2024-06-10 23:50 commit in Ning

configs/stgcn++/stgcn++_ntu120_xsub_hrnet/jnew.py

@@ -0,0 +1,63 @@
+model = dict(
+    type='RecognizerGCN',
+    backbone=dict(
+        type='STGCN',
+        gcn_adaptive='init',
+        gcn_with_res=True,
+        tcn_type='mstcn',
+        graph_cfg=dict(layout='coco', mode='spatial')),
+    cls_head=dict(type='GCNHead', num_classes=10, in_channels=256))
+
+dataset_type = 'PoseDataset'
+ann_file = 'tools/data/Weizman/Wei_xsub_stgn++.pkl'
+train_pipeline = [
+    dict(type='PreNormalize2D'),
+    dict(type='GenSkeFeat', dataset='coco', feats=['j']),
+    dict(type='UniformSample', clip_len=100),
+    dict(type='PoseDecode'),
+    dict(type='FormatGCNInput', num_person=2),
+    dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['keypoint'])
+]
+val_pipeline = [
+    dict(type='PreNormalize2D'),
+    dict(type='GenSkeFeat', dataset='coco', feats=['j']),
+    dict(type='UniformSample', clip_len=100, num_clips=1),
+    dict(type='PoseDecode'),
+    dict(type='FormatGCNInput', num_person=2),
+    dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['keypoint'])
+]
+test_pipeline = [
+    dict(type='PreNormalize2D'),
+    dict(type='GenSkeFeat', dataset='coco', feats=['j']),
+    dict(type='UniformSample', clip_len=100, num_clips=10),
+    dict(type='PoseDecode'),
+    dict(type='FormatGCNInput', num_person=2),
+    dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]),
+    dict(type='ToTensor', keys=['keypoint'])
+]
+data = dict(
+    videos_per_gpu=16,
+    workers_per_gpu=2,
+    test_dataloader=dict(videos_per_gpu=1),
+    train=dict(
+        type='RepeatDataset',
+        times=5,
+        dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xsub_train')),
+    val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xsub_val'),
+    test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xsub_val'))
+
+# optimizer
+optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True)
+optimizer_config = dict(grad_clip=None)
+# learning policy
+lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False)
+total_epochs = 16
+checkpoint_config = dict(interval=1)
+evaluation = dict(interval=1, metrics=['top_k_accuracy'])
+log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')])
+
+# runtime settings
+log_level = 'INFO'
+work_dir = './work_dirs/stgcn++/stgcn++_ntu120_xsub_hrnet/jnew'

tools/data/Weizman/genjson.py

@@ -0,0 +1,78 @@
+import os
+import decord
+import json
+from mmcv import load, dump
+
+from pyskl.smp import mwlines
+
+
+def writeJson(path_train, jsonpath):
+    outpot_list = []
+    trainfile_list = os.listdir(path_train)
+    for train_name in trainfile_list:
+        traindit = {}
+        sp = train_name.split('_')
+        traindit['vid_name'] = train_name.replace('.avi', '')
+        traindit['label'] = int(sp[1].replace('.avi', ''))
+        traindit['start_frame'] = 0
+
+        video_path = os.path.join(path_train, train_name)
+        vid = decord.VideoReader(video_path)
+        traindit['end_frame'] = len(vid)
+        outpot_list.append(traindit.copy())
+    with open(jsonpath, 'w') as outfile:
+        json.dump(outpot_list, outfile)
+
+
+# path是数据集路径 dirpath = '../data/Weizmann'
+# name为生成的list文件名称，这里为 'Weizmann'
+def writeList(dirpath, name):
+    path_train = os.path.join(dirpath, 'train')
+    path_test = os.path.join(dirpath, 'test')
+    trainfile_list = os.listdir(path_train)
+    testfile_list = os.listdir(path_test)
+
+    train = []
+    for train_name in trainfile_list:
+        traindit = {}
+        sp = train_name.split('_')
+
+        traindit['vid_name'] = train_name
+        traindit['label'] = sp[1].replace('.avi', '')
+        train.append(traindit)
+    test = []
+    for test_name in testfile_list:
+        testdit = {}
+        sp = test_name.split('_')
+        testdit['vid_name'] = test_name
+        testdit['label'] = sp[1].replace('.avi', '')
+        test.append(testdit)
+
+    tmpl1 = os.path.join(path_train, '{}')
+    lines1 = [(tmpl1 + ' {}').format(x['vid_name'], x['label']) for x in train]
+
+    tmpl2 = os.path.join(path_test, '{}')
+    lines2 = [(tmpl2 + ' {}').format(x['vid_name'], x['label']) for x in test]
+    lines = lines1 + lines2
+    mwlines(lines, os.path.join(dirpath, name))
+
+
+def traintest(dirpath, pklname, newpklname):
+    os.chdir(dirpath)
+    train = load('train.json')
+    test = load('test.json')
+    annotations = load(pklname)
+    split = dict()
+    split['xsub_train'] = [x['vid_name'] for x in train]
+    split['xsub_val'] = [x['vid_name'] for x in test]
+    dump(dict(split=split, annotations=annotations), newpklname)
+
+
+if __name__ == '__main__':
+    dirpath = './'
+    pklname = 'train.pkl'
+    newpklname = 'Wei_xsub_stgn++.pkl'
+    # writeJson('test', 'test.json')
+    # writeJson('train', 'train.json')
+    traintest(dirpath, pklname, newpklname)
+    # writeList('./', 'Weizmann.list')

tools/data/custom_2d_skeleton.py

@@ -137,7 +137,7 @@ def main():
         my_part = annos
         os.makedirs(args.tmpdir, exist_ok=True)
     else:
-        init_dist('pytorch', backend='nccl')
+        init_dist('pytorch', backend='gloo')
         rank, world_size = get_dist_info()
         if rank == 0:
             os.makedirs(args.tmpdir, exist_ok=True)

tools/data/label_map/Weizman.txt

@@ -0,0 +1,10 @@
+wave2
+bend
+jack
+jump
+pjump
+run
+side
+skip
+walk
+wave1

tools/test.py

@@ -132,7 +132,7 @@ def main():
     cfg.data.test.test_mode = True
 
     if not hasattr(cfg, 'dist_params'):
-        cfg.dist_params = dict(backend='nccl')
+        cfg.dist_params = dict(backend='gloo')
 
     init_dist(args.launcher, **cfg.dist_params)
     rank, world_size = get_dist_info()