_id, sim in enumerate(sims):
-                    corresp_matrix = torch.zeros_like(sim)
-                    for i, j, s in align_paths[b_id]:
-                        if s == 0:
-                            corresp_matrix[i - 1, j - 1] = 1
-                    corresp_matrices.append(corresp_matrix.to(orig_device))
-                    # corresp_matrices.append(corresp_matrix)
-            text_indices = torch.stack([(torch.as_tensor(i-1, dtype=torch.int64)) for i, _, k in align_paths[-1] if k == 0])
-            query_indices = torch.stack([(torch.as_tensor(j-1, dtype=torch.int64)) for _, j, k in align_paths[-1] if k == 0])
-            text_indices, rearrange = torch.sort(text_indices)
-            query_indices = query_indices[rearrange]
-            indices = [(query_indices, text_indices)]
-        #return align_paths, corresp_matrices
-        return indices, _
-
-def build_matcher(args):
-    return DTWMatcher(keep_percentile=args.align_keep_percentile,
-                    top_band_size=args.align_top_band_size,
-                    given_droplines=None,
-                    drop_z=args.align_drop_z,
-                    one_to_many=args.align_one_to_many,
-                    many_to_one=args.align_many_to_one,
-                    contiguous=args.align_contiguous)
-
-
-def build_matcher_simple():
-    return DTWMatcher(keep_percentile=0.5)
-
-if __name__ == '__main__':
-    text_embed = torch.rand(5, 128)
-    event_embed = torch.rand(15, 128)
-    #sim = torch.eye(3, 4)
-    aligner = build_matcher_simple()
-    indices, matrices = aligner(text_embed, event_embed)
-    breakpoint()
diff --git a/backup/pdvc/modules/UniVL_mini.py b/backup/pdvc/modules/UniVL_mini.py
deleted file mode 100644
index 8c9d6e960cc742b2eed92827f568734ae91073ce..0000000000000000000000000000000000000000
--- a/backup/pdvc/modules/UniVL_mini.py
+++ /dev/null
@@ -1,1292 +0,0 @@
-# coding=utf-8
-# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
-# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""PyTorch BERT model."""
-
-import copy
-import math
-import logging
-import collections
-import unicodedata
-import os
-from urllib.parse import urlparse
-from typing import Optional, Tuple, Union, IO, Callable, Set
-from pathlib import Path
-import shutil
-import tempfile
-import json
-from hashlib import sha256
-from functools import wraps
-import boto3
-from botocore.exceptions import ClientError
-import requests
-from tqdm import tqdm
-
-
-import torch
-from torch import nn
-
-
-logger = logging.getLogger(__name__)
-
-PYTORCH_PRETRAINED_BERT_CACHE = Path(os.getenv('PYTORCH_PRETRAINED_BERT_CACHE',
-                                               Path.home() / '.pytorch_pretrained_bert'))
-
-PRETRAINED_MODEL_ARCHIVE_MAP = {
-    'bert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased.tar.gz",
-    'bert-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased.tar.gz",
-    'bert-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased.tar.gz",
-    'bert-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased.tar.gz",
-    'bert-base-multilingual-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased.tar.gz",
-    'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased.tar.gz",
-    'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese.tar.gz",
-}
-
-CONFIG_NAME = 'bert_config.json'
-WEIGHTS_NAME = 'pytorch_model.bin'
-
-PRETRAINED_VOCAB_ARCHIVE_MAP = {
-    'bert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt",
-    'bert-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-vocab.txt",
-    'bert-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-vocab.txt",
-    'bert-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-vocab.txt",
-    'bert-base-multilingual-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased-vocab.txt",
-    'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-vocab.txt",
-    'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-vocab.txt",
-}
-PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP = {
-    'base-uncased': 512,
-    'large-uncased': 512,
-    'base-cased': 512,
-    'large-cased': 512,
-    'base-multilingual-uncased': 512,
-    'base-multilingual-cased': 512,
-    'base-chinese': 512,
-}
-VOCAB_NAME = 'vocab.txt'
-
-
-def load_vocab(vocab_file):
-    """Loads a vocabulary file into a dictionary."""
-    vocab = collections.OrderedDict()
-    index = 0
-    with open(vocab_file, "r", encoding="utf-8") as reader:
-        while True:
-            token = reader.readline()
-            if not token:
-                break
-            token = token.strip()
-            vocab[token] = index
-            index += 1
-    return vocab
-
-def split_s3_path(url: str) -> Tuple[str, str]:
-    """Split a full s3 path into the bucket name and path."""
-    parsed = urlparse(url)
-    if not parsed.netloc or not parsed.path:
-        raise ValueError("bad s3 path {}".format(url))
-    bucket_name = parsed.netloc
-    s3_path = parsed.path
-    # Remove '/' at beginning of path.
-    if s3_path.startswith("/"):
-        s3_path = s3_path[1:]
-    return bucket_name, s3_path
-
-def s3_request(func: Callable):
-    """
-    Wrapper function for s3 requests in order to create more helpful error
-    messages.
-    """
-
-    @wraps(func)
-    def wrapper(url: str, *args, **kwargs):
-        try:
-            return func(url, *args, **kwargs)
-        except ClientError as exc:
-            if int(exc.response["Error"]["Code"]) == 404:
-                raise FileNotFoundError("file {} not found".format(url))
-            else:
-                raise
-
-    return wrapper
-
-@s3_request
-def s3_etag(url: str) -> Optional[str]:
-    """Check ETag on S3 object."""
-    s3_resource = boto3.resource("s3")
-    bucket_name, s3_path = split_s3_path(url)
-    s3_object = s3_resource.Object(bucket_name, s3_path)
-    return s3_object.e_tag
-
-@s3_request
-def s3_get(url: str, temp_file: IO) -> None:
-    """Pull a file directly from S3."""
-    s3_resource = boto3.resource("s3")
-    bucket_name, s3_path = split_s3_path(url)
-    s3_resource.Bucket(bucket_name).download_fileobj(s3_path, temp_file)
-
-def url_to_filename(url: str, etag: str = None) -> str:
-    """
-    Convert `url` into a hashed filename in a repeatable way.
-    If `etag` is specified, append its hash to the url's, delimited
-    by a period.
-    """
-    url_bytes = url.encode('utf-8')
-    url_hash = sha256(url_bytes)
-    filename = url_hash.hexdigest()
-
-    if etag:
-        etag_bytes = etag.encode('utf-8')
-        etag_hash = sha256(etag_bytes)
-        filename += '.' + etag_hash.hexdigest()
-
-    return filename
-
-def http_get(url: str, temp_file: IO) -> None:
-    req = requests.get(url, stream=True)
-    content_length = req.headers.get('Content-Length')
-    total = int(content_length) if content_length is not None else None
-    progress = tqdm(unit="B", total=total)
-    for chunk in req.iter_content(chunk_size=1024):
-        if chunk: # filter out keep-alive new chunks
-            progress.update(len(chunk))
-            temp_file.write(chunk)
-    progress.close()
-
-def get_from_cache(url: str, cache_dir: Union[str, Path] = None) -> str:
-    """
-    Given a URL, look for the corresponding dataset in the local cache.
-    If it's not there, download it. Then return the path to the cached file.
-    """
-    if cache_dir is None:
-        cache_dir = PYTORCH_PRETRAINED_BERT_CACHE
-    if isinstance(cache_dir, Path):
-        cache_dir = str(cache_dir)
-
-    os.makedirs(cache_dir, exist_ok=True)
-
-    # Get eTag to add to filename, if it exists.
-    if url.startswith("s3://"):
-        etag = s3_etag(url)
-    else:
-        response = requests.head(url, allow_redirects=True)
-        if response.status_code != 200:
-            raise IOError("HEAD request failed for url {} with status code {}"
-                          .format(url, response.status_code))
-        etag = response.headers.get("ETag")
-
-    filename = url_to_filename(url, etag)
-
-    # get cache path to put the file
-    cache_path = os.path.join(cache_dir, filename)
-
-    if not os.path.exists(cache_path):
-        # Download to temporary file, then copy to cache dir once finished.
-        # Otherwise you get corrupt cache entries if the download gets interrupted.
-        with tempfile.NamedTemporaryFile() as temp_file:
-            logger.info("%s not found in cache, downloading to %s", url, temp_file.name)
-
-            # GET file object
-            if url.startswith("s3://"):
-                s3_get(url, temp_file)
-            else:
-                http_get(url, temp_file)
-
-            # we are copying the file before closing it, so flush to avoid truncation
-            temp_file.flush()
-            # shutil.copyfileobj() starts at the current position, so go to the start
-            temp_file.seek(0)
-
-            logger.info("copying %s to cache at %s", temp_file.name, cache_path)
-            with open(cache_path, 'wb') as cache_file:
-                shutil.copyfileobj(temp_file, cache_file)
-
-            logger.info("creating metadata file for %s", cache_path)
-            meta = {'url': url, 'etag': etag}
-            meta_path = cache_path + '.json'
-            with open(meta_path, 'w') as meta_file:
-                json.dump(meta, meta_file)
-
-            logger.info("removing temp file %s", temp_file.name)
-
-    return cache_path
-
-def cached_path(url_or_filename: Union[str, Path], cache_dir: Union[str, Path] = None) -> str:
-    """
-    Given something that might be a URL (or might be a local path),
-    determine which. If it's a URL, download the file and cache it, and
-    return the path to the cached file. If it's already a local path,
-    make sure the file exists and then return the path.
-    """
-    if cache_dir is None:
-        cache_dir = PYTORCH_PRETRAINED_BERT_CACHE
-    if isinstance(url_or_filename, Path):
-        url_or_filename = str(url_or_filename)
-    if isinstance(cache_dir, Path):
-        cache_dir = str(cache_dir)
-
-    parsed = urlparse(url_or_filename)
-
-    if parsed.scheme in ('http', 'https', 's3'):
-        # URL, so get it from the cache (downloading if necessary)
-        return get_from_cache(url_or_filename, cache_dir)
-    elif os.path.exists(url_or_filename):
-        # File, and it exists.
-        return url_or_filename
-    elif parsed.scheme == '':
-        # File, but it doesn't exist.
-        raise FileNotFoundError("file {} not found".format(url_or_filename))
-    else:
-        # Something unknown
-        raise ValueError("unable to parse {} as a URL or as a local path".format(url_or_filename))
-
-def whitespace_tokenize(text):
-    """Runs basic whitespace cleaning and splitting on a peice of text."""
-    text = text.strip()
-    if not text:
-        return []
-    tokens = text.split()
-    return tokens
-
-
-class BertTokenizer(object):
-    """Runs end-to-end tokenization: punctuation splitting"""
-
-    def __init__(self, vocab_file, do_lower_case=True, max_len=None, never_split=("[UNK]", "[SEP]", "[MASK]", "[CLS]")):
-        if not os.path.isfile(vocab_file):
-            raise ValueError(
-                "Can't find a vocabulary file at path '{}'. To load the vocabulary from a Google pretrained "
-                "model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`".format(vocab_file))
-        self.vocab = load_vocab(vocab_file)
-        self.ids_to_tokens = collections.OrderedDict(
-            [(ids, tok) for tok, ids in self.vocab.items()])
-        self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case, never_split=never_split)
-        self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
-        self.max_len = max_len if max_len is not None else int(1e12)
-
-    def tokenize(self, text):
-        split_tokens = []
-        for token in self.basic_tokenizer.tokenize(text):
-            for sub_token in self.wordpiece_tokenizer.tokenize(token):
-                split_tokens.append(sub_token)
-        return split_tokens
-
-    def convert_tokens_to_ids(self, tokens):
-        """Converts a sequence of tokens into ids using the vocab."""
-        ids = []
-        for token in tokens:
-            if token not in self.vocab:
-                ids.append(self.vocab["[UNK]"])
-                logger.error("Cannot find token '{}' in vocab. Using [UNK] insetad".format(token))
-            else:
-                ids.append(self.vocab[token])
-        if len(ids) > self.max_len:
-            raise ValueError(
-                "Token indices sequence length is longer than the specified maximum "
-                " sequence length for this BERT model ({} > {}). Running this"
-                " sequence through BERT will result in indexing errors".format(len(ids), self.max_len)
-            )
-        return ids
-
-    def convert_ids_to_tokens(self, ids):
-        """Converts a sequence of ids in tokens using the vocab."""
-        tokens = []
-        for i in ids:
-            tokens.append(self.ids_to_tokens[i])
-        return tokens
-
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name, cache_dir=None, *inputs, **kwargs):
-        """
-        Instantiate a PreTrainedBertModel from a pre-trained model file.
-        Download and cache the pre-trained model file if needed.
-        """
-        vocab_file = os.path.join(os.path.dirname(os.path.abspath(__file__)), pretrained_model_name)
-        if os.path.exists(vocab_file) is False:
-            if pretrained_model_name in PRETRAINED_VOCAB_ARCHIVE_MAP:
-                vocab_file = PRETRAINED_VOCAB_ARCHIVE_MAP[pretrained_model_name]
-            else:
-                vocab_file = pretrained_model_name
-        if os.path.isdir(vocab_file):
-            vocab_file = os.path.join(vocab_file, VOCAB_NAME)
-        # redirect to the cache, if necessary
-        print(vocab_file)
-        try:
-            resolved_vocab_file = cached_path(vocab_file, cache_dir=cache_dir)
-        except FileNotFoundError:
-            logger.error(
-                "Model name '{}' was not found. "
-                "We assumed '{}' was a path or url but couldn't find any file "
-                "associated to this path or url.".format(
-                    pretrained_model_name,
-                    vocab_file))
-            return None
-        if resolved_vocab_file == vocab_file:
-            logger.info("loading vocabulary file {}".format(vocab_file))
-        else:
-            logger.info("loading vocabulary file {} from cache at {}".format(
-                vocab_file, resolved_vocab_file))
-        if pretrained_model_name in PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP:
-            # if we're using a pretrained model, ensure the tokenizer wont index sequences longer
-            # than the number of positional embeddings
-            max_len = PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP[pretrained_model_name]
-            kwargs['max_len'] = min(kwargs.get('max_len', int(1e12)), max_len)
-            kwargs['never_split'] = ("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]")
-
-        # Instantiate tokenizer.
-        tokenizer = cls(resolved_vocab_file, *inputs, **kwargs)
-
-        return tokenizer
-
-    def add_tokens(self, new_tokens, model):
-        """
-        Add a list of new tokens to the tokenizer class. If the new tokens are not in the
-        vocabulary, they are added to it with indices starting from length of the current vocabulary.
-        Args:
-            new_tokens: list of string. Each string is a token to add. Tokens are only added if they are not already in the vocabulary (tested by checking if the tokenizer assign the index of the ``unk_token`` to them).
-        Returns:
-            Number of tokens added to the vocabulary.
-        Examples::
-            # Let's see how to increase the vocabulary of Bert model and tokenizer
-            tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-            model = BertModel.from_pretrained('bert-base-uncased')
-            num_added_toks = tokenizer.add_tokens(['new_tok1', 'my_new-tok2'])
-            print('We have added', num_added_toks, 'tokens')
-            model.resize_token_embeddings(len(tokenizer))  # Notice: resize_token_embeddings expect to receive the full size of the new vocabulary, i.e. the length of the tokenizer.
-        """
-
-        to_add_tokens = []
-        for token in new_tokens:
-            assert isinstance(token, str)
-            to_add_tokens.append(token)
-            # logger.info("Adding %s to the vocabulary", token)
-
-        vocab = collections.OrderedDict()
-        for token in self.vocab.keys():
-            vocab[token] = self.vocab[token]
-        for token in to_add_tokens:
-            vocab[token] = len(vocab)
-        self.vocab = self.wordpiece_tokenizer.vocab = vocab
-        self.ids_to_tokens = collections.OrderedDict(
-            [(ids, tok) for tok, ids in self.vocab.items()])
-
-        model.resize_token_embeddings(new_num_tokens=len(vocab))
-
-class BasicTokenizer(object):
-    """Runs basic tokenization (punctuation splitting, lower casing, etc.)."""
-
-    def __init__(self, do_lower_case=True, never_split=("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]")):
-        """Constructs a BasicTokenizer.
-
-        Args:
-          do_lower_case: Whether to lower case the input.
-        """
-        self.do_lower_case = do_lower_case
-        self.never_split = never_split
-
-    def tokenize(self, text):
-        """Tokenizes a piece of text."""
-        text = self._clean_text(text)
-        # This was added on November 1st, 2018 for the multilingual and Chinese
-        # models. This is also applied to the English models now, but it doesn't
-        # matter since the English models were not trained on any Chinese data
-        # and generally don't have any Chinese data in them (there are Chinese
-        # characters in the vocabulary because Wikipedia does have some Chinese
-        # words in the English Wikipedia.).
-        text = self._tokenize_chinese_chars(text)
-        orig_tokens = whitespace_tokenize(text)
-        split_tokens = []
-        for token in orig_tokens:
-            if self.do_lower_case and token not in self.never_split:
-                token = token.lower()
-                token = self._run_strip_accents(token)
-            split_tokens.extend(self._run_split_on_punc(token))
-
-        output_tokens = whitespace_tokenize(" ".join(split_tokens))
-        return output_tokens
-
-    def _run_strip_accents(self, text):
-        """Strips accents from a piece of text."""
-        text = unicodedata.normalize("NFD", text)
-        output = []
-        for char in text:
-            cat = unicodedata.category(char)
-            if cat == "Mn":
-                continue
-            output.append(char)
-        return "".join(output)
-
-    def _run_split_on_punc(self, text):
-        """Splits punctuation on a piece of text."""
-        if text in self.never_split:
-            return [text]
-        chars = list(text)
-        i = 0
-        start_new_word = True
-        output = []
-        while i < len(chars):
-            char = chars[i]
-            if _is_punctuation(char):
-                output.append([char])
-                start_new_word = True
-            else:
-                if start_new_word:
-                    output.append([])
-                start_new_word = False
-                output[-1].append(char)
-            i += 1
-
-        return ["".join(x) for x in output]
-
-    def _tokenize_chinese_chars(self, text):
-        """Adds whitespace around any CJK character."""
-        output = []
-        for char in text:
-            cp = ord(char)
-            if self._is_chinese_char(cp):
-                output.append(" ")
-                output.append(char)
-                output.append(" ")
-            else:
-                output.append(char)
-        return "".join(output)
-
-    def _is_chinese_char(self, cp):
-        """Checks whether CP is the codepoint of a CJK character."""
-        # This defines a "chinese character" as anything in the CJK Unicode block:
-        #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
-        #
-        # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
-        # despite its name. The modern Korean Hangul alphabet is a different block,
-        # as is Japanese Hiragana and Katakana. Those alphabets are used to write
-        # space-separated words, so they are not treated specially and handled
-        # like the all of the other languages.
-        if ((cp >= 0x4E00 and cp <= 0x9FFF) or  #
-                (cp >= 0x3400 and cp <= 0x4DBF) or  #
-                (cp >= 0x20000 and cp <= 0x2A6DF) or  #
-                (cp >= 0x2A700 and cp <= 0x2B73F) or  #
-                (cp >= 0x2B740 and cp <= 0x2B81F) or  #
-                (cp >= 0x2B820 and cp <= 0x2CEAF) or
-                (cp >= 0xF900 and cp <= 0xFAFF) or  #
-                (cp >= 0x2F800 and cp <= 0x2FA1F)):  #
-            return True
-
-        return False
-
-    def _clean_text(self, text):
-        """Performs invalid character removal and whitespace cleanup on text."""
-        output = []
-        for char in text:
-            cp = ord(char)
-            if cp == 0 or cp == 0xfffd or _is_control(char):
-                continue
-            if _is_whitespace(char):
-                output.append(" ")
-            else:
-                output.append(char)
-        return "".join(output)
-
-class WordpieceTokenizer(object):
-    """Runs WordPiece tokenization."""
-
-    def __init__(self, vocab, unk_token="[UNK]", max_input_chars_per_word=100):
-        self.vocab = vocab
-        self.unk_token = unk_token
-        self.max_input_chars_per_word = max_input_chars_per_word
-
-    def tokenize(self, text):
-        """Tokenizes a piece of text into its word pieces.
-
-        This uses a greedy longest-match-first algorithm to perform tokenization
-        using the given vocabulary.
-
-        For example:
-          input = "unaffable"
-          output = ["un", "##aff", "##able"]
-
-        Args:
-          text: A single token or whitespace separated tokens. This should have
-            already been passed through `BasicTokenizer`.
-
-        Returns:
-          A list of wordpiece tokens.
-        """
-
-        output_tokens = []
-        for token in whitespace_tokenize(text):
-            chars = list(token)
-            if len(chars) > self.max_input_chars_per_word:
-                output_tokens.append(self.unk_token)
-                continue
-
-            is_bad = False
-            start = 0
-            sub_tokens = []
-            while start < len(chars):
-                end = len(chars)
-                cur_substr = None
-                while start < end:
-                    substr = "".join(chars[start:end])
-                    if start > 0:
-                        substr = "##" + substr
-                    if substr in self.vocab:
-                        cur_substr = substr
-                        break
-                    end -= 1
-                if cur_substr is None:
-                    is_bad = True
-                    break
-                sub_tokens.append(cur_substr)
-                start = end
-
-            if is_bad:
-                output_tokens.append(self.unk_token)
-            else:
-                output_tokens.extend(sub_tokens)
-        return output_tokens
-
-def _is_whitespace(char):
-    """Checks whether `chars` is a whitespace character."""
-    # \t, \n, and \r are technically contorl characters but we treat them
-    # as whitespace since they are generally considered as such.
-    if char == " " or char == "\t" or char == "\n" or char == "\r":
-        return True
-    cat = unicodedata.category(char)
-    if cat == "Zs":
-        return True
-    return False
-
-
-def _is_control(char):
-    """Checks whether `chars` is a control character."""
-    # These are technically control characters but we count them as whitespace
-    # characters.
-    if char == "\t" or char == "\n" or char == "\r":
-        return False
-    cat = unicodedata.category(char)
-    if cat.startswith("C"):
-        return True
-    return False
-
-
-def _is_punctuation(char):
-    """Checks whether `chars` is a punctuation character."""
-    cp = ord(char)
-    # We treat all non-letter/number ASCII as punctuation.
-    # Characters such as "^", "$", and "`" are not in the Unicode
-    # Punctuation class but we treat them as punctuation anyways, for
-    # consistency.
-    if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or
-            (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)):
-        return True
-    cat = unicodedata.category(char)
-    if cat.startswith("P"):
-        return True
-    return False
-
-
-def gelu(x):
-    """Implementation of the gelu activation function.
-        For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
-        0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
-    """
-    return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
-
-def swish(x):
-    return x * torch.sigmoid(x)
-
-ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish}
-
-class LayerNorm(nn.Module):
-    def __init__(self, hidden_size, eps=1e-12):
-        """Construct a layernorm module in the TF style (epsilon inside the square root).
-        """
-        super(LayerNorm, self).__init__()
-        self.weight = nn.Parameter(torch.ones(hidden_size))
-        self.bias = nn.Parameter(torch.zeros(hidden_size))
-        self.variance_epsilon = eps
-
-    def forward(self, x):
-        u = x.mean(-1, keepdim=True)
-        s = (x - u).pow(2).mean(-1, keepdim=True)
-        x = (x - u) / torch.sqrt(s + self.variance_epsilon)
-        return self.weight * x + self.bias
-    
-class PretrainedConfig(object):
-
-    pretrained_model_archive_map = {}
-    config_name = ""
-    weights_name = ""
-
-    @classmethod
-    def get_config(cls, pretrained_model_name, cache_dir, type_vocab_size, state_dict, task_config=None):
-        archive_file = os.path.join(os.path.dirname(os.path.abspath(__file__)), pretrained_model_name)
-        if os.path.exists(archive_file) is False:
-            if pretrained_model_name in cls.pretrained_model_archive_map:
-                archive_file = cls.pretrained_model_archive_map[pretrained_model_name]
-            else:
-                archive_file = pretrained_model_name
-
-        # redirect to the cache, if necessary
-        try:
-            resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir)
-        except FileNotFoundError:
-            if task_config is None or task_config.local_rank == 0:
-                logger.error(
-                    "Model name '{}' was not found in model name list. "
-                    "We assumed '{}' was a path or url but couldn't find any file "
-                    "associated to this path or url.".format(
-                        pretrained_model_name,
-                        archive_file))
-            return None
-        if resolved_archive_file == archive_file:
-            if task_config is None or task_config.local_rank == 0:
-                logger.info("loading archive file {}".format(archive_file))
-        else:
-            if task_config is None or task_config.local_rank == 0:
-                logger.info("loading archive file {} from cache at {}".format(
-                    archive_file, resolved_archive_file))
-        tempdir = None
-        if os.path.isdir(resolved_archive_file):
-            serialization_dir = resolved_archive_file
-        else:
-            # Extract archive to temp dir
-            tempdir = tempfile.mkdtemp()
-            if task_config is None or task_config.local_rank == 0:
-                logger.info("extracting archive file {} to temp dir {}".format(
-                    resolved_archive_file, tempdir))
-            with tarfile.open(resolved_archive_file, 'r:gz') as archive:
-                archive.extractall(tempdir)
-            serialization_dir = tempdir
-        # Load config
-        config_file = os.path.join(serialization_dir, cls.config_name)
-        config = cls.from_json_file(config_file)
-        config.type_vocab_size = type_vocab_size
-        if task_config is None or task_config.local_rank == 0:
-            logger.info("Model config {}".format(config))
-
-        if state_dict is None:
-            weights_path = os.path.join(serialization_dir, cls.weights_name)
-            if os.path.exists(weights_path):
-                state_dict = torch.load(weights_path, map_location='cpu')
-            else:
-                if task_config is None or task_config.local_rank == 0:
-                    logger.info("Weight doesn't exsits. {}".format(weights_path))
-
-        if tempdir:
-            # Clean up temp dir
-            shutil.rmtree(tempdir)
-
-        return config, state_dict
-
-    @classmethod
-    def from_dict(cls, json_object):
-        """Constructs a `BertConfig` from a Python dictionary of parameters."""
-        config = cls(vocab_size_or_config_json_file=-1)
-        for key, value in json_object.items():
-            config.__dict__[key] = value
-        return config
-
-    @classmethod
-    def from_json_file(cls, json_file):
-        """Constructs a `BertConfig` from a json file of parameters."""
-        with open(json_file, "r", encoding='utf-8') as reader:
-            text = reader.read()
-        return cls.from_dict(json.loads(text))
-
-    def __repr__(self):
-        return str(self.to_json_string())
-
-    def to_dict(self):
-        """Serializes this instance to a Python dictionary."""
-        output = copy.deepcopy(self.__dict__)
-        return output
-
-    def to_json_string(self):
-        """Serializes this instance to a JSON string."""
-        return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
-    
-class BertConfig(PretrainedConfig):
-    """Configuration class to store the configuration of a `BertModel`.
-    """
-    pretrained_model_archive_map = PRETRAINED_MODEL_ARCHIVE_MAP
-    config_name = CONFIG_NAME
-    weights_name = WEIGHTS_NAME
-
-    def __init__(self,
-                 vocab_size_or_config_json_file,
-                 hidden_size=768,
-                 num_hidden_layers=12,
-                 num_attention_heads=12,
-                 intermediate_size=3072,
-                 hidden_act="gelu",
-                 hidden_dropout_prob=0.1,
-                 attention_probs_dropout_prob=0.1,
-                 max_position_embeddings=512,
-                 type_vocab_size=2,
-                 initializer_range=0.02):
-        """Constructs BertConfig.
-
-        Args:
-            vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `BertModel`.
-            hidden_size: Size of the encoder layers and the pooler layer.
-            num_hidden_layers: Number of hidden layers in the Transformer encoder.
-            num_attention_heads: Number of attention heads for each attention layer in
-                the Transformer encoder.
-            intermediate_size: The size of the "intermediate" (i.e., feed-forward)
-                layer in the Transformer encoder.
-            hidden_act: The non-linear activation function (function or string) in the
-                encoder and pooler. If string, "gelu", "relu" and "swish" are supported.
-            hidden_dropout_prob: The dropout probabilitiy for all fully connected
-                layers in the embeddings, encoder, and pooler.
-            attention_probs_dropout_prob: The dropout ratio for the attention
-                probabilities.
-            max_position_embeddings: The maximum sequence length that this model might
-                ever be used with. Typically set this to something large just in case
-                (e.g., 512 or 1024 or 2048).
-            type_vocab_size: The vocabulary size of the `token_type_ids` passed into
-                `BertModel`.
-            initializer_range: The sttdev of the truncated_normal_initializer for
-                initializing all weight matrices.
-        """
-        if isinstance(vocab_size_or_config_json_file, str):
-            with open(vocab_size_or_config_json_file, "r", encoding='utf-8') as reader:
-                json_config = json.loads(reader.read())
-            for key, value in json_config.items():
-                self.__dict__[key] = value
-        elif isinstance(vocab_size_or_config_json_file, int):
-            self.vocab_size = vocab_size_or_config_json_file
-            self.hidden_size = hidden_size
-            self.num_hidden_layers = num_hidden_layers
-            self.num_attention_heads = num_attention_heads
-            self.hidden_act = hidden_act
-            self.intermediate_size = intermediate_size
-            self.hidden_dropout_prob = hidden_dropout_prob
-            self.attention_probs_dropout_prob = attention_probs_dropout_prob
-            self.max_position_embeddings = max_position_embeddings
-            self.type_vocab_size = type_vocab_size
-            self.initializer_range = initializer_range
-        else:
-            raise ValueError("First argument must be either a vocabulary size (int)"
-                             "or the path to a pretrained model config file (str)")
-
-class PreTrainedModel(nn.Module):
-    """ An abstract class to handle weights initialization and
-        a simple interface for dowloading and loading pretrained models.
-    """
-    def __init__(self, config, *inputs, **kwargs):
-        super(PreTrainedModel, self).__init__()
-        # if not isinstance(config, PretrainedConfig):
-        #     raise ValueError(
-        #         "Parameter config in `{}(config)` should be an instance of class `PretrainedConfig`. "
-        #         "To create a model from a Google pretrained model use "
-        #         "`model = {}.from_pretrained(PRETRAINED_MODEL_NAME)`".format(
-        #             self.__class__.__name__, self.__class__.__name__
-        #         ))
-        self.config = config
-
-    def init_weights(self, module):
-        """ Initialize the weights.
-        """
-        if isinstance(module, (nn.Linear, nn.Embedding)):
-            # Slightly different from the TF version which uses truncated_normal for initialization
-            # cf https://github.com/pytorch/pytorch/pull/5617
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-        elif isinstance(module, LayerNorm):
-            if 'beta' in dir(module) and 'gamma' in dir(module):
-                module.beta.data.zero_()
-                module.gamma.data.fill_(1.0)
-            else:
-                module.bias.data.zero_()
-                module.weight.data.fill_(1.0)
-        if isinstance(module, nn.Linear) and module.bias is not None:
-            module.bias.data.zero_()
-
-    def resize_token_embeddings(self, new_num_tokens=None):
-        raise NotImplementedError
-
-    @classmethod
-    def init_preweight(cls, model, state_dict, prefix=None, task_config=None):
-        old_keys = []
-        new_keys = []
-        for key in state_dict.keys():
-            new_key = None
-            if 'gamma' in key:
-                new_key = key.replace('gamma', 'weight')
-            if 'beta' in key:
-                new_key = key.replace('beta', 'bias')
-            if new_key:
-                old_keys.append(key)
-                new_keys.append(new_key)
-        for old_key, new_key in zip(old_keys, new_keys):
-            state_dict[new_key] = state_dict.pop(old_key)
-
-        if prefix is not None:
-            old_keys = []
-            new_keys = []
-            for key in state_dict.keys():
-                old_keys.append(key)
-                new_keys.append(prefix + key)
-            for old_key, new_key in zip(old_keys, new_keys):
-                state_dict[new_key] = state_dict.pop(old_key)
-
-        missing_keys = []
-        unexpected_keys = []
-        error_msgs = []
-        # copy state_dict so _load_from_state_dict can modify it
-        metadata = getattr(state_dict, '_metadata', None)
-        state_dict = state_dict.copy()
-        if metadata is not None:
-            state_dict._metadata = metadata
-
-        def load(module, prefix=''):
-            local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
-            module._load_from_state_dict(
-                state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs)
-            for name, child in module._modules.items():
-                if child is not None:
-                    load(child, prefix + name + '.')
-
-        load(model, prefix='')
-
-        if prefix is None and (task_config is None or task_config.local_rank == 0):
-            logger.info("-" * 20)
-            if len(missing_keys) > 0:
-                logger.info("Weights of {} not initialized from pretrained model: {}"
-                            .format(model.__class__.__name__, "\n   " + "\n   ".join(missing_keys)))
-            if len(unexpected_keys) > 0:
-                logger.info("Weights from pretrained model not used in {}: {}"
-                            .format(model.__class__.__name__, "\n   " + "\n   ".join(unexpected_keys)))
-            if len(error_msgs) > 0:
-                logger.error("Weights from pretrained model cause errors in {}: {}"
-                             .format(model.__class__.__name__, "\n   " + "\n   ".join(error_msgs)))
-
-        return model
-
-    @property
-    def dtype(self):
-        """
-        :obj:`torch.dtype`: The dtype of the module (assuming that all the module parameters have the same dtype).
-        """
-        try:
-            return next(self.parameters()).dtype
-        except StopIteration:
-            # For nn.DataParallel compatibility in PyTorch 1.5
-            def find_tensor_attributes(module: nn.Module):
-                tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)]
-                return tuples
-
-            gen = self._named_members(get_members_fn=find_tensor_attributes)
-            first_tuple = next(gen)
-            return first_tuple[1].dtype
-
-    @classmethod
-    def from_pretrained(cls, config, state_dict=None,  *inputs, **kwargs):
-        """
-        Instantiate a PreTrainedModel from a pre-trained model file or a pytorch state dict.
-        Download and cache the pre-trained model file if needed.
-        """
-        # Instantiate model.
-        model = cls(config, *inputs, **kwargs)
-        if state_dict is None:
-            return model
-        model = cls.init_preweight(model, state_dict)
-
-        return model
-
-class BertEmbeddings(nn.Module):
-    """Construct the embeddings from word, position and token_type embeddings.
-    """
-    def __init__(self, config):
-        super(BertEmbeddings, self).__init__()
-        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size)
-        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
-        self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
-
-        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
-        # any TensorFlow checkpoint file
-        self.LayerNorm = LayerNorm(config.hidden_size, eps=1e-12)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-    def forward(self, input_ids, token_type_ids=None):
-        seq_length = input_ids.size(1)
-        position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device)
-        position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
-        if token_type_ids is None:
-            token_type_ids = torch.zeros_like(input_ids)
-
-        words_embeddings = self.word_embeddings(input_ids)
-        position_embeddings = self.position_embeddings(position_ids)
-        token_type_embeddings = self.token_type_embeddings(token_type_ids)
-
-        embeddings = words_embeddings + position_embeddings + token_type_embeddings
-        embeddings = self.LayerNorm(embeddings)
-        embeddings = self.dropout(embeddings)
-        return embeddings
-
-
-class BertSelfAttention(nn.Module):
-    def __init__(self, config):
-        super(BertSelfAttention, self).__init__()
-        if config.hidden_size % config.num_attention_heads != 0:
-            raise ValueError(
-                "The hidden size (%d) is not a multiple of the number of attention "
-                "heads (%d)" % (config.hidden_size, config.num_attention_heads))
-        self.num_attention_heads = config.num_attention_heads
-        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
-        self.all_head_size = self.num_attention_heads * self.attention_head_size
-
-        self.query = nn.Linear(config.hidden_size, self.all_head_size)
-        self.key = nn.Linear(config.hidden_size, self.all_head_size)
-        self.value = nn.Linear(config.hidden_size, self.all_head_size)
-
-        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
-
-    def transpose_for_scores(self, x):
-        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
-        x = x.view(*new_x_shape)
-        return x.permute(0, 2, 1, 3)
-
-    def forward(self, hidden_states, attention_mask):
-        mixed_query_layer = self.query(hidden_states)
-        mixed_key_layer = self.key(hidden_states)
-        mixed_value_layer = self.value(hidden_states)
-
-        query_layer = self.transpose_for_scores(mixed_query_layer)
-        key_layer = self.transpose_for_scores(mixed_key_layer)
-        value_layer = self.transpose_for_scores(mixed_value_layer)
-
-        # Take the dot product between "query" and "key" to get the raw attention scores.
-        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
-        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
-        # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
-        attention_scores = attention_scores + attention_mask
-
-        # Normalize the attention scores to probabilities.
-        attention_probs = nn.Softmax(dim=-1)(attention_scores)
-
-        # This is actually dropping out entire tokens to attend to, which might
-        # seem a bit unusual, but is taken from the original Transformer paper.
-        attention_probs = self.dropout(attention_probs)
-
-        context_layer = torch.matmul(attention_probs, value_layer)
-        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
-        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
-        context_layer = context_layer.view(*new_context_layer_shape)
-        return context_layer
-
-
-class BertSelfOutput(nn.Module):
-    def __init__(self, config):
-        super(BertSelfOutput, self).__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.LayerNorm = LayerNorm(config.hidden_size, eps=1e-12)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-    def forward(self, hidden_states, input_tensor):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states + input_tensor)
-        return hidden_states
-
-
-class BertAttention(nn.Module):
-    def __init__(self, config):
-        super(BertAttention, self).__init__()
-        self.self = BertSelfAttention(config)
-        self.output = BertSelfOutput(config)
-
-    def forward(self, input_tensor, attention_mask):
-        self_output = self.self(input_tensor, attention_mask)
-        attention_output = self.output(self_output, input_tensor)
-        return attention_output
-
-
-class BertIntermediate(nn.Module):
-    def __init__(self, config):
-        super(BertIntermediate, self).__init__()
-        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
-        self.intermediate_act_fn = ACT2FN[config.hidden_act] \
-            if isinstance(config.hidden_act, str) else config.hidden_act
-
-    def forward(self, hidden_states):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.intermediate_act_fn(hidden_states)
-        return hidden_states
-
-
-class BertOutput(nn.Module):
-    def __init__(self, config):
-        super(BertOutput, self).__init__()
-        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
-        self.LayerNorm = LayerNorm(config.hidden_size, eps=1e-12)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-    def forward(self, hidden_states, input_tensor):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states + input_tensor)
-        return hidden_states
-
-
-class BertLayer(nn.Module):
-    def __init__(self, config):
-        super(BertLayer, self).__init__()
-        self.attention = BertAttention(config)
-        self.intermediate = BertIntermediate(config)
-        self.output = BertOutput(config)
-
-    def forward(self, hidden_states, attention_mask):
-        attention_output = self.attention(hidden_states, attention_mask)
-        intermediate_output = self.intermediate(attention_output)
-        layer_output = self.output(intermediate_output, attention_output)
-        return layer_output
-
-
-class BertEncoder(nn.Module):
-    def __init__(self, config):
-        super(BertEncoder, self).__init__()
-        layer = BertLayer(config)
-        self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(config.num_hidden_layers)])
-
-    def forward(self, hidden_states, attention_mask, output_all_encoded_layers=True):
-        all_encoder_layers = []
-        for layer_module in self.layer:
-            hidden_states = layer_module(hidden_states, attention_mask)
-            if output_all_encoded_layers:
-                all_encoder_layers.append(hidden_states)
-        if not output_all_encoded_layers:
-            all_encoder_layers.append(hidden_states)
-        return all_encoder_layers
-
-
-class BertPooler(nn.Module):
-    def __init__(self, config):
-        super(BertPooler, self).__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.activation = nn.Tanh()
-
-    def forward(self, hidden_states):
-        # We "pool" the model by simply taking the hidden state corresponding
-        # to the first token.
-        first_token_tensor = hidden_states[:, 0]
-        pooled_output = self.dense(first_token_tensor)
-        pooled_output = self.activation(pooled_output)
-        return pooled_output
-
-
-class BertPredictionHeadTransform(nn.Module):
-    def __init__(self, config):
-        super(BertPredictionHeadTransform, self).__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.transform_act_fn = ACT2FN[config.hidden_act] \
-            if isinstance(config.hidden_act, str) else config.hidden_act
-        self.LayerNorm = LayerNorm(config.hidden_size, eps=1e-12)
-
-    def forward(self, hidden_states):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.transform_act_fn(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states)
-        return hidden_states
-
-
-class BertLMPredictionHead(nn.Module):
-    def __init__(self, config, bert_model_embedding_weights):
-        super(BertLMPredictionHead, self).__init__()
-        self.transform = BertPredictionHeadTransform(config)
-
-        # The output weights are the same as the input embeddings, but there is
-        # an output-only bias for each token.
-        self.decoder = nn.Linear(bert_model_embedding_weights.size(1),
-                                 bert_model_embedding_weights.size(0),
-                                 bias=False)
-        self.decoder.weight = bert_model_embedding_weights
-        self.bias = nn.Parameter(torch.zeros(bert_model_embedding_weights.size(0)))
-
-    def forward(self, hidden_states):
-        hidden_states = self.transform(hidden_states)
-        hidden_states = self.decoder(hidden_states) + self.bias
-        return hidden_states
-
-
-class BertOnlyMLMHead(nn.Module):
-    def __init__(self, config, bert_model_embedding_weights):
-        super(BertOnlyMLMHead, self).__init__()
-        self.predictions = BertLMPredictionHead(config, bert_model_embedding_weights)
-
-    def forward(self, sequence_output):
-        prediction_scores = self.predictions(sequence_output)
-        return prediction_scores
-
-
-class BertOnlyNSPHead(nn.Module):
-    def __init__(self, config):
-        super(BertOnlyNSPHead, self).__init__()
-        self.seq_relationship = nn.Linear(config.hidden_size, 2)
-
-    def forward(self, pooled_output):
-        seq_relationship_score = self.seq_relationship(pooled_output)
-        return seq_relationship_score
-
-
-class BertPreTrainingHeads(nn.Module):
-    def __init__(self, config, bert_model_embedding_weights):
-        super(BertPreTrainingHeads, self).__init__()
-        self.predictions = BertLMPredictionHead(config, bert_model_embedding_weights)
-        self.seq_relationship = nn.Linear(config.hidden_size, 2)
-
-    def forward(self, sequence_output, pooled_output):
-        prediction_scores = self.predictions(sequence_output)
-        seq_relationship_score = self.seq_relationship(pooled_output)
-        return prediction_scores, seq_relationship_score
-
-class BertModel(PreTrainedModel):
-    """BERT model ("Bidirectional Embedding Representations from a Transformer").
-
-    Params:
-        config: a BertConfig class instance with the configuration to build a new model
-
-    Inputs:
-        `type`: a str, indicates which masking will be used in the attention, choice from [`bi`, `seq`, `gen`]
-        `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
-            with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
-            `extract_features.py`, `run_classifier.py` and `run_squad.py`)
-        `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
-            types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
-            a `sentence B` token (see BERT paper for more details).
-        `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
-            selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
-            input sequence length in the current batch. It's the mask that we typically use for attention when
-            a batch has varying length sentences.
-        `output_all_encoded_layers`: boolean which controls the content of the `encoded_layers` output as described below. Default: `True`.
-
-    Outputs: Tuple of (encoded_layers, pooled_output)
-        `encoded_layers`: controled by `output_all_encoded_layers` argument:
-            - `output_all_encoded_layers=True`: outputs a list of the full sequences of encoded-hidden-states at the end
-                of each attention block (i.e. 12 full sequences for BERT-base, 24 for BERT-large), each
-                encoded-hidden-state is a torch.FloatTensor of size [batch_size, sequence_length, hidden_size],
-            - `output_all_encoded_layers=False`: outputs only the full sequence of hidden-states corresponding
-                to the last attention block of shape [batch_size, sequence_length, hidden_size],
-        `pooled_output`: a torch.FloatTensor of size [batch_size, hidden_size] which is the output of a
-            classifier pretrained on top of the hidden state associated to the first character of the
-            input (`CLF`) to train on the Next-Sentence task (see BERT's paper).
-
-    Example usage:
-    ```python
-    # Already been converted into WordPiece token ids
-    input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
-    input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
-    token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
-
-    config = modeling.BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
-        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
-
-    model = modeling.BertModel(config=config)
-    all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
-    ```
-    """
-    def __init__(self, config):
-        super(BertModel, self).__init__(config)
-        self.config = config
-        self.embeddings = BertEmbeddings(config)
-        self.encoder = BertEncoder(config)
-        self.pooler = BertPooler(config)
-        self.apply(self.init_weights)
-
-    def forward(self, input_ids, token_type_ids=None, attention_mask=None, output_all_encoded_layers=True):
-
-        if attention_mask is None:
-            attention_mask = torch.ones_like(input_ids)
-        if token_type_ids is None:
-            token_type_ids = torch.zeros_like(input_ids)
-
-        # We create a 3D attention mask from a 2D tensor mask.
-        # Sizes are [batch_size, 1, 1, to_seq_length]
-        # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
-        # this attention mask is more simple than the triangular masking of causal attention
-        # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
-        extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
-
-        # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
-        # masked positions, this operation will create a tensor which is 0.0 for
-        # positions we want to attend and -10000.0 for masked positions.
-        # Since we are adding it to the raw scores before the softmax, this is
-        # effectively the same as removing these entirely.
-        extended_attention_mask = extended_attention_mask.to(dtype=self.dtype) # fp16 compatibility
-        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
-
-        embedding_output = self.embeddings(input_ids, token_type_ids)
-        encoded_layers = self.encoder(embedding_output,
-                                      extended_attention_mask,
-                                      output_all_encoded_layers=output_all_encoded_layers)
-        sequence_output = encoded_layers[-1]
-        pooled_output = self.pooler(sequence_output)
-        if not output_all_encoded_layers:
-            encoded_layers = encoded_layers[-1]
-        return encoded_layers, pooled_output
-
-
-def build_UniVL_text_encoder(dict):
-    bert_config = BertConfig.from_dict(dict)
-    bert = BertModel(bert_config)
-
-    return bert
-
-def build_UniVL_tokenizer():
-    return BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True)
-
-
-
-def load_pretrained_UniVL(args, device, n_gpu, local_rank, init_model=None):
-
-    if init_model:
-        model_state_dict = torch.load(init_model, map_location='cpu')
-    else:
-        model_state_dict = None
-
-    # Prepare model
-    cache_dir = args.cache_dir if args.cache_dir else os.path.join(str(PYTORCH_PRETRAINED_BERT_CACHE), 'distributed')
-    model = UniVL.from_pretrained('bert-base-uncased', 'visual-base', 'cross-base', 'decoder-base',
-                                   cache_dir=cache_dir, state_dict=model_state_dict, task_config=args)
-
-    model.to(device)
-
-    return model
-
-if __name__ == '__main__':
-    bert_config_dict = {
-    "attention_probs_dropout_prob": 0.1,
-    "hidden_act": "gelu",
-    "hidden_dropout_prob": 0.1,
-    "hidden_size": 768,
-    "initializer_range": 0.02,
-    "intermediate_size": 3072,
-    "max_position_embeddings": 512,
-    "num_attention_heads": 12,
-    "num_hidden_layers": 12,
-    "type_vocab_size": 2,
-    "vocab_size": 30522
-    }
-    tokenizer = build_UniVL_tokenizer()
-    bert = build_UniVL_text_encoder(bert_config_dict)
-    words = ["[CLS]"] + ['you', 'love', 'you'] + ["[SEP]"]
-    #input_ids = tokenizer.convert_tokens_to_ids(words)
-    #masked_tokens = words.copy()
-    #masked_token_ids = tokenizer.convert_tokens_to_ids(masked_tokens)
-    token_type_ids = None
-    breakpoint()
-    encoded_layers, _ = bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=True)
-    sequence_output = encoded_layers[-1]
-
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diff --git a/backup/pdvc/modules/beam.py b/backup/pdvc/modules/beam.py
deleted file mode 100644
index eff1d961ef393e03a3c9105022b1047f5ea7133d..0000000000000000000000000000000000000000
--- a/backup/pdvc/modules/beam.py
+++ /dev/null
@@ -1,116 +0,0 @@
-"""
-Manage beam search info structure.
-Heavily borrowed from OpenNMT-py.
-For code in OpenNMT-py, please check the following link (maybe in oldest version):
-https://github.com/OpenNMT/OpenNMT-py/blob/master/onmt/Beam.py
-"""
-
-import torch
-
-class Constants():
-    def __init__(self):
-        self.PAD = 0
-        self.UNK = 1
-        self.BOS = 2
-        self.EOS = 3
-        self.PAD_WORD = '[PAD]'
-        self.UNK_WORD = '[UNK]'
-        self.BOS_WORD = '[CLS]'
-        self.EOS_WORD = '[SEP]'
-
-    @classmethod
-    def from_tokenizer(cls, tokenizer):
-        instance = cls()
-        instance.PAD = tokenizer.vocab[instance.PAD_WORD]
-        instance.UNK = tokenizer.vocab[instance.UNK_WORD]
-        instance.BOS = tokenizer.vocab[instance.BOS_WORD]
-        instance.EOS = tokenizer.vocab[instance.EOS_WORD]
-        return instance
-
-class Beam():
-    ''' Beam search '''
-
-    def __init__(self, size, device=False, tokenizer=None):
-        if tokenizer is None:
-            self.constants = Constants()
-        else:
-            self.constants = Constants.from_tokenizer(tokenizer)
-
-        self.size = size
-        self._done = False
-        # The score for each interface on the beam.
-        self.scores = torch.zeros((size,), dtype=torch.float, device=device)
-        self.all_scores = []
-
-        # The backpointers at each time-step.
-        self.prev_ks = []
-
-        # The outputs at each time-step.
-        self.next_ys = [torch.full((size,), self.constants.BOS, dtype=torch.long, device=device)]
-
-    def get_current_state(self):
-        "Get the outputs for the current timestep."
-        return self.get_tentative_hypothesis()
-
-    def get_current_origin(self):
-        "Get the backpointers for the current timestep."
-        return self.prev_ks[-1]
-
-    @property
-    def done(self):
-        return self._done
-
-    def advance(self, word_prob, word_length=None):
-
-        "Update beam status and check if finished or not."
-        num_words = word_prob.size(1)
-        # Sum the previous scores.
-        if len(self.prev_ks) > 0:
-            beam_lk = word_prob + self.scores.unsqueeze(1).expand_as(word_prob)
-        else:
-            beam_lk = word_prob[0]
-        flat_beam_lk = beam_lk.view(-1)
-        best_scores, best_scores_id = flat_beam_lk.topk(self.size, 0, True, True) # 1st sort
-        self.all_scores.append(self.scores)
-        self.scores = best_scores
-        # bestScoresId is flattened as a (beam x word) array,
-        # so we need to calculate which word and beam each score came from
-        prev_k = best_scores_id // num_words
-        self.prev_ks.append(prev_k)
-        self.next_ys.append(best_scores_id - prev_k * num_words)
-        # End condition is when top-of-beam is EOS.
-        if self.next_ys[-1][0].item() == self.constants.EOS:
-            self._done = True
-
-        return self._done
-
-    def sort_scores(self):
-        "Sort the scores."
-        return torch.sort(self.scores, 0, True)
-
-    def get_the_best_score_and_idx(self):
-        "Get the score of the best in the beam."
-        scores, ids = self.sort_scores()
-        return scores[1], ids[1]
-
-    def get_tentative_hypothesis(self):
-        "Get the decoded sequence for the current timestep."
-
-        if len(self.next_ys) == 1:
-            dec_seq = self.next_ys[0].unsqueeze(1)
-        else:
-            _, keys = self.sort_scores()
-            hyps = [self.get_hypothesis(k) for k in keys]
-            hyps = [[self.constants.BOS] + h for h in hyps]
-            dec_seq = torch.LongTensor(hyps)
-
-        return dec_seq
-
-    def get_hypothesis(self, k):
-        """ Walk back to construct the full hypothesis. """
-        hyp = []
-        for j in range(len(self.prev_ks) - 1, -1, -1):
-            hyp.append(self.next_ys[j+1][k])
-            k = self.prev_ks[j][k]
-
-        return list(map(lambda x: x.item(), hyp[::-1]))
diff --git a/backup/pdvc/modules/bert-base-uncased/bert_config.json b/backup/pdvc/modules/bert-base-uncased/bert_config.json
deleted file mode 100644
index fca794a5f07ff8f963fe8b61e3694b0fb7f955df..0000000000000000000000000000000000000000
--- a/backup/pdvc/modules/bert-base-uncased/bert_config.json
+++ /dev/null
@@ -1,13 +0,0 @@
-{
-  "attention_probs_dropout_prob": 0.1,
-  "hidden_act": "gelu",
-  "hidden_dropout_prob": 0.1,
-  "hidden_size": 768,
-  "initializer_range": 0.02,
-  "intermediate_size": 3072,
-  "max_position_embeddings": 512,
-  "num_attention_heads": 12,
-  "num_hidden_layers": 12,
-  "type_vocab_size": 2,
-  "vocab_size": 30522
-}
diff --git a/backup/pdvc/modules/bert-base-uncased/vocab.txt b/backup/pdvc/modules/bert-base-uncased/vocab.txt
deleted file mode 100644
index fb140275c155a9c7c5a3b3e0e77a9e839594a938..0000000000000000000000000000000000000000
--- a/backup/pdvc/modules/bert-base-uncased/vocab.txt
+++ /dev/null
@@ -1,30522 +0,0 @@
-[PAD]
-[unused0]
-[unused1]
-[unused2]
-[unused3]
-[unused4]
-[unused5]
-[unused6]
-[unused7]
-[unused8]
-[unused9]
-[unused10]
-[unused11]
-[unused12]
-[unused13]
-[unused14]
-[unused15]
-[unused16]
-[unused17]
-[unused18]
-[unused19]
-[unused20]
-[unused21]
-[unused22]
-[unused23]
-[unused24]
-[unused25]
-[unused26]
-[unused27]
-[unused28]
-[unused29]
-[unused30]
-[unused31]
-[unused32]
-[unused33]
-[unused34]
-[unused35]
-[unused36]
-[unused37]
-[unused38]
-[unused39]
-[unused40]
-[unused41]
-[unused42]
-[unused43]
-[unused44]
-[unused45]
-[unused46]
-[unused47]
-[unused48]
-[unused49]
-[unused50]
-[unused51]
-[unused52]
-[unused53]
-[unused54]
-[unused55]
-[unused56]
-[unused57]
-[unused58]
-[unused59]
-[unused60]
-[unused61]
-[unused62]
-[unused63]
-[unused64]
-[unused65]
-[unused66]
-[unused67]
-[unused68]
-[unused69]
-[unused70]
-[unused71]
-[unused72]
-[unused73]
-[unused74]
-[unused75]
-[unused76]
-[unused77]
-[unused78]
-[unused79]
-[unused80]
-[unused81]
-[unused82]
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-!
-"
-#
-$
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-appearance
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-45
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-35
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-80
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-3rd
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-1943
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-1956
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-1958
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-1959
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-1941
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-1940
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-1948
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-1950
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-33
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-1949
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-1960s
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-1951
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-1936
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-1920
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-##天
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-##宀
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-##安
-##宗
-##定
-##宣
-##宮
-##家
-##宿
-##寺
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-##小
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-##山
-##岡
-##島
-##崎
-##川
-##州
-##巿
-##帝
-##平
-##年
-##幸
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-##弘
-##張
-##彳
-##後
-##御
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-##心
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-##志
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-##我
-##戦
-##戸
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-##政
-##文
-##新
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-##日
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-##歌
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-##水
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-##瀬
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-##生
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-##禾
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-##野
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-##阝
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-##陳
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-##青
-##面
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-##香
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-##龍
-##龸
-##fi
-##fl
-##！
-##（
-##）
-##，
-##－
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-##／
-##：
-##？
-##～
diff --git a/backup/pdvc/modules/cross-base/cross_config.json b/backup/pdvc/modules/cross-base/cross_config.json
deleted file mode 100644
index 8a4807695d56a3aea97a55a9db97ba753e960748..0000000000000000000000000000000000000000
--- a/backup/pdvc/modules/cross-base/cross_config.json
+++ /dev/null
@@ -1,12 +0,0 @@
-{
-  "attention_probs_dropout_prob": 0.1,
-  "hidden_act": "gelu",
-  "hidden_dropout_prob": 0.1,
-  "hidden_size": 768,
-  "initializer_range": 0.02,
-  "intermediate_size": 3072,
-  "max_position_embeddings": 1024,
-  "num_attention_heads": 12,
-  "num_hidden_layers": 2,
-  "vocab_size": 768
-}
\ No newline at end of file
diff --git a/backup/pdvc/modules/decoder-base/decoder_config.json b/backup/pdvc/modules/decoder-base/decoder_config.json
deleted file mode 100644
index 91c46b63eba081afb28085a6d53f390ada5a5cfe..0000000000000000000000000000000000000000
--- a/backup/pdvc/modules/decoder-base/decoder_config.json
+++ /dev/null
@@ -1,14 +0,0 @@
-{
-  "attention_probs_dropout_prob": 0.1,
-  "hidden_act": "gelu",
-  "hidden_dropout_prob": 0.1,
-  "hidden_size": 768,
-  "initializer_range": 0.02,
-  "intermediate_size": 3072,
-  "num_attention_heads": 12,
-  "num_hidden_layers": 12,
-  "type_vocab_size": 2,
-  "vocab_size": 30522,
-  "num_decoder_layers": 1,
-  "max_target_embeddings": 512
-}
diff --git a/backup/pdvc/modules/file_utils.py b/backup/pdvc/modules/file_utils.py
deleted file mode 100644
index 43fa8ca87e20ee5333dd84a09795a743bbf3f183..0000000000000000000000000000000000000000
--- a/backup/pdvc/modules/file_utils.py
+++ /dev/null
@@ -1,239 +0,0 @@
-"""
-Utilities for working with the local dataset cache.
-This file is adapted from the AllenNLP library at https://github.com/allenai/allennlp
-Copyright by the AllenNLP authors.
-"""
-
-import os
-import logging
-import shutil
-import tempfile
-import json
-from urllib.parse import urlparse
-from pathlib import Path
-from typing import Optional, Tuple, Union, IO, Callable, Set
-from hashlib import sha256
-from functools import wraps
-
-from tqdm import tqdm
-
-import boto3
-from botocore.exceptions import ClientError
-import requests
-
-logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
-
-PYTORCH_PRETRAINED_BERT_CACHE = Path(os.getenv('PYTORCH_PRETRAINED_BERT_CACHE',
-                                               Path.home() / '.pytorch_pretrained_bert'))
-
-
-def url_to_filename(url: str, etag: str = None) -> str:
-    """
-    Convert `url` into a hashed filename in a repeatable way.
-    If `etag` is specified, append its hash to the url's, delimited
-    by a period.
-    """
-    url_bytes = url.encode('utf-8')
-    url_hash = sha256(url_bytes)
-    filename = url_hash.hexdigest()
-
-    if etag:
-        etag_bytes = etag.encode('utf-8')
-        etag_hash = sha256(etag_bytes)
-        filename += '.' + etag_hash.hexdigest()
-
-    return filename
-
-
-def filename_to_url(filename: str, cache_dir: Union[str, Path] = None) -> Tuple[str, str]:
-    """
-    Return the url and etag (which may be ``None``) stored for `filename`.
-    Raise ``FileNotFoundError`` if `filename` or its stored metadata do not exist.
-    """
-    if cache_dir is None:
-        cache_dir = PYTORCH_PRETRAINED_BERT_CACHE
-    if isinstance(cache_dir, Path):
-        cache_dir = str(cache_dir)
-
-    cache_path = os.path.join(cache_dir, filename)
-    if not os.path.exists(cache_path):
-        raise FileNotFoundError("file {} not found".format(cache_path))
-
-    meta_path = cache_path + '.json'
-    if not os.path.exists(meta_path):
-        raise FileNotFoundError("file {} not found".format(meta_path))
-
-    with open(meta_path) as meta_file:
-        metadata = json.load(meta_file)
-    url = metadata['url']
-    etag = metadata['etag']
-
-    return url, etag
-
-
-def cached_path(url_or_filename: Union[str, Path], cache_dir: Union[str, Path] = None) -> str:
-    """
-    Given something that might be a URL (or might be a local path),
-    determine which. If it's a URL, download the file and cache it, and
-    return the path to the cached file. If it's already a local path,
-    make sure the file exists and then return the path.
-    """
-    if cache_dir is None:
-        cache_dir = PYTORCH_PRETRAINED_BERT_CACHE
-    if isinstance(url_or_filename, Path):
-        url_or_filename = str(url_or_filename)
-    if isinstance(cache_dir, Path):
-        cache_dir = str(cache_dir)
-
-    parsed = urlparse(url_or_filename)
-
-    if parsed.scheme in ('http', 'https', 's3'):
-        # URL, so get it from the cache (downloading if necessary)
-        return get_from_cache(url_or_filename, cache_dir)
-    elif os.path.exists(url_or_filename):
-        # File, and it exists.
-        return url_or_filename
-    elif parsed.scheme == '':
-        # File, but it doesn't exist.
-        raise FileNotFoundError("file {} not found".format(url_or_filename))
-    else:
-        # Something unknown
-        raise ValueError("unable to parse {} as a URL or as a local path".format(url_or_filename))
-
-
-def split_s3_path(url: str) -> Tuple[str, str]:
-    """Split a full s3 path into the bucket name and path."""
-    parsed = urlparse(url)
-    if not parsed.netloc or not parsed.path:
-        raise ValueError("bad s3 path {}".format(url))
-    bucket_name = parsed.netloc
-    s3_path = parsed.path
-    # Remove '/' at beginning of path.
-    if s3_path.startswith("/"):
-        s3_path = s3_path[1:]
-    return bucket_name, s3_path
-
-
-def s3_request(func: Callable):
-    """
-    Wrapper function for s3 requests in order to create more helpful error
-    messages.
-    """
-
-    @wraps(func)
-    def wrapper(url: str, *args, **kwargs):
-        try:
-            return func(url, *args, **kwargs)
-        except ClientError as exc:
-            if int(exc.response["Error"]["Code"]) == 404:
-                raise FileNotFoundError("file {} not found".format(url))
-            else:
-                raise
-
-    return wrapper
-
-
-@s3_request
-def s3_etag(url: str) -> Optional[str]:
-    """Check ETag on S3 object."""
-    s3_resource = boto3.resource("s3")
-    bucket_name, s3_path = split_s3_path(url)
-    s3_object = s3_resource.Object(bucket_name, s3_path)
-    return s3_object.e_tag
-
-
-@s3_request
-def s3_get(url: str, temp_file: IO) -> None:
-    """Pull a file directly from S3."""
-    s3_resource = boto3.resource("s3")
-    bucket_name, s3_path = split_s3_path(url)
-    s3_resource.Bucket(bucket_name).download_fileobj(s3_path, temp_file)
-
-
-def http_get(url: str, temp_file: IO) -> None:
-    req = requests.get(url, stream=True)
-    content_length = req.headers.get('Content-Length')
-    total = int(content_length) if content_length is not None else None
-    progress = tqdm(unit="B", total=total)
-    for chunk in req.iter_content(chunk_size=1024):
-        if chunk: # filter out keep-alive new chunks
-            progress.update(len(chunk))
-            temp_file.write(chunk)
-    progress.close()
-
-
-def get_from_cache(url: str, cache_dir: Union[str, Path] = None) -> str:
-    """
-    Given a URL, look for the corresponding dataset in the local cache.
-    If it's not there, download it. Then return the path to the cached file.
-    """
-    if cache_dir is None:
-        cache_dir = PYTORCH_PRETRAINED_BERT_CACHE
-    if isinstance(cache_dir, Path):
-        cache_dir = str(cache_dir)
-
-    os.makedirs(cache_dir, exist_ok=True)
-
-    # Get eTag to add to filename, if it exists.
-    if url.startswith("s3://"):
-        etag = s3_etag(url)
-    else:
-        response = requests.head(url, allow_redirects=True)
-        if response.status_code != 200:
-            raise IOError("HEAD request failed for url {} with status code {}"
-                          .format(url, response.status_code))
-        etag = response.headers.get("ETag")
-
-    filename = url_to_filename(url, etag)
-
-    # get cache path to put the file
-    cache_path = os.path.join(cache_dir, filename)
-
-    if not os.path.exists(cache_path):
-        # Download to temporary file, then copy to cache dir once finished.
-        # Otherwise you get corrupt cache entries if the download gets interrupted.
-        with tempfile.NamedTemporaryFile() as temp_file:
-            logger.info("%s not found in cache, downloading to %s", url, temp_file.name)
-
-            # GET file object
-            if url.startswith("s3://"):
-                s3_get(url, temp_file)
-            else:
-                http_get(url, temp_file)
-
-            # we are copying the file before closing it, so flush to avoid truncation
-            temp_file.flush()
-            # shutil.copyfileobj() starts at the current position, so go to the start
-            temp_file.seek(0)
-
-            logger.info("copying %s to cache at %s", temp_file.name, cache_path)
-            with open(cache_path, 'wb') as cache_file:
-                shutil.copyfileobj(temp_file, cache_file)
-
-            logger.info("creating metadata file for %s", cache_path)
-            meta = {'url': url, 'etag': etag}
-            meta_path = cache_path + '.json'
-            with open(meta_path, 'w') as meta_file:
-                json.dump(meta, meta_file)
-
-            logger.info("removing temp file %s", temp_file.name)
-
-    return cache_path
-
-
-def read_set_from_file(filename: str) -> Set[str]:
-    '''
-    Extract a de-duped collection (set) of text from a file.
-    Expected file format is one item per line.
-    '''
-    collection = set()
-    with open(filename, 'r', encoding='utf-8') as file_:
-        for line in file_:
-            collection.add(line.rstrip())
-    return collection
-
-
-def get_file_extension(path: str, dot=True, lower: bool = True):
-    ext = os.path.splitext(path)[1]
-    ext = ext if dot else ext[1:]
-    return ext.lower() if lower else ext
diff --git a/backup/pdvc/modules/modeling.py b/backup/pdvc/modules/modeling.py
deleted file mode 100644
index 9551b488c16d04fad65dcdaeba7d73d7740f2902..0000000000000000000000000000000000000000
--- a/backup/pdvc/modules/modeling.py
+++ /dev/null
@@ -1,429 +0,0 @@
-# coding=utf-8
-# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
-# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""PyTorch BERT model."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import logging
-import numpy as np
-
-import torch
-from torch import nn
-import torch.nn.functional as F
-from torch.nn import CrossEntropyLoss, MSELoss
-
-from pdvc.modules.until_module import PreTrainedModel, LayerNorm, CrossEn, MILNCELoss, MaxMarginRankingLoss
-from pdvc.modules.module_bert import BertModel, BertConfig, BertOnlyMLMHead
-from pdvc.modules.module_visual import VisualModel, VisualConfig, VisualOnlyMLMHead
-from pdvc.modules.module_cross import CrossModel, CrossConfig
-from pdvc.modules.module_decoder import DecoderModel, DecoderConfig
-
-logger = logging.getLogger(__name__)
-
-
-class UniVLPreTrainedModel(PreTrainedModel, nn.Module):
-    """ An abstract class to handle weights initialization and
-        a simple interface for dowloading and loading pretrained models.
-    """
-    def __init__(self, bert_config, visual_config, cross_config, decoder_config, *inputs, **kwargs):
-        # utilize bert config as base config
-        super(UniVLPreTrainedModel, self).__init__(bert_config)
-        self.bert_config = bert_config
-        self.visual_config = visual_config
-        self.cross_config = cross_config
-        self.decoder_config = decoder_config
-
-        self.bert = None
-        self.visual = None
-        self.cross = None
-        self.decoder = None
-
-    @classmethod
-    def from_pretrained(cls, pretrained_bert_name, visual_model_name, cross_model_name, decoder_model_name,
-                        state_dict=None, cache_dir=None, type_vocab_size=2, *inputs, **kwargs):
-
-        task_config = None
-        if "task_config" in kwargs.keys():
-            task_config = kwargs["task_config"]
-            if not hasattr(task_config, "local_rank"):
-                task_config.__dict__["local_rank"] = 0
-            elif task_config.local_rank == -1:
-                task_config.local_rank = 0
-        print(pretrained_bert_name, cache_dir, type_vocab_size, state_dict, task_config)
-        bert_config, state_dict = BertConfig.get_config(pretrained_bert_name, cache_dir, type_vocab_size, state_dict, task_config=task_config)
-        visual_config, _ = VisualConfig.get_config(visual_model_name, cache_dir, type_vocab_size, state_dict=None, task_config=task_config)
-        cross_config, _ = CrossConfig.get_config(cross_model_name, cache_dir, type_vocab_size, state_dict=None, task_config=task_config)
-        decoder_config, _ = DecoderConfig.get_config(decoder_model_name, cache_dir, type_vocab_size, state_dict=None, task_config=task_config)
-
-        model = cls(bert_config, visual_config, cross_config, decoder_config, *inputs, **kwargs)
-
-        assert model.bert is not None
-        assert model.visual is not None
-
-        if state_dict is not None:
-            model = cls.init_preweight(model, state_dict, task_config=task_config)
-
-        return model
-
-class NormalizeVideo(nn.Module):
-    def __init__(self, task_config):
-        super(NormalizeVideo, self).__init__()
-        self.visual_norm2d = LayerNorm(task_config.video_dim)
-
-    def forward(self, video):
-        video = torch.as_tensor(video).float()
-        video = video.view(-1, video.shape[-2], video.shape[-1])
-        video = self.visual_norm2d(video)
-        return video
-
-def show_log(task_config, info):
-    if task_config is None or task_config.local_rank == 0:
-        logger.warning(info)
-
-def update_attr(target_name, target_config, target_attr_name, source_config, source_attr_name, default_value=None):
-    if hasattr(source_config, source_attr_name):
-        if default_value is None or getattr(source_config, source_attr_name) != default_value:
-            setattr(target_config, target_attr_name, getattr(source_config, source_attr_name))
-            show_log(source_config, "Set {}.{}: {}.".format(target_name,
-                                                            target_attr_name, getattr(target_config, target_attr_name)))
-    return target_config
-
-def check_attr(target_name, task_config):
-    return hasattr(task_config, target_name) and task_config.__dict__[target_name]
-
-class UniVL(UniVLPreTrainedModel):
-    def __init__(self, bert_config, visual_config, cross_config, decoder_config, task_config):
-        super(UniVL, self).__init__(bert_config, visual_config, cross_config, decoder_config)
-        self.task_config = task_config
-        self.ignore_video_index = -1
-
-        assert self.task_config.max_words <= bert_config.max_position_embeddings
-        assert self.task_config.max_words <= decoder_config.max_target_embeddings
-        assert self.task_config.max_frames <= visual_config.max_position_embeddings
-        assert self.task_config.max_words + self.task_config.max_frames <= cross_config.max_position_embeddings
-
-        self._stage_one = True
-        self._stage_two = False
-
-        if check_attr('stage_two', self.task_config):
-            self._stage_one = False
-            self._stage_two = self.task_config.stage_two
-        show_log(task_config, "Stage-One:{}, Stage-Two:{}".format(self._stage_one, self._stage_two))
-
-        self.train_sim_after_cross = False
-        if self._stage_one and check_attr('train_sim_after_cross', self.task_config):
-            self.train_sim_after_cross = True
-            show_log(task_config, "Test retrieval after cross encoder.")
-
-        # Text Encoder ===>
-        bert_config = update_attr("bert_config", bert_config, "num_hidden_layers",
-                                   self.task_config, "text_num_hidden_layers")
-        # print('=================The bert config:==========/n',bert_config)
-        # print('=================The task config:==========/n',self.task_config)
-        self.bert = BertModel(bert_config)
-        bert_word_embeddings_weight = self.bert.embeddings.word_embeddings.weight
-        bert_position_embeddings_weight = self.bert.embeddings.position_embeddings.weight
-        # <=== End of Text Encoder
-
-        # Video Encoder ===>
-        visual_config = update_attr("visual_config", visual_config, "num_hidden_layers",
-                                    self.task_config, "visual_num_hidden_layers")
-        self.visual = VisualModel(visual_config)
-        visual_word_embeddings_weight = self.visual.embeddings.word_embeddings.weight
-        # <=== End of Video Encoder
-
-        if self._stage_one is False or self.train_sim_after_cross:
-            # Cross Encoder ===>
-            cross_config = update_attr("cross_config", cross_config, "num_hidden_layers",
-                                        self.task_config, "cross_num_hidden_layers")
-            self.cross = CrossModel(cross_config) 
-            # <=== End of Cross Encoder
-
-            if self.train_sim_after_cross is False:
-                # Decoder ===>
-                decoder_config = update_attr("decoder_config", decoder_config, "num_decoder_layers",
-                                           self.task_config, "decoder_num_hidden_layers")
-                self.decoder = DecoderModel(decoder_config, bert_word_embeddings_weight, bert_position_embeddings_weight)
-                # <=== End of Decoder
-
-            if self.task_config.do_pretrain:
-                self.cls = BertOnlyMLMHead(bert_config, bert_word_embeddings_weight)
-                self.cls_visual = VisualOnlyMLMHead(visual_config, visual_word_embeddings_weight)
-                self.alm_loss_fct = CrossEntropyLoss(ignore_index=-1)
-                
-            self.similarity_dense = nn.Linear(bert_config.hidden_size, 1)
-            self.decoder_loss_fct = CrossEntropyLoss(ignore_index=-1)
-
-        self.normalize_video = NormalizeVideo(task_config)
-
-        mILNCELoss = MILNCELoss(batch_size=task_config.batch_size // task_config.n_gpu, n_pair=task_config.n_pair, )
-        maxMarginRankingLoss = MaxMarginRankingLoss(margin=task_config.margin,
-                                                    negative_weighting=task_config.negative_weighting,
-                                                    batch_size=task_config.batch_size // task_config.n_gpu,
-                                                    n_pair=task_config.n_pair,
-                                                    hard_negative_rate=task_config.hard_negative_rate, )
-
-        if task_config.use_mil:
-            self.loss_fct = CrossEn() if self._stage_two else mILNCELoss
-            self._pretrain_sim_loss_fct = mILNCELoss
-        else:
-            self.loss_fct = CrossEn() if self._stage_two else maxMarginRankingLoss
-            self._pretrain_sim_loss_fct = maxMarginRankingLoss
-
-        self.apply(self.init_weights)
-
-    def forward(self, input_ids, token_type_ids, attention_mask, video, video_mask=None,
-                pairs_masked_text=None, pairs_token_labels=None, masked_video=None, video_labels_index=None,
-                input_caption_ids=None, decoder_mask=None, output_caption_ids=None):
-
-        input_ids = input_ids.view(-1, input_ids.shape[-1])
-        token_type_ids = token_type_ids.view(-1, token_type_ids.shape[-1])
-        attention_mask = attention_mask.view(-1, attention_mask.shape[-1])
-        video_mask = video_mask.view(-1, video_mask.shape[-1])
-        video = self.normalize_video(video)
-
-        if input_caption_ids is not None:
-            input_caption_ids = input_caption_ids.view(-1, input_caption_ids.shape[-1])
-            decoder_mask = decoder_mask.view(-1, decoder_mask.shape[-1])
-
-        sequence_output, visual_output = self.get_sequence_visual_output(input_ids, token_type_ids, attention_mask,
-                                                                         video, video_mask, shaped=True)
-
-        if self.training:
-            loss = 0.
