update

LaSA/.vscode/launch.json

@@ -10,7 +10,7 @@
             "cwd": "/root/autodl-tmp/workshop/LaSA/",
             "args": [
                 "--dataset",
-                "PKU-subject",
+                "BABEL3",
                 "--cuda",
                 "0"
             ]

LaSA/config/BABEL3/config.yaml

@@ -2,7 +2,7 @@ dataset: BABEL3
 dataset_dir: ./dataset
 csv_dir: ./csv
 
-batch_size: 4
+batch_size: 8
 boundary_th: 0.5
 ce: true
 ce_weight: 1.0
@@ -14,11 +14,11 @@ gstmse: true
 gstmse_index: feature
 gstmse_weight: 1.0
 lambda_b: 0.1
-learning_rate: 0.001
+learning_rate: 0.0001
 optimizer: Adam
 momentum: 0.9
 dampening: 0.0
-weight_decay: 0.0001
+weight_decay: 0.001
 nesterov: true
 tmse: false
 tmse_weight: 0.15
@@ -33,7 +33,7 @@ n_stages_asb: 2
 n_stages_brb: 3
 SFI_layer: [1, 2 ,3, 4, 5, 6, 7, 8, 9]
 
-max_epoch: 300
+max_epoch: 150
 num_workers: 0
 iou_thresholds:
 - 0.1

LaSA/evaluate.py

@@ -133,7 +133,7 @@ def main():
     if args.model is not None:
         state_dict = torch.load(args.model,map_location=lambda storage, loc: storage.cuda(device))
     else:
-        state_dict = torch.load(os.path.join(result_path, "best_test_F1_0.5_model.prm"), map_location=lambda storage, loc: storage.cuda(device))
+        state_dict = torch.load(os.path.join(result_path, "best_test_map_model.prm"), map_location=lambda storage, loc: storage.cuda(device))
         
     model.load_state_dict(state_dict, False)
 

LaSA/libs/metric.py

@@ -312,7 +312,7 @@ class ScoreMeter(object):
             seg = getActLoc(self.frm_preds)
             print (len(seg))
             dmap_list = []
-            annotation_path = './dataset/BABEL1/val_split1.pkl'
+            annotation_path = './dataset/BABEL3/val_split3.pkl'
             for iou in (0.1,0.2,0.3,0.4,0.5):
                 print("Testing for IoU %f" % iou)
                 dmap_list.append(
@@ -605,7 +605,7 @@ def getActLoc(
                             outer_score = np.mean(vid_cas[outer_temp_list])
                         c_score = inner_score - 0.6 * outer_score
                         vid_cls_proposal.append([i, s[j], e[j] + 1, c_score])
-            pick_idx = NonMaximumSuppression(np.array(vid_cls_proposal), 0.2)
+            pick_idx = NonMaximumSuppression(np.array(vid_cls_proposal), 0.5)
             nms_vid_cls_proposal = [vid_cls_proposal[k] for k in pick_idx]
             c_temp += nms_vid_cls_proposal
         if len(c_temp) > 0:

LaSA/run.sh

@@ -1,3 +1,5 @@
 python train.py --dataset PKU-subject --cuda 0 
 
-python train.py --dataset BABEL1 --cuda 0 
+python train.py --dataset BABEL3 --cuda 0 
+python train.py --dataset BABEL1 --cuda 0 
+

LaSA/train.py

@@ -23,6 +23,8 @@ from libs.optimizer import get_optimizer
 from libs.transformer import TempDownSamp, ToTensor
 from prompt.text_prompt import TextCLIP, text_prompt_for_class, text_prompt_for_joint
 
+import numpy as np
+
 def get_arguments() -> argparse.Namespace:
     """
     parse all the arguments from command line inteface
@@ -49,7 +51,7 @@ def import_class(import_str):
     except AttributeError:
         raise ImportError('Class %s cannot be found (%s)' % (class_str, traceback.format_exception(*sys.exc_info())))
 
