Add causal person fusion and MuVAP stage-3 baselines
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model: 'MambaA'
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data_name: 'YTConv_ANTICIPATION'
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data_info_name: 'shard_0'
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task: 'ANTICIPATION'
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loss: 'ANTICIPATION' # NONUNIFORM, originally
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metric: 'AP' # AP or cAP
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optimizer: 'AdamW'
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device: 'cuda'
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feature_pretrained: 'resnet50'
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root_path: './data/YTConv/shard_0'
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rgb_type: 'rgb_kinetics_resnet50'
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flow_type: 'audio_wav2vec2' # or 'none'?
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annotation_type: 'target_perframe/case1'
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video_list_path: 'data_info/YTConv_info.json'
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output_path: './output_cfg'
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window_size: 128
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batch_size: 32
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test_batch_size: 1
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# loss_offset: 4 # loss weight a_t when L - {loss_offset} <= t <= L
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num_epoch: 30
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lr: 0.0001
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weight_decay: 0.05
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num_workers: 4
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dropout: 0.20
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num_classes: 2 # for YTConv pretraining, there is no
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embedding_dim: 2048
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hidden_dim: 1024
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num_layers: 1
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stride: 4
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anticipation_length: 10
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actionness: False
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ignore_index: -100
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@@ -0,0 +1,32 @@
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model: 'MiniROADA'
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data_name: 'YTConv_ANTICIPATION'
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data_info_name: 'shard_0'
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task: 'ANTICIPATION'
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loss: 'ANTICIPATION' # NONUNIFORM, originally
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metric: 'AP' # AP or cAP
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optimizer: 'AdamW'
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device: 'cuda'
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feature_pretrained: 'resnet50'
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root_path: './data/YTConv/shard_0'
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rgb_type: 'rgb_kinetics_resnet50'
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flow_type: 'audio_wav2vec2'
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annotation_type: 'target_perframe'
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video_list_path: 'data_info/YTConv_info.json'
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output_path: './output_cfg'
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window_size: 128
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batch_size: 64
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test_batch_size: 1
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# loss_offset: 4 # loss weight a_t when L - {loss_offset} <= t <= L
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num_epoch: 30
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lr: 0.0001
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weight_decay: 0.05
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num_workers: 4
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dropout: 0.20
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num_classes: 2 # for pretraining, there is no target speaker
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embedding_dim: 2048
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hidden_dim: 1024
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num_layers: 1
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stride: 4
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anticipation_length: 10
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actionness: False
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ignore_index: -100
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