-            if self._stage_one:
-                sim_matrix = self.get_similarity_logits(sequence_output, visual_output, attention_mask,
-                                                        video_mask, shaped=True)
-                sim_loss = self.loss_fct(sim_matrix)
-                loss += sim_loss
-
-            if self._stage_two:
-                if self.task_config.do_pretrain:
-                    pairs_masked_text = pairs_masked_text.view(-1, pairs_masked_text.shape[-1])
-                    pairs_token_labels = pairs_token_labels.view(-1, pairs_token_labels.shape[-1])
-
-                    masked_video = self.normalize_video(masked_video)
-                    video_labels_index = video_labels_index.view(-1, video_labels_index.shape[-1])
-
-                    sequence_output_alm, visual_output_alm = self.get_sequence_visual_output(pairs_masked_text, token_type_ids,
-                                                                                             attention_mask, masked_video, video_mask, shaped=True)
-
-                    cross_output, pooled_output, concat_mask = self._get_cross_output(sequence_output_alm, visual_output_alm, attention_mask, video_mask)
-                    sequence_cross_output, visual_cross_output = torch.split(cross_output, [attention_mask.size(-1), video_mask.size(-1)], dim=1)
-
-                    alm_loss = self._calculate_mlm_loss(sequence_cross_output, pairs_token_labels)
-                    loss += alm_loss
-
-                    nce_loss = self._calculate_mfm_loss(visual_cross_output, video, video_mask, video_labels_index)
-                    loss += nce_loss
-
-                    sim_matrix = self.get_similarity_logits(sequence_output, visual_output, attention_mask, video_mask,
-                                                            shaped=True, _pretrain_joint=True)
-                    sim_loss_joint = self._pretrain_sim_loss_fct(sim_matrix)
-                    loss += sim_loss_joint
-
-                if (input_caption_ids is not None) and \
-                        (self.task_config.do_pretrain
-                         or (self.task_config.do_pretrain is False and self.task_config.task_type == "caption")):
-                    if self.task_config.do_pretrain:
-                        decoder_scores, res_tuples = self._get_decoder_score(sequence_output_alm, visual_output_alm,
-                                                                             input_ids, attention_mask, video_mask,
-                                                                             input_caption_ids, decoder_mask, shaped=True)
-                    elif self.task_config.task_type == "caption":
-                        decoder_scores, res_tuples = self._get_decoder_score(sequence_output, visual_output,
-                                                                             input_ids, attention_mask, video_mask,
-                                                                             input_caption_ids, decoder_mask, shaped=True)
-                    else:
-                        raise NotImplementedError
-
-                    output_caption_ids = output_caption_ids.view(-1, output_caption_ids.shape[-1])
-                    decoder_loss = self.decoder_loss_fct(decoder_scores.view(-1, self.bert_config.vocab_size), output_caption_ids.view(-1))
-                    loss += decoder_loss
-
-                if self.task_config.do_pretrain or self.task_config.task_type == "retrieval":
-                    if self.task_config.do_pretrain:
-                        sim_matrix_text_visual = self.get_similarity_logits(sequence_output_alm, visual_output_alm,
-                                                                            attention_mask, video_mask, shaped=True)
-                    elif self.task_config.task_type == "retrieval":
-                        sim_matrix_text_visual = self.get_similarity_logits(sequence_output, visual_output,
-                                                                            attention_mask, video_mask, shaped=True)
-                    else:
-                        raise NotImplementedError
-
-                    sim_loss_text_visual = self.loss_fct(sim_matrix_text_visual)
-                    loss += sim_loss_text_visual
-
-            return loss
-        else:
-            return None
-
-    def _calculate_mlm_loss(self, sequence_output_alm, pairs_token_labels):
-        alm_scores = self.cls(sequence_output_alm)
-        alm_loss = self.alm_loss_fct(alm_scores.view(-1, self.bert_config.vocab_size), pairs_token_labels.view(-1))
-        return alm_loss
-
-    def _calculate_mfm_loss(self, visual_output_alm, video, video_mask, video_labels_index):
-        afm_scores = self.cls_visual(visual_output_alm)
-        afm_scores_tr = afm_scores.view(-1, afm_scores.shape[-1])
-
-        video_tr = video.permute(2, 0, 1)
-        video_tr = video_tr.view(video_tr.shape[0], -1)
-
-        logits_matrix = torch.mm(afm_scores_tr, video_tr)
-        video_mask_float = video_mask.to(dtype=torch.float)
-        mask_matrix = torch.mm(video_mask_float.view(-1, 1), video_mask_float.view(1, -1))
-        masked_logits = logits_matrix + (1. - mask_matrix) * -1e8
-
-        logpt = F.log_softmax(masked_logits, dim=-1)
-        logpt = torch.diag(logpt)
-        nce_loss = -logpt
-
-        video_labels_index_mask = (video_labels_index != self.ignore_video_index)
-        nce_loss = nce_loss.masked_select(video_labels_index_mask.view(-1))
-        nce_loss = nce_loss.mean()
-        return nce_loss
-
-    def get_sequence_visual_output(self, input_ids, token_type_ids, attention_mask, video, video_mask, shaped=False):
-        if shaped is False:
-            input_ids = input_ids.view(-1, input_ids.shape[-1])
-            token_type_ids = token_type_ids.view(-1, token_type_ids.shape[-1])
-            attention_mask = attention_mask.view(-1, attention_mask.shape[-1])
-            video_mask = video_mask.view(-1, video_mask.shape[-1])
-            video = self.normalize_video(video)
-        encoded_layers, _ = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=True)
-        sequence_output = encoded_layers[-1]
-
-        visual_layers, _ = self.visual(video, video_mask, output_all_encoded_layers=True)
-        visual_output = visual_layers[-1]
-
-        return sequence_output, visual_output
-
-    def _get_cross_output(self, sequence_output, visual_output, attention_mask, video_mask):
-        concat_features = torch.cat((sequence_output, visual_output), dim=1)  # concatnate tokens and frames
-        concat_mask = torch.cat((attention_mask, video_mask), dim=1)
-        text_type_ = torch.zeros_like(attention_mask)
-        video_type_ = torch.ones_like(video_mask)
-        concat_type = torch.cat((text_type_, video_type_), dim=1)
-
-        cross_layers, pooled_output = self.cross(concat_features, concat_type, concat_mask, output_all_encoded_layers=True)
-        cross_output = cross_layers[-1]
-
-        return cross_output, pooled_output, concat_mask
-
-    def _mean_pooling_for_similarity(self, sequence_output, visual_output, attention_mask, video_mask,):
-        attention_mask_un = attention_mask.to(dtype=torch.float).unsqueeze(-1)
-        attention_mask_un[:, 0, :] = 0.
-        sequence_output = sequence_output * attention_mask_un
-        text_out = torch.sum(sequence_output, dim=1) / torch.sum(attention_mask_un, dim=1, dtype=torch.float)
-
-        video_mask_un = video_mask.to(dtype=torch.float).unsqueeze(-1)
-        visual_output = visual_output * video_mask_un
-        video_mask_un_sum = torch.sum(video_mask_un, dim=1, dtype=torch.float)
-        video_mask_un_sum[video_mask_un_sum == 0.] = 1.
-        video_out = torch.sum(visual_output, dim=1) / video_mask_un_sum
-
-        return text_out, video_out
-
-    def _cross_similarity(self, sequence_output, visual_output, attention_mask, video_mask):
-        b_text, s_text, h_text = sequence_output.size()
-        b_visual, s_visual, h_visual = visual_output.size()
-
-        retrieve_logits_list = []
-        step_size = 5
-
-        split_size = [step_size] * (b_text // step_size)
-        release_size = b_text - sum(split_size)
-        if release_size > 0:
-            split_size += [release_size]
-
-        sequence_output_splits = torch.split(sequence_output, split_size, dim=0)
-        attention_mask_splits = torch.split(attention_mask, split_size, dim=0)
-        for i in range(len(split_size)):
-            sequence_output_row = sequence_output_splits[i]
-            attention_mask_row = attention_mask_splits[i]
-            sequence_output_l = sequence_output_row.unsqueeze(1).repeat(1, b_visual, 1, 1)
-            sequence_output_l = sequence_output_l.view(-1, s_text, h_text)
-            attention_mask_l = attention_mask_row.unsqueeze(1).repeat(1, b_visual, 1)
-            attention_mask_l = attention_mask_l.view(-1, s_text)
-
-            step_truth = sequence_output_row.size(0)
-            visual_output_r = visual_output.unsqueeze(0).repeat(step_truth, 1, 1, 1)
-            visual_output_r = visual_output_r.view(-1, s_visual, h_visual)
-            video_mask_r = video_mask.unsqueeze(0).repeat(step_truth, 1, 1)
-            video_mask_r = video_mask_r.view(-1, s_visual)
-
-            cross_output, pooled_output, concat_mask = \
-                self._get_cross_output(sequence_output_l, visual_output_r, attention_mask_l, video_mask_r)
-            retrieve_logits_row = self.similarity_dense(pooled_output).squeeze(-1).view(step_truth, b_visual)
-
-            retrieve_logits_list.append(retrieve_logits_row)
-        retrieve_logits = torch.cat(retrieve_logits_list, dim=0)
-        return retrieve_logits
-
-    def get_similarity_logits(self, sequence_output, visual_output, attention_mask, video_mask, shaped=False, _pretrain_joint=False):
-        if shaped is False:
-            attention_mask = attention_mask.view(-1, attention_mask.shape[-1])
-            video_mask = video_mask.view(-1, video_mask.shape[-1])
-
-        if (self._stage_two and _pretrain_joint is False) or self.train_sim_after_cross:
-            retrieve_logits = self._cross_similarity(sequence_output, visual_output, attention_mask, video_mask)
-        else:
-            text_out, video_out = self._mean_pooling_for_similarity(sequence_output, visual_output, attention_mask, video_mask)
-            if self.task_config.use_mil is False:
-                text_out = F.normalize(text_out, dim=-1)
-                video_out = F.normalize(video_out, dim=-1)
-            retrieve_logits = torch.matmul(text_out, video_out.t())
-
-        return retrieve_logits
-
-    def _get_decoder_score(self, sequence_output, visual_output, input_ids, attention_mask, video_mask, input_caption_ids, decoder_mask, shaped=False):
-
-        if shaped is False:
-            input_ids = input_ids.view(-1, input_ids.shape[-1])
-            attention_mask = attention_mask.view(-1, attention_mask.shape[-1])
-            video_mask = video_mask.view(-1, video_mask.shape[-1])
-
-            input_caption_ids = input_caption_ids.view(-1, input_caption_ids.shape[-1])
-            decoder_mask = decoder_mask.view(-1, decoder_mask.shape[-1])
-
-        res_tuples = ()
-        cross_output, pooled_output, concat_mask = self._get_cross_output(sequence_output, visual_output, attention_mask, video_mask)
-        decoder_scores = self.decoder(input_caption_ids, encoder_outs=cross_output, answer_mask=decoder_mask, encoder_mask=concat_mask)
-
-        return decoder_scores, res_tuples
-
-    def decoder_caption(self, sequence_output, visual_output, input_ids, attention_mask, video_mask, input_caption_ids, decoder_mask,
-                        shaped=False, get_logits=False):
-        if shaped is False:
-            input_ids = input_ids.view(-1, input_ids.shape[-1])
-            attention_mask = attention_mask.view(-1, attention_mask.shape[-1])
-            video_mask = video_mask.view(-1, video_mask.shape[-1])
-
-            input_caption_ids = input_caption_ids.view(-1, input_caption_ids.shape[-1])
-            decoder_mask = decoder_mask.view(-1, decoder_mask.shape[-1])
-
-        decoder_scores, _ = self._get_decoder_score(sequence_output, visual_output,
-                                                    input_ids, attention_mask, video_mask,
-                                                    input_caption_ids, decoder_mask, shaped=True)
-
-        if get_logits:
-            return decoder_scores
-
-        _, decoder_scores_result = torch.max(decoder_scores, -1)
-
-        return decoder_scores_result
\ No newline at end of file
diff --git a/backup/pdvc/modules/module_bert.py b/backup/pdvc/modules/module_bert.py
deleted file mode 100644
index aa376657fdf271f11978379665a67897c2cc5943..0000000000000000000000000000000000000000
--- a/backup/pdvc/modules/module_bert.py
+++ /dev/null
@@ -1,447 +0,0 @@
-# coding=utf-8
-# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
-# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""PyTorch BERT model."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import os
-import copy
-import json
-import math
-import logging
-import tarfile
-import tempfile
-import shutil
-
-import torch
-from torch import nn
-import torch.nn.functional as F
-from .file_utils import cached_path
-from .until_config import PretrainedConfig
-from .until_module import PreTrainedModel, LayerNorm, ACT2FN
-
-logger = logging.getLogger(__name__)
-
-PRETRAINED_MODEL_ARCHIVE_MAP = {
-    'bert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased.tar.gz",
-    'bert-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased.tar.gz",
-    'bert-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased.tar.gz",
-    'bert-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased.tar.gz",
-    'bert-base-multilingual-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased.tar.gz",
-    'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased.tar.gz",
-    'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese.tar.gz",
-}
-
-CONFIG_NAME = 'bert_config.json'
-WEIGHTS_NAME = 'pytorch_model.bin'
-
-
-class BertConfig(PretrainedConfig):
-    """Configuration class to store the configuration of a `BertModel`.
-    """
-    pretrained_model_archive_map = PRETRAINED_MODEL_ARCHIVE_MAP
-    config_name = CONFIG_NAME
-    weights_name = WEIGHTS_NAME
-
-    def __init__(self,
-                 vocab_size_or_config_json_file,
-                 hidden_size=768,
-                 num_hidden_layers=12,
-                 num_attention_heads=12,
-                 intermediate_size=3072,
-                 hidden_act="gelu",
-                 hidden_dropout_prob=0.1,
-                 attention_probs_dropout_prob=0.1,
-                 max_position_embeddings=512,
-                 type_vocab_size=2,
-                 initializer_range=0.02):
-        """Constructs BertConfig.
-
-        Args:
-            vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `BertModel`.
-            hidden_size: Size of the encoder layers and the pooler layer.
-            num_hidden_layers: Number of hidden layers in the Transformer encoder.
-            num_attention_heads: Number of attention heads for each attention layer in
-                the Transformer encoder.
-            intermediate_size: The size of the "intermediate" (i.e., feed-forward)
-                layer in the Transformer encoder.
-            hidden_act: The non-linear activation function (function or string) in the
-                encoder and pooler. If string, "gelu", "relu" and "swish" are supported.
-            hidden_dropout_prob: The dropout probabilitiy for all fully connected
-                layers in the embeddings, encoder, and pooler.
-            attention_probs_dropout_prob: The dropout ratio for the attention
-                probabilities.
-            max_position_embeddings: The maximum sequence length that this model might
-                ever be used with. Typically set this to something large just in case
-                (e.g., 512 or 1024 or 2048).
-            type_vocab_size: The vocabulary size of the `token_type_ids` passed into
-                `BertModel`.
-            initializer_range: The sttdev of the truncated_normal_initializer for
-                initializing all weight matrices.
-        """
-        if isinstance(vocab_size_or_config_json_file, str):
-            with open(vocab_size_or_config_json_file, "r", encoding='utf-8') as reader:
-                json_config = json.loads(reader.read())
-            for key, value in json_config.items():
-                self.__dict__[key] = value
-        elif isinstance(vocab_size_or_config_json_file, int):
-            self.vocab_size = vocab_size_or_config_json_file
-            self.hidden_size = hidden_size
-            self.num_hidden_layers = num_hidden_layers
-            self.num_attention_heads = num_attention_heads
-            self.hidden_act = hidden_act
-            self.intermediate_size = intermediate_size
-            self.hidden_dropout_prob = hidden_dropout_prob
-            self.attention_probs_dropout_prob = attention_probs_dropout_prob
-            self.max_position_embeddings = max_position_embeddings
-            self.type_vocab_size = type_vocab_size
-            self.initializer_range = initializer_range
-        else:
-            raise ValueError("First argument must be either a vocabulary size (int)"
-                             "or the path to a pretrained model config file (str)")
-
-class BertEmbeddings(nn.Module):
-    """Construct the embeddings from word, position and token_type embeddings.
-    """
-    def __init__(self, config):
-        super(BertEmbeddings, self).__init__()
-        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size)
-        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
-        self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
-
-        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
-        # any TensorFlow checkpoint file
-        self.LayerNorm = LayerNorm(config.hidden_size, eps=1e-12)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-    def forward(self, input_ids, token_type_ids=None):
-        seq_length = input_ids.size(1)
-        position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device)
-        position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
-        if token_type_ids is None:
-            token_type_ids = torch.zeros_like(input_ids)
-
-        words_embeddings = self.word_embeddings(input_ids)
-        position_embeddings = self.position_embeddings(position_ids)
-        token_type_embeddings = self.token_type_embeddings(token_type_ids)
-
-        embeddings = words_embeddings + position_embeddings + token_type_embeddings
-        embeddings = self.LayerNorm(embeddings)
-        embeddings = self.dropout(embeddings)
-        return embeddings
-
-
-class BertSelfAttention(nn.Module):
-    def __init__(self, config):
-        super(BertSelfAttention, self).__init__()
-        if config.hidden_size % config.num_attention_heads != 0:
-            raise ValueError(
-                "The hidden size (%d) is not a multiple of the number of attention "
-                "heads (%d)" % (config.hidden_size, config.num_attention_heads))
-        self.num_attention_heads = config.num_attention_heads
-        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
-        self.all_head_size = self.num_attention_heads * self.attention_head_size
-
-        self.query = nn.Linear(config.hidden_size, self.all_head_size)
-        self.key = nn.Linear(config.hidden_size, self.all_head_size)
-        self.value = nn.Linear(config.hidden_size, self.all_head_size)
-
-        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
-
-    def transpose_for_scores(self, x):
-        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
-        x = x.view(*new_x_shape)
-        return x.permute(0, 2, 1, 3)
-
-    def forward(self, hidden_states, attention_mask):
-        mixed_query_layer = self.query(hidden_states)
-        mixed_key_layer = self.key(hidden_states)
-        mixed_value_layer = self.value(hidden_states)
-
-        query_layer = self.transpose_for_scores(mixed_query_layer)
-        key_layer = self.transpose_for_scores(mixed_key_layer)
-        value_layer = self.transpose_for_scores(mixed_value_layer)
-
-        # Take the dot product between "query" and "key" to get the raw attention scores.
-        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
-        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
-        # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
-        attention_scores = attention_scores + attention_mask
-
-        # Normalize the attention scores to probabilities.
-        attention_probs = nn.Softmax(dim=-1)(attention_scores)
-
-        # This is actually dropping out entire tokens to attend to, which might
-        # seem a bit unusual, but is taken from the original Transformer paper.
-        attention_probs = self.dropout(attention_probs)
-
-        context_layer = torch.matmul(attention_probs, value_layer)
-        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
-        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
-        context_layer = context_layer.view(*new_context_layer_shape)
-        return context_layer
-
-
-class BertSelfOutput(nn.Module):
-    def __init__(self, config):
-        super(BertSelfOutput, self).__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.LayerNorm = LayerNorm(config.hidden_size, eps=1e-12)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-    def forward(self, hidden_states, input_tensor):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states + input_tensor)
-        return hidden_states
-
-
-class BertAttention(nn.Module):
-    def __init__(self, config):
-        super(BertAttention, self).__init__()
-        self.self = BertSelfAttention(config)
-        self.output = BertSelfOutput(config)
-
-    def forward(self, input_tensor, attention_mask):
-        self_output = self.self(input_tensor, attention_mask)
-        attention_output = self.output(self_output, input_tensor)
-        return attention_output
-
-
-class BertIntermediate(nn.Module):
-    def __init__(self, config):
-        super(BertIntermediate, self).__init__()
-        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
-        self.intermediate_act_fn = ACT2FN[config.hidden_act] \
-            if isinstance(config.hidden_act, str) else config.hidden_act
-
-    def forward(self, hidden_states):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.intermediate_act_fn(hidden_states)
-        return hidden_states
-
-
-class BertOutput(nn.Module):
-    def __init__(self, config):
-        super(BertOutput, self).__init__()
-        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
-        self.LayerNorm = LayerNorm(config.hidden_size, eps=1e-12)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-    def forward(self, hidden_states, input_tensor):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states + input_tensor)
-        return hidden_states
-
-
-class BertLayer(nn.Module):
-    def __init__(self, config):
-        super(BertLayer, self).__init__()
-        self.attention = BertAttention(config)
-        self.intermediate = BertIntermediate(config)
-        self.output = BertOutput(config)
-
-    def forward(self, hidden_states, attention_mask):
-        attention_output = self.attention(hidden_states, attention_mask)
-        intermediate_output = self.intermediate(attention_output)
-        layer_output = self.output(intermediate_output, attention_output)
-        return layer_output
-
-
-class BertEncoder(nn.Module):
-    def __init__(self, config):
-        super(BertEncoder, self).__init__()
-        layer = BertLayer(config)
-        self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(config.num_hidden_layers)])
-
-    def forward(self, hidden_states, attention_mask, output_all_encoded_layers=True):
-        all_encoder_layers = []
-        for layer_module in self.layer:
-            hidden_states = layer_module(hidden_states, attention_mask)
-            if output_all_encoded_layers:
-                all_encoder_layers.append(hidden_states)
-        if not output_all_encoded_layers:
-            all_encoder_layers.append(hidden_states)
-        return all_encoder_layers
-
-
-class BertPooler(nn.Module):
-    def __init__(self, config):
-        super(BertPooler, self).__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.activation = nn.Tanh()
-
-    def forward(self, hidden_states):
-        # We "pool" the model by simply taking the hidden state corresponding
-        # to the first token.
-        first_token_tensor = hidden_states[:, 0]
-        pooled_output = self.dense(first_token_tensor)
-        pooled_output = self.activation(pooled_output)
-        return pooled_output
-
-
-class BertPredictionHeadTransform(nn.Module):
-    def __init__(self, config):
-        super(BertPredictionHeadTransform, self).__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.transform_act_fn = ACT2FN[config.hidden_act] \
-            if isinstance(config.hidden_act, str) else config.hidden_act
-        self.LayerNorm = LayerNorm(config.hidden_size, eps=1e-12)
-
-    def forward(self, hidden_states):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.transform_act_fn(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states)
-        return hidden_states
-
-
-class BertLMPredictionHead(nn.Module):
-    def __init__(self, config, bert_model_embedding_weights):
-        super(BertLMPredictionHead, self).__init__()
-        self.transform = BertPredictionHeadTransform(config)
-
-        # The output weights are the same as the input embeddings, but there is
-        # an output-only bias for each token.
-        self.decoder = nn.Linear(bert_model_embedding_weights.size(1),
-                                 bert_model_embedding_weights.size(0),
-                                 bias=False)
-        self.decoder.weight = bert_model_embedding_weights
-        self.bias = nn.Parameter(torch.zeros(bert_model_embedding_weights.size(0)))
-
-    def forward(self, hidden_states):
-        hidden_states = self.transform(hidden_states)
-        hidden_states = self.decoder(hidden_states) + self.bias
-        return hidden_states
-
-
-class BertOnlyMLMHead(nn.Module):
-    def __init__(self, config, bert_model_embedding_weights):
-        super(BertOnlyMLMHead, self).__init__()
-        self.predictions = BertLMPredictionHead(config, bert_model_embedding_weights)
-
-    def forward(self, sequence_output):
-        prediction_scores = self.predictions(sequence_output)
-        return prediction_scores
-
-
-class BertOnlyNSPHead(nn.Module):
-    def __init__(self, config):
-        super(BertOnlyNSPHead, self).__init__()
-        self.seq_relationship = nn.Linear(config.hidden_size, 2)
-
-    def forward(self, pooled_output):
-        seq_relationship_score = self.seq_relationship(pooled_output)
-        return seq_relationship_score
-
-
-class BertPreTrainingHeads(nn.Module):
-    def __init__(self, config, bert_model_embedding_weights):
-        super(BertPreTrainingHeads, self).__init__()
-        self.predictions = BertLMPredictionHead(config, bert_model_embedding_weights)
-        self.seq_relationship = nn.Linear(config.hidden_size, 2)
-
-    def forward(self, sequence_output, pooled_output):
-        prediction_scores = self.predictions(sequence_output)
-        seq_relationship_score = self.seq_relationship(pooled_output)
-        return prediction_scores, seq_relationship_score
-
-class BertModel(PreTrainedModel):
-    """BERT model ("Bidirectional Embedding Representations from a Transformer").
-
-    Params:
-        config: a BertConfig class instance with the configuration to build a new model
-
-    Inputs:
-        `type`: a str, indicates which masking will be used in the attention, choice from [`bi`, `seq`, `gen`]
-        `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
-            with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
-            `extract_features.py`, `run_classifier.py` and `run_squad.py`)
-        `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
-            types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
-            a `sentence B` token (see BERT paper for more details).
-        `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
-            selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
-            input sequence length in the current batch. It's the mask that we typically use for attention when
-            a batch has varying length sentences.
-        `output_all_encoded_layers`: boolean which controls the content of the `encoded_layers` output as described below. Default: `True`.
-
-    Outputs: Tuple of (encoded_layers, pooled_output)
-        `encoded_layers`: controled by `output_all_encoded_layers` argument:
-            - `output_all_encoded_layers=True`: outputs a list of the full sequences of encoded-hidden-states at the end
-                of each attention block (i.e. 12 full sequences for BERT-base, 24 for BERT-large), each
-                encoded-hidden-state is a torch.FloatTensor of size [batch_size, sequence_length, hidden_size],
-            - `output_all_encoded_layers=False`: outputs only the full sequence of hidden-states corresponding
-                to the last attention block of shape [batch_size, sequence_length, hidden_size],
-        `pooled_output`: a torch.FloatTensor of size [batch_size, hidden_size] which is the output of a
-            classifier pretrained on top of the hidden state associated to the first character of the
-            input (`CLF`) to train on the Next-Sentence task (see BERT's paper).
-
-    Example usage:
-    ```python
-    # Already been converted into WordPiece token ids
-    input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
-    input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
-    token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
-
-    config = modeling.BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
-        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
-
-    model = modeling.BertModel(config=config)
-    all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
-    ```
-    """
-    def __init__(self, config):
-        super(BertModel, self).__init__(config)
-        self.embeddings = BertEmbeddings(config)
-        self.encoder = BertEncoder(config)
-        self.pooler = BertPooler(config)
-        self.apply(self.init_weights)
-
-
-    def forward(self, input_ids, token_type_ids=None, attention_mask=None, output_all_encoded_layers=True):
-
-        if attention_mask is None:
-            attention_mask = torch.ones_like(input_ids)
-        if token_type_ids is None:
-            token_type_ids = torch.zeros_like(input_ids)
-
-        # We create a 3D attention mask from a 2D tensor mask.
-        # Sizes are [batch_size, 1, 1, to_seq_length]
-        # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
-        # this attention mask is more simple than the triangular masking of causal attention
-        # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
-        extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
-
-        # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
-        # masked positions, this operation will create a tensor which is 0.0 for
-        # positions we want to attend and -10000.0 for masked positions.
-        # Since we are adding it to the raw scores before the softmax, this is
-        # effectively the same as removing these entirely.
-        extended_attention_mask = extended_attention_mask.to(dtype=self.dtype) # fp16 compatibility
-        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
-
-        embedding_output = self.embeddings(input_ids, token_type_ids)
-        encoded_layers = self.encoder(embedding_output,
-                                      extended_attention_mask,
-                                      output_all_encoded_layers=output_all_encoded_layers)
-        sequence_output = encoded_layers[-1]
-        pooled_output = self.pooler(sequence_output)
-        if not output_all_encoded_layers:
-            encoded_layers = encoded_layers[-1]
-        return encoded_layers, pooled_output
\ No newline at end of file
diff --git a/backup/pdvc/modules/module_cross.py b/backup/pdvc/modules/module_cross.py
deleted file mode 100644
index 8ff41910a2c62e1c79ab3f843bef3c54171bb026..0000000000000000000000000000000000000000
--- a/backup/pdvc/modules/module_cross.py
+++ /dev/null
@@ -1,394 +0,0 @@
-# coding=utf-8
-# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
-# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""PyTorch BERT model."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import os
-import copy
-import json
-import math
-import logging
-import tarfile
-import tempfile
-import shutil
-
-import torch
-from torch import nn
-import torch.nn.functional as F
-from .file_utils import cached_path
-from .until_config import PretrainedConfig
-from .until_module import PreTrainedModel, LayerNorm, ACT2FN
-
-logger = logging.getLogger(__name__)
-
-PRETRAINED_MODEL_ARCHIVE_MAP = {}
-CONFIG_NAME = 'cross_config.json'
-WEIGHTS_NAME = 'cross_pytorch_model.bin'
-
-
-class CrossConfig(PretrainedConfig):
-    """Configuration class to store the configuration of a `CrossModel`.
-    """
-    pretrained_model_archive_map = PRETRAINED_MODEL_ARCHIVE_MAP
-    config_name = CONFIG_NAME
-    weights_name = WEIGHTS_NAME
-    def __init__(self,
-                 vocab_size_or_config_json_file,
-                 hidden_size=768,
-                 num_hidden_layers=12,
-                 num_attention_heads=12,
-                 intermediate_size=3072,
-                 hidden_act="gelu",
-                 hidden_dropout_prob=0.1,
-                 attention_probs_dropout_prob=0.1,
-                 max_position_embeddings=512,
-                 type_vocab_size=2,
-                 initializer_range=0.02):
-        """Constructs CrossConfig.
-
-        Args:
-            vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `CrossModel`.
-            hidden_size: Size of the encoder layers and the pooler layer.
-            num_hidden_layers: Number of hidden layers in the Transformer encoder.
-            num_attention_heads: Number of attention heads for each attention layer in
-                the Transformer encoder.
-            intermediate_size: The size of the "intermediate" (i.e., feed-forward)
-                layer in the Transformer encoder.
-            hidden_act: The non-linear activation function (function or string) in the
-                encoder and pooler. If string, "gelu", "relu" and "swish" are supported.
-            hidden_dropout_prob: The dropout probabilitiy for all fully connected
-                layers in the embeddings, encoder, and pooler.
-            attention_probs_dropout_prob: The dropout ratio for the attention
-                probabilities.
-            max_position_embeddings: The maximum sequence length that this model might
-                ever be used with. Typically set this to something large just in case
-                (e.g., 512 or 1024 or 2048).
-            type_vocab_size: The vocabulary size of the `token_type_ids` passed into
-                `CrossModel`.
-            initializer_range: The sttdev of the truncated_normal_initializer for
-                initializing all weight matrices.
-        """
-        if isinstance(vocab_size_or_config_json_file, str):
-            with open(vocab_size_or_config_json_file, "r", encoding='utf-8') as reader:
-                json_config = json.loads(reader.read())
-            for key, value in json_config.items():
-                self.__dict__[key] = value
-        elif isinstance(vocab_size_or_config_json_file, int):
-            self.vocab_size = vocab_size_or_config_json_file
-            self.hidden_size = hidden_size
-            self.num_hidden_layers = num_hidden_layers
-            self.num_attention_heads = num_attention_heads
-            self.hidden_act = hidden_act
-            self.intermediate_size = intermediate_size
-            self.hidden_dropout_prob = hidden_dropout_prob
-            self.attention_probs_dropout_prob = attention_probs_dropout_prob
-            self.max_position_embeddings = max_position_embeddings
-            self.type_vocab_size = type_vocab_size
-            self.initializer_range = initializer_range
-        else:
-            raise ValueError("First argument must be either a vocabulary size (int)"
-                             "or the path to a pretrained model config file (str)")
-
-
-class CrossEmbeddings(nn.Module):
-    """Construct the embeddings from word, position and token_type embeddings.
-    """
-    def __init__(self, config):
-        super(CrossEmbeddings, self).__init__()
-
-        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
-        self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
-
-        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
-        # any TensorFlow checkpoint file
-        self.LayerNorm = LayerNorm(config.hidden_size, eps=1e-12)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-    def forward(self, concat_embeddings, concat_type=None):
-
-        batch_size, seq_length = concat_embeddings.size(0), concat_embeddings.size(1)
-        if concat_type is None:
-            concat_type = torch.zeros(batch_size, concat_type).to(concat_embeddings.device)
-
-        position_ids = torch.arange(seq_length, dtype=torch.long, device=concat_embeddings.device)
-        position_ids = position_ids.unsqueeze(0).expand(concat_embeddings.size(0), -1)
-
-        token_type_embeddings = self.token_type_embeddings(concat_type)
-        position_embeddings = self.position_embeddings(position_ids)
-
-        embeddings = concat_embeddings + position_embeddings + token_type_embeddings
-        embeddings = self.LayerNorm(embeddings)
-        embeddings = self.dropout(embeddings)
-        return embeddings
-
-class CrossSelfAttention(nn.Module):
-    def __init__(self, config):
-        super(CrossSelfAttention, self).__init__()
-        if config.hidden_size % config.num_attention_heads != 0:
-            raise ValueError(
-                "The hidden size (%d) is not a multiple of the number of attention "
-                "heads (%d)" % (config.hidden_size, config.num_attention_heads))
-        self.num_attention_heads = config.num_attention_heads
-        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
-        self.all_head_size = self.num_attention_heads * self.attention_head_size
-
-        self.query = nn.Linear(config.hidden_size, self.all_head_size)
-        self.key = nn.Linear(config.hidden_size, self.all_head_size)
-        self.value = nn.Linear(config.hidden_size, self.all_head_size)
-
-        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
-
-    def transpose_for_scores(self, x):
-        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
-        x = x.view(*new_x_shape)
-        return x.permute(0, 2, 1, 3)
-
-    def forward(self, hidden_states, attention_mask):
-        mixed_query_layer = self.query(hidden_states)
-        mixed_key_layer = self.key(hidden_states)
-        mixed_value_layer = self.value(hidden_states)
-
-        query_layer = self.transpose_for_scores(mixed_query_layer)
-        key_layer = self.transpose_for_scores(mixed_key_layer)
-        value_layer = self.transpose_for_scores(mixed_value_layer)
-
-        # Take the dot product between "query" and "key" to get the raw attention scores.
-        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
-        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
-        # Apply the attention mask is (precomputed for all layers in CrossModel forward() function)
-        attention_scores = attention_scores + attention_mask
-
-        # Normalize the attention scores to probabilities.
-        attention_probs = nn.Softmax(dim=-1)(attention_scores)
-
-        # This is actually dropping out entire tokens to attend to, which might
-        # seem a bit unusual, but is taken from the original Transformer paper.
-        attention_probs = self.dropout(attention_probs)
-
-        context_layer = torch.matmul(attention_probs, value_layer)
-        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
-        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
-        context_layer = context_layer.view(*new_context_layer_shape)
-        return context_layer
-
-
-class CrossSelfOutput(nn.Module):
-    def __init__(self, config):
-        super(CrossSelfOutput, self).__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.LayerNorm = LayerNorm(config.hidden_size, eps=1e-12)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-    def forward(self, hidden_states, input_tensor):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states + input_tensor)
-        return hidden_states
-
-
-class CrossAttention(nn.Module):
-    def __init__(self, config):
-        super(CrossAttention, self).__init__()
-        self.self = CrossSelfAttention(config)
-        self.output = CrossSelfOutput(config)
-
-    def forward(self, input_tensor, attention_mask):
-        self_output = self.self(input_tensor, attention_mask)
-        attention_output = self.output(self_output, input_tensor)
-        return attention_output
-
-
-class CrossIntermediate(nn.Module):
-    def __init__(self, config):
-        super(CrossIntermediate, self).__init__()
-        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
-        self.intermediate_act_fn = ACT2FN[config.hidden_act] \
-            if isinstance(config.hidden_act, str) else config.hidden_act
-
-    def forward(self, hidden_states):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.intermediate_act_fn(hidden_states)
-        return hidden_states
-
-
-class CrossOutput(nn.Module):
-    def __init__(self, config):
-        super(CrossOutput, self).__init__()
-        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
-        self.LayerNorm = LayerNorm(config.hidden_size, eps=1e-12)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-    def forward(self, hidden_states, input_tensor):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states + input_tensor)
-        return hidden_states
-
-
-class CrossLayer(nn.Module):
-    def __init__(self, config):
-        super(CrossLayer, self).__init__()
-        self.attention = CrossAttention(config)
-        self.intermediate = CrossIntermediate(config)
-        self.output = CrossOutput(config)
-
-    def forward(self, hidden_states, attention_mask):
-        attention_output = self.attention(hidden_states, attention_mask)
-        intermediate_output = self.intermediate(attention_output)
-        layer_output = self.output(intermediate_output, attention_output)
-        return layer_output
-
-
-class CrossEncoder(nn.Module):
-    def __init__(self, config):
-        super(CrossEncoder, self).__init__()
-        layer = CrossLayer(config)
-        self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(config.num_hidden_layers)])
-
-    def forward(self, hidden_states, attention_mask, output_all_encoded_layers=True):
-        all_encoder_layers = []
-        for layer_module in self.layer:
-            hidden_states = layer_module(hidden_states, attention_mask)
-            if output_all_encoded_layers:
-                all_encoder_layers.append(hidden_states)
-        if not output_all_encoded_layers:
-            all_encoder_layers.append(hidden_states)
-        return all_encoder_layers
-
-
-class CrossPooler(nn.Module):
-    def __init__(self, config):
-        super(CrossPooler, self).__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.activation = nn.Tanh()
-
-    def forward(self, hidden_states):
-        # We "pool" the model by simply taking the hidden state corresponding
-        # to the first token.
-        first_token_tensor = hidden_states[:, 0]
-        pooled_output = self.dense(first_token_tensor)
-        pooled_output = self.activation(pooled_output)
-        return pooled_output
-
-
-class CrossPredictionHeadTransform(nn.Module):
-    def __init__(self, config):
-        super(CrossPredictionHeadTransform, self).__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.transform_act_fn = ACT2FN[config.hidden_act] \
-            if isinstance(config.hidden_act, str) else config.hidden_act
-        self.LayerNorm = LayerNorm(config.hidden_size, eps=1e-12)
-
-    def forward(self, hidden_states):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.transform_act_fn(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states)
-        return hidden_states
-
-
-class CrossLMPredictionHead(nn.Module):
-    def __init__(self, config, cross_model_embedding_weights):
-        super(CrossLMPredictionHead, self).__init__()
-        self.transform = CrossPredictionHeadTransform(config)
-
-        # The output weights are the same as the input embeddings, but there is
-        # an output-only bias for each token.
-        self.decoder = nn.Linear(cross_model_embedding_weights.size(1),
-                                 cross_model_embedding_weights.size(0),
-                                 bias=False)
-        self.decoder.weight = cross_model_embedding_weights
-        self.bias = nn.Parameter(torch.zeros(cross_model_embedding_weights.size(0)))
-
-    def forward(self, hidden_states):
-        hidden_states = self.transform(hidden_states)
-        hidden_states = self.decoder(hidden_states) + self.bias
-        return hidden_states
-
-
-class CrossOnlyMLMHead(nn.Module):
-    def __init__(self, config, cross_model_embedding_weights):
-        super(CrossOnlyMLMHead, self).__init__()
-        self.predictions = CrossLMPredictionHead(config, cross_model_embedding_weights)
-
-    def forward(self, sequence_output):
-        prediction_scores = self.predictions(sequence_output)
-        return prediction_scores
-
-
-class CrossOnlyNSPHead(nn.Module):
-    def __init__(self, config):
-        super(CrossOnlyNSPHead, self).__init__()
-        self.seq_relationship = nn.Linear(config.hidden_size, 2)
-
-    def forward(self, pooled_output):
-        seq_relationship_score = self.seq_relationship(pooled_output)
-        return seq_relationship_score
-
-
-class CrossPreTrainingHeads(nn.Module):
-    def __init__(self, config, cross_model_embedding_weights):
-        super(CrossPreTrainingHeads, self).__init__()
-        self.predictions = CrossLMPredictionHead(config, cross_model_embedding_weights)
-        self.seq_relationship = nn.Linear(config.hidden_size, 2)
-
-    def forward(self, sequence_output, pooled_output):
-        prediction_scores = self.predictions(sequence_output)
-        seq_relationship_score = self.seq_relationship(pooled_output)
-        return prediction_scores, seq_relationship_score
-
-
-class CrossModel(PreTrainedModel):
-    def __init__(self, config):
-        super(CrossModel, self).__init__(config)
-        self.embeddings = CrossEmbeddings(config)
-        self.encoder = CrossEncoder(config)
-        self.pooler = CrossPooler(config)
-        self.apply(self.init_weights)
-
-    def forward(self, concat_input, concat_type=None, attention_mask=None, output_all_encoded_layers=True):
-
-        if attention_mask is None:
-            attention_mask = torch.ones(concat_input.size(0), concat_input.size(1))
-        if concat_type is None:
-            concat_type = torch.zeros_like(attention_mask)
-
-        # We create a 3D attention mask from a 2D tensor mask.
-        # Sizes are [batch_size, 1, 1, to_seq_length]
-        # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
-        # this attention mask is more simple than the triangular masking of causal attention
-        # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
-        extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
-
-        # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
-        # masked positions, this operation will create a tensor which is 0.0 for
-        # positions we want to attend and -10000.0 for masked positions.
-        # Since we are adding it to the raw scores before the softmax, this is
-        # effectively the same as removing these entirely.
-        extended_attention_mask = extended_attention_mask.to(dtype=self.dtype) # fp16 compatibility
-        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
-
-        embedding_output = self.embeddings(concat_input, concat_type)
-        encoded_layers = self.encoder(embedding_output,
-                                      extended_attention_mask,
-                                      output_all_encoded_layers=output_all_encoded_layers)
-        sequence_output = encoded_layers[-1]
-        pooled_output = self.pooler(sequence_output)
-        if not output_all_encoded_layers:
-            encoded_layers = encoded_layers[-1]
-        return encoded_layers, pooled_output
diff --git a/backup/pdvc/modules/module_decoder.py b/backup/pdvc/modules/module_decoder.py
deleted file mode 100644
index 25622d1e4c0e9a0d19fe2b4986f7267ba1526823..0000000000000000000000000000000000000000
--- a/backup/pdvc/modules/module_decoder.py
+++ /dev/null
@@ -1,406 +0,0 @@
-# coding=utf-8
-# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
-# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""PyTorch BERT model."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import os
-import copy
-import json
-import math
-import logging
-import tarfile
-import tempfile
-import shutil
-import numpy as np
-
-import torch
-from torch import nn
-from .file_utils import cached_path
-from .until_config import PretrainedConfig
-from .until_module import PreTrainedModel, LayerNorm, ACT2FN
-
-logger = logging.getLogger(__name__)
-
-PRETRAINED_MODEL_ARCHIVE_MAP = {}
-CONFIG_NAME = 'decoder_config.json'
-WEIGHTS_NAME = 'decoder_pytorch_model.bin'
-
-
-class DecoderConfig(PretrainedConfig):
-    """Configuration class to store the configuration of a `DecoderModel`.
-    """
-    pretrained_model_archive_map = PRETRAINED_MODEL_ARCHIVE_MAP
-    config_name = CONFIG_NAME
-    weights_name = WEIGHTS_NAME
-    def __init__(self,
-                 vocab_size_or_config_json_file,
-                 hidden_size=768,
-                 num_hidden_layers=12,
-                 num_attention_heads=12,
-                 intermediate_size=3072,
-                 hidden_act="gelu",
-                 hidden_dropout_prob=0.1,
-                 attention_probs_dropout_prob=0.1,
-                 type_vocab_size=2,
-                 initializer_range=0.02,
-                 max_target_embeddings=128,
-                 num_decoder_layers=1):
-        """Constructs DecoderConfig.
-
-        Args:
-            vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `DecoderModel`.
-            hidden_size: Size of the encoder layers and the pooler layer.
-            num_hidden_layers: Number of hidden layers in the Transformer encoder.
-            num_attention_heads: Number of attention heads for each attention layer in
-                the Transformer encoder.
-            intermediate_size: The size of the "intermediate" (i.e., feed-forward)
-                layer in the Transformer encoder.
-            hidden_act: The non-linear activation function (function or string) in the
-                encoder and pooler. If string, "gelu", "relu" and "swish" are supported.
-            hidden_dropout_prob: The dropout probabilitiy for all fully connected
-                layers in the embeddings, encoder, and pooler.
-            attention_probs_dropout_prob: The dropout ratio for the attention
-                probabilities.
-            type_vocab_size: The vocabulary size of the `token_type_ids` passed into
-                `DecoderModel`.
-            initializer_range: The sttdev of the truncated_normal_initializer for
-                initializing all weight matrices.
-            max_target_embeddings: The maximum sequence length that this model might
-                ever be used with. Typically set this to something large just in case
-                (e.g., 512 or 1024 or 2048).
-            num_decoder_layers:
-        """
-        if isinstance(vocab_size_or_config_json_file, str):
-            with open(vocab_size_or_config_json_file, "r", encoding='utf-8') as reader:
-                json_config = json.loads(reader.read())
-            for key, value in json_config.items():
-                self.__dict__[key] = value
-        elif isinstance(vocab_size_or_config_json_file, int):
-            self.vocab_size = vocab_size_or_config_json_file
-            self.hidden_size = hidden_size
-            self.num_hidden_layers = num_hidden_layers
-            self.num_attention_heads = num_attention_heads
-            self.hidden_act = hidden_act
-            self.intermediate_size = intermediate_size
-            self.hidden_dropout_prob = hidden_dropout_prob
-            self.attention_probs_dropout_prob = attention_probs_dropout_prob
-            self.type_vocab_size = type_vocab_size
-            self.initializer_range = initializer_range
-            self.max_target_embeddings = max_target_embeddings
-            self.num_decoder_layers = num_decoder_layers
-        else:
-            raise ValueError("First argument must be either a vocabulary size (int)"
-                             "or the path to a pretrained model config file (str)")
-
-
-class BertSelfOutput(nn.Module):
-    def __init__(self, config):
-        super(BertSelfOutput, self).__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.LayerNorm = LayerNorm(config.hidden_size, eps=1e-12)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-    def forward(self, hidden_states, input_tensor):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states + input_tensor)
-        return hidden_states
-
-class BertIntermediate(nn.Module):
-    def __init__(self, config):
-        super(BertIntermediate, self).__init__()
-        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
-        self.intermediate_act_fn = ACT2FN[config.hidden_act] \
-            if isinstance(config.hidden_act, str) else config.hidden_act
-
-    def forward(self, hidden_states):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.intermediate_act_fn(hidden_states)
-        return hidden_states
-
-
-class BertOutput(nn.Module):
-    def __init__(self, config):
-        super(BertOutput, self).__init__()
-        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
-        self.LayerNorm = LayerNorm(config.hidden_size, eps=1e-12)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-    def forward(self, hidden_states, input_tensor):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states + input_tensor)
-        return hidden_states
-
-
-class BertPredictionHeadTransform(nn.Module):
-    def __init__(self, config):
-        super(BertPredictionHeadTransform, self).__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.transform_act_fn = ACT2FN[config.hidden_act] \
-            if isinstance(config.hidden_act, str) else config.hidden_act
-        self.LayerNorm = LayerNorm(config.hidden_size, eps=1e-12)
-
-    def forward(self, hidden_states):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.transform_act_fn(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states)
-        return hidden_states
-
-
-class BertLMPredictionHead(nn.Module):
-    def __init__(self, config, decoder_model_embedding_weights):
-        super(BertLMPredictionHead, self).__init__()
-        self.transform = BertPredictionHeadTransform(config)
-
-        # The output weights are the same as the input embeddings, but there is
-        # an output-only bias for each token.
-        self.decoder = nn.Linear(decoder_model_embedding_weights.size(1),
-                                 decoder_model_embedding_weights.size(0),
-                                 bias=False)
-        self.decoder.weight = decoder_model_embedding_weights
-        self.bias = nn.Parameter(torch.zeros(decoder_model_embedding_weights.size(0)))
-
-    def forward(self, hidden_states):
-        hidden_states = self.transform(hidden_states)
-        hidden_states = self.decoder(hidden_states) + self.bias
-        return hidden_states
-
-
-class BertOnlyMLMHead(nn.Module):
-    def __init__(self, config, decoder_model_embedding_weights):
-        super(BertOnlyMLMHead, self).__init__()
-        self.predictions = BertLMPredictionHead(config, decoder_model_embedding_weights)
-
-    def forward(self, sequence_output):
-        prediction_scores = self.predictions(sequence_output)
-        return prediction_scores
-
-class MultiHeadAttention(nn.Module):
-    ''' Multi-Head Attention module '''
-
-    def __init__(self, config):
-        super(MultiHeadAttention, self).__init__()
-
-        if config.hidden_size % config.num_attention_heads != 0:
-            raise ValueError(
-                "The hidden size (%d) is not a multiple of the number of attention "
-                "heads (%d)" % (config.hidden_size, config.num_attention_heads))
-        self.num_attention_heads = config.num_attention_heads
-        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
-        self.all_head_size = self.num_attention_heads * self.attention_head_size
-
-        self.query = nn.Linear(config.hidden_size, self.all_head_size)
-        self.key = nn.Linear(config.hidden_size, self.all_head_size)
-        self.value = nn.Linear(config.hidden_size, self.all_head_size)
-
-        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
-
-    def transpose_for_scores(self, x):
-        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
-        x = x.view(*new_x_shape)
-        return x.permute(0, 2, 1, 3)
-
-    def forward(self, q, k, v, attention_mask):
-        mixed_query_layer = self.query(q)
-        mixed_key_layer = self.key(k)
-        mixed_value_layer = self.value(v)
-
-        query_layer = self.transpose_for_scores(mixed_query_layer)
-        key_layer = self.transpose_for_scores(mixed_key_layer)
-        value_layer = self.transpose_for_scores(mixed_value_layer)
-
-        # Take the dot product between "query" and "key" to get the raw attention scores.
-        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
-        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
-        # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
-        attention_scores = attention_scores + attention_mask
-
-        # Normalize the attention scores to probabilities.
-        attention_probs = nn.Softmax(dim=-1)(attention_scores)
-
-        # This is actually dropping out entire tokens to attend to, which might
-        # seem a bit unusual, but is taken from the original Transformer paper.
-        attention_probs = self.dropout(attention_probs)
-
-        context_layer = torch.matmul(attention_probs, value_layer)
-        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
-        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
-        context_layer = context_layer.view(*new_context_layer_shape)
-
-        return context_layer, attention_scores
-
-class PositionwiseFeedForward(nn.Module):
-    ''' A two-feed-forward-layer module '''
-
-    def __init__(self, d_in, d_hid, dropout=0.1):
-        super().__init__()
-        self.w_1 = nn.Conv1d(d_in, d_hid, 1) # position-wise
-        self.w_2 = nn.Conv1d(d_hid, d_in, 1) # position-wise
-        self.layer_norm = nn.LayerNorm(d_in)
-        self.dropout = nn.Dropout(dropout)
-
-    def forward(self, x):
-        residual = x
-        output = x.transpose(1, 2)
-        output = self.w_2(ACT2FN["gelu"](self.w_1(output)))
-        output = output.transpose(1, 2)
-        output = self.dropout(output)
-        output = self.layer_norm(output + residual)
-        return output
-
-class DecoderAttention(nn.Module):
-    def __init__(self, config):
-        super(DecoderAttention, self).__init__()
-        self.att = MultiHeadAttention(config)
-        self.output = BertSelfOutput(config)
-
-    def forward(self, q, k, v, attention_mask):
-        att_output, attention_probs = self.att(q, k, v, attention_mask)
-        attention_output = self.output(att_output, q)
-        return attention_output, attention_probs
-
-class DecoderLayer(nn.Module):
-    def __init__(self, config):
-        super(DecoderLayer, self).__init__()
-        self.slf_attn = DecoderAttention(config)
-        self.enc_attn = DecoderAttention(config)
-        self.intermediate = BertIntermediate(config)
-        self.output = BertOutput(config)
-
-    def forward(self, dec_input, enc_output, slf_attn_mask=None, dec_enc_attn_mask=None):
-        slf_output, _ = self.slf_attn(dec_input, dec_input, dec_input, slf_attn_mask)
-        dec_output, dec_att_scores = self.enc_attn(slf_output, enc_output, enc_output, dec_enc_attn_mask)
-        intermediate_output = self.intermediate(dec_output)
-        dec_output = self.output(intermediate_output, dec_output)
-        return dec_output, dec_att_scores
-
-class DecoderEmbeddings(nn.Module):
-    """Construct the embeddings from word, position and token_type embeddings.
-    """
-    def __init__(self, config, decoder_word_embeddings_weight, decoder_position_embeddings_weight):
-        super(DecoderEmbeddings, self).__init__()
-        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size)
-        self.position_embeddings = nn.Embedding(config.max_target_embeddings, config.hidden_size)
-        self.word_embeddings.weight = decoder_word_embeddings_weight
-        self.position_embeddings.weight = decoder_position_embeddings_weight
-
-        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
-        # any TensorFlow checkpoint file
-        self.LayerNorm = LayerNorm(config.hidden_size, eps=1e-12)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-    def forward(self, input_ids):
-        seq_length = input_ids.size(1)
-        position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device)
-        position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
-
-        words_embeddings = self.word_embeddings(input_ids)
-        position_embeddings = self.position_embeddings(position_ids)
-
-        embeddings = words_embeddings + position_embeddings
-        embeddings = self.LayerNorm(embeddings)
-        embeddings = self.dropout(embeddings)
-        return embeddings
-
-class Decoder(nn.Module):
-    def __init__(self, config):
-        super(Decoder, self).__init__()
-        layer = DecoderLayer(config)
-        self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(config.num_decoder_layers)])
-
-    def forward(self, hidden_states, encoder_outs, self_attn_mask, attention_mask, output_all_encoded_layers=False):
-        dec_att_scores = None
-        all_encoder_layers = []
-        all_dec_att_probs = []
-        for layer_module in self.layer:
-            hidden_states, dec_att_scores = layer_module(hidden_states, encoder_outs, self_attn_mask, attention_mask)
-            if output_all_encoded_layers:
-                all_encoder_layers.append(hidden_states)
-                all_dec_att_probs.append(dec_att_scores)
-        if not output_all_encoded_layers:
-            all_encoder_layers.append(hidden_states)
-            all_dec_att_probs.append(dec_att_scores)
-        return all_encoder_layers, all_dec_att_probs
-
-class DecoderClassifier(nn.Module):
-    def __init__(self, config, embedding_weights):
-        super(DecoderClassifier, self).__init__()
-        self.cls = BertOnlyMLMHead(config, embedding_weights)
-
-    def forward(self, hidden_states):
-        cls_scores = self.cls(hidden_states)
-        return cls_scores
-
-class DecoderModel(PreTrainedModel):
-
-    """
-    Transformer decoder consisting of *args.decoder_layers* layers. Each layer
-    is a :class:`TransformerDecoderLayer`.
-
-    Args:
-        args (argparse.Namespace): parsed command-line arguments
-        final_norm (bool, optional): apply layer norm to the output of the
-            final decoder layer (default: True).
-    """
-
-    def __init__(self, config, decoder_word_embeddings_weight, decoder_position_embeddings_weight):
-        super(DecoderModel, self).__init__(config)
-        self.config = config
-        self.max_target_length = config.max_target_embeddings
-        self.embeddings = DecoderEmbeddings(config, decoder_word_embeddings_weight, decoder_position_embeddings_weight)
-        self.decoder = Decoder(config)
-        self.classifier = DecoderClassifier(config, decoder_word_embeddings_weight)
-        self.apply(self.init_weights)
-
-    def forward(self, input_ids, encoder_outs=None, answer_mask=None, encoder_mask=None):
-        """
-        Args:
-            input_ids (LongTensor): previous decoder outputs of shape `(batch, tgt_len)`, for input feeding/teacher forcing
-            encoder_outs (Tensor, optional): output from the encoder, used for encoder-side attention
-
-        Returns:
-            tuple:
-                - the last decoder layer's output of shape `(batch, tgt_len, vocab)`
-                - the last decoder layer's attention weights of shape `(batch, tgt_len, src_len)`
-        """
-        embedding_output = self.embeddings(input_ids)
-
-        extended_encoder_mask = encoder_mask.unsqueeze(1).unsqueeze(2)   # b x 1 x 1 x ls
-        extended_encoder_mask = extended_encoder_mask.to(dtype=self.dtype) # fp16 compatibility
-        extended_encoder_mask = (1.0 - extended_encoder_mask) * -10000.0
-
-        extended_answer_mask = answer_mask.unsqueeze(1).unsqueeze(2)
-        extended_answer_mask = extended_answer_mask.to(dtype=self.dtype)  # fp16 compatibility
-
-        sz_b, len_s, _ = embedding_output.size()
-        subsequent_mask = torch.triu(torch.ones((len_s, len_s), device=embedding_output.device, dtype=embedding_output.dtype), diagonal=1)
-        self_attn_mask = subsequent_mask.unsqueeze(0).expand(sz_b, -1, -1).unsqueeze(1)  # b x 1 x ls x ls
-        slf_attn_mask = ((1.0 - extended_answer_mask) + self_attn_mask).gt(0).to(dtype=self.dtype)
-        self_attn_mask = slf_attn_mask * -10000.0
-
-        decoded_layers, dec_att_scores = self.decoder(embedding_output,
-                                      encoder_outs,
-                                      self_attn_mask,
-                                      extended_encoder_mask,
-                                      )
-        sequence_output = decoded_layers[-1]
-        cls_scores = self.classifier(sequence_output)
-
-        return cls_scores
diff --git a/backup/pdvc/modules/module_visual.py b/backup/pdvc/modules/module_visual.py
deleted file mode 100644
index b9a43f8a74c1e5e020c8b4daec33d7adb5d3b840..0000000000000000000000000000000000000000
--- a/backup/pdvc/modules/module_visual.py
+++ /dev/null
@@ -1,425 +0,0 @@
-# coding=utf-8
-# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
-# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""PyTorch BERT model."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import os
-import copy
-import json
-import math
-import logging
-import tarfile
-import tempfile
-import shutil
-
-import torch
-from torch import nn
-import torch.nn.functional as F
-from .file_utils import cached_path
-from .until_config import PretrainedConfig
-from .until_module import PreTrainedModel, LayerNorm, ACT2FN
-
-logger = logging.getLogger(__name__)
-
-PRETRAINED_MODEL_ARCHIVE_MAP = {}
-CONFIG_NAME = 'visual_config.json'
-WEIGHTS_NAME = 'visual_pytorch_model.bin'
-
-
-class VisualConfig(PretrainedConfig):
-    """Configuration class to store the configuration of a `VisualModel`.
-    """
-    pretrained_model_archive_map = PRETRAINED_MODEL_ARCHIVE_MAP
-    config_name = CONFIG_NAME
-    weights_name = WEIGHTS_NAME
-    def __init__(self,
-                 vocab_size_or_config_json_file=4096,
-                 hidden_size=768,
-                 num_hidden_layers=3,
-                 num_attention_heads=12,
-                 intermediate_size=3072,
-                 hidden_act="gelu",
-                 hidden_dropout_prob=0.1,
-                 attention_probs_dropout_prob=0.1,
-                 max_position_embeddings=512,
-                 initializer_range=0.02):
-        """Constructs VisualConfig.
-
-        Args:
-            vocab_size_or_config_json_file: Size of the encoder layers and the pooler layer.
-            hidden_size: Size of the encoder layers and the pooler layer.
-            num_hidden_layers: Number of hidden layers in the Transformer encoder.
-            num_attention_heads: Number of attention heads for each attention layer in
-                the Transformer encoder.
-            intermediate_size: The size of the "intermediate" (i.e., feed-forward)
-                layer in the Transformer encoder.
-            hidden_act: The non-linear activation function (function or string) in the
-                encoder and pooler. If string, "gelu", "relu" and "swish" are supported.
-            hidden_dropout_prob: The dropout probabilitiy for all fully connected
-                layers in the embeddings, encoder, and pooler.
-            attention_probs_dropout_prob: The dropout ratio for the attention
-                probabilities.
-            max_position_embeddings: The maximum sequence length that this model might
-                ever be used with. Typically set this to something large just in case
-                (e.g., 512 or 1024 or 2048).
-            initializer_range: The sttdev of the truncated_normal_initializer for
-                initializing all weight matrices.
-        """
-        if isinstance(vocab_size_or_config_json_file, str):
-            with open(vocab_size_or_config_json_file, "r", encoding='utf-8') as reader:
-                json_config = json.loads(reader.read())
-            for key, value in json_config.items():
-                self.__dict__[key] = value
-        elif isinstance(vocab_size_or_config_json_file, int):
-            self.vocab_size = vocab_size_or_config_json_file
-            self.hidden_size = hidden_size
-            self.num_hidden_layers = num_hidden_layers
-            self.num_attention_heads = num_attention_heads
-            self.hidden_act = hidden_act
-            self.intermediate_size = intermediate_size
-            self.hidden_dropout_prob = hidden_dropout_prob
-            self.attention_probs_dropout_prob = attention_probs_dropout_prob
-            self.max_position_embeddings = max_position_embeddings
-            self.initializer_range = initializer_range
-        else:
-            raise ValueError("First argument must be either a vocabulary size (int)"
-                             "or the path to a pretrained model config file (str)")
-
-class VisualEmbeddings(nn.Module):
-    """Construct the embeddings from word, position and token_type embeddings.
-    """
-    def __init__(self, config):
-        super(VisualEmbeddings, self).__init__()
-
-        self.word_embeddings = nn.Linear(config.vocab_size, config.hidden_size)
-        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
-
-        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
-        # any TensorFlow checkpoint file
-        self.LayerNorm = LayerNorm(config.hidden_size, eps=1e-12)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-    def forward(self, input_embeddings):
-        seq_length = input_embeddings.size(1)
-        position_ids = torch.arange(seq_length, dtype=torch.long, device=input_embeddings.device)
-        position_ids = position_ids.unsqueeze(0).expand(input_embeddings.size(0), -1)
-
-        words_embeddings = self.word_embeddings(input_embeddings)
-        # words_embeddings = self.transform_act_fn(words_embeddings)
-
-        position_embeddings = self.position_embeddings(position_ids)
-        embeddings = words_embeddings + position_embeddings
-
-        embeddings = self.LayerNorm(embeddings)
-        embeddings = self.dropout(embeddings)
-        return embeddings
-
-class VisualSelfAttention(nn.Module):
-    def __init__(self, config):
-        super(VisualSelfAttention, self).__init__()
-        if config.hidden_size % config.num_attention_heads != 0:
-            raise ValueError(
-                "The hidden size (%d) is not a multiple of the number of attention "
-                "heads (%d)" % (config.hidden_size, config.num_attention_heads))
-        self.num_attention_heads = config.num_attention_heads
-        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
-        self.all_head_size = self.num_attention_heads * self.attention_head_size
-
-        self.query = nn.Linear(config.hidden_size, self.all_head_size)
-        self.key = nn.Linear(config.hidden_size, self.all_head_size)
-        self.value = nn.Linear(config.hidden_size, self.all_head_size)
-
-        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
-
-    def transpose_for_scores(self, x):
-        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
-        x = x.view(*new_x_shape)
-        return x.permute(0, 2, 1, 3)
-
-    def forward(self, hidden_states, attention_mask):
-        mixed_query_layer = self.query(hidden_states)
-        mixed_key_layer = self.key(hidden_states)
-        mixed_value_layer = self.value(hidden_states)
-
-        query_layer = self.transpose_for_scores(mixed_query_layer)
-        key_layer = self.transpose_for_scores(mixed_key_layer)
-        value_layer = self.transpose_for_scores(mixed_value_layer)
-
-        # Take the dot product between "query" and "key" to get the raw attention scores.
-        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
-        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
-        # Apply the attention mask is (precomputed for all layers in VisualModel forward() function)
-        attention_scores = attention_scores + attention_mask
-
-        # Normalize the attention scores to probabilities.
-        attention_probs = nn.Softmax(dim=-1)(attention_scores)
-
-        # This is actually dropping out entire tokens to attend to, which might
-        # seem a bit unusual, but is taken from the original Transformer paper.
-        attention_probs = self.dropout(attention_probs)
-
-        context_layer = torch.matmul(attention_probs, value_layer)
-        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
-        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
-        context_layer = context_layer.view(*new_context_layer_shape)
-        return context_layer
-
-
-class VisualSelfOutput(nn.Module):
-    def __init__(self, config):
-        super(VisualSelfOutput, self).__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.LayerNorm = LayerNorm(config.hidden_size, eps=1e-12)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-    def forward(self, hidden_states, input_tensor):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states + input_tensor)
-        return hidden_states
-
-
-class VisualAttention(nn.Module):
-    def __init__(self, config):
-        super(VisualAttention, self).__init__()
-        self.self = VisualSelfAttention(config)
-        self.output = VisualSelfOutput(config)
-
-    def forward(self, input_tensor, attention_mask):
-        self_output = self.self(input_tensor, attention_mask)
-        attention_output = self.output(self_output, input_tensor)
-        return attention_output
-
-
-class VisualIntermediate(nn.Module):
-    def __init__(self, config):
-        super(VisualIntermediate, self).__init__()
-        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
-        self.intermediate_act_fn = ACT2FN[config.hidden_act] \
-            if isinstance(config.hidden_act, str) else config.hidden_act
-
-    def forward(self, hidden_states):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.intermediate_act_fn(hidden_states)
-        return hidden_states
-
-
-class VisualOutput(nn.Module):
-    def __init__(self, config):
-        super(VisualOutput, self).__init__()
-        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
-        self.LayerNorm = LayerNorm(config.hidden_size, eps=1e-12)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-    def forward(self, hidden_states, input_tensor):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states + input_tensor)
-        return hidden_states
-
-
-class VisualLayer(nn.Module):
-    def __init__(self, config):
-        super(VisualLayer, self).__init__()
-        self.attention = VisualAttention(config)
-        self.intermediate = VisualIntermediate(config)
-        self.output = VisualOutput(config)
-
-    def forward(self, hidden_states, attention_mask):
-        attention_output = self.attention(hidden_states, attention_mask)
-        intermediate_output = self.intermediate(attention_output)
-        layer_output = self.output(intermediate_output, attention_output)
-        return layer_output
-
-
-class VisualEncoder(nn.Module):
-    def __init__(self, config):
-        super(VisualEncoder, self).__init__()
-        layer = VisualLayer(config)
-        self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(config.num_hidden_layers)])
-
-    def forward(self, hidden_states, attention_mask, output_all_encoded_layers=True):
-        all_encoder_layers = []
-        for layer_module in self.layer:
-            hidden_states = layer_module(hidden_states, attention_mask)
-            if output_all_encoded_layers:
-                all_encoder_layers.append(hidden_states)
-        if not output_all_encoded_layers:
-            all_encoder_layers.append(hidden_states)
-        return all_encoder_layers
-
-
-class VisualPooler(nn.Module):
-    def __init__(self, config):
-        super(VisualPooler, self).__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.activation = nn.Tanh()
-
-    def forward(self, hidden_states):
-        # We "pool" the model by simply taking the hidden state corresponding
-        # to the first token.
-        first_token_tensor = hidden_states[:, 0]
-        pooled_output = self.dense(first_token_tensor)
-        pooled_output = self.activation(pooled_output)
-        return pooled_output
-
-
-class VisualPredictionHeadTransform(nn.Module):
-    def __init__(self, config):
-        super(VisualPredictionHeadTransform, self).__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.transform_act_fn = ACT2FN[config.hidden_act] \
-            if isinstance(config.hidden_act, str) else config.hidden_act
-        self.LayerNorm = LayerNorm(config.hidden_size, eps=1e-12)
-
-    def forward(self, hidden_states):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.transform_act_fn(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states)
-        return hidden_states
-
-
-class VisualLMPredictionHead(nn.Module):
-    def __init__(self, config, visual_model_embedding_weights):
-        super(VisualLMPredictionHead, self).__init__()
-        self.transform = VisualPredictionHeadTransform(config)
-
-        # The output weights are the same as the input embeddings, but there is
-        # an output-only bias for each token.
-        self.weight = visual_model_embedding_weights
-        self.bias = nn.Parameter(torch.zeros(visual_model_embedding_weights.size(1)))
-
-    def forward(self, hidden_states):
-        hidden_states = self.transform(hidden_states)
-        hidden_states = hidden_states.matmul(self.weight) + self.bias
-        return hidden_states
-
-
-class VisualOnlyMLMHead(nn.Module):
-    def __init__(self, config, visual_model_embedding_weights):
-        super(VisualOnlyMLMHead, self).__init__()
-        self.predictions = VisualLMPredictionHead(config, visual_model_embedding_weights)
-
-    def forward(self, sequence_output):
-        prediction_scores = self.predictions(sequence_output)
-        return prediction_scores
-
-
-class VisualOnlyNSPHead(nn.Module):
-    def __init__(self, config):
-        super(VisualOnlyNSPHead, self).__init__()
-        self.seq_relationship = nn.Linear(config.hidden_size, 2)
-
-    def forward(self, pooled_output):
-        seq_relationship_score = self.seq_relationship(pooled_output)
-        return seq_relationship_score
-
-
-class VisualPreTrainingHeads(nn.Module):
-    def __init__(self, config, visual_model_embedding_weights):
-        super(VisualPreTrainingHeads, self).__init__()
-        self.predictions = VisualLMPredictionHead(config, visual_model_embedding_weights)
-        self.seq_relationship = nn.Linear(config.hidden_size, 2)
-
-    def forward(self, sequence_output, pooled_output):
-        prediction_scores = self.predictions(sequence_output)
-        seq_relationship_score = self.seq_relationship(pooled_output)
-        return prediction_scores, seq_relationship_score
-
-
-class VisualModel(PreTrainedModel):
-    """Visual model ("Bidirectional Embedding Representations from a Transformer").
-
-    Params:
-        config: a VisualConfig class instance with the configuration to build a new model
-
-    Inputs:
-        `type`: a str, indicates which masking will be used in the attention, choice from [`bi`, `seq`, `gen`]
-        `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
-            with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
-            `extract_features.py`, `run_classifier.py` and `run_squad.py`)
-        `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
-            types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
-            a `sentence B` token (see  paper for more details).
-        `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
-            selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
-            input sequence length in the current batch. It's the mask that we typically use for attention when
-            a batch has varying length sentences.
-        `output_all_encoded_layers`: boolean which controls the content of the `encoded_layers` output as described below. Default: `True`.
-
-    Outputs: Tuple of (encoded_layers, pooled_output)
-        `encoded_layers`: controled by `output_all_encoded_layers` argument:
-            - `output_all_encoded_layers=True`: outputs a list of the full sequences of encoded-hidden-states at the end
-                of each attention block (i.e. 12 full sequences for Visual-base, 24 for Visual-large), each
-                encoded-hidden-state is a torch.FloatTensor of size [batch_size, sequence_length, hidden_size],
-            - `output_all_encoded_layers=False`: outputs only the full sequence of hidden-states corresponding
-                to the last attention block of shape [batch_size, sequence_length, hidden_size],
-        `pooled_output`: a torch.FloatTensor of size [batch_size, hidden_size] which is the output of a
-            classifier pretrained on top of the hidden state associated to the first character of the
-            input (`CLF`) to train on the Next-Sentence task (see 's paper).
-
-    Example usage:
-    ```python
-    # Already been converted into WordPiece token ids
-    input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
-    input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
-
-    config = modeling.VisualConfig(vocab_size_or_config_json_file=4096, hidden_size=768,
-        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
-
-    model = modeling.VisualModel(config=config)
-    all_encoder_layers, pooled_output = model(video, video_mask)
-    ```
-    """
-    def __init__(self, config):
-        super(VisualModel, self).__init__(config)
-        self.embeddings = VisualEmbeddings(config)
-        self.encoder = VisualEncoder(config)
-        self.pooler = VisualPooler(config)
-        self.apply(self.init_weights)
-
-    def forward(self, video, attention_mask=None, output_all_encoded_layers=True):
-
-        if attention_mask is None:
-            attention_mask = torch.ones(video.size(0), video.size(1))
-
-        # We create a 3D attention mask from a 2D tensor mask.
-        # Sizes are [batch_size, 1, 1, to_seq_length]
-        # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
-        # this attention mask is more simple than the triangular masking of causal attention
-        # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
-        extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
-
-        # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
-        # masked positions, this operation will create a tensor which is 0.0 for
-        # positions we want to attend and -10000.0 for masked positions.
-        # Since we are adding it to the raw scores before the softmax, this is
-        # effectively the same as removing these entirely.
-        extended_attention_mask = extended_attention_mask.to(dtype=self.dtype) # fp16 compatibility
-        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
-
-        embedding_output = self.embeddings(video)
-        encoded_layers = self.encoder(embedding_output,
-                                      extended_attention_mask,
-                                      output_all_encoded_layers=output_all_encoded_layers)
-        sequence_output = encoded_layers[-1]
-        pooled_output = self.pooler(sequence_output)
-        if not output_all_encoded_layers:
-            encoded_layers = encoded_layers[-1]
-        return encoded_layers, pooled_output
\ No newline at end of file
diff --git a/backup/pdvc/modules/optimization.py b/backup/pdvc/modules/optimization.py
deleted file mode 100644
index 264c57c7d8f213004b4ee82a8861e0ae6103c906..0000000000000000000000000000000000000000
--- a/backup/pdvc/modules/optimization.py
+++ /dev/null
@@ -1,168 +0,0 @@
-# coding=utf-8
-# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""PyTorch optimization for BERT model."""
-
-import math
-import torch
-from torch.optim import Optimizer
-from torch.optim.optimizer import required
-from torch.nn.utils import clip_grad_norm_
-import logging
-
-logger = logging.getLogger(__name__)
-
-def warmup_cosine(x, warmup=0.002):
-    if x < warmup:
-        return x/warmup
-    return 0.5 * (1.0 + torch.cos(math.pi * x))
-
-def warmup_constant(x, warmup=0.002):
-    """ Linearly increases learning rate over `warmup`*`t_total` (as provided to BertAdam) training steps.
-        Learning rate is 1. afterwards. """
-    if x < warmup:
-        return x/warmup
-    return 1.0
-
-def warmup_linear(x, warmup=0.002):
-    """ Specifies a triangular learning rate schedule where peak is reached at `warmup`*`t_total`-th (as provided to BertAdam) training step.
-        After `t_total`-th training step, learning rate is zero. """
-    if x < warmup:
-        return x/warmup
-    return max((x-1.)/(warmup-1.), 0)
-
-SCHEDULES = {
-    'warmup_cosine':   warmup_cosine,
-    'warmup_constant': warmup_constant,
-    'warmup_linear':   warmup_linear,
-}
-
-
-class BertAdam(Optimizer):
-    """Implements BERT version of Adam algorithm with weight decay fix.
-    Params:
-        lr: learning rate
-        warmup: portion of t_total for the warmup, -1  means no warmup. Default: -1
-        t_total: total number of training steps for the learning
-            rate schedule, -1  means constant learning rate. Default: -1
-        schedule: schedule to use for the warmup (see above). Default: 'warmup_linear'
-        b1: Adams b1. Default: 0.9
-        b2: Adams b2. Default: 0.999
-        e: Adams epsilon. Default: 1e-6
-        weight_decay: Weight decay. Default: 0.01
-        max_grad_norm: Maximum norm for the gradients (-1 means no clipping). Default: 1.0
-    """
-    def __init__(self, params, lr=required, warmup=-1, t_total=-1, schedule='warmup_linear',
-                 b1=0.9, b2=0.999, e=1e-6, weight_decay=0.01,
-                 max_grad_norm=1.0):
-        if lr is not required and lr < 0.0:
-            raise ValueError("Invalid learning rate: {} - should be >= 0.0".format(lr))
-        if schedule not in SCHEDULES:
-            raise ValueError("Invalid schedule parameter: {}".format(schedule))
-        if not 0.0 <= warmup < 1.0 and not warmup == -1:
-            raise ValueError("Invalid warmup: {} - should be in [0.0, 1.0[ or -1".format(warmup))
-        if not 0.0 <= b1 < 1.0:
-            raise ValueError("Invalid b1 parameter: {} - should be in [0.0, 1.0[".format(b1))
-        if not 0.0 <= b2 < 1.0:
-            raise ValueError("Invalid b2 parameter: {} - should be in [0.0, 1.0[".format(b2))
-        if not e >= 0.0:
-            raise ValueError("Invalid epsilon value: {} - should be >= 0.0".format(e))
-        defaults = dict(lr=lr, schedule=schedule, warmup=warmup, t_total=t_total,
-                        b1=b1, b2=b2, e=e, weight_decay=weight_decay,
-                        max_grad_norm=max_grad_norm)
-        super(BertAdam, self).__init__(params, defaults)
-
-    def get_lr(self):
-        lr = []
-        for group in self.param_groups:
-            for p in group['params']:
-                if p.grad is None:
-                    continue
-                state = self.state[p]
-                if len(state) == 0:
-                    return [0]
-                if group['t_total'] != -1:
-                    schedule_fct = SCHEDULES[group['schedule']]
-                    lr_scheduled = group['lr'] * schedule_fct(state['step']/group['t_total'], group['warmup'])
-                else:
-                    lr_scheduled = group['lr']
-                lr.append(lr_scheduled)
-        return lr
-
-    def step(self, closure=None):
-        """Performs a single optimization step.
-        Arguments:
-            closure (callable, optional): A closure that reevaluates the model
-                and returns the loss.
-        """
-        loss = None
-        if closure is not None:
-            loss = closure()
-
-        for group in self.param_groups:
-            for p in group['params']:
-                if p.grad is None:
-                    continue
-                grad = p.grad.data
-                if grad.is_sparse:
-                    raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead')
-
-                state = self.state[p]
-
-                # State initialization
-                if len(state) == 0:
-                    state['step'] = 0
-                    # Exponential moving average of gradient values
-                    state['next_m'] = torch.zeros_like(p.data)
-                    # Exponential moving average of squared gradient values
-                    state['next_v'] = torch.zeros_like(p.data)
-
-                next_m, next_v = state['next_m'], state['next_v']
-                beta1, beta2 = group['b1'], group['b2']
-
-                # Add grad clipping
-                if group['max_grad_norm'] > 0:
-                    clip_grad_norm_(p, group['max_grad_norm'])
-
-                # Decay the first and second moment running average coefficient
-                # In-place operations to update the averages at the same time
-                # next_m.mul_(beta1).add_(1 - beta1, grad) --> pytorch 1.7
-                next_m.mul_(beta1).add_(grad, alpha=1 - beta1)
-                # next_v.mul_(beta2).addcmul_(1 - beta2, grad, grad) --> pytorch 1.7
-                next_v.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
-                update = next_m / (next_v.sqrt() + group['e'])
-
-                # Just adding the square of the weights to the loss function is *not*
-                # the correct way of using L2 regularization/weight decay with Adam,
-                # since that will interact with the m and v parameters in strange ways.