-def change_label_score(best_test, train_loss, epoch, cls_acc, edit_score, f1s):
+def change_label_score(best_test, train_loss, epoch, cls_acc, edit_score, f1s,map = [0]):
 
     best_test['train_loss'] = train_loss
     best_test['epoch'] = epoch
@@ -60,6 +62,7 @@ def change_label_score(best_test, train_loss, epoch, cls_acc, edit_score, f1s):
     best_test['f1s@0.5'] = f1s[2]
     best_test['f1s@0.75'] = f1s[3]
     best_test['f1s@0.9'] = f1s[4]
+    best_test['map'] = np.mean(map)
 
 def main() -> None:
 
@@ -208,7 +211,7 @@ def main() -> None:
     best_test_acc =  {'epoch':0,'train_loss':0,'cls_acc':0,'edit':0,'f1s@0.1':0,'f1s@0.25':0,'f1s@0.5':0,'f1s@0.75':0,'f1s@0.9':0,'mAP':0}
     best_test_F1_10 =  best_test_acc.copy()
     best_test_F1_50 =  best_test_acc.copy()
-
+    best_test_MAP =  best_test_acc.copy()
     log = pd.DataFrame(columns=columns)
     # ['epoch', 'lr', 'train_loss', 'val_loss', 'cls_acc', 'edit', 'f1s@0.1', 'f1s@0.25', 'f1s@0.5', 'f1s@0.75', 'f1s@0.9', 'bound_acc', 'precision', 'recall', 'bound_f1s'] [Columns: [epoch, lr, train_loss, val_loss, cls_acc, edit, f1s@0.1, f1s@0.25, f1s@0.5, f1
     if args.resume:
@@ -351,7 +354,12 @@ def main() -> None:
                         model.state_dict(),
                         os.path.join(result_path, 'best_test_F1_0.5_model.prm')
                     )
- 
+                if np.mean(maps) > best_test_MAP['mAP']:
+                    change_label_score(best_test_MAP, train_loss, epoch, cls_acc, edit_score, segment_f1s,maps)
+                    torch.save(
+                        model.state_dict(),
+                        os.path.join(result_path, 'best_test_map_model.prm')
+                    )
         # save checkpoint every epoch
         save_checkpoint(result_path, epoch, model, optimizer, best_loss)
 
@@ -412,6 +420,8 @@ def main() -> None:
     print('{}'.format(best_test_F1_10))
     print('\n---------------------------best_test_F1_50---------------------------\n')
     print('{}'.format(best_test_F1_50))
+    print('\n---------------------------best_test_MAP---------------------------\n')
+    print('{}'.format(best_test_MAP))
     print('\n---------------------------all_train_time---------------------------\n')
     print('all_train_time: {:.2f}min'.format((time.time() - start_start) / 60))
 

test.txt

@@ -1 +0,0 @@
-test

workload_check.sh

@@ -1,35 +0,0 @@
-#!/bin/bash
-
-# Set your Git username or email (must match your Git commits)
-AUTHOR="qiushuocheng"
-
-echo "📊 Git Activity Report (Author: $AUTHOR)"
-echo "========================================"
-
-# Initialize counters
-total_add=0
-total_del=0
-
-# Loop through the last 7 days (from 6 days ago to today)
-for i in {6..0}; do
-    day=$(date -v -${i}d "+%Y-%m-%d")
-
-    # Get additions and deletions for that day
-    stats=$(git log --since="$day 00:00" --until="$day 23:59" --author="$AUTHOR" --pretty=tformat: --numstat |
-        awk '{ add += $1; del += $2 } END { print add, del }')
-
-    add=$(echo $stats | cut -d' ' -f1)
-    del=$(echo $stats | cut -d' ' -f2)
-    sum=$((add + del))
-
-    printf "📅 %s | ➕ %-5s | ➖ %-5s | 🧮 Total: %-5s\n" "$day" "$add" "$del" "$sum"
-
-    total_add=$((total_add + add))
-    total_del=$((total_del + del))
-done
-
-echo "========================================"
-echo "🧾 Summary for Last 7 Days:"
-echo "✅ Total Added: $total_add lines"
-echo "❌ Total Deleted: $total_del lines"
-echo "📦 Total Changed: $((total_add + total_del)) lines"