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Types & classes25 in github.com/TimSeizinger/Bokehlicious

⨍ Functions 80◇ Types & classes 25
↓ 5 callersClassIdentityMod
method/nn_util.py:12
↓ 3 callersClassBokehlicious
method/model.py:12
↓ 3 callersClassDWConv2d
method/nn_util.py:68
↓ 3 callersClassPatchEmbedIR
r""" Image to Patch Embedding Args: embed_dim (int): Number of linear projection output channels. norm_layer (nn.Module, optional
method/nn_util.py:321
↓ 3 callersClassPatchUnEmbedIR
r""" Image to Patch Unembedding Args: embed_dim (int): Number of linear projection output channels.
method/nn_util.py:341
↓ 3 callersClassRealBokeh
Dataset class for the RealBokeh dataset. :param data_path: Path to the dataset directory with train/val/test subdirectories. Only test is
dataset/loader.py:11
↓ 2 callersClassBlockMod
method/blocks.py:153
↓ 2 callersClassEBB
dataset/loader.py:104
↓ 2 callersClassLayerNorm2d
method/nn_util.py:236
↓ 1 callersClassApertureAttentionBlock
method/blocks.py:123
↓ 1 callersClassApertureAwareAttention
method/nn_util.py:131
↓ 1 callersClassApplyVectorWeights
method/nn_util.py:33
↓ 1 callersClassBasicLayer
method/blocks.py:87
↓ 1 callersClassChannelEmbeddingCompression
method/nn_util.py:40
↓ 1 callersClassConcatTensors
method/nn_util.py:19
↓ 1 callersClassDynRelPos2d
method/nn_util.py:249
↓ 1 callersClassFeedForwardNetwork
method/blocks.py:248
↓ 1 callersClassInvertedConvolution
method/nn_util.py:53
↓ 1 callersClassResidualBlock
Args: depth (int): Number of blocks. num_heads (int): Number of attention heads. drop_path (float | tuple[float], option
method/blocks.py:19
↓ 1 callersClassSimpleGate
method/nn_util.py:204
↓ 1 callersClassSkipConnection
method/nn_util.py:26
ClassChannelAttention
Channel attention module with optional attention weights.
method/nn_util.py:83
ClassLayerNormFunction
method/nn_util.py:209
ClassMode
dataset/util.py:11
ClassSimplifiedChannelAttention
method/nn_util.py:110