-                #
-                # Instead we want to decay the weights in a manner that doesn't interact
-                # with the m/v parameters. This is equivalent to adding the square
-                # of the weights to the loss with plain (non-momentum) SGD.
-                if group['weight_decay'] > 0.0:
-                    update += group['weight_decay'] * p.data
-
-                if group['t_total'] != -1:
-                    schedule_fct = SCHEDULES[group['schedule']]
-                    progress = state['step']/group['t_total']
-                    lr_scheduled = group['lr'] * schedule_fct(progress, group['warmup'])
-                else:
-                    lr_scheduled = group['lr']
-
-                update_with_lr = lr_scheduled * update
-                p.data.add_(-update_with_lr)
-
-                state['step'] += 1
-
-        return loss
\ No newline at end of file
diff --git a/backup/pdvc/modules/tokenization.py b/backup/pdvc/modules/tokenization.py
deleted file mode 100644
index 183c81000f82aae59295f8d8572b6bcf67891790..0000000000000000000000000000000000000000
--- a/backup/pdvc/modules/tokenization.py
+++ /dev/null
@@ -1,408 +0,0 @@
-# coding=utf-8
-# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Tokenization classes."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import collections
-import unicodedata
-import os
-import sys
-import logging
-
-from .file_utils import cached_path
-
-logger = logging.getLogger(__name__)
-PRETRAINED_VOCAB_ARCHIVE_MAP = {
-    'bert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt",
-    'bert-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-vocab.txt",
-    'bert-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-vocab.txt",
-    'bert-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-vocab.txt",
-    'bert-base-multilingual-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased-vocab.txt",
-    'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-vocab.txt",
-    'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-vocab.txt",
-}
-PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP = {
-    'base-uncased': 512,
-    'large-uncased': 512,
-    'base-cased': 512,
-    'large-cased': 512,
-    'base-multilingual-uncased': 512,
-    'base-multilingual-cased': 512,
-    'base-chinese': 512,
-}
-VOCAB_NAME = 'vocab.txt'
-
-
-def load_vocab(vocab_file):
-    """Loads a vocabulary file into a dictionary."""
-    vocab = collections.OrderedDict()
-    index = 0
-    with open(vocab_file, "r", encoding="utf-8") as reader:
-        while True:
-            token = reader.readline()
-            if not token:
-                break
-            token = token.strip()
-            vocab[token] = index
-            index += 1
-    return vocab
-
-
-def whitespace_tokenize(text):
-    """Runs basic whitespace cleaning and splitting on a peice of text."""
-    text = text.strip()
-    if not text:
-        return []
-    tokens = text.split()
-    return tokens
-
-
-class BertTokenizer(object):
-    """Runs end-to-end tokenization: punctuation splitting"""
-
-    def __init__(self, vocab_file, do_lower_case=True, max_len=None, never_split=("[UNK]", "[SEP]", "[MASK]", "[CLS]")):
-        if not os.path.isfile(vocab_file):
-            raise ValueError(
-                "Can't find a vocabulary file at path '{}'. To load the vocabulary from a Google pretrained "
-                "model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`".format(vocab_file))
-        self.vocab = load_vocab(vocab_file)
-        self.ids_to_tokens = collections.OrderedDict(
-            [(ids, tok) for tok, ids in self.vocab.items()])
-        self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case, never_split=never_split)
-        self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
-        self.max_len = max_len if max_len is not None else int(1e12)
-
-    def tokenize(self, text):
-        split_tokens = []
-        for token in self.basic_tokenizer.tokenize(text):
-            for sub_token in self.wordpiece_tokenizer.tokenize(token):
-                split_tokens.append(sub_token)
-        return split_tokens
-
-    def convert_tokens_to_ids(self, tokens):
-        """Converts a sequence of tokens into ids using the vocab."""
-        ids = []
-        for token in tokens:
-            if token not in self.vocab:
-                ids.append(self.vocab["[UNK]"])
-                logger.error("Cannot find token '{}' in vocab. Using [UNK] insetad".format(token))
-            else:
-                ids.append(self.vocab[token])
-        if len(ids) > self.max_len:
-            raise ValueError(
-                "Token indices sequence length is longer than the specified maximum "
-                " sequence length for this BERT model ({} > {}). Running this"
-                " sequence through BERT will result in indexing errors".format(len(ids), self.max_len)
-            )
-        return ids
-
-    def convert_ids_to_tokens(self, ids):
-        """Converts a sequence of ids in tokens using the vocab."""
-        tokens = []
-        for i in ids:
-            tokens.append(self.ids_to_tokens[i])
-        return tokens
-
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name, cache_dir=None, *inputs, **kwargs):
-        """
-        Instantiate a PreTrainedBertModel from a pre-trained model file.
-        Download and cache the pre-trained model file if needed.
-        """
-        vocab_file = os.path.join(os.path.dirname(os.path.abspath(__file__)), pretrained_model_name)
-        if os.path.exists(vocab_file) is False:
-            if pretrained_model_name in PRETRAINED_VOCAB_ARCHIVE_MAP:
-                vocab_file = PRETRAINED_VOCAB_ARCHIVE_MAP[pretrained_model_name]
-            else:
-                vocab_file = pretrained_model_name
-        if os.path.isdir(vocab_file):
-            vocab_file = os.path.join(vocab_file, VOCAB_NAME)
-        # redirect to the cache, if necessary
-        print(vocab_file)
-        try:
-            resolved_vocab_file = cached_path(vocab_file, cache_dir=cache_dir)
-        except FileNotFoundError:
-            logger.error(
-                "Model name '{}' was not found. "
-                "We assumed '{}' was a path or url but couldn't find any file "
-                "associated to this path or url.".format(
-                    pretrained_model_name,
-                    vocab_file))
-            return None
-        if resolved_vocab_file == vocab_file:
-            logger.info("loading vocabulary file {}".format(vocab_file))
-        else:
-            logger.info("loading vocabulary file {} from cache at {}".format(
-                vocab_file, resolved_vocab_file))
-        if pretrained_model_name in PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP:
-            # if we're using a pretrained model, ensure the tokenizer wont index sequences longer
-            # than the number of positional embeddings
-            max_len = PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP[pretrained_model_name]
-            kwargs['max_len'] = min(kwargs.get('max_len', int(1e12)), max_len)
-            kwargs['never_split'] = ("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]")
-
-        # Instantiate tokenizer.
-        tokenizer = cls(resolved_vocab_file, *inputs, **kwargs)
-
-        return tokenizer
-
-    def add_tokens(self, new_tokens, model):
-        """
-        Add a list of new tokens to the tokenizer class. If the new tokens are not in the
-        vocabulary, they are added to it with indices starting from length of the current vocabulary.
-        Args:
-            new_tokens: list of string. Each string is a token to add. Tokens are only added if they are not already in the vocabulary (tested by checking if the tokenizer assign the index of the ``unk_token`` to them).
-        Returns:
-            Number of tokens added to the vocabulary.
-        Examples::
-            # Let's see how to increase the vocabulary of Bert model and tokenizer
-            tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-            model = BertModel.from_pretrained('bert-base-uncased')
-            num_added_toks = tokenizer.add_tokens(['new_tok1', 'my_new-tok2'])
-            print('We have added', num_added_toks, 'tokens')
-            model.resize_token_embeddings(len(tokenizer))  # Notice: resize_token_embeddings expect to receive the full size of the new vocabulary, i.e. the length of the tokenizer.
-        """
-
-        to_add_tokens = []
-        for token in new_tokens:
-            assert isinstance(token, str)
-            to_add_tokens.append(token)
-            # logger.info("Adding %s to the vocabulary", token)
-
-        vocab = collections.OrderedDict()
-        for token in self.vocab.keys():
-            vocab[token] = self.vocab[token]
-        for token in to_add_tokens:
-            vocab[token] = len(vocab)
-        self.vocab = self.wordpiece_tokenizer.vocab = vocab
-        self.ids_to_tokens = collections.OrderedDict(
-            [(ids, tok) for tok, ids in self.vocab.items()])
-
-        model.resize_token_embeddings(new_num_tokens=len(vocab))
-
-class BasicTokenizer(object):
-    """Runs basic tokenization (punctuation splitting, lower casing, etc.)."""
-
-    def __init__(self, do_lower_case=True, never_split=("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]")):
-        """Constructs a BasicTokenizer.
-
-        Args:
-          do_lower_case: Whether to lower case the input.
-        """
-        self.do_lower_case = do_lower_case
-        self.never_split = never_split
-
-    def tokenize(self, text):
-        """Tokenizes a piece of text."""
-        text = self._clean_text(text)
-        # This was added on November 1st, 2018 for the multilingual and Chinese
-        # models. This is also applied to the English models now, but it doesn't
-        # matter since the English models were not trained on any Chinese data
-        # and generally don't have any Chinese data in them (there are Chinese
-        # characters in the vocabulary because Wikipedia does have some Chinese
-        # words in the English Wikipedia.).
-        text = self._tokenize_chinese_chars(text)
-        orig_tokens = whitespace_tokenize(text)
-        split_tokens = []
-        for token in orig_tokens:
-            if self.do_lower_case and token not in self.never_split:
-                token = token.lower()
-                token = self._run_strip_accents(token)
-            split_tokens.extend(self._run_split_on_punc(token))
-
-        output_tokens = whitespace_tokenize(" ".join(split_tokens))
-        return output_tokens
-
-    def _run_strip_accents(self, text):
-        """Strips accents from a piece of text."""
-        text = unicodedata.normalize("NFD", text)
-        output = []
-        for char in text:
-            cat = unicodedata.category(char)
-            if cat == "Mn":
-                continue
-            output.append(char)
-        return "".join(output)
-
-    def _run_split_on_punc(self, text):
-        """Splits punctuation on a piece of text."""
-        if text in self.never_split:
-            return [text]
-        chars = list(text)
-        i = 0
-        start_new_word = True
-        output = []
-        while i < len(chars):
-            char = chars[i]
-            if _is_punctuation(char):
-                output.append([char])
-                start_new_word = True
-            else:
-                if start_new_word:
-                    output.append([])
-                start_new_word = False
-                output[-1].append(char)
-            i += 1
-
-        return ["".join(x) for x in output]
-
-    def _tokenize_chinese_chars(self, text):
-        """Adds whitespace around any CJK character."""
-        output = []
-        for char in text:
-            cp = ord(char)
-            if self._is_chinese_char(cp):
-                output.append(" ")
-                output.append(char)
-                output.append(" ")
-            else:
-                output.append(char)
-        return "".join(output)
-
-    def _is_chinese_char(self, cp):
-        """Checks whether CP is the codepoint of a CJK character."""
-        # This defines a "chinese character" as anything in the CJK Unicode block:
-        #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
-        #
-        # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
-        # despite its name. The modern Korean Hangul alphabet is a different block,
-        # as is Japanese Hiragana and Katakana. Those alphabets are used to write
-        # space-separated words, so they are not treated specially and handled
-        # like the all of the other languages.
-        if ((cp >= 0x4E00 and cp <= 0x9FFF) or  #
-                (cp >= 0x3400 and cp <= 0x4DBF) or  #
-                (cp >= 0x20000 and cp <= 0x2A6DF) or  #
-                (cp >= 0x2A700 and cp <= 0x2B73F) or  #
-                (cp >= 0x2B740 and cp <= 0x2B81F) or  #
-                (cp >= 0x2B820 and cp <= 0x2CEAF) or
-                (cp >= 0xF900 and cp <= 0xFAFF) or  #
-                (cp >= 0x2F800 and cp <= 0x2FA1F)):  #
-            return True
-
-        return False
-
-    def _clean_text(self, text):
-        """Performs invalid character removal and whitespace cleanup on text."""
-        output = []
-        for char in text:
-            cp = ord(char)
-            if cp == 0 or cp == 0xfffd or _is_control(char):
-                continue
-            if _is_whitespace(char):
-                output.append(" ")
-            else:
-                output.append(char)
-        return "".join(output)
-
-class WordpieceTokenizer(object):
-    """Runs WordPiece tokenization."""
-
-    def __init__(self, vocab, unk_token="[UNK]", max_input_chars_per_word=100):
-        self.vocab = vocab
-        self.unk_token = unk_token
-        self.max_input_chars_per_word = max_input_chars_per_word
-
-    def tokenize(self, text):
-        """Tokenizes a piece of text into its word pieces.
-
-        This uses a greedy longest-match-first algorithm to perform tokenization
-        using the given vocabulary.
-
-        For example:
-          input = "unaffable"
-          output = ["un", "##aff", "##able"]
-
-        Args:
-          text: A single token or whitespace separated tokens. This should have
-            already been passed through `BasicTokenizer`.
-
-        Returns:
-          A list of wordpiece tokens.
-        """
-
-        output_tokens = []
-        for token in whitespace_tokenize(text):
-            chars = list(token)
-            if len(chars) > self.max_input_chars_per_word:
-                output_tokens.append(self.unk_token)
-                continue
-
-            is_bad = False
-            start = 0
-            sub_tokens = []
-            while start < len(chars):
-                end = len(chars)
-                cur_substr = None
-                while start < end:
-                    substr = "".join(chars[start:end])
-                    if start > 0:
-                        substr = "##" + substr
-                    if substr in self.vocab:
-                        cur_substr = substr
-                        break
-                    end -= 1
-                if cur_substr is None:
-                    is_bad = True
-                    break
-                sub_tokens.append(cur_substr)
-                start = end
-
-            if is_bad:
-                output_tokens.append(self.unk_token)
-            else:
-                output_tokens.extend(sub_tokens)
-        return output_tokens
-
-def _is_whitespace(char):
-    """Checks whether `chars` is a whitespace character."""
-    # \t, \n, and \r are technically contorl characters but we treat them
-    # as whitespace since they are generally considered as such.
-    if char == " " or char == "\t" or char == "\n" or char == "\r":
-        return True
-    cat = unicodedata.category(char)
-    if cat == "Zs":
-        return True
-    return False
-
-
-def _is_control(char):
-    """Checks whether `chars` is a control character."""
-    # These are technically control characters but we count them as whitespace
-    # characters.
-    if char == "\t" or char == "\n" or char == "\r":
-        return False
-    cat = unicodedata.category(char)
-    if cat.startswith("C"):
-        return True
-    return False
-
-
-def _is_punctuation(char):
-    """Checks whether `chars` is a punctuation character."""
-    cp = ord(char)
-    # We treat all non-letter/number ASCII as punctuation.
-    # Characters such as "^", "$", and "`" are not in the Unicode
-    # Punctuation class but we treat them as punctuation anyways, for
-    # consistency.
-    if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or
-            (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)):
-        return True
-    cat = unicodedata.category(char)
-    if cat.startswith("P"):
-        return True
-    return False
diff --git a/backup/pdvc/modules/until_config.py b/backup/pdvc/modules/until_config.py
deleted file mode 100644
index 596c157aa23c82eb33c1fb2e07d9b006a52990e9..0000000000000000000000000000000000000000
--- a/backup/pdvc/modules/until_config.py
+++ /dev/null
@@ -1,126 +0,0 @@
-# coding=utf-8
-# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
-# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""PyTorch BERT model."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import os
-import copy
-import json
-import logging
-import tarfile
-import tempfile
-import shutil
-import torch
-from .file_utils import cached_path
-
-logger = logging.getLogger(__name__)
-
-class PretrainedConfig(object):
-
-    pretrained_model_archive_map = {}
-    config_name = ""
-    weights_name = ""
-
-    @classmethod
-    def get_config(cls, pretrained_model_name, cache_dir, type_vocab_size, state_dict, task_config=None):
-        archive_file = os.path.join(os.path.dirname(os.path.abspath(__file__)), pretrained_model_name)
-        if os.path.exists(archive_file) is False:
-            if pretrained_model_name in cls.pretrained_model_archive_map:
-                archive_file = cls.pretrained_model_archive_map[pretrained_model_name]
-            else:
-                archive_file = pretrained_model_name
-
-        # redirect to the cache, if necessary
-        try:
-            resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir)
-        except FileNotFoundError:
-            if task_config is None or task_config.local_rank == 0:
-                logger.error(
-                    "Model name '{}' was not found in model name list. "
-                    "We assumed '{}' was a path or url but couldn't find any file "
-                    "associated to this path or url.".format(
-                        pretrained_model_name,
-                        archive_file))
-            return None
-        if resolved_archive_file == archive_file:
-            if task_config is None or task_config.local_rank == 0:
-                logger.info("loading archive file {}".format(archive_file))
-        else:
-            if task_config is None or task_config.local_rank == 0:
-                logger.info("loading archive file {} from cache at {}".format(
-                    archive_file, resolved_archive_file))
-        tempdir = None
-        if os.path.isdir(resolved_archive_file):
-            serialization_dir = resolved_archive_file
-        else:
-            # Extract archive to temp dir
-            tempdir = tempfile.mkdtemp()
-            if task_config is None or task_config.local_rank == 0:
-                logger.info("extracting archive file {} to temp dir {}".format(
-                    resolved_archive_file, tempdir))
-            with tarfile.open(resolved_archive_file, 'r:gz') as archive:
-                archive.extractall(tempdir)
-            serialization_dir = tempdir
-        # Load config
-        config_file = os.path.join(serialization_dir, cls.config_name)
-        config = cls.from_json_file(config_file)
-        config.type_vocab_size = type_vocab_size
-        if task_config is None or task_config.local_rank == 0:
-            logger.info("Model config {}".format(config))
-
-        if state_dict is None:
-            weights_path = os.path.join(serialization_dir, cls.weights_name)
-            if os.path.exists(weights_path):
-                state_dict = torch.load(weights_path, map_location='cpu')
-            else:
-                if task_config is None or task_config.local_rank == 0:
-                    logger.info("Weight doesn't exsits. {}".format(weights_path))
-
-        if tempdir:
-            # Clean up temp dir
-            shutil.rmtree(tempdir)
-
-        return config, state_dict
-
-    @classmethod
-    def from_dict(cls, json_object):
-        """Constructs a `BertConfig` from a Python dictionary of parameters."""
-        config = cls(vocab_size_or_config_json_file=-1)
-        for key, value in json_object.items():
-            config.__dict__[key] = value
-        return config
-
-    @classmethod
-    def from_json_file(cls, json_file):
-        """Constructs a `BertConfig` from a json file of parameters."""
-        with open(json_file, "r", encoding='utf-8') as reader:
-            text = reader.read()
-        return cls.from_dict(json.loads(text))
-
-    def __repr__(self):
-        return str(self.to_json_string())
-
-    def to_dict(self):
-        """Serializes this instance to a Python dictionary."""
-        output = copy.deepcopy(self.__dict__)
-        return output
-
-    def to_json_string(self):
-        """Serializes this instance to a JSON string."""
-        return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
\ No newline at end of file
diff --git a/backup/pdvc/modules/until_module.py b/backup/pdvc/modules/until_module.py
deleted file mode 100644
index d550638157f8aeb2116a9cce022b2c563fd3491b..0000000000000000000000000000000000000000
--- a/backup/pdvc/modules/until_module.py
+++ /dev/null
@@ -1,251 +0,0 @@
-# coding=utf-8
-# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
-# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""PyTorch BERT model."""
-
-import logging
-import numpy as np
-import torch
-from torch import nn
-import torch.nn.functional as F
-import math
-from pdvc.modules.until_config import PretrainedConfig
-
-logger = logging.getLogger(__name__)
-
-def gelu(x):
-    """Implementation of the gelu activation function.
-        For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
-        0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
-    """
-    return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
-
-def swish(x):
-    return x * torch.sigmoid(x)
-
-ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish}
-
-class LayerNorm(nn.Module):
-    def __init__(self, hidden_size, eps=1e-12):
-        """Construct a layernorm module in the TF style (epsilon inside the square root).
-        """
-        super(LayerNorm, self).__init__()
-        self.weight = nn.Parameter(torch.ones(hidden_size))
-        self.bias = nn.Parameter(torch.zeros(hidden_size))
-        self.variance_epsilon = eps
-
-    def forward(self, x):
-        u = x.mean(-1, keepdim=True)
-        s = (x - u).pow(2).mean(-1, keepdim=True)
-        x = (x - u) / torch.sqrt(s + self.variance_epsilon)
-        return self.weight * x + self.bias
-
-class PreTrainedModel(nn.Module):
-    """ An abstract class to handle weights initialization and
-        a simple interface for dowloading and loading pretrained models.
-    """
-    def __init__(self, config, *inputs, **kwargs):
-        super(PreTrainedModel, self).__init__()
-        if not isinstance(config, PretrainedConfig):
-            raise ValueError(
-                "Parameter config in `{}(config)` should be an instance of class `PretrainedConfig`. "
-                "To create a model from a Google pretrained model use "
-                "`model = {}.from_pretrained(PRETRAINED_MODEL_NAME)`".format(
-                    self.__class__.__name__, self.__class__.__name__
-                ))
-        self.config = config
-
-    def init_weights(self, module):
-        """ Initialize the weights.
-        """
-        if isinstance(module, (nn.Linear, nn.Embedding)):
-            # Slightly different from the TF version which uses truncated_normal for initialization
-            # cf https://github.com/pytorch/pytorch/pull/5617
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-        elif isinstance(module, LayerNorm):
-            if 'beta' in dir(module) and 'gamma' in dir(module):
-                module.beta.data.zero_()
-                module.gamma.data.fill_(1.0)
-            else:
-                module.bias.data.zero_()
-                module.weight.data.fill_(1.0)
-        if isinstance(module, nn.Linear) and module.bias is not None:
-            module.bias.data.zero_()
-
-    def resize_token_embeddings(self, new_num_tokens=None):
-        raise NotImplementedError
-
-    @classmethod
-    def init_preweight(cls, model, state_dict, prefix=None, task_config=None):
-        old_keys = []
-        new_keys = []
-        for key in state_dict.keys():
-            new_key = None
-            if 'gamma' in key:
-                new_key = key.replace('gamma', 'weight')
-            if 'beta' in key:
-                new_key = key.replace('beta', 'bias')
-            if new_key:
-                old_keys.append(key)
-                new_keys.append(new_key)
-        for old_key, new_key in zip(old_keys, new_keys):
-            state_dict[new_key] = state_dict.pop(old_key)
-
-        if prefix is not None:
-            old_keys = []
-            new_keys = []
-            for key in state_dict.keys():
-                old_keys.append(key)
-                new_keys.append(prefix + key)
-            for old_key, new_key in zip(old_keys, new_keys):
-                state_dict[new_key] = state_dict.pop(old_key)
-
-        missing_keys = []
-        unexpected_keys = []
-        error_msgs = []
-        # copy state_dict so _load_from_state_dict can modify it
-        metadata = getattr(state_dict, '_metadata', None)
-        state_dict = state_dict.copy()
-        if metadata is not None:
-            state_dict._metadata = metadata
-
-        def load(module, prefix=''):
-            local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
-            module._load_from_state_dict(
-                state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs)
-            for name, child in module._modules.items():
-                if child is not None:
-                    load(child, prefix + name + '.')
-
-        load(model, prefix='')
-
-        if prefix is None and (task_config is None or task_config.local_rank == 0):
-            logger.info("-" * 20)
-            if len(missing_keys) > 0:
-                logger.info("Weights of {} not initialized from pretrained model: {}"
-                            .format(model.__class__.__name__, "\n   " + "\n   ".join(missing_keys)))
-            if len(unexpected_keys) > 0:
-                logger.info("Weights from pretrained model not used in {}: {}"
-                            .format(model.__class__.__name__, "\n   " + "\n   ".join(unexpected_keys)))
-            if len(error_msgs) > 0:
-                logger.error("Weights from pretrained model cause errors in {}: {}"
-                             .format(model.__class__.__name__, "\n   " + "\n   ".join(error_msgs)))
-
-        return model
-
-    @property
-    def dtype(self):
-        """
-        :obj:`torch.dtype`: The dtype of the module (assuming that all the module parameters have the same dtype).
-        """
-        try:
-            return next(self.parameters()).dtype
-        except StopIteration:
-            # For nn.DataParallel compatibility in PyTorch 1.5
-            def find_tensor_attributes(module: nn.Module):
-                tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)]
-                return tuples
-
-            gen = self._named_members(get_members_fn=find_tensor_attributes)
-            first_tuple = next(gen)
-            return first_tuple[1].dtype
-
-    @classmethod
-    def from_pretrained(cls, config, state_dict=None,  *inputs, **kwargs):
-        """
-        Instantiate a PreTrainedModel from a pre-trained model file or a pytorch state dict.
-        Download and cache the pre-trained model file if needed.
-        """
-        # Instantiate model.
-        model = cls(config, *inputs, **kwargs)
-        if state_dict is None:
-            return model
-        model = cls.init_preweight(model, state_dict)
-
-        return model
-
-##################################
-###### LOSS FUNCTION #############
-##################################
-class CrossEn(nn.Module):
-    def __init__(self,):
-        super(CrossEn, self).__init__()
-
-    def forward(self, sim_matrix):
-        logpt = F.log_softmax(sim_matrix, dim=-1)
-        logpt = torch.diag(logpt)
-        nce_loss = -logpt
-        sim_loss = nce_loss.mean()
-        return sim_loss
-
-class MILNCELoss(nn.Module):
-    def __init__(self, batch_size=1, n_pair=1,):
-        super(MILNCELoss, self).__init__()
-        self.batch_size = batch_size
-        self.n_pair = n_pair
-        torch_v = float(".".join(torch.__version__.split(".")[:2]))
-        self.bool_dtype = torch.bool if torch_v >= 1.3 else torch.uint8
-
-    def forward(self, sim_matrix):
-        mm_mask = np.eye(self.batch_size)
-        mm_mask = np.kron(mm_mask, np.ones((self.n_pair, self.n_pair)))
-        mm_mask = torch.tensor(mm_mask).float().to(sim_matrix.device)
-
-        from_text_matrix = sim_matrix + mm_mask * -1e12
-        from_video_matrix = sim_matrix.transpose(1, 0)
-
-        new_sim_matrix = torch.cat([from_video_matrix, from_text_matrix], dim=-1)
-        logpt = F.log_softmax(new_sim_matrix, dim=-1)
-
-        mm_mask_logpt = torch.cat([mm_mask, torch.zeros_like(mm_mask)], dim=-1)
-        masked_logpt = logpt + (torch.ones_like(mm_mask_logpt) - mm_mask_logpt) * -1e12
-
-        new_logpt = -torch.logsumexp(masked_logpt, dim=-1)
-
-        logpt_choice = torch.zeros_like(new_logpt)
-        mark_ind = torch.arange(self.batch_size).to(sim_matrix.device) * self.n_pair + (self.n_pair//2)
-        logpt_choice[mark_ind] = 1
-        sim_loss = new_logpt.masked_select(logpt_choice.to(dtype=self.bool_dtype)).mean()
-        return sim_loss
-
-class MaxMarginRankingLoss(nn.Module):
-    def __init__(self,
-                 margin=1.0,
-                 negative_weighting=False,
-                 batch_size=1,
-                 n_pair=1,
-                 hard_negative_rate=0.5,
-        ):
-        super(MaxMarginRankingLoss, self).__init__()
-        self.margin = margin
-        self.n_pair = n_pair
-        self.batch_size = batch_size
-        easy_negative_rate = 1 - hard_negative_rate
-        self.easy_negative_rate = easy_negative_rate
-        self.negative_weighting = negative_weighting
-        if n_pair > 1 and batch_size > 1:
-            alpha = easy_negative_rate / ((batch_size - 1) * (1 - easy_negative_rate))
-            mm_mask = (1 - alpha) * np.eye(self.batch_size) + alpha
-            mm_mask = np.kron(mm_mask, np.ones((n_pair, n_pair)))
-            mm_mask = torch.tensor(mm_mask) * (batch_size * (1 - easy_negative_rate))
-            self.mm_mask = mm_mask.float()
-
-    def forward(self, x):
-        d = torch.diag(x)
-        max_margin = F.relu(self.margin + x - d.view(-1, 1)) + \
-                     F.relu(self.margin + x - d.view(1, -1))
-        if self.negative_weighting and self.n_pair > 1 and self.batch_size > 1:
-            max_margin = max_margin * self.mm_mask.to(max_margin.device)
-        return max_margin.mean()
\ No newline at end of file
diff --git a/backup/pdvc/modules/visual-base/visual_config.json b/backup/pdvc/modules/visual-base/visual_config.json
deleted file mode 100644
index 324fcb6e7ba63166767adf9afa82324412247a48..0000000000000000000000000000000000000000
--- a/backup/pdvc/modules/visual-base/visual_config.json
+++ /dev/null
@@ -1,12 +0,0 @@
-{
-  "attention_probs_dropout_prob": 0.1,
-  "hidden_act": "gelu",
-  "hidden_dropout_prob": 0.1,
-  "hidden_size": 768,
-  "initializer_range": 0.02,
-  "intermediate_size": 3072,
-  "max_position_embeddings": 512,
-  "num_attention_heads": 12,
-  "num_hidden_layers": 1,
-  "vocab_size": 1024
-}
diff --git a/backup/pdvc/ops/MultiScaleDeformableAttention.egg-info/PKG-INFO b/backup/pdvc/ops/MultiScaleDeformableAttention.egg-info/PKG-INFO
deleted file mode 100644
index 5f86c9097b3b6f4b7f50b9d70f7cd58b2f386871..0000000000000000000000000000000000000000
--- a/backup/pdvc/ops/MultiScaleDeformableAttention.egg-info/PKG-INFO
+++ /dev/null
@@ -1,6 +0,0 @@
-Metadata-Version: 2.1
-Name: MultiScaleDeformableAttention
-Version: 1.0
-Summary: PyTorch Wrapper for CUDA Functions of Multi-Scale Deformable Attention
-Home-page: https://github.com/fundamentalvision/Deformable-DETR
-Author: Weijie Su
diff --git a/backup/pdvc/ops/MultiScaleDeformableAttention.egg-info/SOURCES.txt b/backup/pdvc/ops/MultiScaleDeformableAttention.egg-info/SOURCES.txt
deleted file mode 100644
index cc251e74aff93cae99a730109d3f696ef326b210..0000000000000000000000000000000000000000
--- a/backup/pdvc/ops/MultiScaleDeformableAttention.egg-info/SOURCES.txt
+++ /dev/null
@@ -1,13 +0,0 @@
-setup.py
-/cpfs01/user/liuhuabin/PDVC/pdvc/ops/src/vision.cpp
-/cpfs01/user/liuhuabin/PDVC/pdvc/ops/src/cpu/ms_deform_attn_cpu.cpp
-/cpfs01/user/liuhuabin/PDVC/pdvc/ops/src/cuda/ms_deform_attn_cuda.cu
-MultiScaleDeformableAttention.egg-info/PKG-INFO
-MultiScaleDeformableAttention.egg-info/SOURCES.txt
-MultiScaleDeformableAttention.egg-info/dependency_links.txt
-MultiScaleDeformableAttention.egg-info/top_level.txt
-functions/__init__.py
-functions/ms_deform_attn_func.py
-modules/__init__.py
-modules/ms_deform_attn.py
-modules/ms_deform_attn_for_caption.py
\ No newline at end of file
diff --git a/backup/pdvc/ops/MultiScaleDeformableAttention.egg-info/dependency_links.txt b/backup/pdvc/ops/MultiScaleDeformableAttention.egg-info/dependency_links.txt
deleted file mode 100644
index 8b137891791fe96927ad78e64b0aad7bded08bdc..0000000000000000000000000000000000000000
--- a/backup/pdvc/ops/MultiScaleDeformableAttention.egg-info/dependency_links.txt
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/backup/pdvc/ops/MultiScaleDeformableAttention.egg-info/top_level.txt b/backup/pdvc/ops/MultiScaleDeformableAttention.egg-info/top_level.txt
deleted file mode 100644
index 25d8f7790d14d04a74c6acec779aedb3688ef630..0000000000000000000000000000000000000000
--- a/backup/pdvc/ops/MultiScaleDeformableAttention.egg-info/top_level.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-MultiScaleDeformableAttention
-functions
-modules
diff --git a/backup/pdvc/ops/__init__.py b/backup/pdvc/ops/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/backup/pdvc/ops/__pycache__/__init__.cpython-37.pyc b/backup/pdvc/ops/__pycache__/__init__.cpython-37.pyc
deleted file mode 100644
index 5ed3d8ddca46efead59543bfd2f1961790abdc96..0000000000000000000000000000000000000000
Binary files a/backup/pdvc/ops/__pycache__/__init__.cpython-37.pyc and /dev/null differ
diff --git a/backup/pdvc/ops/__pycache__/__init__.cpython-38.pyc b/backup/pdvc/ops/__pycache__/__init__.cpython-38.pyc
deleted file mode 100644
index 0c722836c6716e99f5a33542ebc2461e4540b9c0..0000000000000000000000000000000000000000
Binary files a/backup/pdvc/ops/__pycache__/__init__.cpython-38.pyc and /dev/null differ
diff --git a/backup/pdvc/ops/build/lib.linux-x86_64-cpython-37/MultiScaleDeformableAttention.cpython-37m-x86_64-linux-gnu.so b/backup/pdvc/ops/build/lib.linux-x86_64-cpython-37/MultiScaleDeformableAttention.cpython-37m-x86_64-linux-gnu.so
deleted file mode 100644
index affe1b85a7c92a8c1ecfca0d0b2c329ce77bf383..0000000000000000000000000000000000000000
--- a/backup/pdvc/ops/build/lib.linux-x86_64-cpython-37/MultiScaleDeformableAttention.cpython-37m-x86_64-linux-gnu.so
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:da5720c8c8f59f4168baf51ec63ba9c5f5e90d5abb998c0fbdd6170547d23a13
-size 7942000
diff --git a/backup/pdvc/ops/build/lib.linux-x86_64-cpython-37/functions/__init__.py b/backup/pdvc/ops/build/lib.linux-x86_64-cpython-37/functions/__init__.py
deleted file mode 100644
index 2f682455af45d3687f0266acce6018741fe7c303..0000000000000000000000000000000000000000
--- a/backup/pdvc/ops/build/lib.linux-x86_64-cpython-37/functions/__init__.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# ------------------------------------------------------------------------------------------------
-# 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_func import MSDeformAttnFunction, ms_deform_attn_core_pytorch
-
diff --git a/backup/pdvc/ops/build/lib.linux-x86_64-cpython-37/functions/ms_deform_attn_func.py b/backup/pdvc/ops/build/lib.linux-x86_64-cpython-37/functions/ms_deform_attn_func.py
deleted file mode 100644
index c59ddc33cf54f23c8b38e192c1421f0c79ebd38b..0000000000000000000000000000000000000000
--- a/backup/pdvc/ops/build/lib.linux-x86_64-cpython-37/functions/ms_deform_attn_func.py
+++ /dev/null
@@ -1,71 +0,0 @@
-# ------------------------------------------------------------------------------------------------
-# 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 __future__ import absolute_import
-from __future__ import print_function
-from __future__ import division
-
-import torch
-import torch.nn.functional as F
-from torch.autograd import Function
-from torch.autograd.function import once_differentiable
-
-try:
-    import MultiScaleDeformableAttention as MSDA
-except:
-    pass
-
-class MSDeformAttnFunction(Function):
-    @staticmethod
-    def forward(ctx, value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, im2col_step):
-        # sampling_locations:(...,2), the first item of last dim means x axis corresponding to w, and second item of the last dim means y, corresponding to h.
-        ctx.im2col_step = im2col_step
-        output = MSDA.ms_deform_attn_forward(
-            value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, ctx.im2col_step)
-        ctx.save_for_backward(value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights)
-        return output
-
-    @staticmethod
-    @once_differentiable
-    def backward(ctx, grad_output):
-        value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights = ctx.saved_tensors
-        grad_value, grad_sampling_loc, grad_attn_weight = \
-            MSDA.ms_deform_attn_backward(
-                value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, grad_output, ctx.im2col_step)
-
-        return grad_value, None, None, grad_sampling_loc, grad_attn_weight, None
-
-
-def ms_deform_attn_core_pytorch(value, value_spatial_shapes, sampling_locations, attention_weights, return_value=False):
-    # for debug and test only,
-    # need to use cuda version instead
-    N_, S_, M_, D_ = value.shape    # N_: batch size , S_: \sum_H*W, M_ : head number, D_: feature dim of each head
-
-    _, Lq_, M_, L_, P_, _ = sampling_locations.shape  # Lq_: \sum H*W, L_: multi-scale number, P_: number of sampled key points
-
-    value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes], dim=1)
-    sampling_grids = 2 * sampling_locations - 1 # convert value from range[0,1] to [-1, 1]
-    sampling_value_list = []
-    for lid_, (H_, W_) in enumerate(value_spatial_shapes):
-        # N_, H_*W_, M_, D_ -> N_, H_*W_, M_*D_ -> N_, M_*D_, H_*W_ -> N_*M_, D_, H_, W_
-        value_l_ = value_list[lid_].flatten(2).transpose(1, 2).reshape(N_*M_, D_, H_, W_)
-        # N_, Lq_, M_, P_, 2 -> N_, M_, Lq_, P_, 2 -> N_*M_, Lq_, P_, 2
-        sampling_grid_l_ = sampling_grids[:, :, :, lid_].transpose(1, 2).flatten(0, 1)
-        # sampling_grid_l_: (...,2), the first item of last dim means x axis corresponding to w, and second item of the last dim means y, corresponding to h.
-        # N_*M_, D_, Lq_, P_
-        sampling_value_l_ = F.grid_sample(value_l_, sampling_grid_l_,
-                                          mode='bilinear', padding_mode='border', align_corners=False)
-        sampling_value_list.append(sampling_value_l_)
-    # (N_, Lq_, M_, L_, P_) -> (N_, M_, Lq_, L_, P_) -> (N_, M_, 1, Lq_, L_*P_)
-    attention_weights = attention_weights.transpose(1, 2).reshape(N_*M_, 1, Lq_, L_*P_)
-
-    if return_value:
-        return torch.stack(sampling_value_list, dim=-2)
-    #(N_ * M_, D_, Lq_, L_* P_) * (N_*M_, 1, Lq_, L_*P_) --> (N_*M_, D_, Lq_)
-    output = (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights).sum(-1).view(N_, M_*D_, Lq_)
-    return output.transpose(1, 2).contiguous()
diff --git a/backup/pdvc/ops/build/lib.linux-x86_64-cpython-37/modules/__init__.py b/backup/pdvc/ops/build/lib.linux-x86_64-cpython-37/modules/__init__.py
deleted file mode 100644
index ceef895ac021db2b6b1762dda3d65c433e09e6e9..0000000000000000000000000000000000000000
--- a/backup/pdvc/ops/build/lib.linux-x86_64-cpython-37/modules/__init__.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# ------------------------------------------------------------------------------------------------
-# 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
-from .ms_deform_attn_for_caption import MSDeformAttnCap
\ No newline at end of file
diff --git a/backup/pdvc/ops/build/lib.linux-x86_64-cpython-37/modules/ms_deform_attn.py b/backup/pdvc/ops/build/lib.linux-x86_64-cpython-37/modules/ms_deform_attn.py
deleted file mode 100644
index 7983d9f64fcff74e89823ad6d7164255f26dda52..0000000000000000000000000000000000000000
--- a/backup/pdvc/ops/build/lib.linux-x86_64-cpython-37/modules/ms_deform_attn.py
+++ /dev/null
@@ -1,126 +0,0 @@
-# ------------------------------------------------------------------------------------------------
-# 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 __future__ import absolute_import
-from __future__ import print_function
-from __future__ import division
-
-import warnings
-import math
-
-import torch
-from torch import nn
-import torch.nn.functional as F
-from torch.nn.init import xavier_uniform_, constant_
-
-from ..functions import MSDeformAttnFunction, ms_deform_attn_core_pytorch
-
-
-def _is_power_of_2(n):
-    if (not isinstance(n, int)) or (n < 0):
-        raise ValueError("invalid input for _is_power_of_2: {} (type: {})".format(n, type(n)))
-    return (n & (n-1) == 0) and n != 0
-
-
-class MSDeformAttn(nn.Module):
-    def __init__(self, d_model=256, n_levels=4, n_heads=8, n_points=4):
-        """
-        Multi-Scale Deformable Attention Module
-        :param d_model      hidden dimension
-        :param n_levels     number of feature levels
-        :param n_heads      number of attention heads
-        :param n_points     number of sampling points per attention head per feature level
-        """
-        super().__init__()
-        if d_model % n_heads != 0:
-            raise ValueError('d_model must be divisible by n_heads, but got {} and {}'.format(d_model, n_heads))
-        _d_per_head = d_model // n_heads
-        # you'd better set _d_per_head to a power of 2 which is more efficient in our CUDA implementation
-        if not _is_power_of_2(_d_per_head):
-            warnings.warn("You'd better set d_model in MSDeformAttn to make the dimension of each attention head a power of 2 "
-                          "which is more efficient in our CUDA implementation.")
-
-        self.im2col_step = 64
-
-        self.d_model = d_model
-        self.n_levels = n_levels
-        self.n_heads = n_heads
-        self.n_points = n_points
-
-        self.sampling_offsets = nn.Linear(d_model, n_heads * n_levels * n_points )
-        self.attention_weights = nn.Linear(d_model, n_heads * n_levels * n_points)
-        self.value_proj = nn.Linear(d_model, d_model)
-        self.output_proj = nn.Linear(d_model, d_model)
-
-        self._reset_parameters()
-
-    def _reset_parameters(self):
-        constant_(self.sampling_offsets.weight.data, 0.)
-        thetas = torch.arange(self.n_heads, dtype=torch.float32) * (2 * math.pi / self.n_heads)
-        grid_init = torch.stack([thetas.cos(), thetas.sin()], -1)
-        grid_init = (grid_init / grid_init.abs().max(-1, keepdim=True)[0]).view(self.n_heads, 1, 1, 2)
-        grid_init = grid_init[..., 0].repeat(1, self.n_levels, self.n_points)
-        for i in range(self.n_points):
-            grid_init[:, :, i] *= i + 1
-        with torch.no_grad():
-            self.sampling_offsets.bias = nn.Parameter(grid_init.view(-1))
-        constant_(self.attention_weights.weight.data, 0.)
-        constant_(self.attention_weights.bias.data, 0.)
-        xavier_uniform_(self.value_proj.weight.data)
-        constant_(self.value_proj.bias.data, 0.)
-        xavier_uniform_(self.output_proj.weight.data)
-        constant_(self.output_proj.bias.data, 0.)
-
-    def forward(self, query, reference_points, input_flatten, input_spatial_shapes, input_level_start_index, input_padding_mask=None):
-        """
-        :param query                       (N, Length_{query}, C)
-        :param reference_points            (N, Length_{query}, n_levels, 1), range in [0, 1], including padding area
-                                        or (N, Length_{query}, n_levels, 2), add additional (c, l) to form reference boxes
-        :param input_flatten               (N, \sum_{l=0}^{L-1} T_l, C)
-        :param input_spatial_shapes        (n_levels ), [T_0, T_1, ..., T_{L-1}]
-        :param input_level_start_index     (n_levels ), [0, 1_0, T_0+T_1, ...]
-        :param input_padding_mask          (N, \sum_{l=0}^{L-1} H_l \cdot W_l), True for padding elements, False for non-padding elements
-
-        :return output                     (N, Length_{query}, C)
-        """
-        N, Len_q, _ = query.shape
-        N, Len_in, _ = input_flatten.shape
-        assert input_spatial_shapes.sum() == Len_in
-
-        value = self.value_proj(input_flatten)
-        if input_padding_mask is not None:
-            value = value.masked_fill(input_padding_mask[..., None], float(0))
-        value = value.view(N, Len_in, self.n_heads, self.d_model // self.n_heads)
-        sampling_offsets = self.sampling_offsets(query).view(N, Len_q, self.n_heads, self.n_levels, self.n_points)
-        attention_weights = self.attention_weights(query).view(N, Len_q, self.n_heads, self.n_levels * self.n_points)
-        attention_weights = F.softmax(attention_weights, -1).view(N, Len_q, self.n_heads, self.n_levels, self.n_points)
-        # N, Len_q, n_heads, n_levels, n_points, 2
-        if reference_points.shape[-1] == 1:
-            offset_normalizer = input_spatial_shapes
-            sampling_locations = reference_points[:, :, None, :, None, 0] \
-                                 + sampling_offsets / offset_normalizer[None, None, None, :, None]
-        elif reference_points.shape[-1] == 2:
-            sampling_locations = reference_points[:, :, None, :, None, 0] \
-                                 + sampling_offsets / self.n_points * reference_points[:, :, None, :, None, 1] * 0.5
-        else:
-            raise ValueError(
-                'Last dim of reference_points must be 1 or 2, but get {} instead.'.format(reference_points.shape[-1]))
-
-        if True:
-            sampling_locations = torch.stack(
-                (sampling_locations, 0.5 * sampling_locations.new_ones(sampling_locations.shape)), -1)
-            input_spatial_shapes = torch.stack([input_spatial_shapes.new_ones(input_spatial_shapes.shape), input_spatial_shapes], -1)
-
-        if query.device.type == 'cuda':
-            output = MSDeformAttnFunction.apply(
-                value, input_spatial_shapes, input_level_start_index, sampling_locations, attention_weights,
-                self.im2col_step)
-        else:
-            output = ms_deform_attn_core_pytorch(value, input_spatial_shapes, sampling_locations, attention_weights)
-        output = self.output_proj(output)
-        return output
diff --git a/backup/pdvc/ops/build/lib.linux-x86_64-cpython-37/modules/ms_deform_attn_for_caption.py b/backup/pdvc/ops/build/lib.linux-x86_64-cpython-37/modules/ms_deform_attn_for_caption.py
deleted file mode 100644
index a6fdc1c220e13146864818a0f79225ca47c7394f..0000000000000000000000000000000000000000
--- a/backup/pdvc/ops/build/lib.linux-x86_64-cpython-37/modules/ms_deform_attn_for_caption.py
+++ /dev/null
@@ -1,123 +0,0 @@
-# ------------------------------------------------------------------------------------------------
-# 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 __future__ import absolute_import
-from __future__ import print_function
-from __future__ import division
-
-import warnings
-import math
-
-import torch
-from torch import nn
-import torch.nn.functional as F
-from torch.nn.init import xavier_uniform_, constant_
-
-from ..functions import MSDeformAttnFunction, ms_deform_attn_core_pytorch
-
-
-def _is_power_of_2(n):
-    if (not isinstance(n, int)) or (n < 0):
-        raise ValueError("invalid input for _is_power_of_2: {} (type: {})".format(n, type(n)))
-    return (n & (n-1) == 0) and n != 0
-
-
-class MSDeformAttnCap(nn.Module):
-    def __init__(self, d_model=256, n_levels=4, n_heads=8, n_points=4,):
-        """
-        Multi-Scale Deformable Attention Module
-        :param d_model      hidden dimension
-        :param n_levels     number of feature levels
-        :param n_heads      number of attention heads
-        :param n_points     number of sampling points per attention head per feature level
-        """
-        super().__init__()
-        if d_model % n_heads != 0:
-            raise ValueError('d_model must be divisible by n_heads, but got {} and {}'.format(d_model, n_heads))
-        _d_per_head = d_model // n_heads
-        # you'd better set _d_per_head to a power of 2 which is more efficient in our CUDA implementation
-        if not _is_power_of_2(_d_per_head):
-            warnings.warn("You'd better set d_model in MSDeformAttn to make the dimension of each attention head a power of 2 "
-                          "which is more efficient in our CUDA implementation.")
-
-        self.im2col_step = 64
-        self.d_model = d_model
-        self.n_levels = n_levels
-        self.n_heads = n_heads
-        self.n_points = n_points
-
-        self.sampling_offsets = nn.Linear(2 * d_model, n_heads * n_levels * n_points)
-        self.attention_weights = nn.Linear(2 * d_model, n_heads * n_levels * n_points)
-        self.value_proj = nn.Linear(d_model, d_model)
-        self.output_proj = nn.Linear(d_model, d_model)
-        self._reset_parameters()
-
-    def _reset_parameters(self):
-        constant_(self.sampling_offsets.weight.data, 0.)
-        thetas = torch.arange(self.n_heads, dtype=torch.float32) * (2.0 * math.pi / self.n_heads)
-        grid_init = torch.stack([thetas.cos(), thetas.sin()], -1)
-        grid_init = (grid_init / grid_init.abs().max(-1, keepdim=True)[0]).view(self.n_heads, 1, 1, 2)
-        grid_init = grid_init[..., 0].repeat(1, self.n_levels, self.n_points)
-        for i in range(self.n_points):
-            grid_init[:, :, i] *= i + 1
-        grid_init = grid_init - grid_init.mean(2, keepdim=True)
-        with torch.no_grad():
-            self.sampling_offsets.bias = nn.Parameter(grid_init.view(-1))
-        constant_(self.attention_weights.weight.data, 0.)
-        constant_(self.attention_weights.bias.data, 0.)
-        xavier_uniform_(self.value_proj.weight.data)
-        constant_(self.value_proj.bias.data, 0.)
-        xavier_uniform_(self.output_proj.weight.data)
-        constant_(self.output_proj.bias.data, 0.)
-
-    def forward(self, query, reference_points, input_flatten, input_spatial_shapes, input_level_start_index, input_padding_mask=None):
-        """
-        :param query                       (N, Length_{query}, C)
-        :param reference_points            (N, Length_{query}, n_levels, 1), range in [0, 1], including padding area
-                                        or (N, Length_{query}, n_levels, 2), add additional (c, l) to form reference boxes
-        :param input_flatten               (N, \sum_{l=0}^{L-1} T_l, C)
-        :param input_spatial_shapes        (n_levels ), [T_0, T_1, ..., T_{L-1}]
-        :param input_level_start_index     (n_levels ), [0, 1_0, T_0+T_1, ...]
-        :param input_padding_mask          (N, \sum_{l=0}^{L-1} H_l \cdot W_l), True for padding elements, False for non-padding elements
-
-        :return output                     (N, Length_{query}, C)
-        """
-        N, Len_q, _ = query.shape
-        N, Len_in, _ = input_flatten.shape
-        assert input_spatial_shapes.sum() == Len_in
-
-        value = self.value_proj(input_flatten)
-        if input_padding_mask is not None:
-            value = value.masked_fill(input_padding_mask[..., None], float(0))
-        value = value.view(N, Len_in, self.n_heads, self.d_model // self.n_heads)
-        sampling_offsets = self.sampling_offsets(query).view(N, Len_q, self.n_heads, self.n_levels, self.n_points)
-        attention_weights = self.attention_weights(query).view(N, Len_q, self.n_heads, self.n_levels * self.n_points)
-        attention_weights = F.softmax(attention_weights, -1).view(N, Len_q, self.n_heads, self.n_levels, self.n_points)
-        # N, Len_q, n_heads, n_levels, n_points, 1
-        if reference_points.shape[-1] == 1:
-            offset_normalizer = input_spatial_shapes
-            sampling_locations = reference_points[:, :, None, :, None, 0] \
-                                 + sampling_offsets / offset_normalizer[None, None, None, :, None]
-        elif reference_points.shape[-1] == 2:
-            sampling_locations = reference_points[:, :, None, :, None, 0] \
-                                 + sampling_offsets / self.n_points * reference_points[:, :, None, :, None, 1] * 0.5
-        else:
-            raise ValueError(
-                'Last dim of reference_points must be 1 or 2, but get {} instead.'.format(reference_points.shape[-1]))
-
-
-
-        if True:
-            sampling_locations = torch.stack(
-                (sampling_locations, 0.5 * sampling_locations.new_ones(sampling_locations.shape)), -1)
-            input_spatial_shapes = torch.stack([input_spatial_shapes.new_ones(input_spatial_shapes.shape), input_spatial_shapes], -1)
-
-        output = ms_deform_attn_core_pytorch(value, input_spatial_shapes, sampling_locations, attention_weights,
-                                             return_value=True)
-
-        return output
diff --git a/backup/pdvc/ops/build/temp.linux-x86_64-cpython-37/.ninja_deps b/backup/pdvc/ops/build/temp.linux-x86_64-cpython-37/.ninja_deps
deleted file mode 100644
index 2bef29d420f02b4282644cba394698912212dab8..0000000000000000000000000000000000000000
Binary files a/backup/pdvc/ops/build/temp.linux-x86_64-cpython-37/.ninja_deps and /dev/null differ
diff --git a/backup/pdvc/ops/build/temp.linux-x86_64-cpython-37/.ninja_log b/backup/pdvc/ops/build/temp.linux-x86_64-cpython-37/.ninja_log
deleted file mode 100644
index fd78ae63cd064bb569f9279931f2e0668833f50d..0000000000000000000000000000000000000000
--- a/backup/pdvc/ops/build/temp.linux-x86_64-cpython-37/.ninja_log
+++ /dev/null
@@ -1,4 +0,0 @@
-# ninja log v5
-0	2930	1685020146224081877	/cpfs01/user/liuhuabin/PDVC/pdvc/ops/build/temp.linux-x86_64-cpython-37/cpfs01/user/liuhuabin/PDVC/pdvc/ops/src/cpu/ms_deform_attn_cpu.o	8f7db54445222f0
-0	10580	1685020153869972218	/cpfs01/user/liuhuabin/PDVC/pdvc/ops/build/temp.linux-x86_64-cpython-37/cpfs01/user/liuhuabin/PDVC/pdvc/ops/src/cuda/ms_deform_attn_cuda.o	91f10249ca524b9b
-0	13795	1685020157081510628	/cpfs01/user/liuhuabin/PDVC/pdvc/ops/build/temp.linux-x86_64-cpython-37/cpfs01/user/liuhuabin/PDVC/pdvc/ops/src/vision.o	3e48c35d2c631cee
diff --git a/backup/pdvc/ops/build/temp.linux-x86_64-cpython-37/build.ninja b/backup/pdvc/ops/build/temp.linux-x86_64-cpython-37/build.ninja
deleted file mode 100644
index 9d156fb45877ed14f310b8ae1f889c048fe0fa2b..0000000000000000000000000000000000000000
--- a/backup/pdvc/ops/build/temp.linux-x86_64-cpython-37/build.ninja
+++ /dev/null
@@ -1,30 +0,0 @@
-ninja_required_version = 1.3
-cxx = c++
-nvcc = /usr/local/cuda/bin/nvcc
-
-cflags = -pthread -B /home/liuhuabin/miniconda3/envs/PDVC/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -DWITH_CUDA -I/cpfs01/user/liuhuabin/PDVC/pdvc/ops/src -I/home/liuhuabin/miniconda3/envs/PDVC/lib/python3.7/site-packages/torch/include -I/home/liuhuabin/miniconda3/envs/PDVC/lib/python3.7/site-packages/torch/include/torch/csrc/api/include -I/home/liuhuabin/miniconda3/envs/PDVC/lib/python3.7/site-packages/torch/include/TH -I/home/liuhuabin/miniconda3/envs/PDVC/lib/python3.7/site-packages/torch/include/THC -I/usr/local/cuda/include -I/home/liuhuabin/miniconda3/envs/PDVC/include/python3.7m -c
-post_cflags = -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_gcc"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1011"' -DTORCH_EXTENSION_NAME=MultiScaleDeformableAttention -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++14
-cuda_cflags = -DWITH_CUDA -I/cpfs01/user/liuhuabin/PDVC/pdvc/ops/src -I/home/liuhuabin/miniconda3/envs/PDVC/lib/python3.7/site-packages/torch/include -I/home/liuhuabin/miniconda3/envs/PDVC/lib/python3.7/site-packages/torch/include/torch/csrc/api/include -I/home/liuhuabin/miniconda3/envs/PDVC/lib/python3.7/site-packages/torch/include/TH -I/home/liuhuabin/miniconda3/envs/PDVC/lib/python3.7/site-packages/torch/include/THC -I/usr/local/cuda/include -I/home/liuhuabin/miniconda3/envs/PDVC/include/python3.7m -c
-cuda_post_cflags = -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options ''"'"'-fPIC'"'"'' -DCUDA_HAS_FP16=1 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_gcc"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1011"' -DTORCH_EXTENSION_NAME=MultiScaleDeformableAttention -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_80,code=compute_80 -gencode=arch=compute_80,code=sm_80 -std=c++14
-ldflags = 
-
-rule compile
-  command = $cxx -MMD -MF $out.d $cflags -c $in -o $out $post_cflags
-  depfile = $out.d
-  deps = gcc
-
-rule cuda_compile
-  depfile = $out.d
-  deps = gcc
-  command = $nvcc --generate-dependencies-with-compile --dependency-output $out.d $cuda_cflags -c $in -o $out $cuda_post_cflags
-
-
-
-build /cpfs01/user/liuhuabin/PDVC/pdvc/ops/build/temp.linux-x86_64-cpython-37/cpfs01/user/liuhuabin/PDVC/pdvc/ops/src/cpu/ms_deform_attn_cpu.o: compile /cpfs01/user/liuhuabin/PDVC/pdvc/ops/src/cpu/ms_deform_attn_cpu.cpp
-build /cpfs01/user/liuhuabin/PDVC/pdvc/ops/build/temp.linux-x86_64-cpython-37/cpfs01/user/liuhuabin/PDVC/pdvc/ops/src/cuda/ms_deform_attn_cuda.o: cuda_compile /cpfs01/user/liuhuabin/PDVC/pdvc/ops/src/cuda/ms_deform_attn_cuda.cu
-build /cpfs01/user/liuhuabin/PDVC/pdvc/ops/build/temp.linux-x86_64-cpython-37/cpfs01/user/liuhuabin/PDVC/pdvc/ops/src/vision.o: compile /cpfs01/user/liuhuabin/PDVC/pdvc/ops/src/vision.cpp
-
-
-
-
-
diff --git a/backup/pdvc/ops/build/temp.linux-x86_64-cpython-37/cpfs01/user/liuhuabin/PDVC/pdvc/ops/src/cpu/ms_deform_attn_cpu.o b/backup/pdvc/ops/build/temp.linux-x86_64-cpython-37/cpfs01/user/liuhuabin/PDVC/pdvc/ops/src/cpu/ms_deform_attn_cpu.o
deleted file mode 100644
index d30f1ff54acc23e3e0f5ea22b3a8828fdd2c44b7..0000000000000000000000000000000000000000
--- a/backup/pdvc/ops/build/temp.linux-x86_64-cpython-37/cpfs01/user/liuhuabin/PDVC/pdvc/ops/src/cpu/ms_deform_attn_cpu.o
+++ /dev/null
@@ -1,3 +0,0 @@
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diff --git a/backup/pdvc/ops/build/temp.linux-x86_64-cpython-37/cpfs01/user/liuhuabin/PDVC/pdvc/ops/src/cuda/ms_deform_attn_cuda.o b/backup/pdvc/ops/build/temp.linux-x86_64-cpython-37/cpfs01/user/liuhuabin/PDVC/pdvc/ops/src/cuda/ms_deform_attn_cuda.o
deleted file mode 100644
index d9274a1b895a7c123eab8231e2e24c2ea6629581..0000000000000000000000000000000000000000
--- a/backup/pdvc/ops/build/temp.linux-x86_64-cpython-37/cpfs01/user/liuhuabin/PDVC/pdvc/ops/src/cuda/ms_deform_attn_cuda.o
+++ /dev/null
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diff --git a/backup/pdvc/ops/build/temp.linux-x86_64-cpython-37/cpfs01/user/liuhuabin/PDVC/pdvc/ops/src/vision.o b/backup/pdvc/ops/build/temp.linux-x86_64-cpython-37/cpfs01/user/liuhuabin/PDVC/pdvc/ops/src/vision.o
deleted file mode 100644
index e771be34bcbacfa86a2e41f1728b9d0b2fef3a85..0000000000000000000000000000000000000000
--- a/backup/pdvc/ops/build/temp.linux-x86_64-cpython-37/cpfs01/user/liuhuabin/PDVC/pdvc/ops/src/vision.o
+++ /dev/null
@@ -1,3 +0,0 @@
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diff --git a/backup/pdvc/ops/dist/MultiScaleDeformableAttention-1.0-py3.7-linux-x86_64.egg b/backup/pdvc/ops/dist/MultiScaleDeformableAttention-1.0-py3.7-linux-x86_64.egg
deleted file mode 100644
index dc5bbc86e1f4304b490711416d30dbeecec3a2b8..0000000000000000000000000000000000000000
--- a/backup/pdvc/ops/dist/MultiScaleDeformableAttention-1.0-py3.7-linux-x86_64.egg
+++ /dev/null
@@ -1,3 +0,0 @@
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-oid sha256:64ad69121c719dc533912a5233ee2ba4d895fd745283dc122601f20b0da2a519
-size 2223428
diff --git a/backup/pdvc/ops/functions/__init__.py b/backup/pdvc/ops/functions/__init__.py
deleted file mode 100644
index 2f682455af45d3687f0266acce6018741fe7c303..0000000000000000000000000000000000000000
--- a/backup/pdvc/ops/functions/__init__.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# ------------------------------------------------------------------------------------------------
-# 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_func import MSDeformAttnFunction, ms_deform_attn_core_pytorch
-
diff --git a/backup/pdvc/ops/functions/__pycache__/__init__.cpython-37.pyc b/backup/pdvc/ops/functions/__pycache__/__init__.cpython-37.pyc
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diff --git a/backup/pdvc/ops/functions/__pycache__/ms_deform_attn_func.cpython-37.pyc b/backup/pdvc/ops/functions/__pycache__/ms_deform_attn_func.cpython-37.pyc
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diff --git a/backup/pdvc/ops/functions/__pycache__/ms_deform_attn_func.cpython-38.pyc b/backup/pdvc/ops/functions/__pycache__/ms_deform_attn_func.cpython-38.pyc
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diff --git a/backup/pdvc/ops/functions/ms_deform_attn_func.py b/backup/pdvc/ops/functions/ms_deform_attn_func.py
deleted file mode 100644
index c59ddc33cf54f23c8b38e192c1421f0c79ebd38b..0000000000000000000000000000000000000000
--- a/backup/pdvc/ops/functions/ms_deform_attn_func.py
+++ /dev/null
@@ -1,71 +0,0 @@
-# ------------------------------------------------------------------------------------------------
-# 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 __future__ import absolute_import
-from __future__ import print_function
-from __future__ import division
-
-import torch
-import torch.nn.functional as F
-from torch.autograd import Function
-from torch.autograd.function import once_differentiable
-
-try:
-    import MultiScaleDeformableAttention as MSDA
-except:
-    pass
-
-class MSDeformAttnFunction(Function):
-    @staticmethod
-    def forward(ctx, value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, im2col_step):
-        # sampling_locations:(...,2), the first item of last dim means x axis corresponding to w, and second item of the last dim means y, corresponding to h.
-        ctx.im2col_step = im2col_step
-        output = MSDA.ms_deform_attn_forward(
-            value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, ctx.im2col_step)
-        ctx.save_for_backward(value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights)
-        return output
-
-    @staticmethod
-    @once_differentiable
-    def backward(ctx, grad_output):
-        value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights = ctx.saved_tensors
-        grad_value, grad_sampling_loc, grad_attn_weight = \
-            MSDA.ms_deform_attn_backward(
-                value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, grad_output, ctx.im2col_step)
-
-        return grad_value, None, None, grad_sampling_loc, grad_attn_weight, None
-
-
-def ms_deform_attn_core_pytorch(value, value_spatial_shapes, sampling_locations, attention_weights, return_value=False):
-    # for debug and test only,
-    # need to use cuda version instead
-    N_, S_, M_, D_ = value.shape    # N_: batch size , S_: \sum_H*W, M_ : head number, D_: feature dim of each head
-
-    _, Lq_, M_, L_, P_, _ = sampling_locations.shape  # Lq_: \sum H*W, L_: multi-scale number, P_: number of sampled key points
-
-    value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes], dim=1)
-    sampling_grids = 2 * sampling_locations - 1 # convert value from range[0,1] to [-1, 1]
-    sampling_value_list = []
-    for lid_, (H_, W_) in enumerate(value_spatial_shapes):
-        # N_, H_*W_, M_, D_ -> N_, H_*W_, M_*D_ -> N_, M_*D_, H_*W_ -> N_*M_, D_, H_, W_
-        value_l_ = value_list[lid_].flatten(2).transpose(1, 2).reshape(N_*M_, D_, H_, W_)
-        # N_, Lq_, M_, P_, 2 -> N_, M_, Lq_, P_, 2 -> N_*M_, Lq_, P_, 2
-        sampling_grid_l_ = sampling_grids[:, :, :, lid_].transpose(1, 2).flatten(0, 1)
-        # sampling_grid_l_: (...,2), the first item of last dim means x axis corresponding to w, and second item of the last dim means y, corresponding to h.
-        # N_*M_, D_, Lq_, P_
-        sampling_value_l_ = F.grid_sample(value_l_, sampling_grid_l_,
-                                          mode='bilinear', padding_mode='border', align_corners=False)
-        sampling_value_list.append(sampling_value_l_)
-    # (N_, Lq_, M_, L_, P_) -> (N_, M_, Lq_, L_, P_) -> (N_, M_, 1, Lq_, L_*P_)
-    attention_weights = attention_weights.transpose(1, 2).reshape(N_*M_, 1, Lq_, L_*P_)
-
-    if return_value:
-        return torch.stack(sampling_value_list, dim=-2)
-    #(N_ * M_, D_, Lq_, L_* P_) * (N_*M_, 1, Lq_, L_*P_) --> (N_*M_, D_, Lq_)
-    output = (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights).sum(-1).view(N_, M_*D_, Lq_)
-    return output.transpose(1, 2).contiguous()
diff --git a/backup/pdvc/ops/make.sh b/backup/pdvc/ops/make.sh
deleted file mode 100644
index a7e4320108ecd2f02d1824505849850b0c69d319..0000000000000000000000000000000000000000
--- a/backup/pdvc/ops/make.sh
+++ /dev/null
@@ -1,9 +0,0 @@
-#!/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/backup/pdvc/ops/modules/__init__.py b/backup/pdvc/ops/modules/__init__.py
deleted file mode 100644
index ceef895ac021db2b6b1762dda3d65c433e09e6e9..0000000000000000000000000000000000000000
--- a/backup/pdvc/ops/modules/__init__.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# ------------------------------------------------------------------------------------------------
-# 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
-from .ms_deform_attn_for_caption import MSDeformAttnCap
\ No newline at end of file
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diff --git a/backup/pdvc/ops/modules/__pycache__/ms_deform_attn_for_caption.cpython-38.pyc b/backup/pdvc/ops/modules/__pycache__/ms_deform_attn_for_caption.cpython-38.pyc
deleted file mode 100644
index 6a23f676c0714c277a628441a7459d2724f62b61..0000000000000000000000000000000000000000
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diff --git a/backup/pdvc/ops/modules/ms_deform_attn.py b/backup/pdvc/ops/modules/ms_deform_attn.py
deleted file mode 100644
index 7983d9f64fcff74e89823ad6d7164255f26dda52..0000000000000000000000000000000000000000
--- a/backup/pdvc/ops/modules/ms_deform_attn.py
+++ /dev/null
@@ -1,126 +0,0 @@
-# ------------------------------------------------------------------------------------------------
-# 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 __future__ import absolute_import
-from __future__ import print_function
-from __future__ import division
-
-import warnings
-import math
-
-import torch
-from torch import nn
-import torch.nn.functional as F
-from torch.nn.init import xavier_uniform_, constant_
-
-from ..functions import MSDeformAttnFunction, ms_deform_attn_core_pytorch
-
-
-def _is_power_of_2(n):
-    if (not isinstance(n, int)) or (n < 0):
-        raise ValueError("invalid input for _is_power_of_2: {} (type: {})".format(n, type(n)))
-    return (n & (n-1) == 0) and n != 0
-
-
-class MSDeformAttn(nn.Module):
-    def __init__(self, d_model=256, n_levels=4, n_heads=8, n_points=4):
-        """
-        Multi-Scale Deformable Attention Module
-        :param d_model      hidden dimension
-        :param n_levels     number of feature levels
-        :param n_heads      number of attention heads
-        :param n_points     number of sampling points per attention head per feature level
-        """
-        super().__init__()
-        if d_model % n_heads != 0:
-            raise ValueError('d_model must be divisible by n_heads, but got {} and {}'.format(d_model, n_heads))
-        _d_per_head = d_model // n_heads
-        # you'd better set _d_per_head to a power of 2 which is more efficient in our CUDA implementation
-        if not _is_power_of_2(_d_per_head):
-            warnings.warn("You'd better set d_model in MSDeformAttn to make the dimension of each attention head a power of 2 "
-                          "which is more efficient in our CUDA implementation.")
-
-        self.im2col_step = 64
-
-        self.d_model = d_model
-        self.n_levels = n_levels
-        self.n_heads = n_heads
-        self.n_points = n_points
-
-        self.sampling_offsets = nn.Linear(d_model, n_heads * n_levels * n_points )
-        self.attention_weights = nn.Linear(d_model, n_heads * n_levels * n_points)
-        self.value_proj = nn.Linear(d_model, d_model)
-        self.output_proj = nn.Linear(d_model, d_model)
-
-        self._reset_parameters()
-
-    def _reset_parameters(self):
-        constant_(self.sampling_offsets.weight.data, 0.)
-        thetas = torch.arange(self.n_heads, dtype=torch.float32) * (2 * math.pi / self.n_heads)
-        grid_init = torch.stack([thetas.cos(), thetas.sin()], -1)
-        grid_init = (grid_init / grid_init.abs().max(-1, keepdim=True)[0]).view(self.n_heads, 1, 1, 2)
-        grid_init = grid_init[..., 0].repeat(1, self.n_levels, self.n_points)
-        for i in range(self.n_points):
-            grid_init[:, :, i] *= i + 1
-        with torch.no_grad():
-            self.sampling_offsets.bias = nn.Parameter(grid_init.view(-1))
-        constant_(self.attention_weights.weight.data, 0.)
-        constant_(self.attention_weights.bias.data, 0.)
-        xavier_uniform_(self.value_proj.weight.data)
-        constant_(self.value_proj.bias.data, 0.)
-        xavier_uniform_(self.output_proj.weight.data)
-        constant_(self.output_proj.bias.data, 0.)
-
-    def forward(self, query, reference_points, input_flatten, input_spatial_shapes, input_level_start_index, input_padding_mask=None):
-        """
-        :param query                       (N, Length_{query}, C)
-        :param reference_points            (N, Length_{query}, n_levels, 1), range in [0, 1], including padding area
-                                        or (N, Length_{query}, n_levels, 2), add additional (c, l) to form reference boxes
-        :param input_flatten               (N, \sum_{l=0}^{L-1} T_l, C)
-        :param input_spatial_shapes        (n_levels ), [T_0, T_1, ..., T_{L-1}]
-        :param input_level_start_index     (n_levels ), [0, 1_0, T_0+T_1, ...]
-        :param input_padding_mask          (N, \sum_{l=0}^{L-1} H_l \cdot W_l), True for padding elements, False for non-padding elements
-
-        :return output                     (N, Length_{query}, C)
-        """
-        N, Len_q, _ = query.shape
-        N, Len_in, _ = input_flatten.shape
-        assert input_spatial_shapes.sum() == Len_in
-
-        value = self.value_proj(input_flatten)
-        if input_padding_mask is not None:
-            value = value.masked_fill(input_padding_mask[..., None], float(0))
-        value = value.view(N, Len_in, self.n_heads, self.d_model // self.n_heads)
-        sampling_offsets = self.sampling_offsets(query).view(N, Len_q, self.n_heads, self.n_levels, self.n_points)
-        attention_weights = self.attention_weights(query).view(N, Len_q, self.n_heads, self.n_levels * self.n_points)
-        attention_weights = F.softmax(attention_weights, -1).view(N, Len_q, self.n_heads, self.n_levels, self.n_points)
-        # N, Len_q, n_heads, n_levels, n_points, 2
-        if reference_points.shape[-1] == 1:
-            offset_normalizer = input_spatial_shapes
-            sampling_locations = reference_points[:, :, None, :, None, 0] \
-                                 + sampling_offsets / offset_normalizer[None, None, None, :, None]
-        elif reference_points.shape[-1] == 2:
-            sampling_locations = reference_points[:, :, None, :, None, 0] \
-                                 + sampling_offsets / self.n_points * reference_points[:, :, None, :, None, 1] * 0.5
-        else:
-            raise ValueError(
-                'Last dim of reference_points must be 1 or 2, but get {} instead.'.format(reference_points.shape[-1]))
-
-        if True:
-            sampling_locations = torch.stack(
-                (sampling_locations, 0.5 * sampling_locations.new_ones(sampling_locations.shape)), -1)
-            input_spatial_shapes = torch.stack([input_spatial_shapes.new_ones(input_spatial_shapes.shape), input_spatial_shapes], -1)
-
-        if query.device.type == 'cuda':
-            output = MSDeformAttnFunction.apply(
-                value, input_spatial_shapes, input_level_start_index, sampling_locations, attention_weights,
-                self.im2col_step)
-        else:
-            output = ms_deform_attn_core_pytorch(value, input_spatial_shapes, sampling_locations, attention_weights)
-        output = self.output_proj(output)
-        return output
diff --git a/backup/pdvc/ops/modules/ms_deform_attn_for_caption.py b/backup/pdvc/ops/modules/ms_deform_attn_for_caption.py
deleted file mode 100644
index a6fdc1c220e13146864818a0f79225ca47c7394f..0000000000000000000000000000000000000000
--- a/backup/pdvc/ops/modules/ms_deform_attn_for_caption.py
+++ /dev/null
@@ -1,123 +0,0 @@
-# ------------------------------------------------------------------------------------------------
-# 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 __future__ import absolute_import
-from __future__ import print_function
-from __future__ import division
-
-import warnings
-import math
-
-import torch
-from torch import nn
-import torch.nn.functional as F
-from torch.nn.init import xavier_uniform_, constant_
-
-from ..functions import MSDeformAttnFunction, ms_deform_attn_core_pytorch
-
-
-def _is_power_of_2(n):
-    if (not isinstance(n, int)) or (n < 0):
-        raise ValueError("invalid input for _is_power_of_2: {} (type: {})".format(n, type(n)))
-    return (n & (n-1) == 0) and n != 0
-
-
-class MSDeformAttnCap(nn.Module):
-    def __init__(self, d_model=256, n_levels=4, n_heads=8, n_points=4,):
-        """
-        Multi-Scale Deformable Attention Module
-        :param d_model      hidden dimension
-        :param n_levels     number of feature levels
-        :param n_heads      number of attention heads
-        :param n_points     number of sampling points per attention head per feature level
-        """
-        super().__init__()
-        if d_model % n_heads != 0:
-            raise ValueError('d_model must be divisible by n_heads, but got {} and {}'.format(d_model, n_heads))
-        _d_per_head = d_model // n_heads
-        # you'd better set _d_per_head to a power of 2 which is more efficient in our CUDA implementation
-        if not _is_power_of_2(_d_per_head):
-            warnings.warn("You'd better set d_model in MSDeformAttn to make the dimension of each attention head a power of 2 "
-                          "which is more efficient in our CUDA implementation.")
-
-        self.im2col_step = 64
-        self.d_model = d_model
-        self.n_levels = n_levels
-        self.n_heads = n_heads
-        self.n_points = n_points
-
-        self.sampling_offsets = nn.Linear(2 * d_model, n_heads * n_levels * n_points)
-        self.attention_weights = nn.Linear(2 * d_model, n_heads * n_levels * n_points)
-        self.value_proj = nn.Linear(d_model, d_model)
-        self.output_proj = nn.Linear(d_model, d_model)
-        self._reset_parameters()
-
-    def _reset_parameters(self):
-        constant_(self.sampling_offsets.weight.data, 0.)
-        thetas = torch.arange(self.n_heads, dtype=torch.float32) * (2.0 * math.pi / self.n_heads)
-        grid_init = torch.stack([thetas.cos(), thetas.sin()], -1)
-        grid_init = (grid_init / grid_init.abs().max(-1, keepdim=True)[0]).view(self.n_heads, 1, 1, 2)
-        grid_init = grid_init[..., 0].repeat(1, self.n_levels, self.n_points)
-        for i in range(self.n_points):
-            grid_init[:, :, i] *= i + 1
-        grid_init = grid_init - grid_init.mean(2, keepdim=True)
-        with torch.no_grad():
-            self.sampling_offsets.bias = nn.Parameter(grid_init.view(-1))
-        constant_(self.attention_weights.weight.data, 0.)
-        constant_(self.attention_weights.bias.data, 0.)
-        xavier_uniform_(self.value_proj.weight.data)
-        constant_(self.value_proj.bias.data, 0.)
-        xavier_uniform_(self.output_proj.weight.data)
-        constant_(self.output_proj.bias.data, 0.)
