↓ 4 callersMethodget_resnet_layer(self, block=BasicBlock, n_blocks=[2,2,2,2], channels=[64, 128, 256, 512], stride = 1)
feature_extraction/visual/extract_imagenet_embedding.py:88
↓ 3 callersMethod__init__(self, block_b, block_m, block_a, layers, num_classes=12666)
feature_extraction/visual/manet/model/manet.py:167
↓ 3 callersFunctiontrain_or_eval_model(args, model, cl_proj, losses, dataloader, optimizer=None, train=False, task='all')
main_frame_val_text_missing.py:84
↓ 1 callersMethod__init__(self, embed_dim, num_heads, layers, attn_dropout=0.0, relu_dropout=0.0, res_dropout=0.0,
emb
toolkit/models/modules/transformers_encoder/transformer.py:24
↓ 1 callersMethod__init__(self, output_dim1=1, output_dim2=1, layers='256,128', dropout=0.3)
feature_extraction/llm4wav/extract_wavlm_vicuna.py:188
↓ 1 callersMethod__init__(self, output_dim1=1, output_dim2=1, layers='256,128', dropout=0.3)
feature_extraction/llm4wav/extract_wavlm_vicuna_hd.py:188
↓ 1 callersFunctionextract(model_name, audio_files, save_dir, feature_level, layer_ids=None, gpu=None)
feature_extraction/audio/extract_transformers_embedding.py:29
↓ 1 callersFunctionextract(input_dir, process_type, save_dir, face_dir, hog_dir, pose_dir)
feature_extraction/visual/extract_openface.py:48
↓ 1 callersFunctionextract_embedding(model_name, trans_dir, save_dir, feature_level, gpu=-1, punc_case=None, language='chinese', model_dir=None)
feature_extraction/llm4wav/extract_wavlm_vicuna.py:272
↓ 1 callersFunctionextract_embedding(model_name, trans_dir, save_dir, feature_level, gpu=-1, punc_case=None, language='chinese', model_dir=None)
feature_extraction/llm4wav/extract_wavlm_vicuna_hd.py:284
↓ 1 callersFunctionextract_embedding(model_name, trans_dir, save_dir, feature_level, gpu=-1, punc_case=None, language='chinese', model_dir=None)
feature_extraction/text/extract_text_embedding_huggingface.py:139