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github.com/RetroCirce/HTS-Audio-Transformer
/ types & classes
Types & classes
59 in github.com/RetroCirce/HTS-Audio-Transformer
⨍
Functions
248
◇
Types & classes
59
↓ 115 callers
Class
ConvBlock
models.py:26
↓ 9 callers
Class
ConvPreWavBlock
models.py:2190
↓ 9 callers
Class
LeeNetConvBlock
models.py:1606
↓ 9 callers
Class
LeeNetConvBlock2
models.py:1697
↓ 4 callers
Class
ConvBlock5x5
models.py:72
↓ 4 callers
Class
DaiNetResBlock
models.py:1804
↓ 4 callers
Class
HTSAT_Swin_Transformer
r"""HTSAT based on the Swin Transformer Args: spec_size (int | tuple(int)): Input Spectrogram size. Default 256 patch_size (int |
model/htsat.py:380
↓ 4 callers
Class
SEDDataset
data_generator.py:21
↓ 3 callers
Class
ESC_Dataset
data_generator.py:185
↓ 3 callers
Class
SCV2_Dataset
data_generator.py:233
↓ 3 callers
Class
SEDWrapper
sed_model.py:35
↓ 3 callers
Class
_ResNet
models.py:698
↓ 3 callers
Class
data_prep
main.py:41
↓ 2 callers
Class
DESED_Dataset
data_generator.py:280
↓ 2 callers
Class
_ResNetWav1d
models.py:2002
↓ 1 callers
Class
AsymmetricLoss
utils.py:28
↓ 1 callers
Class
AttBlock
models.py:109
↓ 1 callers
Class
BasicLayer
A basic Swin Transformer layer for one stage. Args: dim (int): Number of input channels. input_resolution (tuple[int]): Input res
model/htsat.py:311
↓ 1 callers
Class
DropPath
Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
model/layers.py:52
↓ 1 callers
Class
Ensemble_SEDWrapper
sed_model.py:259
↓ 1 callers
Class
Mlp
MLP as used in Vision Transformer, MLP-Mixer and related networks
model/layers.py:94
↓ 1 callers
Class
PatchEmbed
2D Image to Patch Embedding
model/layers.py:62
↓ 1 callers
Class
SwinTransformerBlock
r""" Swin Transformer Block. Args: dim (int): Number of input channels. input_resolution (tuple[int]): Input resulotion. n
model/htsat.py:145
↓ 1 callers
Class
WindowAttention
r""" Window based multi-head self attention (W-MSA) module with relative position bias. It supports both of shifted and non-shifted window. Ar
model/htsat.py:60
Class
Cnn10
models.py:485
Class
Cnn14
models.py:140
Class
Cnn14_16k
models.py:2543
Class
Cnn14_8k
models.py:2640
Class
Cnn14_DecisionLevelAtt
models.py:3216
Class
Cnn14_DecisionLevelAvg
models.py:3111
Class
Cnn14_DecisionLevelMax
models.py:3013
Class
Cnn14_emb128
models.py:1124
Class
Cnn14_emb32
models.py:1215
Class
Cnn14_emb512
models.py:1033
Class
Cnn14_mel128
models.py:2921
Class
Cnn14_mel32
models.py:2830
Class
Cnn14_mixup_time_domain
models.py:2737
Class
Cnn14_no_dropout
models.py:315
Class
Cnn14_no_specaug
models.py:231
Class
Cnn6
models.py:400
Class
DaiNet19
models.py:1870
Class
InvertedResidual
models.py:1424
Class
LeeNet11
models.py:1631
Class
LeeNet24
models.py:1731
Class
MobileNetV1
models.py:1306
Class
MobileNetV2
models.py:1476
Class
PatchMerging
r""" Patch Merging Layer. Args: input_resolution (tuple[int]): Resolution of input feature. dim (int): Number of input channels.
model/htsat.py:269
Class
Predictor
predict.py:14
Class
Res1dNet31
models.py:2080
Class
Res1dNet51
models.py:2135
Class
ResNet22
models.py:772
Class
ResNet38
models.py:859
Class
ResNet54
models.py:946
Class
Wavegram_Cnn14
models.py:2227
Class
Wavegram_Logmel128_Cnn14
models.py:2429
Class
Wavegram_Logmel_Cnn14
models.py:2315
Class
_ResnetBasicBlock
models.py:581
Class
_ResnetBasicBlockWav1d
models.py:1944
Class
_ResnetBottleneck
models.py:639