-
-    def forward(self, query, reference_points, input_flatten, input_spatial_shapes, input_level_start_index, input_padding_mask=None):
-        """
-        :param query                       (N, Length_{query}, C)
-        :param reference_points            (N, Length_{query}, n_levels, 1), range in [0, 1], including padding area
-                                        or (N, Length_{query}, n_levels, 2), add additional (c, l) to form reference boxes
-        :param input_flatten               (N, \sum_{l=0}^{L-1} T_l, C)
-        :param input_spatial_shapes        (n_levels ), [T_0, T_1, ..., T_{L-1}]
-        :param input_level_start_index     (n_levels ), [0, 1_0, T_0+T_1, ...]
-        :param input_padding_mask          (N, \sum_{l=0}^{L-1} H_l \cdot W_l), True for padding elements, False for non-padding elements
-
-        :return output                     (N, Length_{query}, C)
-        """
-        N, Len_q, _ = query.shape
-        N, Len_in, _ = input_flatten.shape
-        assert input_spatial_shapes.sum() == Len_in
-
-        value = self.value_proj(input_flatten)
-        if input_padding_mask is not None:
-            value = value.masked_fill(input_padding_mask[..., None], float(0))
-        value = value.view(N, Len_in, self.n_heads, self.d_model // self.n_heads)
-        sampling_offsets = self.sampling_offsets(query).view(N, Len_q, self.n_heads, self.n_levels, self.n_points)
-        attention_weights = self.attention_weights(query).view(N, Len_q, self.n_heads, self.n_levels * self.n_points)
-        attention_weights = F.softmax(attention_weights, -1).view(N, Len_q, self.n_heads, self.n_levels, self.n_points)
-        # N, Len_q, n_heads, n_levels, n_points, 1
-        if reference_points.shape[-1] == 1:
-            offset_normalizer = input_spatial_shapes
-            sampling_locations = reference_points[:, :, None, :, None, 0] \
-                                 + sampling_offsets / offset_normalizer[None, None, None, :, None]
-        elif reference_points.shape[-1] == 2:
-            sampling_locations = reference_points[:, :, None, :, None, 0] \
-                                 + sampling_offsets / self.n_points * reference_points[:, :, None, :, None, 1] * 0.5
-        else:
-            raise ValueError(
-                'Last dim of reference_points must be 1 or 2, but get {} instead.'.format(reference_points.shape[-1]))
-
-
-
-        if True:
-            sampling_locations = torch.stack(
-                (sampling_locations, 0.5 * sampling_locations.new_ones(sampling_locations.shape)), -1)
-            input_spatial_shapes = torch.stack([input_spatial_shapes.new_ones(input_spatial_shapes.shape), input_spatial_shapes], -1)
-
-        output = ms_deform_attn_core_pytorch(value, input_spatial_shapes, sampling_locations, attention_weights,
-                                             return_value=True)
-
-        return output
diff --git a/backup/pdvc/ops/setup.py b/backup/pdvc/ops/setup.py
deleted file mode 100644
index a0131bc21cf1b45b90fcf174e2c53e4c08e9c641..0000000000000000000000000000000000000000
--- a/backup/pdvc/ops/setup.py
+++ /dev/null
@@ -1,71 +0,0 @@
-# ------------------------------------------------------------------------------------------------
-# 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
-# ------------------------------------------------------------------------------------------------
-
-import os
-import glob
-
-import torch
-
-from torch.utils.cpp_extension import CUDA_HOME
-from torch.utils.cpp_extension import CppExtension
-from torch.utils.cpp_extension import CUDAExtension
-
-from setuptools import find_packages
-from setuptools import setup
-
-requirements = ["torch", "torchvision"]
-
-def get_extensions():
-    this_dir = os.path.dirname(os.path.abspath(__file__))
-    extensions_dir = os.path.join(this_dir, "src")
-
-    main_file = glob.glob(os.path.join(extensions_dir, "*.cpp"))
-    source_cpu = glob.glob(os.path.join(extensions_dir, "cpu", "*.cpp"))
-    source_cuda = glob.glob(os.path.join(extensions_dir, "cuda", "*.cu"))
-
-    sources = main_file + source_cpu
-    extension = CppExtension
-    extra_compile_args = {"cxx": []}
-    define_macros = []
-
-    if torch.cuda.is_available() and CUDA_HOME is not None:
-        extension = CUDAExtension
-        sources += source_cuda
-        define_macros += [("WITH_CUDA", None)]
-        extra_compile_args["nvcc"] = [
-            "-DCUDA_HAS_FP16=1",
-            "-D__CUDA_NO_HALF_OPERATORS__",
-            "-D__CUDA_NO_HALF_CONVERSIONS__",
-            "-D__CUDA_NO_HALF2_OPERATORS__",
-        ]
-    else:
-        raise NotImplementedError('Cuda is not availabel')
-
-    sources = [os.path.join(extensions_dir, s) for s in sources]
-    include_dirs = [extensions_dir]
-    ext_modules = [
-        extension(
-            "MultiScaleDeformableAttention",
-            sources,
-            include_dirs=include_dirs,
-            define_macros=define_macros,
-            extra_compile_args=extra_compile_args,
-        )
-    ]
-    return ext_modules
-
-setup(
-    name="MultiScaleDeformableAttention",
-    version="1.0",
-    author="Weijie Su",
-    url="https://github.com/fundamentalvision/Deformable-DETR",
-    description="PyTorch Wrapper for CUDA Functions of Multi-Scale Deformable Attention",
-    packages=find_packages(exclude=("configs", "tests",)),
-    ext_modules=get_extensions(),
-    cmdclass={"build_ext": torch.utils.cpp_extension.BuildExtension},
-)
diff --git a/backup/pdvc/ops/src/cpu/ms_deform_attn_cpu.cpp b/backup/pdvc/ops/src/cpu/ms_deform_attn_cpu.cpp
deleted file mode 100644
index e1bf854de1f3860d20b6fef5c1a17817c268e70a..0000000000000000000000000000000000000000
--- a/backup/pdvc/ops/src/cpu/ms_deform_attn_cpu.cpp
+++ /dev/null
@@ -1,41 +0,0 @@
-/*!
-**************************************************************************************************
-* 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/backup/pdvc/ops/src/cpu/ms_deform_attn_cpu.h b/backup/pdvc/ops/src/cpu/ms_deform_attn_cpu.h
deleted file mode 100644
index 81b7b58a3d9502bbb684dc84687a526dedf94cae..0000000000000000000000000000000000000000
--- a/backup/pdvc/ops/src/cpu/ms_deform_attn_cpu.h
+++ /dev/null
@@ -1,33 +0,0 @@
-/*!
-**************************************************************************************************
-* 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
-**************************************************************************************************
-*/
-
-#pragma once
-#include <torch/extension.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);
-
-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);
-
-
diff --git a/backup/pdvc/ops/src/cuda/ms_deform_attn_cuda.cu b/backup/pdvc/ops/src/cuda/ms_deform_attn_cuda.cu
deleted file mode 100644
index d6d583647cce987196d5ad1968a8a365a379e774..0000000000000000000000000000000000000000
--- a/backup/pdvc/ops/src/cuda/ms_deform_attn_cuda.cu
+++ /dev/null
@@ -1,153 +0,0 @@
-/*!
-**************************************************************************************************
-* 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/backup/pdvc/ops/src/cuda/ms_deform_attn_cuda.h b/backup/pdvc/ops/src/cuda/ms_deform_attn_cuda.h
deleted file mode 100644
index c7ae53f99c820ce6193b608ad344550348a0b42c..0000000000000000000000000000000000000000
--- a/backup/pdvc/ops/src/cuda/ms_deform_attn_cuda.h
+++ /dev/null
@@ -1,30 +0,0 @@
-/*!
-**************************************************************************************************
-* 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
-**************************************************************************************************
-*/
-
-#pragma once
-#include <torch/extension.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);
-
-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);
-
diff --git a/backup/pdvc/ops/src/cuda/ms_deform_im2col_cuda.cuh b/backup/pdvc/ops/src/cuda/ms_deform_im2col_cuda.cuh
deleted file mode 100644
index 5635be7822e7cbfb8b5524185f213a9368a91dce..0000000000000000000000000000000000000000
--- a/backup/pdvc/ops/src/cuda/ms_deform_im2col_cuda.cuh
+++ /dev/null
@@ -1,1328 +0,0 @@
-/*!
-**************************************************************************
-* 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>
-
-// 使用相同间隔分配block
-#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/backup/pdvc/ops/src/ms_deform_attn.h b/backup/pdvc/ops/src/ms_deform_attn.h
deleted file mode 100644
index ac0ef2ec25f7d0ee51ca2d807b159ddf85652017..0000000000000000000000000000000000000000
--- a/backup/pdvc/ops/src/ms_deform_attn.h
+++ /dev/null
@@ -1,62 +0,0 @@
-/*!
-**************************************************************************************************
-* 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
-**************************************************************************************************
-*/
-
-#pragma once
-
-#include "cpu/ms_deform_attn_cpu.h"
-
-#ifdef WITH_CUDA
-#include "cuda/ms_deform_attn_cuda.h"
-#endif
-
-
-at::Tensor
-ms_deform_attn_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)
-{
-    if (value.type().is_cuda())
-    {
-#ifdef WITH_CUDA
-        return ms_deform_attn_cuda_forward(
-            value, spatial_shapes, level_start_index, sampling_loc, attn_weight, im2col_step);
-#else
-        AT_ERROR("Not compiled with GPU support");
-#endif
-    }
-    AT_ERROR("Not implemented on the CPU");
-}
-
-std::vector<at::Tensor>
-ms_deform_attn_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)
-{
-    if (value.type().is_cuda())
-    {
-#ifdef WITH_CUDA
-        return ms_deform_attn_cuda_backward(
-            value, spatial_shapes, level_start_index, sampling_loc, attn_weight, grad_output, im2col_step);
-#else
-        AT_ERROR("Not compiled with GPU support");
-#endif
-    }
-    AT_ERROR("Not implemented on the CPU");
-}
-
diff --git a/backup/pdvc/ops/src/vision.cpp b/backup/pdvc/ops/src/vision.cpp
deleted file mode 100644
index 2201f63a51dca16d0b31148ed2c9e8e47ec15bdc..0000000000000000000000000000000000000000
--- a/backup/pdvc/ops/src/vision.cpp
+++ /dev/null
@@ -1,16 +0,0 @@
-/*!
-**************************************************************************************************
-* 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 "ms_deform_attn.h"
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
-  m.def("ms_deform_attn_forward", &ms_deform_attn_forward, "ms_deform_attn_forward");
-  m.def("ms_deform_attn_backward", &ms_deform_attn_backward, "ms_deform_attn_backward");
-}
diff --git a/backup/pdvc/ops/test.py b/backup/pdvc/ops/test.py
deleted file mode 100644
index 8dbf6d5547d131f01a8c5c28b76557bd27a9334b..0000000000000000000000000000000000000000
--- a/backup/pdvc/ops/test.py
+++ /dev/null
@@ -1,89 +0,0 @@
-# ------------------------------------------------------------------------------------------------
-# 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 __future__ import absolute_import
-from __future__ import print_function
-from __future__ import division
-
-import time
-import torch
-import torch.nn as nn
-from torch.autograd import gradcheck
-
-from functions.ms_deform_attn_func import MSDeformAttnFunction, ms_deform_attn_core_pytorch
-
-
-N, M, D = 1, 2, 2
-Lq, L, P = 2, 2, 2
-shapes = torch.as_tensor([(6, 4), (3, 2)], dtype=torch.long).cuda()
-level_start_index = torch.cat((shapes.new_zeros((1, )), shapes.prod(1).cumsum(0)[:-1]))
-S = sum([(H*W).item() for H, W in shapes])
-
-
-torch.manual_seed(3)
-
-
-@torch.no_grad()
-def check_forward_equal_with_pytorch_double():
-    value = torch.rand(N, S, M, D).cuda() * 0.01
-    sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda()
-    attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5
-    attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True)
-    im2col_step = 2
-    output_pytorch = ms_deform_attn_core_pytorch(value.double(), shapes, sampling_locations.double(), attention_weights.double()).detach().cpu()
-    output_cuda = MSDeformAttnFunction.apply(value.double(), shapes, level_start_index, sampling_locations.double(), attention_weights.double(), im2col_step).detach().cpu()
-    fwdok = torch.allclose(output_cuda, output_pytorch)
-    max_abs_err = (output_cuda - output_pytorch).abs().max()
-    max_rel_err = ((output_cuda - output_pytorch).abs() / output_pytorch.abs()).max()
-
-    print(f'* {fwdok} check_forward_equal_with_pytorch_double: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}')
-
-
-@torch.no_grad()
-def check_forward_equal_with_pytorch_float():
-    value = torch.rand(N, S, M, D).cuda() * 0.01
-    sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda()
-    attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5
-    attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True)
-    im2col_step = 2
-    output_pytorch = ms_deform_attn_core_pytorch(value, shapes, sampling_locations, attention_weights).detach().cpu()
-    output_cuda = MSDeformAttnFunction.apply(value, shapes, level_start_index, sampling_locations, attention_weights, im2col_step).detach().cpu()
-    fwdok = torch.allclose(output_cuda, output_pytorch, rtol=1e-2, atol=1e-3)
-    max_abs_err = (output_cuda - output_pytorch).abs().max()
-    max_rel_err = ((output_cuda - output_pytorch).abs() / output_pytorch.abs()).max()
-
-    print(f'* {fwdok} check_forward_equal_with_pytorch_float: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}')
-
-
-def check_gradient_numerical(channels=4, grad_value=True, grad_sampling_loc=True, grad_attn_weight=True):
-
-    value = torch.rand(N, S, M, channels).cuda() * 0.01
-    sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda()
-    attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5
-    attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True)
-    im2col_step = 2
-    func = MSDeformAttnFunction.apply
-
-    value.requires_grad = grad_value
-    sampling_locations.requires_grad = grad_sampling_loc
-    attention_weights.requires_grad = grad_attn_weight
-
-    gradok = gradcheck(func, (value.double(), shapes, level_start_index, sampling_locations.double(), attention_weights.double(), im2col_step))
-
-    print(f'* {gradok} check_gradient_numerical(D={channels})')
-
-
-if __name__ == '__main__':
-    check_forward_equal_with_pytorch_double()
-    check_forward_equal_with_pytorch_float()
-
-    for channels in [30, 32, 64, 71, 1025, 2048, 3096]:
-        check_gradient_numerical(channels, True, True, True)
-
-
-
diff --git a/backup/pdvc/pdvc.py b/backup/pdvc/pdvc.py
deleted file mode 100644
index 4f7ffe3067b2a1382a79c0efc5a8ac828baa9c03..0000000000000000000000000000000000000000
--- a/backup/pdvc/pdvc.py
+++ /dev/null
@@ -1,1305 +0,0 @@
-# ------------------------------------------------------------------------
-# PDVC
-# ------------------------------------------------------------------------
-# Modified from Deformable DETR(https://github.com/fundamentalvision/Deformable-DETR)
-# Copyright (c) 2020 SenseTime. All Rights Reserved.
-# ------------------------------------------------------------------------
-# Modified from DETR (https://github.com/facebookresearch/detr)
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-# ------------------------------------------------------------------------
-
-import json
-import torch
-import torch.nn.functional as F
-from torch import nn
-import math
-import time
-
-from misc.detr_utils import box_ops
-from misc.detr_utils.misc import (inverse_sigmoid)
-
-from .matcher import build_matcher
-
-from .deformable_transformer import build_deforamble_transformer
-from pdvc.CaptioningHead import build_captioner
-import copy
-from .criterion import AlignCriterion, SetCriterion, ContrastiveCriterion
-# from .rl_tool import init_scorer
-from misc.utils import decide_two_stage
-from .base_encoder import build_base_encoder
-# from .video_segmentation import segment_video_into_steps, alignment_to_boundary, to_center_duration, align_frame_into_steps
-from .video_segmentation import *
-# from transformers import AutoModel, BertConfig
-# from transformers.models.bert.modeling_bert import BertEncoder
-import numpy as np
-from itertools import chain
-# from .UniVL import load_pretrained_UniVL
-
-
-def _get_clones(module, N):
-    return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
-
-class PDVC(nn.Module):
-    """ This is the PDVC module that performs dense video captioning """
-
-    def __init__(self, base_encoder, transformer, captioner, num_classes, num_queries, num_feature_levels,
-                 aux_loss=True, with_box_refine=False, opt=None, translator=None):
-        """ Initializes the model.
-        Parameters:
-            transformer: torch module of the transformer architecture. See transformer.py
-            captioner: captioning head for generate a sentence for each event queries
-            num_classes: number of foreground classes
-            num_queries: number of event queries. This is the maximal number of events
-                         PDVC can detect in a single video. For ActivityNet Captions, we recommend 10-30 queries.
-            aux_loss: True if auxiliary decoding losses (loss at each decoder layer) are to be used.
-            with_box_refine: iterative bounding box refinement
-            opt: all configs
-        """
-        super().__init__()
-        self.opt = opt
-        self.base_encoder = base_encoder
-        self.transformer = transformer
-        self.caption_head = captioner
-        num_pred_text = 0
-
-        # if opt.matcher_type == 'DTW' or opt.use_pseudo_box:
-        #     self.text_encoder = text_encoder
-        #     text_encoder_hidden_dim = self.text_encoder.config.hidden_size
-        #     num_pred_text += 1
-
-        hidden_dim = transformer.d_model
-        text_hidden_dim = opt.text_hidden_dim
-        
-        if self.opt.use_anchor:
-            # self.tgt_embed = nn.Embedding(num_queries, hidden_dim)
-            self.anchor_embed = nn.Embedding(num_queries, 2) # num_queries, 2 (center, duration) 
-            self.query_embed = self.transformer.prepare_init_anchor_and_query(self.anchor_embed, hidden_dim, \
-                                                                        random_anchor_init=True, prior_anchor_duration_init=True, \
-                                                                        prior_duration=0.048)
-            self.query_embed = nn.Parameter(self.query_embed, requires_grad=True)
-        else:
-            self.query_embed = nn.Embedding(num_queries, hidden_dim * 2)
-
-        self.class_head = nn.Linear(hidden_dim, num_classes)
-        self.class_refine_head = nn.Linear(hidden_dim, num_classes) # For refine pseudo box if use additional score layer
-        self.count_head = nn.Linear(hidden_dim, opt.max_eseq_length + 1)
-        self.bbox_head = MLP(hidden_dim, hidden_dim, 2, 3)
-
-        self.num_feature_levels = num_feature_levels
-        self.aux_loss = aux_loss
-        self.with_box_refine = with_box_refine
-        self.share_caption_head = opt.share_caption_head
-
-        # initialization
-        prior_prob = 0.01
-        bias_value = -math.log((1 - prior_prob) / prior_prob)
-        self.class_head.bias.data = torch.ones(num_classes) * bias_value
-        self.class_refine_head.bias.data = torch.ones(num_classes) * bias_value
-        nn.init.constant_(self.bbox_head.layers[-1].weight.data, 0)
-        nn.init.constant_(self.bbox_head.layers[-1].bias.data, 0)
-
-        if self.opt.matcher_type == 'DTW' or self.opt.matcher_type == 'Sim' \
-            or self.opt.use_pseudo_box:
-            self.load_text_embed = True
-        else:
-            self.load_text_embed = False
-
-
-        num_pred = transformer.decoder.num_layers
-        if self.share_caption_head:
-            print('all decoder layers share the same caption head')
-            self.caption_head = nn.ModuleList([self.caption_head for _ in range(num_pred)])
-        else:
-            print('do NOT share the caption head')
-            self.caption_head = _get_clones(self.caption_head, num_pred)
-
-        if self.opt.use_additional_cap_layer:
-            self.caption_head_refine = _get_clones(captioner, self.opt.refine_pseudo_stage_num)
-
-        if with_box_refine:
-            self.class_head = _get_clones(self.class_head, num_pred)
-            self.count_head = _get_clones(self.count_head, num_pred)
-            self.bbox_head = _get_clones(self.bbox_head, num_pred)
-            nn.init.constant_(self.bbox_head[0].layers[-1].bias.data[1:], -2)
-            # hack implementation for iterative bounding box refinement
-            self.transformer.decoder.bbox_head = self.bbox_head
-        else:
-            nn.init.constant_(self.bbox_head.layers[-1].bias.data[1:], -2)
-            self.class_head = nn.ModuleList([self.class_head for _ in range(num_pred)])
-            self.count_head = nn.ModuleList([self.count_head for _ in range(num_pred)])
-            self.bbox_head = nn.ModuleList([self.bbox_head for _ in range(num_pred)])
-            self.transformer.decoder.bbox_head = None
-
-        self.class_refine_head = _get_clones(self.class_refine_head, self.opt.refine_pseudo_stage_num)
-        # if opt.matcher_type == 'DTW' or opt.use_pseudo_box:
-        if opt.disable_contrastive_projection:
-            projection_event = nn.Identity()
-            projection_text = nn.Identity()
-        else:
-            projection_event = nn.Linear(hidden_dim, opt.contrastive_hidden_size)
-            projection_text = nn.Linear(text_hidden_dim, opt.contrastive_hidden_size)
-        self.contrastive_projection_event = nn.ModuleList(
-            [projection_event for _ in range(num_pred)])
-        self.contrastive_projection_text = nn.ModuleList(
-            [projection_text for _ in range(num_pred)])
-        if opt.enable_bg_for_cl:
-            self.background_embed = nn.Parameter(torch.randn(1, opt.contrastive_hidden_size), requires_grad=True)
-        else:
-            self.background_embed = None
-            
-
-        self.translator = translator
-
-        self.disable_mid_caption_heads = opt.disable_mid_caption_heads
-        if self.disable_mid_caption_heads:
-            print('only calculate caption loss in the last decoding layer')
-        
-        self.pseudo_boxes = {}
-        
-
-    def get_filter_rule_for_encoder(self):
-        filter_rule = lambda x: 'input_proj' in x \
-                                or 'transformer.encoder' in x \
-                                or 'transformer.level_embed' in x \
-                                or 'base_encoder' in x
-        return filter_rule
-
-    def encoder_decoder_parameters(self):
-        filter_rule = self.get_filter_rule_for_encoder()
-        enc_paras = []
-        dec_paras = []
-        for name, para in self.named_parameters():
-            if filter_rule(name):
-                print('enc: {}'.format(name))
-                enc_paras.append(para)
-            else:
-                print('dec: {}'.format(name))
-                dec_paras.append(para)
-        return enc_paras, dec_paras
-
-    # def text_encoding(self, text_encoder_input):
-    #     '''
-    #     Produce the text embedding for each caption
-    #     :param text_encoder_input: a dict of input for text encoder
-    #     '''
-    #     if self.opt.pretrained_language_model == 'UniVL' or self.opt.use_pseudo_box:
-    #         # breakpoint()
-    #         dtype = next(self.parameters()).dtype
-    #         enable_grad = False
-    #         use_amp = False
-    #         with torch.cuda.amp.autocast(enabled=use_amp):
-    #             with torch.set_grad_enabled(enable_grad):
-    #                 text_embed = self.text_encoder(**text_encoder_input, output_all_encoded_layers=True)[0][-1]
-    #         text_embed = text_embed.to(dtype=dtype) # num_sentence, num_word, dim
-    #         attention_mask = text_encoder_input['attention_mask'].unsqueeze(-1).to(dtype=dtype) # num_sentence, num_word, 1
-    #         attention_mask[:,0,:] = 0. # This operation follows from the UniVL 
-    #         text_embed = text_embed * attention_mask # num_sentence, num_word, dim
-    #         text_embed = text_embed.sum(dim=1) / attention_mask.sum(dim=1) # num_sentence, dim
-    #         raw_text_embed = text_embed
-    #         # if video_name:
-    #         #     text_feature_path = '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2/UniVL_features/UniVL_text'
-    #         #     np.save('{}/{}.npy'.format(text_feature_path, video_name), text_embed.detach().cpu().numpy())
-    #         text_embed = self.contrastive_projection_text[-1](text_embed)
-            
-    #     else:
-    #         dtype = next(self.parameters()).dtype
-    #         enable_grad = False
-    #         use_amp = False
-    #         with torch.cuda.amp.autocast(enabled=use_amp):
-    #             with torch.set_grad_enabled(enable_grad):
-    #                 text_embed = self.text_encoder(**text_encoder_input)
-    #         text_embed = text_embed['pooler_output'].to(dtype=dtype) # num_sentence, dim
-    #         text_embed = self.contrastive_projection_text[-1](text_embed) # num_sentence, dim_contrastive_learning
-    #         # TODO: add more paradigm to generate the text_embedding
-
-    #     return text_embed, raw_text_embed
-
-    def forward(self, dt, criterion, contrastive_criterion, eval_mode=False):
-        transformer_input_type = self.opt.transformer_input_type
-        vf = dt['video_tensor']  # (N, L, C)
-        mask = ~ dt['video_mask']  # (N, L)
-        duration = dt['video_length'][:, 1]
-        video_name = dt['video_key'][0][2:]
-        # text_encoder_input = dt['text_encoder_input'] if (self.opt.matcher_type=='DTW' or self.opt.use_pseudo_box) else None
-        N, L, C = vf.shape
-        # assert N == 1, "batch size must be 1."s
-
-        srcs, masks, pos = self.base_encoder(vf, mask, duration)
-
-        src_flatten, temporal_shapes, level_start_index, valid_ratios, lvl_pos_embed_flatten, mask_flatten = self.transformer.prepare_encoder_inputs(
-            srcs, masks, pos)
-        memory = self.transformer.forward_encoder(src_flatten, temporal_shapes, level_start_index, valid_ratios,
-                                                  lvl_pos_embed_flatten, mask_flatten)
-
-        two_stage, disable_iterative_refine, proposals, proposals_mask = decide_two_stage(transformer_input_type,
-                                                                                                dt, criterion)
-        if two_stage:
-            if transformer_input_type == 'prior_proposals':
-                if self.opt.prior_manner == 'add':
-                    #print('Insert the prior knowledge by adding the prior proposals to the query embed')
-                    init_query_embed = self.query_embed.weight
-                    _, tgt = torch.chunk(init_query_embed, 2, dim=1)
-                    tgt = tgt.unsqueeze(0).expand(N, -1, -1)
-                    init_reference, _, reference_points, query_embed = self.transformer.prepare_decoder_input_prior(proposals, num_queries = self.query_embed.weight.shape[0])
-                    proposals_mask = torch.ones(N, self.query_embed.weight.shape[0], device=query_embed.device).bool()
-                else:
-                    init_reference, tgt, reference_points, query_embed = self.transformer.prepare_decoder_input_prior(proposals, num_queries = self.query_embed.weight.shape[0])
-                    proposals_mask = torch.ones(N, self.query_embed.weight.shape[0], device=query_embed.device).bool()
-            else:
-                init_reference, tgt, reference_points, query_embed = self.transformer.prepare_decoder_input_proposal(
-                    proposals)
-        else:
-            if self.opt.use_anchor:
-                # tgt = self.tgt_embed.weight
-                anchor = self.anchor_embed.weight # num_queries, 2
-                query_anchor = (self.query_embed, anchor)
-                proposals_mask = torch.ones(N, self.query_embed.shape[0], device=self.query_embed.device).bool()
-                init_reference, tgt, reference_points, query_embed = self.transformer.prepare_decoder_input_anchor(memory, query_anchor)
-            else:            
-                query_embed = self.query_embed.weight
-                proposals_mask = torch.ones(N, query_embed.shape[0], device=query_embed.device).bool()
-                init_reference, tgt, reference_points, query_embed = self.transformer.prepare_decoder_input_query(memory,
-                                                                                                              query_embed)
-        hs, inter_references = self.transformer.forward_decoder(tgt, reference_points, memory, temporal_shapes,
-                                                                level_start_index, valid_ratios, query_embed,
-                                                                mask_flatten, proposals_mask, disable_iterative_refine)
-        # hs: [num_decoder_layer, bs, num_query, feat_dim]
-
-        # breakpoint()
-        # project to co-embedding space
-        if self.load_text_embed and eval_mode==False:
-            # text_embed, raw_text_embed = self.text_encoding(text_encoder_input)
-            # text_embed = [text_embed] * hs.shape[0]
-            # text_embed = torch.stack(text_embed, dim=0)
-            raw_text_embed = dt['cap_embed'] * hs.shape[0]# dt['caption_embedding'] returns a tuple(list)
-            # text_embed: [num_decoder_layer, num_sentence, contrastive_dim]
-            event_embed = torch.stack([self.contrastive_projection_event[i](hs_i) for i, hs_i in enumerate(hs)])
-            text_embed = torch.stack([self.contrastive_projection_text[j](hs_j.cuda()) for j, hs_j in enumerate(raw_text_embed)])
-            # breakpoint()
-            # event_embed: [num_decoder_layer, num_query, contrastive_dim]
-        else:
-            raw_text_embed = None
-            text_embed = None
-            event_embed = hs
-        # breakpoint()
-        if self.opt.use_pseudo_box and self.training:
-            # breakpoint()
-            # print('use pseudo box')
-            video_frame_num = dt['video_length'][:,0].cpu().numpy() # [feature_len, raw_video_len, video_len]
-            video_name = dt['video_key'][0]
-            if self.pseudo_boxes.get(video_name) is not None and 'box' in self.pseudo_boxes[video_name].keys() and 'loss' in self.pseudo_boxes[video_name].keys():
-                # if self.opt.pseudo_box_type == 'similarity_op_order_v2' or self.opt.pseudo_box_type == 'similarity_op_v2':
-                video_step_alignment = [self.pseudo_boxes[video_name]['box']]
-
-            else:
-                if self.opt.pseudo_box_type == 'align':
-                    video_step_segment = [segment_video_into_steps(dt['video_tensor'][i], raw_text_embed[i].to(memory.device)) for i in range(N)]
-                    bbox_alignment = [torch.tensor(alignment_to_boundary(video_step_segment[i], video_frame_num)).to(memory.device) for i in range(N)]
-                # elif self.opt.pseudo_box_type == 'similarity':
-                #     video_step_alignment = [align_frame_into_steps(dt['video_tensor'][i], raw_text_embed[i].to(memory.device)) for i in range(N)]
-                #     bbox_alignment = [(torch.tensor(video_step_alignment[i]) / video_frame_num).to(memory.device).to(torch.float32) for i in range(N)]
-                # breakpoint()
-                elif self.opt.pseudo_box_type == "similarity":
-                    # breakpoint()
-                    if self.opt.width_ratio < 0:
-                        video_step_alignment = [align_frame_into_steps(dt['video_tensor'][i], raw_text_embed[i].to(memory.device), \
-                                                                    topk=self.opt.top_frames, w=self.opt.window_size, mode=self.opt.statistic_mode) for i in range(N)]
-                    else:
-                        video_step_alignment = [align_frame_into_steps_order(dt['video_tensor'][i], raw_text_embed[i].to(memory.device), \
-                                                                            topk=self.opt.top_frames, w=self.opt.window_size, mode=self.opt.statistic_mode, ratio=self.opt.width_ratio) for i in range(N)]
-                elif self.opt.pseudo_box_type == 'similarity_op':
-                    video_step_alignment = [align_frame_into_steps_op(dt['video_tensor'][i], raw_text_embed[i].to(memory.device), \
-                                                                    topk=self.opt.top_frames, scale=self.opt.width_ratio, beta=1, order=False, num_iterations=self.opt.iteration) for i in range(N)]
-                elif self.opt.pseudo_box_type == 'similarity_op_order':
-                    video_step_alignment = [align_frame_into_steps_op(dt['video_tensor'][i], raw_text_embed[i].to(memory.device), topk=self.opt.top_frames, scale=self.opt.width_ratio, beta=1, order=True, num_iterations=self.opt.iteration) for i in range(N)]
-                elif self.opt.pseudo_box_type == 'similarity_op_order_v1':
-                    video_step_alignment = [align_frame_into_steps_op_v1(dt['video_tensor'][i], raw_text_embed[i].to(memory.device), topk=self.opt.top_frames, scale=self.opt.width_ratio, beta=1, order=True, num_iterations=self.opt.iteration) for i in range(N)]  
-                elif self.opt.pseudo_box_type == 'similarity_op_order_v2':
-                    video_step_alignment = [align_frame_into_steps_op_order_v2(dt['video_tensor'][i], raw_text_embed[i].to(memory.device), topk=self.opt.top_frames, threshold=self.opt.width_th, ratio=self.opt.width_ratio, iteration=self.opt.iteration) for i in range(N)]
-                elif self.opt.pseudo_box_type == 'similarity_op_v2':
-                    video_step_alignment = [align_frame_into_steps_op_v2(dt['video_tensor'][i], raw_text_embed[i].to(memory.device), topk=self.opt.top_frames, threshold=self.opt.width_th, ratio=self.opt.width_ratio, iteration=self.opt.iteration) for i in range(N)]
-                elif self.opt.pseudo_box_type == 'weight_sim':
-                    if self.opt.width_ratio < 0:
-                        video_step_alignment = [step_retrieval_weight_sim(dt['video_tensor'][i], raw_text_embed[i].to(memory.device), \
-                                                                    topk=self.opt.top_frames, w=self.opt.window_size) for i in range(N)]
-                    else:
-                        # breakpoint()
-                        video_step_alignment = [step_retrieval_weight_sim_order(dt['video_tensor'][i], raw_text_embed[i].to(memory.device), \
-                                                                    topk=self.opt.top_frames, w=self.opt.window_size, ratio=self.opt.width_ratio) for i in range(N)]
-
-                elif self.opt.pseudo_box_type == 'weight_index':
-                    video_step_alignment = [step_retrieval_weight_index(dt['video_tensor'][i], raw_text_embed[i].to(memory.device), \
-                                                                        topk=self.opt.top_frames, w=self.opt.window_size) for i in range(N)]
-                elif self.opt.pseudo_box_type == 'modeframe':
-                    video_step_alignment = [align_frame_into_steps_mode(dt['video_tensor'][i], raw_text_embed[i].to(memory.device), \
-                                                                        topk=self.opt.top_frames, w=self.opt.window_size, ratio=self.opt.width_ratio) for i in range(N)]
-                elif self.opt.pseudo_box_type == 'uniform':
-                    video_step_alignment = [uniform_box(dt['video_tensor'][i], raw_text_embed[i].to(memory.device)) for i in range(N)]
-                    # breakpoint()
-                else:
-                    raise NotImplementedError('pseudo_box_type {} is not implemented'.format(self.opt.pseudo_box_type))
-                
-
-                if self.opt.pseudo_box_type != 'align':
-                    if self.opt.pseudo_box_type == 'similarity_op_order_v2' or self.opt.pseudo_box_type == 'similarity_op_v2':
-                        # breakpoint()
-                        video_step_alignment, loss_op = [out[0] for out in video_step_alignment], [out[1] for out in video_step_alignment]
-                        self.pseudo_boxes[video_name] = {'box': video_step_alignment[0], 'loss': loss_op[0].item()}
-                    else:
-                        self.pseudo_boxes[video_name] = {'box': video_step_alignment[0]}
-            
-            if self.opt.pseudo_box_type != 'align':
-                bbox_alignment = [(torch.tensor(video_step_alignment[i]) / video_frame_num).to(memory.device).to(torch.float32) for i in range(N)]
-            else:
-                bbox_alignment = [torch.tensor(alignment_to_boundary(video_step_segment[i], video_frame_num)).to(memory.device) for i in range(N)]
-
-                
-                # self.pseudo_boxes[video_name] = video_step_alignment[0]
-                # self.pseudo_boxes[video_name] = video_step_alignment[0]
-                # bbox_alignment = [torch.tensor(alignment_to_boundary(video_step_segment[i], video_frame_num)).to(memory.device) for i in range(N)]
-
-            bbox_alignment = to_center_duration(bbox_alignment)
-
-
-            for sample in range(len(dt['video_target'])):
-                dt['video_target'][sample]['boxes_pseudo'] = bbox_alignment[sample]
-                # dt['video_target'][sample]['boxes'] = bbox_alignment[sample]
-        # else:
-        #     print('use gt box')
-
-        #breakpoint()
-        others = {'memory': memory,
-                  'mask_flatten': mask_flatten,
-                  'spatial_shapes': temporal_shapes,
-                  'level_start_index': level_start_index,
-                  'valid_ratios': valid_ratios,
-                  'proposals_mask': proposals_mask,
-                  'text_embed': text_embed,
-                  'event_embed': event_embed}
-        # breakpoint()
-        if eval_mode or self.opt.caption_loss_coef == 0:
-            out, loss = self.parallel_prediction_full(dt, criterion, hs, init_reference, inter_references, others,
-                                                      disable_iterative_refine, transformer_input_type)
-        else:
-            if self.opt.refine_pseudo_box and self.opt.use_pseudo_box:
-                # print('refine')
-                out, loss = self.parallel_prediction_refine_matched(dt, criterion, contrastive_criterion, hs, init_reference, inter_references, others,
-                                                         disable_iterative_refine, transformer_input_type)
-            else:
-                # print('no refine')
-                out, loss = self.parallel_prediction_matched(dt, criterion, contrastive_criterion, hs, init_reference, inter_references, others,
-                                                         disable_iterative_refine, transformer_input_type)
-        return out, loss
-
-    def predict_event_num(self, counter, hs_lid):
-        hs_lid_pool = torch.max(hs_lid, dim=1, keepdim=False)[0]  # [bs, feat_dim]
-        outputs_class0 = counter(hs_lid_pool)
-        return outputs_class0
-
-    def parallel_prediction_full(self, dt, criterion, hs, init_reference, inter_references, others,
-                                 disable_iterative_refine, transformer_input_type='queries'):
-        '''
-        hs: [decoder_layer, bs, num_query, feat_dim]
-        init_reference: [bs, num_query, 1]
-        inter_references: [decoder_layer, bs, num_query, 2]
-        '''
-        outputs_classes = []
-        outputs_classes0 = []
-        outputs_coords = []
-        outputs_cap_losses = []
-        outputs_cap_probs = []
-        outputs_cap_seqs = []
-        num_pred = hs.shape[0]
-        #breakpoint()
-        for l_id in range(hs.shape[0]):
-            if l_id == 0:
-                reference = init_reference
-            else:
-                reference = inter_references[l_id - 1]  # [decoder_layer, batch, query_num, ...]
-            hs_lid = hs[l_id]
-            outputs_class = self.class_head[l_id](hs_lid)  # [bs, num_query, N_class]
-            output_count = self.predict_event_num(self.count_head[l_id], hs_lid)
-            n_pred_sentence = output_count.argmax(dim=-1).clamp(min=1).item()
-            tmp = self.bbox_head[l_id](hs_lid)  # [bs, num_query, 4]
-
-            # if self.opt.disable_mid_caption_heads and (l_id != hs.shape[0] - 1):
-            if l_id != hs.shape[0] - 1:
-                cap_probs, seq = self.caption_prediction_eval(
-                    self.caption_head[l_id], dt, hs_lid, reference, others, 'none')
-            else:
-                cap_probs, seq = self.caption_prediction_eval(
-                    self.caption_head[l_id], dt, hs_lid, reference, others, self.opt.caption_decoder_type)  # Only output caption in the last decoding layer
-
-            # if self.opt.use_anchor:
-            #     outputs_coord = reference
-            # else:
-            if disable_iterative_refine:
-                outputs_coord = reference
-            else:
-                reference = inverse_sigmoid(reference)
-                if self.opt.matcher_type == 'DTW':
-                    assert reference.shape[-1] == 2 and tmp.shape[-1] == 2
-                if reference.shape[-1] == 2:
-                    tmp += reference
-                else:
-                    assert reference.shape[-1] == 1
-                    tmp[..., :2] += reference
-                outputs_coord = tmp.sigmoid()  # [bs, num_query, 2]
-
-            outputs_classes.append(outputs_class)
-            outputs_classes0.append(output_count)
-            outputs_coords.append(outputs_coord)
-            outputs_cap_probs.append(cap_probs)
-            outputs_cap_seqs.append(seq)
-        outputs_class = torch.stack(outputs_classes)  # [decoder_layer, bs, num_query, N_class]
-        output_count = torch.stack(outputs_classes0)
-        outputs_coord = torch.stack(outputs_coords)  # [decoder_layer, bs, num_query, 4]
-
-        all_out = {'pred_logits': outputs_class,
-                   'pred_count': output_count,
-                   'pred_boxes': outputs_coord,
-                   'caption_probs': outputs_cap_probs,
-                   'seq': outputs_cap_seqs}
-        out = {k: v[-1] for k, v in all_out.items()}
-
-        if self.aux_loss:
-            ks, vs = list(zip(*(all_out.items())))
-            out['aux_outputs'] = [{ks[i]: vs[i][j] for i in range(len(ks))} for j in range(num_pred - 1)]
-
-        # loss, _, _ = criterion(out, dt['video_target'], others)
-        return out, []
-
-    def parallel_prediction_refine_matched(self, dt, criterion, contrastive_criterion, hs, init_reference, inter_references, others,
-                                    disable_iterative_refine, transformer_input_type='queries'):
-        
-        outputs_classes = []
-        outputs_counts = []
-        outputs_coords = []
-        outputs_cap_costs = []
-        outputs_cap_losses = []
-        outputs_cap_probs = []
-        outputs_cap_seqs = []
-        cl_match_mats = []
-
-        num_pred = hs.shape[0]
-        if self.opt.pseudo_box_aug:
-            assert self.opt.use_pseudo_box
-            num_sentence = dt['gt_boxes'].size(-2)
-            assert num_sentence == len(dt['cap_raw'][0])
-            if self.opt.pseudo_box_aug_num * num_sentence > self.opt.num_queries:
-                aug_num = self.opt.num_queries // num_sentence
-            else:
-                aug_num = self.opt.pseudo_box_aug_num
-            if self.opt.refine_pseudo_box:
-                ori_dt_cap_tensor = copy.deepcopy(dt['cap_tensor'])
-                ori_dt_cap_mask = copy.deepcopy(dt['cap_mask'])
-            cap_dim = dt['cap_tensor'].shape[-1] #(num_sen, num_max_word)
-            dt['cap_tensor'] = dt['cap_tensor'].repeat(1, aug_num).reshape(-1, cap_dim)
-            dt['cap_mask'] = dt['cap_mask'].repeat(1, aug_num).reshape(-1, cap_dim)
-
-        for l_id in range(num_pred):
-            hs_lid = hs[l_id]
-            reference = init_reference if l_id == 0 else inter_references[
-                l_id - 1]  # [decoder_layer, batch, query_num, ...]
-            outputs_class = self.class_head[l_id](hs_lid)  # [bs, num_query, N_class]
-            outputs_count = self.predict_event_num(self.count_head[l_id], hs_lid)
-            tmp = self.bbox_head[l_id](hs_lid)  # [bs, num_query, 2]
-            
-            cost_caption, loss_caption, cap_probs, seq = self.caption_prediction(self.caption_head[l_id], dt, hs_lid,
-                                                                                 reference, others, 'none')
-
-            if disable_iterative_refine:
-                outputs_coord = reference
-            else:
-                reference = inverse_sigmoid(reference)
-                if reference.shape[-1] == 2:
-                    tmp += reference
-                else:
-                    assert reference.shape[-1] == 1
-                    tmp[..., :1] += reference
-                outputs_coord = tmp.sigmoid()  # [bs, num_query, 4]
-
-            # Processing the text embed and event embed for alignment
-            if self.load_text_embed or self.opt.disable_contrastive_projection:
-                assert others['text_embed'].shape[0] == num_pred, \
-                    'visual features have {} levels, but text have {}'.format(num_pred, others['text_embed'].shape[0])
-                text_embed = others['text_embed'][l_id]   # [num_sentence, contrastive_dim]
-                event_embed = others['event_embed'][l_id] 
-                event_embed = event_embed.reshape(-1, event_embed.shape[-1]) # [num_query, contrastive_dim]
-                # event_embed = event_embed.reshape(-1, event_embed.shape[-1])
-                # TODO: complete the contrastive learning to return the similarity matrices as 'cl_match_mat'
-
-
-            if self.opt.enable_contrastive and self.opt.set_cost_cl > 0:
-                assert len(others['text_embed']) == num_pred, \
-                    'visual features have {} levels, but text have {}'.format(num_pred, len(others['text_embed']))
-                text_embed = torch.cat(others['text_embed'][l_id], dim=0)   # [num_sentence, contrastive_dim]
-                event_embed = others['event_embed'][l_id]
-                event_embed = event_embed.reshape(-1, event_embed.shape[-1]) # [num_query, contrastive_dim]
-                cl_match_mat = contrastive_criterion.forward_logits(text_embed, event_embed, self.background_embed).t()
-                # cl_match_mat: [num_query, num_sentence]
-                cl_match_mats.append(cl_match_mat)
-            else:
-                cl_match_mats.append(0)
-
-            outputs_classes.append(outputs_class)
-            outputs_counts.append(outputs_count)
-            outputs_coords.append(outputs_coord)
-            # outputs_cap_losses.append(cap_loss)
-            outputs_cap_probs.append(cap_probs)
-            outputs_cap_seqs.append(seq)
-
-        outputs_class = torch.stack(outputs_classes)  # [decoder_layer, bs, num_query, N_class]
-        outputs_count = torch.stack(outputs_counts)
-        outputs_coord = torch.stack(outputs_coords)  # [decoder_layer, bs, num_query, 4]
-        # outputs_cap_loss = torch.stack(outputs_cap_losses)
-
-        all_out = {
-            'pred_logits': outputs_class,
-            'pred_count': outputs_count,
-            'pred_boxes': outputs_coord,
-            'caption_probs': outputs_cap_probs,
-            'seq': outputs_cap_seqs,
-            'cl_match_mats': cl_match_mats}
-        out = {k: v[-1] for k, v in all_out.items()}
-
-
-        # ============================= Refine pseudo box here ================================
-        ks, vs = list(zip(*(all_out.items())))
-        out['aux_outputs'] = [{ks[i]: vs[i][j] for i in range(len(ks))} for j in range(num_pred - 1)]
-        mil_dict = {}
-        bag_score_cache = []
-        for stage in range(self.opt.refine_pseudo_stage_num):
-            # Decay augment ratio as the stage increases
-            aug_ratio = self.opt.pseudo_box_aug_ratio * (0.5 ** stage)
-            _, last_indices, aux_indices = criterion(out, dt['video_target'], others, aug_num, aug_ratio)
-            # Only use the last decoder layer output to conduct the pseudo box refinement
-            hs_lid = hs[-1]
-            reference = inter_references[-1] #[1, num_query, 2]
-            indices = last_indices[0] # [tensor(): num_matched_query ,tensor(): num_matched_cap]
-            query_indices = indices[0][0] # the indices of matched query is ordered
-            cap_indices = indices[0][1] # the indices of matched sentence is unordered
-            # breakpoint()
-            # num_sentence = cap_indices.size(0) // self.opt.pseudo_box_aug_num
-            cap_sort = torch.sort(cap_indices)[1]
-            reorder_query_indices = query_indices[cap_sort]
-            if self.opt.use_neg_pseudo_box:
-                neg_query_indices = []
-                neg_cap_indices = torch.arange(0,cap_indices.size(0),aug_num).view(num_sentence,-1).repeat(1,self.opt.num_neg_box).view(-1)
-                for i in range(num_sentence):
-                    # select some negetive indices from reordered query indices
-                    candidates_r = (reorder_query_indices[(i+1)*aug_num:])
-                    candidates_l = (reorder_query_indices[:(i)*aug_num])
-                    if (candidates_r.size(0) > 0) and (candidates_l.size(0) > 0):
-                        candidates = torch.cat((candidates_r, candidates_l))
-                    else:
-                        candidates = candidates_r if candidates_r.size(0) > 0 else candidates_l
-                    if candidates.size(0) == 0:
-                        candidates = reorder_query_indices
-                    if candidates.size(0) < self.opt.num_neg_box:
-                        random_selected_indices = torch.randperm(candidates.size(0))
-                        padding_num = self.opt.num_neg_box - candidates.size(0)
-                        random_selected_indices = torch.cat((random_selected_indices, random_selected_indices[:padding_num]))
-                    else:
-                        random_selected_indices = torch.randperm(reorder_query_indices.size(0)-aug_num)[:self.opt.num_neg_box]
-                    neg_query_indices.append(candidates[random_selected_indices])
-                neg_query_indices = torch.cat(neg_query_indices)
-                neg_indices = [(neg_query_indices, neg_cap_indices)]
-            # query_indices: ordered, cap_indices: unordered
-            # ++++++ <1>. Produce the instance score and classification score
-            if self.opt.use_additional_cap_layer:
-                cap_loss, cap_probs, seq, sentence_cap_prob = self.caption_prediction(self.caption_head_refine[stage], dt, hs_lid, reference,
-                                                                    others, self.opt.caption_decoder_type, indices)
-                if (stage > 0) and self.opt.use_neg_pseudo_box:
-                    _, _, _, neg_cap_prob = self.caption_prediction(self.caption_head_refine[stage], dt, hs_lid, reference,
-                                                                    others, self.opt.caption_decoder_type, neg_indices)
-            else:
-                cap_loss, cap_probs, seq, sentence_cap_prob = self.caption_prediction(self.caption_head[-1], dt, hs_lid, reference,
-                                                                    others, self.opt.caption_decoder_type, indices)
-                if (stage > 0) and self.opt.use_neg_pseudo_box:
-                    _, _, _, neg_cap_prob = self.caption_prediction(self.caption_head[-1], dt, hs_lid, reference,
-                                                                    others, self.opt.caption_decoder_type, neg_indices) 
-            # breakpoint()   
-            # sentence_cap_prob: the caption probility for each matched query torch.Size([num_matched_query])
-            if self.opt.use_additional_score_layer:
-                query_ins_score = self.class_refine_head[stage](hs_lid)[:, query_indices, :]
-            else:
-                query_ins_score = outputs_classes[-1][:, query_indices, :] # [1, num_matched_query, 1]
-            query_pred_boxes = outputs_coord[-1][:, query_indices, :] # [1, num_matched_query, 2]
-            query_pred_boxes = query_pred_boxes[0,:,:][cap_sort].view(-1, 2) # [num_matched_query, 2]
-            # breakpoint()
-            try:
-                query_ins_score = query_ins_score[0,cap_sort,0].view(-1, aug_num) # [num_cap, num_aug]
-            except:
-                breakpoint()
-            if self.opt.norm_ins_score == 'softmax':
-                query_ins_score = torch.softmax(query_ins_score, dim=-1)
-            elif self.opt.norm_ins_score == 'sigmoid':
-                query_ins_score = query_ins_score.sigmoid()
-            else:
-                raise NotImplementedError
-
-            # breakpoint()
-            # sentence_cap_score = cap_probs['cap_prob_train']
-            temperature = 2
-            sentence_cap_prob = sentence_cap_prob[cap_sort].view(-1, aug_num) # [num_cap, num_aug]
-            cap_len = torch.tensor([len(cap.split()) for cap in dt['cap_raw'][0]], device=sentence_cap_prob.device).unsqueeze(1)
-            sentence_cap_score = (sentence_cap_prob / cap_len) ** temperature + 1e-5
-
-            sentence_cap_score[torch.isinf(sentence_cap_score)] = 1e8
-
-            sentence_cap_score = sentence_cap_score.detach()
-            query_ins_score = query_ins_score.detach()
-
-            # breakpoint()
-            query_score = sentence_cap_score + query_ins_score
-            # sentence_score = 
-            # if (stage == 0) or (self.opt.focal_mil == False):
-            #     sentence_cap_prob = torch.softmax(sentence_cap_prob, dim=-1) # Softmax over queries in the same bag
-            # else:
-            #     sentence_cap_prob = sentence_cap_prob.sigmoid()
-
-            # if self.opt.cap_prob_clip:
-            #     query_score = sentence_cap_prob.detach() * query_ins_score # [num_cap, num_aug]
-            # else:
-            #     query_score = sentence_cap_prob * query_ins_score # [num_cap, num_aug]
-
-            # # ++++++ <2>. Calculate the MIL loss and Neg loss
-            bag_score = query_score.sum(dim=-1) # [num_cap]
-            bag_score = bag_score.clamp(0,1)
-            bag_score_cache.append(bag_score)
-            mil_weight = bag_score_cache[stage-1] if self.opt.weighted_mil_loss else torch.ones_like(bag_score).to(bag_score.device)
-            if stage > 0:
-                if self.opt.focal_mil:
-                    focal_weight = (torch.ones_like(bag_score).to(bag_score.device) - bag_score).pow(2)
-                    mil_loss =  - focal_weight * (bag_score + 1e-6).log()
-                    mil_loss = (mil_weight * mil_loss).mean()
-                else:
-                    # breakpoint()
-                    mil_loss = - (mil_weight * bag_score.log()).mean()
-                if self.opt.use_neg_pseudo_box:
-                    neg_cap_prob = neg_cap_prob.sigmoid()
-                    neg_loss = - ((neg_cap_prob).pow(2) * (1- neg_cap_prob).log()).view(num_sentence,-1).mean(dim=-1)
-                    neg_loss = (mil_weight * neg_loss).mean()
-                    mil_loss += neg_loss
-            else:
-                mil_loss = F.binary_cross_entropy(bag_score, torch.ones_like(bag_score).to(bag_score.device))
-            if 'loss_mil' in mil_dict.keys():
-                mil_dict['loss_mil'] += mil_loss
-            else:
-                mil_dict['loss_mil'] =  mil_loss
-            # ++++++ <3>. Merge the pseudo box to generate new pseudo box
-            if self.opt.merge_criterion == 'cap_topk':
-                topk_pseudo_scores, topk_pseudo_indices = torch.topk(sentence_cap_score, k=self.opt.merge_k_boxes, dim=-1) # [num_caption, k]
-            elif self.opt.merge_criterion == 'ins_topk':
-                topk_pseudo_scores, topk_pseudo_indices = torch.topk(query_ins_score, k=self.opt.merge_k_boxes, dim=-1)
-            elif self.opt.merge_criterion == 'ins_cap_topk':
-                topk_pseudo_scores, topk_pseudo_indices = torch.topk(query_score, k=self.opt.merge_k_boxes, dim=-1) # [num_caption, k]
-            else:
-                raise NotImplementedError('merge_criterion {} is not implemented'.format(self.opt.merge_criterion))
-            # breakpoint()
-            topk_pseudo_scores = topk_pseudo_scores / (topk_pseudo_scores.sum(dim=-1, keepdim=True) + 1e-6) # [num_caption, k]
-            weight = topk_pseudo_scores.unsqueeze(-1).repeat(1,1,2) # [num_caption, k, 2]
-            for i in range(len(dt['video_target'])):
-                previous_pseudo_box = dt['video_target'][i]['box_pseudo_aug'] #[num_caption*num_aug, 2]
-                if self.opt.use_query_box_for_refine:
-                    # Use the coordinates of query as part of guidance for refinement
-                    previous_pseudo_box = (previous_pseudo_box + query_pred_boxes) / 2
-                if self.opt.merge_mode == 'weighted_sum':
-                    # Merge top-k boxes with weighted sum
-                    selected_pseudo_box = torch.gather(previous_pseudo_box.view(-1,aug_num,2), 1, \
-                                                    topk_pseudo_indices.unsqueeze(-1).expand(-1,-1,previous_pseudo_box.size(-1))) # [num_caption, k, 2]
-                    refined_pseudo_box = (weight * selected_pseudo_box).sum(dim=1).clamp(0,1) # [num_caption, 2]
-                    dt['video_target'][i]['boxes_pseudo'] = refined_pseudo_box.detach().clone()
-                # I met the following problem with ''targets_cp = copy.deepcopy(targets)'' in criterion.py:
-                # RuntimeError: Only Tensors created explicitly by the user (graph leaves) support the deepcopy protocol at the moment
-                # When I tried to conduct the deepcopy operation with the targets which have been updated with 'boxes_pseudo' keys
-                # So I detach the refined_pseudo_box here to avoid the deepcopy operation here
-                # Commented by Huabin, 2023/9/14
-                elif self.opt.merge_mode == 'interpolate':
-                    # Generate new box with linear interpolation between previous pbox and pbox with max score
-                    max_pseudo_scores = topk_pseudo_scores[:,:1]
-                    max_coef = 0.5 * torch.ones_like(max_pseudo_scores).to(max_pseudo_scores.device) # Set a max coef for box interpolatation
-                    max_pseudo_box = torch.gather(previous_pseudo_box.view(-1,aug_num,2), 1, \
-                                                    topk_pseudo_indices[:,:1].unsqueeze(-1).expand(-1,-1,previous_pseudo_box.size(-1)))
-                    interpolate_coef = torch.min(max_pseudo_scores, max_coef)
-                    refined_pseudo_box = (1-interpolate_coef) * previous_pseudo_box[(aug_num-1)::aug_num, :] \
-                                        + interpolate_coef * max_pseudo_box.squeeze(1)
-                    refined_pseudo_box = refined_pseudo_box.clamp(0,1)
-                    dt['video_target'][i]['boxes_pseudo'] = refined_pseudo_box.detach().clone()
-
-        # ++++++ <4>. End of the refinement, inverse-repeat the dt['cap_tensor'] and dt['cap_mask']
-        dt['cap_tensor'] = ori_dt_cap_tensor
-        dt['cap_mask'] = ori_dt_cap_mask
-        mil_dict['loss_mil'] = mil_dict['loss_mil'] / self.opt.refine_pseudo_stage_num
-        criterion.pseudo_box_aug = False
-        # ================== End of refinement ========================================
-        # breakpoint()
-        if self.aux_loss:
-            ks, vs = list(zip(*(all_out.items())))
-            out['aux_outputs'] = [{ks[i]: vs[i][j] for i in range(len(ks))} for j in range(num_pred - 1)]
-            loss, last_indices, aux_indices = criterion(out, dt['video_target'], others)
-            if self.opt.disable_rematch:
-                # Disable re-matching and directly use the indices with max score in the last stage of refinment
-                selected_indices = query_score.argmax(dim=-1).unsqueeze(-1)
-                query_indices_in_refine = reorder_query_indices.to(selected_indices.device).view(-1, aug_num)
-                query_indices_in_refine = query_indices_in_refine.gather(1, selected_indices)
-                query_indices_in_refine, index_sort = torch.sort(query_indices_in_refine, 0)
-                cap_indices_in_refine = last_indices[0][0][1].sort()[0]
-                last_indices = [[(query_indices_in_refine.view(-1), cap_indices_in_refine[index_sort.view(-1)])], last_indices[1]]
-            loss.update(mil_dict)
-            criterion.pseudo_box_aug = True 
-            for l_id in range(hs.shape[0]):
-                hs_lid = hs[l_id]
-                reference = init_reference if l_id == 0 else inter_references[l_id - 1]
-                indices = last_indices[0] if l_id == hs.shape[0] - 1 else aux_indices[l_id][0]
-                cap_loss, cap_probs, seq, sentence_cap_prob = self.caption_prediction(self.caption_head[l_id], dt, hs_lid, reference,
-                                                                   others, self.opt.caption_decoder_type, indices)
-                l_dict = {'loss_caption': cap_loss}
-                if l_id != hs.shape[0] - 1:
-                    l_dict = {k + f'_{l_id}': v for k, v in l_dict.items()}
-                loss.update(l_dict)
-            out.update({'caption_probs': cap_probs, 'seq': seq})
-        else:
-            loss, last_indices = criterion(out, dt['video_target'], others)
-            criterion.pseudo_box_aug = True
-            l_id = hs.shape[0] - 1
-            reference = inter_references[l_id - 1]  # [decoder_layer, batch, query_num, ...]
-            hs_lid = hs[l_id]
-            indices = last_indices[0]
-            cap_loss, cap_probs, seq, sentence_cap_prob = self.caption_prediction(self.caption_head[l_id], dt, hs_lid, reference,
-                                                               others, self.opt.caption_decoder_type, indices)
-            l_dict = {'loss_caption': cap_loss}
-            loss.update(l_dict)
-
-            out.pop('caption_losses')
-            out.pop('caption_costs')
-            out.update({'caption_probs': cap_probs, 'seq': seq})
-
-
-        return out, loss
-
-    def parallel_prediction_matched(self, dt, criterion, contrastive_criterion, hs, init_reference, inter_references, others,
-                                    disable_iterative_refine, transformer_input_type='queries'):
-        
-        outputs_classes = []
-        outputs_counts = []
-        outputs_coords = []
-        outputs_cap_costs = []
-        outputs_cap_losses = []
-        outputs_cap_probs = []
-        outputs_cap_seqs = []
-        cl_match_mats = []
-
-        num_pred = hs.shape[0]
-
-        if self.opt.pseudo_box_aug:
-            assert self.opt.use_pseudo_box
-            cap_dim = dt['cap_tensor'].shape[-1] # (num_sen, num_max_word)
-            dt['cap_tensor'] = dt['cap_tensor'].repeat(1, self.opt.pseudo_box_aug_num).reshape(-1, cap_dim)
-            dt['cap_mask'] = dt['cap_mask'].repeat(1, self.opt.pseudo_box_aug_num).reshape(-1, cap_dim)
-
-        for l_id in range(num_pred):
-            hs_lid = hs[l_id]
-            reference = init_reference if l_id == 0 else inter_references[
-                l_id - 1]  # [decoder_layer, batch, query_num, ...]
-            outputs_class = self.class_head[l_id](hs_lid)  # [bs, num_query, N_class]
-            outputs_count = self.predict_event_num(self.count_head[l_id], hs_lid)
-            tmp = self.bbox_head[l_id](hs_lid)  # [bs, num_query, 2]
-
-
-            cost_caption, loss_caption, cap_probs, seq = self.caption_prediction(self.caption_head[l_id], dt, hs_lid,
-                                                                                 reference, others, 'none')
-            # if self.opt.use_anchor:
-            #     outputs_coord = reference
-            # else:
-            if disable_iterative_refine:
-                outputs_coord = reference
-            else:
-                reference = inverse_sigmoid(reference)
-                if reference.shape[-1] == 2:
-                    tmp += reference
-                else:
-                    assert reference.shape[-1] == 1
-                    tmp[..., :1] += reference
-                outputs_coord = tmp.sigmoid()  # [bs, num_query, 4]
-
-            # Processing the text embed and event embed for alignment
-            if self.load_text_embed or not self.opt.disable_contrastive_projection:
-                assert others['text_embed'].shape[0] == num_pred, \
-                    'visual features have {} levels, but text have {}'.format(num_pred, others['text_embed'].shape[0])
-                text_embed = others['text_embed'][l_id]   # [num_sentence, contrastive_dim]
-                event_embed = others['event_embed'][l_id] 
-                event_embed = event_embed.reshape(-1, event_embed.shape[-1]) # [num_query, contrastive_dim]
-                # event_embed = event_embed.reshape(-1, event_embed.shape[-1])
-                # TODO: complete the contrastive learning to return the similarity matrices as 'cl_match_mat'
-
-
-            if self.opt.enable_contrastive and self.opt.set_cost_cl > 0:
-                assert len(others['text_embed']) == num_pred, \
-                    'visual features have {} levels, but text have {}'.format(num_pred, len(others['text_embed']))
-                text_embed = torch.cat(others['text_embed'][l_id], dim=0)   # [num_sentence, contrastive_dim]
-                event_embed = others['event_embed'][l_id]
-                event_embed = event_embed.reshape(-1, event_embed.shape[-1]) # [num_query, contrastive_dim]
-                cl_match_mat = contrastive_criterion.forward_logits(text_embed, event_embed, self.background_embed).t()
-                # cl_match_mat: [num_query, num_sentence]
-                cl_match_mats.append(cl_match_mat)
-            else:
-                cl_match_mats.append(0)
-
-            outputs_classes.append(outputs_class)
-            outputs_counts.append(outputs_count)
-            outputs_coords.append(outputs_coord)
-            # outputs_cap_losses.append(cap_loss)
-            outputs_cap_probs.append(cap_probs)
-            outputs_cap_seqs.append(seq)
-
-        outputs_class = torch.stack(outputs_classes)  # [decoder_layer, bs, num_query, N_class]
-        outputs_count = torch.stack(outputs_counts)
-        outputs_coord = torch.stack(outputs_coords)  # [decoder_layer, bs, num_query, 4]
-        # outputs_cap_loss = torch.stack(outputs_cap_losses)
-
-        all_out = {
-            'pred_logits': outputs_class,
-            'pred_count': outputs_count,
-            'pred_boxes': outputs_coord,
-            'caption_probs': outputs_cap_probs,
-            'seq': outputs_cap_seqs,
-            'cl_match_mats': cl_match_mats}
-        out = {k: v[-1] for k, v in all_out.items()}
-
-        if self.aux_loss:
-            ks, vs = list(zip(*(all_out.items())))
-            out['aux_outputs'] = [{ks[i]: vs[i][j] for i in range(len(ks))} for j in range(num_pred - 1)]
-            if transformer_input_type == 'prior_proposals':
-                loss, _, _ = criterion(out, dt['video_target'])
-                # Random select an query from each segment
-                num_sentence = dt['cap_tensor'].shape[0]
-                num_query = hs.shape[-2]
-                num_query_interval = num_query // num_sentence
-                query_indices = []
-                for i in range(num_sentence):
-                    interval_min = i * num_query_interval
-                    interval_max = interval_min + num_query_interval
-                    sample = torch.randint(interval_min, interval_max, (hs.shape[0],))
-                    query_indices.append(sample)
-                query_indices = torch.cat(query_indices, dim=0)
-                gt_indices = torch.arange(num_sentence)
-
-                last_indices = ([(query_indices[::hs.shape[0]], gt_indices)], [None, None])
-                aux_indices = []
-                for l_id in range(hs.shape[0]-1):
-                    aux_indices.append(([(query_indices[(l_id+1)::hs.shape[0]], gt_indices)], [None, None]))
-            else:
-                loss, last_indices, aux_indices = criterion(out, dt['video_target'], others)
-            for l_id in range(hs.shape[0]):
-                hs_lid = hs[l_id]
-                reference = init_reference if l_id == 0 else inter_references[l_id - 1]
-                indices = last_indices[0] if l_id == hs.shape[0] - 1 else aux_indices[l_id][0]
-                cap_loss, cap_probs, seq, sentence_cap_prob = self.caption_prediction(self.caption_head[l_id], dt, hs_lid, reference,
-                                                                   others, self.opt.caption_decoder_type, indices)
-
-                l_dict = {'loss_caption': cap_loss}
-                if (self.opt.matcher_type == 'DTW' or self.opt.matcher_type == 'Sim'):
-                    contrastive_loss = contrastive_criterion(
-                        text_embed = others['text_embed'][l_id],
-                        event_embed = others['event_embed'][l_id],
-                        matching_indices = indices,
-                        bg_embed = self.background_embed,
-                    )
-
-                    l_dict.update({'contrastive_loss': contrastive_loss})
-                if l_id != hs.shape[0] - 1:
-                    l_dict = {k + f'_{l_id}': v for k, v in l_dict.items()}
-                loss.update(l_dict)
-            out.update({'caption_probs': cap_probs, 'seq': seq})
-        else:
-            loss, last_indices = criterion(out, dt['video_target'], others)
-
-            l_id = hs.shape[0] - 1
-            reference = inter_references[l_id - 1]  # [decoder_layer, batch, query_num, ...]
-            hs_lid = hs[l_id]
-            indices = last_indices[0]
-            cap_loss, cap_probs, seq, sentence_cap_prob = self.caption_prediction(self.caption_head[l_id], dt, hs_lid, reference,
-                                                               others, self.opt.caption_decoder_type, indices)
-            l_dict = {'loss_caption': cap_loss}
-            loss.update(l_dict)
-
-            out.pop('caption_losses')
-            out.pop('caption_costs')
-            out.update({'caption_probs': cap_probs, 'seq': seq})
-
-        return out, loss
-
-    def caption_prediction(self, cap_head, dt, hs, reference, others, captioner_type, indices=None):
-        N_, N_q, C = hs.shape
-        # all_cap_num = len(dt['cap_tensor'])
-        # if self.opt.pseudo_box_aug:
-        #     assert self.opt.use_pseudo_box
-        #     cap_dim = dt['cap_tensor'].shape[-1] # (num_sen, num_max_word)
-        #     # breakpoint()
-        #     if indices != None:
-        #         breakpoint()
-        #     dt['cap_tensor'] = dt['cap_tensor'].repeat(1, self.opt.pseudo_box_aug_num).reshape(-1, cap_dim)
-        #     dt['cap_mask'] = dt['cap_mask'].repeat(1, self.opt.pseudo_box_aug_num).reshape(-1, cap_dim)
-        all_cap_num = len(dt['cap_tensor'])
-        query_mask = others['proposals_mask']
-        gt_mask = dt['gt_boxes_mask']
-        mix_mask = torch.zeros(query_mask.sum().item(), gt_mask.sum().item())
-        query_nums, gt_nums = query_mask.sum(1).cpu(), gt_mask.sum(1).cpu()
-        hs_r = torch.masked_select(hs, query_mask.unsqueeze(-1)).reshape(-1, C)
-
-        if indices == None:
-            row_idx, col_idx = 0, 0
-            for i in range(N_):
-                mix_mask[row_idx: (row_idx + query_nums[i]), col_idx: (col_idx + gt_nums[i])] = 1
-                row_idx=row_idx + query_nums[i]
-                col_idx= col_idx + gt_nums[i]
-
-            bigids = mix_mask.nonzero(as_tuple=False)
-            feat_bigids, cap_bigids = bigids[:, 0], bigids[:, 1]
-        else:
-            # breakpoint()
-            feat_bigids = torch.zeros(sum([len(_[0]) for _ in indices])).long()
-            cap_bigids = torch.zeros_like(feat_bigids)
-            total_query_ids = 0
-            total_cap_ids = 0
-            total_ids = 0
-            max_pair_num = max([len(_[0]) for _ in indices])
-            new_hr_for_dsa = torch.zeros(N_, max_pair_num, C)  # only for lstm-dsa
-            cap_seq = dt['cap_tensor']
-            new_seq_for_dsa = torch.zeros(N_, max_pair_num, cap_seq.shape[-1], dtype=cap_seq.dtype)  # only for lstm-dsa
-            for i, index in enumerate(indices):
-                feat_ids, cap_ids = index
-                feat_bigids[total_ids: total_ids + len(feat_ids)] = total_query_ids + feat_ids
-                cap_bigids[total_ids: total_ids + len(feat_ids)] = total_cap_ids + cap_ids
-                new_hr_for_dsa[i, :len(feat_ids)] = hs[i, feat_ids]
-                new_seq_for_dsa[i, :len(feat_ids)] = cap_seq[total_cap_ids + cap_ids]
-                total_query_ids += query_nums[i]
-                total_cap_ids += gt_nums[i]
-                total_ids += len(feat_ids)
-            # if self.opt.pseudo_box_aug:
-            #     # Revise the matched targer ids for pseudo box augmentation to caption id
-            #     cap_bigids = cap_bigids // self.opt.pseudo_box_aug_num
-        cap_probs = {}
-        flag = True
-
-        if captioner_type == 'none':
-            cost_caption = torch.zeros(N_, N_q, all_cap_num,
-                                       device=hs.device)  # batch_size * num_queries * all_caption_num
-            loss_caption = torch.zeros(N_, N_q, all_cap_num, device=hs.device)
-            cap_probs['cap_prob_train'] = torch.zeros(1, device=hs.device)
-            cap_probs['cap_prob_eval'] = torch.zeros(N_, N_q, 3, device=hs.device)
-            seq = torch.zeros(N_, N_q, 3, device=hs.device)
-            return cost_caption, loss_caption, cap_probs, seq
-
-        elif captioner_type in ['light']:
-            clip = hs_r.unsqueeze(1)
-            clip_mask = clip.new_ones(clip.shape[:2])
-            event = None
-        elif self.opt.caption_decoder_type == 'standard':
-            # breakpoint()
-            # assert N_ == 1, 'only support batchsize = 1'
-            if self.training:
-                # breakpoint()
-                seq = dt['cap_tensor'][cap_bigids]
-                if self.opt.caption_cost_type != 'rl':
-                    if self.opt.refine_pseudo_box: # Only training and refine_pseudo_box = True returns the raw_cap_prob
-                        cap_prob, raw_cap_prob = cap_head(hs[:, feat_bigids], reference[:, feat_bigids], others, seq)
-                        # shape: [num_sentence, max_num_word, num_vocab]
-                        # cap_prob is log_softmax(prob), raw_cap_prob is (prob)
-                        cap_probs['cap_prob_train'] = cap_prob
-                        cap_probs['raw_cap_prob'] = raw_cap_prob
-                    else:
-                        cap_prob = cap_head(hs[:, feat_bigids], reference[:, feat_bigids], others, seq) 
-                        # [num_matched_query, max_length_sentence, num_word_in_vocab], e.g., [5, 13, 1608], here 13 is the max length among 5 sentences
-                        cap_probs['cap_prob_train'] = cap_prob
-            else:
-                with torch.no_grad():
-                    cap_prob = cap_head(hs[:, feat_bigids], reference[:, feat_bigids], others,
-                                        dt['cap_tensor'][cap_bigids])
-                    seq, cap_prob_eval = cap_head.sample(hs, reference, others)
-                    if len(seq):
-                        seq = seq.reshape(-1, N_q, seq.shape[-1])
-                        cap_prob_eval = cap_prob_eval.reshape(-1, N_q, cap_prob_eval.shape[-1])
-                    cap_probs['cap_prob_eval'] = cap_prob_eval
-
-            flag = False
-            pass
-
-        if flag:
-            clip_ext = clip[feat_bigids]
-            clip_mask_ext = clip_mask[feat_bigids]
-
-            if self.training:
-                seq = dt['cap_tensor'][cap_bigids]
-                if self.opt.caption_cost_type != 'rl':
-                    cap_prob = cap_head(event, clip_ext, clip_mask_ext, seq)
-                    cap_probs['cap_prob_train'] = cap_prob
-            else:
-                with torch.no_grad():
-                    seq_gt = dt['cap_tensor'][cap_bigids]
-                    cap_prob = cap_head(event, clip_ext, clip_mask_ext, seq_gt)
-                    seq, cap_prob_eval = cap_head.sample(event, clip, clip_mask)
-
-                    if len(seq):
-                        # re_seq = torch.zeros(N_, N_q, seq.shape[-1])
-                        # re_cap_prob_eval = torch.zeros(N_, N_q, cap_prob_eval.shape[-1])
-                        seq = seq.reshape(-1, N_q, seq.shape[-1])
-                        cap_prob_eval = cap_prob_eval.reshape(-1, N_q, cap_prob_eval.shape[-1])
-                    cap_probs['cap_prob_eval'] = cap_prob_eval
-
-        if self.opt.caption_cost_type == 'loss':
-            cap_prob = cap_prob.reshape(-1, cap_prob.shape[-2], cap_prob.shape[-1]) # [num_matched_query, max_length_sentence, num_word_in_vocab], e.g., [5, 13, 1608]
-            caption_tensor = dt['cap_tensor'][:, 1:][cap_bigids] # [num_sentence, max_num_sentence], e.g, [5, 13]
-            caption_mask = dt['cap_mask'][:, 1:][cap_bigids]  # [num_sentence, max_num_sentence], e.g, [5, 13]
-            cap_loss = cap_head.build_loss(cap_prob, caption_tensor, caption_mask) # [num_query]
-            cap_cost = cap_loss
-        else:
-            raise AssertionError('caption cost type error')
-
-        # Calculate caption probs for each query
-        # breakpoint()
-        # if self.opt.refine_pseudo_box:
-        #     sentence_cap_prob = cap_head.build_prob(raw_cap_prob, caption_tensor, caption_mask)
-        # else:
-        sentence_cap_prob = - cap_loss
-
-        if indices:
-            return cap_loss.mean(), cap_probs, seq, sentence_cap_prob
-        # cap_loss.mean(): [num_matched_query] --> [1], 
-        # cap_probs: dict, contains 'cap_prob_train' or 'cap_prob_eval' [num_matched_query, max_length_sentence, num_word_in_vocab]
-        # seq： [num_sentence, max_length_sentence+1], here the '+1' means the 1st col is all '0'
- 
-        cap_id, query_id = cap_bigids, feat_bigids
-        cost_caption = hs_r.new_zeros((max(query_id) + 1, max(cap_id) + 1))
-        cost_caption[query_id, cap_id] = cap_cost
-        loss_caption = hs_r.new_zeros((max(query_id) + 1, max(cap_id) + 1))
-        loss_caption[query_id, cap_id] = cap_loss
-        cost_caption = cost_caption.reshape(-1, N_q,
-                                            max(cap_id) + 1)  # batch_size * num_queries * all_caption_num
-        loss_caption = loss_caption.reshape(-1, N_q, max(cap_id) + 1)
-        return cost_caption, loss_caption, cap_probs, seq
-
-    def caption_prediction_eval(self, cap_head, dt, hs, reference, others, decoder_type, pred_num=None, indices=None):
-        assert indices == None
-        N_, N_q, C = hs.shape
-        query_mask = others['proposals_mask']
-        gt_mask = dt['gt_boxes_mask']
-        mix_mask = torch.zeros(query_mask.sum().item(), gt_mask.sum().item())
-        query_nums, gt_nums = query_mask.sum(1).cpu(), gt_mask.sum(1).cpu()
-        hs_r = torch.masked_select(hs, query_mask.unsqueeze(-1)).reshape(-1, C)
-
-        row_idx, col_idx = 0, 0
-        for i in range(N_):
-            mix_mask[row_idx: (row_idx + query_nums[i]), col_idx: (col_idx + gt_nums[i])] = 1
-            row_idx = row_idx + query_nums[i]
-            col_idx = col_idx + gt_nums[i]
-
-        cap_probs = {}
-
-        if decoder_type in ['none']:
-            cap_probs['cap_prob_train'] = torch.zeros(1, device=hs.device)
-            cap_probs['cap_prob_eval'] = torch.zeros(N_, N_q, 3, device=hs.device)
-            seq = torch.zeros(N_, N_q, 3, device=hs.device)
-            return cap_probs, seq
-
-        elif decoder_type in ['light']:
-            clip = hs_r.unsqueeze(1)
-            clip_mask = clip.new_ones(clip.shape[:2])
-            event = None
-            seq, cap_prob_eval = cap_head.sample(event, clip, clip_mask)
-            if len(seq):
-                seq = seq.reshape(-1, N_q, seq.shape[-1])
-                cap_prob_eval = cap_prob_eval.reshape(-1, N_q, cap_prob_eval.shape[-1])
-            cap_probs['cap_prob_eval'] = cap_prob_eval
-
-        elif decoder_type in ['standard']:
-            assert N_ == 1, 'only support batchsize = 1'
-            with torch.no_grad():
-                if self.opt.transformer_input_type == 'prior_proposals':
-                    # hs: [bs, num_query, feat_dim]  
-                    # reference: [bs, num_query, 2]
-                    if pred_num:
-                        num_cap =  pred_num
-                    else:
-                        num_cap =  dt['cap_tensor'].shape[0]
-                    interval = N_q // num_cap
-                    pool_layer = torch.nn.AvgPool1d(interval,stride=interval)
-                    hs = pool_layer(hs.permute(0,2,1)).permute(0,2,1)[:,:num_cap,:] # [batch, num_sentence, dim]
-                    reference = pool_layer(reference.permute(0,2,1)).permute(0,2,1)[:,:num_cap,:] # # [batch, num_sentence, 2]
-                    seq, cap_prob_eval = cap_head.sample(hs, reference, others)
-                    if len(seq):
-                        seq = seq.reshape(-1, num_cap, seq.shape[-1]) #
-                        cap_prob_eval = cap_prob_eval.reshape(-1, num_cap, cap_prob_eval.shape[-1])
-                    cap_probs['cap_prob_eval'] = cap_prob_eval
-                else:
-                    seq, cap_prob_eval = cap_head.sample(hs, reference, others)
-                    if len(seq):
-                        seq = seq.reshape(-1, N_q, seq.shape[-1]) #
-                        cap_prob_eval = cap_prob_eval.reshape(-1, N_q, cap_prob_eval.shape[-1])
-                    cap_probs['cap_prob_eval'] = cap_prob_eval
-        return cap_probs, seq
-
-
-class PostProcess(nn.Module):
-    """ This module converts the model's output into the format expected by the coco api"""
-
-    def __init__(self, opt):
-        super().__init__()
-        self.opt = opt
-
-    @torch.no_grad()
-    def forward(self, outputs, target_sizes, loader):
-        """ Perform the computation
-        Parameters:
-            outputs: raw outputs of the model
-            target_sizes: tensor of dimension [batch_size] containing the size of each video of the batch
-        """
-        out_logits, out_bbox = outputs['pred_logits'], outputs['pred_boxes']
-        N, N_q, N_class = out_logits.shape
-        assert len(out_logits) == len(target_sizes)
-        prob = out_logits.sigmoid() # batch, num_queries, 1
-
-        if self.opt.transformer_input_type == 'prior_proposals':
-            #topk_values = prob.view(N, N_q)
-            #topk_indexes = torch.arange(N_q, device=prob.device).unsqueeze(0).repeat(N, 1)
-            topk_values, topk_indexes = torch.topk(prob.view(out_logits.shape[0], -1), outputs['seq'].shape[1], dim=1)
-        else:
-            topk_values, topk_indexes = torch.topk(prob.view(out_logits.shape[0], -1), N_q, dim=1)
-        scores = topk_values
-        # topk_boxes = topk_indexes // out_logits.shape[2]
-        topk_boxes = torch.div(topk_indexes, out_logits.shape[2], rounding_mode='floor')
-        labels = topk_indexes % out_logits.shape[2]
-        boxes = box_ops.box_cl_to_xy(out_bbox)
-        raw_boxes = copy.deepcopy(boxes)
-        boxes[boxes < 0] = 0
-        boxes[boxes > 1] = 1
-        boxes = torch.gather(boxes, 1, topk_boxes.unsqueeze(-1).repeat(1, 1, 2))
-
-        scale_fct = torch.stack([target_sizes, target_sizes], dim=1)
-        boxes = boxes * scale_fct[:, None, :]
-        seq = outputs['seq']  # [batch_size, num_queries, max_Cap_len=30]
-        cap_prob = outputs['caption_probs']['cap_prob_eval']  # [batch_size, num_queries]
-        eseq_lens = outputs['pred_count'].argmax(dim=-1).clamp(min=1)
-
-        if len(seq):
-            mask = (seq > 0).float()
-            # cap_scores = (mask * cap_prob).sum(2).cpu().numpy().astype('float') / (
-            #         1e-5 + mask.sum(2).cpu().numpy().astype('float'))
-            cap_scores = (mask * cap_prob).sum(2).cpu().numpy().astype('float')
-            seq = seq.detach().cpu().numpy().astype('int')  # (eseq_batch_size, eseq_len, cap_len)
-            caps = [[loader.dataset.translator.rtranslate(s) for s in s_vid] for s_vid in seq]
-            if self.opt.transformer_input_type != 'prior_proposals':
-                caps = [[caps[batch][idx] for q_id, idx in enumerate(b)] for batch, b in enumerate(topk_boxes)]  # Re-arrange the caption order accroding to the logits
-                cap_scores = [[cap_scores[batch, idx] for q_id, idx in enumerate(b)] for batch, b in enumerate(topk_boxes)]
-        else:
-            bs, num_queries = boxes.shape[:2]
-            cap_scores = [[-1e5] * num_queries] * bs
-            caps = [[''] * num_queries] * bs
-
-        results = [
-            {'scores': s, 'labels': l, 'boxes': b, 'raw_boxes': b, 'captions': c, 'caption_scores': cs, 'query_id': qid,
-             'vid_duration': ts, 'pred_seq_len': sl} for s, l, b, rb, c, cs, qid, ts, sl in
-            zip(scores, labels, boxes, raw_boxes, caps, cap_scores, topk_boxes, target_sizes, eseq_lens)]
-        return results
-
-
-class MLP(nn.Module):
-    """ Very simple multi-layer perceptron (also called FFN)"""
-
-    def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
-        super().__init__()
-        self.num_layers = num_layers
-        h = [hidden_dim] * (num_layers - 1)
-        self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))
-
-    def forward(self, x):
-        for i, layer in enumerate(self.layers):
-            x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
-        return x
-
-
-def build(args):
-    device = torch.device(args.device)
-    base_encoder = build_base_encoder(args)
-    # For text encoder when using DTW matcher
-    # if args.matcher_type == 'DTW' or args.use_pseudo_box:
-    #     if args.pretrained_language_model == 'UniVL':
-    #         print('Load pretrained UniVL model weights')
-    #         text_encoder = load_pretrained_UniVL()
-    #     else:
-    #         for i in range(10):
-    #             try:
-    #                 text_encoder = AutoModel.from_pretrained(args.pretrained_language_model, cache_dir=args.huggingface_cache_dir)
-    #                 break
-    #             except:
-    #                 print('download error in AutoModel, retry...')
-    #                 time.sleep(1)
-    # else:
-    #     text_encoder = None
-
-    transformer = build_deforamble_transformer(args)
-    captioner = build_captioner(args)
-
-    model = PDVC(
-        base_encoder,
-        transformer,
-        captioner,
-        num_classes=args.num_classes,
-        num_queries=args.num_queries,
-        num_feature_levels=args.num_feature_levels,
-        aux_loss=args.aux_loss,
-        with_box_refine=args.with_box_refine,
-        opt=args
-    )
-
-    matcher = build_matcher(args)
-    if args.matcher_type == 'DTW' and args.use_anchor:
-        weight_dict = {'loss_ce': args.cls_loss_coef,
-                    'loss_bbox': args.bbox_loss_coef,
-                    'loss_giou': args.giou_loss_coef,
-                    'loss_self_iou': args.self_iou_loss_coef,
-                    'loss_ref_rank': args.ref_rank_loss_coef,
-                    'loss_counter': args.count_loss_coef,
-                    'loss_caption': args.caption_loss_coef,
-                    'contrastive_loss': args.contrastive_loss_start_coef,
-                    }
-    else:
-        weight_dict = {'loss_ce': args.cls_loss_coef,
-                    'loss_bbox': args.bbox_loss_coef,
-                    'loss_giou': args.giou_loss_coef,
-                    'loss_counter': args.count_loss_coef,
-                    'loss_caption': args.caption_loss_coef,
-                    'contrastive_loss': args.contrastive_loss_start_coef,
-                    }
-    if args.refine_pseudo_box:
-        weight_dict.update({'loss_mil': args.mil_loss_coef})
-    # TODO this is a hack
-    if args.aux_loss:
-        aux_weight_dict = {}
-        for i in range(args.dec_layers - 1):
-            aux_weight_dict.update({k + f'_{i}': v for k, v in weight_dict.items()})
-        weight_dict.update(aux_weight_dict)
-
-    losses = ['labels', 'boxes', 'cardinality']
-
-    if args.matcher_type == 'DTW' or args.matcher_type == 'Sim':
-        criterion = AlignCriterion(args.num_classes, matcher, weight_dict, losses, focal_alpha=args.focal_alpha,
-                                focal_gamma=args.focal_gamma, opt=args)
-        contrastive_criterion = ContrastiveCriterion(temperature=args.contrastive_loss_temperature,
-                                                 enable_cross_video_cl=args.enable_cross_video_cl,
-                                                 enable_e2t_cl = args.enable_e2t_cl,
-                                                 enable_bg_for_cl = args.enable_bg_for_cl)
-        contrastive_criterion.to(device)
-    else:
-        criterion = SetCriterion(args.num_classes, matcher, weight_dict, losses, focal_alpha=args.focal_alpha,
-                                focal_gamma=args.focal_gamma, opt=args)
-        contrastive_criterion = None
-    
-    criterion.to(device)
-    postprocessors = {'bbox': PostProcess(args)}
-
-    return model, criterion, contrastive_criterion, postprocessors
-
-
diff --git a/backup/pdvc/position_encoding.py b/backup/pdvc/position_encoding.py
deleted file mode 100644
index 2cb71befd6e4397bd4d5a30c7a43861cea158cc7..0000000000000000000000000000000000000000
--- a/backup/pdvc/position_encoding.py
+++ /dev/null
@@ -1,76 +0,0 @@
-# ------------------------------------------------------------------------
-# 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
-# ------------------------------------------------------------------------
-
-"""
-Various positional encodings for the transformer.
-"""
-import math
-import torch
-from torch import nn
-
-from misc.detr_utils.misc import NestedTensor
-
-
-class PositionEmbeddingSine(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
-        self.max_duration = 256
-        self.duration_embed_layer = nn.Linear(self.max_duration, self.max_duration)
-
-    def forward(self, tensor_list: NestedTensor):
-        x = tensor_list.tensors
-        mask = tensor_list.mask
-        duration = tensor_list.duration
-        assert mask is not None
-        not_mask = ~mask
-        x_embed = not_mask.cumsum(1, dtype=torch.float32)
-        if self.normalize:
-            eps = 1e-6
-            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)
-        dim_t = self.temperature ** (2 * (torch.div(dim_t, 2, rounding_mode='floor')) / self.num_pos_feats)
-        pos_x = x_embed[:, :, None] / dim_t
-        pos_x = torch.stack((pos_x[:, :, 0::2].sin(), pos_x[:, :, 1::2].cos()), dim=3).flatten(2)
-
-        dur_embed = self.duration_embedding(duration).reshape(-1,1,self.max_duration).expand_as(pos_x)
-        pos = torch.cat((pos_x, dur_embed), dim=2).permute(0, 2, 1)
-        return pos
-
-    def duration_embedding(self, durations):
-        out = torch.zeros(len(durations), self.max_duration, device=durations.device)
-        durations = durations.int()
-        for ii in range(len(durations)):
-            out[ii, :durations[ii]] = 1
-        out = self.duration_embed_layer(out)
-        return out
-
-
-
-def build_position_encoding(position_embedding, N_steps):
-    if position_embedding in ('v2', 'sine'):
-        # TODO find a better way of exposing other arguments
-        position_embedding = PositionEmbeddingSine(N_steps, normalize=True)
-    else:
-        raise ValueError(f"not supported {position_embedding}")
-
-    return position_embedding
diff --git a/backup/pdvc/util.py b/backup/pdvc/util.py
deleted file mode 100644
index 7e489c1bce356a96116e2c13fcabc1c84d132711..0000000000000000000000000000000000000000
--- a/backup/pdvc/util.py
+++ /dev/null
@@ -1,72 +0,0 @@
-import torch
-import numpy as np
-
-# def find_center_index(array: np.ndarray) -> np.ndarray:
-#     """
-#     Given a array with shape [steps, topk], find the center index between topk indexes
-#     which has the minimal average distance with other indexes.
-
-#     Args:
-#     - array: numpy array representing the input array with shape [steps, topk]
-
-#     Returns:
-#     - center_indexes: numpy array of center indexes for each step
-#     """
-
-#     distances = np.sum(np.abs(array[:, np.newaxis, :] - array[:, :, np.newaxis]), axis=2)
-#     center_indexes = np.argmin(distances, axis=1)
-
-#     return center_indexes
-
-def find_center_value(arr):
-    # Compute pairwise distances between all values
-    distances = np.abs(arr[:, np.newaxis] - arr[np.newaxis, :])
-    
-    # Sum distances for each value
-    sum_distances = np.sum(distances, axis=1)
-    
-    # Find the index of the value with the smallest sum distance
-    center_index = np.argmin(sum_distances)
-    
-    # Get the center value
-    center_value = arr[center_index]
-    
-    return center_value
-
-
-def compute_overlap(center_t, boundary_t, center_t_minus_1, boundary_t_minus_1):
-    """
-    Compute the overlap of boundaries between time t and t-1 for each element in the arrays.
-
-    Args:
-    - center_t: numpy array representing the center at time t with shape [N,]
-    - boundary_t: numpy array representing the boundary at time t with shape [N,1, candidates]
-    - center_t_minus_1: numpy array representing the center at time t-1 with shape [N,]
-    - boundary_t_minus_1: numpy array representing the boundary at time t-1 with shape [N,]
-
-    Returns:
-    - overlap: numpy array representing the overlap of boundaries with shape [N,]
-    """
-
-    boundary_t = boundary_t.squeeze(1)
-    boundary_t_minus_1 = boundary_t_minus_1.squeeze(1)
-    center_t = center_t[:, np.newaxis]
-    # breakpoint()
-    center_t_minus_1 = center_t_minus_1[:, np.newaxis]
-    # boundary_t_minus_1 = boundary_t_minus_1[:, np.newaxis]
-
-
-    # Calculate the start and end positions of the boundaries at time t and t-1
-    start_t = center_t - 0.5 * boundary_t
-    end_t = center_t + 0.5 * boundary_t
-    start_t_minus_1 = center_t_minus_1 - 0.5 * boundary_t_minus_1
-    end_t_minus_1 = center_t_minus_1 + 0.5 * boundary_t_minus_1
-
-    # Calculate the intersection and union of the boundaries
-    intersection = np.maximum(0, np.minimum(end_t, end_t_minus_1) - np.maximum(start_t, start_t_minus_1))
-    union = boundary_t + boundary_t_minus_1 - intersection
-
-    # Compute the overlap using the Intersection over Union (IoU) formula
-    overlap = intersection / union
-
-    return overlap
\ No newline at end of file
diff --git a/backup/pdvc/video_segmentation.py b/backup/pdvc/video_segmentation.py
deleted file mode 100644
index dfa7f74cf0a858fc7dc8a929638fc294fc9bfc13..0000000000000000000000000000000000000000
--- a/backup/pdvc/video_segmentation.py
+++ /dev/null
@@ -1,976 +0,0 @@
-import torch
-import numpy as np
-
-
-from pdvc.dp.exact_dp import drop_dtw, double_drop_dtw
-from pdvc.dp.dp_utils import compute_sim
-import statistics
-from sklearn.cluster import KMeans
-from pdvc.util import find_center_value, compute_overlap
-# from config import CONFIG
-
-''' configs of original file '''
-config_eval_l2norm = True 
-config_eval_keep_percentile = 0.48
-config_eval_fixed_drop_sim = -1
-
-
-''' 
-return value:
-frame features: [num_frames, feature_dim] -> optimal_assignment: [num_steps], -1 means no match, otherwise means the index of the matched step/caption/query
-
-'''
-# filter_threshold = 0.5
-
-def clip_array(arr, threshold):
-    clipped_arr = np.where(arr > threshold, arr, threshold)
-    return clipped_arr
-
-
-# def compute_filtered_indices(topk_indices_list, topk_values_list, scale=0.5):
-#     # center_indices = []
-#     # boundary_widths = []
-#     filtered_indices_list = []
-#     for topk_indices, topk_values in zip(topk_indices_list, topk_values_list):
-#         center_index = find_center_value(topk_indices)
-#         std_index = (sum((topk_indices - center_index) ** 2 * topk_values) / sum(topk_values)) ** 0.5
-#         boundary_width = std_index * scale
-#         filtered_indices = [i for i in topk_indices if abs(i - center_index) <= boundary_width]
-#         filtered_indices_list.append(filtered_indices)
-#         # center_indices.append(center_index)
-#         # boundary_widths.append(boundary_width)
-
-#     return filtered_indices_list
-
-def compute_filtered_indices(topk_indices, topk_values, threshold=0.5):
-    center_index = find_center_value(np.array(topk_indices))
-    std_index = (sum((topk_indices - center_index) ** 2 * topk_values) / (sum(topk_values) + 1e-5)) ** 0.5
-    boundary_width = std_index * threshold
-    filtered_indices = [i for i in topk_indices if abs(i - center_index) <= boundary_width]
-    return filtered_indices 
-
-def compute_bbox_loss(index_list, box, similarity_values):
-    left, right = box
-    distances = []
-
-    for i, index in enumerate(index_list):
-        if left <= index <= right:
-            distance = -min(index - left, right - index)
-        else:
-            distance = max(left - index, index - right)
-        
-        weighted_distance = similarity_values[i] * distance
-        distances.append(weighted_distance)
-
-    return sum(distances)
-
-
-
-
-
-def remove_outliers(indices, threshold, mode, w):
-    # Calculate the mean and standard deviation of the indices
-    if mode == 'median':
-        median = statistics.median(indices)
-    elif mode == 'mean':
-        mean = sum(indices) / len(indices)
-    elif mode == 'mode':
-        count_dict = {}
-        for p in range(min(indices), max(indices) + 1):
-            # print(p)
-            count = sum(1 for c in indices if p - w <= c <= p + w)
-            count_dict[p] = count
-
-        max_count = max(count_dict.values())
-        best_p_values = [p for p, count in count_dict.items() if count == max_count]
-        if len(best_p_values) % 2 == 0:
-            best_p_values.pop()
-        
-        mode_value = statistics.median(best_p_values)
-    std_dev = (sum((x - mean) ** 2 for x in indices) / len(indices)) ** 0.5
-
-    # if mode == 'mode':
-    #     '''get mode-similar statistics'''
-    #     count_dict = {}
-    #     for p in range(min(indices), max(indices) + 1):
-    #         # print(p)
-    #         count = sum(1 for c in indices if p - w <= c <= p + w)
-    #         count_dict[p] = count
-
-    #     max_count = max(count_dict.values())
-    #     best_p_values = [p for p, count in count_dict.items() if count == max_count]
-    #     if len(best_p_values) % 2 == 0:
-    #         best_p_values.pop()
-        
-    #     mode_value = statistics.median(best_p_values)
-
-    # Calculate the threshold for identifying outliers
-    threshold_value = threshold * std_dev
-
-    # Filter out indices that are far from the mean
-    # breakpoint()
-
-    if mode == 'median':
-        filtered_indices = [i for i in indices if abs(i - median) <= threshold_value]
-    elif mode == 'mode':
-        filtered_indices = [i for i in indices if abs(i - mode_value) <= threshold_value]
-    return filtered_indices
-
-
-def remove_outliers_v1(indices, threshold):
-    pass 
-
-def get_mode(indices, w):
-    count_dict = {}
-    for p in range(min(indices), max(indices) + 1):
-        # print(p)
-        count = sum(1 for c in indices if p - w <= c <= p + w)
-        count_dict[p] = count
-
-    max_count = max(count_dict.values())
-    best_p_values = [p for p, count in count_dict.items() if count == max_count]
-    if len(best_p_values) % 2 == 0:
-        best_p_values.pop()
-    
-    mode_value = statistics.median(best_p_values)
-    return mode_value
-
-def get_mode_box(sim, topk, w, ratio): # topk选择20 ratio 1 
-    ''' 注意这里算中心的时候使用前topk是因为更相信前topk的准确率 但是确定中心以后需要找边界 就需要使用全部的'''
-    avg_caption_length = sim.shape[1] // sim.shape[0]
-    sorted_idx = torch.argsort(-sim, dim=1)
-    top_indices = sorted_idx[:, :topk]
-    # top_values, top_indices = torch.topk(sim, topk, dim=1, largest=True, sorted=True)
-    # top_indices_half = top_indices[:, :topk//2]
-    top_cap_indices = sorted_idx[:, :avg_caption_length]
-    # sorted_idx = torch.argsort(-sim, dim=1)
-    width = int(ratio * avg_caption_length / 2) # ratio选择1
-    
-    bbox = []
-    for i in range(top_indices.shape[0]):
-        # index_list = top_indices[i].tolist()
-        mode_value = get_mode(top_indices[i].tolist(), w)
-        filtered_indices = [i for i in top_cap_indices[i].tolist() if abs(i - mode_value) <= width]
-
-        # if len(filtered_indices) == 0:
-        #     filtered_indices = remove_outliers(sim[i].tolist(), top_indices[i].tolist(), 0.5, mode='median', w=w)
-        #     if len(filtered_indices) == 0:
-        #         bbox.append([0, sim.shape[1] - 1])
-        #         continue
-        if len(filtered_indices) == 0:
-            bbox.append([mode_value-width, mode_value+width])
-        else:
-            bbox.append([min(filtered_indices), max(filtered_indices)])
-    return bbox
-
-def compute_threshold(data, threshold):
-    mean = sum(data) / len(data)
-    std_dev = (sum((x - mean) ** 2 for x in data) / len(data)) ** 0.5
-    threshold_value = threshold * std_dev
-    return threshold_value
-
-
-# using similarity as weight to find center
-''' find center globally, then find the boundary locally. 
-    1. find center: use the similarity as weight to find the center
-    2. find boundary: use the center to find the boundary. steps are '''
-def step_retrieval_weight_sim(frame_features, step_features, topk=15, threshold=0.5, w=2):
-    # breakpoint()
-    if step_features.shape[0] == 0:
-        return -np.ones(frame_features.shape[0])
-    
-    similarity_matrix = compute_sim(step_features, frame_features, config_eval_l2norm).cpu()
-    # sim sum along a window
-    window_sums = torch.nn.functional.conv1d(similarity_matrix.unsqueeze(1), torch.ones(1, 1, 2 * w + 1)).squeeze()
-
-    if len(window_sums.shape) == 1:
-        window_sums = window_sums.unsqueeze(0)
-        flag = 1
-    else:
-        flag = 0 
-
-    top_values, top_indices = torch.topk(window_sums, topk, dim=1, largest=True, sorted=True)
-    # breakpoint()
-
-    # Find the frame with the maximum sum in each step
-    _, step_center_frames = window_sums.max(dim=1)
-    step_center_frames = step_center_frames.squeeze()
-
-    if flag == 1:
-        step_center_frames = step_center_frames.unsqueeze(0).tolist()
-    else:
-        step_center_frames = step_center_frames.tolist()
-
-    bbox = []
-    for i in range(top_indices.shape[0]):
-        threshold_value = compute_threshold(top_indices[i].tolist(), threshold)
-        filtered_indices = [frame for frame in top_indices[i].tolist() if abs(frame - step_center_frames[i]) <= threshold_value]
-        if len(filtered_indices) == 0:
-            bbox.append([step_center_frames[i] - w, step_center_frames[i] + w])
-        else:
-            bbox.append([w + min(filtered_indices), w + max(filtered_indices)])
-    
-    return bbox
-
-''' TODO: get the right weight using index'''
-def step_retrieval_weight_index(frame_features, step_features, topk=15, threshold=0.5, w=2):
-    # breakpoint()
-    if step_features.shape[0] == 0:
-        return -np.ones(frame_features.shape[0])
-    
-    similarity_matrix = compute_sim(step_features, frame_features, config_eval_l2norm).cpu()
-    sorted_idx = torch.argsort(-similarity_matrix, dim=1)
-    # sim sum along a window
-    window_sums = torch.nn.functional.conv1d(similarity_matrix.unsqueeze(1), torch.ones(1, 1, 2 * w + 1)).squeeze()
-
-    top_values, top_indices = torch.topk(window_sums, topk, dim=1, largest=True, sorted=True)
-    # breakpoint()
-
-    # Find the frame with the maximum sum in each step
-    _, step_center_frames = window_sums.max(dim=1)
-    step_center_frames = step_center_frames.squeeze().tolist()
-
-    bbox = []
-    for i in range(top_indices.shape[0]):
-        threshold_value = compute_threshold(top_indices[i].tolist(), threshold)
-        filtered_indices = [frame for frame in top_indices[i].tolist() if abs(frame - step_center_frames[i]) <= threshold_value]
-        bbox.append([w + min(filtered_indices), w + max(filtered_indices)])
-    
-    return bbox
-
-def uniform_box(frame_features, step_features, topk=15, threshold=0.5, w=2, mode='median'):
-    if step_features.shape[0] == 0:
-        return -np.ones(frame_features.shape[0])
-    
-    uniform_boxes = uniform_window(frame_features.shape[0], step_features.shape[0])
-    return uniform_boxes
-    
-
-def align_frame_into_steps(frame_features, step_features, topk=15, threshold=0.5, w=2, mode='median'):
-    # breakpoint()
-    if step_features.shape[0] == 0:
-        return -np.ones(frame_features.shape[0])
-    
-    sim = compute_sim(step_features, frame_features, config_eval_l2norm).cpu()
-    average_width = int(sim.shape[1] // sim.shape[0] / 2)
-    # frame_features, step_features = frame_features.cpu(), step_features.cpu()
-    # bbox = get_mode_box(sim, topk, w, ratio)
-
-    top_values, top_indices = torch.topk(sim, topk, dim=1, largest=True, sorted=True)
-    bbox = []
-    for i in range(top_indices.shape[0]):
-        filtered_indices = remove_outliers(top_indices[i].tolist(), threshold, mode=mode, w=w)
-        if len(filtered_indices) < 2:
-            filtered_indices = remove_outliers(top_indices[i].tolist(), 2*threshold, mode=mode, w=w)
-            if len(filtered_indices) == 0:
-                bbox.append([top_indices[0] - average_width, top_indices[0] + average_width])
-                continue
-        bbox.append([min(filtered_indices), max(filtered_indices)])
-    return bbox
-
-# use optimization to compute pseudo boundary
-def align_frame_into_steps_op(frame_features, step_features, topk=15, num_iterations=4, beta=1, order=False, scale=1):
-    # frame_features:  torch.Size([200, 768])
-    augment_ratio_list = np.arange(0.5, 2, 0.1)
-
-    if step_features.shape[0] == 0:
-        return -np.ones(frame_features.shape[0])
-    
-    # breakpoint()
-    # [#step, #frame]
-    similarity_matrix = compute_sim(step_features, frame_features, config_eval_l2norm).cpu().numpy()
-
-    num_steps, num_frames = similarity_matrix.shape
-
-    # Select top-k frames for each caption [#step, #topk]
-    sorted_indices = np.argsort(similarity_matrix, axis=1)
-    # top_indices = np.argsort(similarity_matrix, axis=1)[:, -topk:]
-    # top_values = np.take_along_axis(similarity_matrix, top_indices, axis=1)
-
-    # Compute center indexes [#step, 1]
-    
-
-    # Update boundary width 
-    initial_boundary_width = num_frames / num_steps # 1
-    # boundary_width = initial_boundary_width * np.ones(num_steps, 1, 1) # 1 
-    # overlap = np.zeros(num_steps)
-
-    for i in range(num_iterations):
-        if i == 0 and not order:
-            boundary_width_last = np.full(num_steps, initial_boundary_width).reshape(-1, 1, 1)
-            topk_indices = [index[-topk:] for index in sorted_indices]
-            topk_values = [similarity_matrix[i][index] for i, index in enumerate(topk_indices)]
-
-
-            center_indexes = np.array([find_center_value(index) for index in topk_indices]) 
-            previous_index_center = None
-        #     # overlap_weight = 0
-        else:
-            if i == 0:
-                segment_boundary = np.linspace(0, num_frames, num_steps + 1).round().astype(int)
-                start_indices, end_indices = segment_boundary[:-1], segment_boundary[1:]
-                start_indices = np.clip(start_indices - initial_boundary_width * scale, 0, num_frames)
-                end_indices = np.clip(end_indices + initial_boundary_width * scale, 0, num_frames)
-                boundary_width_last = (end_indices - start_indices).reshape(-1, 1, 1)
-
-                filtered_indices = [sorted_indices[i][(sorted_indices[i] >= start_indices[i]) & (sorted_indices[i] <= end_indices[i])] for i in range(num_steps)]
-                if sum(len(index) for index in filtered_indices) < topk * num_steps * 0.4:
-                    boundary_width_last = np.full(num_steps, initial_boundary_width).reshape(-1, 1, 1)
-                    topk_indices = [index[-topk:] for index in sorted_indices]
-                    topk_values = [similarity_matrix[i][index] for i, index in enumerate(topk_indices)]
-
-
-                    center_indexes = np.array([find_center_value(index) for index in topk_indices]) 
-                    previous_index_center = None
-            else:
-                boundary_width_last = boundary_width.reshape(-1, 1, 1)
-                start_indices = np.clip(center_indexes - boundary_width // 2 - initial_boundary_width * scale, 0, num_frames)
-                end_indices = np.clip(center_indexes + boundary_width // 2 + initial_boundary_width * scale, 0, num_frames)
-
-            topk_indices = []
-            topk_values = []
-            for j, (start, end) in enumerate(zip(start_indices, end_indices)):
-                # breakpoint()
-                filtered_indices = sorted_indices[j][(sorted_indices[j] >= start) & (sorted_indices[j] <= end)]
-                topk_index = filtered_indices[-topk:]
-                topk_indices.append(topk_index)
-                topk_values.append(similarity_matrix[j][topk_index])
-            previous_index_center = center_indexes.copy() if i > 0 else None
-            center_indexes = np.array([find_center_value(index) for index in topk_indices]) 
-
-            # top_indices = sorted_indices[:, ]
-            # previous_index_center = center_indexes
-            # # overlap_weight = 0.5 * np.sum(overlap)
-        
-        boundary_width_candidates = augment_ratio_list * boundary_width_last # [#steps, 1, #candidates]
-        # breakpoint()
-
-        index_distance = [np.abs(index - center_indexes[i] + 1e-3)[:, np.newaxis] for i, index in enumerate(topk_indices)] # [[topk, 1]]
-
-        loss_candidates_list = [value[:, np.newaxis] * (np.abs(index_distance[i] - 0.5 * boundary_width_candidates[i])) for i, value in enumerate(topk_values)] # [[topk, candidates]]
-        # loss_candidates_list = [value[:, np.newaxis] / index_distance[i] * (np.abs(index_distance[i] - 0.5 * boundary_width_candidates[i])) for i, value in enumerate(topk_values)] # [[topk, candidates]]
-
-
-        # index_distance = np.abs(topk_indices - center_indexes)[:, :, np.newaxis] # [#step, #topk, 1]
-
-        # loss_sim = np.sum(top_values[:, :, np.newaxis] / index_distance * (np.abs(index_distance - 0.5 * boundary_width_candidates)), axis=1) # [#step, #candidates]
-        loss_sim = np.array([np.mean(loss, axis=0) for loss in loss_candidates_list]) # [#step, #candidates]
-
-        if i == 0:            
-            loss = loss_sim
-            # print('loss shape:', loss_sim.shape, loss.shape)
-        else:
-            # measure the overlap between boundaries given center and boundary width
-            overlap = compute_overlap(center_indexes, boundary_width_candidates, previous_index_center, boundary_width_last) # [#step, #candidates]
-            # breakpoint()
-            # print(loss_sim.shape, overlap.shape)
-            loss = loss_sim + beta * overlap 
-            # print("ratio of overlap:", np.sum(overlap) / np.sum(loss_sim))
-            # print('loss shape:', loss_sim.shape, overlap.shape, loss.shape)
-        # find the best boundary width
-        # breakpoint()
-        best_boundary_width_index = np.argmin(loss, axis=1) # [#step]
-
-        # Use broadcasting to create row indices corresponding to each row
-        # row_indices = np.arange(num_steps)[:, np.newaxis]
-        # breakpoint()
-        # print(loss.shape, best_boundary_width.shape, boundary_width_candidates.shape)
-        boundary_width = [boundary_width_candidates[i, 0][best_boundary_width_index[i]] for i in range(num_steps)] # [#step]
-        # boundary_width = boundary_width_candidates[:,0][row_indices, best_boundary_width_index] # [#step]
-        boundary_width = np.array(boundary_width)
-        # print(boundary_width.shape)
-
-    bbox = []
-    left_bound = np.clip(center_indexes - boundary_width // 2, 0, num_frames)
-    right_bound = np.clip(center_indexes + boundary_width // 2, 0, num_frames)
-    # breakpoint()
-    bbox = np.stack([left_bound, right_bound], axis=1).round().astype(int)
-
-    return bbox.tolist()
-
-# use optimization to compute pseudo boundary
-def align_frame_into_steps_op_v1(frame_features, step_features, topk=15, num_iterations=4, beta=1, order=False, scale=1):
-    # frame_features:  torch.Size([200, 768])
-    augment_ratio_list = np.arange(0.5, 2, 0.1)
-
-    if step_features.shape[0] == 0:
-        return -np.ones(frame_features.shape[0])
-    
-    # breakpoint()
-    # [#step, #frame]
-    similarity_matrix = compute_sim(step_features, frame_features, config_eval_l2norm).cpu().numpy()
-
-    num_steps, num_frames = similarity_matrix.shape
-
-    # Select top-k frames for each caption [#step, #topk]
-    sorted_indices = np.argsort(similarity_matrix, axis=1)
-    # top_indices = np.argsort(similarity_matrix, axis=1)[:, -topk:]
-    # top_values = np.take_along_axis(similarity_matrix, top_indices, axis=1)
-
-    # Compute center indexes [#step, 1]
-    
-
-    # Update boundary width 
-    initial_boundary_width = num_frames / num_steps # 1
-    # boundary_width = initial_boundary_width * np.ones(num_steps, 1, 1) # 1 
-    # overlap = np.zeros(num_steps)
-
-    for i in range(num_iterations):
-        if i == 0 and not order:
-            boundary_width_last = np.full(num_steps, initial_boundary_width).reshape(-1, 1, 1)
-            topk_indices = [index[-topk:] for index in sorted_indices]
-            topk_values = [similarity_matrix[i][index] for i, index in enumerate(topk_indices)]
-
-
-            center_indexes = np.array([find_center_value(index) for index in topk_indices]) 
-            previous_index_center = None
-        #     # overlap_weight = 0
-        else:
-            if i == 0:
-                segment_boundary = np.linspace(0, num_frames, num_steps + 1).round().astype(int)
-                start_indices, end_indices = segment_boundary[:-1], segment_boundary[1:]
-                start_indices = np.clip(start_indices - initial_boundary_width * scale, 0, num_frames)
-                end_indices = np.clip(end_indices + initial_boundary_width * scale, 0, num_frames)
-                boundary_width_last = (end_indices - start_indices).reshape(-1, 1, 1)
-
-                filtered_indices = [sorted_indices[i][(sorted_indices[i] >= start_indices[i]) & (sorted_indices[i] <= end_indices[i])] for i in range(num_steps)]
-                if sum(len(index) for index in filtered_indices) < topk * num_steps * 0.4:
-                    boundary_width_last = np.full(num_steps, initial_boundary_width).reshape(-1, 1, 1)
-                    topk_indices = [index[-topk:] for index in sorted_indices]
-                    topk_values = [similarity_matrix[i][index] for i, index in enumerate(topk_indices)]
-
-
-                    center_indexes = np.array([find_center_value(index) for index in topk_indices]) 
-                    previous_index_center = None
-            else:
-                boundary_width_last = boundary_width.reshape(-1, 1, 1)
-                start_indices = np.clip(center_indexes - boundary_width // 2 - initial_boundary_width * scale, 0, num_frames)
-                end_indices = np.clip(center_indexes + boundary_width // 2 + initial_boundary_width * scale, 0, num_frames)
-
-            topk_indices = []
-            topk_values = []
-            for j, (start, end) in enumerate(zip(start_indices, end_indices)):
-                # breakpoint()
-                filtered_indices = sorted_indices[j][(sorted_indices[j] >= start) & (sorted_indices[j] <= end)]
-                topk_index = filtered_indices[-topk:]
-                topk_indices.append(topk_index)
-                topk_values.append(similarity_matrix[j][topk_index])
-            previous_index_center = center_indexes.copy() if i > 0 else None
-            center_indexes = np.array([find_center_value(index) for index in topk_indices]) 
-
-            # top_indices = sorted_indices[:, ]
-            # previous_index_center = center_indexes
-            # # overlap_weight = 0.5 * np.sum(overlap)
-        
-        boundary_width_candidates = augment_ratio_list * boundary_width_last # [#steps, 1, #candidates]
-        # breakpoint()
-
-        index_distance = [np.abs(index - center_indexes[i] + 1e-3)[:, np.newaxis] for i, index in enumerate(topk_indices)] # [[topk, 1]]
-        
-        weight_distance = [clip_array(index_distance[i], 0.5 * boundary_width_candidates[i]) for i in range(len(topk_indices))] # [[topk, 1]]
-
-        loss_candidates_list = [value[:, np.newaxis] / weight_distance[i] * (np.abs(index_distance[i] - 0.5 * boundary_width_candidates[i])) for i, value in enumerate(topk_values)] # [[topk, candidates]]
-        # loss_candidates_list = [value[:, np.newaxis] / index_distance[i] * (np.abs(index_distance[i] - 0.5 * boundary_width_candidates[i])) for i, value in enumerate(topk_values)] # [[topk, candidates]]
-
-
-        # index_distance = np.abs(topk_indices - center_indexes)[:, :, np.newaxis] # [#step, #topk, 1]
-
-        # loss_sim = np.sum(top_values[:, :, np.newaxis] / index_distance * (np.abs(index_distance - 0.5 * boundary_width_candidates)), axis=1) # [#step, #candidates]
-        loss_sim = np.array([np.mean(loss, axis=0) for loss in loss_candidates_list]) # [#step, #candidates]
-
-        if i == 0:            
-            loss = loss_sim
-            # print('loss shape:', loss_sim.shape, loss.shape)
-        else:
-            # measure the overlap between boundaries given center and boundary width
-            overlap = compute_overlap(center_indexes, boundary_width_candidates, previous_index_center, boundary_width_last) # [#step, #candidates]
-            # breakpoint()
-            # print(loss_sim.shape, overlap.shape)
-            loss = loss_sim + beta * overlap 
-            # print("ratio of overlap:", np.sum(overlap) / np.sum(loss_sim))
-            # print('loss shape:', loss_sim.shape, overlap.shape, loss.shape)
-        # find the best boundary width
-        # breakpoint()
-        best_boundary_width_index = np.argmin(loss, axis=1) # [#step]
-
-        # Use broadcasting to create row indices corresponding to each row
-        # row_indices = np.arange(num_steps)[:, np.newaxis]
-        # breakpoint()
-        # print(loss.shape, best_boundary_width.shape, boundary_width_candidates.shape)
-        boundary_width = [boundary_width_candidates[i, 0][best_boundary_width_index[i]] for i in range(num_steps)] # [#step]
-        # boundary_width = boundary_width_candidates[:,0][row_indices, best_boundary_width_index] # [#step]
-        boundary_width = np.array(boundary_width)
-        # print(boundary_width.shape)
-
-    bbox = []
-    left_bound = np.clip(center_indexes - boundary_width // 2, 0, num_frames)
-    right_bound = np.clip(center_indexes + boundary_width // 2, 0, num_frames)
-    # breakpoint()
-    bbox = np.stack([left_bound, right_bound], axis=1).round().astype(int)
-
-    return bbox.tolist()
-
-
-
-
-
-# # use optimization to compute pseudo boundary
-# def align_frame_into_steps_op_order(frame_features, step_features, topk=15, threshold=0.5, num_iterations=4, beta=1):
-#     # frame_features:  torch.Size([200, 768])
-#     augment_ratio_list = np.arange(0.5, 2, 0.1)
-
-#     if step_features.shape[0] == 0:
-#         return -np.ones(frame_features.shape[0])
-    
-#     # breakpoint()
-#     # [#step, #frame]
-#     similarity_matrix = compute_sim(step_features, frame_features, config_eval_l2norm).cpu().numpy()
-
-#     num_steps, num_frames = similarity_matrix.shape
-
-#     # Select top-k frames for each caption [#step, #topk]
-#     top_indices = np.argsort(similarity_matrix, axis=1)[:, -topk:]
-#     top_values = np.take_along_axis(similarity_matrix, top_indices, axis=1)
-
-#     # Compute center indexes [#step, 1]
-#     center_indexes = find_center_index(top_indices)[:, np.newaxis]
-
-#     # Update boundary width 
-#     initial_boundary_width = num_frames / num_steps # 1
-#     # boundary_width = initial_boundary_width * np.ones(num_steps, 1, 1) # 1 
-#     # overlap = np.zeros(num_steps)
-
-#     for i in range(num_iterations):
-#         if i == 0:
-#             boundary_width_last = np.full(num_steps, initial_boundary_width).reshape(-1, 1, 1)
-#         #     previous_index_center = None
-#         #     # overlap_weight = 0
-#         else:
-#             boundary_width_last = boundary_width.reshape(-1, 1, 1)
-#             previous_index_center = center_indexes
-#             # overlap_weight = 0.5 * np.sum(overlap)
-        
-#         boundary_width_candidates = augment_ratio_list * boundary_width_last # [#steps, 1, #candidates]
-
-#         index_distance = np.abs(top_indices - center_indexes)[:, :, np.newaxis] # [#step, #topk, 1]
-
-#         loss_sim = np.sum(top_values[:, :, np.newaxis] / index_distance * (np.abs(index_distance - 0.5 * boundary_width_candidates)), axis=1) # [#step, #candidates]
-
-#         if i == 0:
-#             loss = loss_sim # # [#step, #candidates]
-#             print('loss shape:', loss_sim.shape, loss.shape)
-#         else:
-#             # measure the overlap between boundaries given center and boundary width
-#             overlap = compute_overlap(center_indexes, boundary_width_candidates, previous_index_center, boundary_width_last) # [#step, #candidates]
-#             loss = loss_sim + beta * overlap 
-#             print('loss shape:', loss_sim.shape, overlap.shape, loss.shape)
-#         # find the best boundary width
-#         # breakpoint()
-#         best_boundary_width = np.argmin(loss, axis=1) # [#step]
-#         # print(loss.shape, best_boundary_width.shape, boundary_width_candidates.shape)
-#         boundary_width = boundary_width_candidates[:,0][np.arange(num_steps), best_boundary_width] # [#step]
-#         # print(boundary_width.shape)
-
-#     return center_indexes, boundary_width
-# based on original code but change the method to compute center and std
-def align_frame_into_steps_op_order_v2(frame_features, step_features, topk=15, threshold=0.5, ratio=1, iteration=3):
-    # breakpoint()
-    if step_features.shape[0] == 0:
-        return -np.ones(frame_features.shape[0])
-    
-    sim = compute_sim(step_features, frame_features, config_eval_l2norm).cpu()
-    sorted_index = torch.argsort(-sim, dim=1)
-    top_indices_list_global = [sorted_index[i][:topk] for i in range(sim.shape[0])]
-    top_values_list_global = [sim[i][top_indices_list_global[i]] for i in range(sim.shape[0])]
-
-
-    uniform_boxes = uniform_window(frame_features.shape[0], step_features.shape[0])
-
-    iter_bbox_loss = {}
-    for iter in range(iteration):
-        if iter == 0:
-            refined_uniform_boxes = expand_window(uniform_boxes, frame_features.shape[0], step_features.shape[0], ratio)
-        else:
-            refined_uniform_boxes = expand_window(bbox, frame_features.shape[0], step_features.shape[0], ratio) # last bbox
-
-
-        # global: from all frames, local: from refined uniform boxes
-    
-        top_indices_list_local = [sorted_index[i][(sorted_index[i] >= refined_uniform_boxes[i][0]) & (sorted_index[i] <= refined_uniform_boxes[i][1])][:topk] for i in range(sim.shape[0])]
-        top_values_list_local = [sim[i][top_indices_list_local[i]] for i in range(sim.shape[0])]
-
-        size_local = [len(top_indices_list_local[i]) for i in range(sim.shape[0])]
-        if sum(size_local) < (topk-2) * len(size_local):
-            top_indices_list = top_indices_list_global
-            top_values_list = top_values_list_global
-        else:
-            top_indices_list = top_indices_list_local
-            top_values_list = top_values_list_local
-
-        # top_indices_list = [top_indices_list_global[i] if len(top_indices_list_local[i]) < topk else top_indices_list_local[i] for i in range(sim.shape[0])]
-
-        bbox = []
-        for i in range(len(top_indices_list)):
-            filtered_indices = compute_filtered_indices(top_indices_list[i].tolist(), top_values_list[i].tolist(), threshold)
-            if len(filtered_indices) == 0:
-                filtered_indices = compute_filtered_indices(top_indices_list_global[i].tolist(), top_indices_list_global[i].tolist(), threshold)
-                if len(filtered_indices) == 0:
-                    bbox.append(uniform_boxes[i])
-                    continue
-            bbox.append([min(filtered_indices), max(filtered_indices)])
-
-        # compute bbox loss
-        bbox_loss_list = [compute_bbox_loss(top_indices_list[i], bbox[i], top_values_list[i]) for i in range(len(top_indices_list))]
-        bbox_loss = sum(bbox_loss_list)
-        iter_bbox_loss[iter] = {'loss': bbox_loss, 'bbox': bbox}
-
-    # select the minimum bbox loss and bbox as output
-    min_loss_iter = min(iter_bbox_loss.keys(), key=lambda k: iter_bbox_loss[k]['loss'])
-    min_loss = iter_bbox_loss[min_loss_iter]['loss']
-    best_bbox = iter_bbox_loss[min_loss_iter]['bbox']
-        
-
-    return (best_bbox, min_loss)
-
-def align_frame_into_steps_op_v2(frame_features, step_features, topk=15, threshold=0.5, ratio=1, iteration=3):
-    # breakpoint()
-    if step_features.shape[0] == 0:
-        return -np.ones(frame_features.shape[0])
-    
-    sim = compute_sim(step_features, frame_features, config_eval_l2norm).cpu()
-    sorted_index = torch.argsort(-sim, dim=1)
-    top_indices_list_global = [sorted_index[i][:topk] for i in range(sim.shape[0])]
-    top_values_list_global = [sim[i][top_indices_list_global[i]] for i in range(sim.shape[0])]
-
-
-    uniform_boxes = uniform_window(frame_features.shape[0], step_features.shape[0])
-
-    iter_bbox_loss = {}
-    for iter in range(iteration):
-        # if iter == 0:
-        #     refined_uniform_boxes = expand_window(uniform_boxes, frame_features.shape[0], step_features.shape[0], ratio)
-        # else:
-        #     refined_uniform_boxes = expand_window(bbox, frame_features.shape[0], step_features.shape[0], ratio) # last bbox
-
-
-        # global: from all frames, local: from refined uniform boxes
-    
-        # top_indices_list_local = [sorted_index[i][(sorted_index[i] >= refined_uniform_boxes[i][0]) & (sorted_index[i] <= refined_uniform_boxes[i][1])][:topk] for i in range(sim.shape[0])]
-        # top_values_list_local = [sim[i][top_indices_list_local[i]] for i in range(sim.shape[0])]
-
-        # size_local = [len(top_indices_list_local[i]) for i in range(sim.shape[0])]
-        # if sum(size_local) < (topk-2) * len(size_local):
-        #     top_indices_list = top_indices_list_global
-        #     top_values_list = top_values_list_global
-        # else:
-        #     top_indices_list = top_indices_list_local
-        #     top_values_list = top_values_list_local
-
-        # top_indices_list = [top_indices_list_global[i] if len(top_indices_list_local[i]) < topk else top_indices_list_local[i] for i in range(sim.shape[0])]
-
-        bbox = []
-        for i in range(len(top_indices_list_global)):
-            filtered_indices = compute_filtered_indices(top_indices_list_global[i].tolist(), top_values_list_global[i].tolist(), threshold)
-            if len(filtered_indices) == 0:
-                filtered_indices = compute_filtered_indices(top_indices_list_global[i].tolist(), top_indices_list_global[i].tolist(), threshold)
-                if len(filtered_indices) == 0:
-                    bbox.append(uniform_boxes[i])
-                    continue
-            bbox.append([min(filtered_indices), max(filtered_indices)])
-
-        # compute bbox loss
-        bbox_loss_list = [compute_bbox_loss(top_indices_list_global[i], bbox[i], top_values_list_global[i]) for i in range(len(top_indices_list_global))]
-        bbox_loss = sum(bbox_loss_list)
-        iter_bbox_loss[iter] = {'loss': bbox_loss, 'bbox': bbox}
-
-    # select the minimum bbox loss and bbox as output
-    min_loss_iter = min(iter_bbox_loss.keys(), key=lambda k: iter_bbox_loss[k]['loss'])
-    min_loss = iter_bbox_loss[min_loss_iter]['loss']
-    best_bbox = iter_bbox_loss[min_loss_iter]['bbox']
-        
-
-    return (best_bbox, min_loss)
-
-
-
-# pesudo box 4: based on fixed window. the result is bad. give up
-def align_frame_into_steps_mode(frame_features, step_features, topk=15, w=2, ratio=1):
-    # breakpoint()
-    if step_features.shape[0] == 0:
-        return -np.ones(frame_features.shape[0])
-    
-    sim = compute_sim(step_features, frame_features, config_eval_l2norm).cpu()
-    # frame_features, step_features = frame_features.cpu(), step_features.cpu()
-
-
-    bbox = get_mode_box(sim, topk, w, ratio)
-    return bbox
-
-def uniform_window(frame_num, step_num):
-    uniform_timestamps = torch.linspace(0, frame_num, step_num + 1)
-    uniform_timestamps = torch.round(uniform_timestamps).int().tolist()
-    bbox = []
-    for i in range(step_num):
-        bbox.append([uniform_timestamps[i], uniform_timestamps[i+1] - 1])
-
-    # window_size = frame_num // step_num
-    # bbox = []
-    # for i in range(step_num):
-    #     bbox.append([i * window_size, (i + 1) * window_size - 1])
-    # bbox[-1][1] = frame_num - 1
-    return bbox 
-
-def expand_window(uniform_bbox, frame_num, step_num, ratio=1):
-    '''ratio: gt box相对uniform box的波动范围 超过这个范围视为不可能 ratio单位为一个caption的平均长度'''
-    window_size = frame_num // step_num
-    refined_bbox = []
-    for bbox in uniform_bbox:
-        start = max(0, bbox[0] - ratio * window_size)
-        end = min(frame_num - 1, bbox[1] + ratio * window_size)
-        refined_bbox.append([start, end])
-    return refined_bbox
-
-# pesudo box 3: based on sim, consider the order of steps
-def align_frame_into_steps_order(frame_features, step_features, unordered=False, topk=15, threshold=2, w=2, mode='median', ratio=1):
-    # breakpoint()
-    if step_features.shape[0] == 0:
-        return -np.ones(frame_features.shape[0])
-    
-    sim = compute_sim(step_features, frame_features, config_eval_l2norm).cpu()
-
-    uniform_boxes = uniform_window(frame_features.shape[0], step_features.shape[0])
-    refined_uniform_boxes = expand_window(uniform_boxes, frame_features.shape[0], step_features.shape[0], ratio)
-    
-    # old setting (index is wrong)
-    # # frame_features, step_features = frame_features.cpu(), step_features.cpu()
-    # index_sim_list = [sim[i][refined_uniform_boxes[i][0]: refined_uniform_boxes[i][1]] for i in range(sim.shape[0])]
-    # top_indices_list = [torch.topk(index_sim, k, dim=0, largest=True, sorted=True)[1] for index_sim in index_sim_list]
-    # # top_values, top_indices = torch.topk(sim, k, dim=1, largest=True, sorted=True)
-
-    sorted_index = torch.argsort(-sim, dim=1)
-    # global: from all frames, local: from refined uniform boxes
-    top_indices_list_global = [sorted_index[i][:topk] for i in range(sim.shape[0])]
-    top_indices_list_local = [sorted_index[i][(sorted_index[i] >= refined_uniform_boxes[i][0]) & (sorted_index[i] <= refined_uniform_boxes[i][1])][:topk] for i in range(sim.shape[0])]
-
-    size_local = [len(top_indices_list_local[i]) for i in range(sim.shape[0])]
-    if sum(size_local) < (topk-2) * len(size_local):
-        top_indices_list = top_indices_list_global
-    else:
-        top_indices_list = top_indices_list_local
-
-    # top_indices_list = [top_indices_list_global[i] if len(top_indices_list_local[i]) < topk else top_indices_list_local[i] for i in range(sim.shape[0])]
-
-    bbox = []
-    for i in range(len(top_indices_list)):
-        filtered_indices = remove_outliers(top_indices_list[i].tolist(), threshold, mode=mode, w=w)
-        if len(filtered_indices) == 0:
-            filtered_indices = remove_outliers(top_indices_list_global[i].tolist(), 0.5, mode=mode, w=w)
-            if len(filtered_indices) == 0:
-                bbox.append(uniform_boxes[i])
-                continue
-        bbox.append([min(filtered_indices), max(filtered_indices)])
-
-    return bbox
-
-
-
-
-# based on pbox3, if ratio 1 has enough value, use it otherwise
-def align_frame_into_steps_order_adapt(frame_features, step_features, unordered=False, topk=15, threshold=2, w=2, mode='median', ratio=1):
-    # breakpoint()
-    if step_features.shape[0] == 0:
-        return -np.ones(frame_features.shape[0])
-    
-    sim = compute_sim(step_features, frame_features, config_eval_l2norm).cpu()
-
-    uniform_boxes = uniform_window(frame_features.shape[0], step_features.shape[0])
-    refined_uniform_boxes = expand_window(uniform_boxes, frame_features.shape[0], step_features.shape[0], ratio)
-    
-    # old setting (index is wrong)
-    # # frame_features, step_features = frame_features.cpu(), step_features.cpu()
-    # index_sim_list = [sim[i][refined_uniform_boxes[i][0]: refined_uniform_boxes[i][1]] for i in range(sim.shape[0])]
-    # top_indices_list = [torch.topk(index_sim, k, dim=0, largest=True, sorted=True)[1] for index_sim in index_sim_list]
-    # # top_values, top_indices = torch.topk(sim, k, dim=1, largest=True, sorted=True)
-
-    sorted_index = torch.argsort(-sim, dim=1)
-    # global: from all frames, local: from refined uniform boxes
-    top_indices_list_global = [sorted_index[i][:topk] for i in range(sim.shape[0])]
-    top_indices_list_local = [sorted_index[i][(sorted_index[i] >= refined_uniform_boxes[i][0]) & (sorted_index[i] <= refined_uniform_boxes[i][1])][:topk] for i in range(sim.shape[0])]
-
-    size_local = [len(top_indices_list_local[i]) for i in range(sim.shape[0])]
-    if sum(size_local) < (topk-1) * len(size_local):
-        flag = 0
-        for i in range(4):
-            refined_uniform_boxes = expand_window(uniform_boxes, frame_features.shape[0], step_features.shape[0], ratio+i*0.5)
-            top_indices_list_local = [sorted_index[i][(sorted_index[i] >= refined_uniform_boxes[i][0]) & (sorted_index[i] <= refined_uniform_boxes[i][1])][:topk] for i in range(sim.shape[0])]
-            size_local = [len(top_indices_list_local[i]) for i in range(sim.shape[0])]
-            if sum(size_local) >= (topk-1) * len(size_local):
-                flag = 1
-                break
-        if flag == 0:
-            top_indices_list = top_indices_list_global
-        else:
-            top_indices_list = top_indices_list_local
-
-    else:
-        top_indices_list = top_indices_list_local
-
-    # top_indices_list = [top_indices_list_global[i] if len(top_indices_list_local[i]) < topk else top_indices_list_local[i] for i in range(sim.shape[0])]
-
-    bbox = []
-    for i in range(len(top_indices_list)):
-        filtered_indices = remove_outliers(top_indices_list[i].tolist(), threshold, mode=mode, w=w)
-        if len(filtered_indices) == 0:
-            filtered_indices = remove_outliers(top_indices_list_global[i].tolist(), 0.5, mode=mode, w=w)
-            if len(filtered_indices) == 0:
-                bbox.append(uniform_boxes[i])
-                continue
-        bbox.append([min(filtered_indices), max(filtered_indices)])
-
-    return bbox
-
-def step_retrieval_weight_sim_order(frame_features, step_features, unordered=False, topk=15, threshold=2, w=2, ratio=1):
-    # breakpoint()
-    if step_features.shape[0] == 0:
-        return -np.ones(frame_features.shape[0])
-    
-    sim = compute_sim(step_features, frame_features, config_eval_l2norm).cpu()
-    # breakpoint()
-
-    window_sums = torch.nn.functional.conv1d(sim.unsqueeze(1), torch.ones(1, 1, 2 * w + 1)).squeeze()
-    if len(window_sums.shape) == 1:
-        window_sums = window_sums.unsqueeze(0)
-
-
-    sorted_index = torch.argsort(-window_sums, dim=1) + w
-    
-    
-
-    uniform_boxes = uniform_window(frame_features.shape[0], step_features.shape[0])
-    refined_uniform_boxes = expand_window(uniform_boxes, frame_features.shape[0], step_features.shape[0], ratio)
-
-    top_indices_list_global = [sorted_index[i][:topk] for i in range(sim.shape[0])]
-    top_indices_list_local = [sorted_index[i][(sorted_index[i] >= refined_uniform_boxes[i][0]) & (sorted_index[i] <= refined_uniform_boxes[i][1])][:topk] for i in range(sim.shape[0])]
-    
-
-    size_local = [len(top_indices_list_local[i]) for i in range(sim.shape[0])]
-    if sum(size_local) < (topk-2) * len(size_local):
-        top_indices_list = top_indices_list_global
-    else:
-        top_indices_list = top_indices_list_local
-
-    # top_indices_list = [top_indices_list_global[i] if len(top_indices_list_local[i]) < topk else top_indices_list_local[i] for i in range(sim.shape[0])]
-
-    bbox = []
-    for i in range(len(top_indices_list)):
-        threshold_value = compute_threshold(top_indices_list[i].tolist(), threshold)
-        filtered_indices = [frame for frame in top_indices_list[i].tolist() if abs(frame - top_indices_list[i][0]) <= threshold_value]
-        if len(filtered_indices) == 0:
-            bbox.append([top_indices_list[i] - w, top_indices_list[i] + w])
-        else:
-            bbox.append([min(filtered_indices), max(filtered_indices)])
-
-    return bbox
-
-# pesudo box 0: based on dtw
-def segment_video_into_steps(frame_features, step_features, unordered=False):
-    if step_features.shape[0] == 0:
-        return -np.ones(frame_features.shape[0])
-
-    sim = compute_sim(step_features, frame_features, config_eval_l2norm).cpu()
-    frame_features, step_features = frame_features.cpu(), step_features.cpu()
-
-    k = max([1, int(torch.numel(sim) * config_eval_keep_percentile)])
-    baseline_logit = torch.topk(sim.reshape([-1]), k).values[-1].detach()
-    baseline_logits = baseline_logit.repeat([1, sim.shape[1]])[0]  # making it of shape [1, N]
-    zx_costs, drop_costs = -sim, -baseline_logits # base其实是从相似度矩阵中选择了一个中间值作为drop cost 这个中间值就是你认为匹配也可以 drop也可以的那个值
-    zx_costs, drop_costs = [t.detach().cpu().numpy() for t in [zx_costs, drop_costs]]
-    sim = sim.detach().cpu().numpy()
-
-    if unordered:
-        max_vals, optimal_assignment = np.max(sim, axis=0), np.argmax(sim, axis=0)  # 直接找与每个step最匹配的frame 这样原则上是一对一匹配
-        optimal_assignment[max_vals < baseline_logit.item()] = -1
-    else:
-        optimal_assignment = drop_dtw(zx_costs, drop_costs, return_labels=True) - 1 # 调节drop cost的大小 从而调节匹配的严格程度
-    return optimal_assignment
-
-def align_query_into_steps(query_features, step_features, unordered=False):
-    if step_features.shape[0] == 0:
-        return -np.ones(query_features.shape[0])
-
-    sim = compute_sim(step_features, query_features, config_eval_l2norm).cpu()
-    query_features, step_features = query_features.cpu(), step_features.cpu()
-
-    k = max([1, int(torch.numel(sim) * config_eval_keep_percentile)])
-    baseline_logit = torch.topk(sim.reshape([-1]), k).values[-1].detach()
-    baseline_logits = baseline_logit.repeat([1, sim.shape[1]])[0]  # making it of shape [1, N]
-    zx_costs, drop_costs = -sim, -baseline_logits # base其实是从相似度矩阵中选择了一个中间值作为drop cost 这个中间值就是你认为匹配也可以 drop也可以的那个值
-    zx_costs, drop_costs = [t.detach().cpu().numpy() for t in [zx_costs, drop_costs]]
-    sim = sim.detach().cpu().numpy()
-
-    if unordered:
-        max_vals, optimal_assignment = np.max(sim, axis=0), np.argmax(sim, axis=0)  # 直接找与每个step最匹配的frame 这样原则上是一对一匹配
-        optimal_assignment[max_vals < baseline_logit.item()] = -1
-    else:
-        optimal_assignment = drop_dtw(zx_costs, drop_costs, one_to_one=True, return_labels=True) - 1 # 调节drop cost的大小 从而调节匹配的严格程度
-    return optimal_assignment
-
-# inference时 video和slots之间的匹配
-def segment_video_into_slots(video_features, pred_steps):
-    sim = compute_sim(pred_steps, video_features, l2_norm=config_eval_l2norm).detach()
-    if config_eval_fixed_drop_sim == -1:
-        k = max([1, int(torch.numel(sim) * config_eval_keep_percentile)])
-        baseline_logit = torch.topk(sim.reshape([-1]), k).values[-1].detach()
-    else:
-        baseline_logit = torch.tensor(config_eval_fixed_drop_sim)
-    baseline_logits = baseline_logit.repeat([1, sim.shape[1]])  # making it of shape [1, N]
-    x_drop_costs = -baseline_logits.squeeze()
-    zx_costs = -sim
-
-    z_drop_costs = -baseline_logit.repeat([1, sim.shape[0]]).squeeze()
-    zx_costs = zx_costs - z_drop_costs[0].reshape([1, 1])
-    z_drop_costs = z_drop_costs - z_drop_costs[0]
-    x_drop_costs = x_drop_costs - x_drop_costs[0]
-    segmentation = double_drop_dtw(zx_costs.numpy(), x_drop_costs.numpy(), z_drop_costs.numpy(), return_labels=True) - 1
-    return segmentation
-
-
-# get_index and alignment_to_boundary are used for 'align' based manner
-def get_index(alignment):
-    start_idx, end_idx = [], []
-    for i in range(len(alignment)):
-        if alignment[i] == -1:
-            if i != 0 and alignment[i-1] != -1:
-                end_idx.append(i-1)
-            continue
-        if i == 0:
-            start_idx.append(i)
-        elif alignment[i] != alignment[i-1]:
-            start_idx.append(i)
-            if alignment[i-1] != -1:
-                end_idx.append(i-1)
-        if i == len(alignment) - 1:
-            end_idx.append(i)
-    assert len(start_idx) == len(end_idx)
-    for s, e in zip(start_idx, end_idx):
-        assert alignment[s] <= alignment[e]
-    return start_idx, end_idx
-
-def alignment_to_boundary(alignment, video_frame_num):
-    start_idx, end_idx = get_index(alignment)
-    start_time = start_idx / video_frame_num
-    end_time = end_idx / video_frame_num
-    boundaries = list(zip(start_time, end_time))
-
-    return np.float32(np.stack(boundaries, axis=0))
-
-
-def to_center_duration(alignments):
-    new_alignments = []
-    for alignment in alignments:
-        start, end = alignment[:, 0], alignment[:, 1]
-        center = (start + end) / 2
-        duration = end - start
-        alignment[:, 0], alignment[:, 1] = center, duration
-        new_alignments.append(alignment)
-    return new_alignments
\ No newline at end of file
diff --git a/backup/pdvc/video_segmentation_ori.py b/backup/pdvc/video_segmentation_ori.py
deleted file mode 100644
index 9d06e59f3b5a80fb4e8a765d20287175b03568d4..0000000000000000000000000000000000000000
--- a/backup/pdvc/video_segmentation_ori.py
+++ /dev/null
@@ -1,127 +0,0 @@
-import torch
-import numpy as np
-import statistics
-
-from pdvc.dp.exact_dp import drop_dtw
-from pdvc.dp.dp_utils import compute_sim
-import statistics
-from sklearn.cluster import KMeans
-
-
-config_eval_l2norm = True 
-config_eval_keep_percentile = 0.48 # Calculated from the data
-config_eval_fixed_drop_sim = -1 
-
-def segment_video_into_steps(frame_features, step_features, unordered=False):
-    if step_features.shape[0] == 0:
-        return -np.ones(frame_features.shape[0])
-
-    sim = compute_sim(step_features, frame_features, l2_norm=True).cpu()
-    frame_features, step_features = frame_features.cpu(), step_features.cpu()
-
-    k = max([1, int(torch.numel(sim) * config_eval_keep_percentile)])
-    baseline_logit = torch.topk(sim.reshape([-1]), k).values[-1].detach()
-    baseline_logits = baseline_logit.repeat([1, sim.shape[1]])[0]  # making it of shape [1, N]
-    zx_costs, drop_costs = -sim, -baseline_logits
-    zx_costs, drop_costs = [t.detach().cpu().numpy() for t in [zx_costs, drop_costs]]
-    sim = sim.detach().cpu().numpy()
-
-    if unordered:
-        max_vals, optimal_assignment = np.max(sim, axis=0), np.argmax(sim, axis=0)
-        optimal_assignment[max_vals < baseline_logit.item()] = -1
-    else:
-        optimal_assignment = drop_dtw(zx_costs, drop_costs, return_labels=True) - 1
-    return optimal_assignment # [num_frames]
-
-def get_index(alignment):
-    start_idx, end_idx = [], []
-    for i in range(len(alignment)):
-        if alignment[i] == -1:
-            if i != 0 and alignment[i-1] != -1:
-                end_idx.append(i-1)
-            continue
-        if i == 0:
-            start_idx.append(i)
-        elif alignment[i] != alignment[i-1]:
-            start_idx.append(i)
-            if alignment[i-1] != -1:
-                end_idx.append(i-1)
-        if i == len(alignment) - 1:
-            end_idx.append(i)
-    assert len(start_idx) == len(end_idx)
-    for s, e in zip(start_idx, end_idx):
-        assert alignment[s] <= alignment[e]
-    return start_idx, end_idx
-
-def get_index_update(alignment):
-    optimal_alignment = np.append(np.insert(alignment, 0, -1), -1)
-    diff_optimal_alignment = np.diff(optimal_alignment)
-
-    optimal_alignment_end = optimal_alignment.copy()
-    optimal_alignment_end[optimal_alignment_end==-1] = max(optimal_alignment_end) + 1
-    diff_optimal_alignment_end = np.diff(optimal_alignment_end)
-
-    start_idx = np.where(diff_optimal_alignment>0)[0]
-    end_idx = np.where(diff_optimal_alignment_end>0)[0] - 1
-    return start_idx, end_idx
-
-def alignment_to_boundary(alignment, video_frame_num):
-    start_idx, end_idx = get_index(alignment)
-    start_time = start_idx / video_frame_num
-    end_time = end_idx / video_frame_num
-    boundaries = list(zip(start_time, end_time))
-
-    return np.float32(np.stack(boundaries, axis=0))
-
-
-def to_center_duration(alignments):
-    new_alignments = []
-    for alignment in alignments:
-        start, end = alignment[:, 0], alignment[:, 1]
-        center = (start + end) / 2
-        duration = end - start
-        alignment[:, 0], alignment[:, 1] = center, duration
-        new_alignments.append(alignment)
-    return new_alignments
-
-
-def remove_outliers(indices, threshold):
-    # Calculate the mean and standard deviation of the indices
-    median = statistics.median(indices)
-    mean = sum(indices) / len(indices)
-    std_dev = (sum((x - mean) ** 2 for x in indices) / len(indices)) ** 0.5
-
-    # Calculate the threshold for identifying outliers
-    threshold_value = threshold * std_dev
-
-    # Filter out indices that are far from the mean
-    filtered_indices = [i for i in indices if abs(i - median) <= threshold_value]
-
-    return filtered_indices
-
-
-def align_frame_into_steps(frame_features, step_features, unordered=False, k=15, threshold=0.5):
-    if step_features.shape[0] == 0:
-        return -np.ones(frame_features.shape[0])
-    
-    sim = compute_sim(step_features, frame_features, True).cpu()
-    frame_features, step_features = frame_features.cpu(), step_features.cpu()
-
-    top_values, top_indices = torch.topk(sim, k, dim=1, largest=True, sorted=True)
-    bbox = []
-    for i in range(top_indices.shape[0]):
-        filtered_indices = remove_outliers(top_indices[i].tolist(), threshold)
-        bbox.append([min(filtered_indices), max(filtered_indices)])
-    return bbox
-
-if __name__ == '__main__':
-    # frame_features = torch.randn(100, 768)
-    # text_features = torch.randn(8, 768)
-    # alignment = segment_video_into_steps(frame_features, text_features)
-    # breakpoint()
-    arr = [-1,-1,0,1,2,2,2,-1,-1,3,4,4,-1,-1,5,5,5,-1,6,6,7,-1,-1, 8, 8, 9]
-    start, end = get_index(arr)
-    start_1, end_1 = get_index_update(arr)
-    # start = [2, 3, 4, 8, 9, 13, 16, 18]
-    # end = [2, 3, 5, 8, 10, 15, 17, 18]
-    breakpoint()
diff --git a/backup/test.py b/backup/test.py
deleted file mode 100644
index e1dcf9d7be821a3db142566cb23914ea96f1c064..0000000000000000000000000000000000000000
--- a/backup/test.py
+++ /dev/null
@@ -1,64 +0,0 @@
-# from pdvc.video_segmentation import align_frame_into_steps_op
-# import torch 
-
-# # create two tensors
-# frame = torch.rand(200, 768)
-# steps = torch.rand(10, 768)
-
-# bboxs = align_frame_into_steps_op(frame, steps, order=False)
-# # breakpoint()
-# print('done!')
-
-
-# ==================================================================
-# import json 
-
-# filepath = "/mnt/data/pjlab-3090-sport/wuhao/logs/dibs/yc2_ori_pbox(similarity_op_order)_CLIP/similarity_op_order_topf20_beta1_iter3_r1/info.json"
-# with open(filepath, 'r') as f:
-#     data = json.load(f)
-
-# val_history = data['history']['val_result_history']
-
-# metric_sum = {}
-# metrics = ['METEOR', 'CIDEr', 'soda_c', 'Precision', 'Recall']
-# for k, v in val_history.items():
-#     metric_sum[k] = sum([v['eval_score'][metric] for metric in metrics])
-#     print(f"{k}: {metric_sum[k]}")
-
-# best_epoch = max(metric_sum, key=metric_sum.get)
-# print(val_history[best_epoch]['eval_score'])
-# # write the val_history to a file
-# with open('val.log', 'w') as f:
-#     for k, v in val_history[best_epoch]['eval_score'].items():
-#         f.write(f"{k}: {v}\n")
-# # print(metric_sum)
-# # breakpoint()
-# print('done!')
-
-# ==================================================================
-import os 
-import json
-import sys 
-sys.path.append('/mnt/data/Gvlab/wuhao/code/dibs')
-from misc.utils import create_logger
-save_folder = "/mnt/data/pjlab-3090-sport/wuhao/logs/dibs/yc2_ori_pbox(similarity_op_order)_CLIP/similarity_op_order_topf20_beta1_iter3_r1"
-
-val_logger = create_logger(save_folder, 'val.log')
-infos_path = os.path.join(save_folder, 'info.json')
-
-with open(infos_path, 'r') as f:
-    data = json.load(f)
-val_history = data['history']['val_result_history']
-
-metric_sum = {}
-metrics = ['METEOR', 'CIDEr', 'soda_c', 'Precision', 'Recall']
-for k, v in val_history.items():
-    metric_sum[k] = sum([v['eval_score'][metric] for metric in metrics])
-    # print(f"{k}: {metric_sum[k]}")
-
-best_epoch = max(metric_sum, key=metric_sum.get)
-best_val_score = val_history[best_epoch]['eval_score']
-val_logger.info(f"Best epoch: {best_epoch}")
-print_info = '\n'.join([key + ":" + str(best_val_score[key]) for key in best_val_score.keys()])
-val_logger.info('\nBest Model Performance:\n' + print_info)
-val_logger.info('\nBest Overall Score epoch{}: {}\n'.format(best_epoch, metric_sum[best_epoch]))
\ No newline at end of file
diff --git a/backup/train.py b/backup/train.py
deleted file mode 100644
index 43c0c73fd63d66eb7055f913723dd086ab80d288..0000000000000000000000000000000000000000
--- a/backup/train.py
+++ /dev/null
@@ -1,671 +0,0 @@
-# coding:utf-8
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import json
-import time
-import torch
-import os
-import sys
-import collections
-import numpy as np
-from tqdm import tqdm
-import torch.optim as optim
-from torch.utils.data import DataLoader
-from os.path import dirname, abspath
-
-pdvc_dir = dirname(abspath(__file__))
-sys.path.insert(0, pdvc_dir)
-sys.path.insert(0, os.path.join(pdvc_dir, 'densevid_eval3'))
-sys.path.insert(0, os.path.join(pdvc_dir, 'densevid_eval3/SODA'))
-# print(sys.path)
-CUDA_LAUNCH_BLOCKING=1
-
-os.environ["TOKENIZERS_PARALLELISM"] = "false" # To avoid warning of tokenizer
-from eval_utils import evaluate
-import opts
-from tensorboardX import SummaryWriter
-from misc.utils import print_alert_message, build_folder, create_logger, backup_envir, print_opt, set_seed
-from data.video_dataset import PropSeqDataset, collate_fn
-from pdvc.pdvc import build
-from collections import OrderedDict
-from transformers import AutoTokenizer, get_linear_schedule_with_warmup, get_cosine_schedule_with_warmup
-import copy
-import random
-import numpy as np 
-
-a100_folder = ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/Tasty/features', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Tasty/UniVL_feature', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features']
-r3090_folder = ['/mnt/data/Gvlab/wuhao/features/yc2', '/mnt/data/Gvlab/wuhao/features/tasty', '/mnt/data/Gvlab/wuhao/features/tasty/univl', '/mnt/data/Gvlab/wuhao/features/anet', '/mnt/data/Gvlab/wuhao/features/howto100m']
-
-# /cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features -> /mnt/data/Gvlab/wuhao/features/howto100m
-# /cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip -> /mnt/data/Gvlab/wuhao/features/howto100m/clip_features
-# /cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL -> /mnt/data/Gvlab/wuhao/features/howto100m/univl_features
-
-def construct_save_path(opt, save_folder="/mnt/data/pjlab-3090-sport/wuhao/code/dibs/pbox"):
-    elements = []
-    # breakpoint()    
-    if len(opt.train_caption_file) == 2:
-        if 'puyu' in opt.train_caption_file[0]:
-            elements.append('howto_puyu')
-        elif 'mixlm' in opt.train_caption_file[0]:
-            elements.append('howto_mixlm')
-        else:
-            elements.append('howto_llama2')
-        elements.append('howto')
-        if 'yc2' in opt.train_caption_file[1]:            
-            elements.append('yc2')
-        elif 'anet' in opt.train_caption_file[1]:
-            elements.append('anet')
-    else:
-        if 'yc2' in opt.train_caption_file:
-            elements.append('yc2')
-        elif 'anet' in opt.train_caption_file:
-            elements.append('anet')
-        elif 'howto' in opt.train_caption_file:
-            if 'puyu' in opt.train_caption_file:
-                elements.append('howto_puyu')
-            elif 'mixlm' in opt.train_caption_file:
-                elements.append('howto_mixlm')
-            else:
-                elements.append('howto_llama2')
-            # elements.append('howto')
-
-    if 'clip' in opt.visual_feature_folder[0] or 'CLIP' in opt.visual_feature_folder[0]:
-        elements.append('clip')
-    elif 'UniVL' in opt.visual_feature_folder[0] or 'univl' in opt.visual_feature_folder[0]:
-        elements.append('univl')
-    # add pbox parameters
-    pbox_type = "simop_v2" if opt.pseudo_box_type == "similarity_op_order_v2" else "simop"
-    elements.append(pbox_type)
-    elements.append(f"top{opt.top_frames}")
-    elements.append(f"r{opt.width_ratio}")
-    elements.append(f"iter{opt.iteration}")
-    elements.append(f"th{opt.width_th}")
-    return os.path.join(save_folder, '_'.join(elements) + '.json')
-    
-
-
-def seed_worker(worker_id):
-    worker_seed = torch.initial_seed() % 2**32
-    np.random.seed(worker_seed)
-    random.seed(worker_seed)
-
-def map_path(path):
-    path_backup = copy.deepcopy(path)
-    # breakpoint()
-    for i, folder in enumerate(a100_folder):
-        if folder in path:
-            path = path.replace(folder, r3090_folder[i])
-            
-            
-    if path == path_backup:
-        if path.startswith('/mnt/data'):
-            pass
-        else:
-            # path = '/mnt' + path[6:]
-            print('map failed')
-            exit(1)
-    return path
-
-def train(opt):
-    set_seed(opt.seed)
-    save_folder = build_folder(opt)
-    logger = create_logger(save_folder, 'train.log')
-    tf_writer = SummaryWriter(os.path.join(save_folder, 'tf_summary'))
-
-    # if use mixlm model
-    saved_path = construct_save_path(opt)
-
-    if 'mixlm' in saved_path:
-        # text_feature_folder_mixlm = os.path.join(save_folder, 'text_feature')
-        mixlm_pbox_path = construct_save_path(opt, save_folder='test').replace('.json', '').replace('test/', '')
-        text_feature_folder_mixlm = os.path.join('/mnt/data/Gvlab/wuhao/code/tmp', 'mix_text_feature', mixlm_pbox_path)
-        os.makedirs(text_feature_folder_mixlm, exist_ok=True)
-        if 'clip' in save_folder or 'CLIP' in save_folder:
-            text_feature_folder_llama2 = map_path('/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip/text_proj')
-            text_feature_folder_puyu = '/mnt/data/Gvlab/wuhao/code/clip_frame_feature_extraction/features/howto100m/clip'
-        elif 'univl' in save_folder or 'UniVL' in save_folder or 'Uni' in save_folder:
-            text_feature_folder_llama2 = map_path('/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL/text')
-            text_feature_folder_puyu = '/mnt/data/pjlab-3090-sport/wuhao/features/howto100m/univl_features/text_puyu'
-
-        if not os.path.exists(saved_path):        
-            llama2_pbox_path = saved_path.replace('mixlm', 'llama2')
-            puyu_pbox_path = saved_path.replace('mixlm', 'puyu')
-            with open(llama2_pbox_path, 'r') as f:
-                llama2_pbox = json.load(f)
-            with open(puyu_pbox_path, 'r') as f:
-                puyu_pbox = json.load(f)
-            
-            mixlm_pbox = {}
-            for video_key in llama2_pbox.keys():
-                if llama2_pbox.get(video_key) is None and puyu_pbox.get(video_key) is None:
-                    mixlm_pbox[video_key] = None
-                elif llama2_pbox.get(video_key) is None:
-                    mixlm_pbox[video_key] = {'box': puyu_pbox[video_key]['box'], 'loss': puyu_pbox[video_key]['loss'], 'llm': 'puyu'}
-                elif puyu_pbox.get(video_key) is None:
-                    mixlm_pbox[video_key] = {'box': llama2_pbox[video_key]['box'], 'loss': llama2_pbox[video_key]['loss'], 'llm': 'llama2'}
-                else:
-                    if llama2_pbox[video_key]['loss'] < puyu_pbox[video_key]['loss']:
-                        mixlm_pbox[video_key] = {'box': llama2_pbox[video_key]['box'], 'loss': llama2_pbox[video_key]['loss'], 'llm': 'llama2'}
-                    else:
-                        mixlm_pbox[video_key] = {'box': puyu_pbox[video_key]['box'], 'loss': puyu_pbox[video_key]['loss'], 'llm': 'puyu'}
-            with open(saved_path, 'w') as f:
-                json.dump(mixlm_pbox, f)
-        
-        with open(saved_path, 'r') as f:
-            mixlm_pbox = json.load(f)
-        with open('data/howto/captiondata/howto100m_train_puyu.json', 'r') as f:
-            meta_puyu = json.load(f)
-        with open('data/howto/captiondata/howto100m_train.json', 'r') as f:
-            meta_llama2 = json.load(f)
-        
-        meta_mixlm = {}
-        for video_key in mixlm_pbox.keys():
-            if mixlm_pbox.get(video_key) is not None and (meta_llama2.get(video_key) is not None or meta_puyu.get(video_key) is not None):
-                if mixlm_pbox[video_key]['llm'] == 'llama2':
-                    meta_mixlm[video_key] = meta_llama2[video_key]
-                    llama2_feature_path = os.path.join(text_feature_folder_llama2, video_key + '.npy')
-                    if not os.path.exists(llama2_feature_path):
-                        continue
-                    # if os.path.exists(llama2_feature_path):
-                    #     os.unlink(llama2_feature_path)
-                    # if not os.path.exists(llama2_feature_path):                        
-                    #     os.symlink(llama2_feature_path, os.path.join(text_feature_folder_mixlm, video_key + '.npy'))
-                    soft_link_path = os.path.join(text_feature_folder_mixlm, video_key + '.npy')
-                    # if os.path.exists(soft_link_path):
-                    #     os.unlink(soft_link_path)
-                    if not os.path.exists(soft_link_path):
-                        # print(os.path.exists(soft_link_path), os.path.exists(llama2_feature_path))
-                        os.symlink(llama2_feature_path, soft_link_path)
-                    # text_feature = np.load(llama2_feature_path)
-                    # if text_feature.shape[0] != len(meta_llama2[video_key]['sentences']):
-                    #     print(f"{video_key} has {text_feature.shape[0]} sentences, but {len(meta_llama2[video_key]['sentences'])} sentences found in meta file")
-                else:
-                    meta_mixlm[video_key] = meta_puyu[video_key]
-                    puyu_feature_path = os.path.join(text_feature_folder_puyu, video_key + '.npy')
-                    if not os.path.exists(puyu_feature_path):
-                        continue
-
-                    soft_link_path = os.path.join(text_feature_folder_mixlm, video_key + '.npy')
-
-                    # if os.path.exists(soft_link_path):
-                    #     os.unlink(soft_link_path)
-                    if not os.path.exists(soft_link_path):
-                        os.symlink(puyu_feature_path, soft_link_path)
-                    # text_feature = np.load(puyu_feature_path)
-                    # if text_feature.shape[0] != len(meta_puyu[video_key]['sentences']):
-                    #     print(f"{video_key} has {text_feature.shape[0]} sentences, but {len(meta_puyu[video_key]['sentences'])} sentences found in meta file")
-        with open(os.path.join(save_folder, 'train_caption_mixlm.json'), 'w') as f:
-            json.dump(meta_mixlm, f)
-        opt.train_caption_file[0] = os.path.join(save_folder, 'train_caption_mixlm.json')
-        opt.text_feature_folder[0] = text_feature_folder_mixlm       
-        # pass
-
-
-    if not opt.start_from:
-        backup_envir(save_folder, opt)
-        logger.info('backup evironment completed !')
-
-    saved_info = {'best': {}, 'last': {}, 'history': {}, 'eval_history': {}}
-
-    # continue training
-    if opt.start_from:
-        opt.pretrain = False
-        infos_path = os.path.join(save_folder, 'info.json')
-        with open(infos_path) as f:
-            logger.info('Load info from {}'.format(infos_path))
-            saved_info = json.load(f)
-            prev_opt = saved_info[opt.start_from_mode[:4]]['opt']
-
-            exclude_opt = ['start_from', 'start_from_mode', 'pretrain']
-            for opt_name in prev_opt.keys():
-                if opt_name not in exclude_opt:
-                    vars(opt).update({opt_name: prev_opt.get(opt_name)})
-                if prev_opt.get(opt_name) != vars(opt).get(opt_name):
-                    logger.info('Change opt {} : {} --> {}'.format(opt_name, prev_opt.get(opt_name),
-                                                                   vars(opt).get(opt_name)))
-    print(opt.text_feature_folder)
-    print(opt.train_caption_file)
-    if len(opt.visual_feature_folder) == 2:
-        train_dataset_1 = PropSeqDataset(opt.train_caption_file[0],
-                                            [opt.visual_feature_folder[0]],
-                                            [opt.text_feature_folder[0]],
-                                            opt.dict_file, True, 'gt',
-                                            opt)
-        train_dataset_2 = PropSeqDataset(opt.train_caption_file[1],
-                                            [opt.visual_feature_folder[1]],
-                                            [opt.text_feature_folder[1]],
-                                            opt.dict_file, True, 'gt',
-                                            opt)
-        train_dataset = torch.utils.data.ConcatDataset([train_dataset_1, train_dataset_2])
-        train_dataset.translator = train_dataset_1.translator
-
-    else:
-        train_dataset = PropSeqDataset(opt.train_caption_file,
-                                    opt.visual_feature_folder,
-                                    opt.text_feature_folder,
-                                    opt.dict_file, True, 'gt',
-                                    opt)
-
-    # val_dataset = PropSeqDataset(opt.val_caption_file,
-    #                              opt.visual_feature_folder,
-    #                              opt.text_feature_folder,
-    #                              opt.dict_file, False, 'gt',
-    #                              opt)
-    if not hasattr(opt, 'dict_file_val'):
-        opt.dict_file_val = opt.dict_file
-        opt.vocab_size_val = opt.vocab_size
-
-    val_dataset = PropSeqDataset(opt.val_caption_file,
-                                opt.visual_feature_folder_val,
-                                opt.text_feature_folder_val,
-                                opt.dict_file, False, 'gt',
-                                opt)
-    g = torch.Generator()
-    g.manual_seed(0)
-
-    train_loader = DataLoader(train_dataset, batch_size=opt.batch_size,
-                              shuffle=True, num_workers=opt.nthreads, collate_fn=collate_fn, worker_init_fn=seed_worker, generator=g)
-
-    val_loader = DataLoader(val_dataset, batch_size=opt.batch_size_for_eval,
-                            shuffle=False, num_workers=opt.nthreads, collate_fn=collate_fn, worker_init_fn=seed_worker, generator=g)
-
-    epoch = saved_info[opt.start_from_mode[:4]].get('epoch', 0)
-    iteration = saved_info[opt.start_from_mode[:4]].get('iter', 0)
-    best_val_score = saved_info[opt.start_from_mode[:4]].get('best_val_score', -1e5)
-    val_result_history = saved_info['history'].get('val_result_history', {})
-    loss_history = saved_info['history'].get('loss_history', {})
-    lr_history = saved_info['history'].get('lr_history', {})
-    opt.current_lr = vars(opt).get('current_lr', opt.lr)
-
-    # Build model
-
-    model, criterion, contrastive_criterion, postprocessors = build(opt)
-    model.translator = train_dataset.translator
-    model.train()
-
-    # try to load saved pbox
-    if os.path.exists(saved_path):
-        try:
-            with open(saved_path, 'r') as f:
-                model.pseudo_boxes = json.load(f)
-        except:
-            # delete the bad file
-            os.remove(saved_path)
-
-    # Recover the parameters
-    if opt.start_from and (not opt.pretrain):
-        if opt.start_from_mode == 'best':
-            model_pth = torch.load(os.path.join(save_folder, 'model-best.pth'))
-        elif opt.start_from_mode == 'last':
-            model_pth = torch.load(os.path.join(save_folder, 'model-last.pth'))
-        logger.info('Loading pth from {}, iteration:{}'.format(save_folder, iteration))
-        model.load_state_dict(model_pth['model'])
-
-    # Load the pre-trained model
-    if opt.pretrain and (not opt.start_from):
-        logger.info('Load pre-trained parameters from {}'.format(opt.pretrain_path))
-        model_pth = torch.load(opt.pretrain_path, map_location=torch.device(opt.device))
-        # query_weight = model_pth['model'].pop('query_embed.weight')
-        if opt.pretrain == 'encoder':
-            encoder_filter = model.get_filter_rule_for_encoder()
-            encoder_pth = {k:v for k,v in model_pth['model'].items() if encoder_filter(k)}
-            model.load_state_dict(encoder_pth, strict=True)
-        elif opt.pretrain == 'decoder':
-            encoder_filter = model.get_filter_rule_for_encoder()
-            decoder_pth = {k:v for k,v in model_pth['model'].items() if not encoder_filter(k)}
-            model.load_state_dict(decoder_pth, strict=True)
-            pass
-        elif opt.pretrain == 'full':
-            # model_pth = transfer(model, model_pth)
-            model.load_state_dict(model_pth['model'], strict=True)
-        else:
-            raise ValueError("wrong value of opt.pretrain")
-        
-
-    model.to(opt.device)
-
-    # Decide which parameters need to be trained
-    # if (opt.matcher_type =='DTW' or opt.use_pseudo_box) and opt.text_encoder_learning_strategy == 'frozen':
-    #     for _, p in model.text_encoder.named_parameters():
-    #         p.requires_grad = False
-    #         text_encoder_params = list(map(id, model.text_encoder.parameters()))
-    #         other_params = filter(lambda p: id(p) not in text_encoder_params, model.parameters())
-    # else:
-    #     other_params = model.parameters()
-    other_params = model.parameters()
-
-    training_params = [{'params': other_params, 'lr': opt.lr}]
-
-    if opt.optimizer_type == 'adam':
-        optimizer = optim.Adam(training_params, weight_decay=opt.weight_decay)
-
-    elif opt.optimizer_type == 'adamw':
-        optimizer = optim.AdamW(training_params, weight_decay=opt.weight_decay)
-
-    milestone = [opt.learning_rate_decay_start + opt.learning_rate_decay_every * _ for _ in range(int((opt.epoch - opt.learning_rate_decay_start) / opt.learning_rate_decay_every))]
-    lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestone, gamma=opt.learning_rate_decay_rate)
-
-    # Load tokenizer for text encoder
-    # for i in range(10):
-    #     try:
-    #         if opt.pretrained_language_model == 'UniVL':
-    #             tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
-    #         else:
-    #             tokenizer = AutoTokenizer.from_pretrained(opt.pretrained_language_model)
-    #         break
-    #     except:
-    #         print('download error in AutoTokenizer, retry...')
-    #         time.sleep(1)
-
-    if opt.start_from:
-        optimizer.load_state_dict(model_pth['optimizer'])
-        lr_scheduler.step(epoch-1)
-
-    # print the args for debugging  
-    print_opt(opt, model, logger)
-    print_alert_message('Strat training !', logger)
-
-    loss_sum = OrderedDict()
-    bad_video_num = 0
-
-    start = time.time()
-    # breakpoint()
-    weight_dict = criterion.weight_dict
-    logger.info('loss type: {}'.format(weight_dict.keys()))
-    logger.info('loss weights: {}'.format(weight_dict.values()))
-
-    # Epoch-level iteration
-    refine_pseudo_box_copy = copy.deepcopy(opt.refine_pseudo_box)
-    pseudo_box_aug_copy = copy.deepcopy(opt.pseudo_box_aug)
-
-    while True:
-        # if epoch > opt.start_refine_epoch:
-        #     opt.refine_pseudo_box = refine_pseudo_box_copy
-        #     opt.pseudo_box_aug = pseudo_box_aug_copy
-        #     criterion.refine_pseudo_box = refine_pseudo_box_copy
-        #     criterion.pseudo_box_aug = pseudo_box_aug_copy
-        #     model.opt = opt 
-        # else:
-        #     opt.refine_pseudo_box = False
-        #     opt.pseudo_box_aug = False
-        #     criterion.refine_pseudo_box = False
-        #     criterion.pseudo_box_aug = False
-        #     model.opt = opt
-        
-        if True:
-            # scheduled sampling rate update
-            if epoch > opt.scheduled_sampling_start >= 0:
-                frac = (epoch - opt.scheduled_sampling_start) // opt.scheduled_sampling_increase_every
-                opt.ss_prob = min(opt.basic_ss_prob + opt.scheduled_sampling_increase_prob * frac,
-                                  opt.scheduled_sampling_max_prob)
-                model.caption_head.ss_prob = opt.ss_prob
-
-            print('lr:{}'.format(float(opt.current_lr)))
-            pass
-
-        # breakpoint()
-        # Batch-level iteration
-        trained_samples = 0
-        for dt in tqdm(train_loader, disable=opt.disable_tqdm):
-            # if dt['video_key'][0] != 'LGArj9Do0xc':
-            #     continue
-            # # for fast debugging
-            if opt.test:
-                if trained_samples > 5:
-                    break
-                else:
-                    trained_samples += 1
-            # if trained_samples < 1714:
-            #     trained_samples += 1
-            #     continue
-            if opt.device=='cuda':
-                torch.cuda.synchronize(opt.device)
-            if opt.debug:
-                # each epoch contains less mini-batches for debugging
-                if (iteration + 1) % 5 == 0:
-                    iteration += 1
-                    break
-            iteration += 1
-
-            optimizer.zero_grad()
-            dt = {key: _.to(opt.device) if isinstance(_, torch.Tensor) else _ for key, _ in dt.items()}
-            dt['video_target'] = [
-                {key: _.to(opt.device) if isinstance(_, torch.Tensor) else _ for key, _ in vid_info.items()} for vid_info in
-                dt['video_target']]
-
-            # Add text encoder
-            # if opt.matcher_type == 'DTW' or opt.use_pseudo_box:
-            #     captions = list()
-            #     for video_sents in dt['cap_raw']:  # dt['cap_raw']: [[sent_1, sent_2, ..., sent_n]]
-            #         captions.extend(video_sents) 
-            #     text_encoder_input = tokenizer(captions, return_tensors='pt', truncation=True, padding=True, max_length=opt.max_text_input_len)
-            #     text_encoder_input = {key: _.to(opt.device) if isinstance(_, torch.Tensor) else _ for key, _ in text_encoder_input.items()} 
-            #     # text_encoder_input: {'input_ids': tensor([[  101,  1996,  2307,  ...,     0,     0,     0],...]),  'attention_mask': tensor([[1, 1, 1,  ..., 0, 0, 0],...])}
-            #     # len(text_encoder_input['input_ids']) = n * max_text_input_len
-            #     dt['text_encoder_input'] = text_encoder_input
-
-            # dt = collections.defaultdict(lambda: None, dt) # Commented to 
-            try:
-                output, loss = model(dt, criterion, contrastive_criterion)
-            except Exception as e:
-                print(e)
-                print(dt['video_key'])
-                continue
-            final_loss = sum(loss[k] * weight_dict[k] for k in loss.keys() if k in weight_dict)
-            # breakpoint()
-            final_loss.backward()
-            torch.nn.utils.clip_grad_norm_(model.parameters(), opt.grad_clip)
-
-            optimizer.step()
-
-            for loss_k,loss_v in loss.items():
-                loss_sum[loss_k] = loss_sum.get(loss_k, 0)+ loss_v.item()
-            loss_sum['total_loss'] = loss_sum.get('total_loss', 0) + final_loss.item()
-
-            if opt.device=='cuda':
-                torch.cuda.synchronize()
-
-            losses_log_every = int(len(train_loader) / 10)
-
-            if opt.debug:
-                losses_log_every = 6
-
-            if iteration % losses_log_every == 0:
-                end = time.time()
-                for k in loss_sum.keys():
-                    loss_sum[k] = np.round(loss_sum[k] /losses_log_every, 3).item()
-
-                logger.info(
-                    "ID {} iter {} (epoch {}), \nloss = {}, \ntime/iter = {:.3f}, bad_vid = {:.3f}"
-                        .format(opt.id, iteration, epoch, loss_sum,
-                                (end - start) / losses_log_every, bad_video_num))
-
-                tf_writer.add_scalar('lr', opt.current_lr, iteration)
-                for loss_type in loss_sum.keys():
-                    tf_writer.add_scalar(loss_type, loss_sum[loss_type], iteration)
-                loss_history[iteration] = loss_sum
-                lr_history[iteration] = opt.current_lr
-                loss_sum = OrderedDict()
-                start = time.time()
-                bad_video_num = 0
-                torch.cuda.empty_cache()
-
-        # evaluation
-        if (epoch % opt.save_checkpoint_every == 0) and (epoch >= opt.min_epoch_when_save):
-            
-            # Save model
-            saved_pth = {'epoch': epoch,
-                         'model': model.state_dict(),
-                         'optimizer': optimizer.state_dict()}
-
-            if opt.save_all_checkpoint:
-                checkpoint_path = os.path.join(save_folder, 'model_iter_{}.pth'.format(iteration))
-            else:
-                checkpoint_path = os.path.join(save_folder, 'model-last.pth')
-
-            torch.save(saved_pth, checkpoint_path)
-
-            model.eval()
-            result_json_path = os.path.join(save_folder, 'prediction',
-                                         'num{}_epoch{}.json'.format(
-                                             len(val_dataset), epoch))
-            #eval_score, eval_loss = evaluate(model, criterion,  postprocessors, val_loader, result_json_path, logger=logger, args=opt, alpha=opt.ec_alpha, device=opt.device, debug=opt.debug)
-            eval_score, _ = evaluate(model, criterion,  postprocessors, val_loader, result_json_path, logger=logger, args=opt, alpha=opt.ec_alpha, device=opt.device, debug=opt.debug)
-            if opt.caption_decoder_type == 'none':
-                current_score = 2./(1./eval_score['Precision'] + 1./eval_score['Recall'])
-            else:
-                if opt.criteria_for_best_ckpt == 'dvc':
-                    current_score = np.array(eval_score['METEOR']).mean() + np.array(eval_score['soda_c']).mean()
-                elif opt.criteria_for_best_ckpt == 'overall':
-                    current_score = np.array(eval_score['Bleu_4']).mean() + \
-                    np.array(eval_score['CIDEr']).mean() + \
-                    np.array(eval_score['METEOR']).mean() + \
-                    2./(1./eval_score['Precision'] + 1./eval_score['Recall'])
-                else:
-                    current_score = np.array(eval_score['para_METEOR']).mean() + np.array(eval_score['para_CIDEr']).mean() + np.array(eval_score['para_Bleu_4']).mean()
-
-            # add to tf summary
-            for key in eval_score.keys():
-                tf_writer.add_scalar(key, np.array(eval_score[key]).mean(), iteration)
-
-            # Huabin comment this part for avoiding reporting losses during evaluation
-            # for loss_type in eval_loss.keys():
-            #     tf_writer.add_scalar('eval_' + loss_type, eval_loss[loss_type], iteration)
-            # breakpoint()
-            _ = [item.append(np.array(item).mean()) for item in eval_score.values() if isinstance(item, list)]
-            print_info = '\n'.join([key + ":" + str(eval_score[key]) for key in eval_score.keys()])
-            logger.info('\nValidation results of iter {}:\n'.format(iteration) + print_info)
-            logger.info('\noverall score of iter {}: {}\n'.format(iteration, current_score))
-            val_result_history[epoch] = {'eval_score': eval_score}
-            logger.info('Save model at iter {} to {}.'.format(iteration, checkpoint_path))
-
-            # save the model parameter and  of best epoch
-            if current_score >= best_val_score:
-                best_val_score = current_score
-                best_epoch = epoch
-                saved_info['best'] = {'opt': vars(opt),
-                                      'iter': iteration,
-                                      'epoch': best_epoch,
-                                      'best_val_score': best_val_score,
-                                      'result_json_path': result_json_path,
-                                      'avg_proposal_num': eval_score['avg_proposal_number'],
-                                      'Precision': eval_score['Precision'],
-                                      'Recall': eval_score['Recall']
-                                      }
-
-                # suffix = "RL" if sc_flag else "CE"
-                torch.save(saved_pth, os.path.join(save_folder, 'model-best.pth'))
-                logger.info('Save Best-model at iter {} to checkpoint file.'.format(iteration))
-
-            saved_info['last'] = {'opt': vars(opt),
-                                  'iter': iteration,
-                                  'epoch': epoch,
-                                  'best_val_score': best_val_score,
-                                  }
-            saved_info['history'] = {'val_result_history': val_result_history,
-                                     'loss_history': loss_history,
-                                     'lr_history': lr_history,
-                                     # 'query_matched_fre_hist': query_matched_fre_hist,
-                                     }
-            with open(os.path.join(save_folder, 'info.json'), 'w') as f:
-                json.dump(saved_info, f)
-            logger.info('Save info to info.json')
-
-            model.train()
-
-        epoch += 1
-
-        if epoch == 1 and model.pseudo_boxes is not None and 'mixlm' not in opt.train_caption_file[0]:
-            # save the pseudo boxes
-            pbox_save_path = construct_save_path(opt)
-            if not os.path.exists(pbox_save_path):
-                with open(pbox_save_path, 'w') as f:
-                    json.dump(model.pseudo_boxes, f)
-
-        lr_scheduler.step()
-        opt.current_lr = optimizer.param_groups[0]['lr']
-        torch.cuda.empty_cache()
-        # Stop criterion
-        if epoch >= opt.epoch:
-            # save the pesudo box
-
-
-
-            # # ===============================old code==============================================
-            # # load Best model and conduct evaluation
-            # print('====== Conduct the Final Evaluation to test Best Checkpoint ======')
-            # val_logger = create_logger(save_folder, 'val.log')
-            # loaded_pth = torch.load(os.path.join(save_folder, 'model-best.pth'), map_location='cuda')
-            # model.load_state_dict(loaded_pth['model'], strict=True)
-            # model.eval()
-            # result_json_path = saved_info['best']['result_json_path']
-            # eval_score, _ = evaluate(model, criterion,  postprocessors, val_loader, result_json_path, logger=logger, args=opt, alpha=opt.ec_alpha, device=opt.device, debug=opt.debug)
-            # if opt.caption_decoder_type == 'none':
-            #     current_score = 2./(1./eval_score['Precision'] + 1./eval_score['Recall'])
-            # else:
-            #     if opt.criteria_for_best_ckpt == 'dvc':
-            #         current_score = np.array(eval_score['METEOR']).mean() + np.array(eval_score['soda_c']).mean()
-            #     else:
-            #         current_score = np.array(eval_score['para_METEOR']).mean() + np.array(eval_score['para_CIDEr']).mean() + np.array(eval_score['para_Bleu_4']).mean()
-
-            # _ = [item.append(np.array(item).mean()) for item in eval_score.values() if isinstance(item, list)]
-            # print_info = '\n'.join([key + ":" + str(eval_score[key]) for key in eval_score.keys()])
-            # val_logger.info('Best-model is saved at iter {}.\n'.format(saved_info['best']['iter']))
-            # val_logger.info('\nBest Model Performance:\n' + print_info)
-            # val_logger.info('\nBest Overall Score {}: {}\n'.format(iteration, current_score))
-
-            # tf_writer.close()
-            # break
-            # =================================new code=========================================================
-            val_logger = create_logger(save_folder, 'val.log')
-            infos_path = os.path.join(save_folder, 'info.json')
-
-            with open(infos_path, 'r') as f:
-                data = json.load(f)
-            val_history = data['history']['val_result_history']
-
-            metric_sum = {}
-            metrics = ['METEOR', 'CIDEr', 'soda_c', 'Precision', 'Recall']
-            for k, v in val_history.items():
-                metric_sum[k] = sum([v['eval_score'][metric] for metric in metrics])
-                # print(f"{k}: {metric_sum[k]}")
-
-            best_epoch = max(metric_sum, key=metric_sum.get)
-            best_val_score = val_history[best_epoch]['eval_score']
-            val_logger.info(f"Best epoch: {best_epoch}")
-            print_info = '\n'.join([key + ":" + str(best_val_score[key]) for key in best_val_score.keys()])
-            val_logger.info('\nBest Model Performance:\n' + print_info)
-            val_logger.info('\nBest Overall Score epoch{}: {}\n'.format(best_epoch, metric_sum[best_epoch]))
-
-            break           
-            
-
-    return saved_info
-
-
-if __name__ == '__main__':
-    opt = opts.parse_opts()
-
-    if not hasattr(opt, 'visual_feature_folder_val'):
-        opt.visual_feature_folder_val = opt.visual_feature_folder
-        opt.text_feature_folder_val = opt.text_feature_folder
-    # breakpoint()
-    if opt.map:
-        opt.visual_feature_folder = [map_path(path) for path in opt.visual_feature_folder]
-        opt.text_feature_folder = [map_path(path) for path in opt.text_feature_folder]
-        opt.visual_feature_folder_val = [map_path(path) for path in opt.visual_feature_folder_val]
-        opt.text_feature_folder_val = [map_path(path) for path in opt.text_feature_folder_val]
-
-    # breakpoint()
-
-    if opt.gpu_id:
-        os.environ["CUDA_VISIBLE_DEVICES"] = ",".join([str(i) for i in opt.gpu_id])
-    if opt.disable_cudnn:
-        torch.backends.cudnn.enabled = False
-
-    os.environ['KMP_DUPLICATE_LIB_OK'] = 'True' # to avoid OMP problem on macos
-    # breakpoint()
-    train(opt)
-
diff --git a/backup/train_fewshot.py b/backup/train_fewshot.py
deleted file mode 100644
index d35b3feefc80f1a87e4fb30394702c28d04472d6..0000000000000000000000000000000000000000
--- a/backup/train_fewshot.py
+++ /dev/null
@@ -1,482 +0,0 @@
-# use ft_gt_percent to control the percentage of gt proposals used for finetuning
-
-# coding:utf-8
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import json
-import time
-import torch
-import os
-import sys
-import collections
-import numpy as np
-from tqdm import tqdm
-import torch.optim as optim
-from torch.utils.data import DataLoader
-from os.path import dirname, abspath
-
-pdvc_dir = dirname(abspath(__file__))
-sys.path.insert(0, pdvc_dir)
-sys.path.insert(0, os.path.join(pdvc_dir, 'densevid_eval3'))
-sys.path.insert(0, os.path.join(pdvc_dir, 'densevid_eval3/SODA'))
-# print(sys.path)
-CUDA_LAUNCH_BLOCKING=1
-
-os.environ["TOKENIZERS_PARALLELISM"] = "false" # To avoid warning of tokenizer
-from eval_utils import evaluate
-import opts
-from tensorboardX import SummaryWriter
-from misc.utils import print_alert_message, build_folder, create_logger, backup_envir, print_opt, set_seed
-from data.video_dataset import PropSeqDataset, collate_fn, PercentageSubsetDataset
-from pdvc.pdvc import build
-from collections import OrderedDict
-from transformers import AutoTokenizer, get_linear_schedule_with_warmup, get_cosine_schedule_with_warmup
-import copy
-import random
-import numpy as np 
-
-a100_folder = ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/Tasty/features', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Tasty/UniVL_feature', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features']
-r3090_folder = ['/mnt/data/Gvlab/wuhao/features/yc2', '/mnt/data/Gvlab/wuhao/features/tasty', '/mnt/data/Gvlab/wuhao/features/tasty/univl', '/mnt/data/Gvlab/wuhao/features/anet', '/mnt/data/Gvlab/wuhao/features/howto100m']
-
-# /cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features -> /mnt/data/Gvlab/wuhao/features/howto100m
-# /cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip -> /mnt/data/Gvlab/wuhao/features/howto100m/clip_features
-# /cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL -> /mnt/data/Gvlab/wuhao/features/howto100m/univl_features
-
-def seed_worker(worker_id):
-    worker_seed = torch.initial_seed() % 2**32
-    np.random.seed(worker_seed)
-    random.seed(worker_seed)
-
-def map_path(path):
-    path_backup = copy.deepcopy(path)
-    # breakpoint()
-    for i, folder in enumerate(a100_folder):
-        if folder in path:
-            path = path.replace(folder, r3090_folder[i])
-            return path
-    if path == path_backup:
-        print('map failed')
-        exit(1)
-
-
-def train(opt):
-    set_seed(opt.seed)
-    save_folder = build_folder(opt)
-    logger = create_logger(save_folder, 'train.log')
-    tf_writer = SummaryWriter(os.path.join(save_folder, 'tf_summary'))
-
-    if not opt.start_from:
-        backup_envir(save_folder, opt)
-        logger.info('backup evironment completed !')
-
-    saved_info = {'best': {}, 'last': {}, 'history': {}, 'eval_history': {}}
-
-    # continue training
-    if opt.start_from:
-        opt.pretrain = False
-        infos_path = os.path.join(save_folder, 'info.json')
-        with open(infos_path) as f:
-            logger.info('Load info from {}'.format(infos_path))
-            saved_info = json.load(f)
-            prev_opt = saved_info[opt.start_from_mode[:4]]['opt']
-
-            exclude_opt = ['start_from', 'start_from_mode', 'pretrain']
-            for opt_name in prev_opt.keys():
-                if opt_name not in exclude_opt:
-                    vars(opt).update({opt_name: prev_opt.get(opt_name)})
-                if prev_opt.get(opt_name) != vars(opt).get(opt_name):
-                    logger.info('Change opt {} : {} --> {}'.format(opt_name, prev_opt.get(opt_name),
-                                                                   vars(opt).get(opt_name)))
-    if len(opt.visual_feature_folder) == 2:
-        train_dataset_1 = PropSeqDataset(opt.train_caption_file[0],
-                                            [opt.visual_feature_folder[0]],
-                                            [opt.text_feature_folder[0]],
-                                            opt.dict_file, True, 'gt',
-                                            opt)
-        train_dataset_2 = PropSeqDataset(opt.train_caption_file[1],
-                                            [opt.visual_feature_folder[1]],
-                                            [opt.text_feature_folder[1]],
-                                            opt.dict_file, True, 'gt',
-                                            opt)
-        train_dataset = torch.utils.data.ConcatDataset([train_dataset_1, train_dataset_2])
-        train_dataset.translator = train_dataset_1.translator
-
-    else:
-        train_dataset_target = PropSeqDataset(opt.train_caption_file,
-                                    opt.visual_feature_folder,
-                                    opt.text_feature_folder,
-                                    opt.dict_file, True, 'gt',
-                                    opt)
-        train_dataset = PercentageSubsetDataset(train_dataset_target, opt.ft_gt_percent)
-
-    # val_dataset = PropSeqDataset(opt.val_caption_file,
-    #                              opt.visual_feature_folder,
-    #                              opt.text_feature_folder,
-    #                              opt.dict_file, False, 'gt',
-    #                              opt)
-    if not hasattr(opt, 'dict_file_val'):
-        opt.dict_file_val = opt.dict_file
-        opt.vocab_size_val = opt.vocab_size
-
-    val_dataset = PropSeqDataset(opt.val_caption_file,
-                                opt.visual_feature_folder_val,
-                                opt.text_feature_folder_val,
-                                opt.dict_file, False, 'gt',
-                                opt)
-    g = torch.Generator()
-    g.manual_seed(0)
-
-    train_loader = DataLoader(train_dataset, batch_size=opt.batch_size,
-                              shuffle=True, num_workers=opt.nthreads, collate_fn=collate_fn, worker_init_fn=seed_worker, generator=g)
-
-    val_loader = DataLoader(val_dataset, batch_size=opt.batch_size_for_eval,
-                            shuffle=False, num_workers=opt.nthreads, collate_fn=collate_fn, worker_init_fn=seed_worker, generator=g)
-
-    epoch = saved_info[opt.start_from_mode[:4]].get('epoch', 0)
-    iteration = saved_info[opt.start_from_mode[:4]].get('iter', 0)
-    best_val_score = saved_info[opt.start_from_mode[:4]].get('best_val_score', -1e5)
-    val_result_history = saved_info['history'].get('val_result_history', {})
-    loss_history = saved_info['history'].get('loss_history', {})
-    lr_history = saved_info['history'].get('lr_history', {})
-    opt.current_lr = vars(opt).get('current_lr', opt.lr)
-
-    # Build model
-
-    model, criterion, contrastive_criterion, postprocessors = build(opt)
-    model.translator = train_dataset.translator
-    model.train()
-
-    # Recover the parameters
-    if opt.start_from and (not opt.pretrain):
-        if opt.start_from_mode == 'best':
-            model_pth = torch.load(os.path.join(save_folder, 'model-best.pth'))
-        elif opt.start_from_mode == 'last':
-            model_pth = torch.load(os.path.join(save_folder, 'model-last.pth'))
-        logger.info('Loading pth from {}, iteration:{}'.format(save_folder, iteration))
-        model.load_state_dict(model_pth['model'])
-
-    # Load the pre-trained model
-    if opt.pretrain and (not opt.start_from):
-        logger.info('Load pre-trained parameters from {}'.format(opt.pretrain_path))
-        model_pth = torch.load(opt.pretrain_path, map_location=torch.device(opt.device))
-        # query_weight = model_pth['model'].pop('query_embed.weight')
-        if opt.pretrain == 'encoder':
-            encoder_filter = model.get_filter_rule_for_encoder()
-            encoder_pth = {k:v for k,v in model_pth['model'].items() if encoder_filter(k)}
-            model.load_state_dict(encoder_pth, strict=True)
-        elif opt.pretrain == 'decoder':
-            encoder_filter = model.get_filter_rule_for_encoder()
-            decoder_pth = {k:v for k,v in model_pth['model'].items() if not encoder_filter(k)}
-            model.load_state_dict(decoder_pth, strict=True)
-            pass
-        elif opt.pretrain == 'full':
-            # model_pth = transfer(model, model_pth)
-            model.load_state_dict(model_pth['model'], strict=True)
-        else:
-            raise ValueError("wrong value of opt.pretrain")
-        
-
-    model.to(opt.device)
-
-    # Decide which parameters need to be trained
-    # if (opt.matcher_type =='DTW' or opt.use_pseudo_box) and opt.text_encoder_learning_strategy == 'frozen':
-    #     for _, p in model.text_encoder.named_parameters():
-    #         p.requires_grad = False
-    #         text_encoder_params = list(map(id, model.text_encoder.parameters()))
-    #         other_params = filter(lambda p: id(p) not in text_encoder_params, model.parameters())
-    # else:
-    #     other_params = model.parameters()
-    other_params = model.parameters()
-
-    training_params = [{'params': other_params, 'lr': opt.lr}]
-
-    if opt.optimizer_type == 'adam':
-        optimizer = optim.Adam(training_params, weight_decay=opt.weight_decay)
-
-    elif opt.optimizer_type == 'adamw':
-        optimizer = optim.AdamW(training_params, weight_decay=opt.weight_decay)
-
-    milestone = [opt.learning_rate_decay_start + opt.learning_rate_decay_every * _ for _ in range(int((opt.epoch - opt.learning_rate_decay_start) / opt.learning_rate_decay_every))]
-    lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestone, gamma=opt.learning_rate_decay_rate)
-
-    # Load tokenizer for text encoder
-    # for i in range(10):
-    #     try:
-    #         if opt.pretrained_language_model == 'UniVL':
-    #             tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
-    #         else:
-    #             tokenizer = AutoTokenizer.from_pretrained(opt.pretrained_language_model)
-    #         break
-    #     except:
-    #         print('download error in AutoTokenizer, retry...')
-    #         time.sleep(1)
-
-    if opt.start_from:
-        optimizer.load_state_dict(model_pth['optimizer'])
-        lr_scheduler.step(epoch-1)
-
-    # print the args for debugging  
-    print_opt(opt, model, logger)
-    print_alert_message('Strat training !', logger)
-
-    loss_sum = OrderedDict()
-    bad_video_num = 0
-
-    start = time.time()
-    # breakpoint()
-    weight_dict = criterion.weight_dict
-    logger.info('loss type: {}'.format(weight_dict.keys()))
-    logger.info('loss weights: {}'.format(weight_dict.values()))
-
-    # Epoch-level iteration
-    refine_pseudo_box_copy = copy.deepcopy(opt.refine_pseudo_box)
-    pseudo_box_aug_copy = copy.deepcopy(opt.pseudo_box_aug)
-
-    while True:
-        # if epoch > opt.start_refine_epoch:
-        #     opt.refine_pseudo_box = refine_pseudo_box_copy
-        #     opt.pseudo_box_aug = pseudo_box_aug_copy
-        #     criterion.refine_pseudo_box = refine_pseudo_box_copy
-        #     criterion.pseudo_box_aug = pseudo_box_aug_copy
-        #     model.opt = opt 
-        # else:
-        #     opt.refine_pseudo_box = False
-        #     opt.pseudo_box_aug = False
-        #     criterion.refine_pseudo_box = False
-        #     criterion.pseudo_box_aug = False
-        #     model.opt = opt
-        
-        if True:
-            # scheduled sampling rate update
-            if epoch > opt.scheduled_sampling_start >= 0:
-                frac = (epoch - opt.scheduled_sampling_start) // opt.scheduled_sampling_increase_every
-                opt.ss_prob = min(opt.basic_ss_prob + opt.scheduled_sampling_increase_prob * frac,
-                                  opt.scheduled_sampling_max_prob)
-                model.caption_head.ss_prob = opt.ss_prob
-
-            print('lr:{}'.format(float(opt.current_lr)))
-            pass
-
-        # breakpoint()
-        # Batch-level iteration
-        trained_samples = 0
-        for dt in tqdm(train_loader, disable=opt.disable_tqdm):
-            # if dt['video_key'][0] != 'LGArj9Do0xc':
-            #     continue
-            # # for fast debugging
-            # if trained_samples > 5:
-            #     break
-            # else:
-            #     trained_samples += 1
-            # if trained_samples < 1714:
-            #     trained_samples += 1
-            #     continue
-            if opt.device=='cuda':
-                torch.cuda.synchronize(opt.device)
-            if opt.debug:
-                # each epoch contains less mini-batches for debugging
-                if (iteration + 1) % 5 == 0:
-                    iteration += 1
-                    break
-            iteration += 1
-
-            optimizer.zero_grad()
-            dt = {key: _.to(opt.device) if isinstance(_, torch.Tensor) else _ for key, _ in dt.items()}
-            dt['video_target'] = [
-                {key: _.to(opt.device) if isinstance(_, torch.Tensor) else _ for key, _ in vid_info.items()} for vid_info in
-                dt['video_target']]
-
-            # Add text encoder
-            # if opt.matcher_type == 'DTW' or opt.use_pseudo_box:
-            #     captions = list()
-            #     for video_sents in dt['cap_raw']:  # dt['cap_raw']: [[sent_1, sent_2, ..., sent_n]]
-            #         captions.extend(video_sents) 
-            #     text_encoder_input = tokenizer(captions, return_tensors='pt', truncation=True, padding=True, max_length=opt.max_text_input_len)
-            #     text_encoder_input = {key: _.to(opt.device) if isinstance(_, torch.Tensor) else _ for key, _ in text_encoder_input.items()} 
-            #     # text_encoder_input: {'input_ids': tensor([[  101,  1996,  2307,  ...,     0,     0,     0],...]),  'attention_mask': tensor([[1, 1, 1,  ..., 0, 0, 0],...])}
-            #     # len(text_encoder_input['input_ids']) = n * max_text_input_len
-            #     dt['text_encoder_input'] = text_encoder_input
-
-            # dt = collections.defaultdict(lambda: None, dt) # Commented to 
-
-            output, loss = model(dt, criterion, contrastive_criterion)
-            final_loss = sum(loss[k] * weight_dict[k] for k in loss.keys() if k in weight_dict)
-            # breakpoint()
-            final_loss.backward()
-            torch.nn.utils.clip_grad_norm_(model.parameters(), opt.grad_clip)
-
-            optimizer.step()
-
-            for loss_k,loss_v in loss.items():
-                loss_sum[loss_k] = loss_sum.get(loss_k, 0)+ loss_v.item()
-            loss_sum['total_loss'] = loss_sum.get('total_loss', 0) + final_loss.item()
-
-            if opt.device=='cuda':
-                torch.cuda.synchronize()
-
-            losses_log_every = int(len(train_loader) / 10)
-
-            if opt.debug:
-                losses_log_every = 6
-
-            if iteration % losses_log_every == 0:
-                end = time.time()
-                for k in loss_sum.keys():
-                    loss_sum[k] = np.round(loss_sum[k] /losses_log_every, 3).item()
-
-                logger.info(
-                    "ID {} iter {} (epoch {}), \nloss = {}, \ntime/iter = {:.3f}, bad_vid = {:.3f}"
-                        .format(opt.id, iteration, epoch, loss_sum,
-                                (end - start) / losses_log_every, bad_video_num))
-
-                tf_writer.add_scalar('lr', opt.current_lr, iteration)
-                for loss_type in loss_sum.keys():
-                    tf_writer.add_scalar(loss_type, loss_sum[loss_type], iteration)
-                loss_history[iteration] = loss_sum
-                lr_history[iteration] = opt.current_lr
-                loss_sum = OrderedDict()
-                start = time.time()
-                bad_video_num = 0
-                torch.cuda.empty_cache()
-
-        # evaluation
-        if (epoch % opt.save_checkpoint_every == 0) and (epoch >= opt.min_epoch_when_save):
-            
-            # Save model
-            saved_pth = {'epoch': epoch,
-                         'model': model.state_dict(),
-                         'optimizer': optimizer.state_dict()}
-
-            if opt.save_all_checkpoint:
-                checkpoint_path = os.path.join(save_folder, 'model_iter_{}.pth'.format(iteration))
-            else:
-                checkpoint_path = os.path.join(save_folder, 'model-last.pth')
-
-            torch.save(saved_pth, checkpoint_path)
-
-            model.eval()
-            result_json_path = os.path.join(save_folder, 'prediction',
-                                         'num{}_epoch{}.json'.format(
-                                             len(val_dataset), epoch))
-            #eval_score, eval_loss = evaluate(model, criterion,  postprocessors, val_loader, result_json_path, logger=logger, args=opt, alpha=opt.ec_alpha, device=opt.device, debug=opt.debug)
-            eval_score, _ = evaluate(model, criterion,  postprocessors, val_loader, result_json_path, logger=logger, args=opt, alpha=opt.ec_alpha, device=opt.device, debug=opt.debug)
-            if opt.caption_decoder_type == 'none':
-                current_score = 2./(1./eval_score['Precision'] + 1./eval_score['Recall'])
-            else:
-                if opt.criteria_for_best_ckpt == 'dvc':
-                    current_score = np.array(eval_score['METEOR']).mean() + np.array(eval_score['soda_c']).mean()
-                elif opt.criteria_for_best_ckpt == 'overall':
-                    current_score = np.array(eval_score['Bleu_4']).mean() + \
-                    np.array(eval_score['CIDEr']).mean() + \
-                    np.array(eval_score['METEOR']).mean() + \
-                    2./(1./eval_score['Precision'] + 1./eval_score['Recall'])
-                else:
-                    current_score = np.array(eval_score['para_METEOR']).mean() + np.array(eval_score['para_CIDEr']).mean() + np.array(eval_score['para_Bleu_4']).mean()
-
-            # add to tf summary
-            for key in eval_score.keys():
-                tf_writer.add_scalar(key, np.array(eval_score[key]).mean(), iteration)
-
-            # Huabin comment this part for avoiding reporting losses during evaluation
-            # for loss_type in eval_loss.keys():
-            #     tf_writer.add_scalar('eval_' + loss_type, eval_loss[loss_type], iteration)
-
-            _ = [item.append(np.array(item).mean()) for item in eval_score.values() if isinstance(item, list)]
-            print_info = '\n'.join([key + ":" + str(eval_score[key]) for key in eval_score.keys()])
-            logger.info('\nValidation results of iter {}:\n'.format(iteration) + print_info)
-            logger.info('\noverall score of iter {}: {}\n'.format(iteration, current_score))
-            val_result_history[epoch] = {'eval_score': eval_score}
-            logger.info('Save model at iter {} to {}.'.format(iteration, checkpoint_path))
-
-            # save the model parameter and  of best epoch
-            if current_score >= best_val_score:
-                best_val_score = current_score
-                best_epoch = epoch
-                saved_info['best'] = {'opt': vars(opt),
-                                      'iter': iteration,
-                                      'epoch': best_epoch,
-                                      'best_val_score': best_val_score,
-                                      'result_json_path': result_json_path,
-                                      'avg_proposal_num': eval_score['avg_proposal_number'],
-                                      'Precision': eval_score['Precision'],
-                                      'Recall': eval_score['Recall']
-                                      }
-
-                # suffix = "RL" if sc_flag else "CE"
-                torch.save(saved_pth, os.path.join(save_folder, 'model-best.pth'))
-                logger.info('Save Best-model at iter {} to checkpoint file.'.format(iteration))
-
-            saved_info['last'] = {'opt': vars(opt),
-                                  'iter': iteration,
-                                  'epoch': epoch,
-                                  'best_val_score': best_val_score,
-                                  }
-            saved_info['history'] = {'val_result_history': val_result_history,
-                                     'loss_history': loss_history,
-                                     'lr_history': lr_history,
-                                     # 'query_matched_fre_hist': query_matched_fre_hist,
-                                     }
-            with open(os.path.join(save_folder, 'info.json'), 'w') as f:
-                json.dump(saved_info, f)
-            logger.info('Save info to info.json')
-
-            model.train()
-
-        epoch += 1
-        lr_scheduler.step()
-        opt.current_lr = optimizer.param_groups[0]['lr']
-        torch.cuda.empty_cache()
-        # Stop criterion
-        if epoch >= opt.epoch:
-            # load Best model and conduct evaluation
-            print('====== Conduct the Final Evaluation to test Best Checkpoint ======')
-            val_logger = create_logger(save_folder, 'val.log')
-            loaded_pth = torch.load(os.path.join(save_folder, 'model-best.pth'), map_location='cuda')
-            model.load_state_dict(loaded_pth['model'], strict=True)
-            model.eval()
-            result_json_path = saved_info['best']['result_json_path']
-            eval_score, _ = evaluate(model, criterion,  postprocessors, val_loader, result_json_path, logger=logger, args=opt, alpha=opt.ec_alpha, device=opt.device, debug=opt.debug)
-            if opt.caption_decoder_type == 'none':
-                current_score = 2./(1./eval_score['Precision'] + 1./eval_score['Recall'])
-            else:
-                if opt.criteria_for_best_ckpt == 'dvc':
-                    current_score = np.array(eval_score['METEOR']).mean() + np.array(eval_score['soda_c']).mean()
-                else:
-                    current_score = np.array(eval_score['para_METEOR']).mean() + np.array(eval_score['para_CIDEr']).mean() + np.array(eval_score['para_Bleu_4']).mean()
-
-            _ = [item.append(np.array(item).mean()) for item in eval_score.values() if isinstance(item, list)]
-            print_info = '\n'.join([key + ":" + str(eval_score[key]) for key in eval_score.keys()])
-            val_logger.info('Best-model is saved at iter {}.\n'.format(saved_info['best']['iter']))
-            val_logger.info('\nBest Model Performance:\n' + print_info)
-            val_logger.info('\nBest Overall Score {}: {}\n'.format(iteration, current_score))
-
-            tf_writer.close()
-            break
-
-    return saved_info
-
-
-if __name__ == '__main__':
-    opt = opts.parse_opts()
-
-    if not hasattr(opt, 'visual_feature_folder_val'):
-        opt.visual_feature_folder_val = opt.visual_feature_folder
-        opt.text_feature_folder_val = opt.text_feature_folder
-    # breakpoint()
-    if opt.map:
-        opt.visual_feature_folder = [map_path(path) for path in opt.visual_feature_folder]
-        opt.text_feature_folder = [map_path(path) for path in opt.text_feature_folder]
-        opt.visual_feature_folder_val = [map_path(path) for path in opt.visual_feature_folder_val]
-        opt.text_feature_folder_val = [map_path(path) for path in opt.text_feature_folder_val]
-
-    if opt.gpu_id:
-        os.environ["CUDA_VISIBLE_DEVICES"] = ",".join([str(i) for i in opt.gpu_id])
-    if opt.disable_cudnn:
-        torch.backends.cudnn.enabled = False
-
-    os.environ['KMP_DUPLICATE_LIB_OK'] = 'True' # to avoid OMP problem on macos
-    # breakpoint()
-    train(opt)
-
diff --git a/backup/train_ft.py b/backup/train_ft.py
deleted file mode 100644
index bdcc497f763607f28dfb1e0a687705c42e448a09..0000000000000000000000000000000000000000
--- a/backup/train_ft.py
+++ /dev/null
@@ -1,513 +0,0 @@
-# coding:utf-8
-
-''' 
-train_seq2.py is different from train_seq.py in the following aspects:
-
-1. train_seq2.py uses the same dataset for pretraining and target task
-2. the pretrain dataset and target dataset is not trained one after another in a single epoch. train pretrain dataset for 10 epochs then train target dataset for 20 epochs
-3. the vocabulary is always the same for pretrain and target task i.e. combined vocabulary of pretrain and target task
-4. checkpoint is located in save howto_yc2_* or howto_tasty_* 
-5. cfg use howto-tasty_tasty_* or howto-yc2_yc2_*
-'''
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import json
-import time
-import torch
-import os
-import sys
-import collections
-import numpy as np
-from tqdm import tqdm
-import torch.optim as optim
-from torch.utils.data import DataLoader
-from os.path import dirname, abspath
-import re 
-
-pdvc_dir = dirname(abspath(__file__))
-sys.path.insert(0, pdvc_dir)
-sys.path.insert(0, os.path.join(pdvc_dir, 'densevid_eval3'))
-sys.path.insert(0, os.path.join(pdvc_dir, 'densevid_eval3/SODA'))
-# print(sys.path)
-
-
-os.environ["TOKENIZERS_PARALLELISM"] = "false" # To avoid warning of tokenizer
-from eval_utils import evaluate
-import opts
-from tensorboardX import SummaryWriter
-from misc.utils import print_alert_message, build_folder, create_logger, backup_envir, print_opt, set_seed
-from data.video_dataset import PropSeqDataset, collate_fn
-from pdvc.pdvc import build
-from collections import OrderedDict
-from transformers import AutoTokenizer, get_linear_schedule_with_warmup, get_cosine_schedule_with_warmup
-import copy
-
-a100_folder = ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/Tasty/features', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Tasty/UniVL_feature', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features']
-r3090_folder = ['/mnt/data/Gvlab/wuhao/features/yc2', '/mnt/data/Gvlab/wuhao/features/tasty', '/mnt/data/Gvlab/wuhao/features/tasty/univl', '/mnt/data/Gvlab/wuhao/features/anet', '/mnt/data/Gvlab/wuhao/features/howto100m']
-
-# /cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features -> /mnt/data/Gvlab/wuhao/features/howto100m
-# /cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip -> /mnt/data/Gvlab/wuhao/features/howto100m/clip_features
-# /cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL -> /mnt/data/Gvlab/wuhao/features/howto100m/univl_features
-
-def _init_fn(worker_id):
-    np.random.seed(12 + worker_id)
-
-def map_path(path):
-    path_backup = copy.deepcopy(path)
-    # breakpoint()
-    for i, folder in enumerate(a100_folder):
-        if folder in path:
-            path = path.replace(folder, r3090_folder[i])
-            return path
-    if path == path_backup:
-        print('map failed')
-        exit(1)
-
-
-def train(opt):
-    set_seed(opt.seed)
-    save_folder = build_folder(opt)
-    opt.epoch = 20
-
-    # breakpoint()
-    if 'howto-tasty_tasty' in save_folder:
-        checkpoint_folder = re.sub(r"_seq2-ft.*", "", save_folder.replace('howto-tasty_tasty', 'howto_tasty')) # .replace('_seq2-ft', '')
-    elif 'howto-yc2_yc2' in save_folder:
-        checkpoint_folder = re.sub(r"_seq2-ft.*", "", save_folder.replace('howto-yc2_yc2', 'howto_yc2')) # .replace('_seq2-ft', '')
-    elif 'howto-anet_anet' in save_folder:
-        checkpoint_folder = re.sub(r"_seq2-ft.*", "", save_folder.replace('howto-anet_anet', 'howto_anet'))
-    else:
-        print('the script only support settings howto-XXX_XXX')
-        exit(1)
-
-    if not os.path.exists(checkpoint_folder):
-        print('the checkpoint folder does not exist')
-        exit(1)
-    else:
-        if not os.path.exists(os.path.join(checkpoint_folder, 'val.log')):
-            # print('the checkpoint folder has no val.log, denoting the setting is not fully trained')
-            for i in range(1, 100):
-                if os.path.exists(f'{checkpoint_folder}_{i}'):
-                    if os.path.exists(os.path.join(f'{checkpoint_folder}_{i}', 'val.log')):
-                        checkpoint_folder = f'{checkpoint_folder}_{i}'
-                        break
-                    else:
-                        continue
-                else:
-                    print(f'{checkpoint_folder}_{i} does not exist')
-                    print('the checkpoint folder does not exist')
-                    exit(1)
-
-    logger = create_logger(save_folder, 'train.log')
-    tf_writer = SummaryWriter(os.path.join(save_folder, 'tf_summary'))
-
-    if not opt.start_from:
-        backup_envir(save_folder)
-        logger.info('backup evironment completed !')
-
-    saved_info = {'best': {}, 'last': {}, 'history': {}, 'eval_history': {}}
-
-    # # continue training
-    # if opt.start_from:
-    #     opt.pretrain = False
-    #     infos_path = os.path.join(save_folder, 'info.json')
-    #     with open(infos_path) as f:
-    #         logger.info('Load info from {}'.format(infos_path))
-    #         saved_info = json.load(f)
-    #         prev_opt = saved_info[opt.start_from_mode[:4]]['opt']
-
-    #         exclude_opt = ['start_from', 'start_from_mode', 'pretrain']
-    #         for opt_name in prev_opt.keys():
-    #             if opt_name not in exclude_opt:
-    #                 vars(opt).update({opt_name: prev_opt.get(opt_name)})
-    #             if prev_opt.get(opt_name) != vars(opt).get(opt_name):
-    #                 logger.info('Change opt {} : {} --> {}'.format(opt_name, prev_opt.get(opt_name),
-    #                                                                vars(opt).get(opt_name)))
-    if len(opt.visual_feature_folder) == 2:
-        # train_dataset_pretrain = PropSeqDataset(opt.train_caption_file[0],
-        #                                     [opt.visual_feature_folder[0]],
-        #                                     [opt.text_feature_folder[0]],
-        #                                     opt.dict_file, True, 'gt',
-        #                                     opt)
-        train_dataset_target = PropSeqDataset(opt.train_caption_file[1],
-                                            [opt.visual_feature_folder[1]],
-                                            [opt.text_feature_folder[1]],
-                                            opt.dict_file, True, 'gt',
-                                            opt)
-        # train_loader_pretrain = DataLoader(train_dataset_pretrain, batch_size=opt.batch_size,
-        #                       shuffle=True, num_workers=opt.nthreads, collate_fn=collate_fn, worker_init_fn=_init_fn)
-        train_loader_target = DataLoader(train_dataset_target, batch_size=opt.batch_size,
-                                    shuffle=True, num_workers=opt.nthreads, collate_fn=collate_fn, worker_init_fn=_init_fn)
-        
-        # train_dataloaders = [train_loader_pretrain, train_loader_target]
-        # train_dataset = torch.utils.data.ConcatDataset([train_dataset_1, train_dataset_2])
-        # train_dataset.translator = train_dataset_1.translator
-
-    else:
-        print('the script only support two dataset for pretrain and target task respectively')
-        exit(1)
-        train_dataset_target = PropSeqDataset(opt.train_caption_file,
-                                    opt.visual_feature_folder,
-                                    opt.text_feature_folder,
-                                    opt.dict_file, True, 'gt',
-                                    opt)
-        train_loader_target = DataLoader(train_dataset_target, batch_size=opt.batch_size,
-                                shuffle=True, num_workers=opt.nthreads, collate_fn=collate_fn, worker_init_fn=_init_fn)
-        train_dataloaders = [train_loader_target]
-
-    # val_dataset = PropSeqDataset(opt.val_caption_file,
-    #                              opt.visual_feature_folder,
-    #                              opt.text_feature_folder,
-    #                              opt.dict_file, False, 'gt',
-    #                              opt)
-    if not hasattr(opt, 'dict_file_val'):
-        opt.dict_file_val = opt.dict_file
-        opt.vocab_size_val = opt.vocab_size
-
-    val_dataset = PropSeqDataset(opt.val_caption_file,
-                                opt.visual_feature_folder_val,
-                                opt.text_feature_folder_val,
-                                opt.dict_file, False, 'gt',
-                                opt)
-
-
-    val_loader = DataLoader(val_dataset, batch_size=opt.batch_size_for_eval,
-                            shuffle=False, num_workers=opt.nthreads, collate_fn=collate_fn, worker_init_fn=_init_fn)
-
-    epoch = saved_info[opt.start_from_mode[:4]].get('epoch', 0)
-    iteration = saved_info[opt.start_from_mode[:4]].get('iter', 0)
-    best_val_score = saved_info[opt.start_from_mode[:4]].get('best_val_score', -1e5)
-    val_result_history = saved_info['history'].get('val_result_history', {})
-    loss_history = saved_info['history'].get('loss_history', {})
-    lr_history = saved_info['history'].get('lr_history', {})
-    opt.current_lr = vars(opt).get('current_lr', opt.lr)
-
-    # Build model
-
-    model, criterion, contrastive_criterion, postprocessors = build(opt)
-    model.translator = train_dataset_target.translator
-    model.train()
-
-
-    # load pretrained model
-
-    # breakpoint()
-    # load pretrained model
-    model_pth = torch.load(os.path.join(checkpoint_folder, 'model-best.pth'))
-    logger.info('Loading pth from {}'.format(checkpoint_folder))
-    model.load_state_dict(model_pth['model'])
-    
-
-    # # Recover the parameters
-    # if opt.start_from and (not opt.pretrain):
-    #     if opt.start_from_mode == 'best':
-    #         model_pth = torch.load(os.path.join(save_folder, 'model-best.pth'))
-    #     elif opt.start_from_mode == 'last':
-    #         model_pth = torch.load(os.path.join(save_folder, 'model-last.pth'))
-    #     logger.info('Loading pth from {}, iteration:{}'.format(save_folder, iteration))
-    #     model.load_state_dict(model_pth['model'])
-
-    # # Load the pre-trained model
-    # if opt.pretrain and (not opt.start_from):
-    #     logger.info('Load pre-trained parameters from {}'.format(opt.pretrain_path))
-    #     model_pth = torch.load(opt.pretrain_path, map_location=torch.device(opt.device))
-    #     # query_weight = model_pth['model'].pop('query_embed.weight')
-    #     if opt.pretrain == 'encoder':
-    #         encoder_filter = model.get_filter_rule_for_encoder()
-    #         encoder_pth = {k:v for k,v in model_pth['model'].items() if encoder_filter(k)}
-    #         model.load_state_dict(encoder_pth, strict=True)
-    #     elif opt.pretrain == 'decoder':
-    #         encoder_filter = model.get_filter_rule_for_encoder()
-    #         decoder_pth = {k:v for k,v in model_pth['model'].items() if not encoder_filter(k)}
-    #         model.load_state_dict(decoder_pth, strict=True)
-    #         pass
-    #     elif opt.pretrain == 'full':
-    #         # model_pth = transfer(model, model_pth)
-    #         model.load_state_dict(model_pth['model'], strict=True)
-    #     else:
-    #         raise ValueError("wrong value of opt.pretrain")
-        
-
-    model.to(opt.device)
-
-    # Decide which parameters need to be trained
-    # if (opt.matcher_type =='DTW' or opt.use_pseudo_box) and opt.text_encoder_learning_strategy == 'frozen':
-    #     for _, p in model.text_encoder.named_parameters():
-    #         p.requires_grad = False
-    #         text_encoder_params = list(map(id, model.text_encoder.parameters()))
-    #         other_params = filter(lambda p: id(p) not in text_encoder_params, model.parameters())
-    # else:
-    #     other_params = model.parameters()
-    other_params = model.parameters()
-
-    training_params = [{'params': other_params, 'lr': opt.lr * 0.5}]
-
-    if opt.optimizer_type == 'adam':
-        optimizer = optim.Adam(training_params, weight_decay=opt.weight_decay)
-
-    elif opt.optimizer_type == 'adamw':
-        optimizer = optim.AdamW(training_params, weight_decay=opt.weight_decay)
-
-    milestone = [opt.learning_rate_decay_start + opt.learning_rate_decay_every * _ for _ in range(int((opt.epoch - opt.learning_rate_decay_start) / opt.learning_rate_decay_every))]
-    lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestone, gamma=opt.learning_rate_decay_rate)
-
-    # Load tokenizer for text encoder
-    # for i in range(10):
-    #     try:
-    #         if opt.pretrained_language_model == 'UniVL':
-    #             tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
-    #         else:
-    #             tokenizer = AutoTokenizer.from_pretrained(opt.pretrained_language_model)
-    #         break
-    #     except:
-    #         print('download error in AutoTokenizer, retry...')
-    #         time.sleep(1)
-
-    # if opt.start_from:
-    optimizer.load_state_dict(model_pth['optimizer'])
-        # lr_scheduler.step(epoch-1)
-
-    # print the args for debugging  
-    print_opt(opt, model, logger)
-    print_alert_message('Strat training !', logger)
-
-    loss_sum = OrderedDict()
-    bad_video_num = 0
-
-    start = time.time()
-
-    weight_dict = criterion.weight_dict
-    logger.info('loss type: {}'.format(weight_dict.keys()))
-    logger.info('loss weights: {}'.format(weight_dict.values()))
-
-    # breakpoint()
-
-    # Epoch-level iteration
-
-    while True:
-        if True:
-            # scheduled sampling rate update
-            if epoch > opt.scheduled_sampling_start >= 0:
-                frac = (epoch - opt.scheduled_sampling_start) // opt.scheduled_sampling_increase_every
-                opt.ss_prob = min(opt.basic_ss_prob + opt.scheduled_sampling_increase_prob * frac,
-                                  opt.scheduled_sampling_max_prob)
-                model.caption_head.ss_prob = opt.ss_prob
-
-            print('lr:{}'.format(float(opt.current_lr)))
-            pass
-
-        # breakpoint()
-        # Batch-level iteration
-        # for train_loader in train_dataloaders:
-        trained_samples = 0
-        for dt in tqdm(train_loader_target, disable=opt.disable_tqdm):
-            # # for fast debugging
-            # if trained_samples > 5:
-            #     break
-            # else:
-            #     trained_samples += 1
-            if opt.device=='cuda':
-                torch.cuda.synchronize(opt.device)
-            if opt.debug:
-                # each epoch contains less mini-batches for debugging
-                if (iteration + 1) % 5 == 0:
-                    iteration += 1
-                    break
-            iteration += 1
-
-            optimizer.zero_grad()
-            dt = {key: _.to(opt.device) if isinstance(_, torch.Tensor) else _ for key, _ in dt.items()}
-            dt['video_target'] = [
-                {key: _.to(opt.device) if isinstance(_, torch.Tensor) else _ for key, _ in vid_info.items()} for vid_info in
-                dt['video_target']]
-
-            # Add text encoder
-            # if opt.matcher_type == 'DTW' or opt.use_pseudo_box:
-            #     captions = list()
-            #     for video_sents in dt['cap_raw']:  # dt['cap_raw']: [[sent_1, sent_2, ..., sent_n]]
-            #         captions.extend(video_sents) 
-            #     text_encoder_input = tokenizer(captions, return_tensors='pt', truncation=True, padding=True, max_length=opt.max_text_input_len)
-            #     text_encoder_input = {key: _.to(opt.device) if isinstance(_, torch.Tensor) else _ for key, _ in text_encoder_input.items()} 
-            #     # text_encoder_input: {'input_ids': tensor([[  101,  1996,  2307,  ...,     0,     0,     0],...]),  'attention_mask': tensor([[1, 1, 1,  ..., 0, 0, 0],...])}
-            #     # len(text_encoder_input['input_ids']) = n * max_text_input_len
-            #     dt['text_encoder_input'] = text_encoder_input
-
-            # dt = collections.defaultdict(lambda: None, dt) # Commented to 
-
-            output, loss = model(dt, criterion, contrastive_criterion)
-            final_loss = sum(loss[k] * weight_dict[k] for k in loss.keys() if k in weight_dict)
-            # breakpoint()
-            final_loss.backward()
-            torch.nn.utils.clip_grad_norm_(model.parameters(), opt.grad_clip)
-
-            optimizer.step()
-
-            for loss_k,loss_v in loss.items():
-                loss_sum[loss_k] = loss_sum.get(loss_k, 0)+ loss_v.item()
-            loss_sum['total_loss'] = loss_sum.get('total_loss', 0) + final_loss.item()
-
-            if opt.device=='cuda':
-                torch.cuda.synchronize()
-
-            losses_log_every = int(len(train_loader_target) / 10)
-
-            if opt.debug:
-                losses_log_every = 6
-
-            if iteration % losses_log_every == 0:
-                end = time.time()
-                for k in loss_sum.keys():
-                    loss_sum[k] = np.round(loss_sum[k] /losses_log_every, 3).item()
-
-                logger.info(
-                    "ID {} iter {} (epoch {}), \nloss = {}, \ntime/iter = {:.3f}, bad_vid = {:.3f}"
-                        .format(opt.id, iteration, epoch, loss_sum,
-                                (end - start) / losses_log_every, bad_video_num))
-
-                tf_writer.add_scalar('lr', opt.current_lr, iteration)
-                for loss_type in loss_sum.keys():
-                    tf_writer.add_scalar(loss_type, loss_sum[loss_type], iteration)
-                loss_history[iteration] = loss_sum
-                lr_history[iteration] = opt.current_lr
-                loss_sum = OrderedDict()
-                start = time.time()
-                bad_video_num = 0
-                torch.cuda.empty_cache()
-
-        # evaluation
-        if (epoch % opt.save_checkpoint_every == 0) and (epoch >= opt.min_epoch_when_save):
-            
-            # Save model
-            saved_pth = {'epoch': epoch,
-                         'model': model.state_dict(),
-                         'optimizer': optimizer.state_dict()}
-
-            if opt.save_all_checkpoint:
-                checkpoint_path = os.path.join(save_folder, 'model_iter_{}.pth'.format(iteration))
-            else:
-                checkpoint_path = os.path.join(save_folder, 'model-last.pth')
-
-            torch.save(saved_pth, checkpoint_path)
-
-            model.eval()
-            result_json_path = os.path.join(save_folder, 'prediction',
-                                         'num{}_epoch{}.json'.format(
-                                             len(val_dataset), epoch))
-            #eval_score, eval_loss = evaluate(model, criterion,  postprocessors, val_loader, result_json_path, logger=logger, args=opt, alpha=opt.ec_alpha, device=opt.device, debug=opt.debug)
-            eval_score, _ = evaluate(model, criterion,  postprocessors, val_loader, result_json_path, logger=logger, args=opt, alpha=opt.ec_alpha, device=opt.device, debug=opt.debug)
-            if opt.caption_decoder_type == 'none':
-                current_score = 2./(1./eval_score['Precision'] + 1./eval_score['Recall'])
-            else:
-                if opt.criteria_for_best_ckpt == 'dvc':
-                    current_score = np.array(eval_score['METEOR']).mean() + np.array(eval_score['soda_c']).mean()
-                else:
-                    current_score = np.array(eval_score['para_METEOR']).mean() + np.array(eval_score['para_CIDEr']).mean() + np.array(eval_score['para_Bleu_4']).mean()
-
-            # add to tf summary
-            for key in eval_score.keys():
-                tf_writer.add_scalar(key, np.array(eval_score[key]).mean(), iteration)
-
-            # Huabin comment this part for avoiding reporting losses during evaluation
-            # for loss_type in eval_loss.keys():
-            #     tf_writer.add_scalar('eval_' + loss_type, eval_loss[loss_type], iteration)
-
-            _ = [item.append(np.array(item).mean()) for item in eval_score.values() if isinstance(item, list)]
-            print_info = '\n'.join([key + ":" + str(eval_score[key]) for key in eval_score.keys()])
-            logger.info('\nValidation results of iter {}:\n'.format(iteration) + print_info)
-            logger.info('\noverall score of iter {}: {}\n'.format(iteration, current_score))
-            val_result_history[epoch] = {'eval_score': eval_score}
-            logger.info('Save model at iter {} to {}.'.format(iteration, checkpoint_path))
-
-            # save the model parameter and  of best epoch
-            if current_score >= best_val_score:
-                best_val_score = current_score
-                best_epoch = epoch
-                saved_info['best'] = {'opt': vars(opt),
-                                      'iter': iteration,
-                                      'epoch': best_epoch,
-                                      'best_val_score': best_val_score,
-                                      'result_json_path': result_json_path,
-                                      'avg_proposal_num': eval_score['avg_proposal_number'],
-                                      'Precision': eval_score['Precision'],
-                                      'Recall': eval_score['Recall']
-                                      }
-
-                # suffix = "RL" if sc_flag else "CE"
-                torch.save(saved_pth, os.path.join(save_folder, 'model-best.pth'))
-                logger.info('Save Best-model at iter {} to checkpoint file.'.format(iteration))
-
-            saved_info['last'] = {'opt': vars(opt),
-                                  'iter': iteration,
-                                  'epoch': epoch,
-                                  'best_val_score': best_val_score,
-                                  }
-            saved_info['history'] = {'val_result_history': val_result_history,
-                                     'loss_history': loss_history,
-                                     'lr_history': lr_history,
-                                     # 'query_matched_fre_hist': query_matched_fre_hist,
-                                     }
-            with open(os.path.join(save_folder, 'info.json'), 'w') as f:
-                json.dump(saved_info, f)
-            logger.info('Save info to info.json')
-
-            model.train()
-
-        epoch += 1
-        lr_scheduler.step()
-        opt.current_lr = optimizer.param_groups[0]['lr']
-        torch.cuda.empty_cache()
-        # Stop criterion
-        if epoch >= opt.epoch:
-            # load Best model and conduct evaluation
-            print('====== Conduct the Final Evaluation to test Best Checkpoint ======')
-            val_logger = create_logger(save_folder, 'val.log')
-            loaded_pth = torch.load(os.path.join(save_folder, 'model-best.pth'), map_location='cuda')
-            model.load_state_dict(loaded_pth['model'], strict=True)
-            model.eval()
-            result_json_path = saved_info['best']['result_json_path']
-            eval_score, _ = evaluate(model, criterion,  postprocessors, val_loader, result_json_path, logger=logger, args=opt, alpha=opt.ec_alpha, device=opt.device, debug=opt.debug)
-            if opt.caption_decoder_type == 'none':
-                current_score = 2./(1./eval_score['Precision'] + 1./eval_score['Recall'])
-            else:
-                if opt.criteria_for_best_ckpt == 'dvc':
-                    current_score = np.array(eval_score['METEOR']).mean() + np.array(eval_score['soda_c']).mean()
-                else:
-                    current_score = np.array(eval_score['para_METEOR']).mean() + np.array(eval_score['para_CIDEr']).mean() + np.array(eval_score['para_Bleu_4']).mean()
-
-            _ = [item.append(np.array(item).mean()) for item in eval_score.values() if isinstance(item, list)]
-            print_info = '\n'.join([key + ":" + str(eval_score[key]) for key in eval_score.keys()])
-            val_logger.info('Best-model is saved at iter {}.\n'.format(saved_info['best']['iter']))
-            val_logger.info('\nBest Model Performance:\n' + print_info)
-            val_logger.info('\nBest Overall Score {}: {}\n'.format(iteration, current_score))
-
-            tf_writer.close()
-            break
-
-    return saved_info
-
-
-if __name__ == '__main__':
-    opt = opts.parse_opts()
-    opt.id = 'seq2-ft'
-
-    if not hasattr(opt, 'visual_feature_folder_val'):
-        opt.visual_feature_folder_val = opt.visual_feature_folder
-        opt.text_feature_folder_val = opt.text_feature_folder
-
-    if opt.map:
-        opt.visual_feature_folder = [map_path(path) for path in opt.visual_feature_folder]
-        opt.text_feature_folder = [map_path(path) for path in opt.text_feature_folder]
-        opt.visual_feature_folder_val = [map_path(path) for path in opt.visual_feature_folder_val]
-        opt.text_feature_folder_val = [map_path(path) for path in opt.text_feature_folder_val]
-
-    if opt.gpu_id:
-        os.environ["CUDA_VISIBLE_DEVICES"] = ",".join([str(i) for i in opt.gpu_id])
-    if opt.disable_cudnn:
-        torch.backends.cudnn.enabled = False
-
-    os.environ['KMP_DUPLICATE_LIB_OK'] = 'True' # to avoid OMP problem on macos
-    # breakpoint()
-    train(opt)
-
diff --git a/backup/train_ft2_gt.py b/backup/train_ft2_gt.py
deleted file mode 100644
index b767f5c2525ed10b6551ba02a5551bafe0f1737e..0000000000000000000000000000000000000000
--- a/backup/train_ft2_gt.py
+++ /dev/null
@@ -1,588 +0,0 @@
-# coding:utf-8
-
-''' 
-similar to train_ft_gt.py. it fine-tunes the model on the target dataset with ground-truth annotations. but the pretrain data includes both pretrain and target data (only use captions)
-
-set pretrain_data_mode to 'single', it is same as train_ft_gt.py.
-
-使用全部的howto subset数据进行pretrain， 然后用部分的gt数据进行fine-tune
-'''
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import json
-import time
-import torch
-import os
-import sys
-import collections
-import numpy as np
-from tqdm import tqdm
-import torch.optim as optim
-from torch.utils.data import DataLoader
-from os.path import dirname, abspath
-import re 
-
-pdvc_dir = dirname(abspath(__file__))
-sys.path.insert(0, pdvc_dir)
-sys.path.insert(0, os.path.join(pdvc_dir, 'densevid_eval3'))
-sys.path.insert(0, os.path.join(pdvc_dir, 'densevid_eval3/SODA'))
-# print(sys.path)
-
-
-os.environ["TOKENIZERS_PARALLELISM"] = "false" # To avoid warning of tokenizer
-from eval_utils import evaluate
-import opts
-from tensorboardX import SummaryWriter
-from misc.utils import print_alert_message, build_folder, create_logger, backup_envir, print_opt, set_seed
-from data.video_dataset import PropSeqDataset, collate_fn, PercentageSubsetDataset
-from pdvc.pdvc import build
-from collections import OrderedDict
-from transformers import AutoTokenizer, get_linear_schedule_with_warmup, get_cosine_schedule_with_warmup
-import copy
-
-a100_folder = ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/Tasty/features', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Tasty/UniVL_feature', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features']
-r3090_folder = ['/mnt/data/Gvlab/wuhao/features/yc2', '/mnt/data/Gvlab/wuhao/features/tasty', '/mnt/data/Gvlab/wuhao/features/tasty/univl', '/mnt/data/Gvlab/wuhao/features/anet', '/mnt/data/Gvlab/wuhao/features/howto100m']
-
-pretrain_data_mode = 'mix' # 'mix' or 'seq' or 'single'
-
-# /cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features -> /mnt/data/Gvlab/wuhao/features/howto100m
-# /cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip -> /mnt/data/Gvlab/wuhao/features/howto100m/clip_features
-# /cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL -> /mnt/data/Gvlab/wuhao/features/howto100m/univl_features
-
-def _init_fn(worker_id):
-    np.random.seed(12 + worker_id)
-
-
-def map_path(path):
-    path_backup = copy.deepcopy(path)
-    # breakpoint()
-    for i, folder in enumerate(a100_folder):
-        if folder in path:
-            path = path.replace(folder, r3090_folder[i])
-            
-            
-    if path == path_backup:
-        if path.startswith('/mnt/data'):
-            pass
-        else:
-            # path = '/mnt' + path[6:]
-            print('map failed')
-            exit(1)
-    return path
-
-
-def train(opt):
-    set_seed(opt.seed)
-    save_folder = build_folder(opt)
-    opt.epoch = 20
-    opt.use_pseudo_box = False
-    opt.refine_pseudo_box = False
-    opt.pseudo_box_aug = False
-    # breakpoint()
-
-    # breakpoint()
-    if 'howto-tasty_tasty' in save_folder:
-        if pretrain_data_mode == 'mix':
-            checkpoint_folder = re.sub(r"_seq2-ft.*", "", save_folder)
-        elif pretrain_data_mode == 'seq':
-            checkpoint_folder = re.sub(r"_seq2-ft.*", "_seq-train", save_folder) # .replace('_seq2-ft', '')
-        elif pretrain_data_mode == 'single':
-            checkpoint_folder = re.sub(r"_seq2-ft.*", "", save_folder.replace('howto-tasty_tasty', 'howto_tasty')) # .replace('_seq2-ft', '')
-    elif 'howto-yc2_yc2' in save_folder:
-        if pretrain_data_mode == 'mix':
-            checkpoint_folder = re.sub(r"_seq2-ft.*", "", save_folder)
-        elif pretrain_data_mode == 'seq':
-            checkpoint_folder = re.sub(r"_seq2-ft.*", "_seq-train", save_folder)
-        elif pretrain_data_mode == 'single':
-            checkpoint_folder = re.sub(r"_seq2-ft.*", "", save_folder.replace('howto-yc2_yc2', 'howto_yc2')) # .replace('_seq2-ft', '')
-    elif 'vlep-yc2_yc2' in save_folder:
-        if pretrain_data_mode == 'mix':
-            checkpoint_folder = re.sub(r"_seq2-ft.*", "", save_folder)
-        elif pretrain_data_mode == 'seq':
-            checkpoint_folder = re.sub(r"_seq2-ft.*", "_seq-train", save_folder)
-        elif pretrain_data_mode == 'single':
-            checkpoint_folder = re.sub(r"_seq2-ft.*", "", save_folder.replace('vlep-yc2_yc2', 'vlep_yc2')) # .replace('_seq2-ft', '')
-    elif 'howto-anet_anet' in save_folder:
-        if pretrain_data_mode == 'mix':
-            checkpoint_folder = re.sub(r"_seq2-ft.*", "", save_folder)
-        elif pretrain_data_mode == 'seq':
-            checkpoint_folder = re.sub(r"_seq2-ft.*", "_seq-train", save_folder)
-        elif pretrain_data_mode == 'single':
-            checkpoint_folder = re.sub(r"_seq2-ft.*", "", save_folder.replace('howto-anet_anet', 'howto_anet'))
-    
-    else:
-        print('the script only support settings howto-XXX_XXX')
-        exit(1)
-    # breakpoint()
-
-    if opt.id_ori != '':
-        checkpoint_folder = checkpoint_folder + '_' + opt.id_ori
-    # breakpoint()
-    # if opt.id == "":
-    #     pass
-    # else:
-    #     checkpoint_folder = checkpoint_folder + '_' + opt.id
-
-    if not os.path.exists(checkpoint_folder) and not os.path.exists(checkpoint_folder + '_es20'):
-        print('the checkpoint folder {} does not exist'.format(checkpoint_folder))
-        exit(1)
-    else:
-        if not os.path.exists(os.path.join(checkpoint_folder, 'val.log')):
-            # print('the checkpoint folder has no val.log, denoting the setting is not fully trained')
-            for i in range(1, 100):
-                if os.path.exists(f'{checkpoint_folder}_{i}'):
-                    if os.path.exists(os.path.join(f'{checkpoint_folder}_{i}', 'val.log')):
-                        checkpoint_folder = f'{checkpoint_folder}_{i}'
-                        break
-                    else:
-                        continue
-                else:
-                    print(f'{checkpoint_folder}_{i} does not exist')
-                    print('the checkpoint folder does not exist')
-                    exit(1)
-
-    logger = create_logger(save_folder, 'train.log')
-    tf_writer = SummaryWriter(os.path.join(save_folder, 'tf_summary'))
-
-    if not opt.start_from:
-        backup_envir(save_folder, opt)
-        logger.info('backup evironment completed !')
-
-    saved_info = {'best': {}, 'last': {}, 'history': {}, 'eval_history': {}}
-
-    # # continue training
-    # if opt.start_from:
-    #     opt.pretrain = False
-    #     infos_path = os.path.join(save_folder, 'info.json')
-    #     with open(infos_path) as f:
-    #         logger.info('Load info from {}'.format(infos_path))
-    #         saved_info = json.load(f)
-    #         prev_opt = saved_info[opt.start_from_mode[:4]]['opt']
-
-    #         exclude_opt = ['start_from', 'start_from_mode', 'pretrain']
-    #         for opt_name in prev_opt.keys():
-    #             if opt_name not in exclude_opt:
-    #                 vars(opt).update({opt_name: prev_opt.get(opt_name)})
-    #             if prev_opt.get(opt_name) != vars(opt).get(opt_name):
-    #                 logger.info('Change opt {} : {} --> {}'.format(opt_name, prev_opt.get(opt_name),
-    #                                                                vars(opt).get(opt_name)))
-    if len(opt.visual_feature_folder) == 2:
-        # train_dataset_pretrain = PropSeqDataset(opt.train_caption_file[0],
-        #                                     [opt.visual_feature_folder[0]],
-        #                                     [opt.text_feature_folder[0]],
-        #                                     opt.dict_file, True, 'gt',
-        #                                     opt)
-        train_dataset_target = PropSeqDataset(opt.train_caption_file[1],
-                                            [opt.visual_feature_folder[1]],
-                                            [opt.text_feature_folder[1]],
-                                            opt.dict_file, True, 'gt',
-                                            opt)
-        subset_data = PercentageSubsetDataset(train_dataset_target, opt.ft_gt_percent)
-        # train_loader_pretrain = DataLoader(train_dataset_pretrain, batch_size=opt.batch_size,
-        #                       shuffle=True, num_workers=opt.nthreads, collate_fn=collate_fn, worker_init_fn=_init_fn)
-        train_loader_target = DataLoader(subset_data, batch_size=opt.batch_size,
-                                    shuffle=True, num_workers=opt.nthreads, collate_fn=collate_fn, worker_init_fn=_init_fn)
-        
-        # train_dataloaders = [train_loader_pretrain, train_loader_target]
-        # train_dataset = torch.utils.data.ConcatDataset([train_dataset_1, train_dataset_2])
-        # train_dataset.translator = train_dataset_1.translator
-
-    else:
-        # print('the script only support two dataset for pretrain and target task respectively')
-        # exit(1)
-        train_dataset_target = PropSeqDataset(opt.train_caption_file,
-                                    opt.visual_feature_folder,
-                                    opt.text_feature_folder,
-                                    opt.dict_file, True, 'gt',
-                                    opt)
-        subset_data = PercentageSubsetDataset(train_dataset_target, opt.ft_gt_percent)
-        train_loader_target = DataLoader(subset_data, batch_size=opt.batch_size,
-                                shuffle=True, num_workers=opt.nthreads, collate_fn=collate_fn, worker_init_fn=_init_fn)
-        # train_dataloaders = [train_loader_target]
-
-    # val_dataset = PropSeqDataset(opt.val_caption_file,
-    #                              opt.visual_feature_folder,
-    #                              opt.text_feature_folder,
-    #                              opt.dict_file, False, 'gt',
-    #                              opt)
-    if not hasattr(opt, 'dict_file_val'):
-        opt.dict_file_val = opt.dict_file
-        opt.vocab_size_val = opt.vocab_size
-
-    val_dataset = PropSeqDataset(opt.val_caption_file,
-                                opt.visual_feature_folder_val,
-                                opt.text_feature_folder_val,
-                                opt.dict_file, False, 'gt',
-                                opt)
-
-
-    val_loader = DataLoader(val_dataset, batch_size=opt.batch_size_for_eval,
-                            shuffle=False, num_workers=opt.nthreads, collate_fn=collate_fn, worker_init_fn=_init_fn)
-
-    epoch = saved_info[opt.start_from_mode[:4]].get('epoch', 0)
-    iteration = saved_info[opt.start_from_mode[:4]].get('iter', 0)
-    best_val_score = saved_info[opt.start_from_mode[:4]].get('best_val_score', -1e5)
-    val_result_history = saved_info['history'].get('val_result_history', {})
-    loss_history = saved_info['history'].get('loss_history', {})
-    lr_history = saved_info['history'].get('lr_history', {})
-    opt.current_lr = vars(opt).get('current_lr', opt.lr)
-
-    # Build model
-
-    model, criterion, contrastive_criterion, postprocessors = build(opt)
-    model.translator = train_dataset_target.translator
-    model.train()
-
-
-    # load pretrained model
-
-    # breakpoint()
-    # load pretrained model
-    model_pth = torch.load(os.path.join(checkpoint_folder, 'model-best.pth'))
-    logger.info('Loading pth from {}'.format(checkpoint_folder))
-    model.load_state_dict(model_pth['model'])
-    
-
-    # # Recover the parameters
-    # if opt.start_from and (not opt.pretrain):
-    #     if opt.start_from_mode == 'best':
-    #         model_pth = torch.load(os.path.join(save_folder, 'model-best.pth'))
-    #     elif opt.start_from_mode == 'last':
-    #         model_pth = torch.load(os.path.join(save_folder, 'model-last.pth'))
-    #     logger.info('Loading pth from {}, iteration:{}'.format(save_folder, iteration))
-    #     model.load_state_dict(model_pth['model'])
-
-    # # Load the pre-trained model
-    # if opt.pretrain and (not opt.start_from):
-    #     logger.info('Load pre-trained parameters from {}'.format(opt.pretrain_path))
-    #     model_pth = torch.load(opt.pretrain_path, map_location=torch.device(opt.device))
-    #     # query_weight = model_pth['model'].pop('query_embed.weight')
-    #     if opt.pretrain == 'encoder':
-    #         encoder_filter = model.get_filter_rule_for_encoder()
-    #         encoder_pth = {k:v for k,v in model_pth['model'].items() if encoder_filter(k)}
-    #         model.load_state_dict(encoder_pth, strict=True)
-    #     elif opt.pretrain == 'decoder':
-    #         encoder_filter = model.get_filter_rule_for_encoder()
-    #         decoder_pth = {k:v for k,v in model_pth['model'].items() if not encoder_filter(k)}
-    #         model.load_state_dict(decoder_pth, strict=True)
-    #         pass
-    #     elif opt.pretrain == 'full':
-    #         # model_pth = transfer(model, model_pth)
-    #         model.load_state_dict(model_pth['model'], strict=True)
-    #     else:
-    #         raise ValueError("wrong value of opt.pretrain")
-        
-
-    model.to(opt.device)
-
-    # Decide which parameters need to be trained
-    # if (opt.matcher_type =='DTW' or opt.use_pseudo_box) and opt.text_encoder_learning_strategy == 'frozen':
-    #     for _, p in model.text_encoder.named_parameters():
-    #         p.requires_grad = False
-    #         text_encoder_params = list(map(id, model.text_encoder.parameters()))
-    #         other_params = filter(lambda p: id(p) not in text_encoder_params, model.parameters())
-    # else:
-    #     other_params = model.parameters()
-    other_params = model.parameters()
-
-    training_params = [{'params': other_params, 'lr': opt.lr * 0.5}]
-
-    if opt.optimizer_type == 'adam':
-        optimizer = optim.Adam(training_params, weight_decay=opt.weight_decay)
-
-    elif opt.optimizer_type == 'adamw':
-        optimizer = optim.AdamW(training_params, weight_decay=opt.weight_decay)
-
-    milestone = [opt.learning_rate_decay_start + opt.learning_rate_decay_every * _ for _ in range(int((opt.epoch - opt.learning_rate_decay_start) / opt.learning_rate_decay_every))]
-    lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestone, gamma=opt.learning_rate_decay_rate)
-
-    # Load tokenizer for text encoder
-    # for i in range(10):
-    #     try:
-    #         if opt.pretrained_language_model == 'UniVL':
-    #             tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
-    #         else:
-    #             tokenizer = AutoTokenizer.from_pretrained(opt.pretrained_language_model)
-    #         break
-    #     except:
-    #         print('download error in AutoTokenizer, retry...')
-    #         time.sleep(1)
-
-    # if opt.start_from:
-    optimizer.load_state_dict(model_pth['optimizer'])
-        # lr_scheduler.step(epoch-1)
-
-    # print the args for debugging  
-    print_opt(opt, model, logger)
-    print_alert_message('Strat training !', logger)
-
-    loss_sum = OrderedDict()
-    bad_video_num = 0
-
-    start = time.time()
-
-    weight_dict = criterion.weight_dict
-    logger.info('loss type: {}'.format(weight_dict.keys()))
-    logger.info('loss weights: {}'.format(weight_dict.values()))
-
-    # breakpoint()
-
-    # Epoch-level iteration
-    # opt.use_pseudo_box = False
-
-    while True:
-        if True:
-            # scheduled sampling rate update
-            if epoch > opt.scheduled_sampling_start >= 0:
-                frac = (epoch - opt.scheduled_sampling_start) // opt.scheduled_sampling_increase_every
-                opt.ss_prob = min(opt.basic_ss_prob + opt.scheduled_sampling_increase_prob * frac,
-                                  opt.scheduled_sampling_max_prob)
-                model.caption_head.ss_prob = opt.ss_prob
-
-            print('lr:{}'.format(float(opt.current_lr)))
-            pass
-
-        # breakpoint()
-        # Batch-level iteration
-        # for train_loader in train_dataloaders:
-        trained_samples = 0
-        for dt in tqdm(train_loader_target, disable=opt.disable_tqdm):
-            # # # for fast debugging
-            # if trained_samples > 5:
-            #     break
-            # else:
-            #     trained_samples += 1
-            if opt.device=='cuda':
-                torch.cuda.synchronize(opt.device)
-            if opt.debug:
-                # each epoch contains less mini-batches for debugging
-                if (iteration + 1) % 5 == 0:
-                    iteration += 1
-                    break
-            iteration += 1
-
-            optimizer.zero_grad()
-            dt = {key: _.to(opt.device) if isinstance(_, torch.Tensor) else _ for key, _ in dt.items()}
-            dt['video_target'] = [
-                {key: _.to(opt.device) if isinstance(_, torch.Tensor) else _ for key, _ in vid_info.items()} for vid_info in
-                dt['video_target']]
-
-            # Add text encoder
-            # if opt.matcher_type == 'DTW' or opt.use_pseudo_box:
-            #     captions = list()
-            #     for video_sents in dt['cap_raw']:  # dt['cap_raw']: [[sent_1, sent_2, ..., sent_n]]
-            #         captions.extend(video_sents) 
-            #     text_encoder_input = tokenizer(captions, return_tensors='pt', truncation=True, padding=True, max_length=opt.max_text_input_len)
-            #     text_encoder_input = {key: _.to(opt.device) if isinstance(_, torch.Tensor) else _ for key, _ in text_encoder_input.items()} 
-            #     # text_encoder_input: {'input_ids': tensor([[  101,  1996,  2307,  ...,     0,     0,     0],...]),  'attention_mask': tensor([[1, 1, 1,  ..., 0, 0, 0],...])}
-            #     # len(text_encoder_input['input_ids']) = n * max_text_input_len
-            #     dt['text_encoder_input'] = text_encoder_input
-
-            # dt = collections.defaultdict(lambda: None, dt) # Commented to 
-
-            output, loss = model(dt, criterion, contrastive_criterion)
-            final_loss = sum(loss[k] * weight_dict[k] for k in loss.keys() if k in weight_dict)
-            # breakpoint()
-            final_loss.backward()
-            torch.nn.utils.clip_grad_norm_(model.parameters(), opt.grad_clip)
-
-            optimizer.step()
-
-            for loss_k,loss_v in loss.items():
-                loss_sum[loss_k] = loss_sum.get(loss_k, 0)+ loss_v.item()
-            loss_sum['total_loss'] = loss_sum.get('total_loss', 0) + final_loss.item()
-
-            if opt.device=='cuda':
-                torch.cuda.synchronize()
-
-            losses_log_every = int(len(train_loader_target) / 10)
-
-            if opt.debug:
-                losses_log_every = 6
-
-            if iteration % losses_log_every == 0:
-                end = time.time()
-                for k in loss_sum.keys():
-                    loss_sum[k] = np.round(loss_sum[k] /losses_log_every, 3).item()
-
-                logger.info(
-                    "ID {} iter {} (epoch {}), \nloss = {}, \ntime/iter = {:.3f}, bad_vid = {:.3f}"
-                        .format(opt.id, iteration, epoch, loss_sum,
-                                (end - start) / losses_log_every, bad_video_num))
-
-                tf_writer.add_scalar('lr', opt.current_lr, iteration)
-                for loss_type in loss_sum.keys():
-                    tf_writer.add_scalar(loss_type, loss_sum[loss_type], iteration)
-                loss_history[iteration] = loss_sum
-                lr_history[iteration] = opt.current_lr
-                loss_sum = OrderedDict()
-                start = time.time()
-                bad_video_num = 0
-                torch.cuda.empty_cache()
-
-        # evaluation
-        if (epoch % opt.save_checkpoint_every == 0) and (epoch >= opt.min_epoch_when_save):
-            
-            # Save model
-            saved_pth = {'epoch': epoch,
-                         'model': model.state_dict(),
-                         'optimizer': optimizer.state_dict()}
-
-            if opt.save_all_checkpoint:
-                checkpoint_path = os.path.join(save_folder, 'model_iter_{}.pth'.format(iteration))
-            else:
-                checkpoint_path = os.path.join(save_folder, 'model-last.pth')
-
-            torch.save(saved_pth, checkpoint_path)
-
-            model.eval()
-            result_json_path = os.path.join(save_folder, 'prediction',
-                                         'num{}_epoch{}.json'.format(
-                                             len(val_dataset), epoch))
-            #eval_score, eval_loss = evaluate(model, criterion,  postprocessors, val_loader, result_json_path, logger=logger, args=opt, alpha=opt.ec_alpha, device=opt.device, debug=opt.debug)
-            eval_score, _ = evaluate(model, criterion,  postprocessors, val_loader, result_json_path, logger=logger, args=opt, alpha=opt.ec_alpha, device=opt.device, debug=opt.debug)
-            if opt.caption_decoder_type == 'none':
-                current_score = 2./(1./eval_score['Precision'] + 1./eval_score['Recall'])
-            else:
-                if opt.criteria_for_best_ckpt == 'dvc':
-                    current_score = np.array(eval_score['METEOR']).mean() + np.array(eval_score['soda_c']).mean()
-                else:
-                    current_score = np.array(eval_score['para_METEOR']).mean() + np.array(eval_score['para_CIDEr']).mean() + np.array(eval_score['para_Bleu_4']).mean()
-
-            # add to tf summary
-            for key in eval_score.keys():
-                tf_writer.add_scalar(key, np.array(eval_score[key]).mean(), iteration)
-
-            # Huabin comment this part for avoiding reporting losses during evaluation
-            # for loss_type in eval_loss.keys():
-            #     tf_writer.add_scalar('eval_' + loss_type, eval_loss[loss_type], iteration)
-
-            _ = [item.append(np.array(item).mean()) for item in eval_score.values() if isinstance(item, list)]
-            print_info = '\n'.join([key + ":" + str(eval_score[key]) for key in eval_score.keys()])
-            logger.info('\nValidation results of iter {}:\n'.format(iteration) + print_info)
-            logger.info('\noverall score of iter {}: {}\n'.format(iteration, current_score))
-            val_result_history[epoch] = {'eval_score': eval_score}
-            logger.info('Save model at iter {} to {}.'.format(iteration, checkpoint_path))
-
-            # save the model parameter and  of best epoch
-            if current_score >= best_val_score:
-                best_val_score = current_score
-                best_epoch = epoch
-                saved_info['best'] = {'opt': vars(opt),
-                                      'iter': iteration,
-                                      'epoch': best_epoch,
-                                      'best_val_score': best_val_score,
-                                      'result_json_path': result_json_path,
-                                      'avg_proposal_num': eval_score['avg_proposal_number'],
-                                      'Precision': eval_score['Precision'],
-                                      'Recall': eval_score['Recall']
-                                      }
-
-                # suffix = "RL" if sc_flag else "CE"
-                torch.save(saved_pth, os.path.join(save_folder, 'model-best.pth'))
-                logger.info('Save Best-model at iter {} to checkpoint file.'.format(iteration))
-
-            saved_info['last'] = {'opt': vars(opt),
-                                  'iter': iteration,
-                                  'epoch': epoch,
-                                  'best_val_score': best_val_score,
-                                  }
-            saved_info['history'] = {'val_result_history': val_result_history,
-                                     'loss_history': loss_history,
-                                     'lr_history': lr_history,
-                                     # 'query_matched_fre_hist': query_matched_fre_hist,
-                                     }
-            with open(os.path.join(save_folder, 'info.json'), 'w') as f:
-                json.dump(saved_info, f)
-            logger.info('Save info to info.json')
-
-            model.train()
-
-        epoch += 1
-        lr_scheduler.step()
-        opt.current_lr = optimizer.param_groups[0]['lr']
-        torch.cuda.empty_cache()
-        # Stop criterion
-        if epoch >= opt.epoch:
-            # # load Best model and conduct evaluation
-            # print('====== Conduct the Final Evaluation to test Best Checkpoint ======')
-            # val_logger = create_logger(save_folder, 'val.log')
-            # loaded_pth = torch.load(os.path.join(save_folder, 'model-best.pth'), map_location='cuda')
-            # model.load_state_dict(loaded_pth['model'], strict=True)
-            # model.eval()
-            # result_json_path = saved_info['best']['result_json_path']
-            # eval_score, _ = evaluate(model, criterion,  postprocessors, val_loader, result_json_path, logger=logger, args=opt, alpha=opt.ec_alpha, device=opt.device, debug=opt.debug)
-            # if opt.caption_decoder_type == 'none':
-            #     current_score = 2./(1./eval_score['Precision'] + 1./eval_score['Recall'])
-            # else:
-            #     if opt.criteria_for_best_ckpt == 'dvc':
-            #         current_score = np.array(eval_score['METEOR']).mean() + np.array(eval_score['soda_c']).mean()
-            #     else:
-            #         current_score = np.array(eval_score['para_METEOR']).mean() + np.array(eval_score['para_CIDEr']).mean() + np.array(eval_score['para_Bleu_4']).mean()
-
-            # _ = [item.append(np.array(item).mean()) for item in eval_score.values() if isinstance(item, list)]
-            # print_info = '\n'.join([key + ":" + str(eval_score[key]) for key in eval_score.keys()])
-            # val_logger.info('Best-model is saved at iter {}.\n'.format(saved_info['best']['iter']))
-            # val_logger.info('\nBest Model Performance:\n' + print_info)
-            # val_logger.info('\nBest Overall Score {}: {}\n'.format(iteration, current_score))
-
-            # tf_writer.close()
-            # break
-
-            val_logger = create_logger(save_folder, 'val.log')
-            infos_path = os.path.join(save_folder, 'info.json')
-
-            with open(infos_path, 'r') as f:
-                data = json.load(f)
-            val_history = data['history']['val_result_history']
-
-            metric_sum = {}
-            metrics = ['METEOR', 'CIDEr', 'soda_c', 'Precision', 'Recall']
-            for k, v in val_history.items():
-                metric_sum[k] = sum([v['eval_score'][metric] for metric in metrics])
-                # print(f"{k}: {metric_sum[k]}")
-
-            best_epoch = max(metric_sum, key=metric_sum.get)
-            best_val_score = val_history[best_epoch]['eval_score']
-            val_logger.info(f"Best epoch: {best_epoch}")
-            print_info = '\n'.join([key + ":" + str(best_val_score[key]) for key in best_val_score.keys()])
-            val_logger.info('\nBest Model Performance:\n' + print_info)
-            val_logger.info('\nBest Overall Score epoch{}: {}\n'.format(best_epoch, metric_sum[best_epoch]))
-
-            break 
-
-    return saved_info
-
-
-if __name__ == '__main__':
-    opt = opts.parse_opts()
-    opt.id_ori = opt.id
-    
-    
-    opt.id = 'seq2-ft({})-gt_percent-{}'.format(pretrain_data_mode, opt.ft_gt_percent)
-    if opt.id_ori != '':
-        opt.id = opt.id + '_' + opt.id_ori
-    assert opt.ft_gt_percent <= 1.0 and opt.ft_gt_percent >= 0.0
-
-
-    if not hasattr(opt, 'visual_feature_folder_val'):
-        opt.visual_feature_folder_val = opt.visual_feature_folder
-        opt.text_feature_folder_val = opt.text_feature_folder
-
-    if opt.map:
-        opt.visual_feature_folder = [map_path(path) for path in opt.visual_feature_folder]
-        opt.text_feature_folder = [map_path(path) for path in opt.text_feature_folder]
-        opt.visual_feature_folder_val = [map_path(path) for path in opt.visual_feature_folder_val]
-        opt.text_feature_folder_val = [map_path(path) for path in opt.text_feature_folder_val]
-
-    if opt.gpu_id:
-        os.environ["CUDA_VISIBLE_DEVICES"] = ",".join([str(i) for i in opt.gpu_id])
-    if opt.disable_cudnn:
-        torch.backends.cudnn.enabled = False
-
-    os.environ['KMP_DUPLICATE_LIB_OK'] = 'True' # to avoid OMP problem on macos
-    # breakpoint()
-    train(opt)
-
diff --git a/backup/train_ft_gt.py b/backup/train_ft_gt.py
deleted file mode 100644
index b481c6eb9a19299b401fbe8ce82d10716a846a7c..0000000000000000000000000000000000000000
--- a/backup/train_ft_gt.py
+++ /dev/null
@@ -1,516 +0,0 @@
-# coding:utf-8
-
-''' 
-train_seq2.py is different from train_seq.py in the following aspects:
-
-1. train_seq2.py uses the same dataset for pretraining and target task
-2. the pretrain dataset and target dataset is not trained one after another in a single epoch. train pretrain dataset for 10 epochs then train target dataset for 20 epochs
-3. the vocabulary is always the same for pretrain and target task i.e. combined vocabulary of pretrain and target task
-4. checkpoint is located in save howto_yc2_* or howto_tasty_* 
-5. cfg use howto-tasty_tasty_* or howto-yc2_yc2_*
-'''
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import json
-import time
-import torch
-import os
-import sys
-import collections
-import numpy as np
-from tqdm import tqdm
-import torch.optim as optim
-from torch.utils.data import DataLoader
-from os.path import dirname, abspath
-import re 
-
-pdvc_dir = dirname(abspath(__file__))
-sys.path.insert(0, pdvc_dir)
-sys.path.insert(0, os.path.join(pdvc_dir, 'densevid_eval3'))
-sys.path.insert(0, os.path.join(pdvc_dir, 'densevid_eval3/SODA'))
-# print(sys.path)
-
-
-os.environ["TOKENIZERS_PARALLELISM"] = "false" # To avoid warning of tokenizer
-from eval_utils import evaluate
-import opts
-from tensorboardX import SummaryWriter
-from misc.utils import print_alert_message, build_floder, create_logger, backup_envir, print_opt, set_seed
-from data.video_dataset import PropSeqDataset, collate_fn, PercentageSubsetDataset
-from pdvc.pdvc import build
-from collections import OrderedDict
-from transformers import AutoTokenizer, get_linear_schedule_with_warmup, get_cosine_schedule_with_warmup
-import copy
-
-a100_folder = ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/Tasty/features', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Tasty/UniVL_feature', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features']
-r3090_folder = ['/mnt/data/Gvlab/wuhao/features/yc2', '/mnt/data/Gvlab/wuhao/features/tasty', '/mnt/data/Gvlab/wuhao/features/tasty/univl', '/mnt/data/Gvlab/wuhao/features/anet', '/mnt/data/Gvlab/wuhao/features/howto100m']
-
-# /cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features -> /mnt/data/Gvlab/wuhao/features/howto100m
-# /cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip -> /mnt/data/Gvlab/wuhao/features/howto100m/clip_features
-# /cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL -> /mnt/data/Gvlab/wuhao/features/howto100m/univl_features
-
-def _init_fn(worker_id):
-    np.random.seed(12 + worker_id)
-
-def map_path(path):
-    path_backup = copy.deepcopy(path)
-    # breakpoint()
-    for i, folder in enumerate(a100_folder):
-        if folder in path:
-            path = path.replace(folder, r3090_folder[i])
-            return path
-    if path == path_backup:
-        print('map failed')
-        exit(1)
-
-
-def train(opt):
-    set_seed(opt.seed)
-    save_folder = build_floder(opt)
-    opt.epoch = 20
-    opt.use_pseudo_box = False
-
-    # breakpoint()
-    if 'howto-tasty_tasty' in save_folder:
-        checkpoint_folder = re.sub(r"_seq2-ft.*", "", save_folder.replace('howto-tasty_tasty', 'howto_tasty')) # .replace('_seq2-ft', '')
-    elif 'howto-yc2_yc2' in save_folder:
-        checkpoint_folder = re.sub(r"_seq2-ft.*", "", save_folder.replace('howto-yc2_yc2', 'howto_yc2')) # .replace('_seq2-ft', '')
-    else:
-        print('the script only support settings howto-XXX_XXX')
-        exit(1)
-
-    if not os.path.exists(checkpoint_folder):
-        print('the checkpoint folder {} does not exist'.format(checkpoint_folder))
-        exit(1)
-    else:
-        if not os.path.exists(os.path.join(checkpoint_folder, 'val.log')):
-            # print('the checkpoint folder has no val.log, denoting the setting is not fully trained')
-            for i in range(1, 100):
-                if os.path.exists(f'{checkpoint_folder}_{i}'):
-                    if os.path.exists(os.path.join(f'{checkpoint_folder}_{i}', 'val.log')):
-                        checkpoint_folder = f'{checkpoint_folder}_{i}'
-                        break
-                    else:
-                        continue
-                else:
-                    print(f'{checkpoint_folder}_{i} does not exist')
-                    print('the checkpoint folder does not exist')
-                    exit(1)
-
-    logger = create_logger(save_folder, 'train.log')
-    tf_writer = SummaryWriter(os.path.join(save_folder, 'tf_summary'))
-
-    if not opt.start_from:
-        backup_envir(save_folder)
-        logger.info('backup evironment completed !')
-
-    saved_info = {'best': {}, 'last': {}, 'history': {}, 'eval_history': {}}
-
-    # # continue training
-    # if opt.start_from:
-    #     opt.pretrain = False
-    #     infos_path = os.path.join(save_folder, 'info.json')
-    #     with open(infos_path) as f:
-    #         logger.info('Load info from {}'.format(infos_path))
-    #         saved_info = json.load(f)
-    #         prev_opt = saved_info[opt.start_from_mode[:4]]['opt']
-
-    #         exclude_opt = ['start_from', 'start_from_mode', 'pretrain']
-    #         for opt_name in prev_opt.keys():
-    #             if opt_name not in exclude_opt:
-    #                 vars(opt).update({opt_name: prev_opt.get(opt_name)})
-    #             if prev_opt.get(opt_name) != vars(opt).get(opt_name):
-    #                 logger.info('Change opt {} : {} --> {}'.format(opt_name, prev_opt.get(opt_name),
-    #                                                                vars(opt).get(opt_name)))
-    if len(opt.visual_feature_folder) == 2:
-        # train_dataset_pretrain = PropSeqDataset(opt.train_caption_file[0],
-        #                                     [opt.visual_feature_folder[0]],
-        #                                     [opt.text_feature_folder[0]],
-        #                                     opt.dict_file, True, 'gt',
-        #                                     opt)
-        train_dataset_target = PropSeqDataset(opt.train_caption_file[1],
-                                            [opt.visual_feature_folder[1]],
-                                            [opt.text_feature_folder[1]],
-                                            opt.dict_file, True, 'gt',
-                                            opt)
-        subset_data = PercentageSubsetDataset(train_dataset_target, opt.ft_gt_percent)
-        # train_loader_pretrain = DataLoader(train_dataset_pretrain, batch_size=opt.batch_size,
-        #                       shuffle=True, num_workers=opt.nthreads, collate_fn=collate_fn, worker_init_fn=_init_fn)
-        train_loader_target = DataLoader(subset_data, batch_size=opt.batch_size,
-                                    shuffle=True, num_workers=opt.nthreads, collate_fn=collate_fn, worker_init_fn=_init_fn)
-        
-        # train_dataloaders = [train_loader_pretrain, train_loader_target]
-        # train_dataset = torch.utils.data.ConcatDataset([train_dataset_1, train_dataset_2])
-        # train_dataset.translator = train_dataset_1.translator
-
-    else:
-        print('the script only support two dataset for pretrain and target task respectively')
-        exit(1)
-        train_dataset_target = PropSeqDataset(opt.train_caption_file,
-                                    opt.visual_feature_folder,
-                                    opt.text_feature_folder,
-                                    opt.dict_file, True, 'gt',
-                                    opt)
-        train_loader_target = DataLoader(train_dataset_target, batch_size=opt.batch_size,
-                                shuffle=True, num_workers=opt.nthreads, collate_fn=collate_fn, worker_init_fn=_init_fn)
-        train_dataloaders = [train_loader_target]
-
-    # val_dataset = PropSeqDataset(opt.val_caption_file,
-    #                              opt.visual_feature_folder,
-    #                              opt.text_feature_folder,
-    #                              opt.dict_file, False, 'gt',
-    #                              opt)
-    if not hasattr(opt, 'dict_file_val'):
-        opt.dict_file_val = opt.dict_file
-        opt.vocab_size_val = opt.vocab_size
-
-    val_dataset = PropSeqDataset(opt.val_caption_file,
-                                opt.visual_feature_folder_val,
-                                opt.text_feature_folder_val,
-                                opt.dict_file, False, 'gt',
-                                opt)
-
-
-    val_loader = DataLoader(val_dataset, batch_size=opt.batch_size_for_eval,
-                            shuffle=False, num_workers=opt.nthreads, collate_fn=collate_fn, worker_init_fn=_init_fn)
-
-    epoch = saved_info[opt.start_from_mode[:4]].get('epoch', 0)
-    iteration = saved_info[opt.start_from_mode[:4]].get('iter', 0)
-    best_val_score = saved_info[opt.start_from_mode[:4]].get('best_val_score', -1e5)
-    val_result_history = saved_info['history'].get('val_result_history', {})
-    loss_history = saved_info['history'].get('loss_history', {})
-    lr_history = saved_info['history'].get('lr_history', {})
-    opt.current_lr = vars(opt).get('current_lr', opt.lr)
-
-    # Build model
-
-    model, criterion, contrastive_criterion, postprocessors = build(opt)
-    model.translator = train_dataset_target.translator
-    model.train()
-
-
-    # load pretrained model
-
-    # breakpoint()
-    # load pretrained model
-    model_pth = torch.load(os.path.join(checkpoint_folder, 'model-best.pth'))
-    logger.info('Loading pth from {}'.format(checkpoint_folder))
-    model.load_state_dict(model_pth['model'])
-    
-
-    # # Recover the parameters
-    # if opt.start_from and (not opt.pretrain):
-    #     if opt.start_from_mode == 'best':
-    #         model_pth = torch.load(os.path.join(save_folder, 'model-best.pth'))
-    #     elif opt.start_from_mode == 'last':
-    #         model_pth = torch.load(os.path.join(save_folder, 'model-last.pth'))
-    #     logger.info('Loading pth from {}, iteration:{}'.format(save_folder, iteration))
-    #     model.load_state_dict(model_pth['model'])
-
-    # # Load the pre-trained model
-    # if opt.pretrain and (not opt.start_from):
-    #     logger.info('Load pre-trained parameters from {}'.format(opt.pretrain_path))
-    #     model_pth = torch.load(opt.pretrain_path, map_location=torch.device(opt.device))
-    #     # query_weight = model_pth['model'].pop('query_embed.weight')
-    #     if opt.pretrain == 'encoder':
-    #         encoder_filter = model.get_filter_rule_for_encoder()
-    #         encoder_pth = {k:v for k,v in model_pth['model'].items() if encoder_filter(k)}
-    #         model.load_state_dict(encoder_pth, strict=True)
-    #     elif opt.pretrain == 'decoder':
-    #         encoder_filter = model.get_filter_rule_for_encoder()
-    #         decoder_pth = {k:v for k,v in model_pth['model'].items() if not encoder_filter(k)}
-    #         model.load_state_dict(decoder_pth, strict=True)
-    #         pass
-    #     elif opt.pretrain == 'full':
-    #         # model_pth = transfer(model, model_pth)
-    #         model.load_state_dict(model_pth['model'], strict=True)
-    #     else:
-    #         raise ValueError("wrong value of opt.pretrain")
-        
-
-    model.to(opt.device)
-
-    # Decide which parameters need to be trained
-    # if (opt.matcher_type =='DTW' or opt.use_pseudo_box) and opt.text_encoder_learning_strategy == 'frozen':
-    #     for _, p in model.text_encoder.named_parameters():
-    #         p.requires_grad = False
-    #         text_encoder_params = list(map(id, model.text_encoder.parameters()))
-    #         other_params = filter(lambda p: id(p) not in text_encoder_params, model.parameters())
-    # else:
-    #     other_params = model.parameters()
-    other_params = model.parameters()
-
-    training_params = [{'params': other_params, 'lr': opt.lr * 0.5}]
-
-    if opt.optimizer_type == 'adam':
-        optimizer = optim.Adam(training_params, weight_decay=opt.weight_decay)
-
-    elif opt.optimizer_type == 'adamw':
-        optimizer = optim.AdamW(training_params, weight_decay=opt.weight_decay)
-
-    milestone = [opt.learning_rate_decay_start + opt.learning_rate_decay_every * _ for _ in range(int((opt.epoch - opt.learning_rate_decay_start) / opt.learning_rate_decay_every))]
-    lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestone, gamma=opt.learning_rate_decay_rate)
-
-    # Load tokenizer for text encoder
-    # for i in range(10):
-    #     try:
-    #         if opt.pretrained_language_model == 'UniVL':
-    #             tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
-    #         else:
-    #             tokenizer = AutoTokenizer.from_pretrained(opt.pretrained_language_model)
-    #         break
-    #     except:
-    #         print('download error in AutoTokenizer, retry...')
-    #         time.sleep(1)
-
-    # if opt.start_from:
-    optimizer.load_state_dict(model_pth['optimizer'])
-        # lr_scheduler.step(epoch-1)
-
-    # print the args for debugging  
-    print_opt(opt, model, logger)
-    print_alert_message('Strat training !', logger)
-
-    loss_sum = OrderedDict()
-    bad_video_num = 0
-
-    start = time.time()
-
-    weight_dict = criterion.weight_dict
-    logger.info('loss type: {}'.format(weight_dict.keys()))
-    logger.info('loss weights: {}'.format(weight_dict.values()))
-
-    # breakpoint()
-
-    # Epoch-level iteration
-    # opt.use_pseudo_box = False
-
-    while True:
-        if True:
-            # scheduled sampling rate update
-            if epoch > opt.scheduled_sampling_start >= 0:
-                frac = (epoch - opt.scheduled_sampling_start) // opt.scheduled_sampling_increase_every
-                opt.ss_prob = min(opt.basic_ss_prob + opt.scheduled_sampling_increase_prob * frac,
-                                  opt.scheduled_sampling_max_prob)
-                model.caption_head.ss_prob = opt.ss_prob
-
-            print('lr:{}'.format(float(opt.current_lr)))
-            pass
-
-        # breakpoint()
-        # Batch-level iteration
-        # for train_loader in train_dataloaders:
-        trained_samples = 0
-        for dt in tqdm(train_loader_target, disable=opt.disable_tqdm):
-            # # for fast debugging
-            # if trained_samples > 5:
-            #     break
-            # else:
-            #     trained_samples += 1
-            if opt.device=='cuda':
-                torch.cuda.synchronize(opt.device)
-            if opt.debug:
-                # each epoch contains less mini-batches for debugging
-                if (iteration + 1) % 5 == 0:
-                    iteration += 1
-                    break
-            iteration += 1
-
-            optimizer.zero_grad()
-            dt = {key: _.to(opt.device) if isinstance(_, torch.Tensor) else _ for key, _ in dt.items()}
-            dt['video_target'] = [
-                {key: _.to(opt.device) if isinstance(_, torch.Tensor) else _ for key, _ in vid_info.items()} for vid_info in
-                dt['video_target']]
-
-            # Add text encoder
-            # if opt.matcher_type == 'DTW' or opt.use_pseudo_box:
-            #     captions = list()
-            #     for video_sents in dt['cap_raw']:  # dt['cap_raw']: [[sent_1, sent_2, ..., sent_n]]
-            #         captions.extend(video_sents) 
-            #     text_encoder_input = tokenizer(captions, return_tensors='pt', truncation=True, padding=True, max_length=opt.max_text_input_len)
-            #     text_encoder_input = {key: _.to(opt.device) if isinstance(_, torch.Tensor) else _ for key, _ in text_encoder_input.items()} 
-            #     # text_encoder_input: {'input_ids': tensor([[  101,  1996,  2307,  ...,     0,     0,     0],...]),  'attention_mask': tensor([[1, 1, 1,  ..., 0, 0, 0],...])}
-            #     # len(text_encoder_input['input_ids']) = n * max_text_input_len
-            #     dt['text_encoder_input'] = text_encoder_input
-
-            # dt = collections.defaultdict(lambda: None, dt) # Commented to 
-
-            output, loss = model(dt, criterion, contrastive_criterion)
-            final_loss = sum(loss[k] * weight_dict[k] for k in loss.keys() if k in weight_dict)
-            # breakpoint()
-            final_loss.backward()
-            torch.nn.utils.clip_grad_norm_(model.parameters(), opt.grad_clip)
-
-            optimizer.step()
-
-            for loss_k,loss_v in loss.items():
-                loss_sum[loss_k] = loss_sum.get(loss_k, 0)+ loss_v.item()
-            loss_sum['total_loss'] = loss_sum.get('total_loss', 0) + final_loss.item()
-
-            if opt.device=='cuda':
-                torch.cuda.synchronize()
-
-            losses_log_every = int(len(train_loader_target) / 10)
-
-            if opt.debug:
-                losses_log_every = 6
-
-            if iteration % losses_log_every == 0:
-                end = time.time()
-                for k in loss_sum.keys():
-                    loss_sum[k] = np.round(loss_sum[k] /losses_log_every, 3).item()
-
-                logger.info(
-                    "ID {} iter {} (epoch {}), \nloss = {}, \ntime/iter = {:.3f}, bad_vid = {:.3f}"
-                        .format(opt.id, iteration, epoch, loss_sum,
-                                (end - start) / losses_log_every, bad_video_num))
-
-                tf_writer.add_scalar('lr', opt.current_lr, iteration)
-                for loss_type in loss_sum.keys():
-                    tf_writer.add_scalar(loss_type, loss_sum[loss_type], iteration)
-                loss_history[iteration] = loss_sum
-                lr_history[iteration] = opt.current_lr
-                loss_sum = OrderedDict()
-                start = time.time()
-                bad_video_num = 0
-                torch.cuda.empty_cache()
-
-        # evaluation
-        if (epoch % opt.save_checkpoint_every == 0) and (epoch >= opt.min_epoch_when_save):
-            
-            # Save model
-            saved_pth = {'epoch': epoch,
-                         'model': model.state_dict(),
-                         'optimizer': optimizer.state_dict()}
-
-            if opt.save_all_checkpoint:
-                checkpoint_path = os.path.join(save_folder, 'model_iter_{}.pth'.format(iteration))
-            else:
-                checkpoint_path = os.path.join(save_folder, 'model-last.pth')
-
-            torch.save(saved_pth, checkpoint_path)
-
-            model.eval()
-            result_json_path = os.path.join(save_folder, 'prediction',
-                                         'num{}_epoch{}.json'.format(
-                                             len(val_dataset), epoch))
-            #eval_score, eval_loss = evaluate(model, criterion,  postprocessors, val_loader, result_json_path, logger=logger, args=opt, alpha=opt.ec_alpha, device=opt.device, debug=opt.debug)
-            eval_score, _ = evaluate(model, criterion,  postprocessors, val_loader, result_json_path, logger=logger, args=opt, alpha=opt.ec_alpha, device=opt.device, debug=opt.debug)
-            if opt.caption_decoder_type == 'none':
-                current_score = 2./(1./eval_score['Precision'] + 1./eval_score['Recall'])
-            else:
-                if opt.criteria_for_best_ckpt == 'dvc':
-                    current_score = np.array(eval_score['METEOR']).mean() + np.array(eval_score['soda_c']).mean()
-                else:
-                    current_score = np.array(eval_score['para_METEOR']).mean() + np.array(eval_score['para_CIDEr']).mean() + np.array(eval_score['para_Bleu_4']).mean()
-
-            # add to tf summary
-            for key in eval_score.keys():
-                tf_writer.add_scalar(key, np.array(eval_score[key]).mean(), iteration)
-
-            # Huabin comment this part for avoiding reporting losses during evaluation
-            # for loss_type in eval_loss.keys():
-            #     tf_writer.add_scalar('eval_' + loss_type, eval_loss[loss_type], iteration)
-
-            _ = [item.append(np.array(item).mean()) for item in eval_score.values() if isinstance(item, list)]
-            print_info = '\n'.join([key + ":" + str(eval_score[key]) for key in eval_score.keys()])
-            logger.info('\nValidation results of iter {}:\n'.format(iteration) + print_info)
-            logger.info('\noverall score of iter {}: {}\n'.format(iteration, current_score))
-            val_result_history[epoch] = {'eval_score': eval_score}
-            logger.info('Save model at iter {} to {}.'.format(iteration, checkpoint_path))
-
-            # save the model parameter and  of best epoch
-            if current_score >= best_val_score:
-                best_val_score = current_score
-                best_epoch = epoch
-                saved_info['best'] = {'opt': vars(opt),
-                                      'iter': iteration,
-                                      'epoch': best_epoch,
-                                      'best_val_score': best_val_score,
-                                      'result_json_path': result_json_path,
-                                      'avg_proposal_num': eval_score['avg_proposal_number'],
-                                      'Precision': eval_score['Precision'],
-                                      'Recall': eval_score['Recall']
-                                      }
-
-                # suffix = "RL" if sc_flag else "CE"
-                torch.save(saved_pth, os.path.join(save_folder, 'model-best.pth'))
-                logger.info('Save Best-model at iter {} to checkpoint file.'.format(iteration))
-
-            saved_info['last'] = {'opt': vars(opt),
-                                  'iter': iteration,
-                                  'epoch': epoch,
-                                  'best_val_score': best_val_score,
-                                  }
-            saved_info['history'] = {'val_result_history': val_result_history,
-                                     'loss_history': loss_history,
-                                     'lr_history': lr_history,
-                                     # 'query_matched_fre_hist': query_matched_fre_hist,
-                                     }
-            with open(os.path.join(save_folder, 'info.json'), 'w') as f:
-                json.dump(saved_info, f)
-            logger.info('Save info to info.json')
-
-            model.train()
-
-        epoch += 1
-        lr_scheduler.step()
-        opt.current_lr = optimizer.param_groups[0]['lr']
-        torch.cuda.empty_cache()
-        # Stop criterion
-        if epoch >= opt.epoch:
-            # load Best model and conduct evaluation
-            print('====== Conduct the Final Evaluation to test Best Checkpoint ======')
-            val_logger = create_logger(save_folder, 'val.log')
-            loaded_pth = torch.load(os.path.join(save_folder, 'model-best.pth'), map_location='cuda')
-            model.load_state_dict(loaded_pth['model'], strict=True)
-            model.eval()
-            result_json_path = saved_info['best']['result_json_path']
-            eval_score, _ = evaluate(model, criterion,  postprocessors, val_loader, result_json_path, logger=logger, args=opt, alpha=opt.ec_alpha, device=opt.device, debug=opt.debug)
-            if opt.caption_decoder_type == 'none':
-                current_score = 2./(1./eval_score['Precision'] + 1./eval_score['Recall'])
-            else:
-                if opt.criteria_for_best_ckpt == 'dvc':
-                    current_score = np.array(eval_score['METEOR']).mean() + np.array(eval_score['soda_c']).mean()
-                else:
-                    current_score = np.array(eval_score['para_METEOR']).mean() + np.array(eval_score['para_CIDEr']).mean() + np.array(eval_score['para_Bleu_4']).mean()
-
-            _ = [item.append(np.array(item).mean()) for item in eval_score.values() if isinstance(item, list)]
-            print_info = '\n'.join([key + ":" + str(eval_score[key]) for key in eval_score.keys()])
-            val_logger.info('Best-model is saved at iter {}.\n'.format(saved_info['best']['iter']))
-            val_logger.info('\nBest Model Performance:\n' + print_info)
-            val_logger.info('\nBest Overall Score {}: {}\n'.format(iteration, current_score))
-
-            tf_writer.close()
-            break
-
-    return saved_info
-
-
-if __name__ == '__main__':
-    opt = opts.parse_opts()
-    opt.id = 'seq2-ft-gt_percent-{}'.format(opt.ft_gt_percent)
-    assert opt.ft_gt_percent <= 1.0 and opt.ft_gt_percent >= 0.0
-
-
-    if not hasattr(opt, 'visual_feature_folder_val'):
-        opt.visual_feature_folder_val = opt.visual_feature_folder
-        opt.text_feature_folder_val = opt.text_feature_folder
-
-    if opt.map:
-        opt.visual_feature_folder = [map_path(path) for path in opt.visual_feature_folder]
-        opt.text_feature_folder = [map_path(path) for path in opt.text_feature_folder]
-        opt.visual_feature_folder_val = [map_path(path) for path in opt.visual_feature_folder_val]
-        opt.text_feature_folder_val = [map_path(path) for path in opt.text_feature_folder_val]
-
-    if opt.gpu_id:
-        os.environ["CUDA_VISIBLE_DEVICES"] = ",".join([str(i) for i in opt.gpu_id])
-    if opt.disable_cudnn:
-        torch.backends.cudnn.enabled = False
-
-    os.environ['KMP_DUPLICATE_LIB_OK'] = 'True' # to avoid OMP problem on macos
-    # breakpoint()
-    train(opt)
-
diff --git a/backup/train_pre_ft_gt.py b/backup/train_pre_ft_gt.py
deleted file mode 100644
index 9440eb8b4b86d2123a997285686e704425519a3f..0000000000000000000000000000000000000000
--- a/backup/train_pre_ft_gt.py
+++ /dev/null
@@ -1,537 +0,0 @@
-# coding:utf-8
-
-''' 
-similar to train_ft_gt.py. it fine-tunes the model on the target dataset with ground-truth annotations. but the pretrain data includes both pretrain and target data (only use captions)
-
-set pretrain_data_mode to 'single', it is same as train_ft_gt.py.
-
-
-'''
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import json
-import time
-import torch
-import os
-import sys
-import collections
-import numpy as np
-from tqdm import tqdm
-import torch.optim as optim
-from torch.utils.data import DataLoader
-from os.path import dirname, abspath
-import re 
-
-pdvc_dir = dirname(abspath(__file__))
-sys.path.insert(0, pdvc_dir)
-sys.path.insert(0, os.path.join(pdvc_dir, 'densevid_eval3'))
-sys.path.insert(0, os.path.join(pdvc_dir, 'densevid_eval3/SODA'))
-# print(sys.path)
-
-
-os.environ["TOKENIZERS_PARALLELISM"] = "false" # To avoid warning of tokenizer
-from eval_utils import evaluate
-import opts
-from tensorboardX import SummaryWriter
-from misc.utils import print_alert_message, build_folder, create_logger, backup_envir, print_opt, set_seed
-from data.video_dataset import PropSeqDataset, collate_fn, PercentageSubsetDataset
-from pdvc.pdvc import build
-from collections import OrderedDict
-from transformers import AutoTokenizer, get_linear_schedule_with_warmup, get_cosine_schedule_with_warmup
-import copy
-
-a100_folder = ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/Tasty/features', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Tasty/UniVL_feature', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features']
-r3090_folder = ['/mnt/data/Gvlab/wuhao/features/yc2', '/mnt/data/Gvlab/wuhao/features/tasty', '/mnt/data/Gvlab/wuhao/features/tasty/univl', '/mnt/data/Gvlab/wuhao/features/anet', '/mnt/data/Gvlab/wuhao/features/howto100m']
-
-pretrain_data_mode = 'mix' # 'mix' or 'seq' or 'single'
-
-# /cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features -> /mnt/data/Gvlab/wuhao/features/howto100m
-# /cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip -> /mnt/data/Gvlab/wuhao/features/howto100m/clip_features
-# /cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL -> /mnt/data/Gvlab/wuhao/features/howto100m/univl_features
-
-def _init_fn(worker_id):
-    np.random.seed(12 + worker_id)
-
-def map_path(path):
-    path_backup = copy.deepcopy(path)
-    # breakpoint()
-    for i, folder in enumerate(a100_folder):
-        if folder in path:
-            path = path.replace(folder, r3090_folder[i])
-            return path
-    if path == path_backup:
-        print('map failed')
-        exit(1)
-
-
-def train(opt):
-    set_seed(opt.seed)
-    save_folder = build_folder(opt)
-    opt.epoch = 20
-    opt.use_pseudo_box = False
-    opt.refine_pseudo_box = False
-    opt.pseudo_box_aug = False
-
-    # breakpoint()
-    if 'howto-tasty_tasty' in save_folder:
-        if pretrain_data_mode == 'mix':
-            checkpoint_folder = re.sub(r"_seq2-pre.*", "_seq-pre_perc-{}".format(opt.pre_percent), save_folder)
-        # elif pretrain_data_mode == 'seq':
-        #     checkpoint_folder = re.sub(r"_seq2-pre.*", "_seq-train", save_folder) # .replace('_seq2-ft', '')
-        elif pretrain_data_mode == 'single':
-            checkpoint_folder = re.sub(r"_seq2-pre.*", "_seq-pre_perc-{}".format(opt.pre_percent), save_folder.replace('howto-tasty_tasty', 'howto_tasty')) # .replace('_seq2-ft', '')
-    elif 'howto-yc2_yc2' in save_folder:
-        if pretrain_data_mode == 'mix':
-            checkpoint_folder = re.sub(r"_seq2-pre.*", "_seq-pre_perc-{}".format(opt.pre_percent), save_folder)
-        # elif pretrain_data_mode == 'seq':
-        #     checkpoint_folder = re.sub(r"_seq2-ft.*", "_seq-train", save_folder)
-        elif pretrain_data_mode == 'single':
-            checkpoint_folder = re.sub(r"_seq2-pre.*", "_seq-pre_perc-{}".format(opt.pre_percent), save_folder.replace('howto-yc2_yc2', 'howto_yc2')) # .replace('_seq2-ft', '')
-    elif 'howto-anet_anet' in save_folder:
-        if pretrain_data_mode == 'mix':
-            checkpoint_folder = re.sub(r"_seq2-pre.*", "_seq-pre_perc-{}".format(opt.pre_percent), save_folder)
-        # elif pretrain_data_mode == 'seq':
-        #     checkpoint_folder = re.sub(r"_seq2-ft.*", "_seq-train", save_folder)
-        elif pretrain_data_mode == 'single':
-            checkpoint_folder = re.sub(r"_seq2-pre.*", "_seq-pre_perc-{}".format(opt.pre_percent), save_folder.replace('howto-anet_anet', 'howto_anet'))
-    else:
-        print('the script only support settings howto-XXX_XXX')
-        exit(1)
-
-    if not os.path.exists(checkpoint_folder) and not os.path.exists(checkpoint_folder + '_test'):
-        print('the checkpoint folder {} does not exist'.format(checkpoint_folder))
-        exit(1)
-    else:
-        if not os.path.exists(os.path.join(checkpoint_folder, 'val.log')):
-            # print('the checkpoint folder has no val.log, denoting the setting is not fully trained')
-            for i in range(1, 100):
-                if os.path.exists(f'{checkpoint_folder}_{i}'):
-                    if os.path.exists(os.path.join(f'{checkpoint_folder}_{i}', 'val.log')):
-                        checkpoint_folder = f'{checkpoint_folder}_{i}'
-                        break
-                    else:
-                        continue
-                else:
-                    print(f'{checkpoint_folder}_{i} does not exist')
-                    print('the checkpoint folder does not exist')
-                    exit(1)
-
-    logger = create_logger(save_folder, 'train.log')
-    tf_writer = SummaryWriter(os.path.join(save_folder, 'tf_summary'))
-
-    if not opt.start_from:
-        backup_envir(save_folder, opt)
-        logger.info('backup evironment completed !')
-
-    saved_info = {'best': {}, 'last': {}, 'history': {}, 'eval_history': {}}
-
-    # # continue training
-    # if opt.start_from:
-    #     opt.pretrain = False
-    #     infos_path = os.path.join(save_folder, 'info.json')
-    #     with open(infos_path) as f:
-    #         logger.info('Load info from {}'.format(infos_path))
-    #         saved_info = json.load(f)
-    #         prev_opt = saved_info[opt.start_from_mode[:4]]['opt']
-
-    #         exclude_opt = ['start_from', 'start_from_mode', 'pretrain']
-    #         for opt_name in prev_opt.keys():
-    #             if opt_name not in exclude_opt:
-    #                 vars(opt).update({opt_name: prev_opt.get(opt_name)})
-    #             if prev_opt.get(opt_name) != vars(opt).get(opt_name):
-    #                 logger.info('Change opt {} : {} --> {}'.format(opt_name, prev_opt.get(opt_name),
-    #                                                                vars(opt).get(opt_name)))
-    if len(opt.visual_feature_folder) == 2:
-        # train_dataset_pretrain = PropSeqDataset(opt.train_caption_file[0],
-        #                                     [opt.visual_feature_folder[0]],
-        #                                     [opt.text_feature_folder[0]],
-        #                                     opt.dict_file, True, 'gt',
-        #                                     opt)
-        train_dataset_target = PropSeqDataset(opt.train_caption_file[1],
-                                            [opt.visual_feature_folder[1]],
-                                            [opt.text_feature_folder[1]],
-                                            opt.dict_file, True, 'gt',
-                                            opt)
-        # subset_data = PercentageSubsetDataset(train_dataset_target, opt.ft_gt_percent)
-        # train_loader_pretrain = DataLoader(train_dataset_pretrain, batch_size=opt.batch_size,
-        #                       shuffle=True, num_workers=opt.nthreads, collate_fn=collate_fn, worker_init_fn=_init_fn)
-        train_loader_target = DataLoader(train_dataset_target, batch_size=opt.batch_size,
-                                    shuffle=True, num_workers=opt.nthreads, collate_fn=collate_fn, worker_init_fn=_init_fn)
-        
-        # train_dataloaders = [train_loader_pretrain, train_loader_target]
-        # train_dataset = torch.utils.data.ConcatDataset([train_dataset_1, train_dataset_2])
-        # train_dataset.translator = train_dataset_1.translator
-
-    else:
-        print('the script only support two dataset for pretrain and target task respectively')
-        exit(1)
-        train_dataset_target = PropSeqDataset(opt.train_caption_file,
-                                    opt.visual_feature_folder,
-                                    opt.text_feature_folder,
-                                    opt.dict_file, True, 'gt',
-                                    opt)
-        train_loader_target = DataLoader(train_dataset_target, batch_size=opt.batch_size,
-                                shuffle=True, num_workers=opt.nthreads, collate_fn=collate_fn, worker_init_fn=_init_fn)
-        train_dataloaders = [train_loader_target]
-
-    # val_dataset = PropSeqDataset(opt.val_caption_file,
-    #                              opt.visual_feature_folder,
-    #                              opt.text_feature_folder,
-    #                              opt.dict_file, False, 'gt',
-    #                              opt)
-    if not hasattr(opt, 'dict_file_val'):
-        opt.dict_file_val = opt.dict_file
-        opt.vocab_size_val = opt.vocab_size
-
-    val_dataset = PropSeqDataset(opt.val_caption_file,
-                                opt.visual_feature_folder_val,
-                                opt.text_feature_folder_val,
-                                opt.dict_file, False, 'gt',
-                                opt)
-
-
-    val_loader = DataLoader(val_dataset, batch_size=opt.batch_size_for_eval,
-                            shuffle=False, num_workers=opt.nthreads, collate_fn=collate_fn, worker_init_fn=_init_fn)
-
-    epoch = saved_info[opt.start_from_mode[:4]].get('epoch', 0)
-    iteration = saved_info[opt.start_from_mode[:4]].get('iter', 0)
-    best_val_score = saved_info[opt.start_from_mode[:4]].get('best_val_score', -1e5)
-    val_result_history = saved_info['history'].get('val_result_history', {})
-    loss_history = saved_info['history'].get('loss_history', {})
-    lr_history = saved_info['history'].get('lr_history', {})
-    opt.current_lr = vars(opt).get('current_lr', opt.lr)
-
-    # Build model
-    # breakpoint()
-
-    model, criterion, contrastive_criterion, postprocessors = build(opt)
-    model.translator = train_dataset_target.translator
-    model.train()
-
-
-    # load pretrained model
-
-    # breakpoint()
-    # load pretrained model
-    model_pth = torch.load(os.path.join(checkpoint_folder, 'model-best.pth'))
-    logger.info('Loading pth from {}'.format(checkpoint_folder))
-    model.load_state_dict(model_pth['model'])
-    
-
-    # # Recover the parameters
-    # if opt.start_from and (not opt.pretrain):
-    #     if opt.start_from_mode == 'best':
-    #         model_pth = torch.load(os.path.join(save_folder, 'model-best.pth'))
-    #     elif opt.start_from_mode == 'last':
-    #         model_pth = torch.load(os.path.join(save_folder, 'model-last.pth'))
-    #     logger.info('Loading pth from {}, iteration:{}'.format(save_folder, iteration))
-    #     model.load_state_dict(model_pth['model'])
-
-    # # Load the pre-trained model
-    # if opt.pretrain and (not opt.start_from):
-    #     logger.info('Load pre-trained parameters from {}'.format(opt.pretrain_path))
-    #     model_pth = torch.load(opt.pretrain_path, map_location=torch.device(opt.device))
-    #     # query_weight = model_pth['model'].pop('query_embed.weight')
-    #     if opt.pretrain == 'encoder':
-    #         encoder_filter = model.get_filter_rule_for_encoder()
-    #         encoder_pth = {k:v for k,v in model_pth['model'].items() if encoder_filter(k)}
-    #         model.load_state_dict(encoder_pth, strict=True)
-    #     elif opt.pretrain == 'decoder':
-    #         encoder_filter = model.get_filter_rule_for_encoder()
-    #         decoder_pth = {k:v for k,v in model_pth['model'].items() if not encoder_filter(k)}
-    #         model.load_state_dict(decoder_pth, strict=True)
-    #         pass
-    #     elif opt.pretrain == 'full':
-    #         # model_pth = transfer(model, model_pth)
-    #         model.load_state_dict(model_pth['model'], strict=True)
-    #     else:
-    #         raise ValueError("wrong value of opt.pretrain")
-        
-
-    model.to(opt.device)
-
-    # Decide which parameters need to be trained
-    # if (opt.matcher_type =='DTW' or opt.use_pseudo_box) and opt.text_encoder_learning_strategy == 'frozen':
-    #     for _, p in model.text_encoder.named_parameters():
-    #         p.requires_grad = False
-    #         text_encoder_params = list(map(id, model.text_encoder.parameters()))
-    #         other_params = filter(lambda p: id(p) not in text_encoder_params, model.parameters())
-    # else:
-    #     other_params = model.parameters()
-    other_params = model.parameters()
-
-    training_params = [{'params': other_params, 'lr': opt.lr * 0.5}]
-
-    if opt.optimizer_type == 'adam':
-        optimizer = optim.Adam(training_params, weight_decay=opt.weight_decay)
-
-    elif opt.optimizer_type == 'adamw':
-        optimizer = optim.AdamW(training_params, weight_decay=opt.weight_decay)
-
-    milestone = [opt.learning_rate_decay_start + opt.learning_rate_decay_every * _ for _ in range(int((opt.epoch - opt.learning_rate_decay_start) / opt.learning_rate_decay_every))]
-    lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestone, gamma=opt.learning_rate_decay_rate)
-
-    # Load tokenizer for text encoder
-    # for i in range(10):
-    #     try:
-    #         if opt.pretrained_language_model == 'UniVL':
-    #             tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
-    #         else:
-    #             tokenizer = AutoTokenizer.from_pretrained(opt.pretrained_language_model)
-    #         break
-    #     except:
-    #         print('download error in AutoTokenizer, retry...')
-    #         time.sleep(1)
-
-    # if opt.start_from:
-    optimizer.load_state_dict(model_pth['optimizer'])
-        # lr_scheduler.step(epoch-1)
-
-    # print the args for debugging  
-    print_opt(opt, model, logger)
-    print_alert_message('Strat training !', logger)
-
-    loss_sum = OrderedDict()
-    bad_video_num = 0
-
-    start = time.time()
-
-    weight_dict = criterion.weight_dict
-    logger.info('loss type: {}'.format(weight_dict.keys()))
-    logger.info('loss weights: {}'.format(weight_dict.values()))
-
-    # breakpoint()
-
-    # Epoch-level iteration
-    # opt.use_pseudo_box = False
-
-    while True:
-        if True:
-            # scheduled sampling rate update
-            if epoch > opt.scheduled_sampling_start >= 0:
-                frac = (epoch - opt.scheduled_sampling_start) // opt.scheduled_sampling_increase_every
-                opt.ss_prob = min(opt.basic_ss_prob + opt.scheduled_sampling_increase_prob * frac,
-                                  opt.scheduled_sampling_max_prob)
-                model.caption_head.ss_prob = opt.ss_prob
-
-            print('lr:{}'.format(float(opt.current_lr)))
-            pass
-
-        # breakpoint()
-        # Batch-level iteration
-        # for train_loader in train_dataloaders:
-        trained_samples = 0
-        for dt in tqdm(train_loader_target, disable=opt.disable_tqdm):
-            # # for fast debugging
-            # if trained_samples > 5:
-            #     break
-            # else:
-            #     trained_samples += 1
-            if opt.device=='cuda':
-                torch.cuda.synchronize(opt.device)
-            if opt.debug:
-                # each epoch contains less mini-batches for debugging
-                if (iteration + 1) % 5 == 0:
-                    iteration += 1
-                    break
-            iteration += 1
-
-            optimizer.zero_grad()
-            dt = {key: _.to(opt.device) if isinstance(_, torch.Tensor) else _ for key, _ in dt.items()}
-            dt['video_target'] = [
-                {key: _.to(opt.device) if isinstance(_, torch.Tensor) else _ for key, _ in vid_info.items()} for vid_info in
-                dt['video_target']]
-
-            # Add text encoder
-            # if opt.matcher_type == 'DTW' or opt.use_pseudo_box:
-            #     captions = list()
-            #     for video_sents in dt['cap_raw']:  # dt['cap_raw']: [[sent_1, sent_2, ..., sent_n]]
-            #         captions.extend(video_sents) 
-            #     text_encoder_input = tokenizer(captions, return_tensors='pt', truncation=True, padding=True, max_length=opt.max_text_input_len)
-            #     text_encoder_input = {key: _.to(opt.device) if isinstance(_, torch.Tensor) else _ for key, _ in text_encoder_input.items()} 
-            #     # text_encoder_input: {'input_ids': tensor([[  101,  1996,  2307,  ...,     0,     0,     0],...]),  'attention_mask': tensor([[1, 1, 1,  ..., 0, 0, 0],...])}
-            #     # len(text_encoder_input['input_ids']) = n * max_text_input_len
-            #     dt['text_encoder_input'] = text_encoder_input
-
-            # dt = collections.defaultdict(lambda: None, dt) # Commented to 
-
-            output, loss = model(dt, criterion, contrastive_criterion)
-            final_loss = sum(loss[k] * weight_dict[k] for k in loss.keys() if k in weight_dict)
-            # breakpoint()
-            final_loss.backward()
-            torch.nn.utils.clip_grad_norm_(model.parameters(), opt.grad_clip)
-
-            optimizer.step()
-
-            for loss_k,loss_v in loss.items():
-                loss_sum[loss_k] = loss_sum.get(loss_k, 0)+ loss_v.item()
-            loss_sum['total_loss'] = loss_sum.get('total_loss', 0) + final_loss.item()
-
-            if opt.device=='cuda':
-                torch.cuda.synchronize()
-
-            losses_log_every = int(len(train_loader_target) / 10)
-
-            if opt.debug:
-                losses_log_every = 6
-
-            if iteration % losses_log_every == 0:
-                end = time.time()
-                for k in loss_sum.keys():
-                    loss_sum[k] = np.round(loss_sum[k] /losses_log_every, 3).item()
-
-                logger.info(
-                    "ID {} iter {} (epoch {}), \nloss = {}, \ntime/iter = {:.3f}, bad_vid = {:.3f}"
-                        .format(opt.id, iteration, epoch, loss_sum,
-                                (end - start) / losses_log_every, bad_video_num))
-
-                tf_writer.add_scalar('lr', opt.current_lr, iteration)
-                for loss_type in loss_sum.keys():
-                    tf_writer.add_scalar(loss_type, loss_sum[loss_type], iteration)
-                loss_history[iteration] = loss_sum
-                lr_history[iteration] = opt.current_lr
-                loss_sum = OrderedDict()
-                start = time.time()
-                bad_video_num = 0
-                torch.cuda.empty_cache()
-
-        # evaluation
-        if (epoch % opt.save_checkpoint_every == 0) and (epoch >= opt.min_epoch_when_save):
-            
-            # Save model
-            saved_pth = {'epoch': epoch,
-                         'model': model.state_dict(),
-                         'optimizer': optimizer.state_dict()}
-
-            if opt.save_all_checkpoint:
-                checkpoint_path = os.path.join(save_folder, 'model_iter_{}.pth'.format(iteration))
-            else:
-                checkpoint_path = os.path.join(save_folder, 'model-last.pth')
-
-            torch.save(saved_pth, checkpoint_path)
-
-            model.eval()
-            result_json_path = os.path.join(save_folder, 'prediction',
-                                         'num{}_epoch{}.json'.format(
-                                             len(val_dataset), epoch))
-            #eval_score, eval_loss = evaluate(model, criterion,  postprocessors, val_loader, result_json_path, logger=logger, args=opt, alpha=opt.ec_alpha, device=opt.device, debug=opt.debug)
-            eval_score, _ = evaluate(model, criterion,  postprocessors, val_loader, result_json_path, logger=logger, args=opt, alpha=opt.ec_alpha, device=opt.device, debug=opt.debug)
-            if opt.caption_decoder_type == 'none':
-                current_score = 2./(1./eval_score['Precision'] + 1./eval_score['Recall'])
-            else:
-                if opt.criteria_for_best_ckpt == 'dvc':
-                    current_score = np.array(eval_score['METEOR']).mean() + np.array(eval_score['soda_c']).mean()
-                else:
-                    current_score = np.array(eval_score['para_METEOR']).mean() + np.array(eval_score['para_CIDEr']).mean() + np.array(eval_score['para_Bleu_4']).mean()
-
-            # add to tf summary
-            for key in eval_score.keys():
-                tf_writer.add_scalar(key, np.array(eval_score[key]).mean(), iteration)
-
-            # Huabin comment this part for avoiding reporting losses during evaluation
-            # for loss_type in eval_loss.keys():
-            #     tf_writer.add_scalar('eval_' + loss_type, eval_loss[loss_type], iteration)
-
-            _ = [item.append(np.array(item).mean()) for item in eval_score.values() if isinstance(item, list)]
-            print_info = '\n'.join([key + ":" + str(eval_score[key]) for key in eval_score.keys()])
-            logger.info('\nValidation results of iter {}:\n'.format(iteration) + print_info)
-            logger.info('\noverall score of iter {}: {}\n'.format(iteration, current_score))
-            val_result_history[epoch] = {'eval_score': eval_score}
-            logger.info('Save model at iter {} to {}.'.format(iteration, checkpoint_path))
-
-            # save the model parameter and  of best epoch
-            if current_score >= best_val_score:
-                best_val_score = current_score
-                best_epoch = epoch
-                saved_info['best'] = {'opt': vars(opt),
-                                      'iter': iteration,
-                                      'epoch': best_epoch,
-                                      'best_val_score': best_val_score,
-                                      'result_json_path': result_json_path,
-                                      'avg_proposal_num': eval_score['avg_proposal_number'],
-                                      'Precision': eval_score['Precision'],
-                                      'Recall': eval_score['Recall']
-                                      }
-
-                # suffix = "RL" if sc_flag else "CE"
-                torch.save(saved_pth, os.path.join(save_folder, 'model-best.pth'))
-                logger.info('Save Best-model at iter {} to checkpoint file.'.format(iteration))
-
-            saved_info['last'] = {'opt': vars(opt),
-                                  'iter': iteration,
-                                  'epoch': epoch,
-                                  'best_val_score': best_val_score,
-                                  }
-            saved_info['history'] = {'val_result_history': val_result_history,
-                                     'loss_history': loss_history,
-                                     'lr_history': lr_history,
-                                     # 'query_matched_fre_hist': query_matched_fre_hist,
-                                     }
-            with open(os.path.join(save_folder, 'info.json'), 'w') as f:
-                json.dump(saved_info, f)
-            logger.info('Save info to info.json')
-
-            model.train()
-
-        epoch += 1
-        lr_scheduler.step()
-        opt.current_lr = optimizer.param_groups[0]['lr']
-        torch.cuda.empty_cache()
-        # Stop criterion
-        if epoch >= opt.epoch:
-            # load Best model and conduct evaluation
-            print('====== Conduct the Final Evaluation to test Best Checkpoint ======')
-            val_logger = create_logger(save_folder, 'val.log')
-            loaded_pth = torch.load(os.path.join(save_folder, 'model-best.pth'), map_location='cuda')
-            model.load_state_dict(loaded_pth['model'], strict=True)
-            model.eval()
-            result_json_path = saved_info['best']['result_json_path']
-            eval_score, _ = evaluate(model, criterion,  postprocessors, val_loader, result_json_path, logger=logger, args=opt, alpha=opt.ec_alpha, device=opt.device, debug=opt.debug)
-            if opt.caption_decoder_type == 'none':
-                current_score = 2./(1./eval_score['Precision'] + 1./eval_score['Recall'])
-            else:
-                if opt.criteria_for_best_ckpt == 'dvc':
-                    current_score = np.array(eval_score['METEOR']).mean() + np.array(eval_score['soda_c']).mean()
-                else:
-                    current_score = np.array(eval_score['para_METEOR']).mean() + np.array(eval_score['para_CIDEr']).mean() + np.array(eval_score['para_Bleu_4']).mean()
-
-            _ = [item.append(np.array(item).mean()) for item in eval_score.values() if isinstance(item, list)]
-            print_info = '\n'.join([key + ":" + str(eval_score[key]) for key in eval_score.keys()])
-            val_logger.info('Best-model is saved at iter {}.\n'.format(saved_info['best']['iter']))
-            val_logger.info('\nBest Model Performance:\n' + print_info)
-            val_logger.info('\nBest Overall Score {}: {}\n'.format(iteration, current_score))
-
-            tf_writer.close()
-            break
-
-    return saved_info
-
-
-if __name__ == '__main__':
-    opt = opts.parse_opts()
-    
-    opt.id = 'seq2-pre-{}-ft({})-gt'.format(opt.pre_percent, pretrain_data_mode)
-    assert opt.pre_percent <= 1.0 and opt.pre_percent >= 0.0
-
-
-    if not hasattr(opt, 'visual_feature_folder_val'):
-        opt.visual_feature_folder_val = opt.visual_feature_folder
-        opt.text_feature_folder_val = opt.text_feature_folder
-
-    if opt.map:
-        opt.visual_feature_folder = [map_path(path) for path in opt.visual_feature_folder]
-        opt.text_feature_folder = [map_path(path) for path in opt.text_feature_folder]
-        opt.visual_feature_folder_val = [map_path(path) for path in opt.visual_feature_folder_val]
-        opt.text_feature_folder_val = [map_path(path) for path in opt.text_feature_folder_val]
-
-    if opt.gpu_id:
-        os.environ["CUDA_VISIBLE_DEVICES"] = ",".join([str(i) for i in opt.gpu_id])
-    if opt.disable_cudnn:
-        torch.backends.cudnn.enabled = False
-
-    os.environ['KMP_DUPLICATE_LIB_OK'] = 'True' # to avoid OMP problem on macos
-    # breakpoint()
-    train(opt)
-
diff --git a/backup/train_pre_perc.py b/backup/train_pre_perc.py
deleted file mode 100644
index 15f50480e382fc5704c5a6e019594b9478bcca11..0000000000000000000000000000000000000000
--- a/backup/train_pre_perc.py
+++ /dev/null
@@ -1,593 +0,0 @@
-# coding:utf-8
-'''
-cfgs is the same as train.py, but need add an extra argument: pre_percent
-recommend value: 0.1, 0.2, 0.4, 0.6, 0.8, 1
-'''
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import json
-import time
-import torch
-import os
-import sys
-import collections
-import numpy as np
-from tqdm import tqdm
-import torch.optim as optim
-from torch.utils.data import DataLoader
-from os.path import dirname, abspath
-
-pdvc_dir = dirname(abspath(__file__))
-sys.path.insert(0, pdvc_dir)
-sys.path.insert(0, os.path.join(pdvc_dir, 'densevid_eval3'))
-sys.path.insert(0, os.path.join(pdvc_dir, 'densevid_eval3/SODA'))
-# print(sys.path)
-CUDA_LAUNCH_BLOCKING=1
-
-os.environ["TOKENIZERS_PARALLELISM"] = "false" # To avoid warning of tokenizer
-from eval_utils import evaluate
-import opts
-from tensorboardX import SummaryWriter
-from misc.utils import print_alert_message, build_folder, create_logger, backup_envir, print_opt, set_seed
-from data.video_dataset import PropSeqDataset, collate_fn, PercentageSubsetDataset
-from pdvc.pdvc import build
-from collections import OrderedDict
-from transformers import AutoTokenizer, get_linear_schedule_with_warmup, get_cosine_schedule_with_warmup
-import copy
-import random
-import numpy as np 
-
-a100_folder = ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/Tasty/features', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Tasty/UniVL_feature', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features']
-r3090_folder = ['/mnt/data/Gvlab/wuhao/features/yc2', '/mnt/data/Gvlab/wuhao/features/tasty', '/mnt/data/Gvlab/wuhao/features/tasty/univl', '/mnt/data/Gvlab/wuhao/features/anet', '/mnt/data/Gvlab/wuhao/features/howto100m']
-
-# /cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features -> /mnt/data/Gvlab/wuhao/features/howto100m
-# /cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip -> /mnt/data/Gvlab/wuhao/features/howto100m/clip_features
-# /cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL -> /mnt/data/Gvlab/wuhao/features/howto100m/univl_features
-
-def construct_save_path(opt, save_folder="/mnt/data/pjlab-3090-sport/wuhao/code/dibs/pbox"):
-    elements = []
-    # breakpoint()    
-    if len(opt.train_caption_file) == 2:
-        if 'puyu' in opt.train_caption_file[0]:
-            elements.append('howto_puyu')
-        elif 'mixlm' in opt.train_caption_file[0]:
-            elements.append('howto_mixlm')
-        else:
-            elements.append('howto_llama2')
-        elements.append('howto')
-        if 'yc2' in opt.train_caption_file[1]:            
-            elements.append('yc2')
-        elif 'anet' in opt.train_caption_file[1]:
-            elements.append('anet')
-    else:
-        if 'yc2' in opt.train_caption_file:
-            elements.append('yc2')
-        elif 'anet' in opt.train_caption_file:
-            elements.append('anet')
-        elif 'howto' in opt.train_caption_file:
-            if 'puyu' in opt.train_caption_file:
-                elements.append('howto_puyu')
-            elif 'mixlm' in opt.train_caption_file:
-                elements.append('howto_mixlm')
-            else:
-                elements.append('howto_llama2')
-            # elements.append('howto')
-
-    if 'clip' in opt.visual_feature_folder[0] or 'CLIP' in opt.visual_feature_folder[0]:
-        elements.append('clip')
-    elif 'UniVL' in opt.visual_feature_folder[0] or 'univl' in opt.visual_feature_folder[0]:
-        elements.append('univl')
-    # add pbox parameters
-    pbox_type = "simop_v2" if opt.pseudo_box_type == "similarity_op_order_v2" else "simop"
-    elements.append(pbox_type)
-    elements.append(f"top{opt.top_frames}")
-    elements.append(f"r{opt.width_ratio}")
-    elements.append(f"iter{opt.iteration}")
-    elements.append(f"th{opt.width_th}")
-    return os.path.join(save_folder, '_'.join(elements) + '.json')
-
-def seed_worker(worker_id):
-    worker_seed = torch.initial_seed() % 2**32
-    np.random.seed(worker_seed)
-    random.seed(worker_seed)
-
-def map_path(path):
-    path_backup = copy.deepcopy(path)
-    # breakpoint()
-    for i, folder in enumerate(a100_folder):
-        if folder in path:
-            path = path.replace(folder, r3090_folder[i])
-            return path
-    if path == path_backup:
-        print('map failed')
-        exit(1)
-
-
-def train(opt):
-    set_seed(opt.seed)
-    save_folder = build_folder(opt)
-    logger = create_logger(save_folder, 'train.log')
-    tf_writer = SummaryWriter(os.path.join(save_folder, 'tf_summary'))
-
-    # if use mixlm model
-    saved_path = construct_save_path(opt)
-
-    if 'mixlm' in saved_path:
-        text_feature_folder_mixlm = os.path.join(save_folder, 'text_feature')
-        os.makedirs(text_feature_folder_mixlm, exist_ok=True)
-        if 'clip' in save_folder:
-            text_feature_folder_llama2 = map_path('/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip/text_proj')
-            text_feature_folder_puyu = '/mnt/data/Gvlab/wuhao/code/clip_frame_feature_extraction/features/howto100m/clip'
-        elif 'univl' in save_folder:
-            text_feature_folder_llama2 = '/mnt/data/Gvlab/wuhao/features/howto100m/univl_features'
-            text_feature_folder_puyu = '/mnt/data/Gvlab/wuhao/features/howto100m/univl_features'
-
-        if not os.path.exists(saved_path):        
-            llama2_pbox_path = saved_path.replace('mixlm', 'llama2')
-            puyu_pbox_path = saved_path.replace('mixlm', 'puyu')
-            with open(llama2_pbox_path, 'r') as f:
-                llama2_pbox = json.load(f)
-            with open(puyu_pbox_path, 'r') as f:
-                puyu_pbox = json.load(f)
-            
-            mixlm_pbox = {}
-            for video_key in llama2_pbox.keys():
-                if llama2_pbox[video_key] is None and puyu_pbox[video_key] is None:
-                    mixlm_pbox[video_key] = None
-                else:
-                    if llama2_pbox[video_key]['loss'] < puyu_pbox[video_key]['loss']:
-                        mixlm_pbox[video_key] = {'pbox': llama2_pbox[video_key]['pbox'], 'loss': llama2_pbox[video_key]['loss'], 'llm': 'llama2'}
-                    else:
-                        mixlm_pbox[video_key] = {'pbox': puyu_pbox[video_key]['pbox'], 'loss': puyu_pbox[video_key]['loss'], 'llm': 'puyu'}
-            with open(saved_path, 'w') as f:
-                json.dump(mixlm_pbox, f)
-        
-        with open(saved_path, 'r') as f:
-            mixlm_pbox = json.load(f)
-        for video_key in mixlm_pbox.keys():
-            if mixlm_pbox[video_key] is not None:
-                if mixlm_pbox[video_key]['llm'] == 'llama2':
-                    llama2_feature_path = os.path.join(text_feature_folder_llama2, video_key + '.npy')
-                    os.symlink(llama2_feature_path, os.path.join(text_feature_folder_mixlm, video_key + '.npy'))
-                else:
-                    puyu_feature_path = os.path.join(text_feature_folder_puyu, video_key + '.npy')
-                    os.symlink(puyu_feature_path, os.path.join(text_feature_folder_mixlm, video_key + '.npy'))
-        opt.text_feature_folder[0] = text_feature_folder_mixlm       
-
-    if not opt.start_from:
-        backup_envir(save_folder, opt)
-        logger.info('backup evironment completed !')
-
-    saved_info = {'best': {}, 'last': {}, 'history': {}, 'eval_history': {}}
-
-    # continue training
-    if opt.start_from:
-        opt.pretrain = False
-        infos_path = os.path.join(save_folder, 'info.json')
-        with open(infos_path) as f:
-            logger.info('Load info from {}'.format(infos_path))
-            saved_info = json.load(f)
-            prev_opt = saved_info[opt.start_from_mode[:4]]['opt']
-
-            exclude_opt = ['start_from', 'start_from_mode', 'pretrain']
-            for opt_name in prev_opt.keys():
-                if opt_name not in exclude_opt:
-                    vars(opt).update({opt_name: prev_opt.get(opt_name)})
-                if prev_opt.get(opt_name) != vars(opt).get(opt_name):
-                    logger.info('Change opt {} : {} --> {}'.format(opt_name, prev_opt.get(opt_name),
-                                                                   vars(opt).get(opt_name)))
-    if len(opt.visual_feature_folder) == 2:
-        train_dataset_1 = PropSeqDataset(opt.train_caption_file[0],
-                                            [opt.visual_feature_folder[0]],
-                                            [opt.text_feature_folder[0]],
-                                            opt.dict_file, True, 'gt',
-                                            opt)
-        train_dataset_subdata = PercentageSubsetDataset(train_dataset_1, opt.pre_percent)
-        train_dataset_2 = PropSeqDataset(opt.train_caption_file[1],
-                                            [opt.visual_feature_folder[1]],
-                                            [opt.text_feature_folder[1]],
-                                            opt.dict_file, True, 'gt',
-                                            opt)
-        train_dataset = torch.utils.data.ConcatDataset([train_dataset_subdata, train_dataset_2])
-        train_dataset.translator = train_dataset_1.translator
-
-    else:
-        train_dataset_all = PropSeqDataset(opt.train_caption_file,
-                                    opt.visual_feature_folder,
-                                    opt.text_feature_folder,
-                                    opt.dict_file, True, 'gt',
-                                    opt)
-        train_dataset = PercentageSubsetDataset(train_dataset_all, opt.pre_percent)
-
-    # val_dataset = PropSeqDataset(opt.val_caption_file,
-    #                              opt.visual_feature_folder,
-    #                              opt.text_feature_folder,
-    #                              opt.dict_file, False, 'gt',
-    #                              opt)
-    if not hasattr(opt, 'dict_file_val'):
-        opt.dict_file_val = opt.dict_file
-        opt.vocab_size_val = opt.vocab_size
-
-    val_dataset = PropSeqDataset(opt.val_caption_file,
-                                opt.visual_feature_folder_val,
-                                opt.text_feature_folder_val,
-                                opt.dict_file, False, 'gt',
-                                opt)
-    g = torch.Generator()
-    g.manual_seed(0)
-
-    train_loader = DataLoader(train_dataset, batch_size=opt.batch_size,
-                              shuffle=True, num_workers=opt.nthreads, collate_fn=collate_fn, worker_init_fn=seed_worker, generator=g)
-
-    val_loader = DataLoader(val_dataset, batch_size=opt.batch_size_for_eval,
-                            shuffle=False, num_workers=opt.nthreads, collate_fn=collate_fn, worker_init_fn=seed_worker, generator=g)
-
-    epoch = saved_info[opt.start_from_mode[:4]].get('epoch', 0)
-    iteration = saved_info[opt.start_from_mode[:4]].get('iter', 0)
-    best_val_score = saved_info[opt.start_from_mode[:4]].get('best_val_score', -1e5)
-    val_result_history = saved_info['history'].get('val_result_history', {})
-    loss_history = saved_info['history'].get('loss_history', {})
-    lr_history = saved_info['history'].get('lr_history', {})
-    opt.current_lr = vars(opt).get('current_lr', opt.lr)
-
-    # Build model
-
-    model, criterion, contrastive_criterion, postprocessors = build(opt)
-    model.translator = train_dataset.translator
-    model.train()
-
-    # Recover the parameters
-    if opt.start_from and (not opt.pretrain):
-        if opt.start_from_mode == 'best':
-            model_pth = torch.load(os.path.join(save_folder, 'model-best.pth'))
-        elif opt.start_from_mode == 'last':
-            model_pth = torch.load(os.path.join(save_folder, 'model-last.pth'))
-        logger.info('Loading pth from {}, iteration:{}'.format(save_folder, iteration))
-        model.load_state_dict(model_pth['model'])
-
-    # Load the pre-trained model
-    if opt.pretrain and (not opt.start_from):
-        logger.info('Load pre-trained parameters from {}'.format(opt.pretrain_path))
-        model_pth = torch.load(opt.pretrain_path, map_location=torch.device(opt.device))
-        # query_weight = model_pth['model'].pop('query_embed.weight')
-        if opt.pretrain == 'encoder':
-            encoder_filter = model.get_filter_rule_for_encoder()
-            encoder_pth = {k:v for k,v in model_pth['model'].items() if encoder_filter(k)}
-            model.load_state_dict(encoder_pth, strict=True)
-        elif opt.pretrain == 'decoder':
-            encoder_filter = model.get_filter_rule_for_encoder()
-            decoder_pth = {k:v for k,v in model_pth['model'].items() if not encoder_filter(k)}
-            model.load_state_dict(decoder_pth, strict=True)
-            pass
-        elif opt.pretrain == 'full':
-            # model_pth = transfer(model, model_pth)
-            model.load_state_dict(model_pth['model'], strict=True)
-        else:
-            raise ValueError("wrong value of opt.pretrain")
-        
-
-    model.to(opt.device)
-
-    # Decide which parameters need to be trained
-    # if (opt.matcher_type =='DTW' or opt.use_pseudo_box) and opt.text_encoder_learning_strategy == 'frozen':
-    #     for _, p in model.text_encoder.named_parameters():
-    #         p.requires_grad = False
-    #         text_encoder_params = list(map(id, model.text_encoder.parameters()))
-    #         other_params = filter(lambda p: id(p) not in text_encoder_params, model.parameters())
-    # else:
-    #     other_params = model.parameters()
-    other_params = model.parameters()
-
-    training_params = [{'params': other_params, 'lr': opt.lr}]
-
-    if opt.optimizer_type == 'adam':
-        optimizer = optim.Adam(training_params, weight_decay=opt.weight_decay)
-
-    elif opt.optimizer_type == 'adamw':
-        optimizer = optim.AdamW(training_params, weight_decay=opt.weight_decay)
-
-    milestone = [opt.learning_rate_decay_start + opt.learning_rate_decay_every * _ for _ in range(int((opt.epoch - opt.learning_rate_decay_start) / opt.learning_rate_decay_every))]
-    lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestone, gamma=opt.learning_rate_decay_rate)
-
-    # Load tokenizer for text encoder
-    # for i in range(10):
-    #     try:
-    #         if opt.pretrained_language_model == 'UniVL':
-    #             tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
-    #         else:
-    #             tokenizer = AutoTokenizer.from_pretrained(opt.pretrained_language_model)
-    #         break
-    #     except:
-    #         print('download error in AutoTokenizer, retry...')
-    #         time.sleep(1)
-
-    if opt.start_from:
-        optimizer.load_state_dict(model_pth['optimizer'])
-        lr_scheduler.step(epoch-1)
-
-    # print the args for debugging  
-    print_opt(opt, model, logger)
-    print_alert_message('Strat training !', logger)
-
-    loss_sum = OrderedDict()
-    bad_video_num = 0
-
-    start = time.time()
-    # breakpoint()
-    weight_dict = criterion.weight_dict
-    logger.info('loss type: {}'.format(weight_dict.keys()))
-    logger.info('loss weights: {}'.format(weight_dict.values()))
-
-    # Epoch-level iteration
-    refine_pseudo_box_copy = copy.deepcopy(opt.refine_pseudo_box)
-    pseudo_box_aug_copy = copy.deepcopy(opt.pseudo_box_aug)
-
-    while True:
-        # if epoch > opt.start_refine_epoch:
-        #     opt.refine_pseudo_box = refine_pseudo_box_copy
-        #     opt.pseudo_box_aug = pseudo_box_aug_copy
-        #     criterion.refine_pseudo_box = refine_pseudo_box_copy
-        #     criterion.pseudo_box_aug = pseudo_box_aug_copy
-        #     model.opt = opt 
-        # else:
-        #     opt.refine_pseudo_box = False
-        #     opt.pseudo_box_aug = False
-        #     criterion.refine_pseudo_box = False
-        #     criterion.pseudo_box_aug = False
-        #     model.opt = opt
-        
-        if True:
-            # scheduled sampling rate update
-            if epoch > opt.scheduled_sampling_start >= 0:
-                frac = (epoch - opt.scheduled_sampling_start) // opt.scheduled_sampling_increase_every
-                opt.ss_prob = min(opt.basic_ss_prob + opt.scheduled_sampling_increase_prob * frac,
-                                  opt.scheduled_sampling_max_prob)
-                model.caption_head.ss_prob = opt.ss_prob
-
-            print('lr:{}'.format(float(opt.current_lr)))
-            pass
-
-        # breakpoint()
-        # Batch-level iteration
-        trained_samples = 0
-        for dt in tqdm(train_loader, disable=opt.disable_tqdm):
-            # # for fast debugging
-            # if trained_samples > 5:
-            #     break
-            # else:
-            #     trained_samples += 1
-
-            if opt.device=='cuda':
-                torch.cuda.synchronize(opt.device)
-            if opt.debug:
-                # each epoch contains less mini-batches for debugging
-                if (iteration + 1) % 5 == 0:
-                    iteration += 1
-                    break
-            iteration += 1
-
-            optimizer.zero_grad()
-            dt = {key: _.to(opt.device) if isinstance(_, torch.Tensor) else _ for key, _ in dt.items()}
-            dt['video_target'] = [
-                {key: _.to(opt.device) if isinstance(_, torch.Tensor) else _ for key, _ in vid_info.items()} for vid_info in
-                dt['video_target']]
-
-            # Add text encoder
-            # if opt.matcher_type == 'DTW' or opt.use_pseudo_box:
-            #     captions = list()
-            #     for video_sents in dt['cap_raw']:  # dt['cap_raw']: [[sent_1, sent_2, ..., sent_n]]
-            #         captions.extend(video_sents) 
-            #     text_encoder_input = tokenizer(captions, return_tensors='pt', truncation=True, padding=True, max_length=opt.max_text_input_len)
-            #     text_encoder_input = {key: _.to(opt.device) if isinstance(_, torch.Tensor) else _ for key, _ in text_encoder_input.items()} 
-            #     # text_encoder_input: {'input_ids': tensor([[  101,  1996,  2307,  ...,     0,     0,     0],...]),  'attention_mask': tensor([[1, 1, 1,  ..., 0, 0, 0],...])}
-            #     # len(text_encoder_input['input_ids']) = n * max_text_input_len
-            #     dt['text_encoder_input'] = text_encoder_input
-
-            # dt = collections.defaultdict(lambda: None, dt) # Commented to 
-
-            output, loss = model(dt, criterion, contrastive_criterion)
-            final_loss = sum(loss[k] * weight_dict[k] for k in loss.keys() if k in weight_dict)
-            # breakpoint()
-            final_loss.backward()
-            torch.nn.utils.clip_grad_norm_(model.parameters(), opt.grad_clip)
-
-            optimizer.step()
-
-            for loss_k,loss_v in loss.items():
-                loss_sum[loss_k] = loss_sum.get(loss_k, 0)+ loss_v.item()
-            loss_sum['total_loss'] = loss_sum.get('total_loss', 0) + final_loss.item()
-
-            if opt.device=='cuda':
-                torch.cuda.synchronize()
-
-            losses_log_every = int(len(train_loader) / 10)
-
-            if opt.debug:
-                losses_log_every = 6
-
-            if iteration % losses_log_every == 0:
-                end = time.time()
-                for k in loss_sum.keys():
-                    loss_sum[k] = np.round(loss_sum[k] /losses_log_every, 3).item()
-
-                logger.info(
-                    "ID {} iter {} (epoch {}), \nloss = {}, \ntime/iter = {:.3f}, bad_vid = {:.3f}"
-                        .format(opt.id, iteration, epoch, loss_sum,
-                                (end - start) / losses_log_every, bad_video_num))
-
-                tf_writer.add_scalar('lr', opt.current_lr, iteration)
-                for loss_type in loss_sum.keys():
-                    tf_writer.add_scalar(loss_type, loss_sum[loss_type], iteration)
-                loss_history[iteration] = loss_sum
-                lr_history[iteration] = opt.current_lr
-                loss_sum = OrderedDict()
-                start = time.time()
-                bad_video_num = 0
-                torch.cuda.empty_cache()
-
-        # evaluation
-        if (epoch % opt.save_checkpoint_every == 0) and (epoch >= opt.min_epoch_when_save):
-            
-            # Save model
-            saved_pth = {'epoch': epoch,
-                         'model': model.state_dict(),
-                         'optimizer': optimizer.state_dict()}
-
-            if opt.save_all_checkpoint:
-                checkpoint_path = os.path.join(save_folder, 'model_iter_{}.pth'.format(iteration))
-            else:
-                checkpoint_path = os.path.join(save_folder, 'model-last.pth')
-
-            torch.save(saved_pth, checkpoint_path)
-
-            model.eval()
-            result_json_path = os.path.join(save_folder, 'prediction',
-                                         'num{}_epoch{}.json'.format(
-                                             len(val_dataset), epoch))
-            #eval_score, eval_loss = evaluate(model, criterion,  postprocessors, val_loader, result_json_path, logger=logger, args=opt, alpha=opt.ec_alpha, device=opt.device, debug=opt.debug)
-            eval_score, _ = evaluate(model, criterion,  postprocessors, val_loader, result_json_path, logger=logger, args=opt, alpha=opt.ec_alpha, device=opt.device, debug=opt.debug)
-            if opt.caption_decoder_type == 'none':
-                current_score = 2./(1./eval_score['Precision'] + 1./eval_score['Recall'])
-            else:
-                if opt.criteria_for_best_ckpt == 'dvc':
-                    current_score = np.array(eval_score['METEOR']).mean() + np.array(eval_score['soda_c']).mean()
-                elif opt.criteria_for_best_ckpt == 'overall':
-                    current_score = np.array(eval_score['Bleu_4']).mean() + \
-                    np.array(eval_score['CIDEr']).mean() + \
-                    np.array(eval_score['METEOR']).mean() + \
-                    2./(1./eval_score['Precision'] + 1./eval_score['Recall'])
-                else:
-                    current_score = np.array(eval_score['para_METEOR']).mean() + np.array(eval_score['para_CIDEr']).mean() + np.array(eval_score['para_Bleu_4']).mean()
-
-            # add to tf summary
-            for key in eval_score.keys():
-                tf_writer.add_scalar(key, np.array(eval_score[key]).mean(), iteration)
-
-            # Huabin comment this part for avoiding reporting losses during evaluation
-            # for loss_type in eval_loss.keys():
-            #     tf_writer.add_scalar('eval_' + loss_type, eval_loss[loss_type], iteration)
-
-            _ = [item.append(np.array(item).mean()) for item in eval_score.values() if isinstance(item, list)]
-            print_info = '\n'.join([key + ":" + str(eval_score[key]) for key in eval_score.keys()])
-            logger.info('\nValidation results of iter {}:\n'.format(iteration) + print_info)
-            logger.info('\noverall score of iter {}: {}\n'.format(iteration, current_score))
-            val_result_history[epoch] = {'eval_score': eval_score}
-            logger.info('Save model at iter {} to {}.'.format(iteration, checkpoint_path))
-
-            # save the model parameter and  of best epoch
-            if current_score >= best_val_score:
-                best_val_score = current_score
-                best_epoch = epoch
-                saved_info['best'] = {'opt': vars(opt),
-                                      'iter': iteration,
-                                      'epoch': best_epoch,
-                                      'best_val_score': best_val_score,
-                                      'result_json_path': result_json_path,
-                                      'avg_proposal_num': eval_score['avg_proposal_number'],
-                                      'Precision': eval_score['Precision'],
-                                      'Recall': eval_score['Recall']
-                                      }
-
-                # suffix = "RL" if sc_flag else "CE"
-                torch.save(saved_pth, os.path.join(save_folder, 'model-best.pth'))
-                logger.info('Save Best-model at iter {} to checkpoint file.'.format(iteration))
-
-            saved_info['last'] = {'opt': vars(opt),
-                                  'iter': iteration,
-                                  'epoch': epoch,
-                                  'best_val_score': best_val_score,
-                                  }
-            saved_info['history'] = {'val_result_history': val_result_history,
-                                     'loss_history': loss_history,
-                                     'lr_history': lr_history,
-                                     # 'query_matched_fre_hist': query_matched_fre_hist,
-                                     }
-            with open(os.path.join(save_folder, 'info.json'), 'w') as f:
-                json.dump(saved_info, f)
-            logger.info('Save info to info.json')
-
-            model.train()
-
-        epoch += 1
-        lr_scheduler.step()
-        opt.current_lr = optimizer.param_groups[0]['lr']
-        torch.cuda.empty_cache()
-        # Stop criterion
-        if epoch >= opt.epoch:
-            # # load Best model and conduct evaluation
-            # print('====== Conduct the Final Evaluation to test Best Checkpoint ======')
-            # val_logger = create_logger(save_folder, 'val.log')
-            # loaded_pth = torch.load(os.path.join(save_folder, 'model-best.pth'), map_location='cuda')
-            # model.load_state_dict(loaded_pth['model'], strict=True)
-            # model.eval()
-            # result_json_path = saved_info['best']['result_json_path']
-            # eval_score, _ = evaluate(model, criterion,  postprocessors, val_loader, result_json_path, logger=logger, args=opt, alpha=opt.ec_alpha, device=opt.device, debug=opt.debug)
-            # if opt.caption_decoder_type == 'none':
-            #     current_score = 2./(1./eval_score['Precision'] + 1./eval_score['Recall'])
-            # else:
-            #     if opt.criteria_for_best_ckpt == 'dvc':
-            #         current_score = np.array(eval_score['METEOR']).mean() + np.array(eval_score['soda_c']).mean()
-            #     else:
-            #         current_score = np.array(eval_score['para_METEOR']).mean() + np.array(eval_score['para_CIDEr']).mean() + np.array(eval_score['para_Bleu_4']).mean()
-
-            # _ = [item.append(np.array(item).mean()) for item in eval_score.values() if isinstance(item, list)]
-            # print_info = '\n'.join([key + ":" + str(eval_score[key]) for key in eval_score.keys()])
-            # val_logger.info('Best-model is saved at iter {}.\n'.format(saved_info['best']['iter']))
-            # val_logger.info('\nBest Model Performance:\n' + print_info)
-            # val_logger.info('\nBest Overall Score {}: {}\n'.format(iteration, current_score))
-
-            # tf_writer.close()
-            # break
-
-            val_logger = create_logger(save_folder, 'val.log')
-            infos_path = os.path.join(save_folder, 'info.json')
-
-            with open(infos_path, 'r') as f:
-                data = json.load(f)
-            val_history = data['history']['val_result_history']
-
-            metric_sum = {}
-            metrics = ['METEOR', 'CIDEr', 'soda_c', 'Precision', 'Recall']
-            for k, v in val_history.items():
-                metric_sum[k] = sum([v['eval_score'][metric] for metric in metrics])
-                # print(f"{k}: {metric_sum[k]}")
-
-            best_epoch = max(metric_sum, key=metric_sum.get)
-            best_val_score = val_history[best_epoch]['eval_score']
-            val_logger.info(f"Best epoch: {best_epoch}")
-            print_info = '\n'.join([key + ":" + str(best_val_score[key]) for key in best_val_score.keys()])
-            val_logger.info('\nBest Model Performance:\n' + print_info)
-            val_logger.info('\nBest Overall Score epoch{}: {}\n'.format(best_epoch, metric_sum[best_epoch]))
-
-            break  
-
-    return saved_info
-
-
-if __name__ == '__main__':
-    opt = opts.parse_opts()
-    opt.id = 'seq-pre_perc-{}'.format(opt.pre_percent)
-    assert opt.pre_percent <= 1.0 and opt.pre_percent >= 0.0
-
-    if not hasattr(opt, 'visual_feature_folder_val'):
-        opt.visual_feature_folder_val = opt.visual_feature_folder
-        opt.text_feature_folder_val = opt.text_feature_folder
-    # breakpoint()
-    if opt.map:
-        opt.visual_feature_folder = [map_path(path) for path in opt.visual_feature_folder]
-        opt.text_feature_folder = [map_path(path) for path in opt.text_feature_folder]
-        opt.visual_feature_folder_val = [map_path(path) for path in opt.visual_feature_folder_val]
-        opt.text_feature_folder_val = [map_path(path) for path in opt.text_feature_folder_val]
-
-    if opt.gpu_id:
-        os.environ["CUDA_VISIBLE_DEVICES"] = ",".join([str(i) for i in opt.gpu_id])
-    if opt.disable_cudnn:
-        torch.backends.cudnn.enabled = False
-
-    os.environ['KMP_DUPLICATE_LIB_OK'] = 'True' # to avoid OMP problem on macos
-    # breakpoint()
-    train(opt)
-
diff --git a/backup/train_seq.py b/backup/train_seq.py
deleted file mode 100644
index 6a415e180bf2506f1cbef5ce6d0f6f4205e76203..0000000000000000000000000000000000000000
--- a/backup/train_seq.py
+++ /dev/null
@@ -1,457 +0,0 @@
-# coding:utf-8
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import json
-import time
-import torch
-import os
-import sys
-import collections
-import numpy as np
-from tqdm import tqdm
-import torch.optim as optim
-from torch.utils.data import DataLoader
-from os.path import dirname, abspath
-
-pdvc_dir = dirname(abspath(__file__))
-sys.path.insert(0, pdvc_dir)
-sys.path.insert(0, os.path.join(pdvc_dir, 'densevid_eval3'))
-sys.path.insert(0, os.path.join(pdvc_dir, 'densevid_eval3/SODA'))
-# print(sys.path)
-
-
-os.environ["TOKENIZERS_PARALLELISM"] = "false" # To avoid warning of tokenizer
-from eval_utils import evaluate
-import opts
-from tensorboardX import SummaryWriter
-from misc.utils import print_alert_message, build_floder, create_logger, backup_envir, print_opt, set_seed
-from data.video_dataset import PropSeqDataset, collate_fn
-from pdvc.pdvc import build
-from collections import OrderedDict
-from transformers import AutoTokenizer, get_linear_schedule_with_warmup, get_cosine_schedule_with_warmup
-import copy
-
-a100_folder = ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/Tasty/features', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Tasty/UniVL_feature', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features']
-r3090_folder = ['/mnt/data/Gvlab/wuhao/features/yc2', '/mnt/data/Gvlab/wuhao/features/tasty', '/mnt/data/Gvlab/wuhao/features/tasty/univl', '/mnt/data/Gvlab/wuhao/features/anet', '/mnt/data/Gvlab/wuhao/features/howto100m']
-
-# /cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features -> /mnt/data/Gvlab/wuhao/features/howto100m
-# /cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip -> /mnt/data/Gvlab/wuhao/features/howto100m/clip_features
-# /cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL -> /mnt/data/Gvlab/wuhao/features/howto100m/univl_features
-
-def _init_fn(worker_id):
-    np.random.seed(12 + worker_id)
-
-def map_path(path):
-    path_backup = copy.deepcopy(path)
-    # breakpoint()
-    for i, folder in enumerate(a100_folder):
-        if folder in path:
-            path = path.replace(folder, r3090_folder[i])
-            return path
-    if path == path_backup:
-        print('map failed')
-        exit(1)
-
-
-def train(opt):
-    set_seed(opt.seed)
-    save_folder = build_floder(opt)
-    logger = create_logger(save_folder, 'train.log')
-    tf_writer = SummaryWriter(os.path.join(save_folder, 'tf_summary'))
-
-    if not opt.start_from:
-        backup_envir(save_folder)
-        logger.info('backup evironment completed !')
-
-    saved_info = {'best': {}, 'last': {}, 'history': {}, 'eval_history': {}}
-
-    # continue training
-    if opt.start_from:
-        opt.pretrain = False
-        infos_path = os.path.join(save_folder, 'info.json')
-        with open(infos_path) as f:
-            logger.info('Load info from {}'.format(infos_path))
-            saved_info = json.load(f)
-            prev_opt = saved_info[opt.start_from_mode[:4]]['opt']
-
-            exclude_opt = ['start_from', 'start_from_mode', 'pretrain']
-            for opt_name in prev_opt.keys():
-                if opt_name not in exclude_opt:
-                    vars(opt).update({opt_name: prev_opt.get(opt_name)})
-                if prev_opt.get(opt_name) != vars(opt).get(opt_name):
-                    logger.info('Change opt {} : {} --> {}'.format(opt_name, prev_opt.get(opt_name),
-                                                                   vars(opt).get(opt_name)))
-    if len(opt.visual_feature_folder) == 2:
-        train_dataset_pretrain = PropSeqDataset(opt.train_caption_file[0],
-                                            [opt.visual_feature_folder[0]],
-                                            [opt.text_feature_folder[0]],
-                                            opt.dict_file, True, 'gt',
-                                            opt)
-        train_dataset_target = PropSeqDataset(opt.train_caption_file[1],
-                                            [opt.visual_feature_folder[1]],
-                                            [opt.text_feature_folder[1]],
-                                            opt.dict_file, True, 'gt',
-                                            opt)
-        train_loader_pretrain = DataLoader(train_dataset_pretrain, batch_size=opt.batch_size,
-                              shuffle=True, num_workers=opt.nthreads, collate_fn=collate_fn, worker_init_fn=_init_fn)
-        train_loader_target = DataLoader(train_dataset_target, batch_size=opt.batch_size,
-                                    shuffle=True, num_workers=opt.nthreads, collate_fn=collate_fn, worker_init_fn=_init_fn)
-        
-        train_dataloaders = [train_loader_pretrain, train_loader_target]
-        # train_dataset = torch.utils.data.ConcatDataset([train_dataset_1, train_dataset_2])
-        # train_dataset.translator = train_dataset_1.translator
-
-    else:
-        train_dataset_target = PropSeqDataset(opt.train_caption_file,
-                                    opt.visual_feature_folder,
-                                    opt.text_feature_folder,
-                                    opt.dict_file, True, 'gt',
-                                    opt)
-        train_loader_target = DataLoader(train_dataset_target, batch_size=opt.batch_size,
-                                shuffle=True, num_workers=opt.nthreads, collate_fn=collate_fn, worker_init_fn=_init_fn)
-        train_dataloaders = [train_loader_target]
-
-    # val_dataset = PropSeqDataset(opt.val_caption_file,
-    #                              opt.visual_feature_folder,
-    #                              opt.text_feature_folder,
-    #                              opt.dict_file, False, 'gt',
-    #                              opt)
-    if not hasattr(opt, 'dict_file_val'):
-        opt.dict_file_val = opt.dict_file
-        opt.vocab_size_val = opt.vocab_size
-
-    val_dataset = PropSeqDataset(opt.val_caption_file,
-                                opt.visual_feature_folder_val,
-                                opt.text_feature_folder_val,
-                                opt.dict_file, False, 'gt',
-                                opt)
-
-
-    val_loader = DataLoader(val_dataset, batch_size=opt.batch_size_for_eval,
-                            shuffle=False, num_workers=opt.nthreads, collate_fn=collate_fn, worker_init_fn=_init_fn)
-
-    epoch = saved_info[opt.start_from_mode[:4]].get('epoch', 0)
-    iteration = saved_info[opt.start_from_mode[:4]].get('iter', 0)
-    best_val_score = saved_info[opt.start_from_mode[:4]].get('best_val_score', -1e5)
-    val_result_history = saved_info['history'].get('val_result_history', {})
-    loss_history = saved_info['history'].get('loss_history', {})
-    lr_history = saved_info['history'].get('lr_history', {})
-    opt.current_lr = vars(opt).get('current_lr', opt.lr)
-
-    # Build model
-
-    model, criterion, contrastive_criterion, postprocessors = build(opt)
-    model.translator = train_dataset_target.translator
-    model.train()
-
-    # Recover the parameters
-    if opt.start_from and (not opt.pretrain):
-        if opt.start_from_mode == 'best':
-            model_pth = torch.load(os.path.join(save_folder, 'model-best.pth'))
-        elif opt.start_from_mode == 'last':
-            model_pth = torch.load(os.path.join(save_folder, 'model-last.pth'))
-        logger.info('Loading pth from {}, iteration:{}'.format(save_folder, iteration))
-        model.load_state_dict(model_pth['model'])
-
-    # Load the pre-trained model
-    if opt.pretrain and (not opt.start_from):
-        logger.info('Load pre-trained parameters from {}'.format(opt.pretrain_path))
-        model_pth = torch.load(opt.pretrain_path, map_location=torch.device(opt.device))
-        # query_weight = model_pth['model'].pop('query_embed.weight')
-        if opt.pretrain == 'encoder':
-            encoder_filter = model.get_filter_rule_for_encoder()
-            encoder_pth = {k:v for k,v in model_pth['model'].items() if encoder_filter(k)}
-            model.load_state_dict(encoder_pth, strict=True)
-        elif opt.pretrain == 'decoder':
-            encoder_filter = model.get_filter_rule_for_encoder()
-            decoder_pth = {k:v for k,v in model_pth['model'].items() if not encoder_filter(k)}
-            model.load_state_dict(decoder_pth, strict=True)
-            pass
-        elif opt.pretrain == 'full':
-            # model_pth = transfer(model, model_pth)
-            model.load_state_dict(model_pth['model'], strict=True)
-        else:
-            raise ValueError("wrong value of opt.pretrain")
-        
-
-    model.to(opt.device)
-
-    # Decide which parameters need to be trained
-    # if (opt.matcher_type =='DTW' or opt.use_pseudo_box) and opt.text_encoder_learning_strategy == 'frozen':
-    #     for _, p in model.text_encoder.named_parameters():
-    #         p.requires_grad = False
-    #         text_encoder_params = list(map(id, model.text_encoder.parameters()))
-    #         other_params = filter(lambda p: id(p) not in text_encoder_params, model.parameters())
-    # else:
-    #     other_params = model.parameters()
-    other_params = model.parameters()
-
-    training_params = [{'params': other_params, 'lr': opt.lr}]
-
-    if opt.optimizer_type == 'adam':
-        optimizer = optim.Adam(training_params, weight_decay=opt.weight_decay)
-
-    elif opt.optimizer_type == 'adamw':
-        optimizer = optim.AdamW(training_params, weight_decay=opt.weight_decay)
-
-    milestone = [opt.learning_rate_decay_start + opt.learning_rate_decay_every * _ for _ in range(int((opt.epoch - opt.learning_rate_decay_start) / opt.learning_rate_decay_every))]
-    lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestone, gamma=opt.learning_rate_decay_rate)
-
-    # Load tokenizer for text encoder
-    # for i in range(10):
-    #     try:
-    #         if opt.pretrained_language_model == 'UniVL':
-    #             tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
-    #         else:
-    #             tokenizer = AutoTokenizer.from_pretrained(opt.pretrained_language_model)
-    #         break
-    #     except:
-    #         print('download error in AutoTokenizer, retry...')
-    #         time.sleep(1)
-
-    if opt.start_from:
-        optimizer.load_state_dict(model_pth['optimizer'])
-        lr_scheduler.step(epoch-1)
-
-    # print the args for debugging  
-    print_opt(opt, model, logger)
-    print_alert_message('Strat training !', logger)
-
-    loss_sum = OrderedDict()
-    bad_video_num = 0
-
-    start = time.time()
-
-    weight_dict = criterion.weight_dict
-    logger.info('loss type: {}'.format(weight_dict.keys()))
-    logger.info('loss weights: {}'.format(weight_dict.values()))
-
-    # Epoch-level iteration
-
-    while True:
-        if True:
-            # scheduled sampling rate update
-            if epoch > opt.scheduled_sampling_start >= 0:
-                frac = (epoch - opt.scheduled_sampling_start) // opt.scheduled_sampling_increase_every
-                opt.ss_prob = min(opt.basic_ss_prob + opt.scheduled_sampling_increase_prob * frac,
-                                  opt.scheduled_sampling_max_prob)
-                model.caption_head.ss_prob = opt.ss_prob
-
-            print('lr:{}'.format(float(opt.current_lr)))
-            pass
-
-        # breakpoint()
-        # Batch-level iteration
-        for train_loader in train_dataloaders:
-            trained_samples = 0
-            for dt in tqdm(train_loader, disable=opt.disable_tqdm):
-                # # for fast debugging
-                # if trained_samples > 25:
-                #     break
-                # else:
-                #     trained_samples += 1
-                if opt.device=='cuda':
-                    torch.cuda.synchronize(opt.device)
-                if opt.debug:
-                    # each epoch contains less mini-batches for debugging
-                    if (iteration + 1) % 5 == 0:
-                        iteration += 1
-                        break
-                iteration += 1
-
-                optimizer.zero_grad()
-                dt = {key: _.to(opt.device) if isinstance(_, torch.Tensor) else _ for key, _ in dt.items()}
-                dt['video_target'] = [
-                    {key: _.to(opt.device) if isinstance(_, torch.Tensor) else _ for key, _ in vid_info.items()} for vid_info in
-                    dt['video_target']]
-
-                # Add text encoder
-                # if opt.matcher_type == 'DTW' or opt.use_pseudo_box:
-                #     captions = list()
-                #     for video_sents in dt['cap_raw']:  # dt['cap_raw']: [[sent_1, sent_2, ..., sent_n]]
-                #         captions.extend(video_sents) 
-                #     text_encoder_input = tokenizer(captions, return_tensors='pt', truncation=True, padding=True, max_length=opt.max_text_input_len)
-                #     text_encoder_input = {key: _.to(opt.device) if isinstance(_, torch.Tensor) else _ for key, _ in text_encoder_input.items()} 
-                #     # text_encoder_input: {'input_ids': tensor([[  101,  1996,  2307,  ...,     0,     0,     0],...]),  'attention_mask': tensor([[1, 1, 1,  ..., 0, 0, 0],...])}
-                #     # len(text_encoder_input['input_ids']) = n * max_text_input_len
-                #     dt['text_encoder_input'] = text_encoder_input
-
-                # dt = collections.defaultdict(lambda: None, dt) # Commented to 
-
-                output, loss = model(dt, criterion, contrastive_criterion)
-                final_loss = sum(loss[k] * weight_dict[k] for k in loss.keys() if k in weight_dict)
-                # breakpoint()
-                final_loss.backward()
-                torch.nn.utils.clip_grad_norm_(model.parameters(), opt.grad_clip)
-
-                optimizer.step()
-
-                for loss_k,loss_v in loss.items():
-                    loss_sum[loss_k] = loss_sum.get(loss_k, 0)+ loss_v.item()
-                loss_sum['total_loss'] = loss_sum.get('total_loss', 0) + final_loss.item()
-
-                if opt.device=='cuda':
-                    torch.cuda.synchronize()
-
-                losses_log_every = int(len(train_loader) / 10)
-
-                if opt.debug:
-                    losses_log_every = 6
-
-                if iteration % losses_log_every == 0:
-                    end = time.time()
-                    for k in loss_sum.keys():
-                        loss_sum[k] = np.round(loss_sum[k] /losses_log_every, 3).item()
-
-                    logger.info(
-                        "ID {} iter {} (epoch {}), \nloss = {}, \ntime/iter = {:.3f}, bad_vid = {:.3f}"
-                            .format(opt.id, iteration, epoch, loss_sum,
-                                    (end - start) / losses_log_every, bad_video_num))
-
-                    tf_writer.add_scalar('lr', opt.current_lr, iteration)
-                    for loss_type in loss_sum.keys():
-                        tf_writer.add_scalar(loss_type, loss_sum[loss_type], iteration)
-                    loss_history[iteration] = loss_sum
-                    lr_history[iteration] = opt.current_lr
-                    loss_sum = OrderedDict()
-                    start = time.time()
-                    bad_video_num = 0
-                    torch.cuda.empty_cache()
-
-        # evaluation
-        if (epoch % opt.save_checkpoint_every == 0) and (epoch >= opt.min_epoch_when_save):
-            
-            # Save model
-            saved_pth = {'epoch': epoch,
-                         'model': model.state_dict(),
-                         'optimizer': optimizer.state_dict()}
-
-            if opt.save_all_checkpoint:
-                checkpoint_path = os.path.join(save_folder, 'model_iter_{}.pth'.format(iteration))
-            else:
-                checkpoint_path = os.path.join(save_folder, 'model-last.pth')
-
-            torch.save(saved_pth, checkpoint_path)
-
-            model.eval()
-            result_json_path = os.path.join(save_folder, 'prediction',
-                                         'num{}_epoch{}.json'.format(
-                                             len(val_dataset), epoch))
-            #eval_score, eval_loss = evaluate(model, criterion,  postprocessors, val_loader, result_json_path, logger=logger, args=opt, alpha=opt.ec_alpha, device=opt.device, debug=opt.debug)
-            eval_score, _ = evaluate(model, criterion,  postprocessors, val_loader, result_json_path, logger=logger, args=opt, alpha=opt.ec_alpha, device=opt.device, debug=opt.debug)
-            if opt.caption_decoder_type == 'none':
-                current_score = 2./(1./eval_score['Precision'] + 1./eval_score['Recall'])
-            else:
-                if opt.criteria_for_best_ckpt == 'dvc':
-                    current_score = np.array(eval_score['METEOR']).mean() + np.array(eval_score['soda_c']).mean()
-                else:
-                    current_score = np.array(eval_score['para_METEOR']).mean() + np.array(eval_score['para_CIDEr']).mean() + np.array(eval_score['para_Bleu_4']).mean()
-
-            # add to tf summary
-            for key in eval_score.keys():
-                tf_writer.add_scalar(key, np.array(eval_score[key]).mean(), iteration)
-
-            # Huabin comment this part for avoiding reporting losses during evaluation
-            # for loss_type in eval_loss.keys():
-            #     tf_writer.add_scalar('eval_' + loss_type, eval_loss[loss_type], iteration)
-
-            _ = [item.append(np.array(item).mean()) for item in eval_score.values() if isinstance(item, list)]
-            print_info = '\n'.join([key + ":" + str(eval_score[key]) for key in eval_score.keys()])
-            logger.info('\nValidation results of iter {}:\n'.format(iteration) + print_info)
-            logger.info('\noverall score of iter {}: {}\n'.format(iteration, current_score))
-            val_result_history[epoch] = {'eval_score': eval_score}
-            logger.info('Save model at iter {} to {}.'.format(iteration, checkpoint_path))
-
-            # save the model parameter and  of best epoch
-            if current_score >= best_val_score:
-                best_val_score = current_score
-                best_epoch = epoch
-                saved_info['best'] = {'opt': vars(opt),
-                                      'iter': iteration,
-                                      'epoch': best_epoch,
-                                      'best_val_score': best_val_score,
-                                      'result_json_path': result_json_path,
-                                      'avg_proposal_num': eval_score['avg_proposal_number'],
-                                      'Precision': eval_score['Precision'],
-                                      'Recall': eval_score['Recall']
-                                      }
-
-                # suffix = "RL" if sc_flag else "CE"
-                torch.save(saved_pth, os.path.join(save_folder, 'model-best.pth'))
-                logger.info('Save Best-model at iter {} to checkpoint file.'.format(iteration))
-
-            saved_info['last'] = {'opt': vars(opt),
-                                  'iter': iteration,
-                                  'epoch': epoch,
-                                  'best_val_score': best_val_score,
-                                  }
-            saved_info['history'] = {'val_result_history': val_result_history,
-                                     'loss_history': loss_history,
-                                     'lr_history': lr_history,
-                                     # 'query_matched_fre_hist': query_matched_fre_hist,
-                                     }
-            with open(os.path.join(save_folder, 'info.json'), 'w') as f:
-                json.dump(saved_info, f)
-            logger.info('Save info to info.json')
-
-            model.train()
-
-        epoch += 1
-        lr_scheduler.step()
-        opt.current_lr = optimizer.param_groups[0]['lr']
-        torch.cuda.empty_cache()
-        # Stop criterion
-        if epoch >= opt.epoch:
-            # load Best model and conduct evaluation
-            print('====== Conduct the Final Evaluation to test Best Checkpoint ======')
-            val_logger = create_logger(save_folder, 'val.log')
-            loaded_pth = torch.load(os.path.join(save_folder, 'model-best.pth'), map_location='cuda')
-            model.load_state_dict(loaded_pth['model'], strict=True)
-            model.eval()
-            result_json_path = saved_info['best']['result_json_path']
-            eval_score, _ = evaluate(model, criterion,  postprocessors, val_loader, result_json_path, logger=logger, args=opt, alpha=opt.ec_alpha, device=opt.device, debug=opt.debug)
-            if opt.caption_decoder_type == 'none':
-                current_score = 2./(1./eval_score['Precision'] + 1./eval_score['Recall'])
-            else:
-                if opt.criteria_for_best_ckpt == 'dvc':
-                    current_score = np.array(eval_score['METEOR']).mean() + np.array(eval_score['soda_c']).mean()
-                else:
-                    current_score = np.array(eval_score['para_METEOR']).mean() + np.array(eval_score['para_CIDEr']).mean() + np.array(eval_score['para_Bleu_4']).mean()
-
-            _ = [item.append(np.array(item).mean()) for item in eval_score.values() if isinstance(item, list)]
-            print_info = '\n'.join([key + ":" + str(eval_score[key]) for key in eval_score.keys()])
-            val_logger.info('Best-model is saved at iter {}.\n'.format(saved_info['best']['iter']))
-            val_logger.info('\nBest Model Performance:\n' + print_info)
-            val_logger.info('\nBest Overall Score {}: {}\n'.format(iteration, current_score))
-
-            tf_writer.close()
-            break
-
-    return saved_info
-
-
-if __name__ == '__main__':
-    opt = opts.parse_opts()
-    opt.id = 'seq-train'
-
-    if not hasattr(opt, 'visual_feature_folder_val'):
-        opt.visual_feature_folder_val = opt.visual_feature_folder
-        opt.text_feature_folder_val = opt.text_feature_folder
-
-    if opt.map:
-        opt.visual_feature_folder = [map_path(path) for path in opt.visual_feature_folder]
-        opt.text_feature_folder = [map_path(path) for path in opt.text_feature_folder]
-        opt.visual_feature_folder_val = [map_path(path) for path in opt.visual_feature_folder_val]
-        opt.text_feature_folder_val = [map_path(path) for path in opt.text_feature_folder_val]
-
-    if opt.gpu_id:
-        os.environ["CUDA_VISIBLE_DEVICES"] = ",".join([str(i) for i in opt.gpu_id])
-    if opt.disable_cudnn:
-        torch.backends.cudnn.enabled = False
-
-    os.environ['KMP_DUPLICATE_LIB_OK'] = 'True' # to avoid OMP problem on macos
-    # breakpoint()
-    train(opt)
-
diff --git a/backup/train_seq_gt.py b/backup/train_seq_gt.py
deleted file mode 100644
index 235ae3a83169787f2b2db87e71f0fabe2dbc2dc1..0000000000000000000000000000000000000000
--- a/backup/train_seq_gt.py
+++ /dev/null
@@ -1,480 +0,0 @@
-# coding:utf-8
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import json
-import time
-import torch
-import os
-import sys
-import collections
-import numpy as np
-from tqdm import tqdm
-import torch.optim as optim
-from torch.utils.data import DataLoader
-from os.path import dirname, abspath
-
-pdvc_dir = dirname(abspath(__file__))
-sys.path.insert(0, pdvc_dir)
-sys.path.insert(0, os.path.join(pdvc_dir, 'densevid_eval3'))
-sys.path.insert(0, os.path.join(pdvc_dir, 'densevid_eval3/SODA'))
-# print(sys.path)
-
-
-os.environ["TOKENIZERS_PARALLELISM"] = "false" # To avoid warning of tokenizer
-from eval_utils import evaluate
-import opts
-from tensorboardX import SummaryWriter
-from misc.utils import print_alert_message, build_floder, create_logger, backup_envir, print_opt, set_seed
-from data.video_dataset import PropSeqDataset, collate_fn, PercentageSubsetDataset
-from pdvc.pdvc import build
-from collections import OrderedDict
-from transformers import AutoTokenizer, get_linear_schedule_with_warmup, get_cosine_schedule_with_warmup
-import copy
-
-a100_folder = ['/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/youcook2', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/Tasty/features', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Tasty/UniVL_feature', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/huabin/dataset/Anet', '/cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features']
-r3090_folder = ['/mnt/data/Gvlab/wuhao/features/yc2', '/mnt/data/Gvlab/wuhao/features/tasty', '/mnt/data/Gvlab/wuhao/features/tasty/univl', '/mnt/data/Gvlab/wuhao/features/anet', '/mnt/data/Gvlab/wuhao/features/howto100m']
-
-# /cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features -> /mnt/data/Gvlab/wuhao/features/howto100m
-# /cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/clip -> /mnt/data/Gvlab/wuhao/features/howto100m/clip_features
-# /cpfs01/shared/Gvlab-A100/Gvlab-A100_hdd/wuhao/howto100m/features/UniVL -> /mnt/data/Gvlab/wuhao/features/howto100m/univl_features
-
-def _init_fn(worker_id):
-    np.random.seed(12 + worker_id)
-
-def map_path(path):
-    path_backup = copy.deepcopy(path)
-    # breakpoint()
-    for i, folder in enumerate(a100_folder):
-        if folder in path:
-            path = path.replace(folder, r3090_folder[i])
-            return path
-    if path == path_backup:
-        print('map failed')
-        exit(1)
-
-
-def train(opt):
-    set_seed(opt.seed)
-    save_folder = build_floder(opt)
-    logger = create_logger(save_folder, 'train.log')
-    tf_writer = SummaryWriter(os.path.join(save_folder, 'tf_summary'))
-
-    if not opt.start_from:
-        backup_envir(save_folder)
-        logger.info('backup evironment completed !')
-
-    saved_info = {'best': {}, 'last': {}, 'history': {}, 'eval_history': {}}
-
-    # continue training
-    if opt.start_from:
-        opt.pretrain = False
-        infos_path = os.path.join(save_folder, 'info.json')
-        with open(infos_path) as f:
-            logger.info('Load info from {}'.format(infos_path))
-            saved_info = json.load(f)
-            prev_opt = saved_info[opt.start_from_mode[:4]]['opt']
-
-            exclude_opt = ['start_from', 'start_from_mode', 'pretrain']
-            for opt_name in prev_opt.keys():
-                if opt_name not in exclude_opt:
-                    vars(opt).update({opt_name: prev_opt.get(opt_name)})
-                if prev_opt.get(opt_name) != vars(opt).get(opt_name):
-                    logger.info('Change opt {} : {} --> {}'.format(opt_name, prev_opt.get(opt_name),
-                                                                   vars(opt).get(opt_name)))
-    if len(opt.visual_feature_folder) == 2:
-        train_dataset_pretrain = PropSeqDataset(opt.train_caption_file[0],
-                                            [opt.visual_feature_folder[0]],
-                                            [opt.text_feature_folder[0]],
-                                            opt.dict_file, True, 'gt',
-                                            opt)
-        train_dataset_target = PropSeqDataset(opt.train_caption_file[1],
-                                            [opt.visual_feature_folder[1]],
-                                            [opt.text_feature_folder[1]],
-                                            opt.dict_file, True, 'gt',
-                                            opt)
-        # Create the dataset with the specified percentage
-        subset_data = PercentageSubsetDataset(train_dataset_target, opt.ft_gt_percent)
-
-        # # Create a DataLoader for the subset dataset
-        # subset_dataloader = DataLoader(subset_data, batch_size=64, shuffle=True)
-        
-        train_loader_pretrain = DataLoader(train_dataset_pretrain, batch_size=opt.batch_size,
-                              shuffle=True, num_workers=opt.nthreads, collate_fn=collate_fn, worker_init_fn=_init_fn)
-        train_loader_target = DataLoader(subset_data, batch_size=opt.batch_size,
-                                    shuffle=True, num_workers=opt.nthreads, collate_fn=collate_fn, worker_init_fn=_init_fn)
-        
-        train_dataloaders = [train_loader_pretrain, train_loader_target]
-        # train_dataset = torch.utils.data.ConcatDataset([train_dataset_1, train_dataset_2])
-        # train_dataset.translator = train_dataset_1.translator
-
-    else:
-        print(f'the script only support two dataset training while {len(opt.visual_feature_folder)} dataset folders are provided')
-        exit(1)
-        train_dataset_target = PropSeqDataset(opt.train_caption_file,
-                                    opt.visual_feature_folder,
-                                    opt.text_feature_folder,
-                                    opt.dict_file, True, 'gt',
-                                    opt)
-        train_loader_target = DataLoader(train_dataset_target, batch_size=opt.batch_size,
-                                shuffle=True, num_workers=opt.nthreads, collate_fn=collate_fn, worker_init_fn=_init_fn)
-        train_dataloaders = [train_loader_target]
-
-    # val_dataset = PropSeqDataset(opt.val_caption_file,
-    #                              opt.visual_feature_folder,
-    #                              opt.text_feature_folder,
-    #                              opt.dict_file, False, 'gt',
-    #                              opt)
-    if not hasattr(opt, 'dict_file_val'):
-        opt.dict_file_val = opt.dict_file
-        opt.vocab_size_val = opt.vocab_size
-
-    val_dataset = PropSeqDataset(opt.val_caption_file,
-                                opt.visual_feature_folder_val,
-                                opt.text_feature_folder_val,
-                                opt.dict_file, False, 'gt',
-                                opt)
-
-
-    val_loader = DataLoader(val_dataset, batch_size=opt.batch_size_for_eval,
-                            shuffle=False, num_workers=opt.nthreads, collate_fn=collate_fn, worker_init_fn=_init_fn)
-
-    epoch = saved_info[opt.start_from_mode[:4]].get('epoch', 0)
-    iteration = saved_info[opt.start_from_mode[:4]].get('iter', 0)
-    best_val_score = saved_info[opt.start_from_mode[:4]].get('best_val_score', -1e5)
-    val_result_history = saved_info['history'].get('val_result_history', {})
-    loss_history = saved_info['history'].get('loss_history', {})
-    lr_history = saved_info['history'].get('lr_history', {})
-    opt.current_lr = vars(opt).get('current_lr', opt.lr)
-
-    # Build model
-
-    model, criterion, contrastive_criterion, postprocessors = build(opt)
-    model.translator = train_dataset_target.translator
-    model.train()
-
-    # Recover the parameters
-    if opt.start_from and (not opt.pretrain):
-        if opt.start_from_mode == 'best':
-            model_pth = torch.load(os.path.join(save_folder, 'model-best.pth'))
-        elif opt.start_from_mode == 'last':
-            model_pth = torch.load(os.path.join(save_folder, 'model-last.pth'))
-        logger.info('Loading pth from {}, iteration:{}'.format(save_folder, iteration))
-        model.load_state_dict(model_pth['model'])
-
-    # Load the pre-trained model
-    if opt.pretrain and (not opt.start_from):
-        logger.info('Load pre-trained parameters from {}'.format(opt.pretrain_path))
-        model_pth = torch.load(opt.pretrain_path, map_location=torch.device(opt.device))
-        # query_weight = model_pth['model'].pop('query_embed.weight')
-        if opt.pretrain == 'encoder':
-            encoder_filter = model.get_filter_rule_for_encoder()
-            encoder_pth = {k:v for k,v in model_pth['model'].items() if encoder_filter(k)}
-            model.load_state_dict(encoder_pth, strict=True)
-        elif opt.pretrain == 'decoder':
-            encoder_filter = model.get_filter_rule_for_encoder()
-            decoder_pth = {k:v for k,v in model_pth['model'].items() if not encoder_filter(k)}
-            model.load_state_dict(decoder_pth, strict=True)
-            pass
-        elif opt.pretrain == 'full':
-            # model_pth = transfer(model, model_pth)
-            model.load_state_dict(model_pth['model'], strict=True)
-        else:
-            raise ValueError("wrong value of opt.pretrain")
-        
-
-    model.to(opt.device)
-
-    # Decide which parameters need to be trained
-    # if (opt.matcher_type =='DTW' or opt.use_pseudo_box) and opt.text_encoder_learning_strategy == 'frozen':
-    #     for _, p in model.text_encoder.named_parameters():
-    #         p.requires_grad = False
-    #         text_encoder_params = list(map(id, model.text_encoder.parameters()))
-    #         other_params = filter(lambda p: id(p) not in text_encoder_params, model.parameters())
-    # else:
-    #     other_params = model.parameters()
-    other_params = model.parameters()
-
-    training_params = [{'params': other_params, 'lr': opt.lr}]
-
-    if opt.optimizer_type == 'adam':
-        optimizer = optim.Adam(training_params, weight_decay=opt.weight_decay)
-
-    elif opt.optimizer_type == 'adamw':
-        optimizer = optim.AdamW(training_params, weight_decay=opt.weight_decay)
-
-    milestone = [opt.learning_rate_decay_start + opt.learning_rate_decay_every * _ for _ in range(int((opt.epoch - opt.learning_rate_decay_start) / opt.learning_rate_decay_every))]
-    lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestone, gamma=opt.learning_rate_decay_rate)
-
-    # Load tokenizer for text encoder
-    # for i in range(10):
-    #     try:
-    #         if opt.pretrained_language_model == 'UniVL':
-    #             tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
-    #         else:
-    #             tokenizer = AutoTokenizer.from_pretrained(opt.pretrained_language_model)
-    #         break
-    #     except:
-    #         print('download error in AutoTokenizer, retry...')
-    #         time.sleep(1)
-
-    if opt.start_from:
-        optimizer.load_state_dict(model_pth['optimizer'])
-        lr_scheduler.step(epoch-1)
-
-    # print the args for debugging  
-    print_opt(opt, model, logger)
-    print_alert_message('Strat training !', logger)
-
-    loss_sum = OrderedDict()
-    bad_video_num = 0
-
-    start = time.time()
-
-    weight_dict = criterion.weight_dict
-    logger.info('loss type: {}'.format(weight_dict.keys()))
-    logger.info('loss weights: {}'.format(weight_dict.values()))
-
-    # Epoch-level iteration
-
-    while True:
-        if True:
-            # scheduled sampling rate update
-            if epoch > opt.scheduled_sampling_start >= 0:
-                frac = (epoch - opt.scheduled_sampling_start) // opt.scheduled_sampling_increase_every
-                opt.ss_prob = min(opt.basic_ss_prob + opt.scheduled_sampling_increase_prob * frac,
-                                  opt.scheduled_sampling_max_prob)
-                model.caption_head.ss_prob = opt.ss_prob
-
-            print('lr:{}'.format(float(opt.current_lr)))
-            pass
-
-        # breakpoint()
-        # Batch-level iteration
-        opt.use_pseudo_box = False # True for howto, False for yc2/tasty,
-        opt.pseudo_box_aug = False
-        opt.refine_pseudo_box = False
-        # breakpoint()
-
-        for train_loader in train_dataloaders:
-            opt.use_pseudo_box = not opt.use_pseudo_box
-            opt.
-            criterion.opt = opt 
-            criterion.matcher.use_pseudo_box = opt.use_pseudo_box
-
-            # if opt.use_pseudo_box:
-            #     print('howto dataset')
-            # else:
-            #     print('target dataset')
-            trained_samples = 0
-            for dt in tqdm(train_loader, disable=opt.disable_tqdm):
-                # # for fast debugging
-                # if trained_samples > 25:
-                #     break
-                # else:
-                #     trained_samples += 1
-                if opt.device=='cuda':
-                    torch.cuda.synchronize(opt.device)
-                if opt.debug:
-                    # each epoch contains less mini-batches for debugging
-                    if (iteration + 1) % 5 == 0:
-                        iteration += 1
-                        break
-                iteration += 1
-
-                optimizer.zero_grad()
-                dt = {key: _.to(opt.device) if isinstance(_, torch.Tensor) else _ for key, _ in dt.items()}
-                dt['video_target'] = [
-                    {key: _.to(opt.device) if isinstance(_, torch.Tensor) else _ for key, _ in vid_info.items()} for vid_info in
-                    dt['video_target']]
-
-                # Add text encoder
-                # if opt.matcher_type == 'DTW' or opt.use_pseudo_box:
-                #     captions = list()
-                #     for video_sents in dt['cap_raw']:  # dt['cap_raw']: [[sent_1, sent_2, ..., sent_n]]
-                #         captions.extend(video_sents) 
-                #     text_encoder_input = tokenizer(captions, return_tensors='pt', truncation=True, padding=True, max_length=opt.max_text_input_len)
-                #     text_encoder_input = {key: _.to(opt.device) if isinstance(_, torch.Tensor) else _ for key, _ in text_encoder_input.items()} 
-                #     # text_encoder_input: {'input_ids': tensor([[  101,  1996,  2307,  ...,     0,     0,     0],...]),  'attention_mask': tensor([[1, 1, 1,  ..., 0, 0, 0],...])}
-                #     # len(text_encoder_input['input_ids']) = n * max_text_input_len
-                #     dt['text_encoder_input'] = text_encoder_input
-
-                # dt = collections.defaultdict(lambda: None, dt) # Commented to 
-
-                output, loss = model(dt, criterion, contrastive_criterion)
-                final_loss = sum(loss[k] * weight_dict[k] for k in loss.keys() if k in weight_dict)
-                # breakpoint()
-                final_loss.backward()
-                torch.nn.utils.clip_grad_norm_(model.parameters(), opt.grad_clip)
-
-                optimizer.step()
-
-                for loss_k,loss_v in loss.items():
-                    loss_sum[loss_k] = loss_sum.get(loss_k, 0)+ loss_v.item()
-                loss_sum['total_loss'] = loss_sum.get('total_loss', 0) + final_loss.item()
-
-                if opt.device=='cuda':
-                    torch.cuda.synchronize()
-
-                losses_log_every = int(len(train_loader) / 10)
-
-                if opt.debug:
-                    losses_log_every = 6
-
-                if iteration % losses_log_every == 0:
-                    end = time.time()
-                    for k in loss_sum.keys():
-                        loss_sum[k] = np.round(loss_sum[k] /losses_log_every, 3).item()
-
-                    logger.info(
-                        "ID {} iter {} (epoch {}), \nloss = {}, \ntime/iter = {:.3f}, bad_vid = {:.3f}"
-                            .format(opt.id, iteration, epoch, loss_sum,
-                                    (end - start) / losses_log_every, bad_video_num))
-
-                    tf_writer.add_scalar('lr', opt.current_lr, iteration)
-                    for loss_type in loss_sum.keys():
-                        tf_writer.add_scalar(loss_type, loss_sum[loss_type], iteration)
-                    loss_history[iteration] = loss_sum
-                    lr_history[iteration] = opt.current_lr
-                    loss_sum = OrderedDict()
-                    start = time.time()
-                    bad_video_num = 0
-                    torch.cuda.empty_cache()
-
-        # evaluation
-        if (epoch % opt.save_checkpoint_every == 0) and (epoch >= opt.min_epoch_when_save):
-            
-            # Save model
-            saved_pth = {'epoch': epoch,
-                         'model': model.state_dict(),
-                         'optimizer': optimizer.state_dict()}
-
-            if opt.save_all_checkpoint:
-                checkpoint_path = os.path.join(save_folder, 'model_iter_{}.pth'.format(iteration))
-            else:
-                checkpoint_path = os.path.join(save_folder, 'model-last.pth')
-
-            torch.save(saved_pth, checkpoint_path)
-
-            model.eval()
-            result_json_path = os.path.join(save_folder, 'prediction',
-                                         'num{}_epoch{}.json'.format(
-                                             len(val_dataset), epoch))
-            #eval_score, eval_loss = evaluate(model, criterion,  postprocessors, val_loader, result_json_path, logger=logger, args=opt, alpha=opt.ec_alpha, device=opt.device, debug=opt.debug)
-            eval_score, _ = evaluate(model, criterion,  postprocessors, val_loader, result_json_path, logger=logger, args=opt, alpha=opt.ec_alpha, device=opt.device, debug=opt.debug)
-            if opt.caption_decoder_type == 'none':
-                current_score = 2./(1./eval_score['Precision'] + 1./eval_score['Recall'])
-            else:
-                if opt.criteria_for_best_ckpt == 'dvc':
-                    current_score = np.array(eval_score['METEOR']).mean() + np.array(eval_score['soda_c']).mean()
-                else:
-                    current_score = np.array(eval_score['para_METEOR']).mean() + np.array(eval_score['para_CIDEr']).mean() + np.array(eval_score['para_Bleu_4']).mean()
-
-            # add to tf summary
-            for key in eval_score.keys():
-                tf_writer.add_scalar(key, np.array(eval_score[key]).mean(), iteration)
-
-            # Huabin comment this part for avoiding reporting losses during evaluation
-            # for loss_type in eval_loss.keys():
-            #     tf_writer.add_scalar('eval_' + loss_type, eval_loss[loss_type], iteration)
-
-            _ = [item.append(np.array(item).mean()) for item in eval_score.values() if isinstance(item, list)]
-            print_info = '\n'.join([key + ":" + str(eval_score[key]) for key in eval_score.keys()])
-            logger.info('\nValidation results of iter {}:\n'.format(iteration) + print_info)
-            logger.info('\noverall score of iter {}: {}\n'.format(iteration, current_score))
-            val_result_history[epoch] = {'eval_score': eval_score}
-            logger.info('Save model at iter {} to {}.'.format(iteration, checkpoint_path))
-
-            # save the model parameter and  of best epoch
-            if current_score >= best_val_score:
-                best_val_score = current_score
-                best_epoch = epoch
-                saved_info['best'] = {'opt': vars(opt),
-                                      'iter': iteration,
-                                      'epoch': best_epoch,
-                                      'best_val_score': best_val_score,
-                                      'result_json_path': result_json_path,
-                                      'avg_proposal_num': eval_score['avg_proposal_number'],
-                                      'Precision': eval_score['Precision'],
-                                      'Recall': eval_score['Recall']
-                                      }
-
-                # suffix = "RL" if sc_flag else "CE"
-                torch.save(saved_pth, os.path.join(save_folder, 'model-best.pth'))
-                logger.info('Save Best-model at iter {} to checkpoint file.'.format(iteration))
-
-            saved_info['last'] = {'opt': vars(opt),
-                                  'iter': iteration,
-                                  'epoch': epoch,
-                                  'best_val_score': best_val_score,
-                                  }
-            saved_info['history'] = {'val_result_history': val_result_history,
-                                     'loss_history': loss_history,
-                                     'lr_history': lr_history,
-                                     # 'query_matched_fre_hist': query_matched_fre_hist,
-                                     }
-            with open(os.path.join(save_folder, 'info.json'), 'w') as f:
-                json.dump(saved_info, f)
-            logger.info('Save info to info.json')
-
-            model.train()
-
-        epoch += 1
-        lr_scheduler.step()
-        opt.current_lr = optimizer.param_groups[0]['lr']
-        torch.cuda.empty_cache()
-        # Stop criterion
-        if epoch >= opt.epoch:
-            # load Best model and conduct evaluation
-            print('====== Conduct the Final Evaluation to test Best Checkpoint ======')
-            val_logger = create_logger(save_folder, 'val.log')
-            loaded_pth = torch.load(os.path.join(save_folder, 'model-best.pth'), map_location='cuda')
-            model.load_state_dict(loaded_pth['model'], strict=True)
-            model.eval()
-            result_json_path = saved_info['best']['result_json_path']
-            eval_score, _ = evaluate(model, criterion,  postprocessors, val_loader, result_json_path, logger=logger, args=opt, alpha=opt.ec_alpha, device=opt.device, debug=opt.debug)
-            if opt.caption_decoder_type == 'none':
-                current_score = 2./(1./eval_score['Precision'] + 1./eval_score['Recall'])
-            else:
-                if opt.criteria_for_best_ckpt == 'dvc':
-                    current_score = np.array(eval_score['METEOR']).mean() + np.array(eval_score['soda_c']).mean()
-                else:
-                    current_score = np.array(eval_score['para_METEOR']).mean() + np.array(eval_score['para_CIDEr']).mean() + np.array(eval_score['para_Bleu_4']).mean()
-
-            _ = [item.append(np.array(item).mean()) for item in eval_score.values() if isinstance(item, list)]
-            print_info = '\n'.join([key + ":" + str(eval_score[key]) for key in eval_score.keys()])
-            val_logger.info('Best-model is saved at iter {}.\n'.format(saved_info['best']['iter']))
-            val_logger.info('\nBest Model Performance:\n' + print_info)
-            val_logger.info('\nBest Overall Score {}: {}\n'.format(iteration, current_score))
-
-            tf_writer.close()
-            break
-
-    return saved_info
-
-
-if __name__ == '__main__':
-    opt = opts.parse_opts()
-    opt.id = 'seq-gt_percent_{}'.format(opt.ft_gt_percent)
-    assert opt.ft_gt_percent <= 1.0 and opt.ft_gt_percent >= 0.0
-
-    if not hasattr(opt, 'visual_feature_folder_val'):
-        opt.visual_feature_folder_val = opt.visual_feature_folder
-        opt.text_feature_folder_val = opt.text_feature_folder
-
-    if opt.map:
-        opt.visual_feature_folder = [map_path(path) for path in opt.visual_feature_folder]
-        opt.text_feature_folder = [map_path(path) for path in opt.text_feature_folder]
-        opt.visual_feature_folder_val = [map_path(path) for path in opt.visual_feature_folder_val]
-        opt.text_feature_folder_val = [map_path(path) for path in opt.text_feature_folder_val]
-
-    if opt.gpu_id:
-        os.environ["CUDA_VISIBLE_DEVICES"] = ",".join([str(i) for i in opt.gpu_id])
-    if opt.disable_cudnn:
-        torch.backends.cudnn.enabled = False
-
-    os.environ['KMP_DUPLICATE_LIB_OK'] = 'True' # to avoid OMP problem on macos
-    # breakpoint()
-    train(opt)
-